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Case Creve Couer Pizza, Inc Essay

The Emerald Research Register for this journal is available at www. emeraldinsight. com/researchregister The current issue and full text archive of this journal is available at www. emeraldinsight. com/1741-0401. htm An empirical study of performance measurement in manufacturing ? rms Maurice Gosselin ? ? School of Accountancy, Universite Laval, Quebec City, Canada Abstract Purpose – The recent performance measurement literature suggests that organizations should put more emphasis on non-? ancial measures in their performance measurement systems, that organizations must use new performance measurement approaches such as the balanced scorecard and that measures should be aligned with contextual factors such as strategy and organizational structure. The purpose of this paper is to assess the extent to which organizations are following these prescriptions. Design/methodology/approach – A survey of a sample of Canadian manufacturing ? rms was conducted. In the questionnaire, organizations had to indicate the extent to which they use 73 performance measures.

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They also had to respond to questions about determinants such as strategy, organizational structure and environmental uncertainty. More than 100 organizations responded to the survey. The response rate was 50. 5 percent. Findings – The results show that manufacturing ? rms continue to use ? nancial performance measures. Despite the recommendations from experts and academics, the proportion of ? rms that implement a balanced scorecard or integrated performance measurement systems is low. Furthermore, organizations that use these approaches are not employing more extensively non-? ancial measures than those which are applying traditional performance measurement approaches. This research project also shows that there are some signi? cant relationships between the types of measures and contextual factors like strategy, decentralization and environmental uncertainty. This research ? nally demonstrates clearly that there is a need to develop a theory that explains how ? rms can use their performance measurement system to enhance their performance. Originality/value – This paper provides information on performance measures used by organizations and their association with organizational eterminants. Keywords Performance measures, Performance management, Balanced scorecard Paper type Research paper Study of performance measurement 419 1. Introduction Since the beginning of the 1990s, performance measurement has become an important issue for academics and practitioners. The professional literature has suggested that managers should design new performance measurement systems that include ? nancial and non-? nancial measures. Kaplan and Norton (1992, 1993, 1996) advocated in favor of the design of balanced scorecards. Dixon et al. (1990) and Nanni et al. 1992) proposed the use of integrated performance measurement systems. All these systems would put more emphasis on non-? nancial measures and would enable organizations to give more weight to customers and internal processes in their performance The author acknowledges the ? nancial assistance provided by the FQRSC of the Gouvernement ? du Quebec. International Journal of Productivity and Performance Management Vol. 54 No. 5/6, 2005 pp. 419-437 q Emerald Group Publishing Limited 1741-0401 DOI 10. 1108/17410400510604566 IJPPM 54,5/6 420 measurement systems. Overall, this change would help ? ms to improve ultimately their performance. These suggestions have been in general well received in the accounting community according to the large number of books, seminars and professional articles on performance measurement. However, there is not much information on the extent to which ? rms actually use these performance measurement “innovations”. A few studies have been conducted recently and have revealed that organizations are implementing to some extent performance measurement innovations such as the balanced scorecard (Ax and Bjornenak, 2000; Ittner and Larcker, 1998; Malmi, 2000).

These studies have provided some explanations of the diffusion process for this innovation but have not attempted to establish a closer link between the measures used, the innovation and some contextual factors. The purpose of this paper is to examine what are the measures that manufacturing ? rms use, classify these measures into categories, assess the extent to which ? rms use performance measurement innovations such as the balanced scorecard and integrated performance measurement system and examine the association between the measures and contextual factors like strategy, decentralization and environmental uncertainty.

A survey was mailed to 200 randomly selected Canadian manufacturing ? rms to collect data on their performance measurement systems. After extensive follow-up procedures, 101 responses were received. The results show that traditional measures are still widely used and that the proportion of ? rms that have decided to implement new performance measurement approaches is relatively low. Furthermore, the level of performance measurement competence does not seem to be as high as one could expect. More speci? ally, the results show that the types of performance measures used by ? rms are seldom associated to strategy, environmental uncertainty and decentralization. This paper is organized as follows. A brief review of the literature is completed in Section 2. The questionnaire and the data collection process are described in Section 3. The results are described and discussed in Section 4. 2. Empirical research on performance measurement and hypotheses Research on performance measurement has gone through several phases during the last 30 years.

In the 1970s, researchers examined how organizations used management accounting systems especially budgeting as tools for performance measurement. In the 1980s, the focus was put essentially on the budgeting process and its impact on performance. The scope of the research on performance measurement began to broaden in the beginning of the 1990s. Dixon et al. (1990) and Kaplan and Norton (1992, 1993, 1996) developed new perspectives and frameworks to organize performance measurement systems. Nanni et al. (1992) suggested that ? ms should increase their level of performance measurement competence. The degree of competence would depend on the ? t between the design of the performance measurement system and the strategy of the ? rm. Kaplan and Norton suggested that the performance of a ? rm would increase with the use of a balanced scorecard. Surprisingly, only a few empirical studies were conducted during the 1990s and they have not really been able to test the extent to which these prescriptions are followed by organizations and their impact on the performance.

Traditionally, management accountants have relied on the use of ? nancial measures to evaluate the performance of cost centers. Since the end of the 1980s, academics, consulting ? rms and practitioners have all emphasized the need to give more weight to non-? nancial measures in performance measurement systems. Despite these recommendations, we may expect that organizations, especially in the manufacturing industries, will still rely mainly on ? nancial measures. Therefore, the ? rst hypothesis is: H1. Firms tend to use more frequently ? nancial measures than non-? ancial measures. New approaches to performance measurement suggest that organizations should use more non-? nancial measures than traditional performance measurement systems. Thus, we may expect that the extent to which organizations use non-? nancial measures will be higher in ? rms that have implemented innovations if performance measurement systems such as the balanced scorecard or integrated performance measurement system. H2. Firms that have implemented a balanced scorecard or an integrated performance measurement system use more frequently non-? nancial measures.

The concept of performance measurement competence suggests that organizations use types of measures that ? t with their strategy, their organizational structure and the environmental uncertainty that they face. The type of strategy employed by a ? rm should in? uence the design of the performance measurement system. Miles and Snow (1978, 1994) identi? ed four strategic types of organizations according to the rate at which they change their products and markets: prospectors, defenders, analyzers and reactors. The fundamental difference among these types is the rate of change in the organizational domain.

