Excerpts From:
MOTIVATING CRITICAL COMPUTER SYSTEMS OPERATORS:
JOB CHARACTERISTICS, CONTROLS, AND RELATIONSHIPS
A THESIS
SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL
OF THE UNIVERSITY OF MINNESOTA
BY
DALE HARRISON MCKNIGHT
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Chair: Gordon B. Davis Co-Advisors: Norman L. Chervany and Fred D. Davis
Committee Members: Frank Miller, Akbar Zaheer
December, 1997
Note -- These excerpts include:
--The first in-depth examination of the Critical Information Systems Operator job
--Incrementing the Job Characteristics Model with Relationships/Trust
--Incrementing Management Contols theory with Relationships/Trust
--New Grounded Theory validity methods
--Demonstrations of thorough survey Construct Validity methods
--Empirical results that explain what motivates critical systems operators
--An explanation of the paradoxical results found for managerial controls
Copyright Dale Harrison McKnight 1997
All Rights Reserved
MOTIVATING CRITICAL COMPUTER SYSTEMS OPERATORS:
JOB CHARACTERISTICS, CONTROLS, AND RELATIONSHIPS
ABSTRACT
This study expands the explanatory power of two theories of motivation: the
Hackman and Oldham Job Characteristics Model (JCM) and the economics-based
Management Controls model (MCM). The JCM predicts worker motivation as a function
of the worker’s job characteristics (e.g., skill variety), while the MCM predicts
motivation as a function of managerial controls (e.g., incentives). These motivation
theories each omit an explicit account of the roles of: a) supervisor/subordinate
relationships, and b) workplace fairness perceptions, relying instead on how the job or its
incentives are structured. This study adds explanatory power to these theories through
two constructs: ‘Relationships’ (worker trust and liking towards the supervisor) and
‘System Trust’ (worker beliefs about the fairness structures of the workplace). The target
application of this research is the critical computer systems operator. ‘Critical’ means the
extent to which business transactions are interrupted when these systems are not available
to their users.
This research was conducted in two phases at one site. Phase I explored factors
important to keeping critical computer systems available to users almost 100% of the
time. “Grounded theory” methods were used to analyze the semi-structured interviews.
In Phase II, a questionnaire was administered to eighty-six operators to test the extent to
which adding Relationships and System Trust to the JCM and MCM helped these models
predict operator motivation.
i
The study contributes to research in four ways. First, Relationships and System
Trust added predictive power to the JCM. Second, Relationships and System Trust added
predictive power to the MCM. Relationships and System Trust supplement traditional
views that job characteristics or management controls alone produce motivated workers.
Third, the study validates measures for two newly conceptualized constructs:
Relationships and System Trust. Fourth, it describes the highly motivating nature of the
critical computer systems operator job.
This study also contributes to practice. Two paradigms have dominated recent
corporate motivation practices: worker empowerment (based on the JCM) and incentive
pay (based on the MCM). This research suggests that these paradigms will yield
inadequate results unless worker/manager relationships and workplace fairness are also
considered.
ii
MOTIVATING CRITICAL COMPUTER SYSTEMS OPERATORS:
JOB CHARACTERISTICS, CONTROLS, AND RELATIONSHIPS
TABLE OF CONTENTS
Abstract
Page
ii
Chapter One: Introduction and Overview
Overview and Research Question
The Nature of the Critical Systems Operator Job
Advancing the Job Characteristics Model
Advancing the Management Controls Model
Summary and Contributions
Roadmap for the Study
1
1
4
6
9
12
13
Chapter Two: Methodology and Construct Validation
Approach of the Study
Phase I Conceptual Model Building Methodology
 Grounded Theory
 Phase I Research Framework
Phase II Model Building and Testing Methodology
 Measurable Constructs Used
 Instrument Pretest
 Instrument Pilot
Construct Validation Results
Hypothesis Testing Methodology
Research Site for the Study
15
15
20
20
22
24
25
27
31
33
45
49
Chapter Three: Nature of the Critical Systems Operator Job
Theory Building
 Nature and Importance of the Critical Computer System
 Management Information Systems Literature
 Management of Technology Literature
 Conceptual Model Building
 Hypotheses
Methodology Detail
Results of Hypothesis Testing
Discussion of Results
55
56
56
59
62
64
66
76
79
84
iii
TABLE OF CONTENTS (continued)
Chapter Four: Job Characteristic Model--Adding Relationships
Theory Building
 JCM Related Research
 JCM Hypotheses
 Relationships and System Trust Related Hypotheses
Methodology Detail
Results of Hypothesis Testing
Discussion of Results
88
89
89
90
91
96
96
100
Chapter Five: Incentive Controls--Adding Relationships
Theory Building
 Definitions
 Controls Theory Overview
 Conceptual Model Building-Incentives
 Scientific Model Building-Incentives
 Hypotheses-Incentives
Methodology Detail
Results of Hypothesis Testing
Discussion of Results
103
104
104
105
107
110
119
123
125
127
Chapter Six: Other Controls--Adding Relationships
Theory Building
 Conceptual Model Building-Accountability
 Scientific Model Building-Accountability
 Conceptual Model Building-Feedback
 Scientific Model Building-Feedback
 Conceptual Model Building-Micromanagement
 Scientific Model Building- Micromanagement
 Conceptual Model Building-Autonomy
 Scientific Model Building- Autonomy
 Scientific Model Building-Work Outcomes
 System Trust’s Impact on Motivation
Methodology Detail
Results of Hypothesis Testing
Discussion of Results
133
134
134
135
137
137
140
141
142
142
143
145
145
146
153
iv
TABLE OF CONTENTS (continued)
Chapter Seven: Contributions, Limitations, and Future Research
Contributions
 To Theory
 To Practice
Study Limitations
 External Validity
Future Research
157
158
158
160
163
164
167
References
172
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Appendix F
Appendix G
Appendix H
Appendix I
Appendix J
Examples of open and axial coding
Questionnaire Items by Construct
Operator Questionnaire
Supervisor Questionnaire
Pretest Instrument A--Matching
Pretest Instrument B--Categorization
Pretest Instrument C--Sorting
Pairwise Intercorrelation Matrices
Descriptive Statistics
Pairwise Intercorrelation Matrices--High Level Concepts
191
193
201
224
227
228
229
230
244
245
v
LIST OF TABLES
Page
26
Table 1
Instruments for Testing Management Controls/Relationships
Model
Table 2
Pilot Reliability Analysis
32
Table 3
Construct Level Cronbach’s Alpha Reliabilities
34
Table 4
Intercorrelations of Trust Constructs and Liking
38
Table 5
Mono-Trait, Mono-Method Analysis for Autonomy
41
Table 6
Correlations among CTE, Performance, and Two Autonomy Types 43
Table 7
Correlations among JCM Variables and Two Autonomy Types
43
Table 8
Reliabilities for High Level (Second Order) Concepts
46
Table 9
Intrinsic Motivation Orientation (IMO) Scale
78
Table 10
Job Characteristics Comparisons
79
Table 11
Intrinsic Versus Extrinsic Factors Reported
81
Table 12
Correlations between Less Secure Group and Other Attributes
82
Table 13
Job Characteristics Model Test Results
97
Table 14
Relationships and System Trust Test Results
99
Table 15
Effects of Extrinsic Motivation on Intrinsically Motivating Tasks
115
Table 16
Management Controls / Relationships Model—Correlation Tables
147
Table 17
Management Controls / Relationships Model—Regression Results 148
Table 18
Sensitivity Analysis for Relationships Moderation of Accountability 150
Table 19
Sensitivity Analysis for Relationships Moderation of Feedback
151
vi
LIST OF TABLES (continued)
Table 20
Table 21
Sensitivity Analysis for Relationships Moderation of
Micromanagement
151
Sensitivity Analysis for Relationships Moderation of Autonomy
152
COMMONLY USED ABBREVIATIONS
JCM
Job Characteristics Model
MCM
Management Controls model
CSO
Critical (computer) System Operator
MIS
Management Information Systems
XYZCo
Organization for the research site
GNS
Growth Need Strength
CPS
Critical Psychological States
vii
LIST OF FIGURES
Page
2
Figure 1
Job Characteristics Model (JCM)
Figure 2
Motivating Nature of the Critical Systems Operator Job
6
Figure 3
Expanding the Job Characteristics Model
9
Figure 4
Management Controls Model
10
Figure 5
Advancing the Management Controls Model
12
Figure 6
Roadmap for the Study
14
Figure 7
The Operations Research Model
19
Figure 8
Phase I Research Framework
23
Figure 9
Management Controls / Relationships Model—Detailed Level
25
Figure 10
Job Characteristics Model (JCM)—Detailed Level
27
Figure 11
Nomological Network for Trust Constructs
37
Figure 12
Model of Construct Creation
192
Figure 13
Model of Construct Linkages
192
viii
CHAPTER ONE: INTRODUCTION AND OVERVIEW
This chapter previews the topic, propositions, general methodology, and
contributions of the study. It begins with a research overview that introduces the research
question. Then it creates the broad propositions that later chapters will test in detail.
Finally, it summarizes the contributions of the study and presents a guide that organizes
the contents of later chapters.
OVERVIEW AND RESEARCH QUESTION
This study introduces two constructs, Relationships and System Trust, that
improve the predictive power of the Job Characteristics Model (JCM) and the
Management Controls model (MCM) of worker motivation. System Trust means the
belief that proper impersonal structures are in place to enable one to anticipate a
successful endeavor (Lewis & Weigert, 1985; Shapiro, 1987; Zucker, 1986). In this
study, the Systems Trust construct was operationalized as the worker’s belief that
structures (i.e., processes, procedures) support or encourage fairness in one’s work
environment.
Relationships means the extent to which one holds positive feelings,
beliefs and intentions towards another person.
The Relationships construct was
operationalized as trust in, and liking of, one’s supervisor. The Relationships definition
carries a quality-of-relation focus that differs from the traditional definitions of
relationships in: a) sociology, which focus more on behavioral and role interdependence
(e.g., Blau, 1964), and b) social psychology, which focuses more on the ability of parties
to influence each other (e.g., Berscheid, 1983).
As depicted in Figure 1, the Hackman and Oldham (1975) Job Characteristics
1
Model (JCM) posits that worker perceptions of their Job Characteristics (e.g., Skill
Variety) lead to Critical Psychological States (e.g., Felt Responsibility) that, in turn, lead
to motivational Work Outcomes (e.g., Job Satisfaction).
These model linkages are
moderated by the worker’s Growth Need Strength, an individual characteristic variable.
The JCM focuses on the nature of the job itself, ignoring social or structural aspects of
the worker’s environment.
Figure 1 Job Characteristics Model (JCM)
Job Characteristics
Growth Need Strength
Critical
Psychological
States (CPS)
Work
Outcomes
In contrast, Management Controls models (e.g., Ouchi, 1979) posit that incentives
or other controls improve worker motivation. The term “controls” means methods of
attempting to ensure desired outcomes by trying to influence other people (Anthony,
1965; Lawler & Rhode, 1976). Management control occurs when managers use methods
to try to influence employees to behave in certain ways. Control models generally ignore
social and structural factors, focusing instead on extrinsic rewards or behavior control.
For example, managers try to entice employees to work faster by offering them contingent
financial incentives.
This study’s subjects were critical systems operators. Critical systems are
computer systems that must be kept available to users, or else numerous business or
operational transactions are interrupted. Transaction processing systems, used to conduct
2
a firm’s daily business, often fall in the category of critical systems (Laudon & Laudon,
1995). Managers of critical systems try to keep their systems continuously available to
system users.
Hence, critical systems operators (CSOs) must be constantly alert to
problems that might threaten the system. When a critical systems crashes, the operators
are charged with restoring it within seconds or minutes, not hours. The researcher studied
critical computer systems operators (CSOs) in two stages: exploratory (Phase I) and
confirmatory (Phase II). The systems these operators ran were considered critical because
thousands of users required that the systems be continuously available so they could
perform their daily job function.
During the study’s Phase I interviews, it became evident that CSOs were clearly
motivated by the nature of their job, but that controls and incentives did not have
consistent, positive motivational effects on CSOs. In analyzing Phase I data, it became
clear that worker relationships with superiors and their beliefs about the work
environment also influenced their motivation. Some evidence for this effect also comes
from the management literature (e.g., Cook & Wall, 1980; Locke, Latham & Erez, 1988).
Therefore, the study’s research question is:
Do operator/supervisor Relationships and System Trust improve the ability of
the Job Characteristics Model and the Management Controls model to
predict critical systems operator motivation and motivational outcomes?
In other words, this study tested the extent to which operator/supervisor Relationships
and System Trust added predictive value to the JCM and the MCM in the critical systems
operator context.
3
THE NATURE OF THE CRITICAL SYSTEMS OPERATOR JOB
The critical computer systems operator (CSO) is a subset of the class of
information systems workers called “computer operators.” A literature search revealed
that very little research has been done on computer operators.
The management
information system (MIS) literature focuses on system development, implementation,
maintenance, and use issues, while covering few system operation issues (Berkeley, 1984;
Ives, Hamilton and Davis, 1980; Swanson & Ramiller, 1993). Lyytinen & Hirschheim's
(1987) exhaustive review of the MIS failure literature reported almost no research on
system operation issues.
In fact, in the 1970s and 1980s, the traditional computer operator job was viewed
as a quasi-clerical function that did not merit intensive study (Couger & Zawacki, 1980).
In their survey of over 1200 computer operations employees, Couger and Zawacki (1980:
33) reported that “employees in DP operations perceive their jobs to be deficient in the
key characteristics that produce motivation and lead to increased productivity. The
motivating potential score (MPS) of these jobs is lower than that of any of the other 500
jobs in the Hackman/Oldham data base.” MPS, derived from the scores of the five JCM
job characteristics, represents how motivating a job is. Describing computer operations
as a data processing ‘stepchild,’ Couger and Zawacki suggested that only “the
‘sledgehammer’ of a catastrophic event such as a flood or bombing” could “draw
attention to computer operations.” (1980: 34)
This study draws attention to the job of the critical systems operator—a job that
does not fit the Couger and Zawacki computer operator profile. In the critical system
4
context, the threat of catastrophic system downtime is so large that it produced motivating
potential scores for the eighty-six critical systems operators in this study that were more
than double that of the traditional computer operator Couger and Zawacki measured.
This study’s informants operated three critical transaction processing systems at a large
U. S. corporation fictionally name XYZCo. During Phase I interviews, critical systems
operators (CSOs) at XYZCo were found to be highly skilled and motivated individuals
who performed an extremely interesting and challenging job. For example, the task of
diagnosing and fixing system outages was reported to be exhilarating, satisfying, and yet
full of pressure. These CSOs were found to be primarily intrinsically motivated, in that
they more often mentioned that they enjoyed their job and its challenge than they
mentioned extrinsic job rewards. From Phase I data (discussed in more detail in Chapter
Three), it was proposed that (see Figure 2):
Proposition 1:
The nature of the critical systems operator (CSO) job is such
that: a) JCM measures for the CSO will be significantly higher than was found among
traditional computer operators in the Couger & Zawacki (1980) study; and b) CSOs will
be more intrinsically motivated than extrinsically motivated.
Figure 2 Motivating Nature of the Critical Systems Operator Job
Nature of the
Critical Systems
Operator Job
High Levels of
Motivation
5
The CSO job is therefore considerably different from the jobs of the traditional
computer operators Couger and Zawacki (1980) studied. The CSO subjects of this study
are not representative of computer operators in general, but are representative of operators
of computer (and other) systems that are required to stay available nearly 100% of the
time. Therefore, rather than generalizing to the job of computer operators, the results of
this study will shed light on: a) the jobs of critical computer system operators (e.g., for
transaction processing systems—Laudon & Laudon, 1995; Weick, 1990); and b) the jobs
of those who operate critical systems like nuclear power plants or aircraft carriers (e.g.,
Perrow, 1984; Weick & Roberts, 1993).
ADVANCING THE JOB CHARACTERISTICS MODEL
The Job Characteristics Model posits that jobs may be designed to maximize
motivation (e.g., Hackman, 1980).
JCM forms the theoretical basis for worker
empowerment (e.g., Peters, 1992) and the related process “reengineering” (Hammer &
Champy, 1993) paradigms, which have dominated recent motivation practices of
corporations. JCM has also been widely adopted and discussed in the Management and
MIS literatures (e.g., Couger & Zawacki, 1980; Roberts & Glick, 1981). Evidence
developed by those who have studied information systems jobs (e.g., Couger & Zawacki,
1980; Ives & Chervany, 1983; Lending, 1996) generally supports the application of the
JCM to the information systems worker. Therefore, (see Figure 1)
6
Proposition 2:
The job characteristics of critical systems operators will be
positively associated with their Critical Psychological States (CPS), which, in turn, will
be positively associated with their Work Outcomes. Both linkages will be moderated by
Growth Need Strength (GNS).
Over the past twenty years, significant evidence has accumulated that social
relationships also motivate workers. The original JCM (Hackman & Lawler, 1971)
contained social needs factors that were later removed, probably because they did not
receive as much empirical support as did the job characteristics part of the model
(Lending, 1996). However, some researchers have continued to include some aspect of
sociality in their testing of the JCM (e.g., Couger & Zawacki, 1980; Lending, 1996).
Further, Salancik & Pfeffer (1978) offered their Social Information Processing (SIP)
model as a JCM alternative.
SIP posits that worker perceptions of their jobs are
influenced through social cognitive processes rather than through job characteristics.
Lending (1996) and Couger & Zawacki (1980) used forms of social needs in their studies,
based on Hackman & Lawler (1971). These needs have not always been found to be
closely related to CPS or Work Outcomes.
Note that measurements of social needs or social cognitions are indirect ways of
measuring the ‘goodness’ of relationships between people in the work place. That is,
measuring social need fulfillment refers to how well a relationship fulfills a person’s
need, rather than measuring the quality of the relationship (i.e., trust and liking between
the people) directly. Similarly, social cognition embodies how cognitive frames are
7
formed, but does not directly measure people relationships. However, if social need
fulfillment and social cognition are important to motivation, then it seems reasonable that
people relationships measured directly could be even more important. In fact, Smits,
McLean and Tanner (1997) found that the relationship with one’s supervisor was one of
the two most significant predictors of the motivational variable called organizational
commitment.
Similarly, Lending (1996) found that one relationship variable,
“Satisfaction with Supervisor,” improved her ten-factor JCM index’s prediction of system
developer Job Satisfaction from an adjusted R-squared of .22 to .33. System Trust,
because it is part of the family of trust variables that are positively related to motivation
(Locke, Latham & Erez, 1988), is also likely to be related to motivational outcomes. For
example, how one feels about the structures encouraging equity in the work environment
(System Trust) should be related to one’s Job Satisfaction (a Work Outcome). Therefore
(see Figure 3):
Proposition 3:
In the critical systems operator job, operator/supervisor
Relationships will be predictive of CPS and Work Outcomes beyond the predictive power
of JCM constructs.
System Trust will be predictive of Work Outcomes beyond the
predictive power of JCM constructs.
Figure 3 Expanding the Job Characteristics Model
Job Characteristics
Growth Need Strength
Critical
Psychological
States (CPS)
Work
Outcomes
8
Relationships
System Trust
In light of the strong job characteristics motivation of the CSO job (Proposition
1), Proposition 3 is a strong test.
Proposition 1 implies that CSOs will be highly
motivated by job characteristics. The strong salience of the job characteristics factors
makes it less likely that, in the presence of job characteristics factors, Relationships and
System Trust will be significant predictors of CPS and Work Outcomes. That is, in the
CSO context, job characteristics factors are more likely to dwarf the effects of
Relationships and Systems Trust than would occur in another work context. Thus,
Proposition 3 is a strong test of the efficacy of Relationships and System Trust.
ADVANCING THE MANAGEMENT CONTROLS MODEL
Management Controls research (e.g., Ouchi, 1979; Eisenhardt, 1985) has typically
linked controls to desired outcomes like motivation. For example, accountability control
should lead to higher motivation (Tetlock, 1985). Also, agency theory proposes that, to
be successful, principals should contract with the agent such that: a) their objectives are
aligned (typically through offering the agent incentives); or, b) the agent’s behavior can
be monitored. The latter constitutes a behavioral control, while the former is an outcome
control (Kirsch, 1992).
The typical Management Controls model (MCM) is economics-based, and
assumes that people are self-interested and not socially influenced. The MCM is a
9
theoretical basis for the long-standing paradigm of incentive compensation that permeates
corporate America today (see Figure 4). The logic is that incentives provide employees
the proper motivation for achieving such motivational outcomes as improved market
share, profitability, and stock price. Accountability and Feedback (e.g., Cusella, 1982)
also positively influence motivation, which in turn affect motivational outcomes. Thus:
Proposition 4: In the critical systems environment, Management Controls will be
positively associated with CSO Motivation, which will, in turn, be positively associated
with Motivational Outcomes.
Figure 4 Management Controls Model
Management
Controls
Motivation
Motivational
Outcomes
Paradoxically, Management Controls have sometimes had negative outcomes.
Whereas incentives, or other controls, have sometimes been found to improve worker
motivation and performance (e.g., Henderson & Lee, 1992), they have also been found to
have dysfunctional side effects (e.g., Lawler & Rhode, 1976; Simons, 1995).
For
example, Powers and Dickson (1973) found negative perceived effects of project controls
on system development outcomes. However, they did not explain why this occurred.
10
Phase I data indicated that the worker relationship with the manager is likely to
have an effect on the worker’s motivation. In two Phase I instances, the relationship
moderated the effects of controls on worker motivation.
In another instance, the
relationship directly affected the worker’s motivation.
Some evidence exists in the literature that Relationships can moderate the effect
of Controls on Motivation. For example, Steers & Porter (1979) said that merit pay
systems work best when management and workers have a good relationship. Lawler
(1971) said that pay-for-performance systems don’t work when worker/management trust
is low. Tetlock (1985) and Cummings and Anton (1990) also found evidence that
accountability is motivating only when the relationship between the two parties is
positive.
Hence, Relationships moderates the effects of Management Controls on
Motivation. System Trust will also likely be a motivator. As operationalized here,
System Trust relates closely to structural workplace fairness.
Logically, a worker’s
perceptions of workplace fairness could affect the worker’s motivation. Because System
Trust relates to structural fairness, System Trust will be positively related to Motivation.
Therefore (see Figure 5):
Proposition 5: In the critical systems environment, operator/supervisor
Relationships will moderate the effects of Management Controls on Motivation. System
Trust will be predictive of Motivation beyond the predictive power of Management
Controls.
11
Figure 5 Advancing the Management Controls Model
Management
Controls
Motivation
Relationships
Motivational
Outcomes
System Trust
SUMMARY AND POTENTIAL CONTRIBUTIONS
The introduction presents the study as a test to see if Relationships and System
Trust add predictive power to the popular JCM and MCM theories. Just as Hirschman
(1984) argued that adding variables to economic models that are too parsimonious can
improve understanding, so this study argues that adding Relationships and System Trust
to the JCM and the MCM can improve prediction of motivation.
The primary research contributions of the study are:
 improving the prediction of the dependent variables of the JCM by using
Relationships and System Trust as independent variables;
 improving the prediction of the dependent variables of the MCM by
using Relationships and System Trust as independent variables;
 describing for the first time the nature of the critical computer systems
operator job; and
 validating the new conceptualizations of Relationships and System
Trust.
12
The primary practical contributions of the study are:
 exposing worker/manager relationships and structural workplace fairness
as critical understanding gaps that need to be filled to successfully
implement practices like incentive awards, reengineering, and
empowerment, which stem from the JCM and MCM;
 explaining the relative importance of the JCM, MCM, relationships, and
workplace fairness factors for the motivation of CSOs; and
 explaining that incentives may actually de-motivate, rather than
motivate, workers. The detailed understanding this study provides
of one organization’s experiences with incentives can help guide a
reasoned use of incentives in organizations with similar conditions.
Roadmap for the Study
Figure 6 maps Propositions 1-5 (“Prop.”) and related models to the chapters
13
(“Ch”) that address them. The roadmap will be repeated at the beginning of Chapters
Three through Seven.
Figure 6 Roadmap for the Study
Ch
Prop:
Content or Model
2
--
Methodology and Construct Validation
3
1
Nature of the
Critical Systems
Operator Job
4
High Levels of
Motivation
2, 3
Job Characteristics
Growth Need Strength
Critical
Psychological
States (CPS)
Relationships
5
Work
Outcomes
System Trust
4, 5
Incentive
Controls
Motivational
Effect
Relationships
6
4, 5
Other
Controls
Relationships
7
--
Motivation
Motivational
Outcomes
System Trust
Contributions, Limitations, and Future Research
14
CHAPTER TWO: METHODOLOGY AND CONSTRUCT VALIDATION
First, this chapter outlines and justifies the general approach taken in the study.
Next, the methodologies for Phases I and II are discussed. This is followed by the results
of construct validation efforts. Finally, a brief description of the research site is given.
APPROACH OF THE STUDY
Research models may be built in at least two different ways. Using Method 1, a
researcher searches the scientific literature for what has been done in the area of interest
(e.g., Kaplan, 1964). By analysis of what has already been done, a researcher deductively
builds a model for testing. Using Method 2, a researcher visits the research site and
observes what is happening (e.g., Glaser & Strauss, 1967; Glaser, 1978). By analyzing
some subset of the complex phenomenon, the researcher inductively creates a conceptual
model of the phenomenon. Method 1 has the advantages that it builds upon earlier work
and results in a readily testable model. Its disadvantage is that the model may not
adequately reflect what is occurring in the research setting. Method 2 has the advantage
of more closely matching the phenomenon chosen. Its disadvantages are that it can create
models that are: a) hard to connect with existing models in the literature, and b) difficult
to test scientifically. This study combines Methods 1 and 2 to take advantage of the
benefits of each.
This study was conducted in two phases. Phase I explored the research problem
using semi-structured interview data analyzed via grounded theory methods (Glaser &
Strauss, 1967; Strauss, 1987). Phase II tested the model produced by Phase I, using
15
telephone questionnaire data primarily analyzed with correlation and regression
techniques.
Why was this two-phased approach was taken? First, from an initial literature
search, no studies were found that addressed the critical systems operator’s (CSO’s) job
within the context of the related management controls and people relationships. This
decreased the researcher’s confidence that hypotheses developed from the literature
would hold; rather, the judgment was that such hypotheses would be conjectural. Given
this judgment, it would be likely that, even after testing, the resulting model would not
explain many of the interacting factors found in this area of practice. This issue is a
concern because both MIS and reference discipline scholars have said that complex,
interacting factors determine system reliability (Hale & Glendon, 1987; Lyytinen &
Hirschheim, 1987).
For example, Lucas (1975) said, “...a number of variables are
involved in the design and operation of successful systems. The complex relationships
among technical, behavioral, situational, and personal factors all must be considered. If
any variable is ignored, systems are likely to fail.” (1975: 110) Second, an exclusively
deductive model building approach would likely lead to “Type III” errors (Kirk & Miller,
1986), which occur when a researcher misses important issues for study in the setting.
This is especially important to new fields of study, such as the critical computer system.
Third, the contribution of a deductive model building / model testing effort is
likely to be very limited. To make a major contribution, one needs to go beyond a small,
incremental addition to the literature, which Weick compared to swimming toward “the
white cliffs of the obvious” (Mintzberg, 1979). Meehl (1978) argued that “science does
16
not, and cannot, proceed by incremental gains achieved through statistical significance
testing of hypotheses” (Kaplan & Duchon, 1988: 572). Mintzberg argued that serious
exploratory work is needed for progress to be made: “Simplification squeezes out the
very thing on which the research should focus” (1979: 586). Further, solely deductive
research tends to prevent the discovery of new insights (Kirk & Miller, 1986).
For these reasons, the researcher felt it important to first develop conceptual
models of the phenomenon through an inductive approach. A conceptual model may, or
may not, be quantitatively testable. Often, these models are developed at a high level of
abstraction that needs further delineation in order to be tested. At the least, a conceptual
model provides a clear description of what factors are important in explaining the target
outcomes of the study. This approach lies within the tradition of creating models from
case study work (Applegate, 1991; Eisenhardt, 1989a). The resulting conceptual models
need to be: a) made testable and b) tested empirically. This is important if the resulting
models are to add to the body of scientifically tested knowledge. Through literature
searches, the researcher can make the conceptual models specific enough to be tested.
This is done by justifying variable-level hypotheses that can be tested by existing or new
quantitative scales. Hence, this intensive study builds theory by integrating the strengths
of exploratory and testing methods, much as Lee (1991a) recommended integrating
positivist with post-positivist research.
This study's overall structure can be understood in terms of Sagasti & Mitroff's
(1973) diamond model, which represents four "bases" of research (Figure 7). The bases
are (from "3rd base" clockwise to "home plate"): (1) the real world problem; (2) the
17
conceptual model of the problem; (3) the scientific model; and (4) the model's solution.
Sagasti & Mitroff argued that the four bases are connected by four scientific research
processes--conceptualization, modeling, model solving/testing, and implementation (see
Figure 7--[a],[b],[c],[d]). By linking these four bases, one can produce, from everyday
reality, [a] conceptual models that can be refined into [b] scientific models that, when [c]
tested, can be used as helpful input [d] to the problem again.
The danger of not pursuing part [a] of the process is producing irrelevant or
unrealistic models (Mintzberg, 1979). As Dubin said, “observation and description of the
real world are the essential points of origin for theories” (1976: 18). Warning against the
use of reality-starved methodologies, Cook & Campbell (1979: 92) remarked that
exclusive reliance on statistical or experimental methods can have “disastrous” effects on
a study. Crozier said that “premature rigor” can keep a theory “from being adequately
comprehensive.” (1964: 5). Oversimplifying phenomena through excessive mathematical
modeling eliminates key elements, such that “every similarity to reality is gone”
(Hofstede, 1967: 89). Researchers should preserve reality by resisting models that are not
founded on a thorough prior understanding of the real world phenomenon.
18
Figure 7 The Operations Research Model
Science
Research Processes:
Conceptual
Model
[a]
[a] = Conceptualization
[b]
[b] = Modeling
[c] = Model Solving/Testing
Reality,
Problem
Situation
[e]
Scientific
Model
[d] = Implementation
[e] = Validation
[d]
[c]
Source: Sagasti & Mitroff, 1973
Solution
In order to stay true to the critical systems context, the research undertaken in this
study includes three of the four scientific processes indicated in Figure 7:
 [a] building conceptual models of real world critical computer systems
situations through interviews, using inductive analysis,
 [b] creating a scientific model by Hegelian (dialectic) contrast of the
conceptual models and the literature (Crozier, 1964), and
 [c] testing the scientific model through questionnaire data, analyzed with
regression analysis.
This study’s approach to the dialectic of inductive and deductive theory building does not
rely completely on the qualitative data (as do grounded theorists—Glaser, 1992), but
synthesizes the grounded empirical results and the existing literature into testable models.
Research step [a] ensures that the resulting theoretical contribution is grounded in real
19
world situations. Step [b] ensures that conceptual models are translated into scientific
models that [c] are rigorously tested. Following these steps strengthens the study’s
contribution, because the resulting models will be applicable to practice ([a]) and the
study will add to the body of scientifically validated models ([b] and [c]).
PHASE I CONCEPTUAL MODEL BUILDING METHODOLOGY
Phase I data consisted of transcripts of twenty semi-structured interviews of
managers and operators at a computer site described in the last section of Chapter Two.
Observations of operators in action were limited to two cases of less than thirty minutes
each. The interviewees consisted of a convenience sample selected in consultation with
research site management. A grounded theory approach (Glaser & Strauss, 1967; Strauss
& Corbin, 1990) was used to develop the conceptual model that resulted in the
controls/relationships model (Figure 5), but without the System Trust construct. Due to
time constraints, only six of the twenty interviews were analyzed with grounded theory
methods to produce the model. The six were selected because they were felt to be the
richest sources of what seemed key concepts in Phase I: controls, motivation, teamwork,
and relationships.
Grounded Theory
Grounded theory is a qualitative method from sociology (Glaser & Strauss, 1967)
that enables one to build theory from a rigorous analysis of observational or interview
data.
Grounded theory employs the “usual canons of ‘good science’...significance,
theory-observation compatibility, generalizability, consistency, reproducibility, precision,
20
and verification” (Denzin, 1994: 508), and has been used effectively in MIS research
(Orlikowski, 1993).
A full grounded theory study was not done; rather, the researcher used three
methods from grounded theory: theoretical sensitivity, open coding, and axial coding.
Theoretical sensitivity means that the researcher modifies the specific research topics as
key aspects become apparent from the data already gathered. This is especially important
to exploratory research like Phase I. The researcher used theoretical sensitivity to focus
attention on specific research concepts (e.g., motivation, controls) that seemed important,
based on the initial few interviews at the research site. Using a modifiable interview
instrument facilitated use of theoretical sensitivity. That is, the researcher added and
deleted specific questions from one interview to the next in order to focus on the key
concepts. Open coding means that the researcher abstracted theoretical concepts from
segments of the transcribed interview data. This was done by reading a transcribed
sentence, phrase, or word and asking questions like, “What is this an instance of?”
(Kearney, Murphy & Rosenbaum, 1994: 353), or “What kind of concept does this refer
to?” Axial coding means to analyze the data a second time, relating one concept to
another. Through axial coding, the relationships between concepts that form a conceptual
model are developed. Examples from the research data of open and axial coding are
included in Appendix A.
