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Nonlinear and asymmetric returns on customer satisfaction: do they vary across situations and consumers?

Abstract

Customer satisfaction is generally acknowledged as a key determinant of the value a customer contributes to a firm. Following the widespread recognition that the relationship is nonlinear and possibly even asymmetric, the authors develop a framework for understanding and predicting functional differences across consumers and situations. The resultant conceptualization proposes two general categories of moderating factors: Type I moderators, which induce functional changes by impacting the underlying comparison standards employed in the CS formation process, and Type II moderators, which cause the function to change by altering the interplay of cognitive and affective modes of the satisfaction experience. The authors employ firm reputation as an example of a Type I moderation and customer involvement as an example of a Type II moderation to illustrate differences between these types of moderation and to highlight how exactly each type of moderation changes the functional nature of the focal relationship. Specifically, a firm with a strong reputation benefits from a broader zone of tolerance than a firm of minor reputation, and highly involved customers react more intensively to extreme changes in satisfaction than do low involvement consumers.

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Notes

  1. There is a small bump within medium CS levels, which at first glance lets the flat part of the function look less smooth and less of a linear form. Note, however, that despite this bump the confidence bands within this region still allow describing the function in that area with a linear line. Hence, the bump does not reflect any systematic nonlinearity, but rather random variation. In general, it must be recognized that the estimated function is subject to a certain level of uncertainty. Probably, as a consequence of the relatively small sample size and various exogenous influences (e.g., marketing activities of competitors targeted at these customers) which are hard to control for in a real setting, the corresponding 95%-confidence bands are relatively broad, particularly around extreme CS levels.

  2. Note that this argumentation is also in line with assimilation-contrast theory (Hovland et al. 1957; Sherif and Hovland 1961). If differences between the realized and expected satisfaction level are small such that they fall within the zone of tolerance, the realized satisfaction level will assimilate toward expectations. Thus, the resulting behavioral impact of CS on customer value contribution will be small. In contrast, if differences are large such that they fall outside of the zone of tolerance, the realized satisfaction level will be contrasted (i.e., weighted more heavily). Thus, the resulting behavioral impact of CS on customer value contribution will be large. Moreover, on that basis Coughlan and Connolly (2001) argue that outside of the zone, consumption experiences will either lead to a disappointment effect (if experiences fall short of expectations) or to a surprise effect (if experiences exceed expectations).

  3. To rule out that the manipulation is confounded by variables associated with the general experience or familiarity with any of the chosen tour operators, we asked the respondents to state whether the presented scenarios with the given tour operator were easy to imagine (“The presented scenarios for [Brand X] were easy to imagine”; the scale ranged from 1 = “strongly disagree” to 7 = “strongly agree”). There were no significant differences between the answers of respondents belonging to different conditions (Mann–Whitney-U test: Z = −1.203, p = 0.23). Hence, if we accept that scenarios are easier to imagine for a respondent who is more familiar with an operator, we can be confident that our manipulation is not confounded by brand familiarity or related issues.

  4. The WTP measure was not subject to any restrictions. Hence, participants were also allowed to indicate that they would pay zero euros for the city weekend getaway

  5. Homburg et al. (2005) cross-validated their initial results based on this direct measure in a second experiment by using the more complex BDM method to assess WTP. Finally, their results revealed that neither the sign nor the functional form of the CS/WTP-link is affected by the type of WTP measure

  6. In order to estimate our model in (1), we followed Wood’s (2006a,b) approach that builds on the idea of transferring (nonparametric) GAMM component functions such as f ref and f dif into a parametric mixed model representation and then using standard mixed model methodology for model estimation

  7. The reported WTP differences do not include any possible direct involvement effects on WTP. If direct effects were included the estimated WTP differences between high and low involvement participants would be even higher.

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Acknowledgments

The authors thank a European logistics company for sharing the customer data used in this study. They acknowledge the helpful comments of participants at the 2010 Marketing Science Conference; and seminar participants at the University of Groningen and the University of Münster.

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Correspondence to Maik Eisenbeiss.

Appendices

Appendix 1: details of the pilot study

Sample and research design

In collaboration with a leading European provider of rail freight transport and logistics services, we conducted a telephone survey among key decision makers of its business customers in yearly iterations for a series of 3 years with 1-year time spans between the surveys. The sampling frame consisted of the service provider’s entire customer base (i.e., 850 business customers) ranging from small businesses (with less than 50 employees), over medium-sized businesses (with less than 500 employees), to large businesses (with 500 or more employees). The customers could be classified according to the following industries: ore, coal, and steel; construction; chemicals; industrial and agricultural; and manufacturing. While all customers used the provider’s physical transportation services, a few of them used additional logistic services such as packaging or warehousing.

To maximize response rates, the logistics service provider first sent out written invitation letters together with the printed version of the questionnaire to the key decision makers in every year of the survey announcing the telephone interview. In a second step, we contacted the decision makers by phone for the interview in the course of the following 2 weeks. The average response rate was 60%.