Prospectors are characterized by their dynamism in seeking market opportunities, their capability to develop and produce new products to meet customers’ needs, their investment in large amounts of ? nancial resources related to research and development, and their enhancement of teamwork. They are usually innovators that create change in their respective industries. Defenders have a strategy that is the polar opposite from prospectors. They operate within a narrow product-market domain characterized by high production volume and low product diversity. Defenders compete aggressively on price, quality and ustomer service. They engage in little or no product/market development and stress ef? ciency of operations. Defenders are likely to face a lower level of environmental uncertainty than prospectors (Slocum et al. , 1985; Govindarajan, 1986). Analyzers stand between these two categories, sharing characteristics of both prospectors and defenders. Reactors do not follow a conscious strategy. They are viewed as a dysfunctional organizational type. The premise of the Miles and Snow typology is that prospector, defender and analyzer strategies, if properly implemented, can lead to effective performance.

Since prospectors search continually for market opportunities and have a broad product-market domain, they will tend to adapt their performance measurement systems to their strategy and, therefore, focus on non-? nancial measures pertaining to customers, products, employees and quality. Defenders will tend to put more emphasis on ? nancial measures such as variances. Therefore, we may hypothesize that: Study of performance measurement 421 IJPPM 54,5/6 H3. Prospectors tend to use more frequently non-? nancial measures while defenders tend to use more frequently ? nancial measures.

Centralization has been used as a proxy for organizational structure in most empirical studies in management accounting. Centralization represents the extent to which the decision-process pertaining to the management of divisions or subsidiaries is centralized. The link between centralization (decentralization) and management accounting systems has been investigated in many management accounting studies (Gordon and Narayanan, 1984; Chenhall and Morris, 1986; Govindarajan, 1988; Gul and Chia, 1994). This research showed that centralization plays a key role in the design of management accounting systems.

In the area of performance measurement, Anthony and Govindarajan (1995) suggested that ? nancial measures are more important at higher hierarchical levels and non-? nancial measures at lower levels such as at work centers. Therefore, we may elaborate the following hypothesis: H4. Firms that are more centralized tend to use more ? nancial measures while decentralized ? rms tend to use more non-? nancial measures. Environmental uncertainty is considered as one of the factors that in? uence the design of management accounting system and performance measurement systems.

This construct has been studied in several management accounting studies (Tymon et al. , 1998). Gordon and Narayanan (1984) found that organizational structure and perceived environmental uncertainty are closely related and that high levels of PEU are positively associated with organic structures and the perceived importance of broad scope information. Chenhall and Morris (1986) reached the same conclusion. Gul (1991) concluded that, when environmental uncertainty is high, sophisticated managerial accounting systems enhance performance. This research suggests that managers of ? ms operating in a volatile environment attribute more importance to information which is deemed relevant for decision making as opposed to information from traditional systems which is generally produced for the purpose of coordinating and controlling and not for planning. The following hypothesis is based on these ? ndings. H5. Firms that face a higher level of environmental uncertainty tend to use more frequently non-? nancial measures in comparison to ? rms that face a lower level of environmental uncertainty. 3. Survey instrument A questionnaire was designed and administered to a sample of 200 Canadian manufacturing.

The instrument was divided into ? ve sections. The ? rst section included a list of 73 ? nancial and non-? nancial measures widely used by organizations and mentioned in the professional and the academic literature. A list of the measures is included in Table I. The second section pertained to changes in the performance measurement system. Respondents were asked if they had adopted the balanced scorecard or an integrated performance measurement approach, their overall evaluation of the success of the approach and the degree of ownership of the performance measurement initiative.

The third section included Chenhall and Morris’ (1986) instrument on management accounting system. In the fourth section, respondents needed to evaluate on a ? ve-point scale the level of environmental uncertainty and decentralization in their business. The instrument to measure environmental 422 Performance measures Net pro? t Gross pro? t margin Total sales of revenues Pro? t before tax Cost of goods sold Total expenses Total costs by department Amount of raw material inventory Cost per unit produced Amount of ? nished good inventory Total operating cash ? ws Number of worker injuries Total net cash ? ows Inventory turnover ratio Rate of incidence of injuries Backlog in the delivery schedule Number of customer complaints Account receivable turnover Amount of work in process inventory Length of time from order delivery Materials price variance Number of units produced Rate of production capacity or resources used Amount of material scrap produced Return on sales Number of employee hours Number of units of ? nished goods in the inventory Number of customer orders received Rate of incidence of production defects Labor ef? iency variance Level of absenteeism Number of machine or plant hours used Return on investment (ROI) Total of cash receipts Number of customer orders completed Number of unit of material components in the inventory Current ratio Total sales per region Cost reduction resulting from quality product improvement Total of cash disbursements Unit of output per hours of labor used Total sales per employee Number and length of down time Market share Cost quality Return on equity (ROE) Number of warranty claims Mean 4. 76 4. 71 4. 69 4. 57 4. 30 4. 22 4. 20 4. 13 4. 09 4. 08 4. 08 4. 07 4. 05 4. 2 3. 98 3. 95 3. 90 3. 90 3. 82 3. 75 3. 68 3. 67 3. 66 3. 65 3. 62 3. 58 3. 57 3. 56 3. 55 3. 51 3. 50 3. 49 3. 43 3. 43 3. 42 3. 42 3. 40 3. 29 3. 28 3. 27 3. 26 3. 21 3. 20 3. 19 3. 16 3. 16 3. 15 Standard deviation 0. 59 0. 75 0. 77 0. 85 1. 13 1. 04 0. 95 1. 22 1. 14 1. 15 1. 21 1. 27 1. 20 1. 16 1. 29 1. 36 1. 32 1. 20 1. 25 1. 38 1. 46 1. 39 1. 31 1. 49 1. 52 1. 24 1. 50 1. 40 1. 36 1. 53 1. 31 1. 38 1. 56 1. 55 1. 47 1. 48 1. 39 1. 58 1. 32 1. 50 1. 43 1. 44 1. 40 1. 33 1. 38 1. 58 1. 64 (continued) Study of performance measurement 423 Table I. Performance measures IJPPM 54,5/6

