AICG WORKING PAPER
2003-10
WHY DOES AN ORGANIZATION CHOOSE ONE TYPE OF
GAINSHARING PLAN OVER ANOTHER?
A CONGRUENCE PERSPECTIVE
Dong-One Kim
Associate Professor
Korea University
College of Business Administration
Anam-dong, Sungbuk-gu
Seoul, South Korea 136-701
Tel.: (02)3290-1949
Fax: (02)3290-2526
e-mail: dokim@korea.ac.kr
WHY DOES AN ORGANIZATION CHOOSE ONE TYPE OF
GAINSHARING PLAN
OVER ANOTHER?
A CONGRUENCE PERSPECTIVE
Dong-One Kim
Associate Professor
Korea University
College of Business Administration
Anam-dong, Sungbuk-gu
Seoul, South Korea 136-701
Tel.: (02)3290-1949
Fax: (02)3290-2526
e-mail: dokim@korea.ac.kr
August 2002
The author wishes to thank Paula Voos, Kenneth Mericle, Anne Miner, and John Lund for their
invaluable input and comments.
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BIOGRAPHY
Dong-One Kim is Associate Professor of Employment Relations at Korea University, Seoul,
Korea. He received a Ph.D. degree from the University of Wisconsin-Madison. Prior to joining
the faculty of Korea University in 1997, he was on the faculty of the School of Business at the
State University of New York at Oswego. His research interests are workplace innovations,
employee involvement, and comparative employment relations. His articles have been published
in Industrial and Labor Relations Review, Industrial Relations, Industrial Relations/Relations
Industrielles, Advances in Industrial and Labor Relations, Human Resource Management
Handbook, and Journal of Applied Behavioral Sciences.
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ABSTRACT
Utilizing multinomial logit analyses of survey data from 217 organizations with experience of
gainsharing plans in North America, congruence explanations of the choice of a particular type
of gainsharing plan were examined. The choice of a particular type of gainsharing plan was
found to be influenced by situational factors such as labor intensity, organizational size, and
nonmanufacturing. Program goals such as reducing nonlabor costs and improving labor relations
were also related to the probability of program adoption. Theoretical and managerial
implications are discussed.
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Introduction
As gainsharing programs, such as the Scanlon, Multicost Scanlon, Rucker, and Improshare plans,
have been increasingly adopted by American corporations in the last decade, several studies have
examined the effect of gainsharing on organizational outcomes (Hatcher & Ross, 1991; Kaufman,
1992), the factors influencing such outcomes (Bullock & Tubbs, 1990; Cooper, Dyck, &
Frohlich, 1992; Gowen & Jennnings, 1991; Kim, 1996; Kim & Voos, 1997), and the survival of
gainsharing programs (Kim, 1999). There has, however, been very little empirical research on
what makes an organization more likely to choose one particular type of gainsharing plan over
another. Although there is some evidence that the choice of gainsharing plan is related to
program success (Kim, 1996), previous literature has generally been silent on this issue.
When implementing a gainsharing program, formula determination is the leading cause
of disagreement among corporate officials, local management, human resource management,
union representatives, and outside consultants (Bazerman & Graham-Moore, 1983; GrahamMoore, 1990). Indeed, one long-term observer has argued that “the one-third of gainsharing
installations that failed did so in the first year.... typically, the blame is placed on the formula”
(Graham-Moore, 1990, p. 49). Clearly, human resource management (HRM) practitioners need
to be aware of the patterns of program adoption and the reasons for those patterns so as to be
able to better select the plan that would be appropriate for their organization. The purpose of this
study is to identify factors systematically associated with the choice of a particular gainsharing
plan once an organization has decided to adopt gainsharing.
Various hypotheses were developed from a congruence perspective to explain the design
of gainsharing programs. While previous theoretical models explain the design of various pay
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systems, such as the agency, transaction cost, resource dependence, and institutional models (see
Barringer & Milkovich, 1998 for details), they do not fully explain the design of gainsharing
plans. The first three models (i.e., the agency, transaction cost, and resource dependence models)
mainly address the efficiency merit of a particular type of innovation, while the last model (i.e.,
the institutional model) describes how institutional pressures influence the design of an
innovation. None of these models, however, deals with interactions between design features, and
program goals and situational conditions. Although these dimensions are among the most
important factors that gainsharing consultants pay attention to in the process of designing a
gainsharing plan, I found that most existing theoretical models do not incorporate these issues in
their frameworks. This was one of the reasons why I planned this research. Thus, the present
study focuses on an alternative perspective – the congruence approach – in explaining the design
of gainsharing programs.
I examined survey data from 217 organizations with actual experience of gainsharing
plans in the U.S. and Canada. To test the congruence explanations, I used a multinomial logit
model to capture the choice between five major types of gainsharing: the Simple Scanlon,
Multicost Scanlon, Rucker, Improshare, and customized plans.
Types of Gainsharing
Gainsharing typically involves a contingent group compensation scheme combined with
an employee involvement (EI) component. With gainsharing, each member of a gainsharing
group receives a bonus based on the output of the group as a whole, as opposed to one based on
the employee’s individual output. Whereas profit-sharing provides employees with a companywide bonus based on some percentage of company profits or profits beyond some fixed
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minimum, gainsharing can be implemented on an establishment or departmental basis, and
employees are generally paid a portion of the savings generated when costs fall below a
predetermined level. Gainsharing can also be implemented for companies as a whole.
Gainsharing programs can be classified into five major categories, depending mainly on
how bonuses are calculated. Simple Scanlon plans are based on labor-cost ratios, Multicost
Scanlon plans on production-cost ratios, Rucker plans on value-added ratios, and Improshare
plans on unit-per-hour ratios. Customized plans, on the other hand, use formulae tailored to the
specific needs of individual organizations, often taking into account other factors besides the
efficiency of labor (such as quality, safety, customer satisfaction, attendance records, and ontime delivery records).
