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The study investigates the influence of managerial discretion over accruals on banks' financial reporting quality. Furthermore, it examines the role of ownership in shaping managerial incentives to manipulate banks’ reporting quality in a developing economy.

The sample includes 37 Indian public- and private-sector banks from the fiscal year 2001–2022. The discretionary LLP (DLLP) is used to examine various managerial incentives and accounting quality. The models are estimated using panel fixed-effect regression and the system generalized method of moments. The results survive several sensitivity checks.

The results exhibit a low quality of financial reporting in public-sector banks, which is evident through the higher use of DLLP for income smoothing and signaling. In contrast, the low-capitalized private-sector banks employ DLLP to manage capital.

The study’s sample size is relatively small and focuses on a single country. Future researchers can investigate other emerging economies to better generalize the findings of this study.

The study highlights the influential role of ownership in shaping managerial incentives in the banking industry. Moreover, the study is of utmost importance for governments, regulators and policymakers in devising policies that reduce agency conflicts and improve financial stability in emerging economies.

The study subscribes to the growing literature on the role of ownership in influencing the banks’ financial reporting quality. To the best of the author’s knowledge, this is one of the limited studies in the context of government-owned vs private-owned banks in an emerging economy.

The complexity of citizenship behavior in organizations has long been a focus of research. Traditional methodologies have been predominantly used to address this complexity. This paper aims to tackle the problem using a cutting-edge technological tool: business process mining. The objective is to enhance citizenship behaviors by leveraging primary data collected from 326 white-collar employees in the Indian service industry.

The study focuses on two main processes: training and creativity, with the ultimate goal of fostering organizational citizenship behavior (OCB), both in its overall manifestation (OCB-O) and its individual components (OCB-I). Seven different machine learning algorithms were used: artificial neural, behavior, prediction network, linear discriminant classifier, K-nearest neighbor, support vector machine, extreme gradient boosting (XGBoost), random forest and naive Bayes. The approach involved mining the most effective path for predicting the outcome and automating the entire process to enhance efficiency and sustainability.

The study successfully predicted the OCB-O construct, demonstrating the effectiveness of the approach. An optimized path for prediction was identified, highlighting the potential for automation to streamline the process and improve accuracy. These findings suggest that leveraging automation can facilitate the prediction of behavioral constructs, enabling the customization of policies for future employees.

The findings have significant implications for organizations aiming to enhance citizenship behaviors among their employees. By leveraging advanced technological tools such as business process mining and machine learning algorithms, companies can develop more effective strategies for fostering desirable behaviors. Furthermore, the automation of these processes offers the potential to streamline operations, reduce manual effort and improve predictive accuracy.

This study contributes to the existing literature by offering a novel approach to addressing the complexity of citizenship behavior in organizations. By combining business process mining with machine learning techniques, a unique perspective is provided on how technological advancements can be leveraged to enhance organizational outcomes. Moreover, the findings underscore the value of automation in refining existing processes and developing models applicable to future employees, thus improving overall organizational efficiency and effectiveness.

Numerous prior studies highlight the importance of social media adoption (SMA) in nongovernmental organizations (NGOs) in the disaster preparedness phase (DPP). However, in India, social media is underused by NGOs in their attempts to mitigate the adverse impact of the disaster. Therefore, this study aims to seek to empirically investigate the relationship between factors influencing the SMA in NGOs in the DPP in India.

The “Technology-Organization-Environment (TOE)” framework, integrated with organizational creativity (OC), forms the theoretical foundation of this study. Data were collected from 266 respondents representing 120 Indian NGOs using a seven-point Likert scale. To test the hypotheses, this study used a variance-based structural equation modeling technique.

The empirical findings show that relative advantage, organizational readiness (OR), top management support and government support positively influenced the SMA in NGOs during the DPP. However, compatibility and complexity do not affect the SMA. In addition, OC moderates the relationship between OR and SMA in NGOs. These results underscore the need for NGOs to develop an organizational culture that is more forward-thinking and technology oriented.

This study fills an important research gap in the literature by developing a research model designed to improve the SMA in NGOs during the DPP in India. Furthermore, the authors integrated OC into the TOE framework to develop and examine the relationship between factors that impact SMA.

This study aims to understand how brand personality affects purchase intention through brand love and perceived quality in e-commerce.

Three hundred forty-eight users of e-commerce sites in New Delhi, India, were surveyed for the study. The data set was examined using confirmatory factor analysis, and the research hypotheses were assessed using structural equation modeling.

Two important conclusions emerged from the study. First, brand love and perceived quality have been considerably and favorably influenced by all six dimensions of brand personality of e-commerce brands. Second, the purchase intention toward the e-commerce sites is significantly and positively impacted by brand love and perceived quality.

This study by exploring various dimensions of brand personality, will assist e-commerce executives in increasing purchase intention toward the e-retailing sites.

This research is supposed to be the foremost to look at how brand personality, through brand love and perceived quality affects purchase intention toward e-commerce websites. The attachment theory is used in this study as a theoretical foundation for linking e-commerce brand personality to customers’ purchase intentions via brand love and perceived quality.

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.

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.