The Role of Quality Tools in Improving Satisfaction with Government

Foster article 6/18/02 10:01 AM Page 20 The Role of Quality Tools in Improving Satisfaction with Government S. THOMAS FOSTER JR., BOISE STATE UNIVERS...
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Foster article 6/18/02 10:01 AM Page 20

The Role of Quality Tools in Improving Satisfaction with Government S. THOMAS FOSTER JR., BOISE STATE UNIVERSITY LARRY W. HOWARD, MIDDLE TENNESSEE STATE UNIVERSITY PATRICK SHANNON, BOISE STATE UNIVERSITY © 2002, ASQ

This article presents the results of a study in a city in the western United States. The authors found that city employees believed that quality knowledge was necessary for improving quality. Results show that departmental leadership was positively associated with teamwork, process improvement, and employee satisfaction. Quality knowledge, if followed up with application, can be effective in improving processes. Leadership is necessary to the development of quality tools knowledge. Therefore, both leadership and teamwork are important contextual variables for quality improvement in the public sector. Key words: leadership, organizational context, quality management, quality tools, teamwork

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INTRODUCTION Much has been written about infrastructural and environmental variables in quality improvement in business (Adam 1994; Saraph, Benson, and Schroeder 1989; Flynn, Schroeder, and Sakakibara 1995). Most of this research has focused on antecedents to outcomes such as market share, return on investment, customer satisfaction, and self-reported measures of quality improvement. Interestingly, there is much disagreement among these research models regarding the variables leading to positive quality outcomes. This has led some authors to adopt the contingency-based view that organizational quality improvement can occur in a variety of ways—depending upon organizational context (Mallick, Ritzman, and Safizadeh 1999). While there is an established quality management literature in business, there is relatively little relating to quality improvement in government. Much of the existing literature is anecdotal (Foster and Viano 1996). As a result, there is little understanding of the variables leading to quality improvement in government. There are significant differences in environmental variables of business vs. government. A primary difference is the lack of profit in government. W. Edwards Deming (1986) often alluded to the profit issue as a differentiator resulting in necessarily different choices in quality improvement methods between government and business. For example, infrastructural, labor-related practices differ in government. Employees have more job security in government than in business. To compensate for this, government wages often lag the private sector. Government entities often have a difficult time

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The Role of Quality Tools in Improving Satisfaction with Government

Model and Hypotheses Development Figure 1 shows a model of quality improvement that motivated this research. The structure of the model and the included variables are based upon the literature, including works by Saraph, Benson, and Schroeder (1989), Adam (1994), the Malcolm Baldrige National Quality Award Criteria for Performance Excellence (2000), Sematech Quality Maturity Grid (1998), and other sources. These variables are categorized as context variables, enabler variables, and outcome variables. Context variables refer to organizational context (Benson, Saraph, and Schroeder 1991). Organizational context is the organization’s state of being at the time quality improvement occurs. Organizational context includes external and internal factors surrounding the production system. Internal context variables include leadership and company organization, such as the extent teamwork and collaborative decision-making is used for improvement. Enabler variables make organizational change possible and are necessary for effective improvement. These are critical factors that affect quality outcomes. For example, knowledge is a fundamental enabler that all employees need to do their jobs. Specifically, knowledge of quality tools is required before these tools can be applied. The extent that quality tools are then used to make improvement affects quality outcomes. This could include understanding and using statistical process control, basic tools, automation, and supplier involvement in improvement (Benson, Saraph, and Schroeder 1991). Figure 1 A priori quality improvement model. Contextual

