THE DECISION TO IMPLEMENT GAINSHARING: THE ROLE OF WORK CLIMATE, EXPECTED OUTCOMES, AND UNION STATUS

PERSONNEL PSYCHOLOGY 1993,46

THE DECISION TO IMPLEMENT GAINSHARING: THE ROLE OF WORK CLIMATE, EXPECTED OUTCOMES, AND UNION STATUS

DENIS COLLINS School of Business

University of Wisconsin-Madison

LARRY HATCHER Winthrop University

TIMOTHY L. ROSS Ross Gainsharing Institute

This study empirically examines several factors that may predict management decisions to implement high-involvement gainsharing plans, interventions that pay company-wide bonuses for performance improvements and encourage employee involvement in work decisions. It addresses the question: why does one facility that considers implementing a gainsharing plan decide to do so, while another facility does not? In 59 facilities (32 nonunion and 27 union) considering the implementation of a gainsharing plan, 485 upper-level managers evaluated the work climate in their facilities (participation, identity, cooperation, and expected plan support) and rated the anticipated outcomes of the intervention. Union status had no direct relationship with the implementation decision. A significant interaction showed that implementation was positively related to participation and expected outcomes in the nonunion facilities, but was unrelated to these variables in the unionized facilities. The results provided little support for a “transformation’’ model of plan implementation and moderate support for a “compatibility” model.

Organization theorists have noted the paucity of empirical research on gainsharing, a systematic organization intervention technique that is undergoing a resurgence (Bullock & Lawler, 1984; Hammer, 1988; Lawler, 1988; ODell & McAdams, 1987). Gainsharing, a derivation of the Scanlon plan, typically entails institutional changes in organizational structure, communication flow, and financial incentives (Graham- Moore & Ross, 1990; Lawler, 1986; Schuster, 1987). This type of organizational design has been presented in the participatory management

An earlier draft of this article was presented at the 1989 Academy of Management

Correspondence and requests for reprints should be sent to Denis Collins, School of Meeting. We thank three anonymous reviewers for their very helpful suggestions.

Business, University of Wisconsin-Madison, Madison, WI 53706

COPYRIGHT 0 1993 PERSONNEL PSYCHOLOGY, INC

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78 PERSONNEL PSYCHOLOGY

literature as an example of Theory Y assumptions in practice (McGre- gor, 1960), System 4 processes (Likert, 1967), an open systems model in operation (Katz & Kahn, 1966), and as being representative of the op- timal quadrant on the Blake and Mouton managerial grid, a quadrant wherein managerial concern for production and people are both bal- anced and maximized (Blake & Mouton, 1964). Although gainsharing plans have been around for over 40 years, many fundamental research questions remain, such as why do facilities implement gainsharing? how does gainsharing generate beneficial outcomes? and what are the outcomes of gainsharing?

The purpose of this study is to empirically examine the initiating concern: Why does one facility that considers implementing a gainsharing plan decide to implement it, while another facility does not? Through- out this article the term “facility” will be used rather than “company” or “firm” because many companies or firms have multiple facilities, some of which may have gainsharing and some may not. In regards to initiating concerns, the gainsharing implementation literature primarily con- sists of individual case studies (Graham-Moore & Ross, 1990; Moore & Ross, 1978; Ross & Collins, 1987), general surveys of managers at facilities that have implemented gainsharing (O’Dell & McAdams, 1987), and the experience-based intuitions of organizational consultants who implement gainsharing plans (Graham-Moore & Ross, 1990; Lawler, 1988; Schuster, 1987). There are no studies that compare distinguishing fea- tures between facilities that have decided to implement gainsharing and those that have not.

This study focused on the experience of 59 manufacturing facilities that were seriously considering the implementation of a gainsharing plan. Throughout this article, the term “implementation” refers to the institutionalizing of department teams composed of nonmanagement employees, a review board composed of both management and nonmanagement employees, and a group-based bonus system. In each facility upper-level managers evaluated several aspects of work climate and rated their beliefs that the plan would be instrumental in achieving a number of desirable organizational outcomes. Of the 59 facilities, 17 eventually implemented gainsharing plans. Using the facility as the unit of analysis, two models of organizational change-a compatibility model and a transformation model-were investigated, as was the decision to implement a plan.

Gainsharing

Gainsharing is a generic term that has been used to describe avariety of organizational interventions such as ImproshareR, Rucker plans, and

DENIS COLLINS ET AL. 79

Scanlon plans. These plans differ according to the degree of employee involvement and types of financial incentives. According to Bullock and Lawler (1984) “the basic principle of gainsharing is to establish effective structures and processes of employee involvement and a fair means of rewarding system-wide performance improvements (p. 24).” Through- out this article, the term gainsharing will be used to refer to a modified version of Scanlon plans as described below (Graham-Moore & Ross, 1990; Lawler, 1986; Schuster, 1987).

First, gainsharing implementation typically requires two major changes in organizational stnccture: the formation of department teams and a review board. The department teams, which have substantial decision-making abilities, usually consist of the department supervisor and three to seven nonmanagement employees who are elected by their peers. Team members solicit and evaluate production-related suggestions offered by nonmanagement employees in their work area. Some- times other employee involvement mechanisms-such as quality circles, problem-solving teams, and task forces-are linked to the department teams. These teams typically meet either weekly or monthly. The team is empowered to implement any production-related suggestion that meets three criteria: (a) there is a favorable consensus, (b) implementation costs fall within the team’s monthly budget (usually about $400), and (c) the suggestion does not directly affect the functioning of other departments. Suggestions that do not receive a favorable consensus are often rejected or revised.

Another major structural change is the creation of a review board. Each department team elects one of its members to sit on the review board. In addition, the plant manager typically assigns an equal number of high-ranking management employees to the review board. The review board members discuss and evaluate all of the suggestions that have been forwarded to it by the department teams. Three types of suggestions are forwarded to the review board: (a) those accepted by a team that exceed its budget, (b) those affecting multiple work areas, and (c) those rejected by the department teams but appealed by the suggestion-giver. Any suggestion that receives a favorable consensus at the review board meeting is implemented.

