Search Results (432)

This study investigates the spatiotemporal evolution and key obstacle factors of urban flood resilience in the Shenyang Metropolitan Area, aiming to inform regional flood resilience planning and management. A comprehensive assessment indicator system was established, integrating natural, economic, social, and infrastructure dimensions to capture the multifaceted nature of flood resilience. The long short-term memory (LSTM) network with an attention mechanism, combined with the obstacle degree model, was employed to analyze resilience trends and diagnose limiting factors from 2001 to 2023. The findings reveal a sustained increase in the regional flood resilience index, rising from 0.255 in 2001 to 0.574 in 2023. Spatially, the resilience pattern evolved from a monocentric core diffusion to a dual-core leadership and multi-city collaborative structure, driven by basin-wide management and differentiated development between mountainous and plain areas. Disparities in resilience levels across cities narrowed over time. At the criterion level, infrastructure was the primary obstacle before 2010, while social factors became increasingly significant thereafter. At the indicator level, the main limiting factors varied among cities and shifted over time, reflecting local development dynamics. These results provide a theoretical basis and practical guidance for enhancing urban flood resilience in the Shenyang Metropolitan Area and offer insights applicable to other rapidly urbanizing regions. Full article

Digital transformation (DT) has become a strategic imperative for sustaining competitiveness in global supply chains. This study situates DT within the frameworks of Dynamic Capabilities Theory (DCT) and Structural Contingency Theory (SCT) to explain how leadership, culture, and institutional contexts shape adoption pathways in Brazil and Germany. Using a sequential mixed-methods approach, it combines a tertiary literature review with expert elicitation and Interpretive Structural Modelling (ISM), supported by DEMATEL and MICMAC analyses, to uncover hierarchical relationships among barriers and foundational technologies—Big Data Analytics (BDA), the Internet of Things (IoT), and cloud computing. The results reveal distinct causal structures: in Germany, workforce deficits and economic-risk perceptions act as root barriers that constrain managerial and cultural adaptation; in Brazil, executive sponsorship drives workforce capability and analytics development, activating subsequent IoT and cloud adoption. Across both contexts, BDA consistently emerges as the foundational enabler, indicating a layered sequence of capability accumulation. The findings demonstrate that effective digital transformation depends on leadership-enabled alignment between organisational structure and environmental contingencies. This study contributes a comparative framework linking DCT’s dynamic routines with SCT’s structural fit, providing theoretical, methodological, and policy insights for context-sensitive digitalisation strategies. Full article

This study aimed to understand the contributing factors to cohesion in women’s refugee resettlement networks in Northern Ireland and the Republic of Ireland. In 2022, forty women across four networks participated in semi-structured, in-depth interviews in an exploratory study into a social phenomenon of community-building with refugee women resettling on the island of Ireland. We identified four themes through reflexive thematic analysis. Motherhood and Strength of Relationships related to the networks’ interactions and effect on members’ lives, whereas Leadership Dynamics and Goal Setting and Problem Solving related to the networks’ structure and practices. This study offers evidence of how members viewed their networks as having a transformative power in the process of resettling their lives in Ireland for themselves and their families. Full article

The wildland fire management system is increasingly complex and uncertain, which challenges suppression actions and increases stress on an already strained system. Researchers and managers have called for the use of strategic, risk-informed decision making and decision support tools (DSTs) in wildfire management to manage complexity and mitigate uncertainty. This paper evaluated the use of an emerging wildfire DST, the Risk Management Assistance (RMA) dashboard, during the 2021 and 2022 wildfire seasons. We used a mixed-method approach, consisting of an online survey and in-depth interviews with fire managers. Our objectives were the following: (1) to determine what factors at multiple scales facilitated and frustrated the adoption of RMA; and (2) to identify actionable recommendations to facilitate uptake of RMA. We situate our findings within the diffusion of innovations literature and use-inspired research. Most respondents indicated RMA tools were easy to use, accurate, and relevant to decision-making processes. We found evidence that the tools were used throughout the fire management cycle. Previous experience with RMA and training in risk management, trust in models, leadership support, and perceptions of current and future fire risk affected RMA adoption. Recommendations to improve RMA included articulating how the tools integrate with existing wildland fire DSTs, new tools that consider dynamic forecasting of risk, and both formal and informal learning opportunities in the pre-season, during incidents, and in post-fire reviews. We conclude with research and management considerations to increase the use of RMA and other DSTs in support of safe, effective, and informed wildfire decision making. Full article

