customer loyalty research paper

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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Customer satisfaction and firm performance: insights from over a quarter century of empirical research

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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 ).

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Vikas Mittal

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Kyuhong Han

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Durham University Business School, Durham University, Durham, UK

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Kelley School of Business, Indiana University, Bloomington, IN, USA

Muzeeb Shaik

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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 loyalty in green marketing research: a systematic review.

© 2023 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license ( http://creativecommons.org/licenses/by/4.0/ ).

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At present, green marketing attract the attention of academics and professionals around the world. Research on green marketing/sustainable marketing has increased in recent years. Through a systematics review, this study aims to analyze the development and trends of research with the theme customer loyalty within the scope of green marketing/sustainable marketing last 10 years (2013-2023). This study uses Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Analysis is carried out based on productive countries, authors, institutions, journals, and distribution phrases. This study also analyzed articles based on the methods and variables used, along with outcomes. 33 selected documents were analysed using Wordstat. The results showed that United States is the most productive country that publishes articles on related topics. Green marketing, customer loyalty, customer satisfaction, green products, green image are phrases that often appear with a frequency limit of 200. Structural Equation Modelling (SEM) is a method that is often used.

customer loyalty, green marketing, environment, sustainability

Currently, green marketing is a hot topic for companies, consumers, society, and the government in creating sustainable consumption [1]. Global warming, climate change, and air pollution, which are happening today, are some of the reasons that can be attributed to the shift to a “green” economy, which is considered a “policy and conceptual framework for sustainability” [2]. Every sector is required to have a good impact on the environment to create sustainability in the future, including from the business sector. The company, in terms of marketing, implements “green marketing”.

Green marketing encompasses the entire process of addressing environmental concerns, from product planning ideas to product distribution [3]. Its main objective is to promote environmental responsibility and contribute to a sustainable economy. The aim is not solely to enhance the company’s image or maximize profits, but also to encourage eco-friendly, green consumption [4]. The latter is an action where consumers purchase and consume products because the products are environmentally friendly (e.g., recyclable, sustainable, renewable, low energy consumption, low pollution, non-disturbing to the environment) [5].

In the company’s perspective, the application of green marketing is useful for changing the company’s organization, developing technology, helping companies create social responsibility for the environment [6]. In the study, companies oriented towards implementing green marketing have an impact on the creation of green image [7]. In another study, it was said that green marketing has a significant positive effect on brand loyalty [8]. In the perspective of society and government, green marketing can reduce and provide solutions to environment problems, because green marketing integrates the concepts and practices of the environment, ecology, and social activities [1]. The majority of environmental problems are caused by the industrial sector which needs to be controlled [9].

In the business sector, marketing management is the art and science of choosing target markets and getting, keeping, and growing customer [10]. Companies that successfully retain consumers will create loyalty which is seen as a commitment from consumers to repurchase products in the future consistently [11]. Loyalty is created from companies that create products according to consumer needs [10].

Consumer loyalty is related to consumer satisfaction with the product, satisfied consumers will create loyalty to the product [12]. Satisfaction relates to the good impression of consumers on the product, because the performance of the product matches or exceeds the expectations of consumers [13]. In another study, it was found that consumer loyalty has an impact on positive Word of Mouth (WOM) [14]. WOM is the most effective from of marketing [15]. Positive WOM can have a good image impact on companies and products, and companies get “free” marketing from consumers.

Based on the explanation above, it is known that consumer loyalty is an important and interesting thing to research. However, there are still few published figures that discuss consumer loyalty in the scope of environmentally friendly/sustainable marketing. Therefore, through a systematics review, this research aims to analyse the development and trends of research on the topic customer loyalty in the scope of green marketing for the past 10 years from 2013 - 2023. The systematic review aims to provide a comprehensive, unbiased synthesis of many relevant studies in a single document [16]. Research developments and trends will be seen based on the number of publications per year, authors, journals that publish, most productive countries, affiliations, and subject areas. The data analyzed in this research was sourced from Scopus database. This study will also specifically explore the following research questions spanning from 2013 to 2013 (RQ):

RQ1: What are the trends of customer loyalty in the scope of green marketing research?

RQ2: Which specific phrases frequently emerge within the realm of customer loyalty in the context of green marketing research?

RQ3: What are the commonly used methods and variables in customer loyalty within the scope of green marketing research?

RQ4: What are the purposes and findings of customer loyalty in the scope of green marketing research?