Secondly, gainsharing typically entails substantial process changes. The decision-making process is decentralized in that the department teams have the decision-making and budgetary authority to implement suggestions. The review board establishes consensus decision making between management and nonmanagement employees at a high organizational level. Nonmanagement employees are provided with feedback regarding their suggestions. Many of the suggestions pertain to process concerns such as redesigning jobs and reorganizing an area’s work flow.


In addition, gainsharing facilities often hold monthly facility-wide meet- ings where the plant manager discusses the bonus results, facility operations, and economic conditions with nonmanagement employees.

Lastly, gainsharing implementation often requires a substantial change in a facility’s financial incentive system. Facility employees can earn both monthly and yearly bonuses if actual costs are less than expected costs over a particular period of time. Expected costs are often calculated based upon the facility’s performance over the previous 3-5 years. For instance, if the calculation is based exclusively on labor costs and historical labor costs are 15% of the sales value of production then the employees earn a bonus when labor costs are less than this historical ratio. Assume that labor costs are 12% of the sales value of production for a particular month. The 3% gains in production are shared between the facility and its employees. Anywhere from 25% to 75% of the gains in production is set-aside as a bonus to all facility employees while the remainder is returned to the facility. Next, some of the employee share is distributed in that month’s paycheck while the remainder is put into a year-end reserve pool that accounts for months when actual costs exceed expected costs. The reserve pool amount is then distributed to all employees at the end of the year. Other factors that serve as the basis for the group-based bonus calculation include profit improvements, time required for production output, or any other production expense that employees directly affect. The decision by top management to implement gainsharing is usually cautiously made because it is likely to affect job design and work flow, and it often entails significant changes in an organization’s structure, decision-making processes, and employee financial incentives.

Governing Factors and Competing Models

What are the factors that may influence the managerial decision to implement gainsharing? A long list of factors has been proposed by organization theorists who are either involved in gainsharing implementation (Graham-Moore & Ross, 1990; Lawler, 1981; O’Dell & McAdams, 1987; Schuster, 1987) or who have conducted research on factors that may predict gainsharing success (Bullock & Lawler, 1984; Hatcher, Ross & Collins, 1989, 1991; Ross, Hatcher & Collins, 1992; Ruh, Wallace, & Frost, 1973; Welbourne & Gomez-Mejia, 1988; White, 1979; White & Ruh, 1973). According to Lawler (1981) and Schuster (1987) gainsharing is most feasible in facilities that have less than 500 employees, single product lines, workforce interdependence, and underutilized employees, among other factors. White (1979), in a study of 22 gainsharing facilities, found that gainsharing success was a function of managerial


FACILlTY FACTORS

Accounting Methods

Age Capital Investment Plans Financial Stability Human Resource Policies INDUSTRY* Labor-Management History Overtime History Productivity Performance Seasonality of Business SIZE* Structure Style of Management Support Services UNION STATUS** WORK CLIMATE**

(Cooperation, Identity Participation, Support for Change, Consensus, etc .)

INTERVENTION FACTORS

CHANGE AGENT* EXPECTED OUTCOMES** FINANCIAL BONUS SYSTEM* Legal Implications PROCESS* STRUCTURE*

* Factor controlled for in this study ** Factor empirically examined in this study

INDIVIDUAL FACTORS

Abilities and Skills Aversion to Risk Creativityhitiative Educational Level Job Involvement Locus of Control Motivation Need for Change Philosophy About Work Philosophy About Others Prosocial Behaviors Utilizstion of Abilities and Skills Values Willingness to Change

JOB FACTORS

Hierarchical Status Task Complexity Technology Work Floor Interdependence Work FlowlCycle

PRODUCT FACTORS

I costs I Market Capabilities

Quality

Figure 1: Factors Governing the Decision to Implement Gainsharing

attitudes, expected level of success, and the average level of participation in decision making reported by employees, among other factors. As shown in Figure 1, these factors may be grouped into the following five categories: factors that pertain to the nature of the (a) facility, (b) job,


(c) employees, (d) product, and (e) intervention. All or some of these factors may govern the implementation decision. Importantly, some of the individual factors can be aggregated at the facility level of analysis. For instance, the implementation decision may be a function of one particular person’s values and perceived need for change, such as those of the plant manager, or it may be a function of the values and perceived need for change that exists throughout the facility.

Lawler (1988) notes that there is a “cart before the horse” phe- nomenon regarding gainsharing implementation. Managers may be inclined to implement gainsharing because they believe that the organizational intervention is compatible with the existing facility. This framework of analysis represents a compatibilily model which predicts that gainsharing implementation will most likely occur in facilities with the fewest restraining forces (Lewin, 1947). In this sense, gainsharing intervention boosts the facility from a relatively good level of operations to a relatively better level of operations.

On the other hand, managers may be inclined to implement gainsharing because they believe that the organizational intervention provides a necessary transformation in facility operations. This framework of analysis represents a transformation model which predicts that gainsharing implementation will most likely occur in facilities with the greatest restraining forces. In this sense, the gainsharing intervention transforms the facility from a relatively poor level of operations to a relatively better level of operations. It is consistent with other models of organizational change which state that innovations are more likely to be adopted and/or succeed when there is a strong perception of a need for change (e.g., Fenwick & Olson, 1986; Greiner, 1967; Kochan & Dyer, 1976; Pasmore, Francis, Haldeman, & Shani, 1982).

Each of the governing factors listed in Figure 1 could serve as a restraining force. We limited our exploratory analysis to three governing factors that are often highlighted by gainsharing researchers, namely (a) work climate, (b) union status, and (c) expected outcomes (Graham- Moore & Ross, 1990; Lawler, 1981; O’Dell & McAdams, 1987; Schuster, 1987; White, 1979). As will be discussed later, two other prominent governing factors-industry type and size-were controlled for as a result of the self-selection process used to develop the sample. The first two governing factors-work climate and union status-will be used to differentiate between the two competing models. The compatibility model, in comparison to the transformation model, predicts that gainsharing will be implemented at facilities with more favorable levels of work climate and a nonunion status. The third governing factor will not be used to


H4

Implement u Figure 2: Heuristic Decision Making Model

differentiate between the two models. Managers who choose to implement gainsharing are likely to expect that the organization intervention will generate desirable organizational outcomes regardless of whether the intervention is compatible with the facility or will transform it. Fig- ure 2 is an heuristic model that illustrates the proposed relationships among the three governing factors and the implementation decision.

