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842 Received July 1996 Revised May 1997

An international study of quality improvement approach and firm performance Everett E. Adam Jr University of Missouri-Columbia, USA

Lawrence M. Corbett Victoria University of Wellington, New Zealand

Benito E. Flores Texas A&M University, USA

Norma J. Harrison University of Technology-Sydney, Australia

T.S. Lee Chinese University of Hong Kong, Hong Kong

Boo-Ho Rho Sogang University, Korea

Jaime Ribera University of Navarra, Spain

Danny Samson University of Melbourne, Australia and

Roy Westbrook London Business School, London, UK

International Journal of Operations & Production Management, Vol. 17 No. 9, 1997, pp. 842-873. © MCB University Press, 0144-3577

Improving the quality of an organization’s products and services is fundamental to business success. The once-held view that product or service specification is static and readily achievable is gone. Managers in world-class companies realize that customer wants and desires are changing, that customers’ expectations must be clearly understood, and that their firm must conform to customer wishes. This customer focus requires continual quality improvement, creating a dynamic business situation. If customers are demanding improved quality, then what quality improvement approach leads to the best organization performance? What managerial actions encourage the highest quality and financial performance? Further, is the quality improvement approach that works best in one nation or region the best in other parts of the world? This study is directed to these

questions, with data collected from 977 business firms located in the major industrialized regions of the world – Asia/South Pacific, Europe, and North America. Literature review We consider briefly three literatures in relation to this study. First considered is the literature on the concept of quality and total quality management, leading to the importance of design and conformance, and customer determined specifications. Next is the literature on improvement approach and performance, leading to how firms improve their quality. Finally, we review the literature on international quality practices and comparative management studies of quality. The quality concept Quality is defined and measured differently, largely dependent on one’s viewpoint. In an excellent summary of the various definitions proposed, Reeves and Bednor (1994) identify quality as having been defined as: excellence, value, conformance to specifications, and meeting or exceeding customers’ expectations (including Juran’s term “fitness for use”). They then discuss the strengths and weaknesses of each definition. The last-named definition of quality has emerged, perhaps in the last ten years, as the key definition from a managerial perspective. Unfortunately, as Reeves and Bednor (1994) indicate, this is probably the most complex definition and is difficult to measure (p. 437). Roth and Griffi (1994) attempted to deal with this by including both design (specification to customer desires) and conformance (adaptation to design criteria) as important dimensions of this definition of quality. The customer determines specifications, rather than engineering or others inside the firm. The customer specifies quality, and his or her satisfaction is the basis for measuring quality performance. What constitutes a comprehensive set of quality practices (often called total quality management, or TQM) has been the subject of so much debate that there is often confusion about what should be included and how quality performance should be improved. Dean and Bowen (1994) believe a comprehensive approach to quality improvement can be characterized by three principles: customer focus, continuous improvement and teamwork: Each of these principles is implemented through a set of practices, which are simply activities such as collecting customer information or analyzing processes. The practices are, in turn, supported by a wide array of techniques (i.e. specific step-by-step methods intended to make the practice effective) (p. 394).

The survey instrument used in this paper was developed prior to this article, but it includes items related to all the elements of Dean and Bowen’s principles. In this study we are most concerned with the operations management viewpoint; we accept both a design and conformance dimension of quality, and we accept performance measurement as critical in determining quality levels

Quality improvement approach 843

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(Adam and Swamidass, 1989; Flynn et al., 1994; Maani and Sluti, 1990; Roth and Miller, 1990). Quality improvement as an operations management objective is accepted where quality improvement is also thought to provide the additional benefit of cost reduction as waste is eliminated (Crosby, 1979, 1984; Deming, 1986; Juran, 1982, 1989).

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Improvement approach and performance This study is a direct expansion of Adam’s work in the USA (1994), which was tied most directly to the empirical work of Sluti (1992) and summarized by Maani et al., (1994). Adam (1994) also confirmed and extended the quality work of Benson et al. (1991), leading to a profile for the organization as to what improvement technique might be most useful for improving quality, operating, and/or financial performance. His results have been in agreement with other US studies, specifically reports from the PIMS database (Maani, 1988), the Malcolm Baldrige National Quality Award finalists (US General Accounting Office, 1991), a system-structure model of quality management (Benson et al., 1991), and the International American Quality Foundation and Ernst & Young report (1992). The characteristics of a quality management system are influenced by the external factors (context) that surround the firm. Such a system could be of the form suggested by Benson et al. (1991), in which the organizational quality context is defined by: the external quality demands; past quality performance; corporate direction and support in the area of quality; and the competitive forces that have a bearing on quality. The organizational quality context forces the determination of the changes needed to ensure or at least improve the probability of survival of the firm. Benson et al. (1991) include an extension of the system-structural view of quality management. In their model, the organizational quality context (managerial knowledge, corporate support, marketplace contextual variables, product/process, etc.) affects quality management behaviour. In particular, they investigate the link between organizational quality context and actual and ideal quality management. The results suggest that the organizational context influences managers’ perceptions of both ideal and actual quality management. In the problem formulation part of their model, they hypothesize that if the discrepancies between actual and ideal management were not significant, then the organization would provide no response. On the other hand if they were significant, then a problem-solving mode would ensue and create a strategy and a response by the firm. This paper can be seen as an extension of the work of Benson et al. inasmuch as it is a study of the normal response of the firm to reduce the differences between the actual and ideal quality management. What is hypothesized is that the response of the firm to those differences is the development of a strategy which evolves into quality improvement programmes that will decrease the discrepancies between ideal and actual quality management. In this study we

investigate both the quality improvement process and the impact of that process on actual quality. In addition, the results of these actions on business performance variables is addressed. Figure 1 presents a schematic of a systemstructural model of quality management such as the one addressed herein. In another study, Sluti (1992), as summarized by Maani et al. (1994), looked at an empirical study of quality improvement in manufacturing in New Zealand. Their study used structural equation modelling to validate the direction and magnitude of the hypothesized relationships. The quality performance model that was posed linked quality to business performance via manufacturing. The quality of a firm’s products (and its costs such as scrap and rework, returns, etc.) is linked through the manufacturing process performance (equipment downtime, worker and manufacturing idle time, etc.) to the business performance. In the Maani et al. (1994) study, the impact of quality improvement practices on business performance is studied. The difference between this study and Maani et al.’s study is that the survey used here reflects quality improvement practices and relates those practices to quality improvement, manufacturing results, and business performance. Therefore, this study extends the results of Benson et al.’s model by collecting information on how firms improve their quality to decrease the

Organizational quality context

Actual quality management Ideal quality management


Problem formulation Are discrepancies significant?

No

Organizational response not required

Yes

Problem solving Determine organizational response Strategy plans Action Quality improvement programmes

Performance Quality results and financial results

Figure 1. A system-structural model of quality management

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differences between ideal and actual quality management. It is proposed that the firm will try to correct this difference via quality improvement practices. These quality improvement practices will be the ones that the company is more familiar with or has in its repertoire of actions. These practices form part of the quality management process. Then, practices are evaluated to see which most positively affect performance – quality and business performance. Worldwide quality practices Worldwide quality practices vary, although quality continually emerges as a top competitive priority within the firm (American Quality Foundation and Ernst & Young, 1992; Corbett, 1993; Kim and Miller, 1992). Perhaps the most documented international quality practice comparisons are Japan/USA (Cole, 1981; Ebrahimpour and Lee, 1988; Garvin, 1986). Japan has also been compared to Korea and Denmark by Dahlgaard et al. (1990). In addition to comparisons, studies that focus on performance quality within one culture help us better understand differences among cultures (Benson et al., 1991; Maani et al., 1994; Roth and Giffi, 1994; Sluti, 1992). Comparative management studies It has been suggested that national culture has a considerable influence in the strategies chosen, structures set up, and performance achieved by managers. Our intent is to narrow this focus to find out to what extent national culture affects quality management and performance. In the field of comparative management research, there have been three main approaches: the “culture-free”, the “convergence”, and the “culture-specific” hypotheses. Previous empirical work has been aimed towards testing the “culture-free” hypothesis (e.g. Child and Kieser, 1979; Haire et al., 1966). The “culture-free” hypothesis suggests managers located in different societies, when faced with similar reasons for change, would behave similarly. The feelings, beliefs, mores, and so forth from different cultural backgrounds would not influence their behaviour; they are “culture-free”. The “convergence” hypothesis (e.g. Form, 1994) asserts that learning will lead managers from different cultures to adopt the same efficient management practices. Competitive market forces will eliminate those who resist convergence, so that only the convergent will survive. Consequently, with the increased dissemination about good quality practices around the world, one would expect each country’s respondents to be moving or to have moved to approach the pattern of behaviour of their overseas counterparts. The “culture-specific” argument (Hofstede, 1980) claims that even if managers located in different societies face similar imperatives for change, deep-seated cultural factors will still affect the way managers actually adjust their behaviour, and react to the need for change. Hofstede (1980) surveyed 116,000 employees of one large US multinational company which was operating in 40 different countries. Using factor analysis techniques on their replies to an extended list of propositions about behaviour in organizations, he extracted

