The accelerating convergence of artificial intelligence, advanced analytics, and competitive intelligence is redefining how traditional industries generate knowledge and make strategic decisions. Across manufacturing, energy, mining, and other asset-intensive sectors, recent research demonstrates a decisive shift from fragmented data analysis toward integrated, AI-powered decision support systems that enable organisations to anticipate change rather than merely respond to it (e.g., Lu et al., 2025; Omar et al., 2025; Mantouzi & Youssef, 2025). The literature further highlights the sector-specific embedding of AI-powered competitive intelligence. In manufacturing, AI enhances production planning, quality control, and lifecycle management, while in extractive industries data-driven models support strategic planning, production scheduling, and equipment utilisation. In the energy and oil and gas sectors, AI-enabled platforms provide real-time operational intelligence and improve resource allocation. Across these contexts, competitive intelligence is no longer limited to market analysis but extends to operational, technological, and sustainability domains, supporting organisational performance and adaptability (e.g., Mantouzi & Youssef, 2025; Torres Vásquez et al., 2024). AI-enhanced decision support systems now play a pivotal role in improving organisational responsiveness, innovation capacity, and resource allocation, and are becoming embedded in organisational routines as the backbone of intelligence-driven enterprises (e.g., Lu et al., 2025; Omar et al., 2025). At the same time, despite the clear benefits associated with AI-enabled competitive intelligence, organisations continue to face persistent implementation challenges in the context of digital transformation. Data quality, standardisation, and interoperability remain critical constraints for generating reliable intelligence outputs, while the shortage of analytical and digital competencies limits the effective use of AI-driven insights (e.g., Peng & Yu, 2025; Pasas-Farmer & Jain, 2025). The literature also highlights the need to foster organisational cultures that support knowledge sharing and the integration of competitive intelligence into decision-making processes, alongside requirements for explainability and interpretability to ensure trust in AI-supported decisions (Kostopoulos et al., 2024). Looking ahead, future research emphasises the importance of human–AI collaboration and cross-sectoral learning. Human-in-the-loop approaches are expected to play a critical role in balancing analytical precision with contextual judgement, while cross-industry comparisons may further refine decision support frameworks and enhance organisational learning (e.g., Lu et al., 2025; Torres Vásquez et al., 2024). In parallel, AI-powered competitive intelligence is increasingly linked to sustainability and ESG objectives, particularly through applications in resource optimisation, operational efficiency, and environmental monitoring in asset-intensive industries. The contributions in this issue underline a broader transformation reflected in the emergence of intelligence-driven organisations capable of converting data into foresight and foresight into strategic action. For traditional industries, this transition represents not only a technological upgrade but a fundamental shift in how competitiveness is understood and sustained in the data economy.
Cross-border price bargaining remains a persistent managerial challenge in culturally distant, relationship-oriented markets. This study examines how perceived cultural distance and relationship orientation shape negotiation strategy and pricing outcomes when Swedish managers negotiate business-to-business (B2B) projects with counterparts in Latin America. Framed through a market intelligence lens, the article focuses on how managers interpret, mobilize, and time information-such as technical documentation, service scope, contractual terms, and long-term support commitments-to steer bargaining processes and protect price integrity. The study adopts a qualitative case study design based on semi-structured interviews with Sweden-based managers who have negotiated with partners in Brazil, Chile, and Uruguay, complemented by negotiation-related documents (e.g., proposals, contracts, and process records). Thematic analysis reveals that relationship-building and trust formation typically precede substantive price discussions, influencing bargaining rhythms, concession patterns, and assessments of price flexibility. Cultural differences in communication style, time orientation, and conflict management further condition how market intelligence is interpreted and deployed across negotiation phases. Findings also show that documentation and scope framing operate as critical negotiation levers: when introduced and positioned strategically, they safeguard pricing; when delayed or ambiguously framed, they intensify price pressure. The study contributes to international negotiation and pricing research by integrating cultural distance, relationship orientation, and intelligence use into a unified explanation of process-level price bargaining mechanisms. It also offers actionable guidance for managers on sequencing relational investments and intelligence-driven framing to improve cross-border negotiation outcomes.
This study investigates the role of strategic management in achieving competitive advantage through electronic governance (e-governance) within the telecommunications sector of the Republic of Yemen. Drawing upon the Resource-Based View, Dynamic Capabilities Theory, and Institutional Theory, the research develops and tests a conceptual model linking strategic management, e-governance, and competitive advantage. A quantitative explanatory research design was employed, utilizing data from 152 managerial respondents across major Yemeni telecommunications companies. Structural equation modeling (PLS-SEM) was applied to assess measurement and structural models. The results reveal that strategic management has a significant positive impact on both e-governance adoption and competitive advantage. E-governance also exerts a direct effect on competitive advantage while partially mediating the relationship between strategic management and competitive outcomes. These findings highlight the importance of aligning strategic processes with digital governance practices to enhance organizational efficiency, transparency, and resilience in fragile contexts. The study contributes to strategic management and digital governance literature by extending their application to a conflict-affected developing economy and offers practical insights for policymakers and industry leaders seeking to strengthen the competitiveness of Yemen’s telecommunications sector.
