Grounded Benchmarks for Item Level Service Quality Metrics. Michael Vogelpoel, Anne Sharp, University of South Australia

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1 Grounded Benchmarks for Item Level Service Quality Metrics Michael Vogelpoel, Anne Sharp, University of South Australia Abstract It is still commonly assumed by industry and much of the marketing literature that service quality scores vary considerably between competing brands and that brand scores change over time. This paper empirically examines the extent of service quality score variation between brands in an industry and for individual brands over time. Building on initial findings that overall service quality scores do not vary greatly between brands or change much over time (Eddy, 2001), we extend the research to examine change at the service quality item level. Service quality items contribute to the make-up of overall quality perceptions. We find that the generalisations, in the main, hold at the service quality item level. This pattern, which runs counter to that assumed by service quality literature, is established across three diverse industries, a four year time frame, and more than consumer evaluations. Our findings will help to set more realistic expected performance levels and targets for improvement for service quality performance. Introduction Service quality has been a widely researched stream of marketing for over three decades now, likely due to the impact the services sector is having on the world economy. Currently, service sectors account for 70 percent of GDP and 65 percent of total employment in OECD countries, of which Australia is a member (OECD, 2007). Despite the importance of the services sector, academic research has, for the most part, paid little attention to the crucial issue of interpretation of service quality scores. Instead, service quality literature has been largely focussed on attempts to develop and refine new instruments for service quality measurement, e.g. SERVQUAL and SERVPERF, along with numerous less popular instruments. The majority of research in this area has sought to critique, validate, and/or test these instruments (see Carrillat, Jaramillo and Mulki, 2007; Seth, Deshmukh and Vrat, 2005; Morrison Coulthard, 2004; Brady, Cronin and Brand, 2002; Buttle, 1996), rather than looking for empirical patterns in and establishing benchmarks for consumers evaluations gathered using these instruments. As such, it is still commonly assumed that service quality scores vary considerably between competing brands and that this, at least in part, accounts for differences in metrics such as customer loyalty, profitability, and market share (e.g. Anderson, 1993; Rust, 1993, Rust, 1995). Other areas of marketing, such as brand performance (Uncles, 1995; Ehrenberg and Kennedy, 2002; Ehrenberg, 1972) or attitudes (eg Bird, Channon and Ehrenberg 1970; Sharp and Romaniuk, 2000) have established patterns in their data and used these grounded benchmarks to forecast expected performance, set realistic targets for improvement, and more accurately assess performance over time, and relative to competitors. In the absence of such knowledge in the service quality area, managers have made an assortment of assumptions about the way in which service quality scores move over time, and presumably based decision-making on this seemingly uninformed viewpoint. For example, it is often assumed that competing brands have very different service quality scores (and hence is an explanation for differences in profitability, market share etc), or that a firm can take action to dramatically increase their service quality ratings within a short time. A recent example highlighting this is two Australian state Governments setting a target of a 10% 1

2 performance improvement in service quality metrics by 2010 for all their departments (Government of South Australia, 2008). In addition, several authors have noted the dangers associated with the practice of attributing diagnostic meaning to service quality measures based solely on the measure (eg rating scale) and have called for the development of interpretative norms (Brown, 1997; Danaher and Haddrell, 1996). Developing grounded benchmarks & norms Eddy, Sharp, Page and Dawes (2001) established a number of interesting patterns in overall measures of service quality. One was that there is very little variation in overall service quality scores for competing brands. For any chosen industry, there was typically between 0.1 to 0.4 scale-points difference in the 11-point scales that are often used for service quality measurement. A second important finding was that the overall evaluations of a brand are highly stable over time. While there is no consensus about the exact dimensions that make up overall service quality, a commonly used conceptualisation (Parasuraman, Zeithaml and Berry, 1988) puts forward 22 items. These 22 items each fall under one of five key dimensions of service quality: tangibles; reliability; responsiveness; assurance; and empathy. For example, I receive prompt service from company X and Employees of company X are willing to help customers would be two items relating to the dimension of responsiveness. We extend the examination of patterns in service quality scores from overall quality to item level. Specifically we examine the level of variation in service quality scores between competing brands in an industry at the item level and look at the stability of item level scores for individual brands over time. While it has been established that overall service quality perceptions do not vary much between brands, the individual items that make up this overall evaluation have, to date, not been examined. It may be that the observed stability at the aggregate (overall) service quality level is underpinned by significant item-level variability. Methodology Data relating to different service quality items was gathered using an 11-point agreement scale, where 0 had the verbal anchor of completely disagree and 10 equated to completely agree. Sourcing data that covers multiple brands within the same industry is rare, as organisations tend only to measure service quality amongst their own customer base, rather than across the industry. Even when this data is obtained, there is a lack commonality in the items that measured across different data sets, as questionnaires are typically altered between rounds of data collection. As a result, our data is restricted to 13 items out of the possible 22 that were conceptualised in the work of Parasuraman et al (1988) to cover all five different dimensions of service quality. However, the items we examine cover four of the five possible dimensions of service quality (ie reliability, responsiveness, empathy, and assurance) for the first research question (between brand score variation) and all five dimensions for the second research question (individual brand variation over time), making this a robust test in terms of breadth. Three industries are covered by the data: financial institutions; postal services; and concrete supply. These industries are distinct from each other and so we would not expect any variation (or lack of) to be a result of shared industry characteristics. These industries were also used in the original work of Eddy et al (2001) that established the generalisations that this paper seeks to test in the new item level context. This again strengthens our analyses, as we are able to examine the new research questions within the same original context. This ability 2

