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The present research sought to analyze the effects of customer delight on both internal and external financial structures of publicly traded, service firms.

Primary (i.e. survey) and secondary (i.e. financial records) data sources were gathered. A total of 685 participants responded to one questionnaire focusing on hotels and another one focused on restaurants, both of which measured levels of customer delight and satisfaction. Financial data were gathered from Center for Research in Security Prices, CRSP/COMPUSTAT.

Results of MANOVA revealed that there was a significant difference in the net profit margin (NPM) based on customer delight. Canonical correlation results exposed a significant correlation between satisfaction and delight combined and the financial performance measures (net profit margin, cash flow margin, return on assets and b-beta) combined.

By delighting their customers, managers will achieve higher profit margins. However, these are not likely to result in improved cash flow margin or return on assets. The effects of COVID-19 can alter yearly returns; thus, longitudinal research is needed to continue testing for the effects on delight on financial performance.

The relationship between delight and financial measures had not been previously determined (notwithstanding a few studies using substitute measures for financial performance). The present study uses actual data from the financial filings to empirically test their relationship to customer delight.

The purpose of this study is to identify the structural determinants of e-returns service interactions, examine their impact on online shoppers' loyalty and propose returns service interventions from an interactive marketing perspective to facilitate consumer interaction and repeat purchase intentions with e-retailers.

This study empirically tests the research hypotheses based on cross-sectional survey data collected from Chinese online consumers who experienced interactions during the product returns process.

E-return service interaction includes three determinants: access support, friendly interaction and communication support. These interactions positively impact ease of return, returns satisfaction and customer loyalty. Returns satisfaction is a key mediator in the relationship between return service interaction and loyalty. Access support and friendly interaction have both direct and indirect effects on loyalty, while communication support has only an indirect effect.

This study contributes to understanding e-returns service interaction by analyzing its structural determinants, providing a robust scale foundation and analytical framework for future empirical research. Additionally, this study explores the driving role of e-returns service interaction in forming e-customer loyalty, offering a theoretical basis for the service recovery function of e-returns service interaction. It enriches the application of service recovery theory and relationship marketing theory in the field of interactive marketing.

This article examines customer satisfaction in using banking services through Artificial Intelligence (AI) in India. It addresses two questions: first, will customers perceive AI technology as a reliable and efficient alternative to traditional banking practices; second, will AI save customers’ time.

The quantitative research method based on regression analysis models was adopted for hypothesis testing, with data collected from a survey of 189 banking customers from four banks, i.e., State Bank of India, Axis Bank, Punjab National Bank, and HDFC Bank in India.

AI improves banking customers’ experiences by making banking more accessible and enjoyable. Satisfied customers are quick to use cutting-edge AI tools. However, human service is more satisfying than digital service. AI has great potential but works alongside humans rather than replacing them. Even though AI’s novel architecture is helpful, human bank tellers are still needed in enhancing customer satisfaction.

AI’s integration in Indian banking, propelled by customer satisfaction, foresees a transformative landscape. This study uncovers AI’s role in saving time and improving customer satisfaction. While AI revolutionizes financial processes, its harmonious coexistence with human expertise emphasizes personalized and efficient services. This study provides insights for optimal AI utilization in shaping the future of banking.

This study explores the effect of language on service satisfaction among Finland-Swedes, a national minority language group in Finland, in the context of early childhood education. Models of public service satisfaction hold standard process and outcome related factors, such as availability and quality, as drivers of the satisfaction. However, although research has shown significant variation in satisfaction between different groups of citizens (race, ethnicity, age etc.), research has largely overlooked group specific factors as explanations for the satisfaction.

A randomized survey experiment with a 2 × 2 × 2 factorial design analyzed the impacts of language, service accessibility, and quality on service satisfaction. The data was analyzed with ANOVA.

The results revealed that language significantly impacts Swedish speakers’ satisfaction, suggesting that for minority groups, language may override typical satisfaction determinants like quality and accessibility. Interestingly, special linguistic needs are relatively more pertinent in low-quality services than in higher-quality ones.

The study shows how group related factors of public service, in our case language, in an important factor explaining satisfaction with the service. The findings have implications for the literature on citizens’ satisfaction with public services with demographic and identity facets, especially in a typical Nordic welfare state.

E-commerce expands product and service reach, emphasizing the need for strategic market approaches to enhance e-service quality and drive sales growth. This paper aims to assess the relationship between the perceived quality of e-commerce platforms (characterized by measures of order and return convenience), customer satisfaction with online shopping and repurchase intention from online stores as well as examine whether demographic variables such as age, gender and area of residency (urban/rural) influence the ratings of each of these variables.

An online, self-administered survey gathered 108 valid responses from e-commerce customers. Data were analyzed in Statistical Package for the Social Sciences (SPSS) and Analysis of Moment Structures (AMOS) through principal component analysis, confirmatory factor analysis and structural equation modeling (SEM) as well as correlation, descriptive statistics, difference of means tests and nonlinear regression.

