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This research aims to investigate the effects of e-service quality on customer satisfaction and customer loyalty. The moderating effects of web design and trust in the relationship between e-service quality and customer satisfaction are also explored.

A conceptual model involving various dimensions of e-service quality, customer satisfaction, loyalty, price perception and web design is developed, and hypothesized relationships are tested using Hayes’s PROCESS macros. A survey instrument and data from 650 respondents who are regular e-buyers from the southern part of India are used.

The results indicate that e-service quality positively relates to customer satisfaction and loyalty. The findings also suggest that price perception and trust are vital in enhancing customer satisfaction. Further, the indirect effect of e-service quality on customer loyalty through customer satisfaction is supported. Finally, web design (first moderator) and trust (second moderator) significantly influence the relationship between e-service quality and customer satisfaction.

This research underscores the importance of e-service quality, web design, and trust in influencing customer satisfaction. Based on the findings from this study, e-retailers are suggested to discover the methods of building and improving customer trust and create a web design that is appealing to the customer to enhance satisfaction and loyalty. Some of the limitations of this study include common method bias and social desirability bias. However, the authors have taken adequate care to minimize these biases.

This paper used the e-service quality model and investigated the consequences of e-service quality during the post-pandemic new normal period in a developing country (India). The double moderation of web design and trust is a novel idea that previous researchers have not explored to the best of the authors' knowledge and makes a significant contribution to service marketing. In addition to providing resounding evidence of direct relationships, the three-way interaction investigated in this study makes this study unique and pivotal.

There is a growing trend in online shopping in developing and underdeveloped economies. Measuring web retail service quality is vital to enhance and retain online customers for higher profits. As a result, it is essential to understand e-service quality and its relationship with other e-commerce-related variables identified as customer satisfaction, word-of-mouth (WOM), loyalty and repurchase intention. The main objective of this research is to validate the psychometric properties of E-TailQ and ES-Qual scales to measure e-service quality in the context of an emerging economy.

The study was administered by surveying online customers through a convenience sampling approach. The sample data consisted of 329 valid responses. The data were analyzed using confirmatory factor analysis (CFA) and structural equation modeling (SEM).

The results suggest that both the scales are valid, reliable and adequate and confirm the psychometric properties of E-TailQ and ES-Qual scales in an emerging economy context. There is a positive and significant relationship between latent variables (E-TailQ and ES-Qual) and customer outcome variables like satisfaction, loyalty, WOM and repurchase intention. Additionally, the coefficients of the E-TailQ scale have higher values than ES-Qual scales in all dimensions. The perceived quality proved to be a precedent of satisfaction to a lesser extent than other scales' constructs, and it proved to be a superior predictor of WOM for the E-TailQ scale and loyalty for the ES-Qual scale. Overall, when measured by the ES-Qual scale, perceived quality could explain much of the sample's satisfaction, loyalty and repurchase intention compared to E-TailQ.

One of the significant limitations of this study is related to the data collection, as individual responses were collected at one particular point in time. Thus, it is impossible to verify the development of perceived service quality outcomes over a specific time. Another possible limitation is the selection of only two widely used scales, although other options are available in the literature. Finally, developing a new scale to address future challenges might be another alternative.

The results obtained in the study will help managers assess customer satisfaction, the reputation of the business through WOM, and customer intentions for the repurchase that will lead to loyalty by emphasizing e-service quality. It will enable them to design strategies for improving business performance. The developed instrument will assist in identifying the gaps and describing how service quality can achieve higher levels of customer satisfaction.

Several studies in the literature have used E-TailQ and ES-Qual scales to measure e-service quality in developed countries. Only a few studies addressed the validity of these widely used competing scales in an emerging economy like Pakistan. This study addresses the literature gap by addressing the reliability and validity of these scales and assesses the impact of e-commerce-related variables. Besides, the study reveals insights into the literature by addressing multiple dimensions of e-service quality and summarizes a deep understanding of each scale item that affects the perceived quality in a developing economy like Pakistan.

The purpose of this study is to explore the interplay between electronic service quality, user experience (UX) and overall customer satisfaction. Additionally, it aims to assess the suitability of E-S-QUAL and UX metrics within the cultural context of Greece.