Performance measures Customer satisfaction: survey ratings Materials quantity variance Labor rate variance Number of doubtful account receivable Amount of training expenses Total sales per sale representative Unit of output per machine hours used Number of new employees Number of employee hours per shift Percentage of key staff turnover Number of new products Tonnage of production waste produced Quantity of energy consumed Number of new customers Cost per damaged unit produced Unit of output per unit of raw materials used Time-to-market for new products Number of new customer contacts Earnings per share Average sales order Number of lines or products Stock price Unit of output per square foot used Number of removed products Price-earnings ratio Rate of products removal Mean 3. 12 3. 12 3. 10 3. 06 3. 03 2. 94 2. 85 2. 82 2. 75 2. 75 2. 71 2. 69 2. 67 2. 61 2. 53 2. 52 2. 48 2. 40 2. 40 2. 36 2. 27 1. 90 1. 84 1. 81 1. 80 1. 51 Standard deviation 2. 46 1. 51 1. 63 1. 46 1. 37 1. 53 1. 37 1. 34 1. 40 1. 42 1. 40 1. 54 1. 36 1. 37 1. 38 1. 42 1. 32 1. 27 1. 75 1. 28 1. 30 1. 68 1. 04 1. 05 1. 58 0. 80 424 Table I. uncertainty developed by Gordon and Narayanan (1984) was employed.

It included six items pertaining to price, purchase, labor, product diversity, selling and distribution costs and quality while the instrument for decentralization referred to 12 decisions. The Miles and Snow (1978) instrument for strategy typing was included in section ? ve. The last section, section ? ve, included questions on background information. The population surveyed consisted of Canadian manufacturing ? rms included in the SIC codes 30-39. A list of ? rms was drawn from the Financial Post “CanCorp” CD-ROM database. A sample of 200 ? rms was randomly drawn from this list. The questionnaire was sent to vice-president ? nances, CFO or controllers. The names and addresses of managers were extracted from the CanCorp database.

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To insure that the response rate would be acceptable and to avoid non-responses biases, extensive data collection procedures were performed. An initial copy of the questionnaire was sent with a pre-paid and preaddressed envelope. A follow-up letter was sent three weeks later and another questionnaire was sent six weeks after the initial mail out. A research assistant called non respondents to attempt to know why they had not responded to the survey. These procedures yielded a response rate of 50. 5 percent, 101 responses for an initial sample of 200 ? rms. 4. Results A preliminary analysis of the results of the survey show that manufacturing ? rms are still using to a large extent ? nancial measures. In Table I, the 73 measures were ranked ccording to the mean result of the extent to which respondent use each of the measures. The use of each metrics was measured on a ? ve-point scale ranging from rarely (1) to frequently (5). The results show that ? nancial measures are more frequently used by manufacturing ? rms in the sample. Therefore, H1 is con? rmed. The ? rst 11 measures are ? nancial. These measures are: net pro? t, gross pro? t margin, total sales of revenues, pro? t before tax, cost of goods sold, total expenses, total cost by department, amount of raw material inventory, cost per unit produced, amount of ? nished good inventory, total operating cash ? ows. The ? rst non-? nancial measure is the number of worker injuries.

The next non-? nancial measure that is ranked 14th is the rate of incidence of injuries. These results show clearly that despite all the recommendations to put more emphasis on non-? nancial measures, management in manufacturing ? rms is still giving much more weight in the performance measurement system to ? nancial measures. Since this project is exploratory, a factor analysis with Varimax was used to classify the measures into categories and to investigate if there were any patterns among the 73 measures. The factor analysis identi? ed 12 factors with eigenvalues greater than 1 that in total explained 68. 04 percent of the variations in the 73 measures.

Table II includes the factor loading for each of the measure. Table III includes the list of the 12 factors that were derived from the factor analysis and the measures that were included in each one of them. The ? rst factor was labeled customer and product. It included 12 measures related to customers and products. The second factor pertained to production. This factor includes ? nancial and non-? nancial measures such number of units produced and amount of raw material in inventory. Net pro? t, a classical ? nancial measure was also included in this category. The third factor, ? nancial ratio, included nine measures that are typical ? nancial ratios.

The fourth factor, employee, is composed of eight measures related to employees such as the level of absenteeism or the number of work injuries. The ? fth factor is a set of non-? nancial measures pertaining to production. Factor 6, material and labor variances, is made of a group of measures that are typically ? nancial. Factor 7 is labeled stock market measures. Factors 8 and 9 are derived from order and delivery and quality measures. Factors 10-12 are groups of ? nancial measures: revenues and pro? t, return on investment and account receivable. The factor analysis enabled the researcher to identify 12 dimensions that are considered in manufacturing ? rms.

