Vimal Kumar

The integration of Environmental, Social, and Governance (ESG) practices in family firms is gaining importance, yet research in this domain remains theoretically fragmented and empirically inconclusive. This study presents a multi‐method systematic review of 73 peer‐reviewed articles, integrating bibliometric analysis with the Theory, Context, Characteristics, and Methodology (TCCM) framework and a nomological network to synthesise the intellectual landscape of ESG research in family firms. Five thematic clusters are identified‐corporate governance, socioemotional wealth, ESG disclosure, sustainability performance, financial performance, and ownership composition‐reflecting a broader disciplinary shift from governance‐centric to sustainability‐oriented inquiry. Socioemotional wealth and stewardship theory emerge as the dominant theoretical lenses explaining ESG adoption in family firms. The findings carry meaningful implications for investors, policymakers, and family firm executives seeking to align ownership values with contemporary sustainability demands. This study offers the first comprehensive synthesis of this intersection and charts a structured agenda for future research.

This study investigates how cultural intelligence (CQ), e‐leadership (EL) and psychological well‐being (PWB) influence perceived job performance (PJP) within hybrid work environments. It also examines the mediating effects of EL and PWB, offering insights into workplace adaptability and psychological development in culturally diverse settings. A quantitative survey was conducted with 303 full‐time employees in India who work in hybrid arrangements. The snowball sampling method was used for data collection, where existing subjects recruit future subjects from among their acquaintances. Using Partial Least Squares Structural Equation Modelling (PLS‐SEM), the study tested both direct and indirect pathways among CQ, EL, PWB and PJP. The analysis revealed that CQ significantly enhances EL and PWB. Both EL and PWB positively influence PJP, and they partially mediate the relationship between CQ and PJP. These results demonstrate how culturally and psychologically adaptive traits shape performance in modern work environments. Situated within Social Cognitive Theory and enriched by principles of developmental psychology, the study positions CQ, EL and PWB as adaptive resources that support personal growth, self‐efficacy and role performance across the adult life course in hybrid professional settings. The findings may help educational institutions design staff‐development programmes focusing on CQ, digital leadership and PWB. However, further research with education‐sector employees is needed to confirm their applicability. These findings inform not only managerial practice but also the broader conversation on employee development in the digital age. This study bridges organizational behaviour and human development theory, offering a holistic view of how culturally intelligent, psychologically resilient individuals thrive in hybrid environments. It emphasizes psychological adaptability as a key component of workforce sustainability and developmental success.

Decarbonising freight transport and warehousing is now central to national climate commitments, yet the firm‐level enablers that move green logistics from intention to reported performance remain unevenly understood across economic contexts. This study examines how seven enabler constructs influence green logistics performance and whether their effects and configurations differ between developing and emerging economies. Drawing on a survey of 220 logistics and supply chain managers in India and China, the study applies partial least squares structural equation modelling (PLS‐SEM) with multi‐group analysis (MGA) to estimate net effects, complemented by fuzzy‐set qualitative comparative analysis (fsQCA) to surface equifinal configurations. The seven enabler constructs were operationalised by inverting the empirically validated barrier dimensions established in a companion Total Interpretive Structural Modelling (TISM) study, a grounding that the authors argue strengthens construct validity. Top management commitment and green supply chain integration emerge as the proximate drivers of performance, while knowledge and skill readiness unlock digital infrastructure readiness. MGA reveals that the digital‐integration path is significantly stronger in emerging economies, whereas the regulatory‐to‐management path is stronger in developing economies. Endogeneity checks and comparisons against reversed specifications support the modelled directions. fsQCA identifies three sufficient configurations in developing economies and two in emerging economies, all built on a shared core of integration and managerial commitment, and demonstrates that no single enabler is necessary. The configurations diverge in what completes that core, with eased cost constraints and regulatory obligation forming substitutable routes in developing economies and cost ceasing to discriminate in emerging ones. By integrating a symmetric variance‐based method with an asymmetric configurational method across two economy types, and by grounding the enabler set in a prior structural model of barriers, the study offers a context‐sensitive account of how green logistics performance is achieved and provides differentiated guidance for policymakers and managers.

Purpose: The study looks at the ways that digital readiness affects the adoption of artificial intelligence (AI) and, in turn, the improvement of livelihoods for informal supply-chain workers. It focuses on the mediating functions of collaborative strategies and trust in AI tools. Design/methodology/approach: A cross-sectional survey design was used to gather information from informal supply-chain workers. Within the suggested framework, hypotheses were examined for direct and indirect effects using the structural equation modeling (SEM)-based Smart PLS technique. Findings: The results support the hypothesized model. Digital readiness had a significant positive influence on trust in AI tools, collaborative strategies, and AI adoption. Both trust and collaboration were found to be significant direct antecedents of AI adoption. Crucially, the relationships between digital readiness and AI adoption were partially mediated by trust and collaborative strategies, confirming that these factors are vital pathways. Finally, AI adoption showed a positive and significant relationship with improvement in overall livelihoods. However, there was a statistically nonsignificant direct relationship between digital readiness and livelihood improvement. This suggests that livelihood gains are achieved mostly by successful implementation of AI-supported tools and applications rather than through digital readiness alone. Research limitations/implications: The outcomes of this study are particularly important for frontline workers, managers, and industry practitioners in the supply chain and strategy domains, as they show how integrating digital technologies into everyday work practices can improve efficiency, resilience, and long-term livelihood outcomes. Originality/value: Although the use of AI in informal supply chains is still modest, it offers a significant opportunity to improve livelihoods. Understanding the collaborative enablers that promote AI adoption among informal workers, who frequently operate with limited resources, represents a critical research gap.

