How Control Systems Influence the Salesperson’s
Objective Performance:
An Empirical Investigation
Vincent Onyemah, Boston University, USA
Erin Anderson, INSEAD, France
Boston University
School of Management
Working Paper #2005-06
ABSTRACT
Based on the analyses of archival performance data matched with a
survey of 1,290 salespeople working in Africa, Europe, and North America,
this paper demonstrates the existence of a curvilinear relationship between
salesperson performance and sales force control system. Contrary to
expectations informed by extant literature, there is evidence of situations
where behavior-based control systems yield better sales performance than
outcome-based control systems. Further, mixed control systems-- offshoots of
outcome- and behavior-based control philosophies have both strengths and
weaknesses which directly reflect on salesperson performance. Analysis using
a varying parameter model shows that the above relationships remain after
controlling for the moderating influence of sales manager’s personal and
professional investment orientation toward the salesperson. The consequences
of steering a middle ground with the aid of mixed control systems are
investigated and discussed. This paper closes with a discussion of its findings
and the implications of these for research and management practice.
1
INTRODUCTION
The achievement of sales results is vital for business organizations.
Salespeople play a crucial role in this regard. Most firms rely on sales force
control systems-- procedures and practices aimed at coordinating the activities
of salespeople. This paper is concerned with the sensitivity of salespeople’s
performance to different control systems.
The importance, complexity, and peculiarity of the selling function
give rise to difficulties that make the management of salesforces one of the
most demanding jobs in many organizations (Anderson and Oliver 1987). In
addition, the control of salespeople is of critical importance to top
management because the sales force accounts for the largest portion of
marketing budget in many organizations (Cravens et al. 1993). This paper
reviews the literature on sales force control systems and concludes that not
only is there a dearth of studies investigating the sensitivity of salesperson
performance (sales results) to control systems but also, the predictive power of
existing normative theories is weak and inconclusive. Consequently till date
there is no clear indication as to which sales force control system performs the
best (with respect to actual sales results) and why. Further, given that most
organizations use a combination of control systems that are offshoots of
different control philosophies (Oliver and Anderson 1995), no study has yet
investigated the advisability of such practices.
Analyses of sales data matched with survey responses of 1,290
salespeople revealed a direct relationship between salesperson performance
and control system type. This relationship is however not linear but quadratic.
2
There are firms in which mixed control systems produce the best performance
and other firms in which pure control systems (outcome- or behavior-based
controls) yield the best performance. This paper makes additional contribution
to the literature by demonstrating via a varying parameter model that the link
between control systems and salesperson performance is sensitive to the level
of caring exhibited by sales managers.
The rest of this paper is organized as follows. First the literature on
sales force control systems is reviewed. This is followed by a discussion of the
rationale behind the tradeoffs inherent in the choice of sales force control
systems. Against this background a set of hypotheses are proposed, followed
by a presentation of a varying parameter model specification. The third section
contains data description and analyses. The final section contains results and
discussion of findings.
LITERATURE REVIEW
Sales Force Control Systems, a Subset of Management Control Systems
Management control systems are a continuous process of setting
performance goals, evaluating performance, rewarding, and correcting (Futrell
and Schul 1978, Anderson and Oliver 1987). Sales force control system is the
subset of an organization’s control system aimed at the selling function. A
sales force control system is an organization’s set of procedures for recruiting,
training, monitoring, supervising, evaluating, and compensating its
salespeople (Anderson and Oliver 1987).
Given the relatively entrepreneurial and unstructured nature of the
selling function, management is often concerned about the degree of control to
exert on salespeople, while at the same time holding them accountable for the
3
results of their actions (Oliver and Anderson 1994). Control can be visualized
as being distributed along a continuum anchored on autocratic and democratic
control systems (McMahon and Perritt 1973). In autocratic systems, control is
distributed hierarchically with the focus of decision making in the upper
organizational levels (Weber 1947, Gulick 1937, Mooney 1947, Urwick
1944). In democratic systems, control is exercised by lower organizational
levels closer to the work (Argyris 1957, Bennis 1966, Shepard 1965). Between
these two extremes lie control philosophies that are mixes of autocratic and
democratic control systems.