four underlying cultural dimensions: power-distance (acceptance of power), uncertainty avoidance (including avoidance of conflict, dissent and a penchant for rules), individualist/collectivist, and masculinity (orientation to task achievement versus social or group functioning). He compared managerial behaviour in these countries to these four cultural characteristics. It was found that the different subsidiaries of the multinational had a very different culture, even though their task environments and formal structures were similar, and even though a strong set of cultural values had been disseminated from the parent. He seriously questions the universal validity of some management theories (motivation, leadership, and organization) developed in one country – in most instances the USA. This may have consequences for quality performance in different countries. In a recent study of management in New Zealand (Campbell-Hunt et al., 1993), it is noted that New Zealand (and Australian) respondents to the Hofstede (1980) survey differed very little from North Atlantic norms, so one might conclude that there is very little difference between these management practices and New Zealand culture. This would be incorrect on at least three counts. First, the norms of the tangata whenua (native people) would differ substantially on some of these dimensions. Second, New Zealanders who work for US multinationals are not necessarily representative of the general population. Third, some important aspects of normative practice (e.g. on levels of participation and group responsibility) are now being sourced from Japan. New Zealand cultural heritage may impede adoption of these powerful techniques (Campbell-Hunt et al., 1993, p. 115). Two comparative management research projects which focus on manufacturing strategies and practices are the Manufacturing Futures Project (De Meyer et al., 1989) and the Global Manufacturing Research Group (Whybark and Rho, 1988). The former has not combined the data from participating countries, but has rather presented results looking at trends and changes in importance of key competitive capabilities and manufacturing improvement plans within a country. Quality, and various dimensions of it, have always emerged as a highly-ranked competitive priority. The latter group does combine their data on manufacturing practices in the non-fashion textiles and small machine tool industries. Unfortunately, they have not included quality practices in their questionnaire to date. Nevertheless, one finding has relevance to the development of our hypotheses. The researchers found that manufacturing practice differences between regions within industries were generally much larger than the differences between industries within regions (Vastag and Whybark, 1991). We conclude that comparative management studies have considerable interest for researchers on international quality practices. The “culture-free” hypothesis suggests culture really does not matter; it will not impact on performance quality. The “convergence” hypothesis would suggest that they will be different. The manufacturing studies cited above would tend to agree with the latter. One justification for this study then is to confirm the differences,


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and examine what the differences mean in terms of operational and financial performance. This study focuses on alternative approaches to quality improvement practices in Asia/South Pacific, Europe, and North America and relates quality improvement practice to actual quality, quality costs, and financial performance. Our study expands Adam’s work by first, including a larger set of items relating to quality improvement and second, conducting the study in nine nations within Asia/South Pacific, Europe, and North America. This research was conducted in parallel with, not in comparison to, published models by Flynn et al. (1994) or Roth and Giffi (1994). Research question, design and methodology Research question There are many approaches to quality improvement, including different types of behavioural intervention, management practices, problem-solving methods, and statistical control techniques. Which approaches lead to the best quality and financial performance in different countries and regions of the world? On the issue of geographical variation, our expectation was that different approaches would be taken between and within regions, and that the choices would be culture-specific. In some cultures a very direct, authoritarian approach is expected, while in other cultures participation and shared responsibility are the norm. For example, within North America an authoritarian approach might prevail in Mexico (Riding, 1989) – based on the roots of the nation. By contrast, in the USA we might find the total quality management (TQM) approach, which is more participative and implies the involvement of the entire organization. TQM is itself not a single approach so much as a coherent set of approaches such as customer focus, top-management leadership, statistical thinking, continuous improvement, and teamwork (Dean and Bowen, 1994; Evans and Lindsay, 1993, pp. 32-3). Our contention is that nations or regions can be expected to vary in approach, depending on culture and competitiveness. Within a region, such as North America and a country, such as Mexico or the USA, individual firms chose different approaches towards quality improvement – either explicitly or implicitly. When improving quality a wide range of productivity improvement approaches are frequently used in parallel. Approaches include traditional cost reduction, industrial engineering work and process analysis, wage incentives, and management practices. Contemporary production/operations managers have available inventory reduction (via just-in-time (JIT) or material requirements planning (MRP)), increased speed of product/process design, and flexible manufacturing, to name but a few options. These are often thought to improve quality as well, yet empirical studies demonstrating a quality improvement are somewhat rare (Adam and Swamidass, 1989; Chen and Adam, 1991).

This study’s primary interest is quality improvement. Proponents suggest that through quality improvement, operating and financial performance are enhanced as costs are reduced. Experts also suggest that when design quality improves, revenues and market share increase (Deming, 1986; Garvin, 1988). These linkages lead to questions as to whether overall performance is always improved through quality improvement alone or whether productivity improvement techniques are utilized in tandem with quality improvement techniques to achieve the performance gains. Since culture has not been systematically evaluated in quality management, the literature suggests a “culture-free” hypothesis, that these relationships hold regardless of geographic region or culture. This study examines the “culture-free” hypothesis. The principal aim of this study is to identify quality improvement factors that can predict quality and financial performance, both across and within different international environments. More specifically, the study examines the following hypotheses: H1: A company’s approach to quality improvement correlates to product and service quality. The literature regarding quality improvement approach is broad and somewhat prescriptive (Crosby, 1984; Deming, 1986; Juran, 1989; US Department of Commerce, Baldrige Award guidelines, 1991). This hypothesis builds on the empirical database that identifies quality improvement items and factors that specifically improve product and service quality (Adam, 1994; Benson et al. 1991; Maani et al. 1994). Items tested are primarily from empirical studies, and secondarily from the prescriptive quality literature and the productivity improvement literature. There is substantial evidence that his hypothesis will hold true. H2: A company’s approach to quality improvement correlates to financial performance. Deming (1986) reasons that as quality improves, waste is eliminated, costs are reduced, and financial performance improves. This is referred to as the Deming Chain in the prescriptive quality research. Advocates of Japanese management, especially zero inventory and JIT, accept a similar reasoning that quality is improved as waste is eliminated. Quality must improve as inventory is removed throughout the firm. Then, inventory improvement and quality improvement result in improved financial performance. The empirical relationships between quality and financial performance are few, but important (Baldrige finalists, US General Accounting Office, 1991; Maani et al., 1994; and the PIMS database, Maani, 1988). Empirical studies suggest a weak, but significant, direct relationship between quality improvement and financial improvement. There is substantial doubt that this hypothesis will hold true. H3: The relationships between quality improvement and performance (above) are the same across and within geographical regions.


