The current issue and full text archive of this journal is available at www.emeraldinsight.com/0956-4233.htm

IJSIM 19,2

176 Received 31 August 2007 Revised 8 January 2008 Accepted 8 January 2008

SELECTED PAPER FROM QUIS 10 2007

Alternative perspectives on service quality and customer satisfaction: the role of BPM V. Kumar, P.A. Smart, H. Maddern and R.S. Maull School of Business and Economics, University of Exeter, Exeter, UK Abstract Purpose – The purpose of this paper is to further investigate the linkages between business process management (BPM) and customer satisfaction. Also, to challenge the dominance of the customer contact perspectives on service processes and to propose a more systemic focus on the totality of service design. Design/methodology/approach – The research builds on the existing work of Maddern et al. through the use of structured equation modelling (SEM) tool. The multiple SEM models described here provide a more robust statistical approach for confirming/refuting the constructs found in the earlier research. Findings – This paper presents the results of an empirical analysis, based on longitudinal data from a large UK bank on drivers of customer satisfaction. The results confirm that process management is a critical driver of technical service quality. This suggests that companies with reliability/dependability issues should not emphasise customer satisfaction programmes based on SERVQUAL intangibles until substantial improvements in process design have been achieved. Research limitations/implications – The research is limited to a single case study of a UK bank over a five year period. The generalisibility of these findings is therefore limited. Further work in other sectors and over longer periods would establish the reliability of the findings. The paper also highlights some limitations in the service operations literature, particularly the emphasis on customer presence within the service process. Originality/value – The paper uses time series data to identify the importance of BPM in achieving higher levels of customer satisfaction. The authors provide a platform for further research based on the design of service delivery systems and their impact on customer satisfaction. Keywords Customer satisfaction, Mathematical modelling, Customer services quality Paper type Research paper

International Journal of Service Industry Management Vol. 19 No. 2, 2008 pp. 176-187 q Emerald Group Publishing Limited 0956-4233 DOI 10.1108/09564230810869720

Introduction In an era of increased competition, the importance of achieving high levels of customer satisfaction has gained the attention of researchers and practitioners alike. This is especially the case in the service sector, where many companies are focusing upon service quality improvement issues in order to drive high levels of customer satisfaction. A review of the literature highlights that a number of common factors have been identified as critical drivers of customer satisfaction. The service profit chain, Heskett et al. (1994) is one of the most widely supported theories of customer satisfaction. In brief, it proposes a positive linear relationship between staff satisfaction, service quality and customer satisfaction leading, ultimately, to profitability. Parasuraman et al. (1985) also recognise the significance of staff satisfaction and service quality as

drivers of customer satisfaction in developing their SERVQUAL measurement tool. However, they differentiate the service quality construct distinguishing between functional service quality (FSQ) (doing things nicely) and technical service quality (TSQ) (doing things right). Priority has however been afforded to FSQ. More recently, some researchers have suggested that business process management (BPM) may have an important role to play in driving customer satisfaction (Frei et al., 1997; Tsikriktsis and Heineke, 2004). The research presented in this paper builds on previous work (Maddern et al., 2007) which attempted to illustrate, through multivariate quantitative analysis, a link between process management and customer satisfaction. This research extends the work of Maddern et al. (2007) through an attempt to challenge the concepts presented in this previous work. This is achieved by a holistic assessment of the constructs, using structured equation modelling (SEM). This work therefore seeks to verify or refute these previous findings through the application of an alternative analytical method. The paper is organized in the following sections. The next section examines the relevant literature. Followed by the section that describes the research objectives and gives an overview of the core methodology, SEM. The next section discusses the findings from the research. Finally, the penultimate section offers some conclusions from the research and suggests potential future research directions. Literature review Previous research has indicated that high levels of customer satisfaction are related to the service quality provided through customer interactions (van der Wiele et al., 2002; Vilares and Coehlo, 2003). The service profit chain (Heskett et al., 1994) specifically identifies a relationship between employee satisfaction, service quality and customer satisfaction. Research investigating these relationships has subsequently generated support for this model (Loveman, 1998; Anderson and Mittal, 2000; Voss et al., 2004). The “satisfaction mirror” (Schlesinger and Heskett, 1991; Normann and Ramirez, 1993) has also been presented as a model for understanding the relationship between internal aspects of service delivery with external customer satisfaction. Service quality has formed a nucleus of research incorporating many dimensions of service outcome and the parameters for achieving these outcomes: costs, profitability, customer’s satisfaction, customer retention, and service guarantee (Sohail, 2003); corporate marketing and financial performance (Buttle, 1996). Definitions of service quality have been found in abundance. For example, conformance to customer expectations (Berry et al., 1988), the difference between customer expectation and perceived service (Parasuraman et al., 1985).This latter perspective suggests that dissatisfaction occurs if expectations are greater than actual performance. As a result, evaluations are not based solely on the outcome of the service, the technical quality; they also involve the process of service delivery or functional quality (Gro¨nroos, 1984). SERVQUAL has been subject to a number of criticisms (Cronin and Taylor, 1992; Buttle, 1996). In particular, the priority afforded to FSQ within SERVQUAL has been subject to challenge. In an investigation into service quality in UK banking, Newman (2001) reports that effective delivery on “hard” factors is a necessary pre-condition for overall service quality. “Where hard quality, especially reliability of service delivery, is low, then ‘soft’ quality cannot compensate.” Similarly, Lassar et al. (2000) in a study of private banking customers, find a much stronger relationship between

