Search Results (99)

Shift organizations in automotive manufacturing often rely on manual task allocation, resulting in inefficiencies, human error, and increased workload for supervisors. This research introduces an automated solution using the Kuhn-Munkres algorithm, integrated with the Moodle learning management system, to optimize task assignments based on operator qualifications and task complexity. Simulations conducted with real industrial data demonstrate that the proposed method meets operational requirements, both logically and mathematically. The system improves the start of shifts by assigning simpler tasks initially, enhancing operator confidence and reducing the need for assistance. It also ensures that task assignments align with required training levels, improving quality and process reliability. For industrial practitioners, the approach provides a practical tool to reduce planning time, human error, and supervisory burden, while increasing shift productivity. From an academic perspective, the study contributes to applied operations research and workforce optimization, offering a replicable model grounded in real-world applications. The integration of algorithmic task allocation with training systems enables a more accurate matching of workforce capabilities to production demands. This study aims to support data-driven decision-making in shift management, with the potential to enhance operational efficiency and encourage timely start of work, thereby possibly contributing to smoother production flow and improved organizational performance. Full article

Collaborative business ecosystems (CBEs) are increasingly exposed to disruptive events (e.g., pandemics, supply chain breakdowns, cyberattacks) that challenge organizational adaptability and value creation. Traditional approaches to resilience and robustness often fail to capture the full range of systemic responses. This study introduces a unified mathematical framework to evaluate four crisis response modes—plasticity, resilience, transformative resilience, and antifragility—within complex adaptive networks. Grounded in complex systems and collaborative network theory, our model formalizes both internal organizational capabilities (e.g., adaptability, learning, innovation, structural flexibility) and strategic interventions (e.g., optionality, buffering, information sharing, fault-injection protocols), linking them to pre- and post-crisis performance via dynamic adjustment functions. A composite performance score is defined across four dimensions (Innovation, Contribution, Prestige, and Responsiveness to Business Opportunities), using capability–strategy interaction matrices, weighted performance change functions, and structural transformation modifiers. The sensitivity analysis and scenario simulations enable a comparative evaluation of organizational configurations, strategy impacts, and phase-transition thresholds under crisis. This indicator-based formulation provides a quantitative bridge between resilience theory and practice, facilitating evidence-based crisis management in networked business environments. Full article

Amid structural changes driven by the greening and digital transformation of the maritime industry, the demand for career transitions of seafarers with onboard experience to shore-based positions—particularly ship superintendents—is steadily increasing. However, the current lack of a systematic education and career development framework to support such transitions poses a critical challenge for shipping companies seeking to secure sustainable human resources. The aim of this study was to develop a competency-based training program that facilitates the effective transition of seafarers to shore-based ship superintendent roles. We integrated a developing a curriculum (DACUM) analysis with competency-based job analysis to achieve this aim. The core competencies required for ship superintendent duties were identified through three expert consultations. In addition, social network analysis (SNA) was used to quantitatively assess the structure and priority of the training content. The analysis revealed that convergent competencies, such as digital technology literacy, responsiveness to environmental regulations, multicultural organizational management, and interpretation of global maritime regulations, are essential for a successful career shift. Based on these findings, a modular training curriculum comprising both common foundational courses and specialized advanced modules tailored to job categories was designed. The proposed curriculum integrated theoretical instruction, practical training, and reflective learning to enhance both applied understanding and onsite implementation capabilities. Furthermore, the concept of a Seafarer Success Support Platform was proposed to support a lifecycle-based career development pathway that enables rotational mobility between sea and shore positions. This digital learning platform was designed to offer personalized success pathways aligned with the career stages and competency needs of maritime personnel. Its cyclical structure, comprising career transition, competency development, field application, and performance evaluation, enables seamless career integration between shipboard- and shore-based roles. Therefore, the platform has the potential to evolve into a practical educational model that integrates training, career development, and policies. This study contributes to maritime human resource development by integrating the DACUM method with a competency-based framework and applying social network analysis (SNA) to quantitatively prioritize training content. It further proposes the Seafarer Success Support Platform as an innovative model to support structured career transitions from shipboard roles to shore-based supervisory positions. Full article

