Redesigning computer call center work: a ... - Wiley Online Library

Journal of Organizational Behavior J. Organiz. Behav. 25, 317–337 (2004) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/job.247

Redesigning computer call center work: a longitudinal field experiment MICHAEL WORKMAN1* AND WILLIAM BOMMER2 1

School of Information Studies, Florida State University, Tallahassee, Florida, U.S.A. Department of Management and Labor Relations, College of Business Administration, Cleveland State University, Cleveland, Ohio, U.S.A. 2

Summary

Computer technology call centers provide technical assistance to customers via the telephone to solve computer hardware and software problems. The simultaneous demands for technical and customer service skills often place strain on call center employees, frequently producing poor job attitudes. We utilized a field experiment (N ¼ 149) with a randomly assigned pretest– posttest and control group design to compare three interventions’ effectiveness on employee job attitudes in a computer technology call center: Intervention 1 focused on aligning organizational structures; Intervention 2 focused on increasing employee involvement in work processes (high-involvement); and Intervention 3 implemented autonomous work teams. We found that high-involvement work processes produced the most potent effects on job satisfaction and organizational commitment attitudes, as well as on performance (i.e., improved customer satisfaction scores, increased closed problems, reduced problems escalated, and fewer repeat calls). Further, we found that group work preference moderated the results between the group-oriented interventions and employees’ job satisfaction. Under high involvement and in autonomous work teams, high preferences for group work resulted in greater job satisfaction than when employees had lower preferences for group work. However, preferences for group work were not associated with increased organizational commitment in either intervention. Copyright # 2004 John Wiley & Sons, Ltd.

Concurrent with the increasing use of computer technology in businesses, there have been continuous efforts by employers to make technology-oriented jobs more appealing to employees while simultaneously improving performance. These efforts have been particularly strong in call centers that provide computer hardware and software support, and where the technology-oriented demands placed on employees are further complicated by customer-service demands (Knapp, 1999). Computer call centers, also known as support centers or help desks, provide technical assistance to customers via the telephone to solve computer-related hardware and software failures. As the American economy becomes increasingly service-oriented, call centers are rapidly proliferating (Callaghan & Thompson, 2002), as is the growing body of scholarly literature about organizational challenges, such as poor job attitudes that exist within call centers (Nelson, Nadkarni, Narayanan, & Ghods, 2000; Vandenberg, Richardson, & Eastman, 1999). The environment in computer technology call centers can be repetitious and problem-oriented, and it demands both interpersonal and technical skills (Wallace, Eagleson, & Waldersee, 2000). These workers perform emotional labor (Callaghan & Thompson, 2002; Pugh, 2001) that simultaneously * Correspondence to: Michael Workman, School of Information Studies, Florida State University, B. Louis Shores Building, Tallahassee, FL 32306-2100, U.S.A. E-mail: [email protected]

Copyright # 2004 John Wiley & Sons, Ltd.

Received 20 January 2003 Accepted 21 October 2003

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requires rapid problem solving while inducing an emotional state of customer satisfaction (Ashforth & Humphrey, 1993). These dual environmental demands make research into call centers particularly relevant. Research into computer technology call centers (Holman, Chissick, & Totterdell, 2002; Nelson et al., 2000; Silvestro, 2002; Spreitzer, Cohen & Ledford, 1999) has reported particularly odious effects from this setting on employee job satisfaction, which indicates how employees feel about the work they do (Warr, Cook, & Wall, 1979), and affective organizational commitment attitudes that reflect how employees feel about their company (McCaul, Hinsz, & McCaul, 1995). These negative attitudes have been associated with reduced productivity (Vandenberg et al., 1999), poor customer service (Sagie, 1998; Silvestro, 2002; Wallace et al., 2000), turnover (Rogg, Schmidt, Shull, & Schmidt, 2001), and adverse financial impacts to the company (Hatcher, 1999), particularly if specialized knowledge, the type needed in computer support, is involved (Stewart & Barrick, 2000). Managers in the field have relied upon multiple ‘solutions’ to these problems, which have consisted of job redesigns that align personal motivations with organizational objectives; or the utilization of high-involvement work processes; or autonomous team-based work (Batt, 1999; Vandenberg et al., 1999). Each approach offers potential remedies to call center challenges, but they have neither been simultaneously investigated nor have they been well understood individually (Hatcher, 1999). The extant literature has exclusively concentrated on one type of organizational design or intervention, such as structural alignment or team implementations, or has been ex post facto—leading to incommensurability about which intervention might have greater positive outcomes (Nelson et al., 2000; Silvestro, 2002). For instance, Tesluk, Vance, and Mathieu (1999) stated that ‘In contrast to these impressive claims . . . employee participation has, at best, consistent but small effects on performance and satisfaction’ (p. 272). Researchers are thus left with an unclear picture of how these interventions affect job attitudes. To address this existing deficit, the current study was undertaken to simultaneously investigate the effects of alignment job design (AJD), high-involvement work processes (HIWP), and autonomous work teams (AWT) upon employee job attitudes. In this study, we examined how three interventions affected job attitudes, focusing on whether a person’s high preference for group work moderated the success of the interventions, as has been previously suggested (e.g., Dormann & Zapf, 2001; Stewart & Barrick, 2000).

Theory and Hypotheses Organizational designs and interventions Technology support work involves complex conceptual tasks (e.g., Stewart & Barrick, 2000; Wallace et al., 2000), and it demands the concurrent demonstration of technical, self-management, and interpersonal skills by employees (Wooton, 2001). As one employee we interviewed described, ‘This is a difficult place to work. You have to be able to solve problems quickly and be nice to customers even when they are rude.’ In this setting, employees are externally constrained ‘by physical and resource limitations, peak/off-peak fluctuations in the level of customer demand, conflicting and ambiguous role demands from customers, peers, and management’ (Ashforth & Humphrey, 1993, p. 96). Characteristic of technology call centers is their continuous performance monitoring and individualoriented focus on numeric quotas for efficiency and speed (Batt, 1999; Holman et al., 2002). Such an environment generally dissuades thorough problem investigation, and creates a competitive rather than Copyright # 2004 John Wiley & Sons, Ltd.