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technical quality and satisfaction than functional quality and satisfaction. Nevertheless, SERVQUAL remains the most widely applied measure of service quality today (Sivadas and Baker-Prewitt, 2000). Indeed, Woodall (2001) considers that “service quality has effectively become SERVQUAL and vice versa”. However, he goes on to note that a growing number of companies are focusing on process management in order to ensure effective performance on hard quality dimensions. This thinking builds upon earlier work by Roth and Jackson (1995), who found that “business process capabilities had a larger impact on service quality than did people capabilities”. Frei et al. (1997) also suggest that processes have an important role to play in driving service quality and customer satisfaction. Banks with good, consistent processes enjoy higher financial performance. Critically, it is the performance of the overall “basket” of processes, rather than performance of one or two individual processes, which determines satisfaction levels. Similarly, in their investigation into customer satisfaction in US Airlines, Tsikriktsis and Heineke (2004) find that “reduction of customer dissatisfaction depends upon improvement in process quality”. Process management has attracted the attention of many businesses (Armistead et al., 1999). While the concept of managing by “process” intensified during the re-engineering era initiated by Hammer (1990), it has also, arguably, been and integral component of many of the prominent, historical, thematic initiatives (Smart et al., 2004). A strong re-emergence of “process” is now found in the guise of BPM although a substantial amount of variety in the semantics attributed to this management theme (Al-Mashari, 2002). The continued interest in process management can be found in a number of different contexts, evidenced from a review of current literature. A business-centric (as opposed to an IT-centric) characterisation of BPM has been undertaken by Smart et al. (2007) who identify five common application components: process strategy; process architecture; process measurement; process ownership; process improvement. Most importantly however, they suggest that these components should be undertaken in the context of three conceptual components which form a pre-requisite process mindset. These include: conscious process management (the extent to which process management is pursued as a conscious activity rather than happening by default); macro process management (which recognises that processes exist in a hierarchy at different levels of abstraction/decomposition); centrality of process (where business processes are inextricably linked to the customer). The recognition of these concepts facilitates the development of a “process mindset” which attempts to evaluate the means by which value is created for the customer. From this perspective, a proposition that process management impacts upon customer satisfaction can be logically derived. A number of questions arise from the literature which underpin this research: . What are the key drivers of customer satisfaction? . How significant is the role of process management as one of the drivers of customer satisfaction? . Does process management drive TSQ? . Is customer satisfaction positively correlated with FSQ, staff satisfaction, and TSQ? . Do FSQ and TSQ have positive correlations with staff satisfaction? . Which is the best conceptual model to describe these relations?