Grounded theory was selected because:
 It is considered a rigorous method (Denzin, 1994), compared with other
qualitative research techniques;
21
 It is widely used in the social sciences (Denzin, 1994) and in MIS
research (e.g., Kaplan & Duchon, 1988);
 It is well suited for building models (Strauss & Corbin, 1990), that
reflect reality; and,
 The use of the theoretical sensitivity technique enables researchers to
follow the line of study that appears most important in the research
setting.
Phase I Research Framework
Before entering the field to collect data, the researcher documented the research
framework guiding Phase I interviews (Figure 8). At this point, the research design was
not fully specified, as is common in studies combining qualitative and quantitative
methods (Kaplan & Duchon, 1988).
This framework assumes that the systems approach to understanding the complex
and interactive causes of computer failure is the most productive one (Lyytinen &
Hirschheim, 1987). In particular, several complex systems (sets of factors) interact in the
setting to produce the system availability1 results. To understand the interactive effects of
management strategies, the researcher used the framework shown in Figure 8, which
synthesizes the frameworks of Bostrom & Heinen (1977) and Orlikowski (1992). The
framework assumes that the effects of management strategies on system availability will
be mediated by the interacting systems shown. In particular, the effects of strategies are
1
For simplicity, availability is defined to be measured at the central computer site. Availability equals the total time
possible (24 hours/day, 7 days/week) minus the summed duration of all computer site outages (planned or unplanned),
divided by total time possible.
22
translated into performance (i.e., system availability) by these systems’ processes and
interactive effects. The Technical System includes the computer system, its physical
environment, and the tools the operators use to run it. The Social System refers to the
informal interaction roles and relationships that exist among workers and management.
The Structural system means the formal aspects of organizations (e.g., official roles,
procedures, and official measurement/incentive systems).
The Individual System is
comprised of the perceptions, traits, knowledge, and capabilities of people.
Figure 8 Phase I Research Framework
Technical System
Management
Strategy
System Availability
Social System
Structural System
Individual System
Organizational/
Technical Context
Based on the above framework, the original semi-structured questionnaire covered
management strategies that related to keeping the system running, the roles of operators,
team relationships, and technical issues important to keeping the system running. As the
researcher learned more about the environment from initial interviews, the questionnaire
began to focus on management controls, worker/management relationships, worker
motivation, and teamwork issues, since these seemed most important to keeping the
systems running. Phase I resulted in the high level conceptual model shown in Figure 5
23
(without System Trust). This Controls/Relationships model is considered “high level”
because each model concept is broad and needs further specification before measurement
can be done. For example, in the literature, the term “Controls” can refer to many
different things--from incentives to budgeting systems to surveillance. Specifics on the
creation of the conceptual and testable versions of Figure 5 are contained in later
chapters.
PHASE II MODEL BUILDING AND TESTING METHODOLOGY
In general, Phase II refined the Controls/Relationships model by decomposing it
into measurable form. This was done by: a) decomposing the high level concepts into
measurable constructs,2 each associated with a questionnaire instrument, and b)
developing testable hypotheses, based on a combination of literature search and
qualitative analysis of the Phase I interviews. The reasons for choosing the particular
constructs is explained in the theory building sections of Chapters Three through Six.
Similarly, the JCM concepts shown in Figure 3 were broken down according to the JCM
literature. The researcher telephoned one hundred operators for the phone questionnaire.
Eighty-six of the one hundred participated. Only fourteen declined.
Measurable Constructs Used
This section describes how instruments were developed for testing the hypotheses,
which are presented in Chapters Three through Six. First, midrange constructs were
taken from the literature to form constitutive parts of the high level concepts of Figure 5
2
In general, the term “concept” refers to the high level entities (e.g., Motivation) comprised of several
measured constructs (e.g., Intrinsic Motivation, Job Satisfaction). The term “construct” refers to
measurable (mid-range) entities (Autonomy, Feedback, Trusting Intention,...).
24
(see Figure 9). A questionnaire instrument was found for each construct, generally
adapted from existing instruments (see Table 1). Each construct was measured with
either three, four, or five items. Most scales had seven points, from Strongly Agree to
Strongly Disagree. Two scales used five point scales because they were worded in terms
of amount instead of agree/disagree. Final items and questionnaire item order are shown
in Appendix C for the operator questionnaire.
Figure 9 Management Controls / Relationships Model—Detailed Level
Worker Relationship
with Superior
Management
Controls
Feedback
Autonomy
Accountability
Micromanagement
Computer
Worker
Motivation
Liking
Trusting Intention
Trusting Belief- Benevolence
Trusting Belief-Competence
Intrinsic Motiv.-Enjoyment
Intrinsic Motiv.-Self-Esteem
Experienced Meaningfulness
Job Satisfaction
Organizational Commitment
Individual
Contribution
to Team
Effectiveness
Contribution to
Contribution to
Contribution to
Contribution to
Communication
Conflict Resolution
Cooperation
Team Effectiveness
Individual
Performance
Table 1 Instruments for Testing Management Controls / Relationships Model
Construct
Feedback
Autonomy
Accountability
Micromanagement
Liking
Trusting Intention
Trusting Belief-Benevolence
Trusting Belief-Competence
System Trust
Intrinsic Motivation-Enjoyment
Instrument Source
Henderson & Lee, 1992
Aiken & Hage, 1966
Van de Ven & Ferry, 1980
Van de Ven & Ferry, 1980
Rubin, 1973
Dobing, 1993
Wrightsman, 1991
Wrightsman, 1991
New
New
25
Intrinsic Motivation-Self-Esteem
Experienced Meaningfulness
Job Satisfaction
Organizational Commitment
Contribution to Communication
Contribution to Conflict Resolution
Contribution to Cooperation
Contribution to Team Effectiveness
Individual Performance
Lawler & Hall, 1970; Van de Ven & Ferry, 1980
Hackman, 1980
Hackman, 1980
Mowday, Steers & Porter, 1979
O’Reilly & Roberts, 1975
New
Georgopoulos & Mann, 1962
New
New
Respondents for Contribution to Team Effectiveness (CTE) items consisted of the
direct supervisors of the operators. CTE means the extent to which a worker contributes
to team proficiency in key team attributes. These measures were formulated to represent
three key attributes of team effectiveness—communication, cooperation, and conflict
resolution. Each CTE construct was measured with two items, using two methods (see
Appendix D). The first method employed the same seven point Likert scale used in the
operator questionnaire. The second method was to have the supervisor rank the operators
best-to-worst on the construct. Individual Performance was also measured by asking the
supervisors to rank the operators best-to-worst on performance.
This was a quasi-
objective measure. That is, the supervisor was asked to give the report based on the
group’s latest official best-to-worst rankings. Supervisors with small groups reported the
ranking from memory. The others were heard accessing a ranking file as they prepared to
answer over the phone.
Similarly, the detail constructs shown in Figure 10 enabled the JCM to be
measured. Items from the Hackman/Oldham instrument were transformed into only
26
positively-phrased items, in order to avoid the problems found in Idaszak & Drasgow
(1987—also see Lending, 1996).
Figure 10 Job Characteristics Model (JCM)—Detailed Level
Growth Need Strength (GNS)
Job Characteristics
Critical
Psychological
States (CPS)
Skill Variety
Task Identity
Job Significance
Experienced Work
Meaningfulness
Autonomy
Felt Responsibility
Job Feedback
Knowledge of Results
Work
Outcomes
Intrinsic Motivation
Job Satisfaction
Work Performance
Instrument Pretest
This section describes how instruments were refined. In order to assure that the
instruments would provide reliable and valid measures of the theoretical constructs,
several pretests and a pilot were conducted. The pretest entailed the following steps,
based on Davis (1989):
1. Created a document listing each construct’s definition and items.
The
researcher and three faculty members successively reviewed this document for face
validity. Changes were made and the document revised after each of the four reviews.
Most changes were wording items that clarified or simplified the items. For example, an
27
item that was found to address two ideas was simplified to only address one idea. Since
the Job Characteristics and Motivation instruments had already undergone significant
testing by others (e.g., see Lending, 1996; Mowday, Steers & Porter, 1979; Van de Ven &
Ferry, 1980), the next pretest steps concentrated on improving the Controls and
Relationships constructs.
2. Pretest instrument A was a matching instrument (Appendix E). This instrument
was given to four Ph. D. Students and one department clerical person. Respondents were
asked to match items to construct names/definitions and then to point out which items (up
to three items) didn’t fit well with the definition. Pretest A was analyzed in terms of the
number of respondents who incorrectly categorized each item. Respondent comments
about which items didn’t fit were quantified by assigning points to each of the items. A
worst item comment was given a 3, second worst item a 2, and third worst a 1. An
overall ranking of best-to-worst items was developed by equally weighting the results of
these two analyses. Those items within each construct that had low rankings were
reworded.
3. Pretest B was a categorization exercise (Appendix F). The two Pretest B
versions (one each for trust and control) were each administered to forty-eight MBA
students. Respondents were asked to place sixteen statements into three to five categories
by placing A, B, C, D, or E next to the statement. At the bottom of the page, respondents
were asked to define each construct. The questionnaire included improved directions
versus the previous pretest, and asked for the item numbers that were difficult to analyze.
Pretest B was analyzed for number of respondents correctly categorizing each item and
28
for the items identified as hard to categorize. Eighty-nine percent of the Relationship
items and seventy percent of the Controls items were categorized correctly. The major
problem with Controls was the two negatively worded items that caused respondents to
categorize in terms of degree of control instead of type of control. These were reworded
positively. Several other changes were made based on Pretest B.
4. Pretest C was drafted as an item sorting exercise. Forty-one MBA students
were given an envelope with fifteen slips of paper with items on them--twenty-four
respondents for trust constructs and seventeen for control. The students were asked to
sort the items into three to five categories and then to tell what the categories mean
(Appendix G). The data were analyzed for difficult items and changes to the instruments
were made. The trust instruments (ninety-two percent correctly classified) again did
better than the controls instruments (seventy-two percent correct).
5. After the instrument changes were made, the questions were ordered by major
topic (e.g., Job Characteristics) and by construct within topic for the pilot. All items of a
construct were asked together, in order to improve internal consistency (Davis &
Venkatesh, 1994). In addition, a preface sentence introducing each construct was placed
before the first question in the series. For example, before the Feedback questions, the
interviewer said, “The next few questions relate to supervisory feedback.” (see Appendix
C for other examples). The questionnaire mechanics were based on Dillman’s (1978)
recommendations. In particular, respondents were first asked whether they agreed with,
disagreed with, or were neutral toward, the statement; then they were asked whether they
29
(dis)agreed strongly, moderately, or slightly. This technique enabled use of a seven point
scale without producing cognitive overload among respondents (Appendix C).
The questionnaire was done by telephone because telephone interviews, per
Dillman (1978):
 have high
 allow
response rates, both for individual items and the entire instrument;
the researcher to control fully the sequence of questions;
 are less
expensive to conduct than face-to-face interviews;
 are almost
 facilitate
 enable
unlimited in terms of the number of items one may ask;
transitions that indicate when a new construct is being covered;
researchers to gauge the feelings of the respondents;
 provide less
social desirability bias than face-to-face interviews, and about the
same as written questionnaires;
 facilitate
use of open-ended questions.
Because social desirability was still considered a possible validity threat, the researcher
included in the questionnaire’s introduction assurances that: a) there were no right or
wrong answers to the questionnaire; b) he was not an agent of management; and c) the
respondents’ answers would not be shared with anyone else (see Appendix C).
Instrument Pilot
The pilot consisted of administering the revised pretest instruments in full
telephone questionnaire form to ten computer troubleshooters in another company (not
the research site). The pilot group consisted of troubleshooters organized into a selfdirected team. These troubleshooters were not actually computer operators, but were the
30
technical support people for a number of software products. When customers called the
help desk with difficult software problems, debugging tasks were assigned to these
troubleshooters.
Hence, their job functions were somewhat similar to those of the
operators at the research site, providing a realistic pilot test for the instruments. The
researcher made notes of respondent difficulties with, or comments about, the individual
items. For example, if the respondent paused before answering, the researcher wrote
“pause” by the question. These notes were then analyzed to identify items needing
rework. To further identify rework items, those items were identified whose average
score varied the most from the average of all item scores for the construct. Reliability
analysis of each construct also identified rework items. Table 2 displays reliability results
from the pilot.
From the pilot, a number of changes were made.
First, several items were
reworded slightly (e.g., in Job Significance, Task Identity). Second, since respondents
seemed to have trouble with the first set of questions of the questionnaire (i.e., Job
Significance was the first set in the Job Characteristics series), the researcher placed the
most reliable Job Characteristics construct (Skill Variety) at the beginning of the
questionnaire. Third, items were substituted in some instruments (e.g., Accountability),
in order to improve reliability. The instrument was administered to eighty-six computer
operators at the research site.
Table 2 Pilot Reliability Analysis
31
Construct
Liking
Trusting Belief-Benevolence
Trusting Belief-Competence
Trusting Intention
System Trust
Feedback
Autonomy
Accountability
Skill Variety
Job Significance
Task Identity
Job Feedback
Experienced Meaningfulness
Knowledge of Results
Felt Responsibility
Job Satisfaction
Growth Need Strength
Organizational Commitment
Intrinsic Motivation-Self-Esteem
Intrinsic Motivation-Enjoyment
Cronbach’s
Alpha
.95
.92
.99
.99
.60
.87
.74
.66
.72
.14
.48
.98
.60
.81
.66
.63
.60
.78
.79
.93
CONSTRUCT VALIDATION RESULTS
The psychometric tests consisted of internal consistency reliability and simple
construct validity tests on the data from the eighty-six questionnaires. Nomological
validity was done for the trust constructs and mono-method bias was tested for the
Autonomy construct.
Reliability. Cronbach’s alpha (Cronbach, 1951) was used as the indicator of
internal consistency reliability. Reliability refers to the ratio of “true” variance to total
variance in a set of measures obtained from a respondent (Schwab, 1980). True variance
32
means systematic, error-free variance. While the true variance can’t be calculated, it can
be estimated by assuming that the available items are a random sample of a population of
items that would give a true measure of the variable if all the items were answered
(Cronbach, 1951). Reliability is a necessary, but not a sufficient, condition for construct
validity. This is because unreliable measures cannot be depended upon to consistently
reflect the same conceptual meaning. Table 3 shows that nearly all the constructs were
unidimensional at, or almost at, the 0.70 level generally endorsed (Nunnally, 1978).
Only Job Significance and Growth Need Strength (GNS) did not come close to
0.70. Both constructs appeared to have reached a ceiling effect, with very low variances.
On seven point scales, GNS and Job Significance items had average means of 6.82 and
6.77, respectively. Their standard deviations were 0.36 and 0.45. Most other constructs
had standard deviations above 1.0. Because of these high means and low standard
deviations, the researcher decided to use GNS and Job Significance as unitary constructs,
even though their internal consistency score was low.
Descriptive statistics for all
constructs are shown in Appendix I.
Table 3 Construct Level Cronbach’s Alpha Reliabilities
(n=86 research site respondents; number of items in parentheses
CONTROLS:
Autonomy Granted (4 items)
Micro Management (4)
Feedback (4)
Job Accountability (2*)
Alpha
0.79
0.85
0.98
0.69
RELATIONSHIPS:
Liking (4 items)
Trusting Intention (4)
0.94
0.99
JOB CHARACTERISTICS:
(not included elsewhere)
Skill Variety (3 items)
Job Significance (3*)
Task Identity (3)
Job Feedback (3)
Knowledge of Results (3)
Growth Need Strength (3)
Felt Responsibility (3)
Alpha
0.67
0.62
0.77
0.85
0.92
0.44
0.73
33
Trusting Belief--Benevolence (4)
0.97
Trusting Belief--Competence (3*)
0.95
MOTIVATION:
Experienced Meaningfulness (3*)
Organizational Commitment (4)
Intrinsic Motivation--Enjoyment (4)
Intrinsic Motivation--Self-Esteem (4)
Job Satisfaction (3*)
0.92
0.84
0.92
0.77
0.86
CONTRIBUTION TO TEAM
EFFECTIVENESS:**
Contrib. to Overall Team Effectiveness (2)
Contrib. to Coordination Effectiveness (2)
Contrib. to Communication Effectivns. (2)
Contrib. to Conflict Resolution (2)
0.70
0.71
0.68
0.67
OTHER TRUST-RELATED:
System Trust (4)
Dispositional Trust (3)
0.94
0.91
*Questionnaire contained additional items that did not highly correlate with items in the constructs shown.
**Note: These alphas are probably deflated because two different methods were used to collect them.
Construct Validity. Adequate construct validity means that the measures of a
variable correspond closely to the conceptual meaning of the variable (Schwab, 1980).
Construct validity addresses “the approximate validity with which we can make
generalizations about higher-order constructs from research operations” (Cook &
Campbell, 1979: 38). This is important because no true implications can be drawn at the
construct level from measures that do not adequately represent the meaning of the
construct. Reliability is generally considered a necessary, but not sufficient, condition for
construct validity. Further evidence is required, in terms of convergent and discriminant
validity.
Convergent validity means the extent to which responses from different
measurements of the same construct are highly correlated (Schwab, 1980). Discriminant
validity means the extent to which a construct is distinct from other constructs.
Therefore, discriminant validity means one construct’s measurements should be distinct
from measurements of other constructs.
34
Convergent and discriminant construct validity were demonstrated by pairwise
intercorrelation matrices of constructs within each high level concept (Appendix H). For
example, the first pair contrasts correlations within and between Autonomy and
Micromanagement, two Controls constructs.
The intra-construct correlations are
consistently higher than the correlations between constructs. Appendix H reports the
intra- and inter-correlation averages, and highlights intercorrelations that exceed the
smallest intra-construct correlation. This analysis was done to show, in the simplest
possible fashion, how the constructs hold together internally while being distinguished
from similar constructs, much as a factor analysis would do. This method was chosen
over factor analysis because factor analysis is based on correlation analysis, but uses
somewhat arbitrary cut-off values that may obscure what the actual correlations indicate.
These results show that each construct is internally cohesive (convergent validity) and
differs from similar constructs (discriminant validity).
This is a strong test of
discriminant validity, since one would expect high correlations among four different types
of Motivation, for example.
Of the Controls constructs, only Accountability shows construct validity problems
(see bold highlighting of items in Appendix H). However, when item 4 is removed, the
construct demonstrated discriminant validity.
For hypothesis testing, the researcher
dropped item 4 and treated Accountability as a two item construct.3
The resulting
reliability improved from 0.65 to 0.69 when this was done. Among the Relationship
constructs, Liking, Trusting Intention, and Trusting Belief-Benevolence had high
3
Item two had already been removed in pilot testing.
35
intercorrelations with each other.
However, the average intracorrelations were
consistently higher than the average inter-correlations, providing evidence that these
constructs can be distinguished. These constructs were also kept separate at this point
because of the theoretical basis for treating them as separate constructs (McKnight &
Chervany, 1996; McKnight, Cummings & Chervany, 1996).
The intercorrelation
matrices for the Motivation and Job Characteristics constructs provide significant
evidence that these are unitary constructs.
Nomological Validity. Because System Trust is a new operationalization and the
other trust constructs are re-formulations, nomological validity of these constructs was
analyzed. Nomological validity means that one assesses (theoretically and empirically)
the relationships between a construct and other constructs (Schwab, 1980).
Hence,
nomological validity is also tested in later chapters, when the hypotheses are tested. In
this chapter, the researcher looked at nomological validity in terms of the relationships
among System Trust and other trust-related variables.
McKnight & Chervany (1996) and McKnight, Cummings & Chervany (1997)
hypothesized the relationships among trust variables shown in Figure 11. This theory has
not previously been tested, so all the links are tentative. Trusting Belief-Benevolence and
Trusting Belief-Competence were selected for this study because of their importance to
the trust literature in general (e.g., Barber, 1983; Mayer, Davis & Schoorman, 1995) and
the technical worker specifically (Crozier, 1964).
Figure 11
Nomological Network for Trust Constructs
36
System Trust
Dispositional
Trust
Trusting Intention
Trusting Belief-Benevolence
Trusting Belief-Competence
Other empirical work has shown that Trusting Beliefs are related to Trusting
Intention (e.g., Dobing, 1993). Tests of the links from Dispositional Trust have had
mixed results (e.g., Johnson-George & Swap, 1982), so these are shown as weak links
using dotted lines. In addition to the relationships shown in Figure 11, Liking should be
highly related with the Trusting Beliefs and Trusting Intention, but less highly related
with System- and Dispositional Trust (since the latter are not social constructs). Liking
was selected because it has traditionally been an important interpersonal variable that
generalizes much of the emotional tie one person has for another (Rubin, 1973). Table 4
shows the correlations among these variables.
Table 4 Intercorrelations of Trust Constructs and Liking
(correlation / [significance])
Trusting
Intention
Trusting
Intention
1.0
Trusting
BeliefBenevolence
.85/[.000]
Trusting
BeliefBenevolence
Trusting
BeliefCompetence
System
Trust
Dispositional
Trust
Liking
1.0
37
Trusting
BeliefCompetence
System
Trust
.75/[.000]
.79/[.000]
1.0
.51/[.000]
.59/[.000]
.42/[.000]
1.0
Dispositional
Trust
.18/[.049]
.05/[.340]
.04/[.345]
.16/[.066]
1.0
Liking
.82/[.000]
.80/[.000]
.83/[.000]
.42/[.000]
.15/[.087]
In general, the results support nomological validity.
1.0
System Trust and the
Trusting Beliefs are highly correlated with Trusting Intention, as expected. Dispositional
Trust is correlated with System Trust at p=.066. However, instead of being related with
Trusting Beliefs, Dispositional Trust is related directly (at p=.049) to Trusting Intention.
This is a little surprising, and indicates that Dispositional Trust can be a determinant of
one’s willingness to depend on the other party (Trusting Intention) irrespective of one’s
Trusting Beliefs in that party.
The relationships between Liking and the trust constructs are as expected, in that
Liking is highly related to the Trusting Beliefs and Trusting Intention, but very little
related with Dispositional Trust. However, the fact that Liking is significantly related
with System Trust indicates that the study’s operationalization of System Trust ties it
more closely to feelings about one’s supervisor than the theory projects. This is probably
because System Trust was operationalized to represent structures supporting fairness in
one’s environment, and the supervisor is one of the prime administrators of fairness in the
work environment. The high correlations between System Trust and the Trusting Beliefs
38
constructs may be explained in the same way. So while the theoretical System Trust
variable is quite impersonal, the operationalization of it is quite closely related with
operator feelings regarding their supervisor. Note that System Trust does not equate to
fairness or equity, such as constructs in the organizational justice literature (e.g.,
Greenberg, 1993), but is the belief that the workplace has features that encourage
fairness.
Mono-method bias. For purposes of this study, mono-method bias refers to the
use of a single informant type: the CSO or the supervisor. Though mono-method bias
has been pointed out as a potential problem with JCM research (Roberts & Glick, 1981),
most researchers have accepted it as a given, since employees are the best informants of
their own beliefs and feelings about their own job characteristics and related motivation.
Although this argument has significant merit, the laissez-faire approach of accepting it
fully is not completely satisfying. Thus, two separate efforts addressed mono-method
bias in the study.
First, the Contribution to Team Effectiveness and Individual
Performance dependent variables used supervisors as informants, while the JCM,
Controls, Relationships, System Trust, and Motivation variables had CSOs as informants.
This means that tests of links between constructs gathered from these two different
sources constituted stronger tests. However, it also means that tests of links within
informant constituted relatively weaker tests. The relative weakness or strength of these
tests is demonstrated by the very high correlation (see Chapter Four) between the
supervisor variables Contribution to Team Effectiveness and Individual Performance
versus the weak correlation between CSO-informed variables and Individual
39
Performance. This result emphasizes the large difference a different informant can make.
But it leaves unanswered the question of which informant’s view is most correct.
Second, the researcher tested the results when one variable (Autonomy) was
measured with both methods. Table 5 displays a variation of the Campbell & Fiske
(1959) multitrait-multimethod analysis (cf. Henderson & Lee, 1992). The informant is
represented as a method, while the item is represented as a trait.
Note that the
correlations within methods (in bold) are generally higher than the correlations between
methods. Average correlations are also shown, as in the pairwise matrices of Appendix
H. From Table 5, the two methods appear to be related (based on the cross-correlations),
but also appear to be somewhat separate constructs from each other (based on higher
within-method correlations). Exploring further, we did Cronbach’s Alpha measures for
each of the methods separately, and one that joined them. The result was that joining
them raised the alpha from .75 (Supervisor informant) or .79 (CSO informant) to .80
(combined).
Since joining the constructs together as one did not degrade internal
consistency, they are probably not two distinct constructs. The average intercorrelation of
Table 5 items overall is .41. This is a significant correlation, and is higher than the
average correlation among Motivation constructs (.36), which was treated as one second
order construct. Based on this analysis, the Autonomy items from both informants could
effectively form one construct.
Table 5: Mono-Trait, Mono-Method Analysis for Autonomy
40
(Methods)
Method 1:
Supervisor
Report
Method 2:
Operator
Report
(Traits)
Item 1
Item 2
Item 1
Item 2
Item 3
Item 4
Method 1:
Method 2:
Supervisor Report
Operator Report
Item 1
Item 2
Item 1 Item 2
Item 3
Item 4
1.00
Means: Supervr. Operator
Cross
1.00
.65
.55
.29
.65
.14
.26
.30
.42
.23
.36
.25
.32
1.00
.74
.41
.39
Overall
.41
1.00
.60
.51
1.00
.62
1.00
A look at the wording used for each informant (Appendix B) revealed that the
Supervisor question was worded in more general terms than were the Operator questions.
Each of the four Operator questions was worded specifically and differed slightly from
each other. The differences between these constructs is also accentuated in that while the
Operator measure of Autonomy used a seven-point agree/disagree/neutral format only,
the Supervisor measure used both the seven-point scale and a one-to-N ranking of the
employee against all other employees in the supervisor group (see Appendix B, B.
Questions Asked Supervisors).
These wording and scaling differences probably
accentuate the level of overall method bias that exists.
Since CTE and Individual Performance used the same informant as the
Supervisor-reported Autonomy construct, a correlation was run among these constructs
and Operator-reported Autonomy, in order to isolate how much difference the informant
method would make to the correlation. Table 6 shows the result. Supervisor-reported
Autonomy was correlated with CTE and Individual Performance almost as strongly (.72,
.79) as CTE and Performance are with each other (.84), while CSO-reported Autonomy
41
was only correlated with CTE at .37 and Individual Performance at .30. Similarly (Table
7), Operator-reported Autonomy was correlated with other (Operator-reported) job
characteristics and CPS variables at an average of r = .20, while Supervisor-reported
Autonomy was correlated with the same job characteristics and CPS variables at only an
average of r =.10. So the autonomy construct was, on average, twice as highly correlated
with the JCM variable if it had the same informant as the JCM variable. This is another
indication that some level of method bias exists, and needs to be addressed in future
research of this kind. To see if using the combined Autonomy variable mattered to the
prediction of Motivation, the six items were merged into one variable.
Combined
Autonomy only correlated with Motivation at r = .069, which means combined Autonomy
did not predict Motivation any better than did CSO-reported Autonomy (see Chapter Six).
Table 6 Correlations among CTE, Performance, and Two Autonomy Types
Supv-reported
Autonomy
Supvreported
CSO-reported
Autonomy
Autonomy
1.00
Contribution to
Team
Effectiveness
CSO-reported
Autonomy
.40
1.00
Contribution to
Team
Effectiveness
Individual
Performance
.72
.37
1.00
.79
.30
.84
Individual
Performance
1.00
42
Table 7 Correlations among JCM Variables and Two Autonomy Types
(CSO-reported) JCM variables
Job Significance
Job Identity
Job Feedback
Skill Variety
Experienced Meaningfulness
Knowledge of Results
Supv-reported Autonomy
-.03
-.01
.16
.22
-.05
.14
|Mean| = .10
CSO-reported Autonomy
-.03
.33
.35
.16
.00
.32
|Mean| = .20
In summary, mono-method (common informant) bias is a concern that was
addressed for equations predicting CTE and Performance, but only partly tested for
equations predicting Motivation, CPS, and Work Outcomes. The testing of Autonomy
for mono-method bias revealed some differences between methods. Overall, however,
the items from the two methods can be successfully merged into a single construct,
lending confidence to the results of this study. Just as important, this study was more
concerned with the operator’s own perceptions of their JCM, Motivation, Relationships,
and System Trust constructs. It is highly doubtful that the supervisor can accurately
report on what the operator perceives and feels about these constructs. Hence, for the
purposes of this study, the CSO-reported data was justifiably used for these constructs.
First-Order versus Second-Order Concept Formation. Once the reliability and
validity of the constructs were tested, the items were summed to their respective
constructs. Because the Figures 3 and 5 models are shown at two levels—second-order
(concept) level (e.g., Controls) and the first-order (construct) level that defines the
concept operationally (e.g., Feedback, Autonomy)--the researcher needed to determine at
which level to test the model. An analysis was performed similar to that done to analyze
43
second order factor models (e.g., Hunter & Gerbing, 1982; Kumar & Dillon, 1990). It
was decided that those concepts (e.g., Controls) whose construct components (e.g.,
Feedback, Autonomy, Accountability, and Micromanagement) were internally consistent
and had convergent validity as a set would be tested at the second-order (concept) level.
This was judged by performing reliability and intercorrelation matrix analyses on the
constructs within each high level category. The first rule for use of the second-order
concept was for the concepts to be internally consistent at the Cronbach’s alpha  .70
level, just as for the operational constructs. For this test, each construct was treated like
an item and a reliability analysis was performed (see Table 8) for the set of constructs.
The second rule was for the concepts to display the same kind of convergent and
discriminant validity among similar concepts as was demonstrated in Appendix H for
items.
For this test, Appendix J displays the intercorrelation matrix analysis. The
reliability analysis demonstrated adequate support for treating Relationships, Motivation
and Contribution to Team Effectiveness as unitary constructs, with alphas of .94, .73, and
.97, respectively. By comparing internal to cross-correlations, Appendix J shows that
Relationships, Motivation and Contribution to Team Effectiveness are internally cohesive
and separate from the other concepts. In contrast, based on the same intercorrelation
analysis, Controls, Job Characteristics, Critical Psychological States, and Work Outcomes
are not unitary. Further they had alphas of .52, .44, .48, and .22, respectively. Hence, the
researcher chose to test hypotheses using Relationships, Motivation and CTE as unitary
concepts, while Controls, Job Characteristics, CPS, and Work Outcomes were tested at
the construct level.
44
HYPOTHESIS TESTING METHODOLOGY
To test the hypotheses, correlation and regression analyses were done, using
SPSS. In some cases, qualitative analysis supplemented the correlational analyses, as
detailed in Chapters 3-6. The model relationships were tested with regression. The
coefficient beta used by regression gives a clear interpretation of the magnitude of the
effect of each independent variable on the dependent variable. All hypotheses are stated
and tested at the individual operator level of analysis.
Specific hypothesis testing
techniques will be discussed in detail in succeeding chapters.
Table 8 Reliabilities for High Level (Second Order) Concepts
(N=86)
Model Concepts
# Items
4
Alpha
.52
Relationships5
4
.94
Motivation6
5
.73
Contribution to Team
Effectiveness7
4
.97
Job Characteristics8
5
.44
Critical Psychological States9
3
.48
Controls
4
4
Item Intercorrelation
.31
.35 .12
.18 .24 .37
.82
.80 .85
.83 .75 .79
.22
.49 .44
.32 .22 .41
.51 .47 .34 .17
.86
.85 .85
.90 .92 .86
.16
.45 -.09
-.15 .14 .09
.16 -.03 .35 .33
.40
Controls = Autonomy, Feedback, Accountability, and Micromanagement
Relationships = Liking, Trusting Intention, Trusting Belief-Benevolence, Trusting Belief-Competence
6
Motivation = Experienced Work Meaningfulness, Organizational Commitment, Intrinsic MotivationEnjoyment, Intrinsic Motivation-Self-Esteem, Job Satisfaction
7
Contribution to Team Effectiveness = Contribution to Team Coordination Effectiveness, Contribution to
Communication, Contribution to Conflict Resolution, Contribution to Overall Team Effectiveness
8
Job Characteristics = Skill Variety, Job Significance, Job Feedback, Job Identity, Autonomy
45
5
Work Outcomes
10
3
.22
.24 .34
.10
.17 .12
In general, the hypotheses in this study were tested at the alpha = .05 level. This
level is appropriate for three reasons.
First, this is a field study, not a controlled
experiment. Because of the complexity of things happening in a field setting, links
between variables will be harder to find than in an experiment, in which alpha = .01 may
be appropriate. Second, this study covers virgin conceptual territory through new, and
sometimes speculative, hypotheses. Third, the use of an alpha of .01 or .001, while
decreasing the chance of a Type I error, severely increases the chance of a Type II error.
That is, using a very small alpha decreases the chance that researchers will think they
have found an effect when they didn’t (Type I error). But it greatly increases the chance
that one will think there is no effect when there really is one (Type II error), per Cohen
(1988), who recommends a moderate choice of alpha for significance testing. Cohen
illustrated this using an alpha of .001 for a given scenario. In Cohen’s scenario, the .001
significance level for Type I errors implied that the Type II rate is .90. The ratio of
importance between the two is .90 divided by .001, or 900 to one. Hence, this assumes
that “mistakenly rejecting the null hypothesis…is 900 times more serious than mistakenly
accepting it” (Cohen, 1988: 5). Cohen gave a more moderate scenario that used an alpha
of .05, in which the ratio was .20 divided by .05, or four to one. Using alpha = .05
9
Critical Psychological States = Experienced Work Meaningfulness, Felt Responsibility, Knowledge of
Results
10
Work Outcomes = Job Satisfaction, Individual Performance, Intrinsic Motivation-Self-Esteem
46
reflects this Type II moderation while still providing a challenging alpha for study of new
phenomena in a field setting.
