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Customer satisfaction, loyalty behaviors, and firm financial performance: what 40 years of research tells us

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• Published: 03 March 2023
• Volume 34 , pages 171–187, ( 2023 )

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• Vikas Mittal 1 ,
• Kyuhong Han 2 ,
• Carly Frennea 3 ,
• Markus Blut 4 ,
• Muzeeb Shaik 5 ,
• Narendra Bosukonda 6 &
• Shrihari Sridhar 6  

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The authors synthesize research on the relationship of customer satisfaction with customer- and firm-level outcomes using a meta-analysis based on 535 correlations from 245 articles representing a combined sample size of 1,160,982. The results show a positive association of customer satisfaction with customer-level outcomes (retention, WOM, spending, and price) and firm-level outcomes (product-market, accounting, and financial-market performance). A moderator analysis shows the association varies due to many contextual factors and measurement characteristics. The results have important theoretical and managerial implications.

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1 Introduction

Oliver ( 2014 , p. 8) defines customer satisfaction (CS) as “a judgment that a product/service feature or the product or service itself provided (or is providing) a pleasurable level of consumption-related fulfillment, including levels of under- or over-fulfillment.” Similarly, Anderson and Sullivan ( 1993 , p. 126) characterize CS as a “post-purchase evaluation of product quality given repurchase expectations.” Thus, CS is a customer’s evaluative summary judgment of consumption experiences that is associated with customer- and firm-level outcomes.

Although we may theoretically know and expect that CS will have a positive association with many outcomes such as retention, WOM, and sales, a systematic and large-scale meta-analysis can provide important insights. First, it is important to compare differences in the strength of relationship across different customer- and firm-level outcomes (e.g., CS-retention vs. CS-sales). Second, it is important to examine the considerable variation in the magnitude of these relationships across studies. For example, some studies find the CS-retention correlation to be nonsignificant (e.g., van Birgelen, de Jong, and de Ruyter 2006 ) while others find a strong positive association (e.g., Anderson and Sullivan 1993 ).

Understanding the reasons behind these systematic differences can yield new and important research questions and insights. For example, is the association between CS and customer-level consequences stronger (or weaker) for business-to-consumer (B2C) markets relative to business-to-business (B2B) markets? What is the theoretical reason behind this difference, and what are its practical implications? Answering these questions can suggest more nuanced testable hypotheses and guide practitioners as well.

This study investigates the association of CS with 14 outcomes in a meta-analytic framework (see Fig. 1 , panel A). These outcomes include customer outcomes, product-market performance, accounting performance, and financial-market performance. These outcomes are of great importance to a firm’s chief marketing officer (CMO), chief sales officer (CSO), chief financial officer (CFO), and chief executive officer (CEO) (see Fig. 1 , panel B).

Customer satisfaction and its outcomes

As shown in Table 1 , there have been three meta-analyses of CS published in marketing journals. Szymanski and Henard ( 2001 ) conducted the first meta-analysis including 50 studies. Among them, 15 studies examined three CS outcomes (complaining, negative WOM, and repurchase) while 35 examined antecedents of CS. No studies investigated CS and firm-level outcomes.

Curtis et al. ( 2011 ) focused on CS and three customer-level outcomes, retention behavior, retention intention, and loyalty, with no firm-level outcomes. They showed that the positive association of CS with retention and loyalty varies across exchanges (goods vs. services), markets (B2C vs. B2B), and locations of study (North America vs. Europe vs. others).

The most recent meta-analysis by Otto, Szymanski, and Varadarajan ( 2020 ) did not examine any customer-level outcomes and included only five out of ten firm-level outcomes examined in the current study. While they included moderators such as goods vs. services and ACSI vs. non-ACSI metrics, factors such as location of study and scale points were not included.

This meta-analysis uses 535 effect sizes from 245 articles representing a combined sample size of 1,160,982 units, examines 14 effects, and includes nine moderators. It is the most comprehensive meta-analysis to date with a much larger number of articles, customer- and firm-level outcomes, and moderators (see Table 1 ).

2 Theoretical framework

Within the attitude-intentions-behavior framework (Fishbein and Ajzen 1975 ), satisfaction judgments are a function of expectations, disconfirmation, and performance (see, for a review, Oliver 2014 ). Satisfaction judgments drive customers’ behavioral intentions, which in turn guide subsequent actions such as WOM, repurchase, and spending. As customers repeatedly engage in these behaviors, their satisfaction judgments, intentions, and action are reinforced. The result of this process is a cumulative satisfaction judgment (Anderson, Fornell, and Lehmann 1994 ) and associated outcomes. This general process undergirds the framework in Fig. 1 , panel A. Note the current meta-analysis examines CS and its outcomes (and not antecedents).

2.1 Customer- and firm-level outcomes of customer satisfaction

Extant research has linked CS to four customer-level outcomes (retention, WOM, price outcomes, and spending outcomes) and ten firm-level outcomes (e.g., sales, cash flow, stock returns, and Tobin’s q ). Their definition, measures, and respective calculations are shown in Table 2 , panel A.

2.2 Moderators of the CS-outcomes relationship

Table 2 , panel B reports the nine moderators examined in this meta-analysis. These include (1) contextual factors such as type of exchange and location of study and (2) measurement characteristics including the number of items and the number of scale points in the CS measure, the source of CS measure (e.g., ACSI), the calculation of CS score (e.g., top-box score), and the measurement of outcome (e.g., behavior). Footnote 1

3 Methodology

3.1 literature search.

We identified studies using computerized searches of Web of Knowledge, ScienceDirect, and EBSCO with the keywords “customer satisfaction” and “consumer satisfaction.” We examined each issue of the major marketing journals in the USA and Europe starting from 1980. Footnote 2 Prior to 1980, CS research focused on its antecedents. We also reviewed and included pertinent articles from the three meta-analyses in Table 1 .

3.2 Criteria for inclusion/exclusion

A study was excluded if it: (1) measured satisfaction with specific attributes but not overall satisfaction, (2) used a composite measure of multiple outcomes (e.g., latent construct of repurchase and recommendation), and (3) did not report correlations or information that could be converted to correlations. Footnote 3 When a study provided multiple effect sizes, either for separate samples or relationships, we treated effects as independent. When a study provided multiple effect sizes for the same relationship (e.g., for subsets of the same sample), we calculated the average effect size. The final analyses use 535 correlations from 245 articles ( N = 1,160,982).

3.3 Approach to analysis

We calculate inverse-variance-weighted reliability-adjusted correlations between CS and each outcome (Hunter and Schmidt 2004 ). To adjust for reliability, we use Cronbach’s alpha (Nunally 1978 ) as a reliability measure and divide the raw correlations by the square root of the product of reliabilities of CS and the outcome. We are unable to correct for reliability for firm-level outcomes because they use a single metric based on archival financial data. We then transform the reliability-adjusted correlations to Fisher’s z coefficients and weight them by the inverse variance (i.e., 1/[ N  – 3]). Finally, we transform the Fisher’s z coefficients back to correlations to arrive at the weighted reliability-adjusted correlations. Footnote 4 The analyses use a random effects approach for effect size integration.

3.3.1 Publication bias

To address the file-drawer problem, we report the fail-safe N (FSN). This calculates the number of studies that would have to be missing from the analysis to nullify an effect or reduce it to a level that is not theoretically or practically significant (Orwin 1983 ). A funnel plot shows minimal publication bias (Fig. A 1 in Web Appendix A).

3.3.2 Homogeneity and moderator analysis

The Q test assesses between-study variability in the population effect size estimated by the individual studies. Footnote 5 In Table 3 , a statistically significant Q statistic suggests the need for subgroup analysis (e.g., Pick and Eisend 2014 ). Thus, we compare effect sizes across different levels of each moderator.