In an era defined by volatility, data abundance, and intensifying competition, the foundations of competitive advantage are undergoing a fundamental shift. It is no longer sufficient for organizations to possess valuable resources; increasingly, advantage depends on how effectively firms interpret, integrate, and act upon information. Within this evolving landscape, Business Intelligence (BI) and Competitive Intelligence (CI) have emerged not merely as analytical tools, but as strategic capabilities that shape innovation trajectories and long-term competitiveness. Traditionally, BI has been associated with internal data processing-transforming structured and unstructured data into actionable insights that support operational and strategic decision-making. Yet, its role has expanded significantly. Contemporary BI systems enable organizations to align data-driven insights with strategic objectives, enhance operational efficiency, and support customer-centric strategies through advanced analytics and artificial intelligence (Rahman, 2021; Natarajan et al., 2024; Omar et al., 2025). BI contributes not only to short-term performance improvements but also to the development of innovation capabilities that are critical for sustaining competitive advantage. Complementing this internal focus, CI provides an outward-looking perspective by systematically collecting and analyzing information about competitors, markets, and broader environmental dynamics. It enables organizations to anticipate market shifts, identify emerging opportunities, and interpret weak signals in uncertain environments (Marcão & Santos, 2025; Salguero et al., 2019). More importantly, CI strengthens strategic decision-making by transforming fragmented external data into coherent intelligence, thereby enhancing organizational resilience and adaptability. While BI and CI offer distinct contributions, their true strategic value lies in their integration. Organizations that successfully combine internal analytics with external intelligence gain a holistic understanding of their competitive environment. This integration reduces uncertainty, improves decision quality, and fosters continuous innovation – key conditions for sustainable competitive advantage (Kazemi et al., 2024). BI and CI function not as isolated systems but as interdependent components of a broader intelligence ecosystem. The theoretical foundations of this integration can be understood through several complementary perspectives. The Resource-Based View (RBV) conceptualizes BI and CI as strategic resources that are valuable, rare, and difficult to imitate, particularly when embedded within organizational routines (Meraz-Sepulveda, 2024; Al Derei & Fam, 2023). However, RBV alone does not fully capture how organizations respond to dynamic environments. This gap is addressed by Dynamic Capabilities Theory, which emphasizes the ability to sense, seize, and transform opportunities. BI and CI play a critical role in enabling these capabilities by facilitating data interpretation, strategic foresight, and business model innovation (Shao et al., 2025; Marcão & Santos, 2025). From a knowledge-centric perspective, the Knowledge-Based View (KBV) highlights that the strategic value of BI and CI lies in their ability to generate, share, and apply knowledge. Empirical research demonstrates that knowledge-sharing processes often mediate the relationship between intelligence systems and innovation outcomes (Eidizadeh et al., 2017; Al Derei & Fam, 2023). Similarly, absorptive capacity theory explains how organizations transform external intelligence into innovation by acquiring, assimilating, and exploiting knowledge – an area where CI plays a particularly significant role (Hassani & Mosconi, 2021). Beyond these resource- and capability-based perspectives, socio-technical frameworks such as Diffusion of Innovation (DOI) and Actor-Network Theory (ANT) provide insights into how BI systems are adopted and embedded within organizations. These frameworks emphasize that the effectiveness of intelligence systems depends not only on technology, but also on organizational context, human actors, and inter-organizational relationships (Naznen & Lim, 2023). In parallel, systems theory underscores the importance of integrating diverse information flows into a coherent decision-making architecture (Bartes, 2010). Importantly, the competitive advantage derived from BI and CI is not purely technological. While advances in artificial intelligence and machine learning have significantly enhanced analytical capabilities, the decisive factor remains the organization’s ability to interpret and operationalize insights. This involves organizational culture, leadership, and knowledge management practices that enable the translation of data into strategic action. In other words, data do not create advantage – interpretation and application do. The role of BI and CI is increasingly extending into the domain of sustainability. CI, for instance, supports the integration of corporate social responsibility (CSR) and sustainability considerations into strategic decision-making, thereby contributing to reputational differentiation and long-term value creation (Oyenuga, 2025). This reflects a broader shift in how competitive advantage is conceptualized – not only in economic terms, but also in terms of social and environmental impact. Future research should move beyond isolated theoretical perspectives and develop integrative frameworks that capture the dynamic interplay between resources, capabilities, knowledge processes, and environmental factors. Only through such a holistic approach can we fully understand how intelligence systems translate into enduring organizational success.
Digital transformation has become an urgent need for universities in Indonesia to improve academic and operational competitiveness. Business Intelligence plays a strategic role in supporting sustainable data-based decision-making. This study aims to formulate a sustainable strategy on Business Intelligence factors that contribute to the success of digital transformation in the university environment. The research method used is a qualitative approach with in-depth interview techniques with stakeholders in five selected state and private universities in Indonesia. The analysis was carried out using two approaches: (1) SWOT analysis to identify the strengths, weaknesses, opportunities, and threats of Business Intelligence implementation in the context of digital transformation; and (2) McFarlan Strategic Grid to map the priorities of Business Intelligence strategies and applications based on their strategic and operational values. The results of the study indicate that the success of digital transformation is greatly influenced by the integration of Business Intelligence into academic and administrative processes, digital leadership, and data and IT infrastructure readiness. The proposed sustainable strategies include strengthening data governance capabilities, increasing data literacy at all levels of the organization, and developing a BI application roadmap based on the “Strategic” and “Factory” categories in the McFarlan Grid. This research contributes to the formulation of BI’s long-term strategy to support adaptive and value-based digital transformation in Indonesian universities.
