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The relationship between quality management, strategic control systems and financial performance of Malaysia local government.


As with other Malaysia governmental institutions, most of local governments in Malaysia, over the past years have seriously practiced the Quality Management (QM). As widely reported in the QM scholarly literature, QM has contributed to the betterment of many organizations worldwide. As documented by Hunt (1995), among the benefits that can be gained from the implementation of QM into local government is a better financial performance, particularly a larger cost savings. However, in an empirical study, Khairul et al., (2001) reported that not all of QM practices were significantly related to the financial performance of Malaysia local government. The insignificant relationship between most of the quality management practices and financial performance in the study by Khairul et al. (2001), perhaps, can be linked with the premise of contingency theory. As postulated by the contingency theory, the unsuccessful implementation of a strategy is due to the mismatch or misfit between the strategy and the control systems. As proven in the literature, the implementation of QM, that is a universal strategy for the survival of many organizations, requires the QM organization to implement QM focused control systems or Strategic Control Systems (SCS) (Andersen et al., 2004). In line with the strategic management as well as contingency theory scholars (Selto et al., 1995; Van de Ven & Drazin, 1985), this present study postulates that the strategy and controls systems must somehow fit together if the strategy to perform well. Moving from the findings of study by Khairul et al., (2001) as well as the perspective of 'fit as mediation' as discussed by Venkatraman (1989), this present study revisit the issue of the relationship between QM and financial performance (FP) of Malaysia local government by investigating the mediating role of SCS in explaining the relationship between QM and FP. The rest of this paper is reported as follows. The next section describes the problem statement, followed by the research objectives. This paper then presents literature review and proposing the hypotheses of the study. This is followed by the research model depicting the interrelationships between the variables understudy. Research methodology and statistical analysis are then detailed. This is followed by the discussion of findings. The last three sections of this paper present limitation of the study, suggestion for future study as well as implications and conclusion of the study.

Problem Statement

Although the factors that contribute to the financial performance of local government are various and complex, but the QM has been widely recognized in the literature as among the important determinant of good financial performance. Therefore, the relationship between QM and financial performance of local government provides an interesting phenomenon for scientific research. Although the amount of study on the relationship between QM and financial performance that reported in the literature are encouraging, but the issue of the interrelationship between QM, control systems and performance was not fully explored. Given to the shortcomings in the literature concerning the issue of control systems in explaining the relationship between QM and financial performance, this study undertakes to explore: To what extent QM is related to SCS to gain good financial performance?

Research Objectives

In order to shed light on the issue of the study, this study aimed to achieve the following objectives: (1) to investigate the relationship between QM and FP, (2) to determine the relationship between QM and SCS, (3) to assess the relationship between SCS and FP, and (4) to study the structural relationship between QM, SCS and FP.

Literature Review and Hypotheses Development

Essentially, financial performance of an organization can be enhanced by two ways, namely: increasing revenue or decreasing cost. As proven in the QM literature, both of these approaches could be fulfilled by implementing QM (Flynn et al., 1995; Powell, 1995). As elaborated by QM scholars, the institutionalization of QM encourages an organization to continually seeking for better ways to achieve better financial performance (Juran, 1988). By practicing QM, an organization should be able to improve the performance of service delivered and in turn to satisfy their customer reasonable expectation. For a local government, a satisfied customer, perhaps, would have better local sense and further more willing to pay assessment tax, parking charge, summonses bills and the like. Additionally, a local government with a good service would be able to attract new developers to establish new development areas, new residents from the higher socioeconomics as well as new investors to set up factory, office or shop, which in turn contributes to higher tax revenue for a local government. As documented in the scholarly literature, the institutionalization of QM would significantly lead to less work error, less rework, as well as better work process (Flynn et al., 1995), which in turn lead to higher cost saving. Given to the superb benefits that can be gained from the QM implementation as promoted in the literature, particularly for increasing revenue and decreasing cost, this study hypothesizes that:

