We undertake a comprehensive evaluation of stress and its determinants for Indian banks, from the viewpoint of opportunity cost incurred under diverse stress events. Using a sample of 39 banks in India over the 2005-22 period, the study reveals size, ownership and operating inefficiencies as the principal determinants of stress. A sharp rise in delinquencies draws proactive policy intervention, which mitigates stress at subsequent time periods. Higher quantum of corporate loan exposure was found to relieve banking stress. The study meaningfully augments extant literature landscape on banking default prediction by establishing efficacy of stress score as a potent Early Warning Indicator (EWI) of banking distress. Our findings pinpoint the need for rigorous monitoring of regulatory capital, liquidity and credit screening standards of Indian banks. The study offers valuable guidance for proactive policy intervention, aimed at minimizing the adverse impacts of stress on banking stability.

This research investigates how Hofstede's cultural dimensions impact the application of AI technology in fulfilling Environmental, Social, and Governance (ESG) objectives by concentrating on organizational attitude and policy as moderating factors. The framework formulated using cultural theory attempts to explain the influence that power distance, uncertainty avoidance, and individualism have on the application of AI to various sociocultural settings. This study used a sequential exploratory mixed-methods design involving qualitative grounded theory and in-depth interviews with stakeholders and key informants from different domains to identify culturally relevant constructs pertaining to AI use for ESG objectives in the exploratory phase. In the confirmatory phase, the relationships formulated in the previous step were tested using PLS-SEM with survey data from 448 respondents in India, processed with SMART PLS 4.0, ensuring the results met the various PLS-SEM validity criteria of reliability, validity, and theoretical rigor. It is noted that positive organizational attitudes increase the effects of AI on ESG outcomes, while formal policies, save for a few, do not exert any meaningful moderating influence because they are too rigid and lack context. This research contributes to culture theory by illustrating the intersections of AI adoption and ESG performance, which are relatively under-discussed in modern academic research. Addressing this gap highlights the inadequacy of culturally sensitive attitude-centered frameworks tailored for responsible organizational leadership shaped by sustainable ethical guiding global principles.

As urban logistics demand grows, traditional truck-based delivery faces significant challenges, including traffic congestion and difficulty meeting time-sensitive customer needs. With their flexibility and mobility, drones offer a promising complement to traditional last-mile delivery using trucks. Hybrid truck-drone delivery is gaining traction as a solution for last-mile logistics. However, integrating trucks and drones into a cohesive routing system is complicated by varying service capabilities, diverse customer requirements, dynamic traffic conditions, and strict delivery time windows. Motivated by these challenges, this paper introduces a time-dependent vehicle routing problem with drones under time windows and customer service constraints (TDVRPD-TW-CS). We model dynamic road conditions using a time-dependent function based on travel speed and incorporate time window penalty costs into the objective function. The problem is formulated as a Markov decision process, and an end-to-end deep reinforcement learning (DRL) algorithm is developed to solve it. Comparative experiments with the Gurobi optimizer, variable neighborhood search (VNS), and existing DRL methods, conducted using instances of varying scales, demonstrate the superiority of the proposed algorithm in solution quality, efficiency, and robustness. Thus, with a novel modeling and solution approach, we contribute to the literature on the vehicle routing problem with drones. Our work provides valuable insights for intelligent urban delivery planning, accounting for realistic operational constraints.

PurposeThis study remains focused on the evaluation of perceived value, satisfaction, attitude, trust, and habit as determinants impacting consumers' intention to continue using and advocating for online food delivery services. Thus, illuminating these constructs within Technology Continuance Theory (TCT). The intent is to analyze the competition and technological advancement engagement advocacy landscape in relation to the aforementioned factors in question.Design/methodology/approachA quantitative cross-sectional design was implemented for this study, along with a purposive sampling method from 420 users who were considered seasoned over a significant period of time with the use of online food delivery applications. All hypothesized relationships, alongside moderating effects, were tested using PLS-Structural Equation Modeling (PLS-SEM).FindingsThe results suggest that perceived value increases satisfaction, which positively influences attitudes and continuance intentions. Trust also acts as a moderator between continuance intention and recommendation intention by enhancing advocacy at higher levels of trust. Stronger positive attitudes propel improved continuance intentions, while trust bolsters the relationship between continuance and recommendation intentions, reinforcing advocacy when trust is high. Contrarily, this model demonstrates no moderation from habit, which implies that promotional efforts are not triggered by habitual usage.Originality/valueThis study deepens the body of knowledge by applying TCT to online food ordering systems and focusing on the trust and habit formation dynamics of referring users to the service. As noted in the practical implications, trust must be earned alongside a sustained delivery of value for brands to transform their loyal consumers into advocates.

