Search Results (5,426)

Background: Small- and medium-sized enterprises (SMEs) constitute a large portion of the industrial energy demand in the emerging economies, but their shift to renewable energy is not well comprehended at the firm level. Bangladesh is a special case, since the country has adopted national commitments to Sustainable Development Goal 7 on clean energy, but the uptake of renewable energy by SMEs remains minimal due to complex socio-economic factors. Most of the literature has concentrated on household access to energy or national policy models, leaving a gap in empirically validated models of firm-level adoption in the manufacturing sector. Method: Based on the diffusion of innovation theory, institutional theory, and the resource-based view, this research paper formulates and empirically verifies a combined socio-economic model of renewable energy adoption. Partial least squares structural equation modeling (PLS-SEM) was used to analyze a cross-sectional survey of 426 owners and managers of manufacturing SMEs in Bangladesh’s textile and food processing sub-sectors. Findings: Four out of five hypothesized direct relationships were supported. The most important drivers were environmental orientation (β = 0.467, p < 0.001, f² = 0.413), market competitiveness (β = 0.287, p < 0.001, f² = 0.413), policy and institutional factors (β = 0.211, p < 0.001, f² = 0.413), and access to finance (β = 0.096, p = 0.004). Perceptions of cost did not become significant (β= −0.036, p = 0.279). Top management support significantly and negatively moderated the relationship between environmental orientation and adoption (β = −0.093, p = 0.003), possibly because it moderates the substitution mechanism in SME decision-making, which is highly centralized. The model accounted for 64.5% of the variation in renewable energy adoption (R² = 0.645). Conclusion: The results show that attitudinal and institutional factors tend to be more important than financial barriers in determining SMEs’ energy transitions. Environmental consciousness, market incentives, and streamlined institutional access should be the focus of policy interventions to hasten inclusive low-carbon transitions in emerging manufacturing economies. Full article

Corporate financial policy requires managers to balance financing, investment, and payout decisions while maintaining sufficient financial flexibility to respond to unexpected shocks and investment opportunities. Despite the importance of financial flexibility, limited empirical evidence exists on its determinants in African capital markets. Using panel data from 106 non-financial firms listed on the Johannesburg Stock Exchange over the period 2000–2019, this study examines the determinants of financial flexibility. Financial flexibility is identified by comparing actual and predicted leverage and classifying firms with persistent spare debt capacity as financially flexible. The main empirical model is estimated as a random-effects linear probability model with heteroscedasticity-robust standard errors. The results show that financial flexibility is significantly negatively associated with leverage and Tobin’s Q, indicating that firms with higher debt levels and stronger growth opportunities are less likely to preserve borrowing capacity. Retained earnings and financing cost show weak negative associations at the 10% significance level, while dividend payout, profitability, cash holdings, and tangibility are statistically insignificant. The study contributes to the corporate finance literature by providing new evidence from an African emerging market context, incorporating payout policy into the financial flexibility framework, and showing how leverage discipline and growth-related financing demands shape firms’ financial flexibility. Full article

Small and medium-sized enterprises (SMEs) are critical actors in promoting environmentally sustainable supply chains, particularly in emerging economies where their collective environmental footprint is substantial. Despite growing attention to green supply chain management (GSCM), research has predominantly focused on large firms, leaving the motivational drivers shaping GSCM implementation in SMEs underexplored. Addressing this gap, the present study develops and empirically tests a motivation-based framework to examine how four organizational motives, cost, market, ethical, and legitimacy, drive the depth of GSCM implementation in SMEs. In addition, environmental uncertainty is conceptualized as a key contextual contingency moderating the effectiveness of these motives. Drawing on survey data from Vietnamese SMEs, the findings reveal that all four motives positively influence implementation depth, with ethical motives exerting the strongest effect. Furthermore, environmental uncertainty significantly amplifies these relationships. By integrating multiple theoretical perspectives and emphasizing the contingent role of environmental uncertainty, this study advances GSCM research by providing a nuanced, context-sensitive understanding of how SMEs operationalize sustainability practices in dynamic and resource-constrained environments. Full article

