Investigating the link between service quality, value, satisfaction and behavioural intentions in a public sector bank in India

The study uses Structural Equation Modelling (LISREL 8.5) to compare four competing models of linkage between service quality, satisfaction, value and behavioural intentions of repurchase and recommendation in public sector bank context of India. The indirect effects model was selected due to parsimony and best fit. Service quality was found to significantly impact customer satisfaction and value perceptions. Satisfaction and value were significant antecedents of word of mouth and value was significant antecedent of repurchase intentions. Value has a significant but negative effect on satisfaction. The indirect effect of service quality on behavioural intentions via value and satisfaction is significant. The results indicate the need for adopting a comprehensive approach to managing customer loyalty. Future studies of antecedents of behavioural intentions need to incorporate the effect of value and its components to develop better insight into its role in service encounter outcomes.customer satisfaction; customer value; behavioural intentions; customer loyalty; word of mouth; repurchase intentions; India; retail banking; emerging markets; public sector banks; linear structural relations; structural equation modelling; LISREL 8.5; linkage; recommendations; indirect effects; customer perceptions; significant antecedents; negative effects; service encounter outcomes; economics; service industries; service quality management; developing nations; newly industrialised nations.

Systematic review of artificial intelligence in higher education (2000-2020) and future research directions

The goal of this study is to synthesize the findings, methodology and research themes of peer reviewed studies on Artificial Intelligence in higher education, published between 2000 to 2020. Twenty-nine articles were selected for review by following the PRISMA approach. The demographical and thematic trends suggest that most research is skewed towards few geographical locations (USA, Europe, India, China, Hong-Kong) and recent time periods (2018-2020) and scattered across publications from varied disciplinary traditions. Taiwan and United States contributed most to the number of studies, with 2017 being the most fruitful year. Vectors as well as decision trees were the most often used machine learning algorithms. Mechanization, cognitive process assessment, prediction models, integrated learning systems, and tackling potential problems in the use of big data and learning analytics were among the most commonly explored topics. Expanding geographical variety, adopting advanced algorithmic approaches including Bayesian as well as fuzzy logic techniques in educational machine learning work; applications for knowledge-based systems, and personalized learning were suggested for future search. Conclusions are drawn and future research directions identified. Potential research recommendations emphasize the expansion of geographical, topical, and methodological variety

Customer value in retailing (2000-2020): A narrative review and future research directions

The purpose of this paper is to synthesize and categorize the literature on customer value in retailing through a narrative review of literature by segregating and arranging the accrued knowledge into a thematic framework. The 27 papers extracted were selected from indexed databases through a systematic multi-stage process by applying exclusion and inclusion criteria. The theoretical perspectives adopted in the studies were summarized and the findings from these studies were then categorized and classified and emergent themes were discussed to draw future research directions. The classification framework adopted consisted of the themes of “Theoretical perspectives”, “Dimensions of Customer Value”, “Antecedents, and Outcomes of Customer Value”. The conclusions are drawn and future research directions have been proposed

An exploratory study of cognitive, social and normative dimensions of female entrepreneurship within transition economies: Evidence from India and Vietnam

Prior research conducted on budding female entrepreneurship did not highlight the transition nations such as Asia experiencing relatively faster economic growth and socioeconomic transformation. The role of contextual environment and motivation type in female entrepreneurship has not been adequately researched. This exploratory study of the association between the cognitive, social, and normative determinants of nascent female entrepreneurship activities in the two transition economies of India and Vietnam. This comparative study used the Global Entrepreneurship Monitor (GEM) database of 2015 for data collection. Data analysis of individual surveys from the database was conducted for selected categorical variables by using nonparametric tests of association for cross-tabulation (Chi-square test). The results show that the cognitive, social, and normative factors have a significant role in nascent female entrepreneurial levels in India and Vietnam. Socio-cultural influences play a dominant role in influencing and shaping women's entrepreneurial career choices to start a business. Moreover, women's entrepreneurial intentions is a product of the dynamic interaction of cognitive, social, and cultural factors at the individual (micro), social (meso), and cultural (macro) levels. This comparative country study of two middle-income transition South Asian economies creates insight into the gender and motivation specific contextualized roles of cognitive creates insight into the gender and motivation specific contextualized roles of cognitive and normative factors associated with nascent entrepreneurship at the country level. No prior other study has evaluated the contextualized role of women entrepreneurship's cognitive and social determinants by comparing two countries with similar economic and socio-cultural environments. The findings of the study can be used by policymakers to make better-informed decisions to promote women entrepreneurs by curating contextspecific policies

Consumer Segmentation in the Fashion Industry Using Social Media: An Empirical Analysis

Social media has developed into a symbolic channel that affects consumer behavior due to the remarkable marketing and ecommerce opportunities that the internet has provided. This study is based on segmentation of consumers into different categories in the world of fashion using social media. This paper proposes two conceptual models (the FC-CBR model of consumer brand relationship and the FC-CBP model of consumer brand perception) for exploring further into these constructs and developing a more coherent theoretical framework. The study identifies factors of Consumer Brand Relationship (CBR) and Consumer Brand Perception (CBP) using social media in the fashion world. Subsequently, the manuscript groups fashion consumers into clusters using K-means cluster analysis based on consumer brand relationship and consumer brand perception. The manuscript demonstrates how the clusters can be used in the development of efficient targeting and positioning strategies by practitioners