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Specifically, the same relationships hold for the regions studied: Asia, Europe, and North America. International studies do not empirically address the impact of quality improvement on quality and financial performance, suggesting a “culture-free” hypothesis. Other than Japan/USA quality practice comparisons, the international literature is weak. The few empirical studies that exist were within a culture (such as Adam, 1994; Sluti, 1992) and this adds little sense of direction regarding this hypothesis. The international literature does tend to descriptively identify the differing degrees to which quality is emphasized in various regions and nations (American Quality Foundation and Ernst & Young, 1992; Corbett, 1993; Kim and Miller, 1992). Our understanding of differences in culture (“culture-specific”, Hofstede, 1980) suggest this hypothesis will not hold. That is, there will be significant differences within regions compared to all regions as a group. Experimental design Testing the first hypothesis requires a delineation of quality improvement approaches and measures for quality. There are alternative approaches to quality improvement in the quality research literature and in practice. For example, Deming (1986) stresses statistical thinking, managing through data analysis, and some principles or guidelines that summarize his philosophy for improvement. Ishikawa (1976) also stresses statistical analysis and problem solving. Juran (1982, 1989) agrees with Deming and Ishikawa on the use of statistics but stresses management processes, including a spiral of continuous improvement and quality project teams. Crosby (1979, 1984) suggests a behavioural approach, stressing employee and management attitudes as crucial to quality improvement. The Baldrige criteria, a popular US guideline for firms desiring to improve performance quality, identified seven categories of quality improvement and under the seven categories suggests 32 items. Combined, these approaches and items are prescriptive regarding organization performance. Dean and Bowen (1994, p. 297) suggest total quality is almost completely prescriptive and our search for items that affect performance supports that contention. A comprehensive listing of items thought to improve quality was developed from these sources and the broader quality improvement research literature. The items are presented later in the paper where they are reduced into broader factors (Table III). Our original thinking was that these items could be broadly grouped as statistical process control, behavioural (including operative employees and managers and encompassing rewards and training), a customer focus, projects emphasized, and a focus on design and/or conformance to design specifications. We found no support in the literature for such a taxonomy. The literature does, however, suggest that many productivity improvement techniques are used in tandem with quality improvement approaches to achieve improved operating performance. Therefore, we

included items such as traditional industrial engineering process analyses and work measurement, inventory reduction, employee selection, decentralizing decision making, and providing objective feedback on performance (see Kopelman, 1986). Table I sets forth the quality measures – per cent defective, the cost of quality, and customer satisfaction. To test the second hypothesis, we relate the approaches to quality improvement (the factors from Table III) to financial performance measures set forth in Table I. Financial performance measures included net profit as a per cent of sales, return-on-assets for the past year and for the average of the past three years, as well as sales growth, as an average of the past three years. Net profit as a per cent of sales is a measure that is a surrogate for actual productivity, the total output of goods and services divided by all resources used to provide that output. Net profit could be thought of a measure of operating performance rather than a financial measure. The third hypothesis recognizes the need to examine for differences by geographic region. The intent is to see if the first two hypotheses indicate the same quality improvement processes and profiles across and within the regions Asia/South Pacific, Europe, and North America. In summary, the study reflects an experimental variable, quality improvement approach, which is correlated to two dependent variables – actual quality and financial performance. The design includes replication in three geographical regions. Multiple levels of the experimental variable and multiple measures exist. The research design, then, is (quality improvement approach × geographic region) with dependent variables quality and financial performance.

Dependent variable

Measure

Performance quality

Average per cent items defective Cost of quality (as per cent of sales) scrap rework inspection training and development returns and warranty total cost of quality Customer satisfaction

Financial performance

Net profit as per cent of sales, past year Return on assets, past year Return on assets, average past three years Sales growth, average past three years Annual employee turnover rate

Other


Table I. Dependent variables

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Methods and procedure Procedure. A general procedure was established and communicated to each investigator. A pre-test of the procedure and questionnaire had been conducted in the USA. In the USA investigators surveyed practising manufacturing firms. The primary source of companies was SIC and geographical listings where the company’s address and telephone number were available. A secondary source was membership in the Operations Management Association, an association of production/operations management academics and executives. Only the executives were sampled concerning their company. The general procedure was to telephone the company and ask for the chief production or operations officer. Firms were systematically selected and investigators made up to four telephone calls to solicit participation. They mailed a questionnaire to those agreeing to participate. One follow-up call was made to those who agreed to participate but did not return the questionnaire. In the UK and Spain a similar procedure was followed. In the UK the sample (47) was confined to manufacturing companies known to the UK investigator. Each was telephoned to secure participation, and then again if late in responding. The subgroup of Spanish firms surveyed was selected based on feelings of accessibility and variety (in terms of sectors and sizes represented). The group was selected to include some very large companies as well as small/local firms. Manufacturing and service firms were included. The first contact was by telephone to a company manager, followed by a mailed questionnaire with a cover letter. In a majority of cases there existed a relationship between the investigator’s university and the firm. Up to three follow-up calls were made after the mailing. Australia, Hong Kong, Korea, Taiwan, and New Zealand conducted a mail survey. The Australian data were collected through a random sample of manufacturing companies chosen from a database held by the government of all manufacturing companies. The sample is considered to be a good representation of the population of Australian companies with the exception of the small business sector, which was excluded from the sample because of its poor response rate. For those firms selected that did not respond a follow-up letter and second questionnaire were sent after four weeks; this had a minor effect on the response rate. In Hong Kong and Taiwan, surveys were mailed in each respective region. The Taiwan survey involved a random sample of manufacturing companies. The Hong Kong population was professional managers in the part-time MBA programme of the Chinese University of Hong Kong (CUHK) and the Diploma Course in Small Company Business Management at CUHK. Hong Kong respondents reflect manufacturing practices where they produce, both in Hong Kong and the southern provinces of the People’s Republic of China. In Korea, members of the Korean Standards Association were surveyed. In New Zealand, two local organizations assisted in the survey by allowing access to their members. A random sample of members of the Wellington Manufacturers’ Association and the Wellington Quality Improvement Network

was drawn and a questionnaire and conveying letter was sent to the contact person in the selected companies. One follow-up phone call was made to those who had not responded after four weeks. The sampling procedure in Mexico utilized undergraduate business students who were asked to collect the information from businesses. Respondents were from many cities in Mexico and a variety of firms. Cities included Pueblo, Saltillo, and Mexico City, where 25, 21, and 13 firms were interviewed respectively. The State of Tlaxcala and the State of Mexico provided 17 and 11 firms respectively. Firms were nearly exclusively from the interior of Mexico, with but a few at Maquilladora border towns (Chihuahua [1], Monterrey [4]). As the sampling procedure is most different in Mexico, the procedure deserves explanation. Mail survey procedures in Mexico obtain lower returns than in the USA because of limited financial resources in a developing country for responding; non-professional (primarily in education) managers and familyowned small businesses who see limited value in responding. If professionals attempt to obtain first-hand information they are unlikely to get on to the premises, and if so, face extended waits to be seen. Most companies, besides being small, are not traded on a stock exchange. Prior to the mid-1980s companies operated in a closed market (high tariffs) and kept poor (if any) records. This is gradually changing with the North American Free Trade Agreement (NAFTA), although current economic conditions in Mexico are not the best for gathering data. It is estimated that at this time only 10-20 per cent of firms have professional management, good information, and keep reasonable records. The students we asked to participate in the information gathering process are from families in the top 25 per cent of income in Mexico. They usually have relative contacts within firms, they are in their senior year at a well-recognized university, they are enrolled in or have completed an operations management or management science course, and they are gathering data under supervision in a course. Students were instructed on the data, their meaning and format, and were committed to the instructor and institution (university) in the data gathering process. The instructors reviewed all data for consistency and completeness. Although it would be desirable to have professionals collect the data, we were required to utilize this alternative procedure, a procedure we view as prudent in design and execution in the Mexican environment. In summary, three countries followed the recommended telephone contact/mail survey approach. Five countries relied on a mail survey approach, deleting the initial telephone contact which was intended to increase the response rate of the mail survey. Interestingly, all five countries are from the Asia/South Pacific region. The choice to skip the telephone contact was made independently in each country to reflect the local customs and business practices. Mexico represented the most different procedure, with students personally making contact with the individual respondents and collecting the completed survey. Mail was not used.