This research focuses on analyzing research developments and trends with the topic about customer loyalty within the scope of green marketing. The method used in this study is Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), with the aim to help the authors improve the reporting of systematic reviews and meta-analyses [17]. The data in this study is based on internationally accredited articles published in the Scopus database from 2013 to 2023 using a systematic approach to select articles with keywords in the title or abstract. The Scopus database, created in 2004, (a product from Elsevier), is one of the largest curated databases covering scientific journals, books, conference proceedings, etc. [18]. The database of Scopus was utilized as the primary resource of information because academics regard it as a credible resource of scholarly papers [19]. The data obtained was then processed using the Wordstat application for keyword analysis. Furthermore, the process of searching for data is discussed in the subchapter of search strategies.

2.1 Search strategy

The follow were the methodological steps used for this research:

1. The results are filtered via a Boolean search on the basis of their relevance to the objective of this work.

2. Duplicates are eliminated and literature stored as per their respective keywords in titles and abstracts, further introducing restrictions that would limit the search to only the relevant fields based on years, language, etc. (as shown in Table 1).

3. Select articles that are appropriate for the purpose of the study.

4. Complete analyzes of the articles and summarize the results reported by the authors related to customer loyalty in green marketing.

5. Parsing and further elaboration of the findings in the context of the section.

Figure 1 shows “The PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” used by this study to conduct a systematic literature review. This study utilized the keywords “green marketing”, “sustainable marketing”, “Eco Marketing”, and “customer loyalty” in the title, abstract, and keywords of the author to acquire relevant data from Scopus database. The search query option used in data mining was as follows (TITLE-ABS-KEY (“green marketing” AND “customer loyalty”) OR TITLE-ABS-KEY (“sustainable marketing” AND “customer loyalty”) OR TITLE-ABS-KEY (“Eco Marketing” AND “customer loyalty”) AND (LIMIT-TO (Year, “2013-2023”)) AND (LIMIT-TO (Document Type, “Article”)) AND (LIMIT-TO (Source Type, “Journal”) AND LIMIT-TO (Language, “English”). We discovered 33 articles in this stage.

2.2 Study selection

Table 1 describes the inclusion criteria and exclusion criteria used in selecting articles in the Scopus database.

Figure 1. PRISMA

Source: Authors

Table 1. Inclusion criteria and exclusion criteria

3.1 Publications per year

Figure 2 shows the development of publications about customer loyalty within the scope of sustainable marketing published in Scopus database from 2013-2023.

Based on Figure 2, 2019 registered the highest number of publications (9 publications) on the researched topic. After 2019, the number of publications on customer loyalty within the scope of sustainable marketing on the Scopus database has continued to decline. There was an increase in publications in 2021, from 3 to 5, but after that, the number of publications decreased again. We suspect this to be due to the effect of the Covid-19 pandemic which has affected the research productivity in all fields of knowledge [20]. After the government announced the end of the pandemic period, it is expected that there will be an increase in future publications about green marketing in Indonesia.

Figure 2. Annual publications

Source: Scopus database

3.2 Publications by journal

Table 2 shows the distribution of publications on customer loyalty within the scope of sustainable marketing during the analyzed period.

Sustainability, published by the Multidisciplinary Digital Publishing Institute (Switzerland), is the journal with the highest number of publications on related topics, with a total of three publications. After that, two journals that have two publications each with related topics are the International Journal of Sustainable Development and World Ecology from Taylor and Francis Ltd (United Kingdom) and Quality - Access To Success from Societatea Romana Pentru Asigurarea Calitatii (Romania). The data in Table 2 can serve as a valuable journal reference for researchers who are interested in publishing articles within the scope of green marketing, sustainability marketing, or environmental studies.

Table 2. Documents by Journal

Note: TP=Total Publications

3.3 Publications by author and country/territory

Table 3 shows the top five authors who published the most articles in the Scopus database on the topic of customer loyalty within the scope of sustainable marketing spanning the period 2013 - 2023.

Based on the information in Table 3, we can analyze that the productive authors in the field of “customer loyalty” papers in the area of “Green Marketing” are Athanasios Krystallis, Norazah Mohd Suki, Lalinthorn Marakanon, Erifili Papista, and Vinai Panjakajornsak, each with 2 publications. Other authors have produced 1 publication each.

Table 4 provides an explanation of publications related to customer loyalty within the scope of green marketing. These publications are categorized based on countries/territories that have made a significant contribution.

