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This paper aims to examine the influence of sustainability reporting on bank performance. Furthermore, this study investigates the impact of the country’s economic development, financial system and crisis in moderating sustainability reporting and bank performance relationship.

The sample consists of 400 listed banks from 19 countries over the 2009–2022 period. Panel fixed-effect regression is applied, and System Generalized Method of Moments is used as robustness to address endogeneity concerns. The results are robust and survive several sensitivity tests.

The results, aligning with legitimacy and agency theories, suggest a negative relationship between sustainability reporting and bank performance. Based on further classifications, results suggest the negative (positive) impact of country’s financial system (economic development) in moderating the sustainability reporting and bank performance nexus. Finally, this study documents the positive influence of sustainability reporting on bank performance during the crisis period. Overall, the findings fail to support the reduced information asymmetry accruing from higher sustainability disclosures in developing and bank-based economies.

This study has important implications for regulators, policymakers and other stakeholders, especially in light of recent banking scandals that have deteriorated stakeholders' faith in financial institutions' reporting quality.

This study extends the scant literature on sustainability reporting in banking from a cost-benefit vantage point. Furthermore, to the best of the author’s knowledge, no previous research has examined the moderating role of the country’s financial structure and crisis in sustainability reporting and bank performance relationship.

The prevailing conceptualization of information system (IS) capabilities, rooted in the resource-based view (RBV) framework, tends to focus on unique firm resources. In the digital age, as emphasized by dynamic capabilities (DC), resource reconfiguration is critical in maintaining strategic advantage. This paper focuses on big data analytics capabilities (BDAC) from a DC perspective to present a novel conceptualization of BDAC–DC. We examine its effects on product, business model and business process innovation, including the effects of enterprise architecture (EA) on the BDAC business model innovation relationship.

This research presents a novel DC-based BDAC conceptualization, operationalized as a hierarchical construct. A survey-based approach is used for data collection and data analysis is done using partial least squares structural equation modeling (PLS-SEM).

The novel conceptualization and the effects of BDAC DC on BDA sensing-seizing and reconfiguration capacities support BDAC’s functional and evolutionary roleplay. Empirical results confirm the positive effects of BDAC–DC on first-order value targets (innovation) and the moderating effects of EA.

The novel BDAC–DC conceptualization has several implications for BDAC, DC, EA and business value research. Practicing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation.

Practicing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation for synergistic outcomes.

To the best of our knowledge, this research is the first attempt toward DC-based BDAC conceptualization, empirical validation of first-order effects on various forms of innovation and the often-overlooked role of critical EA capability.

Adopting and implementing robotic technology applications in the library is a significant technological up-gradation today. The purpose of this study was to evaluate selected literature focused mainly on robotics technology applications in the field of libraries and to assess the online social attention to research publications.

The study employed Scientometric and altmetric tools to evaluate the research publications. The bibliographic data of research publications were downloaded from Scopus database and scrutinized one by one and 71 articles were selected which mainly focused on robotic technology in libraries. Altmetric data were collected from the Dimensions.ai database. The analysis was performed using MS Excel, Tableau, Biblioshiny, VOSviewer and SPSS software.

Research on robotic technology in the field of libraries has been experiencing a gradual increase, marked by an annual growth rate of 12.93%. The United States has prominently led the way as the most active participant and collaborator in this advancement. Among the various journals, Library Hi Tech has notably stood out as a significant contributor to this field. However, the research articles have garnered limited social attention and impact. Furthermore, the patterns of authorship collaboration have demonstrated relatively modest levels within the field, and a weak correlation has been observed between the social attention received and the Scopus citation metrics of the publications.

The research needs to be disseminated more through various social media platforms to increase its visibility. Sharing research information through social media can bridge the gap between academia and society. The findings of this study can serve as a valuable reference for researchers and policymakers.

This study presents a Scientometric analysis of the selected published literature on robotics technology applications in the field of libraries, highlighting the progress and development of worldwide research in this area.

In the era of Industry 5.0, understanding the balance between environmental, social, and governance (ESG) and firm performance is crucial for mitigating climate change and enhancing financial outcomes. This paper aims to analyze the effect of ESG disclosure on the financial performance (FP) of renewable and clean energy (RCE) companies, focusing on the combined ESG disclosure and individual E, S, and G disclosure scores.

The study analyzed a panel data sample from 2015–2021, covering 41 RCE companies. By applying the K-means++ clustering technique, the research also explored how firm-specific features influence the relationship between ESG disclosure and FP. The Bloomberg database and audited financial reports were used to gather the data for the study.

The findings indicate that increased ESG disclosure positively influences FP. Further, a significant positive relationship exists between FP and a company’s E and S disclosure. However, firm-specific characteristics significantly influence this relationship. Findings suggest that a company’s commitment to comprehensive ESG efforts enhances financial efficiency rather than increasing costs.

This study adds to the ESG-FP literature by emphasizing global RCE companies, a key player in sustainability. Further, to the best of the author’s knowledge, the study’s uniqueness is attributed to its application of a two-step approach, combining ESG-FP analysis with K-means++ clustering to account for firm-specific characteristics. It also uniquely examines the individual impact of E, S, and G disclosure on the FP of global RCE companies. The findings offer valuable insights for businesses and policymakers in developing strategies that improve profitability while addressing climate change risks.

Generative artificial intelligence (GAI) can disrupt how consumers search for information on retail products/services online by reducing information overload. However, the risk associated with GAI is high, and its widespread adoption for product/service information search purposes is uncertain. This study examined psychological drivers that impact consumer adoption of GAI platforms for retail information search.

We conducted 31 in-depth, semi-structured interviews with the lead GAI users regarding product/service information search. The data were analysed using a grounded theory paradigm and thematic analysis.

Results show that consumers experience uncertainty about GAI’s functioning. Their trust in GAI impacts the adoption and usage of this technology for information search. GAI provides unique settings to investigate potential additional factors, leveraging UTAUT as a theoretical basis. This study identified three overarching themes – technology characteristics, technology readiness and information characteristics – as possible drivers of adoption.

Consumers seek exhaustive and reliable information for purchase decisions. Due to the abundance of online information, they experience information overload. GAI platforms reduce information overload by providing synthesized and customized product/service search results. However, its reliability, trustworthiness and accuracy have been questioned. The functioning of GAI is opaque; the popular technology adoption model such as UTAUT is general and is unlikely to explain in totality the adoption and usage of GAI. Hence, this research provides the adoption drivers for this unique technology context. It identifies the determinants/antecedents of relevant UTAUT variables and develops an integrated conceptual model explaining GAI adoption for retail information search.

This study aims to assess the current state and impact of the RegTech industry on financial regulation and compliance by providing a comprehensive overview of its evolution and identifying key challenges and opportunities.

A hybrid review approach was employed, involving a detailed bibliometric analysis of 89 scholarly articles and a content analysis of 47 key studies, covering the period from 2010 to 2023.

The research identifies critical trends and challenges within the RegTech industry, focusing on the roles of regulatory bodies and technological innovations. It explores four major themes: (1) RegTech applications in FinTech, financial services and banking regulations; (2) RegTech’s role in compliance management and fraud prevention; (3) the impact of digital transformation, governance and regulations; and (4) the integration of Big Data, AI, ML and blockchain in regulatory systems.

This study provides a comprehensive framework for understanding the complicated applications of RegTech, highlighting its potential to enhance compliance efficiency, mitigate risks and foster innovation within the financial sector. The insights provided are valuable for policymakers and financial institutions aiming to develop more robust regulatory frameworks and practices.

This study uniquely integrates bibliometric and content analysis to provide an up-to-date and nuanced overview of RegTech, focusing on recent advancements in AI, ML and blockchain technologies. It not only maps current trends but also identifies research gaps and offers new directions for future research.

This study aims to understand the kind of content and context that effectively create higher fan social media engagement (SME) through pre-match content posted by sports teams.

This research examines the effect of inspirational, informational, entertaining and warmth content appeal on affective and cognitive responses from fans in the form of likes and shares. Messages on X (previously Twitter), chosen as a representative social media platform, from the teams participating in the Indian Premier League, were analysed using regression models to validate the proposed model empirically.

For sports clubs, entertaining, warmth and inspirational content is more effective than information content in generating likes on social media. Content with high vividness is effective only for sports teams with high performance. Fans of low-performance teams exhibit higher responsiveness to content with inspirational appeal.

This research contributes to the sports marketing literature by examining the influential role of warmth and inspirational content in generating higher SME in the pre-match context.

This study provides prescriptions to sports clubs for leveraging social media platforms to engage their fans through appropriate content. Given the growth of sports leagues in developing and developed countries, this study provides guidelines to sports clubs for effective social media marketing.

To the best of the authors’ knowledge, this study is among the first to integrate social identity theory and elaboration likelihood model theoretical frameworks to study fan engagement with social media content posted by sports clubs.