- Email: [email protected]
Understanding Customer Quality Requirements
Understanding Customer Quality Requirements Model and Application
Eric Hansen Robert J. Bush We integrate previous work regarding service and product quality to develop and demonstrate tools useful in evaluating total product quality. Our premise is that competitive advantage can be gained through quality, even in mature commodity industries, but that a quality-based competitive strategy will be successful only if managers understand both how quality is perceived by their customers and their company’s level of quality performance. We used information from questionnaires sent to buyers of softwood dimension lumber to develop an empirical model of customer-perceived total product quality. This model and additional data from suppliers were then subjected
to Importance–Performance Analysis. Respondents identified quality performance differences, based on five dimensions, between their least- and most-preferred suppliers. The dimension representing physical product characteristics was rated as the most important. Results indicate that the empirical model can be used with Importance–Performance Analysis to develop information necessary to pursue a successful quality-based competitive strategy. © 1999 Elsevier Science Inc. All rights reserved.
Address correspondence to Eric Hansen, Department of Forest Products, College of Forestry, Oregon State University, Corvallis, OR 97331. This is paper 3195, Forest Research Laboratory, Oregon State University. This material is based upon work supported by the Cooperative State Research Service, U.S. Department of Agriculture, under Agreement No. 91-37103-6543.
Quality has become something of a cliché. Advertisers would have us believe that every company produces a high-quality product. In fact, the term “quality” has been so overused that, to many, it has begun to lose its mean-
Industrial Marketing Management 28, 119–130 (1999) © 1999 Elsevier Science Inc. All rights reserved. 655 Avenue of the Americas, New York, NY 10010
INTRODUCTION
0019-8501/99/$–see front matter PII S0019-8501(98)00007-8
ing. This has resulted in confusion among customers, as well as within companies. What is quality? Many people have difficulty answering this. A common response is, “I can’t define quality, but I know it when I see it.” Inability to define quality does not, however, preclude interest in, and strong opinions regarding the concept. This has resulted in a variety of approaches to quality and its definition. For example, quality has been defined as “fitness for use” [15, p. 2-2] and “conformance to requirements” [4, p. 40]. These definitions are lacking, however, because a company that designs a product and produces it according to the design criteria is not necessarily producing a quality product, as judged by customers. A product will be perceived as high quality only if it conforms to the requirements of the customer. Thus, the customer is the key to defining quality, and a company’s internal definition of quality is meaningless if it fails to reflect consumer requirements. It is this aspect of quality (i.e., the need to match internal definitions of quality to customer requirements) that is perhaps the greatest impediment to gaining a competitive advantage through quality. Companies make the quality claim, because they see it as a potential source of competitive advantage. The claim itself, however, is insufficient to ensure advantage. Achieving competitive advantage through quality requires an understanding of the quality requirements of the customer, a method of measuring efforts to provide for these quality requirements, and a commitment to do both. In this paper, we provide tools that can be used by managers to address these requirements. We describe the development and use of a measure of customer-perceived quality, a measure based upon previous work, but differing from such work, because it includes both product and service aspects of quality. In addition, we describe the use of Importance–Performance Analysis, a tool that can be used in conjunction with the measure to quantify and monitor customer perceptions of the quality delivered by a company.
ERIC HANSEN is Assistant Professor/Extension Specialist in the Department of Forest Products, College of Forestry, Oregon State University, Corvallis, OR.
BACKGROUND Quality Strategies Developing an effective quality strategy for a business is one of the most important challenges faced by top management [3]. According to O’Neal and LaFief [19, p. 134], “. . . quality is increasingly cited as a primary strategic variable for effectively competing in the global marketplace.” Some experts believe that quality improvements are essential merely to maintain competitive position [31]; others claim that quality competition has taken the place of price competition [28]. In any case, high product quality has been associated with improved return on investment and higher market share [14, 21, 24]. Research indicates that accurate and periodic measurement of performance is requisite if a company is to maintain high product quality [24, 31]. Customer perceptions are key to measuring quality performance [8, 16, 20, 22, 29]. Typically, research concerning quality has dealt either with physical products or with services. For example, Garvin’s 1984 study [8] focuses on product quality, whereas Parasuraman et al. [20] outline a model of service quality. This product-service separation provides an incomplete perspective on quality, because every physical product is associated with an array of services (e.g., credit and delivery). Consequently, both the physical product and its associated services (i.e., the total product) must be considered when defining quality from the customer’s perspective. According to Sinclair et al. [25, p. 75], “. . . the services offered and the quality of those services significantly impact the perceived quality of the entire company and its product(s)” and “companies may well be perceived through people and through the services they provide as much as or more than through the actual physical product.” Sonnenburg [27] cites a survey that found poor service (i.e., low-quality service) to be the primary reason that customers switched to competing companies. Kaspar and Lemmink [16] claim that product and service strategies should receive equal attention, because the industrial consumer evaluates the quality of both the physical product and the service associated with that product. Such statements suggest that service quality is essential to a physical product’s success in the marketplace. Quality Measurement
ROBERT BUSH is Associate Professor in the Department of Wood Science and Forest Products, Virginia Polytechnic Institute and State University, Blacksburg, VA.
120
According to Zeithaml [33], the conceptualization and measurement of quality have not been adequately studied. Still, a variety of researchers have investigated the
You must understand customer quality requirements. nature of quality and developed techniques for its measurement [8, 12, 15, 20, 28]. Many of these researchers have approached the construct by investigating, either empirically or theoretically, its dimensionality. For example, the model developed by Parasuraman et al. [20], the SERVQUAL model, explains service quality with five dimensions (Table 1): Reliability, Responsiveness, Assurance, Empathy, and Tangibles. A number of studies have evaluated the SERVQUAL model, with mixed results (see Dabholkar et al. [6] for summary). Garvin [8, p. 41], however, suggests that quality is “multifaceted” and “appears in many forms,” and proposes a model of quality that includes eight dimensions (Table 2). Grönroos [11] claims that service quality can be described by two primary dimensions, technical quality and functional quality. Technical quality refers to what the customer of a service actually receives; whereas, functional quality refers to how the customer receives that service. Holmlund and Kock [13], studying industrial services, expand on the ideas of Grönroos [11] by adding a third dimension, economic service quality, which incorporates the idea that businesses must be profitable. The Industrial Setting The empirical work reported in this paper was conducted within the U.S. softwood dimension lumber industry and its associated markets. Softwood dimension includes lumber two–five inches thick and two or more
TABLE 1 The Dimensions of Service Quality, as Identified in the SERVQUAL Model [20] Dimension
Definition
Reliability
Ability to perform the promised service dependably and accurately Willingness to help customers and provide prompt service Knowledge and courtesy of employees and their ability to inspire trust and confidence Caring, individualized attention to customers Physical facilities, equipment, and appearance of personnel.