The attributes of the major types of gainsharing programs are listed in Table I. Some
plans are considerably more complex to administer than others and require more information
and/or administrative expertise: in general, Simple Scanlon plans require the least information,
Rucker plans require somewhat more information, Multicost Scanlon plans require even more,
and Improshare plans require even greater organizational information and expertise. Some plans
put more emphasis on reducing labor costs (Simple Scanlon and Improshare plans), whereas
others emphasize reducing other costs (Multicost Scanlon and Rucker plans), and still others
(customized plans) have even more complex goals. Some put a greater emphasis on formal
employee involvement and employee votes (Scanlon plans in general) and others may or may not
have a formal involvement component (Improshare plans typically place the least emphasis on
employee involvement). The various types of plans also differ in the way gains are split between
labor and management, with Scanlon Plans generally providing a greater proportion of gains to
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employees.
Theoretical Issues and Hypotheses
Previous Theoretical Models
A few theoretical models can be identified in the previous literature explaining the
adoption and diffusion of HRM programs. For example, Williamson (1981) argues that
management’s decisions about pensions might be related to transaction costs. Pfeffer and DavisBlake (1987) apply resource dependence theory in explaining compensation systems for
university administrators. Goodstein (1994), and Ingram and Simons (1995) provide institutional
and resource dependence models in analyzing the adoption of work family programs. Eisenhardt
(1988) utilizes agency and institutional models to explain the adoption of various pay systems for
salespeople. Barringer and Milkovich (1998), explaining the adoption of various flexible benefit
programs, provide a comprehensive theoretical discussion and formulate four theoretical lenses:
the agency, transaction cost, resource dependence, and institutional models. The main thesis of
the first three models is that the purpose of program design is to maximize the efficiency
potentials of the program. Thus, all four models can be reclassified into two groups: efficiency
(including the agency, transaction cost, and resource dependence models) and institutional
models.
The efficiency models assume that organizations actively manage environmental
constraints by adopting organizational structures or programs that ensure the efficient flow of
resources and minimize costs. Specifically, agency cost theorists argue that the “classical
capitalist firm” is established in order to circumvent the free-riding problem by assigning the task
of monitoring to a specialist whose incentive to monitor is his/her claim to the team’s “residual”
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income. That is, if the monitor is to receive any residual profits above prescribed amounts, he/she
will have a strong incentive not to shirk his/her duty as a monitor (Alchian & Demsetz, 1972).
The obvious agency costs are those associated with the reduced incentives for the user to
maintain the asset properly and to guard it from theft, and the increased incentives to misuse it
(Jensen & Meckling, 1976). The agency theory implies that human resource programs should be
designed to motivate employees to act in the best interests of the principal (Kim & Voos, 1997).
Based on economic reasoning, transaction cost theorists argue that organizations establish
structures that minimize the costs of their transactions with employees when the employees’
potential for opportunistic behavior is high. That is, where organizational-specific skills are
critical to the organization, turnover can be costly, and organizations adopt internal governance
structures to minimize the transaction costs incurred in the negotiation process (Williamson,
1975). According to this view, organizations tend to adopt HRM structures that can stabilize
employment and provide incentives for employees to act in the organizations’ interests
(Williamson, 1981).
The resource dependence model assumes that managerial decisions are influenced by
internal and external agents who control and distribute critical resources such as funds, personnel,
or both (Pfeffer & Salancik, 1978). These agents can explicitly or implicitly exert pressure on an
organization to adopt certain innovations or structures by linking compliance with resource
allocation. Thus, organizational structures and HRM systems tend to be shaped by agents who
control critical resources. Whereas the resource dependence model, in particular that part of it
concerning external pressures, is very similar to the institutional model’s “coercive pressures”
(which will be discussed next), the resource dependence model assumes to a greater extent than
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does the institutional model that organizations respond strategically and autonomously to
external pressures (Barringer & Milkovich, 1998). Hence, the primary consideration behind
management decisions from these perspectives is expected efficiency gains.
The institutional model assumes that organizations do not exercise active choice; rather,
they passively conform to environments. Organizations theoretically adopt structures that
enhance their legitimacy in the external environments, regardless of the impact on the technical
efficiency of internal operations. According to this view, organizations adopt a particular type of
innovation through three isomorphic processes: coercive, mimetic, and/or normative
isomorphism. Thus, institutional pressures and modeling behaviors are the primary factor
influencing management decisions about innovations (Barringer & Milkovich, 1998).
The common theme of the above four models is the attention they give to human factors.
The first two models (the agency and transaction cost models) mainly deal with how to manage
human resources within an organization, whereas the latter two models (the resource dependence
and institutional models) focus in general on the issue of how to satisfy powerful outside
agencies or gain legitimacy in the eyes of outsiders. Interestingly, all these models ignore the
influence of environments and situations faced by organizations, and the goal of program
implementation. Thus, they do not seem to explain factors that are actually considered by
gainsharing practitioners and consultants in the design process.
Congruence Hypothesis
The present study hypothesizes that organizations naturally choose a program which fits
with program goals and situational conditions. Organizations are expected to design a program
which is consistent with the goal of the program as long as they have unambiguous goals.
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Organizations are also expected to adopt practices that compliment existing environmental
constraints because this type of congruence enhances the efficiency gain by maximizing synergy
effects with existing components of the organization.
Even though one may not be sure that the congruent system enhances organizational
efficiency, it is obvious that a program that is inconsistent with program goals and situational
factors will not accomplish the intended objectives. If the design features of the program do not
match program goals, situational factors, or both, the program is unlikely to be fully
implemented, effectively operated, and persist over time. Indeed, the previous literature indicates
that the unique design aspects of an organizational development program themselves may
contribute to its survival or discontinuation. Throughout the life of a gainsharing program, the
strengths and weaknesses of each program interact with organizational goals and strategies, and
situational and environmental factors. During this process, some design features may facilitate
the long-term viability of the program, while others may contribute to program failure (Kim,
1999). Therefore, in order to improve the efficiency gains and longevity of a gainsharing
program, organizations should design a gainsharing program that is consistent with the program
goals and situational conditions.
Other studies implicitly and explicitly assume the influence of congruence (in terms of
situational factors or program goals) in program adoption. For example, Lawler (1988) argues
that organizations must choose an employee involvement program that is congruent with existing
situational factors such as the nature of the work and technology, the values and goals of the
organization, and the organization’s current management approach. Osterman (1994) also found
that firms considering employee commitment as an important goal are less likely to use
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temporary employees and more likely to invest in innovative work practices such as skills
training and incentive compensation.
