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.
This study investigates how companies respond to international market innovation challenges by analyzing the interplay between managerial practices, business intelligence (BI), and innovation performance. Using a quantitative approach with data from 100 SME managers, the research highlights critical dynamics in achieving innovation objectives through strategic orientations and BI mediation. Data analysis revealed two major significant effects. First, companies react positively to the improvement of their innovation performance. Second, a strongly observed link marking the positive mediation effect of business intelligence practices, which indicates that it acts as an appropriate means for achieving innovation objectives. This research suggests a new conceptual model that emphasizes the dynamics of innovation within companies. The methodological approach is based on structural equation methods and uses Smart PLS software as part of an experimental study.
This paper addresses the problem of misalignment between Competitive Intelligence (CI) systems and the actual intelligence needs of organizations. Many firms implement CI tools without first identifying what specific intelligence is required to support strategic decision-making. To overcome this gap, the paper introduces a decision-oriented framework for prioritizing Competitive Intelligence Needs (CIN). Unlike well-established (CI) practices, which have received greater attention in both research and application, CIN represents an upstream construct that guides the design, implementation, and strategic alignment of CI practices with actual organizational requirements. This orientation is particularly relevant in resource-constrained environments, such as emerging economies, where formal intelligence infrastructures are often lacking. Grounded in contingency theory and operationalized through the Analytic Hierarchy Process (AHP), the CIN framework is structured around four core intelligence sub-needs: data collection, information analysis, dissemination, and protection, each linked to internal and external contingency factors. Empirical results, based on expert evaluations, show that data collection and analysis are top priorities, primarily driven by environmental uncertainty. In contrast, dissemination and protection are more strongly influenced by organizational size and task interdependence. Internal contingencies emerge as equally important as external ones, challenging traditional models focused mainly on external threats, a legacy of CI’s military origins. Although the framework was developed using input from Moroccan experts, it is designed as a transferable diagnostic model for aligning intelligence priorities with organizational context across diverse sectors and geographical settings.
This issue brings together the authors’ research, which reveals contemporary topics in the context of studying CI, which resonate with contemporary scientific development trends and dynamics of change, and relate to recent discoveries in the field. Artificial intelligence (AI) and advanced data analytics are increasingly shaping how organizations generate intelligence and support strategic decision-making. In contemporary management research, strategic intelligence is no longer viewed solely as information gathering but as an integrative capability that combines analytical technologies, knowledge management, and strategic foresight. The integration of AI into strategic intelligence systems enhances the ability of organizations to process complex data and extract meaningful insights. Consequently, AI-supported intelligence systems facilitate knowledge-driven decision-making and contribute to improved organizational performance (e.g., Maitra et al., 2025; Seremeti & Anastasiadou, 2025). Several theoretical perspectives help explain how the integration of strategic intelligence and AI contributes to organizational effectiveness. Decision Theory provides an important foundation by explaining how organizations evaluate alternatives and make choices under uncertainty. Empirical evidence indicates that AI-enabled decision systems can significantly increase decision accuracy while reducing decision-making time, thereby improving the efficiency of strategic processes (e.g., Sikhakolli et al., 2025; Seremeti & Anastasiadou, 2025). The successful adoption of AI technologies can also be understood through the Technology Acceptance Model (TAM), which emphasizes that perceived usefulness and ease of use influence the adoption of new technologies within organizations. Consequently, organizational readiness, digital capabilities, and user acceptance become key determinants of effective AI integration (Abuzaid, 2024; Maitra et al., 2025). Another important theoretical lens is Information Processing Theory, which highlights the role of organizational capacity to process and interpret information in complex environments. Modern organizations face increasing information overload due to the exponential growth of digital data. AI technologies expand organizational information-processing capacity by automating data analysis, integrating diverse data sources, and delivering real-time insights that support more informed and timely decisions. These capabilities significantly enhance the analytical foundation of strategic intelligence systems. From a dynamic perspective, Organizational Learning Theory emphasizes the role of knowledge creation, knowledge sharing, and continuous adaptation in shaping organizational performance. AI contributes to organizational learning by extracting insights from large datasets and supporting evidence-based strategy development. By enabling organizations to identify patterns, evaluate outcomes, and refine strategies, AI strengthens adaptive learning processes and promotes innovation-driven growth (Alami & Al-Masaeid, 2025; Sposato, 2025). AI-driven analytics enables organizations to process large volumes of data, identify patterns, and generate predictive insights that improve decision quality and strategic responsiveness (Vincent, 2021; Weiser & von Krogh, 2023). In highly dynamic contexts, AI-based modeling and scenario simulations help organizations anticipate environmental changes and reduce uncertainty in strategic planning. At the same time, research emphasizes that the most effective outcomes emerge when AI-generated insights are combined with human judgment and intuition in decision processes (Vincent, 2021). Consequently, AI-supported intelligence systems enhance strategic agility and strengthen organizations’ ability to respond to emerging challenges and opportunities in omplex environments (Weiser & von Krogh, 2023; Asmar & Al-Rob, 2024). Beyond theoretical explanations, AI-enabled intelligence systems improve knowledge-driven decision making by transforming data into action-able insights that support evidence-based stra-tegic choices (Sikhakolli et al., 2025; Abdeljaber et al., 2025). In addition, AI contributes to operational efficiency by automating analytical processes and optimizing resource utilization, allowing managers to focus on strategic and innovative activities (Okafor & Murphy, 2025). Research increasingly emphasizes the impor-tance of human–AI collaboration, where AI systems augment rather than replace managerial expertise. Human oversight remains essential for interpreting analytical outputs, ensuring ethical accountability, and aligning technological insights with organizational values and strategic objectives (Štrukelj & Dankova, 2025; Alami & Al-Masaeid, 2025). Future research should continue to explore hybrid intelligence models that combine technological capabilities with human expertise to support sustainable and responsible decision making in increasingly complex organizational environments.