PurposeFII, the sought-after capital infuser by emerging economies, faces an agency issue as a minority investor. The dividend payout policy of an investee firm plays a critical role in regulating this issue. At the same time, firms may use it to attract the investors. This study explores the relationship between foreign institutional investors (FII) presence as a shareholder and their shareholding proportion (independent variables) with dividend payout policy in terms of dividend-paying behavior (dividend payer) and dividend payout proportion (dependent variable) and otherwise.Design/methodology/approachConsidering the sample of 4,044 firm years during 2013-2023, the study uses the Tobit and Probit regression model to examine the relation between dividend-paying behavior and dividend payouts against FII presence and FII shareholding proportion. The study applies 2SLS for controlling endogeneity as a robustness check.FindingsThe results demonstrate that FII prefer firms that pay high dividends and self-select them. The Tobit model shows that FII presence induces dividend decisions, and the Probit models show that dividend payouts attract foreign shareholding. These effects are evident in firms with high profits, potential growth opportunities, higher leverage and lower cash flows. The FII influence is more significant when they have substantial shareholding. Firms with higher FII investments have more dividend payout and firms with higher dividend payout attract more FII investments. The study indicates that firms can use dividends as a signal for obtaining capital investments from FII.Originality/valueThis is the first study checking such a bidirectional relationship. For emerging economies, this can serve as a tool for attracting the capital. The research is also helpful for policymakers for regulatory purposes, considering the monitoring role of FII, executed through investment decisions in emerging economies.

The issue of executive financial fraud has attracted growing attention from research communities, especially after serious corporate scandals in recent times. This research paper offers an extensive summary of existing literature in executive financial misconduct literature. Employing topic modeling and bibliographic coupling techniques, the study identifies major themes and research frontiers in the area. We find that major topics range from incentives to behavioral drivers of executive misconduct. The study consolidates the fragmented body of literature to intellectually map the research area and propose a conceptual framework that focuses on the drivers and moderators of executive misconduct. Finally, the study determines gaps in the current body of literature and proposes a way forward for academic research, while providing valuable insights for regulators and practitioners.

Context: Agile Software Development (ASD) emphasizes adaptability, collaboration, and rapid, iterative delivery to address evolving client needs and manage interdependent roles effectively. Though extant literature has explored ASD and its various dimensions, intersections, and dependencies among various well-defined ASD job roles, it remains underexplored. Objective: This study examines the implementation barriers faced by ASD teams and identifies the best practices adopted across key ASD roles-Scrum Masters, Product Owners, and Developers-as well as senior leadership, to overcome them. Method: This study employs a Grounded Theory (GT) methodology to explore the complex, role-based challenges in ASD inductively. Drawing on qualitative insights from 23 industry practitioners representing multiple ASD roles, the research delves into the ground realities of ASD projects to surface emergent patterns and contextual nuances. Through iterative coding and constant comparison, the study develops a role-contextual understanding of barriers to ASD implementation and synthesizes practitioner-validated best practices for overcoming them. Results: The study identifies a spectrum of barriers-role-specific, inter-role, and leadership-related-spanning strategic, operational, and interpersonal dimensions. It proposes a Role-Contextual Framework that categorizes these barriers at triadic, dyadic, and intra-role levels and links them to practitioner-validated best practices. Conclusion: The findings contribute to ASD research by highlighting the contextual and systemic nature of ASD barriers and underscoring the need for adaptive, role-based strategies, such as strategic alignment and leadership involvement, to facilitate the successful implementation of ASD.