Regression analysis assumes that multicollinearity and non-constant variance are
not present (Neter, Wasserman & Kutner, 1990). Multi-collinearity of the independent
variables was checked for each regression using the variance inflation factor (VIF)
statistic in SPSS. The few regressions found to have multi-collinearity are reported in the
Results sections of Chapters Three through Six. Non-constant variance was analyzed
through a test devised by Weisberg (1985). None of the study’s equations had the
problem of non-constant variance. These tests provided evidence that the regression
assumptions were met.
To test the model’s moderation structure, regression was used as outlined by
Baron & Kenny (1986). For moderation (X=independent variable, Y=dependent variable,
Z=moderator variable), the study assumes linear effects of X on Y. So Y is regressed on
X, Z and XZ. Moderator effects were considered to be indicated if XZ is significant
while X and Z are controlled.
The interaction terms were created by multiplying
standardized terms together, as recommended by Aiken & West (1991), in order to
minimize multicollinearity.
Because moderation effects are much more difficult to demonstrate for field data
than for laboratory data (McClelland & Judd, 1993), an additional moderation analysis
was done for the Management Controls / Relationships model. The data were split into
two groups reflecting respondents with high- and low-Relationships scores. For example,
in the high Relationships conditions, a means test was performed to see whether
47
Motivation was higher or lower for those in low or high Controls (e.g., Autonomy)
groups. Two-by-two tables were constructed to show the Motivation means under the
four Relationships (HI-LO) and Controls (HI-LO) conditions.
An interaction was
considered to have taken place when, under conditions of HI Relationships, the effect of
Controls was high; yet under conditions of LO Relationships, the effects of Controls was
low. If the effects of Controls did not significantly differ under the two Relationships
levels, then there was no interaction.
Cook & Campbell (1979) said that researchers who eliminate plausible
alternatives to the constructs in their model increase the likelihood that they have found a
valid relationship among constructs (‘internal validity’). To improve confidence in the
internal validity of this study’s findings, a number of plausible alternatives were added to
the models tested to see if they added predictive power. These included the normal
demographic variables (e.g., age, gender, and education) and several others suited as
alternatives to the constructs used in the study (see Appendix B). These were entered into
the JCM- and Management Controls-related models to see if they improved the models’
prediction of the motivation-related dependent variables.
RESEARCH SITE FOR THE STUDY
This study intensively researches one field site, because:
 the literature lacks a reasonably complete understanding of the critical
computer systems phenomenon;
48
 understanding the detailed context in which the operator phenomenon
exists will improve whatever knowledge exists of how the
phenomenon really works (Kaplan & Duchon, 1988; Lending,
1996; Van Maanen, 1979b);
 logically, a more complete understanding would have the effect of
generating more--and better focused--research; and, therefore,
 studying a single site intensively would enable this deeper level of initial
understanding (Mintzberg, 1979), laying a firmer foundation for
future research.
A general description of the research site is presented below. The details of the
research site are discussed throughout Chapters 3-6 in order both to provide a context for
the study and to support hypotheses.
The description of the research site supplied vital data about the context in which
the critical systems phenomenon takes place, aiding the researcher in asking the right
questions.
Lending (1996), for example, found that the organizational context was
important in her study of systems analyst use of CASE tools. The organizational context
can be a major factor in how work is accomplished. For example, in stressful jobs like
nuclear plant operation or air traffic control, the context can influence individual worker
ability to accomplish the work (Mowday & Sutton, 1993). Context is also important
because questionnaire data can best be understood in light of the specific environment of
the organization (Mintzberg, 1979). The contextual environment includes both the nature
49
of the tasks done by the workers and the manner in which the company is run by
management. It also includes the workers’ norms and customs and assumptions. Because
time was limited, the researcher did not do a complete ethnographic study. Therefore, the
description of these environmental factors is incomplete. The site description effort was
guided by Barley (1990), Denzin (1978, 1989), Lincoln & Guba (1985), Lofland (1971),
Sanjek (1990), and Van Maanen (1979a,b).
The research site studied was the computer operations department (comprised of
approximately 100 hardware and software operators and their nine supervisors), which
operates a large host computer system in a firm to be called XYZCo. “Host” computer
system means that thousands of workers throughout XYZCo’s industry continually use
the system for daily business transactions--not just employees within the host system’s
parent corporation. XYZCo employees take pride in the high level of system availability-better than 99.9% up-time at the computer site. The operations department also operates
several other smaller systems. The largest of these is the test/development system, used
by the company to test new and modified applications software, most of which is
developed in-house. Finally, one subgroup within the organization operates another
company’s system. Those who support the main system are separated into hardware and
software groups. Hardware operators are assigned to a single shift, but the software
operators for the largest of the systems rotate among the three shifts. This means that the
set of people watching for system problems on a given shift differs from day to day.
Specific duties (e.g., watching monitors, handling utilities) are also shifted among
50
hardware and software operators on a given shift. But hardware operators do not handle
software duties, and software operators do not handle hardware duties.
Because the system is used internationally, operators try to keep the system fully
available not only on first shift, but also during second and third shifts. Over the past ten
or fifteen years, system unavailability due to scheduled maintenance (on second or third
shift) has been greatly reduced. Unavailability due to system outages has also been
significantly reduced.
Nearly every year for the past ten years, the average system
availability has improved over the prior year.
XYZCo is very customer-conscious; it tries to please every user of the system.
Hence, the operators take pride in their role in keeping the system available. The
computer division of XYZCo incorporated Total Quality Management practices in 1991.
A quality improvement team meets after each operator-caused outage to analyze the root
causes of the outage and to discuss what can be done to prevent this type of outage from
happening again.
XYZCo has traditionally provided operators a favorable workplace. CSOs have
typically stayed in their positions for many years.
Some have transferred due to
promotions or lateral opportunities, both of which have been adequate or even plentiful.
During Phase I, the researcher noted a (then) recent management decision to
automate many of the hardware operator functions. The researcher expected this action to
increase the level of job insecurity among hardware operators.
In addition, a new
management incentive system was installed as Phase I began that encouraged stricter cost
controls by management. The incentive system provided operators financial bonuses,
51
which had previously only been provided to managers. The awarding of these bonuses
was contingent upon the profitability of the overall organization.
The role of the critical system operator (CSO) is interesting and challenging.
Depending on the situation, the CSO plays three primary roles, which resemble the roles
of a detective, a doctor and a fire fighter. In the detective role, the CSO monitors the
computer system, proactively looking for problems that could potentially harm the
system. In this role, the CSO uses both the system’s monitoring consoles and system
“dumps” that report small or large internal computer events. CSOs may also interface
with system help desk personnel to try to head off individual-level problems that could be
symptoms of larger system problems. The curious, persistent, somewhat suspicious CSO
is best suited to the detective’s task.
When the system incurs a serious, but unknown problem, the CSO becomes like a
frenzied doctor trying to diagnose a patient who will die within minutes if not properly
treated. In the doctor role, CSOs try to very quickly diagnose the cause of the problem.
This task can involve interpretation of “dumps,” system monitor cues, or help desk news.
In those cases in which a new problem arises (Weick, 1990), the diagnosis task requires
intensely imaginative brainstorming. In this highly unstructured situation, some who are
not as good at everyday tasks excel. Some CSOs can imagine the step-by-step process
the computer takes as it undergoes various problems (Weick & Roberts, 1993).
Describing the creative scenario-generating prowess of one of the software operators, a
co-worker said, “[name] can BE the computer.”
The abstract thinker with a great
imagination does the diagnosis task best.
52
Once the root problem is confidently diagnosed, the CSO becomes like a fire
fighter. When the building is burning, every second counts. Realizing the urgency of
restoring the system which thousands are impatiently waiting to use, the CSO takes rapid,
but calculated, actions. The proactive, quick-to-act, experience-assured CSO performs
the fire fighting task best.
It should be noted that the CSO job often involves technological equivoque,
which Weick (1990: 2) defined as “something that admits of several possible or plausible
interpretations and therefore can be esoteric, subject to misunderstandings, uncertain,
complex, and recondite.” Weick said that technological equivoque occurs for three
reasons. First, because stochastic, random events occur to cause the system problems.
Second, Weick explained that the randomness of these events causes worse problems
when the event is not understood. Hence, a large store of knowledge and skill is required
among CSOs and the technical specialists they must immediately access during a system
outage. Third, since the internal workings of these critical systems are obscure and hard
to visualize (Brooks, 1987; Weick, 1990), operators must deal with abstract, almost
incomprehensible, events. Because of the nature of critical systems, CSOs need an
aptitude for “high attention to work processes, rapid response to emergencies, ability to
stay calm in tense environments, and early detection of malfunctions” (Weick, 1990: 13).
53
CHAPTER THREE:
NATURE OF THE CRITICAL SYSTEMS OPERATOR JOB
Ch
Prop:
Content or Model
2
--
Methodology and Construct Validation
3
1
4
2, 3
Nature of the
Critical Systems
Operator Job
Job Characteristics
High Levels of
Motivation
Growth Need Strength
Critical
Psychological
States (CPS)
Relationships
5
Work
Outcomes
System Trust
4, 5
Incentive
Controls
Motivational
Effect
Relationships
6
4, 5
Other
Controls
Relationships
7
--
Motivation
Motivational
Outcomes
System Trust
Contributions, Limitations, and Future Research
54
Chapter Three first reviews the nature of critical systems from the research
literature. From this review and from Phase I data, hypotheses are developed. The
methods used to test the specific hypotheses are outlined. The results are then presented
and discussed.
THEORY BUILDING
The Nature and Importance of the Critical Computer System
To understand the CSO job, one must first understand what critical computer
systems are like. The critical nature, importance, and complexity of these systems is now
discussed.
Critical Nature. Some computer systems are so critical to the operations of an
organization that when they become unavailable, they create major problems. One class
of such systems is the transaction processing system (TPS). "A transaction processing
system is a computerized system that performs and records the daily routine transactions
necessary to conduct the business..." (Laudon & Laudon, 1995: 37) As an example of
system criticality, Lucas (1975: 16) said that “...interruption of on-line service in a
reservation system can drastically affect the functioning of other departments in the
organization.”
Now that airlines share their reservation systems with travel agents,
system downtime can interrupt thousands of travel businesses across the globe. As
another example, automated bank teller systems can be critical for conducting personal
business (e.g., withdrawing money from a bank). In terms of the need for continuous
operation (Weick, 1990), the critical computer system resembles the critical nature of
nuclear power plants, air traffic control systems, or the aircraft carrier. Hence, critical
55
computer systems can be classified as a member of the general family of critical
technology systems (Perrow, 1984; Rasmussen, 1986; Weick & Roberts, 1993).
However, the critical computer systems studied here did not have life-threatening
consequences.
Instead, their consequences consisted of work stoppages for large
numbers of people and significant customer inconveniences, both of which could hurt
those businesses that depended on the systems.
Importance. When a TPS fails, a company may lose sales, upset important
customers, or decrease productivity for itself and its customers. Upsetting customers is
becoming dangerous as more and more firms compete on the basis of service quality
(Schlesinger & Heskett, 1991). The decreased productivity caused by TPS failures is
costly--and ironic, since companies use such systems to try to increase productivity
(Drucker, 1991). But TPS failures are even more harmful because they often cause total
work stoppages for those dependent on the system (Weick, 1990; Zuboff, 1985). Without
the use of its TPS, a company may not be able to transact its daily business. In the
extreme case, "...TPS failure for a few hours can spell the demise of a firm and perhaps
other firms linked to it" (Laudon & Laudon, 1995: 37). An interesting parallel to the
critical nature of the TPS failure was found in Crozier’s (1964) account of plant machine
stoppages. Crozier said these stoppages were crucial because: a) they are unpredictable;
b) impersonal rules can’t be applied to fix them; and c) only skilled maintenance people
can cope with them.
Internet-related computer systems are also becoming more critical to business.
For example, well over 1 million people use America Online (AOL) to conduct business
56
(Reuters News Service, 1996). These people have suffered such business interruptions as
a nineteen hour outage on August 7, 1996 (Wall Street Journal, 1996). Because of this
and subsequent AOL availability problems, the Wall Street Journal online edition hosted
a discussion group regarding AOL problems for several months during 1996 and 1997,
further accentuating the public relations problems related to the AOL outages. Another
recent example of the business consequences resulting from system failure involved a
small Internet provider used by a number of firms to transact business (personal
communication with a subscriber). During the summer of 1996, this provider incurred a
series of long outages.
A number of businesses, that depended on the network’s
continuous availability to be able to conduct daily operations, were severely hurt. Soon,
this internet provider lost about 10% of its customer base and a higher percentage of its
revenues.
Complexity.
Like all large computer systems, critical systems are generally
complex. As Brooks (1987: 11) said, “Software entities are more complex for their size
than perhaps any other human construct because no two parts are alike…In this respect,
software systems differ profoundly from computers, buildings, or automobiles, where
repeated elements abound.” Complexities of systems are compounded when: a) systems
interact with each other, and b) systems are operated by automated systems (Zuboff,
1985, 1988). Weick (1990) posited that technology can be very hard to control when it is
comprised of many automated, interacting parts.
Complexity places a burden of
escalating cognitive demands on operators that can lead to operator errors.
Interactive effects between complexity and criticality are also possible. Critical
57
systems have the added burden of having many people dependent upon them. Zuboff
(1985: 13-14) warned: “Such dependence on automation means that the problems of
reliability will be critical. Automatic controls that can provide fail-safe measures to
guard against systems errors will be needed, since the ripple effects of such failures can
escalate with alarming speed in a highly automatic and interdependent machine system.”
Management Information Systems Literature
The MIS literature contains several research streams that relate to computer
operations. These bodies of research helped inform Phase I exploration of the critical
computer systems operators phenomenon. A large body of literature (e.g., Bailey, 1982;
Galitz, 1980; Norman, 1983) addresses what might be called “user interface
requirements,” (Davis & Olson, 1985) or the engineering of computers to match “human
factors” (Shneiderman, 1980). User interface research addresses an important issue: how
to design systems with which humans can effectively and efficiently interact.
This
literature stresses the importance of designing appropriate computer interfaces for
operators in order to minimize errors at the operator console.
Davis & Olson (1985) mentioned three types of controls to improve information
system availability:
physical facilities control (to prevent the risk of access to the
computer site by undesirables), terminal access control (to protect against illegal access),
and backup and recovery controls (to recover from errors). Further, Davis & Olson
discussed procedures and duties performed by information systems personnel to monitor
system quality in terms of errors, downtime, reruns, and application repair maintenance.
They also discussed preventive controls (i.e., quality application development and
58
adequate testing) and detective controls (e.g., redundant parity bits). These controls
emphasize ways to protect the system. They encompass both technical and structural
approaches.
DeGreene (1970) discussed the Semi-Automatic Ground Environment (SAGE) air
defense system set up by the Air Force to detect and destroy enemy bomber aircraft. This
system was the “granddaddy” of the electronic control systems (DeGreene, 1970: 12).
However, DeGreene almost exclusively discussed the system in terms of the lessons
learned from developing—not operating--SAGE. Similarly, Lyytinen & Hirschheim’s
(1987) detailed review of system failures reports almost no studies of operations failures.
It does, however, point out the importance of operations in terms of keeping the system
running, because “errors...are hard to pin down and correct” when systems are so
complex (1987: 281).
Perhaps closest to this study’s domain is research in technical support and
computer maintenance. Amit Das (1994) and Brian Pentland (1992) studied the technical
support done at computer user help desks. Das took a problem solving (i.e., Simon,
1981) approach to technical support work, explaining that the failure mode leads to the
types of tasks and problem solving moves used. Pentland’s work described how technical
problem solvers interpret and coordinate (e.g., assign, refer, escalate) the trouble calls
they receive. Das and Pentland showed that helping users is a dynamic process that
requires effective teamwork.
In the area of computer maintenance, Lientz & Swanson (1980) and Swanson &
Beath (1989) have taken a combined technical and organizational approach.
For
59
example, Swanson (1984) has looked at the impact of alternate organizational designs on
software maintenance. Others have also taken up the topic of software maintenance (e.g.,
Slaughter, 1995; Banker, Datar, Kemerer & Zweig, 1993), particularly in terms of the
economics of enhancing and maintaining software.
These studies have primarily
addressed the enhancement of application software, while this study researches large
infrastructure systems that include both the systems software and the related applications,
and focuses on pure maintenance (fixing the system when it breaks).
Couger and Zawacki (1980) surveyed over 1200 computer operations employees.
From analysis of their data, Couger and Zawacki concluded that the computer operator
job is one of the least motivating jobs in industry. By contrast, Couger and Zawacki
found that the job of the system developer was more motivating than the average industry
job.
In sum, the MIS literature is helpful in framing the boundaries for this study, and
introducing the researcher to the complex nature of the task, the opportunity for human
error, and the importance of teamwork in the critical systems operator context. The
Couger & Zawacki (1980) study provided a benchmark view of the traditional operator
job that could be compared with Phase I findings regarding the critical systems operator.
Management of Technology Literature
Perrow (1984) described the Three Mile Island nuclear power disaster as a
“normal accident” that occurs when complex, interacting, and hard-to-visualize systems
combine with human limitations.
Perrow argued (1984: 31) that neither “better
organization..., [nor] more money and resources for better people and equipment” will
60
help reduce the risk of accident in such systems. Per Perrow, only taking steps to
simplify the system will help. This is a structural view of system problems that says
people, interpersonal relationships, and the organization of workers’ roles do not matter:
simplification is the only possible answer.
Rasmussen, like Perrow, studied the operation of nuclear power plants.
Rasmussen focused on the human-system interactions, primarily using cognitive decisionmaking as his research paradigm (e.g., Rasmussen, 1986).
However, some studies
sponsored or reported by Rasmussen allude to the importance of people relationships or
motivation in keeping nuclear plants operating (e.g., Quantanilla, 1987). This alerted the
researcher to the importance of social issues in keeping systems running.
Zuboff (1985, 1988) discussed how computers affect people and management in
terms of controls and power issues. Computers may be used by management to both
control or even replace people, making the computer divisive to the worker-management
relationship. Highlighting power issues between workers and management, Zuboff (1985)
explained that giving information to workers takes away some measure of manager
control, constituting a threat to management. These descriptions helped the researcher be
aware that:
a) automating the critical systems operator function may have some
drawbacks and b) power issues may exist between technicians and their management.
Weick & Roberts (1993) explained how things work on the flight deck of a
military aircraft carrier. The flight deck is extremely complex, interactive and risky, “a
million accidents waiting to happen.” In this context, Weick & Roberts portrayed the
people and interactive roles as the glue that kept things together, “but only a few
61
[accidents] do [happen].” Weick & Roberts posited that the combined problem solving
capability of the high-reliability organization enables it to hold at bay its potentially
hazardous environment. In contrast to Perrow’s belief that the environment limits people,
Weick & Roberts posited that the combination of people, organization, and relationships
is able to overcome almost any degree of structural complexity. Weick & Roberts’ belief
in the ability of teams to handle complex situations inspired the researcher to wonder if
highly alert, motivated, and team-oriented operators might be a key to keeping critical
systems running.
This dissertation research fills an important gap by examining how computer
operator motivation and teamwork function within an organizational and social context.
To some extent, researchers have studied critical computer systems (DeGreene, 1970),
beginning with air defense systems. But they have primarily focused on technical or
human/computer interaction factors. The same focus permeates the analogous literatures
on industrial safety (Hale & Glendon, 1987) and nuclear plant availability (Rasmussen,
1986; Rasmussen, Duncan, & Leplat, 1987), leaving a gap in the area of
social/organizational issues. This gap is important to fill, since people and organizations
are part of the interacting systems components that determine the success of computer
systems (e.g., Lucas, 1975; Lyytinen & Hirschheim, 1987; see Figure 8 in Chapter Two).
Conceptual Model Building
In part, reducing computer outages is complex because computer system
components are themselves complex (Brooks, 1987). This was confirmed at XYZCo.
The main system consisted of many thousands of interacting segments of computer code.
62
Once each week, many new and revised segments were implemented, each carrying the
potential to take the system down through programming errors. Pre-implementation
testing of software was extremely rigorous.
Phase I found that each operator must learn as much as possible to be prepared for
almost any contingency. This is because: a) operators cannot predict the type of system
problem they will next face; b) system repair knowledge is highly specialized and
dispersed among numerous people; c) differing levels of experience, abilities, motivation,
and teamwork exist among operators; and, d) because of rotating shifts among software
operators and the unpredictability of the timing of outages, management cannot predict
which set of operators will be onsite when an outage occurs.
The operators take pride in being key to keeping the system going. But, since
system outages damage so many peoples’ ability to do their job, they also feel intense
pressure from the task to do things right. Most of the hardware and software operators
have college degrees, but they primarily obtained their technical knowledge on the job.
The operators continuously and alertly monitor the health of the system,
proactively investigating anything that could bring the system down. They act on such
cues as messages on the operator console and calls from the help desk. When the system
does crash, they immediately respond in cohesive team fashion to bring the system up
again--hopefully in a matter of a few minutes. One operator said, “...an outage looks very
chaotic. But everybody basically knows what to do...” The researcher found that what one
does during an outage also depended on who else was present and what the outage
symptoms were. Operators reported that adrenaline rushes are common as they fix
63
system problems. Total attention is focused on getting the system running. When asked
about how the presence of management felt in an outage, one interviewee replied,
Respondent: That's tough, because when you are in the middle of an outage, I
think that the pressure is so great that you don't particularly think of it. I don't
particularly care for popcorn, okay? But if you give me a bag of popcorn in
the middle of an outage, I'll eat the whole thing.
Interviewer: Just because you're so nervous?
Respondent: Right. Well, it's not even.. [pause].. It's like energy.
Another XYZCo employee said the operator job is “an adrenaline junkie’s
dream.” While system outages occurred more frequently during the 1980s, they now only
occur about once every two weeks. Between fixes, some team members search for
potential problems in existing software or in hardware or software that will soon be
brought online. Others “tune” the large body of application and system software for
increased efficiency. Still others monitor the utilities that create off-line files for testing
and disaster-recovery storage.
The operators are proud of the fact that the system has historically improved to
over 99% availability.
In many cases, their greatest motivation appears to be the
challenge of keeping the system continuously up and running. In talking with several
employees, this feeling of pride is especially strong among the “old-timers.” One of these
(whom we’ll call Tom), for example, began with the company in the early 1960s, when
64
the system was in its infancy. After working as a software operators for about twenty
years, Tom moved to the applications group in the 1980s. In spite of this job change, he
has maintained a close relationship with the current operators. Tom also spends about
half an hour each day looking for things that might harm the system, much as he did when
in the operator group.
Hypotheses
These findings can be related to the Hackman/Oldham JCM (see Figure 2). These
hypotheses expand upon Proposition 1 (see Chapter One), which stated that CSOs will be
more highly motivated than traditional computer operators. Each job characteristic will
be italicized in the following discussion. Based on the findings at XYZCo, the job of the
critical systems operators appeared to be highly significant to the operators, because the
entire organization and thousands of customers depend on each individual operator to
keep the system running. Arguably, these jobs would be more job significant than the job
of a system developer, since developers typically have fewer continually dependent
constituents. Because so many different activities are possible during outages and periods
of calm, the job appeared to be replete with skill variety. Critical systems operator skill
variety is probably higher than that of system developers, who tend to work on one task
for a longer period of time. Because most CSOs possess significant knowledge and skill,
they appeared to be given much autonomy by management in carrying out their complex
functions.
Because the system itself tells CSOs immediately whether or not they
succeeded in fixing an outage, the task potentially carries high levels of job feedback.
Job feedback should be higher than for system developers, who do not receive feedback
65
as often, and don’t receive complete feedback until their system is tested and
implemented. Given the significant pressure on the critical systems operator and the
length of time it takes to learn the job, CSOs will probably have higher levels of growth
need strength than will systems developers or traditional operators. Therefore:
66
Hypothesis 1: The nature of the critical systems operator job is such that the
levels of Job Significance, Skill Variety, Autonomy, Job Feedback, Growth Need
Strength, and Motivating Potential Score will be significantly higher for XYZCo
operators than was found among traditional:
a) computer operators and b) system
developers in the Couger & Zawacki (1980) study.
Note that systems developers have been found to be highly motivated employees
(e.g., Couger & Zawacki, 1980; Lending, 1986). For example, Couger and Zawacki
found that system developers had an average Motivating Potential Score that was fiftyfive percent higher than that of the computer operator. Hence, the comparison of CSOs to
Couger & Zawacki operators is an easy test, but the comparison of CSOs to Couger &
Zawacki system developers constitutes a difficult test.
Also note that if CSOs are
strongly motivated by job characteristics, they are probably not as strongly motivated by
other factors, such as social relationships. Thus, Hypothesis 1, if true, enables a strong
test of whether Relationships and System Trust add to the JCM’s prediction of motivation
constructs (tested in Chapter Four).
The Lending (1996) study of system developers was included as a comparison
group to help remove the objection that the time period (1980 to 1997) was the major
differentiating factor between the CSO measures and those of the Couger & Zawacki
(1980) computer operator study.
Hackman and Oldham’s Task Identity construct refers to the extent to which
workers see the task as a whole or complete task, as opposed to some component part of
67
an entire task. Since XYZCo workers often get interrupted by outages or new potential
problems to explore, their Task Identity should be relatively low. Therefore:
Hypothesis 2: The nature of the critical systems operator job is such that the
levels of Task Identity will be significantly lower for XYZCo operators than was found
among traditional: a) computer operators and b) system developers in the Couger &
Zawacki (1980) study.
Based on JCM theory, these four job characteristics—job significance, skill
variety, autonomy, and job feedback--should result in high levels of experienced
meaningfulness, felt responsibility, and knowledge of results.
In fact, interviews
indicated that operators feel their job is very meaningful and that they feel keenly their
responsibility to keep the system available. Note that these four will far outweigh the
effects of Job Identity, which is hypothesized to go the other direction. Therefore:
Hypothesis 3: The nature of the critical systems operator job is such that the
levels of Experienced Meaningfulness, Felt Responsibility, and Knowledge of Results
will be significantly higher for XYZCo operators than was found among traditional: a)
computer operators and b) system developers in the Couger & Zawacki (1980) study.
Based on the JCM, the Critical Psychological States will lead to motivational
Work Outcomes. Supporting this theory, Phase I interviews found significant levels of
68
job satisfaction and intrinsic motivation. Therefore:
Hypothesis 4: The nature of the critical systems operator job is such that the
levels of intrinsic motivation and job satisfaction will be significantly higher for XYZCo
operators than was found among traditional:
a) computer operators and b) system
developers in the Couger & Zawacki (1980) study.
To motivate its workers effectively, management should know the extent to which
the workers are motivated by intrinsic factors (i.e., the characteristics of the job) or
extrinsic factors (e.g., incentives) (Steers & Porter, 1977). Hackman and Oldham found
that the worker’s Growth Need Strength moderated the effects of Job Characteristics on
their motivating psychological states (Figure 1). In Phase I interviews, workers were
asked what motivated them to do a good job. In the vast majority of cases, the answers
were intrinsic, rather than extrinsic, in nature. This provided initial evidence that critical
computer systems operators are more intrinsically motivated than extrinsically motivated.
In Phase II (Appendix C, questions 48. and 49.), CSOs were asked to compare their
current level of commitment to work hard for the company to their commitment level
three years previously (or less, if they had less than three years of tenure). While CSOs
probably had trouble remembering their commitment level of three years ago, this
question broadly measured the upward or downward trend in their commitment levels.
The CSOs were then asked why they are more (or less) committed today. Their answers
69
provided qualitative data on what motivates the CSOs to be committed. Because this
question asks about their motivation indirectly, the responses should be less subject to
social desirability bias than would result from a direct question about their motivation.
Therefore:
Hypothesis 5: When asked why they are more (or less) committed to work hard
for the organization today than they were three years ago, most of the reasons CSOs
provide will be intrinsic, rather than extrinsic, in nature.
To the Phase I question of what motivated them to do a good job, some CSO
answers indicated that people relationships, either with supervisors or peers, motivated
them. From this, the researcher projected that a significant portion of the responses to the
question of why the CSO is more/less committed today would involve people
relationships. If over half of the responses are expected to refer to intrinsic factors
(Hypothesis 5), it is reasonable to assume that a significant portion of the remaining
responses would be about twenty percent of the remaining responses, or ten percent of the
total responses. Therefore:
Hypothesis 6: When asked why they are more or less committed to work hard for
the organization today than they were three years ago, greater than ten percent of CSO
responses will indicate that critical systems operators are motivated by people
relationships.
70
Age, grade level, and job security probably affect the worker’s choice between
intrinsic and extrinsic factors. Herzberg (1966) said that what motivates someone is what
they want that they don’t have (Steers & Porter, 1977). Hence, a junior employee is more
likely to be motivated by money or promotions (extrinsic factors) because they are usually
at a lower salary and grade level early in their career. Similarly, the employee with a low
grade level is more likely to choose extrinsic factors like promotion over a challenging
job. Employees with low job security are likely to want job security, or will at least want
to be compensated for the lack of job security by receiving greater compensation or
promotional opportunities. Moreover, based on needs-based motivation theories (e.g.,
Herzberg, 1976; Maslow, 1954), the lack of job security (a low-order need) will direct
operator attention from higher order needs like job satisfaction to lower order needs like
compensation. Therefore:
Hypothesis 7: When asked what motivates them to work hard and do a good job,
those critical systems operators a) who possess a higher grade level, and b) have greater
job security will be more likely to choose intrinsic over extrinsic motivators.
While XYZCo has found that improved technical tools helped, the proactive
teamwork of the highly motivated operations “fire-fighters” has contributed much to
improving uptime. For example, one management person said that the number one factor
for keeping the system up was “lots of teamwork.” In part, this is because the system is
71
so complex and has so many interacting parts that, as one operator said, “There’s too
much for one person alone, for even just a handful of people alone. Everybody has their
part.” Hence, teamwork is essential to CSO performance.
In the CSO setting, teamwork is essential because no one knows what problem is
going to threaten system availability next. Some members of the team specialize in
particular parts of the system (e.g., data base software or system utilities), since no one
can comprehend it all. Thus, diagnosing and fixing the system often requires the onsite
personnel to access by phone a virtual network of experts. Knowing who to call for what
purpose and being quick to respond to a call become incredibly important as the seconds
of downtime tick into minutes. Those interviewed on-site named many people who were
critical to keeping the systems available. The names of those critical came from many
different parts of the organization--from on-site hardware vendor technicians, to system
engineering department gurus who specialized in tape or DASD (Direct Access Storage
Devices), to applications programmers like Tom. In spite of being in a new job in a
different city, Tom still reported getting four or five calls from the software group per
month.
Given how important such teamwork is, management probably views an
operator’s contribution to team effectiveness as an essential ingredient in the operator’s
individual performance. Therefore:
Hypothesis 8:
Because of the nature of the critical systems operator task,
teamwork will be highly valued at XYZCo. This will be manifested by a high correlation
72
between the supervisor’s evaluation of the operator’s contribution to team effectiveness
and the operator’s performance rating.
Pfeffer (1981) said that those workers who are most critical in meeting an
organizational contingency will have the most power, particularly when they are hard to
replace in the function. Pfeffer cited Crozier’s (1964) study of French maintenance
engineers who controlled “the one remaining uncertainty confronting the organization,
the breakdown of machinery” (Pfeffer, 1981: 113). Crozier found that maintenance
engineers held significant power over assistant plant managers, for example, and were
able to exert more influence on the plant manager than did the assistant manager. As a
group, the operators of XYZCo’s critical systems hold this type of power.
However, levels of power differ between software and hardware operators.
Software operators have significant expert power (French & Raven, 1968) because: a)
they have no written procedures for diagnosing problems, since the possible problems are
too complex to document, and b) the amount of knowledge it takes to become relatively
competent at this task can only be learned on-the-job over a period of about two years.
To become expert at the job requires much more time, however. The interview with Tom
highlighted this. Though no longer a CSO, he still was frequently called on issues within
his area of expertise. Tom also pointed out three software operators who, in Tom’s
opinion, were “becoming” expert. Each of the three had been software operators for ten
years or more!
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Hardware operator power levels appears to be lower than for software operators.
While the task of the hardware operators is also complex, portions of the hardware job are
sufficiently standardized to make automation possible. Hence, management has pursued
a “lights dim” initiative to replace enough hardware functions to make it unnecessary to
have operators physically located in the computer room.
By making the hardware
operators partially replaceable, “lights dim” efforts constituted a threat to hardware
operator power (McKnight, 1996). One informant reported that the operators resisted
programmers who came to the operators to develop specifications for automating their
function. This resistance demonstrates the use of operator power against actions that
threatened their power.
Arguably, the worker/management relationship will begin to erode when
management makes attempts to eliminate worker jobs or erode worker power. This
relationship erosion will be manifest in terms of the trust and liking of the workers for
their management. Even though such policies generally come from higher management
levels, such actions will likely taint hardware operators’ relationships with their
immediate supervisors. Therefore:
Hypothesis 9: Critical systems operators in groups whose jobs will likely be
eliminated over time will have lower levels of trust and liking toward their supervisors
than will operators in other groups.
