Search Results (954)

The digital transformation of the textile industry poses unique challenges due to its labor-intensive processes, complex global supply chains, and coexistence of traditional methods and emerging technologies. Despite the urgency of this transition, existing digital maturity models lack sector-specific frameworks and often fail to integrate multi-criteria decision-making (MCDM) methodologies for quantitative performance assessment. This study addresses these gaps by proposing a novel digital maturity model tailored specifically to the textile sector. The research employs an integrated decision-making framework using the Method Based on the Removal Effects of Criteria (MEREC) to determine objective criterion weights and the Operational Competitiveness Rating Analysis (OCRA) method to rank firm-level digital maturity performance. The findings indicate that Strategy is the most influential dimension, whereas Technology receives the lowest weight. At the sub-criterion level, Management Support, Market Analysis, and Vision and Strategic Awareness are the most critical factors, while Technology Usage Competency is less influential. The performance evaluation shows that Company A3 achieves the highest level of digital maturity, whereas Company A2 ranks lowest. The robustness of the proposed framework is comprehensively validated through a scenario-based sensitivity analysis and a comparative evaluation using the Additive Ratio Assessment System (ARAS) method. Overall, the results suggest that successful digital transformation in the textile sector depends primarily on strategic vision and managerial support rather than on technological infrastructure alone. Full article

Background: The COVID-19 pandemic disrupted global dairy supply chains and threatened business continuity from farms to retail outlets. There is limited understanding of how operational-level managerial decisions supported resilience in this perishable sector. Methods: This study applies a systematic literature review based on PRISMA 2020 guidelines. It analyses 21 peer-reviewed studies published between 2019 and 2025 across 19 countries. Results: The findings identify 8 primary supply chain challenges. Adaptive responses are classified into traditional and innovative managerial adaptations. Traditional adaptations rely on established practices such as production adjustments, cross-training, and product reallocation to stabilise short-term performance. Innovative adaptations involve structural and analytical approaches such as network optimisation, digital coordination, and scenario planning to support long-term resilience. The results also reveal differences between developed and developing economies. Conclusions: Resilient dairy supply chains require both operational continuity and structural innovation. This study proposes a sector-specific classification of managerial adaptations and highlights directions for future research. Full article

Banquet services are a strategically important yet underexplored component of full-service hotel operations, characterized by event-based delivery and complex coordination. Existing service quality frameworks inadequately capture the operational, spatial, and experiential features of banquet contexts. This study developed and validated a banquet-specific service quality measurement scale for full-service hotels. Using a modified DINESERV instrument, data were collected from 216 U.S. respondents who had attended a hotel banquet within the previous 36 months. Exploratory factor analysis identified four dimensions, Facilities & Operations, Service Performance, Guest Care, and Venue Quality, with acceptable-to-strong reliability. The findings conceptualized banquet services as integrated, episodic service systems rather than transactional dining encounters. Managerially, the scale provides a practical tool for prioritizing hygiene, operational reliability, and venue functionality while supporting sustainable service improvement. This study offers an empirically grounded framework that advances banquet service quality research and practice. Full article

The construction industry is under mounting pressure to enhance its sustainability performance. Increasing project complexity and risk require real-time data collection, monitoring, and assistance in decision making via the Internet of Things (IoT). IoT has emerged as a critical enabling technology to overcome these hurdles. This study provides a bibliometric and thematic overview of IoT applications in sustainable construction project management to identify research trends, key themes, and practical implications for project managers. We used a structured screening process to analyze peer-reviewed journal papers, conference articles, and book chapters listed in the Scopus database. We identified 77 publications published between 2019 and 2025. Using VOSviewer_1.6.20_exe, we analyzed publication trends, source influences, geographical dispersion, and keyword co-occurrence patterns. Since 2023, research output and citation impact have increased dramatically, with sustainability, project management, and IoT serving as the main conceptual foundations recorded. Real-time monitoring, wireless sensor networks, safety improvement, BIM and digital twin integration, and resource and energy optimization are the five main application domains recognized using thematic synthesis. This shows a marked transition from standalone sensing applications to integrated, intelligent, and predictive systems that enable data-driven decision making throughout the construction lifecycle. This review highlights the ongoing difficulties associated with data quality, sensor dependability, system interoperability, and energy limitations. IoT is progressing from a support technology to a core operational and managerial infrastructure for sustainable construction, with major consequences for project management and future research. Full article