In addressing these questions, the research aims to offer both the practitioner and research communities a better understanding of the critical components, and their relative significance, for attaining high levels of customer satisfaction. Research objectives and methodology The importance of building customer loyalty is paramount to the success of service firms. Existing models, such as the service profit chain, suggest that this loyalty can be achieved through the attainment of high levels of customer satisfaction which is affected by service quality. A review of current models also suggests that the major components of the drivers of customer satisfaction include: staff satisfaction; TSQ; and FSQ. Maddern et al. (2007), conducted empirical work to identify the drivers of customer satisfaction. The central argument of this work suggests that process management impacts upon TSQ, and subsequently affects customer satisfaction (Maddern et al., 2007). An additional relationship found in this work is a relationship between staff satisfaction and TSQ. This construct remains substantially unexplained. The objective of this research is therefore twofold: to verify, or refute, the constructs presented in the Maddern et al. (2007) model; to explain the relationship between staff satisfaction and TSQ. The propositions tested by Maddern et al. (2007) indicate a degree of co-relationship between the drivers of customer satisfaction: Maddern et al. (2007) tested six propositions but only four of them were considered to be supporting after carrying out the multivariate analysis. However, during the analysis it was attempted to test all the propositions. The four main propositions considered are: P1.

TSQ is positively correlated with BPM.

P2.

Customer satisfaction is positively correlated with FSQ.

P3.

Customer satisfaction is positively correlated with TSQ.

P4.

Customer satisfaction is positively correlated with staff satisfaction.

The utility of the approach adopted in this work (investigating each hypothesis individually) may therefore be questioned. To address these co-relationships (multiple dependents), and to challenge the existing constructs, structural equation modelling (SEM) may be used. SEM has been widely used to examine the dimensionality of the service quality scale and to test relationships among variables and casual models that involve both observable and unobservable variables (Kang et al., 2002; Kline, 1998). This has led to the growth in the popularity of the technique (Shah and Goldstein, 2006). There are numerous advantages of using SEM in place of conventional statistical methods such as multiple regression, correlation, and covariance. While SEM is similar to multiple regression, it also includes the modelling of interactions, nonlinearities, correlated independents, measurement error, correlated error terms, multiple latent independents, each measured by multiple indicators, together with one or more latent dependents. SEM provides a more appropriate analytical method to investigate the co-related hypotheses outlined in previous work. It uses confirmatory factor analysis to reduce measurement error by having multiple indicators per latent variable. The SEM also has an attractive graphical modelling interface which assists in the interpretation of the model. In addition, SEM models the mediating variables, error terms, and handles difficult data such as time series data with auto-correlated error and non-normal data.

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Moreover, where regression is highly susceptible to error of interpretation by misspecification, the SEM strategy of comparing alternative models to assess relative model fit makes it more robust. Unlike other statistical methods in SEM the researcher can analyze the entire theoretical model in one analysis. SEM has the ability to handle intangible concepts in the analysis such as intelligence, loyalty or satisfaction (Fitzgerald and Johnson, 2002). Fitzgerald and Johnson (2002) also showed that SEM allows researchers to identify the attributes that predicts overall satisfaction or loyalty in their research on application of SEM in predicting loyalty. The SEM’s multi-iterated modelling approach allows understanding of the particular variable in terms of key influencing factors in detailed. This provides a more robust approach for confirming/refuting the constructs found in the previous model. Data for this model were collected as part of an extensive, longitudinal case study of a large UK bank. Within the sector, customer satisfaction is seen as a key differentiator (Newman, 2001) and recent deregulation has led to a “shift in strategic focus from price to service quality” (Frei et al., 1997). The selected company had experienced significant variation in service quality performance, following a series of mergers and acquisitions in the late 1990 s and had introduced an extensive BPM programme to address perceived service weaknesses. The company had robust data for all the key constructs over a five year period, based upon collection and analysis techniques which met requirements specified by Heskett et al. (1994), for example, the use of external agencies and adequate, consistent samples. Analysis of this data supported a demand from the research community for more longitudinal analysis, in contrast to the prevailing cross sectional approaches which explore relationships at a single point in time (Voss et al., 2002). Data sources are outlined below: . Customer satisfaction. Throughout the period, the company had engaged a professional research group to conduct 15,000 telephone interviews a month in which customer were asked, “overall, how satisfied are you with the company?” . Staff satisfaction. Again an independent research group had been commissioned to measure levels of staff satisfaction over the five year period. In addition to an annual survey of all staff, quarterly surveys, based on statistically robust sample sizes, were carried out. . FSQ. The monthly customer interviews also asked questions regarding key features of the encounter such as staff empathy and expertise. These questions conform to the variables found in SERVQUAL, specifically those identified by Gro¨nroos (1984) as reflecting FSQ. . TSQ. Kang and James (2004) recognise the challenges associated with operationalizing the TSQ concept. For this research, TSQ was analysed using a range of operational metrics such as complaints and adherence to service levels. . BPM. Using criteria established by Maddern et al. (2004), measures of BPM were identified in a series of facilitated staff workshops. Table I show the quarterly results over a five year period for each of the key constructs. Qualitative analysis, based on interviews and a range of secondary data such as presentations, minutes and internal magazines, provided further insight and validation of the quantitative findings shown below.