Although there are no investigations that directly explore the decision to implement gainsharing, the balance of the indirect evidence, which is reviewed below, tends to support the compatibility model. Therefore, the hypotheses which follow are derived from the framework that implementation will most likely occur when the intervention is “loaded for success” (Shepard, 1985), easing management fears about plan failure.

A facility’s work climate is often mentioned as a factor that is likely to govern the implementation decision. If employee relations tradition- ally have been adversarial, then a substantial amount of time and effort is required to develop the trust and cooperation said to be requisite for a successful gainsharing plan (Bullock & Lawler, 1984; Frost, Wakeley, & Ruh, 1974; White, 1979). Similarly, if currently there is little participation in decision making, then supervisors may be threatened by the higher levels of employee involvement promoted under gainsharing (Collins, Ross, & Ross, 1989; Hatcher & Ross, 1986; Ross, Hatcher, & Collins, 1992). On the other hand, if current participation is high then plan implementation will be seen as being less risky and management


should be more likely to implement. Research suggests that other work climate restraining forces include existing levels of employee involvement, employee identity, and managerial support (Bullock & Bullock, 1982; Cummings & Malloy, 1977; Gilson & Lefcowitz, 1957; Green- wood, 1977; Ruh, Wallace & Frost, 1973).

Hypothesis I : Facilities that implement gainsharing will exhibit more favorable work climates than facilities that do not.

The presence of a union can also be expected to serve as a restraining force inhibiting management’s decision to actually adopt gainsharing. First, management typically perceives a union as an additional, adversarial institutional voice whose support for plan intervention is crit- ical (Hammer & Stem, 1986; Levitan & Johnson, 1982). Gainsharing implementation is subject to collective bargaining because it affects employee wages and often results in the redesign of some job tasks. Thus unions must be included in the gainsharing implementation decision for legal and practical considerations. Ross and Collins (1987) report that at one union facility two gainsharing coordinators were employed, one appointed by the union and the other appointed by management. This demonstrates not only the additional hurdles that need to be dealt with at union facilities, but also the extra administrative expenses that may be incurred. Management also needs to consider the possibility that unions will make their support for gainsharing contingent upon other union issues (Hammer & Stern, 1986).

Secondly, union leadership may be skeptical about management’s intentions for gainsharing implementation. In a survey of trade union leadership, the two primary reasons for union opposition to gainsharing plans were: (a) management may try to substitute it for equitable wages and (b) management cannot be trusted (Ross, Hatcher, & Adams, 1985). In this sense, union leaders fear that gainsharing may be used as a substitute for, rather than a supplement to, collective bargaining (Kochan, 1984; Kochan, Katz, & Mower, 1984; Schuster, 1984). The fact that management attempts to avoid unionization is no secret (Freed- man, 1985; Freeman & Medoff, 1984; Kochan, Katz, & McKersie, 1986; Verma & Kochan, 1985). Nor is it surprising that, based upon its own self-interests, management attempts to decertify unions. Union leaders were particularly sensitive to these issues with the steady decline of union membership during the 1980s (the years in which data were collected) (Curme, Hirsch, & Macpherson, 1990). Thus some union leaders have expressed concern that the institutional voice which gainsharing provides to nonmanagement employees may be a strategy for diverting worker allegiances away from the union and undermining its influence


(Kochan, Katz, & Mower, 1984; Schuster, 1984). In short, unionized facilities have the potential for unique restraining forces which simply do not exist in nonunion settings. Nonetheless, there is a growing interest among unions for increased union-management cooperation (Banks & Metzgar, 1989; Schuster, 1983, 1984). Based upon the compatibility model, it is proposed that the presence of a union is a restraining force.

Hypothesis 2: Facilities without unions will be more likely to implement gainsharing than facilities with unions.

The third restraining force is the expected outcomes of gainsharing implementation. There is evidence that gainsharing is often successful in improving levels of productivity, product quality, improved service, communication, and cooperation (Bullock & Lawler, 1984; O’Dell & McAdams, 1987). It is predicted that facilities whose managers believe that plan implementation will result in a number of generally valued organizational outcomes are more likely to implement gainsharing than those facilities whose managers do not hold this belief. This is based on survey research data and general expectancy theory. O’Dell and McAdams (1987) surveyed executives at facilities that had implemented gainsharing and found that management’s desire to improve productivity was the most important reason for implementing gainsharing. Accord- ing to general expectancy theory, individuals are motivated to engage in behaviors that are instrumental in attaining valued outcomes (Vroom, 1964). Of more direct relevance is the work of Sproull and Hofmeister (1986), who have argued that the mental representations that individuals form concerning organizational innovations have behavioral con- sequences for the implementation of those innovations. For example, there is evidence that employee beliefs about the expected outcomes of quality control circles and work redesign projects are predictive of their behavioral support for those interventions (Anderson & Terborg, 1988; Dean, 1985). In the same way, it is expected that managers will be more likely to implement gainsharing when they believe it will lead to desirable organizational outcomes.

Hypothesis 3: Managers in facilities that eventually implement gainsharing will report a greater likelihood for the intervention to achieve organizational improvements than managers in facilities that do not implement a plan.

As shown in the heuristic model that appears in Figure 2, it is expected that the work climate and expected outcome measures will be positively related to each other as well as to the implementation decision (Schneider & Reichers, 1983). For instance, Ruh, Wallace, and


Frost (1973) found that managers at 10 companies that kept gainsharing plans, in comparison to managers at 8 companies that abandoned gainsharing plans, perceived production employees as possessing greater de- pendability, innovativeness, willingness to change, pride in performance, long-term perspective, alertness, and sense of responsibility. Funda- mentally, it is predicted that managers at facilities with more favorable work climates will believe that there is a greater likelihood of their facilities achieving desirable gainsharing outcomes than managers at facilities with less favorable work climates.

Hypothesis 4: Work climate will be positively related to expected outcomes.