Universities are increasingly expected to bridge the gap between higher education, skills development, and graduate employability, yet evidence-based approaches to curriculum–labour market alignment remain limited in Communication Studies. This study examines which ESCO-mapped occupational profiles and transversal competencies are represented in official curricula of leading Spanish Communication programmes (RQ1), how demand for communication-related occupations evolved in Spain over 2018–2023 (RQ2), and where the most salient alignment gaps emerge to inform curriculum redesign (RQ3). We used an explanatory sequential mixed-methods design combining documentary analysis of programme verification reports and national disciplinary documentation, an ESCO-based mapping of curricular profiles, and labour-market intelligence from 2,701,503 job postings (2018–2023) mapped to ESCO to analyse demand dynamics, volatility, and skill patterns. Results show strong curricular convergence around a shared core of ESCO profiles (71.8% of identified codes shared across institutions) alongside institution-specific specialisations (28.2%). Labour demand fluctuated markedly across the period and exhibited heterogeneous volatility by occupation, and transversal competency patterns differed significantly across professional groupings, supporting segment-specific interpretations of alignment and mismatch. Overall, ESCO combined with job-posting analytics provides a replicable framework for continuous curriculum calibration and employability-oriented programme redesign, particularly for hybrid profiles that integrate technical, analytical, relational, and ethically grounded capabilities. Full article

This article investigates the housing precarity of EU migrant workers in the Dutch–German border region, focusing on the Venlo Greenport area. Drawing on documentary analysis, 28 interviews, field observations, and stakeholder engagement, it explores how local governance, market dynamics, and framing practices shape housing outcomes. While EU law guarantees free movement, housing remains excluded from the EU rights frameworks, leaving workers dependent on employer-linked or agency-controlled short-stay facilities. These arrangements—often overcrowded, surveilled, and formally temporary—become long-term solutions, producing what we term short-stay sedentarism: prolonged residence in housing designed to deny permanence. The study conceptualises the local “battleground” where municipalities, employers, housing providers, NGOs, and residents negotiate competing interests. Seven interpretive frames—nuisance/disorder, cowboys, human rights, NIMBY, shadow power, integration, and unwanted accumulation—structure these debates, legitimising certain strategies while obscuring structural deficiencies. Findings reveal that certification and enforcement, while intended to improve standards, often entrench precariousness by sustaining the short-stay model. Emerging integration-oriented policies signal a shift but remain fragile amid economic imperatives and spatial constraints. The paper argues that addressing housing precarity requires structural reforms: expanding access to regular housing, reducing employer dependency, and recognising migrant workers as long-term residents rather than temporary labour inputs. Full article

This paper reviews recent work on dynamic virtual power plants (DVPPs) using an Energy–Information–Market framework. It addresses the important problem of how DVPPs can support low-inertia power system operation and feeder-level stability under high renewable penetration. First, system-level studies on low-inertia operation and frequency control are used to frame quantitative requirements on rate of change of frequency, nadir, and quasi-steady-state limits. Second, energy-layer models are surveyed, including participation-factor-based DVPP controllers, grid-forming architectures, model-free frequency regulation, and robust frequency-constrained scheduling for allocating virtual inertia and fast frequency response (FFR) across distributed energy resource fleets. Third, information-layer and market-layer models are reviewed, covering stochastic and robust bidding, distribution locational marginal price-based clearing, peer-to-peer and community markets, privacy-preserving coordination, and emerging governance and cybersecurity schemes for DVPP participation. Across these strands, much of the literature remains centred on steady-state active and reactive power dispatch, with dynamic security enforced as constraints rather than formulated as verifiable and tradable services. This review identifies gaps in dynamic metrics and benchmarks, forecasting of available inertia and FFR capacity, market-physics co-design, multi-aggregator interaction, and experimentally validated DVPP implementations. These findings suggest that DVPPs can “sell stability” at the feeder level only through co-designed control, information, and market mechanisms and outline a research roadmap for this purpose. Full article