Customer satisfaction

Staff satisfaction

66.00 66.33 66.55 67.30 67.27 65.97 65.77 65.53 66.33 67.17 66.43 66.73 67.03 67.40 67.33 67.30 67.93 67.73 68.10 68.00

50 45 45 44 50 45 45 49 49 41 46 51 62 62 66 61 68 69 67 67

2000-Q1 2000-Q2 2000-Q3 2000-Q4 2001-Q1 2001-Q2 2001-Q3 2001-Q4 2002-Q1 2002-Q2 2002-Q3 2002-Q4 2003-Q1 2003-Q2 2003-Q3 2003-Q4 2004-Q1 2004-Q2 2004-Q3 2004-Q4

Functional service Technical service quality Complaints quality BPM 61.50 61.11 62.06 62.48 62.35 60.93 60.71 60.36 61.35 61.97 60.79 61.12 61.30 61.72 60.68 60.61 61.08 60.89 61.77 60.80

4,657 5,056 4,419 4,587 5,458 6,513 8,070 6,954 6,041 4,511 4,051 3,249 3,497 3,711 3,331 3,476 3,467 3668 3409 3409

0.000215 0.000198 0.000226 0.000218 0.000183 0.000154 0.000124 0.000144 0.000166 0.000222 0.000247 0.000308 0.000286 0.000269 0.000300 0.000288 0.000288 0.000273 0.000293 0.000293

36.00 39.00 48.00 48.00 48.00 45.00 51.00 51.00 51.00 67.00 67.00 76.00 79.00 79.00 80.00 80.00 81.00 81.00 86.00 86.00

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Table I. The quarterly results over a five year period for each of the key constructs

Research findings For this research the SEM analysis was run on LISREL 8.54. The relationships between the drivers of customer satisfaction were analysed and the conceptual model was tested using the SEM technique. During the analysis, a number of models were tested, and their fitness values compared, in order to identify the best fit model; an identification of appropriate constructs. The comparative fitness measures are shown in Table II. As indicated in Table II, a total of four best models have been compared against standard fitness measures. Appropriate fitness ranges for the indices are based on those suggested in previous literatures (Green et al., 2006; Zeynep and Berry, 1996; Cheung and Rensvold, 2002; Driscoll et al., 2005). As can be seen from the comparative analysis, Model 4 accounts for the best fit on all of the fitness indices. Previous research using SEM suggests that it is advantageous to compare more than two models, in order to find the best conceptual model. The modelling started with the null hypotheses model with additional iterative tests seeking to identify better fitness values. A range of models illustrating this process, and the subsequent findings, are shown in Figure 1, with the best fit model being shown in Figure 2. Whilst the models tested have very close fitness measures, Models 1, 2, and 3 do not conform to the main theoretical arguments.

Model

x2

GFI (. 0.90)

AGFI ($0.90)

RMR (0.00)

RMSEA (#0.05)

NFI ($0.90)

NNFI (1)

RFI (1)

IFI (. 0.90)

1 2 3 4

0.33 4.66 0.49 0.97

0.99 0.94 0.99 0.99

0.97 0.77 0.95 0.95

0.025 0.14 0.059 0.012

0.00 0.00 0.00 0.00

0.97 0.60 0.96 0.97

5.31 2 0.03 5.49 3.12

0.91 0.0018 0.87 0.93

1.29 1.05 1.29 1.11

Table II. Fitness indices of the models

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FSQ

0.97

FSQ

0.95

TSQ

0.28

– 0.18 – 0.18 1.02

SS SS

0.70

0.49 – 0.27

TSQ

0.00

0.02

182

0.56

0.90 0.95

0.06

BPM

0.43

BPM

0.23

0.36

0.79

CS

Figure 1. Graphical representation of Models 1 and 2

Chi-Square=0.33, df=3, P-value=0.95498, RMSEA=0.000

CS

0.26

0.72

Chi-Square=4.66, df=6, P-value=0.58775, RMSEA=0.000

(b) Model 2

(a) Model 1

1.00

–0.28

0.92

FSQ

SS 0.19

0.26

TSQ 0.44 0.88

0.77 0.31

1.00

Figure 2. Graphical representation of best fit SEM (Model 4)

BPM

0.67

CS

0.13

Chi-Square=0.97, df=3, P-value=0.61671, RMSEA=0.000

The constructs within Model 4 provide support for the previous findings of Maddern et al. (2007). These results therefore provide further evidence highlighting BPM as an important factor in attaining high levels of customer satisfaction. We find it disconcerting, however, that there appears to be a lack of clarity surrounding the process concept, within both the service operations and the broader service management literature. It is also interesting that, given the prominence of both SERVQUAL and process management over a prolonged period, the two concepts have not been more strongly associated in previous research. In pursuit of an explanation, we draw on some of our parallel research (Ponsignon et al., 2007) which has highlighted customer centricity as the dominant paradigm within services research. It is arguable that many of the frameworks and conceptual models found within the services literature, for example: SERVQUAL (Parasuraman et al., 1985, 1988, 1991); the service profit chain (Heskett et al., 1994); and service operations frameworks (Chase, 1978; Schmenner, 1986; Silvestro et al., 1992) have been strongly influenced by an emphasis on “customer contact”. Furthermore, our observations suggest that, although the “service process” construct

has been widely adopted by both services marketing and service operations as a convenient unit of analysis, it is often interpreted as a “customer process” a part of the process where the customer is involved, or a service facility where the customer is present. This is consistent with a customer contact emphasis within aforementioned conceptual models. The findings presented here, particularly the relationship between BPM and TSQ does not correspond to this emphasis. Our representation of BPM within this research corresponds to a holistic perspective of process management; the entire set of activities and their interrelationships which deliver services to customers. We therefore highlight the differentiation between holistic approaches to the design of service delivery systems and reductionist perspectives, often termed (in our view) the “service process”. Holistic approaches, we would suggest, should be informed by the plethora of work identifiable within the systems paradigm (Ackoff, 1980; Checkland, 1981; Katz and Khan, 1966; von Bertalanffy, 1968). The findings of this research are also important for practitioners. It suggests that performance problems, particularly those associated with customer dissatisfaction induced by reneging on service promises, cannot be resolved by allocating more empathetic staff at customer contact points. If companies have problems in maintaining service promises, there is a requirement to review the service delivery system: a system of interconnected processes which deliver value to the customer. Conclusions The results have important consequences for both practitioners and the research community. Whilst the results recognise the role of staff satisfaction and both elements of service quality, they highlight the significance of BPM as a critical factor in driving customer satisfaction. They suggest that practitioners should focus on process management to impact upon TSQ rather than simply addressing service quality from a functional perspective. They also suggest that more research is needed to identify different configurations of processes within service systems, and to align these configurations with different outcome measures. This research, through triangulation and reinforcement of recent findings has provided a much needed longitudinal perspective on this important topic. These findings provide a platform for future research on the design of service delivery systems; a move to more holistic approaches which are not constrained by activities limited by customer contact. While we would stress the value of exploring the use of alternative analytical methods (such as SEM) to explore complex issues and to verify previous statistical relationships and models, the research is subject to limitations. Findings are based on a single case and a limited data set, requiring bias in the form of exaggeration of the salience of the data and the prominence of the specific contextual parameters to be recognised. Our research does not address alternative drivers of customer satisfaction (e.g. culture, leadership, etc.) other than those limited by the propositions and dataset. The SEM methodology used is also affected by sample size. In addition, SEM is a confirmatory approach which requires established theory about the relationships of key parameters as a pre-requisite. These relationships have been derived from an analysis of extant models relating to customer satisfaction, synthesised with a process management perspective on value creation. Future research is therefore required: to apply the model in different contexts; to identify additional parameters that are not present in our current hypotheses; and to attempt to falsify the findings which suggest that BPM is a key driver of customer satisfaction.