74
Workers whose jobs will be eliminated will likely have lower organizational
commitment and job satisfaction. They are likely to get less enjoyment from their job
because of job insecurity, as they focus more on lower-order, rather than higher-order,
needs (Deci & Ryan, 1985; Maslow, 1954)
Hypothesis 10: Critical systems operators in groups whose jobs will likely be
eliminated over time will have lower levels of Organizational Commitment, Intrinsic
Motivation-Enjoyment, and Job Satisfaction than will operators in other groups.
METHODOLOGY DETAIL
Hypotheses 1-4 (motivation levels comparisons with other groups) were tested by
means comparison tests.
Hypotheses 5 and 6 (intrinsic and relationships motivation orientation) were tested
by grounded theory’s open coding method (Glaser & Strauss, 1967). The qualitative
responses were open coded into categories. Once the categories were identified from the
data, the researcher went back through the coding process again, making several minor
coding changes. Hypothesis 5 indicates that a majority of comments would be intrinsic.
For testing purposes, “majority” was assumed to be 50% or more. Hypothesis 6’s
“significant percentage” was interpreted to be over 10% for testing purposes. Given that
over 50% were projected to be intrinsic, then 10% is at least 20% of the remaining 50%
of the comments.
75
Hypothesis 7 (grade/job security  motivation orientation) was tested by
correlating grade and secure group membership with a construct developed to represent
the operator’s choice of intrinsic versus extrinsic motivation for why they work hard and
do a good job. This construct was named IMO, for Intrinsic Motivation Orientation. The
data for IMO was gathered by asking questions #50-52 in Appendix B, as shown in Table
9. Questions 50-51 present four choices for why the employee works hard, two intrinsic
and two extrinsic.
Respondents are also offered the choice of “5. Something else
(specify:)___” The intrinsic and extrinsic choices were selected from theory. Question 52
offers three extrinsic choices, choice “5. Something else,” and one choice that mixes
extrinsic and intrinsic: “Appreciation from your boss.” This choice is partly extrinsic
(Kohn, 1993b), in that the stimulus comes from outside the worker, and partly intrinsic in
that appreciation relates to the employee’s self-esteem. Responses that indicated “5.
Something else” were further probed, and the responses recorded on the questionnaire.
These answers were coded as intrinsic, extrinsic, or mixed. IMO was placed on a 1-7
scale as follows. To the minimum score of 1.0, two points were added for each answer
on questions 50-52 that was an intrinsic motivator. Exceptions: a) one point was added
for “Appreciation from your boss;” b) the scores from mixed answers to “5. Something
else” probes were scored through open coding methods explained above. Since IMO was
formulated by summing the intrinsic responses to each of the three questions, IMO could
not be tested for reliability.
Table 9 Intrinsic Motivation Orientation (IMO) Scale
Q#
Question Text
Category
76
50.
From the following list, please select the one reason that best represents
why you try to work hard and do a good job:
1. Opportunities for a promotion
2. The challenge of the task
3. Merit pay increases
4. A feeling of accomplishment
5. Something else (specify)
51.
From the following list, please select the one reason that best represents
why you try to work hard and do a good job:
1. [incentive plan name] bonuses
2. Solving the incident, outage, or potential problem
3. Achievement award programs
4. Enjoyment of the job
5. Something else (specify)
52.
From the following list, please select the one reason that best represents
why you try to work hard and do a good job:
1. Opportunities for a Promotion
2. Appreciation from your boss
3. Merit pay increases
4. [incentive plan name] bonuses
5. Something else (specify)
Extrinsic
Intrinsic
Extrinsic
Intrinsic
Either
Extrinsic
Intrinsic
Extrinsic
Intrinsic
Either
Extrinsic
Extrinsic/Intrinsic
Extrinsic
Extrinsic
Either
Hypotheses 8 (CTE importance) and 9-10 (job security effects on relationships,
motivation) were tested using correlations, with a one-tailed significance test. Hence,
Hypotheses 9-10 tests for differences between employees whose functions were going to
be eliminated and those in the other groups by correlating insecure group membership
with Relationships and Motivation variables.
RESULTS OF HYPOTHESIS TESTING
Table 10 shows the results related to Hypotheses 1-4 (motivation levels).
Table 10 Job Characteristics Comparisons (averages)
TYPE OF JOB:
H:
Support
?
STUDY:
SYSTEM
DEVELOPMENT
Lending, Couger &
Zawacki,
1996
1980
COMPUTER
OPERATIONS
Couger &
XYZCo,
Zawacki,
1997
1980
77
1
1
1
1
1
1
2
3
3
3
4
4
Yes
Job Significance
5.37
5.75
5.62
6.77
Yes
Skill Variety
5.76
5.55
3.98
6.28
Yes
Autonomy
5.31
5.31
4.08
5.97
Yes
Job Feedback
5.09
5.20
4.62
5.95
Yes
Growth Need Strength
5.29
5.91*
5.78
6.82
Yes
Motivating Potential Score
150
154*
99
216
No
Task Identity
4.98
5.37
4.53
4.83
Yes
Felt Responsibility
n/a
5.31
4.08
6.88
Yes
Experienced Meaningfulness n/a
5.56
4.71
6.62
Yes
Knowledge of Results
n/a
4.59
4.33
6.38
Yes
Job Satisfaction
5.10
5.10
4.94
6.29
Yes
Intrinsic Motivation
5.70
n/a
5.71
6.46
*Based on combined programmers and analysts; other column entries are analysts only
 Lending results shown here only to demonstrate that System Development scores have not
changed greatly from 1980 (Couger & Zawacki study) to 1996 (Lending study). Means tests did not
involve Lending results.
Hypothesis 1 (CSO JCM measures higher than those of comparison groups) was
consistently supported.
Each Hypothesis 1 job characteristics measure for critical
systems operators at XYZCo is nominally higher than the Lending or Couger results.
Note that the contrast is greatest between XYZCo CSOs and Couger’s computer
operators. For each variable, a T-test was performed, comparing the XYZCo results to
the Couger & Zawacki System Developer score (Keller, Warrack & Bartel, 1988). At
alpha = .05, each variable showed a significant mean difference. XYZCo’s figures are
also significantly higher than the operator figures, since the System Developer figures
each exceeded Couger & Zawacki’s operator figure. The CSO Motivating Potential
Score was more than double that of the computer operator, and over sixty points higher
than that of the System Developers. Hence, Hypothesis 1 was fully supported.
Hypothesis 2 (CSO Task Identity lower than those of comparison groups) was
partially supported. XYZCo’s Task Identity mean was nominally lower than the means of
Lending and Couger & Zawacki system developers, but was higher than that of the
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Couger & Zawacki computer operators. T-test results showed that XYZCo’s average
Task Identity score of 4.83 was significantly (alpha = .05) lower than the Couger &
Zawacki average for system developers (5.37), supporting Hypothesis 2. However, Ttests showed that XYZCo’s average Task Identity score of 4.83 was not significantly
different from the Couger & Zawacki average for Computer Operators (4.53) or the
Lending average for system developers (4.98).
Based on alpha = .05 significance T-tests, Hypotheses 3 and 4 (CSO CPS and
Work Outcome measures higher than those of comparison groups) were also supported.
While most T-tests compared the XYZCo mean with that of Couger & Zawacki’s System
Developers, the Intrinsic Motivation T-test compared the XYZCo mean with that of the
Couger & Zawacki operators, since that was the only number available.
T-tests were also done to compare Hypotheses 1 and 4 XYZCo results with those
of Lending (1996). For each variable, the average XYZCo score was significantly higher
than that of Lending. Hypothesis 2 Lending results were reported above, and Lending did
not report data on Hypothesis 3 variables.
Table 11 presents the strongly supportive results of Hypotheses 5 and 6 (intrinsic
and relationships motivation orientation). In support of Hypothesis 5, Table 11 shows
that intrinsic factors were strongly favored over extrinsic factors—52.9% to 8.9%. In
support of Hypothesis 6, the worker’s relationship with either his/her boss or coworkers
was mentioned almost 20% of the time, which was more than twice as often as extrinsic
factors. Even the sum of job security and hygiene factors was a higher percentage than
extrinsic factors overall.
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Table 11 Intrinsic versus Extrinsic Factors Reported
(Hypotheses 5 and 6 Results)
%
52.9%
8.9%
19.5%
8.0%
3.0%
7.7%
100.0%
Factor Reported
Intrinsic factors (job related)
Extrinsic factors (pay, promotions, bonuses)
People relationship factors
Job security
Shift work (hygiene factor)
Other hygiene factors (overtime, work conditions)
Hypothesis 7 (grade/job security  motivation orientation) was supported. Grade
level was correlated with IMO (r = .200) with a significance of p=.033. This provides
evidence that those with higher grade levels are more likely to choose intrinsic factors.
Being in one of the more job-secure groups was correlated with IMO scores (r = .405), at
p=.000 level. This strongly indicates that lack of job security led hardware operators to
think more in terms of extrinsic rewards, rather than intrinsic ones. As an alternative to
grade level, age was tested. Age was not a factor (r = .073, p=.253).
Hypothesis 8 (CTE importance) was strongly supported. Contribution to Team
Effectiveness was highly correlated with Individual Performance (r = .84, p=.000). In
terms of prediction, CTE predicted Individual Performance at an adjusted R-squared level
of .71. A caveat of this result is that CTE and Individual Performance were both reported
by the supervisor (see Mono-method bias in Chapter Two).
However, Individual
Performance was based on written performance appraisal documentation.
80
Hypothesis 9 (job security effects on relationships) was supported, but Hypothesis
10 (job security effects on motivation) was not (Table 12). That is, those in less secure
groups had lower levels of trust in their supervisor. Yet their motivation levels were no
different than those in more secure groups.
Table 12 Correlations between Less Secure Group and Other Attributes
(Hypotheses 9 and 10)
Less Secure Group correlations with:
Trusting Belief-Benevolence
Trusting Belief-Competence
Liking
IM-Enjoyment
Organizational Commitment
Job Satisfaction
r
p
.261
.219
.258
-.035
-.076
.135
.008
.021
.008
.376
.244
.108
Eliminating Plausible Alternatives
In order to establish these hypotheses’ internal validity with greater confidence,
the researcher entered a number of plausible alternatives into the equations predicting the
CPS and Work Outcomes. These included demographic variables (age, grade level,
education), individual situation variables (number of recent promotions, number of recent
pay raises, percent of time keeping systems available, duration of time worked with
supervisor), and variables providing possible alternative explanations (interaction with
team members, interaction with supervisor, relationship with team members). These
variables added little predictive value to the Work Outcomes models’ most significant
equations (i.e., predictions of IMSE and JobSat). Only grade level helped ‘predict’
81
Performance. However, good Performance is more likely to cause higher grade levels
than vice-versa. So grade level was eliminated from consideration as a Performance
predictor. Because these plausible alternatives were eliminated, one can have greater
confidence in the internal validity of the best equations for the Work Outcomes models
(see Table 14).
While none of the plausible alternatives helped predict Felt Responsibility, two
alternatives successfully entered the equations predicting the other CPS. Education level
was strongly (negatively) predictive of Experienced Meaningfulness (beta = .317, p =
.000), and the CSO’s relationship with team members was predictive of Knowledge of
Results (beta = .244, p = .015). The fact that the CSO’s relationship with the team was
related to Knowledge of Results indicates that having a good relationship with peers
helps CSOs know how they are doing on the job. Perhaps they receive considerable
feedback from their peers, as well as from the job. This was also indicated in the answers
to the questions about pressure on the job. Many respondents, after indicating that they
did not feel significant pressure to perform well from managers or supervisors, said they
felt more pressure from the job itself and from their peers.
The highly negative
correlation between education level and Experienced Meaningfulness can be interpreted
as follows. It is possible that education broadens one’s views of what is important in the
workplace generally. If so, those with more education would be less ‘impressed’ by the
importance or meaningfulness of their current job because they would have more
knowledge of other interesting jobs in the economy.
DISCUSSION OF RESULTS
82
The results of Hypotheses 1-4 underscore the highly motivating nature of the CSO
job. In particular, while it is impressive that XYZCo’s motivating potential score more
than doubled that of Couger’s computer operators, it is even more impressive that the
operator job is significantly more motivating than that of the system developer—a job
which has received much more research attention in the past. These results confirm that
the CSO job is very different from that of the traditional computer operator, placing it in
the general class of critical technology systems jobs (e.g., nuclear plant operators).
Critical systems operators are primarily motivated to work hard for the
organization through intrinsic factors like the job’s challenge (53%) and through people
relationships (20%), rather than through extrinsic motivation (9%). Those with higher
grade levels and secure positions are significantly more intrinsically motivated than their
counterparts. Operator Contribution to Team Effectiveness (CTE) is closely related to
operator Performance rating, showing how important supervisors consider CTE to be.
Operators in secure groups had higher trust and liking towards their supervisor, but did
not report significantly higher levels of motivation than did their counterparts. This latter
finding shows the over-arching power of the critical systems job to motivate the operator.
Apparently, the job’s characteristics are powerful enough to lead to high levels of job
enjoyment, organizational commitment, and job satisfaction in spite of being in an
insecure group. This agrees with the relatively infrequent mention (8%) of job security as
a motivational driver. Additional analysis revealed that Experienced Meaningfulness and
Intrinsic Motivation—Self-Esteem were not significantly correlated with secure group
either.
One explanation of this is that workers who stay in insecure groups (or
83
companies) tend to reconcile their feelings about such groups. This would happen in
order for them to reconcile their feelings about continuing to work there. Some evidence
exists that workers are beginning to adapt to the fact that the American dream of job
security is no longer the same (Wall Street Journal, 1995).
If the CSO job is highly motivating, the question remains: which specific tasks
are CSOs most motivated to do? It was suggested to the researcher by an advisor that a
highly visible and appreciated task like fixing the system may be more attractive to the
CSO than an almost invisible task like preventing problems. This was informally termed
the “Red Adair versus Maytag repair” syndrome. The CSO reward system is likely to
favor fixing the system, as opposed to preventing system problems. At the extreme, a
CSO may feel a disincentive to do preventive maintenance, for three reasons. First,
fixing the system is probably more intrinsically motivating and brings greater job
satisfaction than the prevention job because it is more challenging. Second, the ‘honor
and glory’ is more likely go to the CSO in heroic fire-fighter mode, because of the high
visibility of an outage (and the longer the outage, the more visible it is). Third, the fix-it
task preserves the power of the operator (Crozier, 1964), as discussed earlier. The
researcher found no evidence that this phenomenon was taking place at XYZCo. On the
contrary, the researcher found that when a new manager asked who had fixed a
particularly troubling outage, the supervisor refused to give out an individual name,
stating that it was a team effort. The fact that fix-it successes were identified as team,
rather than individual, successes suggests that the Red Adair versus Maytag repair
syndrome may not exist at XYZCo.
84
In sum, the picture of the critical systems operator job reflects:
 extremely high motivating potential,
 the job itself (intrinsic factors) as the primary motivator and relationships
the secondary motivator,
 extrinsic and job security factors less important,
 job security positively related to operator/supervisor relationships, and
 Contribution to Team Effectiveness a paramount virtue in supervisors’
eyes.
The highly intrinsically motivating nature of the operator job is likely to impact
additional parts of this study. For example, because this job is so highly motivating, job
characteristics are likely to be especially important to worker motivational outcomes.
Hence, the basic tenets of the JCM (tested in Chapter Four) are likely to hold. For the
same reason, however, Relationships and System Trust are not likely to be as important to
worker motivational outcomes as job characteristics. Chapter Three evidence on what
motivates operators supports this prediction.
Even though about 20% of comments
mentioned relationship issues, nearly three times as many referred to job-related /
intrinsic motivational factors. Chapter Four examines further the relative importance of
relationship and intrinsic factors.
85
CHAPTER FOUR:
JOB CHARACTERISTICS MODEL--ADDING RELATIONSHIPS
Ch
Prop:
Content or Model
2
--
Methodology and Construct Validation
3
1
Nature of the
Critical Systems
Operator Job
4
2, 3
High Levels of
Motivation
Growth Need Strength
Job Characteristics
Critical
Psychological
States (CPS)
Relationships
5
Work
Outcomes
System Trust
4, 5
Incentive
Controls
Motivational
Effect
Relationships
6
4, 5
Other
Controls
Relationships
7
--
Motivation
Motivational
Outcomes
System Trust
Contributions, Limitations, and Future Research
86
THEORY BUILDING
Chapter Four first builds hypotheses regarding the Job Characteristics Model
(JCM), expanding upon Chapter One’s Propositions 2 and 3. These hypotheses are
supplemented by hypotheses on the incremental predictive power of Relationships and
System Trust. The methodology for testing the hypotheses is detailed and the research
results are presented and discussed.
JCM Related Research
The hypotheses in Chapter Four primarily come from literature, supplemented by
Phase I results. The Hackman and Oldham Job Characteristics Model posits that the
worker’s perceptions of five job characteristics (Job Significance, Task Identity, Skill
Variety, Autonomy, and Job Feedback) will predict the Critical Psychological States that,
in turn, affect Work Outcomes. The Critical Psychological States are Experienced Work
Meaningfulness, Felt Responsibility, and Knowledge of Results.
While the Job Characteristics Model has had some criticism (e.g., Roberts &
Glick, 1981), significant amounts of evidence support the model (e.g., Hackman &
Oldham, 1976; Hackman, 1980). In MIS research, Couger and Zawacki (1980) applied
the Job Characteristics Model. In general, their research supported the tenets of the JCM.
The recent dissertation study of Lending (1996) also supported the basic premises of the
JCM.
Lending also confirmed earlier research findings that combining the job
characteristics in an additive way predicts better than in the multiplicative way that
Hackman & Oldham prescribed. Note that Hackman & Oldham’s (1975) version of the
JCM focuses solely on job characteristics without employing the effects of controls or
87
relationships, as does the Management Controls / Relationships model. Lending (1996)
pointed out that the JCM originally (Hackman & Lawler, 1971) included two
interpersonal characteristics (friendship opportunities, dealing with others) that were later
removed.
The alternative offered to the JCM by Salancik and Pfeffer (1978), Social
Information Processing (SIP), says that task attitudes are socially constructed from
organizational influences rather than from the characteristics of the job, as the JCM
posits. While the JCM theory has generally found more support than SIP (Glick, Jenkins
& Gupta, 1986), the void created by removing interpersonal issues was partially filled by
SIP. Rather than looking directly at the effects of management controls and relationships
between people, as this study does, however, SIP looks at how people’s perceptions of
their jobs are influenced socially through cognitive processes. Because some have found
that the leader/worker relationship is as important to job motivation as the task itself (e.g.,
McIntosh, 1990), this study later tests the effects of relationships on motivation.
JCM Hypotheses
The hypotheses
of this
section follow the detailed version of the
Hackman/Oldham model, as depicted in Figure 10 in Chapter Two. Justification for the
original hypotheses may be found in the Hackman/Oldham studies (e.g., Hackman, 1980;
Hackman & Oldham, 1975). Chapter 2 reported that the constructs in the JCM did not
summarize reliably at the concept level (see Table 8). Hence, instead of testing some
hypotheses at Figure 1’s level of Job Characteristics, Critical Psychological States, and
88
Work Outcomes, JCM hypotheses were formed at the construct level (e.g., Felt
Responsibility, Job Satisfaction—see Figure 10 in Chapter Two). Therefore:
Hypothesis 11: Skill Variety, Task Identity and Job Significance will each be
positively associated with Experienced Work Meaningfulness, moderated by Growth
Need Strength.
Hypothesis 12: Autonomy will be positively associated with Felt Responsibility,
moderated by Growth Need Strength.
Hypothesis 13: Job Feedback will be positively associated with Knowledge of
Results, moderated by Growth Need Strength.
Hypothesis 14:
Experienced Meaningfulness, Felt Responsibility, and
Knowledge of Results will each be positively associated with Intrinsic Motivation-SelfEsteem, Job Satisfaction, and Work Performance, moderated by Growth Need Strength.
Relationships- and System Trust Related Hypotheses
As briefly discussed in Chapter One, both the originators of the JCM (Hackman &
Lawler, 1971) and its competitors (Salancik & Pfeffer, 1978) have recommended the use
of social factors in predicting work motivation. Hackman & Lawler (1971) used social
89
needs (dealing with others during work, friendship opportunities) to help predict
motivational outcomes. While these social needs were found to correlate significantly
with job satisfaction, they did not correlate with other work outcomes, defined as
motivation, performance, and reduced absenteeism (Lending, 1996).
In MIS studies, social aspects of Hackman & Oldham’s (1975) Job Diagnostic
Survey (JDS) have been employed. Couger & Zawacki (1980) used feedback from
supervisors, and dealing with others to represent the social side. Lending (1996) used
three JDS social variables in her study, dealing with others, friendship opportunities, and
feedback from agents. Lending grouped these with other job characteristics into a tenfactor index. She did not report the individual predictive power of these social variables.
They probably added predictive power, however, because the ten-factor index outpredicted the traditional five-factor index. This was especially true for Job Satisfaction,
in that the ten-factor index’s adjusted R-squared was .22, while the additive five-factor
index had an R-squared of only .10. However, Lending’s exploratory model building
included a construct called “Satisfaction with Supervisor” that raised the ten-factor index
explanation of Job Satisfaction from an adjusted R-squared of .22 to .33. Expanding her
model with a Job Security construct further increased the adjusted R-squared to .36.
Lending reported that both Satisfaction with Supervisor and Job Security worked best as
moderators in these equations. Couger & Zawacki (1980) did not report large predictive
power from dealing with others, compared to the core job characteristics variables.
However, Couger & Zawacki reported that feedback from supervisors was correlated
highly (r= .41) with internal work motivation, which was higher than correlations of any
90
of the core job characteristics variables. Thus, evidence from Lending (1996) and Couger
& Zawacki (1980) provided strong incentive to employ worker/supervisor relationships as
a factor in predicting operator motivation.
Salancik & Pfeffer (1978) related social cognitive processes to worker perceptions
of the job. They (and others—e.g., Griffin, 1983) have found support for their Social
Information Processing (SIP) model. However, overall, the Hackman/Oldham model,
which excluded social needs, had greater predictive power than the SIP model, based on
Taber and Taylor’s (1990) meta-analysis.
In exploring why social factors inconsistently predicted work outcomes in the
original JCM and SIP theories, two aspects of the JCM and SIP’s treatment of sociality
became clear. First, those variables did poorly that looked at employee lateral (peer)
sociality, rather than the vertical (employee/boss) relationship. In contrast, Couger &
Zawacki’s (1980) vertical construct, feedback from supervisors, did better than the peerrelated variables. Second, each theory primarily considers relationships indirectly. SIP
examined how cognition is influenced socially. The original JCM tested how social
needs influenced motivational outcomes rather than directly examining the relationships
between people.
This research is based upon the premise that looking directly at the
worker/supervisor relationship could add greater incremental predictive power than the
approaches employed by the original JCM or SIP studies. Some evidence from the
literature encouraged this thinking.
First, such relationship variables as trust
demonstrated a surprisingly strong impact in some organizational settings (e.g., Atwater,
91
1988).
Worker/supervisor relationships has been found to be significantly correlated
with organizational commitment (e.g., Tansky, 1993), a motivational outcome. Second,
Smits, McLean and Tanner (1997) found that the worker/supervisor relationship was one
of the two most significant predictors of organizational commitment of information
systems people. This was particularly impressive because their study included a large
number of other independent variables. Therefore:
Hypothesis 15:
Critical systems operator/supervisor Relationships will be
predictive of CPS and Work Outcomes (in the positive direction) beyond the predictive
power of Job Characteristics Model variables.
Relationships and System Trust were defined in Chapter Two.
This study
conceptualizes Relationships in terms of Liking and three types of trust (see Table 3 in
Chapter Two). Liking represents the affective dimension of the relationship, while the
two Trusting Beliefs represent the cognitive dimension.
Finally, Trusting Intention
reflects one’s willingness to depend on the supervisor.
Hence, Relationships is a
balanced set of variables that represent how one feels, believes, and intends to act toward,
one’s supervisor. System Trust means a person’s belief about the structures supporting
success in the work environment. In a sense, System Trust communicates what the
operator believes about the organization or organizational subgroup of which s/he is a
part. For this reason, one might say that System Trust reflects an operator’s relationship
with the organization. Just as the Relationships construct means the extent to which one
92
holds positive feelings, beliefs and intentions towards another person, so System Trust
refers to one’s feelings/beliefs about the organization.
How one feels about the
organization probably motivates one to be committed to it or to desire to work hard for it.
Therefore, System Trust will likely be positively associated with such motivational Work
Outcomes as contained in the JCM.
System Trust will probably not be strongly related to CPS, however, since CPS
reflect how one feels towards specific aspects of the job, not the overall work situation.
For example, Experienced Meaningfulness means that one experiences the work as being
important. Similarly, Knowledge of Results means that one understands the outcomes of
one’s job. These are narrowly focused on job-related psychological states. System Trust,
which focuses on general perceptions of the work environment, is more likely to be
related with the Work Outcomes of CPS, since Work Outcomes reflect less narrow views
of the worker’s motivation, such as Job Satisfaction or Intrinsic Motivation.
Hypothesis 16:
System Trust will be predictive of Work Outcomes (in the
positive direction) beyond the predictive power of Job Characteristics. System Trust will
not add to Job Characteristics’ prediction of Critical Psychological States.
Given how strongly predictive job characteristics were (see Chapter Three),
Hypotheses 15 and 16 represent strong tests of the impact of Relationships and System
Trust in a work environment. That is, in a work environment that is so intrinsically
motivating, the effects of social or relationship constructs are very likely to be
93
overpowered by the intrinsic motivators present. Therefore, if Hypotheses 15 and 16 are
affirmed in this environment, they are even more likely to hold in less intrinsically
motivating environments.
METHODOLOGY DETAIL
Hypotheses 11-16 were tested using regression analysis. To avoid the
multicollinearity problem, the interaction terms were created by multiplying standardized
terms together (Aiken & West, 1991).
RESULTS OF HYPOTHESIS TESTING
Table 13 summarizes the results of the tests of Hypotheses 11-14. Hypothesis 11
(Job Characteristics  Experienced Meaningfulness) was strongly supported. All three
of the hypothesized job characteristics, GNS, and one interaction entered the equation
significantly. Hypothesis 12 (Autonomy  Felt Responsibility) was not supported. Since
Job Feedback predicted Knowledge of Results, Hypothesis 13 (Job Feedback 
Knowledge of Results) was supported. Hypothesis 14 (CPS  Work Outcomes) was
partially supported. Both Job Satisfaction and Intrinsic Motivation were predicted by
some combination of Experienced Meaningfulness and GNS. However, Performance
was not predicted by any of the CPS or GNS.
Table 13 Job Characteristics Model Test Results
H#
11
Independent Variables
Skill Variety
+ Job Identity
+ Job Significance
+ GNS
+ interactions
Dependent
Variables
Experienced
Meaningfulns.
R2adj
Fstat
.366
.000
Significant
Constructs
Job Significance
Skill Variety
Job Identity
GNS
Skill Variety X GNS

.323
.180
.217
.211
.242
p
.005
.049
.017
.049
.019
94
12
13
14
14
14
Autonomy
+ GNS
+ interaction
Job Feedback
+ GNS
+ interaction
Experienced Meaningfulns.
+ Felt Responsibility
+ Knowledge of Results
+ GNS
+ interactions
Experienced Meaningfulns.
+ Felt Responsibility
+ Knowledge of Results
+ GNS
+ interactions
Experienced Meaningfulns.
+ Felt Responsibility
+ Knowledge of Results
+ GNS
+ interactions
Felt
Responsibility
-.014
.616
--
--
--
Knowledge of
Results
.204
.000
Job Feedback
.466
.000
Intrinsic
Motivation
.170
.003
Experienced
Meaningfulns.
.425
.002
GNS
.256
.023
.725
.000
.399
.001
--
--
Job
Satisfaction
Individual
Performance
.343
-.035
.000
.760
Experienced
Meaningfulns.
GNS X Experienced
Meaningfulns.
--
Table 14 shows the results from Hypotheses 15 and 16. For Hypothesis 15,
Relationships with supervisors enters the equations predicting Experienced Work
Meaningfulness (15a) and Felt Responsibility (15b). However, Table 14’s equation
predicting Felt Responsibility is still nonsignificant, based on the F-statistic. In the
equation predicting Experienced Meaningfulness (15a), the beta for Relationships is
higher than that of any other variable, and raises the adjusted R-squared from .366 to
.492. Separate from Table 14, however, it was found that when the interaction terms and
the non-significant Skill Variety construct are removed from this equation, Job
Significance has a higher beta (.455) than Relationships (.335). So Relationships is not as
predictive as Job Significance. In the equation predicting Knowledge of Results (15c),
Relationships is not significant because it is highly correlated with Job Feedback (r =
95
.465). Relationships does not predict the Work Outcomes (15d,e,f), except Performance.
Based on the F-statistic, however, the Performance equation (15f) is not significant.
In support of Hypothesis 16, System Trust enters the equations predicting Job
Satisfaction (16b) and Performance (16c). However, based on the F-statistic, the equation
predicting Performance is not significant overall.
System Trust does not help the
prediction of Intrinsic Motivation (16a). As predicted, System Trust did not predict CPS
(16d,e,f).
System Trust has only modest predictive power for Experienced
Meaningfulness (16d), and no predictive power for Felt Responsibility (16e) and
Knowledge of Results (16f).
96
Table 14 Relationships and System Trust Test Results
H#
15a
15b
15c
15d
15e
15f
16a
16b
16c
Independent Variables
Skill Variety
+ Job Identity
+ Job Significance
+ GNS + interactions
+ Relationships
Autonomy
+ GNS + interaction
+ Relationships
Job Feedback
+ GNS + interaction
+ Relationships
Experienced Meaningfulns.
+ Felt Responsibility
+ Knowledge of Results
+ GNS + interactions
+ Relationships
Experienced Meaningfulns.
+ Felt Responsibility
+ Knowledge of Results
+ GNS + interactions
+ Relationships
Experienced Meaningfulns.
+ Felt Responsibility
+ Knowledge of Results
+ GNS + interactions
+ Relationships
Experienced Meaningfulns.
+ Felt Responsibility
+ Knowledge of Results
+ GNS + interactions
+ System Trust
Experienced Meaningfulns.
+ Felt Responsibility
+ Knowledge of Results
+ GNS + interactions
+ System Trust
Experienced Meaningfulns.
+ Felt Responsibility
+ Knowledge of Results
+ GNS + interactions
+ System Trust
Dependent
Variables
Experienced
Meaningfulns.
.000
Significant
Constructs
Job Significance
Job Identity
GNS
Skill Variety X GNS
Relationships

.355
.198
.258
.340
.394
p
.001
.016
.008
.000
.000
.023
.209
Relationships
.220
.045
Knowledge of
Results
.194
.000
Job Feedback
.471
.000
Intrinsic
Motivation
.177
.003
Experienced
Meaningfulns.
.355
.016
GNS
.243
.030
Experienced
Meaningfulns.
.713
.000
GNS X Experienced
Meaningfulns.
.392
.001
R2adj
Fstat
.492
Felt
Responsibility
Job
Satisfaction
.335
.000
Individual
Performance
.029
.247
Relationships
.297
.016
Intrinsic
Motivation
.165
.004
Experienced
Meaningfulns.
.405
.004
GNS
.266
.020
.648
.000
.354
.284
.001
.002
.234
.046
Job
Satisfaction
Individual
Performance
.415
.006
.000
.395
Experienced
Meaningfulns.
GNS X Experienced
Meaningfulns.
System Trust
System Trust
97
Table 14 (Continued)
16d Skill Variety
+ Job Identity
+ Job Significance
+ GNS + interactions
+ System Trust
16e Autonomy
+ GNS + interaction
+ System Trust
16f Job Feedback
+ GNS + interaction
+ System Trust
Experienced
Meaningfulns.
.393
.000
Job Significance
Job Identity
GNS
Skill Variety X GNS
System Trust
.337
.184
.267
.266
.197
.003
.041
.014
.009
.038
Felt
Responsibility
.013
.282
--
--
--
Knowledge of
Results
.194
.000
Job Feedback
.468
.000
DISCUSSION OF RESULTS
First, the hypotheses related to the JCM itself are discussed. Since most of the
equations predicting CPS and Work Outcomes are significant, the basic tenets of the Job
Characteristics Model were largely supported by the study’s data. The fact that few
interaction terms were significant in the six models is consistent with earlier findings
(Griffin, Walsh & Moorhead, 1981). In the critical systems environment, Experienced
Work Meaningfulness emerged as a very important psychological state in terms of
predicting two of the three Work Outcomes. Job Significance was the most important
predictor of Experienced Work Meaningfulness. Job Feedback successfully predicted
Knowledge of Results. But neither Knowledge of Results nor Felt Responsibility had any
effect on the Work Outcomes. However, exploratory regression analysis revealed that
Knowledge of Results was significantly correlated with Felt Responsibility, which in turn
was significantly correlated with Experienced Meaningfulness.
Second, hypotheses concerning Relationships and System Trust are discussed.
Relationships and System Trust have significant predictive effect in two of the equations.
98
In light of the highly intrinsically motivating job studied, this finding is remarkable.
