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In mobile banking (m-banking), understanding the factors contributing to customer satisfaction is crucial for bank managers to design effective strategies for enhancing the uptake of mobile banking services. This study assesses the relationships between quality, technology acceptance and credibility factors and behavioural outcomes (actual use, continuance intention and loyalty) and satisfaction with m-banking. It further investigates the moderating influence of economy type, innovation level, connectivity level and sample size on all these relationships.

The study employs a meta-analysis technique and reviews 54 published studies to investigate the antecedents and consequences of satisfaction with m-banking.

The study finds a significant relationship between satisfaction with m-banking and quality, technology acceptance and credibility factors and behavioural outcomes. It concludes that the moderating effect of economy type, innovation level, connectivity level and sample size partially moderate the majority of the hypothesized relationships.

Drawing on a comprehensive literature review, this study presents a novel framework elucidating the antecedents and behavioural outcomes of satisfaction with mobile banking. It contributes to the literature by exploring the moderating effects of sample size and country context on the relationships between these factors, presenting important implications for future mobile banking research.

This study has practical implications for m-banking service providers, offering insights into the factors that drive user satisfaction with mobile banking and highlighting the need for tailored strategies in different country contexts.

This study examines the effects of factors leading to satisfaction and the subsequent outcomes within the context of m-banking. The findings offer fresh perspectives that can be valuable for managers and policymakers, enabling them to enhance customer satisfaction in the realm of m-banking.

This paper aims to bridge the extended reality framework and the luxury hospitality literature by providing insights into how immersive technologies using artificial intelligence (AI) can shape luxury value and consumer differentiation.

The authors conducted three experimental studies comparing immersive AI versus traditional hospitality across luxury contexts (hotels, restaurants and spas). Study 1 investigates the effect of immersive AI (vs traditional hospitality) on customers’ behavioral intentions and the need for differentiation using virtual-assisted reality. Study 2 tests the underlying mechanism of the need for differentiation and luxury value in an augmented reality context. Study 3 provides additional support for the proposed underlying mechanism using virtual-assisted reality in luxury hospitality.

The findings reveal that immersive AI (vs traditional) luxury hospitality reduces customers’ behavioral intentions of using such services and perceived luxury value. Moreover, the findings indicate that the intention to use immersive AI (vs traditional) luxury hospitality services is contingent upon customers’ need for differentiation.

The findings have important theoretical and managerial implications for immersive technologies in luxury hospitality. They shed light on the dynamics between integrating immersive AI into luxury hospitality and its impact on customers’ differentiation motives and perceived luxury value. The findings reveal the detrimental effect of using immersive AI (vs traditional hospitality) within this context.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.

The study aims to analyze factors impacting firms’ success and persistence in a digital platform competition using the structure-conduct-performance (SCP) framework. The study also includes real-life cases that are beneficial to academicians and practitioners to understand and develop strategies for success and persistence during uncertainty.

A literature review to identify the factors that impact success and persistence in a digital platform competition was conducted following Webster and Watson (2002). Findings were integrated into a SCP framework to examine and understand the identified factors’ relational impact.

While analyzing factors under the SCP framework, all factors were divided into three categories: those impacting positively, those impacting negatively and those with ambiguous impact on the success and persistence in digital platform competition. Digital platform firms can exploit the positively impacting factors to increase market share by being distinctive from other digital platform firms and becoming dominant by withstanding competition. On the other hand, negatively impacting factors increase barriers to entry, intensify competition and reduce the distinctiveness of digital platform firms. Lastly, a few factors may have either a positive or a negative impact depending upon the particular characteristics of the firm/industry.

The study opens the scope for future research on empirically testing the developed conceptual framework and relationships by developing propositions to posit the possible impact of these factors on digital platforms’ success and persistence.

The study contributed to the existing literature by using SCP framework to analyze the factors affecting firm’s success and persistence in a digital platform competition. Also, the study has discussed the relational impact of factors rather than their impact in isolation.

Co-working spaces bundle all real estate services into a package and leverage shared economy trend to create new opportunities for growth. This sector is anticipated to expand significantly due to changes in mobility and office design driven by the development of remote or hybrid work settings. The current study attempts to identify key motivating factors for users in emerging economies in choosing co-working spaces.

Using analytic hierarchy process (AHP) methodology and the self-determination theory framework, a total of 4 criteria-level factors, along with 13 sub-criteria level factors were identified as key motivators for adapting to co-working spaces.

The study highlights a few factors and their relative importance, which could help firms/organizations to start or offer co-working spaces within emerging economies.

The study contributes to literature by advancing the understanding of key motivators for users of co-working spaces within the ambits of emerging economies. In the process, the authors enlist a few factors vis-à-vis their relative importance, which could help firms/organizations to start or offer co-working spaces within emerging markets.

This study has two major objectives. First, comprehensively review the literature on transparency in supply chain management. Second, based on a critical analysis of literature, identify the attributes and sub-attributes of supply chain transparency and develop a numerical measure to quantify transparency in supply chains.

A systematic literature review (SLR) was conducted using the PRISMA approach. Utilizing SCOPUS database past eighteen-year papers search resulted in 249 papers to understand major developments in the domain of supply chain transparency. Subsequently, graph theoretic approach is applied to quantify transparency in supply chain and the proposed index is evaluated for case supply chains from pharma and dairy sectors.

It can be concluded from SLR that supply chain transparency research has evolved from merely tracking and tracing of the product towards sustainable development of the whole value chain. The research identifies four major attributes and their sub-attributes that influence transparency in supply chains, which are used to develop transparency index. The proposed index for two sectors helps to understand areas that need immediate attention to improve transparency in the case supply chains.

This paper attempts to understand the development of transparency research in supply chain using the PRISMA approach for SLR. In addition, development of mathematical model to quantify supply chain transparency is a novel attempt that would help benchmark best practices in the industry. Further, transparency index would help to understand specific areas that need attention to improve transparency in supply chains.

This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19).

A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities.

Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed.

The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample.

Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach.

This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation.