Search Results (3,243)

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

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

Objective: This in vitro study aimed to compare the layer-matched color compatibility of three 3Y/4Y/5Y multilayer zirconia grades marketed in shade A2. Materials and Methods: Disc specimens (18 mm × 1.5 mm) were milled from pre-shaded multilayer zirconia blanks (Katana™ Multi-Layered Zirconia; Kuraray Noritake Dental Inc., Tokyo, Japan) in three grades: UTML (5Y), STML (4Y), and HTML (3Y). Twelve discs per grade were polished and measured on a neutral-gray background (Munsell N7) using a dental spectrophotometer (VITA Easyshade Advance 4.0; VITA Zahnfabrik, Bad Säckingen, Germany) at the incisal, middle, and cervical thirds. Color differences were calculated using CIEDE2000 (ΔE₀₀). Yttria content (wt%) was determined using EDS (JSM-7800F; JEOL Ltd., Tokyo, Japan), and phases were assessed using XRD (X’Pert PRO; Malvern Panalytical, Almelo, The Netherlands); microstructure and grain size were examined using FE-SEM after thermal etching. Statistics: A two-way mixed-design ANOVA with Bonferroni adjustment (α = 0.05) was conducted. Results: A significant incisal-to-cervical gradient was observed within each grade (p < 0.001), whereas layer-matched inter-material differences were small (all ΔE00 < 1.0), i.e., below the commonly accepted perceptibility threshold. EDS confirmed the expected stepwise decrease in Y₂O₃ from UTML to HTML, accompanied by corresponding changes in phase constitution and grain size. Conclusions: Despite compositional and microstructural differences, the three multilayer zirconia grades showed no clinically perceptible layer-matched color differences, supporting their combined use in extended rehabilitations while maintaining the natural-like color gradient across the multilayer blank. Full article

This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over 30% of the S&P 500—the analysis confronts a small-N, large-P environment where economically structured dimensionality reduction is essential. Using 154 firm-level characteristics categorized into 13 economic themes, we evaluate linear, penalized, tree-based, and neural network models in a small-N, large-P setting. Unrestricted models suffer substantial overfitting and fail to outperform the historical average benchmark out-of-sample. In contrast, theme-based models generate economically meaningful and regime-dependent predictive gains. Short-Term Reversal and seasonality exhibit stronger expansion-period predictability, while size and profitability perform better during recessions. Regularized linear models provide the most stable performance in limited-data environments, whereas nonlinear ensemble methods improve only when training windows are extended. The findings underscore the importance of economically structured dimensionality reduction and adaptive factor allocation in managing concentration risk among systemically important mega-cap firms. Full article

This study explores how board characteristics impact corporate cash flow risk in an emerging market setting. While previous research has examined firm risk, crash risk, and earnings quality, there is limited evidence on cash flow risk and its governance factors, especially in developing economies. To fill this gap, this study differentiates between volatility-based and distortion-based measures of cash flow risk and assesses how board attributes influence these aspects. Using a balanced panel of 327 non-financial firms listed in Vietnam from 2013 to 2023, cash flow risk is measured by the rolling five-year volatility of operating cash flows and short-term distortions shown in earnings–cash flow mismatches. To address endogeneity and dynamic persistence, the analysis uses the system generalized method of moments estimator, along with fixed-effects and feasible generalized least squares models for robustness. The findings suggest that board independence, gender diversity, and financial expertise are linked to lower cash flow risk, highlighting the importance of effective monitoring. Conversely, board meeting frequency is positively linked to risk, suggesting that boards tend to increase meeting frequency as a reactive response to heightened uncertainty. Board size and CEO duality do not show consistent effects. Focusing on Vietnam’s institutional context, this study provides evidence that governance mechanisms influence different dimensions of cash flow risk through separate channels, offering valuable insights for enhancing board effectiveness in emerging markets. Full article

Smallholder poultry farming contributes enormously to rural livelihoods, food security, and nutrition in South Africa, yet the poultry industry remains constrained by limited participation and low ICT utilisation. This study investigated the socioeconomic and demographic factors influencing decisions and choices of smallholder poultry farmers towards the adoption of ICT and market engagement in Jozini Local Municipality, KwaZulu-Natal. A cross-sectional research design was used to collect primary data from respondents. Data were collected through face-to-face surveys from 162 participants, who were randomly selected. Descriptive statistics were employed to profile the use and extent of ICT, while the multivariate probit model was used to analyse the determinants of ICT adoption and market engagement. The findings revealed that most farmers own ICT tools such as mobile phones (98.15%), which they mainly use for communication purposes (98.77%) rather than for accessing production and market related information. Smallholder characteristics like age, faming experience, marital status, and household size significantly influenced farmers decisions and choices to adopt ICT and participate in markets. The study recommends improving the traditional extension through digital integration and farmer support by means of training on ICT and formal market linkages. These interventions can significantly market participation and profitability in smallholder poultry farming, stabilising rural economic development. Full article

This article analyzes the scale, fluctuations and geographical distribution of olive (Olea europaea) cultivation in Palestine over 550 years, from the Late Mamluk period (1300–1517), through the Ottoman era (1517–1917), until the end of the British Mandate in 1947. Although olive oil played a dominant role in the diet and the local economy, there is currently no research that measures and quantifies the number of olive trees or the number of villages and towns that cultivated olive trees and produced olive oil. We reconstruct the agricultural landscape with its vast olive groves and examine the cultural history of olive tree farming, the growth of the olive oil industries and their economic role and importance. The earliest figures we have, that are from the year 1596, show that 400 villages cultivated 1,400,794 olive trees. By 1943, there were 6,053,367 olive trees that were cultivated by 644 villages. We found a strong correlation (R² = 0.96, p < 0.01) between the number of olive trees and the number of villages, indicating that olive oil demand and the olive oil industry align with population size. The research data derives from a variety of medieval local chroniclers, as well as diaries by European, North African and Middle Eastern travelers who provide descriptions of olive groves and the olive oil industry. Among the most important sources are the 1596 Ottoman tax registers. The tax registers are the first document that present clear-cut figures on the numbers of olive trees, olive presses and the names of the villages that cultivated olive groves. The main sources for the last period dealt with in this study are the British Mandate maps (1943), which display the acreage of the different crops across Palestine. The data from the maps is supplemented by two modern works on olive cultivation written by agronomists Assaf Goor (b. 1894) and Ali Nasouh (b. 1906) who were born in Palestine and employed by the British department of agriculture. The analysis of data shows that demands of local and oversea markets; the olive oil soap industry, which was based on the local olive oil; as well as competing agricultural crops like sugarcane, cotton and citrus, contributed to a complex economic structure. Olive tree cultivation did not depend on government investment. Olive groves in Palestine were rain fed, and, except for the harvest, they required relatively few working days a year. Hence, moderate policies (low taxation during periods of drought and low yields) adopted by enterprising local rulers and the central British government created a unique and relatively balanced relationship between rulers and farmers, which encouraged olive cultivation and led to a constant increase in the number of olive trees and the development of the olive oil industry. Full article

International tourism plays an important role in the global service economy, contributing to trade, employment, and regional development. For this reason, identifying the factors that influence tourist flows is an important issue for tourism policy, market strategy, and infrastructure planning. A large body of research has applied gravity models to analyze tourism flows between countries. While this approach provides a clear economic interpretation, it is usually based on linear specifications and may therefore capture only part of the relationships present in tourism data. This study examines the economic and geographic determinants of international tourism flows to Mongolia using a framework that combines a traditional gravity model with machine learning techniques. Mongolia serves as an instructive empirical setting, a landlocked, geographically peripheral destination whose inbound demand determinants have received limited systematic empirical attention. The analysis uses panel data for 27 origin countries covering the period from 2000 to 2024. In the first stage, a gravity model is estimated to assess how tourism flows relate to economic size and geographic distance. The results show that tourism flows tend to increase with the economic size of origin and destination countries, while greater geographical distance is associated with lower tourism flows. The estimated distance elasticity ranges from approximately −1.85 to −2.10 across model specifications, which is larger in absolute terms than the values typically reported in cross-country studies. This result is consistent with the relatively high travel cost barriers associated with Mongolia’s geographic location. These findings are consistent with the distance decay relationship commonly reported in the tourism literature. In the second stage, machine learning algorithms, including Random Forest, LightGBM, and XGBoost, are used as complementary interpretive instruments rather than forecasting tools to explore possible nonlinear relationships among the explanatory variables. To make the results more interpretable, the contribution of individual variables is examined using SHAP (Shapley Additive Explanations). The machine learning results indicate that some relationships in tourism demand may be nonlinear and not fully captured by the linear gravity specification. Specifically, distance sensitivity is approximately 6.5 times greater in nearby markets than in long-haul markets, with a structural inflexion at around 5700 km. Further analysis suggests that the influence of geographical distance is not uniform across all markets. In particular, tourism flows originating from middle-income countries appear to be more sensitive to increases in travel distance than those from higher-income countries. Overall, the findings indicate that economic size and geographical distance remain key determinants of international tourism flows to Mongolia. At the same time, the use of machine learning methods provides additional insight into potential nonlinear patterns in tourism demand. By combining econometric modelling with explainable machine learning techniques, the study offers an integrated analytical perspective for examining international tourism flows at geographically peripheral destinations where standard gravity assumptions may be insufficient. Full article

