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International Journal of Productivity and Performance Management The performance consequence of multiple performance measures usage: Evidence from the Malaysian manufacturers Ruzita Jusoh Daing Nasir Ibrahim Yuserrie Zainuddin

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To cite this document: Ruzita Jusoh Daing Nasir Ibrahim Yuserrie Zainuddin, (2008),"The performance consequence of multiple performance measures usage", International Journal of Productivity and Performance Management, Vol. 57 Iss 2 pp. 119+ - 136 Permanent link to this document: http://dx.doi.org/10.1108/17410400810847393 Downloaded on: 14 June 2016, At: 00:46 (PT) References: this document contains references to 47 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 3360 times since 2008*

Users who downloaded this article also downloaded: (2013),"Understanding the features of performance measurement system: a literature review", Measuring Business Excellence, Vol. 17 Iss 4 pp. 102-121 http://dx.doi.org/10.1108/MBE-05-2012-0031 (2008),"Competitive strategy and performance measurement in the Malaysian context: An exploratory study", Management Decision, Vol. 46 Iss 1 pp. 5-31 http://dx.doi.org/10.1108/00251740810846716 (2005),"An empirical study of performance measurement in manufacturing firms", International Journal of Productivity and Performance Management, Vol. 54 Iss 5/6 pp. 419-437 http:// dx.doi.org/10.1108/17410400510604566

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The performance consequence of multiple performance measures usage Evidence from the Malaysian manufacturers Ruzita Jusoh University of Malaya, Kuala Lumpur, Malaysia, and

Multiple performance measures 119 Received February 2007 Revised April 2007 Accepted April 2007

Daing Nasir Ibrahim and Yuserrie Zainuddin Downloaded by University of Malaya At 00:46 14 June 2016 (PT)

Universiti Sains Malaysia, Penang, Malaysia Abstract Purpose – The purpose of this paper is to contribute to the body of knowledge in the area of performance measurement systems, particularly the BSC framework, by investigating empirically the extent of multiple performance measures usage and their effects on the performance of Malaysian manufacturers. Design/methodology/approach – The paper used a mail-survey of companies listed in the Directory of the Federation of Malaysian Manufacturers (FMM), year 2003. The FMM Directory provides a database of over 2,000 manufacturing firms of various sizes producing a broad range of products. The FMM Directory was utilized because it specifically covers manufacturers and manufacturing-related services. A simple random sample of 975 companies located in West Malaysia was drawn. Companies with at least 25 employees and annual sales turnover of at least RM10 million were selected. A total of 120 usable responses were gathered and used in the data analysis. Findings – The findings suggest that the use of non-financial measures, particularly, internal business process and innovation and learning measures, appears to be important as it enhances firm performance. More interesting, the findings reveal that the use of multiple performance measures via overall BSC measures contributes to a more positive outcome. Research limitations/implications – The paper shows that the FMM Directory is not an exhaustive list, and may not represent the whole population of Malaysian manufacturers. The sample size is not overwhelming and confined to the manufacturing sector only. Furthermore, the use of cross-sectional data could not find consistent association between non-financial measures and future performance. Originality/value – The paper shows that one important practical implication is for the designers of control and performance measurement systems to emphasize the use of multiple performance measures that are fundamental to the success of organizations. Keywords Performance measurements, Balanced Scorecard, Malaysia Paper type Research paper

Introduction Performance measurement is an issue of growing importance among academicians, practitioners and researchers where it still remains a critical and much debated issue. Management control system and performance measurement literatures have found that traditional performance measures, such as profits and return on investment, were insufficient for decision-making, planning and controlling operations in today’s rapidly

International Journal of Productivity and Performance Management Vol. 57 No. 2, 2008 pp. 119-136 q Emerald Group Publishing Limited 1741-0401 DOI 10.1108/17410400810847393

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changing and hyper-competitive environment. The traditional performance measurement system is under serious challenge since its emphasis is on financial measures in order to satisfy the regulatory and accounting reporting requirements. Traditional financial measures are criticized because they are short-term rather than long-term focus, measuring the past rather than future. Besides, they tend to be obsolete and easily manipulated by managers. Therefore, in order to overcome the shortcomings of using traditional performance measurement system, Kaplan and Norton (1992, 1996a, b, c, 2001) have introduced the balanced scorecard (has been BSC) offering a superior combination of non-financial and financial performance measures. Balanced scorecard implemented by a number of organizations worldwide in response to the new global competitive environment. In the UK for example, according to Business Intelligence, 71 per cent of big companies use it, while in the US, almost 50 per cent of 1,400 global businesses apply some kind of BSC (Paladino, 2000). Also, as noted by Brewer (2002), 50 per cent of the Fortune 1,000 and 40 to 45 per cent of larger companies in Europe use the BSC. Meanwhile, a survey by Kald and Nilsson (2000) on 236 Nordic multi-business companies shows that 61 companies use scorecards and another 140 planned to adopt the model within the next two years. In Malaysia, the percentage of Malaysian manufacturing companies adopting BSC is not overwhelming. This study found that about 30 per cent of the companies surveyed have adopted balanced scorecard as a performance measurement system either wholly or partially. Since many respondents said that they do not know what BSC is all about, that could explain the reason why many companies do not adopt BSC. Despite widespread practitioner interest in BSC, thus far, little empirical research has been conducted on the implementation or performance consequences of its concept (Ittner and Larcker, 1998a). Later, Ittner and Larcker (2001, p. 375) also noted that the “performance effects of the balanced scorecard and other value driver techniques remain open issues”. Consequently, this study attempts to contribute to the body of knowledge in the area of performance measurement system by focusing on issues relating to multiple performance measures, which are conceptualized according to the BSC framework. First, the aim is to investigate the extent of multiple performance measures usage among Malaysian manufacturing firms. Second, an attempt is made to explore the performance consequences by investigating the effect of multiple performance measures usage on firm performance. The paper is organized as follows. First, a brief review of the literature on performance measurement system, in general, and the BSC, in particular, is presented. In addition, hypothesis pertaining to the relationship between multiple performance measures usage and firm performance is developed based on evidence from the relevant literature. Second, the research methodology is described, followed by a discussion of the results and findings. Finally, a conclusion is presented by discussing the implications of the research findings to designers of performance measurement systems, as well as limitations of the current study and fertile avenues for future research. Literature review and hypothesis development Traditional performance measurement system Performance measurement system is important for an organization as it plays a key role in developing strategic plans, evaluating the achievement of organizational objectives, and compensating managers (Ittner and Larcker, 1998a). Over the past