Sales force control systems have attracted many researches in the last
two decades (Anderson and Oliver 1987, Jaworski 1988, Lusch and Jaworski
1991, Jaworski et al. 1993, Cravens et al. 1993, Oliver and Anderson 1994,
1995, Challagalla and Shervani 1996, Stathakopoulos 1996, Piercy et al. 1998)
but there is weak empirical evidence on the existence and nature of the
relationship between salesperson performance (sales) and the choice of control
system (Lusch and Jaworski 1991, Cravens et al. 1993, Jaworski et al. 1993,
Oliver and Anderson 1994, 1995, Challagalla and Shervani 1996, Piercy et al.
1998). This could be due to a number of factors-- (1) inappropriate model
specifications that attempt to capture nonlinear relationship with linear ones;
(2) use of sales manager reported performance data in place of objective
customer generated sales performance data; (3) mixing objective and
subjective performance data in the same analysis; (4) use of aggregate level
performance data rather than individual sales performance data; (5) use of
control system indicators supplied by sales managers on behalf of the
salespeople rather than self reported indicators; (6) use of sample from one
4
company and, or industry; and (7) common method variance. This paper
makes an attempt to address some of the foregoing limitations.
Sales Force Control System Configuration
Many configurations for sales force control systems exist (Mahon and
Perritt 1973, Anderson and Oliver 1987, Jaworski 1988, Challagalla and
Shervani 1996, Stathakopoulos 1996). This paper chooses the configuration
proposed by McMahon and Perritt (1973) and Anderson and Oliver (1987)
because of its modeling appeal. Besides it is the configuration that has been
investigated the most in the sales force literature (Leigh et al. 2001). Using
this configuration thus makes it easier to situate the findings of this paper in
extant literature.
Anderson and Oliver (1987) describe, contrast, and evaluate two forms
of control systems, Outcome-based Control (OC) and Behavior-based Control
(BC). The authors define OC “as a system in which the achievement of results
is entirely the responsibility of the salesperson.” The salesperson is free to
select the methods for achieving results. On the other hand, BC “rests entire
responsibility on management. In BC systems, management makes
salespeople conform to a given set of ideas that are behavioral in nature, in the
belief that results will naturally come.” Table 1 captures these philosophies on
a continuum (McMahon and Perritt 1973, Anderson and Oliver 1987, Oliver
and Anderson 1995).
The components of the control system configuration (table 1) are
distinct but related. The thesis is that the type of measure kept by an
organization on the activity of salespeople reveals the organization’s control
philosophy. What is measured reveals the preference for what matters. The
5
nature of every other component derives from the decision on what to
measure. Hence in table 1, the top line indicates what the firm measures and
the remaining six components are consequences of this.
Consider a firm FA which operates an OC system (extreme left of the
OC-BC continuum, table 1). While it is true that FA could find many potential
outcomes that are objectively measurable, because of the complexity and cost
of gathering information, it will most likely not base evaluation on everything
that is conceivably measurable. Instead, it will base its performance evaluation
on a few observable results, primarily driven by customers. Hence the firm
needs relatively little monitoring and fewer managers. Knowing they are held
responsible for the bottom line, the salespeople will often disobey sales
managers whose directives constitute impediments. In the absence of many
managers, the salespeople do not have much contact with management.
Consider another firm FB which operates a pure BC system (extreme
right of the OC-BC continuum, table 1). Input (behavior) rather than output
(result) is paramount. The firm is convinced it knows what brings about good
results, albeit in the long run. Thus its metric addresses call plans, number of
calls, presentation skills, detailing style and techniques, call reports, route
plans, correspondence with clients, etc. It has an operating manual which
specifies in detail how selling should be conducted. It practices close
monitoring in order to ensure that salespeople work to plan. Thus frequent
contact with management is encouraged. This translates into higher need for
sales managers. The firm bears most of the business risks and manages these
via intense coaching partly to safeguard, among other things, the big
investment in fixed salary.
6
Preferences for Sales Force Control Systems: Choices and Tradeoffs
Indications that firms use mixed control systems abound (Coughlan
and Sen 1986, Oliver and Anderson 1995, Kerin et al 2003 p. 548).
Conceivably firms use mixed control systems in order to take advantage of the
benefits associated with OC and BC systems while avoiding their inherent
drawbacks. But are companies better off with this combination plan? Is the
attainment of sales results best served by combination plans rather than pure
OC or BC systems? How do salespeople’s performances respond to mixed
control systems?