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References Ackoff, R.L. (1980), “The systems revolution”, in Lockett, M. and Spear, R. (Eds), Organisations as Systems, The Open University Press, Milton Keynes, pp. 26-33. Al-Mashari, M. (2002), “Editorial”, Business Process Management Journal, Vol. 8 No. 1, pp. 1-2. Anderson, E.W. and Mittal, V. (2000), “Strengthening the satisfaction-profit chain”, Journal of Service Research, Vol. 3 No. 2, pp. 107-20. Armistead, C., Pritchard, J.P. and Machin, S. (1999), “Strategic business process management for organisational effectiveness”, Long Range Planning, Vol. 32 No. 1, pp. 96-106. Berry, L.L., Zeithaml, V.A. and Parasuraman, A. (1988), “Communication and control processes in the delivery of service quality”, Journal of Marketing, Vol. 52, pp. 35-48. Buttle, F. (1996), “SERVQUAL: review, critique, research agenda”, European Journal of Marketing, Vol. 30 No. 1, pp. 8-32. Chase, R.B. (1978), “Where does the customer fit in a service operation?”, Harvard Business Review, Vol. 56 No. 6, pp. 137-42. Checkland, P. (1981), Systems Thinking, Systems Practice, Wiley, New York, NY. Cheung, G.W. and Rensvold, R.B. (2002), “Evaluating goodness-of-fit indexes for testing measurement invariance”, Structural Equation Modelling: A Multidisciplinary Journal, Vol. 9 No. 2, pp. 233-55. Cronin, J.J. Jr and Taylor, S.A. (1992), “Measuring service quality: a re-examination and extension”, Journal of Marketing, Vol. 56, pp. 55-68. Driscoll, H., Campbell, A. and Muncer, S. (2005), “Confirming the structure of a ten-item Expagg scale using confirmatory factor analysis”, Current Research in Social Psychology, Vol. 10, pp. 222-34. Fitzgerald, A. and Johnson, C. (2002), “Data use: uncovering customer loyalty drivers using structural equation modelling”, Quirks Marketing Research Review, available at: www. quirks.com/articles/a2002/20021003.aspx?searchID ¼ 3330500&sort ¼ 7&pg ¼ 1 Frei, F.X., Kalakota, R. and Marx, L.M. (1997), “Process variation as a determinant of service quality and bank performance: evidence from the retail banking study”, Working Paper 97-36, The Wharton Financial Institutions Center, Philadelphia, PA. Green, J.L., Camilli, G. and Elmore, P.B. (2006), Handbook of Complementary Methods in Education Research, Routledge, London, published for the American Educational Research Association, Lawrence Erlbaum Associates, Mahwah, NJ. Gro¨nroos, C. (1984), “A service quality model and its marketing implications”, European Journal of Marketing, Vol. 18 No. 4, pp. 36-44. Hammer, M. (1990), “Re-engineering work: don’t automate; obliterate”, Harvard Business Review, June, pp. 4-112. Heskett, J.L., Jones, T.O., Loveman, G.W., Sasser, W.E. Jr and Schlesinger, L.A. (1994), “Putting the service-profit chain to work”, Harvard Business Review, March/April, pp. 164-74. Kang, G.D. and James, J. (2004), “Service quality dimensions: an examination of Gronroos’s service quality model”, Managing Service Quality, Vol. 14 No. 4, pp. 266-77. Kang, G.D., James, J. and Alexandris, K. (2002), “Measurement of internal service quality: application of the SERVQUAL battery to internal service quality”, Managing Service Quality, Vol. 12 No. 5, pp. 278-91. Katz, D. and Khan, R. (1966), The Social Psychology of Organizations, Wiley, New York, NY. Kline, R.B. (1998), Principles and Practice of Structural Equation Modelling, The Guildford Press, New York, NY.