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cooperative environment (Semler, 1997; Wallace et al., 2000). Job redesign interventions have taken many forms in different organizational contexts including job rotation, enlargement, and enrichment, which have had mixed results across a variety of outcome measures (Gerhart, 1987; Silvestro, 2002). In computer call centers, many interventions have attempted to align work structures including measurement and reward systems with organizational goals (Davis, 2001; Tesluk et al., 1999). An AJD intervention is derived from alignment theory (Semler, 1997) in which efforts are directed at measurement and reward structures to align organizational and individual goals. Because the typical organizational design in computer call centers is beneath the capabilities of personnel that effectively creates an ‘underemployed’ condition (Batt, 1999; Callaghan & Thompson, 2002; Holman et al., 2002), AJD is also compatible with Hackman and Oldham’s (1980) job characteristics model, which targets skill variety, task identity, individual-level autonomy, and individual-level feedback. AJD configures the measurement and reward systems to correspond to organizational processes and objectives such that it measures and rewards strategic organizational goals attainment, and it provides a structure supporting and complementing individual end-to-end completion and efforts toward these objectives (Hackman & Oldham, 1980; Semler, 1997). AJD assumes that people working in their perceived selfinterests strive toward goals that harmonize with those of the organization. Such compatibility among personal and organizational goal seeking, monitoring, and performance feedback has been found to increase job satisfaction in call center personnel (Holman et al., 2002), and it is surmised to have a positive effect on shallow problem investigation by creating structures that encourage expending the time and effort needed to accurately solve problems (Hacker, Newton, & Akinyele, 2001; Zenger & Marshall, 2000). Like AJD, HIWP also attend to the call center environment through structural changes (Batt, 1999), and like AJD, HIWP is also compatible with Hackman and Oldham’s (1980) job characteristics model. However, where AJD seeks to achieve ‘systematic agreement among strategic goals, tactical behaviors, performance and reward systems’ (Semler, 1997, p. 23), HIWP derive from co-optimized systems theory (Fox, 1995), which evolved from socio-technical systems theory (Trist, 1971). Cooptimized systems theory concentrates on the joint adaptation of systemic interrelationships among technical, socio-cultural, and other organizational systems constituents (Fox, 1995). Hence, in HIWP, employee participation and buy-in are seen as critical to the alignment process; it seeks to increase system-level feedback and address cooperation versus competition (Fox, 1995; Lawler, 1999). Vandenberg et al. (1999) characterized HIWP as consisting of: (1) the elevation of member–leader participation; (2) establishing customer and business feedback loops; (3) expanding member knowledge of the total work system; and (4) creating structural alignment. Conceived this way, HIWP raise individual discretion and involvement in the development of organizational structures through teamoriented practices, problem-solving groups, or quality circles, but it leaves the supervisory structure in place (Davis, 2001; Knapp, 1999). The majority of the research examining the effects of HIWP has been conducted at the organizational level of analysis (e.g., Arthur, 1994; Huselid, 1995). This more macro-oriented research has employed a resource-based view of the firm and has focused upon creating competitive advantage. While the research regarding HIWP has been encouraging, suggesting that HIWP improve employee retention (Arthur, 1994; Huselid, 1995), relatively little fieldwork has focused on the effects of HIWP at the work group level of analysis. Also derived from co-optimized systems theory (Fox, 1995) and moving to the end of the employee involvement continuum from traditional organizational structuring and management, AWT utilize group-level autonomy and redistribute the control of the group structure and processes to the group members (Osterman, 1994; Spreitzer et al., 1999), replacing traditional management with a cooperative of interdependent peers (Batt, 1999). The cooperative is surmised to maximize work and knowledge sharing (Griffin, Patterson, & West, 2001; Tesluk et al., 1999), particularly in cases where the Copyright # 2004 John Wiley & Sons, Ltd.

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level of task interdependence is high (Kirkman & Shapiro, 2001). This team-based work has the dual effects of increasing skills capacity and creating non-linear problem solving (Knapp, 1999; Thomas & Thomas, 1990). AWT not only enables the mixing of members with needed expertise, but the cooperative work enables expertise to be shared (Griffin et al., 2001; Tesluk et al., 1999). The cooperative environment derived from team-based work is perceived as important because, regardless of the extent of training, individual employees working in hierarchical groups have great difficulty duplicating the learning that occurs in teams. In particular, traditional work settings constrain an employee’s ability to solve a broad range of complex problems due to the ‘silo’ structure forcing workers to become increasingly specialized and narrow in their skills (Nelson et al., 2000). Teams increase interdependence and broaden learning opportunities (Moses & Stahelski, 1999), which have made AWT a popular intervention in technology environments (Spreitzer et al., 1999; Stewart & Barrick, 2000). It is interesting to note, however, that the effectiveness of autonomous work teams has not been universally positive (Zemke, 1993). While team members tend to report higher levels of job satisfaction, autonomous work teams have often encountered higher absenteeism and turnover rates than found in traditional work structures (cf. Cordery, Mueller, & Smith, 1991; Wall, Kemp, Jackson & Clegg, 1986).

Interventions’ effects on job attitudes Organizational commitment and job satisfaction indicate various aspects of how people feel about their work environment. While different ‘types’ of organizational commitment have been conceived (Allen & Meyer, 1990), affective commitment has received the lion’s share of the attention in the literature. Allen and Meyer (1990) described affective commitment as an emotional attachment to the organization in which employees remain with their organization because they want to. This made affective commitment particularly relevant for the current study. More specifically, workers in the technology field at the time of this study had numerous options and employee retention was a priority for most organizations employing these workers, so the affective and behavioral intention component of commitment as it is commonly measured made this ideal for our study. Job satisfaction concerns how people feel about their jobs, and it influences job-related behaviors such as productivity, turnover, and absenteeism (Taber, 1991; Hatcher, 1999). Several structured measurement procedures have been developed that tap into global, overall job satisfaction (Gillet & Schwab, 1975; Taber, 1991; Warr et al., 1979). However, overall job satisfaction may be thought of as an aggregate of extrinsic features, such as pay, physical working conditions, and job security; and intrinsic features, such as autonomy, recognition, and opportunities to use one’s abilities (Warr et al., 1979). Characteristic of call centers is their continuous performance monitoring and focus on numeric quotas for efficiency and speed. This creates a structure incongruent with thorough problem research, which takes time, and the devotion to the development of technical skills that comes from such research efforts (cf. Hatcher, 1999; Holman et al., 2002; Wallace et al., 2000). Further, this focus on numeric outcomes also makes the call center environment incompatible with intrinsic job aspects found important to technology workers such as growth, the opportunity to teach others, accomplishment, and professional development (Nelson et al., 2000). Alignment focuses on removing motivational impediments related to four components from the job characteristics model (Hackman & Oldham, 1980): skill variety, task identity, individual-level autonomy, and individual-level feedback (Semler, 1997). It seeks to establish measurement and reward systems that correspond to organizational processes and objectives in such a way that it measures and rewards strategic organizational goals attainment and it provides a structure supportive of and Copyright # 2004 John Wiley & Sons, Ltd.