Big data analytics (BDA) has emerged as a crucial strategic asset for organizations aiming to enhance their sustainable company performance; nevertheless, empirical information elucidating the correlation between analytics and sustainability results is scarce, especially in developing nations. This study examines the influence of big data analytics (BDA) on sustainable firm performance (SFP) within the Libyan telecommunications sector, focusing on the mediating roles of organizational learning (OL) and process-oriented dynamic capabilities (PODCs), utilizing dynamic capability and organizational learning theories. A quantitative, cross-sectional research design was utilized. A systematic questionnaire was used to collect data from personnel at five different managerial and functional levels in the Libyan telecoms sector. There were 354 valid replies from a group of 5400 professionals who worked in the managerial, technical, and strategic areas. We used Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS 4.0 to look at the proposed research model. We used measurement scales from previous investigations. The findings demonstrate that BDA exerts a positive and statistically significant influence on SFP. Nonetheless, this direct effect is quite minor when juxtaposed with the indirect effects conveyed by OL and PODCs. Both organizational learning and process-oriented dynamic capabilities significantly and partially mediate the relationship between big data analytics (BDA) and sustainable performance. This shows that analytics-driven sustainability outcomes depend heavily on a company’s ability to learn from data and change how it does things. This study enhances the Business and Management literature by elucidating the inadequacy of analytics investments in producing robust sustainability outcomes. It emphasizes the essential function of supplementary organizational capabilities in converting data-driven insights into enduring economic, environmental, and social value. From a practical standpoint, the findings indicate that managers and policymakers in developing economies ought to prioritize learning systems and adaptive process capabilities in conjunction with digital investments to fully harness the sustainability potential of big data analytics. Full article

Despite the rapid expansion of AI and machine-learning techniques in bankruptcy prediction, classical statistical methods such as discriminant analysis and logistic regression remain relevant because of their transparency and interpretability. These characteristics are crucial for stakeholders who require understandable decision-making tools, especially in NACE Rev. 2 Section I—Accommodation and Food Service Activities, a sector characterized by high operating leverage, vulnerability to economic shocks, and strong macroeconomic importance. The study aims to evaluate and compare the predictive performance of discriminant analysis and logistic regression for bankruptcy prediction and to identify key predictors that can serve as managerial early-warning signals for companies in crisis across B9 countries. The sample of 4395 companies was used. The classification ability of all models is assessed using multiple performance metrics, including overall accuracy, sensitivity, specificity, precision, the F1-score, the F2-score, the Matthews correlation coefficient, and the area under the receiver operating characteristic curve. The results show that both approaches achieve consistently high predictive performance, with all major metrics exceeding 0.92 on the test sample of prosperous and non-prosperous enterprises. Six significant bankruptcy predictors are identified for each method, with three common indicators: financial leverage, total liabilities to assets, and return on costs. The comparative analysis results in a methodological “draw,” confirming comparable predictive power. These findings reaffirm the relevance of classical prediction models and identify key financial indicators that can be used as practical early-warning signals by managers in the sector. Full article

With the rapid acceleration of technological revolutions and industrial upgrading, firms are increasingly exposed to environmental uncertainty, intensified competition, and continuous technological disruption. Under such conditions, sustainable corporate development depends not only on innovation performance, but on the ability to sustain innovation activities over time. Innovation resilience, defined as the capacity to withstand shocks, reconfigure resources, and maintain innovation momentum, therefore represents a critical foundation of corporate sustainability. Using panel data from Chinese A-share listed firms from 2009 to 2024, this study examines how CEO power shapes sustainable innovation resilience. Drawing on upper echelons theory and signaling theory, we investigate the direct effect of CEO power, the mediating role of corporate reputation, and the moderating role of artificial intelligence adoption. Fixed-effects regression results indicate that CEO power is positively associated with sustainable innovation resilience, and this relationship is partially mediated by corporate reputation. Furthermore, artificial intelligence adoption strengthens the positive association between CEO power and innovation resilience. By linking executive governance, reputational mechanisms, and digital transformation to sustained innovation capacity, this study advances understanding of the organizational foundations of corporate sustainability under uncertainty. The findings provide theoretical insights and managerial implications for designing governance structures that support long-term sustainable development. Full article

