Search Results (346)

Over the past decade the industry has developed technical, contractual and managerial tools for road construction without recognizing their capacity as solutions to land access restrictions. This systematic review analyzes the strategies for executing road projects under such restrictions. Following the PRISMA 2020 protocol, 51 indexed articles (2015–2026) retrieved from Scopus and Web of Science were examined and classified into land access restrictions, execution strategies (four families: technical–constructive, project management, social–institutional and contractual–legal) and impact on performance. Strategies are documented in 64.7% of the studies, but only 25.5% analyze them as an object, which explains why they have not been recognized as available solutions. The four families are not alternatives: they operate at different moments of the cycle, and their effectiveness depends on the type of restriction, the moment of activation and the institutional capacity, a determining variable that the corpus does not measure. Treating the restriction as a planning variable, rather than a contingency, distinguishes the projects with positive impact. Full article

South Africa’s public hospital system faces mounting pressure from ageing infrastructure, rising patient demand, and constrained maintenance budgets. While significant investment has been directed toward the construction of new healthcare facilities, the diffusion and adoption of advanced technologies within operations and maintenance (O&M) remain uneven and underdeveloped. This misalignment limits the long-term performance, safety, and sustainability of hospital assets. This study investigates technological diffusion within the O&M environment of a newly commissioned 500-bed regional hospital in Durban, KwaZulu-Natal. A qualitative single-case study approach was adopted, drawing on semi-structured interviews with 14 stakeholders across project delivery and facility management functions. Data were analysed thematically to identify systemic patterns and operational constraints. Findings reveal a persistent reliance on manual, reactive maintenance practices, with minimal integration of digital tools, including building management systems, predictive maintenance technologies, and real-time monitoring platforms. Key barriers include unclear institutional roles, inadequate handover processes, limited technical capacity, and the absence of strategic leadership to drive innovation. A critical disconnect was also identified between managerial expectations and operational realities. The study argues that technological adoption in hospital O&M is not merely a technical challenge but an institutional one. It recommends targeted capacity development, structured transition frameworks, and stronger governance mechanisms to enable sustainable digital integration. Full article

Artificial intelligence (AI) is increasingly embedded in marketing work, yet employees who use AI tools may not always disclose AI’s role in producing their outputs. This study examines AI disclosure silence, defined as employees’ intentional withholding of information about the use, role, or contribution of AI tools in work-related outputs after AI has already been used. Unlike AI avoidance or resistance, this construct concerns post-adoption concealment; unlike general employee silence, it focuses on the hidden technological contribution behind visible work. Drawing on Conservation of Resources Theory and Psychological Safety Theory, the study investigates how threat-based conditions, safety and governance conditions, and AI-related capability are associated with AI disclosure silence. Data were collected through a two-wave survey of 635 marketing employees who actively used AI tools at work. The analysis combined measurement validation, Necessary Condition Analysis (NCA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and explainable machine learning. The findings show that no single condition operated as a strong necessary bottleneck. Instead, AI disclosure silence appeared through multiple pathways involving AI anxiety, fear of negative evaluation, perceived creativity threat, perceived job insecurity, low trust in management, weak psychological safety, and unclear AI policy. SHapley Additive exPlanations (SHAP)-based interpretation further indicated that fear of negative evaluation, AI anxiety, perceived creativity threat, and trust in management had the strongest model-based predictive relevance. The study contributes to workplace AI and employee silence research by positioning AI disclosure silence as an emerging post-adoption disclosure construct. It also highlights the need for clear AI disclosure norms, non-punitive managerial responses, AI-assisted authorship guidelines, and psychologically safe AI-governance practices. The findings should be interpreted as configurational and predictive evidence rather than causal effects, and further scale validation across sectors and cultures is encouraged. Full article

Background: The transition from linear to circular supply chains (CSC) is critical for advancing sustainability, resilience, and resource efficiency, while supporting the UN Sustainable Development Goals (SDGs). However, existing studies rarely integrate internal operational performance with external PESTEL factors under the Benefits, Opportunities, Costs, and Risks (BOCR) perspective, limiting the ability to prioritize circular strategies holistically. Methods: This study develops a decision-making framework that combines the Best-Worst Method (BWM) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), enabling reliable prioritization of interdependent sustainability criteria. Results: A case analysis in the chemical industry demonstrates the applicability of the framework, enhancing transparency and reducing subjectivity in CSC evaluation. Findings highlight quality as the key operational attribute and social as the dominant PESTEL dimension, reinforcing the integration of internal and external factors toward SDG-oriented strategies. Conclusions: The study contributes theoretically by bridging operational and contextual dimensions in CSC evaluation under the BOCR perspective, and methodologically by advancing hybrid MCDM applications to address uncertainty. Managerially, the framework provides a structured tool for aligning circular supply chain strategies with organizational objectives and SDGs, supporting decision-making that strengthens environmental sustainability, stakeholder legitimacy, and resilience. Full article