The congruence approach in the present study is different from the internal fit and
external fit approaches found in strategic HRM literature (Arthur, 1994; Huselid, 1995). Internal
fit refers to the tendency for firms to adopt HRM practices that complement and support each
other, whereas external fit suggests that firms match HRM practices with competitive strategies.
The congruence model, which addresses the relationship between an HRM program and its
immediate goal, is narrower than the external fit concept. On the other hand, the concept of the
congruence model, which stresses the congruence between an HRM program and its technical
environments, is broader than that of internal fit.
The congruence hypothesis is similar to the efficiency models (i.e., the agency,
transaction cost, and resource dependence models) because it basically tries to improve
efficiency. Thus, the congruence model can be considered another type of efficiency model. The
present theoretical framework should be considered as a complementary model to the existing
four theoretical models because it does not deny the existing models.
In the following, the seven congruence hypotheses will be discussed in turn. The
situational conditions of the congruence model considered in the present study include labor
intensity, organizational size, and industry type.
Labor Intensity. Labor-intensive organizations are expected to be more interested in the
efficient use of the labor force, other things being equal, because the saving of labor costs is so
important in these organizations. Thus, labor-intensive organizations are more likely to adopt
plans emphasizing the saving of labor costs, and less likely to adopt plans focusing on other
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goals (e.g., the saving of material cost and overhead, and quality improvement), other things
being equal.
For this reason, it was hypothesized that labor-intensive organizations are more likely to
choose the Simple Scanlon or Improshare plans over the Multicost Scanlon plan because these
plans put the greatest emphasis on reducing labor costs. In general, customized plans put the
least emphasis on reducing labor costs. Thus, I hypothesize that they are least likely to be chosen
by labor-intensive organizations.
Hypothesis 1: Labor-intensive organizations are more likely to choose labor-cost focused
plans (i.e., the Simple Scanlon or Improshare plans) than other plans (i.e., the Multicost
Scanlon plan and customized plans).
Organization Size. Gainsharing plans differ in terms of the administrative complexity.
The choice of gainsharing plan is constrained by the organization’s ability to produce various
data in a timely fashion and maintain an appropriate management system. I hypothesized that
differences in administrative difficulty among various gainsharing plans affected adoption
probabilities.
The administration of the Simple Scanlon plan is relatively easy: only basic accounting
skills for dealing with payroll data and production data are needed. The administration of the
Rucker plan requires some accounting expertise and a cost accounting system which can provide
payroll data, production data, and the costs of outside supplies. The Multicost Scanlon plan is
more complicated than the Simple Scanlon or Rucker plans as it requires an accounting system
that provides a wider variety of data (e.g., data concerning payroll, sales, revenues, inventory,
material cost, overhead, and other costs) on a monthly basis. The administration of the
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Improshare plan requires industrial engineering expertise as well as cost accounting. The need
for detailed time standards probably requires the most sophisticated management information
system of any of the standardized plans.
Although customized plans are good at incorporating diverse business strategies into the
gainsharing bonus formula, they are generally more difficult to develop than standard plans.
There is accumulated knowledge regarding how to operate a standard gainsharing plan – for
instance, how to measure and evaluate group performance (see, for example, Bazerman &
Graham-Moore, 1983; Moore & Ross, 1978; Ross & Ross, 1990). In contrast, the development
and installation of a customized plan requires considerable design work, trial and error,
administrative tasks, expertise in accounting and work systems, and a sophisticated management
information system that can generate different types of data sets from those related to standard
programs.
In the present study, the size of the organization was considered as a proxy of the
organization’s capacity to implement and administer gainsharing plans. Such a capacity may
include the ability to deal with administrative complexity, the availability of specialized full-time
personnel to manage the program, and the ability of the management information system to
provide various data on time. Small organizations, which usually do not possess such a capacity
to implement and administer more complex programs (even with the help of consultants), are
more likely to rely more on administratively simpler plans (e.g., the Simple Scanlon and Rucker
plans). On the other hand, large organizations will not have such constraints and should be able
to manage gainsharing plans requiring more administrative complexity and a more sophisticated
management information system (e.g., the Multicost Scanlon, Improshare, and customized plans).
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For this reason, I hypothesize that small organizations are more likely to choose the Simple
Scanlon or Rucker plans over the Multicost Scanlon, Improshare, or customized plans.
Hypothesis 2: Small organizations are more likely to prefer administratively simpler
plans (i.e., the Simple Scanlon or Rucker plans) to more complex plans (i.e., the
Multicost Scanlon, Improshare, or customized plans).
Industry Differences. Although gainsharing originated in, and has been traditionally used
in, manufacturing,1 it has recently spread to nonmanufacturing: to mining, communications,
retail, financial services, hospitals, and even government sectors (e.g., Markham, Scott, & Little,
1992; Jarrett, 1990; Ross, 1990).
While nonmanufacturing organizations can use both standard and customized plans,
some standard plans are more difficult than others to apply to nonmanufacturing settings.
Specifically, the Improshare plan is based on the use of detailed work measures for each task.
Because detailed time standards must be available to implement the Improshare plan, it tends to
be more applicable to quasi-manufacturing service operations, such as large-scale laundries (e.g.,
hospital laundries) and information-processing operations, where operation is not interrupted by
customer contacts of unpredictable length (Ross, 1990). It can be difficult to apply the
Improshare plan to typical nonmanufacturing settings where it is hard to establish a baseline time
standard due to unpredictable customer contacts.
Other standard gainsharing plans (e.g., the Simple Scanlon, Multicost Scanlon, and
Rucker plans) are relatively easy to implement in a variety of nonmanufacturing settings since
these plans use a dollar-based measure, as opposed to a time-based one. For this reason, I
hypothesize that nonmanufacturing organizations are less likely to choose the Improshare plan
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than other gainsharing plans.
Hypothesis 3: Nonmanufacturing organizations are less likely to prefer the Improshare
plan to other gainsharing plans.
The most popular goals that organizations pursue when implementing gainsharing
include: improvement of labor productivity, reduction of nonlabor costs, improvement of quality,
and improvement of labor relations (for some examples of this, see Cotton [1993], Schuster
[1984], and Gowen [1991]). I expected that organizations with different goals would choose
different plans, other things being equal, given that different plans emphasize particular
outcomes. For example, consider two organizations with a similarly high degree of labor
intensity. For the economic reasons just discussed, both organizations should be more likely to
choose those gainsharing plans that focus on reducing labor costs. However, if one of the two
organizations has the explicit goal of improving product quality (and the other does not), that
should additionally influence the relative probabilities of plan selection. It is in this sense that I
tested whether or not conscious organizational goals were important factors in plan selection.
