Purpose This study aims to determine the moderating role of competitive intelligence in the relationship between perceived environmental uncertainty and the choice of competitive strategies. Addressing a gap in the hospitality literature, this study investigates how competitive intelligence enables hotels to adapt their strategies in turbulent environments, a critical challenge in under-researched contexts such as boutique hotels. Design/methodology/approach This study collected survey data from 149 top-level managers of 4/5-star and boutique/cave hotels in Nevsehir, Türkiye. The hypothesized relationships, including the moderating effects of competitive intelligence, were tested using partial least squares structural equation modeling (PLS-SEM). Findings The findings reveal distinct relationships: perceived heterogeneity negatively impacts differentiation but positively influences focus strategies, whereas hostility negatively affects cost leadership. Crucially, this study demonstrates the significant moderating role of competitive intelligence. Specifically, competitive intelligence components such as information gathering were found to alter the impact of environmental hostility and heterogeneity on firms’ strategic choices, sometimes buffering negative effects and at other times amplifying them. This highlights that the effectiveness of a strategy under uncertainty is contingent upon a firm's intelligence capabilities. Research limitations/implications The findings suggest that hotel managers should develop robust competitive intelligence systems as a strategic enabler. These systems inform strategic decision-making by revealing the limited viability of cost leadership strategies in hostile environments and encouraging the adoption of focus strategies in heterogeneous markets. This research theoretically establishes competitive intelligence as a critical moderating variable in the environment-strategy relationship. The research shows that a firm's response to turbulence depends on its information processing capabilities. Practical implications Hotel managers should prioritize the development of robust competitive intelligence systems to navigate environmental uncertainty. Specifically, these systems can guide strategic choice by highlighting the risks of cost leadership strategies in hostile markets while revealing opportunities for focus strategies in heterogeneous ones. For boutique and cave hotels, this involves implementing scalable digital tools for monitoring competitor pricing and customer feedback to enable agile strategic adjustments. Originality/value This study fills a gap in the literature by examining the moderating role of competitive intelligence in the relationship between perceived environmental uncertainty and competitive strategies. Unlike previous research, this study specifically focuses on accommodation businesses, a context that has been largely overlooked. This study offers a novel perspective by integrating competitive intelligence into this relationship, providing both theoretical contributions and practical implications for the hospitality industry.

From Data to Strategy: A Public Market Framework for Competitive Intelligence

Knowledge management has strong importance in innovative processes, furthermore, it can be stated that competitive intelligence and knowledge generation systems for top managers have effect on business innovation. This article establishes a theoretical framework based on the Resourced-Based View (RBV) to analyze the role of top managers’ strategic vision in the success of innovation processes and to provide some empirical evidence of the value of business intelligence systems on the innovation performance in the organizations. Results show there is a strong relationship between top managers’ vision and innovation performance; moreover, from the relevance of the top managers’ vision derives the value of the business intelligence systems, which is the top managers’ main tool for learning and knowledge creation.

Purpose: This paper suggests a digital mental health (DMH) and smart tourism model with cultural adaptation, to assist in maintaining the psychological health and the culture adjustment of mainland Chinese students studying in Hong Kong. Its objective is to intertwine the elements of mental health with the mobility of the city, way finding technologies, and peer interaction to turn the day-to-day navigation process into a source of emotional management, learning culture, and institutional smartness. Methodology: A systematic literature review was performed based on PRISMA 2020 guidelines. Predefined keywords that were used include digital mental health, cultural adaptation, mobility, and smart tourism, which were searched in Scopus, Web of Science, PubMed, and Google Scholar. The inclusion was based on the PICOS framework and 55 studies published between 2008 and 2025 were included. Coding and reliability checks with thematic synthesis facilitated with qualitative content analysis (NVivo 14) were supplemented with data triangulation, making the methodology robust. Originality/Value: The study contributes to existing body of knowledge by incorporating DMH interventions with mobility and smart tourism technologies - which have not been combined to help students to cross-border to date. It also places the framework in the context of sustainable competitive intelligence on student mobility and engagement data as an institutional benefit. Key Results: It is demonstrated that the use of culturally adapted DMH tools leads to anxiety and depressive symptoms decreasing in students, whereas guided exploration of the city and the use of AR-guided navigation improve mobility confidence and sociocultural adaptation. Peer-social capabilities help to decrease isolation and create a sense of belonging. Collectively these elements can provide real-time institutional intelligence that can help in early detection, service planning and long term strategic competitiveness. Contributions: The study proposes an interdisciplinary model connecting the mental health, mobility, tourism technologies, and competitive intelligence. It shows the way universities may use smart-city settings to encourage well-being, cultural inclusiveness, and evidence-informed decision-making.