Enablers Q-tools application

H2 Teamwork H1b

H1a Leadership

H3 Q-tools knowledge H1c H1d

Outcomes

H4

Process improvement H5 Employee satisfaction H6a

H6b

Customer satisfaction

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identifying the customer. In business, the customer often ends up owning the product. Some authors have posited that the customer is the “one who pays the bills” (Evans and Lindsay 1999). However, who is the customer in government? Is it the taxpayer, the elected leader (for example, executive branch), the legislature (they allocate resources), or the individuals who directly access government services (such as the licensee to a motor-vehicle division)? In fact, government entities may have a number of customers who cannot be defined with the simple internal and external designations. Although there is limited research in government quality management, the need for more research is great. There are a number of reasons for this. First, demands are increasing for government services, while budgets are stagnant or decreasing. Therefore, process simplification is needed to respond to increasing demands. Second, there is increasing competitive pressure on government service providers as pressure mounts to privatize government services. Third, leaders in government have moved to improve and reinvent government. Finally, government employees are internally motivated to provide service that is on par with the private sector. It is not clear, however, that quality practices can be transferred from the private sector to the public sector. While basic quality tools are used commonly in industry, research has not demonstrated the efficacy of these tools in improving government service. In fact, Deming cautioned against applying modern quality management approaches to government (Deming 1986). This article presents results from a study performed in a city government. The city in question had been implementing teams and quality improvement tools over a number of years. Quality tools, while ubiquitous in the practitioner literature, have received little attention in research. The primary research question is, “Were the applications of quality tools effective in improving quality-related outcomes in this city government.” As a result of this study and analysis, the authors propose a model of quality tool usage in government. The primary contribution of this article is to examine the role of quality tools in effective implementation of quality improvement in a government setting.

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The Role of Quality Tools in Improving Satisfaction with Government Outcome variables represent the desired outcomes of quality tools application. Outcomes often mentioned in the literature include process improvement, employee satisfaction, and customer satisfaction. The following paragraphs address the relationships between context variables, enabler variables, and outcome variables as described in the quality improvement model shown in Figure 1.

Contextual Variables Leadership. Leadership is generally regarded as essential for quality improvement. Leadership provides the foundation for improvement, as leaders hold both the positional and monetary authority to oversee improvement. In a case study of the Office of Administrative Services, Department of the Interior, Keck (1996) found leadership to be necessary for successfully completing process improvement projects. Scully (1993) stated that leadership is needed to initiate the process of change in government. Rago (1996) developed a deductive leadership model of government improvement with leadership enacting purpose, coordination, communication, and empowerment. Leadership promotes the implementation of teamwork by providing required resources and assets, and by symbolically communicating top-level commitment to quality tools application. Leadership is considered in the literature to be an antecedent to process improvement (Deming 1986). Also, positive leadership is associated with employee satisfaction (Howard and Foster 1999). By inference, perceptions of leadership commitment to quality should also influence the satisfaction of those affected by satisfied employees and improved processes—customers. Hypothesis 1a: There is a positive relationship between leadership and teamwork. Hypothesis 1b: There is a positive relationship between leadership and process improvement. Hypothesis 1c: There is a positive relationship between leadership and employee satisfaction. Hypothesis 1d: There is a positive relationship between leadership and customer satisfaction.

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Teamwork. The second contextual variable is teamwork. As with the manufacturing and services sectors of the private sector, teams have been widely adopted in government. There are several reasons for this. One of the main reasons is complexity in the workplace (Wenger and Snyder 2000). Given the large volumes of data available to managers, unilateral decision-making is less effective. Also, businesses are transforming from “command and control” to collaboration. Collaboration is needed as complexity drives workers from performing manual work to knowledge work or work that involves the development and transmission of knowledge and information. Knowledge work implies a greater amount of ambiguity, searching, researching, and on-the-job learning. As a result, organizations are using teams more frequently in their normal operations and in their problem-solving and process improvement efforts. For the authors’ purposes, a team is defined as a finite number of individuals who are united in a common purpose. Selander and Cross (1999) view the team component as essential for business process redesign.

Enabler Variables Quality tools knowledge. The first enabler variable is quality tools knowledge. Before quality tools are applied, training is often provided so employees learn what quality tools are available and how to use them. The quality tools referred to in this research include the basic seven tools of quality (that is, flowcharts, control charts, histograms, scatter plots, Ishikawa diagrams, run charts, Pareto charts, and checksheets) and selected advanced tools (affinity diagrams, surveys). Ceridwen (1992) identified flowcharting, Ishikawa diagrams, control charts, and scatter diagrams as the most useful tools for quality improvement. Foster and Viano (1996) demonstrated how basic quality tools were used in the Internal Revenue Service to improve service quality. As teams begin to work on process improvement, they have more opportunity to apply quality management tools and to use teamwork to solve problems. The more the team works together, knowledge of how and when to apply the quality tools is reinforced. Working in teams increases the value of sharing quality knowledge. It is expected that as people work in teams, they are more likely to be facilitated by other team

