This paper examines how senior executives perceive the conditions under which an organization becomes data driven, and how those perceptions can be organised, through an explicit procedure, into candidate strategic orientations. An exploratory descriptive survey of twenty-five items built on five pillars of the data-driven organization was completed by fifty executives across ten sectors. Frequencies are read as indicating the prevalence of a reported perception within this purposive panel, not as measures of capability, importance or effect; factors are selected and classified under written rules, with every judgement reported alongside its alternative. The conditions most widely reported were descriptive analytics, cloud or hybrid infrastructure, training and awareness programmes, and data being available daily or in real time; the constraints most often reported were a data strategy not strongly aligned with the business strategy, data accuracy problems, resistance to change, a shortage of qualified talent, and data fragmentation. Respondent-level comparisons qualify two of the study’s own interpretations. Crossing these conditions through the TOWS matrix yields four candidate orientations; because the external quadrants are thinly evidenced, the two that cross the single threat are the most provisional. The output is a perception-based conceptual framework that distinguishes what respondents reported, what the researchers coded, what was inferred and what remains to be validated, and it requires external validation.
Artificial intelligence (AI) is reshaping organizational decision-making, managerial roles, and accountability, yet responsible AI governance remains dominated by principles, technical controls, and regulatory compliance. This conceptual paper develops a human leadership-based framework for translating responsible AI principles into organizational governance. In the framework, the responsible leader is an accountable human organizational actor—such as a board member, senior executive, business leader, or designated decision owner—while AI is treated as a governed technological system or operational agent rather than the accountable leader. Human responsible leadership influences responsible AI governance (RAIG) capability through six mediating organizational processes: stakeholder orientation, ethical sensemaking, accountability architecture, meaningful human oversight, inclusive participation, and long-term value orientation. RAIG capability is defined as the higher-order organizational capacity to institutionalize and adapt lifecycle governance arrangements; it enables observable governance practices, which are conceptually separated from downstream responsible AI outcomes. AI operational autonomy is proposed as a focal moderator: the leadership-to-RAIG pathway should become stronger when AI can initiate or execute consequential actions with limited contemporaneous human approval, particularly where actions are difficult to reverse, trace, or interrupt. The framework offers testable propositions and a research agenda, but it is conceptual and requires empirical validation; it is not a technical safety guarantee.
Generative artificial intelligence (GenAI) gives tourism small and medium-sized enterprises scalable cognitive and creative support, but access alone does not ensure sustained advantage. This conceptual article asks how firms can realize productivity gains while preserving the human and place-based capabilities required for future adaptation. An integrative, theory-building review connects tourism research with resource and asset orchestration, AI readiness, human-AI complementarity, organizational routines, and dynamic capabilities. Human-AI orchestration is proposed as a workflow-level configuration of task decomposition, contextualization, verification, and learning. Its contribution lies in coordinating established mechanisms rather than claiming that its dimensions are individually new. The prompt-to-performance framework distinguishes a capability-accumulation loop from a delegation-driven erosion loop. A proposed stock-and-flow mechanism explains switching between them: erosion dominates when losses from displaced practice and knowledge discontinuity exceed learning and replenishment; verification becomes vulnerable when retained capability falls below task-dependent requirements. Seven proposition families specify untested mediating, moderating, nonlinear, multilevel, and temporal relationships, with disconfirming observations. Sector-specific boundary conditions concern perishable service commitments, situated experience, partner interdependence, and concentrated expertise. Four configurations and a foresight matrix support contingent organizational choices. The contribution is a temporally explicit extension of automation-augmentation theory that separates immediate assisted performance from the evolving capacity to verify, recover, and adapt.
Porter’s Five Forces remains a foundational framework in strategic management, but its application is often qualitative and can be difficult to compare across industries shaped by digitalization, network effects, and intangible capital. This study examines the K-Index (K) through a comparative review of published estimates of scale elasticity across five Japanese industries: automotive, printing, retail, financial services, and information services. K is treated as a quantitative indicator of latent competitive structure, particularly related to entry barriers and concentration pressure. The reviewed evidence shows that information services exhibit the strongest positive K values, ranging from 1.197 to 1.643, and that the automotive industry also shows a positive K value of 0.695. Metropolitan retail tends toward positive K, whereas printing, rural retail, and financial services remain in the non-positive-K category. These patterns suggest that K may complement Porter’s framework as an indicator for comparing competitive structures across industries. To be cautious, this review does not constitute an external empirical validation of K. The findings, including those about positive- and non-positive-K categories, should be interpreted as exploratory because the available evidence is limited to a small number of Japanese sectors. Further research should expand the sectoral and international scope and examine how changes in K are associated with observed competitive outcomes, such as concentration, pricing, entry, and innovation.
The purpose of this study is to examine how apparel SMEs in less-developed regions of China adapt to long-term retail digitalization and how these adaptations relate to business resilience and the creation of consumer and community value. This two-and-a-half-year longitudinal qualitative study (February 2023–June 2025) combined repeated prospective observations with retrospective accounts from later-added firms through interviews and field visits with apparel businesses and consumers. By 2025, the participating businesses had been in operation for an average of 25 years. Data were analyzed using grounded-theory coding, with themes organized using the Gioia data structure. Findings show that, as digital technologies increasingly reshaped retailing, brick-and-mortar SMEs became proactive in selectively adopting accessible technologies, including social media for marketing and consumer interaction and digital payment systems for transaction management. This differed from their earlier response to e-commerce, which had transformed retailing since the 2000s but was initially met with passive adaptation because of resource and knowledge constraints. SMEs also adjusted product assortments and targeted niche consumer segments, gradually forming hybrid physical–digital business models. Participants retrospectively reported more stable customer and revenue trajectories after 2017, although adaptation did not guarantee survival. SMEs retained physical-retail strengths, including personalized service, trust-based relationships, and tangible product evaluation. This study shows how selective digital adaptation and market repositioning may support resilience among resource-constrained SMEs.
