Search Results (491)

Standardization alliance networks serve as crucial channels for firms to sponsor standards and access external resources, exerting a substantial impact on their standard-setting competitiveness and their ability to foster a sustainable innovation ecosystem. The technology–organization–environment (TOE) framework offers an integrated theoretical perspective for studying the causal recipes of how the technological composition, the network structure, and the environment features of standardization alliance network affect firms’ capabilities to dominate and support technological standardization. Based on the data of 88 vehicle manufacturers with experience in participating in standardization alliances, the results of fuzzy-set Qualitative Comparative Analysis (fsQCA) show that: (1) large-scale networks with low density and high technological diversity can simultaneously explain firms’ high level capabilities to dominate and support standardization; (2) supporting standardization is highly dependent on network scale, as firms embedded in large-scale alliance networks with high technological diversity or proximity both enjoy advantages in supporting standardization; (3) dominating standardization shows relatively low dependence on network scale, a firm can enhance its standardization dominance by maintaining an alliance network with low technological proximity in institutional environment with high government intervention. Overall, this study contributes to the theoretical literature on cooperative standard-setting and provides managerial implications for firms looking to enhance capabilities to dominate and support standardization, thereby shaping sustainable technology trajectories and securing their long-term industrial competitiveness in a rapidly evolving technological environment. Full article

Analyzing accident reports to absorb past experiences is crucial for construction site safety. Current methods of processing textual accident reports are time-consuming and labor-intensive. This research applied the LDA topic model to analyze construction accident reports, successfully identifying five main types of accidents: Falls from Height (23.5%), Struck-by and Contact Injuries (22.4%), Slips, Trips, and Falls (21.8%), Hot Work & Vehicle Hazards (18.1%), and Lifting and Machinery Accidents (14.2%). By mining the rich contextual details within unstructured textual descriptions, this research revealed that environmental factors constituted the most prevalent category of contributing causes, followed by human factors. Further analysis traced the root causes to deficiencies in management systems, particularly poor task planning and inadequate training. The LDA model demonstrated superior effectiveness in extracting interpretable topics directly mappable to engineering knowledge and uncovering these latent factors from large-scale, decade-spanning textual data at low computational cost. The findings offer transformative perspectives for improving construction site safety by prioritizing environmental control and management system enhancement. The main theoretical contributions of this research are threefold. First, it demonstrates the efficacy of LDA topic modeling as a powerful tool for extracting interpretable and actionable knowledge from large-scale, unstructured textual safety data, aligning with the growing interest in data-driven safety management in the construction sector. Second, it provides large-scale, empirical evidence that challenges the traditional dogma of “human factor dominance” by systematically quantifying the critical role of environmental and managerial root causes. Third, it presents a transparent, data-driven protocol for transitioning from topic identification to causal analysis, moving from assertion to evidence. Future work should focus on integrating multi-dimensional data for comprehensive accident analysis. Full article

The integration of artificial intelligence (AI) and information technology (IT) is transforming business operations while increasing energy demand. A scalable and nonintrusive method for assessing the adoption of energy-conscious IT governance without direct measurements of energy use is lacking. To address this gap, a machine learning framework is developed and validated that infers the presence of energy-conscious IT governance from five indicators of digital maturity and AI adoption. Enterprise survey data were used to train five classification algorithms—support vector machine, logistic regression, decision tree, neural network, and k-nearest neighbors—to identify organizations implementing energy-efficient IT/AI management. All models achieved strong predictive performance, with SVM achieving 90% test accuracy and an F1 score of 89.8%. The findings demonstrate that an enterprise’s technological profile can serve as a reliable proxy for assessing sustainable IT/AI practices, enabling rapid assessment, benchmarking, and targeted support for green digital transformation. This approach offers significant implications for policy design, ESG reporting, and managerial decision-making in energy-conscious governance, supporting the alignment of digital innovation with environmental objectives. Full article

The home-building industry is crucial to Thailand’s economic growth but unfortunately it is plagued by persistent sustainability problems stemming from fierce competition, cost fluctuations, and evolving consumer expectations. Therefore, this study sought to identify the main drivers of sustainable customer loyalty (CL) in the high-end home-building industry by integrating marketing communications strategy (MCS), service quality (SQ), and company management system (CMS) into a unified structural equation model (SEM). Data were collected from 680 customers registered with Thailand’s Home Builders Association through online and in-person surveys. Path analysis of latent variables using LISREL 9.10 showed that the model had strong explanatory power, explaining 72% of the variance of CL (R² = 0.72). MCS had the greatest total effect (β = 0.82), followed by SQ (β = 0.54) and CMS (β = 0.24). The results also showed how transparent communication interventions add to perceived quality while increasing management efficiency which jointly sustains long-term CL. These findings confirm that strategic communication, service excellence, and good corporate management provide the basis for the sustainable competitive advantage and economic resilience of Thailand’s home-building firms. The validated framework provides theoretical and managerial implications for incorporating sustainability into customer relationship strategies. Full article