The African Continental Free Trade Area presents a unique opportunity for Zimbabwe to accelerate industrialisation and strengthen regional economic integration. While competitive intelligence has been widely acknowledged as a strategic tool in business, its role in informing national policy and industrial strategy in the context of AfCFTA remains underexplored. This study fills this gap by conducting a systematic literature review using the PRISMA framework to examine how CI can shape AfCFTA policy integration and industrialisation in Zimbabwe. Peer-reviewed articles, policy reports, and relevant documents were systematically identified, screened, and analyzed to ensure methodological rigor. Findings reveal that CI supports evidence-based policymaking, enhances industrial planning, reduces information asymmetries, and strengthens Zimbabwe’s competitiveness under AfCFTA. The study’s novelty lies in synthesizing insights specifically for Zimbabwe, highlighting practical recommendations for policymakers and industry stakeholders to leverage CI for sustainable industrial growth and regional integration.
Digital cultural platforms increasingly rely on recommender systems to support access to and engagement with large-scale heritage collections, yet their potential as instruments of strategic intelligence remains underexamined. This study conceptualizes and empirically evaluates value-aware recommender systems as strategic intelligence infrastructures in digital cultural ecosystems. Using a design science research approach and comparative evaluation, a multimodal recommender framework integrating diversity, inclusion, visibility, and strategic priority signals was developed and tested on large-scale open datasets from Europeana, Rijksmuseum, and The Metropolitan Museum of Art. The value-aware model maintains comparable recommendation accuracy while substantially improving cultural representation and visibility balance. Diversity increased by 27%, coverage by 34%, and minority representation from 0.18 to 0.31, while visibility disparities decreased by 38% and the Cultural Representation Score improved from 0.62 to 0.84. Cross-platform and multilingual analyses confirm the robustness of the approach. The results demonstrate that value-aware recommender systems can function as data-driven strategic intelligence tools that support cultural organizations in enhancing visibility, diversity, and evidence-based decision-making.
This study examines the role of national intelligence agencies in fostering gross capital formation in Zimbabwe, with the aim of understanding how intelligence functions can support economic transformation. Employing a sequential mixed-methods design, the research integrates a PRISMA-guided systematic literature review with a three-round Delphi study involving 18 experts from intelligence, economic policy, and investment sectors. Findings reveal that intelligence agencies, traditionally viewed as security-focused, possess untapped potential to enhance investment outcomes through macroeconomic risk forecasting, monitoring trade and geopolitical developments, aligning industrial and infrastructure planning, and detecting illicit financial flows. The study contributes theoretically by extending institutional economics and strategic foresight frameworks to include intelligence as a mechanism for reducing uncertainty and improving policy effectiveness. Practically, it highlights pathways for operationalising intelligence functions to strengthen investor confidence and guide economic policymaking. Limitations include the small Delphi panel and lack of direct empirical measurement of capital formation outcomes, suggesting opportunities for future research to quantify the impact of intelligence-informed interventions. Overall, the study provides both conceptual and actionable insights for enhancing economic governance and investment climates in Zimbabwe and similar contexts.