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The Role of Quality Tools in Improving Satisfaction with Government members in learning about quality tools. Also, some of the quality tools, such as brainstorming, are specifically designed to be used in team settings, so the more people work in teams, the more likely it is they are going to become familiar with these types of quality tools. Hypothesis 2: There is a positive relationship between teamwork and quality tools knowledge. Quality tools application. Quality tools application refers to the continued use of quality tools after the training is completed. Knowledge alone will be inadequate for process improvement unless employees actually apply their knowledge by properly using quality tools. A long-term commitment is required to improve quality— one that will result in a change to a culture of improvement. Foster and Franz (1998) proposed the use of quality tools to improve product quality. They also stated that a method was needed for further expanding the use of these tools. This required both a method for understanding the effects of the quality tools and a means for selecting appropriate quality tools. The more knowledgeable employees are about quality tools, the more likely they are to appropriately select and apply those tools. Hypothesis 3: Quality tools knowledge is positively related to quality tools application.

Outcome Variables Process improvement. Process improvement refers to the extent to which employees and customers perceive that processes have improved. The importance of process improvement has long been emphasized by quality proponents (Deming 1986). Process improvement occurs in a variety of ways, including process redesign, process simplification, and process elimination. Since Deming and others have focused on processes and their role as part of the system, process improvement has received increased attention by decision makers. Most quality tools can be used to improve processes. Tools are used for documenting processes, gathering data about the processes, and proposing, implementing, and evaluating improvements to the processes. Hypothesis 4a: Quality tools application is positively related to process improvement.

Process improvement has long been cited as a probable source of employee satisfaction, and quality improvement has also been shown empirically to be associated with employee satisfaction (Adam and Foster 2001). In a study of federal employees, Yuan (1997) found that organizational characteristics were significantly related to employee satisfaction. Since process and quality improvement efforts increase organizational commitment and communication, it is expected that employee satisfaction will be improved. Quality and process improvement can be used as a career anchor (Leavitt 1996) leading to improved employee satisfaction. Since quality improvement leads to empowerment of employees and a leveling of job responsibilities in government organizations, employees are more satisfied (Stepina and Perrewe 1991). Both the practitioner literature and the authors’ own experience indicate that process improvements are associated with employee satisfaction. Hypothesis 5: There is a positive relationship between process improvement and employee satisfaction. Finally, process improvements should be associated with improvements in customer satisfaction. The focus on quality improvement in both for-profit industries and government has been monitored for the last several years using the American Customer Satisfaction Index (ACSI) (Fornell 1996). Wipper (1994) found a relationship between organizational improvement and customer satisfaction through a performance measurement effort at the Oregon Department of Transportation. Process improvement also promotes a sense of competence, achievement, and meaning among employees in the workplace, contributing to employee job satisfaction. In turn, satisfied employees are able and predisposed to provide good customer service (George 1998). Hypothesis 6a: There is a positive relationship between employee satisfaction and customer satisfaction. Hypothesis 6b: There is a positive relationship between process improvement and customer satisfaction.

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The data for this study were drawn from a general attitude survey of employees working for a municipality in the northwestern United States. The municipal structure included 11 departmental units: airport, community development, customer support, fire, legal, library, mayor’s office, parks, police, public works, and traffic court. Although all respondents worked within a single municipality, it should be noted that substantial employee and situational diversity existed. For instance, respondents’ educational attainment ranged from secondary students to holders of doctoral degrees. Job requirements also varied widely including technical, nontechnical, clerical, managerial personnel, and elected and appointed officials. Departments differed significantly, too. Police and fire personnel were unionized, while other city employees were not. Some departments operated in the downtown administrative offices, while others were located at various field sites. Departments also received funding from various sources, including federal, state, and local taxes, and from user fees. The city’s authority structure is decentralized at the department level. Entire departments operated as teams, similar to other municipal systems using team structures (for example, Coates and Miller 1995; Magee 1997). While this study is from one city, and is thus a limited scope design, the diversity within and between departments is believed to be sufficient for theory development (Eisenhardt 1989). The researchers delivered surveys to one coordinator in each department, who then distributed the surveys to all employees in their respective departments. Each coordinator collected the completed surveys and returned them to the researchers for tabulation. Of the city’s 1205 employees, 659 (55.3 percent) full-time employees participated, representing all 11 departmental units of municipal government. Response rates by department ranged from 37.8 percent in the mayor’s office to 91.9 percent in the legal department. Nearly one-third (32.1 percent) of the employees held bachelor’s degrees, while 11.3 percent also held graduate college degrees. Table 1 summarizes some of the other key demographic characteristics of the respondents. The distributions for of the respondents’ genders, ages, tenure, and education levels matched almost exactly the citywide employee statistics