It is also predicted that the presence of unions will be negatively related to managers’ evaluations of achieving desirable organizational improvements. As noted earlier, managers generally view unions as an interest group that generates conflict within companies (Hammer & Stern, 1986; Levitan & Johnson, 1982). Given the sometimes conflicting goals sought by management and unions such perceptions are to be expected. Managers are generally interested in profit, productivity, and organizational effectiveness, whereas unions are more concerned with job se- curity, wages, and job rights (Lawler & Drexler, 1978). Consequently, managers at unionized facilities typically have less flexibility in making management decisions because unionized facilities are more likely to be managed “by the book” (Freeman & Medoff, 1984). Naturally, this hypothesis assumes that other factors, such as job tasks and technology, are relatively similar. Thus, managers of unionized facilities can be expected to be more conservative in their predictions of plan success.

Hypothesis 5: Managers in facilities without unions will report greater likelihood of achieving desirable outcomes than managers in facilities with unions.

It is further predicted that work climate will be more favorable in nonunion facilities. This prediction is based on research showing that, compared to unionized employees, nonunion workers are more likely to report good relations with their supervisors, more opportunities for involvement in work decisions, more responsibilities, and greater overall job satisfaction (Berger, Olson, & Boudreau, 1983; Freeman & Medoff, 1984; Odewahn & Petty, 1980). Note that the arrow connecting work climate and union status in Figure 2 is bi-directional: at the present time there is evidence that unfavorable employee relations and work conditions may be either a cause or a result of unionization (Cameron, 1982; Feuille & Blandin, 1974; Freeman & Medoff, 1984; Hammer & Berman, 1981).


Hypothesis 6: Managers in facilities without unions will report more favorable work climates than managers in facilities with unions.

Finally, two interactive relationships illustrated in Figure 2 are also hypothesized. A significant amount of research on unions suggests that union status is important not only for its own probable effect on the plan implementation decision, but also for its influence in moderating the relationship between implementation and other antecedent variables (Freeman & Medoff, 1984). For instance, labor researchers have found that unions have a negative effect on job satisfaction (Berger, Olson, & Boudreau, 1983; Leigh, 1986; Odewahn & Petty, 1980; Schwochau, 1987) and job autonomy (Duncan & Stafford, 1980; Kirmeyer & Shi- rom, 1986). In addition, union facilities typically have many more job classifications (Verma & Kochan, 1985) and workers are more prone to dual loyalties (Angle & Perry, 1986; Conlon & Gallagher, 1987). Given these relationships, it follows that the relationship between implementation and certain antecedent variables may be different in union and nonunion facilities.

First, we predict a Union Status x Expected Outcomes interaction in which the positive relationship between expected outcomes and the implementation decision would be stronger in the sample of nonunion facilities than in the sample of unionized facilities. This prediction is based on the assumption, discussed earlier, that unions bring additional restraining forces to bear on the implementation decision that do not exist in nonunion facilities. In addition to the usual concerns, managers in unionized settings must also take into consideration such factors as the stance of the national union leadership on gainsharing, concerns that the plan may interfere with the bargaining agreement, or fears that the plan may undermine the influence of the union (Ross et al., 1985). Other variables (such as management expectations concerning outcomes) may exert less influence in unionized settings because these concerns are likely to be important determinants of implementation. In nonunion settings, however, management’s perceptions of expected outcomes should exert greater influence because fewer variables can be expected to come into Play *

Hypothesis 7 Union status will moderate the relationship between expected outcomes and the implementation decision such that the relationship is stronger among the nonunion facilities than it is among the unionized facilities.

For the same reasons, union status is also expected to moderate the relationship between work climate and the implementation decision. In unionized facilities work climate will be just one of many variables taken


into consideration by managers. Therefore, the relationship between work climate and implementation should be weaker in this sample than in the sample of nonunion facilities.

Hypothesis 8: Union status will moderate the relationship between work climate and the implementation decision so that the relationship is stronger among the nonunion facilities than it is among the unionized facilities.

Method

Facilities and Subjects

The data were collected at 59 facilities between 1983 and 1987 by a research and consulting institute that specialized in gainsharing plans. A self-selection process was used to construct the sample. All 59 facilities contacted the institute to obtain information about gainsharing, invited a consultant from the institute to meet with a cross-section of high-ranking managers and requested that a Managerial Gainsharing Evaluation (MGE) questionnaire be administered. Survey respondents would typically include the plant manager and controller, along with the top production manager, engineering manager, personnel manager, and sales manager, among others. Typically, plant managers rely upon the survey information gathered from the other managers when making the implementation decision. In plants associated with large corporations the plant manager’s decision must also be approved by corporate head- quarters. In unionized facilities the union must be included in the implementation decision. We consider those facilities that administer the MGE survey as demonstrating substantial interest in gainsharing implementation because of the time, effort, and costs incurred in this process.

This self-selection process resulted in questionnaire data from 485 upper-level managers in 59 industrial plants. All of the plants were medium-sized facilities with 200-400 employees. Labor unions represented employees in 27 facilities. The unionized facilities contributed 217 of the 485 surveyed managers. Most of the facilities were publicly- owned (61%) rather than privately-owned, and subsidiaries to a parent organization (77%) rather than independent facilities. Although the facilities were located throughout the United States, most of them were operating in the Midwest (71%). An average of 8.22 managers com- pleted a survey at each facility (SD = 2.80; range = 4-15).

Through follow-up communication between facility management and the consulting institute it was found that management at 17 facilities


(represented by 155 surveyed managers) decided to implement a gainsharing plan consisting of department teams, review board, and group- based financial bonuses. Of the 27 unionized facilities, 6 had implemented a plan; of the 32 nonunion facilities, 11 had implemented gainsharing.