Generative AI models are increasingly adopted in tourism marketing content based on text, image, video, and code, which generates new content as per the needs of users. The potential uses of generative AI are promising; nonetheless, it also raises ethical concerns that affect various stakeholders. Therefore, this research, which comprises two experimental studies, aims to investigate the enablers and the inhibitors of generative AI usage. Studies 1 (n = 403 participants) and 2 (n = 379 participants) applied a 2 x 2 between-subjects factorial design in which cognitive load, personalized recommendations, and perceived controllability were independently manipulated. The initial study examined the probability of reducing the cognitive load (reduction/increase) due to the manual search for tourism information. The second study considers the probability of receiving personalized recommendations using generative AI features on tourism websites. Perceived controllability was treated as a moderator in each study. The impact of the cognitive load produced mixed results (i.e., predicting perceived fairness and environmental well-being), with no responsible AI system constructs explaining trust within Study 1. In study 2, personalized recommendations explained each responsible AI system construct, though only perceived fairness and environmental well-being significantly explained trust in generative AI. Perceived controllability was a significant moderator in all relationships within study 2. Hence, to design and execute generative AI systems in the tourism domain, professionals should incorporate ethical concerns and userempowerment strategies to build trust, thereby supporting the responsible and ethical use of AI that aligns with users and society. From a practical standpoint, the research provides recommendations on increasing user trust through the incorporation of controllability and transparency features in AI-powered platforms within tourism. From a theoretical perspective, it enriches the Technology Threat Avoidance Theory by incorporating ethical design considerations as fundamental factors influencing threat appraisal and trust.

PurposeWith rapid urbanisation and increased rainfall leading to frequent flooding, this research aims to focus on analysing road network performance during disruptions and to assess the effectiveness of various evacuation strategies.Design/methodology/approachThis study uses a microscopic traffic simulation model to analyse a road network comprising 5,159 links, 2,147 nodes and 18 zones. The model is first calibrated using crowdsourced travel time and travel patterns to reflect real-world conditions accurately. Two disruption scenarios are examined: reduced road speeds due to inundation (scenario-1) and network disconnectivity from severe flooding (scenario-2). In addition, four evacuation scenarios are simulated: planned evacuation (scenario-3), contraflow on key corridors (scenario-4), phased evacuation (scenario-5) and a combination of contraflow with staging (scenario-6). Network performance is evaluated based on travel times and evacuation outcomes under these scenarios.FindingsIn the disruption scenarios, scenario-2 results indicate that nearly 65% of origin-destination pairs experience travel times exceeding the base model's maximum. In the evacuation scenarios, the combined contraflow and staging strategy (scenario-6) yields the best results, with most zones completing evacuation in under 60 min.Research limitations/implicationsPublic transportation has not been considered when calibrating the road network, and the driver behaviour used in the model is suitable for daily conditions. It does not reflect behaviour during disruption conditions, which may affect the accuracy of the results.Originality/valueThis study demonstrates the utility of traffic simulation models in assessing urban road network vulnerabilities and evacuation strategies under heterogeneous traffic conditions. It provides insights into effective evacuation planning and highlights the benefits of combining contraflow and staging strategies.

Context: Agile software development (ASD) is gaining prominence as the leading methodology for modern software development organizations because it enables a fast, effective, and customer-centric approach in the current disruptive and dynamic work environment. Objective: Despite increasing interest in ASD as a research area, the extant literature remains scattered and lacks convergence. This study provides a detailed account of all aspects of ASD, including emerging agile concepts, such as agile governance and large-scale agile implementations. Method: A systematic literature review (SLR) technique identifies 208 relevant articles. The study included papers published between 1999 and 2024. Results: This SLR provides a concise overview of the various theories applied in the context of ASD. The study classifies previous literature into numerous different facets of ASD. In addition, the paper has prepared an extensive list of relevant research questions for future investigations in each domain of ASD. Conclusion: This study offers scholars insights into the status of ASD research as well as the current trends in ASD. Furthermore, the proposed future research questions provide researchers with precise direction for delving deeper into different facets of ASD.