Data were collected from 310 Internet users based on their last online purchase from an e-retail website. To evaluate the conceptual model, the authors used partial least squares structural equation modeling (PLS-SEM).

The findings of this study validate the scales' reliability and validity in the realm of electronic commerce (e-commerce) in Greece. The findings also emphasize the favorable association between e-service quality and UX with overall satisfaction, while indicating that e-service quality plays a partial mediating role in the relationship between UX and customer satisfaction.

The authors' study enhances the existing theory by introducing a new multi-dimensional conceptual framework that illuminates the relative importance of the dimensions within the scales. Additionally, it offers valuable insights into the impacts of e-service quality and UX on overall satisfaction, providing managers and practitioners with a tool to evaluate the quality of their electronic services and make necessary adjustments to meet the needs of their customers.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector

The behaviour of audience, consuming video entertainment, has changed intensely over the years. Lately, the consumers have increasingly preferred to watch video programs, through video-on-demand services through over-the-top medium. The service is novel and the consumer’s perception of the service quality is not well explored. As extant literature considers service quality as the construct to determine the sustained growth of a service, the present study has attempted to explore the dimensions to measure service quality of video-on-demand services.

The authors conducted qualitative, semi-structured interviews and focus group discussions amongst the user of the video-on-demand service. The qualitative data was content analysed to furnish thematic dimensions.

The study reveals thematic attributes perceived as dimensions to measure service quality of video-on-demand services.

Considering the exploratory nature of the study, the themes proposed might seem nascent. Hence, it was the authors’ discretion to stop expanding the respondent sample to avoid data saturation. A quantitative establishment of the service quality dimensions was beyond the scope of the current research and would follow in a different study.

The objective of the study is to qualitatively explore service quality dimensions of video-on-demand services. In pursuit of that, the current study explored the consumers’ excerpts, content analysed the data and furnished several themes perceived as service quality dimensions in this context. Such a detailed approach is uncommon in this context.

This study investigates whether and how the service quality of artificial intelligence (AI) chatbots affects customer loyalty to an organization.

Based on the sequential chain model of service quality loyalty, this study first classifies AI chatbot service quality into nine attributes and then develops a research model to explore the internal mechanism of how AI chatbot service quality affects customer loyalty. The analysis of survey data from 459 respondents provided insights into the interrelationships among AI chatbot service quality attributes, perceived value, cognitive and affective trust, satisfaction and customer loyalty.

The results show that AI chatbot service quality positively affects customer loyalty through perceived value, cognitive trust, affective trust and satisfaction.

This study captures the attributes of the service quality of AI chatbots and reveals the significant influence of service quality on customer loyalty. This study develops research on service quality in the information system (IS) field and extends the sequential chain model of quality loyalty to the context of AI services. The findings not only help an organization find a way to improve customers' perceived value, trust, satisfaction and loyalty but also provide guidance in the development, adoption, and post-adoption stages of AI chatbots.

This study aims to develop and validate a scale for measuring accounting service quality (ASQ), which is named ACCOUNTQUAL.

The authors initially investigated the service quality literature in the context of accounting. To develop the scale, the authors carried out three studies. First, the authors conducted 20 in-depth interviews to generate the ASQ items. Then, they undertook a survey with 174 accounting services clients to group these items into factors through exploratory factor analysis (EFA). Finally, the authors carried out another survey with 330 clients to purify and validate the scale through a confirmatory composite analysis (CCA) and structural equation modeling (SEM).

The authors were able to validate the ACCOUNTQUAL scale, which is composed of the following dimensions: efficiency, trust, technological innovation and accountant knowledge, the latter being composed of three aspects: consultative view, technical capacitation and knowledge about clients.

The authors concluded that ASQ is a multidimensional construct that covers the assessment of technical, tooling, relationship and qualification aspects of the accounting service. To the best of the authors’ knowledge, this is the first study that developed and validated a scale for measuring ASQ. If accounting service providers meets the elements presented in the scale, they will potentially deliver a high-quality service.