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complementary toward individual end-to-end task completion while enhancing and improving organizational–individual-level feedback (Hackman & Oldham, 1980; Semler, 1997). Therefore, structuring organizational process, measurements, and rewards supporting individual motivations and job requirements will positively influence job satisfaction by removing obstacles and improving job conditions (Griffin et al., 2001; Semler, 1997; Tesluk et al., 1999). Consequently: Hypothesis 1a: Alignment job redesign will increase employee job satisfaction. In addition to having positive effects on job satisfaction, it is likely that well-conceived AJD interventions should also increase employee organizational commitment. The reduction of member– organization friction as the result of alignment should affect how people view the organization (Davis, 2001). That is, the systematic agreement of processes, reward systems, and strategic goals is important to the enhancement of perceptions of the organization, and this influences employee commitment (Hacker et al., 2001; Semler, 1997; Thomas & Thomas, 1990). To be committed to an organization, a person must feel compatibility with organizational objectives—made possible through the alignment process—and hence working toward these objectives agrees with what one perceives as working toward his or her own self-interests (Fox, 1995; Vandenberg et al., 1999). Therefore: Hypothesis 1b: Alignment job redesign will increase employee commitment. Generally, participation in defining and aligning their work with organizational goals, reward, and measurement systems improves employee perceptions. In particular, joint employee–manager efforts toward optimization and alignment of personal–organizational structures should lead to improved job satisfaction and greater organizational commitment (Fox, 1995). Thus, since HIWP utilize teamoriented development of structures in cooperation with management, and because HIWP creates a forum for the exchange of ideas, expertise, and effort, it should lead to improved job satisfaction and greater commitment (Kahai et al., 1997). Lawler (1999) points out that firms will benefit most from their use of HIWP with front-line employees, who are the subjects of our experiment. In fact, Lawler (1999) suggests that the primary difference between traditional structures and HIWP is ‘how work is managed at the lowest level in the organization’ (p. 28). Importantly, HIWP provide mechanisms through which effort and expertise are shared (Vandenberg et al., 1999) and involve the process of peer teaching, which is important to technology workers (Nelson et al., 2000). Thus: Hypothesis 2a: HIWP will increase employee job satisfaction. Hypothesis 2b: HIWP will increase employee commitment. Although HIWP would appear to improve employee job satisfaction and commitment, there is some reason to believe that these effects may not accrue equally to all employees. Because team-based work involves greater collaboration and cooperation with peers than non-team-centered work, the propensity team members have toward group work as opposed to individual work may affect their attitudes, especially as interdependence in their work requirements increase (Sandberg, 2000; Shaw & BarrettPower, 1998; Sternberg, 1997). Thus: Hypothesis 2c: The improvement in employee job satisfaction associated with HIWP will be moderated by group work orientation such that more group-oriented employees will enjoy greater improvements in satisfaction than will less-group-oriented employees. The above logic can also be applied to an employee’s organizational commitment. More specifically, the alignment associated with improved satisfaction should also increase the commitment of Copyright # 2004 John Wiley & Sons, Ltd.

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an employee to the organization. Thus, if an employee’s job does not align with his or her preferences for group work, it is quite likely that the employee will feel less attachment to the organization (Heald, Contractor, Koehly, & Wasserman, 1998). Therefore: Hypothesis 2d: The increase in employee commitment associated with HIWP will be moderated by group orientation such that more group-oriented employees will enjoy larger commitment increases than will less group-oriented employees. At the other end of the continuum from individual work focus, autonomous team-based work is group-focused. As team autonomy increases, member–member interaction and participation often radically increase (Moses & Stahelski, 1999; Stewart & Barrick, 2000). This high degree of participation has been found to improve the quality of work-life by enabling members to have more control of their work, thereby elevating job satisfaction (Cordery et al., 1991; Heald et al., 1998; Moses & Stahelski, 1999). AWT are also believed to improve job satisfaction because they increase task variety, enrich system-level feedback, and encourage cooperation (Fox, 1995; Moses & Stahelski, 1999). Consequently: Hypothesis 3a: Autonomous work teams will increase employee job satisfaction. It has also been argued that the AWT have positive effects on organizational commitment because workers view this intervention as a company effort to respond to their needs (Cordery et al., 1991; Wall et al., 1986). In the AWT setting, team members perceive a greater role in organizational processes because they are more involved in the broader work systems, along with external coordination and direct communication with other departments (Batt, 1999). This enhanced organizational role creates stronger identification with, and commitment toward, the organization (Heald et al., 1998). In addition, the interaction that takes place among members in autonomous teams has been linked with improved learning and work sharing found important to these employees’ perceptions of organizational efforts to mitigate their work environment (Vandenberg et al., 1999). Thus: Hypothesis 3b: Autonomous work teams will increase employee commitment. Beyond the main effects hypothesized above, a more complex relationship likely exists. The degree of a worker’s group orientation may influence job attitudes in autonomous team-based work since the nature of this setting requires greater collaboration about work and work structures (Heald et al., 1998, Shaw & Barrett-Power, 1998; Sternberg, 1997). Further support for this assertion can be found in the work of Kirkman and Shapiro (2001), who have identified employee individualism as being an important contributor to employee resistance to teams in general, and autonomous work teams in particular. Although Kirkman and Shapiro’s research has concentrated on individualism at the national cultural level, the argument would remain largely unchanged at the organizational level of analysis (Workman, 2001). Thus, employees with strong group work orientations may view AWT as a welcomed opportunity to collaborate with co-workers. Thus: Hypothesis 3c: The improvement in employee job satisfaction associated with AWT will be moderated by employee’s work orientation such that more group-oriented employees will enjoy larger improvements in job satisfaction than will less group-oriented employees. The more complex relationship described above likely also exists between autonomous work teams and organizational commitment. An employee’s group work preference may moderate autonomous team-based work’s relationship with organizational commitment since the setting necessitates increased interpersonal contact (Sternberg, 1997). Thus, for employees with strong group orientations, the increased need for collaboration in an AWT may enhance the attractiveness of the organization. Thus: Copyright # 2004 John Wiley & Sons, Ltd.

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Hypothesis 3d: The increase in employee commitment associated with AWT will be moderated by employee’s work orientation such that more group-oriented employees will enjoy larger commitment increases than will less group-oriented employees.

Organizational Context

The Organization Amalgamated Computer Inc. (a pseudonym) reached its zenith in the 1980s with revenues approaching U.S. $8 billion and 110 000 employees worldwide. Its computer operating systems call center located in Atlanta, Georgia (U.S.A.) was ranked #1 in customer service by a leading trade publication in 1988. In the 1990s and into the twenty-first century, increasing competition and changes in market forces away from mid-range computer servers and toward lower-margin personal computers resulted in a series of mergers and acquisitions concomitant with a higher volume of new problem calls. This led to greater external downward cost pressures, and greater internal pressures to increase productivity. Tasks and Climate The operating systems call center is responsible for resolving complex computer problems over the telephone for customers in government, industry, and education in the areas of operating systems, networking, language compilers, and graphical user interfaces. It is tasked to resolve large numbers of problems as expeditiously and efficiently as possible with two goals in mind: (1) satisfy the customer and (2) keep costs down. Call center support engineers had fallen behind and could not keep up with the problem volume. Customers were complaining that it was taking too long for support engineers to get to their problems, and when they did the solutions they provided were often hasty and inadequate. The turnover rate in the organization was increasing at an alarming rate, exacerbating the problem backlog. Management, who perceived conditions of low morale, high turnover, and low productivity, brought in organizational developers to assist. The Interventions Organizational developers conducted a climate survey and assessment. Three interventions are commonly described individually in the literature to address the problems identified. It was decided to test the three interventions concurrently to determine which was most effective. The three groups, alignment job design (AJD) consisting of 35 support engineers of whom 7 were junior level, 23 mid-level, and 5 senior, high-involvement work processes (HIWP) consisting of 43 support engineers of whom 12 were junior level, 25 mid-level, and 6 senior, autonomous work teams (AWT) consisting of 35 support engineers of whom 10 were junior level, 21 mid-level, and 4 senior, were compared against a control group of 36 support engineers, 8 of whom were junior, 23 mid-level, and 4 senior. The AJD group kept the traditional management structure in place and the work remained pooled interdependent; however, a job rotation was put in place and the organizational–individual performance measurement system was aligned, and the reward system consisting of merit pay and bonuses were awarded based on meeting the aligned goals.