Lassar, M.W., Manolis, C. and Winsor, R.D. (2000), “Service quality perspectives and satisfaction in private banking”, Journal of Services Marketing, Vol. 14 No. 3, pp. 244-71. Loveman, G.W. (1998), “Employee satisfaction, customer loyalty, and financial performance: an empirical examination of the service profit chain in retail banking”, Journal of Service Research, Vol. 1 No. 1, pp. 18-31. Maddern, H., Maull, R. and Smart, P.A. (2004), “Understanding business process management: evidence from UK financial services”, Proceedings of EUROMA Conference, pp. 527-36. Maddern, H., Maull, R. and Smart, P.A. (2007), “Customer satisfaction and service quality in UK financial services’”, International Journal of Operations & Production Management, Vol. 27 No. 9. Newman, K. (2001), “Interrogating SERVQUAL: a critical assessment of service quality measurement in a high street retail bank”, International Journal of Bank Marketing, Vol. 19 No. 3, pp. 126-39. Normann, R. and Ramirez, R. (1993), “From value chain to value constellation: designing interactive strategy”, Harvard Business Review, July/August, pp. 65-77. Parasuraman, A., Berry, L.L. and Zeithaml, V.A. (1991), “Refinement and reassessment of the SERVQUAL scale”, Journal of Retailing, Vol. 67 No. 4, pp. 420-50. Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1985), “A conceptual model of service quality and its implications for future research”, Journal of Marketing, Vol. 49, pp. 41-50. Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1988), “SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality”, Journal of Retailing, Vol. 64 No. 1, pp. 12-40. Ponsignon, F., Smart, P.A. and Maull, R.S. (2007), “A new perspective on service delivery systems: the transformational context”, working paper. Roth, A.V. and Jackson, W.E. (1995), “Strategic determinants of service quality and performance: evidence from the banking industry”, Management Science, Vol. 41 No. 11, pp. 1720-33. Schlesinger, L.A. and Heskett, J.L. (1991), “The service driven company”, Harvard Business Review, September/October, pp. 71-80. Schmenner, R.W. (1986), “How can service businesses survive and prosper?”, Sloan Management Review, Vol. 27 No. 3, pp. 21-32. Shah, R. and Goldstein, S.M. (2006), “Use of structural equation modelling in operations management research: looking back and forward”, Journal of Operations Management, Vol. 24, pp. 148-69. Silvestro, R., Fitzgerald, L., Johnston, R. and Voss, C.A. (1992), “Towards a classification of service processes”, International Journal of Service Industry Management, Vol. 3 No. 3, pp. 62-75. Sivadas, E. and Baker-Prewitt, J.L. (2000), “An examination of the relationship between service quality, customer satisfaction, and store loyalty”, International Journal of Retail & Distribution Management, Vol. 28 No. 2, pp. 73-82. Smart, P.A., Maddern, H. and Maull, R.S. (2007), “Understanding business process management: implications for theory and practice”, working paper series, School of Business and Economics, University of Exeter, Exeter. Smart, P.A., Maull, R.S., Childe, S.J. and Radnor, Z.J. (2004), “Capitalising on thematic initiatives: a framework for process-based change in SMEs”, Production Planning & Control, Vol. 15 No. 1, pp. 2-12. Sohail, S.M. (2003), “Service quality in hospitals than you might think”, Managing Service Quality, Vol. 13 No. 3, pp. 197-206.