Purpose: Manufacturing firms in the textile sector face simultaneous pressure to enhance responsiveness, maintain quality, shorten lead times, and align production with sustainability expectations. In the current Industry 4.0/5.0 environment, flexible manufacturing systems (FMS) are increasingly viewed as strategic enablers of product variety, speed and resource efficiency. However, managers still lack clear evidence on which performance variables deserve priority in textile settings. This study, therefore, identifies, classifies and prioritizes the most influential FMS performance variables for textile manufacturing. Design/methodology/approach: A triangulation design was adopted. First, a focused literature review was undertaken to compile candidate FMS performance variables. Second, semi-structured interviews with 17 experts and a questionnaire survey of 135 domain experts were used to validate the variables for the textile context. Third, 19 validated variables were classified into 3 major categories, quality, productivity and flexibility, and prioritized using the best-worst method (BWM), a multi-criteria decision-making approach selected for its lower comparison burden and stronger consistency. Findings: The study identifies 19 performance variables that are significant for FMS in textile industries and classifies them into quality, productivity and flexibility categories. The results show that quality-related variables dominate the overall ranking; automation emerges as the most influential quality variable, unit manufacturing cost is the leading productivity variable and the use of automated material-handling devices is the most important flexibility variable. These findings offer an evidence-based basis for sequencing FMS investment and improvement efforts. Research limitations/implications: The results are derived from expert judgements and a textile-sector case context and should therefore be interpreted with appropriate contextual caution. Even so, the study offers actionable implications for managers by showing which variables should be prioritized first under limited resources. The findings also suggest that future research should test the framework through larger cross-sector datasets and incorporate broader sustainability and digitalization variables. Originality/value: The originality of the study lies not simply in applying BWM, but in contextualizing FMS prioritization for the textile sector at a time when digitalization, sustainability and circular-economy objectives are reshaping manufacturing choices. The paper shows how textile-specific operational constraints influence the relative importance of FMS performance variables in ways that are not fully captured by prior generic manufacturing studies.

Purpose: This study aims to examine the impact of artificial intelligence (AI)-driven workflows on efficiency and collaboration, shaping employees’ attitudes and intentions. In addition, it theoretically contributes by linking AI adoption to different levels of collaboration, showing how trust and risk influence engagement. Design/methodology/approach: This study conducted a survey with remote work employees in Indian information technology (IT) firms and received 386 respondents. The study further extended the unified theory of acceptance and use of technology (UTAUT2) model, and for a comprehensive analysis, partial least squares structural equation modeling using SmartPLS4 was used. Findings: The study findings underline the significant impact of AI adoption on employees’ attitudes and intentions. Results also demonstrate how trust and risk perceptions determine the depth of collaboration in remote AI-enabled work environments. It further provides insights into how traditional job practices adapt to an AI-integrated work environment. Research limitations/implications: Finally, this study contributes to understanding organizational adaptation in an AI-enabled environment and gives practical and managerial insights for organizational leaders, practitioners and policymakers while ensuring a trust- and ethics-focused AI system in remote work. The findings contribute to collaboration theory by empirically showing how trust enables, and risk constrains, effective collaborative engagement in remote work. Originality/value: The rapid use of AI in remote work scenarios in Indian IT firms influences collaboration and work efficiency. However, this scenario is hindered by certain challenges related to stakeholder and employee trust and ethical concerns. This study provides a novel integration of UTAUT2 with collaboration frameworks, emphasizing the theoretical link between AI adoption, trust, risk and collaboration levels.

Type of the article: Research ArticleAbstractGamified marketing has become an increasingly critical strategy in digital commerce, yet the full pathway from consumer attitude through engagement to purchase behavioral intention remains insufficiently examined, particularly across demographic cohorts in emerging markets. This study investigates how gamification elements shape consumer attitudes, engagement and purchase behavioral intention among digital consumers in the Delhi National Capital Region of India. This quantitative study employs a structured survey administered between July and November 2025, yielding a final usable sample of 643 (56.8% male, 43.2% female). The study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM) within a Partial Least Squares Structural Equation Modelling (PLS-SEM) framework. The results demonstrate that perceived agility (β = 0.307, p < 0.001), perceived ease of use (β = 0.278, p < 0.001), perceived usefulness (β = 0.259, p < 0.001), perceived enjoyment (β = 0.248, p < 0.001) and social influence (β = 0.188, p < 0.001) are significant drivers of consumer attitude. Consumer attitude strongly predicts consumer engagement (β = 0.550, p < 0.001) and purchase behavioral intention (β = 0.334, p < 0.001), with engagement partially mediating the attitude–purchase behavioral intention relationship (β = 0.196, p < 0.001). Measurement invariance testing followed by multi-group analysis reveals significant generational and gender-based differences in gamification drivers, underscoring the need for demographically tailored strategies. These findings advance theory by confirming the TAM-UTAUT integrated model in a gamified emerging-market context, and offer practitioners a roadmap for designing differentiated gamification campaigns that enhance engagement and drive purchase intention.