Imagine a firm attracted to an OC system. The following scenario
could occur. The firm realizes that its situation is not completely suited to take
advantage of the benefits of an OC system but it still considers it an interesting
system to have in order to meet sales objectives. At the same time, it is
concerned about the drawbacks of OC systems. The firm is also interested in
its salespeople behaving responsibly. So it establishes multiple indicators of
performance. However the use of these indicators increases the complexity of
the control system, necessitates more record keeping, and involves subjective
judgments when combining several indices into an overall performance index.
The reduction in the simplicity of the original pure OC system translates into a
rightward shift along the OC-BC continuum (figure 1), in search of a safer
middle ground. This paper evaluates the sales-related consequence of this
tendency. Is the firm better off in this middle ground or is it a poor
compromise?
Similarly a firm which admires BC systems might be motivated to take
actions to address its inherent drawbacks. The firm then tries to reduce, for
7
example, the heavy subjectivity characterizing BC systems by tying appraisals
to both subjective and object measures of performance (Jackson, Keith, and
Schlacter 1983). This move suggests a desire to stay away from a pure BC
system, hence a displacement towards the center of the OC-BC continuum
(table 1).
The tendencies described in the two preceding paragraphs have support
in the literature. While the literature on control systems contains a strong
theoretical foundation for the existence of two major types of sales force
control systems (OC and BC), what one often observes in reality is a mix of
the two types (Coughlan and Sen 1986, Oliver and Anderson 1995). The
position in this paper is that even though majority of firms use mixed systems,
it is not necessarily in the best interest of the firms as far as the achievement of
sales performance is concerned. The middle portion of the OC-BC continuum
might be more problem-prone than either of the two extreme points. While the
middle ground is naturally a desirable location, achieving and sustaining the
equilibrium might be too problematic that it renders the mixed plan
dysfunctional (Merchant 1985). It is often easier to adhere strictly to either of
two separate philosophies than to adhere to a third “philosophy” which is a
product of the first two philosophies.
Hypothesis 1: On the outcome-based / behaviour-based control system
continuum, the performance of salespeople closest to the end points of the
continuum will be higher than the performance of salespeople in the middle
portion of the continuum.
Hypothesis 1 implies that the relationship between salesperson
performance and control system is U-shaped. An interesting follow-up
8
question is: Where do salespeople perform the best, in an environment heavy
on OC or in a climate dominated by BC orientation? Anderson and Oliver
(1987, p. 86) argue as follows:
In control systems that are more behavior-based than outcomebased, individual salespeople will perform more poorly on traditional
output measures of individual-level performance. Serving customer
needs (a long-term strategic consideration) and meeting other
organizational goals follow from management’s ability to direct
salespeople to ignore immediate market cues and follow management
directives instead. This emphasis may hurt short-term individual
performance.
It is therefore hypothesized as follows:
Hypothesis 2: On the OC-BC continuum, the performance 1 of salespeople
closest to the outcome-based end of the continuum will be higher than that of
salespeople in the behaviour-based end of the continuum.
The Role of the Sales Manager- Differences across Individual Salesperson
In addition to the direct effects hypothesized, the role played by sales
managers’ personal and professional investment orientation toward the
salesperson is investigated. This construct captures sales managers concern for
the well being (personal and professional) of the salesperson. Sales managers’
actions such as active listening, offering to help, being sensitive to the feelings
of the salespeople, etc., create supportive atmospheres conducive for personal
and professional development. Always being there when s/he (the manager) is
needed will reassure and strengthen the resolve of the subordinates. This type
of support engenders in the salespeople additional extrinsic motivation to keep
trying regardless of circumstances. Salespeople, acting on the principle of
reciprocity, should reward sales managers for their caring attitude. Caring
1
Short term customer generated performance- sales, sales growth, and percentage of sales quota
achieved.
9
superiors create work climates of psychological support, mutual trust and
respect, helpfulness, and friendliness (Gibson et al 1973). Finally, a caring
sales manager constitutes a buffer for the salesperson so that the later is not
overwhelmed by the dysfunctionalities in the organization’s control system.
Consequently, the salesperson performance will be less sensitive to the
dysfunctional side effects control systems might harbour.
Hypothesis 3: The sensitivity of salesperson performance to the control system
experienced by the salesperson is weaker when the sales managers exhibit
more personal and professional investment orientation toward the
salesperson.