In an era of rapid digital transformation, brand activism has emerged as a prominent strategy through which organizations seek to signal social and environmental commitment while engaging increasingly sceptical and digitally empowered consumers. Within this context, perceived authenticity has become a critical evaluative mechanism shaping how digital brand activism is interpreted and whether it contributes to sustainable consumption and trust-based market outcomes. This study examines how perceived authenticity in digital brand activism is associated with consumer trust, attitudes toward socially engaged brands, and behavioral intentions that support sustainable consumption. Grounded in attribution theory and the authentic brand activism framework, the study adopts a quantitative, cross-sectional design based on an online survey of 240 consumers. The findings indicate that perceived authenticity is strongly associated with higher levels of consumer trust and more favorable attitudes toward digitally activist brands, reinforcing authenticity as a key trust-building mechanism in digital environments. Trust and attitudes are, in turn, positively associated with behavioral intentions such as purchasing, recommending, and willingness to pay a premium for sustainable products. However, behavioral intentions are weaker than trust and attitudinal evaluations, providing evidence of a persistent attitude–behavior gap that limits the translation of positive digital evaluations into concrete sustainable consumption outcomes. Exploratory results further suggest that the association between perceived authenticity of brand and behavioral intentions operates primarily through trust and attitudes rather than through a strong direct relationship. By clarifying these indirect pathways, the study advances attribution-based explanations of digital brand activism and contributes to research on smart innovation and digital sustainability by highlighting the role of authenticity in trust-based market outcomes. It also underscores the importance of authentic, data-informed digital strategies for fostering consumer trust, aligning brand activism with ESG principles, and supporting sustainable growth in digitally empowered markets. Full article

This study evaluates the extent and quality of tax transparency reporting among the Top 40 firms listed on the Johannesburg Stock Exchange (JSE), distinguishing between mandatory tax disclosures and voluntary transparency practices. A qualitative, disclosure-based research design was employed, involving content analysis of publicly available annual reports, integrated reports, and sustainability reports. A structured tax transparency framework grounded in stakeholder theory and legitimacy theory, and adapted from prior empirical studies was applied to systematically assess tax-related disclosures. Findings indicate high compliance with mandatory tax disclosure requirements, reflecting strong adherence to accounting standards and regulatory obligations. In contrast, voluntary tax transparency shows considerable variation: firms predominantly provide narrative, policy-oriented, and governance-related information, while detailed, forward-looking, and jurisdiction-specific disclosures remain limited. The discussion highlights that voluntary transparency is shaped by stakeholder expectations, legitimacy concerns, and perceived reputational and commercial risks, leading to selective disclosure. Regulatory compliance emerges as the primary driver of tax reporting, whereas voluntary practices are influenced by firm-specific and contextual factors. The results hold relevance for investors, regulators, and policymakers seeking greater corporate accountability, and for standard-setters aiming to enhance the consistency and depth of tax transparency reporting. Overall, the study enriches the limited literature on corporate tax transparency in emerging markets by offering contemporary empirical evidence from South Africa and identifying key areas requiring improvement in voluntary tax disclosures. Full article

Population aging in middle-income economies produces macroeconomic and distributional consequences that aggregate frameworks cannot detect. This paper develops a multi-household CGE model calibrated to a 26-sector Social Accounting Matrix for Thailand (2024) and traces the labor, saving, and fiscal channels of aging across eleven counterfactual scenarios. Three findings emerge. First, aging’s primary macroeconomic cost operates through capital accumulation, not output contraction: investment falls seven times faster than the GDP under a savings-driven closure, because middle-aged households—the economy’s dominant net savers—compress lifecycle saving in response to aging. The saving channel alone amplifies the labor supply shock four-fold (range: 3.5–4.5). Second, aging can raise elderly welfare. When elderly households retain labor market attachment, wage gains from tighter factor markets outweigh declining capital returns—a welfare reversal invisible to representative agent and OLG frameworks by construction. The critical labor income threshold is ${\alpha }_L^{\ast }=35.5\%$ (range: 34.8–36.2%), confirmed across all participation increments tested (elderly welfare gain: THB 341–521 million). Third, no single instrument satisfies efficiency and equity simultaneously. Pension transfers crowd out investment nonlinearly above 12 percent of tax revenue (range: 10–14%); health demand expansion is the decisive complement that converts redistribution into a near-Pareto improvement. Policy complementarity is an empirical necessity, not a theoretical refinement. Collectively, these results reframe demographic aging as a factor price redistribution mechanism whose welfare incidence is determined by the cohort-level income composition—with direct implications for aging policy in middle-income economies facing rapid demographic transitions under tighter fiscal constraints than for advanced economies encountered at equivalent demographic stages. Full article