These factors will be used later in this paper to test the last three hypotheses. One of the purposes of this paper was to assess the extent to which manufacturing ? rms have adopted new performance measurement approaches such as the balanced scorecard and integrated performance measurement. Table IV shows the results for the questions about the adoption of new performance measurement approaches. Eighteen ? rms mentioned that they had adopted the balanced scorecard while 12 indicated that they used an integrated performance measurement system. One ? rm revealed that it had adopted another new performance measurement approach. The proportion of ? ms that adopted a new performance measurement approach is 30. 7 percent. To test H2, an analysis of the means of the results for each performance measures was completed. The organizations were divided into two groups: performance measurement innovations and non innovators. A comparison of the group means was Study of performance measurement 425 426 IJPPM 54,5/6 Number of customer complaints Number of warranty claims Index of customer satisfaction based on surveys Length of time from order to delivery Backlog in the delivery schedule Number of customer orders received Number of customer orders completed Number of new customer Number of new customer contacts (visits, phone calls, etc. Total sales per employee Number of doubtful account receivable Account receivable turnover 0. 1109 0. 0525 0. 2474 0. 0493 0. 1205 20. 0221 0. 1314 0. 0562 0. 1615 20. 0225 0. 1048 20. 0078 0. 0162 0. 0766 0. 0778 0. 0051 0. 0647 0. 1659 0. 1726 0. 5579 0. 7221 0. 7437 0. 6533 0. 1926 0. 1999 0. 0311 0. 2325 0. 2014 0. 2232 0. 2916 0. 3952 0. 1437 0. 0040 0. 0512 0. 2734 0. 0575 0. 0678 20. 3177 0. 0654 2 0. 0289 0. 3473 0. 0461 0. 1457 0. 1122 20. 0310 2 0. 0235 0. 1709 0. 7545 20. 1461 0. 2787 20. 1700 0. 1481 0. 0155 20. 0242 0. 1192 0. 1658 0. 1361 0. 1464 20. 1231 20. 0978 0. 3101 0. 2509 0. 0036 0. 2237 0. 0507 0. 0746 2 0. 0239 0. 1300 0. 2174 20. 2498 20. 0461 0. 0243 2 0. 764 0. 1322 0. 1648 0. 2123 0. 2102 0. 2558 2 0. 1383 0. 0611 2 0. 0876 0. 2723 0. 1279 0. 2281 0. 0769 0. 0379 0. 2820 0. 2102 20. 0816 20. 0713 0. 1264 0. 1275 0. 4942 0. 1988 0. 2961 0. 1748 2 0. 1063 20. 0890 Table II. Factor loading Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Factor 9 Factor 10 Factor 11 Factor 12 0. 0916 20. 0341 0. 2105 20. 0444 0. 4567 20. 1215 0. 1442 2 0. 0985 0. 0043 0. 0540 0. 0355 2 0. 0253 0. 0492 0. 2507 0. 0696 0. 0115 0. 0123 0. 2127 0. 0646 0. 1306 0. 2858 2 0. 0338 20. 1434 2 0. 0290 0. 1211 0. 1090 0. 5430 0. 4250 0. 4724 (continued) Factor 1 0. 1195 0. 4304 0. 1141 20. 1750 0. 1538 0. 1349 . 3013 0. 3325 0. 6150 20. 0182 0. 6387 20. 0876 0. 3800 20. 0788 20. 0479 0. 4124 0. 1428 Factor 1 0. 0908 0. 1066 20. 3380 20. 0387 0. 0952 2 0. 0491 0. 0109 20. 1876 0. 2024 20. 0350 0. 1453 0. 2713 20. 1555 20. 0936 0. 1776 0. 3259 0. 0550 0. 1399 0. 1894 0. 0654 0. 1162 0. 1761 0. 0504 0. 0952 0. 1955 0. 1324 20. 0138 0. 0758 0. 0692 0. 2847 0. 1303 0. 1497 0. 0827 0. 0900 0. 0987 0. 0949 0. 1253 0. 1562 0. 0793 2 0. 0784 0. 0813 0. 1197 0. 2012 0. 0892 0. 1361 0. 2449 0. 0685 0. 2080 0. 0364 20. 0138 0. 1511 0. 2265 0. 0379 2 0. 0310 0. 0299 20. 1172 0. 0887 0. 1254 0. 1622 2 0. 0722 0. 2975 0. 0227 0. 1381 2 0. 0388 0. 1325 2 0. 0520 0. 046 2 0. 0242 0. 2469 2 0. 1304 20. 0384 2 0. 1134 0. 7230 2 0. 0194 0. 5672 2 0. 1273 0. 0092 2 0. 1182 0. 0232 2 0. 2978 0. 0447 0. 0750 0. 0232 0. 2612 0. 3228 2 0. 0349 0. 3592 2 0. 0416 0. 0603 0. 0151 0. 1061 2 0. 0009 0. 3894 0. 0676 0. 0671 0. 0114 0. 0691 0. 1481 0. 0768 0. 0312 0. 0241 20. 0524 0. 0682 20. 0896 0. 1649 20. 0810 2 0. 0082 20. 2367 0. 0793 0. 1679 0. 2293 2 0. 0028 0. 2272 0. 3968 0. 1469 2 0. 0529 20. 0669 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Factor 9 Factor 10 Factor 11 Factor 12 0. 1407 2 0. 1324 20. 0993 0. 1152 0. 2770 0. 5311 20. 0592 0. 2792 0. 4484 0. 0907 0. 0612 0. 0944 20. 0090 0. 2014 0. 567 0. 0762 20. 1063 0. 0515 0. 0997 0. 2305 0. 1317 0. 1794 0. 0387 0. 2168 0. 2441 0. 1330 0. 2051 0. 0165 20. 1150 0. 0402 0. 2537 0. 1863 0. 0124 (continued) Market share 0. 1165 0. 0018 Total sales per sale representative 0. 4276 0. 0533 Average sales order 0. 6522 0. 1506 Total sales per region 0. 5401 20. 0218 Total sales or revenues 0. 1821 0. 0252 Gross pro? t margin 0. 0109 0. 0260 Number of new products 0. 7461 0. 2366 Time-to-market for 0. 5387 0. 1177 new products Number of lines of 0. 7161 0. 1007 products Number of removed 0. 7585 0. 1482 products Rate of products removal 0. 7210 0. 0450 Number of units of ? nished goods in 0. 562 the inventory 0. 4301 Number of unit of material components in the 0. 5211 inventory 0. 4977 Amount of training expenses 0. 2715 0. 1595 Number of machine or plant hours used 20. 1109 0. 3251 Study of performance measurement 427 Table II. 428 IJPPM 54,5/6 Amount of raw material inventory 0. 0360 Amount of work in process inventory 0. 0788 Amount of ? nished good inventory 0. 2419 Rate of production capacity or resources used 0. 0084 Number and length of down time 0. 0619 Number of new employees 0. 3973 Number of employee hours 0. 1492 Number of employee hours per shift 0. 3951 Number of worker injuries 20. 1798 Rate of incidence of injuries 20. 379 Level of absenteeism 0. 1813 Percentage of key staff turnover 0. 4124 Rate of incidence of production or service defects 0. 1124 Amount of material scrap produced 0. 0366 0. 7872 0. 5876 0. 7485 0. 5253 20. 0577 0. 3940 0. 1291 0. 2794 0. 1362 0. 3088 0. 3194 0. 2281 0. 0307 0. 4314 20. 0780 0. 3826 20. 1062 0. 0314 0. 4539 0. 5022 0. 3522 0. 0156 0. 5922 0. 2246 0. 0898 0. 7186 0. 1561 0. 1765 0. 6985 0. 1059 0. 1410 0. 1480 0. 1781 0. 0594 20. 0458 0. 1139 0. 3504 0. 1642 0. 3364 0. 4978 0. 0001 0. 1159 0. 5246 0. 2589 0. 1912 2 0. 1655 0. 0580 0. 5294 0. 0991 20. 0943 2 0. 0198 0. 0895 0. 2224 0. 3840 0. 1699 0. 0841 0. 0242 0. 0752 0. 0833 0. 3684 0. 3324 0. 456 0. 1386 0. 0726 0. 2532 0. 0992 0. 1392 0. 1159 0. 1627 2 0. 0261 0. 1903 0. 3532 20. 0885 0. 1460 2 0. 0499 0. 0173 0. 1699 0. 1150 0. 0693 2 0. 0026 0. 1711 2 0. 1077 0. 1641 0. 0681 0. 3881 20. 2233 0. 0878 0. 0812 0. 1989 0. 1716 0. 1999 0. 1055 0. 3508 0. 2851 0. 1908 0. 0946 2 0. 0021 20. 0761 0. 0615 0. 0187 0. 1506 20. 0482 0. 2191 2 0. 0185 0. 2789 0. 0634 0. 2790 0. 1595 0. 1405 20. 0725 0. 3019 20. 0828 0. 3355 0. 0122 20. 0283 0. 2519 20. 0871 0. 2016 0. 0676 Table II. Factor 2 0. 0288 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Factor 9 Factor 10 Factor 11 Factor 12 0. 0468 0. 3196 0. 1498 0. 1067 0. 1580 0. 0234 0. 165 0. 0435 0. 0993 0. 2455 20. 0687 0. 0637 20. 0302 0. 0922 0. 0229 0. 1826 0. 1586 2 0. 1022 0. 0638 0. 0216 0. 1049 0. 1277 0. 1573 20. 1597 0. 3177 20. 0643 (continued) 0. 0299 2 0. 0503 20. 0059 2 0. 1826 0. 0689 2 0. 0419 20. 0535 2 0. 0045 Factor 1 Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Factor 9 Factor 10 Factor 11 Factor 12 0. 0726 0. 0592 0. 3587 0. 3038 0. 4826 0. 2272 2 0. 0428 0. 0255 0. 3218 20. 1120 2 0. 0400 0. 1987 0. 0865 0. 7154 0. 2069 2 0. 1444 0. 1554 0. 0832 20. 0865 0. 0330 20. 0350 0. 1240 0. 1511 0. 4021 20. 0180 0. 2703 0. 0146 0. 0798 0. 5443 0. 2830 0. 2815 0. 2965 0. 2653 0. 1191 0. 165 0. 0440 0. 2709 0. 1629 0. 2081 0. 2303 0. 1510 0. 0018 0. 3281 0. 0614 0. 1553 0. 0972 0. 7708 0. 7109 0. 7328 0. 6832 0. 2898 0. 2972 0. 2455 0. 0196 0. 1719 0. 0929 0. 2403 0. 0189 0. 4468 0. 1943 20. 0340 0. 2995 2 0. 2500 0. 0466 0. 2565 0. 1007 20. 0414 0. 6391 20. 1950 0. 0580 0. 1810 0. 6659 0. 2074 2 0. 0040 20. 0065 0. 2714 0. 5042 0. 2728 2 0. 0468 0. 0650 2 0. 0345 0. 1644 0. 0502 0. 7119 0. 2763 0. 0867 0. 0459 0. 0298 20. 1708 2 0. 1185 20. 0251 0. 2707 0. 1045 20. 2189 0. 2058 0. 1577 0. 2552 0. 3765 0. 3480 0. 1920 0. 1002 0. 0074 0. 1482 0. 0051 0. 1798 0. 2904 2 0. 0872 20. 0161 0. 2589 2 0. 1593 0. 1319 0. 2547 20. 0624 0. 879 20. 0186 2 0. 0462 0. 0563 2 0. 0435 20. 0844 0. 1721 0. 2287 0. 1502 0. 1313 0. 0423 0. 0335 0. 0347 0. 1015 0. 0810 0. 0919 0. 0315 0. 1195 0. 1442 0. 0421 20. 0494 0. 0439 2 0. 0740 0. 2195 0. 1445 0. 1533 0. 1674 20. 0871 0. 2673 0. 1950 (continued) Tonnage of production waste produced 0. 1210 Quantity of energy consumed (e. g. fuel, hydro, natural gas) 0. 1571 Production or service yields Unit of output per unit of raw materials used 0. 0295 Unit of output per hours of labor used 20. 0885 Unit of output per machine hours used 20. 0972 Unit of output per square foot used 0. 1863 Total costs by departments 0. 1391 Cost per unit produced 0. 029 Cost per damaged unit produced 0. 3558 Materials price variance 0. 1541 Materials quantity variance 0. 1371 Labor ef? ciency variance 0. 0856 Labor rate variance 0. 2617 Study of performance measurement 429 Table II. 430 IJPPM 54,5/6 Cost reduction resulting from quality product improvements 0. 2099 0. 2899 0. 0366 0. 3399 0. 0514 0. 3281 0. 2010 Cost of quality 0. 2612 0. 4083 0. 1122 0. 2152 0. 2049 20. 0427 0. 1513 Inventory turnover ratio 0. 0444 0. 5800 20. 0013 0. 2553 0. 0729 0. 2387 0. 0507 Net pro? t 20. 1041 0. 4913 0. 3223 0. 0366 0. 0676 0. 0454 0. 1497 Current ratio 0. 2313 0. 2022 0. 5153 20. 1141 0. 2858 20. 1391 2 0. 0185 Cost of goods sold 0. 1594 0. 010 0. 4474 20. 0245 20. 0376 0. 0740 2 0. 0813 Pro? t before tax 0. 0539 0. 2747 0. 4883 20. 1268 20. 0530 0. 1787 0. 1461 Earnings per share 0. 1679 0. 0009 0. 0265 0. 0041 0. 0163 0. 1555 0. 8079 Stock price 0. 1150 20. 0356 0. 1423 0. 1586 0. 0038 0. 0646 0. 8900 Price-earnings ratio 0. 1327 0. 0011 0. 1187 0. 0963 0. 0621 0. 1412 0. 8543 Return on sales 0. 1753 0. 0792 0. 4606 0. 2388 0. 0715 0. 1433 0. 2925 Return of equity (ROE) 0. 0550 0. 0918 0. 2985 0. 1273 0. 1497 0. 1062 0. 3605 Return on 20. 0347 0. 1935 0. 2543 0. 1644 0. 1647 0. 0914 0. 3987 investment (ROI) Total of cash 0. 7447 0. 2018 0. 0817 20. 0330 0. 2113 receipts 0. 1711 20. 078 Total of cash disbursements 0. 1524 20. 0015 0. 7754 0. 1351 0. 1121 20. 0298 0. 1020 Total net cash ? ows 0. 1442 0. 1528 0. 8036 20. 0155 0. 1143 20. 0055 0. 1975 Total operating cash ? ows 0. 0399 0. 1623 0. 7739 0. 0196 0. 2712 0. 0595 0. 1944 Total expenses 0. 2354 0. 0079 0. 6205 0. 0590 0. 0224 0. 2147 2 0. 2445 Number of units produced 0. 1486 0. 6217 0. 1874 0. 0902 0. 1722 0. 1426 2 0. 0395 20. 0546 20. 0183 0. 4307 0. 4427 0. 1099 0. 2295 0. 0171 0. 1188 0. 1156 0. 0299 0. 1221 0. 4147 0. 0941 2 0. 0610 0. 2324 0. 1484 0. 0744 0. 5679 0. 2123 0. 1269 0. 4136 20. 0227 0. 0558 20. 0055 0. 0988 0. 0206 20. 0128 0. 0799 0. 0147 20. 0083 20. 1372 2 0. 549 0. 2372 20. 0945 2 0. 0414 20. 0411 20. 0315 0. 0964 20. 0839 0. 2562 2 0. 0409 20. 0274 0. 2031 2 0. 0077 20. 0481 0. 0612 0. 0116 20. 1245 0. 1528 0. 1050 0. 1222 0. 0541 0. 2362 0. 2761 20. 0500 Table II. Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Factor 9 Factor 10 Factor 11 Factor 12 0. 1248 20. 0082 0. 0485 0. 0708 0. 2786 0. 4521 0. 1438 0. 0635 0. 1874 0. 1358 0. 1934 0. 1164 0. 0037 20. 1332 0. 0324 0. 0205 0. 0920 20. 0284 0. 1607 20. 0108 0. 3780 0. 0806 0. 5452 0. 5778 0. 1851 0. 1037 0. 0966 20. 1049 0. 1377 20. 1239 0. 0403 0. 0953 0. 1510 0. 0257 0. 1678 0. 1710 2 0. 0453 20. 0200 0. 0032 20. 3015 Factor 1