It became apparent that two governing factors-size and type of industry (manufacturing or service)--could not be examined because they were held in common by all 59 facilities that participated in this study. Although the number of employees did range from 200-400, we concluded that the sample would not allow for generalizations based upon size, particularly when monthly and yearly fluctuations in labor markets are taken into account. We also concluded that the sample would not allow for generalizations based upon industry type because all of the facilities were manufacturing facilities; none were service facilities. A break- down based upon two-digit SIC codes showed that these facilities manu- factured the following products: industrial machinery and equipment (21%), rubber and plastics (17%), fabricated metal products (15%), transportation equipment (15%), electronic equipment (14%), textile products (6%), instruments (4%), paper products (2%), furniture products (2%), glass products (2%), and miscellaneous (2%). We considered classifying the facilities according to the level of financial performance they displayed at the time their management teams approached the consulting firm, but we were unable to obtain consistent financial measures across facilities and no objective measures of facility performance were available as an alternative index.

In addition to controlling for size and type of industry, this self- selection methodology provided important controls for four other rival hypotheses: (a) time period, (b) seriousness of intervention consideration, (c) type of intervention-structure, processes, and financial bonus system, and (d) intervention agent. First, the analysis was limited to facilities considering implementing gainsharing during a specific 4-year period. Thus all of the facilities were subject to the same national economic and political conditions. Second, the seriousness of intervention consideration was controlled to the extent that all of the facilities had invested similar amounts of managerial time and financial resources by employing the consulting institute. Third, the intervention structure, process, and financial bonus system were controlled to the extent that the consulting institute presented the same intervention package to all of the management teams. Fourth, the intervention agent was controlled to the extent that each management team was responding to the same consultant.


Work Climate and Expected Outcome Measures

The MGE questionnaire included four a priori scales designed to measure four distinct aspects of work climate: employee participation, employee identity, cooperation, and support. With a 7-item employee participation scale, higher scores indicated greater involvement of employees in work decisions, greater confidence being placed in employees, more open management-nonmanagement communication, and other in- dicators of a generally participative management style. Higher scores on a 4-item identity scale indicated that employees shared the facility’s goals and understood the problems faced by the facility. With a 3-item cooperation scale, higher scores indicated that individuals and groups showed good cooperation in work relationships. Higher scores on a 4-item support scale indicated the belief that managers and nonmanagement employees would probably be supportive of a gainsharing plan if it were implemented. These a priori scales appear in the Appendix. ?Lpically, managers are forthcoming in their responses to the survey items because of the magnitude of organizational change under gainsharing and the substantial weight their input has on the plant manager’s decision.

The MGE also contained seven items used to form an a priori expected outcomes scale. A question asked, “If gainsharing were installed, what do you believe are the most likely outcomes in each of these areas during the first year?” This prompt was followed by (a) increased labor productivity, (b) increased customer service, (c) increased understanding of problems, (d) improved quality, (e) pay bonuses, ( f ) reduced other costs, and (g) improved communications and cooperation (teamwork). Managers rated each of these seven outcomes using a 5-point scale rang- ing from 1 = “no success” to 5 = “good success.”

Nature of the Data

The manner in which the questionnaire data had been recorded at the research and consulting institute permitted some types of statistical analyses and precluded others. The data were not recorded in raw score form, with a separate record of raw data for each individual manager. Instead, tables of response frequencies had been compiled for each facility to indicate how the facility’s managers responded to each questionnaire item. For example, at Facility 1 it was noted that two managers had “strongly disagreed” with item 1, three managers had “disagreed” with item 1, and so forth. This recording method allowed us to calculate the mean and standard deviation of the management teams’ responses to each questionnaire item for each facility.


This method of record keeping precluded many analyses in which the individual manager would have been the unit of analysis. For example, it was not possible to perform a factor analysis with the 485 managers serving as the units of analysis because it was not possible to associate a given manager’s response to item 1 with his or her response to any of the other questionnaire items. Thus most of the analyses used the facility (N = 59) as the unit of analysis. Only the item analyses (reported below) used the individual as the unit of analysis.

Intmclass Correlations

We calculated intraclass correlations (ICC) to assess the reliability of mean ratings provided by the group of raters from each facility (James, 1982). In climate research this is normally done by (a) creating composite scales from questionnaire items, (b) computing each manager’s score on the composite, and (c) calculating the ICC for that scale using the individual manager as the unit of analysis. However, it was not possible to compute scale scores for each manager in the present study because it was not possible to determine how a given manager responded to each item constituting the scale. As an alternative procedure we instead calculated intraclass correlations for each of the 25 individual questionnaire items. These analyses did permit the use of the individual manager as the unit of analysis.

This process began with a series of ANOVAs in which responses to the individual questionnaire item served as the criterion variable and facility (N = 59) served as the predictor variable. The individual manager (N = 485) was the unit of analysis. For 24 of the 25 items, the resulting F ratios were statistically significant at p < .05. One item from the expected outcomes scale (“reduced other costs”) failed to achieve significance so it was dropped from further analysis.

Using information from these ANOVAs we computed an intraclass correlation for each item using the formula: ICC = (MSB-MSW) / MSB, where MSB = mean square between and MSW = mean square within (Bartko, 1976). The mean ICC was .517 (SD = .128) across the 24 surviving questionnaire items. These ICC values estimate reliability at the level of the individual item and not at the level of the total scale. Estimates of total scale reliability are discussed below.

Scale Development

Climate scales. For each facility we determined the average response to each of the 18 items constituting the four a priori work climate scales. This resulted in 18 mean scores for each of the 59 facilities. These


mean scores were subjected to a factor analysis using squared multiple correlations as prior communality estimates. A scree test suggested a four-factor solution that proved to be interpretable. An item was said to demonstrate a significant loading for a factor if its loading was over .50 for that factor and that factor only. This criterion resulted in the elimination of 5 of the 18 items. Each of the remaining 13 items showed a significant loading for the a priori scale to which it had originally been assigned. Items used to measure the four work climate constructs (i.e., items with significant loadings) are in italics in the Appendix. Only these items were included in further analyses.

A facility’s score on a given work climate factor (such as the “participation” factor) was determined by (a) identifying the items with significant loadings for that factor and (b) calculating the mean of the responses to those items. This procedure was used to create the study’s final measures of participation, identity, cooperation, and support. Higher scores represented greater amounts of the construct being assessed for each scale.