The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South. Full article

In the context of accelerating digital transformation and growing sustainability imperatives, entrepreneurial ecosystems increasingly rely on open innovation and strategic collaboration to foster resilient, knowledge-driven growth. This study aims to examine how leadership behaviors and strategic alliances interact as enablers of sustainable innovation across macro (systemic), meso (organizational), and micro (individual) levels. To achieve this, this study employs a literature review, supported by bibliometric analysis, as its core methodological approach. Drawing on 86 influential publications from 1992 to 2024, two major thematic streams emerge: leadership dynamics in entrepreneurial settings and the formation and governance of strategic alliances as vehicles for innovation. The findings underscore the pivotal role of transformational and ethical leadership in cultivating trust-based inter-organizational relationships, facilitating digital knowledge sharing, and catalyzing sustainable value creation. Simultaneously, strategic alliances enhance organizational agility and innovation capacity through co-creation mechanisms, digital platforms, and crowdsourcing, especially in small and medium-sized enterprises (SMEs). This paper highlights a mutually reinforcing relationship: effective leadership strategies empower alliances, while alliance participation enhances leadership capabilities through experiential learning in diverse, digitalized environments. By bridging leadership theory, open innovation practices, and digital transformation, this study offers critical insights for entrepreneurs, managers, and policymakers seeking to drive inclusive and sustainable innovation within interconnected global markets. Therefore, this study provides practical guidance for business leaders aiming to strengthen alliance performance through adaptive leadership and for policymakers seeking to foster innovation ecosystems through supportive regulatory and institutional frameworks. Full article

There is no consensus in existing research on whether the application of digital technology diversification strengthens or weakens export resilience. This study fills this gap by exploring the influence mechanism of digital technology diversity on enterprise export resilience. Based on organizational inertia theory, dynamic capabilities perspective, and organizational learning theory, this study uses data from Chinese listed export manufacturing companies from 2019 to 2023 in order to empirically examine the relationship between digital technology diversity and enterprise export resilience. The results show that digital technology diversity significantly inhibits enterprise export resilience, supply chain finance plays a partially mediating role in this relationship, and digital interlock alleviates the inhibitory effect of digital technology diversity on supply chain finance. The findings provide guidance for the digital technology application strategy of export enterprises in adversity. Full article

This study examines the effect of internal dynamic capabilities i.e., digital leadership, environmental awareness, and organizational learning, on sustainable supply chain performance as studied in the logistics sector. It builds on the Dynamic Capabilities Theory by combining notions of green innovation and sustainability and fills the growing gap in the existing literature. Despite the fact that these domains have been extensively studied independently, there has been limited research examining how internal capabilities contribute to green supply chain innovation (GSCI) that in turn results in sustainability outcomes, especially in the case of emerging markets. Seven hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis of data collected from 312 logistics and supply chain professionals in Jordan. This study shows that each of the three capabilities has a major effect on GSCI and therefore sustainable performance. Linking the most influential predictor of sustainability outcome to sustainable supply chain performance, as indicated by the strongest effect (β = 0.825, p < 0.001) between GSCI and sustainable supply chain performance, and followed by significant coefficients between the sustainable information processing (β = 0.261, p < 0.01), and information capabilities (β = 0.297, p < 0.001), indicates that the theory is more suited to GSCI. In particular, digital leadership had the largest impact on the green innovation (β = 0.481, p < 0.001), indicating that the role of digitally driven leadership is to facilitate eco-innovation. In addition, this intermediate factor, GSCI, serves as a variable that mediates relationships between the capabilities and the sustainability outcomes. As the results here suggest, leveraging internal capabilities is a very tangible channel for green innovation that has important ramifications for practitioners and policymakers facing resource constraints. Full article