[H.sub.1]: There is a relationship between QM and FP Apart from successful organizations, there are considerable organizations reported in the contemporary literature that had not successfully implemented the QM (Madu et al., 1995). Although QM has been widely recognized as an excellent strategy, but the way in which QM may be implemented is another critical determinant for its success. As documented by Madu et al., (1995), there was no generic recipe for implementing QM. Each organization must analyses and understands their unique structure and environment before employing Qm. Given that, the control and monitoring process of QM implementation are necessary. Control systems facilitate the process of organizing the resources of the organization as well as instilling the motivation among the staff to execute the predetermined strategy. Normally, in a big organization, the strategy is formulated at top management level and the responsibility for strategy implementation is given to the lower management level. Consequently, this segregation practice would lead to the problem of lack of goal congruence between the both parties. In order to improve the level of goal congruence among organizational members, the institutionalization of control systems is necessary (Merchant, 1982). The pivotal role of control systems for the purpose of strategy implementation was firmly addressed by Goold & Quinn (1993). According to them, without the presence of suitable control systems, even excellent strategy could easily be blown. As documented in the literature, many scholars have investigated and reported the strong relationship between QM and control systems (e.g., Daniel & Reitsperger, 1991; Ittner & Larcker, 1997). In line with the above discussion, this study hypothesizes that:

[H.sub.2]: There is a relationship between QM and SCS Control is the process by which top managers monitor the activities being performed by organizational members to ensure that organizational plans are being rightly or effectively executed. It involves several processes and each process is interrelated between each other. All of these processes constitute control systems, and strategic control systems refer to strategy focused control systems. However, control systems have been defined in various ways by control systems scholars (e.g. Kaplan & Norton, 1992; Merchant, 1982). Basically, strategic control systems involve communication, linking reward to predetermined objectives, alignment of budget control with predetermined goals, reevaluating the existing work procedure to be aligned with the intended strategy as well as reviewing and monitoring the deviation between required performance and actual performance. Therefore, these functions of communicating, linking, evaluating and reviewing are intended to ensure the achievement of required performance. Any deviation occurred and detected during this monitoring process would be corrected, so that the final negative impact could be prevented before it occurs. Given that, strategic control systems do provide a good vehicle for managers in dealing with unplanned situation that would jeopardize the strategy executed. Therefore, strategic control systems ensure an organization achieves their predetermined goal using the minimum amount of resource or achieves a maximum benefit with a specified amount of resource. In other words, strategic control systems ensure an organization being efficient and effective. Therefore, this study hypothesizes that:

[H.sub.3]: There is a relationship between SCS and FP Empirically, QM researchers have reported that the imitators firms in implementing QM perform worse than the firm who adapt QM to their unique environment (Llorens Montes & Verdu Jover, 2004; Yasin, Alavi, Kunt & Zimmerer, 2004). In other words, their findings provided evidence that the relationship between QM and performance was dependent on the third variable. As documented in an extensive review paper by Sila and Ebrahimpour (2002), the previous studies on the relationship between QM and performance were inconclusive. In discussing the emergence of third variable between the relationship between QM and performance, management accounting literature, among others, is replete with the discussion on the control systems variable. Given to the critical function of control systems for the purpose of strategy implementation, many management accounting scholars had criticized the traditional control systems such as budgetary control systems as discouraged the long term focus of QM (e.g., Otley, 1999; Rangone, 1997). In this regard, a more strategy focused control systems have been proposed as a means to deal with the requirement of QM (Andersen et al., 2004). According to Horovitz and Thietart (1982), the existence of suitable control systems is among the prerequisite for a strategy to be successful. In line with management accounting

Bucharest, Economic and Administrative Series, Nr. 3 (2009) 23-40 researchers (e.g., Moores & Chenhall, 1994; Otley, 1980), the strategic management literature proposes that the relationship between organizational strategy and performance is mediated by the control systems variable (Venkatraman, 1989). Given to wide discussion in the literature that recognizes the structural relationship of strategy [right arrow] control system [right arrow] performance, this study hypothesizes that:

[H.sub.4]: The structural relationship of QM [right arrow] SCS [right arrow] FP has a good fit

Research Model

In consistent with the objectives of the study, the following research model was developed as presented in Figure 1. The research model depicts the relationship between three variables understudy, namely: QM, SCS and FP. These inter relationships were postulated accordingly with the contingency theory. The premise of contingency theory is that the strategy and control systems must somehow 'fit' together if the strategy to be successfully implemented. In discussing the concept of 'fit', strategic management literature is replete with the divergent ideals. However, in this study, the research framework was developed based on the perspective of fit as mediation (Venkatraman, 1989). This perspective assumes the existence of mediating variable between a strategy variable and the performance variable.