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years, there have been critics on the traditional performance measurement system, which was based on the traditional management/cost accounting system. According to Johnson and Kaplan (1987), performance measurement based on traditional cost or management accounting system that was introduced in early 1900s is no longer suitable and less useful in today’s business environment. The main criticism of traditional performance measurement is basically centered on its over-reliance on cost information and other financial data which are short-term in nature, while no or less emphasis is given on long-term value creation activities which are intangible in nature that generate future growth to the organization. Kaplan and Norton (2001) have argued that many organizations nowadays focus on managing intangible assets (e.g. customer relationships, innovative products and services, high-quality and responsive operating processes), which are non-financial in nature, rather than managing tangible assets (e.g. fixed assets and inventory), which are financial in nature. Therefore, the changing nature of value creation complicates the performance measurement process when the performance measurement systems are not kept abreast with this latest phenomenon. Meanwhile, Ghalayini and Noble (1996) highlighted that traditional performance measures are outdated and lagging metrics that are a result of past decision, not related to corporate strategy, not relevant to practice and difficult to understand by the factory shop-floor people, conflict with continuous improvement, inability to meet customer requirements, and emphasis too much on cost reduction efforts. Shortcomings in traditional accounting-based performance measures have led to the development of new performance measurement systems, so called strategic performance measurement systems (SPMS). According to Chenhall (2005), a distinct feature of these SPMS is that they are designed to present managers with financial and non-financial measures covering different perspectives which, in combination, provide a way of translating strategy into a coherent set of performance measures (Chenhall, 2005). In a similar vein, Bruns and McKinnon (1993) argued that the use of multiple performance measures comprising financial and non-financial is generally most fair to both management and the owner where for management, they have the added advantage of providing enhanced protection against the consequences of uncontrollable outside events. Further, Chenhall (2005) argued that it is the integrative nature of SPMS that provide them with the potential to enhance an organization’s strategic competitiveness. In fact, prior studies have shown how non-financial performance measures can be best combined with financial performance measures to obtain the best measurement of performance in a competitive environment (Hemmer, 1996; Shields, 1997; Hoque and James, 2000). One of the famous SPMS is the balanced scorecard (BSC), originated by Kaplan and Norton in 1992. The Balanced Scorecard (BSC) Empirical research on BSC has become prominent and gained momentum in accounting research (e.g. Lingle and Schiemann, 1996; Hoque and James, 2000; Hoque et al., 2001; Maiga and Jacobs, 2003). The use of multiple performance measures in the BSC model is timely in today’s competitive environment as business cannot rely solely on the narrowly focused internal financial measures for performance evaluation. The term “balanced” refers to the balance between financial and non-financial performance measures, between lagging and leading indicators and between internal and external perspective of performance measurement. The BSC measures are linked together on a

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cause-and-effect relationship covering four perspectives, namely, financial, customer, internal business process, and learning and growth. Every performance measure on a BSC attempts to address an aspect of a company’s strategy because it should be a link between performance measures and strategy. The BSC is regarded as a tool for focusing the organization, improving communication, setting organizational objectives, and providing feedback on strategy (Anthony and Govindarajan, 2003). Financial perspective – The main objective of financial perspective is to serve shareholders well. Financial perspective provides the ultimate outcome or bottom-line improvement of the organization where it measures the economic consequences of actions already taken in the learning and growth, internal business process, and customer perspectives. Financial measures are typically related to profitability such as operating income, return-on-investment and economic value-added (EVA), while other financial measures may also include sales growth, cost control, and cash flow. Customer perspective – Customer perspective captures the ability of the organization to provide quality products and services, the effectiveness of its delivery, and overall customer service and satisfaction. Also, customer perspective provides a strategy for creating value and differentiation from the perspective of the customer. This perspective helps an organization to connect its internal business processes with customers in order to improve financial outcomes. This perspective encompasses measures such as customer satisfaction, customer response time, market share, and on-time-delivery. Internal business process perspective – Internal business process perspective focuses on the internal processes that the organization must do well in order to add value to customers through customer satisfaction and generate financial returns to shareholders. According to Kaplan and Norton (1992, p. 78) “A failure to convert operational performance, as measured in the scorecard, into improved financial performance, should send executives back to their drawing boards to rethink the company’s strategy or its implementation plans”. The key performance measures under this perspective may include manufacturing efficiency, quality, defect rate, and cycle time. Learning and growth perspective – Learning and growth perspective focuses on how an organization learns and makes a change and improvement so that long-term value creation can be realized. Thus, learning and growth perspective focuses on the capabilities of people (employees), systems and procedures used in achieving breakthrough performance in internal processes, customer satisfaction and ultimately the financial performance. This perspective measures such things as training and development, employee satisfaction, employee retention, and employee productivity. Despite widespread practitioner interest in the BSC, its empirical support is rather limited and not conclusive. Thus, it is the aim of this study to provide additional empirical evidence pertaining to the BSC measures and their relationships with firm performance. While many previous studies on the use and performance consequences of non-financial measures have produced mixed results (Ittner and Larcker, 1998b; Banker et al., 2000), an attempt to examine the performance consequences of the BSC measures incorporating both financial and non-financial measures is also timely. Multiple performance measures usage and performance linkage Studies by Hoque and James (2000), Sim and Koh (2001), Davis and Albright (2004) and Maiga and Jacobs (2003) provide empirical evidence about BSC measures and