Purpose: This paper will discuss the way in which the Mongolian yak jerky could be re-packaged as a high-end sustainably-positioned snack to the Chinese market that is traditionally consumed as a pastoral item. It is proposed to determine the strategic, value-chain, consumer, and territorial branding variables that allow launching the product at the premium market, and overcome the structural deficiencies in the livestock industry of Mongolia. Methodology/Approach: The study has an integrative qualitative approach that involves a systematic literature review, strategic market intelligence analysis, cross-border regulatory analysis, and value-chain diagnostic report. The secondary sources were supplemented by the synthetic dataset that needed to reflect indicative patterns at the consumer perceptions, processing quality, export preparedness, and benchmark competition. Originality/Relevance: The study is the first to develop a comprehensive framework of the association between sustainable value-chain upgrading, strategic intelligence, and territorial branding to the premiumization of a pastoral meat product in a foreign market. It provides a way forward based on evidence of emerging economies aiming to modernize the traditional livestock systems and establish high value niches in the form of Sustainable Competitive Intelligence. Key Findings: The findings indicate that purity, healthiness, and cultural authenticity are the product attributes based on sustainability that the Chinese consumers value highly. Value-chain diagnostics indicates a high level of positive relationships between hygienic compliance, packaging quality and export preparedness. This means that as indicated by benchmarking, the Mongolian yak jerky is in a potential premium position but needs better packaging, flavors, and brand communication. The partnership between the sustainability, modernization, strategic intelligence, and territorial identity proves to be the key to the successful establishment in the market. Theoretical/Methodological Contributions: The research contributes to the Sustainable Competitive Intelligence theory by showing how the use of intelligence in decision-making can aid in cross-border value-chain upgrading and sustainable market alignment. It is also a contribution to the literature of COO and territorial branding because it demonstrates how ecological authenticity and nomadic identity might be developed into market strength when backed by quality control and processing standardization. In terms of methodology, the research depicts the (analytical) usefulness of synthetic data in conducting exploratory research in under-reported industries.

Background: While competitive intelligence (CI) mostly focuses outwardly, knowledge management (KM) is tasked to draw from external and internal knowledge sources to facilitate the knowledge flow inside the organisation towards innovation and competitive advantage. As a result, KM is perceived to be a key support function for the CI function. As such, it may be worthwhile for KM practitioners to understand key CI resources and plans for the future to offer improved support to CI practitioners. This study thus explores the future research agenda of CI. Objectives: This study aims to map the body of work centred on CI research and to map future research agendas in the CI space. Method: A bibliometric analysis approach was used to identify the latest studies, key sources, authors, keywords and countries that publish work on CI. Results: The research identified the main industries where CI research is done, the focus of the studies, research gaps and the prominent research methodologies in the CI field. Conclusion: Knowledge management practitioners can better support CI practitioners in their functions as they learn where CI is mostly practised and where the field is headed. Contribution: The authors believe that by fulfilling the research aims, CI and KM practitioners would be able to enhance their CI knowledge, tackle pressing CI research needs and reenergise conversations focusing on CI.