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Table 1 Key demographic characteristics of respondents*. Age group

Percentage

under 30

11.90%

30-39

26.60%

40-59

47.70%

over 59

13.80

Years with the city

Percentage

less than 1 year

8.40%

1-5 years

33.20%

6-10 years

21%

more than 10 years

32.70

*Totals may not add up to 100 percent due to some employees not answering certain questions.

provided by the city’s human resources, thereby reducing expectation of nonresponse bias. The research relies on self-report measures. The questionnaire included several survey items that were not part of this research but were of interest to the city’s managers. Some of those items were drawn from employee surveys and training workshops administered previously. Thirty-nine survey items specific to this research were developed by the authors based on the contextual, enabler, and outcome variables included in the a priori model shown in Figure 1. These items and the survey format were pretested for face and content validity using a group of 12 employees and managers. Feedback resulted in some changes, and the revised surveys were then further pretested with a second group of 20 employees and managers, and a consensus was reached regarding content validity. All measures were Likert-type scales, using the summated average of selected items and scored on a range of 1 to 5, with 1 = “strongly disagree” to 5 = “strongly agree.” Five items measuring teamwork were examined with confirmatory factor analysis in an earlier study (see Howard, Foster, and Shannon 2000). The teamwork scale included five items pertaining to employees working together and participating on team projects and on process improvement teams, the extent that

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Procedures and Methods

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The Role of Quality Tools in Improving Satisfaction with Government teams were used in respective departments, and perceived team success. The five items reflected the teamwork variable quite well. While the chi-square statistic, which is vulnerable to large sample sizes, was significant (χ2 = 30.16, df = 6, p < .001), other fit indices confirmed the teamwork factor structure (that is, normed fit index (NFI) = 1.00, comparative fit index (CFI) = 1.00, Tucker-Lewis index (TLI) = 0.99, root mean square resides (RMSEA) < .08). Cronbach’s coefficient alpha, an indicator of internal consistency and reliability, was α = .81. The authors submitted the remaining 34 items to exploratory factor analyses, and rotated the six principle factors to an orthogonal solution. The resulting factor pattern is presented in the appendix. The items are assigned to the factor for which the factor loading is shown in bold type. They retained only those items with factor loadings exceeding .60 on their target factor and with factor loadings less than .40 on any other factor, with two exceptions (items 16 and 34). Both of these items loaded substantially higher on their intended factors than on secondary factors and both items improved the reliabilities of their respective scales. In addition, the authors wanted to maintain a minimum of three items per scale for purposes of justifying the structural equation model without imposing theoretically irrelevant constraints. Item 34 represented the third item in its respective scale. Items 17, 18, and 19 were dropped for cross-loading on multiple factors. The remaining items were used to create scales for six variables, three independent variables, and three dependent variables. Brief descriptions of all measures follow. Independent variables. The authors collected data to measure three independent variables (in addition to teamwork): leadership, quality tools application, and quality tools knowledge. The leadership scale corresponds to factor 1 in the appendix and consists of a weighted average of items 1-12 and includes items reflecting the extent to which leaders listen to ideas, are long-term oriented, and take an active role in quality improvement. The coefficient alpha for this measure was α = .97. Factor 2 corresponds to the variable, quality tools application, and relates to using a formal process to determine root causes, measuring and monitoring

quality tools application, and estimating the extent to which teammates use quality tools. Four items (13-16) produced a coefficient alpha of α = .84 for this scale. The quality tools knowledge scale (factor 3) includes six items (items 20-25) and reflects respondents’ understanding of various quality tools, such as structured brainstorming, unstructured brainstorming, statistics, team building, surveys, and flowcharts. Coefficient alpha was α = .89. Dependent variables. Three dependent variables are examined: perceived customer satisfaction, employee satisfaction, and process improvement. Perceived customer satisfaction is reflected in items 26-28 on factor 4. These items include comparing the customer satisfaction of one’s own department to that of other departments, overall customer satisfaction, and pride in the department’s ability to satisfy customers. The coefficient alpha for these three items was α = .82. Items 29-31 under factor 5 form the variable, employee satisfaction, and reflect self-ratings of improvement in satisfaction over the prior two years, feeling of importance to the city, and pride in being a member of the city government. Coefficient alpha was α = .79. Process improvement (factor 6) consists of a weighted average of items 32-34 relating to whether work is performed better than it was two years ago, whether customer response is better than two years ago, and whether overall customer service is better than two years ago. This approach is consistent with prior research in quality management (Adam 1993). Since continuous improvement methods result in gradual improvement, it takes time for customer satisfaction levels to improve (Narasimham, Ghosh, and Mendez 1994). These reflect self-assessments of the efficacy of process improvement efforts in achieving positive results. Coefficient alpha was α = .83. It is important to note that while some studies have found that employee perceptions of the predictors of team performance are often not the factors that predict actual team performance (Gladstein 1984), other studies have found that measures of perceived performance outcomes correlate positively at moderate-to-strong levels with objective measures of performance (Delaney and Huselid 1996).