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Questionnaire. The questionnaire content reflected the research design and was thus based on the quality improvement research literature. A primary source of questions was Benson et al. (1991), who utilized the instrument designed, verified, and validated by Saraph et al. (1989). The 1991 Malcolm Baldrige National Quality Award (US Department of Commerce, 1991) identified seven categories of quality and under the seven categories evaluated a total of 32 items. Many of those items became candidates for this study. Here 12 items were taken from Baldrige categories: human resource utilization, quality assurance of products and services, leadership, quality results, and customer satisfaction. The basis for selection was consistency with quality improvement research literature, yet not previously selected by Benson et al. (1991) This study is not intended to replicate the Baldrige criteria, which has as its base the judgement of practising quality managers as to which categories and items are included. Kopelman’s 1986 survey of the behavioural/managerial literature provided a summary of items concerning productivity interventions that lead to improvement. Items from that survey included here were employee biographical data in selection, placing decision making at lower levels, providing objective feedback on performance, and employee satisfaction. The survey instrument also included questions that summarized traditional productivity improvement techniques such as industrial engineering and effective inventory planning and control. These items were stated in a style consistent with the overall survey, which was primarily a Likert seven-point scaling. Example questions are provided in the Appendix. Characteristics of the sampled firms. Table II provides geographical representation. A total of 977 firms provided usable responses. Asia provided the highest response (61 per cent of the total), followed by North America (29 per cent) and Europe (10 per cent). The objective was to receive about 100 respondents (about 10 per cent) from each nation, never intending to balance all regions regarding sample size. Hong Kong (200 respondents, 20 per cent) and the USA (187 respondents, 19 per cent) had higher participation, while the UK (47 respondents, 5 per cent) and Spain (46 respondents, 5 per cent) had lower participation. The low number of responses from Europe somewhat restricts regional comparisons. Respondent characteristics suggest companies had been in business a long time, averaging 32 years. From the 977 respondents, the average number of employees was 1,745, and the average annual sales was $US286 million. North America and Europe were similar in number of employees (2,844 and 2,694 respectively) and annual sales (million $US640 and 473 respectively), while Asia firm size was smaller based on number of employees (1,071) and annual sales (million $US79). Median responses were lower than means, especially so for the USA and the UK, where a few large firms skewed the averages upward. Performance quality, as shown in Table II, indicates the average per cent defective was 4.54 per cent and total cost of quality (non-conformance costs) averaged 15.24 per cent of sales. If average annual sales were million $US286,

the annual dollar cost of poor quality averaged $US43.5 million per company. For 977 firms in this study, that would be a $US42.4 billion loss per year. This demonstrates that the cost of non-conformance is quite large and a significant business issue throughout the world. The implication is that if these costs are reduced or eliminated, profits could increase and be shared with owners,


Characteristic

Geographic representation Nation: Australia UK Hong Kong Korea Mexico New Zealand Spain Taiwan USA Region: Asia/South Pacific Europe North America

Measure Number of Respondents

Per cent of total respondents

128 47 200 101 98 71 46 99 187

13 5 20 10 10 8 5 10 19

599 93 285

61 10 29

Respondent characteristics Years company has been in business Number of employees Company sales ($US millions)

Mean 32 1,745 286

Performance quality Per cent items defective Cost of quality as per cent of sales Training and development (per cent) Inspection (per cent) Rework ( per cent) Internal waste/scrap (per cent) Returns and warranty or adjustment (per cent) Total cost of quality (per cent) Per cent of employees involved in quality improvement

Mean 4.54

Financial performance Past year’s net profit as per cent of sales Past year’s return-on-assets (per cent) Past three-years’ return-on-assets (per cent) Past three-years’ average sales growth (per cent)

Mean 10.15 15.48 15.67 14.79

Other Employee annual turnover rate (per cent)

3.06 3.29 3.24 3.61 2.04 15.24 40.4

11.20

Table II. Characteristics of the sampled firms

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employees, and customers. The total cost of quality (as a per cent of sales) varied by region: 17.30 for Asia, 8.21 for Europe, and 13.97 for North America. Financial data in Table II indicate firms were profitable, with the past threeyears’ return-on-assets averaging 15.67 per cent. Profitability and growth were particularly strong in Asia. Hong Kong, for example, experienced a past threeyears’ return-on-assets of 17.8, with past three-years’ average sales growth at 28.9 per cent. Analysis procedures Data included responses to Likert scale questions and ordinal scaled numbers. The responses for each of 977 respondents were displayed in various forms – the raw information, frequency distributions, graphs, means, and standard deviations. We carefully examined the data and discovered and corrected some entry errors. Independent variable constructs for quality are not commonly defined and accepted in the research literature. Therefore, factor analysis was necessary to define these constructs. We conducted factor analysis on the quality improvement responses to determine which items were answered similarly. We selected the SAS factor analysis routine and performed a principal components analysis including an orthogonal transformation with a varimax rotation. Factor scores were the average of the items, with factor loadings exceeding 0.400. After the factor analysis, we conducted a step-wise multiple regression to test the hypotheses. We regressed the independent variables, expressed as factor scores condensed from the item responses, against the dependent variables of quality and financial performance. Results Quality improvement factors Table III identifies the 46 of the 52 factor analysed items, 39 of which are clearly quality improvement indicators and 13 of which are productivity improvement indicators that are thought to indirectly influence quality. Data are after orthogonal transformation and rotation. The factors with eigenvalues greater than 1 resulted in 11 factors that captured all of the variance. Factors 1, 2, and 3 explained cumulative variances of 15, 28, and 39 per cent respectively. Factors 1 through 7 explained 75 per cent of the variance. The orthogonal transformation with a varimax rotation resulted in factors nearly orthogonal, but not totally so. In Table III, the items “applying a formal approach to quality improvement”, “provide objective feedback on performance”, “job training and development for employees”, and “senior executive rewarded for performance” remained in two factors. Nine of the 11 factors and 46 of the 52 items are shown in Table III. Factor 1 is broadly defined by 11 of the 52 items. Quality items that involve employees have strong factor scores: training, addressing employee skills, employees contributing to objectives, and involving employees. Factor 2 captures eight items that reflect strategies and senior executive involvement. Factor 3 reflects employee satisfaction, while Factor 4 encompasses

employees paid for superior performance output-based individual incentive plans reward focused group incentive plans employees recognized for performance

employees satisfied with the company employees satisfied with their jobs employees satisfied with pay employees satisfied with management employees satisfied with co-workers

quality a key strategic opportunity senior executive assumes responsibility for quality senior executive commitment compares to others senior executive creates quality values senior executive rewarded for performance* comparisons and benchmarking strategic quality planning process

Quality improved at my company by… training in total quality concept programmes address employee skill and knowledge employees contribute to objectives involving employees identify and resolve improvement projects adequate resources made available management involvement and responsibility applying a formal approach to quality improvement* provide objective feedback on performance* job training and development for employees* improving quality

Items Factor 1 0.72613 0.64114 0.61944 0.61196 0.60035 0.58029 0.52338 0.51125 0.43171 0.41957 0.41267

0.73541 0.73028 0.68183 0.60159 0.47812 0.45553 0.42530

0.45611

Factor 2

0.76841 0.76174 0.73430 0.68846 0.61424

Factor 3

Factor pattern

0.76581 0.70126 0.69598 0.69526 0.52279 (Continued )

0.43728

Factor 4


Table III. Quality factor analysis

Table III. Note: * These items in two factors; all other items in one factor only

reducing inventories implementing just-in-time production effective inventory planning and control

employee biographical data used in selection pre-employment testing used in employee selection job training and development for employees* responsibility and decision making at lower levels

tried to expand knowledge in quality area familiar with various quality programmes knowledge of quality compares to others

quality practice reflects on both design and conformance quality practice reflects on design quality practice reflects on conformance design and manufacturing engineers work closely together