Relationships adds predictive power to the JCM equation predicting Experienced Work
Meaningfulness. This by itself is important, since Experienced Work Meaningfulness is
the most powerful predictor of Job Satisfaction and Intrinsic Motivation. System Trust
adds predictive power to the JCM equation predicting Job Satisfaction.
In the critical
systems setting, as expected, the characteristics of the job appear to be the most important
factors in predicting Critical Psychological States (CPS).
One CPS, Experienced
Meaningfulness, was the most important factor in predicting two Work Outcomes—Job
Satisfaction and Intrinsic Motivation. Since Relationships and System Trust add
predictive value to two of the more important of the JCM equations, they represent a vital
missing element in the current configuration of the JCM.
While none of the Table 14 equations predicted Performance, exploratory analysis
found that Relationships by itself predicted Performance with an R-Squared of .043, Beta
= .233, and p = .031. Hence, Relationships was a better predictor of Performance than
any of the JCM variables. However, even Relationships did not predict Performance
well. From Chapter Three results, Contribution to Team Effectiveness was a major
predictor of Performance. Performance may also not be predicted by motivation-related
variables because variables not measured, like the CSO’s skill, knowledge, and ability,
were much more important predictors of performance. Another possible explanation is
that supervisors (the informant for Performance) may distinguish employees more via
skill, knowledge, and ability than via motivation levels. This seems plausible in light of
99
the high mean scores and low standard deviations of the CPSs (Felt Responsibility,
Experienced Meaningfulness, and Knowledge of Results--see Appendix I).
In summary, Chapter Four provides evidence that:
 the JCM applies in the Critical Systems Operator (CSO) environment;
 the job’s significance and related Experienced Work Meaningfulness are
the most important factors in predicting CSO intrinsic motivation
and job satisfaction, and, therefore, the work context makes a
difference;
 by increasing the variance explained from .366 to .492 (34%),
Relationships with the supervisor adds significant power to the
JCM’s prediction of Experienced Meaningfulness;
 by increasing the variance explained from .343 to .415 (21%), System
Trust adds significant power to the JCM’s prediction of Job
Satisfaction.
Chapter Four’s evidence that relationships matter beyond the power of the JCM
constructs is especially striking in light of Chapter Three’s evidence that the CSO job is
highly extrinsically motivating.
Further, while Chapter Three demonstrated that
Relationships are qualitatively important to CSO motivation, Chapter Four quantifies that
importance using an entirely different method. Together, these two methods provide
triangulated evidence (Kaplan & Duchon, 1988) for the importance of relationships.
100
CHAPTER FIVE:
INCENTIVE CONTROLS--ADDING RELATIONSHIPS
Ch
Prop:
Content or Model
2
--
Methodology and Construct Validation
3
1
Nature of the
Critical Systems
Operator Job
4
High Levels of
Motivation
2, 3
Job Characteristics
Growth Need Strength
Critical
Psychological
States (CPS)
Relationships
5
Work
Outcomes
System Trust
4, 5
Incentive
Controls
Motivational
Effect
Relationships
6
4, 5
Other
Controls
Relationships
7
--
Motivation
Motivational
Outcomes
System Trust
Contributions, Limitations, and Future Research
Chapter Five first reviews theory about Incentive Controls. Hypotheses are then
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tailored to the XYZCo environment. The methodology for testing the hypotheses is
detailed, and then the results are presented and discussed.
THEORY BUILDING
Definitions
One view of management controls is to measure performance against some
comparison standard (Davis & Olson, 1985: 319). This definition of controls is broad
enough to fit several control mechanisms, such as monitoring, feedback and
accountability. Other controls definitions augment the comparison-versus-standard view
of control. At the organizational level, the term "controls" means the process of assuring
that resources are used effectively to accomplish an organization's goals (Anthony, 1965).
This is a broad, structural definition. At an interpersonal level, the term "controls" means
the processes used "to direct, to influence, or to determine the behavior of someone else"
(Lawler & Rhode, 1976: 1248). Combining the meanings from Anthony and Lawler &
Rhode, this study defines controls as methods of attempting to ensure desired outcomes
by trying to influence other people. Management controls occur when managers use
various methods to try to influence employees to behave in ways that lead to outcomes
desirable to management. This definition is similar to Kirsch’s (1992: 9) interpretation of
Anthony’s view of control:
“motivating individuals to act in accordance with
organizational objectives.”
Control is distinguished from related concepts as follows.
Influence is a
descriptive term that means that one person causes changes in another person’s behavior,
emotions or thoughts (Huston, 1983; Tannenbaum, 1968). Following Huston, this study
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defines power as the ability (whether used or not used) to achieve desired ends through
influence. Translating the researcher’s definition of controls in light of the definitions of
influence and power, control means trying to utilize power through influence attempts.
Dominance exists when influence is asymmetrical over a broad range of activities
(Huston, 1983). Dependence means one’s interest (what is at stake) in satisfactions
provided by the other person (Walton, 1968). Power, controls, influence, dominance, and
dependency exist in actual and perceived form (Walton, 1968).
Controls Theory Overview
Existing controls theories (e.g., organization theory--Ouchi, 1979; agency theory-Eisenhardt, 1989b) have largely been used to test the link between types of controls and
desired outcomes. For example, Ouchi’s (1979) controls theory said that outcome-,
behavior-, and clan controls each produce different outcomes under different kinds of
conditions. Agency Theory says that an agent will do what the principal wants as long as:
a) the contract aligns the objectives of the principal and the agent through bonding
mechanisms and/or b) the principal can monitor the agent’s behavior (Barney & Ouchi,
1986).
The assumptions underlying Ouchi’s control theory, Agency Theory, and
Transaction Cost Economics (Williamson, 1975) are from the long-standing tradition in
economics: people are boundedly rational, probably opportunistic but definitely selfinterested, and not influenced socially. In other words, these theories assume that people
are not to be trusted, and therefore they should be controlled.
Organization theory and agency theory provide significant light about how
controls work in terms of what leads to the use of different kinds of controls, and which
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types of controls work best on what. However, because these theories largely ignore
organizational, social, and interpersonal factors, they have trouble explaining how
controls achieve outcomes. That is, controls work through peoples’ attitudes towards
their job, management, and the company. Controls affect, and are affected by, the
social/organizational issues (e.g., motivation, teamwork, and trust) that are often key to
positive outcomes.
Several researchers have pointed out how important workplace
relationships are toward accomplishing tasks (e.g., Ring & Van de Ven, 1994; Gabarro,
1990). Granovetter (1985) argued that researchers should find a balance between oversocialized (i.e., relationships are paramount) and under-socialized (i.e., relationships
don’t matter) depictions of organizational phenomena. Granovetter maintained that many
controls theories (e.g., agency theory and transaction cost economics) are undersocialized.
To the extent that they are under-socialized, control theories do not
adequately address the linkage between controls, social relationships, and worker
motivation.
Moreover, more work is needed to understand the mechanisms behind the effects
of controls on organizational outcomes. Powers & Dickson (1973) found that system
development project controls were perceived to have negative effects on system
development success.
However, they did not conclude why or how this occurred.
Henderson & Lee (1992) found that controls had positive effects.
However, their
operational definitions of controls primarily reflected only positive types of controls (e.g.,
helping behaviors) instead of the full range of possible control mechanisms. Lawler &
Rhode (1976) discussed briefly the negative impacts of tight financial control
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mechanisms (e.g., budgets) on employee behavior, but did not explain the mechanisms
behind it. Simons (1995) said incentives stimulate initiative and opportunity-seeking, but
may have dysfunctional side-effects. Simons did not explain this statement further.
Conceptual Model Development--Incentives
XYZCo’s context provided some clues about why controls have dysfunctional
side-effects. At XYZCo, management is concerned enough about system availability to
want to control critical systems operator (CSO) behavior in order to improve availability.
The CSOs’ management is active and involved in the day-to-day affairs of the unit. The
senior management team is keenly interested in keeping the system continually available.
The company has norms for doing things right, for succeeding, and for not accepting
excuses for failure. These norms act like controls; management inculcates these norms to
try to influence workers to do things right. The company also has a norm for rapidly
fixing problems that need to be addressed, as well as a norm for promptly reporting the
problem cause and both short-term and long-term solutions to management. Management
requests significant levels of detail on the reasons behind every outage and what is being
done to see that these underlying causes never recur. These actions are a form of the
feedback- and accountability controls treated in Chapter Six.
Based on Phase I data, XYZCo management’s concern to keep the systems
operating primarily manifested itself through five control mechanisms:
incentives,
accountability, feedback, autonomy granting, and management involvement.
The
conceptual and scientific model development and test results for incentive controls will
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be discussed in this chapter. The other control mechanisms will be covered in Chapter
Six.
In 1994, senior corporate management installed a bonus award system for all
employees.
Overall, the bonuses were made contingent on division profitability.
However, each work group also had its own set of performance goals that largely
determined its members’ annual bonus awards. The division was profitable in 1994 and
1995, so bonuses were given. During this period, management focused on cost controls
in order to assure high division profitability. Two interviewees perceived this as a change
from past management strategy, because the division’s first emphasis had always been to
spend enough money on system infrastructure to assure the system would be kept highly
available. In 1996, the new management team changed the bonus distribution from a
team-based method to an individual merit basis. The team-based method tied the bonus
to specific team availability goals, while the individual-based method was not as specific.
Interviewees said that the focus on cost savings that began about 1994 decreased morale
of some operators. In part, morale decreases were due to employees’ perceptions that
management was de-emphasizing quality of their system by spending less on it. CSOs
had always been proud of the system’s quality. Therefore, reduced spending called into
question what had been the highest priority of employees. Also, CSOs translated events
like cuts of budgeted positions11 into job insecurity feelings. However, these slight to
moderate changes in morale did not appear to have seriously jeopardized system
11
No layoffs of any size had taken place.
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availability. When the computer went down, workers still quickly got it going again--in a
very cooperative, highly motivated manner.
However, the incentive system did produce the potential for negative side effects
in the overall company (McKnight, 1996). Groups that had different bases for their
incentives tended to have more conflicts with each other than they did before the
incentives were implemented. For example, the major incentive for the programming
division was now to produce more new systems, with no incentive to maintain old
systems or to keep existing systems running. Hence, the management of the operations
group felt the incentives motivated the programmer group to implement new systems
before they were adequately tested, thereby endangering system availability. However,
the good interpersonal relationships, developed over time, between specific programmers
and operators ameliorated the conflicts that resulted from the incentive system, such that
it appeared to have no negative effects on computer system availability.
This example supports the conceptual version of the controls/relationships model
(Figure 5—Chapter One). In this example, interpersonal relationships moderated the
effects of controls (incentives) on system availability. Evidence in Phase I interview data
lent credence to Relationships’ moderation of the Incentive Controls  Motivation link
in Figure 5. Those interviewees who appeared to have the worst relationship with
management also appeared to be most negatively affected, in terms of morale, by the
incentive plan.
Several interviewees also mentioned that the incentives had made both
management and technicians so cost-conscious that they were afraid to spend money even
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for the “infrastructure” that would keep the system at a high level of availability. If
continued, interviewees noted, this trend could eventually have serious implications. One
interviewee claimed that low infrastructure spending had already resulted in at least one
extended system availability problem. Two other informants said that incentives were
encouraging the operations people to focus only on things that were true outages, but to
ignore things that inconvenienced the customer without being outages for which
operations was accountable.
From the data above, the researcher felt: a) that incentives may have negative, as
well as positive, effects on CSO motivation at XYZCo; b) the changes in the incentive
plan distribution methods may change the plan’s effectiveness; and, c) the
CSO/supervisor relationship may influence how incentives affect CSO motivation.
Scientific Model Development-- Incentives
Earlier research has linked controls and motivation.
Three most applicable
examples follow: a) scientific management theories, b) cognitive choice theories, and, c)
need-motive-value theories. From these theories, the intrinsic motivation literature will
be discussed in more detail.
Scientific Management Theories.
Frederick Taylor’s (1911) research on
“scientific management” of physical tasks resulted in recommendations that workers be
motivated by piece rate incentives. “In Taylor’s view, workers would only respond to
financial incentives based on defined performance standards” (Simons, 1995: 22). To
Taylor, effective controls came through the strategic placement of a quantified carrot.
Taylor’s work coincides with a large body of literature in learning psychology on how
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animals and people can be controlled by offering them reinforcing rewards (e.g., Skinner,
1953). Out of this tradition have come many studies in organizational behavior on related
topics such as operant learning theory (e.g., Hamner, 1974). As shown in Figure 4, many
studies support the proposition that incentive controls improve worker motivation (see
Hamner, 1991).
Cognitive Choice Theories. Control was the theme in early animal experimental
work in psychology. Experimenter control assumed, however, that the animal was merely
a responder to stimuli, not a purposeful, thoughtful being. Only the actions of the
animals were studied, since their thought processes were assumed away for purposes of
empirical rectitude (Hergenhahn & Olson, 1993).
While most early work in the
psychology of motivation studied behavior only, Tolman (1932) hypothesized that
motivating rats and people involves not only behaviors, but behavior supported by
cognitive processes. The cognitive revolution in psychology spawned a number of what
Kanfer’s motivation review article (1990:75) called “cognitive choice” theories of
motivation, such as expectancy/value theory (Vroom, 1964), attribution theory (Weiner,
1974), dynamics of action theory (Atkinson & Birch, 1970) and “self-regulationmetacognition” theories, such as goal setting (Locke, 1968), social learning (Bandura,
1986) and cybernetic controls theories (Carver & Scheier, 1981). These theories do not
agree with the assumptions behind the narrow behavioral view of human motivation
espoused by operant learning- and scientific management theorists.
Need-based Theories. Also at odds with scientific management are the needmotive value motivation theories (Kanfer, 1990).
In his famous Western Electric
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Hawthorne research, Elton Mayo reported that workers were motivated by the support
and sentiment of social interaction in the workplace (Mayo, 1949). Social control by
meeting worker sociality needs was the key motivator to Mayo. The Hawthorne studies
relate to what Kanfer (1990) called the need-motive-value research in motivation. This
broad area of research includes need fulfillment theories (e.g., Maslow, 1943; Alderfer,
1969), intrinsic motivation (e.g, Deci, 1975), and equity theories (e.g., Adams, 1963) of
motivation. For example, Maslow’s hierarchy of needs theory said that people are first
motivated to fulfill basic needs, such as for food and safety. Once these needs are
fulfilled, they no longer motivate. At this point, one desires to fulfill higher order needs,
such as love, self-esteem, and finally, self-actualization.
One underlying difference between the operant learning and need-based
motivation theories is important for this study. The assumptions underlying the scientific
management and operant learning theories are that humans are disconnected from each
other, self-interested, and fully rational (Simons, 1995). Note that these assumptions are
also reflected in the economic controls theories discussed previously (e.g., Eisenhardt,
1989b). By contrast, the need-motive-value theories assume that people are socially
connected, both self- and other-interested, and not always economically rational. In
particular, intrinsic motivation researchers have tried to reconcile the economics-based,
gain-seeking motivation perspective with the idea that people are motivated by other
needs and desires.
Intrinsic Motivation. Intrinsic motivation means motivation that “results from
an individual’s need to be competent and self-determining” (Steers & Porter, 1979: 249).
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Intrinsic motivation signifies that the outcome/reward for the work behavior comes from
inside the person (e.g., personal satisfaction). Extrinsic motivation signifies that the
outcome of the behavior comes from the outside, such as a monetary incentive or a job
promotion (Kanfer, 1990). The inference is that a person is intrinsically motivated when
outside forces are not present in enough force to move one to action. However, “[w]hen
there are strong [external] forces bearing on the individual to perform an activity, there is
little reason to assume that a behavior is self-determined...” (Staw, 1976).
Management controls constitute some of these outside forces. That is, a control
like behavior monitoring can act as an extrinsic motivation factor.
For example,
Strickland found that supervisors who watched their employees (i.e., a behavioral control,
per Kirsch, 1992) more frequently felt that the employees’ good behavior was caused by
the supervisor’s monitoring. Staw (1976) cited Strickland (1958) as an example of how
people interpret another person’s behavior as extrinsically motivated when the other
person is being controlled.
Hence, employing managerial controls can affect the
manager’s views of the worker’s motivation.
Bem (1967) said that this principle can also be applied to self-perception: how
one views the motivation behind one’s own behavior. If one acts in the presence of
strong external rewards, s/he is likely to attribute her/his behavior to external controls. If
these outside rewards are not strong, s/he will probably assume her/his behavior is due to
his/her own interest in the activity. The same point was made by deCharms (1968: 328):
“...when a person perceives the locus of causality for his behavior to be external to
himself (that he is a Pawn), he will consider himself to be extrinsically motivated.” Staw
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(1976) argued that when intrinsic rewards are high and extrinsic rewards (e.g., pay,
bonuses) are low, people will perceive themselves as being intrinsically motivated. That
is, intrinsic motivation is perceived to justify the person’s action to her/himself.
However, when both intrinsic and extrinsic rewards are high, the workers will be faced
with an unstable perception. The perception is unstable because it is “oversufficiently
justified” (Staw, 1976: 255). That is, the person feels more than fully justified for the
action taken. S/he is likely to reason, therefore, that since the external reward by itself
would have justified the action, s/he was extrinsically motivated to perform the activity,
and therefore, the task was not that enjoyable--or intrinsically motivating--after all. This
drop in intrinsic motivation may be crucial if the nature of the task was intrinsically
motivating before:
total motivation may be decreased and task performance may
therefore suffer.
Three early studies provided evidence of this concept (see Table 15).
Table 15 Effects of Extrinsic Motivation on Intrinsically Motivating Tasks
Study
Deci, 1971
Task
Solving puzzles
Subjects
College
students
Lepper,
Greene, and
Nisbett, 1973
Kruglanski,
Freedman and
Zeevi, 1971
Playing with
Magic Markers
Nursery school
children
Creativity and
Memory tasks
Teenagers
Effects of Experimental Condition
Lower intrinsic motivation (i.e., less time playing
with the puzzle during free time) after being paid
for solving a puzzle
Lower intrinsic motivation (i.e., less time playing
with the markers during free-play period) after a
contingent reward
Less satisfaction with the task, less likely to
volunteer for future tasks, lower task performance
after being offered the extrinsic reward (i.e., a free
tour of the laboratory facility)
Deci (1971, 1972) hypothesized that only rewards contingent on a high level of
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task performance will adversely affect intrinsic motivation.
This was supported by
Lepper, Greene & Nisbett (1973) in that those in the unexpected reward condition were
not as affected as those in the expected (i.e., contingent) reward condition. In all three of
the studies summarized in Table 15, what seemed to change the cognitive orientation of
the person from intrinsic to extrinsic was the contingent reward for a given level of
output.
Eisenhardt (1985) challenged Deci’s hypothesis that extrinsic rewards diminish
intrinsic motivation.
Eisenhardt used data from specialty retailers to support her
economics-based hypothesis that incentives motivate.
She found that the intrinsic
motivation of those in sales jobs who were given salient, contingent sales incentives was
not decreased.
Eisenhardt interpreted this result as a contradiction of Deci’s
overjustification hypothesis. However, Eisenhardt did not report the specific levels of
intrinsic motivation her respondents possessed, making it impossible to know whether
their level of intrinsic motivation was high enough for the overjustification hypothesis to
work.
Why Extrinsic Rewards Can Demotivate. The literature suggests two reasons
why extrinsic rewards can demotivate. First, the use of extrinsic rewards may change the
person’s mind about why they are doing the intrinsically motivating task. Before the
reward was offered, the person may have been doing the task primarily because the task
was enjoyable. The extrinsic reward convinces them that they are doing the task for the
sake of obtaining the reward instead of for enjoyment. This causes them to dislike the
task: “In fact, the more you want what has been dangled in front of you, the more you
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may come to dislike whatever you have to do to get it.” (Kohn, 1993b: 83) Second, the
use of extrinsic rewards harms the person’s view of themselves by moving their locus of
control from internal to external. People have a desire to be efficacious and autonomous
(de Charms, 1968). They like to control their own destiny. When the extrinsic reward
moves their locus of control outside of themselves, they may become less interested in the
task. But they also wonder: Why does my manager believe I cannot control myself? This
is not perceived as a compliment! Arguably, those who already have a poor relationship
with their management are more likely to interpret controls negatively than are those with
a good relationship with their management.
Conditions for Proper Application of Cognitive Evaluation Theory. Per Staw
(1976, quoted in Steers & Porter, 1979: 261), researchers still need to take these early
findings and “determine the exact conditions under which they might be expected to
hold.” The following discusses five conditions. These studies help by predicting more
precisely when extrinsic rewards will affect intrinsic motivation.
Condition 1—Incentive Salience. Ross (1975) showed through two experiments
that the reward offered had to be salient, or it would not affect intrinsic motivation. Deci
(1975) explained that rewards may not be salient enough to affect negatively intrinsic
motivation because they are perceived to be informational rather than controlling. This
may occur when the reward is interpreted as providing information related to one’s
competencies--which may enhance, rather than hurt, one’s feelings of control (Kanfer,
1990). Evidence for this was found by Harackiewicz, Manderlink & Sansone (1984).
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Similarly, Freedman, Cunningham & Krismer (1992) noted that the greater the incentive
offered, the more it will decrease intrinsic motivation.
Condition 2—Norms. Staw, Calder & Hess (1976) found that rewards decrease
intrinsic motivation only when there is a situational norm not to give extrinsic rewards
for the task. Fisher (1978) found that the same held for societal norms.
Condition 3—Pre-existing Level of Intrinsic Motivation.
Calder & Staw
(1975) found that extrinsic rewards only hurt intrinsic motivation when intrinsic
motivation is high. When intrinsic motivation is low, the rewards had a reinforcement
effect that increased overall motivation. Staw (1976) commented that most industrial
work settings do not meet the conditions for when extrinsic rewards will hurt intrinsic
motivation, because many work tasks are not highly intrinsically motivating and extrinsic
rewards are the norm.
Condition 4—Competence/Control Impact of Incentive. Two studies found
that incentives can increase intrinsic motivation if they increase the task’s level of
perceived challenge, provide the worker additional competence information, or increase
the perception of personal control over performance (Tripathi, 1991; Lopez, 1981).
Condition 5—Perceived Reason for Incentive. Calder & Staw (1975) also
cautioned that the perception of why the reward being offered is a key. “For example, if a
financial reward is perceived as a bonus for good work rather than as an inducement to
keep people on the job, it may not have a deleterious effect on the valence of intrinsic
outcomes” (Campbell & Pritchard, 1976: 104).
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In summary, how an incentive is perceived is just as important as its objective
attributes. In particular, it is likely that when the situation involves a highly intrinsically
motivating task and extrinsic rewards are not the norm, a salient, contingent extrinsic
reward will lead to lower intrinsic motivation for the task. In contrast, extrinsic rewards
will not negatively affect intrinsic motivation if: a) they are not salient or contingent; b)
they are already the norm; c) the task is not intrinsically motivating; d) the reward is
perceived to increase the worker’s feelings of competence or control; or, e) the reward is
perceived to be a compliment for good work. Applying these factors to XYZCo: a) The
incentive award was potentially large enough in monetary value to be salient. b) The
award was the norm since 1994, but the method of distributing the award changed in
1996 from the previous norm of team-based to individual performance-based; c) The task
is intrinsically motivating in the extreme; d) The incentive award, since no longer tied to
specific actionable measures, did not increase worker feelings of competence or control;
e) The incentive award could probably be more clearly interpreted as a compliment for
individual performance now. But since it was no longer team-based, it could also be
interpreted as more of a management “carrot,” and less of a compliment for good team
performance. These applications to XYZCo will influence the hypotheses tested.
Hypotheses-Incentives
Phase I’s qualitative data indicated that when the original incentive system was
installed in 1994, the incentives were tied to challenging team goals. However, the
method for distributing rewards changed in 1996.
The literature indicates that
challenging goals need to be quantifiable. Quantifiable goals provide the worker with
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greater perceived control over performance (and related rewards). The qualitative data
indicated that the new award distribution method was relatively subjective, making it
likely to be perceived as non-salient. Therefore:
Hypothesis 17: Since the incentive award system was recently changed from
specific, quantifiable team goals to a non-quantified individual performance at XYZCo,
most CSOs will say that the incentive plan goal was not challenging for them. Hence, the
incentive will not be perceived to be highly challenge salient, even though, in absolute
dollar terms, the goal is large enough to be considered monetarily salient.
Because the incentive will probably not be perceived as salient, and because the
workers probably have high levels of intrinsic motivation, the incentive will probably be
considered to have neutral or negative effects on CSO motivation. The changes that were
made to the incentive award structure will probably cause negative reactions in many of
the workers. Therefore:
Hypothesis 18:
When asked: a) if achieving their incentive plan goal was
challenging for them, and b) if the incentive plan has any other effects on them or their
team, the majority of the responses will indicate that the incentive plan has either little-tono effect or a negative effect on motivation.
The recent changes at XYZCo in how XYZCo’s incentive awards are divided will
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also mean the awards will probably not be regarded as being highly motivational. Rather,
they will be considered only “nice-to-have” by some workers, but punitive by those who
receive smaller than expected awards. This is because the workers’ views of an award
will probably change from a bonus for overall good team performance to a vehicle to
reward differentially “good versus bad” employees. The new way of awarding bonuses
may violate employee norms for how things should be done. Therefore:
Hypothesis 19: When asked: a) if the incentive plan has a positive motivating
effect on them and the team; b) if the incentive plan has a positive effect on their own and
the team’s conscientiousness; and, c) if the incentive plan has a positive effect on their
own and the team’s work effort, respondents will be significantly more negative than they
were for the other questions in the survey.
Several researchers have indicated that the relationship between workers and
management impacts the effectiveness of incentives in motivating employees. In the
context of budget controls, Hofstede said that “the interpersonal relation and
communication between superior and subordinate is of much greater importance for the
functioning of the organization than the power relationship” (1967: 58). Steers & Porter
(1979: 547) said that merit pay systems work best when trust and openness exist between
workers and management. Indeed, other studies suggest that pay-for-performance plans
may not work because of a lack of worker/management trust (Lawler, 1971; Steers &
Porter, 1979: 526-531). Lawler said that “No plan can succeed in the face of low trust
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and poor supervision, no matter how valid it may be from the point of view of
mechanics” (1971: 163). Steers & Porter (1979: 386) said that the perceived helpful
intent of controls leads to employee liking of the boss, which leads to greater
productivity.
The literature reviewed above indicated that the effects of incentives also depend
on worker perceptions of why the incentive was given.
Phase I’s qualitative data
indicated that worker perceptions are often influenced by worker relationships with
management.
Hence, worker relationships with management probably moderate the
effects of the incentive on the worker’s motivation, as Figure 5 indicates. Therefore:
Hypothesis 20: Responses to the questions in Hypothesis 19 will be significantly
more positive for those CSOs with a better relationship with management than those with
a worse relationship with management.
Another important factor will be whether the groups have a feeling of security
about their jobs. Workers in groups whose jobs are going away are more likely to feel
less motivated by the incentive than those whose jobs are secure. Therefore:
Hypothesis 21: Responses to the questions in Hypothesis 19 will be significantly
less positive for CSO groups with insecure positions.
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It is likely that respondents’ answers about the incentives’ effect on motivation are
to some extent driven by the level of motivation they possess about the job itself. That is,
how intrinsically motivated they are will probably be related to how they feel about their
own level of motivation. If they are highly intrinsically motivated, they will more likely
feel the incentive plan provides positive motivational effect. This hypothesis makes
intuitive sense, but is speculative, because it is not based on prior research. Therefore:
Hypothesis 22: The CSOs’ intrinsic motivation will be positively associated with
answers about the effects of the incentive plan on them and their team.
METHODOLOGY DETAIL
Chapter Five’s hypotheses were tested through a combination of qualitative and
quantitative methods.
To determine whether the incentives were perceived as challenge salient (H17),
the respondents to the telephone questionnaire were asked (on a 7-point scale) the extent
to which they agreed that achieving their most recent bonus goal was challenging for
them. Scores of four or less were considered “not challenging” responses, while those
over four were coded as challenging. A simple majority of “not challenging” responses
was considered adequate support for Hypothesis 17.
H18 (incentive plan  Motivational effect) was tested by coding the researcher’s
notes from respondents’ open-ended responses to the two questions implied in the
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hypothesis (Appendix C, questions 43 and 90). By open-ended is meant the responses
that CSOs used to comment on their agree/disagree answer to these questions. The
coding of these responses was done twice, once to capture whether answers were positive
or negative toward the incentive, and a second time to capture answers that specifically
stated that the incentive plan had little or no effect on the respondent’s or the team’s
motivation. These questions were asked of the respondents over a period of time from
about the date the award was given to about two months after the award was given.
Hence, the most recent award was fresh in their minds. A simple majority of negative
responses is considered support for Hypothesis 18.
H19 (incentive planMotivational effect) was tested by asking the respondents
the questions implied in the a), b), and c) parts of Hypothesis 19. Each of the three topics
was asked with two items. One item addressed the individual’s feelings about the team,
and the other question asked them to respond about themselves (see Appendix B,
questions 84-89). The six items could be joined into one construct called Motivational
Effect with a Cronbach’s alpha of 0.92. However, for this hypothesis, the answers will
each be analyzed separately by pair of questions. This is because the first pair asks for
general motivation effects, while the second and third ask for two specific types of
motivational effects: conscientiousness and work effort. The hypothesis was tested by
comparing the average responses to these three sets of questions with the average
responses to the other questions in the survey.
Hypothesis 19 will be considered
supported if: a) each of the three mean scores is in the bottom quartile when compared
with the mean scores in Appendix I; b) each of the three mean scores is significantly
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below the average of all mean scores shown in Appendix I. Test a) is probably the
stronger of the two tests. The test for b) will be an alpha = .05 significance T-test of the
difference between two means, as used to test Hypotheses 1-4 (Keller, Warrack & Bartel,
1988).
To test whether relationships with management made a difference (Hypothesis
20), the data were divided at the mean into a good relationship group and a poor
relationship group. The mean scores for opinions on the motivational effect of the
incentive awards were calculated, and then a one-way anova test performed. The same
test was done for those who were in secure versus insecure groups (Hypothesis 21). The
insecure group consisted of the hardware operators, whose functions management had
decided to largely automate.
Hypothesis 22 (Intrinsic MotivationMotivational effect) was tested by
correlating the degree of both enjoyment- and self-esteem-based intrinsic motivation
(Appendix C, average of questions 20-27) with respondents’ beliefs about the effects of
the incentive plan (Motivational Effect--average of questions 84-89 in Appendix C).
RESULTS OF HYPOTHESIS TESTING
Hypothesis 17 (incentive salience): H17 was supported. Fifty of eighty-six
(58.1%) of the respondents felt the goals were not challenging to obtain. Hence, the
incentive was not salient in terms of challenge. Prior year goals appeared to present a
mild to moderate challenge, based on responses. This occurred even though the annual
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award was found to be anywhere from zero to as much as somewhat above ten percent of
employee annual salary. Thus, the incentive was monetarily salient but not challenge
salient.
Hypothesis 18 (negative effect of incentive): H18 was supported. Eighty-four
percent of the responses were negative and 16% of the responses were positive about the
incentive plan in answers to questions 43 and 90. Although the researcher did not
specifically solicit this comment, 44% (37 of 84) of the respondents to these two
questions also stated that the incentive plan had little or no effect on their motivation or
actions, or those of the team.
Hypothesis 19 (Incentive planMotivational Effect):
H19 was strongly
supported. The mean score for the sum of the six questions asked about the incentive was
4.03 on a 1-7 scale, which is very low compared to the mean scores of other variables
(Appendix I). The detailed questions were then analyzed to obtain a more complete view.
On the question of the incentive’s general effect on motivation, scores were somewhat
more positive (mean = 4.58), while they were quite negative on how the incentive
specifically affected conscientiousness (mean = 3.78) and work effort (mean = 3.72).
Comparing these scores to the means in Appendix A, all three Motivational Effect scores
were in the bottom quartile of scores. In fact, excluding Micromanagement (reversescaled), Accountability (five-point scale) and Performance (scaled to be close to 4.0 on
average), Motivational Effect mean scores were the lowest of all the variables in the
study. T-tests revealed that each of the three Motivational Effect scores was significantly
123
below the average mean of all other Appendix I variables, which was 5.74. Hence, H19
was fully supported.
Hypothesis 20 (effect of Relationships): H20 was weakly supported. Responses
were more positive for those employees with better relationships with management (mean
= 4.36; n=43) versus those with worse relationships (mean = 3.69; n=43). However, a
one-way ANOVA revealed that the means were different at the moderately significant
p=.072 level.
Hypothesis 21 (effects of job security): H21 was not supported. Contrary to
prediction, responses were more positive for those in insecure groups (mean = 4.64)
versus secure groups (mean = 3.79). These two means differed at the significant p=.038
level.
H22 (intrinsic motivation  motivational effect) results: The respondents’
intrinsic motivation was significantly correlated with their beliefs about the motivational
effectiveness of the incentive plan (Motivational Effect), at r= .243; p=.012. Intrinsic
Motivation-Enjoyment and Intrinsic Motivation-Self-Esteem were about equally
correlated with Motivational Effect.
DISCUSSION OF RESULTS
Based on the results of Hypotheses 17-19, CSOs felt that since the incentives were
not challenging, the incentives only had general motivational effects rather than
specifically improving their conscientiousness or work effort. Those CSOs with higher
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intrinsic motivation and better relationships with their supervisors felt the incentives had
greater motivational effect. To understand these results better, the following discusses
the findings in light of other qualitative analysis of the questionnaires.
