Supply chain bottlenecks are critical constraints that disrupt the smooth flow of goods, often triggering delays that cascade through interconnected processes and weaken overall resilience. In an era of digital transformation, the ability to anticipate such constraints through real-time data and predictive analytics has become increasingly important, yet the existing literature remains fragmented across transportation, logistics, and information systems, leaving conceptual boundaries blurred and practical insights underdeveloped. Addressing this gap, we conducted a bibliometric review of 3638 Scopus-indexed publications from 2016 to 2025, using Bibliometrix and VOSviewer to analyse publication patterns, keyword clusters, and thematic evolution. The findings reveal a strong concentration in intelligent transportation systems, with traffic congestion emerging as the most frequent theme, and four clusters identified around traffic management and detection, congestion and travel-time modelling, data infrastructure and analytics, and short-horizon flow control. A clear shift is observed between 2016–2020 and 2021–2025, marked by the growing prominence of machine learning and deep learning approaches. The study contributes by distinguishing transport-level prediction from supply chain-level bottleneck management. It further proposes a research agenda that emphasises cross-context validation, prediction-to-action integration, and explicit tests of forecast usefulness, thereby offering both theoretical clarity and practical guidance for strengthening supply chain performance.
Abstract The increasing prevalence of green marketing and growing consumer concern about environmental sustainability has heightened interest in understanding the psychological factors that encourage responsible consumption. However, limited research has simultaneously examined how consumer skepticism toward environmental claims and voluntary simplicity influences reduced consumption and subsequent avoidance behavior, particularly in emerging economies. Drawing on complementary insights from the Theory of Planned Behavior, the Value–Belief–Norm framework, and Sustainable Consumption Theory, this study investigates the relationships among consumer skepticism, voluntary simplicity, reduced consumption, and avoidance behavior. Data were collected from 395 consumers in Pakistan through a structured questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings demonstrate that both consumer skepticism and voluntary simplicity exert significant positive effects on reduced consumption, which subsequently promotes avoidance behavior. Furthermore, reduced consumption significantly mediates the relationships between consumer skepticism and avoidance behavior, as well as between voluntary simplicity and avoidance behavior, highlighting its role as a key behavioral mechanism through which consumers translate cognitive evaluations and value-based lifestyle orientations into sustainable marketplace decisions. By integrating cognitive and value-based perspectives within a sustainable consumption framework, this study extends the understanding of voluntary simplicity behavior and provides empirical evidence from an emerging economy. The findings offer practical guidance for organizations and policymakers seeking to encourage responsible consumption through transparent environmental communication and authentic sustainability practices.
Abstract As artificial intelligence (AI) becomes increasingly integrated into economic activity, its implications for development in sub-Saharan Africa (SSA) require greater empirical attention to country-level differences in productive and institutional capacity. This study examines the relationship between AI and economic growth across 36 SSA countries from 2010 to 2022 using a two-step system generalized method of moments estimator. The results show a positive and significant relationship between AI and economic growth. Mediation analysis indicates that industrial development is an important transmission mechanism, with approximately 23.6% of the total AI–growth effect operating indirectly through industrial development. The results also show that regulatory quality conditions the AI–growth relationship, as the positive interaction between AI and regulatory quality indicates a stronger growth relationship at higher levels of regulatory quality. Heterogeneity analysis reveals no significant AI–growth relationship among low-income countries, whereas the relationship is positive and significant among both lower-middle- and upper-middle-income economies. The study therefore contributes to the emerging macroeconomic literature on AI in SSA by identifying industrial development as a transmission mechanism and regulatory quality as a conditioning factor, while highlighting substantial heterogeneity across income groups. The findings support development strategies that strengthen productive capabilities, institutional quality, and complementary infrastructure alongside AI-related investment.
Abstract Purpose Women form a large and growing share of the global workforce, yet they carry a disproportionate share of unpaid care work and experience higher rates of common mental disorders, which makes their workplace mental health an economic as well as a public-health concern. Evidence on this issue remains fragmented across disciplines, sectors and regions. This review consolidates two decades of research (2006–2026) to map its theories, contexts, characteristics and methods, to develop an integrated conceptual model, and to set a structured agenda for future research. Methodology Following PRISMA 2020, a Scopus search returned 664 records (663 after removing one duplicate), of which 84 studies met the eligibility criteria. Study quality was appraised with the Mixed Methods Appraisal Tool (MMAT 2018), keyword co-occurrence was mapped in VOSviewer, and the evidence was synthesised using the Theory, Context, Characteristics and Methodology (TCCM) framework. Findings Publications have risen sharply since 2020, a period that coincides with the COVID-19 pandemic (2020–2026: n = 51, 60.7% of the corpus). India and the USA (n = 12 each) dominate, followed by Malaysia (n = 8), while Sub-Saharan Africa and Latin America are under-represented. JD-R model (n = 11), Border Theory (n = 8) and COR Theory (n = 6) are most used. Most studies are cross-sectional surveys (60.7%), with few longitudinal (9.5%) and experimental (4.8%). Three thematic clusters are identified: Work-Family Conflict, Burnout and Emotional Distress; Gender, Stress and Mental Health; Occupational Stress and Work-Life Balance. Originality This is the first review to apply the TCCM framework to women’s workplace mental health. TCCM moves the synthesis beyond description by exposing theoretical fragmentation (limited use of intersectional and feminist lenses), contextual blind spots (the Global South and under-studied sectors), under-examined characteristics (moderators and life stage) and methodological gaps (few longitudinal and experimental designs). Anchored in Conservation of Resources theory, an integrated model links antecedent (work demands, family demands, organisational culture, individual characteristics), proximal stressors (work–family conflict and occupational stress), mediators (work-life balance, job satisfaction, emotional intelligence, recovery experiences) and consequences (burnout, turnover intention, psychological well-being, job performance). Implications Because work–family conflict is the main pathway to burnout and psychological distress among working women, organisations can protect women’s mental health by training supervisors in family-supportive behaviours, offering flexible working arrangements backed by anti-presenteeism norms, and giving priority to structural interventions (workload, pay and career equity) over individual coping programmes alone.
Financial inclusion and business resilience in Sabah, Malaysia, continue to face significant challenges, as acceptance of microfinance services among rural micro, small, and medium enterprises (MSMEs) remains uneven. This study analyses the interaction among attitude, subjective norms, perceived behavioural control, perceived benefit, perceived risk, and entrepreneurial motivation to predict high and low intentions to adopt microfinance. A total of 135 rural MSME operators in Sabah were surveyed, and the data were analysed using fuzzy-set Qualitative Comparative Analysis (fsQCA). The necessity analysis reveals that none of the conditions is strictly required for high intention, but attitude and subjective norms are highly relevant. The sufficiency analysis reveals three equifinal configurations that result in high intention, and the core configurational condition that is common across the solutions is favourable attitude. Low intention, on the other hand, is primarily associated with a lack of perceived benefit, suggesting a causal asymmetry between high and low intention. This study has revealed that the intention of the rural MSMEs to adopt microfinance is not based on a single predictor, but on various combinations of social, cognitive, motivational, and risk factors. The study enriches the microfinance and financial inclusion literature by highlighting the importance of a configurational approach and can inform the design of more targeted, trust-building, and capability-enhancing interventions by MFIs and policymakers for rural entrepreneurs.