Identifying abnormal group behavior formed by multi-type participants from large-scale historical industry and tax data is important for regulators to prevent potential criminal activity. We propose an Abnormal Alliance detection framework comprising two methods. For detecting joint behavior among multi-type participants, we present DyCIAComDet, a dynamic community identification and tracking method for large-scale, time-varying bipartite multi-type participant networks, and introduce three community-splitting measurement indicators—cohesion, integration, and leadership—to improve community division. To verify whether joint behavior is abnormal, termed an Abnormal Alliance, we propose BMPS, a frequent-sequence identification algorithm that mines key features along community evolution paths based on bitmap matrices, sequence matrices, prefix-projection matrices, and repeated-projection matrices. The framework is designed to address sampling limitations, temporal issues, and subjectivity that hinder traditional analyses and to remain scalable to large datasets. Experiments on the Southern Women benchmark and a real tax dataset show DyCIAComDet yields a mean modularity Q improvement of 24.6% over traditional community detection algorithms. Compared with PrefixSpan, BMPS improves mean time and space efficiency by up to 34.8% and 35.3%, respectively. Together, DyCIAComDet and BMPS constitute an effective, scalable detection pipeline for identifying abnormal alliances in tax datasets and supporting regulatory analysis. Full article

Although Generative Artificial Intelligence (GenAI) is one of the strategic choices for digital transformation in small and medium-sized enterprises (SMEs), its adoption remains constrained by leadership decision-making that must balance strategic aspirations with resource limitations and organizational inertia. Organizational leadership must face the dynamic and complex characteristics of digital transformation in the knowledge era. Drawing on Complexity Theory and integrating the Technology Acceptance Model (TAM), Temporal Motivation Theory (TMT), and the Resource-Based View (RBV), this study proposes a conceptual framework reflecting distinct strategic leadership orientations. Following a qualitative approach based on semi-structured interviews with SME leaders and an Interpretative Phenomenological Analysis (IPA) this conceptual framework contributes by reframing GenAI adoption as a complex, nonlinear process rather than a straightforward diffusion model, that includes four strategic profiles (Strategic Adopters, Aspiring Adopters, Opportunistic Adopters, and Operational Stabilizers) that affect a dynamic relationship between three key adoption dimensions: intention, motivation, and resource allocation. SME leaders can benefit from a delimitation of their strategic and operational goals while overcoming adoption barriers. Full article

This study examines the impact of Green Entrepreneurial Orientation (GEO), Managerial Environmental Concerns (MECs), and Green Absorptive Capacity (GAC) on the environmental performance of Pakistani SMEs. The Dynamic Capabilities View (DCV) and Natural Resource-Based View (NRBV) demonstrate that innovation focused on sustainability and competitive advantage is enhanced by managerial engagement, knowledge capability, and environmental awareness. A cross-sectional survey involving 350 managers of SMEs in Pakistan utilized covariance-based structural equation modeling (CB-SEM). The MEC-to-GEO direction was insignificant, and this implies that the issue of managerial concern is not a driving force towards the initiation of green entrepreneurial endeavors. The confirmatory factor analysis model for the 350 SMEs demonstrates a satisfactory fit (CFI = 0.947; RMSEA = 0.073), along with reliability and validity. GEO and EP are positively influenced by GAC and MECs, with R² values of 0.204 and 0.526, respectively. The findings indicate that the absorptive and managerial capabilities of SMEs can integrate environmental responsibility into strategic decision-making, exceeding regulatory criteria to foster proactive environmental innovation. The study emphasizes ethical leadership, environmental competitiveness, and social responsibility through green information management and cooperative networks. The sustainability ideas and GEO are enhanced in developing nations by linking global green initiatives with local institutions and cultural contexts. Organizational management and policymakers should promote environmental education, ecological innovations, and sustainable practices within sectors. The limitations of the study include the use of self-reported data and cross-sectoral replication utilizing objective environmental indicators. Full article