At the top of the list is United States with a total of 6 publications, followed by India, Malaysia, Spain, and Taiwan with 3 publications. Based on this data, it is evident that countries from the Asian continent have the highest number of published articles on related topics. Therefore, exploring research in Asian countries would be intriguing for future investigations [21].

Table 3. Documents by author

Table 4. Documents by country/territory

Table 5. Documents by affiliations

3.4 Publication by affiliations

Table 5 displays the affiliates who publish the highest number of articles on related topics.

The top three affiliates consist of three countries: Malaysia, Greece, and Denmark. Universiti Malaysia Sabah from Malaysia achieved the highest number of publications with 4. Following closely are Athens University of Economics and Business from Greece, and Aarhus Universitet from Denmark, each with 2 publications. There are also other affiliations with 1 publication.

Figure 3. Publications by subject areas

3.5 Publications by subject areas

Figure 3 shows a pie chart that reflects research publications by subject areas.

The chart indicates that the majority (35%) of studies are published in the field of business, management, and accounting. This is followed by the domain of social sciences (23%) and environmental science (13%). Other domains include energy (8%); economics, econometrics, and finance (7%); arts and humanities (3%); computer science (3%); engineering (3%); and other (5%). Based on this data, we can conclude that research discussing “customer loyalty” in the context of “green marketing” can be found in several domains.

3.6 Highly cited articles

Table 6 shows the five most cited from 33 articles based on the Scopus database.

Table 6. Top five most cited articles

Note: TC=Total Citations

The article titled “Customer Loyalty: Exploring Its Antecedents from a Green Marketing Perspective” published by the International Journal of Contemporary Hospitality Management was the most cited article, with 146 citations. Patricia Martinez from University of Cantabria is the author of the article. The main purpose of the study was to examine the relationship of three variables namely, green image, green trust, green satisfaction to green loyalty. The samples taken were hotel customers in Spain. The results showed that green image, green trust, and green satisfaction, have a significant influence on green loyalty. Hoteliers, in particular, must be proactive in promoting an environmentally friendly image. This can be achieved through active participation in ecological events and forums, sponsorship of environmental programs, and an increased presence on social media [22]. These managerial implications can help hoteliers establish and maintain a strong eco-friendly reputation.Furthermore, hotels can enhance their positive image by focusing on environmental aspects and taking tangible actions that consumers can trust.Also, companies, especially those in the hospitality industry, need to enhance employee and customer performance. This includes providing information about the environment to increase environmental trust and satisfaction [22].

The article authored by Chen, Y-S is the second most cited, with 130 citations. This is followed by the article by Hur et al, with 118 citations; Papista & Krystallis, with 91 citations, and Chung, K.C., with 87 citations.

3.7 Distribution phrases

Keyword analysis in this study by looking at the distribution phrases (Table 7 and Figure 3).

Distribution phrases in Table 7 and Figure 4 show phrases that are often used by the authors in their articles.

Figure 4. Distribution phrases

Table 7. Distribution phrases

A total of 33 documents were studied to generate distribution phrases. These phrases are limited in frequency to 200. The top five phrases that frequently appeared were green marketing, customer loyalty, customer satisfaction, green products, and green image. Additionally, other phrases that often appeared included Green Brand, Green Trust, Brand Equity, Social Responsibility, Eco Friendly, and Social Media.

3.8 Documents by methods and variables

Table 8 shows the methods and variables used by 33 research articles.

Based on this data, we can conclude that Structural Equation Modelling (SEM) is the most commonly used method in the study, with 28 documents. Researchers can use structural equation modeling (SEM) to specify confirmatory factor analysis models, regression models, and complex path models [23]. SEM is a powerful technique that allows for the combination of complex path models with latent variables, also known as factors. The growing popularity of SEM among academic researchers and social science practitioners is driven by the need for effective methods to understand the structure and interaction of latent phenomena [24]. The research practice in management research is also dominantly based on Structural Equation Modeling (SEM) [25]. Complex models can be discussed simply through this technique [26].

Apart from the Customer loyalty variable, customer satisfaction is a variable that is often used in these 33 documents. Customer satisfaction is a condition reached whenever product performance meets or exceeds customer expectations [10].

3.9 Research outcomes

Table 9 explains the purposes and findings resulting from the five most cited articles listed in Table 6.

The five articles listed in Table 9 consist of four research articles and one conceptual article. The article titled “Towards Green Loyalty: Driving from Green Perceived Value, Green Satisfaction, And Green Trust”, examines three antecedents of green loyalty. In this research it was found that green perceived value, green satisfaction, and green trust have a significant effect in the creation of green loyalty [11].