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performance linkage. Hoque and James (2000) provide support for the main effect of overall BSC usage on firm performance. According to Newing (1995), BSC works well in conjunction with ABC and activity-based management (ABM). More recently, Maiga and Jacobs (2003) found that the implementation of ABC, when combined with BSC, has a significant positive impact on organizational performance where the results indicate that each of the four BSC perspectives interacts with ABC to affect performance. Correlation between learning and growth perspective and financial perspective is shown in Sim and Koh’s (2001) study where the results indicate that innovative technique, new product development time and customer performance measures are related to lower manufacturing costs, higher sales, and greater market share. Meanwhile, Davis and Albright (2004) found evidence that banks report an improvement in internal performance measures after they implement BSC program. Other than previously mentioned studies that deal specifically with BSC, few studies have shown that firms may perform better if multiple measures, in particular non-financial measures, are used for performance evaluation of the firms (e.g. Abernethy and Lilis, 1995; Anderson et al., 1994; Banker et al., 2000; Droge et al., 2000; Ittner and Larcker, 1998b; Said et al., 2003; Bryant et al., 2004). Droge’s et al. (2000) revealed that time-to-market new products as a measure often leads to higher initial prices, greater market share and customer loyalty, as well as significant cost benefits. Meanwhile, Banker et al. (2000) showed that there is a positive relationship between customer satisfaction measures and future accounting performance while Ittner and Larcker (1998b) provided evidence that customer satisfaction measures are leading indicators of non-financial performance (customer purchase behaviour and growth in the number of customers) and accounting performance (profit margins and return on sales). Earlier, Anderson et al. (1994) supported the hypothesis that, on average, customer satisfaction was positively related to contemporaneous accounting return on investment. Furthermore, Abernethy and Lilis (1995) found that greater reliance on non-financial manufacturing measures had a greater positive effect on perceived performance in flexible firms than in non-flexible firms. Meanwhile, Said et al. (2003) revealed that the use of non-financial measures is associated with future accounting and market-based returns. More recently, Bryant et al. (2004) reported that when firms implements a performance measurement system that contain both financial and non-financial measures, they will benefit more than the firms that rely solely on financial measures. Although the previous studies show some positive association between the use of non-financial measures and performance, several other studies reveal the opposite results. Perera et al. (1997) found that there is no association between the use of non-financial measures and perceived performance in plants that follow a customer-focused manufacturing strategy. Also, Ittner and Larcker’s (1998b) study suggests that the inability to link non-financial performance measures with economic performance does exist. Their study found that the ability of executives to relate customer satisfaction measures to accounting or stock price returns is only about 28 and 27 per cent respectively. In addition, Young and Selto (1993) found little evidence that the use of non-financial measures in JIT facility was associated with differences in manufacturing performance. With regard to the ability of financial indicators to measure performance, findings from Govindarajan’s (1988) study indicate that deemphasizing budget evaluative style

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is positively and significantly associated with strategic business unit effectiveness. Budget evaluative style is a control system design, which emphasizes on short-term profit measures, and thus is not adequate to reflect effectiveness and competitiveness. Similarly, evidence from Hayes’s (1977) study also lends support to the inability of financial data to measure performance. His results indicate a general dissatisfaction with such measures and imply that the assessment tool as such is not sufficient. Based on the foregoing discussion, it can be argued that there should be a more flexible technique in measuring performance, which could provide a balance between financial and non-financial perspectives of performance measures. In this regard, BSC framework is considered the most appropriate tool in achieving this objective. It is proposed that the extent to which multiple performance measures, conceptualized as the BSC measures, are used is likely to have a significant positive impact on firm performance. Hence, on the whole, the following hypothesis was developed: Firm performance is positively associated with the extent to which the firm uses: . financial measures; . customer measures; . internal business process measures; and . learning and growth measures. Methodology Sample This study focuses on manufacturing firms from various industries. Manufacturing firms are chosen because the use of performance measurement system in this sector is generally common. Due to greater diversity and complexity in many areas such as product market, technological process, and cost structure (particularly overhead cost), manufacturing companies should place greater concern on their performance measurement systems. Besides, manufacturing sectors in Malaysia is growing and plays a dominant role in the Malaysian economy by being the second largest sector, after services sector, in terms of its share to total GDP. The data used in the analysis were obtained from a mail-survey of companies listed in the directory of Federation of Malaysian Manufacturers (FMM) year 2003. The FMM directory was utilized because it is the only directory that specifically covers manufacturers and manufacturing-related services in Malaysia. It provides a list of over 2,400 manufacturers and manufacturing-related services, and over 8,000 products. A simple random sample of 975 companies located in West Malaysia was drawn. Only firms with at least 25 employees and annual sales turnover of at least RM10 million were selected. Adoption of BSC is not a prerequisite for these targeted companies. The manufacturing companies selected to be surveyed do not necessarily adopt and use the fully-fledged BSC as a tool for performance measurement system as there may not be many of them in Malaysia. As the usage of performance measures is common in any organization, it is expected that there are firms possibly using some elements of BSC measures either knowingly or unknowingly, either wholly or partially or customizing it according to their needs. For those firms that do not adopt BSC either wholly or partially, the use of Key Performance Indicators (KPIs) scorecards are common which may contain some elements of BSC measures. Thus, when conceptualizing multiple performance measures similar to BSC framework, the researcher perceived that firms

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often build its scorecards on the base already established by classifying their existing measurements into the four BSC perspectives. Therefore, in the questionnaire, firms were asked about their usage of performance measures, which are commonly used by many manufacturing firms. A total of 120 usable responses were used in the data analysis, and this sample size corresponds to response rate of 12.3 per cent. For a mail-survey, this low response rate is not unusual in Malaysia. A test of the existence of possible response bias between early and late responses was performed by a t-test. The results reveal that no significant differences exist. Hence, there is reason to believe that non-response bias may not be a problem. Table I Frequency

%

25 18 13 11 7 7 7

21.0 15.1 10.9 9.2 5.9 5.9 5.9

5 4 4

4.2 3.4 3.4

18 119

15.1

Annual sales turnover: Less than RM10 million RM10-RM20 million RM21-RM50 million M51-RM100 Above RM100 million Total

4 17 33 30 35 119

3.4 14.3 27.7 25.2 29.4

Total gross assets: Less than RM50 million RM50-RM70 million RM71-RM100 million RM101-RM150 million Above RM150 million Total