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Factor 1: customer and product sales Average sales order Cost per damaged unit produced Number of lines or products Number of new customer contacts Number of new customers Number of new products Number of removed products Number of warranty claims Rate of products removal Time-to-market for new products Total sales per region Total sales per sale representative Factor 2: production information Account receivable turnover Amount of ? nished good inventory Amount of material scrap produced Amount of raw material inventory Amount of work in process inventory Cost per unit produced Inventory turnover ratio Net pro? t Number and length of down time Number of unit of material components in the inventory Number of units of ? nished goods in the inventory Number of units produced Rate of production capacity or resources used Factor 3: ? nancial ratio Current ratio Pro? t before tax Return on sales Total costs by department Total expenses Total net cash ? ws Total of cash disbursements Total of cash receipts Total operating cash ? ows Factor 4: employee data Amount of training expenses Level of absenteeism Number of employee hours Number of new employees Number of worker injuries Percentage of key staff turnover Rate of incidence of injuries Rate of incidence of production or service defects Factor 5: non-? nancial ratio Number of employee hours per shift Number of machine or plant hours used Quantity of energy consumed Tonnage of production waste produced Unit of output per hours of labour used (continued) Study of performance measurement 431 Table III. Factors based on the factor analysis IJPPM 54,5/6 432 Table III.