The ICC values reported earlier estimate reliability at the level of the individual item rather than at the level of the total scale. To arrive at a more accurate estimate of total scale reliability we (a) computed an average ICC based on the ICCs for the individual items that constituted a given scale and (b) inserted this average ICC, along with the appropriate number of scale items, into the Spearman Brown Prophecy formula. The resulting total scale reliability estimates ranged from .75 to .87 and are reported on the diagonal of Table 1.

Expected outcomes scale. The expected outcomes construct was not expected to share the same factor structure as the work climate scales so a separate factor analysis was performed on responses to these items. The procedure was similar to that used with the work climate items: for each facility we determined the average response to each of the 6 items constituting the a priori expected outcomes scale (one item had been dropped following the ANOVAs reported earlier). This resulted in 6 mean scores for each of the 59 facilities. These means were then subjected to a factor analysis with squared multiple correlations as prior communality estimates. A scree test suggested a one-factor solution. Each of the six items displayed a loading of over .60 for this factor.

An expected outcomes scale was created by averaging responses to these 6 items. For this variable higher scores represent greater expectations of success in achieving organizational objectives if the gainsharing plan was adopted. The reliability of this scale (.83) was also estimated using the Spearman Brown Prophecy formula.


T m L E 1 Means, Standard Deviations, Reliabiliw Estimates, and Intercomlatiom

Variable M SD 1 2 3 4 5 6 7

1. Implementationa .29 .46 (-) 2. Union statusb .46 S O -13 (-) 3. Participation 2.46 .29 20 -54*** (77)

5. Cooperation 3.39 .47 23* -32.’ 58*** 44*** (87) 4. Identity 3.06 .41 21 -34*** 57*** (82)

6. Support 3.82 .38 06 -33*** 52*** 32** 50*** (75) 7. Expectedoutcomes 3.98 .28 11 -21 26” 25* 23* 38*** (83)

Note: N = 59. Reliability estimates appear in parentheses on the diagonal. Decimals

a Coded “0” = did not implement plan, “1” = did implement plan.

*p< .10; **p< .05; ***p< .01

omitted from reliability estimates and intercorrelations.

Coded “0” = nonunion facility, “1” = unionized facility.

Union Status and Plan Implementation Measures

A union status variable was created in which nonunion facilities were coded “0” and union facilities were coded “1.” A plan implementation variable was created in which “0” coded facilities that did not implement gainsharing and “1” coded firms that did implement. Means, standard deviations and intercorrelations for all study variables are presented in Table 1.

Power Analysis

Because most of this study’s statistical tests are based on a relatively small sample of facilities (N = 59), we conducted analyses to assess the power of these tests (Cohen, 1969). The power assessment incorporated Cohen’s suggested value for a “moderate” effect size (population T = .30) and assumed the use of a two-tailed t-test for the significance of T .

These analyses showed that power = .41 whenp = .01, power = .65 when p = .05 and power = .76 when p = .lo. Given these findings we followed the recommendations of Sauley and Bedian (1989) and adopted the p = .10 significance level for the tests reported here.

Results

Predictor Enables and Plan Implementation

Hypotheses 1,2, and 3 posited simple bivariate relationships between plan implementation and the study’s three sets of predictor variables- work climate, union status, and expected outcomes. Hypothesis 1


predicted that facilities implementing gainsharing would demonstrate more favorable work climates than facilities that do not implement. The correlations reported in 'Itible 1 provide very weak support for this hypothesis. Of the four work climate scales only the cooperation measure demonstrated the predicted positive correlation with implementation decisions (r = .23, p < .lo).

Hypothesis 2 predicted that facilities without unions would be more likely to implement gainsharing than facilities with unions. However, a chi-square test of independence failed to identify a significant relationship between implementation and union status x2 (1, N = 59) = 1.05, n.s.

Finally, Hypothesis 3 predicted that managers in facilities that eventually implement gainsharing would report a greater likelihood that the intervention would achieve organizational improvements than managers in facilities that do not implement a plan. However, the bivariate correlation between implementation and expected outcomes reported in lh- ble 1 failed to support this hypothesis ( r = .11, n.s.).

Relationships Between Predictor kriables

Hypotheses 4,5, and 6 dealt with relationships between the study's three sets of predictor variables. Hypothesis 4 predicted a positive relationship between work climate and expected outcomes. The correlations in Bble 1 provide support for this hypothesis. The expected outcomes measure was positively related to participation (r = .26, p < .05), identity (r = .25, p < .lo), cooperation ( r = .23, p < .lo), and support (T = .38, p < .Ol).

Hypothesis 5 predicted that managers in facilities without unions would report greater likelihood of the plan achieving desirable outcomes than managers in facilities with unions. The results failed to support this prediction. Table 1 reports a correlation between union status and expected outcomes of only -.21 (n.s.).

The results provided consistent support for the prediction of Hy- pothesis 6 that managers in nonunion facilities would report more favorable work climates than managers in unionized facilities. Union status demonstrated significant correlations with participation (r = -.54, p < .001), identity (r = -.34, p < .Ol), cooperation ( r = -.32, p < .05), and support (T = -.33, p < .Ol ) .

Union Status as a Moderator

Hypotheses 7 and 8 predicted that union status would moderate the relationship between (a) expected outcomes and plan implementation


TABLE 2 Logistic Regression Results: Predicting Plan Implementation Decisions

Predictors

Model A Model B X2 X2

change if change if Standard deleted Standard deleted

BL error (df = 1) BL error (df = 1)

Union status Participation Identity Cooperation Support Expected outcomes Union x Participation Union X Identity Union x Cooperation Union x Support Union X Expected outcomes (Intercept)

-.09 .36 .06 16.51

.47 .94 .25 .83 1.06 .96 1.34 1.77

.48 1.58 .09 -2.16

-.91 1.02 .81 -.44 .59 1.16 .27 -1.15

-6.26 2.06 .69 5 4

-3.12 -6.10 4.91 - 1.01

8.01 2.63 1.34 1.17 1.28 1.65 2.63 1.34 1.17 1.28 1.65 8.01

6.07* .77 .40

2.60 .12 .51

8.54** 2.69 .35 .18

4.27* -

Note: N = 59. B~ = logistic regression coefficient, representing the change in log odds associated with a one-unit change in the predictor variable. “x2 change if deleted” tests the null hypothesis that the logistic regression coefficient for the corresponding predictor is equal to zero.