Figure 1 represents the conceptual model and a summary of the hypotheses.
Operationalization of Control System and Salesperson Performance
This paper considers the mean of the seven scores (given that there are
seven components) of a salesperson as the central thrust of the control actually
experienced by the salesperson. The attention here is not on what management
intends but on what it actually implements at the level of each salesperson.
Each component is measured on a 7-point scale anchored on 1 (indicating pure
OC) and 7 (indicating pure BC). Thus a salesperson with the set of scores: (5,
7, 5, 3, 4, 6, 5) has a mean of 5, implying that the salesperson experiences a
mixed control system that is largely dominated by a behavior control
philosophy (i.e., the mean is closer to the BC end than to the OC end of the
continuum).
Salesperson performance is operationalized as current customer
generated sales, sales growth, and percentage of sales quota. The appropriate
way to measure performance is a major concern in the sales force literature
10
(Churchill et al 1985, Rich et al 1999). In this paper the choice of performance
measure is consistent with the arguments that formed the basis of the
hypotheses.
Model Specification
A varying parameter model (VPM) specification (Gatignon and
Hanssens 1987) is used to jointly test the three hypotheses. VPM lends itself to
the study of interactions- the process that drives a parameter (value, sign,
variation). Equations 1 and 2 are response and process function respectively,
in which subscript i represents individual salesperson.
(1)
SPi = β 0 + β1,i CS i + β 2 CS i2 + β 3 FPi + υ i
(2)
β 1,i = δ 0 + δ 1 MPi + ε 1,i
Where:
SP = Salesperson sales (customer-generated) performance
CS = Control system philosophy
MP = Manager’s personal and professional investment orientation toward the
salesperson.
FP = Percentage of compensation that is fixed 2
DATA DESCRIPTION AND MODEL ESTIMATION
Data on performance (unit sales, revenues, percentage of sales quota
achieved) and pay plan were obtained from companies’ archives while
information on all other variables were collected via a survey of salespeople
whose organizations participated in this study. There were a total of eighteen
organizations with operations in Africa, Europe, and the US. They come from
food, pharmaceutical, managed healthcare, chemical, information system,
2
Serves as a control variable in the regression model
11
banking, and light engineering industries. The decision to conduct a
multinational and multi industry study is motivated by a quest for strong
external validity. In return for their participation, firms were promised a
summary of the survey.
The firms contacted represent a convenience sample drawn from a list
of companies that participated in executive education seminars at some
leading international business schools. Each participating organization sent a
list (with contact details) of all their salespeople. Prior to survey mailing, the
chief executive or a senior member of management sent a memo to all
salespeople informing them of the study and encouraging them to collaborate.
Questionnaires were available in six languages- English (original version),
Spanish, French, Italian, Hungarian, and German. Multiple two way
translations were done before arriving at the final translated versions.
While the questionnaires were in the field, companies provided data on
sales performance and pay plan for each salesperson. In order to ensure
accurate data matching, each questionnaire was unobtrusively coded. The
codes were destroyed afterwards, consistent with a promise of confidentiality
made to all respondents and companies. Over a period of three and a half
months, the survey yielded 1,290 usable questionnaires (English: 40%,
Spanish: 25%, French: 12%, Italian: 10%, Hungarian: 12%, German: 1%). A
third of the respondents are female and none of the participating organizations
had response rate below 50%. On the surface there is nothing to suggest a
systematic difference on any of the variables of interest between the eighteen
companies that participated and those that declined. A comparison of early and
12
late respondents showed no response bias in all the variables of interest
(Armstrong and Overton 1977).
Development of Measures
The control system is made of seven elements (table 1), each measured
with multi-item scales (table 2). Exploratory factor analysis (EFA) was
conducted in order to verify the unidimensionality of each of the constructs
and to measure the reliability of their scales.
In order to establish the theoretical validity of multi-item latent
constructs developed in the preceding section, confirmatory factor analysis
(CFA) using LISREL 8 was conducted. CFA takes into account measurement
errors and restricts loading of an items on unique constructs. Except for a few
relatively large Chi-square values (table 3) all other statistics were within
acceptable range (Joreskog and Sorbom 1996).