This section describes the methodological issues of the study such as unit of analysis, population and sampling, data collection procedure, questionnaire design as well as measurement.

Unit of Analysis

Unit of analysis of the study were departments attached to City Council (CC) and Municipal Council (MC) in Peninsular of Malaysia. This study used departments attached to CC and MC due to their bigger size and more complex structure than departments attached to District Council. Previous scholars used to select big organizations in studying control systems related issues (e.g. Mohd Yusoff, Daing Nasir & Ahmadu, 2001; Widener, 2004). It is expected that, bigger organizations would have more prevalent control systems, thus appropriate for empirical study. From another perspective, the QM literatures report the significant difference on the relationship between QM and performance across different size of organizations (Gustafsson & Nilson, 2003; Madu et al., 1995). By focusing on the bigger size of local government, the effect of size can be controlled, thus providing more convincing findings.

Population and Sampling

The population of this study consisted of 305 departments attached to seven CC and 25 MC. Eighty five (85) departments belong to CC. Two hundred and twenty (220) departments belong to MC. For the purpose of sampling, the stratified cluster sampling was used. Even the objective of this study was not to differentiate the information between each local government, but stratified cluster sampling approach still could be regarded as the most efficient design following the nature of the study. As discussed by Sekaran (2003), for groups with intragroup heterogeneity and intragroup homogeneity, a cluster sampling is most appropriate. Generally, Malaysia local governments are governed under the same act, Local Government Act 1976 and having similar objectives and types of activities, but within each local government, there are various departments with different kind of objectives, function and activities. However, cluster sampling exposes to larger error than other probability sampling. This larger error occurs because the selection of each sampling unit within the cluster was dependent on the selection of the cluster, although the cluster was randomly selected. Therefore, in order to offset the loss of precision from cluster sampling, selection of cluster was stratified according to the status of local government. This procedure refers to stratified cluster sampling (Davis, 2000), where it select cluster at random from prespecified strata. Therefore, all 85 departments attached to CC were selected due to small number of department involved. The balance numbers of sample, a total of 165 were randomly selected from 220 departments attached to MC. Therefore, 250 departments were selected for this study. Basically, a sample size of 200 would be required to generate valid results for structural equation modeling (SEM) analysis (Marsh et al., 1988). However, by taking into consideration the probability of non response, the higher sample size than 200 was considered necessary. Out of 250 questionnaires distributed, only 205 of it, which is 82% were returned and completed for analysis.

Data Collection Procedure

Data of this study was collected by using personally administered approach. However, to approach each targeted respondent personally, it seems impractical

Bucharest, Economic and Administrative Series, Nr. 3 (2009) 23-40 due to the hectic working schedule of targeted respondents. Therefore, a personal contact was limited between researcher and administration department of each local government. They were requested to be a coordinator to administer the research instruments within their local government. The purpose and the topic of the study were introduced and explained to the coordinator. A cover letter explaining the purpose of this study was also attached. Time frame up to two weeks was given to the targeted respondents. Follow up calls were made to the coordinator. On completion, the research instruments were to be forwarded to the researcher in a 'postal express' envelope provided by the researcher, so that the times taken for reply would be shorter.

Questionnaire Design

Closed ended questions were used in this study, so that respondent would be able to make quick response to choose among the listed alternatives. The questionnaire was also referred to a research method lecturer for his judgmental analysis of the instrument. The questionnaire was printed in a booklet format, so that looks more handy, tidy and professional.


All items were anchored on a five point Likert scales: (1) strongly disagree; (2) disagree; (3) neither agree nor disagree; (4) agree; and (5) strongly agree. An average score was calculated for each of the construct based on their respective items. A higher score indicates a higher practice of QM, a more intense use of SCS and a better FP. For the purpose of SEM, these items were treated as observed variables, while the related constructs were treated as latent variables, as depicted in figure 2. In other words, all of the constructs of the study were treated as one order factor constructs.

Quality Management (QM) was measured using five items derived from the scholarly literature as follows:

* QM1 -Commitment of top management on quality initiatives (Ahire, Golhar & Waller, 1996)

* QM2-Customer feedback is used effectively (Powell, 1995)

* QM5-Engaged in extensive benchmarking practices (Li, Andersen & Harrisson, 2003; Powell, 1995)

* QM4-Quality related training is adequate (Anderson, Rungtusanatham, Schroeder & Devaraj, 1995; Li et al., 2003)

* QM5-Quality related data is well collected (Anderson et al., 1995)

Strategic Control Systems (SCS) was measured using five items derived from control systems literatures as follows:

* SCS1- QM is translated into action that can be communicated (Ittner & Larcker, 1997)

* SCS2- Linking rewards to QM (Goold & Quinn, 1993; Kaplan & Norton, 1996)

* SCS3- Resource allocated based on QM (Goold & Quinn, 1993; Kaplan & Norton, 1996)

* SCS4- Activities are not contributing to QM objectives are eliminated (Kaplan & Norton, 1996)

* SCS5- Management review reports on QM results (Goold & Quinn, 1993; Ittner & Larcker, 1997)

Financial Performance (FP) was measured using three items derived from various scholarly sources as follows:

* FP1 -Having good budget management (Chan, 2004; Kanji, 2002)

* FP2-Operation cost saving (Chan, 2004; Kanji, 2002)

* FP3-Increasing in productivity (Chan, 2004; Van de Ven & Ferry, 1980)

Statistical Analysis

This section reports the results of statistical analysis such as reliability and validity test as well as hypotheses testing. Statistical analysis was performed using software of SPSS (Statistical Package for Social Science) and AMOS (Analysis of Moment Structure).

Reliability and Validity Test

The first step in the statistical procedure that needs to be undertaken in order to test the construct reliability and construct validity is testing unidimensionality of the construct (Dunn, Seaker & Waller, 1994). A widely used method in the literature for the purpose of unidimensionality test is item-construct correlation. In this paper, the item-construct correlation was performed according to the procedure developed by Nunnally (1978). Therefore, the construct score was measured by averaging the score of the items of related construct. For instance, the score for QM was measured by averaging the score for five items of QM construct. As tabulated in table 1, all items (indicated in bold) have the highest correlation with the construct they intend to measure. Statistically, the findings indicated that all items in the research instrument had been appropriately assigned to their respective construct. Therefore, the condition of unidimensionality of the constructs was satisfied.

The reliability of a measurement is primarily a matter of consistency. In other words, if a measurement was reliable, then the same result can be produced when the same object are measured repeatedly. In line with the most scholarly research papers, this study used cronbach alpha coefficient for evaluating the reliability of the instrument. A high cronbach alpha indicates that items of the construct do have equal share in describing that specific construct. As presented in table 2, the cronbach coefficient for all constructs understudy indicating that there was a good level of reliability among the constructs. As tabulated, all alpha values of all constructs exceed the 0.7 cut off level (Nunnally, 1978).

As documented in the research method texts, the reliability is a prerequisite of good measurement but not the sufficient indicator of the goodness of a measurement (Sekaran, 2003). As such, one measurement could have high reliability, but it is not a valid measurement if it did not measure the intended construct. Thus, validity refers to the extent to which a measurement measures what it is intended to measure (Nunnally, 1978). In this study, the construct validity was examined by using factor analysis. The KMO (Kaise-Meyer-Olkin) value of above 0.50 is an indicator to ensure the appropriateness of factor analysis procedure as documented by Kaiser (1970) and then cited by Hair, Anderson, Tatham & Black (1998). As tabulated in table 2, the KMO value ranged between 0.608 and 0.778. In other words, the analysis of KMO shows that the factor analysis was appropriate. Additionally, the results of factor analysis show that all items have a high factor loading on their first order factor. The high factor loading implies that the items are critical to the assigned construct. In assigning factor loading, the minimum threshold of 0.30 was applied to the analysis, as suggested by Hair et al. (1998). Table 2 also presents that first factor of all constructs understudy have an eigenvalue higher than one (1.000).