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The HIWP group implemented quality circles seeded with mentors. Since the organization had been operating with low interdependence, participation training was conducted to help employees adapt to the increased interdependence. A gainsharing reward system was put in place in addition to the individual-based merit pay, where bonuses were given based on meeting group-defined objectives set for customer satisfaction and quality. The AWT group implemented fully autonomous self-managed teams. Responsibilities for problem solving as well as job design were shared. Participation training was conducted and the group devised a team-based merit pay system.

Method Catalyst, setting, and participants This field study was of a large international computer company’s call center that is responsible for resolving computer-related problems over the telephone. Employees expressed negative attitudes, and management perceived the department as having low expertise, poor performance, and low commitment. Turnover was high, which precipitated high recruitment costs and substantial interviewer time. Although the group’s size grew, there was only a slight increase in the number of problems solved, and new employees required extensive training. Pressures reached a crisis point and management sought assistance from outside organizational development consultants, who devised three intervention protocols (as summarized in Table 1): alignment job redesign (AJD group); high-involvement work processes (HIWP group); and an autonomous team approach (AWT group). See Table 1 for a summary of interventions. The call center consisted of 151 support specialists, two of whom left during the study, leaving 149 study participants. Thirty-seven participants were junior support specialist level 1, 41 were level 2 support specialists, 52 were senior support specialists (level 3), and 19 were principal support specialists

Table 1. Intervention targets and practices Alignment job design (AJD) Characteristics Employee involvement Management structure Performance measurement Workflow Interdependence Enhancements Focus Received participation training Team interaction

Low

High-involvement performance work process (HIWP)

Autonomous work teams (AWT)

Traditional structure remained in place Individual

Moderate (problem-solving teams and quality circles) Specialists involved in important decisions Mix (individual þ gain-sharing)

Teams made all important decisions Team (team merit pay)

Pooled Low

Sequential Medium

Reciprocal High

Personnel–systems alignment No

Boundary spanning

Teamwork

Job rotation (hot seat)

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High (self-managed)

Yes: 1 week plus weekly lunch Yes: 1 week and learn sessions Quality circles and mentors Problem-solving teams

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(level 4). Of the 149 participants, 83 were male. Ages ranged from 21 to 56 (mean ¼ 31, SD ¼ 7.1), and tenure ranged from 1 to 9 years (mean ¼ 2.7, SD ¼ 1.6). All of the employees were college educated, primarily in computer science or information technology; 86 per cent held bachelor’s degrees and the remainder had master’s degrees.

Instrumentation and procedures Along with demographics, the data collection gathered the degree of group orientation (eight items), job satisfaction (five items), and organizational commitment (five items). All data were collected using a 7-point Likert-type scale where ‘1’ represented ‘strongly disagree’ and ‘7’ was associated with ‘strongly agree.’ Sternberg’s (1997) Thinking Style Inventory (TSI) for external scope was used to measure group work orientation (a sample item reads: ‘I like to participate in activities where I can interact with others as part of a team’). The data were coded so that high scores were consistent with a strong preference for group work and low scores were consistent with a weak preference for group work. The TSI is a fairly young instrument and has been used most often in educational settings; however, its increasing application in business settings has produced good evidence of content validity (Guastello, Shissler, Driscoll, & Hyde, 1998; Hayes & Allinson, 1998), as well as criterion and construct validity, and internal consistencies ranging from 0.59 to 0.83 (Grigorenko & Sternberg, 1997). Job satisfaction data were collected with Warr et al.’s (1979) intrinsic job satisfaction scale. This instrument taps into job-related (as opposed to company-related) satisfaction and has shown sufficient construct and criterion validity and internal consistency reliability (Griffin et al., 2001; Tesluk et al., 1999; Warr et al., 1979). Organizational commitment was collected with Mowday, Steers, and Porter’s (1979) Organizational Commitment Questionnaire (OCQ), which has been used extensively in the organizational literature. The 149 support specialists were divided into four groups (i.e., the intervention protocols and a control group). The interventions were implemented by outside consultants and the authors were brought in to evaluate the results. Using a random number table, 35 specialists were randomly assigned to the AJD group intervention, and 35 specialists were randomly assigned to the AWT group intervention, which was further subdivided and randomly assigned into five teams of seven support specialists. Forty-three specialists were randomly assigned to the HIWP group intervention, which were subdivided randomly into seven teams. Thirty-six support specialists were assigned to a control group (i.e., their work was unchanged). A follow-up analysis indicated that the specialists’ level of experience, gender, and ages were evenly distributed throughout the groups, suggesting that the random assignment was effective. Prior to the interventions (pre-intervention), support specialists were administered surveys to collect demographics and individual versus group work orientation (TSI for external scope), along with job satisfaction and commitment. The dependent variable instruments were again given 6 months after the interventions (post-intervention). The delay interval was an attempt to address potential novelty and Hawthorne effects (Batt, 1999), but a longer delay was not accommodated due to restrictions placed on the study by the organization. Specifically, company management wanted to come to a conclusion as quickly as feasible, decide on the most effective design, and implement it organization-wide. Performance measures consisting of customer survey scores, problems solved by group, escalations, and repeat calls were tracked for 10 months. Performance measures, however, were not used to test hypotheses in this study because they were collapsed across employees and groups to derive a single measure for each experimental condition. This was the result of objections raised by some of the participants to having their individual performance measures tracked by outside observers and because Copyright # 2004 John Wiley & Sons, Ltd.

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the group-based interventions changed the performance measurements from an individual to a group level.