Responsiveness Assurance Empathy Tangibles
inches wide. Consequently, this category includes common “2 by 4” and “2 by 6” lumber used in construction, primarily for structural purposes. Softwood lumber is produced by a mature industry [26, 30] that has a limited repertoire of competitive tools. However, Shetty [24] claims that, even in mature industries, quality can be enhanced. Although the setting for the study is the softwood dimension lumber industry, the concepts and techniques described can be applied more broadly. For example, the measure described in this paper can be refined for use with other products and industries. In addition, Importance–Performance Analysis, introduced in detail later in this paper, can be used in any industrial setting. METHODS Modification of Existing Models The dimensions suggested by Garvin [8] adequately describe the concept of physical product quality. Accordingly, for this study, we chose to model total product quality based on a combination of the eight dimensions described by Garvin [8] and the five dimensions included in the SERVQUAL model [20]. Despite some variation in results when used in previous studies, the SERVQUAL model fit our purpose, because it is comprehensive and empirically grounded. In addition, the dimensions of SERVQUAL subsume the dimensions of service quality suggested by Grönroos [11]. We did not include the Economic Service Quality dimension suggested by Holmlund and Kock [13] in this study. Clearly, firms must be profitable. However, introducing this dimension would have required that we include price and, consequently, the concept of value in our work. Our objective was to study quality independent of, and without the confounding influence of, value. Several modifications were necessary to ensure that the combined model [8, 20] was applicable to and appropriate for the industrial setting (i.e., softwood dimension lumber). Specifically, we deleted Garvin’s dimension Serviceability (defined as “the speed, competency, and 121
Product quality was measured 11 ways. courtesy of repair”). Because of the manner in which softwood lumber is used in the markets investigated, no mechanism for repair exists. As a result, serviceability is not a consideration or an expectation of customers. In addition, we combined the Performance and Conformance dimensions described by Garvin [8]. This modification reflected our belief that, for a structural product such as softwood lumber, the performance of the product cannot be separated from its conformance (i.e., the extent to which it meets design standards). In other words, performance is dictated by conformance for this product. From a practical standpoint, we were unable to select measurement items that effectively differentiated between these two dimensions. The model resulting from these modifications consists of 11 dimensions, five of which address the service aspect of total product quality and six of which address the product aspect. Item Selection Items were selected to represent each of the 11 dimensions in the resulting model of total product quality. For example, “Supplier’s ability to deliver quickly on short notice” was chosen as one item to measure Responsiveness. Previous research [8, 10, 12, 20, 23] and discussions with university faculty and industry professionals were used for item generation. Nine of the items generated were considered to be important, but did not seem to fit within any of the theorized 11 dimensions. Rather, they seemed to relate to a dimension we termed Coopera-
tiveness/Supplier Services. This dimension was added to the combined model, resulting in a 12-dimension, 80item model (Figure 1), which we theorized could be used to measure total product quality. Population and Sample Industrial consumers of softwood dimension lumber were the population of interest in this study. The sample frame consisted of three major U.S. softwood dimension lumber consumer groups: wood truss manufacturers, wood treaters, and home centers/lumber and buildingmaterials dealers. These three markets were chosen, because they consume a significant volume of softwood dimension lumber, and because they reflect the needs of a variety of final consumers. Individual companies within the sample frame comprised the sample units, and informants were professional softwood lumber buyers. Truss manufacturers and wood treaters were identified through the Directory of the Forest Products Industry [18] and lumber trade association membership lists. We used the 1991 Directory of Home Center Operators & Hardware Chains [5] to identify home centers/lumber and building-materials dealers. Our study sample included 2,040 companies. The Questionnaire Data were obtained by a mail questionnaire that asked respondents to rate the importance of each of 80 items to
TABLE 2 The Dimensions of Product Quality as Defined by Garvin [8] Dimension Performance Features Reliability Conformance Durability Serviceability Aesthetics Perceived quality
122
Description “Primary operating characteristics” of a product; performance for a television would include such characteristics as picture clarity and color. “Bells and whistles” inherent in a product; features can be thought of as a secondary aspect of Performance; the screen-in-screen option on a television is an example of a feature. The probability that a product will fail; reliability can be measured as the mean time to first failure and mean time between failures. The extent to which a product meets its design standards; this is a measure of how well the manufacturing process was carried out. “The amount of use one gets from a product before it physically deteriorates”; expected life of a product would be a measure of durability. The “speed, courtesy, and competance of repair”; this refers to the whole realm of services associated with handling complaints. “How a product looks, feels, sounds, tastes, or smells”, a matter of personal judgment The consumer’s opinion of a product as it has been influenced through “images, advertising, or brand names”; perceived quality is a way of inferring quality when no direct measure is available; things such as reputation and past experience with a company are important here.
over-all quality. Ratings were made on a seven-point scale anchored at the “1” (well below average importance) and “7” (well above average importance) positions. From the sample of 2,040 companies, 867 useable questionnaires were returned, an over-all response rate of 43%.
ANALYSIS AND RESULTS Nonresponse Bias A common concern in survey research is bias resulting from no or low response from a segment of the sample
FIGURE 1. Theorized dimensions of total product quality and corresponding items used for measurement. (Reprinted from Forest Science 42, 407–414. Used with permission, courtesy of the Society of American Foresters.)
123
Five factors had most effect: salesperson/ supplier characteristics; supplier facilities; product performance; product characteristics; supplier characteristics. that differs significantly from the respondents [32]. Relatively high response rates, such as those obtained in this study, decrease the likelihood of this type of bias [2, 7]. However, we investigated this potential bias by comparing the ratings of early and late respondents on each of the 80 items and on the descriptive variables: volume of lumber purchased, sales volume, and total number of employees. Multivariate analysis of variance (MANOVA) was used to test the 80 items, and analysis of variance (ANOVA) was used to test the descriptive variables. No significant differences were found, thus suggesting that nonresponse bias was not a problem in this study. Testing the Model Zeithaml [33] speaks of quality as a higher-order abstract; that is, dimensions of quality should be similar, regardless of the product or the customer group investigated. Accordingly, we tested the fit between the data and the 12-dimension model by means of confirmatory factor analysis with the software package ITAN [9]. Similarity coefficients indicated a lack of external consistency, which means that, as operationalized, the model did not adequately explain the data [1]. Because of the lack of fit between the data and the hypothesized model, we turned to exploratory factor analysis to assess the underlying dimensionality of the data. New Model Development Two tests were used to determine whether or not exploratory factor analysis was an appropriate technique for developing an empirical model: the Kaiser–MeyerOlkin measure of sampling adequacy and Bartlet’s test for sphericity. Both the Kaiser–Meyer-Olkin value of 0.95 and the sphericity value (p , .00) for the data indi124
cated that exploratory factor analysis was an acceptable method of analysis. Principle-axis factoring with oblique (Oblimin) rotation, provided by the Statistical Package for the Social Sciences, was used for the analysis. The choice of nonorthogonal rotation was based on the assumption that all dimensions would measure some aspect of quality, and would, therefore, be correlated. Eigenvalues greater than 1 were obtained for 15 factors that collectively explained 61.4% of the variance. The scree plot indicated a seven-factor model that explained 49.5% of the variance. However, a five-factor model, explaining 41.5% of the variance, provided item groupings with apparent content validity and was the most useful result. This five-factor model was then refined on the basis of factor loadings, content, and reliability. Three items— Convenience of Supplier’s Location, Electronic Data Interchange with Supplier, and Long-Term Price Agreements Offered by Supplier—did not load at a level of 0.4 or greater on any of the five factors and were deleted prior to reliability analysis. Thirty-four of the remaining items loaded on Factor I (Figure 2), Salesperson/Supplier Characteristics, which combined five dimensions of the original model (Service Reliability, Responsiveness, Assurance, Empathy, and Perceived Quality). Factor II, Supplier Facilities, incorporated two items each from the original Tangibles and Features dimensions. Items from the original Performance/Conformance, Product Reliability, Durability, Features, and Aesthetics dimensions were loaded on Factor III, Lumber Performance, and Factor IV, Lumber Characteristics. Finally, Factor V, Supplier Services, primarily consisted of items used to measure the original Cooperativeness/Supplier Services dimension. Because our goal was to develop a parsimonious, reliable, and useful measurement instrument, we respecified some of the dimensions to obtain the best possible reli-
abilities (as measured by Cronbach’s Alpha). The fivefactor model was respecified as follows. Two items, Manufacturing Expertise of Supplier and Product Availability, were moved from Factor IV to Factor I. Although both items displayed similar loadings on Factors I and IV, their reassignment increased the reliability of Factor I
and allowed Factor I to include all items hypothesized to represent four of the five service dimensions in the SERVQUAL model [20]. After this reassignment, seven items (Absence of Hard-Sell Techniques by Supplier’s Salesperson, Bar Coding of Lumber, Square End Trimming, Antistain Treatment, Precision End Trimming,
Respecified five-dimension model of total product quality and corresponding items used for measurement. (Reprinted from Forest Science 42 407–414. Used with permission, courtesy of the Society of American Foresters.) FIGURE 2.