Online shopping on e-commerce platforms is seen as convenient for both placing orders and managing returns. Additionally, consumers express satisfaction with their online shopping experiences and exhibit a strong intention to repurchase. The analysis revealed linear relationships between order convenience and customer satisfaction, between order convenience and repurchase intention and nonlinear relationships between return convenience, customer satisfaction and repurchase intention. No significant difference was found between the way the demographic variables rated the convenience, satisfaction and repurchase intention constructs.

This study contributes to the empirical literature on service quality in e-commerce by providing a streamlined model of the interactions among the factors as well as by isolating the nonlinear relationships and comparing results across three demographic variables. From a managerial standpoint, the findings suggest that strategies aimed at providing complete qualitative information and enhancing order and return convenience improve customer satisfaction and foster repurchase intention.

New product development (NPD) is necessary for business sustenance and customer satisfaction. Six Sigma and Design for Lean Six Sigma (DLSS) efficiently employ the repetitive stages for NPD, leading to quality performance and profitability. This study aims to map the quality performance through NPD attributes through the Lean methodology.

The data on NPD were collected from 267 respondents from manufacturing companies to map the relationship between Six Sigma and DLSS for NPD. Confirmatory factor analysis was employed to confirm model fit, while structural equation modeling was employed to analyze the empirical data for framework testing. The study included nine variables and fourteen hypotheses identified from the literature.

The statistical results of this study show that NPD attributes such as innovation, marketing, organization, customer, product and technology positively influence the Lean Six Sigma structured improvement process (LSSSIP) and DLSS. Moreover, integrating these attributes in Lean planning enhance quality performance. This empirical investigation's findings indicate that ten of the 14 hypotheses were supported, giving the study a strong foundation.

The data collection was limited to northern India; therefore, the results may not be generalizable to other areas of the world.

NPD involves handling technical issues and factors such as cost, operational bottlenecks, economic changes, competitors' strategy and company policy. This study helps understand the various NPD parameters and their relationship to Lean, which enables an effective NPD implementation strategy.

The current philosophy of NPD calls for a concurrent engineering approach; therefore, the entire organization must be part of this process. This study uses the holistic framework by optimizing NPD with Lean Six Sigma (LSS) principles. The study is unique in that, to date, research does not integrate NPD attributes with the objectives of LSS to develop an efficient NPD implementation strategy.

Research on showrooming has focused on investigating its antecedents, motivators and characteristics, leaving a gap regarding its consequences. Our study intends to build a comprehensive framework to examine the role of showrooming behaviour on showrooming satisfaction and customer loyalty while exploring its antecedents.

Data collection took place in Brazil through an electronic form distributed on social media, reaching 393 people. After eliminating responses from those who had no showrooming experience, the final sample consisted of 310 respondents. The questions captured the costs and benefits of showrooming, internet savviness, showrooming behaviour, satisfaction with showrooming and customer loyalty (i.e. repurchase intention and positive word-of-mouth). We performed partial least squares data analysis by means of the statistical package Smart PLS 4.

The results show that showrooming behaviour is positively influenced by internet savviness and the perceived benefits of showrooming but not by the perceived costs of showrooming. In addition, showrooming behaviour positively influences satisfaction with showrooming and customer loyalty (i.e. repurchase intention and positive word-of-mouth). Finally, we identify satisfaction with showrooming as a mediator between showrooming behaviour and customer loyalty.

Given that the implications of showrooming behaviour on customer loyalty remain understudied, this work contributes to the marketing literature by demonstrating the influence of showrooming behaviour on satisfaction with showrooming and its consequential effect on customer loyalty. Moreover, we confirmed the mediating role of customer satisfaction in the relationship between showrooming behaviour and customer loyalty.

Effective customer journey design (ECJD) is considered a key variable in customer experience management and an essential source of brand meaning and pro-brand behavior. Although previous research has confirmed its importance for driving brand attitudes and loyalty, the role of consumer-brand identification as a social identity-based influence in this relationship has not yet been discussed. Drawing on construal level and social identity theories, this paper aims to investigate whether effective journeys and the resulting overall journey experience are equally powerful in driving brand loyalty among customers with different levels of consumer-brand identification.

The present article develops and tests a research model using data from the European and US service sectors (N = 1,454) to investigate how and when ECJD affects service brand loyalty.

Across two cultural contexts, four service industries and 33 service brands, the results reveal that ECJD is a crucial driver of service brand loyalty for customers with low consumer-brand identification. Moreover, the findings show that different aspects of journey effectiveness positively impact the valence of customers’ experience related to those journeys – a process that is ultimately decisive for their brand loyalty.

This study is unique because it generates theoretical and practical knowledge by combining the literature streams of customer journey design, customer experience and branding. Furthermore, this work demonstrates that consumer-brand identification is a critical boundary condition to be considered in the relationship between ECJD and brand loyalty in services.

This paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM).

Using a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM’s capabilities.

Criticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method’s general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method’s efficacy.

We believe the alleged “fallacies” and “untold facts” have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications.

All statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field.

This research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.