Hypotheses Testing

This section discusses on the hypotheses testing. Given that, SEM analysis using software of AMOS was employed for examining the structural model of the study. Prior to test the hypotheses of the study, the model overall fit must be established (Bollen, 1989). In order to evaluate the model overall fit, a series of indexes provided by AMOS were examined. Model fit determines the degree to which structural equation model fits to the data. Model fits indexes that are commonly used are goodness of fit index (GFI), normed fit index (NFI), incremental fit index (IFI), comparative fit index (CFI), as well as Tucker Lewis index (TLI).


Bucharest, Economic and Administrative Series, Nr. 3 (2009) 23-40 Given that, the fit indexes of full model investigated in this study were firstly examined. According to the analysis performed, all indexes were out of accepted range, thus suggesting for model iteration. Therefore, the model of the study was reiterated based on the modification index. The modification index provides researcher with the information to add additional path in the model such as correlation between error variances so that to improve the model overall fit. As presented in Figure 3, three additional paths were added into the model as compared to the earlier model of the study as depicted in Figure 2. In many cases, the iteration is difficult to be theoretically justified. Therefore, the additional path should only be done with cautious. As presented in figure 3, the additional paths were added between the error variances of indicators of strategic control systems. Theoretically, these additional paths perhaps provided us with the evidence of the 'in package' or systems nature of strategic control systems. In other words, each indicator of strategic control systems have synergistic relationship between them. As documented by control systems scholars, control systems involve more than one activity that must be well integrated (Flamholtz et al., 1985; Goold & Quinn, 1993).

Table 3 reports the indexes of the iterated model. As presented in this table, the indexes of the iterated model surpassed or marginally lower than the benchmark value that suggesting the model did fit to the data. Given to this finding, the hypotheses of the study can be tested.


The first hypothesis of the study ([H.sub.1]), states that there is a relationship between QM and FP. As reported in figure 3 as well as table 4, the path relating QM and FP is not significant (CR = 0.987, p > 0.05), thus unable to provide evidence of the relationship between QM and FP. Therefore, [H.sub.1] was rejected. The second hypothesis of the study ([H.sub.2]), asserts that there is a relationship between QM and SCS. As given in figure 3 as well as table 4, the path relating QM and SCS is positive and significant (CR = 3.688, p<0.05), thus providing a strong evidence to support [H.sub.2]. This finding indicates that the higher the level of QM implementation, the higher the level of SCS practices. The third hypothesis of the study ([H.sub.3]), hypothesizes that there is a relationship between SCS and FP. As presented in figure 3 as well as table 4, the path relating SCS and FP is positive and significant (CR = 2.039, p<0.05), thus providing a strong evidence that SCS has a positive effect on FP. The fourth hypothesis of the study ([H.sub.4]), speculates that the structural relationship between QM, SCS and FP is fit to the data. As given in table 3, the index of iterated model had achieved the benchmark level. Therefore, our [H.sub.4] is supported. Additionally, table 5 tabulates the results of direct effect, indirect effect as well as total effect between the constructs understudy. As can be seen, the total effect of QM on FP is higher than the direct effect of QM on FP. The total effect of QM on FP can be calculated by adding the direct effect of QM on FP to indirect effect of QM on FP through the presence of SCS. The indirect effect of QM on FP mediated through SCS is calculated by multiplying direct effect of QM on FP and the direct effect of SCS on FP (0.852 X 0.563 = 0.479). Therefore, the total effect (direct + indirect) of QM on FP is 0.686. This finding provides a strong evidence to indicate that the relationship between QM and FP mediated by SCS is higher than the direct relationship between QM and FP.