125
Kiln Dried, and Absence of Knots) were deleted to improve the reliability of the associated factors. With these respecifications, the dimensions in the empirical model were explainable and consistent combinations of the 12 original dimensions. Figure 2 lists the items in, and reliability of, each of the five dimensions. Utilizing the Model Although the model of total quality described is a significant development, the associated 70-item measure is too cumbersome to use on a day-to-day basis in a strategic quality monitoring and improvement program. Therefore, we developed a smaller and more efficient measure, a 25-item, abridged measure of the total quality associated with softwood lumber (Figure 3). This measure incorporates the most important items (as rated by respon-
FIGURE 3. Condensed measurement model for softwood lumber quality.
126
dents) within each of the model’s dimensions and the items most highly correlated with the dimension. This condensed measure can be used to monitor customer perceptions of the total product quality offered by a company, as well as the quality offered by competitors. Utilizing this measure will, of course, require the collection of data from customers. However, gathering such data, not only will facilitate quality monitoring and improvement, but also will increase supplier/customer interaction, thus fostering stronger relationships. An effective way to monitor efforts to provide quality is to use the abridged measure of quality in an Importance– Performance Analysis [17]. Importance–Performance Analysis is a simple, but useful, method that can help to guide quality-based marketing strategies. The technique involves mapping customer perceptions of each of the five facets of quality on a two-axis grid, where the vertical axis represents how well a company performs relative to its competition and the horizontal axis illustrates the relative importance of the quality dimension. Positions on the axes are assessed through customer surveys, and thus represent customer perceptions. Surveys must be conducted on a regular basis to capture changes in competitive position. In addition, a company should monitor, not only its own standing, but also that of its major competitors. The importance–performance grid is easily interpreted (Figure 4). Companies performing well on dimensions rated as important by their customers are doing a good job (Keep Up The Good Work), and are most likely to obtain a competitive advantage based on quality. Because resources available for addressing perceived quality deficiencies are often limited, companies should concentrate improvement efforts on the dimension(s) rated as highly important, but that rank low in performance (Concentrate Here). Dimensions ranked low in customer importance should be given a relatively low priority in a company’s quality improvement strategy. To demonstrate the use of the abridged measure of quality and the application of Importance–Performance Analysis, respondents to the first portion of the study were sent a second questionnaire. A total of 846 questionnaires were mailed (some companies were not included in this part of the study because of incomplete information or because they had discontinued business), and 331 were returned (a response rate of 39%). In this questionnaire, respondents were asked to rate the importance of each of the five quality dimensions (as described by the items chosen to represent them in the abridged measurement instrument) and the performance of their
FIGURE 4.
Importance-performance grid.
most- and least-preferred suppliers on these dimensions. The importance rating scale was the same as that used in the first questionnaire. Average respondent ratings of the importance of the five dimensions to total product quality are depicted in Figure 5. The dimension representing the physical characteristics of the product (i.e., Lumber Characteristics) was rated highest in importance. Supplier/Salesperson Characteristics was the dimension rated second in importance, followed by Lumber Performance, Supplier Services, and Supplier Facilities. Clearly, physical product characteristics are a significant component of quality, even for a commodity-like product such as lumber. Positions representing the performance of most- and leastpreferred suppliers, mapped in Figure 5, provide evi-
FIGURE 5.
dence of the utility of this model. As would be expected, least-preferred suppliers are in a relatively poor competitive position because of their low average performance rating on all of the dimensions of quality. Perhaps of more interest are the clear differences between the ratings of most- and least-preferred suppliers. These differences suggest that respondents recognized the dimensions of quality in the model represented by the abridged measure and perceived differences among suppliers based on these dimensions. Also, the differences imply that the most-preferred suppliers have been successful in seeking to differentiate themselves (either intentionally or unintentionally) on the basis of quality. Finally, the results suggest that quality can lead to competitive advantage (i.e., becoming a buyer’s “preferred supplier”).
Importance and performance rating for the most- and least-preferred suppliers.