54 20 14 5 21 114

47.4 17.5 12.3 4.4 18.4

Total number of employees: Less than 100 100-200 201-400 401-600 Above 600 Total

13 30 40 16 21 120

10.8 25.0 33.3 13.3 17.5

Primary business activity: Electrical and electronics product manufacturing Iron, steel, and metal product manufacturing Food and beverage manufacturing Rubber and plastic product manufacturing Paper, printing, packaging, and labeling product manufacturing Chemicals and chemical product manufacturing Pharmaceutical, medical equipment, cosmetics, toiletries, and household products Furniture- and wood-related product manufacturing Textile, clothing, footwear and leather manufacturing Machinery and equipment manufacturing Other manufacturing Total

Note: Total figures are not equal due to missing values. RM (Ringgit Malaysia) refers to Malaysian currency

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Table I. Profile of the responding firms (n ¼ 120)

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presents the profile of the responding firms by manufacturing activity, annual sales turnover, total gross assets, and number of employees. Measurement of variables Independent variable – multiple performance measures usage. Using the BSC framework, a total of 29 performance measures representing financial and non-financial measures were identified (see Table II). These are considered generic measures, commonly used by manufacturing firms. In total, 20 items were taken from Hoque et al. (2001), which are originally adopted from Kaplan and Norton (1992), and the remaining nine items were self-constructed from the literature. They were sales revenue, cash flows, manufacturing costs, economic value added (EVA), customer loyalty, defect rate, set-up and changeover time, flexibility, and employee training. A seven-point Likert-type scale ranging from 1 (not at all) to 7 (to a greater extent) was used to assess the extent to which a firm uses each performance measure. The means for each dimension and overall BSC measures were considered in the data analysis.

Performance measures

Table II. Usage frequency of multiple performance measures (n ¼ 120)

Operating income Sales growth Sales revenue ROI Cash flows Manufacturing cost EVA Market share Customer response time On-time delivery Number of customer complaints Number of warranty claims Survey of customer satisfaction Customer loyalty Per cent of shipments returned Number of overdue deliveries Materials efficiency variance Labour efficiency variance Ratio of good output to total output Manufacturing lead time Rate of material scrap loss Defect rate Set-up and change-over time Flexibility Number of new patents Number of new product launches Time-to-market new products Employee satisfaction Employee training

1

2

3

Frequency 4

5

6

7

1 6 3 3 11 6 2 1 3 10 4 3 7 10 1 6 6 7 10 5 9 10 8 14 13 4 5

13 10 10 17 17 11 18 14 12 8 8 10 13 15 17 17 15 14 13 10 10 9 20 23 25 17 24 23 15

18 20 15 31 19 27 32 29 30 19 12 22 27 31 15 20 26 27 21 26 20 15 34 32 25 30 29 31 27

41 55 58 42 40 38 37 45 45 49 42 31 53 39 29 34 33 41 46 41 33 37 29 29 14 18 23 42 53

46 35 36 22 37 40 12 22 28 41 41 22 20 25 30 21 32 24 26 29 35 39 17 13 4 13 7 11 18

1

3 1 1 13 3 7 4 1 1 1 1 1 2 3 25 12 15 4

2 2 1 5 1 2 1 11 11 3 4 15 8 10 5 5 5 9 11 6 6 18 14 8 5 2

Notes: 1 ¼ Not at all; 2 ¼ To minimum extent; 3 ¼ To a slight extent; 4 ¼ To some extent; 5 ¼ To a moderate extent; 6 ¼ To a large extent; 7 ¼ To a greater extent

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As the sample size was larger than 100 (Hair et al., 1998), a principal component analysis (PCA) with varimax rotation was performed for the 29 items on the performance measures to determine their groups according to the BSC’s four perspectives of measures. Each item should load 0.50 or greater on one factor and 0.35 or lower on the other factor (Igbaria et al. (1995). In a sample of 120, factor loadings of 0.50 and above are considered significant (Hair et al. (1998). A total of 12 items were deleted from the analysis due to cross loading and insignificant factor loadings. From factor analysis, five component factors were extracted with eigenvalues exceeding 1, explaining a total of 71.9 per cent of the variance (see Table III). Since a priori expectation was imposed about the number of BSC perspectives, two component factors (Component 1 and 5) were combined together, and were referred to as Customer as both represent measures for customer. The other three component factors were named as Financial (Component 4), Internal Business Process (Component 2), and Innovation and Learning (Component 3) and these results were quite consistent with previous research on the BSC scale (e.g. Hoque et al. 2001; Hoque and James, 2000). As shown in Table IV, a reliability check for each and overall dimensions of BSC measures produced Cronbach alpha values all above the lower limits of normal acceptability (Nunnally, 1978). Dependent variable: firm performance. Firm performance was measured by a self-rating scale using 12 indicators taken from Mia and Clarke (1999) and Govindarajan (1984). The 12 indicators were: productivity, cost, quality, delivery schedule, market share, sales growth rate, operating profit, cash flow from operation,

Eigenvalue

Percentage of variance explained

0.840 0.839 0.817 0.777

5.58

17.95

Manufacturing lead time/cycle time Ratio of good output to total output Labour efficiency variance Flexibility

0.836 0.830 0.659 0.540

2.24

14.53

3

Time-to-market new products Number of new product launches Number of new patents

0.875 0.849 0.815

1.84

14.44

4

Sales revenue Sales growth Operating income

0.910 0.840 0.640

1.42

12.77

5

On-time delivery Customer response time Survey of customer satisfaction

0.840 0.811 0.654

1.15

12.20

Component

Items

1

Percent of shipments returned Number of overdue deliveries Number of warranty claims Number of customer complaints

2

Factor loadings

Notes: Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. A Rotation converged in six iterations

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Table III. Results of the principal component factor analysis for the multiple performance measures

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return on investment, new product development, R&D activity, and personnel development. The scale represents a multiple indicators approach in assessing performance where it incorporates all aspects of quantitative, qualitative, financial and non-financial performance (Mia and Clarke, 1999). Respondents were asked to indicate the changes in the performance in the last three years using the above 12 performance indicators on a scale from 1 ¼ decreased tremendously to 7 ¼ increased tremendously. The performance represented the recent improvements in actual firm performance as perceived by the respondents. The Cronbach alpha value for the overall performance scale is as shown in Table IV, indicating satisfactory internal reliability of the scale. A weighted average performance index was obtained for each firm.