The discriminant validity of the constructs (table 4) was confirmed by
comparing a measurement models with restricted construct-to-construct
correlation with one without restriction. According to Bagozzi, Yi and Phillips
(1991), if the unrestricted model improves significantly overall fit, the two
constructs are distinct from each other, although they can possibly be
significantly correlated. The test statistics (table 4) shows that all the twofactor-measurement-models performed significantly better than their
corresponding one-factor-measurement models. Hence the two-factor models
were selected for further analyses.
Indications of relatedness of among elements of sales force control
systems are shown in table 5. A pair wise correlation of the model variables
13
was also conducted. The correlations are shown in table 6. The size of
coefficients in table 6 is consistent with the findings in Churchill et al (1985).
Estimation of Model Parameters and Results
Pooling tests were conducted prior to model estimation. The ability to
pool together subsets 3 of data requires that a pooling test be performed
(Gatignon 2003). This investigates the extent to which datasets are
homogenous or are generated by the same data generating function. If the
estimated parameters of different subsets of data have the same parameters
then the data is poolable (Gatignon 2003). Data was poolable across all six
languages but not across all eighteen companies. There is a split of data into
two groups of firms each with poolable data (i.e., the signs and values of
parameters obtained for different company cross sections in each group are
statistically the same). The first group contains 620 observations (ten firms)
while the second group contains 670 observations (eight firms). Table 7 shows
estimated parameters for the two groups. Plots of salesperson performance
against control system philosophy are shown in figures 2 and 3 for groups 1
and 2 respectively.
The results in table 7 and figures 2 and 3 suggest there is a direct
relationship between sales performance and control system philosophy. This
relationship is not linear but quadratic—inverted-U as hypothesized (figure 3)
and regular-U contrary to hypothesis 1 (figure 2).
Consistent with hypothesis 1, the signs of the estimated parameters
(table 7) for Group 1suggest that the farther a salesperson is from the end points
of the OC-BC continuum, the worse his or her performance (figure 2). The
3
The data in this paper come from eighteen different organizations. Besides, six language subsets are
represented.
14
reverse is the case in Group 2—the farther a salesperson is from the end points
of the continuum, the better his or her performance (figure 3).
As per hypothesis 2, the supremacy of pure outcome controls (OC) over
pure behavior controls (BC) is group dependent. In Group 1, although the
performance under pure OC system (1.885) is higher than that under pure BC
system (1.873), this difference is marginally significant (figure 2). In Group 2
however, salespeople in pure BC systems clearly outperform (0.971) their
counterparts in pure OC systems (0.059) (figure 3).
Consistent with Hypothesis 3, the strength of the relationship between
sales performance and control system is a function of the level of caring
exhibited by the sales managers. In Group 2, the strength of the link is reduced
in the presence of increasing sales manager’s personal and professional
investment orientation toward the salesperson.
The covariate (proportion of compensation that is fixed) is negative and
significant, thus suggesting that salesperson’s performance increases as the
variable portion of his or her compensation grows. Concerning the variancecovariance structure of the random portion of the specified model, there is
evidence of a significant difference across the cross-section of salespeople
(table 7).
DISCUSSION AND CONCLUSION
The most important finding in this paper is that a direct relationship
between salesperson’s performance and control system philosophy not only
exists but also takes a curvilinear form. These findings are contrary to
dominant claims in the literature—indirect and linear relationships. The
15
discovery of two distinct curvilinear relationships (U and inverted-U) offers
some interesting insights and inferences.
Mixed control systems when properly implemented address a wider
array of control problems and produce outstanding results (Merchant 1985).
This is consistent with the surprise finding reported in Oliver and Anderson
(1995). The authors discovered that salespeople under mixed control systems
perform better than those under pure outcome or pure behaviour control
systems. Figure 3 is yet another empirical evidence of the strength of well
executed mixed control systems.
On the other hand, this paper also draws attention (figure 2) to the dark
side of mixed control systems (Merchant 1985). This had never been
demonstrated empirically. It is conceivably the case that firms in Group 1
suffer from the dysfunctional side effects of mixed control systems, as alluded
to by Merchant (1985). There is no evidence in this paper suggesting that
Merchant’s affirmation about potential negative consequences of mixed
control systems is contingent on types of industries or businesses. The claim
seems to hold regardless of industry sector given that service and product
(industrial and non industrial) firms are represented in the two groups
investigated.