Despite advanced aviation safety systems, recurring operational failures demonstrate that current safety management system (SMS) implementation practices remain predominantly reactive, with organizations adopting SMS frameworks theoretically embracing Safety-II philosophy while continuing Safety-I-oriented reactive management. This study develops an integrated framework for implementing both Safety-I and Safety-II principles in aviation safety management, addressing the gap between SMS theoretical requirements and actual implementation. Using the HEAR (human error analysis and reduction) framework, we analyzed three representative aviation cases involving FMS operation, turbulence response, and aircraft energy management through a qualitative multiple-case study design. Data collection utilized internal safety reports, official investigation reports, and reconstructed operational scenarios. The analysis employed a four-phase approach integrating predetermined categorization with inductive pattern recognition. Results revealed that 87% of all causes were organizational factors—6.7 times higher than individual/task factors (13%)—yet safety management responses primarily target individual behaviors. We defined “flight crew’s resilient behavior” and developed implementation guidelines by integrating the HEAR framework with the LPAC (learn, plan, adapt, coordinate) model and PAM (pressures, adaptations, and manifestations) framework. Effectiveness evaluation demonstrated a transition from 54 discrete contributing factors to 19 systematically related factors with clearer implementation pathways. Our integrated framework enables organizations to systematically implement both Safety-I analytical capabilities and Safety-II adaptive responses, transforming safety management from reactive “failure prevention” to proactive “success expansion”. Full article

The recent transformation in Libya’s banking industry, driven largely by the Central Bank of Libya, has led to increased financial inclusion, enhanced banking services, and the adoption of digital banking technologies. While most banks have rapidly transitioned from traditional data analysis methods to using Artificial Intelligence (AI) for daily transaction analysis, the impact of AI on sustainable innovation performance and organizational learning remains underexplored. This study, grounded in dynamic capabilities theory, investigates the mediating role of organizational learning in the relationship between AI adoption in the banking sector and sustainable innovation performance. Data were collected from 401 employees across Libya’s conventional and Islamic banking sectors using a judgmental sampling technique. Partial Least Squares Structural Equation Modeling (PLS–SEM) was used to analyze the data and assess the relationships among the variables. The findings indicate that AI adoption significantly and positively influences sustainable innovation performance and organizational learning. Additionally, organizational learning was found to have a significant positive effect on sustainable innovation performance and to partially mediate the relationship between AI adoption and innovation performance. The study recommends that bank management teams implement training programs to enhance employees’ understanding of AI applications, sustainability objectives, and innovative financial services to improve overall efficiency. Full article

Amid the rapid advancement of artificial intelligence (AI) and increasing environmental uncertainty, enterprises are facing unprecedented challenges in sustaining innovation. As a key enabler of digital transformation, AI enhances resource allocation efficiency and knowledge acquisition, offering new avenues for continuous innovation under dynamic conditions. Innovation resilience—defined as a firm’s ability to maintain and restore innovation activities during external shocks—has emerged as a critical indicator of organizational adaptability. Leveraging its advantages in data processing, process optimization, and organizational learning, AI is increasingly regarded as a pivotal driver of innovation resilience. This study develops a theoretical framework linking AI technology, dynamic capabilities, and innovation resilience. Using panel data from Chinese A-share listed companies between 2013 and 2023, we conduct an empirical analysis with a two-way fixed effects model. The results reveal that AI technology significantly enhances innovation resilience; dynamic capabilities partially mediate this relationship; and financial constraints positively moderate the effect of AI on innovation resilience. By adopting a dual perspective of technological enablement and capability construction, this research uncovers the internal mechanism through which AI fosters resilient innovation and provides practical insights for enterprises seeking capability upgrading under resource limitations. Full article

The importance of flood fighting is becoming increasingly apparent worldwide in the context of climate change. In light of the often-limited conditions and resources available for the deployment of military and professional rescue teams in emergencies, flood-fighting corps that help protect communities from floods have been organized by community members in numerous regions. This study aims to evaluate the extent to which the capabilities of flood-fighting corps and other participating organizations were developed through a field exercise conducted in Japan involving 55 relevant organizations and 1025 participants. The requisite data were obtained through structured in-person interviews with 18 participants playing key roles in the exercise, who were questioned about the effects of the exercise. The findings showed that the exercise achieved three principal outcomes: (1) the strengthening of inter-organizational collaboration, (2) the learning of authentic capabilities by simulating a real-world flood-fighting environment, and (3) an increase in public awareness of flood fighting through extensive public relations efforts. Furthermore, this study concluded that the national government should take a leading role in organizing such large-scale exercises involving multiple organizations with diverse cultural backgrounds and responsibilities in order to generate these outcomes. Full article