Factor 5 0.81760 0.81654 0.80173 0.56591

0.79579 0.73312 0.70699 0.44396

Factor 6

0.81521 0.81267 0.72651

Factor 7

Factor pattern

0.69038 0.60860 0.53339 0.44066

Factor 8

858

customers received acceptable quality customers perceive our past year’s quality as good customers receive quality expected customers demand quality

Items

0.74461 0.68184 0.60138

Factor 9

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compensation and recognition. Factor 5 reflects customers: what customers received, perceived, and demanded. Respondents were asked to use actual customer data, but in many instances have given their view of the customer rather than actual customer data. Further, customers’ views could be sought regarding what they desire (reflected in design) and what they receive (reflected in conformance), both components of Factor 6. Factor 7 could be viewed as knowledge: the desire to expand knowledge and the extent of knowledge concerning quality. Employee pre-employment data, job training, and decision making comprise Factor 8 while factor 9 reflects inventory reduction. In summary, Factors 1 and 2 are behavioural, Factors 3 and 4 reflect satisfaction and rewards, and Factors 5 and 6 involve customers. Factor 7 addresses knowledge of quality, while Factor 8 is pre-employment screening and Factor 9 inventory reduction. Table IV provides a summary of the factors, with a descriptor (factor name) for each. The means reflect the Likert seven-point scaling (see Appendix). Reliability and construct validity. The 11 factors identified in the factor analysis were tested to determine if the items within any one factor reliably represent the factor. Inter-item analysis is used to check the scales for internal consistency (Nunnally, 1978). Analysis calculated a Cronbach coefficient alpha on the items within each factor. This is a common reliability check more recently being recommended and used in operations management (Flynn et al., 1990; Ward et al., 1995). Two factors had Cronbach alphas below 0.70 and were dropped from the study, along with six items defining the factors. Cronbach alpha scores range from 0.74 to 0.89 as shown in Table IV. These constructs (the nine factors) demonstrate good reliability.


Quality improvement factors related to actual performance The intent of this study is to explain how these quality improvement factors relate to actual quality and financial performance across the major industrialized regions of the world. The stepwise regression summarized in

Factor number

Factor name

1 2 3 4 5 6 7 8 9

Employee involvement Senior executive involvement Employee satisfaction Compensation Customers Design and conformance Knowledge Employee selection and development Inventory reduction

Number of items in factor

Mean

Cronbach’s alpha

11 8 5 6 4 4 3 4 3

5.16 4.99 4.86 3.89 5.68 5.08 5.46 4.43 4.71

0.89 0.86 0.87 0.82 0.84 0.77 0.82 0.74 0.76

Table IV. Quality improvement factors

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Table V defines or explains the dependent variables in terms of the independent variables (Factors 1-9). Only the statistically significant variables at a level of significance less than 0.05 are reported. All but one of the dependent variables are predicted by a significant regression. The R2s appear low, except for the “opinion-based” item – customer satisfaction (R 2 = 0.8051, F ratio = 1173). Eleven of 13 regressions are significant at p < 0.01, the inspection regression was significant at p < 0.05, and

Stepwise regression procedure

Dependent variables

Table V. Quality regression summary

Intercept

Regression component (parameter estimate)

R

R2

F ratio

Performance quality Total cost of quality

64.48

F7 (–4.64) F5(–3.80) F4(2.37) F3(–2.25)

0.3807**

0.1450

10.81

Waste and scrap

20.47

F5(–1.52) F7(–0.77) F3(–1.27) F4(0.80)

0.3157**

0.0997

12.02)

Returns and warranty

12.26

F5(–1.46) F7(–0.69) F2(0.46)

0.3001**

0.0901

12.18)

Inspection

3.61

F4(0.83) F3(–0.74)

0.1341*

0.0180

3.19

Rework

11.41

F3(–0.82) F7(–0.67)

0.13151

0.0173

2.98

Training and development

6.06

F7(–1.82) F2(0.98) F4(0.61)

0.3312**

0.1097

15.94

Average per cent of items defective

20.47

F5(–1.52) F7(–0.77) F3(–1.27) F4(0.80)

0.3157**

0.0997

12.02

Customer satisfaction

–1.06

F3(0.05) F5(1.10)

0.8972**

0.8051

1173.25

(Continued )

Quality improvement approach

Stepwise regression procedure

Dependent variables

Intercept

Regression component (parameter estimate)

R

R2

F ratio

861 Financial Past year’s net profit

20.97

Past year’s return-on-assets

12.85

Past three-years’ average return-on-assets

10.09

Past three-years’ average sales growth Other Annual employee turnover rate

F7(–2.14) F4(1.25) F3(–1.86) F8(1.21) F2(6.14) F7(–2.89) F8(–2.81)

0.2792**

0.0780

7.34

0.2002**

0.0401

3.93

F2(3.94) F7(–2.56)

0.1854**

0.0344

4.74

–6.90

F4(5.72)

0.2267**

0.0514

19.72

26.15

F7(–1.84) F8(–1.07)

0.1630**

0.0266

6.81

Notes: * p < 0.05 ** p < 0.01 1p = 0.052

the remaining one (rework) was nearly significant with p = 0.054. Each regression is a test of a separate model[1]. Here, we concur with Adam (1994) that the total cost of quality is important because of the measure’s broad scope. It emerges as an important dependent variable as it is significantly (p < 0.01) related to Factors 7 (knowledge), 5 (customer), 4 (compensation), and 3 (employee satisfaction) with an R2 of 0.1450. Items within these factors with high loadings are expanding one’s quality knowledge, the quality customers receive and perceive, paying for superior performance, and satisfaction with the company. Notice that the components of total quality (the next five columns of Table V) are explained by these four factors (7, 5, 4, and 3) as well as Factor 2, senior management’s involvement. Relating quality practice to financial performance provides promising results (Table V). Financial results all are significant at p < 0.01 but with low R2s.

Table V.

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Past year’s net profit is used here as a surrogate for total operating costs. The quality literature suggests that as quality is improved, operating costs should decline (profit increase), and this was found to be so, significant at p < 0.01. Profit was explained by a wide variety of quality factors: in decreasing importance, F7 (knowledge), F4 (compensation), F3 (employee satisfaction), and F8 (employee selection and development). Return-on-assets for the past year and the past three years are explained by F2 (senior executive involvement), F7 (knowledge), and F8 (employee selection and development). Past three years’ sales growth is explained by F4 (rewards). One other operations statistic was measured in the survey, employee turnover. Annual employee turnover across all 977 companies averaged 11.2 per cent (Table II). In Table V turnover is significantly explained by Factors 7 and 8 – knowledge, and employee selection and development. Quality improvement and performance by region In this study we define regions as follows: Asia/South Pacific to include Australia, Hong Kong, Korea, New Zealand, and Taiwan; Europe to include the UK and Spain; and North America to include Mexico and the USA. When presenting and discussing results we use these regions as terms to represent the aggregate of firms responding in the nations above. Those who take exception to our inference of these nations representing these regions need to substitute the original nations into their reading of the paper. Table VI provides a direct comparison of each quality improvement factor, region by region. First, we examine the mean scores and standard deviations. Means are generally in the 4.0-5.0 range (on a Likert scale 1-7, with 7 indicating high importance; see Figure 1). A few means fall below the neutral of 4.0 and a few are above 5.0. In Asia, Europe, and North America high importance is placed on a customer emphasis (Factor 5; means 5.48, 5.91, and 6.01), knowledge about quality improvement (Factor 7; 5.13, 5.85, and 5.95) employee involvement (Factor 1; 5.04, 5.23, and 5.37), design and conformance (Factor 6; 5.05, 4.74, and 5.23), and senior executive involvement (Factor 2; 4.94, 4.67, and 5.20). There seems to be the least importance placed on compensation (Factor 4; 3.88, 3.15, and 4.14).The mean scores suggest that in each region a customer focus is most important followed closely by employee’s having knowledge about quality improvement. Then involvement is important – employee and senior executive involvement. Understanding what customers want (design) and providing that (conformance) was valued across each region as well. Table VII summarizes regionally the dependent variables, the quality and financial performance measures. Quality measure “per cent items defective” was highest in Asia (4.74), followed by North America (4.65), and Europe (3.08). In the USA per cent items defective was 3.29, much closer to Europe. Mexico raised the North America per cent defective, with a 7.26 score, a score similar to some Asia countries. Total cost of quality was highest in Asia at 17.30 per cent of sales, followed by North America at 13.97 per cent and Europe at 8.21 per cent. The disparity