The study is a comprehensive bibliometric review of Strategic Intelligence (SI) and Growth Management which are becoming more relevant as they affect the organizational decision-making in highly competitive and digitally driven marketplaces. Although the amount of research work in both fields is increasing, there is still no single and longitudinal mapping of the development of Strategic Intelligence and Growth Management on how these two domains have co-evolved and the intersecting intellectual and thematic frameworks of these two domains. This paper fills this research gap by synthesizing, and visualizing, the fragmented body of research at their intersection in a systematic manner. The paper provides insight into the intersection of these two areas through the identification of the fruitful research domains, tracing the intellectual roots, as well as outlining the new directions in the research in this transdisciplinary field. The quantitative bibliometric strategy is anchored on a dataset of 2,272 indexed journal articles listed in Scopus and published in 2016-2025. Metadata was extracted using bibliometric methods along with performance analysis and creation of science maps. VOSviewer and Bibliometrix R were used in order to create co-word, co-citation, and co-authorship networks. The results indicate that there is great international collaboration as indicated by an international co-authorship percentage of 30.37% and 5,907 authors. The articles have been cited 53,089 times, and this shows increasing scholarly influence in the field of strategic intelligence and growth management. The strategic focus areas are digital intelligence, innovation management, strategic foresight, and performance improvement, and the emerging themes are applying artificial intelligence, big data, and decision intelligence to strategy growth activities. Overall, this research presents the dynamics of intelligent growth strategies as it elucidates the intellectual framework of the field and offers a systematic basis on the future theoretical and empirical studies.
This study investigates the role of AI in improving the efficiency and effectiveness of HRM in the Civil Service Commission of Kuwait. Focusing on five key HRM areas namely recruitment & selection, performance management, compensation, training & development, and promotion; this study explores how AI moderates the relationship between these practices and HRM outcomes. Using a quantitative survey approach, data from 228 employees were analyzed through structural equation modeling. The findings reveal that AI positively moderates the relationship between recruitment, performance management, compensation, and promotion with HRM efficiency and effectiveness. At the same time, no significant effect was observed for training & development. The results underscore AI's potential to reduce biases, enhance decision-making, and increase transparency in HRM processes. However, challenges in integrating AI into training & development highlight the need for a more tailored approach to implementation. Finally, the study refers to the fact that AI can significantly transform HRM in the public sector, particularly by enhancing efficiency and fairness. However, careful consideration is needed for its successful integration across all HR functions.
In an ever-changing world, marked by unpredictable fluctuations in the business environment, Morocco has integrated economic intelligence at the heart of its entrepreneurial strategy since the 2000s, aiming to actively insert itself into the global economy through forward-looking analysis, innovation processes and influencing techniques (Baulant, 2020). Although the private sector is the country's economic engine (Koura et al., 2022), accounting for over 93% of businesses, generating over 55% of wealth and creating over 95% of jobs, they play a key role in promoting economic development and growth. Their contribution is decisive in resolving major issues such as unemployment, poverty, innovation and sustainable development (Eniola and Entebang, 2015). Today, decision-makers increasingly recognise the importance of business intelligence in strengthening their competitiveness (Ghazouani et al., 2020). In addition, several initiatives and structures have been set up that consider economic intelligence to be essential for economic development.
Data analysis constitutes a critical component of organizational decision-making, involving the interpretation of complex datasets to generate meaningful and actionable insights. A central challenge in this process lies in effectively integrating subjective judgment with analytical rigor. This balance is particularly important in the field of Competitive Intelligence, where professionals must operate under conditions of uncertainty, incomplete information, and rapidly changing environments, requiring both analytical precision and contextual adaptability. This study examines how balancing subjective judgment and data-driven analysis in Competitive Intelligence influences decision-making competence and confidence. The findings reveal a moderate positive association between subjective judgment use and data-driven analysis use (r = .45, p < .001), indicating that decision-makers tend to employ both cognitive modes in parallel rather than as alternatives. A strong positive relationship was found between the perceived importance of balancing subjective and analytical approaches and decision-making confidence (r = .70, p < .001), identifying balance as the most influential factor in perceived competence. Training exposure was also positively associated with decision-making confidence (r = .55, p < .001), and regression analysis confirmed that perceived balance (β = .49), training (β = .30), and both subjective (β = .19) and analytical (β = .22) processing significantly predict decision-making confidence (R² = .57). The study demonstrates that effective CI practice depends not only on data collection and management but on the ability to interpret, triangulate, and contextualize information, combining analytical tools with experiential insight. The findings highlight the importance of training programs that explicitly develop the capability to balance subjective and objective inputs, thereby enhancing decision-making confidence and strategic competence.
This study argues that Decision Support Systems (DSS) are necessary for Covid-19 Crisis Management in turbulent economic environments. The study was conducted in Iraqi public hospitals. Data was collected through questionnaires from 140 top managers in these hospitals during Covid-19 crises and analyzed using descriptive and analytical methods. The study found strong evidence that DSS significantly impacts Covid-19 Crisis Management as a total and also in each dimension. The results provide Iraqi public hospitals manager's insights on how DSS enables Covid-19 Crisis Management processes. The study's primary value lies in its ability to provide evidence that DSS plays a significant role in Covid-19 Crisis Management in Iraqi public hospitals particularly. Since there was a lack of such study in the Iraqi context, this study can provide a theoretical basis for future researchers as well as practical implications for managers.
