Search Results (257)

Despite growing interest in AI technologies, there is a lack of integrated models explaining AI adoption in SMEs from a consumer perspective. This study addresses this gap. Although artificial intelligence (AI) has gained traction in digital innovation strategies, especially among SMEs, existing research lacks integrative models that address cognitive, contextual, and emotional factors driving AI adoption. This study addresses this gap by developing a theoretical model based on TAM and UTAUT2, enhanced with passion, workplace integration, and trust. Drawing on the Technology Acceptance Model and consumer trust theories, the study provides empirical insights into how these factors shape behavioral intentions to adopt AI technologies. The findings aim to inform both theory and practice by highlighting how emerging digital tools affect consumer decision making and engagement across personal and professional contexts. The study contributes to both theory and practice by offering empirical evidence on the drivers of AI adoption and by providing managerial recommendations for SMEs to implement AI-driven personalization responsibly. Full article

Data scarcity hampers the implementation of circular economy (CE) in rural historical small towns (HSTs) where traditional agricultural practices persist outside formal monitoring systems. In this regard, this study proposes and tests an estimation framework to quantify agricultural waste flows and energy recovery potential. The methodology combines waste generation coefficients from peer-reviewed literature with administrative data to generate actionable CE assessments. Application to four Sicilian HSTs within the Local Action Group (LAG) “Terre dell’Etna e dell’Alcantara” exhibits substantial waste generation potential despite their small size. The agricultural enterprises generate an estimated 6930–7130 tons of annual agricultural waste under moderate production scenarios, comprising grape pomace (3250 tons), pruning residues (3030 tons), and mixed processing wastes (650–850 tons). The energy recovery potential ranges from 20–30 TJ through direct combustion to 4.9–8.1 TJ via anaerobic digestion. Sensitivity analysis indicates balanced contributions from all three key parameters (enterprise density, yields, and waste coefficients), each accounting for 31–35% of output variance. The framework provides resource-constrained municipalities with a cost-effective tool for preliminary CE assessment, enabling identification of priority interventions without expensive primary data collection. From a managerial perspective, local administrators can leverage this tool to transform routine administrative data into actionable CE strategies. Full article

Background/Objectives: The effective management of dental practices is increasingly recognized as a key factor in ensuring high-quality care, efficient operations, and interdisciplinary collaboration. While many dentists assume managerial responsibilities, formal training in healthcare or dental practice management may influence the quality of these practices. This study aims to evaluate the differences in organization, efficiency, and quality of care between dental clinics managed by dentists who have completed management training and those who have not. It also explores dentists’ knowledge and attitudes regarding dental practice management. Methods: A cross-sectional, observational, case–control study was conducted between 14 April and 14 May 2023, using an online questionnaire distributed to licensed dental practitioners in Romania. A total of 136 dentists participated, divided into a study group (n = 60), who had completed management courses and a control group (n = 76) who had not. Descriptive statistics and comparative analyses (t-tests, Chi-square tests) were performed using SPSS version 24, with significance set at p < 0.05. Results: Dentists with managerial training demonstrated greater implementation of strategic planning, financial performance monitoring, quality management, and use of digital tools. They also reported higher collaboration with interdisciplinary professionals—orthodontist 76.7% in the study group vs. 63.2% in the control group, medical assistant 78.3% in the study group vs. 47.4% in the control group, front desk 43.3% in the study group vs. 18.4% in the control group; better delegation of tasks—61.7% in the study group vs. 27.6% in the control group; and greater concern for team development—95% in the study group vs. 71% in the control group; and patient rights—81.7% in the study group vs. 75% in the control group. Significant differences (p < 0.05) were noted in management practices, opinions about the optimal manager for a dental practice, and the use of software tools. Conclusions: Managerial training equips dentists with critical skills for enhancing operational efficiency and care quality. Integrating management education into dental curricula and continuing professional development can substantially improve the sustainability and performance of dental practices. Full article

This study investigates how pandemic-induced emotional disruption has reshaped sustainable consumer behavior in the digital age, with a focus on the continued influence of safety measures in the restaurant industry. As societies transition beyond COVID-19 restrictions, health-related anxieties persist, driving consumers to prioritize hygiene and risk reduction in their decision-making. Drawing on large-scale data from TripAdvisor and OpenTable, we analyze the effects of digitally communicated safety protocols on restaurant booking behavior across major U.S. cities. Our findings reveal that safety communication remains a salient factor in consumer choice, even after the acute phase of the pandemic. This effect is particularly pronounced in lower-tier restaurants, where visible digital safety signals help build trust and compensate for weaker brand equity. Conversely, in upscale establishments, where baseline hygiene standards are presumed, the marginal benefit of safety signaling is reduced. The study also identifies enduring patterns of emotional expression and anxiety in online reviews, indicating the long-term psychological imprint of the pandemic on consumer sentiment. By situating safety communication as both a psychological reassurance mechanism and a strategic digital marketing tool, this research contributes to the emerging discourse on sustainable marketing in post-crisis contexts. The results offer theoretical and managerial insights into how businesses can integrate health assurance into long-term brand strategies, reinforcing trust and resilience in digitally mediated, post-pandemic consumption environments. Full article

The purpose of this paper is to forecast the sovereign credit risk for Egypt, Morocco, and Saudi Arabia during political crises. Our approach uses machine learning models (Linear Regression, Ridge Regression, Lasso Regression, XGBoost, and Kernel Ridge) and deep learning models (RNN, LSTM, BiLSTM, and GRU) to predict CDS-based implied default probabilities. We compare the predictive accuracy of the tested models with the results showing that Linear Regression outperforms all other techniques, while deep learning architectures, such as RNN and GRU, demonstrate a competitive performance. To validate the sovereign credit risk prediction, we use the forecasted implied default probability from the Linear Regression model to determine the corresponding forecasted implied rating according to the Thomson Reuters StarMine Sovereign Risk model. The results reveal significant differences in the perceived creditworthiness of Egypt, Morocco, and Saudi Arabia, reflecting each country’s economic fundamentals and their ability to manage global shocks, particularly those related to the Russo-Ukrainian war. Specifically, Egypt is perceived as the most vulnerable, Morocco occupies an intermediate position, and Saudi Arabia is seen as having a low credit risk. This study provides valuable managerial insights by enhancing tools for the sovereign credit risk analysis, offering reliable decision-making in volatile global markets. The alignment between forecasted ratings and default probabilities underscores the practical relevance of the results, guiding stakeholders in effectively managing credit risks amidst economic uncertainty. Full article

Selecting an appropriate business development model is central to strategic decision-making in economic and business management. These models shape sustainable growth, long-term scalability, and strategic flexibility. Existing evaluation methods rely on heuristic or qualitative judgments that lack transparency, reproducibility, and sensitivity to evaluation criteria. To address these limitations, this study introduces a hybrid multi-criteria decision-making (MCDM) framework that integrates VIKOR, entropy weighting, and simulation to evaluate 35 business development models derived from 245 real-world cases. The evaluation covers six strategic criteria: scalability, adaptability, risk exposure, financial sustainability, implementation complexity, and market relevance. Entropy weighting assigns criterion importance based on data variability, and simulation generates input sets for sensitivity and stability analysis. Results highlight Cross-Border Investment, Tiered Access, and Crowd-Backed models as top-performing strategies across multiple dimensions. By combining multiple tools in a unified framework, the research advances MCDM methodology and supports strategic business development planning under uncertainty. This contribution strengthens both academic insight and managerial practice in economics and business management. Full article

This study develops and validates an innovation management framework based on the integration of dynamic capabilities and multidimensional innovation process factors. A mixed-methods approach was employed, combining a quantitative survey of 267 respondents from the Porto Digital ecosystem with qualitative interviews conducted in 10 companies. Confirmatory factor analysis (CFA) was used to validate the proposed measurement model, confirming the reliability and validity of the identified constructs. The qualitative findings reinforced the contextual relevance of the framework and provided insights into managerial perceptions of innovation capabilities. The validated framework consolidates ten organizational factors into four hierarchical layers, offering a structured approach to assessing and strengthening innovation management capacity. This study contributes to the literature by proposing an empirically validated model that addresses gaps related to dynamic capabilities and integrated innovation processes. Practical implications are also discussed, providing managers with a diagnostic tool to support strategic innovation initiatives. Full article

The integration of Industry 4.0 technologies with Corporate Social Responsibility (CSR) initiatives offers transformative potential for enhancing safety culture in manufacturing. This study investigates how managerial competence facilitates the alignment of tools like the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics with CSR principles to foster sustainable safety practices. Employing a qualitative methods approach with secondary data from 2010 to 2024, including case studies of some of five leading firms (Siemens, General Electric, Toyota, Bosch, and Ford) and a systematic literature review, this analysis uses thematic and statistical techniques. The results show that strategic integration significantly reduces workplace hazards by 30–50%, boosts employee engagement, and enhances operational efficiency through real-time monitoring, predictive maintenance, and CSR alignment. Managerial competence, encompassing strategic vision, technical expertise, and stakeholder engagement, is critical for aligning these domains, delivering enhanced safety, sustainability, and competitive advantages. Full article

This study focuses on the development of a decision support system for complex production systems. As a promising approach to resource allocation challenges, the application of AI tools, particularly the multi-agent approach, is proposed. It is hypothesized that a decision support system based on multi-agent systems (MASs), grounded in an invariant ontology and utilizing game-theoretic tools, will enhance the effectiveness of managerial decisions by accounting for the inherent multi-agent nature of complex production systems, manifested in diverse objective functions and intricate interaction structures. The work identifies key features of intelligent agent interactions in multi-agent systems. Drawing on interaction types relevant to complex production systems and the selected MAS implementation architecture, an ontological model of multi-agent interactions and an ontological model for managing complex production systems are designed. The proposed models are tested using data from an oil and gas enterprise, and a formalization of utility functions for its agents is provided using game-theoretic tools. The resulting ontological model of multi-agent interactions in complex production systems is practical and easy to implement. It enables the design of MASs for diverse complex production systems, as demonstrated through a resource management case study in the oil and gas industry. Future study will refine the ontological model via industry-specific validation and expand the mathematical and computational tools for model implementation. Full article

This study examines the perceived relationships between circular economy practices, procurement strategies, and organizational competitiveness using a theory-driven model and survey data from future business professionals. Drawing on multiple theoretical perspectives, the research explores how circular economy practices and procurement strategies influence organizational competitiveness and investigates the mediating and moderating roles of organizational culture, stakeholder pressure, resource availability, innovation, and industry characteristics. The findings suggest that circular economy practices not only contribute to environmental goals, but also hold strategic value for competitive advantage. Procurement strategies are found to be more than operational tools; they act as strategic levers that shape sustainable performance. Based on cross-sectional survey data with a sample of 568 respondents, the study sheds light on patterns that highlight the importance of organizational culture, innovation, and resource access in reinforcing the link between sustainability and competitiveness. Although the data reflects perceptions rather than organizational behavior, the study offers conceptual foundations and future-oriented insights into how the systemic interactions between sustainable practices, procurement, and competitiveness are likely to be interpreted and enacted. Therefore, the insights inform both academic research and managerial practice by emphasizing the strategic value of integrating circular economy principles and procurement strategies within a broader sustainability-oriented framework. Full article

The aim of this study is to develop a convenient and effective entropy analysis method for assessing the efficiency of workload distribution among medical institution personnel. This research is based on a model for evaluating employee workload in conditional time units—credits—taking into account time-and-motion studies and the volume of medical services provided or tasks performed over a given period. The model and method developed by the authors enable the consideration of potential losses of working time and coefficients that determine the percentage of effective working time. The method is based on calculating and analyzing the values of normative and actual workloads of employees. The study introduces such indicators as relative workload, workload distribution entropy, and the entropy of free and excessively worked time credits. During the experimental verification of the developed method for analyzing the activities of a dental clinic, it was demonstrated that the method is both convenient and effective for analyzing the performance of individual employees as well as groups of employees. The results of the method are presented in a convenient and intuitively understandable form. Therefore, this method can serve as an effective tool for identifying internal reserves within the institution and making managerial decisions regarding its further operation. Full article