Nevertheless, it might be possible to find individual salesperson
characteristics that explain why some salespeople perform best under a mixed
control system (figure 3) while other salespeople, governed by similar control
philosophy perform worst (figure 2). This is a limitation of this study and
could constitute an important and interesting research endeavour. Findings
thereof might inform firms’ sales staff selection criteria and practices. It could
16
improve the ability of firms to match prospective salesperson to the control
philosophy envisaged by the hiring organization. Concerning current
salespeople, organizations might rely on training and socialization as a way of
providing salespeople with the knowledge and dispositions required to achieve
excellent results given the control system philosophy they face.
An unexpected finding is the setting in which salespeople under pure
behaviour-based control systems achieve better sales performance than their
counterparts under pure outcome-based control systems (figure 3. See also
Cravens et al. 1993). This suggests, contrary to assertions in most management
literatures, that pure behaviour-based control systems are not always inferior
to pure outcome control systems even when the performance indicators of
interest are current customer generated metrics like sales.
An important lesson from this paper is that organizations should
exercise caution when borrowing sales force control practices from other
business entities (within or outside their sectors). If the findings here are
anything to go by, the path to guaranteeing excellent customer-generated sales
performance is not unique. Consequently equifinality theorists might interpret
this finding as yet another empirical proof of their thesis.
While the foregoing is sustainable it is also true that the achievement
of higher sales is not the sole objective of an organization. It is often the case
that firms want to hold their salespeople accountable for the behaviour they
exhibit during their selling assignments. Firms expect that salespeople
contribute positively to their image and reputation. This expectation calls for
more than just achieving higher sales figures.
17
Given the cross-sectional nature of the data used in this paper, causal
statements cannot be made based on the findings. The findings are only
suggestive. Experiments or longitudinal study will be needed in order to
ascertain the causal links suggested by the findings. Another limitation of this
study is the scope of the configuration adopted (Anderson and Oliver 1987).
The sensitivity of sales performance to other types of control systems captured
with other configurations in existing sales force literature should yield useful
insights.
18
Table 1
Schematic Representation of Outcome- and Behavior-based Control System
Continuum
Outcome Control
Intermediate
Control
Behavior Control
Focus of performance
criteria
Tied to outcomes
(output / results)
Tied to behavior (input)
Number of
performance criteria
used
Performance evaluated
on a few observable
criteria results,
primarily controlled by
customer
Tends to be very
transparent
Light monitoring
Tied to both
outcomes (output)
and behaviors (input)
Mixed
Transparency of
evaluation criteria
Monitoring by
management
Degree of contact with
management
Coaching by
management
Intervention from
management
Intermediate
Intermediate
Salespeople have
relatively little contact
with management
Light coaching
Intermediate
Few. Relatively little
supervision and few
managers
Intermediate
Intermediate
Adapted from Oliver and Anderson (1995)
19
Performance evaluated
subjectively by superior
on many criteria,
including nonobservable
Tends to be less
transparent
Very close monitoring
Salespeople have
relatively high contact
with management
Intense coaching
Many. Relatively
greater supervision and
many managers
Figure 1
Conceptual Model
Manager’s Personal and Professional Investment
Orientation toward the Salesperson
H3
Control System
Philosophy Experienced
by the Salesperson
H1 and H2
20
Salesperson
Performance (customer
generated)
Table 2
Exploratory Factor Analysis- Results
Construct Name
Number
of items
Scale Anchors
Coefficicient
Alpha
Relative Importance of
Input and Output Factors
during Evaluation
7
1 (completely disagree) to 7
(completely agree)
0.82
Number of Performance
Criteria
3
1 (completely disagree) to 7
(completely agree)
0.78
Transparency of Evaluation
5
Monitoring by Management
6
1 (completely disagree) to 7
(completely agree)
0.81
Degree of Contact with
Management
4
1 (completely disagree) to 7
(completely agree)
0.79
Coaching by Management
5
1 (completely disagree) to 7
(completely agree)
0.81
Intervention from
Management
4
1 (completely disagree) to 7
(completely agree)
0.69
Manager’s Personal and
Professional Investment
Orientation Toward the
Salesperson
11
1 (completely disagree) to 7
(completely agree)
0.93
Very Imprecise…….Very
precise
Not at all clear……… Very
clear
Subjective……………
Objective
Very Vague……Not at all
Vague
Very partial……Highly
impartial
21
0.85
Table 3
Confirmatory Factor Analysis: Summary of Results
Construct
Chi-Sq.