Goal: Improving Labor Productivity. Whereas the Simple Scanlon and Improshare plans
focus primarily on improving labor productivity, the Multicost Scanlon plan (and Rucker plan, to
a lesser degree) aims to save nonlabor costs as well as labor costs. Customized plans generally
emphasize objectives other than labor productivity (such as quality, safety, customer satisfaction,
attendance records, or on-time delivery records). Consequently, organizations seeking to
improve labor productivity by using gainsharing are more likely to choose the Simple Scanlon
and Improshare plans than the Multicost Scanlon, customized, or Rucker plans.
Hypothesis 4: Organizations seeking to improve labor productivity by using gainsharing
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are more likely to choose the Simple Scanlon and Improshare plans than the Multicost
Scanlon, customized, or Rucker plans.
Goal: Reducing Nonlabor Costs. On the other hand, if the reduction of nonlabor costs is
an important goal when implementing gainsharing, the Multicost Scanlon plan is a clear choice
since it includes most nonlabor costs as well as labor costs in the formula. Alternatively, some
organizations may consider customized plans because some customized plans focus heavily on
nonlabor savings, such as reduction in waste, energy consumption, and overhead costs. Thus, I
expect that organizations trying to reduce nonlabor costs are more likely to prefer the Multicost
Scanlon or customized plans (or the Rucker plan, to a lesser degree) to the Simple Scanlon or
Improshare plans.
Hypothesis 5: Organizations trying to reduce nonlabor costs by using gainsharing are
more likely to choose the Multicost Scanlon or customized plans than the Simple Scanlon
or Improshare plans.
Goal: Improving Quality. Although some customized plans include the measure of
product (or service) quality in the gainsharing formula, standard plans usually do not. Indeed, the
pursuit of improved labor productivity or cost reduction in standard plans sometimes conflicts
with quality improvement. Consequently, I hypothesize that if the improvement of quality is an
important goal when implementing gainsharing, organizations are more likely to choose
customized plans over standard plans.
Hypothesis 6: Organizations trying to improve quality by using gainsharing are more
likely to choose customized plans over other, standard plans.
Goal: Improving Labor Relations. Whereas some plans heavily emphasize a cooperative
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labor-management relationship, others do not. The Scanlon plan has been a symbol of labormanagement cooperation in North America. It has been argued that the key to the Scanlon plan’s
success is not the particular calculation, but rather the spirit of labor-management cooperation,
such as congruence of management and employee objectives, and their commitment to the
success of the plan (U.S. General Accounting Office, 1981). Other plans do not emphasize this
element to the same extent. Consequently, if the improvement of labor relations is an important
goal when implementing gainsharing, organizations should be more likely to prefer Scanlon-type
plans (e.g., the Simple Scanlon or Multicost Scanlon plans) to other plans (e.g., the Rucker,
Improshare, or customized plans).
Hypothesis 7: Organizations trying to improve labor relations by using gainsharing are
more likely to choose Scanlon-type plans (e.g., the Simple Scanlon or Multicost Scanlon
plans) than other plans.
Control Variables
In the present study, I include several control variables that may account for some
variance in the adoption behavior of a particular gainsharing program. These are: unionization,
the existence of a consultant, and a periodic and regional fad.
Unions. Managerial decisions regarding program choice are expected to be influenced by
union preference because labor unions control human resources at the workplace and can exert
their influence through collective action such as strikes. Indeed, the formula determination is the
major cause of disagreement among management and union (Bazerman & Graham-Moore, 1983;
Graham-Moore, 1990), and union opposition to gainsharing has been found to be negatively
related to program success (Kim, 1996).
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I hypothesize that unionized organizations are more likely to choose Scanlon-type plans
(e.g., the Simple Scanlon or Multicost Scanlon plans) than non-Scanlon plans (e.g., the Rucker,
Improshare, or customized plans) for the following reasons. First, the Scanlon plan was
originally developed and popularized by union leaders (e.g., Joseph Scanlon and Frederick
Lesieur), while other plans were developed by accountants (e.g., Allan W. Rucker) or industrial
engineers (e.g., Mitchell Fein). Consequently, many well-known early Scanlon plans were
implemented in unionized organizations (e.g., Empire Steel and LaPointe Machine Tool).
Second, Scanlon plans typically direct a larger portion of the bonus pool (75%) to workers than
do Rucker or Improshare plans (50%). This appeals to union leaders. For these reasons, it is
expected that unionized organizations are more likely to choose Scanlon-type plans than nonScanlon plans.
Hypothesis 8: Unionized organizations are more likely to choose Scanlon-type plans than
non-Scanlon plans.
Consultants. While it is possible to develop gainsharing programs without outside help,
many organizations rely on consultants. Outside consultants can provide expertise in program
design, and their recommendations are often taken seriously by organizations. How does
consultant involvement influence program choice? Although some consultants commonly install
customized plans, they are exceptions. Most consultants in North America specialize in specific
plans or copyrighted standard plans. One reason for this tendency may be the need for
consultants to reduce design and installation costs by adopting well-structured standard plans. In
a similar vein, Abrahamson (1991) indicates that fashion-setting organizations such as consulting
firms may select only those innovations they believe they can market profitably, regardless of
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how technically efficient the technologies would be for organizations. In fact, the Rucker and
Improshare plans are copyrighted ones. The development of customized plans usually requires
internal expertise. For this reason, I expect that organizations with outside consultant
involvement in the design process are more likely to choose standardized plans than customized
plans.
Hypothesis 9: Organizations with outside consultant involvement in the design process
are more likely to choose standardized plans than customized plans.
Periodic and Regional Fads. Although gainsharing has existed in North America for
nearly 100 years in a variety of forms, the interest in these programs increased considerably in
the 1980s. Before the mid-1980s, gainsharing was primarily adopted by “progressive” companies
that had an unusual interest in a participative management model. The rising popularity of
gainsharing in the mid-1980s meant that “normal” companies increasingly introduced
gainsharing during that period along with other popular employee involvement programs. I
expect that these “normal” companies will show different patterns of program choice from the
earlier “progressive” companies.