The article with the title “Assessing the Effects of Perceived Value and Satisfaction on Customer loyalty: A ‘Green’ Perspective” examined the relationship of customer satisfaction to price consciousness. There is still little research that discusses the relationship between these two variables. This article concludes by saying that as consumers become more satisfied with a product, their price-consciousness tends to decrease [27]. Other findings state that customer satisfaction has an effect on increasing consumer loyalty.

The article with the title “Green Marketing Orientation: Archieving Sustainable Development in Green Hotel Management” discusses the benefits of green hotel management in protecting the earth and establishes a green marketing-oriented model. The study found that stakeholders and executive corporate social responsibility hotels could indirectly increase consumer loyalty to the image of the hotel through green marketing [7].

3.10 Study limitation

This article has limitations that should be acknowledged as part of its systematic review. Firstly, the study only utilized data from the Scopus database, which is just one database. Additionally, the study imposed restrictions on language, year, and selected only a few sources from journals. the data in this study was also only sourced from a few domains as listed in Figure 3. It is important to note that incorporating data from other databases, such as WOS or Google Scholar, would yield different results and conclusions. It is recommended that future studies should include additional databases so as to broaden the scope of their research and ensure wider applicability of the obtained results.Secondly, this study solely focuses on discussing Customer loyalty within the realm of green marketing or sustainable marketing. To enhance its comprehensiveness, future authors may consider incorporating additional variables.

Table 8. Documents by methods and variables

Table 9. Research Outcomes from 5 most cited articles

Source : Authors

This study analyzed research developments and trends on the topic of Customer loyalty within the scope of green marketing using the Scopus database. Based on the results of the study, we can conclude:

i. Publications on related themes reached the highest number in 2019, after which the number of publications with related themes decreased. This is suspected to be due to the impact of the Covid-19 pandemic. Sustainability, International Journal of Sustainable Development and World Ecology, and Quality Access to Success are the most published journals on related topics. Athanasios Krystallis, Norazah Mohd Suki, Lalinthorn Marakanon, Erifili Papista, and Vinai Panjakajornsak are the authors who published the most articles on such topics. The United States is the most productive country when it comes to producing articles on related topics. Universiti Malaysia Sabah is the affiliation that produces the most articles with related themes. Business, Management, and Accounting is the domain that is most related to such topics.

ii. Green marketing, customer loyalty, customer satisfaction, green products, and green image are the top five phrases that often appear, with a frequency limit of 200.

iii. Structural Equation Modelling (SEM) is the most widely used research method. Customer satisfaction is the most analyzed variable with regards to relationship to Customer loyalty.

These findings may help future researchers determine the appropriate variables and methods in related themes.

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Please note you do not have access to teaching notes, customer loyalty: an empirical study.

European Journal of Marketing

ISSN : 0309-0566

Article publication date: 19 September 2008

The purpose of this paper is to focus on establishing individuals' levels of loyalty and what sustains and develops their customer loyalty. This paper recognises the importance customer loyalty has for many competitive organisations and industries. However there has been less focus on what value customer's attach to customer loyalty in this context.

Design/methodology/approach

A two‐stage study is presented, establishing individual levels of loyalty and then identifying the role of mediating effects in loyalty development. The first stage involved a postal survey, including a 28‐item scale, designed to measure customer loyalty, and its sustainers and vulnerabilities (mediating effects). The second stage, and the main focus of this paper, uses scores from the loyalty scale (high, medium and low levels of loyalty) to examine what sustains and develops loyalty amongst differing levels of development.

The findings highlight the importance of identifying, understanding and managing mediating effects, in the context of loyalty development. The research emphasises the importance of a differentiated approach to developing and managing customer loyalty by appropriately rewarding customers at different levels. The findings highlight the need to acknowledge the importance of reciprocity in terms of which aspects of service customers value.

Originality/value

The main contribution of this paper is that it uniquely identifies an approach to understanding the sustaining and vulnerability effects mediating customer loyalty development going beyond previous categorisation attempts. Understanding this approach should lead to effective customer loyalty management and greater awareness of managing recognition, reciprocity and rewards.

• Customer loyalty
• Service levels

McMullan, R. and Gilmore, A. (2008), "Customer loyalty: an empirical study", European Journal of Marketing , Vol. 42 No. 9/10, pp. 1084-1094. https://doi.org/10.1108/03090560810891154

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