Building-integrated photovoltaic systems (BIPVs) is a strategy to achieve energy self-sufficiency in buildings. However, photovoltaic (PV) energy production presents challenges due to its intermittent nature, characterized by variations and uncertainties associated with solar radiation and interference from the building’s surroundings. Therefore, building information modeling (BIM) enables energy simulations and solar performance analyses during the design phase of buildings. In this context, this paper aims to identify the key strategies for integrating BIM and photovoltaic energy production systems and how these approaches support the development of sustainable projects. A systematic literature review was conducted, combined with bibliometric analysis, content analysis, and coding of 63 articles. Results indicate an annual research growth rate of 19.62%, with contributions from 268 authors and an international co-authorship rate of 22.22%. Core research trends in the BIM-PV context were identified through thematic maps, highlighting four dimensions—BIPV applications, parametric tools for energy simulation, challenges, and potential benefits—divided into 32 codes. The findings are synthesized into four theoretical propositions structured in an integrative framework. Additionally, five key applications of BIM-PV integration are mapped, along with their addressed problems and the limitations in establishing theoretical and managerial contributions. Full article

The scientific literature and practice have demonstrated that the old linear economic model “extract—produce—use and throw away” is no longer sustainable due to the enormous accumulation of waste and the related production of CO₂. Consequently, there is a need to adopt more sustainable development systems that include recycling resources and producing goods derived from recycled material. The examined literature highlights that SMEs are the least likely to make technological or paradigm changes in favor of sustainable choices due to a lack of resources and managerial competencies. This study presents a mixed-method approach based on qualitative and quantitative analyses. The qualitative analysis aims to identify, in the Italian context, measures that encourage companies to reduce the use of plastics in favor of sustainable alternatives. The quantitative analysis, based on secondary data, aims to identify the characteristics of the firms that benefited from the aid identified in the previous analysis. Thus, this study may support corporate environmental sustainability strategies in Italy by identifying specific characteristics and profiles of those companies willing to obtain public incentives for the use of recycled materials in their business and production processes. The results show that small and micro-sized companies obtained most of the analyzed incentives (almost 76% in terms of number of applications), and the most affected areas by these measures are the agriculture and food industries. Therefore, economic incentives can improve sustainable performance for small and micro-sized enterprises in the wide agri-food sector, while the legislator must adopt different tools, such as bans, Extended Producer Responsibility (EPR), and sustainability reports for medium-large sized companies of other crucial industrial sectors such as construction and automotives. Full article

As a carbon reduction tool that discloses the green quality information of products, a unified standard for the evaluation and identification of carbon labeling has not yet been established. Given the diverse market environments and the different types of carbon labels, selecting an appropriate carbon label can create greater value for both the businesses and the society. This paper constructs a game model involving green and non-green firms to explore the heterogeneous firms’ carbon label choices and green decisions under two market structures. Using a numerical comparative analysis, it examines the social value of carbon labeling schemes in various scenarios. The study finds that (1) enhancing consumer environmental awareness (CEA) and competition intensity contributes to economic and social benefits, while an increased ambiguity in carbon labeling may suppress system efficiency; (2) carbon labeling schemes always generate profits for green firms but do not necessarily benefit non-green firms; (3) in a monopolistic competition market, self-certified labeling (SCL) is more beneficial for improving social welfare, whereas the opposite holds in an oligopolistic competition setting, but in both market structures, third-party certified labeling (TCL) maximizes environmental benefits; and that (4) for the same type of carbon label, environmental improvements vary significantly across market structures, whereas firms’ carbon label choices have a relatively minor impact on environmental benefits. This research provides valuable managerial insights for firms’ green decision-making and carbon label standard selection in different markets. Full article

This article presents a methodology for risk assessment and management in the development of hard-to-recover hydrocarbon reserves. The proposed methodology integrates Monte Carlo simulation and fuzzy logic methods, which allows for the consideration of both quantitative stochastic risks (e.g., fluctuations in oil prices, variability in costs, and production volumes) and qualitative uncertainties (e.g., environmental, social, and technological risks) that are traditionally difficult to formalize. The approach facilitates the incorporation of uncertainties associated with complex field developments and aims to improve managerial decisions through comprehensive risk assessment. The article elaborates on the theoretical aspects of the proposed methodology, including risk identification stages, the formalization of qualitative data using fuzzy logic, and the application of the Monte Carlo method for integrating various risk categories. The results confirm the potential of this methodology as a tool to enhance the resilience and economic efficiency of projects involving the development of hard-to-recover hydrocarbon reserves. Full article