University social incubators have been emerging as relevant instruments within the Third Mission of higher education institutions, as they contribute not only to entrepreneurship and innovation but also to the promotion of the Social and Solidarity Economy (SSE). While the literature on technological and business incubators is extensive, limited attention has been devoted to university social incubators and their role in strengthening SSE initiatives. This article presents partial results of a broader international research project, focusing specifically on the survey data collected in Argentina and Brazil. The comparative analysis highlights that, in both countries, university social incubators are more numerous than in many European contexts. In Brazil, the institutionalization of public programs such as PRONINC and ITCP has enabled universities to consolidate their engagement with SSE, while in Argentina experiences are more fragmented and less supported by stable policy frameworks. The findings show that university social incubators play a distinctive role in supporting cooperative projects, social enterprises, and community-based initiatives. They act as bridges between universities and local territories, fostering inclusive and sustainable development. Finally, the article discusses the managerial implications for incubator managers, universities, and SSE actors, as well as policy recommendations for decision-makers seeking to integrate SSE into innovation and development strategies. In doing so, the study contributes to the international debate on university incubators by moving beyond technology-oriented models and emphasizing their social transformative potential. Full article

The success of civil infrastructure projects hinges on effective project management. Building on the PMBOK^® Guide framework, this study investigates how project management knowledge areas are used in practice and how their use relates to the integration of sustainability and risk-management principles. 272 construction professionals in Türkiye were surveyed and their responses were analyzed using reliability testing, normality checks, and a combination of non-parametric tests (Mann–Whitney U, Kruskal–Wallis) and ANOVA. There were found significant differences in perceived use of knowledge areas by education level, project role, project profile, and prior project-management training; in applied practice, company profile explains variation, whereas project type does not. The results indicate that wider, more systematic adoption—particularly in integration, schedule/time, quality, and risk—supports transparent, traceable processes aligned with sustainability objectives. These behavioral determinants were interpreted as enablers of life-cycle sustainability assessment and risk-informed decision making across civil-infrastructure contexts. There were discussed managerial and policy implications for asset owners and contractors, identifying leverage points for training and capability building, and outlining how standardized use of PMBOK knowledge areas can accelerate sustainability assessment and risk analysis in practice. Full article

The advent of artificial intelligence marks a decisive turning point in the evolution of Industry 5.0, redefining the paradigms of industrial performance. This holistic transformation affects not only technological aspects but also the entire industrial ecosystem. Industrial performance is amplified by AI through two major axes: operational excellence and strategic differentiation of solutions. These drivers of performance are structured around concrete strategic advantages, particularly in terms of technological leadership and operational resilience. However, this transformation raises significant challenges on both the human, technical, and financial levels. The managerial implications require a structured approach to the adoption of AI, supported by appropriate organizational development. Future prospects suggest an ever-deeper integration of AI within the industrial ecosystem, paving the way for new models of performance and innovation. In this paper, we strive to make a scientific contribution aimed at shedding light on the impact of artificial intelligence on Industry 5.0, highlighting its implications for the pillars of industrial transformation: operational efficiency and optimization of industrial processes, technological innovation, and competitiveness. We have opted for a theoretical analysis of research related to the integration of AI into industrial systems, in order to provide a synthetic and critical review of this phenomenon. Full article

In the digitally saturated hospitality environment, research on digital transformation remains dominated by macro-level adoption trends and user-generated content, while the potential of micro-level web-behavioural data remains largely untapped. Recent systematic reviews highlight a fragmented body of literature and note that hospitality studies seldom address first-party behavioural data or big-data analytics capabilities. To address this gap, we collected clickstream, navigation and booking-funnel data from five luxury hotels in the Mediterranean and employed big-data analytics integrated with simulation modelling—specifically fuzzy cognitive mapping (FCM)—to model causal relationships among digital touchpoints, managerial actions and customer outcomes. FCM is a robust simulation tool that captures stakeholder knowledge and causal influences across complex systems. Using a case-study methodology, we show that first-party behavioural data enable real-time insights, support knowledge-based decision-making and drive digital service innovation. Across a 12-month panel, visitor volume was strongly associated with search traffic and social traffic, with the total-visitors model explaining 99.8% of variance. Our findings extend digital-transformation models by embedding micro-level behavioural data flows and simulation modelling. Practically, this study offers a replicable framework that helps managers integrate web-analytics into decision-making and customer-centric innovation. Overall, embedding micro-level web-behavioural analytics within an FCM framework yields a decision-ready, replicable pipeline that translates behavioural evidence into high-leverage managerial interventions. Full article