We included only those companies in the analysis that participated in all three surveys because, for an individual-level analysis of CS outcomes, a certain level of within-group variation (i.e., variation within each customer) must be observable (Kumar and Reinartz 2012). As recommended for panel data structures (Hofmann and Gavin 1998; Raudenbush and Bryk 2002), we also group mean centered the CS scores for each business customer across the three survey years such that CS = 0 could be interpreted as a customer’s average satisfaction level and CS > 0 (CS < 0) as a relatively positive (negative) CS level for that particular customer.

In this way, we derived a sample of 151 customers that participated in all three surveys. The participating companies were distributed as follows over the different industry categories: ore, coal, and steel (50 companies); construction (21 companies); chemicals (38 companies); industrial and agricultural (15 companies); and manufacturing (27 companies). To avoid sampling bias, we tested whether the included companies differed significantly from those that did not meet the described selection criteria (Van Doorn and Verhoef 2008). We did not find any significant differences in terms of CS, share of wallet or industry sector. We finally pooled the data over all companies and years to end up with 3 × 151 = 453 cases in total.

Measurement of variables

In each survey, we measured overall satisfaction with the service using a single-item global measure. Specifically, we asked customers to answer the following question (on a seven-point bipolar rating scale ranging from 1 = “not at all satisfied” to 7 = “completely satisfied”): “How satisfied are you with the overall performance of [name of service provider]?” We measured share of wallet by letting the respondents answer the following question on a six-point categorical scale (1: <10%; 2: 10%–20%; 3: 20%–30%; 4: 30%–40%; 5: 40%–60%; and 6: >60%): “How large is the share of [name of service provider] with respect to the total volume of traffic of your company?” Note that the service provider had applied both measures in previous surveys and wanted to maintain consistency in its measurement approach.

Given the rather small sampling frame, we refrained from including any additional—more complex—multi-item, attribute-specific measures of satisfaction into the questionnaire, because we aimed to develop a rather short questionnaire to maximize response rates. Although measurement theory suggests multi-item measures are conceptually advantageous, there is considerable evidence for preferring single-item measures of satisfaction in the context of field studies (Mittal et al. 1998). LaBarbera and Mazursky (1983) demonstrated that contrary to a single-item satisfaction measure, a multi-item measurement approach substantially reduces the number of customers willing to participate in the survey, and thus may cause a significant nonresponse bias. Thus, despite their conceptual appeal, in field research multi-item scales may actually decrease and not enhance the quality of the satisfaction measure. Finally, single-item global satisfaction measures have been shown to be useful predictors of customer behavior and intentions resulting from satisfaction experiences (Rust et al. 1995) such a customer’s value contribution to the firm. In a review of several single- and multi-item satisfaction scales by Yi (1990) they were also shown to exhibit acceptable test/retest reliabilities (0.55 to 0.84).

Likewise, we adopted a category level scale for the measurement of share of wallet, which had been formerly applied by the service provider, rather than using the actual percentage measure because typically the value share is difficult to assess on a more precise level than by the given categories (Van Doorn and Verhoef 2008). Thus, we believed the return from using a more detailed percentage measure was not worth the burden imposed on respondents.

Descriptive statistics

For our sample, the average satisfaction was 4.47 (SD = 0.97), 4.22 (SD = 1.02), and 4.20 (SD = 1.11) for the first, second, and third survey year, respectively. Likewise, the average share of wallet also decreased over time: 4.13 (SD = 1.69), 4.05 (SD = 1.56), and 3.87 (SD = 1.62) for the first, second, and third survey year, respectively. Moreover, the participating business customers ranged from small business owners (minimum employees = 1) to large companies (maximum employees = 248,853); the average number of employees was equal to 6,177.18 (SD = 28,533.73).

Model

In line with the general nonparametric modeling perspective adopted in our study, we estimated an additive model of the form: SW i = β + f(CS i ) + ε i , where SW i and CS i denote share of wallet and satisfaction for the i-th observation, respectively; and ε i depicts the residual error with ε i ~ N(0,σ 2). According to the results of a Breusch-Pagan test, there was no unobserved heterogeneity between the customers in terms of share of wallet (p = 0.90), supporting that a pooled model of the above form was appropriate. This was probably because share of wallet is a relative, not an absolute metric. Moreover, the inclusion of industry-specific dummy variables did not improve the overall model fit. For details on the resulting model estimates, see Fig. 3 in the manuscript.

Appendix 2

Table 5 Study 1: descriptives of CS and WTP per scenario
Table 6 Study 1: significance of difference in CS for each pair of means

Appendix 3

Table 7 Study 1 and 2: scale items and measurement properties

Appendix 4

Table 8 Study 2: descriptives of CS, WTP, and involvement per scenario
Table 9 Study 2: significance of difference in CS for each pair of means

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Eisenbeiss, M., Cornelißen, M., Backhaus, K. et al. Nonlinear and asymmetric returns on customer satisfaction: do they vary across situations and consumers?. J. of the Acad. Mark. Sci. 42, 242–263 (2014). https://doi.org/10.1007/s11747-013-0366-1

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Keywords

  • Customer satisfaction
  • Share of wallet
  • Willingness to pay
  • Involvement
  • Firm reputation
  • Functional moderation