127
Quality is multidimensional. These results also provide a clear example of the utility of Importance–Performance Analysis. The greatest difference between least- and most-preferred suppliers occurred on the dimension of Supplier/Salesperson Characteristics (Figure 5), the second most important contributor to total quality. All else being equal, this is where a business (i.e., a least-preferred supplier) may choose to concentrate improvement efforts. However, this decision should be balanced with other considerations. The performance differences are fairly large for several of the dimensions. Concentrating improvement efforts on a dimension of lesser importance, but on which the company has more potential for improvement, is a valid strategy and may yield better results. MANAGERIAL IMPLICATIONS AND CONCLUSIONS The results described in this paper have implications regarding how a manager views quality, as well as the methods he or she uses to track and improve the quality offered to customers. Concerning the former, previous research and the results of this work suggest that product quality, as viewed by customers, is, indeed, multidimensional, including dimensions of service as well as dimensions that address the physical product. Adopting the customer’s multidimensional view of quality and linking this view to company resources and abilities provide a promising way for a manager to identify and develop opportunities to differentiate his or her company. Clearly, concentrating on only one or two dimensions of quality (e.g., physical product characteristics), as is often the case in such resource-driven industries as softwood lumber, will result in missed competitive opportunities. Addressing quality as a multidimensional phenomenon aids the manager in understanding where quality improvements can be made and which improvements will be most valued by customers. When approached multidimensionally, customer-perceived quality deficiencies are more specific and, therefore, far more easily addressed. Managers should be aware that, although quality is multidimensional, not all dimensions contribute equally to perceptions of total product quality. Good perfor128
mance on the more important dimensions may be requisite to providing quality; whereas, performance on dimensions of lesser importance may not significantly influence customers. One finding of this study that may be surprising, given the commodity-like characteristics of softwood lumber, is that the dimension representing physical product characteristics (i.e., Lumber Characteristics) was rated as more important to quality than were dimensions representing service characteristics. The physical product quality of a commodity is often thought to be similar among suppliers, which suggests that managers would do well to choose a strategy that concentrates on service quality. Therefore, managers should evaluate productbased differentiation in relation to their particular industry. In the softwood dimension lumber industry, customers perceive that the physical appearance of lumber has declined. Changes in the resource base are often cited as a cause. This likely has created a heightened sensitivity to appearance on the part of the customer, and is an example of the industry-specific factors that must be considered by a manager. In addition to suggestions regarding how a manager views quality, the results of this study provide managers with specific methods and tools that can be used to undertake a quality evaluation and tracking program. The empirical model of quality developed in this study is applicable, perhaps with minor modification, to other commodity-like industries. Similarly, the multi-item measure developed here may be applicable, with wording changes to reflect industry and product terminology. Whether a manager uses the measure developed here or develops his or her own measure of the dimensions of quality, Importance–Performance Analysis, as demonstrated in this paper, is a useful technique. Managers can gather data from current customers and, possibly, potential customers. We suggest that customers be asked to rate both the company conducting the study and the company’s closest competitor. This “closest competitor” may be either specified or, preferably, defined by the respondent. Mapping the results on the importance–performance grid will provide the manager with several types of information. Positioning on the Importance axis will
Commodity producers cannot ignore physical product quality. provide the manager with information concerning the relative contribution of the dimensions (i.e., components) to over-all quality. Positioning on the Performance axis will detail how well the company is meeting the quality expectations of customers. Customer-perceived differences between the company and its “closest competitor” will be evident from vertical distances on the Performance axis and will provide important information regarding the company’s competitive position. Separate mapping of current and potential customer perceptions can help identify promotional needs. That is, what promotion strategy or total product changes might be necessary to improve potential customer perceptions of the company’s quality? Finally, replication of the work will allow a company to track changes in quality performance resulting from improvement efforts and changes in customer quality expectations. With quality dimension importance and performance available, managers can develop a rational improvement plan, one that considers company expertise and resources, as well as investment return. It is quite possible that the greatest success will result from an improvement strategy that concentrates on one dimension of quality, with the company seeking clear leadership on that dimension, rather than from one in which the company improves marginally on several dimensions. In the event that a company finds itself performing better than its competitors on all dimensions, quality measurement and Importance–Performance Analysis will be useful for tracking customer and competitor changes. Because markets, customers, and performance are all dynamic, periodic measurement is essential. REFERENCES 1. Anderson, J. C., and Gerbing, D. W.: Some Methods for Respecifying Measurement Models to Obtain Unidimensional Construct Measurement. Journal of Marketing Research 19, 453–460 (1982). 2. Churchill, G. A., Jr.: Marketing Research Methodological Foundations, 5th edition. The Dryden Press, Chicago, 1991. 3. Cravens, D. W., Holland, C. W., Lamb, C. W., Jr., and Moncrief, W. C.,
III: Marketing’s Role in Product and Service Quality. Industrial Marketing Management 17, 285–304 (1988). 4. Crosby, P. B.: Quality is Free. New American Library, New York, 1979. 5. CSG Information Services: 1991 Directory of Home Center Operators & Hardware Chains. Business Guides, Inc., Tampa, FL, 1991. 6. Dabholkar, P. A., Thorpe, D. I., and Rentz, J. O.: A Measure of Service Quality for Retail Stores: Scale Development and Validation. Journal of the Academy of Marketing Science 24, 3–16 (1996). 7. Fowler, F. J., Jr.: Survey Research Methods. Sage Publications, Newbury Park, CA, 1984. 8. Garvin, D. A.: What Does “Product Quality” Really Mean? Sloan Management Review 26, 25–43 (1984). 9. Gerbing, D. W., and Hunter, J. F.: ITAN: A Statistical Package for Item Analysis with Correlational Data Including Multiple Groups Confirmatory Factor Analysis. Portland State University, Portland, OR, 1988. 10. Govett, R. L., and Sinclair, S. A.: Marketing Research for Primary Processors of Northern Softwood Lumber. Forest Products Journal 34, 13–20 (1984). 11. Grönroos, C.: A Service Quality Model and its Marketing Implications. European Journal of Marketing 18, 36–44 (1984). 12. Hansen, B. G.: An Analysis of Factors Influencing Quality Perceptions and Purchase of Office Furniture. Ph.D. dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA (1990). 13. Holmlund, M., and Kock, S.: Buyer-Perceived Service Quality in Industrial Networks: Industrial Marketing Management 24, 109–121 (1995). 14. Jacobson, R., and Aaker, D. A.: The Strategic Role of Product Quality. Journal of Marketing 51, 31–44 (1987). 15. Juran, J. M.: Quality Control Handbook. McGraw-Hill, New York, 1974. 16. Kaspar, H., and Lemmink, J.: After Sales Service Quality: Views between Industrial Customers and Service Managers. Industrial Marketing Management 18, 199–208 (1989). 17. Martilla, J. A., and James, J. C.: Importance–Performance Analysis. Journal of Marketing 41, 77–79 (1977). 18. Miller Freeman: 1990 Directory of the Forest Products Industry. Miller Freeman, Inc., San Francisco, 1990. 19. O’Neal, C. R., and LaFief, W. C.: Marketing’s Lead Role in Total Quality. Industrial Marketing Management 21, 133–143 (1992). 20. Parasuraman, A., Zeithaml, V. A., and Berry, L. L.: SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality. Journal of Retailing 64, 12–40 (1988). 21. Phillips, L. W., Chang, D. R., and Buzzell, R. D.: Product Quality, Cost Position, and Business Performance: A Test of Some Key Hypotheses. Journal of Marketing 47, 26–43 (1983). 22. Ross, J. E., and Shetty, Y. K.: Making Quality a Fundamental Part of Strategy: Long Range Planning 18, 53–58 (1985). 23. Seward, K. E., and Sinclair, S. A.: Retailer’s Perceptions of Structural Panel Attributes and Market Segments. Forest Products Journal 38, 25–31 (1988).
129
24. Shetty, Y. K.: Product Quality and Competitive Strategy. Business Horizons 30, 46–49 (1987).
29. Takeuchi, H., and Quelch, J. A.: Quality Is More than Making a Good Product. Harvard Business Review 61, 139–145 (1983).
25. Sinclair, S. A., Hansen, B. G., and Fern, E. F.: Industrial Forest Product Quality: An Empirical Test of Garvin’s Eight Quality Dimensions. Wood and Fiber Science 25, 66–76 (1993).
30. Vasconcellos, J.: Key Success Factors in Marketing Mature Products. Industrial Marketing Management 20, 263–278 (1991).
26. Sinclair, S. A., and Stalling, E. C.: Perceptual Mapping: A Tool for Industrial Marketing: A Case Study. The Journal of Business and Industrial Marketing 5, 55–66 (1990). 27. Sonnenburg, F. K.: Service Quality: Forethought, Not Afterthought. Journal of Business Strategy 10, 54–57 (1989). 28. Steenkamp, J. B. E. M.: Conceptual Model of the Quality Perception Process. Journal of Business Research 21, 309–333 (1990).
130
31. Whiting, E., and Walsh, M.: What is Quality and How Much Does It Cost? Accountancy 97, 146–149 (1986). 32. Yu, J., and Cooper, H.: A Quantitative Review of Research Design Effects on Response Rates to Questionnaires. Journal of Marketing Research 20, 36–44 (1983). 33. Zeithaml, V. A.: Consumer Perceptions of Price, Quality, and Value: A Means–End Model and Synthesis of Evidence. Journal of Marketing 52, 2–22 (1988).