Organizations also imitate other organizations that are proximate either geographically or
in terms of their communication network (Abrahamson, 1991). Thus, it is believed that there are
regional/national influences on program choice (i.e., Northcentral states, the Northeast, the South,
the West, and Canada). These regional/national variables may reflect the influence of “model”
companies with respect to gainsharing practices in that area, or a common managerial network in
particular areas.2 Although I did not formulate any specific hypotheses about periodic and
regional fads, I expect that there will be different patterns of program choice between early and
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late adopters of gainsharing, and among organizations located in different regions.
Research Methods
Survey Methods
I tried to obtain as large a sample as possible by investigating several different sources. A
probability sampling technique, although preferable, seemed unrealistic since there was no
existing database on gainsharing programs. Thus, I had to rely on a nonprobability sampling
method and later determine whether or not it was reasonably representative. I judged it to be
quite representative in crucial respects.
Starting in March 1992, I contacted: (1) consulting firms dealing with gainsharing
programs; (2) researchers who had carried out surveys related to gainsharing programs and
published their results in journals; (3) union officials or plant managerial employees who
attended labor education programs on gainsharing; and (4) regional or national offices of
international unions. In addition, I examined (5) publications of the U.S. Department of Labor
dealing with work innovation to identify organizations with gainsharing experience. The above
sampling method provided a list of 622 organizations in the U.S. and Canada that either had
experience of a gainsharing program in the past, or currently had a gainsharing program. The
survey was sent to the industrial relations or human resource manager who was responsible for
operating the gainsharing program. The respondents were told that their answers would be kept
confidential.
Of the 622 surveys initially sent in June 1992, 50 were returned uncompleted – for
example, because the organization had moved or closed. Among the 572 organizations that were
successfully contacted, 334 surveys (58.4%) were eventually completed or partially completed,
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and returned (a response rate achieved through the use of three survey waves and the deletion of
respondents after each wave). After deleting cases in which there were missing data on any of
the variables in the model, 217 remained in the sample.
Representativeness of the Sample
Since nonprobability sampling methods were used in the present study, the
representativeness of the sample had to be evaluated. Previous surveys of gainsharing provided
some clues to the representativeness of this sample. First, Kaufman (1992) found that 23.2% of
organizations in his sample that implemented the Improshare plan eventually discontinued it,
while Fein (cited in Kaufman, 1992) estimates that 15-20% of Improshare programs are
discontinued. It was found that 21.4% of gainsharing programs in the present sample were
discontinued. Second, Globerson and Parsons (1988) found that 53% of 92 companies
implementing Improshare programs were unionized. In the present sample, 52% of the
organizations with gainsharing experience were found to be unionized.
In addition, the present sample can be compared with two previous surveys carried out
by Markham, Scott, and Little (1992), and O’Dell and McAdams (1987).3 Markham, Scott, and
Little (1992) found the ratio between the number of active Improshare plans and that of Scanlon
plans to be 0.90. O’Dell and McAdams (1987) found the ratio between the number of
Improshare plans and that of Scanlon plans to be 1.40. In the present sample, the ratio of
Improshare plans to Scanlon plans was 1.21. The comparison suggests that compared with the
previous surveys, the Scanlon and Improshare plans were not seriously overrepresented or
underrepresented in our sample.
In sum, the above comparisons suggest that although our sample was not random, it was
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reasonably representative in some major respects.4
Measurement
The variables used in this study were measured by questions in the mail survey. Five
mutually exclusive categorical (0-1) variables were constructed from this data using information
from the respondents about the bonus formula used. The respondents were asked to choose the
closest performance measure of the gainsharing program among five categories. If the
respondents chose the ratio of payroll costs to sales value of production as the performance
measure, the gainsharing program was considered to be a Simple Scanlon plan. If the ratio of
payroll, material, and overhead to sales value of production was used, it was considered to be a
Multicost Scanlon plan. If the ratio of payroll costs to value added was used, it was considered to
be a Rucker plan. Programs using the ratio of actual labor hours to standard labor hours were
considered to be Improshare plans. Programs using other measures were considered to be
customized plans.
If the respondents stated that the work system was (heavily) labor intensive, the variable
labor intensity was coded 1. If they stated that the work system was (heavily) capital intensive, it
was coded 0. If an organization had less than 100 employees, the variable, small organization,
was coded 1 (otherwise, 0). The respondents were asked either to provide the SIC code of the
primary product (or service) of the organization, or to describe it. If an organization belonged to
the service, retail trade, transportation, mining, or construction industries, nonmanufacturing was
coded 1 (otherwise, 0).
The variables measuring the goals in adopting gainsharing were created from questions
regarding the relative importance of the following four objectives of the gainsharing program:
4
2
improving labor productivity, reducing nonlabor costs, improving product (service) quality, and
improving labor relations. A response of “very important” on a three-point scale led to the
coding of that goal as present. It should be noted that the four categories were not mutually
exclusive since some respondents considered more than one objective to be very important.
If an organization was unionized, the variable, union, was coded 1 (otherwise, 0). If an
outside consultant was involved in designing the gainsharing program, consultant involvement
was coded 1 (otherwise, 0). If a gainsharing program was begun in or after 1986, the variable,
since 1986, was coded 1; if a program was begun before 1986, it was coded 0. Four
regional/national 0-1 variables were constructed: the Northeast, the South, the West, and Canada
(the benchmarking category was Northcentral states).
Descriptive statistics and correlations among variables are presented in Table II. As the
correlation matrix demonstrates, no serious multicollinearity among independent variables was
detected.
Analysis
To test the hypotheses of the present study, a multinomial logit (MNL) analysis was
utilized. MNL performs maximum-likelihood estimation of models with discrete dependent
variables. It is intended to be used when the dependent variable takes on more than two mutually
exclusive outcomes that have no natural ordering (Greene, 1993). In the present study, the
general form of this MNL equation was:
lneP(J/K) = a + biX + e
(i = 1, 2, 3, ... n),
where P(J/K) is the relative probability of choosing one type of gainsharing (J) over another type
5
2
(K). For example, P(Simple/Impro) is the probability of choosing the Simple Scanlon plan over
the Improshare plan, and P(Rucker/Custom) is the probability of choosing the Rucker plan over
customized plans. X is a vector of exogenous variables. Table III shows the MNL results for the
total sample.