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Results Descriptive statistics and correlation matrix As the main focus of this study is to determine the extent of usage of multiple performance measures, preliminary descriptive statistics for all 29 performance measures was performed to see their frequencies of use. Table II provides the usage frequency of all 29 performance measures as included in the questionnaire on the scale ranging from 1 (to a minimum extent) to 7 (to a greater extent). As can be seen in Table II, as expected, the extent of usage in financial measures such as operating income, sales growth, and sales revenue among responding firms range mostly from “to some extent” to “to a greater extent”, indicates high usage of financial measures. However, EVA (Economic Value Added) was not popularly used when only 12 firms have used it at “to a greater extent”, three firms did not use it at all while 16 firms have used it either at “to a minimum” or “to a slight extent”. EVA did not seem to be popular, probably because it is rather complex and difficult to use, and research on the extent to which it is superior to traditional accounting measures is limited and mixed (Ittner and Larcker, 1998a). Customer measures like market share, on-time delivery, number of customer complaints, survey of customer satisfaction and customer loyalty show that their usage are mostly at the higher end of the scale, with 79 per cent scored at least “to a moderate extent”. Among all customer measures, on-time delivery was very extensively used, while the number of warranty claims and percentage of shipments returned were not used at all by quite a number of firms. The extent of usage of internal business process measures was also high, where majority of firms show high frequencies for scores ranging from “to a moderate extent” to “to a greater extent”. Overall, among the internal business process measures, manufacturing lead-time scored the highest usage with about 81.3 per cent used it at the moderate extent and

Table IV. Descriptive statistics for all variables

BSC: Financial Customer Internal business process Innovation and learning Overall BSC Performance

Minimum

Maximum

Mean

Std deviation

Cronbach Alpha

3.67 3.22 2.00 1.00 3.17 2.99

7.00 7.00 7.00 7.00 7.00 7.00

5.9750 5.3645 5.2638 3.9874 5.1508 4.7823

0.7828 1.0075 1.1118 1.5731 0.8017 0.7762

0.75 0.85 0.88 0.85 0.90 0.88

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above. For innovation and learning measures, on the whole, the usage is considered the lowest compared to the other three perspectives of performance measures. Number of new patents, number of new product launches, and time-to-market new products show the slightest usage. Many firms indicated the usage as ranging from “not at all” to “to some extent”. In fact, many firms did not use these particular measures at all. Table V provides descriptive statistics on minimum, maximum, means, and standard deviations values for all 29 performance measures. When all 29 performance measures were ranked in terms of their mean usage, Table V shows that customer measures are the top four of the list, indicating that the usage of customer measures, particularly on-time delivery, customer response time, number of customer complaints, and survey of customer satisfaction are high among the Malaysian manufacturing companies. On the other hand, non-financial measures such as number of new product launches, time-to-market new products, and number of new patents are ranked at the bottom of the list in term of their extent of usage. These measures, considered as innovation measures by Kaplan and Norton (1992), do not seem to be popularly used by the Malaysian manufacturing companies. An inference to this outcome could be that Malaysian companies do not seem to put high emphasis on the innovation and R&D activities. This outcome is quite consistent with the findings reported by

On-time delivery Sales revenue Operating income Sales growth Manufacturing costs Cash flows Customer response time Number of customer complaints Survey of customer satisfaction Manufacturing lead time/cycle time Defect rate Employee training Market share Ratio of good output to total output ROI Materials efficiency variance Labour efficiency variance Customer loyalty Rate of material scrap loss Employee satisfaction Set-up and change-over time Number of overdue deliveries EVA Per cent of shipments returned Flexibility Number of warranty claims Number of new product lunches Time-to-market new products Number of new patents

Minimum

Maximum

Mean

Std deviation

2.00 3.00 2.00 4.00 2.00 2.00 2.00 1.00 2.00 2.00 1.00 2.00 1.00 1.00 2.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00

5.9916 5.9833 5.9832 5.9583 5.8167 5.7203 5.6639 5.6186 5.5250 5.5085 5.5043 5.4833 5.4746 5.4576 5.4250 5.3898 5.3729 5.3667 5.3559 5.0167 4.9915 4.9912 4.9492 4.8750 4.8448 4.6639 4.2288 4.1849 3.5462

0.9957 0.9165 1.0575 0.8925 1.1226 1.2187 1.0991 1.5901 1.1447 1.3381 1.6275 1.1226 1.2034 1.3815 1.1858 1.5137 1.3700 1.4019 1.6147 1.4022 1.4354 1.6590 1.4313 1.9210 1.4542 1.9799 1.8277 1.7465 1.8354

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Table V. Descriptive statistics (before factor analysis)