The global division of labor system is increasingly refined, and the core components of some manufacturing enterprises are concentrated in a few (or even a single) suppliers, resulting in supply dependence. Excessive concentration of suppliers can lead to a higher risk of supply chain disruption. To this end, taking manufacturing companies listed on the Shanghai and Shenzhen A-share markets in China from 2010 to 2024 as samples and referring to Huazheng ESG rating data, research shows how the ESG performance of manufacturing companies reduces supplier concentration. The research found that (1) the ESG performance of manufacturing enterprises significantly reduces supplier concentration,—this effect is mainly reflected in social responsibility (S dimension)—and firm size has a positive moderating effect; (2) ESG performance has a mediating effect of alleviating financing constraints and enhancing trade credit in the process of reducing supplier concentration; and (3) heterogeneity analysis results show that the inhibitory effect of ESG performance on supplier concentration is more significant in non-state-owned enterprises. Through empirical analysis, the research scope of ESG performance was expanded to the upstream supply chain field, emphasizing the importance of ESG performance in manufacturing enterprises and providing theoretical and empirical evidence for enterprises to achieve high-quality and sustainable development. Full article

This study employs a sample of 25 active and 22 passive AI ETFs to examine several issues surrounding their performance, risk, pricing efficiency, and persistence in pricing discrepancies and their impact on ETFs’ performance combined with the respective impact of intraday volatility. The relationship between AI ETFs’ performance and market factors concerning size, value, profitability, investment and momentum is evaluated too. The results indicate that the passive AI ETFs have outperformed active ones over their entire trade history, without, however, shouldering their investors with materially higher volatility. Moreover, both AI ETF groups trade at a persistent premium to their NAV. The concurrent premium positively affects return, while the one-period lagged premium is negatively related to return. In addition, a negative relationship between return and concurrent intraday volatility and a positive (but less strong) relationship between return and one-period lagged intraday volatility are found. Moreover, the majority of AI ETFs do not generate significant alphas. Finally, market factors effectively explain the performance of AI ETFs. Full article

This research analyzed the role of Green Marketing (GM) and Artificial Intelligence (AI) in promoting Corporate Sustainability (CS) across the environmental, social, and economic dimensions within the industrial sector in the Palestinian territories. Given the limited empirical evidence from developing and resource-constrained contexts, an explanatory sequential mixed-methods design was employed. The quantitative phase involved a survey of 500 valid respondents, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The quantitative findings were complemented by fifteen in-depth semi-structured interviews to further interpret and validate the survey results. The results indicate that GM showed the largest effect size and functions as a strategic approach for embedding sustainability values into organizational activities. AI also demonstrated a positive and supportive role by enhancing operational efficiency and monitoring capabilities within industrial processes. The interaction between AI and GM showed a statistically significant but relatively small effect, particularly in the social sustainability dimension, suggesting that AI may help reinforce the effectiveness of green marketing practices. The qualitative findings further illustrate how GM contributes to internal accountability, eco-design initiatives, stakeholder trust, and competitive positioning, while AI supports waste management, resource optimization, employee safety monitoring, forecasting accuracy, and sustainability reporting verification. Overall, the results suggest that GM and AI jointly contribute to improving corporate sustainability practices, with GM providing strategic direction and AI supporting operational implementation. This study contributes to the literature on sustainability, marketing, and digital transformation by providing empirical evidence on the interaction between green marketing and artificial intelligence in promoting corporate sustainability within a developing-country context. Full article

This study examines the firm-specific factors associated with the level and quality of compliance with the International Integrated Reporting Framework (IIRF) among companies in the United Arab Emirates (UAE), an emerging economy characterized by a growing sustainability-oriented institutional context. Although the Securities and Commodities Authority (SCA) mandates listed companies to publish an integrated report, it does not prescribe a specific reporting framework. As a result, alignment with the IIRF and the depth of disclosure remain largely discretionary. Using a sample of 89 non-financial firms listed on the Dubai Financial Market (DFM) and Abu Dhabi Securities Exchange (ADX), an Integrated Reporting Disclosure Score (IRDS) was constructed through content analysis based on 43 criteria derived from the IIRF. Regression and dominance analyses were employed to examine the relationship between firm characteristics and the level of IIRF compliance. The results indicate that firm size, profitability, board size, and gender diversity are positively associated with higher levels of IIRF alignment and disclosure quality, while financial leverage and board independence are not significantly associated with disclosure levels. The dominance analysis further shows that firm size, board size, gender diversity, and profitability account for the majority of the model’s explanatory power. Overall, the findings contribute to the literature by providing empirical evidence on voluntary compliance with international integrated reporting standards beyond mandatory reporting requirements in an emerging market context. Full article