3 to replicate the context of the original research was the key reason for the choice of industries for this study. There were five brands examined in the financial services industry. These were the largest share brands in the market. Three data sets cover the time periods of 2001, 2004, and For concrete supply there were two brands (again the key leaders in the market) covering a two-year period of 1999 and The nature of the postal services industry in Australia is that there is only one provider. Because of this, we are only able to examine the financial services and concrete supply industries for our first research question (variation between brands in an industry). The postal data covers five periods of 1997, May 98, November 98, June 99, and November 99. The different time periods covered by the different data sets were created by the fact the data was collected on behalf of a different industry sponsor for each industry and they each had different reporting period requirements for the service quality data. This issue of different time periods between data collection across the industries strengthen the testing of the research questions. It can be argued that if any brand were going to change service quality scores significantly, it should happen within such time frames. Overall, the number of individual consumer evaluations analysed was For our analysis of variation between brands, data for each brand in each data collection period was compared back to the industry average for that year. For example, if the average industry score across all brands was 8.2 and Brand X scored 7.8, the variation for Brand X was recorded as 0.4 for that year. For the financial services data, changes in the questionnaires meant that for two dimensions (reliability and responsiveness) three sets of data were available and only two sets for empathy and assurance. The distribution of brand score variation from the relevant industry average was then aggregated across all brands and industries to give a frequency table of variations, shown by dimension of service quality (Table 1). We then examine the variation for an individual brand, over-time, rather than the variation between competing brands. This analysis includes the postal services industry and because of this, the added dimension of tangibles. Again, we present results by service quality dimension, showing the scale point variability distribution across all brands, across all industries, over time. For example, the financial services industry has five brands and three sets of data. This gives two variation scores for each brand across the three data sets (change from survey 1 to 2 and from survey 2 to 3), providing 10 variability scores for each item. In total, 53 time period changes for brands were analysed, involving nine brands and three industries. For interpretation of the results, we look for significant sameness amongst the data. By this, we look for commonality of the pattern displayed in the data rather than applying statistical tests of significance (Bound and Ehrenberg, 1989). This is an appropriate approach when handling multiple sets of data and when conducting exploratory work. It was also adopted in earlier research (Eddy, Sharp, Page and Dawes, 2001). Results & Discussion Table 1 shows results for item variation between brands and their respective industry average for that year. The data has been aggregated across the years and industries to give one frequency table. It is unnecessary to display the actual score each brand received (i.e. 7.2/10), as the purpose of this study was to discover the extent of variation, not to examine service performance per se. The columns present the data by dimension. The rows show the percentage of cases displaying that level of variation. For example, 37% of the item 3

4 measures relating to the dimension of reliability showed 0.1 of a scale point variation from the average. Table 1: Variation between brands at service quality item level Variation (scale points) Service Quality Dimension Reliability Assurance Empathy Responsiveness Total all dimensions Cumulative % TOTAL 100% 100% 100% 100% 100% The final column of Table 1 shows that 87% of all the variability between brands (within the same industry) was 0.4 scale points or less. For reliability, no variability was seen outside of this range. 21% lay outside this range for assurance, 28% for empathy and 11% for responsiveness. It appears that the vast majority of variation is within that seen for overall service quality score evaluations. Table 2 shows the score variation for individual brands, over-time. Again, the columns show the percentage variation by service quality dimension and the rows the percentage of data showing that degree of scale point variability. The final two columns show average variability across all the dimensions. Over seventy percent of all the variability was of the magnitude of 0.4 scale points or less (cumulative percent column). The greatest variability was for the dimension of responsiveness, which had only 45% of the variability being of the magnitude of 0.4 scale points or less. Reliability had 82% within 0.4, assurance 73%, empathy 71% and tangibles 75%. In the main, Table 2 shows the same pattern as was seen for brands within an industry and for service quality scores overall. Table 2: Distribution of Score Variation for Individual Brands Over Time Service Quality Dimension Variation Reliability Assurance Empathy Responsive ness Tangibles All dimensions Cumulative percent 4