Unit of output per machine hours used Unit of output per square foot used Unit of output per unit of raw materials used Factor 6: variance of labour and material Labour ef? ciency variance Labour rate variance Materials price variance Materials quantity variance Factor 7: stock market measures Earnings per share Price-earnings ratio Stock price Factor 8: order and delivery Backlog in the delivery schedule Length of time from order delivery Number of customer orders completed Number of customer orders received Factor 9: quality Cost quality Cost reduction resulting from quality product improvement Market share Number of customer complaints Factor 10: revenues and pro? t Cost of goods sold Gross pro? margin Total sales of revenues Factor 11: return on investment Return on equity (ROE) Return on investment (ROI) Customer satisfaction: survey ratings Factor 12: account receivable Number of doubtful account receivable) Total sales per employee performed. As it is shown in Table V, only a few performance measures were signi? cantly different. Therefore, we cannot con? rm that balanced scorecard and integrated performance system implementers use non-? nancial measures to a larger extent than other ? rms. In the questionnaire, ? rm’s controllers had to type their organizations according to the three statements developed by Miles and Snow (1978). Table VI shows that 35 ? rms were identi? ed as defenders while 21 as prospectors. The remaining 45 ? rms were considered as analyzers (42) or did not respond to this question (3). To test H3, we examine the Spearman correlation coef? ient between the strategy types and the 12 factors that were derived from the factor analysis. The results show that there is a signi? cant negative association between the defender strategy type and factor 1 (customer and product sales), factor 2 (production information) and factor 4 (employee data). Defenders seem to use less frequently these non-? nancial measures. The test shows that defenders are not using more frequently ? nancial measures. Therefore, the results con? rm partially H3. Pearson correlation coef? cients were used to test H4 about the association between centralization and the type of measures used by manufacturing ? rms. Centralization