Accurate tail risk forecasting in emerging markets is frequently compromised by the nonlinear dynamics and time-varying long memory of high-frequency volatility. In this study, we employ multifractal detrended fluctuation analysis (MF-DFA) to decode the complex market behavior, revealing pronounced multifractality and strong persistence that defy the static assumptions of classical linear models. The multifractal analysis is only used for research motivation and model design, not as input features for the model. To bridge the gap between fractal diagnostics and predictive modeling, we propose an attention-based dynamically reweighted SA-HAR-J-Net framework. This architecture uniquely integrates HAR-style multi-horizon inputs with a bidirectional LSTM (BiLSTM) encoder and a temporal self-attention mechanism. Crucially, the attention module functions as a dynamic reweighting system, allowing the model to adaptively emphasize historical patterns that receive higher attention weights under changing market conditions, thereby mimicking the time-varying correlations inherent in multifractal processes. Furthermore, we incorporate jump proxies and realized higher moments to enhance the capture of extreme tail dynamics. Utilizing a strict expanding-window out-of-sample protocol, the proposed method achieves significantly lower quantile loss and superior calibration relative to established econometric and machine learning benchmarks for Value-at-Risk (VaR) forecasting. This work provides a robust framework for tail risk monitoring by effectively aligning deep learning architectures with the stylized facts of multifractal markets. Full article

Systemic digital innovation plays a pivotal role in driving firms’ future growth. As key decision-makers in strategic planning, executives play a critical role in promoting digital innovation. Therefore, how to effectively motivate executives to engage in systemic digital innovation remains an important research question. Drawing on principal-agent theory, this study examines how equity incentives promote systemic digital innovation, a form of firm-level digital technological innovation embedded in organizational governance and resource allocation systems. Using the panel data from Chinese A-share listed firms over 2007–2024, we investigate the governance mechanisms in a major emerging market context. The results show that equity incentives significantly promote systemic digital innovation. Managerial risk-taking and long-term orientation partially mediate this relationship, indicating that incentive alignment reshapes executives’ behavioral orientations toward intertemporal decision-making. Moreover, executives’ IT background strengthens the positive effect of equity incentives, whereas financing constraints weaken it. These findings highlight equity incentives as a governance mechanism that facilitates sustained systemic digital innovation in emerging market firms. Full article

Tourism’s digital transformation has reshaped how travelers search for and evaluate destinations. However, relatively little empirical work has examined how user engagement translates into booking intent, especially under the emergent discovery channels mediated by artificial intelligence (AI). This study tests an engagement-driven referral framework using longitudinal behavioral data from a Mediterranean destination portal (April 2022–January 2026; 1.6 million sessions). Engagement depth, measured as average session time, significantly predicts booking intent click rate. Mobile drives 83% of sessions, but desktop users convert at nearly twice the rate (5.69% vs. 3.37%). High traffic, as it turns out, does not equal high commercial intent. Lower-volume international markets routinely outperform the dominant domestic market. The most striking result concerns AI referrals. Traffic arriving from AI assistants converts at 8.26%, more than double the organic search rate of 3.88%, despite shorter sessions, a pattern consistent with compressed decision-making under generative AI. These findings, grounded in real travel portal data, extend engagement theory beyond transactional settings and shed early light on how referrals from AI assistants like ChatGPT or Gemini differ behaviorally from organic search, with practical implications for portal managers, destination marketing organizations (DMOs), and sustainable demand management. Full article