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Results Table 2 shows descriptive statistics and bivariate correlations for each of the independent and dependent variables. The authors examined hypotheses with correlation coefficients, and then submitted the overall model to a structural equation path model, using SPSS-AMOS. The hypothesis test results are discussed in the following paragraphs. Hypotheses 1a, 1b, 1c, and 1d. These hypotheses involve the relationships between leadership and teamwork (1a), process improvement (1b), employee satisfaction (1c), and customer satisfaction (1d). As shown in Table 2, the correlations between the leadership composite variable and the other variables are significant at r = .62, r = .49, and r = .51, respectively (all p < .0001). This supports the supposition that leadership is an important antecedent to teamwork, process improvement, employee satisfaction, and customer satisfaction in government services. Hypothesis 2. The correlation between teamwork and quality knowledge is significant and positive at r = .31 (p < .0001) (see Table 1). This result supports the hypothesis predicting such a relationship. Hypothesis 3. Hypothesis 3 relates to the relationship between understanding and applying quality tools

knowledge. As shown in Table 2, the correlation between these two variables is r = .26 and statistically significant (p < .0001). While this relationship may seem obvious, the quality tools knowledge gained in this city government was garnered through a structured, long-term training program. This shows that such an approach is correlated with the application of quality tools. Hypotheses 4a, 4b, and 4c. These hypotheses apply to the relationships between quality tools application and the dependent variables of process improvement (4a), employee satisfaction (4b), and customer satisfaction (4c). As reported in Table 2, these relationships are all significant (all p < .0001), r = .49, r = .41, and r = .56, respectively. These results show a positive association between the use of quality tools and desired quality outcomes, as predicted. While these relationships have been assumed in much of the quality literature, they had not been previously tested in a public-sector setting. Hypothesis 5a. The correlation between process improvement and employee satisfaction is positive at r = .58 and significant (p < .0001). This supports the hypothesis that process improvement and employee satisfaction are linked. Hypotheses 6a and 6b. The correlations between customer satisfaction and employee satisfaction

Table 2 Descriptive statistics and correlations for all variables. N

Mean

S.D.

Leadership

Quality tools knowledge

Quality tools application

Teamwork

Process improvement

Leadership

632

3.36

1.08

(97)

Quality tools knowledge

659

2.72

0.92

32

(89)

Quality tools application

628

2.75

0.95

43

26

(84)

Teamwork

662

3.15

0.87

62

31

64

(81)

Process improvement

581

3.38

0.97

49

23

49

57

(83)

Employee satisfaction

656

3.57

0.87

51

19

41

62

58

(79)

Customer satisfaction

663

3.51

0.77

58

20

56

64

58

57

Note: Decimals omitted; numbers on diagonal in parentheses are coefficient alphas; all correlations are significant at p < .001.

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Employee satisfaction

Customer satisfaction

(82)

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Variable

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The Role of Quality Tools in Improving Satisfaction with Government Figure 2 Post hoc model of quality improvement in government. Contextual

Enablers Q-tools application

.18 .19 Teamwork .32

.59 Leadership

Outcomes

.31

.06 Q-tools knowledge .13

.61 .21

Process improvement .27

.37

Employee satisfaction .12

.22

Customer satisfaction

Note: All coefficients are standardized and significant at p < .05 or greater; paths not hypothesized are dotted.