Eric Hansen Robert J. Bush We integrate previous work regarding service and product quality to develop and demonstrate tools useful in evaluating total product quality. Our premise is that competitive advantage can be gained through quality, even in mature commodity industries, but that a quality-based competitive strategy will be successful only if managers understand both how quality is perceived by their customers and their company’s level of quality performance. We used information from questionnaires sent to buyers of softwood dimension lumber to develop an empirical model of customer-perceived total product quality. This model and additional data from suppliers were then subjected
to Importance–Performance Analysis. Respondents identified quality performance differences, based on five dimensions, between their least- and most-preferred suppliers. The dimension representing physical product characteristics was rated as the most important. Results indicate that the empirical model can be used with Importance–Performance Analysis to develop information necessary to pursue a successful quality-based competitive strategy. © 1999 Elsevier Science Inc. All rights reserved.
Address correspondence to Eric Hansen, Department of Forest Products, College of Forestry, Oregon State University, Corvallis, OR 97331. This is paper 3195, Forest Research Laboratory, Oregon State University. This material is based upon work supported by the Cooperative State Research Service, U.S. Department of Agriculture, under Agreement No. 91-37103-6543.
Quality has become something of a cliché. Advertisers would have us believe that every company produces a high-quality product. In fact, the term “quality” has been so overused that, to many, it has begun to lose its mean-
Industrial Marketing Management 28, 119–130 (1999) © 1999 Elsevier Science Inc. All rights reserved. 655 Avenue of the Americas, New York, NY 10010
INTRODUCTION
0019-8501/99/$–see front matter PII S0019-8501(98)00007-8
ing. This has resulted in confusion among customers, as well as within companies. What is quality? Many people have difficulty answering this. A common response is, “I can’t define quality, but I know it when I see it.” Inability to define quality does not, however, preclude interest in, and strong opinions regarding the concept. This has resulted in a variety of approaches to quality and its definition. For example, quality has been defined as “fitness for use” [15, p. 2-2] and “conformance to requirements” [4, p. 40]. These definitions are lacking, however, because a company that designs a product and produces it according to the design criteria is not necessarily producing a quality product, as judged by customers. A product will be perceived as high quality only if it conforms to the requirements of the customer. Thus, the customer is the key to defining quality, and a company’s internal definition of quality is meaningless if it fails to reflect consumer requirements. It is this aspect of quality (i.e., the need to match internal definitions of quality to customer requirements) that is perhaps the greatest impediment to gaining a competitive advantage through quality. Companies make the quality claim, because they see it as a potential source of competitive advantage. The claim itself, however, is insufficient to ensure advantage. Achieving competitive advantage through quality requires an understanding of the quality requirements of the customer, a method of measuring efforts to provide for these quality requirements, and a commitment to do both. In this paper, we provide tools that can be used by managers to address these requirements. We describe the development and use of a measure of customer-perceived quality, a measure based upon previous work, but differing from such work, because it includes both product and service aspects of quality. In addition, we describe the use of Importance–Performance Analysis, a tool that can be used in conjunction with the measure to quantify and monitor customer perceptions of the quality delivered by a company.
ERIC HANSEN is Assistant Professor/Extension Specialist in the Department of Forest Products, College of Forestry, Oregon State University, Corvallis, OR.
BACKGROUND Quality Strategies Developing an effective quality strategy for a business is one of the most important challenges faced by top management [3]. According to O’Neal and LaFief [19, p. 134], “. . . quality is increasingly cited as a primary strategic variable for effectively competing in the global marketplace.” Some experts believe that quality improvements are essential merely to maintain competitive position [31]; others claim that quality competition has taken the place of price competition [28]. In any case, high product quality has been associated with improved return on investment and higher market share [14, 21, 24]. Research indicates that accurate and periodic measurement of performance is requisite if a company is to maintain high product quality [24, 31]. Customer perceptions are key to measuring quality performance [8, 16, 20, 22, 29]. Typically, research concerning quality has dealt either with physical products or with services. For example, Garvin’s 1984 study [8] focuses on product quality, whereas Parasuraman et al. [20] outline a model of service quality. This product-service separation provides an incomplete perspective on quality, because every physical product is associated with an array of services (e.g., credit and delivery). Consequently, both the physical product and its associated services (i.e., the total product) must be considered when defining quality from the customer’s perspective. According to Sinclair et al. [25, p. 75], “. . . the services offered and the quality of those services significantly impact the perceived quality of the entire company and its product(s)” and “companies may well be perceived through people and through the services they provide as much as or more than through the actual physical product.” Sonnenburg [27] cites a survey that found poor service (i.e., low-quality service) to be the primary reason that customers switched to competing companies. Kaspar and Lemmink [16] claim that product and service strategies should receive equal attention, because the industrial consumer evaluates the quality of both the physical product and the service associated with that product. Such statements suggest that service quality is essential to a physical product’s success in the marketplace. Quality Measurement
ROBERT BUSH is Associate Professor in the Department of Wood Science and Forest Products, Virginia Polytechnic Institute and State University, Blacksburg, VA.
120
According to Zeithaml [33], the conceptualization and measurement of quality have not been adequately studied. Still, a variety of researchers have investigated the
You must understand customer quality requirements. nature of quality and developed techniques for its measurement [8, 12, 15, 20, 28]. Many of these researchers have approached the construct by investigating, either empirically or theoretically, its dimensionality. For example, the model developed by Parasuraman et al. [20], the SERVQUAL model, explains service quality with five dimensions (Table 1): Reliability, Responsiveness, Assurance, Empathy, and Tangibles. A number of studies have evaluated the SERVQUAL model, with mixed results (see Dabholkar et al. [6] for summary). Garvin [8, p. 41], however, suggests that quality is “multifaceted” and “appears in many forms,” and proposes a model of quality that includes eight dimensions (Table 2). Grönroos [11] claims that service quality can be described by two primary dimensions, technical quality and functional quality. Technical quality refers to what the customer of a service actually receives; whereas, functional quality refers to how the customer receives that service. Holmlund and Kock [13], studying industrial services, expand on the ideas of Grönroos [11] by adding a third dimension, economic service quality, which incorporates the idea that businesses must be profitable. The Industrial Setting The empirical work reported in this paper was conducted within the U.S. softwood dimension lumber industry and its associated markets. Softwood dimension includes lumber two–five inches thick and two or more
TABLE 1 The Dimensions of Service Quality, as Identified in the SERVQUAL Model [20] Dimension
Definition
Reliability
Ability to perform the promised service dependably and accurately Willingness to help customers and provide prompt service Knowledge and courtesy of employees and their ability to inspire trust and confidence Caring, individualized attention to customers Physical facilities, equipment, and appearance of personnel.