4.1 CS and customer-level outcomes

Table 3 , panel A reports that CS has a strong association with retention ( r = 0.60, p < 0.01) and WOM ( r = 0.68, p < 0.01) and is moderately correlated with spending ( r = 0.28, p < 0.01) and price outcomes ( r = 0.39, p < 0.01). Footnote 6 The statistically significant Q tests ( p s < 0.01) for all four outcomes indicate that effect sizes may vary based on exchange type, market type, location of study, measurement of outcome, scale items, and scale points. Disaggregated results are shown in panel A of Table A 2 in Web Appendix A and discussed next.

4.2 Moderator analysis for customer-level outcomes

4.2.1 exchange.

For retention, the association with CS is stronger for mixed exchanges ( r MIXED = 0.69) than for services ( r SERVICES = 0.56) but not for goods ( r GOODS = 0.57); the association does not differ between goods and services. The association between CS and WOM is statistically not different among goods ( r GOODS = 0.66), services ( r SERVICES = 0.64), and mixed exchanges ( r MIXED = 0.74). For spending outcomes, the association with CS is statistically similar for goods ( r GOODS = 0.38), services ( r SERVICES = 0.22), and mixed exchanges ( r MIXED = 0.27). Finally, the association of CS and price outcomes is also not statistically different across goods ( r GOODS = 0.08), services ( r SERVICES = 0.41), and mixed exchanges ( r MIXED = 0.34). Footnote 7

4.2.2 Market

The CS-retention association is statistically stronger in B2B ( r B2B = 0.66) than in B2C ( r B2C = 0.55) but not in mixed markets ( r MIXED = 0.63). The CS-WOM relationship is stronger in B2B markets than in others ( r B2C = 0.61 vs. r B2B = 0.74 vs. vs. r MIXED = 0.42). The CS-spending outcomes relationship is not statistically different across B2C ( r B2C = 0.33), B2B ( r B2B = 0.16), and mixed markets ( r MIXED = 0.23). Finally, the CS-price outcomes association is statistically similar in B2C and B2B markets ( r B2C = 0.41 vs. r B2B = 0.18).

4.2.3 Location of study

Relative to Europe, North American samples exhibit a stronger association of CS with retention ( r NORTH.AMERICA = 0.63 vs. r EUROPE = 0.51 vs. r ASIA = 0.64 vs. r AFRICA = 0.82), WOM ( r NORTH.AMERICA = 0.71 vs. r EUROPE = 0.57 vs. r ASIA = 0.65 vs. r AFRICA = 0.41), and price outcomes ( r NORTH.AMERICA = 0.75 vs. r EUROPE = 0.35). For spending outcomes, the association with CS does not statistically differ among samples from North America ( r NORTH.AMERICA = 0.25), Europe ( r EUROPE = 0.30), and Asia ( r ASIA = 0.50).

4.2.4 Measurement of outcome

The association with CS is stronger when the outcome is measured as intentions than as behaviors for retention ( r BEHAVIOR = 0.21 vs. r INTENTION = 0.65) and WOM ( r BEHAVIOR = 0.50 vs. r INTENTION = 0.71) but not for spending outcomes ( r BEHAVIOR = 0.24 vs. r INTENTION = 0.41).

4.2.5 Scale items

The association with CS is stronger when a single- vs. a multiple-item CS scale is used for retention ( r SINGLE = 0.66 vs. r MULTI = 0.55) and WOM ( r SINGLE = 0.73 vs. r MULTI = 0.59) but statistically not different for spending outcomes ( r SINGLE = 0.22 vs. r MULTI = 0.31).

4.2.6 Scale points

The association of CS with outcomes is statistically similar for 5-, 7-, 10-, and 100-point scales ( r 5-POINT = 0.62 vs. r 7-POINT = 0.60 vs. r 10-POINT = 0.50 vs. r 100-POINT = 0.54 for retention; r 5-POINT = 0.65 vs. r 7-POINT = 0.71 vs. r 10-POINT = 0.50 vs. r 100-POINT = 0.65 for WOM; r 5-POINT = 0.28 vs. r 7-POINT = 0.33 vs. r 10-POINT = 0.21 vs. r 100-POINT = 0.23 for spending outcomes; and r 5-POINT = 0.24 vs. r 7-POINT = 0.41 for price outcomes).

4.3 CS and firm-level outcomes

The CS-outcomes correlation is smaller at the firm level than at the customer level (see Table 3 , panel B) potentially because firm-level outcomes are more distal than customer-level outcomes. Different than the association of CS with customer-level outcomes, the magnitude of the association of CS with firm-level outcomes can be classified as small to moderate. Footnote 8

Specifically, CS has a positive and statistically significant association with sales ( r = 0.15, p < 0.01), profit ( r = 0.10, p < 0.01), ROA ( r = 0.22, p < 0.01), Tobin’s q ( r = 0.29, p < 0.01), and stock returns ( r = 0.08, p < 0.05); a negative and statistically significant association with cash flow variability ( r = –0.10, p < 0.01), risk ( r = –0.23, p < 0.01), and cost of debt financing ( r = –0.14, p < 0.01). CS has a nonsignificant association with market share ( r = 0.05, p > 0.10) and a weak positive association with cash flow ( r = 0.09, p < 0.10), which may occur because they likely represent multiple subgroups with large between-group variability in the association (Whitener 1990 ). Footnote 9

The Q statistics for all outcomes, except for cost of debt financing, indicate a statistically significant heterogeneity among studies (see Table 3 , panel B). Yet, with a small number of exceptions, the association between CS and firm-level outcomes is not statistically different across subgroups based on different levels of moderators (see panel B of Table A 2 in Web Appendix A). There are several potential reasons for the statistically nonsignificant results. First, for several moderator levels, each outcome has been investigated by a small number of studies (i.e., k in panel B of Table A 2 in Web Appendix A). Second, most of the firm-level studies include samples from multiple industries and preclude us from isolating correlations based on specific industry settings. Finally, published studies typically do not report correlations disaggregated by firm-level moderators such as firm size, advertising and R&D intensity, and industry concentration. Therefore, we report means by subgroups for firm-level outcomes but do not discuss them further.

5 Implications

5.1 research implications.

First, the moderator analysis shows that there is substantial and systematic heterogeneity in the positive association between CS and customer-level outcomes. Yet, we do not understand the different patterns of variability and their implications. As an example, the association of CS with price outcomes is more heterogeneous than its association with spending outcomes across markets, exchange types, and locations of study. Is it because firms have more control on price outcomes but not on spending outcomes? These issues need further research.

Second, studies that simultaneously examine and compare the association of CS with multiple customer-level outcomes under different contexts are needed. Specifically, attention to differences in effect sizes among subgroups as well as their causes and implications is a key research direction.

Third, the association of CS is strongest for WOM, followed by retention, and is the weakest for spending and price outcomes. Future research should develop a conceptual and theoretical framework to understand these relative differences. Thus, is it the case that higher CS is more beneficial for growing new customers than retaining current customers? To the extent that WOM affects the cost of attracting new customers, customer equity research can be expanded by including CS as a contributing factor for retaining current customers and attracting new customers. Third, a wider set of potential moderators including psychological constructs such as trust and commitment as well as structural factors such as company size, industry growth, and competitive intensity should be investigated.

Fourth, these results make a very strong case that consumer behavior scholars should use CS as a consequential dependent variable in their studies. CS has a clear association with actual consumer behaviors and firm-level financial outcomes. Thus, consumer behavior scholars can be reasonably assured that differences in CS are consequential, i.e., predictive of actual consumer behaviors and firm financial outcomes.

Fifth, these results call into question the long-standing insistence on using multi-item scales for measuring CS. The CS-outcomes linkage is impervious to single- vs. multiple-item scales or number of scale points (i.e., 5- vs. 7- vs. 10- vs. 100-point scale). Simple and single item scales suffice; this is an important insight for practitioners who value simplicity to reduce the cost of customer surveys.