Sustainability in the agri-food sector has been increasingly important with regard to the environment, health, and employment of local farmers. The purpose of this research is to analyze the impact of drivers and barriers on consumer behavior toward sustainable agri-food products in India under the Behavioral Reasoning Theory (BRT). This study employs a mixed-methods approach, integrating both exploratory and confirmatory strategies designed to understand the behavioral motivators and barriers in sustainable purchase decisions. The study concludes that environmental concern, health, and support for local farmers positively enhance consumer intention to purchase sustainable agri-food products. Conversely, payment and limited accessibility serve as barriers in highly price-sensitive markets like India. Environmental concern moderates the impact of supportive and negative agri-food reasons by amplifying the positive influence while reducing the negative one. Addressing the barriers alongside the motivators will enable greater achievement of sustainable consumption. This makes well-founded recommendations for businesses and public institutions in developing strategies to promote sustainable agri-food products optimized for hesitant consumers. It also broadens the scope of BRT, portraying consumer decision-making in a less-considered light, and offers a practical approach to fostering green purchase intentions in developing nations.

This study presents a structured decision-making framework for assessing green mergers and acquisitions (M&A) by integrating environmental, social, and governance (ESG) performance with financial return thresholds. Drawing on seven high-profile renewable energy transactions executed between 2020 and 2025, the research applies a four-quadrant analytical model comprising the M&A-ESG Fit Matrix, Capital Allocation Heatmap, IRR-ESG Frontier Graph, and Policy Roadmap Timeline. This framework operationalises the IRR_min constraint aligned with investor mandates and applies Pareto frontier optimisation to identify deals that maximise ESG impact without compromising financial competitiveness. The empirical analysis reveals that transactions positioned on the efficient frontier consistently deliver superior value by balancing sustainability outcomes with robust internal rates of return. The approach aligns with global strategic consulting methodologies, enhancing transparency, capital efficiency, and policy alignment in deal selection. While the study is constrained by data availability, limited sample size, and sectoral concentration in renewables, it offers a replicable template for broader application across industries. Future research should incorporate longitudinal post-acquisition performance tracking, sector-adjusted ESG taxonomies, and dynamic risk modelling. The proposed framework helps bridge the gap between ESG ambitions and disciplined investment performance, providing actionable insights for investors, policymakers, and corporate strategists navigating the evolving green M&A landscape.

PurposeThe importance of key account management (KAM) as a management technique in business-to-business markets has grown in recent years. The success of KAM programmes is highly dependent on the efforts of individual employees, specifically key account managers. Research on KAM at an individual level is important but lacking in the academic domain. This study aims to fill this gap by developing and evaluating a model of key account manager personality traits and how they impact the adoption of artificial intelligence (AI) technologies. The study also depicts the effect of the adoption of AI technologies on competitive advantage and firm performance.Design/methodology/approachThe study examines how the adoption of AI technologies impacts firms' competitive advantage and performance. The study used competitive advantage as a mediator and organisational culture as a moderator. A mixed-method analysis was used to conduct the study. In the first phase, an exploratory study was conducted using interviews with 26 key account managers from the automobile industry and thematic analysis to establish 9 constructs. In the second phase, which is a confirmatory study, 496 respondents finally responded to the questionnaire.FindingsAll constructs are used for confirmatory analysis and validate the data. Our research shows that key account managers' adoption of AI technologies is influenced significantly by personality traits. Extraversion, agreeableness, conscientiousness, neuroticism and openness have substantial links to adopting AI technologies, which impacts firms' competitive advantage and performance. Organisational culture significantly moderates the association between agreeableness and the adoption of AI technologies.Practical implicationsThe findings of this research allow organisations to optimise team composition, customise training programs based on individual traits and incorporate personality assessments into recruitment processes for streamlined technology adoption and improved competitiveness. Overall, these actions aim to enhance AI integration, driving competitive advantage and client satisfaction.Originality/valueThis study stands out as one of the limited inquiries examining how the Big-five personality traits of key account managers influence the integration of AI technologies and its resulting impact on company performance. Therefore, this research makes notable contributions to the realms of organisational psychology and technology adoption studies.