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Tsikriktsis, N. and Heineke, J. (2004), “The impact of process variation on customer dissatisfaction: evidence from the US domestic airline industry”, Decision Sciences, Vol. 35 No. 1, pp. 129-42. van der Wiele, T., Boselie, P. and Hesselink, M. (2002), “Empirical evidence for the relationship between customer satisfaction and business performance”, Managing Service Quality, Vol. 12 No. 3, pp. 184-93. Vilares, M.J. and Coehlo, P.S. (2003), “The employee-customer satisfaction chain in the ESCI model”, European Journal of Marketing, Vol. 37 Nos 11/12, pp. 1703-22. von Bertalanffy, L. (1968), General System Theory: Foundations’, Development, Applications George Braziller, New York, NY. Voss, C., Tsikriktsis, N. and Frohlich, M. (2002), “Case research in operations management”, International Journal of Operations & Production Management, Vol. 22 No. 2, pp. 195-219. Voss, C., Roth, A.V., Rosenzweig, E.D., Blackmon, K. and Chase, R.B. (2004), “A tale of two countries’ conservatism, service quality, and feedback on customer satisfaction”, Journal of Service Research, Vol. 6 No. 3, pp. 212-23. Woodall, T. (2001), “Six sigma and service quality: Christian Gro¨nroos revisited”, Journal of Marketing Management, Vol. 17, pp. 595-607. Zeynep, A. and Berry, J.W. (1996), “Impact of employment-related experiences on immigrants’ psychological well-being and adaption to Canada”, Canadian Journal of Behavioural Science, Vol. 28, pp. 240-51. Further reading Fitzgerald, G. and Siddiqui, F.A. (2002), “Business process reengineering and flexibility: a case for unification”, International Journal of Flexible Manufacturing Systems, Vol. 14, pp. 73-86. About the authors V. Kumar is a PhD student at the Department of Management in the School of Business and Economics at University of Exeter. He graduated with first class degree in Metallurgy and Materials engineering in 2005 from NIFFT, Ranchi, India in 2005. He worked as a part time research assistant at the Research Promotion Cell, Ranchi, India. He was working as a Research Assistant in the Department of Industrial and Manufacturing Systems Engineering at The University of Hong Kong prior to joining University of Exeter. His research interests include service operations, system dynamics, SERVQUAL, application of Senge’s archetypes, SEM modelling, supply chain management, and BPM, etc. He is presently associated with the Exeter Centre of Strategic Processes and Operations at School of Business and Economics, University of Exeter. He has published more than 11 articles in international journals and conferences such as IJPR, JEM, IEEE, ACSOM06 (India), QUIS10 (Florida), OSCM07 (Bangkok), etc. P.A. Smart is the Director of the Exeter Centre for research in Strategic Processes and Operations within the School of Business and Economics, University of Exeter. He is also on the Board of the European Operations Management Association, where he is promoting research in service operations. His post-doctoral research has included projects in the area of business process management for both the private and public sectors, and he has played a lead role in collaborative US/European projects exploring enterprise modelling and integration. He has published over 40 research papers and has acted as a consultant for a number of companies in both the UK and the USA. P.A. Smart is the corresponding author and can be contacted at: [email protected] H. Maddern is a Research Fellow at the Exeter Centre for Research in Strategic Processes and Operations within the School of Business and Economics, University of Exeter. His research has focused on investigating the role of BPM within UK financial services. He has gained

numerous professional qualifications in project management and quality management. He has a wealth of experience as a practitioner in financial services including: total quality management, business process re-engineering, customer relationship management, and activity-based management. R.S. Maull is a Professor in the School of Business and Economics at the University of Exeter. He has published over 100 research papers on, quality management, operations and production management and business process re-engineering. In 2006, he was re-elected as a member of the EPSRC’s Engineering Management College of peers for the fourth time. He has a substantial background in consulting with major organisations.

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The role of BPM

187

Alternative Perspectives on Service Quality and Customer Satisfaction ...

Purpose - The purpose of this paper is to further investigate the linkages between business process management (BPM) and customer satisfaction. Also, to challenge the dominance of the customer contact perspectives on service processes and to propose a more systemic focus on the totality of service design.

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