quite well. This post hoc model is illustrated in Figure 2, along with all standardized parameter estimates. The chi-square statistic, though still significant, was also significantly smaller at χ2 = 65.50, df = 8, p < .001. Other fit indices were as follows: GFI = .97; AGFI = .91; NFI = .96; CFI = .96; TLI = .91; RMSEA = .10. In light of the fact that measures of all variables were taken from the same sources, presenting the possibility of common method bias, the authors conducted a second analysis following the guidelines of Hofmann and Stetzer (1996). That is, they randomly split their sample into two halves and used one half to estimate measures for the outcome variables and the other half to estimate measures for the contextual and enabler variables. Subsequently, they fit these modified data to the post hoc model. These data fit the model nominally better, although direct comparisons are not possible with no difference in degrees of freedom: χ2 = 21.16, df = 8, p < .05, GFI = .99, AGFI = .96, NFI = .98, CFI = .99, TLI = .97, and RMSEA = .05. While this technique compromises the variability in the data, it also suggests that common method bias was probably not a serious problem. These results suggest that the a priori model was under-specified, but with the addition of three more parameters was a reasonable approximation to the empirical data. In summary, the authors found support for their hypotheses. They found positive correlations between leadership and teamwork, process improvement, employee satisfaction, and customer satisfaction, as

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(6a) and process improvement (6b) were positive at r = .57 and r = .58, respectively and significant (both p < .0001). While these results are intuitively satisfying, they should be interpreted cautiously, as all three variables are based on self-reported measures. Structural equation model. The authors examined the efficacy of the overall model by submitting the relationships specified in Figure 1 to a structural equation analysis. While no result would provide definitive proof of a predicted pattern of relationships, because alternative specifications might explain the data as well as theirs, this provides a more rigorous test of the hypotheses as a set. Because SPSS-AMOS will not produce modification indices when processing structural equation models with missing data, the authors substituted mean scores for items missing data prior to their aggregation as summated means scales. The fit indices for the proposed model illustrated by Figure 1 indicate a poor fit to the data. The chi-square statistic was significant (χ2 = 361.73, df = 9, p < .001). Other fit indices are: goodness of fit index (GFI) = .89; adjusted goodness of fit index (AGFI) = .66; NFI = .78; CFI = .78; TLI = .50; RMSEA = .24. Modification indexes produced along with the output, however, suggested that the problem was not that the parameters the authors proposed were improper, but rather that there were additional parameters they did not propose that needed to be accounted for. In particular, they added direct relationships from the teamwork variable to employee satisfaction and to quality tools application, and from the leadership variable to quality tools knowledge. (In retrospect, it would have been consistent with the authors’ a priori model if the authors had proposed direct relationships between both contextual variables – leadership and teamwork – and both enabler variables – quality tools knowledge and quality tools application. In addition, the authors could have anticipated a direct relationship between teamwork and employee satisfaction, since studies have previously reported that employees working in teams were more satisfied with their jobs than employees in the same firms who were not working in teams (Kirkman and Rosen 1999). Had they done so, their a priori model would have been identical to the post hoc model the authors report here.) Subsequent to adding these three parameters, the model fit the data

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The Role of Quality Tools in Improving Satisfaction with Government predicted in hypothesis 1. They found positive relationships between teamwork and quality tools knowledge, and between quality tools knowledge and quality tools application, as predicted in hypotheses 2 and 3, respectively. As predicted by hypothesis 4, the authors also found positive relationships between quality tools application and all three outcome variables, process improvement, employee satisfaction, and perceived customer satisfaction. They also found direct relationships between process improvement and employee satisfaction, as predicted by hypothesis 5. Finally, the authors found positive relationships between employee satisfaction and customer satisfaction, and between process improvement and customer satisfaction, as predicted by hypothesis 6. In addition, the structural equation analyses indicated that there were significant direct relationships between teamwork and quality tools application and employee satisfaction, and between leadership and quality tools knowledge, relationships the authors did not predict.

