Article

Background: With the rapid and widespread adoption of generative artificial intelligence (GenAI) in the tourism sector, scholars have examined this technology from multiple perspectives. However, a comprehensive and systematic overview of GenAI research in tourism remains lacking. Objective: This study aims to map the current research landscape, identify research hotspots, and trace the developmental trajectory of this emerging field using visualization-based scientometric tools. Methods: Relevant literature indexed in the core collection of the Web of Science was selected as the data source. CiteSpace was used to analyse collaboration networks and keyword co-occurrence, HistCite was applied to construct citation chronologies, and Pajek was employed to identify main research paths. Results: A total of 111 publications on GenAI in tourism (2023–2025) were retrieved. The findings reveal an exponential growth in the research output and demonstrate active collaboration among authors, institutions, and countries. Existing studies are mainly focused on GenAI’s technical characteristics, acceptance of technology, and practical applications. The literature exhibits strong cross-citation patterns, and a clear developmental path of knowledge evolution has been identified. Conclusion: Research interest in GenAI within tourism is expected to continue to increase. Nevertheless, cross-institutional and interdisciplinary collaboration requires further strengthening, and the scope of research topics and perspectives needs to be broadened as the field matures. Implications: This study fills an important gap by providing the first visualization-based scientometric review of GenAI research in tourism, offering valuable insights for scholars and practitioners seeking to understand and advance this rapidly evolving field.

Generative artificial intelligence (GenAI) is increasingly influencing strategic decision-making, environmental scanning, scenario development, and organizational adaptation. However, existing research remains fragmented across strategic decision-making, managerial cognition, strategic foresight, strategic agility, human-AI collaboration, and AI governance, limiting understanding of how GenAI-enabled managerial processes translate into firm-level strategic outcomes. This conceptual paper develops a Human-AI Strategic Capability Framework by integrating the dynamic capabilities perspective with managerial cognition. The framework proposes that Generative AI strategic capability enhances augmented managerial cognition, which strengthens strategic foresight, facilitates strategic agility, and ultimately promotes strategic renewal. Human-AI strategic complementarity and AI governance are introduced as boundary conditions that strengthen the effective use of GenAI, while excessive AI reliance is conceptualized as a nonlinear risk that may weaken managerial judgment and critical scrutiny. By connecting individual-level cognitive augmentation with organization-level sensing, adaptation, and reconfiguration, the framework moves strategic management research beyond technology adoption toward a capability-orchestration perspective. The paper contributes to theory by explaining how GenAI can become strategically valuable through governed human-AI complementarity and identifies directions for future empirical research on AI-enabled strategic capabilities, foresight, agility, and renewal.

The increasing concerns about environmental degradation, environmental issues, and green employee behaviour have been attracting constant attention and pressure from business sectors, but the extent to which institutional pressures moderate the effects of green employee support and green job-induced stress on environmental performance has limited number of studies. The study sought to understand how the impact of institutional pressures on environmental performance is largely determined by the firm’s willingness to respond based on their green employee support or reducing green-induced job stress. Focusing on transport and logistics-related firms from both manufacturing and service sectors, the study conducted a survey that collected data from 227 firms using questionnaires as the research instrument. The respondents for the study were top-level managers. The findings revealed that green support and environmental performance are positively related. It was also found that the green induced job stress negatively influences environmental performance. Finally, the effect of green employee support on environmental performance is conditioned by high levels of institutional pressure, but the moderating effect of institutional pressures on the relationship between green-induced job stress and environmental performance was not statistically significant. The study contributes to the environmental literature by demonstrating the importance of green-induced job stress and green employee support in explaining environmental performance. Given certain external conditions, especially customers’ environmental demands and competitors’ organisational green initiatives, a firm with strong green employee behaviour should generate superior performance with its emphasis on the integration of organizational efforts to deliver superior environmental performance.

This study examines the influence of Corporate Social Responsibility (CSR) on corporate reputation in Pakistan’s information technology (IT) sector, incorporating corporate image, knowledge creation, and stakeholder engagement as mediating variables, and organizational culture as a moderating factor. Drawing on stakeholder theory and the knowledge-based view, the study employs a quantitative research design using data collected from 302 managerial respondents across 80 IT firms. Structural Equation Modeling (SEM) via SmartPLS was used to assess the direct, indirect, and moderating relationships among the constructs. The results reveal that CSR has a significant direct effect on corporate reputation and exerts indirect effects through corporate image, knowledge creation, and stakeholder engagement. Furthermore, organizational culture significantly strengthens the positive relationship between CSR and knowledge creation, highlighting the importance of supportive internal environments in amplifying CSR’s strategic value. These findings demonstrate that CSR initiatives that are well-communicated, learning-oriented, and stakeholder-inclusive enhance trust, innovation, and reputational capital. The study contributes to the CSR literature by offering a multidimensional explanatory framework applicable to emerging economies and knowledge-intensive industries. Practical implications are provided for managers and policymakers seeking to embed CSR within cultural, relational, and learning processes to sustain competitive advantage in dynamic market environments.

The increasing use of generative artificial intelligence (GenAI) in organizational work creates a persistent tension between leveraging machine capabilities and preserving human judgment, autonomy, creativity, and accountability. Drawing on paradox theory, this conceptual paper develops a framework explaining how paradoxical leadership can enable organizations to manage this human-AI tension through a both-and approach. The framework proposes two complementary mechanisms. Human-AI complementarity captures the productive integration of distinct human and AI capabilities, whereas responsible AI agency reflects employees' capacity to use AI while retaining critical evaluation, meaningful decision authority, and accountability. The framework also identifies AI literacy as an enabling boundary condition and AI overreliance as a constraining condition that influences whether human-AI collaboration results in augmentation or substitution. By integrating paradoxical leadership, human-AI collaboration, responsible AI, and innovation research, the paper extends paradox theory to AI-enabled work and explains why GenAI may enhance employee innovation under some conditions while generating dependence and diminished human contribution under others. The paper develops 7 propositions to guide future empirical research on leadership in increasingly hybrid human-AI workplaces.