Responsiveness Assurance Empathy Tangibles
inches wide. Consequently, this category includes common “2 by 4” and “2 by 6” lumber used in construction, primarily for structural purposes. Softwood lumber is produced by a mature industry [26, 30] that has a limited repertoire of competitive tools. However, Shetty [24] claims that, even in mature industries, quality can be enhanced. Although the setting for the study is the softwood dimension lumber industry, the concepts and techniques described can be applied more broadly. For example, the measure described in this paper can be refined for use with other products and industries. In addition, Importance–Performance Analysis, introduced in detail later in this paper, can be used in any industrial setting. METHODS Modification of Existing Models The dimensions suggested by Garvin [8] adequately describe the concept of physical product quality. Accordingly, for this study, we chose to model total product quality based on a combination of the eight dimensions described by Garvin [8] and the five dimensions included in the SERVQUAL model [20]. Despite some variation in results when used in previous studies, the SERVQUAL model fit our purpose, because it is comprehensive and empirically grounded. In addition, the dimensions of SERVQUAL subsume the dimensions of service quality suggested by Grönroos [11]. We did not include the Economic Service Quality dimension suggested by Holmlund and Kock [13] in this study. Clearly, firms must be profitable. However, introducing this dimension would have required that we include price and, consequently, the concept of value in our work. Our objective was to study quality independent of, and without the confounding influence of, value. Several modifications were necessary to ensure that the combined model [8, 20] was applicable to and appropriate for the industrial setting (i.e., softwood dimension lumber). Specifically, we deleted Garvin’s dimension Serviceability (defined as “the speed, competency, and 121
Product quality was measured 11 ways. courtesy of repair”). Because of the manner in which softwood lumber is used in the markets investigated, no mechanism for repair exists. As a result, serviceability is not a consideration or an expectation of customers. In addition, we combined the Performance and Conformance dimensions described by Garvin [8]. This modification reflected our belief that, for a structural product such as softwood lumber, the performance of the product cannot be separated from its conformance (i.e., the extent to which it meets design standards). In other words, performance is dictated by conformance for this product. From a practical standpoint, we were unable to select measurement items that effectively differentiated between these two dimensions. The model resulting from these modifications consists of 11 dimensions, five of which address the service aspect of total product quality and six of which address the product aspect. Item Selection Items were selected to represent each of the 11 dimensions in the resulting model of total product quality. For example, “Supplier’s ability to deliver quickly on short notice” was chosen as one item to measure Responsiveness. Previous research [8, 10, 12, 20, 23] and discussions with university faculty and industry professionals were used for item generation. Nine of the items generated were considered to be important, but did not seem to fit within any of the theorized 11 dimensions. Rather, they seemed to relate to a dimension we termed Coopera-
tiveness/Supplier Services. This dimension was added to the combined model, resulting in a 12-dimension, 80item model (Figure 1), which we theorized could be used to measure total product quality. Population and Sample Industrial consumers of softwood dimension lumber were the population of interest in this study. The sample frame consisted of three major U.S. softwood dimension lumber consumer groups: wood truss manufacturers, wood treaters, and home centers/lumber and buildingmaterials dealers. These three markets were chosen, because they consume a significant volume of softwood dimension lumber, and because they reflect the needs of a variety of final consumers. Individual companies within the sample frame comprised the sample units, and informants were professional softwood lumber buyers. Truss manufacturers and wood treaters were identified through the Directory of the Forest Products Industry [18] and lumber trade association membership lists. We used the 1991 Directory of Home Center Operators & Hardware Chains [5] to identify home centers/lumber and building-materials dealers. Our study sample included 2,040 companies. The Questionnaire Data were obtained by a mail questionnaire that asked respondents to rate the importance of each of 80 items to
TABLE 2 The Dimensions of Product Quality as Defined by Garvin [8] Dimension Performance Features Reliability Conformance Durability Serviceability Aesthetics Perceived quality
122
Description “Primary operating characteristics” of a product; performance for a television would include such characteristics as picture clarity and color. “Bells and whistles” inherent in a product; features can be thought of as a secondary aspect of Performance; the screen-in-screen option on a television is an example of a feature. The probability that a product will fail; reliability can be measured as the mean time to first failure and mean time between failures. The extent to which a product meets its design standards; this is a measure of how well the manufacturing process was carried out. “The amount of use one gets from a product before it physically deteriorates”; expected life of a product would be a measure of durability. The “speed, courtesy, and competance of repair”; this refers to the whole realm of services associated with handling complaints. “How a product looks, feels, sounds, tastes, or smells”, a matter of personal judgment The consumer’s opinion of a product as it has been influenced through “images, advertising, or brand names”; perceived quality is a way of inferring quality when no direct measure is available; things such as reputation and past experience with a company are important here.
over-all quality. Ratings were made on a seven-point scale anchored at the “1” (well below average importance) and “7” (well above average importance) positions. From the sample of 2,040 companies, 867 useable questionnaires were returned, an over-all response rate of 43%.
ANALYSIS AND RESULTS Nonresponse Bias A common concern in survey research is bias resulting from no or low response from a segment of the sample
FIGURE 1. Theorized dimensions of total product quality and corresponding items used for measurement. (Reprinted from Forest Science 42, 407–414. Used with permission, courtesy of the Society of American Foresters.)
123
Five factors had most effect: salesperson/ supplier characteristics; supplier facilities; product performance; product characteristics; supplier characteristics. that differs significantly from the respondents [32]. Relatively high response rates, such as those obtained in this study, decrease the likelihood of this type of bias [2, 7]. However, we investigated this potential bias by comparing the ratings of early and late respondents on each of the 80 items and on the descriptive variables: volume of lumber purchased, sales volume, and total number of employees. Multivariate analysis of variance (MANOVA) was used to test the 80 items, and analysis of variance (ANOVA) was used to test the descriptive variables. No significant differences were found, thus suggesting that nonresponse bias was not a problem in this study. Testing the Model Zeithaml [33] speaks of quality as a higher-order abstract; that is, dimensions of quality should be similar, regardless of the product or the customer group investigated. Accordingly, we tested the fit between the data and the 12-dimension model by means of confirmatory factor analysis with the software package ITAN [9]. Similarity coefficients indicated a lack of external consistency, which means that, as operationalized, the model did not adequately explain the data [1]. Because of the lack of fit between the data and the hypothesized model, we turned to exploratory factor analysis to assess the underlying dimensionality of the data. New Model Development Two tests were used to determine whether or not exploratory factor analysis was an appropriate technique for developing an empirical model: the Kaiser–MeyerOlkin measure of sampling adequacy and Bartlet’s test for sphericity. Both the Kaiser–Meyer-Olkin value of 0.95 and the sphericity value (p , .00) for the data indi124
cated that exploratory factor analysis was an acceptable method of analysis. Principle-axis factoring with oblique (Oblimin) rotation, provided by the Statistical Package for the Social Sciences, was used for the analysis. The choice of nonorthogonal rotation was based on the assumption that all dimensions would measure some aspect of quality, and would, therefore, be correlated. Eigenvalues greater than 1 were obtained for 15 factors that collectively explained 61.4% of the variance. The scree plot indicated a seven-factor model that explained 49.5% of the variance. However, a five-factor model, explaining 41.5% of the variance, provided item groupings with apparent content validity and was the most useful result. This five-factor model was then refined on the basis of factor loadings, content, and reliability. Three items— Convenience of Supplier’s Location, Electronic Data Interchange with Supplier, and Long-Term Price Agreements Offered by Supplier—did not load at a level of 0.4 or greater on any of the five factors and were deleted prior to reliability analysis. Thirty-four of the remaining items loaded on Factor I (Figure 2), Salesperson/Supplier Characteristics, which combined five dimensions of the original model (Service Reliability, Responsiveness, Assurance, Empathy, and Perceived Quality). Factor II, Supplier Facilities, incorporated two items each from the original Tangibles and Features dimensions. Items from the original Performance/Conformance, Product Reliability, Durability, Features, and Aesthetics dimensions were loaded on Factor III, Lumber Performance, and Factor IV, Lumber Characteristics. Finally, Factor V, Supplier Services, primarily consisted of items used to measure the original Cooperativeness/Supplier Services dimension. Because our goal was to develop a parsimonious, reliable, and useful measurement instrument, we respecified some of the dimensions to obtain the best possible reli-
abilities (as measured by Cronbach’s Alpha). The fivefactor model was respecified as follows. Two items, Manufacturing Expertise of Supplier and Product Availability, were moved from Factor IV to Factor I. Although both items displayed similar loadings on Factors I and IV, their reassignment increased the reliability of Factor I
and allowed Factor I to include all items hypothesized to represent four of the five service dimensions in the SERVQUAL model [20]. After this reassignment, seven items (Absence of Hard-Sell Techniques by Supplier’s Salesperson, Bar Coding of Lumber, Square End Trimming, Antistain Treatment, Precision End Trimming,
Respecified five-dimension model of total product quality and corresponding items used for measurement. (Reprinted from Forest Science 42 407–414. Used with permission, courtesy of the Society of American Foresters.) FIGURE 2.