Sixth, at the firm level, the mean association of CS with market share ( p > 0.10) and cash flow ( p < 0.10) is nonsignificant to weak (Table 3 , panel B). This may be the case if the association of CS with these outcomes is nonlinear and/or contingent on factors such as firms’ ability to standardize or customize their offerings, the heterogeneity in consumer preferences, and the nature of the offering (e.g., goods vs. services; Anderson, Fornell, and Rust 1997 ). In the same vein, CS has a stronger association with ROA than with cash flow. While we can speculate on the potential reasons for this, more studies are needed to better estimate the effects and explain the differences. Finally, the small number of studies for subgroups within different levels of moderators precluded specific conclusions; clearly, more studies on CS-firm outcomes are needed.

5.2 Implications for firm strategy and senior executives

Figure 1 , panel B organizes the outcomes of CS based on their relevance to CMOs, CSOs, CFOs, and CEOs and board members. CMOs who organize their efforts around CS and make CS as their key metric should be able to make a case for their relevance and contribution to customer retention, WOM, spending, and price outcomes. While CMOs are free to focus on other constructs such as net promoter, this research provides clear, strong, and convincing evidence for using CS as a metric to measure marketing and sales performance and relate it to firm performance. Specifically, CS can provide the basis for CMOs and CSOs to collaboratively grow the current customer base organically as well as expand it through additional sales. The positive association of CS with ROA and cash flow and its negative association with cash flow variability speak to CFOs.

Finally, our work makes a clear case for CEOs and board members to utilize CS as an organizing framework for strategy planning and execution. By making customer value, as measured through CS, the central mechanism for creating and implementing strategy, CEOs can reliably increase Tobin’s q and stock returns while decreasing risk, outcomes for which CEOs are most responsible.

In summary, a focus on CS can align C-suite members (CEO, CFO, CMO, and CSO) using a theoretically sound, conceptually consistent, and empirically validated approach. We hope that senior leaders in firms embrace a satisfaction-based approach to strategy planning and execution based on these results.

6 Concluding comments

CS is a core construct for guiding strategy research and a consequential outcome for consumer-behavior research. This meta-analysis of 535 effect sizes from 245 articles shows that the positive outcomes of CS at the customer- and firm-level vary across different outcomes and across different study characteristics. The results provide guidance for research scholars and show how senior executives can adopt a CS-based framework to develop, guide, and implement firm strategy.

The current research has limitations. First, the results are limited by data availability, which precluded a larger number of outcomes or additional moderators. Second, variation in effect sizes remained even after accounting for contextual and measurement factors, suggesting that sources of variation still exist. Finally, our analysis was based on traditional meta-analytic framework and could not capture nonlinearity in the relationship between two constructs. Studies reporting correlations at different levels of moderators and boundary conditions in the association of CS with its consequences can be helpful in this regard.

Data Availability

Please contact authors for data availability.

We calculated the proportion of studies for each combination of levels in different moderators. Table A1 in Web Appendix A reports the proportions showing adequate variation in study settings.

Journals include Journal of Marketing , Journal of Marketing Research , Marketing Science , Journal of Consumer Research , Journal of Service Research , Journal of Retailing , Journal of the Academy of Marketing Science , Journal of Services Marketing , Journal of Service Industry Management , Journal of Consumer Satisfaction, Dissatisfaction, and Complaining Behavior , Journal of Business Research , and International Journal of Service Industry Management. The list of papers included in the meta-analysis is provided in Web Appendix B.

We contacted 44 authors to request missing correlations for studies, and 17% of them provided the correlations.

We use the Fisher’s z transformation due to potential issues associated with using raw correlations. Specifically, different than Fisher’s z scores, raw correlations may be highly skewed and have a problematic standard error formulation: the standard error is used to compute the inverse variance weight in the meta-analysis (Lipsey and Wilson 2001 ). Still, we computed results using raw correlations. Reassuringly, most of the results remained unchanged when using Fisher’s z or correlations.

The Q statistic is computed by summing the squared deviations of each study’s effect estimate from the overall estimate, weighting each study by the inverse of its variance, and has a chi-square distribution with k – 1 degrees of freedom ( k = number of effect sizes). A statistically significant Q statistic indicates the effect size varies across studies. The Q statistic has low power to detect heterogeneity when the number of studies is small or sample size within studies is low. Thus, it should be interpreted cautiously.

Following Cohen ( 1992 ), we deem a correlation of 0.10 as small, 0.30 as medium, and 0.50 as large. Notably, the magnitude and the statistical significance of the correlations of CS with retention and WOM are similar to those reported in Szymanski and Henard ( 2001 ) and Curtis et al. ( 2011 ).

The very small sample size for goods and mixed exchanges precludes meaningful statistical comparisons.

We use Cohen’s ( 1992 ) standards for effect sizes in our interpretation. It may be the case that higher/more conservative standards are required because lower-level variability influences higher-level effects (e.g., individual-level variability is ignored in the estimate of firm-level effects).

Notably, the magnitude and the statistical significance of the correlations of CS with market share, sales, profit, Tobin’s q , and stock returns are similar to those reported in Otto, Szymanski, and Varadarajan ( 2020 ).

Anderson, E. W. (1998). Customer satisfaction and word of mouth. Journal of Service Research, 1 (1), 5–17.

Article   Google Scholar  

Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer satisfaction, market share, and profitability: findings from Sweden. Journal of Marketing, 58 (3), 53–66.

Anderson, E. W., Fornell, C., & Rust, R. T. (1997). Customer satisfaction, productivity, and profitability: Differences between goods and services. Marketing Science, 16 (2), 129–145.

Anderson, E. W., & Mansi, S. A. (2009). Does customer satisfaction matter to investors? Findings from the bond market. Journal of Marketing Research, 46 (5), 703–714.

Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12 (2), 125–143.

Bhattacharya, A., Morgan, N. A., & Rego, L. L. (2021). Customer satisfaction and firm profits in monopolies: A study of utilities. Journal of Marketing Research, 58 (1), 202–222.

Bolton, R. N., Lemon, K. N., & Verhoef, P. C. (2008). Expanding business-to-business customer relationships: Modeling the customer’s upgrade decision. Journal of Marketing, 72 (1), 46–64.

Cohen, J. (1992). A power primer. Psychological Bulletin, 112 (1), 155–159.

Curtis, T., Abratt, R., Rhoades, D. L., & Dion, P. (2011). Customer loyalty, repurchase and satisfaction: A meta-analytical review. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 24 , 1–26.

Google Scholar  

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research . Addison-Wesley.

Gruca, T. S., & Rego, L. L. (2005). Customer satisfaction, cash flow, and shareholder value. Journal of Marketing, 69 (3), 115–130.

Homburg, C., Wieseke, J., & Hoyer, W. D. (2009). Social identity and the service–profit chain. Journal of Marketing, 73 (2), 38–54.

Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis (2nd ed.). Sage.

Jacobson, R., & Mizik, N. (2009). The financial markets and customer satisfaction: Reexamining possible financial market mispricing of customer satisfaction. Marketing Science, 28 (5), 810–819.

Lipsey, M. W., & Wilson, D. T. (2001). Practical meta-analysis. Applied social research methods series . SAGE.

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, 1 (1), 18–31.

Mittal, V., Han, K., Lee, J., & Sridhar, S. (2021). Improving business-to-business customer satisfaction programs: assessment of asymmetry, heterogeneity, and financial impact. Journal of Marketing Research, 58 (4), 615–643.

Mittal, V., & Kamakura, W. A. (2001). Satisfaction, repurchase intent, and repurchase behavior: Investigating the moderating effect of customer characteristics. Journal of Marketing Research, 38 (1), 131–142.

Mittal, V., Kumar, P., & Tsiros, M. (1999). Attribute-level performance, satisfaction, and behavioral intentions over time: A consumption-system approach. Journal of Marketing, 63 (2), 88–101.

Morgan, N. A., & Rego, L. L. (2006). The value of different customer satisfaction and loyalty metrics in predicting business performance. Marketing Science, 25 (5), 426–439.

Nunally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill Book Company.

Oliver, R. L. (2014). Satisfaction: A behavioral perspective on the consumer . Routledge.

Book   Google Scholar  

Orwin, R., & G. (1983). A fail-safe N for effect size in meta-analysis. Journal of Educational Statistics, 8 (2), 157–159.