Smart grid operators face escalating cyber threats and tight resource constraints, demanding the transparent, defensible prioritization of security controls. This paper asks how to select cybersecurity controls for smart grids while retaining picture fuzzy evidence throughout and supporting policy-sensitive “what-if” analyses. We propose a hybrid Picture Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) and Combinative Distance-based Assessment (CODAS) framework that carries picture fuzzy evidence end-to-end over a domain-specific cost/benefit criteria system and a relative-assessment matrix, complemented by multi-scenario sensitivity analysis. Applied to ten prominent solutions across twenty-nine sub-criteria in four dimensions, the model highlights Performance as the most influential main criterion; at the sub-criterion level, the decisive factors are updating against new threats, threat-detection capability, and policy-customization flexibility; and Zero Trust Architecture emerges as the best overall alternative, with rankings stable under varied weighting scenarios. A managerial takeaway is that foundation controls (e.g., OT-integrated monitoring and ICS-aware detection) consistently remain near the top, while purely deceptive or access-centric options rank lower in this context. The framework contributes an end-to-end picture fuzzy risk-assessment model for smart grid cybersecurity and suggests future work on larger expert panels, cross-utility datasets, and dynamic, periodically refreshed assessments. Full article

In increasingly turbulent environments, organizations must go beyond generic robustness and develop Requisite Resilience, the capacity to align internal variety with environmental variety to sustain core functions during crises. This study situates Requisite Resilience within organizational theory and strategic management, assessing how major theories of the firm contribute to its development. The analysis groups these perspectives into foundational/diagnostic theories, which clarify environmental, structural and institutional constraints and correspond to passive resilience frameworks, and enabling/capability-building theories, which emphasize managerial agency, resource orchestration and adaptive learning, corresponding to active resilience frameworks. Findings indicate that while foundational perspectives offer essential diagnostics, they are insufficient on their own to foster Requisite Resilience. A composite configuration provides the strongest fit: co-evolutionary views offer an integrative backbone, dynamic capabilities and organizational learning enhance sensing, seizing and acting, and resource dependence theory informs the design of permeable boundaries. Full article

All over the world, the problem of ensuring the safety of pedestrians, who are the most vulnerable road users, is becoming more acute due to urbanization and the growth of micromobility. In 2013, according to WHO data, more than 270 thousand pedestrians were dying each year worldwide (accounting for 22% of all traffic accidents). Currently, experts report that around 1.3 million people die every year globally from road crashes. The roads in developing countries are particularly hazardous, according to experts, because the increase in the number of vehicles far exceeds the development of road infrastructure and safety systems. Since the risk of hitting a pedestrian depends on many factors that can have different natures, and the severity of the consequences can be determined by a set of other factors, the risk of an accident can only be reduced by influencing all these factors in a comprehensive manner. The novelty of our approach is to create an intelligent system that will gradually accumulate all the best practices into a single complex aimed at reducing the risk of an accident with pedestrians and the severity of the consequences if an accident does occur. The distinction lies in offering an integrated system where each module addresses a particular task, so by mitigating risks at every stage, one achieves a synergistic outcome. From the analysis of existing and applied developments, it is known that many specialists mainly solve a narrowly focused problem aimed at ensuring the one subsystems sustainability in the “vehicle-infrastructure-driver-pedestrian” system. Some of these ideas are given as practical examples. The relevance of the designated problem increases with the emergence of autonomous vehicles and smart cities, the sustainability of which depends on the sustainable interaction between all road users. As experience shows, only the implementation of comprehensive solutions allows us to solve strategic problems, including improving road safety. Here, by complex solutions we mean solutions that combine technical issues, as well as environmental, social, and managerial aspects. To account for different kinds of effects, indicator systems are developed and composite indices are computed to choose the most rational solution. The novelty of our approach consists in combining within a unified DSS algorithms for assessing the efficiency of the proposed solution with respect to technological soundness, environmental sustainability, economic viability, social acceptability, as well as administrative rationality and computation of interrelated effects resulting from implementing any given project. In our opinion, the proposed system will lead to a synergistic effect due to the integrated application of various developments, which will ensure increased sustainability and safety of the transport system of smart cities. Our paper proposes a conceptual approach to addressing pedestrian safety, and the examples provided illustrate how the same model or algorithm can lead to positive changes from different perspectives. Full article