Results and Discussion
The results generally supported the congruence hypothesis; though there were some
exceptions.
Results for Congruence Hypotheses
The results strongly supported the hypothesis that labor-intensive organizations are more
likely to choose labor-cost focused plans (Hypothesis 1). The variable, labor intensity, had
significant and positive relationships with P(Simple/Custom) and P(Impro/Custom). The
relationship between labor intensity and P(Multicost/Impro) was found to be significant and
negative, as expected. Also, the variable, labor intensity, had positive and significant
relationships with P(Multicost/Custom) and P(Rucker/Custom), which implies that customized
plans are least likely to be chosen by labor-intensive organizations. The reason for this could be
that customized plans usually do not focus on labor costs or labor productivity at all, whereas the
Multicost and Rucker plans include labor costs within their performance measures.
The hypothesis that small organizations are more likely to choose administratively
simpler plans (i.e., the Simple Scanlon or Rucker plans) than complex ones (the Multicost
Scanlon, Improshare, or customized plans) (Hypothesis 2) received strong support. As expected,
the variable, small organization, had significant and positive relationships with P(Simple/Impro),
P(Rucker/Impro), P(Rucker/Custom), and P(Simple/Multicost), and a significant and negative
6
2
relationship with P(Multicost/Rucker). One unexpected finding was the negative and significant
relationship between the variable, small organization, and P(Simple/Rucker), which implies that
small organizations are more likely to choose the Rucker plan than the Simple Scanlon plan.
Since the Rucker plan is more complex and requires more complicated data than does the Simple
Scanlon plan, it is unclear why the Rucker plan is preferred to the Simple Scanlon plan among
small organizations.
The results supported the hypothesis that nonmanufacturing organizations are less likely
to choose the Improshare plan than other plans (Hypothesis 3). The variable, nonmanufacturing,
had a positive relationship with P(Simple/Impro) and a negative relationship with
P(Impro/Custom). The results indicated that nonmanufacturing organizations are less likely to
choose Improshare plans than the Simple Scanlon or customized plans.
The results did not support the hypothesis that organizations trying to improve labor
productivity are more likely to choose the Simple Scanlon or Improshare plans than the
Multicost Scanlon, customized, or Rucker plans (Hypothesis 4). Although the variable, goallabor productivity, showed an expected positive sign in the P(Impro/Custom) equation and an
expected negative sign in the P(Multicost/Impro) equation, these coefficients were not significant.
The results did indicate that organizations trying to reduce nonlabor costs
are more likely to prefer the Multicost Scanlon plan to the Improshare plan, as hypothesized
(Hypothesis 5). The variable, goal-nonlabor costs, had significant and negative relationships with
P(Simple/Custom), P(Impro/Custom), and P(Simple/Multicost), and a significant and positive
relationship with P(Multicost/Impro). These results clearly indicated that the Multicost Scanlon
and customized plans are preferred to the Simple Scanlon and Improshare plans if organizations
7
2
consider the reduction of nonlabor costs as an important goal. Interestingly, it was found that the
Multicost Scanlon plan is more likely to be chosen than the Rucker plan by these organizations.
Goal-nonlabor costs showed a positive and significant sign in the P(Multicost/Rucker) equation.
The reason for this result might be that the emphasis of the Rucker plan on nonlabor savings is
weaker than that of the Multicost Scanlon plan.
At the same time, the results did not support the hypothesis that organizations aiming for
quality improvement are more likely to choose customized plans than standard plans (Hypothesis
6). Although the variable, goal-quality, had the expected negative relationship with
P(Simple/Custom), P(Multicost/Custom), P(Rucker/Custom), and P(Impro/Custom), the
coefficients were all insignificant.
There was weak support for the hypothesis that if the improvement of labor relations
were an important goal when implementing gainsharing, Scanlon-type plans would be preferred
to other plans (Hypothesis 7). The significant and negative relationship between the variable,
goal-labor relations, and P(Multicost/Rucker) implies that organizations aiming to improve labor
relations by gainsharing prefer the Multicost Scanlon plan to the Rucker plan. In addition, the
coefficients for P(Multicost/Custom), P(Simple/Impro), P(Multicost/Impro), and
P(Simple/Rucker) also showed expected, although insignificant, signs.
Results for Control Variables
The results did not substantiate the hypothesis that unionized organizations are more
likely to choose Scanlon-type plans over non-Scanlon plans (Hypothesis 8). The coefficients
were simply not significant. Either unions do not have the strong preferences which I anticipated
or perhaps they are not generally influential regarding the type of gainsharing plan adopted.
8
2
There was some support for the hypothesis that organizations with outside consultant
involvement are more likely to choose standard plans than customized plans (Hypothesis 9). The
variable, consultant, had significant and positive relationships with P(Simple/Custom) and
P(Impro/Custom), suggesting that Simple Scanlon and Improshare plans are preferred to
customized ones when consultants are involved in program design. Simple Scanlon and
Improshare plans seem to be particularly pushed by outside consultants.
The results showed some differences in the choice of gainsharing types between earlier
programs and those introduced since 1986. The variable, since 1986, showed a significant and
positive relationship with P(Rucker/Improshare) and a significant and negative relationship with
P(Simple/Rucker). Although further scrutiny is warranted, these results may reflect the fact that
the Improshare program is the newest plan, developed in 1973 by Mitchell Fein. Thus, there was
a weak indication that there are different patterns of program choice between earlier and later
adopters of gainsharing.
The results showed some significant regional variances in program choice. Organizations
in Canada are more likely to prefer Simple Scanlon, Multicost Scanlon, and Improshare plans to
customized plans than those in the Northcentral part of the U.S. The variable, Canada, was
significantly associated with P(Simple/Custom), P(Multicost/Custom), and P(Impro/Custom).
This trend can be explained by the fact that gainsharing programs in Canada were recently
introduced by consultants, and thus standard programs are more popular in Canada.
Organizations in the South are more likely to choose Simple Scanlon over Rucker plans than are
those in the Northcentral states. Also, organizations in the West are more likely to prefer
Multicost Scanlon to Rucker plans. The variables, South and West, had significant and positive
9
2
relationships with P(Simple/Rucker) and P(Multicost/Rucker), respectively. These
regional/national patterns deserve further scrutiny in future research. In sum, there are different
patterns of program choice among organizations located in different regions.