Number of organizations Adopted a new performance measurement approach Balanced scorecard Integrated performance measurement Other Have not adopted a new performance measurement approach Total 18 12 1 31 70 101 Percent 17. 8 11. 9 1. 0 30. 7 69. 3 100 Study of performance measurement 433 Table IV. Adoption of new performance measures Performance measures Customer satisfaction: survey ratings Amount of training expenses Tonnage of production waste produced Stock price Price-earnings ratio Mean (no PM innovation) 2. 69 2. 83 2. 51 1. 65 1. 59 Mean (PM innovation) 4. 03 3. 38 3. 10 2. 52 2. 31 ( p-value) 0. 0001 0. 0654 0. 0796 0. 0202 0. 0421 Note: The table includes only the measures for which there was a signi? ant difference in the means Table V. Performance measures – t-test for group means Number of organizations Have identi? ed a precise type of strategy Defender Prospector Analyser Have not identi? ed a precise type of strategy Total 35 21 42 98 3 101 Percent 34. 7 20. 8 41. 6 97. 1 2. 9 100 Table VI. Type of strategy used was measured by adding the scores of the hierarchical levels at which 12 decisions are made. The mean and the standard deviation for each of the decisions are included in Table VII. The results in Table VIII show that there is a signi? cant negative association between centralization and factor 6 (non-? nancial measures) which includes eight non-? ancial measures and a signi? cant positive association between centralization and ? nancial measures grouped in factor 12. Therefore, the results con? rm H4. Decentralized ? rms tend to use more non-? nancial measures than centralized organizations. The last hypothesis pertained to the association between environmental uncertainty, measured with the Gordon and Narayanan’s (1984) instrument and the use of ? nancial and non-? nancial measures. Table IX includes the mean and the standard deviation for each of the six items included in the instrument. The results on the correlation between the factors and environmental uncertainty are shown in IJPPM 54,5/6

Decisions (level of authority: 1-5) Decide to design a new product Establish the budget level Choose the methods of work to be used Select machinery or equipment to be used Select suppliers Determine labour force requirements Select type or brand for new equipment Decide what type of costing system will apply Dismiss direct workers Determine sale prices Alter responsibilities or areas of work of a line department Determine personnel rewards Total Mean 4. 10 3. 85 2. 57 2. 83 2. 92 2. 94 3. 55 4. 43 2. 85 4. 23 2. 68 3. 81 40. 75 Standard deviation 1. 28 1. 13 1. 22 1. 15 1. 15 1. 01 1. 04 0. 91 1. 17 1. 02 1. 12 1. 16 7. 99 434 Table VII. Centralization Uncertainty Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Factor 9 Factor 10 Factor 11 Factor 12 Table VIII. Correlation matrix 0. 228 (0. 022)** 0. 18 (0. 861) 0. 178 (0. 076)* 0. 217 (0. 029)** 20. 019 (0. 847) 0. 063 (0. 533) 0. 010 (0. 923) 20. 014 (0. 889) 0. 147 (0. 142) 0. 027 (0. 792) 0. 028 (0. 782) 0. 021 (0. 834) Centralization 0. 133 (0. 184) 0. 020 (0. 846) 0. 090 (0. 368) 0. 120 (0. 230) 2 0. 332 (0. 001)*** 0. 005 (0. 957) 2 0. 148 (0. 139) 0. 090 (0. 370) 0. 047 (0. 638) 0. 022 (0. 828) 0. 125 (0. 214) 0. 194 (0. 052)* Prospector 0. 101 (0. 315) 0. 052 (0. 605) 2 0. 034 (0. 732) 0. 212 (0. 033)** 0. 042 (0. 675) 0. 213 (0. 032)** 0. 078 (0. 438) 2 0. 163 (0. 104) 0. 114 (0. 256) 0. 094 (0. 352) 0. 114 (0. 256) 0. 057 (0. 572) Strategy Analyzer 0. 089 (0. 376) 0. 130 (0. 194) 20. 11 (0. 271) 0. 008 (0. 935) 20. 073 (0. 467) 2 0. 193 (0. 053)* 20. 039 (0. 698) 0. 230 (0. 021)** 0. 002 (0. 983) 20. 125 (0. 212) 0. 027 (0. 791) 0. 057 (0. 575) Defender 2 0. 179 (0. 074)* 2 0. 220 (0. 027)** 0. 100 (0. 320) 2 0. 186 (0. 062)* 2 0. 002 (0. 984) 0. 009 (0. 929) 2 0. 023 (0. 817) 2 0. 020 (0. 844) 2 0. 143 (0. 154) 0. 100 (0. 318) 2 0. 144 (0. 151) 2 0. 117 (0. 244) Notes: *correlation is signi? cant at the 0. 10 level (two-tailed); **correlation is signi? cant at the 0. 05 level (two-tailed); ***correlation is signi? cant at the 0. 01 level (two-tailed) Table IX. The use of factor 1 (non-? nancial customer measures), factor 3 (? ancial ratio) and factor 4 (employee data) is signi? cantly associated to environmental uncertainty. Therefore, ? rms that are in a more unstable environment would tend to use ? nancial measures but also non-? nancial measures pertaining to customers and employees. 5. Discussion and conclusion The purpose of this study was to collect some empirical evidence on the level of performance measurement competence in manufacturing companies. The literature has suggested that ? rms should put much more emphasis on non-? nancial measures in comparison to ? nancial measures in their performance measurement systems. This study shows that, despite these prescriptions, ? ancial measures are much more used by controllers than non-? nancial measures. The results of the study which is cross-sectional do not enable the researcher to assess the extent to which controllers use ? nancial and non-? nancial measures has changed overtime. It only highlights the importance of ? nancial measures. The conclusions may have been different if the questionnaire had been administered to operation managers. The second aim of this research project was to evaluate to which extent controllers use balanced scorecards and integrated performance measurement systems. These recent innovations are already implemented in 30 percent of the respondents’ organizations.