125
Kiln Dried, and Absence of Knots) were deleted to improve the reliability of the associated factors. With these respecifications, the dimensions in the empirical model were explainable and consistent combinations of the 12 original dimensions. Figure 2 lists the items in, and reliability of, each of the five dimensions. Utilizing the Model Although the model of total quality described is a significant development, the associated 70-item measure is too cumbersome to use on a day-to-day basis in a strategic quality monitoring and improvement program. Therefore, we developed a smaller and more efficient measure, a 25-item, abridged measure of the total quality associated with softwood lumber (Figure 3). This measure incorporates the most important items (as rated by respon-
FIGURE 3. Condensed measurement model for softwood lumber quality.
126
dents) within each of the model’s dimensions and the items most highly correlated with the dimension. This condensed measure can be used to monitor customer perceptions of the total product quality offered by a company, as well as the quality offered by competitors. Utilizing this measure will, of course, require the collection of data from customers. However, gathering such data, not only will facilitate quality monitoring and improvement, but also will increase supplier/customer interaction, thus fostering stronger relationships. An effective way to monitor efforts to provide quality is to use the abridged measure of quality in an Importance– Performance Analysis [17]. Importance–Performance Analysis is a simple, but useful, method that can help to guide quality-based marketing strategies. The technique involves mapping customer perceptions of each of the five facets of quality on a two-axis grid, where the vertical axis represents how well a company performs relative to its competition and the horizontal axis illustrates the relative importance of the quality dimension. Positions on the axes are assessed through customer surveys, and thus represent customer perceptions. Surveys must be conducted on a regular basis to capture changes in competitive position. In addition, a company should monitor, not only its own standing, but also that of its major competitors. The importance–performance grid is easily interpreted (Figure 4). Companies performing well on dimensions rated as important by their customers are doing a good job (Keep Up The Good Work), and are most likely to obtain a competitive advantage based on quality. Because resources available for addressing perceived quality deficiencies are often limited, companies should concentrate improvement efforts on the dimension(s) rated as highly important, but that rank low in performance (Concentrate Here). Dimensions ranked low in customer importance should be given a relatively low priority in a company’s quality improvement strategy. To demonstrate the use of the abridged measure of quality and the application of Importance–Performance Analysis, respondents to the first portion of the study were sent a second questionnaire. A total of 846 questionnaires were mailed (some companies were not included in this part of the study because of incomplete information or because they had discontinued business), and 331 were returned (a response rate of 39%). In this questionnaire, respondents were asked to rate the importance of each of the five quality dimensions (as described by the items chosen to represent them in the abridged measurement instrument) and the performance of their
FIGURE 4.
Importance-performance grid.
most- and least-preferred suppliers on these dimensions. The importance rating scale was the same as that used in the first questionnaire. Average respondent ratings of the importance of the five dimensions to total product quality are depicted in Figure 5. The dimension representing the physical characteristics of the product (i.e., Lumber Characteristics) was rated highest in importance. Supplier/Salesperson Characteristics was the dimension rated second in importance, followed by Lumber Performance, Supplier Services, and Supplier Facilities. Clearly, physical product characteristics are a significant component of quality, even for a commodity-like product such as lumber. Positions representing the performance of most- and leastpreferred suppliers, mapped in Figure 5, provide evi-
FIGURE 5.
dence of the utility of this model. As would be expected, least-preferred suppliers are in a relatively poor competitive position because of their low average performance rating on all of the dimensions of quality. Perhaps of more interest are the clear differences between the ratings of most- and least-preferred suppliers. These differences suggest that respondents recognized the dimensions of quality in the model represented by the abridged measure and perceived differences among suppliers based on these dimensions. Also, the differences imply that the most-preferred suppliers have been successful in seeking to differentiate themselves (either intentionally or unintentionally) on the basis of quality. Finally, the results suggest that quality can lead to competitive advantage (i.e., becoming a buyer’s “preferred supplier”).
Importance and performance rating for the most- and least-preferred suppliers.