This study investigates how artificial intelligence (AI) integration enhances competitive intelligence (CI) effectiveness and, in turn, drives corporate growth and sustainability performance in Zimbabwean firms. Employing a mixed methods design, the research combines a quantitative survey of 312 senior managers and strategy professionals from medium and large firms with qualitative data from 28 semi structured interviews across manufacturing, financial services, telecommunications, and retail sectors. Quantitative findings reveal that AI capability significantly predicts CI effectiveness (β = 0.62, p < .001), while CI effectiveness significantly predicts corporate growth (β = 0.51, p < .001) and sustainability performance (β = 0.47, p < .001). Mediation analysis indicates that CI effectiveness partially mediates the relationship between AI capability and both corporate growth and sustainability outcomes. Qualitative analysis using the Gioia methodology further identifies three aggregate dimensions: AI enabled competitive intelligence, strategic decision making and growth, and sustainable value creation, illustrating how AI enhances sensing, analytics, and reporting capabilities, and how these capabilities are embedded into strategic routines. The findings extend the resource based, knowledge based, and dynamic capabilities perspectives by conceptualising CI as a mediating dynamic capability that transforms AI driven data into actionable strategic knowledge. The study contributes to theory and practice by demonstrating that AI delivers strategic value only when integrated into CI processes and organisational routines, enabling firms to achieve sustainable competitive advantage in volatile emerging economy contexts.
Introduction: The research demonstrates the transforming role of AI in email marketing. This points towards the need for advanced strategies which will serve to improve open rates and consumer engagement. The objective of the research is to study the impact of AI on automated emails and consumer familiarity. Literature Review: The key messages emerge on how AI improves engagement through making things timely and personalized, while on the other side, ethical issues reduce consumers who are in a state of mistrust over data privacy or integrity. There is a knowledge gap with regard to AI's long-term impact on trust and its aftermath of ethics. Methodology: This can be a quantitative study through questionnaires; it also includes statistical testing by SPSS. The sample size is 100 for generalization through simple random sampling. Key findings and analyses: While AI increases the rate of engagement and helps people develop trust in a certain brand, it has raised a number of problems in directing privacy concerns. Regression and Chi-square tests confirm significant impacts of personalization on engagement. Conclusion and Limitations: This chapter finally concluded that AI improves the effectiveness of email marketing while ensuring considerate information handling for long-lasting customer relationships. The limitations can be determined by illustrating the sample size which is small with 100 only.
The present economic situation and the level of competitiveness in the global marketplace are increasing at an alarming rate. Many organizations face challenges sustaining their operations during rapid technological advancements and innovations. To address these challenges, this study determined the influence of competitive intelligence (CI) on the sustainability of manufacturing companies in Nigeria, focusing on the novel contribution of product positioning and innovation capability as mediating influences on the established nexus. Three hundred ninetysix (396) employees were surveyed from selected manufacturing companies in Nigeria using a quantitative approach. At the same time, the data obtained was analyzed using the partial least square structural equation model (PLS-SEM) and the Sobel test to determine the association level among the variables captured in this study. The finding proves a positive nexus exists between competitive intelligence (CI) and organizational sustainability. Furthermore, product positioning and innovation capability were significantly and positively correlated with competitive intelligence and organizational sustainability. Similarly, the findings revealed that product positioning and innovation capability mediated the association between CI and organizational sustainability. Hence, companies should integrate product positioning and innovation capability for their business sustainability through competitive intelligence to enhance and maintain their competitive edge in the marketplace.
The current study aims to investigate the impact of strategic intelligence on organizational excellence within private Jordanian universities, with a particular emphasis on the mediating role of innovation and the moderating role of talent management. The study targets a sample of approximately 201 participants, including employees at both top and middle management levels in private Jordanian universities. Data were gathered through structured questionnaires and analyzed using Smart PLS4-SEM. The findings indicate that strategic intelligence and innovation exert a statistically significant impact on organizational excellence in the universities surveyed, with an explanatory power of R2 = 0.881. Also, it was demonstrated that there is a presence of a positive but statistically insignificant impact of talent management in enhancing the impact of strategic intelligence on organizational excellence at universities surveyed. Based on these results, the study provides several recommendations for private Jordanian universities. First, universities are encouraged to adopt strategic intelligence as a critical driver of organizational excellence, given its substantial and demonstrable impact. Furthermore, universities should prioritize the dimensions of strategic intelligence, particularly future vision, which emerged as the most prominently applied dimension. The study also underscores the importance of fostering an organizational culture conducive to innovation. Finally, future researchers are also encouraged to investigate other potential moderating variables, such as strategic ambidexterity and strategic vigilance, to deepen understanding of the factors influencing organizational excellence.