Intervention designs Pre-intervention, and continuing in the control group, support specialists worked independently, receiving incoming calls as assigned by an automated call routing system. The performance measurements were based on how long support specialists spent solving problems (time per problem) and the number of problems closed (problems per specialist). Each specialist also had a variable quota for the number of problems to take based on incoming call volume (percentage of volume). These measurements are typical of those found in call centers (Silvestro, 2002; Wallace et al., 2000). When call volume was high, and when the specialist was unable to solve a problem, he or she would place it into an open problem queue to be revisited later during slack time. If the problem was urgent, and the specialist deemed that he or she could not solve the problem, it was manually ‘escalated’ to a more seniorlevel specialist for resolution. Three objectives were set for the AJD group intervention: Examine the performance measurements and determine their outcomes; adjust them according to strategic organizational objectives; and adapt the structure and reward systems around these new measures. During the examination phase of the AJD group intervention, an inspection of repeat calls (recurring calls from the same customer on the same problem) revealed that at peak call volumes there was a reduction in time spent on each problem. This speed increase had the effect of increasing errors in the solutions given by support specialists who strived to ‘get rid of problems.’ An increase in the escalations was also observed as problems were ‘thrown over the wall’ to other specialists. While this reduced the time per problem for each specialist, it had the consequences of involving multiple support specialists in the resolution effort and extending the duration that the problem went unsolved. As problem volumes continued to increase, specialists reached a point where they were forced to concentrate on simple problems and set the harder problems aside in their open problem queues in hopes of returning to them later. This had the effect of causing the most difficult problems to go unsolved for extended periods of time. The alignment effort was to set performance measures congruent with business objectives, since what is measured becomes a goal or a milestone (Semler, 1997). A key aspect of the alignment would reduce the pressure to quickly solve problems so that specialists could focus on giving correct solutions. The alignment thrust involved adjusting the measurement system toward thorough problem resolution (e.g., looking at the number of repetitive calls and eliminating quotas). Further, the number of escalations was tracked, as was the number of problems in open problem queues. Management continued to track the number of problems solved, but only in the aggregate. The existing management structure was kept in place and managers continued to conduct performance reviews. Performance rewards, however, such as bonuses, raises, and the expressions of management approval, were administered based upon the new measures. In addition to the changes described above, the AJD intervention also included a new process to facilitate learning. Coined the ‘hot seat,’ this job rotation was devised to enable specialists to spend some portion of their time off the phones and working on problems in their open problem queues. Support specialists worked 3 days on the phone (hot seat) and 2 days off the phone working on problems they had been unable to solve. The objectives set for the HIWP intervention followed Vandenberg et al.’s (1999) conceptions of (1) elevating member–leader participation, (2) establishing customer and business feedback loops, (3) expanding member knowledge of the total work system, and (4) creating structural alignment. For the first objective, quality circles in the form of member–leader cross-participation process Copyright # 2004 John Wiley & Sons, Ltd.

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improvement teams (PITs) were devised for ongoing job redesign efforts. The structural changes devised by the PITs included a formal escalation team (research team), into which specialists would rotate off of the phones on a biweekly basis. In the research team, junior specialists were paired with senior specialists who acted as mentors. Since the organization had been working in a mode with little interdependence, to encourage leader– member participation managers and specialists attended a 1-week training seminar that encouraged a participative environment and focused on achieving personal and organizational potential (Pfeiffer, 1985). In addition, a series of ‘lunch and learn participation workshops’ were conducted with managers and support specialists at regular intervals. The lunch and learn sessions were also used to uncover stumbling blocks in the structure and processes, as well as to expose the specialists to company-wide processes to expand their knowledge of the larger work system. In addition, customer survey scores were discussed (good and bad) to increase customer issue awareness. Process improvement teams similarly restructured the performance measurement system to align it with strategic business objectives. Specifically, quotas were centered on the collective efforts of the group, and rather than percentage of volume they incorporated percentage of problems in open queues to drive down the number of difficult problems going unresolved. Gainsharing was implemented where bonuses were given based on meeting PIT-defined objectives set for customer satisfaction and quality. The final intervention, the AWT condition, was entirely group focused, and followed Wellins et al.’s (1990) components of autonomous work teams based on self-management processes whereby the team (1) assigned jobs to members, (2) planned and scheduled work, (3) made service-related decisions, and (4) took action to remedy problems. Management relinquished control of performance measurement and assessment to the group. Team members developed written agreements covering roles and tasks the team would assume, along with peer review criteria. To encourage participation in the teams, specialists attended the same type of 1-week training seminar as the HIWP intervention (Pfeiffer, 1985). In the AWT intervention, the previous measurement and reward structure concentrating on individual production was replaced with team-based measurements and rewards. The new measurement structure focused on thorough problem research and resolution, and problems were worked as a collective in the teams. The teams in this intervention created a work design similar to the ‘hot seat’ used in the AJD group. On a 1-day weekly rotation, two members from each team would ‘man the phones,’ while unsolved problems were passed to those members in the teams who were ‘off the phones.’ These teams also organized themselves into ‘specialties.’ For instance, team 1 focused more on problems involving networking, whereas team 2 tended to focus on problems involving core system components. Team members selected their own specialty. Merit increases were given to each team as a whole based on meeting team-defined quality and productivity objectives.

Analytical procedures So that the hypotheses presented above can be tested in a straightforward manner and the data can be more completely interpreted, a number of procedures were used. First, a multivariate analysis of covariance (i.e., MANCOVA) was conducted to examine the overall effects of the interventions. The MANCOVA was designed to address whether or not there were significant differences among the groups in terms of their simultaneous effects upon job satisfaction and commitment (while using the pre-test scores as covariates). After a brief examination of the multivariate results, the univariate results were explored by analyzing pre- and posttest means to evaluate the main-effect hypotheses (Hypotheses 1, 2a, 2b, 3a, and 3b) and then a series of ANOVAs were used to explore the interactions hypothesized in Hypotheses 2c, 2d, 3c, and 3d. Copyright # 2004 John Wiley & Sons, Ltd.

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Results The means, standard deviations, and intercorrelations among the variables in the study are presented in Table 2. Alpha reliabilities were 0.92 for the TSI, 0.95 for the OCQ, and 0.93 for intrinsic job satisfaction. An examination of Table 2 provides clear evidence that the two outcome measures of interest (i.e., organizational commitment and job satisfaction) are significantly intercorrelated. This suggests that a MANCOVA should be conducted before the univariate results (associated with commitment and satisfaction) are examined. The results obtained from the omnibus MANCOVA are shown in Table 3. At the most general level, the MANCOVA model explains significant variance in both job satisfaction and organizational commitment. Further, the MANCOVA results suggest that the interventions did impact the attitudinal outcomes and the interactions hypothesized were significant in the aggregate. Table 2. Descriptive statistics and intercorrelations of study variables Mean 1. Employee age 31.07 2. Employee tenure 2.71 3. Employee gender (1 ¼ male 2 ¼ female) 1.45 4. TSI 4.19 5. Intrinsic job satisfaction (pretest) 3.52 6. Organizational commitment (pretest) 3.74 7. Intrinsic job satisfaction (posttest) 4.06 8. Organizational commitment (posttest) 3.85

SD

1

2

7.14 1.58 0.49 0.50
0.16
0.11 0.99
0.16
0.12 0.77 0.06 0.10 0.92 0.05 0.18 0.89
0.02
0.01 0.90 0.10 0.12

3

4

5

6

7

0.32 0.12 0.21 0.01 0.19

0.04 0.25 0.30 0.25

0.56 0.29 0.34

d.f.

Mean square

F

0.14 0.64 0.39

Note: N ¼ 149; r > 0.15, p < 0.05; r > 0.21, p < 0.01; r > 26, p < 0.001.

Table 3. MANCOVA results Source

Dependent variablea

Corrected model

Intrinsic job sat. Org. commitment Intrinsic job sat. Org. commitment Intrinsic job sat. Org. commitment Intrinsic job sat. Org. commitment Intrinsic job sat. Org. commitment Intrinsic job sat. Org. commitment Intrinsic job sat.