Otto, A. S., Szymanski, D. M., & Varadarajan, R. (2020). Customer satisfaction and firm performance: Insights from over a quarter century of empirical research. Journal of the Academy of Marketing Science, 48 (3), 543–564.

Pick, D., & Eisend, M. (2014). Buyers’ perceived switching costs and switching: a meta-analytic assessment of their antecedents. Journal of the Academy of Marketing Science, 42 (2), 186–204.

Pritchard, M., & Silvestro, R. (2005). Applying the service profit chain to analyse retail performance. International Journal of Service Industry Management, 16 (4), 337–356.

Szymanski, D. M., & Henard, D. H. (2001). Customer satisfaction: A meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science, 29 (1), 16–35.

Tuli, K. R., & Bharadwaj, S. G. (2009). Customer satisfaction and stock returns risk. Journal of Marketing, 73 (6), 184–197.

van Birgelen, M., de Jong, A., & de Ruyter, K. (2006). Multi-channel service retailing: The effects of channel performance satisfaction on behavioral intentions. Journal of Retailing, 82 (4), 367–377.

Verhoef, P. C. (2003). Understanding the effect of customer relationship management efforts on customer retention and customer share development. Journal of Marketing, 67 (4), 30–45.

Whitener, E. M. (1990). Confusion of confidence intervals and credibility intervals in meta-analysis. Journal of Applied Psychology, 75 (3), 315–321.

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Mittal, V., Han, K., Frennea, C. et al. Customer satisfaction, loyalty behaviors, and firm financial performance: what 40 years of research tells us. Mark Lett 34 , 171–187 (2023). https://doi.org/10.1007/s11002-023-09671-w

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Customer satisfaction is the key factor for successful and depends highly on the behaviors of frontline service providers. Customers should be managed as assets, and that customers vary in their needs, preferences, and buying behavior. This study applied the Taiwan Customer Satisfaction Index model to a tourism factory to analyze customer satisfaction and loyalty. We surveyed 242 customers served by one tourism factory organizations in Taiwan. A partial least squares was performed to analyze and test the theoretical model. The results show that perceived quality had the greatest influence on the customer satisfaction for satisfied and dissatisfied customers. In addition, in terms of customer loyalty, the customer satisfaction is more important than image for satisfied and dissatisfied customers. The contribution of this paper is to propose two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively. Compared with traditional techniques, we believe that our method is more appropriate for making decisions about allocating resources and for assisting managers in establishing appropriate priorities in customer satisfaction management.

Traditional manufacturing factories converted for tourism purposes, have become a popular leisure industry in Taiwan. The tourism factories has experienced significant growth in recent years, and more and more tourism factories emphasized service quality improvement, and customized service that contributes to a tourism factory’s image and competitiveness in Taiwan (Wu and Zheng 2014 ). Therefore, tourism factories has become of greater economic importance in Taiwan. By becoming a tourism factory, companies can establish a connection between consumers and the brand, generate additional income from entrance tickets and on-site sales, and eventually add value to service innovations (Tsai et al. 2012 ). Because of these incentives, the Taiwanese tourism factory industry has become highly competitive. Customer satisfaction is seen as very important in this case.

Numerous empirical studies have indicated that service quality and customer satisfaction lead to the profitability of a firm (Anderson et al. 1994 ; Eklof et al. 1999 ; Ittner and Larcker 1996 ; Fornell 1992 ; Anderson and Sullivan 1993 ; Zeithaml 2000 ). Anderson and Sullivan ( 1993 ) stated that a firm’s future profitability depends on satisfying current customers. Anderson et al. ( 1994 ) found a significant relationship between customer satisfaction and return on assets. High quality leads to high levels of customer retention, increase loyalty, and positive word of mouth, which in turn are strongly related to profitability (Reichheld and Sasser 1990 ). In a tourism factory setting, customer satisfaction is the key factor for successful and depends highly on the behaviors of frontline service providers. Kutner and Cripps ( 1997 ) indicated that customers should be managed as assets, and that customers vary in their needs, preferences, buying behavior, and price sensitivity. A tourism factory remains competitive by increasing its service quality relative to that of competitors. Delivering superior customer value and satisfaction is crucial to firm competitiveness (Kotler and Armstrong 1997 ; Weitz and Jap 1995 ; Deng et al. 2013 ). It is crucial to know what customers value most and helps firms allocating resource utilization for continuously improvement based on their needs and wants. The findings of Customer Satisfaction Index (CSI) studies can serve as predictors of a company’s profitability and market value (Anderson et al. 1994 ; Eklof et al. 1999 ; Chiu et al. 2011 ). Such findings provide useful information regarding customer behavior based on a uniform method of customer satisfaction, and offer a unique opportunity to test hypotheses (Anderson et al. 1997 ).

The basic structure of the CSI model has been developed over a number of years and is based on well-established theories and approaches to consumer behavior, customer satisfaction, and product and service quality in the fields of brands, trade, industry, and business (Fornell 1992 ; Fornell et al. 1996 ). In addition, the CSI model leads to superior reliability and validity for interpreting repurchase behavior according to customer satisfaction changes (Fornell 1992 ). These CSIs are fundamentally similar in measurement model (i.e. causal model), they have some obvious distinctions in model’s structure and variable’s selection. Take full advantages of other nations’ experiences can establish the Taiwan CSI Model which is suited for Taiwan’s characters. Thus, the ACSI and ECSI have been used as a foundation for developing the Taiwan Customer Satisfaction Index (TCSI). The TCSI was developed by Chung Hua University and the Chinese Society for Quality in Taiwan. The TCSI provides Taiwan with a fair and objective index for producing vital information that can help the country, industries, and companies improve competitiveness. Every aspect of the TCSI that influences overall customer satisfaction can be measured through surveys, and every construct has a cause–effect relationship with the other five constructs (Fig.  1 ). The relationships among the different aspects of the TCSI are different from those of the ACSI, but are the same as those of the ECSI (Lee et al. 2005 , 2006 ).

The Taiwan Customer Satisfaction Index model

The traditional CSI model for measuring customer satisfaction and loyalty is restricted and does not consider the performance of firms. Moreover, as theoretical and empirical research has shown, the relationship between attribute-level performance and overall satisfaction is asymmetric. If the asymmetries are not considered, the impact of the different attributes on overall satisfaction is not correctly evaluated (Anderson and Mittal 2000 ; Matzler and Sauerwein 2002 ; Mittal et al. 1998 ; Matzler et al. 2003 , 2004 ). Few studies have investigated CSI models that contain different levels of performance (satisfaction), especially in relation to satisfaction levels of a tourism factory. To evaluate overall satisfaction accurately, the impact of the different levels of performance should be considered (Matzler et al. 2004 ). The purpose of this study is to apply the TCSI model that contains different levels of performance to improve and ensure the understanding of firm operational efficiency by managers in the tourism factory. A partial least squares (PLS) was performed to test the theoretical model due to having been successfully applied to customer satisfaction analysis. The PLS is well suited for predictive applications (Barclay et al. 1995 ) and using path coefficients that regard the reasons for customer satisfaction or dissatisfaction and providing latent variable scores that could be used to report customer satisfaction scores. Our findings provide support for the application of TCSI model to derive tourist satisfaction information.

Literature review

National customer satisfaction index (csi).

The CSI model includes a structural equation with estimated parameters of hidden categories and category relationships. The CSI can clearly define the relationships between different categories and provide predictions. The basic CSI model is a structural equation model with latent variables which are calculated as weighted averages of their measurement variables, and the PLS estimation method calculates the weights and provide maximum predictive power of the ultimate dependent variable (Kristensen et al. 2001 ). Many scholars have identified the characteristics of the CSI (Karatepe et al. 2005 ; Malhotra et al. 1994 ).