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Hongren et al. (2006) where new product time was ranked rather low in terms of its importance in Australia, Japan, and the UK. Meanwhile, Ittner and Larcker (1998b) reported the findings of a study by Towers Perrin Consulting firm, which indicates that a relative weight placed on the learning, and growth among the balanced scorecard adopters is only 5 percent. After factor analysis, descriptive statistics on the four components are displayed in Table IV. Table IV shows that responding firms place a major weight on the usage of financial measures (mean ¼ 5.9750), followed by customer measures (5.3645), internal business process measures (5.2638), and innovation and learning measures (3.9874). These results are similar to the results reported from a survey by the consulting firm Towers Perrin in the US (Lingle and Schiemann, 1996). All the Cronbach alpha coefficients exceeded the lower limit of acceptability, which is usually considered to be 0.70 (Nunnally, 1978). Table VI displays a correlation matrix for all variables. It shows that several BSC dimensions are significantly correlated with each other, suggesting that multi-collinearity is likely. However, after performing tolerance and variation inflation factor (VIF) tests, none of these tests detected multi-collinearity among the variables (VIF , 10, 16). Thus, it indicates no major problem for regression analysis. These correlations also imply the cause-and-effect relationships among the BSC perspectives as suggested by Kaplan and Norton. Hypothesis testing To test the effect of the usage of four dimensions of BSC measures on firm performance, the following multiple regression was run: Y ¼ b0 þ b1 X 1 þ b2 X 2 þ b3 X 3 þ b4 X 4 þ e Where Y ¼ performance; X1 ¼ financial measures; X2 ¼ customer measures; X3 ¼ internal business process measures; X4 ¼ innovation and learning measures; e ¼ error term; b0 ¼ the intercept; and b1, b2, b3 and b4 ¼ the regression coefficients for the four dimensions of the BSC measures. The results presented in Table VII indicate that the coefficients b3 (internal business process measures) and b4 (innovation and learning measures) are both positive and significant (b3 ¼ 0.197, t ¼ 1.762; p ¼ 0.081; b4 ¼ 0.384, t ¼ 4.426, p ¼ 0.000). The whole model is significant (F ¼ 11.739; p ¼ 0.000) and explains 29.5 per cent of the performance variance. These results support the study’s proposition that improved firm performance is positively associated with greater internal business process

Table VI. Correlation matrix

Financial measures Customer measures Internal business process measures Innovation and learning measures Overall BSC measures Performance

1

2

3

4

5

6

1.000 0.301 * 0.277 * 0.050 0.441 * 0.108

1.000 0.536 * 0.258 * 0.845 * 0.280 *

1.000 0.380 * 0.791 * 0.409 *

1.000 0.618 * 0.487 *

1.000 0.472 *

1.000

Note: * Correlation is significant at the 0.01 level (two-tailed)

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measures and innovation and learning measures usage. However, the results reveal that both the usage of financial and customer measures do not contribute significantly towards firm performance. Hence, these results do not support the study’s proposition that greater financial and customer measures usage is associated with increasing firm performance. Overall, the results presented in Table VII partially support the hypothesis proposed earlier. The analysis conducted thus far focuses on four perspectives of the BSC measures individually. The question now is whether the single scalar for the BSC measures has different effects on firm performance compared to those of the four individual perspectives? To address this question, another regression analysis was conducted to test the effect of the usage of overall BSC measures on firm performance. Overall BSC measures usage is represented by an average of the four perspective means. The results are reported in Table VIII. The model is significant (F ¼ 33.354; p ¼ 0.000), and is able to explain 22 per cent of the variance in firm performance. These results show that overall BSC measures usage has positive effect on firm performance and the effect is stronger (Beta ¼ 0.469, t-value ¼ 5.775) when considering each perspective individually.

Multiple performance measures 131

Discussion This paper has examined the effect of the usage of the BSC measures on firm performance. The results fully support the notion that firm performance is positively associated with the overall BSC measures usage. Although this study found support for the positive effect of overall BSC usage on performance, results of this study are rather mixed when taking the four perspectives of BSC measures individually. An interpretation of the results is that manufacturing firms with greater usage of internal business process and innovation and learning measures will experience improvement in performance. However, the usage of financial and customer measures was found to have no significant effect on firm performance. The significant result with regard to innovation and learning measures usage is consistent with the findings from studies by Sim and Koh (2001) and Droge et al. (2000) in which they found that new product development and time-to-market new products are related to increase in performance such as higher sales, greater market share, and lower manufacturing costs. In this

Variables Financial measures Customer measures Internal business process measures Innovation and learning measures R 2 ¼ 0:295

Variables Overall BSC measures R 2 ¼ 0:220

Beta

t-value

p-value

0.000 0.086 0.197 0.384 F ¼ 11.739

0.003 0.783 1.762 4.426 Sig. F ¼ 0.000

n.s. n.s. 0.081 0.000

Beta

t-value

p-value

0.469 F ¼ 33.354

5.775 Sig. F ¼ 0.000

0.000

Table VII. Regression analysis: individual BSC measures and firm performance

Table VIII. Regression analysis: overall BSC measures and firm performance

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respect, managers may want to closely monitor their time to market new products because a slack in this indicator often signals retarding sales or a sluggish market share ahead (Droge et al., 2000). The significant result for the usage of internal business process measures is quite consistent with the findings produced by Swamidas and Newell (1987) in that the higher the manufacturing flexibility (an indicator for internal business process), the better financial performance. The insignificant results are consistent with those of Maiga and Jacobs (2003) where their regression results show that all perspectives of BSC measures, namely, financial, customer, internal business process, and innovation and learning had no significant main effect on performance (product quality, customer satisfaction, and margin on sales), but they did have significant effect when interacted with the ABC (activity-based costing) measures. The non-significant result for financial perspective is due to shortcomings of traditional financial measures in measuring performance effectively and competitively. For example, findings from Hayes’s (1977) study reveal the inability of financial data to measure performance. Thus, the use of financial measures alone is not sufficient. Surprisingly, usage of customer measures is not significantly associated with firm performance. This non-significant result supports the findings of those studies that found non-financial measures were not related with performance (e.g. Perera et al., 1997). Thus, these findings seem to be in congruence with the assertions that the inability to link non-financial performance measures with economic performance does exist (Ittner and Larcker, 1998b) and that the use of short-term performance measures do not reflect the increase or decrease in the organization’s economic performance (Johnson and Kaplan, 1987). Nevertheless, overall, the results of this study lend some support to the arguments that firms may perform better if multiple performance measures are used for performance evaluation of the firms (e.g. Hoque and James, 2000; Ittner et al., 2003; Davis and Albright, 2004; Bryant et al., 2004). For example, Ittner et al. (2003) found that organizational performance is significantly and positively associated with the extent to which the firm measures and uses information related to a diverse set of financial and non-financial performance measures. Conclusion From the survey, apparently, many Malaysian companies still focus heavily on the use of financial measures as compared to non-financial measures. However, the use of non-financial measures is gaining momentum particularly in the use of customer measures. The findings suggest that the use of BSC measures in the form of internal business process and innovation and learning measures proved to have a significant effect on performance. The results also suggest that financial measures alone are not sufficient to measure performance. The results also suggest that when firms use a performance measurement system that comprises all four perspectives of BSC measures, their performance is much better than when they rely solely on an individual perspective. It is evident that BSC is a comprehensive measure of performance that reflects the needs of effective management and provides a guide for improvement. One important practical implication of this study is on the design of control and measurement systems where the designers of control and performance measurement systems need to emphasize the use of multiple performance measures that are fundamental to the