However, the performance measures used in organizations that do balanced scorecards and integrated performance measurement systems are quite similar to those who do not. Only three non-? nancial measures were signi? cantly more frequently used in ? rms that have balanced scorecards and integrated performance measurement systems. There is, at this point of time, a strong need for empirical research on the balanced scorecard to better understand how they are designed and how managers use them. Performance competence is a new concept, introduced by Nanni et al. (1992). It suggests that to be competent, a performance measurement system should measure what is important for the organization and that the measures should correspond to its context.

The results demonstrate that there is an association between strategy and the different factors. For instance, there is a negative association between customer and sales measures and a defender strategy and other hand prospectors use more frequently employee data. This association is a demonstration of performance measurement competence. The association between centralization and performance measures was also examined and again some association was found. Study of performance measurement 435 Type of competition Price Quality-based By diversity of products Bidding for purchases or raw materials For manpower For selling and distribution Total Mean 4. 39 4. 26 3. 20 2. 88 3. 00 3. 8 20. 99 Standard deviation 0. 79 0. 80 1. 12 1. 11 1. 17 1. 22 3. 53 Table IX. Environmental uncertainty IJPPM 54,5/6 436 Despite the fact that the implementation of balanced scorecards and integrated performance systems has been highly recommended in the literature, the majority of organizations is not using these performance measurement innovations and is still relying on traditional ? nancial performance measures. The results shows clearly that there is a need to better understand how organizations design and implement their performance measurement systems and how they manage to improve their competence in performance measurement. Notes 1. 0 – Rubber and miscellaneous plastics products; 31 – Leather and leather products; 32 – Stone, clay and glass products; 33 – Primary metal industry; 34 – Fabricated metal products; 35 – Industrial machinery and equipment; 36 – Electronic and other electric equipment; 37 – Transportation equipment; 38 – Instruments; and 39 – Miscellaneous manufacturing industry. 2. This database is based on CanCorp plus, the CanCorp Canadian Corporations database produced by Micromedia Limited, and comprises data from the Financial Post data group of Canada. It contains ? nancial and management information extracted from the documents of more than 8,000 companies. The database includes major public corporations incorporated in Canada, major subsidiaries, privately held companies, major federal, provincial and municipal crown corporations, all companies listed on the Toronto Stock Exchange and all ? rms in the Report on Business Top 1,000 list. References Anthony, R. N. and Govindarajan, V. 1995), Management Control System, Irwin, Chicago, IL. Ax, C. and Bjornenak, T. (2000), “Bunding and diffusion of management accounting innovations: the case of the balanced scorecard in Scandinavia”, paper presented at the European Accounting Congress, Munich. Chenhall, R. H. and Morris, D. (1986), “The impact of structure, environment, and interdependence on the perceived usefulness of management accounting systems”, The Accounting Review, Vol. 61, pp. 16-35. Dixon, J. R. , Nanni, A. J. and Vollmann, T. E. (1990), The New Performance Challenge: Measuring Operations for World Class Competition, Dow-Jones Irwin, Homewood, IL. Gordon, L. A. and Narayanan, V. K. 1984), “Management accounting systems, perceived environmental uncertainty and organization structure: an empirical investigation”, Accounting, Organizations and Society, Vol. 9 No. 1, pp. 33-47. Govindarajan, V. (1986), “Decentralization, strategy and effectiveness of strategic business unit in multi business organizations”, Academy of Management Review, Vol. 11 No. 4, pp. 844-56. Govindarajan, V. (1988), “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. Gul, F. A. (1991), “The effects of management accounting systems and environmental uncertainty on small business managers’ performance”, Accounting and Business Research, Vol. 22, pp. 57-61. Gul, F. A. and Chia, Y. M. 1994), “The effects of management accounting systems perceived environmental uncertainty and decentralization on managerial performance: a test of three-way interaction”, Accounting, Organizations and Society, Vol. 19, pp. 413-26. Ittner, C. D. and Larcker, D. F. (1998), “Innovations in performance measurement: trends and research implications”, Journal of Management Accounting Research, Vol. 10, pp. 205-38. Kaplan, R. S. and Norton, D. P. (1992), “The balanced scorecard – measures that drive performance”, Harvard Business Review, pp. 71-80. Kaplan, R. S. and Norton, D. P. (1993), “Putting the balanced scorecard to work”, Harvard Business Review, pp. 134-47. Kaplan, R. S. and Norton, D. P. 1996), The Balanced Scorecard, Harvard Business School Press, Boston, MA. Malmi, T. (2000), “Balanced scorecard in ? nnish companies: some empirical evidence”, paper presented at the European Accounting Congress in Munich. Miles, R. E. and Snow, C. C. (1978), Organizational Strategy, Structure and Process, McGraw-Hill, New York, NY. Miles, R. E. and Snow, C. C. (1994), Fit, Failure and the Hall of Fame, Free Press, New York, NY. Nanni, A. J. , Dixon, R. and Vollmann, T. E. (1992), “Integrated performance measurement: management accounting to support the new manufacturing realities”, Journal of Management Accounting Research, Vol. 4, pp. 1-19. Slocum, J. W. Jr, Cron, W. L. Hansen, R. W. and Rawlings, S. (1985), “Business strategy and the management of Plateaued employees”, Academy of Management Journal, Vol. 28, pp. 133-54. Tymon, W. G. , Stout, D. E. and Shaw, K. N. (1998), “Critical analysis and recommendations regarding the role of perceived environmental uncertainty in behavioral accounting research”, Behavioral Research in Accounting, Vol. 10, pp. 23-46. Further reading Gosselin, M. (1997), “The effect of strategy and organizational structure on the adoption and implementation of activity-based costing”, Accounting, Organizations and Society, Vol. 22 No. 2, pp. 105-22. Study of performance measurement 437

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The Emerald Research Register for this journal is available at www. emeraldinsight. com/researchregister The current issue and full text archive of this journal is available at www. emeraldinsight. com/1741-0401. htm An empirical study of performance measurement in manufacturing ? rms Maurice Gosselin ? ? School of Accountancy, Universite Laval, Quebec City, Canada Abstract Purpose – The recent performance measurement literature suggests that organizations should put more emphasis on non-? anc
2018-10-21 16:32:29
Case Creve Couer Pizza, Inc Essay
$ 13.900 2018-12-31
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