Search results

The objective of this study was to investigate the relationship between various drivers of consumer-based brand equity (CBBE) for m-payments and to demonstrate practical relevance. The study examined the indirect relationship between m-payment drivers and satisfaction and their subsequent association with brand equity drivers in the context of m-payments.

A survey was administered to a total of 725 respondents. Structural equation modeling, SPSS AMOS and a multi-mediation model using process macros were used to analyze the primary data.

The results of this study corroborate the post-use driver (satisfaction) and trust mediates drivers of m-payments (perceived usefulness and perceived ease of use) with overall brand equity. Satisfaction and trust exert a positive influence on overall brand equity, and this research will help organizations devise strategies to retain consumers, offer loyalty schemes and brand effectively to bundle services.

Novelty was achieved in this study by extending the technology acceptance model to determine the association between m-payment drivers and satisfaction and their subsequent association with overall brand equity, thus providing practical implications.

This study aims to explore the motivational factors (or value components) using a consumer-centric view to enable consumer make mobile payments (mPayments).

Means-end chain (MEC) theory, an exploratory approach was employed to understand personally relevant reasons for consumer use mPayments in India. In addition, the MEC phenomenon was investigated by examining the relationships among the three layers Attribute-Consequence-Value (A-C-V).

The study revealed the most important attribute to be handling money, followed by ease of use (consequence) and convenience as value. There was a wider recognition and penetration for acceptance of mPayments in smaller amounts or micro-payments with vendors, specifically in urban cities.

This research is the first of its kind wherein the concept of MEC has been utilized to evaluate why consumers make mPayments and demonstrate a novel approach to explore consumer insight into this domain.

The demand for over-the-top (OTT) video streaming services has grown manifold in the last few years. With increasing competition in the OTT market, it is vitally important for businesses to understand OTT retail consumers' continuance intentions. This study aims to understand the antecedents of the continuance intention of OTT platforms and distinguish the most essential and best-performing predictor variables of continuance intention.

The study employs an integrated research model based on the theory of planned behavior (TPB), flow theory and habit. Data collected from OTT retail consumers (n = 383) using the online survey method are analyzed using partial least squares structural equation modeling.

The results elucidated which TPB antecedents and dimensions of flow experience affect OTT platforms' continuance intention. The most important factors affecting continuance intention were habit, attitude and perceived behavioral control, while the best-performing factors were perceived behavioral control, perceived enjoyment and attitude.

The study is unique in harmoniously integrating conscious cognitive intention, intrinsic motivation and habit of performing an activity to explain OTT platforms' continuance intention.

This study aims to examine the impact of big data management on green service production (GSP) and environmental performance (ENPr) while considering green HRM practices (GHRM) in healthcare emergencies.

The authors collected primary data from major healthcare organizations in India by surveying healthcare professionals. The data analysis through structural equation modelling (PLS-SEM) reveals several significant relationships to extricate the underlying dynamics.

Grounded in the theories of service production and natural resource-based view (NRBV), this study conceptualizes GSP with its three dimensions of green procurement (GP), green service design (GSD) and green service practices (GSPr). The study conducted in India's healthcare sector with a sample size limited to healthcare professionals serving in COVID-19 identifies the positive and significant impact of big data management on GSP and ENPr that organizations seek to deploy in such emergencies. The findings of the study explain the moderating effects of GHRM on GSP-ENPr relationships.

The study was conducted in the healthcare sector in India, and its sample size was limited to healthcare professionals serving in COVID-19. The practical ramifications for healthcare administrators and policymakers are suggested, and future avenues of research are discussed.

This paper develops a holistic model of big data analytics, GP, GSD, GSPr, GHRM and ENPr. This study is a first step in investigating how big data management contributes to ENPr in an emergency and establishing the facets of GSP as a missing link in this relationship, which is currently void in the literature. This study contributes to the theory and fills the knowledge gap in this area.

The purpose of this paper is to investigate the role of the “Big Five” personality traits (extraversion, openness, agreeableness, conscientiousness and neuroticism) on the adoption of augmented reality (AR), with a particular focus on the role AR may play in interactive marketing.

A quantitative-based approach was followed by a questionnaire survey, which was completed by 230 respondents comprising graduate and postgraduate students, using structural equation modelling.

While the trait of openness was positively associated with the perceived ease of use of AR, the usefulness of AR and subjective norms, the trait of neuroticism was negatively associated with the perceived ease of use of AR. Extraversion was positively associated with subjective norms. Perceived ease of use of AR, the usefulness of AR and subjective norms were positively associated with attitudes toward AR.

The data gathered will add a valuable contribution to the currently limited data available on empirical consumer behaviour research, particularly in relation to the adoption of AR for interactive marketing.

The findings of this study will benefit academics working on the adoption of technology in rapidly developing fields such as automation and artificial intelligence; the study also contributes to the emerging interdisciplinary domain of psychology, information systems, marketing and human behaviour.

The study is designed to explore the drivers of customer satisfaction of leading mobile network providers in a high‐growth market like Kolkata a metropolitan city in India.

A framework was developed based on earlier study of eminent researchers pertinent to customer satisfaction of mobile network providers in Germany, France, Korea, Canada, the USA and Greece. The construct flexibility was considered as a new determinant for customer satisfaction. For this data were collected from 277 respondents and pertinent analysis were made using multivariate techniques.

The study finds that generic requirements, price, and flexibility are major drivers of customer satisfaction of mobile network providers and brand wise relevance of these key determinants.

The fixed line telephone directory was the sampling frame, and all the respondents considered in the survey had a fixed line but there are situations where customer subscribes only to mobile phones. It is also necessary to study other metropolitan cities of India to validate the results we have obtained for Kolkata. Originality/value – The current research has taken into account new driver of customer satisfaction in a high‐growth market and this is the first study on drivers of customer satisfaction of leading mobile network providers in the city of Kolkata, India.