Incentive-based demand response (DR) programs rely on accurate and trustworthy quantification of customer performance to ensure fair compensation and market efficiency. Estimating the customer baseline load is an important part of this process. It shows how much electricity would be used if there were no DR occurrence. Unlike conventional load forecasting, baseline modeling is inherently unobservable, economically sensitive, and vulnerable to strategic manipulation. With the growing penetration of distributed energy resources, electric vehicles, and intelligent control technologies, traditional baseline estimation approaches face increasing limitations. This paper offers a thorough and future-oriented synthesis of baseline load estimation for incentive-based DR strategies. Current approaches are carefully classified into rule-based, statistical, probabilistic, machine learning (ML), and hybrid intelligence techniques, and their appropriateness for various DR services and client categories is rigorously evaluated. Beyond modeling accuracy, this paper emphasizes market-oriented requirements, including incentive compatibility, simplicity, transparency, privacy preservation, and deployment feasibility. Furthermore, emerging digital trust enablers such as blockchain and FL are reviewed, along with baseline-free and baseline-light alternatives for performance evaluation. Finally, open research challenges and future directions toward interpretable, robust, and market-ready baseline intelligence are discussed. Full article

The global landscape of sustainability challenges has become increasingly complex, characterized by varying regulatory frameworks and market maturity across different nations. The financial significance of environmental, social, and governance (ESG) factors is influenced by industry and firm-specific attributes. Therefore, this study employs an integrated decision support framework that combines grey relational analysis (GRA) models including Deng’s GRA, absolute GRA, and a second synthetic grey relational analysis (SSGRA) with firm-level panel regressions to compare ESG and financial performance linkages across 11 Middle East and Africa (MEA) countries and industrial sectors. Furthermore, the study utilized a sensitivity analysis to check the robustness of SSGRG. Results indicate considerable variability in the relationships between ESG and financial performance across the region. The economies of the Gulf Cooperation Council (GCC) showed the most robust positive relationship between ESG factors and financial performance based on SSGRG, with Kuwait (0.82), Qatar (0.81), and Saudi Arabia (0.80) predominantly influenced by the social and governance dimensions. Conversely, a weak correlation was demonstrated in Egypt (0.54), Nigeria (0.53), and Kenya (0.56). Moreover, financials, communication services, and materials sectors exhibit the greatest integration of ESG factors into financial performance, with composite SSGRG values ranging from 0.75 to 0.78. In contrast, the information technology and energy sectors demonstrate weak association, with composite SSGRG values falling below 0.60. Furthermore, a conservative maximin analysis reveals that corporate governance in Kenya and environmental performance in Oman are identified as the weakest relationship at the country level, while governance in the information technology and energy sectors, environmental management in real estate, and social performance in consumer discretionary sectors are highlighted as weak connections. This study addresses a gap in the literature by developing a novel decision-support framework, providing fresh empirical evidence from emerging markets, and offering theoretical insights into the into influence of stakeholder and institutional factors on ESG value creation. This study provides implications for investors, corporate managers, and policymakers on sustainable finance in emerging markets and presents a decision-making framework that emphasizes ESG initiatives to enhance financial performance. Full article

Vehicle-to-grid (V2G) technology has emerged as a key enabler for coupling large-scale electric vehicle (EV) deployment with the operation of smart energy systems. By allowing bidirectional power and information exchange between EVs and the grid, V2G transforms EVs from passive loads into distributed energy resources capable of supporting grid flexibility, reliability, and renewable energy integration. However, the practical realization of V2G remains challenged by technical complexity, system coordination, user participation, and regulatory constraints. This paper presents a comprehensive review of V2G integration from a system-level perspective. Rather than focusing solely on individual technologies, the review examines how V2G is embedded within smart energy systems, emphasizing the interactions among EVs, aggregators, grid operators, energy markets, and end users. Key enabling technologies, including bidirectional charging, aggregation mechanisms, communication frameworks, and data-driven control strategies, are discussed in relation to their system-level roles and limitations. The impacts of V2G on grid operation, energy management, and market participation are analyzed, with particular attention to reliability, battery lifetime, and user trust. Furthermore, this review identifies critical open issues that hinder large-scale deployment, spanning infrastructure readiness, standardization, economic incentives, and cybersecurity. Emerging application scenarios, such as building-integrated V2G, fleet-based services, and artificial intelligence (AI) supported coordination, are also discussed to illustrate potential evolution pathways. By synthesizing technological developments with system-level impacts and unresolved challenges, this paper aims to provide a structured reference for researchers, system planners, and policymakers seeking to advance the integration of V2G into future smart energy systems. Full article