Conclusion and Implications
Utilizing multinomial logit analyses of survey data from 217 organizations with
experience of gainsharing plans in North America, I examined congruence explanations of the
choice of a particular type of gainsharing plan. The results provide an understanding of the
general pattern of usage of the various gainsharing plans. Clearly, the results show that
congruence factors are important determinants of program choice.
The theoretical implications of the present study are as follows. First and foremost, the
choice of a particular type of gainsharing plan was generally found to be influenced by
congruence factors. Among congruence factors, situational factors (i.e., labor intensity,
organizational size, and nonmanufacturing) were clearly found to be related to the particular type
of gainsharing plan selected by organizations. I found evidence that program goals (such as
reducing nonlabor costs and improving labor relations) have some relationship to the probability
of program adoption; although in general the relationships were weaker than they were for
situational factors. Explicit goals do seem to carry some weight, suggesting that future
researchers should not ignore managerial attitudes and organizational goals.
There have been some attempts to show that congruence (or fit) of management systems
is correlated with organizational performance (Arthur, 1994; Huselid, 1995; Lawler, 1988;
Osterman, 1994), whereas little research has been carried out to examine whether congruence
serves as a criterion in the choice of an innovation. The present study shows that organizations
0
3
do consider congruence factors when they design gainsharing programs.
Second, the present study also found that other factors influence the choice of a
gainsharing plan. Specifically, organizations that had consultant help were more likely to choose
standard plans than customized plans. There were some systematic differences in the choice of
gainsharing types between earlier and later adopters, and different patterns of program choice
among organizations located in different regions.
The results of this study carry a number of practical implications for organizations
considering adopting a gainsharing program, and for union representatives, who are sometimes
involved in those decisions. First, the decision to involve a particular consultant in the decision
process may be implicitly a decision to adopt a particular type of gainsharing (especially the
Simple Scanlon or Improshare plans) that may or may not be the most appropriate one for the
given organization and its goals. Moreover, organizations need to consider more explicitly the
feasibility of particular programs given their ability to produce needed information and their
administrative expertise – there is evidence that smaller organizations may be wise to adopt
simpler types of gainsharing, and that some work processes, particularly in nonmanufacturing,
are not amenable to the type of time-study needed for the Improshare plan.
There is some evidence from the patterns of adoption that different gainsharing programs
are more suitable in labor-intensive settings than in others; although the explicit goals of the
program (e.g., the minimization of nonlabor costs) should also be weighed in program selection.
Getting these issues onto the table in the adoption process can minimize later conflict over the
program and the probability of plan failure in the first year of installation.
There has been little research to date on the reasons why individual organizations adopt
1
3
one gainsharing program over another. I hope that this study will open up this area of research
and will encourage other examinations – possibly of a broader nature than was possible with this
data set – such as into why some organizations adopt gainsharing, others adopt profit-sharing,
and others adopt total quality management initiatives, teams, or other employee involvement
initiatives.
2
3
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3
TABLE I
Characteristics of Major Gainsharing Programs
Plan type
Simple Scanlon
plan
Multicost
Scanlon plan
Rucker plan
Improshare plan
Formula base
for bonus
Payroll / Sales
value of
production
Payroll,
material,
overhead /
Sales value of
production
Payroll /
Value added
Actual hours /
Standard labor
hours
Typical
employee share
75% of gain
75% of gain
50% of gain
50% of gain
Information
needed
Payroll,
production
Payroll,
production,
revenues,
material cost,
overhead, and
other costs
Payroll,
production,
material cost
Time standards
and production
levels
Expertise
needed
Basic
accounting
Advanced
accounting
Intermediate
accounting
Industrial
engineering and
extensive
accounting
Employee vote
in
implementation
decision
Yes
Yes
Yes (optional)
Usually no
Suggestion
system
Formal
Formal
Formal
Not part of plan
but may be
added
Employee
Involvement
Screening and
production
committee
Screening and
production
committee
Screening and
production
committee
(optional but
often used)
Productivity
team (optional)
9
3
TABLE II Descriptive Statistics and Correlations
1. Simple Scanlon
Plan
2. Multicost
Scanlon Plan
3. Rucker Plan
4. Improshare Plan
5. Customized
Plans
6. Labor Intensity
7. Small
Organization
8.
Nonmanufacturing
9. Goal-Labor
Productivity
10. Goal- Nonlabor
Cost
11. GoalQuality
12. GoalLabor Relations
13. Union
14. Consultant
15. Since 86
16. Northcentral
17. Northeast
18. South
19. West
20. Canada
Mean S.D.