From the questionnaire interviews, CSO perceptions of the incentive plan had
changed since it was installed in 1994. While at first it was almost like a profit sharing
award tied to team goals, such as system availability, it became more of a carrot for
management to use to try to influence behavior by rewarding or punishing individual
performance. The reward value came through for those who received medium to large
awards. Qualitative responses from those who were dissatisfied with the incentive bonus
consistently indicated that it had a punitive effect for them, as Kohn (1993a,b) and
Simons (1995: 79) predicted. A number of respondents who were not satisfied with their
own award indicated that they felt the bonus award system was not equitable (e.g., it
worked like a “good old boy” system). Since incentive systems that punish have never
proven to be effective motivators (see review in Hergenhahn & Olson, 1993), XYZCo’s
system has a decidedly negative motivational effect on those who received lower than
expected rewards, in spite of the money devoted to it.
To further understand CSO views on how motivating the incentives were, the
researcher split the data in questions 84-89 by the perceived effect of the incentives on the
CSO versus on the team. Paradoxically, even though 69% of the CSOs were satisfied
(scores above 4.0) with their own recent incentive award, only 28% felt that most of their
co-workers were satisfied. This is probably because the comments they heard around the
shop were primarily negative. If so, this indicates a rumor-mill type of effect that is
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similar to how second-hand knowledge about a person can exaggerate the effects of
various factors on peoples’ trust of that person (Burt & Knez, 1996). The preponderance
of negative (84%) comments given the researcher also supports the rumor-mill effect.
Tests of Hypothesis 19 showed that the incentive plan had more general effects
(e.g., morale boosting--”it’s nice to have a bonus”), as opposed to helping the team’s
specific work effort motivation or its conscientiousness. The qualitative questionnaire
data supported this. Many respondents said they rarely thought about the incentive except
just before and just after it was given. Hence, the incentive probably had very little dayto-day motivational effect, even on those who were positive toward it. Rather, it probably
only acted like a general and temporary morale booster.
The results contradicting Hypothesis 21 (job security  lower motivational effect
of incentives) can be explained as follows. Even though those in the insecure groups are
just as highly intrinsically motivated as those in the other groups (Table 12), Chapter
Three found that those in insecure groups were more likely to explain what motivates
them with extrinsic, rather than intrinsic, factors (p= .000). Because of this tendency,
those in insecure groups were more likely to believe that extrinsic controls have
motivational effect.
Overall, the incentive plan tended to have either neutral or negative effects. While
none of the interviewees suggested that the incentive be done away, the workers’
consensus was two-fold:
a) the incentive did not significantly effect their specific
motivation to work harder or be more conscientious; and b) the 1996 changes made to the
126
incentive plan primarily had negative effects on worker morale. The latter effect was
more pronounced among those who were dissatisfied with their own award.
Incentives fit this study’s definition of controls in that incentives are used by
management to influence the work behavior of employees. Figure 5 posits that the
operator’s relationship with the supervisor moderates how effective the operator felt the
incentive was in motivating her/him and the team. Results from Hypothesis 20 support
the controls/relationship model, in that the mean Incentive Salience for those in the low
group was 3.69, while it was 4.36 for those in the high relationship group. Though the
difference is not significant at p = .05, this provides modest evidence that Relationships
moderate the effects of controls on worker motivation in the critical systems environment.
The literature search pointed out that controls may have either informational or
controlling effects, depending on how they are interpreted. The qualitative data showed
that a few workers felt that incentives were used by management as a carrot. These
employees said things like, “I don’t need a bonus to work hard.” Most employees did not
say this directly.
However, the fact that 44% made the unsolicited comment that
incentives had little or no influence on their work motivation reflects a less than favorable
attitude toward either the bonus or the way it was awarded.
Since this study’s data showed that Relationships may moderate the effect of
incentives on motivation, it makes a step towards resolving a paradox in the literature.
The literature on controls has been mixed on whether, or when, controls improve
motivation. Scholars have tried to explain contradictory results (e.g., Harackiewicz &
Larson, 1986; Pittman, et al., 1980; Ryan, 1982) by describing the feedback as controlling
127
versus informational. But this ignores the relationships between the controller and the
controllee. This study contends that the literature has been unable to unravel this because
of their neglect of interpersonal relationships. Adding personal relationships into the
analysis helps predict when controls will hurt motivation (i.e., when a poor relationship
exists). The relationship probably provides a lens by which the worker views the control
mechanism as either controlling or complimentary/informational.
In sum, Chapter Five found evidence that in XYZCo’s CSO environment:
 the incentive plan was perceived to have more negative or neutral effects
than positive effects;
 the incentive plan’s effects on motivation consisted more of general and
temporary morale boosting than increases in CSO work effort or
conscientiousness;
 CSO relationships with their supervisors modestly moderated the effects
of the incentive plan on perceived work motivation;
 CSOs in insecure groups were more likely to believe the incentives had a
positive effect on worker motivation—probably because they are
more extrinsically oriented;
 those CSOs with higher levels of intrinsic motivation were generally
more positive about the effects of the incentive plan on CSO
motivation;
128
 since CSOs were generally satisfied with their own incentive award,
many of the negative perceptions they had about the incentive
probably related to how they felt other CSOs perceived the
incentive award process, a “rumor-mill” effect.
129
CHAPTER SIX: OTHER CONTROLS—ADDING RELATIONSHIPS
Ch
Prop:
Content or Model
2
--
Methodology and Construct Validation
3
1
Nature of the
Critical Systems
Operator Job
4
High Levels of
Motivation
2, 3
Job Characteristics
Growth Need Strength
Critical
Psychological
States (CPS)
Relationships
5
Work
Outcomes
System Trust
4, 5
Incentive
Controls
Motivational
Effect
Relationships
6
4, 5
Other
Controls
Relationships
7
--
Motivation
Motivational
Outcomes
System Trust
Contributions, Limitations, and Future Research
This chapter first develops hypotheses relating four types of Controls,
Relationships, and System Trust to critical systems operator Motivation. The Motivation
construct consists of Job Satisfaction, Experienced Work Meaningfulness, Organizational
130
Commitment, Intrinsic Motivation--Self-Esteem, and Intrinsic Motivation--Enjoyment.
Then, the methods are detailed and the results are reported and discussed.
THEORY BUILDING
Conceptual Model Building--Accountability
Accountability at XYZCo. Accountability means being held responsible for an
action or event.
This occurs either by receiving some consequence from the event
(Tetlock, 1985), or by being asked to give a verbal or written account or explanation of
the event (Cummings & Anton, 1990). When an outage occurs at XYZCo, someone
begins to account for it immediately. The supervisor is contacted right away, and outages
over five minutes are reported to higher levels of management. At the end of the shift, an
incident report is created by the supervisor to explain why the outage occurred and what
was done about it. Those in higher levels of management felt keenly aware of the need to
account for the health of the system on a daily basis. The Vice President and division
President read a system situation report first thing each morning. However, when the
system went down, these executives required almost minute by minute reporting so that
they could communicate up the line to their corporate leaders.
In other words,
accountability is a constant in this critical systems organization.
AccountabilityMotivation.
This type of accountability appears to have
positive motivational consequences in terms of making workers aware that their job was
important. CSOs appear to interpret constant accountability as a signal of management
interest in system health. Since operators know how keenly management was interested
131
in keeping the system up, they know their jobs are critically important. This raises their
level of pride in their work, and positively affects their self-esteem. Overall, then, this
type of accountability primarily has positive motivational effects at XYZCo.
Relationships as Moderator. However, accountability also had negative effects.
For example, one employee involved in accounting to a disliked boss appeared to be demotivated by the accounting process. This appeared to happen because the process had a
negative effect on her/his self-esteem. Thus, accountability controls can have either
positive or negative effects on motivation. What helped resolve this paradox was the
relationship between the CSO and the manager who held the CSO accountable. When
the CSO had a good relationship with the manager, the accountability control had positive
impacts. When the CSO had a poor relationship with the manager, the accountability
control had a negative impact on the worker’s motivation. Hence, as Figure 5 shows, the
relationship moderates the effects of Management Controls on Motivation.
Scientific Model Building--Accountability
AccountabilityMotivation.
Cummings
&
Anton
(1990)
said
that
accountability leads to felt responsibility (a JCM motivation construct). Steers & Porter
(1979, p. 324) reported that whatever leads to definite expectations leads to felt
responsibility, which results in organizational commitment (another motivation
construct). Since: a) strong accountability leads to definite expectations in terms of
having to account for oneself, and b) definite expectations lead to felt responsibility and
organizational commitment, then c) accountability should lead to felt responsibility and
132
organizational commitment. Tetlock (1985) agreed that accountability should lead to
higher motivation in terms of experienced work meaningfulness. Therefore:
Hypothesis 23: The perception of accountability by the CSO will be positively
related with the CSO’s Motivation.
Relationships as a moderator. Tetlock pointed out that the relationship between
the two parties could make a difference. He said that having a good relationship makes
one want to account for their task. Having a bad relationship makes accountability
threatening, especially if job insecurity is present. Cummings & Anton (1990) came to a
similar conclusion.
They theorized that the worker’s perceptions of management’s
attitude toward them determine whether accountability becomes a mentoring or a
controlling/monitoring system. The person held accountable will respond in accordance
with the motives s/he perceives in management. This suggests that Relationships will
moderate the effects of Accountability on Motivation. Therefore:
Hypothesis 24:
Relationships will moderate the effect of Accountability on
Motivation. Those CSOs with positive Relationships with their supervisors will more
likely have significant positive links between Accountability and Motivation.
Conceptual Model Building—Feedback
Feedback at XYZCo.
In this study’s context, feedback refers to information the
133
supervisor gives the critical systems operator about how s/he is doing on the job. Most
feedback comes to CSOs in an informal way during day-to-day interaction with their
supervisors. Only one instance of feedback was found in the qualitative data. In this
instance, the feedback was negative, and the existing relationship between supervisor and
CSO was negative. The CSO indicated that the experience had a negative effect on the
CSO’s morale.
Scientific Model Building--Feedback
FeedbackMotivation. In general, feedback has been found to have positive
effects on motivation (Gear, Marsh & Sergent, 1985), motivation-related productivity
(Gallegos & Phelan, 1977; Pritchard & Montagno, 1978), and job satisfaction (Sarata &
Jeppesen, 1977). Feedback has been found to be correlated positively with organizational
commitment (Ivancevich & McMahon, 1982). Feedback has had positive correlations
with intrinsic motivation (Cusella, 1982; Ivancevich & McMahon, 1982; Shanab,
Peterson, Dargahi & Deroian, 1981).
Hypothesis 25: Perceived feedback from the supervisor will be positively related
with the CSO’s Motivation.
However, feedback has not always been found to positively affect Motivation.
Formal feedback in the form of performance appraisals caused the organizational
commitment of satisfactory (less than outstanding) employees to drop (Pearce & Porter,
134
1986). Some have found that negative or controlling feedback hurts intrinsic motivation
(Deci, 1972; Harackiewicz & Larson, 1986; Ryan, 1982). These findings suggest that
there may be a need for a moderator in the Feedback  Motivation equation.
Relationships as a Moderator.
Although several treat topics close to Relationships (e.g., social mediation) and
feedback (Guzzo, 1979), Harackiewicz and Larson (1986) come the closest to connecting
feedback, superior/subordinate relationships, and intrinsic motivation. They proposed
that the supervisor’s feedback style impacts intrinsic motivation. A controlling feedback
style will undermine intrinsic motivation, while a supportive feedback style enhances it.
In their experiment, Harackiewicz and Larson operationalized controlling feedback style
by the printed messages subject supervisors chose to give to their subordinates. In this
way, the control mechanism became part of the experiment. Harackiewicz & Larson did
not hypothesize or test any effect of the relationship between the superior and the
subordinate. Interestingly, the Harackiewicz & Larson study found that in the no-rewardfor-subordinate condition, the control mechanism had a positive effect on intrinsic
motivation, contradicting prior results (Pittman, et al., 1980; Ryan, 1982). The authors
gave the plausible explanation that the controlling behavior contained informational
feedback, which may have overpowered the negative effect of the controlling behavior.
No measure of the subordinate’s feeling of being controlled was made.
But some who have read this important study felt there may be more to it. Kanfer
commented on the Harackiewicz & Larson (1986) study: “As suggested by Dyer and
Parker (1975), normative beliefs associated with the broader context in which behavior
135
occurs appear to influence the interpretation of events and intrinsic motivation. In the
Harackiewicz and Larson (1986) study, subordinates performing a novel task might have
construed the situation as one in which the supervisor’s feedback was designed to help
the subordinate master the task, thus reducing the perception that the feedback was
controlling.” (1990: 91) The point here is that the positive social and structural context
surrounding the task can make enough difference to reverse the expected results of this
highly controlled experiment. A worker with a good relationship with the supervisor
would be more likely to interpret the supervisor’s feedback as helpful, rather than
controlling.
This study contends that one of the key contextual variables not being taken into
account in these feedback experiments is the relationship between the superior and
subordinate. There are hints in the literature that relationships are important. Earley
(1986), for example, found that feedback is more effective in influencing a worker’s
performance if the worker trusts the feedback giver.
Earley (1988) found that the
feedback source (i.e., supervisor or computer) influenced a person’s level of trust in the
feedback. Lawler & Rhode (1976) found that, in order to motivate positively, feedback
should come from a trusted source. Therefore:
Hypothesis 26: Relationships will moderate the effects of Feedback on CSO
Motivation. Those CSOs with positive Relationships with their supervisors will more
likely have significant positive links between Feedback and Motivation.
136
Conceptual Model Building--Micromanagement
MicromanagementMotivation. Micromanagement means that a supervisor
gets so deeply involved with the worker’s task that s/he takes over the task. From Phase I
interviews, the extent to which CSOs were micromanaged could not be ascertained. One
worker reported that a former supervisor knew so much about certain topics that s/he
would “take over” for the worker. The interviewee reported that this made him/her feel
bad about being in that job, and he soon found a better position. S/he said that in general
s/he had a good relationship with the boss, but that these actions had put a strain on the
relationship.
Relationships as a Moderator. One interviewee described his/her reactions to
the widely varying management styles of a former and current boss. One of the bosses
used a hands-off approach, while the other was very hands-on. The hands-on manager
would actually come in and take over the job for the operator on occasion.
The
interviewee’s reactions to the hands-on manager was intriguing. Whereas the researcher
would have predicted that this obtrusive management style would demotivate the worker,
s/he reported feeling good about the manager’s actions. S/he interpreted the manager’s
actions as a kind of training/helping function. In further questioning, the researcher found
that the operator had worked with the hands-on supervisor for many years, and had a very
good relationship with the supervisor. This led the researcher to speculate that the
operator’s relationship with the supervisor moderated the potentially negative
motivational effects of the supervisor’s micromanagement actions.
137
Scientific Model Building--Micromanagement
MicromanagementMotivation; Relationships as a Moderator. Standing by
a worker without taking an active part can lead to higher felt responsibility by the worker,
per Steers & Porter (1979:323). But standing over the worker may reduce the worker’s
intrinsic motivation--enjoyment (Steers & Porter, 1979: 323) or job satisfaction (Ouchi &
Maguire, 1975). Similarly, Creed & Miles (1996) said that overmanagement may lead to
lower morale.
It is speculated that Relationships make a difference in how
Micromanagement affects Motivation. Therefore:
Hypothesis 27:
Perceived micromanagement from the supervisor will be
negatively related with the CSO’s Motivation, moderated by Relationships.
138
Conceptual Model Building--Autonomy
AutonomyMotivation. Autonomy is not the opposite of Micromanagement.
Micromanagement refers to doing work, while autonomy refers to decision making. This
study defines Autonomy as the extent to which an employee is allowed to make decisions
pertaining to his/her own job functions. One employee reported that s/he was constrained
from making his/her own decisions. S/he contrasted this with the autonomy s/he was
given by a former boss when s/he was in a less responsible position. S/he said s/he felt
better about his/herself and his/her authority in the less responsible position than s/he felt
now. S/he saw the lack of autonomy in his/her current job as a clear sign that the present
supervisor did not trust him/her. The lack of autonomy granted appeared to have a
negative effect on the worker’s morale.
Scientific Model Building--Autonomy
AutonomyMotivation. Several researchers have found that autonomy leads to
greater job satisfaction (e.g., Jayaratne, Vinokur-Kaplan & Chess, 1995; Kakabadse,
1986). The JCM has connected autonomy with felt responsibility and other general job
attitudes (Hackman & Lawler, 1971; Hackman & Oldham, 1975). Research on Autonomy
has provided fuel for the practical business press on empowering or liberating workers
(e.g., Peters, 1992). Steers & Porter (1977) and Rosin & Korabik (1991) found that
autonomy is positively related to organizational commitment. Deci and Ryan (1987)
found that autonomy promoted intrinsic motivation, as have others (e.g., Goudas, Biddle
& Underwood, 1995; Green & Foster, 1986). Therefore:
139
Hypothesis 28:
Perceived autonomy from the supervisor will be positively
related with the CSO’s Motivation.
Relationships as a Moderator.
As already justified in Chapter Four,
Relationships is proposed to moderate the effect of Autonomy on motivational variables.
Therefore:
Hypothesis 29: The effects of Autonomy on Motivation will be moderated by
Relationships. Those CSOs with positive Relationships with their supervisors will more
likely have significant positive links between Autonomy and Motivation.
Scientific Model Building--Work Outcomes
Motivation Individual Performance. The motivation literature shows that
highly motivated workers are likely to produce better outcomes, both in terms of
productivity and general performance (Steers & Porter, 1979). While this linkage does
not always hold (Griffin, Welsh & Moorhead, 1981), it holds often enough to propose a
link between a CSO’s motivation and individual performance. Therefore:
Hypothesis 30:
CSO Motivation will be positively associated with CSO
Individual Performance.
140
Motivation  Contribution to Team Effectiveness.
This study linked
Motivation to the CSO’s Contribution to Team Effectiveness, with subconstructs relating
to team cooperation, communication, conflict resolution, and overall team effectiveness
(see Figure 9).
Contribution to Team Effectiveness was also linked to Individual
Performance, measured objectively by the supervisor’s recall of the latest official rating
of the CSO.
To date, very little research has been done to see how motivation affects
functioning as a team. Thus, the linkage between Motivation and Contribution to Team
Effectiveness is considered speculative. However, it makes sense that highly motivated
employees will like their jobs and will get along with those with whom they work.
Getting along with others on the job is connected with the construct Contribution to Team
Effectiveness (CTE), because CTE includes communication and cooperation, which are
manifestations of getting along. Therefore,
Hypothesis 31:
CSO Motivation will be positively associated with CSO
Contribution to Team Effectiveness.
Hypothesis 8 already demonstrated the strong linkage between Contribution to
Team Effectiveness (CTE) and Individual Performance.
141
System Trust’s Impact on Motivation
In Chapter 4, it was argued that System Trust will be related to such Work
Outcomes as Job Satisfaction and Intrinsic Motivation, which are included in the
Motivation construct.
Further, System Trust is probably related to Organizational
Commitment, since it reflects how an operator feels towards the work environment.
System Trust will also probably be related to Experienced Meaningfulness, since an
unfair work environment would tend to cast doubt on how important the work is.
Workplace unfairness can reduce general motivation levels. For example, from studying
budget controls, Hofstede (1967: 56) reported that feelings of unfairness regarding
management “are strongly demotivating.” Thus, System Trust will probably be predictive
of Motivation, comprised of the above discussed constructs. Therefore:
Hypothesis 32:
CSO System Trust will be positively associated with CSO
Motivation.
METHODOLOGY DETAIL
Regression analysis was used to test Chapter Six’s hypotheses, including the
moderation effects. The researcher also used a second method to test for moderation.
The researcher divided the questionnaire results into groups:
those with high
Relationship scores and those with low Relationship scores. The means for the Control
and Motivation variables were then calculated and a T-test done to see if there was a
mean difference. This was done with two groups (low-high) and three groups (low142
medium-high) to see how significant, or close to significant, the moderator was.
McClelland and Judd (1993) said that showing a moderation effect using regression
techniques on survey data is many times more difficult than showing a moderation effect
using ANOVA on experimental data. The sensitivity analysis employed here seemed
appropriate given the difficulty in seeing a moderation effect on field data using a
regression analysis.
RESULTS OF HYPOTHESIS TESTING
Table 16 presents a set of correlation matrices of the hypothesized variables.
Table 16 shows at a glance which variables are most closely related with Motivation
(Feedback, Relationships and System Trust). These correlations are not intended to test
the hypotheses, but are shown to provide information clarifying the regression results.
Table 17 presents the results of the related regressions.
Hypotheses 23-32. Relationships and Feedback were the only variables that were
significant in the full regressions for Hypotheses 23-29 (Table 17). Based on Table 17’s
results, only Hypotheses 23, 25, and 32 were supported. Relationships was not found to
significantly moderate the linkage between any of the Controls and Motivation.
However, Relationships by itself was a significant predictor of Motivation (as shown in
Table 17 between H24 and H25). Since Feedback was highly correlated with
Relationships, only one of the two could be used as a predictor without introducing
multicollinearity. Hence, the better model would employ only Feedback, with an
143
Table 16 Management Controls / Relationships Model—Correlation Tables
H#
23
24
24
25
26
26
27
27
27
Motivation
Accountability
Relationships
Accountability X
Relationships
Motivation
Feedback
Relationships
Feedback X
Relationships
Motivation
Micromanagement
Relationships
Micromanagement
Relationships
X
Motivation
1.0
.23
.28
-.27
Accountability
Relationships
Accountability
X Relationships
1.0
.40
-.38
1.0
-.55
1.0
Motivation
1.0
.32
.28
-.15
Feedback
Relationships
Feedback X
Relationships
1.0
.71
-.40
1.0
-.81
1.0
Micromanagement
Relationships
Micromanagement X
Relationships
1.0
.10
-.12
1.0
-.17
1.0
Motivation
1.0
.06
.28
-.01
Autonomy
Relationships
Autonomy X
Relationships
1.0
.06
-.07
1.0
.11
1.0
Motivation
1.0
-.20
.28
-.26
28
29
29
Motivation
Autonomy
Relationships
Autonomy X
Relationships
Performance
Relationships
31
30
Motivation
1.0
.14
.10
.28
CTE
Motivation
CTE
Performance
Relationships
1.0
.84
.28
1.0
.23
1.0
Motivation
1.0
.35
System Trust
Motivation
System Trust
32
1.0
144
adjusted R-squared of .089 and model significance of p=.003. Relationships by itself
produces an adjusted R-squared of .069 and p= .008.
Table 17 Management Controls / Relationships Model—Regression Results
Dependent
Variables
Motivation
Motivation
R2adj
.040
.075
Fstat
.036
.025
Significant
Constructs
Accountability
--

.226
--
p
.036
--
Relationships
Motivation
.069
.008
Relationships
.283
.008
25
26
Feedback
Feedback
+ Relationships
+ Feedback X
Relationships
Motivation
Motivation
.089
.079
.003
.021
Feedback
--
.316
--
.003
--
27
27
Micromanagement
Micromanagement
+ Relationships
+ Micromanagement X
Relationships
Motivation
Motivation
-.012
.074
.961
.026
-Relationships
-.263
-.016
28
29
Autonomy
Autonomy
+ Relationships
+ Autonomy X
Relationships
Motivation
Motivation
-.008
.050
.582
.066
-Relationships
-.285
-.009
30
Motivation
Performance
-.002
.369
--
--
--
31
Motivation
CTE
.007
.205
--
--
--
32
System Trust
Motivation
.112
.001
System Trust
.350
.001
H#
23
24
Independent Variable(s)
Accountability
Accountability
+ Relationships
+ Accountability X
Relationships
145
In Hypotheses 30-31, Motivation predicted neither CTE nor Individual
Performance. In exploratory mode, Relationships by itself was found to be predictive of
CTE, with an R-squared of .070, Beta = .284, and p = .008. Supporting Hypothesis 32,
System Trust was strongly related with Motivation, with a higher R-squared than either
Feedback or Relationships. Only System Trust explained more than ten percent of the
variance in Motivation.
Tables 18 through 21 present the moderation sensitivity analysis by variable.
Tables 18 through 21 show the moderation analysis based on dividing each variable into
halves and then thirds. Using thirds was done in case the moderation effect related only
at the two extreme values of the data.
That is, a moderation effect may only be
significant among those with relatively high or low Relationship scores. Using the top
third and lower third captures this possibility. A Relationships moderation effect is
represented by a table in which there is a significant difference in Motivation means ()
between the left side and the right side of the table. That is, at a given level of the
Controls variable, moving from low to high Relationships resulted in a significant
increase in the mean of the Motivation scores.
For example, in the second part of Table 18, which represents high and low thirds,
one sees that in the case of the low Accountability scores, a change from low to high
Relationships scores produced a significantly higher (p= .04) set of Motivation scores.
Therefore, Relationships is said to have moderated the effects of Accountability on
Motivation. This effect appears in the “thirds” analysis for Accountability and Feedback,
and in the “halves” analysis for Micromanagement. In each case, the difference is only
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significant when the Controls variable is in the “low” condition. This provides modest
evidence that Relationships moderates the effect of these Controls variables on
Motivation, supporting Hypotheses 24, 26, and 27.
Table 18 Sensitivity Analysis for Relationships Moderation of Accountability
Low Half
Accountability
Significance
of Difference
High Half
Accountability
Low Half
Relationships
 = 6.13; n=24
Significance
Of Difference
not significant
(n.s.)
n.s.

= 6.30; n=19
Low Third
Relationships
 = 5.95; n=10
High Half
Relationships
 = 6.45; n=17
n.s.
n.s.

Significance
Of Difference
p = .041
High Third
Relationships
 = 6.51; n=4
= 6.55; n=26
Low Third
Accountability
Significance
n.s. (p=.08)
n.s.
of Difference
High Third
 = 6.36; n=9
n.s.
 = 6.53; n=30
Accountability
Note:  refers to the mean of the Motivation scores in this cell.
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Table 19 Sensitivity Analysis for Relationships Moderation of Feedback
Low Half
Significance
High Half
Relationships
Of Difference Relationships
Low Half
 = 6.11; n=35 n.s.
 = 6.33; n=11
Feedback
Significance
n.s. (p=.09)
n.s.
of Difference
High Half
 = 6.61; n=8
n.s.
 = 6.57; n=32
Feedback
Low Third
Significance
High Third
Relationships
Of Difference Relationships
 = 6.12; n=21 p = .041
 = 6.48; n=9
Low Third
Feedback
Significance
n.s. (p= .053)
n.s.
of Difference
High Third
 = 6.75; n=1
n.s.
 = 6.56; n=35
Feedback
Note:  refers to the mean of the Motivation scores in this cell.
Table 20 Sensitivity Analysis for Relationships Moderation of Micromanagement
Low Half
Micromanagement
Significance
of Difference
High Half
Micromanagement
Low Half
Relationships
 = 6.16;
n=21
n.s.
Significance
Of Difference
p = .01
High Half
Relationships
 = 6.58; n=22

= 6.24;
n=21
n.s.

Low Third
Relationships
 = 5.98; n=8
Significance
Of Difference
n.s. p = .051
High Third
Relationships
 = 6.53; n=11
n.s.
= 6.44; n=22
Low Third
Micromanagement
Significance
n.s.
n.s.
of Difference
High Third
 = 6.26; n=9 n.s.
 = 6.46; n=26
Micromanagement
Note:  refers to the mean of the Motivation scores in this cell.
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Table 21 Sensitivity Analysis for Relationships Moderation of Autonomy
Low Half
Autonomy
Significance
of Difference
High Half
Autonomy
Low Half
Relationships
 = 6.12; n=24
Significance
High Half
Of Difference Relationships
n.s. (p = .069)  = 6.45; n= 20
n.s.
n.s.


= 6.31; n=19 n.s.
Low Third
Relationships
 = 6.12; n=8
Significance
Of Difference
n.s.
= 6.56; n= 23
High Third
Relationships
 = 6.53; n=12
Low Third
Autonomy
Significance
n.s.
n.s.
of Difference
High Third
 = 6.30; n=8
n.s.
 = 6.57; n=22
Autonomy
Note:  refers to the mean of the Motivation scores in this cell.
Eliminating Plausible Alternatives
In order to establish these hypotheses’ internal validity with greater confidence,
the researcher entered a number of plausible alternatives into the equations predicting
Motivation, CTE, and Performance. These included demographic variables (age, grade
level, education), individual situation variables (number of recent promotions, number of
recent pay raises, percent of time keeping systems available, duration of time worked with
supervisor), and variables providing possible alternative explanations (interaction with
team members, interaction with supervisor, relationship with team members). Interaction
with, and duration of time worked with, the supervisor were suggested by research on
trust (e.g., Burt & Knez, 1996). Interaction and relationship with team members was
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suggested by the social needs emphasis of the JCM. None of these variables added any
significant predictive value to the model’s most significant equations (i.e., System Trust
 Motivation; Relationships  CTEs). With these plausible alternatives eliminated,
one can have greater confidence in the internal validity of the best equations for these
models (see Table 17).
DISCUSSION OF RESULTS
Of the Control constructs, only Feedback and Accountability were significantly
related to Motivation. Neither Autonomy nor Micromanagement had any significant
effect on the CSOs’ Motivation. Further analysis revealed that Micromanagement did not
significantly (p=.05, one-tailed test) correlate with any of the motivation-related variables
that comprised Critical Psychological States, Work Outcomes, or Motivation. Autonomy
significantly correlated with only one (Knowledge of Results, r = .323; p<.01). In light of
both the research cited above and the ground swell of practitioner support for
‘empowering’ or ‘liberating’ workers (e.g., Peters, 1992), this finding is very surprising.
This underscores the possibility that empowering by itself may not have as strong of an
effect on motivation as the CSO/supervisor relationship.
In contrast, Feedback from the Supervisor appears to be strongly positively related
to the worker’s Motivation, as is Accountability to a lesser extent. Relationships itself
relates positively to Motivation, and is more strongly related to Motivation than any of
the Controls except Feedback. This is interesting because Feedback is the control that
apparently has the most to do with Relationships, given its high correlation with
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Relationships. This high correlation indicates that either frequency of feedback leads to
good relationships or that good relationships leads to frequent feedback (or both). Hence,
Feedback may be thought of as a characteristic of the relationship. Hence, an important
finding of this study is that operator/supervisor Relationships (and the related Feedback)
have a more power for predicting Motivation than do the Control types. Even though
Relationships has a stronger direct, than interactive, effect on Motivation, the sensitivity
analysis shows that Relationships does have some impact on the strength with which
most of the Controls affect Motivation. In particular, above average Relationships were
associated with significantly higher Motivation among those CSOs with below average
Micromanagement scores. Similarly, top third Relationships scores were associated with
higher Motivation among those with lower third Feedback and Accountability.
The fact that Relationships and Feedback frequency are so highly correlated is in
itself important. Argyris (1975) argued that those who act within controlling
environments will develop poor relationships with the controller and will come to seek
little feedback. It is also likely that a supervisor with a poor relationship with the
employee will have less desire to give feedback.
From the other direction, the
infrequency of feedback will leave questions in the mind of the worker, which will lead to
suspicion and, over time, to lower trust levels (Holmes & Rempel, 1989). The power
differences between the worker and supervisor tend to increase the levels of suspicion and
distrust (Kramer, 1996). Hence, XYZCo data supports the idea that relationships and
feedback tend to reinforce each other over time.
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One possible reason why Autonomy had so little predictive power is that
Autonomy is so crucial in the critical system environment (Weick, 1990) that almost all
CSOs have high degrees of autonomy. The data shows some evidence of this (Appendix
I), in that the mean for Autonomy was 5.97 out of 7.00. Also, while answering the
questionnaire, two supervisors said they give all their CSOs full autonomy on the job.
Also somewhat surprising, Relationships predicted CTE better than did
Motivation. Also, Autonomy predicted CTE and Individual Performance even better than
did Relationships.
It is probable, however, that the causality is the opposite for
Autonomy. That is, it is possible that those who are the best performers are given the
most Autonomy by their supervisors.
Hence, Individual Performance is probably a
predictor of Autonomy, rather than the converse. The finding that Motivation did not
predict Individual Performance parallels the Chapter Four finding that CPS did not
predict Individual Performance. Again, this is probably because other variables, such as
Contribution to Team Effectiveness, skill, knowledge, and ability are more salient
predictors of Individual Performance.
Note that the only Table 17 R-squared that exceeds .10 is the prediction of
Motivation by System Trust, which is only .112. By contrast, Chapter Four’s significant
JCM equations had R-squares in the .20-.49 range. It appears from this that these
controls have far less motivational impact on CSOs than do the characteristics of the job.
Based on Chapter Five, the same is probably true of incentive controls.
In sum, Chapter Six found evidence that:
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 System Trust was the best predictor of Motivation, followed by
Feedback;
 of the other Controls, only Accountability was a significant predictor of
Motivation;
 Relationships itself predicted Motivation better than any Control except
Feedback;
 Relationships had a modest moderating effect on how Controls affect
Motivation, especially for Relationships in the top or bottom third;
 Relationships predicted CTEs better than did Motivation;
 Although a direct comparison cannot be made, Controls appear to
have less effect on CSO motivation than do the JCM variables.