Although the core of the models are in most respects standard, they have some obvious distinctions in model’s structure and variable’s selection so that their results cannot be compared with each other and some variations between the SCSB (Swedish), the ACSI (American), the ECSI (European), the NCSB (Norwegian) and other indices. For example, the image factor is not employed in the ACSI model (Johnson et al. 2001 ); the NCSB eliminated customer expectation and replaced with corporate image; the ECSI model does not include the customer complaint as a consequence of satisfaction. Many scholars have identified the characteristics of the CSI (Karatepe et al. 2005 ; Malhotra et al. 1994 ). The ECSI model distinguishes service quality from product quality (Kristensen et al. 2001 ) and the NCSB model applies SERVQUAL instrument to evaluate service quality (Johnson et al. 2001 ). A quality measure of a single customer satisfaction index is typically developed according to a certain type of culture or the culture of a certain country. When developing a system for measuring or evaluating a certain country or district’s customer satisfaction level, a specialized customer satisfaction index should be developed.

As such, the ACSI and ECSI were used as a foundation to develop the TCSI. The TCSI was developed by Chung Hua University and the Chinese Society for Quality. Every aspect of the TCSI that influences overall customer satisfaction can be measured through surveys, and every construct has a cause–effect relationship with the other five constructs. The TCSI assumes that currently: (1) Taiwan corporations have ability of dealing with customer complaints; customer complaints have already changed from a factor that influences customer satisfaction results to a factor that affects quality perception; (2) The expectations, satisfaction and loyalty of customers are affected by the image of the corporation. The concept that customer complaints are not calculated into the TCSI model is that they were removed based on the ECSI model (Lee et al. 2005 , 2006 , 2014a , b ; Guo and Tsai 2015 ; Tsai et al. 2015a , b ; 2016a ).

TCSI model and service quality

Service quality is frequently used by both researchers and practitioners to evaluate customer satisfaction. It is generally accepted that customer satisfaction depends on the quality of the product or service offered (Anderson and Sullivan 1993 ). Numerous researchers have emphasized the importance of service quality perceptions and their relationship with customer satisfaction by applying the NCSI model (e.g., Ryzin et al. 2004 ; Hsu 2008 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Temizer and Turkyilmaz 2012 ; Mutua et al. 2012 ; Dutta and Singh 2014 ). Ryzin et al. ( 2004 ) applied the ACSI to U.S. local government services and indicated that the perceived quality of public schools, police, road conditions, and subway service were the most salient drivers of satisfaction, but that the significance of each service varied among income, race, and geography. Hsu ( 2008 ) proposed an index for online customer satisfaction based on the ACSI and found that e-service quality was more determinative than other factors (e.g., trust and perceived value) for customer satisfaction. To deliver superior service quality, an online business must first understand how customers perceive and evaluate its service quality. This study developed a basic model for using the TCSI to analyze Taiwan’s tourism factory services. The theoretical model comprised 14 observation variables and the following six constructs: image, customer expectations, perceived quality, perceived value, customer satisfaction, and loyalty.

Research methods

The measurement scale items for this study were primarily designed using the questionnaire from the TCSI model. In designing the questionnaire, a 10-point Likert scale (with anchors ranging from strongly disagree to strongly agree) was used to reduce the statistical problem of extreme skewness (Fornell et al. 1996 ; Qu et al. 2015 ; Tsai 2016 ; Tsai et al. 2016b ; Zhou et al. 2016 ). A total of 14 items, organized into six constructs, were included in the questionnaire. The primary questionnaire was pretested on 30 customers who had visited a tourism factory. Because the TCSI model is preliminary research in the tourism factory, this study convened a focus group to decide final attributes of model. The focus group was composed of one manager of tourism factory, one professor in Hospitality Management, and two customers with experience of tourism factory.

We used the TCSI model (Fig.  1 ) to structure our research. From this structure and the basic theories of the ACSI and ECSI, we established the following hypotheses:

Image has a strong influence on tourist expectations.

Image has a strong influence on tourist satisfaction.

Image has a strong influence on tourist loyalty.

Tourist expectations have a strong influence on perceived quality.

Tourist expectations have a strong influence on perceived values.

Tourist expectations have a strong influence on tourist satisfaction.

Perceived quality has a strong influence on perceived value.

Perceived quality has a strong influence on tourist satisfaction.

Perceived value has a strong influence on tourist satisfaction.

Customer satisfaction has a strong influence on tourist loyalty.

The content of our surveys were separated into two parts; customer satisfaction and personal information. The definitions and processing of above categories are listed below:

Part 1 of the survey assessed customer satisfaction by measuring customer levels of tourism factory image, expectations, quality perceptions, value perceptions, satisfaction, and loyalty toward their experience, and used these constructs to indirectly survey the customer’s overall evaluation of the services provided by the tourism factory.

Part 2 of the survey collected personal information: gender, age, family situation, education, income, profession, and residence.

The six constructs are defined as follows:

Image reflects the levels of overall impression of the tourism factory as measured by two items: (1) word-of-mouth reputation, (2) responsibility toward concerned parties that the tourist had toward the tourism factory before traveling.

Customer expectations refer to the levels of overall expectations as measured by two items: (1) expectations regarding the service of employees, (2) expectations regarding reliability that the tourist had before the experience at the tourism factory.

Perceived quality was measured using three survey measures: (1) the overall evaluation, (2) perceptions of reliability, (3) perceptions of customization that the tourist had after the experience at the tourism factory.

Perceived value was measured using two items: (1) the cost in terms of money and time (2) a comparison with other tourism factories.

Customer satisfaction represents the levels of overall satisfaction was captured by two items: (1) meeting of expectations, (2) closeness to the ideal tourism factory.

Loyalty was measured using three survey measures: (1) the probabilities of visiting the tourism factory again (2) attending another activity held by the tourism factory, (3) recommending the tourism factory to others.

Data collection and analysis

The survey sites selected for this study was the parking lots of one food tourism factory in Taipei, Taiwan. A domestic group package and individual tourists were a major source of respondents who were willing to participate in the survey and completed the questionnaires themselves based on their perceptions of their factory tour experience. Four research assistants were trained to conduct the survey regarding to questionnaire distribution and sampling.

To minimize prospective biases of visiting patterns, the survey was conducted at different times of day and days of week—Tuesday, Thursday, Saturday for the first week; Monday, Wednesday, Friday and Sunday for the next week. The afternoon time period was used first then the morning time period in the following weeks. The data were collected over 1 month period.

Of 300 tourists invited to complete the questionnaire, 242 effective responses were obtained (usable response rate of 80.6 %). The sample of tourists contained more females (55.7 %) than males (44.35 %). More than half of the respondents had a college degree or higher, 28 % were students, and 36.8 % had an annual household income of US $10,000–$20,000. The majority of the respondents (63.7 %) were aged 20–40 years.

Comparison of the TCSI models for satisfied and dissatisfied customers

Researchers have claimed that satisfaction levels differ according to gender, age, socioeconomic status, and residence (Bryant and Cha 1996 ). Moreover, the needs, preferences, buying behavior, and price sensitivity of customers vary (Kutner and Cripps 1997 ). Previous studies have demonstrated that it is crucial to measure the relative impact of each attribute for high and low performance (satisfaction) (Matzler et al. 2003 , 2004 ). To determine the reasons for differences, a satisfaction scale was used to group the sample into satisfied (8–10) and dissatisfied (1–7) customers.

The research model was tested using SmartPLS 3.0 software, which is suited for highly complex predictive models (Wold 1985 ; Barclay et al. 1995 ). In particular, it has been successfully applied to customer satisfaction analysis. The PLS method is a useful tool for obtaining indicator weights and predicting latent variables and includes estimating path coefficients and R 2 values. The path coefficients indicate the strengths of the relationships between the dependent and independent variables, and the R 2 values represent the amount of variance explained by the independent variables. Using Smart PLS, we determined the path coefficients. Figures  2 and 3 show ten path estimates corresponding to the ten research hypothesis of TCSI model for satisfied and dissatisfied customers. Every path coefficient was obtained by bootstrapping the computation of R 2 and performing a t test for each hypothesis. Fornell et al. ( 1996 ) demonstrated that the ability to explain the influential latent variables in a model is an indicator of model performance, in particular the customer satisfaction and customer loyalty variables. From the results shown, the R 2 values for the customer satisfaction were 0.53 vs. 0.50, respectively; and the R 2 value for customer loyalty were 0.64 vs. 0.60, respectively. Thus, the TCSI model explained 53 vs. 50 % of the variance in customer satisfaction; 64 vs. 60 % of that in customer loyalty as well.