While many organizations are increasingly willing to adopt artificial intelligence (AI) to support strategic objectives such as sustainable development, the ESG benefits of such adoption are not consistently realized across firms. This study investigates the boundary conditions under which AI adoption contributes to ESG performance. This study aims to investigate when AI adoption contributes to enhanced ESG outcomes by examining key organizational boundary conditions. Specifically, it addresses (1) the association between AI adoption and ESG performance, (2) the moderating roles of learning capability, digital top management team (digital TMT), and operational slack. Using a unique dataset constructed by integrating AI adoption announcements extracted through natural language processing from Factiva and ESG scores obtained from Bloomberg, this study analyzes 8469 firm-year observations from 941 publicly listed manufacturing firms in North America between 2015 and 2022. The results reveal that AI adoption is positively associated with ESG performance. Moreover, this positive effect is amplified by digital TMTs and strong learning capabilities, but weakened by operational slack. These findings enrich the literature on AI-enabled sustainability by highlighting the contingent nature of ESG outcomes and offers managerial insights for firms seeking to align AI strategies with ESG objectives. Full article

This study investigates how digital transformation, digital culture, innovation capabilities, and organizational resilience influence organizational performance in the post-pandemic era. Grounded in Resilience Theory, the Dynamic Capabilities Framework, and Organizational Learning, this research analyzes how digital capabilities—such as innovation, digitalization, telework, and investment strategies—influence organizational performance. Data were collected through a structured online survey with 320 valid responses from decision-makers across various sectors. Using descriptive statistics, Pearson correlations, and multiple linear regression analysis, the results reveal that innovation, organizational resilience, and investment strategies are significant predictors of performance, together explaining over 52% of its variance. Interestingly, while digitalization correlates strongly with innovation and strategic adaptation, its direct effect on performance was not statistically significant in the regression model. These findings underscore the importance of an integrated approach to digital transformation and resilience-building strategies for navigating crises and fostering long-term performance. The study contributes to the literature on digitalization, crisis response, and strategic management, offering practical insights for managers and policymakers committed to strengthening organizational adaptability in the post-pandemic era. Full article

This study examines the impact of technological integration on sustainable performance in Pakistani manufacturing SMEs from the export sector, focusing on the mediating role of employee capacity building and the moderating role of organizational learning, drawing on dynamic capabilities theory. Data were collected from manufacturing SMEs in Pakistan via a structured questionnaire, and structural equation modeling (SEM) was employed using Smart-PLS (4.1) for analysis. The results reveal that both AI adoption and technological readiness significantly enhance employee capacity building, which, in turn, positively impacts sustainable performance. Employee capacity building significantly mediates the relationships between both AI adoption and sustainable performance as well as between technological readiness and sustainable performance. Notably, organizational learning shows a significant moderating effect between technological readiness and employee capacity building but an insignificant moderating effect between AI adoption and employee capacity building. These findings offer actionable insights for managers in optimizing AI and readiness strategies to enhance capacity building and sustainable outcomes. This study contributes to the literature by being one of the first to integrate AI adoption and technological readiness with employee capacity building as a mediator and organizational learning as a moderator in the context of SMEs. Unlike prior research, which typically examines these factors independently, our framework offers a holistic approach to understanding sustainable performance. Full article

This review synthesizes current knowledge on the transformative impacts of artificial intelligence (AI)—computational systems capable of performing tasks requiring human-like reasoning—on business innovation. It addresses the potential of AI to reshape strategies, operations, and value creation across various industries. Key themes include AI-driven business model innovation, human–AI collaboration, ethical governance, operational efficiency, customer experience personalization, organizational capability development, and adoption disparities. AI enables scalable product development, personalized service delivery, and data-driven strategic decisions. Successful implementations hinge on overcoming technical, cultural, and ethical barriers, with ethical AI adoption enhancing consumer trust and competitiveness, positioning responsible innovation as a strategic imperative. For practitioners, this review offers evidence-based frameworks for aligning AI with business objectives. For academics, it identifies research frontiers, including longitudinal impacts, context-specific roadmaps for small- and medium-sized enterprises, and sustainable innovation pathways. This review conceptualizes AI as a driver of systemic organizational transformation, requiring continuous learning, ethical foresight, and strategic ability for competitive advantage. Full article