Correlation among Quality Variables

20 QMJ VOL. 9, NO. 3/© 2002, ASQ

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 team- work are important contextual variables for quality improvement in the public sector.

Key words: leadership, organizational context, quality management, quality tools, teamwork

INTRODUCTION Much has been written about infrastructural and envi- ronmental variables in quality improvement in busi- ness (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 disagree- ment among these research models regarding the vari- ables 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 con- text (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 exist- ing 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 differ- ence is the lack of profit in government. W. Edwards Deming (1986) often alluded to the profit issue as a dif- ferentiator 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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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 gov- ernment? Is it the taxpayer, the elected leader (for exam- ple, executive branch), the legislature (they allocate resources), or the individuals who directly access govern- ment services (such as the licensee to a motor-vehicle division)? In fact, government entities may have a num- ber 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 pres- sure 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 inter- nally 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 indus- try, research has not demonstrated the efficacy of these tools in improving government service. In fact, Deming cautioned against applying modern quality manage- ment approaches to government (Deming 1986).

This article presents results from a study per- formed in a city government. The city in question had been implementing teams and quality improve- ment 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 quali- ty tools effective in improving quality-related out- comes 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 con- tribution of this article is to examine the role of qual- ity tools in effective implementation of quality improvement in a government setting.

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 possi- ble 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 con- trol, basic tools, automation, and supplier involvement in improvement (Benson, Saraph, and Schroeder 1991).

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Teamwork

Contextual Enablers Outcomes

Leadership

Q-tools application

Q-tools knowledge

Process improvement

Employee satisfaction

Customer satisfaction

H1a

H2

H4

H1d

H1c

H3 H5

H6a H6bH1b

Figure 1 A priori quality improvement model.

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Outcome variables represent the desired outcomes of quality tools application. Outcomes often mentioned in the literature include process improvement, employ- ee satisfaction, and customer satisfaction. The follow- ing paragraphs address the relationships between con- text 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 suc- cessfully 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, coordi- nation, communication, and empowerment. Leadership promotes the implementation of teamwork by providing required resources and assets, and by sym- bolically communicating top-level commitment to quality tools application. Leadership is considered in the literature to be an antecedent to process improve- ment (Deming 1986). Also, positive leadership is asso- ciated with employee satisfaction (Howard and Foster 1999). By inference, perceptions of leadership commit- ment 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.

Teamwork. The second contextual variable is teamwork. As with the manufacturing and services sec- tors 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 num- ber 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, knowl- edge of how and when to apply the quality tools is rein- forced. 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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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 applica- tion refers to the continued use of quality tools after the training is completed. Knowledge alone will be inade- quate 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 improve- ment. Foster and Franz (1998) proposed the use of quali- ty 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 posi- tively 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 pro- ponents (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 orga- nizational 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 sever- al years using the American Customer Satisfaction Index (ACSI) (Fornell 1996). Wipper (1994) found a relationship between organizational improvement and customer satisfaction through a performance meas- urement 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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Procedures and Methods The data for this study were drawn from a general atti- tude 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 employ- ee and situational diversity existed. For instance, respon- dents’ 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 appoint- ed 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 munici- pal 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 suffi- cient 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 coor- dinator 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 par- ticipated, 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 charac- teristics of the respondents. The distributions for of the respondents’ genders, ages, tenure, and education levels matched almost exactly the citywide employee statistics

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 team- work 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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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.

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teams were used in respective departments, and per- ceived team success. The five items reflected the team- work variable quite well. While the chi-square statistic, which is vulnerable to large sample sizes, was signifi- cant (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 coef- ficient 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 princi- ple factors to an orthogonal solution. The resulting fac- tor 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 fac- tors 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 theoreti- cally 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 vari- ables, 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 corre- sponds 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’ under- standing 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 vari- ables 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 stud- ies have found that measures of perceived performance outcomes correlate positively at moderate-to-strong lev- els with objective measures of performance (Delaney and Huselid 1996).

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Results Table 2 shows descriptive statistics and bivariate corre- lations for each of the independent and dependent vari- ables. The authors examined hypotheses with correla- tion coefficients, and then submitted the overall model to a structural equation path model, using SPSS-AMOS. The hypothesis test results are discussed in the follow- ing 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, respec- tively (all p < .0001). This supports the supposition that leadership is an important antecedent to team- work, process improvement, employee satisfaction, and customer satisfaction in government services.

