The aim of this study was to empirically examine the extended unified theory of acceptance and use of technology 2 (UTAUT2) model by adding “personalization” as one of the antecedents, as well as a moderator to determine the key factors for the continuous use intention of mobile news applications (apps). For this study, an online and manual sample survey of 309 respondents, who had used the news app earlier, was collected and analyzed, using quantitative methods such as explanatory and confirmatory factor analysis, structural equation modeling, and Hayes process for finding moderating effects among variables. The findings of the direct effect demonstrated that performance expectancy (PE) has the most influential effect on continuous use intention, followed by habit (HT), hedonic motivation (HM), and facilitating conditions (FC). Furthermore, the outcome of tests for the moderating effect of personalization between UTAUT2 constructs and continuous use intention (CUI) showed that personalization has a significant moderating effect on performance expectancy and habit. Therefore, this research establishes the key role of PE, HT, HM, and FC as main factors that trigger the users’ continuous use intention of news apps and provides an integrated framework to assess the moderating effect of personalization on technology acceptance. The findings of the research expand the existing literature on news applications and provide foundation for future research studies in the area of mobile news apps.

Disclosing information on environmental, social, and governance (ESG) parameters is voluntary for most firms across the world. Companies disclose their performance on ESG datapoints due to two main reasons—(i) to gain the trust of stakeholders through increased transparency and (ii) to comply with regulations imposed by governments and investment houses. Using a dataset of companies disclosing ESG parameters during 2014–2021 from the S&P BSE 500 index, this study investigates the role of ESG disclosure on firm performance. We divide the constituent securities into three factors—size, value, and disclosure to study the premiums generated by firms on each factor using single-, double-, and triple-sorting approaches. We utilize time series regressions along with GRS tests to empirically test the presence of factor premiums. We find the significant role of factors size, value, disclosure, and a dummy variable for the COVID-19 pandemic period to explain the portfolio returns. The study found a negative ESG disclosure premium stating that firms with high levels of disclosure earn less returns compared with the firms with less disclosures. The findings of this study contrast with multiple studies in the past that have found a positive disclosure premium. Our findings help reconcile the mixed evidence on the disclosure–returns relationship.

An agency problem is defined as a conflict of interest arising due to a misalignment of interests among the managers and other stakeholders of the company. This article aims to review the articles addressing the agency problem and their impact on business performance. This article reviews the contributions of prominent theorists on agency problems and agency costs. Using bibliometric attributes of 740 articles from the Scopus database, this study highlights the publishing trend and outlets, along with leading contributors and collaborators in terms of authors, institutions, and countries. This study identifies the clusters through the bibliographic coupling technique and a trend topics analysis. Most researchers have focused on corporate governance and expressed the agency problem as one of the impact areas. This study is unique as no study to date specifically focuses solely on agency theory or the agency problem through the lens of bibliometric analysis. Future research directions on agency problems and their solutions conclude this study.

This study is the first to look at supply chain finance's mediating role in elucidating how fintech adoption leads to improved financial performance. By assessing supply chain finance practices in small and medium enterprises in emerging economies like India, this study aims to bridge the knowledge gap and guarantee the influence of fintech adoption in attaining improved financial performance. The original data was gathered from 270 workers in North India who worked for various small and medium enterprises. The study found that supply chain finance mediates the favorable association between fintech adoption and financial performance. A crucial moderator in the relationship between fintech adoption and financial performance was supply chain risk. The results indicate several applicable ramifications, including the use of fintech by companies, which is eventually enhanced by effective supply chain risk management to improve supply chain finance practices.

Mobile payment has evolved over time and its adoption and usage have proliferated in the new normal subsequent to the COVID-19 pandemic. This study aims to (1) evaluate the performance and (2) map the body of knowledge in order to (3) chart the avenues to advance mobile payment research in the new normal. To achieve its aims, this study adopts and implements a bibliometric methodology on a corpus of 455 publications retrieved from the Web of Science to provide a retrospective and a prospective of mobile payment research. Using performance analysis, this study unpacks the productivity and impact of the contributors (i.e., journals, articles, authors, countries, and institutions) to mobile payment research. Using science mapping, this study reveals the foundational themes and topical trajectories in the extant literature dedicated to mobile payment research. The findings from the performance analysis and science mapping are subsequently used to inform the agenda for future research on mobile payment.

Purpose: In today's rapidly changing digital world, the ability of startups to grow has become a major issue. This research looks at how AI-enhanced Human Decision-Making, AI-powered Entrepreneurial Agility and Ethical AI Governance help startups create an AI-driven Innovation Ecosystem that improves AI Infrastructure Readiness and supports both internal and external startup scalability potential. Design/methodology/approach: The Prolific platform was used to collect data from 274 decision-makers who worked for AI-intensive firms in the US. We used modified Likert-scale items from previous validated research to measure constructs. Partial Least Squares Structural Equation Modelling in SmartPLS4 was used to test the model. Both direct and indirect effects were analysed to examine the hypothesized relationships. Findings: All AI-enabled capabilities had a strong positive association with AI-driven Innovation Ecosystem. AI-driven Innovation Ecosystem significantly influenced AI Infrastructure Readiness, which in turn positively impacted both Startup Scalability Potential – Internal and Startup Scalability Potential – External. Mediation investigations showed that AI-driven Innovation Ecosystems and AI-Infrastructure Readiness serially mediate the impact of AI-enabled capabilities on startup scalability. Practical implications: Theoretically, this study builds on the Dynamic Capabilities Theory by adding AI-driven ecosystem and infrastructure preparedness as sequential mediators. This gives us a new way to look at how businesses use AI to develop quickly. In practice, the results give startup leaders and policymakers useful information on how to build AI adoption plans that are not only new but also follow the rules and fit with the infrastructure. Originality/value: This article presents a new serial mediation paradigm that connects AI capabilities to startup scalability and backs it up with real-world evidence from the US, a nation that is driven by innovation.