55.92 63.84 11.07 4.59 0.32 34.78 9.56 0.00 8.14 0.27 6.30 3.26 4.66

9 9 1 1 1 1 1 1 1 1 3 3 3

6.21 7.09 11.07 4.59 0.32 34.78 9.56 0.00 8.14 0.27 2.10 1.09 1.55

13.96*** 17.50*** 24.86*** 11.33*** 0.73 85.83*** 21.49*** 0.00 18.28*** 0.67 4.72** 2.68* 3.49*

Org. commitment Intrinsic job sat. Org. commitment Intrinsic job sat. Org. commitment Intrinsic job sat. Org. commitment

3.77 61.86 56.33 2568.64 2333.13 117.77 120.17

3 139 139 149 149 148 148

1.26 0.45 0.41

3.10*

Intercept Org. commitment pre Intrinsic job sat. pre Group work preference Intervention Intervention  group work preference Error Total Corrected total

Type III sum of squares

Notes: aDependent measures are posttest values; R2 ¼ 0.48 for intrinsic job satisfaction; R2 ¼ 0.53 for organizational commitment.

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Table 4. Pre- and posttest values Pretest Group

Control (N ¼ 36) Low group preference (N ¼ 20) High group preference (N ¼ 16) Reward alignment (N ¼ 35) Low group preference (N ¼ 17) High group preference (N ¼ 18) HIWP team (N ¼ 43) Low group preference (N ¼ 20) High group preference (N ¼ 23) Autonomous team (N ¼ 35) Low group preference (N ¼ 19) High group preference (N ¼ 16) Totals (N ¼ 149) Low group preference (N ¼ 76) High group preference (N ¼ 73)

Org. com.

Posttest Int. sat.

Org. com.

Effect size (d) Int. sat.

Mean

SD

Mean SD

Mean SD Mean SD

3.72 3.43 4.09 3.80 3.41 4.16 3.59 3.52 3.65 3.89 3.73 4.07 3.74 3.53 3.97

0.96 0.79 1.05 0.86 0.74 0.82 0.89 0.98 0.82 0.98 1.03 0.92 0.92 0.89 0.90

3.49 3.25 3.80 3.54 3.43 3.64 3.49 3.70 3.30 3.57 3.51 3.64 3.52 3.47 3.57

3.58 3.35 3.87 3.86 3.83 3.89 4.14 3.91 4.34 3.77 3.58 4.00 3.85 3.66 4.05

0.77 0.74 0.72 0.86 0.90 0.84 0.73 0.75 0.67 0.75 0.94 0.46 0.77 0.83 0.70

0.96 0.81 1.08 0.76 0.75 0.78 0.90 1.04 0.71 0.91 0.86 0.95 0.90 0.89 0.88

3.46 3.23 3.75 4.31 4.31 4.30 4.61 4.35 4.84 3.74 3.36 4.18 4.06 3.80 4.32

0.75 0.71 0.70 0.68 0.78 0.61 0.85 1.05 0.57 0.76 0.69 0.60 0.89 0.96 0.73

Org. com. Int. sat. d

0.21

d

0.98**

0.75** 1.54*** 0.02

0.26

Notes: Effect sizes were calculated using Carlson and Schmidt’s (1999) effect size formula for a pretest–posttest with control design. The preferences for group work score was dichotomized to illustrate the differences in means.

The specific tests of the hypotheses, however, are based on the detailed univariate results and not the overall multivariate ones. To facilitate hypothesis testing, the effect sizes of the different interventions were calculated and the MANCOVA analysis was followed up with univariate analyses. We were interested in assessing the effectiveness of the three interventions and the interaction between preferences for group work and two of the interventions (i.e., HIWP and AWT). Because the experimental conditions were randomly assigned and an examination of the groups’ composition did not lead us to believe that any meaningful differences existed between the groups, we did not include any demographic covariates in this analysis. The pretest scores for organizational commitment and job satisfaction were the only covariates. The first hypothesis suggested that the reward alignment intervention would increase employee job satisfaction and organizational commitment. An examination of Table 4 allows a direct test of this hypothesis. While the effect size was positive for both outcomes, only job satisfaction showed a statistically significant effect size (d ¼ 0.98, p < 0.01). Thus, Hypothesis 1a was supported, while the results associated with Hypothesis 1b did not reach significance. In other words, employees receiving the AJD intervention experienced increased job satisfaction (compared to the control group), whereas the effect on their commitment was not significant. The second set of hypotheses addressed the efficacy of HIWP. More precisely, Hypothesis 2a suggested that HIWP would be associated with an increase in job satisfaction and Hypothesis 2b posited an increase in organizational commitment. An examination of Table 4 shows a significant effect size (d) of 1.54 ( p < 0.001) for job satisfaction and a significant d score (d ¼ 0.75, p < 0.01) for organizational commitment. Thus, the implementation of HIWP was associated with both improved employee satisfaction and organizational commitment, providing support for Hypotheses 2a and 2b. Thus, the employees in the HIWP condition experienced significantly improved job satisfaction and commitment when compared to the control group on these same measures. Beyond the effects hypothesized for HIWP’s impact on job satisfaction and commitment, we also believed interactions would be present. We hypothesized that employee preference for group work Copyright # 2004 John Wiley & Sons, Ltd.

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Table 5. Analysis of covariance testing the HIWP  group work preference interaction upon intrinsic job satisfaction Source

Type III sum of squares

d.f.

Mean square

41.39 1.41 6.22 9.98 0.04 1.54 34.75 1394.72 76.14

4 1 1 1 1 1 74 79 78

10.35 1.41 6.22 9.98 0.04 1.54 0.47

Corrected model Intercept Group work preference Intrinsic job sat. (pretest) HIWP HIWP  group work pref. Error Total Corrected total

F 22.04*** 3.00 13.24*** 21.25*** 0.09 3.28*

Notes: R2 ¼ 0.54 (adjusted R2 ¼ 0.52). ***p < 0.001; **p < 0.01; *p < 0.05.

would moderate the impact of HIWP upon satisfaction and commitment. To test this, we ran two ANCOVAs (i.e., one for job satisfaction and one for commitment) comparing the results experienced by the people in the HIWP intervention against the results experienced by those people in the control group (See Table 5). Results show that employee preferences for group work did moderate the HIWP intervention’s effect on job satisfaction (F ¼ 3.28, p < 0.05). Specifically, when employees had a high preference for group work they responded more positively to the high-involvement intervention than when they had less group-oriented focus. This finding provides support for Hypothesis 2c. Utilizing the information from Table 4 we can better understand the form of interaction. In particular, employees who took part in HIWP with a high preference for group work had their average job satisfaction raised from an average of 3.30 in the pretest to 4.84 in the posttest, while their counterparts who had low preferences for group work had their mean job satisfaction raised from 3.70 to 4.35. Thus, under the HWIP intervention, the job satisfaction of employees increased more than for employees with high preferences for group work (i.e., 1.54 units) compared to those with low preferences for group work (0.65 units). No support for a significant interaction between high involvement and preference for group work was found when the outcome measure was organizational commitment, failing to support Hypothesis 2d. The final set of hypotheses addressed the relationship between autonomous teams and the job satisfaction and commitment outcomes. More precisely, Hypothesis 3a suggested that employees who were part of the autonomous work teams intervention would show improved job satisfaction. Further, Hypothesis 3b hypothesized that these employees would also raise their organizational commitment after the autonomous teams’ intervention. As evidenced by Table 4, the results failed to support the efficacy of the autonomous teams’ intervention in both cases. Neither Hypothesis 3a nor Hypothesis 3b was supported, meaning that the autonomous work teams’ intervention did not improve employee job satisfaction or commitment (relative to the control group) in our sample. Interactions regarding AWT were also put forth to be tested. We hypothesized that employee preference for individual or group work would moderate the impact of autonomous teams upon satisfaction and commitment. These hypotheses were assessed consistent with the tests conducted for Hypotheses 2c and 2d. The results of these analyses, presented in Table 6, indicate that employee preferences for group work did moderate the autonomous team intervention’s effect on job satisfaction. When employees had high preference for group work, their job satisfaction responded more positively to the autonomous teams intervention (increased from 3.64 to 4.18) than did employees with low team orientation (declined from 3.51 to 3.36) (F ¼ 4.35, p < 0.05). This finding supported Hypothesis 3c. No support, however, was found for the organizational commitment interaction hypothesized in Hypothesis 3d. Copyright # 2004 John Wiley & Sons, Ltd.