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success of firms. The use of multiple performance measures may allow some trade-offs because benefits from their usage cannot be obtained across all the measures all the time. It is important to stress that this study is quite preliminary. In evaluating this study, several limitations should be noted. First, the sample was taken only from the FMM directory where the population is limited to only the manufacturing firms that are members of the association. Thus, the sample was relatively small and not comprehensive enough. Also, confinement of the sample only to manufacturing firms would provide a potential source of bias to generalization. In order to get better understanding of the BSC concepts and its application, future research should examine larger sample size and apply BSC concepts of performance measures beyond manufacturing. However, one must take note of the need that the original architecture of the BSC be modified in order to suit and adapt to the mission and vision of the organization. Second, the firm-level analysis of cross-sectional data would not find consistent associations between non-financial measure and future performance. Therefore, as suggested by Ittner and Larcker’s (1998b), future studies are suggested to use time-series data as an alternative to this cross-sectional study in investigating the impacts of the BSC measures usage on firm performance. Finally, the instrument to measure BSC variables was rather novel and used only selected performance measures. Further study could lead to refinement of the BSC variables whereby other performance measures within the four perspectives proposed by Kaplan and Norton and other perspectives of BSC could be identified in future research. References Abernethy, M. and Lilis, A.M. (1995), “The impact of manufacturing flexibility on management control system design”, Accounting, Organization and Society, Vol. 20 No. 4, pp. 241-58. Anderson, E.W., Fornell, C. and Lehmann, D.R. (1994), “Customer satisfaction, market share, and profitability: findings from Sweden”, Journal of Marketing, Vol. 58 No. 3, pp. 53-66. Anthony, R. and Govindarajan, W. (2003), Management Control Systems, McGraw-Hill, New York, NY. Banker, R.D., Potter, G. and Srinivasan, D. (2000), “An empirical investigation of an incentive plan that includes non-financial performance measures”, The Accounting Review, Vol. 75 No. 1, pp. 65-92. Brewer, P. (2002), “Putting strategy into the balanced scorecard”, Strategic Finance, Vol. 83 No. 7, pp. 44-52. Bruns, W.J. Jr and McKinnon, S.M. (1993), “Information and managers: a field study”, Journal of Management Accounting Research, Vol. 5, pp. 84-123. Bryant, L., Jones, D.A. and Widener, S.K. (2004), “Managing value creation within the firm: an examination of multiple performance measures”, Journal of Management Accounting Research, Vol. 16, pp. 107-31. Chenhall, R.H. (2005), “Integrative strategic performance measurement system, strategic alignment of manufacturing, learning and strategic outcomes: an exploratory study”, Accounting, Organizations and Society, Vol. 30 No. 5, pp. 395-422. Davis, S. and Albright, T. (2004), “An investigation of the effect of balanced scorecard implementation on financial performance”, Management Accounting Research, Vol. 15 No. 2, pp. 135-53.

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Droge, C., Jayaram, J. and Vickery, S. (2000), “The ability to minimize the timing of new product development and introduction: an examination of antecedent factors in the North American automobile supplier industry”, Journal of Production Innovation Management, Vol. 17, pp. 24-40. Ghalayini, A.M. and Noble, J.S. (1996), “The changing basis of performance measurement”, International Journal of Operations & Production Management, Vol. 16 No. 8, pp. 63-80. Govindarajan, V. (1984), “Appropriateness of accounting data in performance evaluation: an empirical examination of environmental uncertainty as an intervening variable”, Accounting, Organizations and Society, Vol. 9 No. 2, pp. 125-35. Govindarajan, V. (1988), “A contingency approach to strategy implementation at the business-unit level: integrating administrative mechanisms with strategy”, Academy of Management Journal, Vol. 31 No. 4, pp. 828-53. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis, Prentice-Hall, Englewood Cliffs, NJ. Hayes, D.C. (1977), “The contingency theory of managerial accounting”, The Accounting Review, Vol. 52 No. 1, pp. 22-39. Hemmer, T. (1996), “On the design and choice of ‘modern’ management accounting measures”, Journal of Management Accounting Research, Vol. 8, pp. 87-116. Hongren, C.T., Foster, G. and Datar, S.M. (2006), Cost Accounting: A Managerial Emphasis, Prentice-Hall, Englewood Cliffs, NJ. Hoque, Z. and James, W. (2000), “Linking balanced scorecard measures to size and market factors: impact on organizational performance”, Journal of Management Accounting Research, Vol. 12, pp. 1-17. Hoque, Z., Mia, L. and Alam, M. (2001), “Market competition, computer-aided manufacturing and use of multiple performance measures: an empirical study”, British Accounting Review, Vol. 33, pp. 23-45. Igbaria, M., Livari, J. and Maragah, H. (1995), “Why do individuals use computer technology? A Finnish case study”, Information and Management, Vol. 29, pp. 227-38. Ittner, C.D. and Larcker, D.F. (1998a), “Innovations in performance measurement: trends and research implications”, Journal of Management Accounting Research, Vol. 10, pp. 205-38. Ittner, C.D. and Larcker, D.F. (1998b), “Are non-financial measures leading indicators of financial performance? An analysis of customer satisfaction”, Journal of Accounting Research, Vol. 36, pp. 1-35. Ittner, C.D. and Larcker, D.F. (2001), “Assessing empirical research in managerial accounting: a value based management perspective”, Journal of Accounting and Economics, Vol. 32 Nos 1-3, pp. 349-410. Ittner, C.D., Larcker, D.F. and Randel, T. (2003), “Performance implications of strategic performance measurement in financial services firms”, Accounting, Organizations and Society, Vol. 28 Nos 7-8, pp. 715-41. Johnson, H.T. and Kaplan, R.S. (1987), Relevance Lost: The Rise and Fall of Management Accounting, Harvard Business School Press, Boston, MA. Kald, M. and Nilsson, F. (2000), “Performance measurement at Nordic companies”, European Management Journal, Vol. 18 No. 1, pp. 113-27. Kaplan, R.S. and Norton, D.P. (1992), “The Balanced Scorecard: measures that drive performance”, Harvard Business Review, Vol. 70 No. 1, pp. 71-9. Kaplan, R.S. and Norton, D.P. (1996a), “Using the Balanced Scorecard as a strategic management system”, Harvard Business Review, Vol. 74 No. 1January-February, pp. 75-85.