Path estimate of the TCSI model for satisfied customers. *p < 0.05; **p < 0.01; ***p < 0.001

Path estimate of the TCSI model for dissatisfied customers. *p < 0.05; **p < 0.01; ***p < 0.001

According to the path coefficients shown in Figs.  2 and 3 , image positively affected customer expectations (β = 0.58 vs. 0.37), the customer satisfaction (β = 0.16 vs. 0.11), and customer loyalty (β = 0.47 vs. 0.16). Therefore, H1–H3 were accepted. Customer expectations were significantly related to perceived quality (β = 0.94 vs. 0.83). However, customer expectations were not significantly related to perceived value shown as dotted line (β = −0.01 vs. −0.20) or the customer satisfaction, shown as dotted line (β = −0.21 vs. −0.32). Thus, H4 was accepted but H5 and H6 were not accepted. Perceived value positively affected the customer satisfaction (β = 0.27 vs. 0.14), supporting H7. Accordingly, the analysis showed that each of the antecedent constructs had a reasonable power to explain the overall customer satisfaction. Furthermore, perceived quality positively affected the customer satisfaction (β = 0.70 vs. 0.62), as did perceived value (β = 0.83 vs. 0.74). These results confirm H8 and H9. The path coefficient between the customer satisfaction and customer loyalty was positive and significant (β = 0.63 vs. 0.53). This study tested the suitability of two TCSI models by analyzing the tourism factories in Taiwan. The results showed that the TCSI models were all close fit for this type of research. This study provides empirical evidence of the causal relationships among perceived quality, image, perceived value, perceived expectations, customer satisfaction, and customer loyalty.

To observe the effects of antecedent constructs of perceived value (e.g., customer expectation and perceived quality), customer expectations were not significantly related to perceived value for either satisfied or dissatisfied customers. Furthermore, satisfied customers were affected more by perceived quality (β = 0.83 vs. 0.74), as shown in Table  1 . Regarding the effect of the antecedents of customer satisfaction (e.g., image, customer expectations, perceived value and perceived quality), the total effects of perceived quality on the customer satisfaction of satisfied and dissatisfied customers were 0.92 and 0.72. The total effects of image on the customer satisfaction of satisfied and dissatisfied customers were 0.45 and 0.19. Thus, the satisfaction level of satisfied customers was affected more by perceived quality. Consequently, regarding customer satisfaction, perceived quality is more important than image for satisfied and dissatisfied customers. Numerous researchers have emphasized the importance of service quality perceptions and their relationship with customer satisfaction by applying the CSI model (e.g., Ryzin et al. 2004 ; Hsu 2008 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Temizer and Turkyilmaz 2012 ; Mutua et al. 2012 ; Dutta and Singh 2014 ). This is consistent with the results of previous research ( O’Loughlin and Coenders 2002 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Chin and Liu 2015 ; Chin et al. 2016 ).

With respect to the effect of the antecedents of customer loyalty (e.g., image and customer satisfaction), the total effects of image on customer loyalty for satisfied and dissatisfied customers were 0.57 and 0.21. In other words, the customer loyalty of satisfied customers was affected more by customer satisfaction. Customer satisfaction was significantly related to the customer loyalty of both satisfied and dissatisfied customers, and satisfied customers were affected more by customer satisfaction ( β  = 0.63 vs. 0.14). Consequently, regarding customer loyalty, customer satisfaction is more important than image for both satisfied and dissatisfied customers. Numerous studies have shown that customer satisfaction is a crucial factor for ensuring customer loyalty (Barsky 1992 ; Smith and Bolton 1998 ; Hallowell 1996 ; Grønholdt et al. 2000 ). This study empirically supports the notion that customer satisfaction is positively related to customer loyalty.

The TCSI model has a predictive capability that can help tourism factory managers improve customer satisfaction based on different performance levels. Our model enables managers to determine the specific factors that significantly affect overall customer satisfaction and loyalty within a tourism factory. This study also helps managers to address different customer segments (e.g., satisfied vs. dissatisfied); because the purchase behaviors of customers differ, they must be treated differently. The contribution of this paper is to propose two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively.

Fornell et al. ( 1996 ) demonstrated that the ability to explain influential latent variables in a model, particularly customer satisfaction and customer loyalty variables, is an indicator of model performance. However, the results of this study indicate that customer expectations were not significantly related to perceived value for either satisfied or dissatisfied customers. Moreover, they were affected more by perceived quality of customer satisfaction. Numerous researchers have found that the construct of customer expectations used in the ACSI model does not significantly affect the level of customer satisfaction (Johnson et al. 1996 , 2001 ; Martensen et al. 2000 ; Anderson and Sullivan 1993 ).

Through the overall effects, this study derived several theoretical findings. First, the factors with the largest influence on customer satisfaction were perceived quality and perceived expectations, despite the results showing that customer expectations were not significantly related to perceived value or customer satisfaction. Hence, customer expectations indirectly affected customer satisfaction through perceived quality. Accordingly, perceived quality had the greatest influence on customer satisfaction. Likewise, our results also show that satisfied customers were affected more by perceived quality than dissatisfied customers. This study determined that perceived quality, whether directly or indirectly, positively influenced customer satisfaction. This result is consistent with those of Cronin and Taylor ( 1992 ), Cronin et al. ( 2000 ), Hsu ( 2008 ), Ladhari ( 2009 ), Terblanche and Boshoff ( 2010 ), Deng et al. ( 2013 ), and Yazdanpanah et al. ( 2013 ).

Second, the factors with the most influence on customer loyalty were image and customer satisfaction. The results of this study demonstrate that the customer loyalty of satisfied customers was affected more by customer satisfaction. Consequently, regarding customer loyalty, customer satisfaction is more important than image for satisfied customers. Lee ( 2015 ) found that higher overall satisfaction increased the possibility that visitors will recommend and reattend tourism factory activities. Moreover, numerous studies have shown that customer satisfaction is a crucial factor for ensuring customer loyalty (Barsky 1992 ; Smith and Bolton 1998 ; Hallowell 1996 ; Su 2004; Deng et al. 2013 ). In initial experiments on ECSI, corporate image was assumed to have direct influences on customer expectation, satisfaction, and loyalty. Subsequent experiments in Denmark proved that image affected only expectation and satisfaction and had no relationship with loyalty (Martensen et al. 2000 ). In early attempts to build the ECSI model, image was defined as a variable involving not only a company’s overall image but products or brand awareness; thus image is readily connected with customer expectation and perception. Therefore, this study contributes to relevant research by providing empirical support for the notion that customer satisfaction is positively related to customer loyalty.

In addition to theoretical implications, this study has several managerial implications. First, the TCSI model has a satisfactory predictive capability that can help tourism factory managers to examine customer satisfaction more closely and to understand explicit influences on customer satisfaction for different customer segments by assessing the accurate causal relationships involved. In contrast to general customer satisfaction surveys, the TCSI model cannot obtain information on post-purchase customer behavior to improve customer satisfaction and achieve competitive advantage.

Second, this study not only indicated that each of the antecedent constructs had reasonable power to explain customer satisfaction and loyalty but also showed that perceived quality exerts the largest influence on the customer satisfaction of Taiwan’s tourism factory industry. Therefore, continually, Taiwan’s tourism factories must endeavor to enhance their customer satisfaction, ideally by improving service quality. Managers of Taiwan’s tourism factories must ensure that service providers deliver consistently high service quality.

Third, this research determined that the factors having the most influence on customer loyalty were image and customer satisfaction. Therefore, managers of Taiwan’s tourism factories should allow customer expectations to be fulfilled through experiences, thereby raising their overall level of satisfaction. Regarding image, which refers to a brand name and its related associations, when tourists regard a tourism factory as having a positive image, they tend to perceive higher value of its products and services. This leads to a higher level of customer satisfaction and increased chances of customers’ reattending tourism factory activities.