459 458 457 450 581 466 529 465 458

1. Employee involvement 2. Senior executive involvement 3. Employee satisfaction 4. Compensation 5. Customers 6. Design and conformance 7. Knowledge 8. Employee selection and development 9. Inventory reduction

Factor

4.51

4.40

5.13

5.05

5.48

3.88

4.69

4.94

5.04

1.31

1.18

1.43

1.07

0.99

1.35

1.03

1.08

1.05

Asia/South Pacific Sample Standard size Mean deviation

83

84

92

75

93

80

65

80

82

Sample size

4.80

4.57

5.85

4.74

5.91

3.15

4.98

4.67

5.23

Mean

Europe

1.43

1.19

1.08

1.06

0.70

1.26

0.90

1.20

1.01

Standard deviation

262

272

280

250

281

253

256

249

256

1.04 1.11 0.99 1.36 0.86 1.12 1.02 1.29 1.24

5.37 5.20 5.13 4.14 6.01 5.23 5.95 4.43 5.03

North America Sample Standard size Mean deviation


Table VI. Regional comparisons of quality improvement factors

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between Asia and Europe is considerable. The 8.21 per cent reported in Europe is not reflected in the quality management literature, where estimates are commonly in the 15 to 30 per cent of sales range. Financial performance tended to be highest in Asia (net profit and sales growth), although past year’s and past three-years’ return-on-assets was slightly higher in North America than Asia. Europe was consistently the lowest region in terms of financial performance. Employee annual turnover was highest in North America (11.78 per cent), followed by Asia (11.47 per cent) and Europe (8.09 per cent). Similar to per cent defective, there was a substantial difference between the USA (9.94) and Mexico (15.70) data within the North America region. Standard deviations were high for each (USA 13.48, Mexico 26.63) indicating considerable variation in the data. Table VII presents a regional comparison of performance quality, selecting but a few dependent variables (seven of the 13) to demonstrate results. First note the differences in sample sizes. Consider total cost of quality. In Asia, for example, only 141 of 599 responding firms provided all components of the total cost of quality. The mean response was 17.30 per cent of sales, and the relationship is significantly explained (p < 0.01, F = 11.43) by knowledge (F7), customers (F5), and senior executive involvement (F2) of Table III. Note that factor components do vary across regions, as do significance levels. Quality dependent variables tend to be significant within each region, while

Dependent variable Performance quality Per cent items defective Cost of quality as per cent of sales Training and development Inspection Rework Internal waste/scrap Return and warranty or adjustment Total cost of quality

Table VII. Regional comparisons of quality and financial performance

Asia/South Pacific

Mean Europe

North America

4.74

3.08

4.65

3.69 3.66 3.69 3.85

1.27 2.13 1.39 2.48

2.43 3.09 3.07 3.61

2.41 17.30

0.94 8.21

1.77 13.97

Financial performance Past-year’s net profit as per cent sales Past year’s return-on-assets Past three-years’ return-on-assets Past three-years’ average sales growth

10.96 15.56 14.69

6.58 11.39 14.99

9.80 17.02 18.26

16.21

11.94

13.36

Other Employee annual turnover rate ( per cent)

11.47

8.09

11.78

financial dependent variables are at times not significant, especially in Europe. Annual employee turnover rate was significant in all regions. Discussion and conclusions Discussion of hypotheses The interest in this study is to identify a set of improvement factors that will predict quality and financial performance across and between international regions. The factors are identified by a “set of improvement items”, activities a firm could engage in to improve quality. A large database (977 participating firms) allowed evaluation of a wide set of improvement items (52), which were reduced to nine factors. The factors as presented in Tables III and IV are quality improvement factors, factors composed of items the research literature suggests relate to quality. H1 states that a company’s approach to quality (the factors as independent variables) correlates to actual product and service quality (the dependent variables). Table V indicates this to be so. In Table V the eight ways that quality could be measured (columns of Table) were each tested as a model, and six were significantly correlated to improvement factors at p < 0.01, one at p < 0.05, and one at p = 0.052. Quality improvement factors do significantly impact on actual quality. Of particular interest are the dependent variables “total cost of quality” and “average per cent of items defective”. From Table V the total cost of quality (R2 = 0.1450, p < 0.01, F = 10.81) and average per cent defective (R2 = 0.0997, p < 0.01, F = 12.02) are significantly explained by knowledge about quality improvement (F7), what quality customers receive and perceive (F5), employee compensation and recognition (F4), and employee satisfaction (F3). As one studies all eight quality dependent variables (columns), it is noted that these factors appear frequently in different models. A factor (F1, employee involvement) and an item (statistical process control (SPC)) we believed to influence quality, primarily from the research literature and our experiences, were not so prevalent, or missing entirely. The regression parameter estimate for knowledge about quality improvement was negative each time it appeared in a model. Customers’ perception was negative each time but one when it appeared. Employee compensation had a positive parameter estimate each time it appeared in a predictive model. This suggests that as employee knowledge about quality and as customers’ perceptions increase, quality measures per cent defective and costs of quality will decrease, i.e. quality gets better. Similarly, as compensation increases in importance, per cent defective and costs of quality increase, i.e. quality gets worse. The latter relationship is difficult to understand. In summary, negative parameter estimates for per cent defective and quality costs dependent variables would lead to improved quality. Our conclusion is that H1 is supported. There is a predictive model that significantly influences quality, even when quality is measured as many as eight different ways.


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H2 states that a company’s approach to quality improvement correlates to financial performance. Results are also shown in Table V. For financial performance (Table V) all four dependent variables were significantly predicted by a model at p < 0.01. The R2s were considerably lower than when quality was the dependent variable, ranging from 0.0344 to 0.0780. The primary factors explaining financial performance were knowledge of quality (F7), senior management involvement (F2), and employee compensation and recognition (F4). The regression parameter estimate was negative for knowledge of quality and the factor appeared in three of the four models. One can readily explain how positive parameter estimates would be expected to increase financial returns for management involvement and compensation, but a negative relationship to knowledge is difficult to understand. There were several negative parameter estimates for each financial model, bringing into question the relationship between quality improvement approach and financial improvement. Average sales growth demonstrated the strongest model, with compensation (F4) positively related to sales growth. Our conclusion is that H2 is at best weakly supported by these data. Predictive models were found that are significant. Yet the models have very low R 2 values and the models do not make predictions that show clearly when quality factors improve, financial performance improves. The relationships are weak at best. This finding was expected and is consistent with prior research. H3 states that relationships between quality improvement and quality performance will be the same across and within geographical regions. To examine this hypothesis, first the factors that improve quality were compared within each region (Table VI) to one another and to the overall factor means (Table IV). The results suggest a common pattern, those factors most important in one region are the same in other regions. The magnitude of the means varies, but the most important factors (such as customers and knowledge) and the least important (such as compensation) remain the same within any one region. Second, the dependent variables, the actual quality and financial results in the firms, are quite different across regions. The data (Table VII) support Asia with the poorest quality performance (highest per cent defective at 4.74 and highest total cost of quality at 17.30 per cent of sales), yet with the highest financial performance (past year’s net profit 10.96 per cent of sales and past three-years’ average sales growth 16.21 per cent). One could argue that North America has the highest financial performance, based on a higher return-onassets than Asia or Europe. However, Europe clearly reports the best quality and consistently lowest financial performance of any region. Where does this leave the region-by-region comparisons? Means seem to tell different stories, so it is to the regression models that we must turn. These models search for statistically significant relationships between quality improvement factors and performance, region by region. H3 states that operationally the 13 models across all regions (in Table V) will be the same respective models within each region (as demonstrated in Table VIII). In Table VIII only seven of the 13 models are repeated for each region, i.e.