As reported in this study, the relationship between QM and financial performance was insignificant. Indeed, previous scholars have reported that not all of QM implementers had gained positive impact of QM strategy (e.g., Madu et al., 1995; Yasin et al., 2004). Although these findings were inconsistent with the mountainous reported literatures that revealed the significant relationship between QM and financial performance (e.g., Powell, 1995; Sanchez-Rodriguez et al., 2004), the unsuccessful story of QM implementation is not surprising. As documented by Kaplan and Norton (2000), 70 to 90 percent of organizations worldwide failed to execute their organizational strategy successfully. A strategy is only a means toward an end (Zakaria, 1999). On the other hand, the control system is a systems that must be institutionalized to monitor, evaluate and measure the progress of intended strategy. The insignificant relationship between QM and financial performance of local governments understudy is not a failure of the QM philosophy, but perhaps could be linked with the need of local governments to pay sufficient attention to their organizational control systems. The finding of the study revealed that the relationship between QM and FP was not in the simple direct relationship, but perhaps mediated by the control systems variable. As suggested by Ehigie and McAndrew (2005), future researchers need to investigate the variable that could influence the successful of QM implementation. Given that, this study also investigated the relationship between QM and strategic control systems. As revealed in this study, there was a significant relationship between QM and SCS. Therefore, this finding provided another evidence to support the findings of previous study. As can be seen in the literature, many previous scholars have shown that the type of organizational strategy had significant relationship with the type of control systems (e.g., Daniel & Reitsperger, 1991; Ittner & Larcker, 1997). The finding may imply that the local governments understudy did attempt to tailor their control systems to suit with the QM strategy, such as the alignment between the resource allocation as stated in the budget and the resource needed for the QM purposes. Indeed, the premise of the need to align the strategic control systems to the intended strategy is well accepted in the literature (Lorange & Murphy, 1984). Our first hypothesis reported the insignificant relationship between QM and FP. Our second hypothesis found the significant relationship between QM and SCS. Given to the significant relationship between QM and SCS, our next step was to investigate the relationship between QM and FP mediated by SCS. This further analysis was important to shed light on the mediating role of SCS in explaining the relationship between QM and FP. Additionally, this further analysis, perhaps, would be able to explain the underlying factor of the insignificant relationship between QM and FP. As revealed in this study, the QM mediated by the SCS had a stronger relationship with FP, as compared to the direct relationship between QM and FP. As tabulated in table 6, the total effect of QM on FP mediated by SCS was 0.686, much higher than the direct effect of QM on FP which was 0.207. Given to this finding, it indicated that the explanatory power of QM toward FP is higher when mediated by SCS than that of QM directly to FP. Therefore, this finding provided support for the earlier study reported by Andersen et al. (2004). As documented by Andersen and his co-authors, organizational strategy could be implemented more successfully if it was been complimented by strategy focused control systems or SCS. For instance, the practice of strategic control systems would be able to protect an organization from unintended events or unexpected problems. As documented by Kaplan and Norton (1996), effective control systems align the personal objectives of each organizational member as well as the objectives of each functional unit to the overall organizational objectives. This alignment process ensures the efforts of each organizational member are consistent with the objectives of organization, so that the unintended actions of employees can be minimized. Consequently, the concerted effort of employees toward the achievement of organizational objective can be instilled, which in turn contributes to the better performance. As revealed in this study, there was a significant relationship between strategic control systems and financial performance of local government understudy.

Limitation of the Study

As with other scientific research, this study is not without limitation. For the greatest benefit, limitations of this study should be considered when interpreting its results. Three important limitations of the study that need to be addressed were generalizability, methodological aspect and causality. In terms of Bucharest, Economic and Administrative Series, Nr. 3 (2009) 23-40 generalizability, this study was limited to the CC and MC in Peninsular Malaysia and excluded those in East Malaysia. In terms of methodology, this study used five point Likert scale to measure financial performance instead of using objective data, thus raised the issue of potential response bias. In terms of causality, this study used a cross sectional sample. While causal relationship can be inferred, but it cannot be strictly proven.

Suggestion for Future Study

Several suggestions that fruitful for future research emerged from this present study. In order to validate the findings of this study, case study is another interesting approach that can be done by future research. Additionally, the research model of this study can be retested by using business organizations, so that the research model can be generalized to other economic sector.