Hypothesis 2. The correlation between team- work and quality knowledge is significant and positive at r = .31 (p < .0001) (see Table 1). This result sup- ports the hypothesis predicting such a relationship.

Hypothesis 3. Hypothesis 3 relates to the relation- ship 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 govern- ment 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 appli- cation 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 predict- ed. While these relationships have been assumed in much of the quality literature, they had not been pre- viously 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

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Table 2 Descriptive statistics and correlations for all variables.

Variable N Mean S.D. Leadership Quality tools Quality tools Teamwork Process Employee Customer knowledge application improvement satisfaction satisfaction

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 (82)

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

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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 vari- ables are based on self-reported measures.

Structural equation model. The authors examined the efficacy of the overall model by submit- ting the relationships specified in Figure 1 to a struc- tural 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 structur- al equation models with missing data, the authors sub- stituted 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 index- es 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 knowl- edge. (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 satisfac- tion, since studies have previously reported that employ- ees 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

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 sig- nificantly 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 compar- isons 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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Teamwork

Contextual Enablers Outcomes

Leadership

Q-tools application

Q-tools knowledge

Process improvement

Employee satisfaction

Customer satisfaction

.59 .61

.19

.31

.21

.37

.13

.06 .27

.12 .22.32

.18

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

Figure 2 Post hoc model of quality improvement in government.

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predicted in hypothesis 1. They found positive relation- ships between teamwork and quality tools knowledge, and between quality tools knowledge and quality tools application, as predicted in hypotheses 2 and 3, respec- tively. 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 relation- ships between process improvement and employee sat- isfaction, 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 pre- dicted by hypothesis 6. In addition, the structural equa- tion analyses indicated that there were significant direct relationships between teamwork and quality tools application and employee satisfaction, and between leadership and quality tools knowledge, rela- tionships 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 knowl- edge was necessary for improving quality. The results showed that departmental leadership was positively asso- ciated 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 lead- ership and teamwork, therefore, are important contex- tual 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

government workers are much like private-sector work- ers 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 com- posed entirely of employee self-reports, therefore subject to common method bias. At the same time as the sur- vey 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, con- vergent 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 generaliz- ability of their results, they believe that the department- team structure of the municipal government organiza- tion 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.

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BIOGRAPHIES

S. Thomas Foster is a professor of quality and operations man- agement at Boise State University. He has a doctorate from the University of Missouri-Columbia. He has been published in jour- nals such as Decision Sciences, International Journal of Produc t ion Research, Journal o f Qual i ty 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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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 foster@boisestate.edu .

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 gen- eral manager with two Fortune 100 companies for six years, and a full-time management consultant for three years. He still con- sults on occasion with public and private organizations around the world in team building, organizational change and develop- ment, 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 Qual i ty

Management, Journal of Education for Business, International Journal of Organizational Analysis, and others. He can be reached by e-mail at lhoward@mtsu.edu .

Patrick W. Shannon is a professor of operations management and department chair of the networking, operations, and infor- mation 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 teach- ing 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 Corporat ion, Hewle t t -Packard, PowerBar Inc. , Pot la tch Corporation, Woodgrain Millwork Inc., J. R. Simplot Company, and others. Shannon has coauthored several university-level text- books inc luding Business S ta t i s t ics : A Decis ion Making Approach, 5th edition; A Course in Business Statistics, 3rd edi- tion; and Introduction to Management Science. He has also pub- l i shed ar t ic les in such journals as Business Hor izons, Transpor ta t ion Research Record, In ter 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 pshannon@boisestate.edu .

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APPENDIX Factor Pattern after Varimax Rotation1.

Factor2

1 2 3 4 5 6

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 30

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 32

11 Dept. head supports improvements in customer service. 75 35

12 Dept. head supports quality improvement teams. 75

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Factor Pattern after Varimax Rotation1. (continued)

Factor2

1 2 3 4 5 6

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 73

16 Dept. has system for measuring customer service. 62 41

17 Dept. effectively communicates with customers. 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. 66 45

28 Overall, my department satisfies our customers. 31 65 30

29 I feel pride when I say I work for city. 76

30 I am an important part of city government. 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 64

34 Work is performed better than two years ago. 43 57

Variance explained 27.5 12.3 11.8 7.8 7.3 6.5

1 Decimals omitted; loadings lower than 30 omitted; bold identifies scale items. 2 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.

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