This study examines how green talent management (GTM) practices foster sustainable supply chains in micro, small, and medium-sized enterprises (MSMEs) in Quito, Ecuador. It analyzes how sustainable leadership, green organizational culture, and sustainability-oriented training influence employees’ pro-environmental motivation, organizational commitment, and sustainability attitudes, which in turn mediate the adoption of green logistics practices, supply chain efficiency, and organizational resilience. A quantitative design was employed, using survey data from 280 MSMEs analyzed through structural equation modeling. The findings demonstrate that GTM enhances employees’ motivation, commitment, and sustainability attitudes, which act as the primary behavioral mechanisms translating managerial practices into sustainability outcomes. Theoretically, the study integrates Green HRM and supply chain research with multiple organizational behavior theories, including Social Exchange Theory, the AMO model, the Theory of Planned Behavior, and the Resource-Based View. Empirically, it contributes novel evidence from Ecuadorian MSMEs, a context often underexplored in sustainability research. Practically, the study highlights leadership, culture, and training as strategic levers for building greener, more efficient, and more resilient supply chains. The results offer actionable recommendations for MSME managers and policymakers in Ecuador, highlighting the importance of investing in people as the foundation of sustainable competitiveness. Full article

This study examines digital hospitality as a socio-technical system in which technological adoption and human resource (HR) practices jointly shape guest experiences and workforce dynamics. The research is situated at CitizenM hotels in Paris, a brand recognized for its integration of mobile applications, automated check-in, and the ambassador model of flexible role design. A mixed-methods approach was applied, combining a guest survey (n = 517) with semi-structured interviews with managers. Exploratory and confirmatory factor analyses confirmed a five-factor structure of guest perceptions: Digital Efficiency, Smart Personalization, Service Satisfaction, Trusted Security, and Digital Loyalty. Structural equation modeling showed that efficiency significantly drives satisfaction, while personalization and security strongly predict loyalty. Managerial insights revealed that these outcomes rely on continuous investment in training, mentorship, and flexible role allocation. Overall, the findings suggest that digital transformation enhances value creation not by substituting but by reconfiguring human service, with technology alleviating routine tasks and enabling employees to focus on relational and creative aspects of hospitality. The study concludes that effective digital hospitality requires the alignment of technological innovation with supportive HR practices, ensuring both guest satisfaction and employee motivation. Full article

This study investigates the determinants of customers’ perception of business model innovation (BMI) and its impact on customer satisfaction (CS), customer loyalty (CL), and firm sustainability (FS) within Ecuadorian family economic groups (EFEGs). It also examines the moderating role of perceived BMI in the relationships between CS, CL, and FS. Data were collected through an online survey yielding 342 valid responses, using a structured instrument that included socio-demographic variables, perceived EFEG characteristics, and nine validated constructs. Reliability and validity were corroborated through exploratory and confirmatory factor analyses, while structural equation modeling (SEM) and multiple regression analyses were employed to test the proposed relationships. The results reveal that socially responsible consumption (SRC), technological/digital customer skills (TCS), value creation innovativeness (VCrI), value proposition innovativeness (VPI), and value capture innovativeness (VCI) significantly influence customers’ perception of BMI. In turn, BMI positively influences CS, CL, and FS, and moderates the relationships between CS and FS, and CL and FS, though it does not significantly moderate the CS–CL relationship. These findings are consistent with previous research on European family firms, emphasizing the relevance of innovation capabilities, entrepreneurial orientation, and socioemotional wealth in enhancing adaptability and performance in family-owned businesses. This study contributes novel empirical evidence on BMI in the context of an emerging economy dominated by family firms. It underscores BMI as a dynamic capability crucial for fostering customer engagement, improving competitiveness, and ensuring long-term sustainability. Managerial implications suggest that EFEG managers should prioritize digital integration, service innovation, and transparency to strengthen customer trust and loyalty. Future research should broaden the scope to include other Latin American contexts, integrate internal organizational perspectives, and explore intergenerational dynamics and digital transformation processes to deepen understanding of BMI in family business ecosystems. Full article

Amid growing environmental challenges associated with self-driving tourism, this study explores destination-level factors beyond the traditional 6A framework that influence tourists’ environmental responsibility behavior (ERB). Using a grounded theory approach supported by expert interviews, this study conducted 20 + 5 (theoretical saturation test) interviews with local government officials, academics and researchers, local tourism industry professionals, and local community representatives. The interview data underwent a three-stage coding process—open, axial, and selective coding. As a result, three additional drivers of ERB were identified: (1) governance capacity for sustainability, (2) green innovation practices, and (3) community-based environmental empathy. Together with the 6A framework, these drivers form a multi-layered, progressive model that explains how destinations shape ERB through three pathways: product and service experience, institutional regulation and technological enablement, and emotional connection as the deepest driver. The study enriches destination behavior theory by integrating tangible, managerial, and cultural mechanisms, offering theoretical advancement and practical strategies for promoting sustainable self-driving tourism. Full article