Different performance levels exist in how tourists express their opinions about various aspects of service quality and satisfaction with tourism factories. Customer segments can have different preferences depending on their needs and purchase behavior. Our findings indicate that tourists belonging to different customer segments (e.g., satisfied vs. dissatisfied) expressed differences toward service quality and customer satisfaction. Thus, the management of Taiwan’s tourism factories must notice the needs of different market segments to meet their individual expectations. This study proposes two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively. Compared with traditional techniques, we believe that our method is more appropriate for making decisions about allocating resources and for assisting managers in establishing appropriate priorities in customer satisfaction management.

Limitations and suggestions for future research

This study has some limitations. First, the tourism factory surveyed in this study was a food tourism factory operating in Taipei, Taiwan, and the present findings cannot be generalized to the all tourism factory industries. Second, the sample size was quite small for tourists (N = 242). Future research should collect a greater number of samples and include a more diverse range of tourists. Third, this study was preliminary research on tourism factories, and domestic group package tourists were a major source of the respondents. Future studies should collect data from international tourists as well.

Anderson EW, Sullivan M (1993) The antecedents and consequences of customer satisfaction for firms. Mark Sci 12:125–143

Article   Google Scholar  

Anderson EW, Mittal V (2000) Strengthening the satisfaction-profit chain. J Serv Res 3(2):107–120

Anderson EW, Fornell C, Lehmann DR (1994) Customer satisfaction, market share, and profitability: findings from Sweden. J Mark 58:53–66

Anderson EW, Fornell C, Rust RT (1997) Customer satisfaction, productivity, and profitability: differences between goods and services. Mark Sci 16(2):129–145

Barclay DW, Higgins C, Thompson R (1995) Interdepartmental conflict in organizational buying: the impact of the organizational context. J Mark Res 28:145–159

Barsky JD (1992) Customer satisfaction in the hotel industry: meaning and measurement. J Hosp Tour Res 16(1):51–73

Google Scholar  

Bryant BE, Cha J (1996) Crossing the threshold. Mark Res 8(4):20

Chin T, Liu RH (2015) Understanding labor conflicts in Chinese manufacturing: A Yin-Yang harmony perspective. Int J Confl Manag 26(3):288–315

Chin T, Liu RH, Yang X (2016) Reverse internationalization in Chinese firms: A study of how global startup OEMs seek to compete domestically. Asia Pac Bus Rev 22(2):201–219. doi: 10.1080/13602381.2015.1055087

Chiu SI, Cheng CC, Yen TM, Hu HY (2011) Preliminary research on customer satisfaction models in Taiwan: a case study from the automobile industry. Expert Syst Appl 38(8):9780–9787

Cronin Jr JJ, Taylor SA (1992) Measuring service quality: a reexamination and extension. J Mark 56(3):55–68

Cronin JJ, Brady MK, Hult GTM (2000) Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. J Retail 76(2):193–218

Deng WJ, Yeh ML, Sung ML (2013) A customer satisfaction index model for international tourist hotels: integrating consumption emotions into the American customer satisfaction index. Int J Hosp Manag 35:133–140

Dutta K, Singh S (2014). Deriving Customer Satisfaction and Loyalty from Organized Retailer’s Sales Promotion Activities in India. ISSN 2045-810X, 21

Eklof JA, Hackl P, Westlund A (1999) On measuring interactions between customer satisfaction and financial results. Total Qual Manag 10(4–5):514–522

Fornell C (1992) A national customer satisfaction barometer: the swedish experience. J Mark 56 (1):6

Fornell C, Johnson MD, Anderson EW, Cha J, Bryant BE (1996) The American customer satisfaction index: nature, purpose, and findings. J Mark 60:7–18

Grønholdt L, Martensen A, Kristensen K (2000) The relationship between customer satisfaction and loyalty: cross-industry differences. Total Qual Manag 11(4–6):509–514

Guo JJ, Tsai SB (2015) Discussing and evaluating green supply chain suppliers: a case study of the printed circuit board industry in China. S Afr J Ind Eng 26(2):56–67

Hallowell R (1996) The relationships of customer satisfaction, customer loyalty, and profitability: an empirical study. Int J Serv Ind Manag 7(4):27–42

Hsu SH (2008) Developing an index for online customer satisfaction: adaptation of American Customer Satisfaction Index. Expert Syst Appl 34(4):3033–3042

Ittner CD, Larcker DF (1996) Measuring the impact of quality initiatives on firm financial performance. Adv Manag Organ Qual 1(1):1–37

Johnson MD, Nader G, Fornell C (1996) Expectations, perceived performance, and customer satisfaction for a complex service: the case of bank loans. J Econ Psychol 17(2):163–182

Johnson MD et al (2001) The evolution and future of national customer satisfaction index models. J Econ Psychol 22(2):217–245

Karatepe OM, Yavas U, Babakus E (2005) Measuring service quality of banks: scales development and validation. J Retail Consum Serv 12(5):373–383

Kotler P, Armstrong G (1997) Marketing: an introduction. Prentice-Hall, New York

Kristensen K, Juhl HJ, Østergaard P (2001) Customer satisfaction: some results for European retailing. Total Qual Manag 12(7–8):890–897

Kutner S, Cripps J (1997) Managing the customer portfolio of healthcare enterprises. Healthc Forum J 40(5):52–54

CAS   PubMed   Google Scholar  

Ladhari R (2009) Service quality, emotional satisfaction, and behavioural intentions. Manag Serv Qual Int J 19(3):308–331

Lee C-F (2015) Tourist satisfaction with factory tour experience. Int J Cult Tourism Hosp Res 9:3

Lee YC, He LY, Jiang JS, Lian QY (2005) Foundation of the Taiwan customer satisfaction index model. Qual Mag 41(12):52–56

Lee YC, He LY, Jiang JS, Lian QY (2006) The Taiwan customer satisfaction index model in related to sample decision. Qual Mag 42(4):74–77

CAS   Google Scholar  

Lee YC, Chen CY, Tsai SB, Wang CT (2014a) Discussing green environmental performance and competitive strategies. Pensee 76(7):190–198

Lee YC, Wu CH, Tsai SB (2014b) Grey system theory and fuzzy time series forecasting for the growth of green electronic materials. Int J Prod Res 299(8):1395–1406

Malhotra NK, Ulgado FM, Agarwal J, Baalbaki IB (1994) International service marketing: a comparative evaluation of the dimensions of service quality between developed and developing countries. Int Mark Rev 11(2):5–15

Martensen A, Gronholdt L, Kristensen K (2000) The drivers of customer satisfaction and loyalty: cross-industry findings from Denmark. Total Qual Manag 11(4–6):544–553

Matzler K, Sauerwein E (2002) The factor structure of customer satisfaction: an empirical test of the importance grid and the penalty-reward-contrast analysis. Int J Serv Ind Manag 13(4):314–332

Matzler K, Sauerwein E, Heischmidt K (2003) Importance-performance analysis revisited: the role of the factor structure of customer satisfaction. Serv Ind J 23(2):112–129

Matzler K, Bailom, F, Hinterhuber HH, Renzl B, Pichler J (2004) The asymmetric relationship between attribute-level performance and overall customer satisfaction: a reconsideration of the importance–performance analysis. Ind Mark Manag 33(4):271–277

Mittal V, Ross WT, Baldasare PM (1998) The asymmetric impact of negative and positive attribute-level performance on overall satisfaction and repurchase intentions. J Mark 62:33–47

Mutua J, Ngui D, Osiolo H, Aligula E, Gachanja J (2012) Consumers satisfaction in the energy sector in Kenya. Energy Policy 48:702–710

O’Loughlin C, Coenders G (2002) Application of the european customer satisfaction index to postal services. Structural equation models versus partial least squares, No. 4. Working Papers of the Department of Economics, University of Girona. Department of Economics, University of Girona