Reliable emission monitoring is essential for effective environmental regulation and the operation of carbon markets. However, high-frequency CO₂ data from Continuous Emission Monitoring Systems (CEMS) and material-based monitoring often contain inconsistencies arising from operational variability, sensor drift, and data-processing errors. This study develops a transparent statistical framework to screen the quality of CO₂ emission data by integrating CEMS measurements with material-based estimates in a coal-fired power plant. A correlation ratio between the two monitoring approaches is used as a process-level indicator, and four statistical tests, Mann–Whitney U, Bootstrap, Levene, and Dip tests, are applied to detect distributional deviations associated with anomalous behavior. Using one year of high-resolution data, we evaluate the influence of reference dataset size, anomaly magnitude, and anomaly duration on detection performance. The results show that approximately 700 reference samples are sufficient to establish a stable baseline. Anomalies corresponding to daily emission deviations of about 4% or higher, when sustained over several days, can be reliably identified as anomalous at the monthly scale. A composite risk score is further developed to support monthly data screening and risk-based verification. The proposed framework provides a practical tool to improve the reliability of emission data and supports more transparent and efficient environmental monitoring and regulatory oversight. Full article

Generative artificial intelligence (GenAI) is increasingly portrayed as a transformative technology capable of simultaneously enhancing operational efficiency and enabling strategic growth. Yet small and medium-sized enterprises (SMEs) experience heterogeneous outcomes, suggesting that GenAI does not generate value uniformly across firms. This study develops and empirically informs a contingency framework explaining how distinct GenAI functions relate to differentiated strategic objectives and how technological, organizational, and environmental (TOE) readiness conditions shape this relationship. Using a three-round Delphi study with an interdisciplinary expert panel, including GenAI consultants, corporate managers, legal experts, academic researchers, and public-sector policymakers, we identify six core GenAI functional domains associated with efficiency-oriented and growth-oriented strategies. The findings suggest that operational automation and data intelligence are more strongly associated with efficiency objectives, whereas market intelligence, market testing, linguistic expansion, and idea generation are more closely related to growth objectives, although none is exclusively linked to a single strategic goal. Importantly, TOE readiness is found to play a key role in shaping the extent to which function-specific GenAI deployment translates into realized strategic value, with organizational readiness appearing more prominent than technological or environmental conditions. By shifting the focus from adoption to function-specific strategic alignment and readiness configurations, this study advances understanding of GenAI-enabled strategic value realization and heterogeneous transformation pathways in SMEs. Full article

This study examines the relationship between house prices and the factors driving their growth during the transition from a long-standing currency board regime to Eurozone membership. The main objective is to identify and quantify the key factors explaining the variation in house price growth in Bulgaria under conditions of prolonged currency convergence. The study applies a set of econometric techniques, including stationarity tests (ADF and KPSS), diagnostic checks for normality, serial correlation and heteroscedasticity, and robustness checks. The study is based on 40 quarterly observations covering the period 2015Q4–2025Q3 and 48 selected predictors of the General house price index. The final ARIMAX(0,2,1) model is estimated using second-differenced data. The model includes a first-order moving average component and three exogenous regressors: the owner-occupiers’ housing expenditures, the actual rentals for housing in Bulgaria and the homeowners’ utility expenses. The model explains 87% of the variation in house price acceleration, with a comparatively low mean squared error. The diagnostic analysis confirms model adequacy. The three exogenous regressors are statistically significant at the 1% level with strong and stable effects on house price dynamics. No statistically significant relationship is found for the set of traditional macroeconomic, demographic, financial, and sectoral factors. The results show that during Bulgaria’s transition from a currency board to the Eurozone, the sustained house price growth was driven by country-specific factors. The three statistically significant determinants of the house price acceleration in Bulgaria reflect, respectively, the active investment behaviour of homeowners in improving existing properties, the rational assessment by housing market participants of the balance between mortgage and rental payments, and the burden of utility and maintenance costs borne by owners and tenants, depending on property size and energy efficiency. The first factor is most influential for homeowners, the second for tenants, and the third has a similarly significant impact on both groups. Full article