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CHAPTER SEVEN:
CONTRIBUTIONS, LIMITATIONS, AND FUTURE RESEARCH
Ch
Prop:
Content or Model
2
--
Methodology and Construct Validation
3
1
Nature of the
Critical Systems
Operator Job
4
High Levels of
Motivation
2, 3
Job Characteristics
Growth Need Strength
Critical
Psychological
States (CPS)
Relationships
5
Work
Outcomes
System Trust
4, 5
Incentive
Controls
Motivational
Effect
Relationships
6
4, 5
Other
Controls
Relationships
7
--
Motivation
Motivational
Outcomes
System Trust
Contributions, Limitations, and Future Research
154
CONTRIBUTIONS
This research contributes to both theory and practice.
To Theory
The current literature lacks models fully explaining the paradoxical effects of
controls.
That is, controls sometimes have positive effects and sometimes negative
effects (e. g., Harackiewicz & Larson, 1986; Powers & Dickson, 1973). This suggests a
hidden moderator is present (Sitkin & Pablo, 1992). While the nature of the feedback
itself has been examined as a moderator, the contextual relationships between parties,
though suggested by Kanfer (1990), has not been examined. This study developed and
tested a model that used relationships as a moderator of the Management
Controls/Motivation link. Building on existing theory, the Controls/Relationship model
helps explain prior paradoxical empirical findings on Controls by including
Relationships. When CSO/supervisor relationships were positive, Management Controls
had a stronger positive influence on motivation than when these relationships were
negative. Adding the Relationships and System Trust constructs improved the predictive
power of the Management Controls model of motivation.
Through the use of Relationships and System Trust, the study also extended the
predictive power of the Job Characteristics Model. Even though the job of the CSO is
highly intrinsically motivating, Relationships and System Trust were found to be
predictive of JCM dependent variables in the presence of job characteristics predictors.
While this study says Relationships and System Trust are important, it does not claim that
155
job characteristics are not important. Indeed, job characteristics were the most salient
predictors of CSO motivation in this study.
This research also fills a key gap in the management information systems domain
by analyzing and describing the critical computer systems operator (CSO) job vis-a-vis
that of traditional computer operators and system developers. The CSO job is critical
because of the urgent need to restore systems that crash. Two recent five-hour e-mail
blackouts at America Online (Quick, 1997) underscore again the need to keep highly used
systems running 100% of the time. In addition, the System Trust and Relationships
constructs are introduced for the first time in psychologically measurable form in this
study.
This study helps explain the paradoxical research findings regarding Management
Controls. While Controls have been found to motivate employees (Eisenhardt, 1985;
Henderson & Lee, 1992; Tetlock, 1985), controls often have dysfunctional effects (e.g.,
Lawler & Rhode, 1976; Powers & Dickson, 1973). Unraveling paradoxes is a highly
recommended theory-building process (e.g., Poole & Van de Ven, 1989). Resolving
paradoxes often requires that researchers incorporate moderator or mediator variables
between independent and dependent variables (e.g., Sitkin & Pablo, 1992). Perhaps one
reason the economics literature has been unable to unravel the control paradox is because
it assumes that interpersonal relationships are not important. This study adds value by
positing Relationships as a moderator of the traditional Management Controls 
Motivation link. From the results of this study, it is proposed that Management Controls
have positive effects on Motivation when worker/supervisor Relationships are positive,
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but negative effects when Relationships are negative. Adding personal relationships into
the analysis helps explain when controls will hurt motivation (when a poor relationship
exists), but also how (through threatening the self-esteem of the worker).
This study also calls for broader-based motivation research approaches. Most
studies of motivation have focused narrowly on one kind of factor (e.g., incentives, job
characteristics) or outcome (e.g., organizational commitment, intrinsic motivation). This
study found that one can measure the relative strength of several motivational factors.
Further, in a given context, one can test to see which motivational paradigm (i.e.,
controls, job characteristics) works best.
To Practice
Two major paradigms of management have dominated U. S. Corporations in
recent years. One, based on the Hackman/Oldham model, says to redesign work to
increase productivity and decrease worker costs.
In the process, managers should
empower or ‘liberate’ (Peters, 1992) the workers by giving them more autonomy to do
their job. Similarly, numerous corporations have pursued “reengineering” (Hammer &
Champy, 1993) with the charge to enrich the worker’s job (e.g., Davenport, 1993)--but
without considering people relationships. The ‘gurus’ of the reengineering movement
now admit that they forgot the people part (Wall Street Journal, November, 1996). This
study points out that autonomy by itself may not be nearly as motivating as simply a good
worker/supervisor relationship.
Further, a reengineered job may motivate, but that
motivation can be enhanced by a positive worker/boss relationship. The other paradigm,
based on economics, said that corporations can be most successful by giving corporate
157
agents salient incentives to encourage them to do the right thing for corporate principals.
Some are now beginning to show that this paradigm does not consistently work either
(e.g., Kohn, 1993a). This study’s findings say that each of these two paradigms is
inadequate! That is, proper management of people relationships and the fairness of the
company’s work environment are also required for effective corporate management. The
“traditional models of authority” often assume that “managers must closely supervise
their employees and cannot trust them” (Tyler & Kramer, 1996: 6). The relationships
findings of this study shed new light on authority models of management by pointing out
that the manager/worker relationship is itself an overlooked key to the worker’s
motivation. Since trust is the key component of Relationships, managers should work to
improve the level of trust between them and their employees. Creed and Miles (1996: 36,
19) pointed out that, by “taking the initiative in trusting,” management plays “a central
role” in determining a unit’s levels of trust. This study’s System Trust findings also
provide evidence that “managers need to create an environment in which workers can be
trusted” (Ibid.)
Popular management books and articles are also beginning to emphasize trust
between workers and managers (e.g., Covey, 1989; Peters, 1992). Further, the role of
workplace justice is being explored in management books: “[Workers] must trust that
you will treat them fairly if they make mistakes” (Campbell, 1997). This is especially
important in light of evidence that worker/management relationships have eroded because
of the layoffs, downsizing and other insecurity-building management practices (e.g.,
Associated Press, 1997). One recent study of 215 companies found that “trust has
158
declined in three out of four workplaces during the past two years” (Jones, 1997: 1). The
lack of worker trust in management is commonly associated with lower worker loyalty to
the company (e.g., Jackson, 1997).
Jackson said that the “new contract” between
employees and employers has left employees feeling like they are on their own. Hence,
worker loyalty , based on trust, is at an all-time premium in the corporate workplace. In
the critical system environment, in which a large store of knowledge and skill must be
kept to face the next unpredictable contingency (Weick, 1990), loyalty and retention of
skilled workers is of paramount importance.
This study also emphasizes that giving out incentives may not motivate. Rather
than just giving incentives, managers need to find out first what motivates their workers
and then try to give it to them (Pinder, 1991). In this study’s research site, the operators
were more strongly motivated by intrinsic factors and Relationships/System Trust than
anything else. Hence, managers of CSOs should concentrate on keeping the CSO job
motivating and developing a good relationship with each CSO. They should also take
steps to assure that CSOs feel that workplace structures encourage fair treatment.
Management should be very careful in how they employ incentive systems or alter
existing incentive systems. Mohrman, Resnick-West, and Lawler (1989) emphasized that
pay for performance must be done correctly, or “the positive advantages …are more than
wiped out…” (1989: 174-175). Lawler (1971) recommended that incentive pay not be
used in situations in which worker/supervisor trust levels are low.
By adding Relationships and System Trust to either the JCM or the Controls
model, organizations can more fully explain and more accurately predict motivation and
159
motivational work outcomes. In the context examined in this research, the study outlines
the significant motivational impacts of Job Characteristics, Relationships, and System
Trust for those operating critical computer systems.
This research also contributes to practice by applying the critical technology
systems paradigm (e.g., Weick, 1990) to the information systems field. What has been
learned in this study largely relates to the very challenging CSO job and its environment.
This researcher echoes Weick’s impression of critical systems operators generally:
“Considering what they face, it is remarkable that operators do as well as they do” (1990:
33).
This study identifies several personal (e.g., knowledge), interpersonal (e.g.,
Relationships), structural (e.g., System Trust), and technical (e.g., testing) factors that
interact in this environment. While these factors each require additional research, the
immediate contribution to practice is that, in managing CSOs, one should consider all of
these interacting aspects of the CSO environment. Otherwise, unforeseen problems may
arise.
STUDY LIMITATIONS
This section discusses limitations and how they were addressed. Most research is
subject to the researcher’s pre-existing biases (Yin, 1984). However, the use of both
inductive and deductive methods helps reduce the effects of bias in this study.
External Validity
The most important limitation of a study of a single organization is that its results
may not be generalizable to other organizations. The fact that the study’s data is drawn
from operators of three separate critical systems helps minimize the site’s uniqueness in
160
terms of critical systems, but does not address the uniqueness of XYZCo as an
organization. This study has argued that it is the unique organizational and systems taskrelated factors at XYZCo that make it an informative site.
The drawback of this
argument is that the results may not have external validity, limiting this study’s
contribution to both science and practice.
However, the researcher believes that many of the principles brought out in this
study have wider applicability. For example, the Controls/Relationships model (Figure 5)
can be applied to many other situations, both in information systems settings, and in
general management situations. Likewise, the issues relating to incentive systems have
broad applicability.
The new instruments developed here were designed for broad
application. The following briefly outlines efforts the researcher has already made to test
the generalizability of the Management Controls / Relationships model and the specific
incentive findings.
First Test for External Validity: Controls/Relationships Model. First, the
Management Controls/Relationships model has been tested in a different setting.
Separate from the dissertation study, the researcher and a colleague have tested the
external validity of a subset of the Controls/Relationships model using an existing data
source, the “world-class manufacturing study.” This study began in 1991 at the University
of Minnesota’s Carlson School of Management. Its researchers collected data on a wide
range of important variables from 110 American, Japanese, and Italian manufacturing
plants.
The
data
included
variables
reflecting
worker/management Relationships, and Motivation.
Management
Controls,
Using these data, a model of
161
Relationships moderating the effects of Controls on Motivation was tested. A variable
for plant nationality was also placed in the model as a covariate. This model was found
to be strongly supported.
This external validity test provides significant additional
evidence for the usefulness of the Management Controls/Relationships model.
Obviously, more testing is needed.
Second Test for External Validity: Incentives Effects on Motivation. The
pilot organization afforded a second site to further explore the implications of incentives
because of how their reward structure differed from that at XYZCo.
At the pilot
company:
 incentives were salient in terms of goal challenge, but not as salient in terms of
objective amount;
 the incentive was tied to a one time productivity goal over a three month period;
 the award was based on the team meeting its goal; and,
 the award was divided evenly among team members.
The researcher hypothesized that the incentive would have negative side effects.
Team members were interviewed about the incentive and their motivation both
during and after the three month award period. The incentive appeared to increase
several aspects of their motivation (and performance) during the award period. However,
performance decreased immediately after the award period to lower-than-normal levels.
In subsequent months, performance gradually increased to about the same as it had been
before the incentive. Post-award interviews confirmed the researcher’s hypothesis that
162
the incentive would have some negative impact. Explaining the reduced post-incentive
performance, several interviewees said that the incentive caused the team to focus so
much on the goal that it neglected other things that were not directly related to the goal.
Having to pursue these neglected items contributed to decreases in goal-related
productivity in the months directly following the goal period. Hence, the pilot site
confirmed that negative effects of controls may occur.
Other Limitations. The fact that most constructs were based on self-report data
makes it difficult to know how valid they are in the face of threats like social desirability
bias (Cook & Campbell, 1979). In spite of Dillman’s (1978) assurances to the contrary
(Chapter Two), this threat may have been exacerbated by the use of a telephone
questionnaire. This possibility was minimized by assurances the interviewer provided
that tend to decrease social desirability bias (see Chapter Two, Appendix C-Introduction).
Another issue is statistical power (Cohen, 1988). Because the sample size was
limited to the number of people available to talk at one research site, power calculations
were not pursued. Intuitively, a sample size of eighty-six is probably adequate to feel safe
about the main effects of the models. However, the interactive effects probably require
additional power.
Mono-method (common informant) bias is a limitation that was addressed for
equations predicting CTE and Performance, but not for equations predicting Motivation,
CPS, and Work Outcomes. Hence, mono-method bias should be considered a caveat for
this study. The testing of Autonomy for mono-method bias revealed some differences
163
between methods, but overall, the items from the two methods could be successfully
merged into a single construct.
Another limitation is that several minor wording changes were made to the JCM
scales in order to increase their reliability. While these were minor, it is possible that this
could have slightly affected the scores for these variables. It is not at all unusual,
however, for JCM constructs to be operationalized in ways that differ somewhat from the
original (Griffin, Welsh & Moorhead, 1981).
FUTURE RESEARCH
This study raises a number of interesting questions for researchers to pursue. The
most obvious question is, “To what extent will these findings apply in similar and diverse
settings?”
A number of direction-of-causality issues were raised in the discussion
sections. This study indicates that a number of processes lie behind these cross-sectional
results; the interactional nature of relationships and motivation is intriguing but not clear
at this point. To begin to answer such questions, the following steps are suggested.
First, the constructs of this study need to be studied in other critical computer
systems settings. Second, the direction of causality of a number of construct-to-construct
relationships needs to be studied. These include the extent to which Controls, System
Trust, and Relationships influence each other.
Third, the interaction between
Relationships, System Trust, and Motivation should be tested longitudinally--both
experimentally and through questionnaire research. Fourth, the surprising strength of
System Trust and Relationships’ trust constructs makes determining the antecedents of
trust a strong imperative (e.g., see Luhmann, 1991). Fifth, while the relationship between
164
workers and direct supervisors is important, some research suggests that the relationship
between workers and higher levels of management may also be important—yet difficult
to manage (e.g., Crozier, 1964: 82-83, 95). The effects of these relationships on worker
motivation should also be studied. Sixth, the relative strength of the JCM and MCM
should be tested. Simultaneous model tests (e.g., Davis, Bagozzi & Warshaw, 1989) help
science advance.
In future research, method bias should be addressed in two ways. First, the social
desirability bias resulting from telephone questionnaire should be compared directly to
that resulting from written questionnaires by splitting the sample. Second, informantrelated bias should be addressed by gathering independent variables from one informant
and dependent variables from the other informants. Alternatively, one set should be
gathered from both informants.
This study found that organizational, people, and relationships issues were
important to the operation of critical computer systems. Abstracting to a higher level, one
might say that this study explores the effects of people and organizational issues on
information systems (people + organizations  systems). This is the opposite of the
trend in the MIS field, which has primarily focused on how information systems affect
people and organizations (systems  people + organizations). For example, George &
King (1991) discussed how computing can drive centralization or decentralization of
organizations, but did not discuss how organizational structure can affect the health of
systems. The results of this study suggest, as emphasized by Kaplan and Duchon (1988:
165
583), that research “designs should consider the impacts of users, the organization, or
society on the computer information system.” As an example, this study’s results about
incentives imply a need to study cross-sectionally the effects of information system
management incentives on system availability.
In particular, the well-being of critical systems needs to be examined in much
more detail than has been done so far. Since system availability is becoming more and
more important, the following research avenues should be pursued:
 The effects of Total Quality Management approaches on critical computer
system availability should be researched.
This was suggested by XYZCo’s quality
improvement teams made up of CSOs. Improving critical system availability to six
sigma (99.9999%) quality is a natural fit for the quality management research paradigm.
 Thompson (1967) predicted that the structural system will address system
uncertainties by protecting the technical system from environmental threats. Phase I
confirmed this proposition at XYZCo (McKnight, 1996).
The effects of structural
technical safeguards like redundant equipment, backup software, system environmental
protections, and testing procedures all contributed to XYZCo’s systems availability.
These should be studied in terms of which factors are the most important to system
availability.
 The complexity and comprehensibility of an operational environment change in
both nature and degree when the environment is computerized (Lee, 1991b; Perrow,
1984; Zuboff, 1988). In light of efforts to automate CSO roles, the effects of operator
166
automation on CSO alertness, comprehension, and job characteristics should be studied
(McKnight, 1996).
 In the Chapter Three Discussion of Results section, the issue of CSO
disincentives to do preventive maintenance was raised. This area needs further research.
In light of this possibility, the extent to which teamwork and trust among CSOs reduces
the tendency to prefer the ‘glory job’ of system repair over the ‘no glory’ job of
preventive maintenance should also be studied.
 As Pentland (1992) found, an individual CSO in troubleshooting mode is likely
to need the help of others on the team to resolve a thorny problem. The network of team
members on whom the CSO can call seems paramount. Research should identify what
attributes of the networked team member are most important to the CSO handling a given
problem type (e.g., particular knowledge, skills), and how the interpersonal relationships
attributes (e.g., Liking, Trusting Beliefs-Benevolence) matter regarding who the CSO
contacts for help.
 The central computing center probably accounts for far fewer system outages, as
experienced by end users, than the combined communications network and end user
premise environments. These should also be studied in terms of system availability.
 While studies have been done of the human-computer interaction (HCI) of
nuclear plant and aircraft carriers operators (e.g., Rasmussen, 1986; Weick & Roberts,
1993), little is known about the HCI of CSOs. At XYZCo, work has been done to
simplify and more clearly present the system control screen information to the CSO. The
167
HCI domain needs to be explored in much more detail in the uniquely frenetic CSO
arena.
 Weick (1990) pointed out that the critical system environment can be a cauldron
of strong emotions. In the context of a critical computer system outage, emotional events
are likely to affect both management, the CSO, and the system user—as they interact.
How these emotions affect, and are affected by, the interpersonal dynamics of the
situation is interesting. How these interactive factors impact CSO performance is an
intriguing question to pursue.
168
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APPENDICES
Appendix A Example of Open and Axial Coding
The following example illustrates the construct creation and linking process.
I. OPEN CODING (to establish constructs and their descriptors--see Figure 12)
A. Interview text: "I think [manager #1 (Mgr1) and manager #2 (Mgr2)]...they cared about people."
A. Construct creation: In general, this has to do with people caring for other people; a construct was created
called "Felt Caring." By comparing this construct with other constructs, "Felt Caring" was found to be similar,
but not identical, to those constructs (e.g., liking, trust) that also expressed something about the personal
relationships between workers. Hence, the construct "Felt Caring" was made a subset of the existing higher
level conceptual category called "Relationships between people." The detail conceptual descriptors of the
construct "Felt Caring" were examined, based on this item. These include: a) Who cares b) about Whom.
B. Interview text: "Mgr1 and Mgr2 like whenever we had outages, even at night, would always show up....I
think there was more interaction between us and Mgr1 and Mgr2....with Mgr1 or Mgr2 you could yak or
kid...they'd be apt to show up [to an outage] in their sweats."
B. Construct creation: A construct called "Outages--Attendance" was created, with Who and When
descriptors. Another construct called "Interaction between people" was created. Conceptual descriptors for this
construct were: a) who (Mgr1/Mgr2); b) when--frequency ("whenever"); c) when--occasion (during outages);
d) when--time of day (night); e) how--mode or medium (in person); f) how--style (formal/informal).
Informality was indicated by "yak or kid," and by the informal attire ("sweats") worn during the interaction.
C. Interview text: "Mgr1 and Mgr2 used to always come down at holiday time, say, you know, 'Happy New
Year,' 'Merry Christmas,'...it's just little things sometimes people do for you that make you know that they
appreciate you."
C. Construct creation: First, a new construct was easily formed: "felt appreciation." It was given who and
about whom descriptors. However, the first half of this text was more problematic. At first, the researcher
coded the part before "Merry Christmas..." as an indicator of the construct "Felt Caring." But the second half
of the quote shows that the interviewee interpreted the holiday greetings as "felt appreciation." By comparing
it to other constructs, he decided to place it in the "Interaction between people" construct, with how--style
(formal/informal) mode and when-occasion descriptors.
D. Interview text: "If Mgr1 or Mgr2 came down [to a computer outage], I probably would have felt easy."
D. Construct creation: The first phrase reflects "Outages--Attendance." The second phrase has to do with how
comfortable or "easy" a person feels with the superior. The researcher placed this in a category called
"Nervous around others," which was also in the larger relationships between people category. Detail
descriptors: a) Who; b) about Whom; c) when [during an outage]; d) Where [in the computer center]; e)
Certainty of easiness [probably]
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Figure 12 Model of Construct Creation
K
K
Interaction
Between People
Relationships
Between People
K
K
Felt
Caring
Felt
Appreciation
I
Outages-Attendance
Nervous
Around Others
I
I
I
I
I
I
Text
A
Text
B
Text
C
Text
D
K = Kind link (one construct is a subset or “kind” of another)
I = Instance link (object in box is an instance of construct in ellipse)
Notation source: Thagard (1992)
II. AXIAL CODING (to establish relationships between constructs)
A. B. C. D. Interview text: [see above]
A. B. C. D. Relationship creation: These four pieces of text are connected by reference to Mgr1 and Mgr2
(and to the interviewee). One causal link is made obvious by the interviewee's comments (Figure 13): informal
holiday interaction led the interviewee to feel appreciated ("it's just little things sometimes people do for you
that make you know that they appreciate you"). The links between interaction and felt caring don't appear to be
causal, but they do seem to be positively associated. The links between interaction/felt caring/felt appreciation
and nervousness were then explored. The evidence in this text only indicates that interaction/caring/felt
appreciation are negatively associated with nervousness around Mgr1 and Mgr2. No causal link can be formed
here.
Figure 13
Model of Construct Linkages
+Ca
Interaction
Between People
Felt
Caring
+A
Felt
Appreciation
+A
Nervous
Around Others
LEGEND:
Ca = Causal
A = Associative
+ = Positive link
- = Negative link
-A
-A
-A
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APPENDIX B
QUESTIONNAIRE ITEMS BY CONSTRUCT
A. QUESTIONS ASKED TECHNICIANS
Note: Question number indicates the order in which questions were asked
JOB CHARACTERISTICS
Skill Variety: The extent to which the job requires the worker to use a diverse set of talents.
1. My job requires me to do many different things at work, using a variety of my skills and talents.
2. This job requires me to use a number of complex or high-level skills.
3. Overall, my tasks are not simple and repetitive.
Job Significance: The extent to which the worker perceives the job as crucial or important to their
own, or the general, workplace.
4. This job is one where a lot of other people, in this organization and other organizations, can be affected
by how well my work gets done.
5. This job is important in that the results of my work can significantly affect other peoples’ ability to do
their work.
6. This job itself is very significant and important in that it facilitates or enables other peoples’ work.
7. My job is very important in the broader scheme of things, that is, in the general workplace.
Task Identity: The extent to which the worker sees the job as a whole or complete set of work, as
opposed to just a component piece of an overall set of work.
8. This job is arranged so that I can usually do an entire piece of work from beginning to end, not just a
small part of an overall piece of work.
9. This job generally provides me the chance to completely finish the pieces of work I begin.
10. My job usually involves a complete piece of work that has an obvious beginning and end.
Job Feedback: The extent to which the job itself provides workers knowledge about how well they
have done a task.
11. This job itself provides me information about my work performance. That is, the actual work itself
provides clues about how well I am doing--aside from any feedback co-workers or supervisors may provide.
12. After I finish a task, I know whether I performed it well.
13. Just doing the work required by this job provides many chances for me to figure out how well I am
doing.
Growth Need Strength: The extent to which a worker desires a job that is challenging or growthproducing.
14. I would like to have stimulating and challenging work.
15. I would like to exercise independent thought and action in my work.
16. I would like to have opportunities for personal growth and development at my work.
Knowledge of Results: The extent to which workers understand how well they are doing on the job.
17. I usually know whether or not my work is satisfactory on this job.
18. I have a pretty good idea of how I am performing my work
19. I can generally tell whether I am doing well or poorly in this job.
MOTIVATION
Intrinsic Motivation—Enjoyment: The degree to which the person perceives that their job gives
them enjoyment or pleasure.
20. I get a lot of enjoyment out of doing my job.
21. When it comes right down to it, I really enjoy my work.
22. Just doing my job gives me a sense of keen satisfaction.
23. Doing my job gives me a very satisfying feeling.
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Intrinsic Motivation--Self-Esteem: The degree to which the person perceives that their job gives
them a feeling of self-esteem (i.e., when they do the job well).
24. When I do my job well, it gives me a feeling of accomplishment.
25. When I perform my work well, it contributes to my personal growth and development.
26. My opinion of myself goes up when I do this job well.
27. Performing this job well reinforces my feelings of self-esteem.
Job Satisfaction: The extent to which one feels pleased or satisfied with one’s job.
32. Generally speaking, I feel satisfied with this job.
33. Overall, I feel satisfied with the kind of work I do in this job.
34. In general, I feel satisfied with my job.
35. I seldom think of finding another job.
Experienced Work Meaningfulness: The degree to which the person experiences the job as one that
is generally meaningful, valuable, and worthwhile.
39. To me, most of the work I do is valuable and important.
40. My work is worthwhile and valuable.
41. In general, the work I do in this job is important.
42. Only a few of the things I do on this job seem useless or trivial.
Organizational Commitment: Willingness of the worker to exert considerable effort/sacrifice on
behalf of the organization. (Note: this is the work/effort component of the overall Organizational
Commitment construct.)
44. I am willing to put in a great deal of effort beyond that normally expected in order to help this
organization be successful.
45. This organization inspires the very best in me in the way of job performance.
46. I show by my actions that I really care about the fate of this organization.
47. I am willing to sacrifice to help this organization meet its goals.
Felt Responsibility: The degree to which one feels personally obligated or responsible for one’s
work.
36. I feel a high degree of personal responsibility for the work I do on this job.
37. Whether or not this job gets done--and done properly--is clearly my responsibility.
38. I feel I should personally take responsibility for the results of my work on this job.
RELATIONSHIPS
Liking: The extent to which a subordinate has positive affect toward the boss.
60. My lead/supv, _____________, is one person I really like.
61. I have a lot of respect for my lead/supv.
62. Overall, I react very favorably to my lead/supv.
63. I admire my lead/supv.
Trusting Intention: A subordinate’s willingness to depend on the supervisor on an issue important to
the subordinate’s career.
64. When an issue that is critical to my career arises, I feel I can depend on my lead/supv.
65. I can always rely on my lead/supv in a career-related issue.
66. My lead/supv is a person on whom I feel I can rely when the issue is important to my career.
67. I feel I can depend on my lead/supv on a career-sensitive issue.
Trusting Belief-Benevolence: The extent to which a subordinate believes that the boss is benevolent
(cares for the welfare of the subordinate and is motivated to act in the subordinate’s interest).
68. When it comes to my well-being, my lead/supv really cares.
69. If I required help, my lead/supv would care enough to help me.
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70. I believe that my lead/supv cares enough to act in my personal best interest.
71. When you get right down to it, my lead/supv cares about what happens to me.
IF HI: You have a good working R w/your lead. Briefly, what is the basis for that good R?
Trusting Belief-Competence: The extent to which a subordinate believes that the boss is capable,
skillful, and/or proficient at work.
76. My lead/supv is skillful and effective in her/his work.
77. My lead/supv performs his/her job very well.
78. Overall, I have a capable and proficient lead/supv.
79. Overall, my lead/supv is competent technically.
Felt Gratitude: The extent to which a subordinate perceives that their boss appreciates the
subordinate’s work.
80. My lead/supv often shows appreciation for me when I do a good job.
81. When I do my task well, my lead/supv often expresses appreciation to me.
TRUST--OTHER
System Trust: The belief that impersonal structures (e.g., regulations, procedures) exist that support
or encourage fairness in one’s work environment.
53. Our workplace has processes that assure that we will be treated fairly and equitably.
54. I work in an environment in which good procedures make things fair and impartial.
55. Fairness to employees is built into how issues are handled in our work environment.
56. In this workplace, sound practices exist that help ensure fair and unbiased treatment of employees.
Dispositional Trust: General tendency of one to believe in the benevolence of other people across
most situations.
57. In general, people really do care about the well-being of others.
58. The typical person is sincerely concerned about the problems of others.
59. Most of the time, people care enough to try to be helpful, rather than just looking out for themselves.
INCENTIVES
Challenge Content of Incentive: The degree to which reaching the contingent performance standard
required substantial effort.
43. Achieving my [incentive plan name] goals during last year’s incentive period was very challenging for
me, based on when the goals were given.
Motivational Effect: The degree to which the incentive awards have positive motivational impact on
the individual and the team.
82. The goal-oriented [plan name] bonuses have a positive motivational effect on the CODWRD team.
83. The goal-oriented [plan name] bonuses have a positive motivational effect on me.
84. The CODWRD team is more conscientious now because of the [plan name] bonuses.
85. I am more conscientious now because of the [plan name] bonuses.
86. The CODWRD team works harder because of the [plan name] bonuses.
87. I work harder now because of the [plan name] bonuses.
Satisfaction with Incentive: The degree to which the person is pleased or content with the incentive
award.
88. Most of my co-workers feel satisfied with the [plan name] bonus they received.
89. I feel satisfied with the [plan name] bonus I received.
Aside from our normal questions, tell me, very briefly, does the [plan name] have any other effects on you
or the team?
191
CONTROLS
Autonomy: The extent to which a boss allows a subordinate to make decisions about their work on
their own.
72. In my work, I usually do not have to refer matters to my lead/supv for a final decision.
73. Usually, my lead/supv does not have to approve my decisions before I can take action.
74. Rather than asking my lead/supv, I usually make my own decisions about what to do on my job.
75. I can usually do what I want on this job without consulting my lead/supv.
Feedback (from Supervisor): The frequency with which a boss gives a subordinate work-related
feedback.
90. My lead/supv gives me a lot of feedback about how I am doing on this job.
91. My lead/supv frequently tells me how I am doing on my job functions.
92. Our lead/supv gives me frequent feedback about my performance.
93. Our lead/supv often let’s me know the extent to which I did a task satisfactorily.
Micromanagement: The extent to which a boss becomes so involved in a subordinate’s task that the
boss does the task for the subordinate.
94. My lead/supv rarely gets so involved that s/he does my task for me.
95. Our lead/supv rarely gets too involved in the activities of my job.
96. I hardly ever see our lead/supv take a larger role in work assigned to me than s/he should.
97. Our lead/supv rarely performs a part of my job for me.
Accountability: The extent to which job holders are held responsible for their work.
101. How much are you held personally responsible for achieving your performance goals or standards?
102. How much are you personally given credit for successes you have on the job?
103. How much are you held personally accountable for the work decisions you make in your job?
104. How much are you held personally responsible for mistakes you make on the job?
In genl, who in mgmt are you held accountable by?______________________________________
Pressure: The extent to which a subordinate feels under stress from the boss when performing their
job.
105. I seldom feel significant pressure from my lead/supv to perform at a consistently high level on this job.
106. I seldom feel significant pressure from my manager to perform at a consistently high level on this job.
PERCEIVED TEAM EFFECTIVENESS
Overall Team Effectiveness: The perception that the team performs its function proficiently.
107. I feel that this team effectively performs its overall task.
108. Overall, this team performs its functions effectively.
109. In general, the CODWRD team is effective in doing its job.
Team Coordination Effectiveness: The perception that the members of the team function in a
cooperative and helpful way so as to accomplish the team’s function.
110. The people who work together on the CODWRD team do their job properly and efficiently without
getting in each other’s way.
111. The people who work together on this team perform their tasks without interfering with each other’s
duties.
112. When it comes to jointly fixing system problems (*or * making sched changes), my activities are wellcoordinated with activities of other CODWRD team members.
113. Team members are willing to assist each other when needed.
114. I feel the various CODWRD team members work together very well.
192
Information Sharing Accuracy: The perception that the information shared among team members is
factual.
115. The information I receive from other team members is seldom inaccurate.
116. I can feel confident that information I receive from other CDWRD team members is correct.
117. I rarely have to go back and check the information I have received from team members.
118. I never have to worry about getting false information from members of the CODWRD team.
Information Sharing Openness: The perception that team members openly share information with
each other.
119. It is easy to talk openly about work-related issues to all members of this team.
120. The members of this team freely discuss various topics important to the CODWRD team.
121. It is easy to ask for information from any member of this team.
122. I feel free to discuss almost any work-related issue in the CODWRD team.
Conflict Resolution: The perception that the group deals with and resolves its internal problems
productively and positively.
125. Conflicts among team members are usually resolved effectively and positively.
126. When disagreements occur, the CODWRD team is good at bringing the issues into the open and
working them out peacefully.
127. When problems between team members do arise, they are handled satisfactorily.
Do you Agree or Disagree, or are you Neutral?
128. Conflict is typically dealt with and resolved constructively by the CODWRD team.
EXPLANATORY/PLAUSIBLE ALTERNATIVES
Interaction with Team Members--98. In general, how much do you interact with other CODWRD team
members. Again, the CODWRD team consists of all those who keep central site CODWRD up and
running:
Interaction with Supervisor--99. In general, how much do you interact with your lead/supv:
Interaction with Manager--100. In general, how much do you interact with your manager:
Effect of Supervisor Interaction on Worker Self-esteem (SEspv)--28. My work-related interactions with
my lead/supv usually have a positive effect on my self-esteem.
29. Interacting with my lead/supv on the job generally reinforces my feelings of self-esteem.
Feelings today versus Earlier--30. I get a greater feeling of accomplishment from my job today than I did
3 years ago.
31. I get more enjoyment from doing my job today than I did 3 years ago.
Reasons?________________________________
Feelings Today versus Earlier--48. When I compare my current level of work commitment to the
company’s success versus three years ago, I am more committed today.
49. I am more committed to work hard for this company today than I was three years ago.
(any reasons you are more/less committed today?)