.15 .36
1
2
3
4
5
6
7
8
9
10
11
.15
.36
-.18
.14
.34
-.16
-.16
.34
.22
.47
.42
-.30
-.23
-.30
-.23
-.28
-.21
-.38
.66
.31
.47
.46
.06
.14
-.01
-.13
.04
.33
.20
-.15
-.30
-.10
.04
.09
.28
.20
-.05
-.03
-.18
.11
-.00
.14
.78
.41
.01
-.05
.09
.09
-.15
.04
.17
-.02
.51
.50
-.05
.20
-.07
-.19
.14
.01
-.07
-.03
.08
.65
.48
-.06
.06
.01
-.10
.10
-.01
.01
-.08
.06
.33
.40
.49
-.01
.09
-.06
-.05
.05
.07
-.01
-.10
.11
.14
.22
.53
.81
.68
.64
.14
.12
.04
.05
.50
.40
.47
.48
.35
.33
.20
.22
-.07
.10
-.06
-.04
.04
-.01
-.04
.06
.02
.02
.02
-.03
.05
-.06
.13
-.04
-.10
.08
.12
.09
.05
-.11
-.08
-.03
.08
.08
-.12
-.05
-.04
.07
-.02
.11
.03
-.25
.07
.04
-.06
.07
.02
-.11
.07
.02
-.08
-.04
.02
.01
.15
-.10
-.24
.15
.02
-.02
.12
-.12
-.01
.07
-.17
.03
.06
-.01
-.04
.01
.16
-.07
-.04
.22
-.12
-.02
.00
.03
.01
-.01
.05
-.03
.03
.06
-.11
-.00
.04
-.00
.04
-.08
.09
.01
.01
-.04
.06
-.05
12
13
14
15
16
17
18
19
.05
.01
.02
.04
-.02
-.04
.10
-.07
-.04
-.04
.07
-.07
-.07
-.03
.11
-.12
-.05
.11
-.11
-.01
.11
.05
-.04
-.06
.09
-.03
-.54
-.50
-.27
-.30
-.15
-.08
-.09
-.07
-.08
-.04
0
4
TABLE III Multinomial Logit Estimates of the Odds of Choosing Types of Gainsharing Plans
Variables
P(Simple/ P(Multicost/
P(Rucker/
P(Impro/
P(Simple/ P(Multicost/ P(Rucker/ P(Simple/ P(Multicost/
Custom)
Custom)
Custom)
Custom)
Impro)
Impro)
Impro)
Rucker)
Rucker)
Labor Intensity
1.47***
.97*
1.91***
2.09
-.62
-1.13**
-1.8
-4.4
-9.4
(.54)
(.52)
(.61)
(.47)
(.52)
(.53)
(.59)
(.64)
(.65)
Small Organization
.45
-.64
1.78***
-.56
1.01*
-0.8
2.33***
-1.33
-2.42***
(.58)
(.65)
(.62)
(.55)
(.53)
(.63)
(.57)
(.63)
(.70)
Nonmanufacturing
.45
-1.06
-1.10
-2.18
2.62**
1.11
1.08
1.54
.03
(.81)
(1.18)
(1.09)
(1.25)
(1.20)
(1.52)
(1.42)
(1.05)
(1.41)
-0.2
-0.5
1.09
.90
-.92
-.95
.19
-1.11
-1.14
Goal-Labor
Productivity
(.61)
(.58)
(.74)
(.55)
(.59)
(.58)
(.72)
(.76)
(.77)
Goal-Nonlabor
-1.05*
.17
-.92
-1.35**
.31
1.52***
.44
-1.3
1.08*
(.56)
(.57)
(.59)
(.48)
(.50)
(.53)
(.53)
(.61)
(.64)
Goal-Quality
-4.5
-4.1
-2.1
-4.2
-0.3
0.1
.21
-.24
-.20
(.57)
(.59)
(.63)
(.50)
(.50)
(.54)
(.54)
(.62)
(.66)
Goal-Labor Relations
-.15
.01
-1.15*
-.57
.43
.58
-.57
1.00
1.15*
(.52)
(.51)
(.59)
(.45)
(.48)
(.49)
(.55)
(.61)
(.63)
Union
-.14
-.23
-0.1
-.22
.08
-0.1
.21
-.13
-.22
(.53)
(.52)
(.59)
(.46)
(.49)
(.50)
(.54)
(.61)
(.63)
Consultant
1.30*
.56
.96
1.06*
.24
-4.9
-0.9
.34
-4.0
(.75)
(.61)
(.76)
(.55)
(.75)
(.64)
(.76)
(.91)
(.83)
Since 1986
-.45
.03
-.96
-.21
-2.4
.24
1.17**
-1.41**
-.93
(.55)
(.55)
(.67)
(.48)
(.48)
(.52)
(.60)
(.66)
(.70)
Northeast
.27
.22
-.32
-.16
.43
.38
-.17
.60
.55
(.71)
(.71)
(.74)
(.65)
(.63)
(.67)
(.65)
(.72)
(.77)
South
-.30
-1.24
-34.68
-.24
.06
-1.00
-34.44
22.38***
21.44
(.71)
(.85)
(.00)
(.59)
(.68)
(.86)
(.00)
(.96)
(----)
West
-.04
.66
-34.57
-1.0
-.06
.76
-34.48
21.54
22.22***
(1.60)
(1.33)
(.00)
(1.33)
(1.41)
(1.13)
(.00)
(----)
(1.45)
Canada
22.87***
21.77**
21.74
23.23***
-.35
-1.46
-1.50
1.14
.04
(1.35)
(1.60)
(----)
(1.21)
(.92)
(1.21)
(1.2)
(1.35)
(1.60)
Constant
-1.24
-.79
-3.30**
-.68
-.56
-.12
-2.62**
2.06
2.50*
(1.07)
(.98)
(1.35)
(.89)
(1.05)
(1.01)
(1.31)
(1.44)
(1.42)
Log Likelihood = -268.44; Chi-square = 130.19 ***; N=217; * p < .10; ** p < .05; *** p < .01, all two-tailed; standard errors in parentheses.
P(Simple/
Multicost
.50
(.59)
1.09*
(.67)
1.51
(1.21)
.03
(.65)
-1.21**
(.62)
-.04
(.62)
-.16
(.56)
0.9
(.58)
.74
(.82)
-.48
(.59)
.05
(.74)
.94
(.96)
-.69
(1.45)
1.10
(1.37)
-.45
(1.18)
1
4
Notes
1. Indeed, there is even a belief that gainsharing will work only in organizations where some
type of tangible product is manufactured (Lawler, 1986). However, studies that have examined
this issue have only obtained insignificant results. Bullock and Tubbs (1990), in their correlation
analysis based on 33 reported case studies, found insignificant associations between the
manufacturing industry and the success of gainsharing. Markham, Scott, and Little (1992), in
their cross-tabulation analysis of 219 active gainsharing programs, did not find that gainsharing
programs in the manufacturing industry were systematically different in terms of bonus payout
records, participant satisfaction, and program longevity from those in other industries.
2. Alternatively, they may pick up the effect of regional/national industry patterns which are not
adequately captured by the manufacturing/nonmanufacturing variable.
3. Since the two surveys used a different definition of gainsharing than that adopted in the
current study, they are not directly comparable. For instance, in both surveys, customized plans
include customized profit sharing plans as well as customized gainsharing plans. Also, O’Dell
and McAdams (1987) did not include any Rucker plans in their sample. Consequently, the only
meaningful comparison among these two surveys and ours is the examination of the relative
proportion between Scanlon-type plans and Improshare plans.
4. As found in the present study, it is generally believed that gainsharing is more prevalent in the
Midwest and Northeast than in any other regions. For example, the survey by Hewitt Associates
also revealed that gainsharing programs were found to be more prevalent in the Midwest and the
Northeast (Hewitt Associates, 1989; Jones, Kato, & Pliskin, 1997).