Corporate governance serves as the institutional foundation that aligns managerial decisions with stakeholder interests and sustainable growth. It provides the accountability mechanisms necessary for translating environmental and social initiatives into measurable firm value. This paper examines how Environmental, Social, and Governance (ESG) pillars individually influence firm performance in Gulf Cooperation Council countries (GCCs). The paper analyses a balance panel dataset comprising 84 listed firms observed over a five-year period from 2019 to 2023 with 392 observations. The paper employs two-way fixed effects with Driscoll–Kraay robust standard errors to ensure consistent inference by correcting for heteroskedasticity, autocorrelation, and cross-sectional dependence. Firm performance is assessed by Tobin’s Q, return on assets (ROAs), and sustainable growth rate (SGR), reflecting market valuation, accounting profitability, and long-term sustainable growth, respectively. Tobin’s Q results show that GCC firms’ performance is enhanced by higher environmental pillar scores, whereas it responds negatively to increases in social and governance scores. Findings remain qualitatively similar for ROA but of a smaller magnitude. These findings challenge the conventional assumption that ESG dimensions uniformly enhance firm value, revealing instead that governance and social investments may impose agency costs or compliance burdens in emerging markets where institutional frameworks and stakeholder expectations differ fundamentally from developed economies. The environmental pillar exhibits a positive and significant association with firms’ long-term sustainable growth, whereas the social pillar exerts an adverse effect. Conversely, assessing firm performance with SGR reveals that the influence of the governance pillar is statistically insignificant. Theoretically, this paper contributes by demonstrating that ESG pillars operate through differentiated value-creation mechanisms in institutional contexts characterised by weak stakeholder activism and nascent ESG disclosure norms. Findings suggest GCC firms should prioritise environmental initiatives while carefully evaluating costs and benefits of governance and social programmes. Full article

This study examines the impact of Environmental Management Accounting (EMA) on Corporate Environmental Performance (CEP). Furthermore, it explores the mediating role of Green Process Innovation (GPI) in this relationship. Data from 558 employees across registered Saudi Arabian oil and natural gas companies were collected and analyzed with Smart-PLS software. Results revealed a significant positive impact of EMA on CEP, with GPI partially mediating this relationship. These insights are valuable for corporate policymakers aiming to improve environmental performance through the strategic implementation of EMA and GPI. Additionally, the study emphasizes the importance of EMA and GPI for managerial strategies, showing how these tools can enhance environmental performance. Overall, this research expands the understanding of EMA’s impact on CEP and recommends integrating EMA and GPI into managerial and policy efforts to address stakeholder sustainability concerns and retain competitive advantage. Full article

This research aims to record hotel owners’ perceptions as subjective measures of the degree of integration of local traditional cheese varieties in the hospitality sector. Within the context of cheese tourism, this specific type of alternative tourism is operationalized through B2B co-creation among tourism businesses and cheese factories, serving as a framework for perceived business development. Specifically, this study fills a gap in the literature by exploring the managerial views on the current state of cheese tourism in relation to the entrepreneurship strengthening, the opportunities, and challenges that could favor cooperation between the two sectors. Descriptive and inductive statistics were conducted, collecting primary data from hotels in the Peloponnese, Greece, which has a long tradition of cheese production. Regional tradition and star rating determine the integration of local cheese. While 4–5-star hotels leverage cheese heritage for differentiation and experiential services, lower-end hotels face cost and supply chain barriers, requiring supporting strategies and cross-sector partnerships. The study offers original knowledge for the development of specific strategic proposals for the use of cheese tourism through co-creation for business development of hotels. Future research is recommended to record the views of all stakeholders and correlate them with objective financial performance. Full article

Digital transformation in commercial banks is a critical enabler of modern financial development. While technological advancement and resource allocation are key drivers, managerial attributes also play a decisive role in shaping transformation trajectories. Managerial myopia—often arising from short-term performance pressures, evolving regulatory expectations, and cyclical macroeconomic conditions—warrants particular attention. This study examines how managerial myopia constrains banks’ digital transformation by analyzing its direct impact, underlying behavioral mechanisms, and contingent boundary conditions. Using panel data from 55 Chinese listed commercial banks from 2010 to 2021, we construct a text-based measure of managerial myopia through linguistic analysis of annual reports and employ fixed-effects models for estimation. The results show that a short-term managerial orientation significantly impedes digital transformation, primarily by reducing banks’ propensity for proactive risk-taking. However, this inhibitory effect weakens when managers anticipate longer tenures, management teams exhibit greater diversity in overseas experience and functional expertise, or the average educational level is higher. Moreover, the adverse effects are less pronounced in larger banks and those with stronger corporate governance. Increased external scrutiny and intensified market competition further mitigate this negative influence. These findings offer actionable insights for banking stakeholders aiming to strengthen governance, extend managerial time horizons, and foster an innovation-oriented culture conducive to sustained digital advancement. Full article