226.98
df
14
p-value
0.0000
RMSEA
0.146
GFI
0.92
Number of Performance Criteria
0
0
1
0.000
1.00
Transparency of Evaluation
100.61
5
0
0.164
0.95
Monitoring by Management
70
9
0
0.098
0.97
Degree of Contact with
Management
1.52
2
0.4678
0.000
1.00
Coaching by Management
24.88
5
0
0.075
0.99
Intervention from Management
7.68
2
0.0215
0.063
0.99
Manager’s Personal and
Professional Investment
Orientation toward the
Salesperson
279.94
44
0
0.087
0.93
Relative Importance of Input and
Output Factors during
Evaluation
22
Table 4
Discriminant Validity Test- Summary of Results
Monitoring
by
Management
/ Contact
with
Management
Monitoring
by
Management
/ Coaching
by
Management
Contact with
Management
/ Coaching
by
Management
Transparency
of Evaluation
/ Relative
Importance
of Input and
Output
Factors
during
Evaluation
Transparency
of Evaluation
/ Number of
Performance
Criteria
Relative
Importance
of Input and
Output
Factors
during
Evaluation /
Number of
Performance
Criteria
X2
1,105
One - Factor Model
df
RMSEA GFI
35
0.210
0.76
X2
172
Two - Factor Model
df
RMSEA GFI
34
0.075
0.95
396
44
0.106
0.91
227
43
0.077
0.95
926
27
0.216
0.78
75
26
0.051
0.98
2,344
54
0.244
0.65
469
53
0.105
0.90
429
20
0.169
0.87
148
19
0.098
0.95
753
35
0.170
0.83
397
34
0.122
0.90
23
Table 5
Control System’s Variable Means, Standard Deviations, and Pearson
Correlation Coefficients
Mean
S.D.
X1
X2
X3
X4
X1
3.56
1.13
1.00
X2
4.69
1.55
.36
1.00
X3
3.33
1.18
-.26
-.11
1.00
X4
4.79
1.09
.15
.11
-.32
1.00
X5
4.58
1.48
.40
.12
-.24
.17
1.00
X6
4.33
1.26
.32
.10
-.36
.63
.26
1.00
X7
3.47
1.12
-.16
-.08
.25
.08
-.26
-.06
All correlation coefficients are significant at p < 0.01
Number of observations (N) = 1290
Legend:
X1 = Input – Output weight in evaluation
X2 = Number of performance criteria
X3 = Transparency of evaluation criteria
X4 = Monitoring by management
X5 = Contact with management
X6 = Coaching by management
X7 = Intervention from management
24
X5
X6
X7
1.00
Table 6
Model’s Variable Means, Standard Deviations, and Pearson Correlation Coefficients
Mean
S.D.
K1
K2
K3
K4
K1
4.11
0.56
1.00
K2
17.19
4.53
0.99
1.00
K3
4.89
1.25
.41
.41
1.00
K4
4.84
1.42
.14
.13
-.01a
1.00
K5
0.00
1.00
-.05*
-.05*
-.06**
-.17
K5
1.00
a : not significant
* Significant at p < .1
** Significant at p < .05
All other correlation coefficients are significant at p < .01
Number of observations = 1290
Legend:
K1 = Control system philosophy (reflected by mean of score of control system
elements)
K2 = Square of control system philosophy
K3 = Manager’s personal and professional investment orientation towards the
salesperson
K4 = Proportion of compensation that is fixed
K5 = Salesperson performance (customer generated)
25
Table 7
Results from Varying Parameter Model Analyses
Group 1
Group 2
(a)
(b)
3.315***
-1.052
(1.237)
(1.525)
-1.634***
1.248**
(.628)
(.727)
0.204***
-0.137*
(.079)
(.086)
Control system philosophy * Manager’s personal and
0.004
-0.025***
professional investment orientation toward the salesperson
(.009)
(.008)
Proportion of compensation that is fixed
-0.040*
-0.242***
(.025)
(.030)
Intercept
Control system philosophy
Square of control system philosophy
620
Number of observations
670
Test for Heteroscedasticity
Intercept
1.037
1.402***
Square of control system philosophy
-0.004
-0.030***
Note: Standard errors are in parentheses
*** significant at p < .01; ** significant at p < .05; *significant at p < .10
26
Figure 2
Plot of the Relationship between Control Philosophy and Salesperson Performance
(Group 1)
Performance versus Control Philosophy
2
1.885
Salesperson Objective
Performance
1.8
1.873
1.6
1.4
1.2
1
0.863
0.8
0.855
0.6
0.4
0.249
0.2
0.245
0.043
0
1
2
3
4
5
6
Control Philosophy (OC-BC Continuum)
27
7
Figure 3
Plot of the Relationship between Control Philosophy and Salesperson Performance
(Group 2)
Salesperson Objective
Performance
Performance versus Control Philosophy
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1.763
1.748
1.504
1.459
0.971
0.896
0.059
1
2
3
4
5
6
Control Philosophy (OC-BC Continuum)
28
7
APPENDIX 1
SCALE ITEMS
Construct
Monitoring by Management
Contact with Management
Coaching by Management
Intervention from
Management
Scale Items
Anchors
* Management tracks my
activities.