This study presents a sociotechnical framework for mitigating ethnic violent conflicts by integrating information and communication technologies (ICTs) with community-based social capital. Drawing on longitudinal case studies from three conflict-prone regions in Kenya, Mt. Elgon, Muhoroni, and the Turkana–West Pokot borderlands, the research examines how ICT-enabled peace networks, particularly the Early Warning and Early Response System (EWERS), mobilize bonding, bridging, and linking social capital to reduce violence. The study employs a multi-phase qualitative design, combining retrospective analysis, key informant interviews, focus group discussions, action participation, and thematic coding of EWERS data collected between 2009 and 2021. This approach enabled the reconstruction of system evolution, stakeholder dynamics, and community responses across diverse socio-political contexts. Findings demonstrate that embedding ICTs within trusted social structures fosters inter-ethnic collaboration, inclusive decision-making, and trust-building. EWERS facilitated confidential reporting, timely alerts, and coordinated interventions, leading to reductions in livestock theft, improved leadership accountability, emergence of inter-ethnic business networks, and enhanced visibility and response to gender-based violence. The system’s effectiveness was amplified by faith-based legitimacy, local governance integration, and adaptive training strategies. The study argues that ICTs can become effective enablers of peace when sensitively contextualized within local norms, relationships, and community trust. Operationalizing social capital through digital infrastructure strengthens community resilience and supports inclusive, sustainale peacebuilding. These insights offer a scalable model for ICT-integrated violence mitigation in low- and middle-income countries. This is among the first studies to operationalize bonding, bridging, and linking social capital within ICT-enabled peace networks in rural African contexts. By embedding digital infrastructure into trusted community relationships, the framework offers an analytical approach that can inform inclusive violence mitigation strategies across low- and middle-income settings. While the framework demonstrates potential for scalability, its outcomes depend on contextual adaptation and cannot be assumed to replicate uniformly across all environments. Full article

The study assesses how effectively succession planning and talent management facilitate the establishment of organizational agility, as well as the moderating influence of organizational learning in the context of Saudi-based banking and finance sectors. Based on the Resource-Based View theory, the study indicates that learning culture and human capital are very important as primary sources of competitiveness in turbulent environments. A stratified sampling was used in the data gathering of 400 respondents and the partial least squares structural equation modeling (PLS-SEM). The result shows that there is a positive and statistically significant relationship between succession planning and organizational agility, and, therefore, a consistent stream of leadership makes an organization more adaptable and resilient. On the other hand, talent development was negatively correlated with agility, which implies that the existing training practices do not match agility needs. Representatives of organizational learning moderated the succession planning–agility, leadership readiness, and adaptability relationship in a positive manner, but moderated the talent development–agility relationship in a negative manner, which implies that the organization has a disconnection between learning and talent strategies. It highlights the necessity to redesign HR practices to make them agile, promote the development of adaptive leadership and a culture of learning, and introduce flexible talent policies. This knowledge adds to the theoretical discussion of the dual nature of organizational learning as a facilitator and constraint as well as providing practical ways to enhance competitiveness in dynamic financial markets. Full article

The accurate assessment of follower readiness remains a challenge within Situational Leadership Theory (SLT), which traditionally emphasizes competence and commitment while overlooking motivational and relational cues. To address this gap, the study examined a structured four-facet model of subjective readiness—Drive, Dare, Decode, and Dialogue—and its association with employee and manager satisfaction and team adaptability. Data from a cross-sectional survey of employees and managers were analyzed using a 12-item 4D readiness scale alongside traditional readiness indicators and established measures of satisfaction and adaptability. The 4D scale showed strong overall reliability and factorial validity, though the Drive facet displayed weaker psychometric properties in the employee sample and should be interpreted cautiously. Overall readiness profiles were positively associated with both satisfaction and adaptability, with Dialogue emerging as a consistent contributor across outcomes. These associations should be interpreted as indicative rather than conclusive, given the study’s correlational design and reliance on self-reported data. Including the 4D facets alongside traditional indicators offered modest yet meaningful incremental explanatory value. Taken together, our findings indicate that a structured subjective readiness framework can enrich SLT’s traditional view of readiness by emphasizing motivational and relational dynamics—although further validation and longitudinal studies are needed to confirm these initial results. Full article

Innovation in the new energy industry serves not only as a key accelerator for the global green and low-carbon energy transition but also as a core driving force of the ongoing energy revolution. This study utilizes data on publications, patents, and the spatial distribution of representative innovation enterprises in the new energy industry of the Yangtze River Delta urban agglomeration from 2009 to 2023 to construct a multilayer science–technology–industry innovation network. Social network analysis is employed to examine its evolutionary dynamics and structural characteristics, and the Quadratic Assignment Procedure (QAP) is used to investigate the factors shaping intercity innovation linkages. The results reveal that the multilayer innovation network has continuously expanded in scale, gradually forming a multi-core radiative structure with Shanghai, Nanjing, and Hangzhou at the center. At the cohesive subgroup level, the scientific and technological layers exhibit clear hierarchical differentiation, where core cities tend to engage in strong mutual collaborations, while the industrial layer shows a hub-and-spoke pattern combining large, medium, and small cities. In terms of layer relationships, the centrality of the scientific layer increasingly surpasses that of the technological and industrial layers. Inter-layer degree correlations and overlaps also display a strengthening trend. Furthermore, differences in regional higher education scale, urban economic density, and geographic proximity are found to exert significant influences on scientific, technological, and industrial innovation linkages among cities. In response, this study recommends enhancing the leadership role of core cities, leveraging the bridging and intermediary functions of peripheral cities, and promoting application-driven cross-regional innovation collaboration, thereby building efficient science–technology–industry networks and enhancing intercity innovation linkages and the flow of innovation resources, and ultimately promoting the high-quality development of the regional new energy industry. Full article