Discussion and Conclusions This article presents a study of quality improvement in a city government setting. The research shows that for this city government, employees believed that quality knowledge was necessary for improving quality. The results showed that departmental leadership was positively associated with teamwork, process improvement, and employee satisfaction. Quality knowledge, if followed up with application, can be effective in improving processes. These improvements, with teamwork, led to improved employee satisfaction and customer satisfaction. From a managerial perspective, the authors find that for quality tools training to be effective, it should be followed up by application through team processes. Leadership is critical to the development of quality tools knowledge, but teamwork is the vehicle through which this knowledge is translated into application. Both leadership and teamwork, therefore, are important contextual concerns for quality management in the public sector. The findings associated with improved employee satisfaction are important for government agencies since budget limitations often require nonmonetary approaches to improve morale. This also suggests that

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government workers are much like private-sector workers in that they want to perform work effectively and they feel satisfaction when they achieve positive results. They also perceive that they are serving the public better as a result of process improvement. Since these results were gathered within a single city government at one point in time, the normal caveats relative to case studies and cross-sectional data apply. Care should be taken in generalizing these results, as they could have been affected by some unique aspect of this city or its context. The authors’ measures were also original and lacking validation evidence, and composed entirely of employee self-reports, therefore subject to common method bias. At the same time as the survey data collection, however, a series of focus groups was conducted in each of the 11 departments. The results of these focus group sessions generally validated the survey results. In addition, results of the exploratory factor analyses offer support for the independence, convergent validity, and discriminant validity of most scales, while the split-sample structural equation analysis suggests that common source bias may not have been a severe problem. Finally, although the authors cannot eliminate limitations to the generalizability of their results, they believe that the departmentteam structure of the municipal government organization in this study is fundamentally similar to other team-based municipal government organizations, and there were no significant historical events that might set this city apart from others. It should be noted that this study was conducted in a city government with an established quality management program, which is not always the case. Larger sample studies from a large group of government agencies are called for to further validate these findings. REFERENCES Adam, E.E. Jr., 1994. Alternative quality improvement practices and organizational performance. Journal of Operations Management 12, no. 1: 27-44. Adam, E. E. Jr., and S. T. Foster Jr. 2001. Quality improvement approach and performance: Multi-site analysis within a firm. Journal of Quality Management 5, no. 1: 1-16. Benson, G., J. Saraph, and R. Schroeder. 1991. The effects of organizational context on quality management. Journal of Operations Management 37, no. 9: 1107-1124.

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Foster, S. T. Jr., and C. R. Franz. 1998. On the differences between information systems users and analysts: Managing Perceptions of Systems Quality. Journal of Quality Management 3, no. 1: 63-77. Foster, S.T. Jr., and R. Viano. 1996. Using quality management to improve customer responsiveness at the internal revenue service. Production and Inventory Management Journal 37, no. 2: 37-43. George, J. M. 1998. Salesperson mood at work: Implications for helping customers. The Journal of Personal Selling 18, no. 3: 23-30. Gladstein, D. L. 1984. Groups in context: A model of task-group effectiveness. Administrative Science Quarterly 29: 499-516. Hofmann, D. A., and A. Stetzer. 1996. A cross-level investigation of factors influencing unsafe behavior and accidents. Personnel Psychology 49: 307-339. Howard, L. W., and S. T. Foster Jr. 1999. The influence of human resource practices on empowerment and employee perceptions of management commitment to quality. Journal of Quality Management 4, no. 1: 5-22. Howard, L. W., S. T. Foster, and P. Shannon. 2000. Team climate and teamwork in government: The power of embedded leadership. In Proceedings of the 4th International Workshop on Teamworking (IWOT4): 83-119. Keck, M. E. 1996. Total quality management teams in the office of administrative services, U. S. department of the interior: A success story. International Journal of Public Administration 19: 1811-1844. Kirkman, B. L., and B. Rosen. 1999. Beyond self-management: Antecedents and consequences of team empowerment. Academy of Management Journal 42, no. 1: 58-74.

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BIOGRAPHIES S. Thomas Foster is a professor of quality and operations management at Boise State University. He has a doctorate from the University of Missouri-Columbia. He has been published in journals such as Decision Sciences, International Journal of Production Research, Journal of Quality Management, International Journal of Quality and Reliability Management, Quality Management Journal, and Quality Progress. Foster has consulted for a number of companies including Hewlett-Packard,

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Foster article 6/18/02 10:01 AM Page 30