Statistic Dependent variables

Number of firms reporting

Performance quality Total cost of quality (% of sales) Asia 144 Europe 25 North America 88 Waste and scrap (% of sales) Asia 200 Europe 35 North America 141 Average per cent of items defective Asia 255 Europe North America

36 145

Financial Past year’s net profit (% of sales) Asia 219 Europe 26 North America 104 Past three years’ return-on-assets Asia 178 Europe 19 North America 69 Past three years’ sales growth Asia 217 Europe 32 North America 114 Other Annual employee turnover rate Asia 300 Europe 42 North America 156 Notes: * p < 0.05 ** p < 0.01 1p = 0.07

Regression Mean component

R

Significance (P ≥ 0.05) F-ratio


17.30 8.21 13.97

F7, F5, F2 F4 F3

0.4422 0.3958 0.1923

** * N.S.

11.43 4.46 3.341

3.85 2.48 3.61

F7, F5 – F3

0.4466 – 0.2313

** N.S. **

24.67 – 7.91

4.74

0.3551

**

7.21

3.08 4.65

F5, F7, F3, F9, F4 F4 F9, F7

0.3615 0.2617

* **

5.26 5.26

10.96 6.58 9.80

F7, F1 F4 F5, F8

0.3861 0.3383 0.2493

** N.S. *

19.01 3.23 3.38

14.69 14.99 18.26

F7, F2 F2 –

0.2968 0.3801 –

** N.S. N.S.

8.50 3.04 –

16.21 11.94 13.36

F4, F9 F5, F9 F3

0.2617 0.4118 0.3000

** N.S. **

7.91 3.06 11.18

11.47 8.09 11.78

F8, F7 F9, F6, F4 F7

0.1682 0.5419 0.2677

* ** **

4.35 5.41 11.97 Table VIII. Quality regressions: selected relationships by region

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21 of the 39 regional models. Studying the two Tables we see the models vary within the regions, depending on what is being measured. Consider, for example, the quality dependent variable “average per cent of items defective”. In Table V (across all regions), the predicting factors are F5, F7, F3, and F4. The Asia region in Table VII is nearly the same, with factor F9 included – F5, F7, F3, F9 and F4. Europe is explained by F4, North America by F9 and F7. Asia and North America are significant at p < 0.01, and Europe significant at p < 0.05. R2s are reasonable. One obvious reason for Asia being closer to the overall model is sample size. Asia with 249 respondents, compared to 36 for Europe and 143 for North America, has the most respondents. However, if the respondents all answered the same, then all regions would be identical. There clearly are differences for this variable. After examining the firms that responded, our expectation was that Europe and North America might be similar due to characteristics of the firms (Table II). We suspected Asia might be different because of firm size, where the smaller firms might not find TQM affordable. Examining the regression components under each dependent variable leads us to the conclusion that there is very little commonality between regions. Factors seem to vary substantially, a factor occasionally appearing in two or three regions. Even then we could not find region pairs that were similar, i.e. Asia-Europe, Asia-North America, or EuropeNorth America. Despite the pattern of factor means being similar (Table VI) our conclusion is that H3 did not hold as stated. Regions and nations. We conducted our analysis and presented results geographically, by region; yet, in each region of the world we are concerned that the sample does not reflect that region adequately. This is most pronounced with Europe, where we are uncomfortable with the UK and Spain representing “Europe”. Somewhat of a concern is the geographical grouping of Asia, where China, Hong Kong, Korea, and Taiwan are included with Australia and New Zealand. Another difficulty with Asia is that Japan and Singapore are not included. We are less concerned with North America, where Canada was excluded. Canada is similar in many ways to the USA, so that is not judged as critical. The analysis in this paper developed formal models for regions. It did not search for clusters of countries that answered questions similarly regardless of region. An examination of demographic data and open-ended questions led us to believe there were some broad similarities to approach for quality improvement and achieved quality. China, Hong Kong, Mexico, and New Zealand appear to have more traditional, inspection driven, management centred (rather than employee centred) approaches to quality improvement. The total cost of quality as a per cent of sales tended to be high, as was the per cent of items defective. On the other hand, Korea, Taiwan, the UK, and the USA exhibited higher quality levels, lower quality costs, and more employee participation. Australia and Spain were somewhere in between.

Study weakness. Future research might address several weaknesses of this study. First, sample size was uneven. The sample from Europe (10 per cent of the total) was particularly small to reach conclusions about that region. Second, the criteria and procedure for sampling varied among regions. All investigators used the same questionnaire and written procedures, yet local conditions demanded variations. To some extent, as in many field research situations, a convenience sampling plan emerged rather than a scientifically predetermined plan. Another weakness of the study is the self-report nature of both the independent and dependent variables. It would be better if the investigators independently observed the actual quality and financial data. For the most part respondents collected those data within their organizations and reported them. In our checking they usually did not have these data readily at hand, yet we really do not know to what extent they could have been estimated. Finally, it would be useful if future international quality improvement studies could include nations not in this study – especially Japan in Asia, Canada in North America, and France, Germany, and Italy in Europe. Conclusions In conclusion, we can say that the quality improvement items suggested from many sources – research, practice, Baldrige criteria – can effectively be reduced to a reasonable set of factors (52 items to nine factors). These factors do significantly relate to quality when quality is measured eight different ways. A reasonable amount of variance is explained in each of these eight models. This study suggests that performance quality can especially be influenced by obtaining knowledge about quality improvement, focusing on customers – what they receive and perceive, and management involvement. Regression parameter estimates were negative, indicating that as each of these factors were increased, the per cent defective and cost of quality would decrease. Increasing the use of these factors improved quality. We were surprised that statistical process control (SPC) did not significantly predict performance quality. It is, however, a welcome finding that the actions that most influence quality are within management’s domain. Quality improvement, based on this study, can be enhanced by a few specific management actions: increasing knowledge about quality, a customer focus, and management involvement. Further, we conclude that although quality improvement approach successfully influences quality, the impact on financial performance is somewhat weak. These findings are consistent with those of Adam (1994) and Sluti (1992). Relationships were statistically significant but did not explain as much variance as when quality was the dependent variable. Perhaps this is suggesting that when quality is improved, quality makes an impact on financial performance, but so do a lot of other variables. Quality improvement alone may not be enough to broadly change financial performance. Proper cash flow management, prudent utilization of automation, effectively matching


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employees to jobs, and so forth, will be necessary as well. That could be the reason for low explained variance. Finally, we concluded that the nine factors that capture quality improvement approach could be used to predict quality – and to some extent financial performance – within any region (Asia, Europe, or North America). However, the explanatory models varied from region to region, and the model varied depending on what was being measured (which of the 13 dependent measures). The models across all regions were dissimilar from any region, and no two regions seemed to be similar to one another, supporting the “culture-specific” hypothesis in international management. This study is in general agreement with the empirical work from the PIMS database (Maani, 1988), the Baldrige finalists (US General Accounting Office, 1991), and system-structure models of quality management (Benson et al., 1991; Flynn et al., 1994; Roth and Giffi, 1994). The strong predictive value of a few quality improvement factors on actual quality provides further refinement of system-structure models of quality management. Yet it is the international extension of the work of Sluti (1992; also reported in Maani et al., 1994) in New Zealand and Adam (1994) in the USA that sets this study apart. Note 1. A lack of statistically rigorous techniques for measuring the associations between Likert scaled variables has led us to use the common (mal)practice of using correlation, regression and other related techniques even though it is acknowledged that some assumptions associated with these measures are violated. References Adam, E.E. Jr ((1994), “Alternative quality improvement practices and organization performance”, Journal of Operations Management, Vol. 12, 1994, pp. 27-44. Adam, E.E. Jr and Swamidass, P.M. (1989), “Assessing operations management from a strategic perspective”, Journal of Management, Vol. 15 No. 2, pp. 181-203. American Quality Foundation and Ernst & Young (1992), The International Quality Study Best Practices Report: An Analysis of Management Practices that Impact on Performance, p. 49. Benson, P.G., Saraph, J.V. and Schroeder, R.G. (1991), “The effects of organizational context on quality management: an empirical investigation”, Management Science, Vol. 31 No. 9, September, pp. 1107-24. Campbell-Hunt, C., Harper, D.A. and Hamilton, R.T. (1993), Islands of Excellence? A Study of Management in New Zealand, NZ Institute of Economic Research, Wellington, New Zealand. Chen, F.F. and Adam, E.E. Jr (1991), “The impact of flexible manufacturing systems on productivity and quality”, IEEE Transactions on Engineering Management, Vol. 38 No. 1, pp. 33-45. Child, J. and Kieser, A. (1979), “Organizational and management roles in British and German companies: an examination of the culture-free thesis”, in Lammus, C.J. and Hickson, D. J. (Eds), Organizations Alike and Unlike, Routledge & Kegan Paul, London, pp. 251-71. Cole, R.E. (1981), “The Japanese lesson in quality”, Technology Review, No. 83, pp. 29-40. Corbett, L.M. (1993), “Manufacturing futures project 1992 international comparisons: a New Zealand perspective”, Special Report Series #6, Graduate School of Business and Government Management, Victoria University of Wellington, New Zealand, p. 17.