Qu Q, Chen KY, Wei YM et al (2015) Using hybrid model to evaluate performance of innovation and technology professionals in marine logistics industry mathematical problems in engineering. Article ID 361275. doi: 10.1155/2015/361275

Reichheld FF, Sasser WE (1990) Zero defections: quality comes to services. Harvard Bus Rev 68(5):105–111

Ryzin GG, Muzzio D, Immerwahr S, Gulick L, Martinez E (2004) Drivers and consequences of citizen satisfaction: an application of the American customer satisfaction index model to New York City. Public Adm Rev 64(3):331–341

Smith AK, Bolton RN (1998) An experimental investigation of customer reactions to service failure and recovery encounters paradox or peril? J Serv Research 1(1):65–81

Temizer L, Turkyilmaz A (2012) Implementation of student satisfaction index model in higher education institutions. Procedia Soc Behav Sci 46:3802–3806

Terblanche NS, Boshoff C (2010) Quality, value, satisfaction and loyalty amongst race groups: A study of customers in the South African fast food industry. S Afr J Bus Manag 41(1):1–9

Tsai SB (2016) Using grey models for forecasting China’s growth trends in renewable energy consumption. Clean Technol Environ Policy 18:563–571

Tsai CH, Peng YJ, Wu HH (2012, October). Evaluating service process satisfaction of a tourism factory—using Brands’ Health Museum as an example. In: 2012 6th international conference on new trends in information science and service science and data mining (ISSDM), pp 244–247

Tsai SB, Chien MF, Xue Y, Li L et al (2015a) Using the fuzzy DEMATEL to determine environmental performance: a case of printed circuit board industry in Taiwan. PLoS ONE 10(6):e0129153. doi: 10.1371/journal.pone.0129153

Article   PubMed   PubMed Central   Google Scholar  

Tsai SB, Saito R, Lin YC, Chen Q et al (2015b) Discussing measurement criteria and competitive strategies of green suppliers from a Green law Perspective. Proc Inst Mech Eng B J Eng Manuf 229(S1):135–145

Tsai SB, Huang CY, Wang CK, Chen Q et al (2016a) Using a mixed model to evaluate job satisfaction in high-tech industries. PLoS ONE 11(5):e0154071. doi: 10.1371/journal.pone.0154071

Tsai SB, Xue Y, Zhang J, Chen Q et al (2016b) Models for forecasting growth trends in renewable energy. Renew Sustain Energy Rev. doi: 10.1016/j.rser.2016.06.001

Weitz BA, Jap SD (1995) Relationship marketing and distribution channels. J Acad Mark Sci 23:305–320

Wold H (1985) Partial least squares. In: Kotz S, Johnson N (eds) Encyclopedia of statistical sciences. Wiley, New York, pp 581–591

Wu SI, Zheng YH (2014) The influence of tourism image and activities appeal on tourist loalty–a study of Tainan City in Taiwan. J Manag Strategy, 5(4):121

Yazdanpanah M, Zamani GH, Hochrainer-Stigler S, Monfared N, Yaghoubi J (2013) Measuring satisfaction of crop insurance a modified American customer satisfaction model approach applied to Iranian Farmers. Int J Disaster Risk Reduct 5:19–27

Zeithaml VA (2000) Service quality, profitability, and the economic worth of customers: what we know and what we need to learn. J Acad Mark Sci 28(1):67–85

Zhou J, Wang Q, Tsai SB et al (2016) How to evaluate the job satisfaction of development personnel. IEEE Trans Syst Man Cybern Syst. doi: 10.1109/TSMC.2016.2519860

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Authors’ contributions

Writing: S-CL; providing case and idea: Y-CL, Y-CW, Y-FH, C-HC; providing revised advice: S-BT, WD. All authors read and approved the final manuscript.

Acknowledgements

Department of Technology Management, Chung-Hua University, Hsinchu, Taiwan. This work was supported by University of Electronic Science Technology of China, Zhongshan Institute (414YKQ01 and 415YKQ08).

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Department of Technology Management, Chung-Hua University, Hsinchu, 300, Taiwan

Yu-Cheng Lee

Department of Business Administration, Chung-Hua University, Hsinchu, 300, Taiwan

Yu-Che Wang

PhD Program of Technology Management, Chung-Hua University, Hsinchu, 300, Taiwan

Shu-Chiung Lu & Chih-Hung Chien

Department of Food and Beverage Management, Lee-Ming Institute of Technology, New Taipei City, 243, Taiwan

Shu-Chiung Lu

Department of Business Administration, Lee-Ming Institute of Technology, New Taipei City, 243, Taiwan

Chih-Hung Chien

Department of Food and Beverage Management, Taipei College of Maritime Technology, New Taipei City, 251, Taiwan

Yi-Fang Hsieh

Zhongshan Institute, University of Electronic Science and Technology of China, Dongguan, 528402, Guangdong, China

Sang-Bing Tsai

School of Economics and Management, Shanghai Maritime University, Shanghai, 201306, China

Law School, Nankai University, Tianjin, 300071, China

School of Business, Dalian University of Technology, Panjin, 124221, China

College of Business Administration, Dongguan University of Technology, Dongguan, 523808, Guangdong, China

Department of Psychology, Universidad Santo Tomas de Oriente y Medio Día, Granada, Nicaragua

School of Economics and Management, Shanghai Institute of Technology, Shanghai, 201418, China

Weiwei Dong

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Lee, YC., Wang, YC., Lu, SC. et al. An empirical research on customer satisfaction study: a consideration of different levels of performance. SpringerPlus 5 , 1577 (2016). https://doi.org/10.1186/s40064-016-3208-z

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Received : 25 April 2016

Accepted : 02 September 2016

Published : 15 September 2016

DOI : https://doi.org/10.1186/s40064-016-3208-z

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• Customer satisfaction
• Tourism factory industry
• Partial least squares
• Business management
• Service management

Impact of Service Quality on the Customer Satisfaction: Case Study at Online Meeting Platforms

Ali, B. J., Saleh, Akoi, S., Abdulrahman, A. A., Muhamed, A. S., Noori, H. N., Anwar, G. (2021). Impact of Service Quality on the Customer Satisfaction: Case study at Online Meeting Platforms. International journal of Engineering, Business and Management, 5(2), 65–77.

13 Pages Posted: 7 May 2021

Bayad Jamal Ali

Komar university for science and technology, pakzad fadel saleh, komar university for science and technology - business administration department, shwana akoi, aram ahmed abdulrahman, awezan shamal muhamed, halwest nowzad noori, govand anwar.

Knowledge University

Date Written: May 3, 2021

As it’s known business rely on the customers it means the profitability of any company change depending on customer demand. Due to that, it’s necessary to treat customer as the king of the market. In another meaning, Customer satisfaction is very important issue to company’s product which it measures the level of probability between company’s product and customer belief in which the happier customer with quality and types of products more products and more profit will occur. The purpose of this project was to determine the Impact of technical and functional service quality on the customer satisfaction and loyalty. Though conducting a survey number of results was collected, and the results belong to their view about different types of platform which they used for online meeting and academic stuff. Different nationality participates in this project including (Kurdish, Turkish, Arabic, Turkmen, Indian, Pakistan, Uzbek, Nigerian and British) with having different position such as head of the faculty, instructor, academic staff, head of the department and etc. in number of universities in Kurdistan region of Iraq. As it’s shown in chapter three Excel software was used to calculate collected data through (co-variance, correlation and regression analysis) methods. And Based on the result fulfillment, privacy issues were affected the customer satisfaction and loyalty.

Keywords: Customer, Satisfaction, Service, Fulfillment and Effect

Suggested Citation: Suggested Citation

Bayad Jamal Ali (Contact Author)

Komar university for science and technology ( email ).

Sulaimani Qularesi Kurdistan, Sulaimani Iraq

Iraq Qularesi Sulaimani, Sulaimani 053 Iraq

Knowledge University ( email )

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A Study on Customer Satisfaction with Reference to Myntra 

2021, International Journal of Social Science and Economic Research

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