This research utilises an extended UTAUT (Unified Theory of Acceptance and Use of Technology) framework that explores the adoption of Information and Communication Technology (ICT) in higher education. With a focus on student engagement in Oman, the research integrates trust as a novel construct and examines the role of gender as a moderator among traditional UTAUT variables. This extension addresses gaps in the literature regarding trust and demographic influences in ICT adoption. Using a quantitative approach and robust statistical techniques, the findings reveal that effort expectancy (EE), performance expectancy (PE), facilitating conditions (FC), social influence (SI), and trust significantly impact behavioral intention (BI) and use behavior (UB). Gender emerged as a key moderator, highlighting nuanced variations in perceptions between male and female students regarding the adoption of ICT tools. The study makes both theoretical and practical contributions by refining UTAUT's predictive capability and offering hands-on insights for policymakers and educators to boost ICT adoption. By addressing infrastructure readiness, promoting trust, and tailoring strategies to gender-specific needs, this research lays a foundation for improved digital integration in education. The findings underscore ICT's transformative potential in creating interactive, equitable, and future-ready learning environments, particularly in emerging economies.

PurposeThis research focuses on developing and testing a conceptual model that explores customer behavioural responses (engagement, experience and recommendation) towards generative artificial intelligence (AI)-enabled chatbots. It highlights the significant influence of anthropomorphic characteristics in enhancing perceptions of competence and warmth, further enhancing perceived authenticity. In addition, this study aims to investigate how the need for social interactions moderates these relationships.Design/methodology/approachThis study used a self-administered questionnaire distributed on Prolific Academic to gather data from 282 eligible participants worldwide. This study uses a structural equation modelling approach to answer the research questions.FindingsThe findings reveal that anthropomorphic characteristics of generative AI-enabled chatbots are positively associated with perceived competence. Moreover, the findings show that the perceived competence and warmth of generative AI-enabled chatbots are significantly associated with perceived authenticity. Furthermore, the results highlight that the perceived authenticity of generative AI-enabled chatbots is positively associated with customer engagement, experience and recommendation. Finally, the results illustrate that the impact of anthropomorphic characteristics on perceived warmth is significantly moderated by the need for social interaction.Originality/valueThis study enriches the generative AI literature and guides organizations in understanding consumer interactions for leveraging generative AI-enabled chatbots. Furthermore, this study contributes to the social response theory literature as this study investigates how user behavioural intentions towards generative AI-enabled chatbots are influenced by their perceived level of anthropomorphic characteristics.

This research aims to address the fragmented perspectives in the literature on anti-consumption by conducting a systematic literature review. The major goal is to offer an integrated and holistic understanding of the essential factors impacting consumer behavior within the anti-consumption domain. By utilizing the SPAR-4-SLR protocol, we examined 86 studies, encompassing diverse theories, contexts, characteristics, and methodologies (TCCM). A conceptual framework that outlines the antecedents, mediators, and outcomes of anti-consumption behavior was developed by combining variables in this study. The results highlight the diverse factors shaping individuals' anti-consumption behaviors, shedding light on the complex dynamics that vary across industries, cultures, and consumer segments. Additionally, this research has pinpointed neglected domains that have received limited attention in this field and put forth pioneering pathways for future research by incorporating the TCCM framework. Specifically, this research proposes potential future research directions, including exploring theories like social learning theory, behavioral reasoning theory, and self-determination theory. Furthermore, we recommend employing advanced methodologies like longitudinal studies, netnography, and neuromarketing to push the boundaries of knowledge in this field. By identifying these theoretical and methodological gaps, the study makes a substantive contribution to advancing academic inquiry and practical applications in anti-consumption research.

Artificial intelligence (AI) is rapidly changing the banking and financial system, taking over fundamental operations to improve operational efficiencies and minimize costs. Although scholarly research has focused either on the implications of machine learning (ML) and deep learning algorithms to improve the performance of the banking and financial sectors or on the applications of AI in specific banking or financial operations, insights into the overall growth of AI in the banking and financial sectors but its applications in diverse functional areas of the banking and financial sectors are relatively scarce. This study aims to present a systematic literature review on the current state of knowledge of AI in banking and the financial sector. A systematic review of 157 articles was conducted to identify, select, assess, and synthesize relevant studies on this phenomenon and was analyzed for emerging themes. The thematic analysis revealed several key themes, including AI as an innovation in customer service, AI applications and customer readiness, AI adoption and financial inclusion, AI adoption and regulation, and AI applications and their explainability. It also identified various applications of, and barriers to, AI adoption. Furthermore, the study proposes a research framework and develops propositions that can be tested in future research. Finally, the study provides a future research agenda based on a theory, context, and methodology (TCM) framework and discusses the study's implications.