The Role of Quality Tools in Improving Satisfaction with Government Trus Joist Macmillan, Cutler-Hammer/Eaton Corp., Heinz Frozen Foods, Qwest Corporation, Healthwise Corporation, and the U. S. Department of Energy. Foster served on the 1996 and 1997 board of examiners for the Malcolm Baldrige National Quality Award. He is the author of Quality Management: An Integrative Approach. Foster is founder of www.freequality.org and was awarded the ASBSU 2000 Outstanding Faculty Award. He can be reached by e-mail at [email protected] . Larry W. Howard is an assistant professor in the management and marketing department of Middle Tennessee State University’s Jennings A. Jones College of Business. He received his doctorate in business administration from the University of Missouri and his bachelor’s and master’s degrees from Western Michigan University. Prior to pursuing doctoral studies, Howard was a general manager with two Fortune 100 companies for six years, and a full-time management consultant for three years. He still consults on occasion with public and private organizations around the world in team building, organizational change and development, and managing organizational justice. Recently, he has been involved in a federal government initiative examining public policy implications of management practices for 21st century leadership and governance. Howard has presented his research at professional conferences and has published book chapters and articles in journals such as Academy of Management Journal, Journal of Business and Psychology, Journal of Quality

Management, Journal of Education for Business, International Journal of Organizational Analysis, and others. He can be reached by e-mail at [email protected] . Patrick W. Shannon is a professor of operations management and department chair of the networking, operations, and information systems department in the College of Business at Boise State University. He teaches graduate and undergraduate courses in business statistics, quality management, and production and operations management, and has received several alumni teaching awards. In addition, Shannon has lectured and consulted in the statistics, operations management, and quality areas for more than 20 years. Among his consulting clients are Boise Cascade Corporation, Hewlett-Packard, PowerBar Inc., Potlatch Corporation, Woodgrain Millwork Inc., J. R. Simplot Company, and others. Shannon has coauthored several university-level textbooks including Business Statistics: A Decision Making Approach, 5th edition; A Course in Business Statistics, 3rd edition; and Introduction to Management Science. He has also published ar ticles in such journals as Business Horizons, Transpor tation Research Record, Inter faces, Journal of Simulation, Journal of Production and Inventory Control, Quality Progress, and Journal of Marketing Research. Shannon has his bachelor’s and master’s degrees from the University of Montana and his doctorate from the University of Oregon. He can be reached by e-mail at [email protected] .

APPENDIX Factor Pattern after Varimax Rotation1. Factor2

1 Dept. head effectively communicates with me.

84

2 Dept. head is willing to change.

83

3 Dept. head takes active role in quality improvement.

83

4 Dept. head inspires employee trust.

83

5 Dept. head is long-term oriented.

81

6 Dept. head has good knowledge of quality concepts.

81

7 Dept. head respects me.

80

8 Dept. head listens to my ideas.

79

9 Dept. head believes in continuous improvement.

78

10 Dept. head is involved in quality planning.

77

11 Dept. head supports improvements in customer service.

75

12 Dept. head supports quality improvement teams.

75

30 QMJ VOL. 9, NO. 3/© 2002, ASQ

2

3

4

5

6

30

32 35

© 2002, ASQ

1

Foster article 6/18/02 10:01 AM Page 31

The Role of Quality Tools in Improving Satisfaction with Government Factor Pattern after Varimax Rotation1. (continued) Factor2 1

2

13 We use the tools of quality.

81

14 We use process to determine cause of problems.

77

15 Dept. members are familiar with tools of quality.

31

16 Dept. has system for measuring customer service.

3

4

41

62 48

54

18 Dept. focuses on satisfying customers.

40

53

31

19 Have effective system for resolving customer complaints.

35

48

47

20 I have been taught/used flowcharts.

81

21 I have been taught/used structured brainstorming.

80

22 I have been taught/used unstructured brainstorming.

80

23 I have been taught/used surveys.

79

24 I have been taught/used team building.

72

25 I have been taught/used customer event diagrams.

67

26 Dept. is ahead of other depts. in customer service.

31

68

27 I am proud of the work performed in department. 31

66

45

65

30

29 I feel pride when I say I work for city.

76 34

65

31 I am more satisfied with my job than two years ago.

61

32 We respond more quickly to customer needs than two years ago.

71

33 Customer service is better than two years ago.

39

34 Work is performed better than two years ago. Variance explained

2

64

43 27.5

12.3

57 11.8

7.8

7.3

6.5

Decimals omitted; loadings lower than 30 omitted; bold identifies scale items. Factor 1 = leadership; factor 2 = quality tools application; factor 3 = quality tools knowledge; factor 4 = perceived customer satisfaction; factor 5 = employee satisfaction; factor 6 = process improvement.

www.asq.org 31

© 2002, ASQ

30 I am an important part of city government.

1

6

73

17 Dept. effectively communicates with customers.

28 Overall, my department satisfies our customers.

5

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