This study aims to analyze the impact of environmental, social, and governance (ESG) disclosure quality on banking risk. Data were collected from the 100 largest commercial banks in the Middle East and Africa over ten years and examined using econometric analysis to measure the influence of ESG disclosure quality on banking risks. The findings indicate that both social and environmental disclosures have high predictability, while governance disclosure shows lower predictability. A significant negative relationship exists between the ESG disclosure quality and risk. Governance disclosure, Tier 1 capital, has a strong influence, and capital adequacy has the least. Managerial and practical implications are based on bank compliance, coverage, and debt. Unlike previous studies, this study moves from ESG performance to its disclosure quality and combines the random forest method (machine learning) with dynamic panel analysis (econometrics), bringing innovation and contribution to knowledge (the stakeholder theory) and practice. Full article

This study investigates the predictability of Environmental, Social, and Governance (ESG) performance using financial fundamentals within the context of Taiwan, a prominent small open economy integrated into global value chains. As global markets transition toward mandatory sustainability reporting, identifying the financial ante-cedents of ESG outcomes is critical for risk management and regulatory oversight. Uti-lizing a decade of firm-level data (2014–2023) from the Taiwan Economic Journal (TEJ), we employ supervised machine learning (ML) architectures-including Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost)-to classify firms into ESG performance tiers based on indicators such as profitability, valuation, and scale. Our empirical results provide robust support for the Slack Resources Hypothesis, identifying Return on Assets (ROA) and Firm Size (SIZE) as the most consistent predictors of ESG excellence across the semiconductor, cement, and steel sectors. Conversely, mar-ket-based indicators (Tobin’s Q) dominate predictive models for the financial industry. Methodologically, XGBoost delivers superior predictive calibration for the financial sector, while Decision Trees offer highly interpretable threshold-based logic for risk screening. Our study contributes a transparent “early-warning” framework, enabling investors and regulators to identify sustainability risks through auditable financial benchmarks. The findings suggest that while financial latitude is a structural prerequisite for ESG engagement, it is not its sole determinant, pointing toward a “virtuous circle” of financial health and managerial quality. Full article

Managerial decision-making is a core component of business management and plays a particularly critical role in Sustainable Business Models (SBMs), where it supports long-term competitiveness, adaptability, and positive environmental and social impact. SBMs are inherently complex, dynamic, and data-intensive, requiring advanced analytical capabilities to continuously monitor and optimize sustainability performance across Environmental, Social, and Governance (ESG) dimensions. Artificial Intelligence (AI) introduces new technological opportunities that fundamentally transform managerial decision-making by enabling advanced modeling, simulation, and the analysis of incomplete and heterogeneous data. The purpose of this research is to systematically analyze and synthesize existing AI-supported decision-making approaches used in sustainable business models, with a focus on how these methods transform traditional managerial decision-making frameworks through the integration of Environmental, Social, and Governance (ESG) criteria, and to assess the key benefits, limitations, and implementation conditions of AI-supported decision systems for achieving long-term organizational sustainability. Using a systematic literature review and comparative synthesis of recent theoretical and empirical studies, the research maps key AI-based decision-making approaches applied in sustainable business models and compares their managerial relevance across ESG dimensions. The results provide a structured overview of how different AI techniques contribute to sustainability monitoring, resource optimization, and risk assessment, while also outlining critical organizational, governance, and ethical constraints affecting their practical deployment. Full article

This study assesses the effectiveness of social media advertising campaigns in the supermarket sector by combining managerial insights with multi-criteria decision analysis (MCDA) to support informed, sustainable decision-making. Considering the ever-increasing complexity of digital communication and the growing need for sustainable marketing resources, supermarkets require effective methods to evaluate social media platforms beyond isolated metrics. The study employs the Visual PROMETHEE program, an MCDA that incorporates qualitative insights from 27 supermarket managers in Northern Greece, along with the PROMETHEE II multi-criteria decision analysis method. At the outset, managers evaluated the importance of thirty-four social media performance factors with a five-point scale. Seven core evaluation criteria are identified by aggregating importance ratings and qualitative analysis: return on investment, revenue contribution, lead generation, engagement, cost efficiency, feedback, electronic word of mouth (eWoM), and reach. The use of these criteria later led to the evaluation of seven major social media platforms. A transparent ranking of platforms is presented, based on the results. The ranking highlights significant performance differences across financial, engagement, and reputational dimensions. The findings demonstrate the importance of integrating managerial guidance with multi-criteria analysis to inform long-lasting and evidence-based marketing decisions in retail. Full article