5 % TOTAL 100% 100% 100% 100% 100% 100% Our results suggest that brands service quality scores across a number of dimensions remain relatively stable over time. However, the results for responsiveness give some indication that this stability may not extend across all dimensions equally. This warrants further research. It is also interesting that the variability between brands is less than that for an individual brand over time. Whether this is because service scores in an industry are all affected by external factors equally or an artefact of the sampling is a further question. Conclusions/Implications This research extends the original conditions of two generalisations about overall service quality scores to the context of item level scores. It establishes that two preliminary patterns, previously established at the aggregate overall service quality evaluation level, also hold at the service quality item level. There is very little variation in service quality scores between competing brands at the item level and there is also little variation for an individual brand, over time, at the item level. That said, there is some evidence that variability is not uniform across all dimensions for brands over time and this warrants further investigation. These results suggest that managers should expect to receive very similar service quality scores to their competitors across a range of items. In addition, they should not expect their service scores to change significantly over time, even with market interventions occurring (for example a new customer service initiative). This should not suggest that the monitoring of service quality is not important. It is quite possible that the similarity between competitive brands is a result of the managers of those brands working very hard at competitive matching. Also, given that it seems a reasonable expectation (based on these results) to assume that service quality scores will not differ by much (either over time or between competitive brands), when analysis of data shows that there is an unusually large variation it should signal something has gone wrong (or very right. This paper s findings should result in more realistic setting of service quality improvement targets. Reference List: Anderson, E., Sullivan, M., The Antecedents and consequences of customer satisfaction for firms. Marketing Science 12 (2), Bird, M., Channon, C., Ehrenberg, A.S.C., Brand image and brand usage. Journal of Marketing Research 7, Bound, J., Ehrenberg, A.S.C., Significant sameness. Journal of the Royal Statistical Society 152 (2),

6 Brady, M., Cronin, J., Brand, R., Performance-only measurement of service quality: a replication and extension. Journal of Business Research 55, Brown, T., 1997 Using Norms to Improve the Interpretation of Service Quality Measures. The Journal of Services Marketing 11 (1), Buttle, F., SERVQUAL: review, critique, research agenda. European Journal of Marketing 30 (1), 8-32 Carrillat, F., Jaramillo, F., Mulki, J., The Validity of the SERVQUAL and SERVPERF scales: A meta-analytic view of 17 years of research across five continents. International Journal of Service Industry Management 18 (5), Danaher, P., Haddrell, V., A comparison of question scales used for measuring customer satisfaction. International Journal of Service Industry Management 7 (4), 4-26 Eddy, C., Industry benchmarking of service quality and satisfaction: a descriptive approach. Masters Thesis, University of South Australia, Adelaide. Eddy, C., Sharp, B., Page, N., Dawes, J., Grounded benchmarks for service quality, Proceedings of ANZMAC, Massey University, New Zealand Ehrenberg, A.S.C, Repeat Buying: Theory and Applications, American Elsevier, New York. Ehrenberg, A.S.C., Kenndey, R., Striving for generalizations. Market Research: Back Talk 14 (2), 42. Morrison Coulthard, L.J., Measuring service quality: A review and critique of research using SERVQUAL. International Journal of Market Research 46, Organisation for Economic Cooperation and Development, Industry issues Service sector. viewed online, 21 st April 2008, URL: Parasuraman, A., Zeithaml, V., Berry, L., SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing 64, Peterson, R., Wilson, W., Measuring customer satisfaction: Fact and artifact. Journal of the Academy of Marketing Science 20 (1), Rust, R., Zahorik, A., Customer satisfaction, customer retention, and market share. Journal of Retailing 69 (2), Rust, R., Zahorik, A., Keiningham, T., Return on quality (ROQ): Making service quality financially accountable. Journal of Marketing 59 (April), Seth, N., Deshmukh, S., Vrat, P., Service quality models: A review. International Journal of Quality and Reliability Management 22 (9), Sharp, A., Romanuik J., The stability of brand and competitor responses, proceedings of the 29 th European Marketing Academy Conference, Rotterdam. 6

7 Government of South Australia, Customer service good practice guide. Government of South Australia. Uncles, M., Ehrenberg, A.S.C., Hammond, K., Patterns of buyer behavior: Regularities, models, and extensions. Marketing Science 14 (3),

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