While higher education institutions strive for academic excellence, they also bear the responsibility of caring for and ensuring the sustainable well-being of their students. After the COVID-19 pandemic, these institutions have transitioned to hybrid and digital education models and have begun to experience the opportunities and threats of digital learning ecosystems. With the introduction of AI technology, this transformation has taken on a new dimension: while students benefit from the flexibility, instant feedback, and personalized learning offered by AI tools, they have also begun to experience new challenges, including cognitive overload, digital fatigue, and social isolation. In this context, the aim of this research is to assess students’ overall psychological well-being and to provide a support system that promotes sustainable well-being by anticipating potential psychological strain and recommending necessary precautions. Accordingly, the purpose of this study, drawing on Self-Determination Theory and Conservation of Resources Theory, is to examine the direct effects of an AI-enabled student support system on sustainable well-being and academic engagement, as well as its indirect effects through self-efficacy and academic resilience. Data will be collected from undergraduate students from a public university in Istanbul. Data will be analyzed in the R statistical environment. We expect that academic resilience, and self-efficacy will mediate the relationship between an AI-enabled student support system and sustainable well-being. At the end of the study, we propose a conceptual model that can be tested empirically by further research. Managerial and further research directions, as well as limitations, are also discussed. Full article

This paper introduces the Normative Lean Performance Score (NLPS) model designed to evaluate lean operational performance using publicly available financial and accounting metrics, without requiring advanced analytics for practical implementation. The study applies an empirical research design based on a longitudinal dataset, where firms are first classified into lean-oriented groups, followed by logistic regression to identify significant indicators and Random Forest models to estimate their relative importance. The resulting index provides an objective, interpretable, and easily implementable performance measure suitable for cross-firm benchmarking and managerial decision support. Empirical testing using automotive manufacturers demonstrates strong alignment with lean classification and efficiency outcomes, providing evidence for the model’s relevance as an accounting-based benchmarking tool. In addition to its practical applicability, the framework contributes to lean performance measurement by translating machine learning insights into a reproducible index that can be applied in data-constrained environments. This approach ensures that the resulting index remains both empirically grounded and practically interpretable, while avoiding reliance on arbitrary or expert-assigned weighting schemes and qualitative assessment-based approaches. The model therefore offers a scalable and transparent alternative for practitioners, analysts, and researchers seeking robust lean performance evaluation when advanced modelling resources are unavailable. The study contributes a transparent, accounting-based normative index that reframes lean performance as a financial configuration rather than an operational maturity construct. The empirical analysis uses quarterly financial data from 17 publicly listed automotive manufacturers over the period 1994Q1–2024Q4. Full article

The integration of AI technologies into management processes is rapidly shaping the digital era. The need to standardize operations and minimize human-related errors has made algorithmic management a strategic tool for contemporary organizations. Despite their importance and growing prevalence in today’s dynamic business environment, employees’ perceptions and attitudes towards these contemporary approaches remain unclear. Accordingly, the purpose of this cross-sectional study, drawing on Social Exchange Theory, is to investigate the direct effect of algorithmic management on innovative workplace behaviors and its indirect effects through organizational trust, trust in management, and algorithmic trust. Moreover, we tested the moderating effect of autonomy on the relationship between algorithmic management and innovative workplace behaviors. Data were collected from 450 gig-economy workers from the service industry in Istanbul. Data were analyzed in the R statistical environment. The findings demonstrated that all trust types, namely algorithmic trust, trust in management, and organizational trust, partially mediate the relationship between algorithmic management and innovative workplace behaviors. Additionally, we found that the relationship between algorithmic management and innovative workplace behaviors is stronger when employees’ perceptions of autonomy are high. Taken together, the findings revealed that algorithmic management enhances innovative workplace behaviors through algorithmic trust when employees perceive high levels of autonomy. The study concludes by outlining managerial and further research directions, as well as limitations. Full article

Background: Knowledge management in global health is essential in response to aging populations, increasing morbidity, and rising expectations of care. The Knowledge Model about Person Care promotes health systems organized around individuals rather than diseases. Within this framework, vulnerability—understood as the risk of physical or moral harm—can be assessed through basic care variables (BCV) that determine individuals’ need and capacity for self-care. Primary care health information systems provide an opportunity to operationalize these variables at the population level. Methods: This study applies deductive methodology to extrapolate community-level health indicator data to population-level vulnerability measures. Using the electronic primary care objective monitoring tool (e-SOAP) from the Community of Madrid, we analyzed health and social care indicators derived from primary care clinical information systems. The mathematical architecture of selected indicators was used as an approximation to model-based systems engineering. Results: Primary care indicators enabled the identification and aggregation of community-level data reflecting BCVs. The system supports multi-level analysis (regional, managerial, institutional, and professional), facilitating grouped and anonymized data extraction for future vulnerability assessment. Conclusions: A minimum set of primary care indicators can effectively estimate community vulnerability, supporting person-centered health system management and informed decision-making. Full article

Assessing the level of circularity in the Hotel, Restaurant and Catering (HoReCa) sector is a significant challenge due to the lack of standardized quantification methods and the absence of structured environmental and material accounting systems, features that are typical of a sector largely composed of micro-enterprises. The technical standard UNI/TS 11820:2024 has developed a set of 71 indicators for the circular economy, structured across six domains (material resources and components; energy and water; waste and emissions; logistics; products and services; and human resources, assets, policies, and sustainability), allowing the assessment of circularity levels in a replicable and comparable manner. The present research measures circularity in a table-service restaurant micro-enterprise, which has voluntarily adopted circular economy practices since its foundation. The purpose is to test the applicability of UNI/TS 11820:2024 in the HoReCa context, improve knowledge about this technical standard, and highlight its strengths and weaknesses from the managerial, methodological and public authorities’ perspective. The overall organization’s circularity score achieved is 31.88%, with performance ranging from 14.40% for “material resources and components” to 56.25% for “human resources, assets and policies”. Although UNI/TS 11820:2024 aims at bridging theoretical and practical gaps towards a harmonized set of measurement tools, sector-specific indicators for the foodservice context remain underrepresented, and public authorities and universities should promote both basic and advanced education in the field of circular economy measurement to support wider adoption. Full article

Background: This study focuses on construction firms undergoing digital transformation, exploring the mechanisms through which Generative AI (GenAI) is associated with employee creativity in a context where knowledge is highly context-dependent, project teams are temporary, and unique safety and schedule pressures prevail. Methods: A mixed-methods approach integrating Partial Least Squares Structural Equation Modeling (PLS-SEM), Importance-Performance Mapping Analysis (IPMA), and Fuzzy Set Qualitative Comparative Analysis (fsQCA) is employed. The proposed model is tested using primary survey data from 268 employees of Chinese construction firms. Results: Generative AI has no significant direct association with construction firm employee creativity (CFEC). Instead, it shows an indirect association through the full mediation of explicit knowledge sharing (EKS) and tacit knowledge sharing (TKS), with TKS having a stronger association with employee creativity. The relationship between GenAI and knowledge sharing is positively moderated by digital self-efficacy. The fsQCA identifies seven equifinal configurations leading to high employee creativity, with ‘explicit knowledge sharing and digital self-efficacy’ constituting the optimal configuration. Conclusions: Construction firms should actively promote knowledge sharing among their staff and provide regular training on GenAI tools, thereby fully harnessing employee creativity. Managerial Implication: Construction CEOs should prioritize building GenAI-supported knowledge sharing systems and improving employees’ digital self-efficacy, rather than expecting direct creativity improvement from GenAI deployment. Full article

AI/IoT initiatives are increasingly adopted in business, yet reported success varies substantially across firms. This study develops and evaluates a firm-level predictive framework for the reported AI/IoT success rate, measured on a bounded 0–100 scale. Using enterprise survey data from Slovakia and the Czech Republic (n = 1250), we compare a regularized linear baseline (Elastic Net) with nonlinear approaches (Decision Tree and Random Forest) under a consistent out-of-sample evaluation framework, and we examine the best-performing model using permutation importance and PDP/ICE tools. Random Forest achieves the strongest out-of-sample predictive performance and reduces absolute errors relative to Elastic Net for most test observations, although diagnostics also reveal a small tail of extreme errors. Across model families, ai_iot_advantage_share emerges as the most stable predictor of reported AI/IoT success. Nonlinear diagnostics indicate a threshold-like transition in predicted success around the mid-range of advantage attribution and a saturation pattern at higher values. Readiness and performance-related variables are associated with higher predicted success, whereas higher barrier levels are associated with lower predicted success. The results position value realization as the most informative predictive signal in the dataset and provide an interpretable basis for enterprise-level screening and managerial reflection rather than causal inference. Full article

Global supply chain disruptions, most acutely demonstrated during the COVID-19 pandemic, have exposed fundamental tensions between efficiency-oriented design and the adaptive capacity required for resilience. This paper addresses a critical gap in the existing literature: the absence of an integrative, operationalisable framework that treats sustainability and resilience as mutually reinforcing strategic objectives rather than competing trade-offs. Employing a systematic literature review guided by PRISMA protocols, complemented by comparative analysis of documented organisational responses across multiple sectors and commodity markets, the study identifies four primary pathways through which sustainability investments generate resilience: structural diversification, information and visibility, social capital and trust, and adaptive capabilities. The principal finding is that sustainability practices, particularly those enhancing supply network visibility, structural diversification, and workforce stability, create option value that becomes strategically decisive during periods of disruption. A decision intelligence framework is proposed that translates these insights into three managerial tools: a sustainability–resilience assessment matrix, a disruption scenario analysis tool, and a capability development roadmap. The framework challenges the prevailing trade-off assumption by demonstrating that efficiency, sustainability, and resilience can function as complementary dimensions of supply chain performance. Findings carry particular relevance for commodity-dependent supply chains, where price volatility, trade structure rigidity, and resource concentration constitute persistent sources of systemic disruption. Theoretical contributions include the integration of supply chain resilience theory, sustainable operations management, and decision science under deep uncertainty. Full article

This paper analyses seven project deliverables from the Interreg Euro-MED “MAST” project to examine its sustainability protocol as a sociotechnical boundary object facilitating ISO 21401:2018 adoption among Mediterranean tourism SMEs. Using Science and Technology Studies (STS) and boundary object theory, we conducted qualitative content analysis (QCA) to map how the protocol translates global standards into SME roadmaps addressing implementation costs, skill gaps, and legitimacy barriers. Results reveal a tension between managerial scripting (actionable tables and KPIs) and relational openings (peer learning and stakeholder prompts). While enabling SME access to certification, the protocol risks “smart compliance” by prioritising formal verification over substantive transformation. Universities emerge as key boundary brokers, potentially translating technical standards into entrepreneurial competencies and curricula. Limited to pre-implementation project documents, the analysis identifies discursive conditions under which standardised tools could support regenerative governance. Findings suggest university–SME partnerships as promising mechanisms for aligning certification with Mediterranean socio-ecological priorities, warranting empirical testing through SME implementation studies. Full article

The purpose of this study is to develop and propose a holistic, stage-based conceptual framework for Customer Journey Management, addressing a critical gap in the literature that lacks a diagnostic and strategic tool to analyze the full journey across all phases, particularly within the tourism sector. Using a conceptual modeling approach grounded in a systematic literature review, the study synthesizes existing theories, Service-Dominant Logic and Customer Experience Theory, to propose a new theoretical-practical model (The Pyramid Model) and a measurement tool (Questionnaire/Grid). The framework integrates cognitive, emotional, and behavioral dimensions across the pre-, during-, and post-consumption stages. A comprehensive Questionnaire/Grid systematically maps and measures the impact of critical touchpoints on customer outcomes such as satisfaction and consumer delight. The model pioneers a quantifiable diagnostic tool that translates theory into managerial action, offering service managers a clear methodology to audit journeys, allocate resources, and drive customer delight and sustainable competitive advantage. Full article

This study reviews and summarizes existing research on how small and medium-sized construction enterprises adopt Building Information Modeling (BIM), while also highlighting potential areas for future investigation. The analyses aimed to address two research questions: RQ1: What research areas are explored in scientific publications on the use of BIM in small and medium-sized enterprises? RQ2: What future research directions should be pursued regarding the implementation and development of BIM in SMEs? A bibliometric analysis and science-mapping analysis was conducted on 162 Scopus-indexed publications (2007–2025) using Excel, VOSviewer and Biblioshiny, complemented by a state-of-the-art review of 69 recent studies (2022–2025). Keyword analyses revealed five thematic clusters: implementation and adaptation, collaboration and integration, construction industry digitalization, project management, and information systems. Within the identified areas, a state-of-the-art review was conducted to indicate the main research domains and directions for future research. Emerging topics include Industry 4.0-enabled digitalization, common data environments, interoperability, decision-making, human resource management, and safety and risk assessment. Future studies should examine managerial competencies, behavioral drivers of adoption and value creation in resource-constrained contexts. Policymakers and professional bodies should combine capacity building, incentives and lightweight interoperable tools to lower entry barriers for SMEs. Integrating bibliometric mapping with qualitative synthesis, this paper offers an evidence-based research agenda and guidance to support BIM diffusion in SMEs. Full article