127
Quality is multidimensional. These results also provide a clear example of the utility of Importance–Performance Analysis. The greatest difference between least- and most-preferred suppliers occurred on the dimension of Supplier/Salesperson Characteristics (Figure 5), the second most important contributor to total quality. All else being equal, this is where a business (i.e., a least-preferred supplier) may choose to concentrate improvement efforts. However, this decision should be balanced with other considerations. The performance differences are fairly large for several of the dimensions. Concentrating improvement efforts on a dimension of lesser importance, but on which the company has more potential for improvement, is a valid strategy and may yield better results. MANAGERIAL IMPLICATIONS AND CONCLUSIONS The results described in this paper have implications regarding how a manager views quality, as well as the methods he or she uses to track and improve the quality offered to customers. Concerning the former, previous research and the results of this work suggest that product quality, as viewed by customers, is, indeed, multidimensional, including dimensions of service as well as dimensions that address the physical product. Adopting the customer’s multidimensional view of quality and linking this view to company resources and abilities provide a promising way for a manager to identify and develop opportunities to differentiate his or her company. Clearly, concentrating on only one or two dimensions of quality (e.g., physical product characteristics), as is often the case in such resource-driven industries as softwood lumber, will result in missed competitive opportunities. Addressing quality as a multidimensional phenomenon aids the manager in understanding where quality improvements can be made and which improvements will be most valued by customers. When approached multidimensionally, customer-perceived quality deficiencies are more specific and, therefore, far more easily addressed. Managers should be aware that, although quality is multidimensional, not all dimensions contribute equally to perceptions of total product quality. Good perfor128
mance on the more important dimensions may be requisite to providing quality; whereas, performance on dimensions of lesser importance may not significantly influence customers. One finding of this study that may be surprising, given the commodity-like characteristics of softwood lumber, is that the dimension representing physical product characteristics (i.e., Lumber Characteristics) was rated as more important to quality than were dimensions representing service characteristics. The physical product quality of a commodity is often thought to be similar among suppliers, which suggests that managers would do well to choose a strategy that concentrates on service quality. Therefore, managers should evaluate productbased differentiation in relation to their particular industry. In the softwood dimension lumber industry, customers perceive that the physical appearance of lumber has declined. Changes in the resource base are often cited as a cause. This likely has created a heightened sensitivity to appearance on the part of the customer, and is an example of the industry-specific factors that must be considered by a manager. In addition to suggestions regarding how a manager views quality, the results of this study provide managers with specific methods and tools that can be used to undertake a quality evaluation and tracking program. The empirical model of quality developed in this study is applicable, perhaps with minor modification, to other commodity-like industries. Similarly, the multi-item measure developed here may be applicable, with wording changes to reflect industry and product terminology. Whether a manager uses the measure developed here or develops his or her own measure of the dimensions of quality, Importance–Performance Analysis, as demonstrated in this paper, is a useful technique. Managers can gather data from current customers and, possibly, potential customers. We suggest that customers be asked to rate both the company conducting the study and the company’s closest competitor. This “closest competitor” may be either specified or, preferably, defined by the respondent. Mapping the results on the importance–performance grid will provide the manager with several types of information. Positioning on the Importance axis will
Commodity producers cannot ignore physical product quality. provide the manager with information concerning the relative contribution of the dimensions (i.e., components) to over-all quality. Positioning on the Performance axis will detail how well the company is meeting the quality expectations of customers. Customer-perceived differences between the company and its “closest competitor” will be evident from vertical distances on the Performance axis and will provide important information regarding the company’s competitive position. Separate mapping of current and potential customer perceptions can help identify promotional needs. That is, what promotion strategy or total product changes might be necessary to improve potential customer perceptions of the company’s quality? Finally, replication of the work will allow a company to track changes in quality performance resulting from improvement efforts and changes in customer quality expectations. With quality dimension importance and performance available, managers can develop a rational improvement plan, one that considers company expertise and resources, as well as investment return. It is quite possible that the greatest success will result from an improvement strategy that concentrates on one dimension of quality, with the company seeking clear leadership on that dimension, rather than from one in which the company improves marginally on several dimensions. In the event that a company finds itself performing better than its competitors on all dimensions, quality measurement and Importance–Performance Analysis will be useful for tracking customer and competitor changes. Because markets, customers, and performance are all dynamic, periodic measurement is essential. REFERENCES 1. Anderson, J. C., and Gerbing, D. W.: Some Methods for Respecifying Measurement Models to Obtain Unidimensional Construct Measurement. Journal of Marketing Research 19, 453–460 (1982). 2. Churchill, G. A., Jr.: Marketing Research Methodological Foundations, 5th edition. The Dryden Press, Chicago, 1991. 3. Cravens, D. W., Holland, C. W., Lamb, C. W., Jr., and Moncrief, W. C.,
III: Marketing’s Role in Product and Service Quality. Industrial Marketing Management 17, 285–304 (1988). 4. Crosby, P. B.: Quality is Free. New American Library, New York, 1979. 5. CSG Information Services: 1991 Directory of Home Center Operators & Hardware Chains. Business Guides, Inc., Tampa, FL, 1991. 6. Dabholkar, P. A., Thorpe, D. I., and Rentz, J. O.: A Measure of Service Quality for Retail Stores: Scale Development and Validation. Journal of the Academy of Marketing Science 24, 3–16 (1996). 7. Fowler, F. J., Jr.: Survey Research Methods. Sage Publications, Newbury Park, CA, 1984. 8. Garvin, D. A.: What Does “Product Quality” Really Mean? Sloan Management Review 26, 25–43 (1984). 9. Gerbing, D. W., and Hunter, J. F.: ITAN: A Statistical Package for Item Analysis with Correlational Data Including Multiple Groups Confirmatory Factor Analysis. Portland State University, Portland, OR, 1988. 10. Govett, R. L., and Sinclair, S. A.: Marketing Research for Primary Processors of Northern Softwood Lumber. Forest Products Journal 34, 13–20 (1984). 11. Grönroos, C.: A Service Quality Model and its Marketing Implications. European Journal of Marketing 18, 36–44 (1984). 12. Hansen, B. G.: An Analysis of Factors Influencing Quality Perceptions and Purchase of Office Furniture. Ph.D. dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA (1990). 13. Holmlund, M., and Kock, S.: Buyer-Perceived Service Quality in Industrial Networks: Industrial Marketing Management 24, 109–121 (1995). 14. Jacobson, R., and Aaker, D. A.: The Strategic Role of Product Quality. Journal of Marketing 51, 31–44 (1987). 15. Juran, J. M.: Quality Control Handbook. McGraw-Hill, New York, 1974. 16. Kaspar, H., and Lemmink, J.: After Sales Service Quality: Views between Industrial Customers and Service Managers. Industrial Marketing Management 18, 199–208 (1989). 17. Martilla, J. A., and James, J. C.: Importance–Performance Analysis. Journal of Marketing 41, 77–79 (1977). 18. Miller Freeman: 1990 Directory of the Forest Products Industry. Miller Freeman, Inc., San Francisco, 1990. 19. O’Neal, C. R., and LaFief, W. C.: Marketing’s Lead Role in Total Quality. Industrial Marketing Management 21, 133–143 (1992). 20. Parasuraman, A., Zeithaml, V. A., and Berry, L. L.: SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality. Journal of Retailing 64, 12–40 (1988). 21. Phillips, L. W., Chang, D. R., and Buzzell, R. D.: Product Quality, Cost Position, and Business Performance: A Test of Some Key Hypotheses. Journal of Marketing 47, 26–43 (1983). 22. Ross, J. E., and Shetty, Y. K.: Making Quality a Fundamental Part of Strategy: Long Range Planning 18, 53–58 (1985). 23. Seward, K. E., and Sinclair, S. A.: Retailer’s Perceptions of Structural Panel Attributes and Market Segments. Forest Products Journal 38, 25–31 (1988).
129
24. Shetty, Y. K.: Product Quality and Competitive Strategy. Business Horizons 30, 46–49 (1987).
29. Takeuchi, H., and Quelch, J. A.: Quality Is More than Making a Good Product. Harvard Business Review 61, 139–145 (1983).
25. Sinclair, S. A., Hansen, B. G., and Fern, E. F.: Industrial Forest Product Quality: An Empirical Test of Garvin’s Eight Quality Dimensions. Wood and Fiber Science 25, 66–76 (1993).
30. Vasconcellos, J.: Key Success Factors in Marketing Mature Products. Industrial Marketing Management 20, 263–278 (1991).
26. Sinclair, S. A., and Stalling, E. C.: Perceptual Mapping: A Tool for Industrial Marketing: A Case Study. The Journal of Business and Industrial Marketing 5, 55–66 (1990). 27. Sonnenburg, F. K.: Service Quality: Forethought, Not Afterthought. Journal of Business Strategy 10, 54–57 (1989). 28. Steenkamp, J. B. E. M.: Conceptual Model of the Quality Perception Process. Journal of Business Research 21, 309–333 (1990).
130
31. Whiting, E., and Walsh, M.: What is Quality and How Much Does It Cost? Accountancy 97, 146–149 (1986). 32. Yu, J., and Cooper, H.: A Quantitative Review of Research Design Effects on Response Rates to Questionnaires. Journal of Marketing Research 20, 36–44 (1983). 33. Zeithaml, V. A.: Consumer Perceptions of Price, Quality, and Value: A Means–End Model and Synthesis of Evidence. Journal of Marketing 52, 2–22 (1988).