*p<.o5; **p< .01

and (b) work climate and plan implementation such that the relationships would be stronger in nonunion facilities than in unionized facilities. This is analogous to positing an interaction between union status and the other variables in the prediction of plan implementation.

It was necessary to analyze the data using logistic regression because the criterion in these analyses (plan implementation) was a dichotomous variable (Hosmer & Lemeshow, 1989). With logistic regression the significance of variables added to a regression equation is tested by calculating a model-improvement chi-square statistic. This provides a test of the null hypothesis that the regression coefficients for all variables added to the model in the most recent step are equal to zero. The model-improvement chi-square is therefore analogous to the F-change test commonly used in linear multiple regression (Norusis, 1990).

In order to conduct a single significance test for the Union Status x Work Climate and Union Status x Expected Outcomes interactions, variables were added to a logistic regression equation in two steps. At step 1, union status, the four work climate scales and the expected outcomes measure were added to an equation containing only the intercept. These predictors failed to produce a significant model-improvement chi- square value, x2 (6, N = 59) = 4.90, ns.. The resulting 6-variable equation will be referred to as Model A and the logistic regression coefficients for this equation are presented under the heading “Model A” in Thble 2.


At step 2, the five cross-product interaction terms were added to Model A (each interaction term was created by multiplying the union status variable by the appropriate climate or outcome variable). The addition of these interaction terms did result in a significant improvement in the model’s ability to predict plan implementation, x2 (5, N = 59) = 13.83, p < .05.

Under the heading “Model B,” n b l e 2 provides the logistic regression coefficients (BL) obtained when plan implementation was simul- taneously regressed on union status, the work climate scales, expected outcomes, and the interaction terms. Norusis (1990) recommends that the significance of individual predictors be tested by building a model with and without the variable of interest and determining the difference between the resulting likelihood-ratio chi-square statistics. The results of these analyses are reported under the heading “x2 change if deleted.” Table 2 shows that just two interaction terms added significantly to the prediction of plan implementation: the Union Status x Participation interaction, x2 (1, N = 59) = 8.54, p < .01, and the Union Status x Expected Outcomes interaction, x2 (1, N = 59) = 4.27, p < .05.

The nature of the interactions is graphed in Figure 3. Data from the nonunion facilities (N= 32) and the unionized facilities (N= 27) were analyzed separately to better understand these interactions. It was found that implementation was positively related to participation in the nonunion facilities (r = .44, p < .05) but not in the unionized facilities ( r = -.26, n.s.). Similarly, implementation was positively related to expected outcomes in the nonunion facilities ( r = .32, p < .lo) but not in the unionized facilities (r = -23, n.s.).

Discussion

Gainsharing has been presented in the participatory management literature as a highly desirable organization system that provides a systematic linkage among an organization’s structure, communication and work processes, and reward system. Although highly desirable, gainsharing implementation requires a significant amount of organizational change. This exploratory study examined governing factors for 59 medium-sized manufacturing facilities that were seriously considering implementing gainsharing. Of these 59 facilities, 17 subsequently implemented a gainsharing plan.

We empirically examined three factors-work climate, union status, and expected outcomes-in order to determine their ability to discrimi- nate between the 17 facilities that implemented gainsharing and the 42 facilities that did not. Contrary to what some gainsharing theorists have suggested, we found that the 32 nonunion facilities, in comparison to the


2.80

2.70

2.60

2.50 s a

n

I

0 - 2.40

2.30

2.20

2.10 J

4.30

4.20

0 4.10

4.00

3 3.90

i 3.80

3.70

3.60

Gainsharing Not Implemented

Gainsharing Implemented

8 I

Nonunion

Unionized

Gainsharing Not Implemented

Gainsharing Implemented

Figure 3: Union Status as a Moderator of the Relationship Between (a) Imple- mentation and Participation, and (b) Implementation and Expected Outcomes

27 union facilities, were not more likely to actually implement gainsharing (Hypothesis 2). We also found that the actual decision to implement was not a function of the degree to which managers believed that gainsharing could obtain desirable outcomes at their facilities (Hypothesis 3). In terms of work climate, we found that only one variable-current level of cooperation-was able to distinguish between these two groups of facilities, yet even this variable received only very modest statistical support (Hypothesis 1).


Statistical analysis did reveal significant positive relationships between union status and each of the four work climate variables (Hypoth- esis 6). Consistent with earlier research on unions (Berger et al., 1983; Cameron, 1982; Odewahn & Petty, 1980), nonunion facilities scored significantly higher regarding the level of participation, employee identity, cooperation, and willingness to support a gainsharing plan. The relationship between union status and level of participation was particularly strong.

Thus, although gainsharing appears to be feasible in union settings, these results suggest that implementation itself may be more problem- atic. Additional time may have to be spent developing the trust and employee involvement which seem to facilitate the decision to implement a plan. Kochan and Dyer’s (1976) model of organizational change in the context of union-management relations may be helpful in this regard. The Kochan-Dyer model discusses the various interests of both management and the union that may or may not be served by a cooperative effort. It describes the stages a firm moves through in developing a project, as well as the various factors that influence the ultimate success of the intervention. Organizations may find the model to be a useful guide for building trust and avoiding pitfalls as they develop a plan.

We found that expected outcomes was positively related to the work climate variables (Hypothesis 4) but not to union status (Hypothesis 5). Nonunion facilities, in comparison to union facilities, were not more likely to expect desirable outcomes from gainsharing implementation. However, facilities with more favorable work climates, in comparison to facilities with less favorable work climates, were more likely to expect desirable outcomes from implementation. The willingness to support a gainsharing plan demonstrated the strongest relationship with expected outcomes. However, caution should be used in interpreting the strength of the relationship between work climate and expected outcomes due to potential problems associated with common method variance.

Most importantly, the statistical analyses revealed that union status moderated the relationship between (a) expected outcomes and plan implementation (Hypothesis 7) and (b) level of participation and plan implementation (Hypothesis 8). For nonunion facilities, implementation was significantly related to both the level of participation and expected outcomes. Nonunion facilities that implemented gainsharing, in comparison to nonunion facilities that did not, had higher levels of participation and expected outcomes. For union facilities, implementation was not significantly related to either participation or the expected outcomes.