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Table 6. Analysis of covariance testing the AWT  group work preference interaction upon intrinsic job satisfaction Source

Type III sum of squares

d.f.

20.12 1.09 5.49 8.62 0.81 1.36 20.62 958.38 40.74

4 1 1 1 1 1 66 71 70

Corrected model Intercept Group work preference Intrinsic job sat. (pretest) Autonomous work team AWT  group work pref. Error Total Corrected total

Mean square 5.03 1.09 5.49 8.62 0.81 1.36 0.31

F 16.10*** 3.48 17.57*** 27.59*** 2.60 4.35*

Notes: R2 ¼ 0.49 (adjusted R2 ¼ 0.46). ***p < 0.001; **p < 0.01; *p < 0.05.

Table 7. Performance measurement results Performance measures/ condition

Pretest measure

Posttest measures Control

Customer service score Problems closed per employee Percentage of calls escalated Percentage of repeat calls

6.3 53.6 14.0 14.6

6.5 61.9 9.6 15.2

AJD 8.5 80.0 1.8 1.0

AWT

HIWP

7.2 50.2 0.0 0.7

9.0 71.5 0.0 0.5

Note: The pretest measures are the average performance across 6 months prior to the intervention and the posttest measures are the average across a 3-month period beginning 3 months after the interventions started (i.e., months 4–6).

As a final analysis of the data, we also conducted a post hoc test to determine whether the employees in the experimental groups (as a whole) had higher satisfaction and commitment than the employees in the control group. The results of this MANCOVA analysis found that the effect for taking action was not significant. Thus, combining these results with those presented above suggests that while specific interventions (i.e., HIWP) did yield returns (on commitment and satisfaction), the act of engaging in a redesign intervention did not in and of itself impact the attitudinal outcomes measured in this study. Due to limitations imposed on the data collection by the organization under study, combined with the shift from individual to group-based measurements in the HIWP and AJD interventions, individual performance measures were not explored. However, in the aggregate, there were indications that performance improved as the result of certain interventions. A summary of these results can be seen in Table 7. Table 7 allows us to make some overall statements regarding the impacts of the interventions upon performance, but these conclusions should be tempered by the knowledge that the data represents single groups. The findings suggested that employees in the HIWP intervention condition received the highest customer service scores, had the second highest number of problems closed per employee, and showed dramatic improvements in both the percentage of escalated calls (none were escalated out of the group) and the percentage of repeat calls handled (i.e., 0.5 per cent). These findings regarding the HIWP intervention condition tell a reasonably consistent story with HIWP’s effects upon job satisfaction and commitment. HIWP was highly successful in terms of both perceptual and performancerelated outcome measures. Much in the same manner that the performance results for HIWP supported the perceptual findings, the findings associated with alignment job design also supported the perceptual results. More Copyright # 2004 John Wiley & Sons, Ltd.

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specifically, the AJD intervention was associated with improved customer satisfaction, the percentage of escalated calls, and the percentage of repeat calls, but not to the extent of improvement seen with HIWP. AJD, however, was linked to the largest increase in the number of problems closed per employee, with employees averaging 80 closures. Similarly, the performance-based results associated with autonomous work teams generally confirmed its perceptual findings. More specifically, autonomous work teams were associated with the smallest rise in customer service and actually resulted in fewer problems being closed per employee. On the positive side, however, the percentage of repeat calls was relatively low in the autonomous work team. At this juncture it should be pointed out that the performance of the control group suggested that no major contamination between the control group and the experimental groups occurred. The control group showed relatively little change from the pretest measures across the four performance-related outcomes and certainly less change than any experimental group.

Discussion Prior to the interventions, managers perceived the employees to have low satisfaction and low commitment. While there are many factors associated with turnover (Sagie, 1998; Workman, 2001), of which job satisfaction is only one, it is in a company’s inherent best interest to take steps to retain its most valued talent. Organizational commitment and job satisfaction-related turnover may be at least partly preventable, and their consequences in this case were causing the company to spend considerable time and energy on interviewing, hiring, and training new personnel. Moreover, employees who remained with the company expressed poor job sentiments and they found little reason to exert more than the minimal effort required. These feelings were summed up by the pre-intervention comment of one employee: ‘This place is a code mine, and the specialists are the dirt from which the code is extracted.’ Three approaches have frequently been used to address call center environments of this nature: AJD, HIWP, and AWT. The literature has been incommensurable in terms of which of these interventions has had what effect on job satisfaction and/or organizational commitment (Hatcher, 1999). This study examined three interventions concurrently, and the results indicate that AJD significantly enhances job satisfaction (Hypothesis 1a), although there was no support for the conclusion that it enhances organizational commitment (Hypothesis 1b, n.s.). It has been posited that although organizational commitment tends to be more stable over time, it may take longer to develop (Sagie, 1998), which may partially account for this disparity. However, in context with the effects of the HIWP intervention in which both job satisfaction and organizational commitment significantly improved (Hypotheses 2a and 2b), it hints that the individual-oriented focus of the AJD intervention may not have significantly improved factors associated with organizational commitment. For instance, since management drove the alignment of measurement and rewards, it appears that the absence of employee involvement may have suppressed commitment to the organization. Working in a high-involvement environment increases member–leader and cross-functional interaction and joint efforts toward a compatible job design, leading to improved commitment and satisfaction (Moses & Stahelski, 1999). The results of this study confirm these propositions. Therefore, improvements in job satisfaction (Hypothesis 3a) and organizational commitment (Hypothesis 3b) may result from the combination of structural alignment and an exchange of essential ideas and effort. In follow-up interviews, several of the support specialists in the HWIP group viewed that more variety and learning from the work setting helped to ameliorate the feeling of redundancy as well as the Copyright # 2004 John Wiley & Sons, Ltd.