Communication Today versus Earlier--123. Compared to the CODWRD team 3 years ago, current
CODWRD team members share information more openly today.
124. Compared to the CODWRD team 3 years ago, team information shared with me by current CODWRD
team members is more accurate today.
members.
Intrinsic Motivation Orientation--50. From the following list, please select the one reason that best
represents why you try to work hard and do a good job: (I’ll read the list again if you would like)
(1st List)
1. Opportunities for a promotion
2. The challenge of the task
3. Merit pay increases
4. A feeling of accomplishment
193
5. Something else (specify)
(would you like me to read the list again?)
51. From the following list, please select the one reason that best represents why you try to work hard and
do a good job:
(2nd List)
1. [incentive plan name] bonuses
2. Solving the incident, outage, or potential problem
3. Achievement award programs
4. Enjoyment of the job
5. Something else (specify)
(shall I read them again?)
52. From the following list, please select the one reason that best represents why you try to work hard and
do a good job:
(3rd List)
1. Opportunities for a Promotion
2. Appreciation from your boss
3. Merit pay increases
4. [incentive plan name] bonuses
5. Something else (specify)
(shall I read them again?)
Relationship with Team Members--129. In general, I have a good relationship with those CODWRD
team members I interact with.
Percent of Time Directly Keeping System Available--130. What percentage of your job (in terms of % of
hours spent) relates directly to keeping CODWRD up and running? ____ By ‘directly related,’ I mean
either fixing CODWRD when it goes down, or working to prevent it from crashing in the first place.
Extent of Time Worked with Supervisor--131. How many years and months have you worked with your
current lead/supv, both now and in past jobs? _____yrs. ____Mths.
DEMOGRAPHIC
Age--132. How old did you turn on your last birthday? ____
Number of recent Promotions--133. How many promotions have you had over the past five years, if any?
____
Number of recent Merit Pay Increases--134. How many base pay increases have you had over the past
five years, if any? ____
Grade Level--135. What is your current grade level? ____
Educational Attainment--136. How many years of academic, vocational, or professional education have
you obtained beyond high school? _____
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B. QUESTIONS ASKED SUPERVISORS
(Same questions [107-128] as above for: Overall Team Effectiveness, Team Coordination Effectiveness,
Information Sharing Accuracy, Information Sharing Openness, Communication Today versus
Earlier, Conflict Resolution)
Questions regarding each person they supervise: (each question was prefaced by a definition)12
Organizational Commitment (defined above)
1. _______________________ typically displays a commitment and desire to work hard and exert
2. Please rate the following employees 1-N, with 1=Most; N=Least, on the following attributes: (again, use
all N numbers, if at all possible)
Amount of Commitment
to Work Hard they display
(If at all possible, please use all N numbers instead of showing “ties” between people.)
#(1-N)
____
______________
Autonomy (defined above)
3. I usually give a lot of decision-making autonomy to _______________________ on matters related to
her/his job.
4. Please rate the following employees 1-N, with 1=Most; N=Least, on the following attributes: (again, use
all N numbers, if at all possible)
Amount of Decision-making
Autonomy I give them in their work
#(1-N)
____
______________
Contribution to Team Coordination Effectiveness: The degree to which a worker enhances the
willing and effective cooperation and helpfulness among team members.
5. ____________typically makes a good contribution to effective team cooperation, both by his/her own
example and by his/her positive influence on other team members.
6. Please rate the following employees 1-N, with:
1=Best at coordinating their activities with others and helping other members of the team when
needed.
N=Worst at coordinating their activities with others and helping other members of the team when
needed.
Contribution to Team Communication Effectiveness: The degree to which a person enhances the
communication effectiveness of the team through example and influence on others.
7. ____________typically makes a good contribution to effective team communication, both by his/her
own example and by his/her positive influence on other team members.
8. Please rate the following employees 1-N, with:
1=Best at communicating openly and accurately with team members and influencing other team
members to do the same.
N=Worst at communicating openly and accurately with team members and influencing other team
members to do the same.
Contribution to Team Conflict Resolution: The degree to which a person enhances the team’s ability
to resolve internal problems through example and influence on others.
12
Question order for supervisor instrument was: 1, 3, 5, 7, 9, 11, 4, 2, 10, 6, 8, 12, 13
195
9. ____________usually enhances the team’s ability to constructively resolve large or small disagreements
or conflicts that arise, both by his/her own example and by his/her positive influence on other team
members.
10. Please rate the following employees 1-N, with:
1=Best at helping the CODWRD team effectively resolve its internal conflicts or disagreements,
large or even small; and
N=Worst at helping the CODWRD team effectively resolve its internal conflicts or disagreements,
large or even small.
Contribution to Overall Team Effectiveness: The degree to which a person enhances the team’s
ability to effectively accomplish its overall goals.
11. ____________typically makes a good contribution to overall team effectiveness, both by his/her own
example and by his/her positive influence on other team members.
12. Please rate the following employees 1-N, with:
1=Best contributor towards overall CODWRD team effectiveness.
N=Worst contributor towards overall CODWRD team effectiveness.
Employee Performance: The extent to which the worker does his/her job functions in a capable
manner.
13. Please rate the following employees 1-N, with 1=Best Overall Performer; N=Worst Overall Performer.
Base the ratings on the most recent official ratings you (and/or others) have done for each person, recent
merit pay evaluations, or ‘write-ups’ for bonuses or special awards given.
(If at all possible, please use all N numbers instead of showing “ties” between people.)
#(1-N)
____
______________
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APPENDIX C
OPERATOR QUESTIONNAIRE
INTRODUCTION
Hi, ________; this is (researcher). Are you having a good day today?_______________________ (Good. I
was hoping to find you in a good mood!) I appreciate the opportunity to talk with you. Just to remind you,
I’m a Management Information Systems researcher from the University of Minnesota. The purpose of the
questionnaire is to study how teams like yours keep systems running, in terms of social and interpersonal
issues.
For the next forty to forty-five minutes, I’ll be asking you some questions about your job and workplace. By
the way, if you get called to do something, we can be interrupted and just resume where we left off. I want
to assure you of a couple of things before we get started.
First, your answers are being captured over here with a pen and piece of paper--not with a tape recorder.
Second, your answers and this conversation will be kept completely confidential. That’s one reason we’re
doing this by phone. The other reason is that it is less expensive for me to conduct research over the phone
than in person. Your specific answers will not be shared with anyone else either within or outside your
organization. I’m not an agent of management. So your answers will not be made available to your
management. Any results of the study will be presented only at a summary level. So you can share your
thoughts freely. All right?
Third, the questions I’m going to ask you do not have ‘right’ or ‘wrong’ answers. In fact, I have no
preconceived notions of the right answers myself. Your opinion is the correct answer, and that’s what I’m
interested in hearing. Your first impressions are usually going to be the best answer. Hence, we will go
through the questions fairly quickly. Let me know if we go too fast, though. Also, if you feel
uncomfortable answering a question, or if you don’t hear or fully understand something I say, please let me
know as we go along. Okay?
The questionnaire has two parts. Part I addresses aspects of the job. Part II addresses people and team
issues. By ‘team,’ I mean the CODWRD team. I’m defining the CODWRD team as those people who keep
the CODWRD system up and running. Based on that definition, you consider yourself part of the
CODWRD team, right? _____ That would also include people in your work group and several other work
groups, right? If the CODWRD team consists of those who keep the central site part of the CODWRD
system up and running, which other groups do you think belong to the CODWRD team?
____________________________________________________________________________________
**1ST TIME/GP: By the way, who do you report to? I mean, do you have a lead or someone who acts as a
kind of supervisor over you?___________Do you call him/her a lead orwhat?______________________
For each of the following statements, I want you to react first by telling me whether you agree or disagree
with the statement. Then I will ask you whether you strongly, moderately, or slightly agree or disagree with
the statement. Slightly means you agree a little. Strongly means you agree a lot. Moderately is in-between;
it means you agree, but not a large or a small amount. You may also tell me if you neither agree nor
disagree, but are completely neutral. However, even if you only slightly agree, you should say that you
agree rather than saying that you are neutral. You may also say “I don’t know” if that’s the appropriate
response.
Okay?
************************************************************************************
Part I-- the nature of your job. These first three questions address the skill variety involved in your job.
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Question: 1. My job requires me to do many different things at work, using a variety of my skills and
talents.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
2. This job requires me to use a number of complex or high-level skills.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
3. Overall, my tasks are not simple and repetitive.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next, we address how important your job is to others in the general workplace--at CODWRD and
elsewhere.
Question: 4. This job is one where a lot of other people, in this organization and other organizations, can be
affected by how well my work gets done.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
5. This job is important in that the results of my work can significantly affect other peoples’ ability
to do their work.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
6. This job itself is very significant and important in that it facilitates or enables other peoples’
work.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
198
7. My job is very important in the broader scheme of things, that is, in the general workplace.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
The next three questions assess the extent to which you do a whole piece of work, as opposed to just doing
part of a larger piece of work.
8. This job is arranged so that I can usually do an entire piece of work from beginning to end, not
just a small part of an overall piece of work.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
9. This job generally provides me the chance to completely finish the pieces of work I begin.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
10. My job usually involves a complete piece of work that has an obvious beginning and end.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
These next questions are about how the job itself informs you about your work performance.
Question 11. This job itself provides me information about my work performance. That is, the actual work
itself provides clues about how well I am doing--aside from any feedback co-workers or supervisors may
provide.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
199
12. After I finish a task, I know whether I performed it well.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
13. Just doing the work required by this job provides many chances for me to figure out how well I
am doing.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
These next questions should be answered without consideration of what your job is like today. Rather, they
cover what you want your ideal job to be like.
14. I would like to have stimulating and challenging work.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
15. I would like to exercise independent thought and action in my work.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
16. I would like to have opportunities for personal growth and development at my work.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
These next 3 questions address how well you typically know your task results.
17. I usually know whether or not my work is satisfactory on this job.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
200
18. I have a pretty good idea of how I am performing my work.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
19. I can generally tell whether I am doing well or poorly in this job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Okay, the next topic relates to the enjoyment or pleasure you get from your job.
20. I get a lot of enjoyment out of doing my job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
21. When it comes right down to it, I really enjoy my work.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
22. Just doing my job gives me a sense of keen satisfaction.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
23. Doing my job gives me a very satisfying feeling.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
The next questions deal with how your job affects your self-concept.
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24. When I do my job well, it gives me a feeling of accomplishment.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
25. When I perform my work well, it contributes to my personal growth and development.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
26. My opinion of myself goes up when I do this job well.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
27. Performing this job well reinforces my feelings of self-esteem.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Here are two similar questions:
28. My work-related interactions with my lead/supv usually have a positive effect on my selfesteem.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
29. Interacting with my lead/supv on the job generally reinforces my feelings of self-esteem.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
The next question compares today with 3 years ago. To prepare to answer this, take just a moment and
think back to what you were doing 3 years ago--that would be March of 1994, who you were working with,
and how you felt about your job and so forth.....Do you recall your workgroup? your manager? your vice
president? Okay, are you ready?
202
30. I get a greater feeling of accomplishment from my job today than I did 3 years ago.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
31. I get more enjoyment from doing my job today than I did 3 years ago.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Reasons?_____________________________________________________________________
Next we ask about your current level of job satisfaction.
32. Generally speaking, I feel satisfied with this job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
33. Overall, I feel satisfied with the kind of work I do in this job.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
34. In general, I feel satisfied with my job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
35. I seldom think of finding another job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next, I’ll ask about the sense of personal obligation or responsibility you feel in doing your job.
203
36. I feel a high degree of personal responsibility for the work I do on this job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
37. Whether or not this job gets done--and done properly--is clearly my responsibility.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
38. I feel I should personally take responsibility for the results of my work on this job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
This next set is about how significant and important you feel your work is.
39. To me, most of the work I do is valuable and important.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
40. My work is worthwhile and valuable.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
41. In general, the work I do in this job is important.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
42. Only a few of the things I do on this job seem useless or trivial.
Agree, Neutral, or Disagree?
204
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Okay, we’ll shift gears for this question:
43. Achieving my [incentive plan name] goals during last year’s incentive period was very
challenging for me, based on when the goals were given.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
The next questions address your relationship with the company in terms of work commitment.
44. I am willing to put in a great deal of effort beyond that normally expected in order to help this
organization be successful.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
45. This organization inspires the very best in me in the way of job performance.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
46. I show by my actions that I really care about the fate of this organization.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
47. I am willing to sacrifice to help this organization meet its goals.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Okay, the next 2 questions compare today to 3 years ago--so think back again for a moment....Ready?
205
48. When I compare my current level of work commitment to the company’s success versus three
years ago, I am more committed today.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
49. I am more committed to work hard for this company today than I was three years ago.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
(any reasons you are more/less committed today? ____________________________________________)
That completes Part I. To give you a little break before we proceed to Part II, I have three questions of a
different type.
50. From the following list, please select the one reason that best represents why you try to work
hard and do a good job: (I’ll read the list again if you would like)
(1st List)
1. Opportunities for a promotion
2. The challenge of the task
3. Merit pay increases
4. A feeling of accomplishment
5. Something else (specify)
(would you like me to read the list again?)
51. From the following list, please select the one reason that best represents why you try to work
hard and do a good job:
(2nd List)
1. [incentive plan name] bonuses
2. Solving the incident, outage, or potential problem
3. Achievement award programs
4. Enjoyment of the job
5. Something else (specify)
(shall I read them again?)
52. From the following list, please select the one reason that best represents why you try to work
hard and do a good job:
(3rd List)
1. Opportunities for a Promotion
2. Appreciation from your boss
3. Merit pay increases
4. [incentive plan name] bonuses
5. Something else (specify)
(shall I read them again?)
Part II covers people and team issues.
First, we’ll talk about the nature of your work environment in terms of structures that encourage fairness to
workers.
206
53. Our workplace has processes that assure that we will be treated fairly and equitably.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
54. I work in an environment in which good procedures make things fair and impartial.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
55. Fairness to employees is built into how issues are handled in our work environment.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
56. In this workplace, sound practices exist that help ensure fair and unbiased treatment of
employees.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next, we’ll ask 3 questions about what you believe about other people in the world generally; not people at
work, but people in general. Okay?
57. In general, people really do care about the well-being of others.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
207
58. The typical person is sincerely concerned about the problems of others.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
59. Most of the time, people care enough to try to be helpful, rather than just looking out for
themselves.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
How you feel towards your lead/supv is the next topic. You said your lead/supv’s name was _____. Right?
62. My lead/supv, _____________, is one person I really like.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
63. I have a lot of respect for my lead/supv.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
64. Overall, I react very favorably to my lead/supv.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question
65. I admire my lead/supv.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Now we go to additional feelings about your lead/supv.
66. When an issue that is critical to my career arises, I feel I can depend on my lead/supv.
208
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
67. I can always rely on my lead/supv in a career-related issue.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
68. My lead/supv is a person on whom I feel I can rely when the issue is important to my career.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
69. I feel I can depend on my lead/supv on a career-sensitive issue.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
These next questions are similar, but relate to issues of caring and concern.
70. When it comes to my well-being, my lead/supv really cares.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
71. If I required help, my lead/supv would care enough to help me.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
72. I believe that my lead/supv cares enough to act in my personal best interest.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
73. When you get right down to it, my lead/supv cares about what happens to me.
209
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
IF HI: You have a good working R w/your lead. Briefly, what is the basis for that good R?
These next questions address the amount of decision-making autonomy you have.
74. In my work, I usually do not have to refer matters to my lead/supv for a final decision.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
75. Usually, my lead/supv does not have to approve my decisions before I can take action.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
76. Rather than asking my lead/supv, I usually make my own decisions about what to do on my
job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
77. I can usually do what I want on this job without consulting my lead/supv.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next we look at other perceptions you have about your lead/supv.
78. My lead/supv is skillful and effective in her/his work.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
79. My lead/supv performs his/her job very well.
Agree, Neutral, or Disagree?
210
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
80. Overall, I have a capable and proficient lead/supv.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
81. Overall, my lead/supv is competent technically.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
82. My lead/supv often shows appreciation for me when I do a good job.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
83. When I do my task well, my lead/supv often expresses appreciation to me.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next a question about the [incentive plan name] bonus’s current affects on the CODWRD team. The
CODWRD team consists of all those (in several groups) who keep central site CODWRD up and running.
84. The goal-oriented [plan name] bonuses have a positive motivational effect on the CODWRD
team.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
85. The goal-oriented [plan name] bonuses have a positive motivational effect on me.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
86. The CODWRD team is more conscientious now because of the [plan name] bonuses.
Agree, Neutral, or Disagree?
211
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
87. I am more conscientious now because of the [plan name] bonuses.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
88. The CODWRD team works harder because of the [plan name] bonuses.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
89. I work harder now because of the [plan name] bonuses.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
89a. Most of my co-workers feel satisfied with the [plan name] bonus they received.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
89b. I feel satisfied with the [plan name] bonus I received.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
90. Aside from our normal questions, tell me, very briefly, does the [plan name] have any other
effects on you or the team?
_____________________________________________________________________________________
_____________________________________________________________________________________
_____________________________________________________________________________________
The next few questions relate to supervisory feedback.
95. My lead/supv gives me a lot of feedback about how I am doing on this job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
212
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
96. My lead/supv frequently tells me how I am doing on my job functions.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
97. Our lead/supv gives me frequent feedback about my performance.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
98. Our lead/supv often let’s me know the extent to which I did a task satisfactorily.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
The next 4 questions relate to supervisory involvement.
99. My lead/supv rarely gets so involved that s/he does my task for me.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
100. Our lead/supv rarely gets too involved in the activities of my job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
101. I hardly ever see our lead/supv take a larger role in work assigned to me than s/he should.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
102. Our lead/supv rarely performs a part of my job for me.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
213
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
These next 4 questions are on a different scale, and address how much you interact with others on the job.
In increasing order, the scale choices are “Not at All,” “A Little,” “Some,” “Quite a Bit,” and “Very
Much.” And I will repeat the scale as we go along.
103. In general, how much do you interact with other CODWRD team members. Again, the
CODWRD team consists of all those who keep central site CODWRD up and running:
Not at All, A Little, Some, Quite a Bit, or Very Much?
104. In general, how much do you interact with your lead/supv:
Not at All, A Little, Some, Quite a Bit, or Very Much?
105. In general, how much do you interact with your manager:
Not at All, A Little, Some, Quite a Bit, or Very Much?
On the same scale, I’ll be asking you about the amount of accountability you feel is present in your job.
107. How much are you held personally responsible for achieving your performance goals or
standards?
Not at All, A Little, Some, Quite a Bit, or Very Much?
108. How much are you personally given credit for successes you have on the job?
Not at All, A Little, Some, Quite a Bit, or Very Much?
109. How much are you held personally accountable for the work decisions you make in your job?
Not at All, A Little, Some, Quite a Bit, or Very Much?
110. How much are you held personally responsible for mistakes you make on the job?
Not at All, A Little, Some, Quite a Bit, or Very Much?
In genl, who in mgmt are you held accountable by?______________________________________
Now two questions on the level of pressure you feel on the job.
214
111. I seldom feel significant pressure from my lead/supv to perform at a consistently high level on
this job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question
112. I seldom feel significant pressure from my manager to perform at a consistently high level on
this job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next we’ll talk about team effectiveness. By ‘team,’ I mean the CODWRD team, those who keep central
site CODWRD up and running.
118. I feel that this team effectively performs its overall task.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
119. Overall, this team performs its functions effectively.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
120. In general, the CODWRD team is effective in doing its job.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next we address internal team coordination effectiveness.
121. The people who work together on the CODWRD team do their job properly and efficiently
without getting in each other’s way.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
215
122. The people who work together on this team perform their tasks without interfering with each
other’s duties.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
123. When it comes to jointly fixing system problems (*or * making sched changes), my activities
are well-coordinated with activities of other CODWRD team members.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
124. Team members are willing to assist each other when needed.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
125. I feel the various CODWRD team members work together very well.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Now I’ll ask about aspects of information sharing among CODWRD team members.
126. The information I receive from other team members is seldom inaccurate.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
127. I can feel confident that information I receive from other CDWRD team members is correct.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
216
128. I rarely have to go back and check the information I have received from team members.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
129. I never have to worry about getting false information from members of the CODWRD team.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Okay, this next set is a bit different from the prior set, but is still about CODWRD team communication
effectiveness.
130. It is easy to talk openly about work-related issues to all members of this team.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
131. The members of this team freely discuss various topics important to the CODWRD team.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
132. It is easy to ask for information from any member of this team.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
133. I feel free to discuss almost any work-related issue in the CODWRD team.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Okay, this next set is a bit different, but is still about CODWRD team communication. Think back again to
your work group and CODWRD team of 3 years ago.
134. Compared to the CODWRD team 3 years ago, current CODWRD team members share
information more openly today.
217
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
135. Compared to the CODWRD team 3 years ago, team information shared with me by current
CODWRD team members is more accurate today.
members.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next we talk about CODWRD team conflict resolution. Once more, the CODWRD team consists of those
who keep CODWRD up and running
136. Conflicts among team members are usually resolved effectively and positively.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next.
137. When disagreements occur, the CODWRD team is good at bringing the issues into the open
and working them out peacefully.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
138. When problems between team members do arise, they are handled satisfactorily.
Do you Agree or Disagree, or are you Neutral?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Next question.
139. Conflict is typically dealt with and resolved constructively by the CODWRD team.
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
This is a different kind of question.
140. In general, I have a good relationship with those CODWRD team members I interact with.
218
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
Finally, a few background questions.
145. What percentage of your job (in terms of % of hours spent) relates directly to keeping
CODWRD up and running? ____ By ‘directly related,’ I mean either fixing CODWRD when it goes down,
or working to prevent it from crashing in the first place.
146. How many years and months have you worked with your current lead/supv, both now and in
past jobs? _____yrs. ____mths.
147. How old did you turn on your last birthday? ____
148. How many promotions have you had over the past five years, if any? ____
151. How many base pay increases have you had over the past five years, if any? ____
152. What is your current grade level? ____
153. How many years of academic, vocational, or professional education have you obtained
beyond high school? _____
THAT COMPLETES THE QUESTIONNAIRE, _____. THANKS VERY MUCH FOR YOUR HELP!
Again, I want to compliment you on being a part of the CODWRD team! I have one request for you. So
that I can obtain consistent results from all team members, I would request that you do not discuss this
questionnaire with other members of the team or those in other departments.
Okay?____________________ Also, to follow up on what I said at the beginning, were there any
questions I asked that made you feel uncomfortable or that I should not have asked? ________________
THANKS AGAIN, AND BEST OF WISHES TO YOU!
219
APPENDIX D
SUPERVISOR QUESTIONNAIRE
First, I’ll ask you to evaluate each of your team members in terms of several attributes. Is that okay? If my
list is correct, I have the following people reporting to you: ________ _______ _______ _______
________ _______ _______ ________ _______ _______ _______ _______ (Reconcile the list)
The first attribute I want you to give me your opinion with respect to your team members is the individual’s
contribution to effective team cooperation. By this, I mean the degree to which an individual enhances the
willing and effective cooperation among members of the CODWRD team. By cooperation, I mean the
person willingly coordinates activities with others and helps them out when needed. In other words, the
degree to which an individual both willingly cooperates with CODWRD team members her- or himself, and
also influences other team members to do the same.
I’ll ask about your folks in alphabetical order, so think ahead a little about some reasonable range of best to
worst workers, such that we can cover a wide range in terms of your best (which will be strongly agree
answers) and your worst (which will be strongly disagree answers). Okay?
1. ____________typically makes a good contribution to effective team cooperation, both by his/her own
example and by his/her positive influence on other team members: (circle)
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
(Repeat 1. For each employee)
Now we’ll talk about the individual’s contribution to effective team communication. By this, I mean the
degree to which an individual enhances communication effectiveness among members of the CODWRD
team. By communication effectiveness, I mean the extent to which a team member talks openly and
accurately to others on work related issues. In other words, these questions address the degree to which an
individual both communicates with CODWRD team members openly and accurately her- or himself, and
also influences other team members to do the same.
2. ____________typically makes a good contribution to effective team communication, both by his/her
own example and by his/her positive influence on other team members: (circle)
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
(Repeat 2. For each employee)
Now we’ll talk about the individual’s contribution to team conflict resolution. By this, I mean the degree to
which an individual enhances the team’s ability to effectively resolve disagreements or conflicts that arise,
large or small. So these questions address the degree to which an individual facilitates the CODWRD
team’s ability to bring issues into the open and work them out peacefully.
220
3. ____________usually enhances the team’s ability to constructively resolve large or small disagreements
or conflicts that arise, both by his/her own example and by his/her positive influence on other team
members: (circle)
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
(Repeat 3. For each employee)
Now we’ll talk about the individual’s contribution to overall team effectiveness. By this, I mean the degree
to which an individual enhances the team’s ability to do its work effectively. By team effectiveness, I mean
the team does its job in a way that allows it to meets its objectives. So these questions address the degree to
which an individual facilitates the effectiveness of the overall CODWRD team.
4. ____________typically makes a good contribution to overall team effectiveness, both by his/her own
example and by his/her positive influence on other team members: (circle)
Agree, Neutral, or Disagree?
AGREE
Do you agree Strongly, Moderately, or Slightly?
DISAGREE
Do you disagree Strongly, Moderately, or Slightly?
(Repeat 4. For each employee)
Please rate the following employees 1-N, with 1=Most; N=Least, on the following attributes: (please use all
N numbers, if at all possible)
1. Individual contribution to
effective team cooperation
#(1-N)
____
______________
____
_____________
____
______________
...
2. Individual contribution to
effective team communication
#(1-N)
____
_____________
____
______________
____
______________
3. Individual contribution to
effective conflict resolution
#(1-N)
____
_____________
____
_____________
____
_____________
4. Individual contribution to
overall team effectiveness
#(1-N)
____
______________
____
_____________
____
______________
...
221
Please rate the following employees 1-N, with 1=Best Overall Performer; N=Worst Overall Performer.
Base the ratings on the most recent official ratings you (and/or others) have done for each person, recent
merit pay evaluations, ‘write-ups’ for bonuses or special awards given, or other information you have about
them.
(If at all possible, please use all N numbers instead of showing “ties” between people.)
#(1-N)
____
______________
____
______________
____
______________
...
222
APPENDIX E
PRETEST INSTRUMENT A--MATCHING
Instructions: First, carefully read all three construct definitions. Match the Item in the left column with
the appropriate Construct in the right column by drawing a straight line from the Item number to the
Construct letter. The ___ in most items refers to a specific person.
ITEMS:
1. If I were faced with a question related to my
professional future, I feel I could depend on ___.
CONSTRUCTS: Definitions
2. When you get right down to it, ___
cares about what happens to me.
3. I can count on ___ to act in my
personal best interest.
4. The typical person is sincerely
concerned about the problems of others.
5. I feel that I could depend on ___, even on
a crucial issue that could affect my career.
A. Dispositional Trust: The general
tendency of one to believe in the
benevolence of other people in
most situations. [Benevolence
means one cares for the welfare
of the other person and is
motivated to act in the other
person’s interests.]
6. ___ is more inclined to help me out
than to look out for him/herself.
7. In general, people really do care
about the well-being of others.
8. ___ is a person on whom I can rely when
the issue is important to my career.
9. If I really needed help with something, I could
always count on ___ to come to my aid.
B. Trusting Belief--Benevolence:
One’s belief that a specific other
person will act with benevolence
towards one. [See benevolence
definition above]
10. Most of the time, people care
enough to help, rather than just
looking out for themselves.
11. When a career-critical issue arises, I
would want to be dependent on ___.
12. Most people do not hesitate to go out of
their way to help someone in trouble.
C. Trusting Intention: One’s willingness
to depend on a specific person on an
issue that is critical to one’s career.
13. Human nature is fundamentally
cooperative.
14. When it comes to things important
to me, ___ really cares.
15. I can always rely on ___ in a
career-related issue.
223
APPENDIX F
PRETEST INSTRUMENT B--CATEGORIZATION
Differentiating Types of Trust
Instructions: Below are statements that related to different types of Trust. Please place them into three to
five categories (Category A, B, C...) such that statements within a category are most similar in meaning to
each other and are dissimilar in meaning from statements in other categories. As you proceed, briefly
describe the meanings of your categories at the bottom of the page. After your initial round of
categorizing, read all the statements by category to verify that they ‘fit’ where you placed them.
CATEGORY
(A, B, C,...)
_____
STATEMENT
If I were faced with an issue related to my professional future, I feel I could depend on
___.
_____
Because of the way employee issues are handled here, I believe we are safe from unfair
or unjust treatment.
_____
When you get right down to it, ___ cares about what happens to me.
_____
Fairness to employees is built into the way issues are handled in our work environment.
_____
I can count on ___ to act in my personal best interest.
_____
I feel that I could depend on ___, even on an issue that could affect my career.
_____
If I required help, ___ would care enough to help me.
_____
In this workplace, safeguards exist that protect us from unfair treatment.
_____
___ is a person on whom I can rely when the issue is important to my career.
_____
I work in an environment in which good procedures make things fair and impartial for
the employees.
_____
If I really needed help with something. I could always count on ___ to come to my aid.
_____
Our workplace has processes that assure that we will be treated fairly and equitably.
_____
When a career-critical issue arises, I would want to be dependent on ___.
_____
When it comes to things important to me, ___ really cares.
_____
I can always rely on ___ in a career-related issue.
_____
This organization treats its employees in a fair, impartial manner.
Category A means:_____________________________________________________________________
Category B means: _____________________________________________________________________
Category C means:_____________________________________________________________________
Category D means:_____________________________________________________________________
Category E means: _____________________________________________________________________
224
APPENDIX G
PRETEST INSTRUMENT C--SORTING
Differentiating Types of Trust
Instructions: Attached are sixteen statements that relate to three to five different types of Trust. Please
sort them into three to five categories (Category A, B, C, D, E) such that statements within a category are
most similar in meaning to each other and are dissimilar in meaning from statements in other categories.
(Sort by conceptual meaning, not by the degree of trust the statement implies). After your initial round of
categorizing, read all the statements by category to verify that they ‘fit’ where you placed them. Adjust
accordingly. Then fill in PART I. Next, briefly describe the meanings of your categories (PART II).
PART I: SORTING SUMMARY
Items placed in Category A (#s):
_____
_____
_____
_____
_____
Items placed in Category B (#s):
_____
_____
_____
_____
_____
Items placed in Category C (#s):
_____
_____
_____
_____
_____
Items placed in Category D (#s):
_____
_____
_____
_____
_____
Items placed in Category E (#s):
_____
_____
_____
_____
_____
PART II: CATEGORY DESCRIPTION
Category A means: ___________________________________________________________________
Category B means: ___________________________________________________________________
Category C means: ___________________________________________________________________
Category D means: ___________________________________________________________________
Category E means: ___________________________________________________________________
List the numbers of any statements you found difficult to categorize:
(#s)___ ___ ___
225
APPENDIX H
PAIRWISE INTERCORRELATION MATRICES
(see Appendix I for key to construct abbreviations)
**not shown**
APPENDIX I DESCRIPTIVE STATISTICS
(ordered by increasing Mean)
Abbrev.
Micr
Acct
Perf
FB
SysTr
Comm
Coord
ConfRes
CTE
TeamEff
JobID
DispTr
TrInt
JobFB
Auton
TrBBn
Rs
OCW
Like
SkVar
IMEnj
JobSat
TrBCp
Mots
KnRes
IMSE
ExMng
JobSig
GNS
FR
Construct
Micromanagement
Accountability
Individual Performance
Feedback
System Trust
Contribution to Communication
Effectiveness
Contribution to Coordination
Effectiveness
Contribution to Conflict Resolution
Contribution to Team Effectiveness
Contribution to Overall Team
Effectiveness
Job Identity
Dispositional Trust
Trusting Intention
Job Feedback
Autonomy
Trusting Belief-Benevolence
Relationships
Organizational Commitment
Liking
Skill Variety
Intrinsic Motivation-Enjoyment
Job Satisfaction
Trusting Belief-Competence
Motivation
Knowledge of Results
Intrinsic Motivation-Self-Esteem
Experienced Meaningfulness
Job Significance
Growth Need Strength
Felt Responsibility
Min
1.00
2.50
1.00
1.00
1.00
1.50
Max
6.75
5.00
7.00
7.00
7.00
7.25
Mean
1.79
4.27
4.31
4.63
4.67
4.70
Mode
1.00
5.00
4.50
6.00
6.00
3.15
StdDev
1.22
0.65
1.96
1.94
1.68
1.50
1.50
7.35
4.73
2.45
1.52
1.50
1.81
1.50
7.25
7.00
7.35
4.74
4.74
4.80
2.95
6.35
2.00
1.40
1.40
1.49
1.00
1.00
1.00
1.67
2.00
1.25
1.21
1.50
1.00
2.00
2.50
2.00
1.33
4.62
2.33
3.50
4.00
4.00
5.33
5.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
4.83
5.55
5.67
5.95
5.97
6.08
6.08
6.13
6.24
6.28
6.28
6.29
6.32
6.36
6.38
6.46
6.62
6.77
6.82
6.88
6.00
6.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
6.67
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
7.00
1.66
1.20
1.72
1.25
1.00
1.40
1.27
1.05
1.21
0.88
0.88
0.88
1.14
0.58
0.94
0.71
0.60
0.45
0.36
0.32
226
APPENDIX J
PAIRWISE INTERCORRELATION MATRICES-- HIGH LEVEL CONCEPTS
**not shown**
227