Living Labs (LLs) are key for collaborative and value-based innovation, though their relational and governance mechanisms are still being explored. This study focuses on examining how relational dynamics and community leadership influence the design, governance, and replicability of a Digital Living Labs (DLLs) methodology. The research examines the DLLs of Catalonia using a combination of 15 qualitative interviews and 104 survey responses, with a mixed-methods design adopted. This regional initiative is based on Quadruple Helix (4-H) collaboration and value-driven innovation. The findings show that inclusive participation is enabled through core relational infrastructures. These relationships are built on trust-building, collaboration, facilitation, and knowledge exchange. Community leaders complemented facilitators through harmonizing institutional objectives with local priorities, reinforcing distributed governance, and generating public value. Inclusion, equity, transparency, and solidarity were essential to engagement and collective ownership. The study’s results indicate that effective DLLs transferability depends more on reinforcing relational foundations and shared values than on replicating fixed structures. Full article

This study examines why Technical Assistance (TA) interventions often fail to foster entrepreneurial resilience in emerging markets, despite substantial expertise and funding. Through a qualitative case study of an African Development Bank export diversification initiative in Lesotho, we analyze how leadership, knowledge management (KM), and transactive memory systems (TMS) shape TA effectiveness. Using participant-observer methods and stakeholder interviews over 16 months, findings reveal that success depends less on formal diagnostics and more on developing shared mental models, collaborative routines, and organizational memory across diverse actors. Fragmented knowledge, weak coordination, and underdeveloped group learning processes constrained the intervention’s sustainability. The originality of this study lies in its empirical analysis of failure dynamics, offering actionable policy insights for redesigning TA programs around adaptive leadership, knowledge transfer, and collaborative learning. Implications are relevant for practitioners, policymakers, and scholars seeking to enhance global development initiatives. Full article

This research examines the drivers of lean implementation in sustainable energy enterprises (SEEs) to balance efficiency, sustainability, and competitiveness. This research investigates the interdependence among lean drivers and classifies them by driving power and dependence. This study followed a novel mixed-method approach combining a systematic literature review for driver identification, interviews with entrepreneurs for expert consensus, and analysis using total interpretive structural modelling (TISM), cross-impact matrix multiplication applied to classification (MICMAC), and a graph-theoretic approach (GTA). The result indicated that leadership commitment, teamwork and collaboration, and time management are high drivers; cost reduction, resource optimization, and continuous improvement are linkage drivers; and customer focus and flexibility are found as dependent drivers, revealing the sustainable outcome. This provides a structured pathway for the SEEs for the lean implementation drivers, where prioritization is required. The exploration adds to the Resource-Based View, dynamic capability theory, system theory, etc. The study calls for policymakers’ interventions in designing capacity-building programmes, leadership training, and collaborations. This research incorporated the antecedents–decisions–outcomes (ADO) framework for highlighting the antecedents, leading to decisions, and the outcomes of the choices, with future research questions connecting with multiple sustainable development goals (SDGs), such as SDG7, SDG9, SDG12, and SDG13. Full article

As data product transactions become increasingly standardized, the operational strategies of data product manufacturers and service providers play a pivotal role in shaping market outcomes. This study develops a game-theoretic framework that incorporates dynamic data updates under alternative power structures to examine the equilibrium performance of pricing, demand, technological investment, update rates, and promotional effort. The results indicate that optimal prices under Stackelberg leadership exceed those in the Nash game, whereas demand, technological investment, update frequency, and promotion are consistently higher in the Nash setting. The effects of these decisions are moderated by end-user preference heterogeneity: when users exhibit stronger promotion preferences, service-provider leadership generates superior outcomes, while stronger quality preferences favor manufacturer leadership. Demand preferences and cost coefficients significantly influence profitability—enhanced preferences improve the leader’s returns, whereas high technological and promotional costs suppress profits for both parties. Cost savings in dynamic updates and increases in perceived value exert strong positive effects on market competitiveness, while higher update investment and data acquisition costs exert negative effects. Overall, this study deepens the theoretical understanding of how power structures interact with dynamic updating and user preferences, providing analytical insights and decision support for optimizing operational strategies in data product markets. Full article