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Kaplan, R.S. and Norton, D.P. (1996b), Translating Strategy into Actions: The Balanced Scorecard, Harvard Business School Press, Boston, MA. Kaplan, R.S. and Norton, D.P. (1996c), “Linking the balanced scorecard to strategy”, California Management Review, Vol. 39 No. 1, pp. 53-79. Kaplan, R.S. and Norton, D.P. (2001), The Strategy-focused Organization: How Balanced Scorecard Companies Thrive in the New Business Environment, Harvard Business School Press, Boston, MA. Lingle, J.H. and Schiemann, W.A. (1996), “From balanced scorecard to strategic gauges: is measurement worth it?”, Management Review, Vol. 85 No. 3, pp. 56-61. Maiga, A.S. and Jacobs, F.A. (2003), “Balanced scorecard, activity-based costing and company performance: an empirical analysis”, Journal of Managerial Issues, Vol. 15 No. 3, pp. 283-301. Mia, L. and Clarke, B. (1999), “Market competition, management accounting systems and business unit performance”, Management Accounting Research, Vol. 10, pp. 137-58. Newing, R. (1995), “Wake up to the Balanced Scorecard”, Management Accounting, Vol. 73 No. 3, pp. 22-5. Nunnally, J.C. (1978), Psychometric Theory, McGraw-Hill, New York, NY. Paladino, B. (2000), “What is strategic-operational misalignment costing your firm each year?”, Journal of Corporate Accounting and Finance, Vol. 11 No. 5, pp. 47-56. Perera, S., Harrison, G. and Poole, M. (1997), “Customer-focused manufacturing strategy and the use of operations-based non-financial performance measures: a research note”, Accounting, Organizations and Society, Vol. 22 No. 6, pp. 557-72. Said, A.A., HassabElnaby, H.R. and Wier, B. (2003), “An empirical investigation of the performance consequences of non-financial measures”, Journal of Management Accounting Research, Vol. 15, pp. 193-223. Shields, M.D. (1997), “Research in management accounting by North Americans in the 1990s”, Journal of Management Accounting Research, Vol. 9, pp. 3-62. Sim, K.L. and Koh, H.C. (2001), “Balanced Scorecard: a rising trend in strategic performance measurement”, Measuring Business Excellence, Vol. 5 No. 2, pp. 18-26. Swamidas, P. and Newell, W. (1987), “Manufacturing strategy, environmental uncertainty and performance: a path analytic model”, Management Science, Vol. 33 No. 4, pp. 509-24. Young, S.M. and Selto, F.H. (1993), “Explaining cross-sectional workgroup performance differences in a JIT facility: a critical appraisal of a field-based study”, Journal of Management Accounting Research, Vol. 5, pp. 300-26. Further reading Amaratunga, D., Baldry, D. and Sarshar, M. (2001), “Process improvement through performance measurement: the balanced scorecard methodology”, Work Study, Vol. 50 No. 5, pp. 179-88. Atkinson, A.A., Balakrishnan, R., Booth, P., Cote, J.M., Groot, T., Malmi, T., Roberts, H., Uliana, E. and Wu, A. (1997), “New directions in management accounting research”, Journal of Management Accounting Research, Vol. 9, pp. 70-108. Lipe, M. and Salterio, S. (2000), “The Balanced Scorecard: judgemental effects of common and unique performance measures”, The Accounting Review, Vol. 75 No. 3, pp. 283-98. Malina, M.A. and Selto, F.H. (2001), “Communicating and controlling strategy: an empirical study of the effectiveness of the Balanced Scorecard”, Journal of Management Accounting Research, Vol. 13, pp. 47-90.

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About the authors Ruzita Jusoh is a lecturer in the Department of Management Accounting and Taxation, Faculty of Business and Accountancy, University of Malaya, Kuala Lumpur, Malaysia. She holds a Doctorate from the School of Management, University Sains Malaysia, specializing in management accounting. She obtained a Masters degree in Accounting from the University of Missouri, Kansas City, USA and a Bachelors degree in Business Administration (Accounting) from University of Alabama, USA. Her research interests include management accounting control systems and strategic performance management systems for all types of organizations. She has published a book and several papers in local and international journals and conference proceedings. Ruzita Jusoh is the corresponding author and can be contacted at: [email protected] or [email protected] Daing Nasir Ibrahim, PhD BBA, MBA, CA (M), FCPA (Australia) is Professor of Accounting and the Dean of the School of Management, Universiti Sains Malaysia (USM). Dr Daing is a Chartered Accountant (Malaysia), a Fellow of the Certified Practicing Accountants of Australia (FCPA) and Vice President of its Malaysian Division. Utilizing his vast knowledge and experiences in the field of accounting and management, he is actively involved in the administration of the University, and at the same time lends his expertise to various other organizations such as professional accounting bodies, business associations and government agencies. He sits on the Council of the Malaysian Institute of Accountants and on several of its committees. His areas of expertise are Management Accounting and Control, Corporate Governance, and Research Methodology. Yuserrie Zainuddin is currently serving as an Associate Professor in the School of Management, Universiti Sains Malaysia. He graduated with Doctor of Business Administration specialised in Accountancy from the Universiti Kebangsaan Malaysia. He is currently teaches management accounting, management control systems, international business management and research methodology for undergraduate, and postgraduate programmes. He also has been publishing numerous articles in local and international journals. He has been a member of journal editorial board of three journals in Indonesia and actively involved as a member and formerly the Honorary Secretary of Asian Academy of Management.

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