Crosby, P.B. (1979), Quality Is Free, New American Library, New York, NY. Crosby, P.B. (1984), Quality without Tears, McGraw-Hill, New York, NY. Dahlgaard J.J., Kanji G.K. and Kristensen K. (1990), “A comparative study of quality control methods and principles in Japan, Korea, and Denmark”, Total Quality Management, Vol. 1 No. 1, pp. 115-32. De Meyer, A., Nakane, J., Miller, J.G. and Ferdows, K. (1989), “Flexibility: the next competitive battle. The manufacturing futures survey”, Strategic Management Journal, Vol. 10 No. 2, pp. 135-45. Dean, J.W. Jr and Bowen, D.E. (1994), “Management theory and total quality: improving research and practice through theory development”, Academy of Management Review, Vol. 19 No. 3, pp. 392-418. Deming, W.E. (1986), Out of the Crisis, Center for Advanced Engineering Study, Cambridge, MA. Ebrahimpour, M. and Lee, S.M. (1988), “Quality management practices of American and Japanese electronic firms in the United States”, Production and Inventory Management Journal, Vol. 29 No. 4, pp. 28-31. Evans, J.R. and Lindsay, W.M. (1993), The Management and Control of Quality, West Publishing Company, Minneapolis, MN. Flynn, B.B., Schroeder, R.G. and Sakakibara, S. (1994), “A framework for quality management research and an associated measurement instrument”, Journal of Operations Management, Vol. 11 No. 4, pp. 339-66. Flynn, B.B., Sakakibara, S., Schroeder, R.G., Bates, K. and Flynn, J. (1990), “Empirical research methods in operations management”, Journal of Operations Management, Vol. 9 No. 2, pp. 250-84. Form, W. (1994), “Comparative industrial sociology and the convergence hypothesis”, Annual Review of Sociology, Vol. 19 No. 3, pp. 1-25. Garvin, D.A. (1986), “Quality problems, policies, and attitudes in the United States and Japan: an exploratory study”, Academy of Management Journal, Vol. 29, pp. 653-73. Garvin, D.A. (1988), Managing Quality, The Free Press, New York, NY. Haire, M., Ghiselli, E.E. and Porter, L.W. (1966), Managerial Thinking: An International Study, Wiley, New York, NY. Hofstede, G.H. (1980), Culture’s Consequences: International Differences in Work-Related Values, Sage Publications, Beverly Hills, CA. Ishikawa, J. (1976), Guide to Quality Control, Nordica International Limited for the Asian Productivity Organization, Hong Kong. Juran, J.M. (1982), Juran on Quality Improvement, Juran Institute, New York, NY. Juran, J.M. (1989), Juran on Leadership for Quality, Juran Institute, New York, NY. Kim, J.S. and Miller, J.G. (1992), “Building the value factory: a progress report for US manufacturing”, a research report of the Boston University School of Management Manufacturing Roundtable, Boston, MA. Kopelman, R.E. (1986), Managing Productivity in Organizations, McGraw-Hill, New York, NY. Maani, K.E. (1988), “Quality and productivity: are they really compatible?”, Proceedings: The ORSA/TIMS Joint National Meeting, October, Denver, CO. Maani, K.E. and Sluti, D.G. “A conformance-performance model: linking quality strategies to business units performance”, in Ettlie, J.E., Burstein, M.C. and Feigenbaum, A. (Eds), Manufacturing Strategy: The Research Agenda for the Next Decade, Kluwer Academic, Boston, MA, pp. 85-96. Maani, K.E., Putterill, M.S. and Sluti, D.G., “Empirical analysis of quality improvement in manufacturing”, International Journal of Quality & Reliability Management, Vol. 11 No. 7, pp. 19-37.


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Nunnally, J.C. (1978), Psychometric Methods, McGraw-Hill, New York, NY. Reeves, C.A. and Bednor, D.A. (1994), “Defining quality: alternatives and implications”, Academy of Management Review, Vol. 19 No. 3, pp. 419-45. Riding, A. (1989), Distant Neighbors: A Portrait of the Mexicans, Vintage Books, New York, NY. Roth, A.V. and Giffi, C.A. (1994), “Critical factors for achieving world class manufacturing governance”, Operations Management Review, Vol. 10 No. 2, pp. 1-29. Roth, A.V. and Miller, J.G. (1990), “Manufacturing strategy, manufacturing strength, managerial success and economic outcomes”, in Ettlie, J.E., Burstein, M.C. and Feigenbaum, A. (Eds), Manufacturing Strategy: The Research Agenda for the Next Decade, Kluwer Academic, Boston, MA, pp. 97-108. Saraph, J.V., Benson, P.G. and Schroeder, R.G. (1989), “An instrument for measuring the critical factors of quality measurement”, Decision Sciences, Vol. 20 No. 4, Fall, pp. 810-29. Sluti, D.G. “Linking process quality with performance: an empirical study of New Zealand manufacturing plants, PhD dissertation, The University of Auckland, Auckland, NZ. US Department of Commerce, (1991), 1991 Application Guidelines Malcolm Baldrige National Quality Award, p. 43. US General Accounting Office (1991), Management Practices: US Companies Improve Performance through Quality Efforts, GAO/NSIAD-91-190, May. Vastag, G. and Whybark, D.C. (1991), “Manufacturing practices: differences that matter”, International Journal of Production Economics, Vol. 23 Nos. 1-3, pp. 251-9. Ward, P.T., Duray, R., Leong, G.K. and Sum, C. (1995), “Business, environment, operations strategy, and performance: an empirical study of Singapore manufacturers”, Journal of Operations Management, Vol. 13 No. 2, pp. 99-115. Whybark, D.C. and Rho B.-H. (1988), “A worldwide survey of manufacturing practices”, Indiana Center for Global Business, Discussion Paper #2, Indiana University. Appendix: Example measurement items for quality and productivity improvement Strongly disagree Quality improvement at this company is best described as… Applying no formal approach Statistical process control (SPC) Involving employees; each employee’s responsibility; behavioural in nature Quality products and services depend on the degree to which a company (1) understands and specifies customer requirements (design) and (2) produces and services these requirements (conformance). At my company… Customers’ opinions and views regarding their needs are actively sought through direct contact; sales calls, focus groups, and so forth Customers regularly and formally receive customer satisfaction questionnaires

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Senior executives (responsible for company profit and loss) create and sustain clear and visible quality values Productivity can be improved by introducing behavioural, engineering, and accounting practices. At my company productivity is improved by… Traditional industrial engineering applications: process flow analyses, work measurement, standards, layouts and so forth Effective inventory planning and control

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