Compatibility and Transfornation Models

The compatibility and transformation models of gainsharing plan implementation made opposite predictions concerning the relationships investigated in this study. Specifically, the compatibility model predicted that implementation would be more likely in facilities with a favorable work climate and with nonunion employees. The transformation model predicted that implementation would be more likely in facilities with a less favorable work climate and with union employees. As discussed above, moderate support was found for the compatibility model and no support was found for the transformation model. The failure to obtain consistent support for either model may indicate that the implementation of a gainsharing plan is a complex process that defies the simplicity of either model taken in isolation. A more accurate understanding of the implementation process may ultimately require combining elements of both models.

While gainsharing is often promoted as a means for achieving greater levels of involvement, the present results suggest that it is most likely to actually be implemented in settings where there already are higher levels of cooperation and in nonunion facilities where there already are higher levels of participation. The positive relationship between work climate and implementation observed here probably reflects a certain amount of informed caution being exercised by organizational decision makers. Because these work climate conditions have been previously shown to be positively associated with plan success (Frost et al., 1974; Lawler, 1981; Ruh et al., 1973; White, 1979), managers in firms with less favorable work climates may be less inclined to proceed because they do not believe that the plan will achieve early success-an important consideration in organization development interventions (French & Bell, 1990; Shepard, 1985).

Limitations

As is often the case when research is conducted using the organization as the unit of analysis, the size of the sample reported here was not large enough to allow for high levels of power in the statistical analyses. These problems were exacerbated in subgroup analyses in which data from unionized and nonunion facilities were analyzed separately.

Importantly, this study did not examine all of the potential factors that might govern the implementation decision. We chose to examine those factors that were most prominently mentioned in the gainsharing literature-work climate, expected outcomes, and union status-while


controlling for some other factors (firm size, time period, type of business, type of intervention, and intervention agent). All of the organizations were medium-sized manufacturing facilities that seriously considered implementing gainsharing during the 1980s. Other factors listed in Figure 1 that we neither tested nor controlled include use of overtime, business stability, number of product changes, type of ownership, financial performance at time of intervention, extent of planned capital investment, and so forth. (Lawler, 1981; White, 1979). Each of these factors could serve as a rival hypothesis to our findings. The importance of these factors on the implementation decision should be examined in future research.

In regards to the measures used in this study, unions were viewed as a restraining force measured on a dichotomous scale. Unions were clas- sified as a restraining force because, in order to implement gainsharing in a union firm, management must convince another institutional agent to agree with the implementation. However, some unions may be more cooperative than others, thus researchers may want to adapt an ordinal scale that reflects varying degrees of union cooperation, rather than a simple dichotomous scale, in future studies.

Conclusion

This study was designed to identify the nature of the relationship between a number of predictor variables and an organization’s decision to implement gainsharing. From our findings, the following conclusions may be drawn: (a) for the entire sample, facilities were more likely to implement a plan when managers reported higher levels of employee cooperation, (b) union status has no direct relationship to the implementation decision, (c) work climates were more favorable at nonunion facilities than at unionized facilities, and (d) a significant interaction showed that implementation was positively related to participation and expected outcomes in the nonunion facilities but was unrelated to these variables in the unionized facilities.

The findings concerning work climate present a troubling paradox: gainsharing would seem to have the most to offer in those facilities demonstrating lower levels of cooperation and less employee involvement, that is, facilities where the performance-improving ideas of the workforce have been largely untapped. These are precisely the organizations that appear to be less likely to implement a plan. Nonetheless, the gainsharing literature contains a number of case studies in which the plan was implemented under conditions of low involvement, mistrust, and poor employee relations, and was successful in improving those conditions (e.g., Ross & Collins, 1987; Ross, Hatcher, & Ross, 1989). In this


regard, the findings suggest two paths for future gainsharing research: (a) examine how the plan can be a genuinely workable instrument of change in the vast majority of organizational settings rather than a low- risk innovation suitable only for already-participative and cooperative climates and (b) examine the effects of more conservative organizational development intervention techniques, such as team building, as an antecedent to gainsharing implementation.

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APPENDIX

Items constituting the a priori climate scales are reproduced below. Items retained for final scales following factor analysis are in italics.

The participation scale was adapted from Likert’s (1967) Profile of Organizational Characteristics. For each of the following items, extreme anchors on the 4-point response scale are included in parentheses: (a) Are subordinates involved in decisions related to their work? (never con- sulted; fully involved); (b) How much confidence and trust is shown in subordinates? (virtually none; a great deal); (c) How free do employees feel to talk to supervisors about theirjobs? (not very free; very free); (d) Where is responsibility felt for achieving organization’s goals? (mostly at top; at all levels); (e) What is the usual direction of information flow? (downward; down, up, & sideways); (f) How accurate is upward communication? (usually inaccurate; almost always accurate); and (g) What are cost, productivity, and other control data used for? (policing, punishment; self-guidance, problem solving).

The remaining climate scales used a 5-point response format where 1 = “strongly disagree” and 5 = “strongly agree.” The identity scale consisted of (a) Most employees here feel that their goals and goals of the company are pretty much the same; (b) Employees here have a pretty good idea about how the economy will affect our business; (c) Our employees understand the problems faced by our company; and (d) The company’s overall goals and objectives are understood by the employees.

The cooperation scale consisted of (a) Managers at various levels cooperate with each other well; (b) Employees cooperate with each other well; and (c) People from different departments cooperate with each other well.

The support scale consisted of (a) If you implement gainsharing, top management will do everything they can to help it succeed; (b) Middle management, in general, wouldput a lot of effort into making the gainshar- ins system successful; (c) Employees here would probably give gainsharing a chance and try to make it work; and (d) Our supervisors would, in general, be supportive of gainsharing.

Documents

THE DECISION TO IMPLEMENT GAINSHARING: THE ROLE OF WORK CLIMATE, EXPECTED OUTCOMES, AND UNION STATUS