Implications and Conclusion of the Study

As a conclusion, issues of the performance of local government will remain an important agenda for political leaders, managers and employees of local government, researchers and community at large. For those who are skeptical as to whether QM could lead to a more cost effective local government, the finding of this study was able to provide another interesting insight. As revealed in this study, the direct relationship between QM and FP was insignificant. Although QM has been widely documented as an excellent improvement strategy, but an excellent strategy per se may not be adequate (Chenhall & Langfield-Smith, 1998). The more critical issue is the alignment between organizational strategy and organizational control systems (Selto et al., 1995). Given to the findings of this study, this paper strongly suggests the establishment of SCS as a complimentary to the practices of QM. Without a presence of suitable control systems, a local government would probably have difficulty in monitoring and evaluating the implementation of QM in their organization.


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Ilias Said *, Abdelnaser Omran *, Zulnaidi Yaacob * & Zakaria Abas ** E-mail: naser_elamroni@

* School of Housing, Building and Planning, Universiti Sains Malaysia, 11800, Minden, pulau pinang, Malaysia

** Faculty of Accountancy, Universiti Utara Malaysia, 06010 Sintok, Kedah Malaysia
Table 1

Item Construct Correlation Test

Constructs   Item     QM     SCS      FP

QM           QM1    0.827   0.678   0.278
             QM2    0.805   0.617   0.324
             QM3    0.732   0.496   0.460
             QM4    0.778   0.773   0.489
             QM5    0.733   0.624   0.684
SCS          SCS1   0.792   0.828   0.427
             SCS2   0.628   0.819   0.616
             SCS3   0.475   0.672   0.336
             SCS4   0.571   0.758   0.665
             SCS5   0.677   0.750   0.640
FP           FP1    0.469   0.592   0.844
             FP2    0.206   0.395   0.812
             FP3    0.714   0.724   0.803

Table 2

Reliability Test and Factor Analysis

Constructs/   Cronbach   Factor loading for items in
No of items    alpha            first factor

QM (5)         0.719     0.805   0.793   0.752   0.767   0.765
SCS (5)        0.819     0.808   0.816   0.634   0.787   0.783
FP (3)         0.769     0.882   0.812   0.799

Constructs/     KMO   Eigen     % of
No of items           value   variance

QM (5)        0.608    3.02    60.31
SCS (5)       0.787    2.95    59.10
FP (3)        0.667    2.07    69.14

Table 3

Indexed of Full Iterated Model

Indexes   Value   Threshold (Hair et al., 1998)      Acceptability

GFI       0.905   [greater than or equal to] 0.900   Acceptable
NFI       0.869   [greater than or equal to] 0.900   Marginally
IFI       0.920   [greater than or equal to] 0.900   Acceptable
CFI       0.919   [greater than or equal to] 0.900   Acceptable
TLI       0.893   [greater than or equal to] 0.900   Marginally

Table 4

Hypotheses Testing

Path                   Standardized   Standardized   CR        p
                         estimate        error

QM [right arrow] FP       0.207          0.425       0.487     0.324
QM [right arrow] SCS      0.852          0.231       3.688*    0.000 **
SCS [right arrow] FP      0.563          0.276       2.039 *   0.009 **

Path                   Hypothesis   Result

QM [right arrow] FP    [H.sub.1]    Reject
QM [right arrow] SCS   [H.sub.2]    Accept
SCS [right arrow] FP   [H.sub.3]    Accept

* Significant at Critical Ratio (CR) > 1.96; ** Significant at p< 0.05

Table 5

Direct and Indirect Effect

Path                   Standardized     Standardized     Total Effect
                       Direct Effect   Indirect Effect

QM [right arrow] FP       0.207             0.479           0.686
QM [right arrow] SCS      0.852               n/a           0.852
SCS [right arrow] FP      0.563               n/a           0.563
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Article Details
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Author:Said, Ilias; Omran, Abdelnaser; Yaacob, Zulnaidi; Abas, Zakaria
Publication:Annals of the University of Bucharest, Economic and Administrative Series
Article Type:Report
Geographic Code:9MALA
Date:Jan 1, 2009
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