Artificial intelligence (AI) has the potential to automate processes in the manufacturing and supply chain domains, thereby assisting in the attainment of the three pillars of circular economy, namely, recycle, reuse, and reduce. Furthermore, implementing transparent and responsible AI is foundational to achieving sustainable and ethical Circular Supply Chains (CSC). This research explores how AI transparency and responsibility mechanisms are critical enablers for aligning technological advancement with sustainable practices in CSC operations. Furthermore, technologies such as Digital Twins, Industrial Internet of Things, and Blockchain provide frameworks for cyber-physical systems that create smart or intelligent factories and contribute to CSC. Following the COVID-19 pandemic, it became crucial for businesses to begin focusing on Society 5.0, which resulted in the development and deployment of strategies based on the Triple Bottom Line (TBL) framework, wherein profit is achieved by keeping people and the environment in mind. Through the lens of the TBL framework, this study compiles and organizes AI in CSC research by identifying the enablers of AI as predictive maintenance, real-time monitoring, society 5.0, sustainable development, and cyber-physical systems and barriers like privacy, transparency, etc. We believe that the benefits of AI technology will outweigh its initial costs. Implications for the future and enablers and barriers under the themes of AI in CSC are also discussed.

PurposeThis study aims to analyse the role of factors responsible for continued telemedicine usage in emerging economies.Design/methodology/approachA sequential mixed-methods approach was used to analyse the data. This included in-depth interviews with telemedicine users to identify the factors influencing their continued telemedicine usage, followed by quantitative analysis to empirically validate the relationship postulated within the research model.FindingsThe primary findings show that attitude, social influence and satisfaction directly impact users' continued intention to use telemedicine services. These factors underscore the importance of both individual perceptions and external influences in shaping intention to continue to use telemedicine services.Practical implicationsThe results show that factors such as satisfaction, attitude and social influence impact the continued usage of telemedicine services in emerging economies. Specific policy initiatives and awareness programs can be implemented to promote long-term telemedicine usage. In addition, improving apps with user-friendly interfaces, local language options and voice commands, alongside enhanced security measures, would serve to build trust in, and improve accessibility to, telemedicine.Social implicationsThe study's findings can be used to improve health-care services in areas where facilities are inaccessible.Originality/valueThis research explains the intention to continue telemedicine usage through a multi-theoretic perspective that combines technology continuance theory and the unified theory of acceptance and use of technology.

PurposeThe purpose of this study is to understand the evolution of digital social entrepreneurship (DSE) as a field of study by a combinatorial approach of systematically reviewing the existing knowledge and doing a pilot study with a selection of experts to propose potential areas for future research. Design/methodology/approachThe authors used a combinatorial approach combining systematic literature review along with interview-based exploration in this study. PRISMA methodology was used to systematically review the existing literature, and a thematic content analysis of the relevant articles was used to identify themes. An interview-based study was conducted subsequently with a set of representative experts to refine potential areas of future research. FindingsBy systematically reviewing the extant literature, the authors identified five key themes - hybrid business models, ecosystem effects, resource orchestration, legitimacy building and founder identity impact that have driven the research directions so far. The discussion with experts confirmed the growing importance of digital in helping social enterprises to scale. Furthermore, the authors have proposed an integrated framework to evaluate the value dimensions of DSE and areas for future research. Research limitations/implicationsThis study is focused on the development of existing knowledge in the area of DSE which is getting enhanced. Hence, a similar study needs to be conducted in two years to review progress. The pilot study was done with limited experts and a future expansion of a similar study must be done. Theoretical implicationsBased on the findings from the discussion with experts and insights from the literature review, the authors propose a four-dimensional value framework to comprehensively understand the DSE value proposition. Further to that, the authors elaborate on a few areas for future research that can benefit the DSE field. Practical implicationsWith the rising need for the social and solidarity economy to find scalable solutions, this study provides valuable inputs for practitioners on how the field of DSE is developing. The study brings valuable insights into the role of institutional support as well as nurturing ecosystem to create a favorable climate for the growth of digital social enterprises. Social implicationsSocial enterprises play a vital part in solving social problems in developing countries. A comprehensive understanding of the DSE field helps in crafting supportive policy decisions. Originality/valueThe present study is one of the first focusing on the area of DSE combining a literature review and qualitative study.