* Management keeps a close
watch on how I spend my time
* Management takes my call and
activity reports seriously
* Management carries out a
detailed examination of my call
and activity reports
* Management here stays
informed of my activities
* Management checks to see if
I’m following its instructions
* I have many opportunities to
interact with management (R)
* I’m isolated from management
* I don’t get day-to-day contact
with management
* Management doesn’t spend
time with me
* Management gives me training
intended to improve my
productivity
* I receive a lot of coaching from
my boss or those I report to
* Management provides a lot of
on-the-job suggestions and tips
on ways they think I can improve
my selling skills and abilities
* There are senior salespeople
designated by management who
offer me a lot of coaching
* Management makes sure I
know how to carry out my
assigned tasks
* Management grants me a great
deal of autonomy
* Management allows me to do
almost as I please
* I take the final decision on
practically everything that has to
do with my selling assignment
* Management allows me
freedom to organize my work
R – Reverse Scored
29
1 (completely disagree) to 7
(completely agree)
1 (completely disagree) to 7
(completely agree)
1 (completely disagree) to 7
(completely agree)
1 (completely disagree) to 7
(completely agree)
APPENDIX 2 (Continued)
SCALE ITEMS
Construct
Transparency of Evaluation
Number of Performance
Criteria
Relative Importance of Input
and Output Factors during
Evaluation
Scale Items
Anchors
* How would you describe the
criteria management seems to use in
evaluating your performance?
* Very Imprecise……..Very precise
* Not at all clear……… Very clear
* Subjective…………… Objective
* Very Vague………….. Not at all Vague
* Very partial…………… Highly impartial
* To get a favorable performance
evaluation, I only need to pay
attention to a few factors
* In my opinion, there are just a
couple of requirements I need to
meet to get a good performance
evaluation
* I need to do well on quite a few
criteria in order to achieve a
favorable performance evaluation (R)
* I think that what really matters
most to management is the results I
achieve, rather than how I achieve
them
* I think management does not care a
great deal about my input into the
job; instead they focus on my output
* In my opinion, management puts a
lot of emphasis on the outcome of
my effort, but puts little weight on
the effort itself
* Only my tangible results matter to
my management
* No matter how well I behave and
how well I struggle to achieve
results, at the end of the day my
promotion and career progress
depend mostly on my bottom line
* I think management considers only
a handful of things when determining
my performance evaluation
* When management rates my
performance, they take many things
into consideration (R)
R – Reverse Scored
30
1 (completely disagree) to 7 (completely
agree)
1 (completely disagree) to 7 (completely
agree)
APPENDIX 2 (Continued)
SCALE ITEMS
Construct
Manager’s Personal and
Professional Investment
Orientation toward the
Salesperson
Scale Items
Anchors
* My manager seems willing to listen
to my problems
* My manager is considerate of my
feelings
* My manager seems to be rather
distant and unapproachable (R)
* My manager and I both have
confidence in one another
* My manager puts a lot of effort into
attending to my personal needs
* My manager is always there when I
need him or her
* My manager really cares about my
well-being
* If given the opportunity, my
manager would take advantage of me
(R)
* My manager encourages me
* My manager has a good rapport
with me
* My manager brings me valuable
feedback, even if we do not have a
good rapport
R – Reverse Scored
31
1 (completely disagree) to 7 (completely
agree)
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