Smart-work has been attracting more attention since the COVID-19 outbreak hit the world in 2020. Smart-working practices do not always run smoothly despite the necessary infrastructure being in place. Taking the quality-value-loyalty chain and information system continuance model as the basis, this study aims to identify how “smart-work support service” leads to employees’ continuance intention toward smart-work. In this study, the smart-work support service refers to the infrastructure making smart-work possible and services needed to support smart-work.

Using data collected from a survey of 406 employees working in Korea, this study verifies the components of smart-work support service quality and dimensions of perceived value through partial least squares analysis and then tests the proposed conceptual model using structural equation modeling.

The results verified that the quality of smart-work support service consists of eight attributes while dividing the perceived value of smart-work into three dimensions and further revealed that the service quality influences the intention to continue smart-working by way of perceived value and satisfaction.

This study sought to explore the relationship among service quality, perceived value and satisfaction by clearly conceptualizing and measuring them in the context of smart-work, thereby ultimately understanding the impact they have on the intention to continue smart-work.

The financial technology (FinTech) era has brought a revolutionary change in the financial sector’s customer experiences at the national and global levels. The importance of artificial intelligence (AI) in the context of FinTech services for enriching customer experiences has become a new norm in this modern era of technological advancement. So, it becomes crucial to understand the customer’s perspective. The current research ranks the factors and sub-factors influencing customers’ perceptions of AI-based FinTech services.

The sample size for this study was decided to be 970 respondents from four Indian cities: Mumbai, Delhi, Kolkata and Chennai. The Fuzzy-AHP technique was used to identify the primary factors and sub-factors influencing customers’ experiences with AI-enabled finance services. The factors considered in the study were service quality, trust commitment, personalization, perceived convenience, relationship commitment, perceived sacrifice, subjective norms, perceived usefulness, attitude and vulnerability. The current research is both empirical and descriptive.

The study’s three top factors are service quality, perceived usefulness and perceived convenience, all of which have a significant impact on customers’ experience with AI-enabled FinTech services discussing sub-criteria three primary criteria for customers’ experience for FinTech services include: “Using FinTech would increase my effectiveness in managing a portfolio (A2)”, “My peer groups and friends have an impact on using FinTech services (SN3)” and “Using FinTech would increase my efficacy in administering portfolio (PU2)”.

The current study is limited to four Indian cities, with 10 factors to understand customers’ preferences in FinTech. Further research can focus on other dimensions like perceived ease of use, familiarity, etc. Future studies can have a broader view of different geographical locations and consider new tech to understand customer perceptions better.

The study’s findings will significantly assist businesses in determining the primary aspects influencing customers’ experiences with AI-enabled financial services. As a result, they will develop strategies and policies to entice clients to use AI-powered FinTech services.

Existing AI research investigated several vital topics in the context of FinTech services. On the other hand, the current study ranked the criteria in understanding customer experiences. The research will substantially assist marketers, business houses, academicians and practitioners in understanding essential facets influencing customer experience and contribute significantly to the literature.

The purpose of this study is to investigate the impact of e-service quality and e-trust on customer e-satisfaction and, subsequently, on customer e-loyalty towards a website in the online shopping environment of Pakistan.

The research employed a quantitative approach and utilised structural equation modelling to investigate the relationship between e-service quality and e-trust on consumers’ e-satisfaction and e-loyalty. The data were collected from 250 individuals who actively use online shopping websites to purchase products in Pakistan.

The findings revealed that e-service quality and e-trust offered on e-commerce websites significantly impacted customer e-loyalty. However, it was found that both e-service quality and e-trust do not have a significant impact on customer e-satisfaction. In addition, the findings showed that customer e-satisfaction positively impacts e-loyalty.

Overall, these findings emphasise the importance of e-service quality, e-trust and customer e-satisfaction and their role in cultivating customer loyalty within the context of the online shopping environment in Pakistan.

This study contributes to the existing literature on online shopping in Pakistan by exploring the factors influencing consumer behaviour in this context. The findings add to the academic understanding of consumer behaviour and provide valuable insights for e-commerce businesses in Pakistan.