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perceived negative effects of the problem-oriented task environment. These views were consistent with Nelson et al.’s (2000) findings that growth and accomplishment derived from challenging work, teaching others, and professional development were important to support specialists’ motivation and commitment. In addition to addressing shallow problem investigation, HIWP enhanced cooperation, which appears to have improved job satisfaction. Moreover, employee involvement in aligning the organizational structure appears to have improved commitment to the organization. Although we did not formally pose a hypothesis regarding the relative potency of the interventions, there is some theoretical basis to believe that the effectiveness of the HIWP intervention should exceed the effectiveness of the AJD intervention. Since the HIWP intervention aligned the reward systems to address shallow problem investigation, and then implemented additional changes to increase cooperation and reduce competition, HIWP could reasonably be believed to possess incremental effectiveness above AJD. To test this assertion, we conducted a post hoc analysis to determine whether one intervention was significantly more effective than the others in terms of its impact upon job satisfaction and organizational commitment. From Table 4, it is clear that the HIWP group intervention had the largest effect sizes, but whether or not they are statistically different from other interventions needed an additional statistical test. Hence, we conducted an analysis whereby the HIWP intervention was directly compared to AJD. The result of this analysis suggested that the HIWP group intervention had an effect size of 0.56 (p < 0.05) over and above the AJD group intervention for organizational commitment. When evaluated in terms of its impact on job satisfaction, the HIWP group intervention’s effect size was 0.44 (p < 0.10). Thus, there is some evidence to suggest that the HIWP intervention was the most effective in improving satisfaction and commitment. Beyond the main effects associated with HIWP, an interaction between employee preferences for group work and HIWP was also found. When high preferences for group work were taken into account, there were even greater improvements in employee job satisfaction resulting from HIWP (Hypothesis 2c), although there was no indication that preferences for group work significantly improved commitment to the organization over employees with low group orientation (Hypothesis 2d, n.s.). Overall, these findings suggest that organizations implementing HIWP may enjoy greater positive satisfaction and commitment if their employees have a high preference for group work in the first place. Interestingly, however, the results of our study do not suggest that employees who have a low preference for group work experience a decrease in commitment or negative impact on job satisfaction. As such, the results of our study suggest that employees will benefit from HIWP interventions even when they do not prefer group-oriented work. A comment from an employee summed up these sentiments by stating that, ‘I hate the group work, but generally, it’s better. Less pressure on numbers.’ In other words, it suggests that the HIWP intervention had a ‘little something for everyone.’ In terms of AWT, it is surmised that a key ingredient to enhancing organizational commitment and job satisfaction involves the perception that one has requisite skills and abilities to perform well (Griffin et al., 2001). Thus, there is a tight connection between organizational structure and learning opportunities on organizational commitment and job satisfaction outcomes. We speculated that autonomous teams should heighten the exchange of workers’ skills and knowledge. This appears to be sensitive to preferences for group work. Without taking group preferences for group work into account, there were no significant improvements in job satisfaction or organizational commitment for the AWT intervention in this study (Hypotheses 3a and 3b, n.s.). Follow-up comments from the employees provided some insight into these findings. It is possible, for example, that when the AWT group changed their focus from individual to group measures, it was perceived that working in one’s best interest (thoroughly solving problems) was consonant with the group emphasis. Nevertheless, the perception of control over the outcome was limited in some instances by some specialists’ perceptions of having inadequate skills Copyright # 2004 John Wiley & Sons, Ltd.

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for solving the more difficult problems. This led to reports of greater desire for learning. The perceived need for learning was coupled with increased pressure among the group members ‘not to escalate’ problems. As a result, many of the ‘junior’ support specialists indicated a feeling of being more upset about performance objectives. Barker (1993) has described this effect as an ‘iron cage’ resulting from concertive (group) control. As an illustration of the iron cage phenomenon, one employee stated that after the intervention ‘there [was] more pressure to solve problems on your own because co-workers make you feel guilty for not sharing the load.’ Further credence is given to this reasoning because inclination toward preferences for group work moderated the job satisfaction results (Hypothesis 3c). Team members who were more inclined toward group work had significantly better job satisfaction than members who preferred working alone. As an indication of the follow-up comments on this point, one employee who had a low preference for group work commented that he was ‘getting tired of [my peers] telling me what to do. I came here to work for my boss, not these guys.’ This can be contrasted with a comment made by a specialist, who had a preference for group work, who stated that the changes were ‘great, love it. My team mates are great, and the work is more varied.’

Limitations and Suggestions for Future Research The findings of this study should not be interpreted as an across-the-board rejection of AWT, or the broad approval of high-involvement work practices. To the contrary, the current study provides one piece to a much larger puzzle and needs further testing and refinement for enhanced practitioner and academic utility. Several additional factors might be inferred. In particular, the diverging results for the three interventions in this study might be partially explained from the differential involvement of the management, which was highest in the HIWP group, followed by the AJD group, with least management involvement in AWT. The degree of management involvement has had differential effects on job satisfaction and organizational commitment (cf. Moses & Stahelski, 1999). Moreover, although the groups were relatively small, both control over performance outcomes and incentives in the AWT group were diffused over a collective effort, which may have diminished its intensity (Zenger & Marshall, 2000). Since the only measure to address shallow problem investigation was in terms of repeat calls, performance measures remained disproportionately geared toward volume, albeit at the group level. This, combined with concertive control, may have muted job satisfaction in the AWT group. Hence, in addition to team member attributes, the ways in which teambased rewards are implemented are important to outcomes (Zenger & Marshall, 2000) and should be more closely investigated. While we benefited from the ecological validity, as with most field quasi-experiments, there were a number of limitations in this study. At the most basic level, our findings need further assessment to determine their generalizability. Our study focused on employees working in a computer technology call center. Thus we raise two questions to consider for future research: would the results of this study be replicated in other computer technology-based call centers? And can these results be replicated in call centers generally? In addition, future research should place a premium on assessing the interventions’ effects upon job performance. Our data allowed us to make some preliminary suggestions regarding job performance, but did not allow us to explore the question with the depth deserved. Typical of threats to validity in field quasi-experiments, we had reason to believe that there was some cross-group contamination as some members discussed their interventions with members of Copyright # 2004 John Wiley & Sons, Ltd.

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other conditions. Future studies may seek additional ways to control this. Also, from the follow-up interviews, there were some indications that the delay interval of 6 months did not fully address group and novelty effects, particularly in AWT, which may require a longer period-of-time to ‘gel’ compared to more moderate team-oriented implementations. Restrictions placed on the research by the company made the short delay unavoidable. Of particular future interest would be the consequences of different interventions upon different types of employees. While we examined preferences for group work of employees as a moderator of intervention effectiveness, undoubtedly there are additional individual and group-level factors that might influence the success or failure of such interventions. It is likely that the treatment by person interactions will need to be much more fully understood before the promise of organizational intervention effectiveness can be fully realized.

Author biographies Michael Workman received his PhD from Georgia State University and is an Assistant Professor of Information Sciences at Florida State University. His research area investigates how to exploit technologies, tasks, and human factors to improve how well people work. William Bommer earned his PhD in Organizational Behavior at Indiana University. He is an Assistant Professor in the Management and Labor Relations Department at Cleveland State University. His primary research areas include leadership development, organizational citizenship, and reward system alignment.

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