This paper addresses the basic issues with using classical Price Elasticity of Demand (PED) models for commercial applications, particularly due to their inability to apply effective constraints under dynamic market conditions. Classical models usually lead to theoretically optimal, but impractical price recommendations (e.g. unlimited price increases for inelastic goods). This study proposes the Holistic Pricing Approach (HPA), a multi-variable method that unifies data inputs from multiple sources into a single recommendation engine that helps overcome the classical model shortcomings. The HPA method employs a data fusion system linking three unique data sources: internal economics (e.g., product cost, target gross margins), competitive intelligence (competitor’s prices) and macroeconomic factors (e.g., inflation). These inputs are standardized with an interoperability layer to drive a four-step algorithmic heuristic. This includes a margin anchor price that is subject to adjustments by “competitive boundary checks” and “volume guardrails” to avoid excessive demand erosion. The effectiveness of the HPA was validated through a theoretical simulation with truncation that was compared to a classic elasticity model. The results showed that the traditional approach maximized margin at the expense of significant volume (20% lost), while HPA successfully balanced preserving margins and market share (5% volume loss). Furthermore, the total profit dollar amount was greater for the HPA strategy, which confirms that the HPA methodology drives increasing economic value. This study demonstrates that to protect revenue integrity, pricing must be approached as an interoperable ecosystem of constraints rather than a single dimensional elasticity calculation. This approach offers a roadmap for business leaders who, in the face of inflation, need to strike the right balance between increasing prices and preserving market share.

Global landscape of PROTAC: Perspectives from patents, drug pipelines, clinical trials, and licensing transactions.

The rising popularity of halal tourism in Indonesia has led to have stronger competition among hotels aiming to attract to have Muslim travelers. Various challenges in implementing to have Halal Integrity (HI) and Competitive Intelligence (CI) are found to have frequently, especially within the components that form to have their Halal Tourism Innovation Strategy (HTIS). In this study, the focus is to have on exploring to have how the Halal Orientation Strategy (HOS) influences to have and impacts to have HTIS. A total of 310 questionnaires were distributed to have to managers of halal hotels in Indonesia, and quantitative analysis was conducted to have using SmartPLS on 271 responses that were validated to have and confirmed to have as valid. According to the findings, HOS is to have the key mediating factor that connects to have HI and CI—both to one another and to HTIS. While HI promotes to have the implementation of halal principles, CI helps to have hotels stay to have informed about industry trends and competitor movements. By combining to have the strengths of CI and HI, HOS facilitates to have a more effective development and implementation of HTIS, ultimately giving to have halal hotels a stronger competitive position in the market. The findings suggest to have that halal hotels need to utilize to have CI in order to understand to have consumer preferences and market competition more effectively, while simultaneously strengthening to have their own HI through strict adherence to established halal practices. Hotel management also must devise to have HOS-based strategies that incorporate to have halal values into every aspect of their operations, thereby enhancing to have the overall effectiveness and efficiency of HTIS.

Objective of the study: This research is designed to analyze the relationship between competitive intelligence, innovation, and performance in the perception of world workers, considering the significance of these three constructs and their connections. Methodology/approach: This research was developed through a survey, with the application of a structured questionnaire applied to a primary database. In total, 508 valid responses were processed and analyzed using PLS-SEM model fitting approach. Main results: The findings reveal a cyclical pattern: competitive intelligence significantly enhances innovation levels; higher innovation levels, in turn, lead to improved performance across two distinct dimensions, strategic growth and operational efficiency; and elevated performance levels subsequently reinforce competitive intelligence. The bifactorial structure of performance and the unidimensional structures of innovation and competitive intelligence provide a coherent measrement model aligned with employees’ perceptions. Theoretical/methodological contributions: The study empirically validates and refines existing models, demonstrating that multidimensional constructs may operate more cohesively in practice. It advances theoretical understanding of the continuous feedback loop between competitive intelligence, innovation, and performance, while offering validated instruments for cross-sector assessment. Practical implications: Organizations can leverage these insights to strategically align competitive intelligence and innovation initiatives with targeted performance outcomes, optimizing both growth and operational responsiveness. The results also offer a reliable framework for monitoring and improving organizational practices across diverse sectors. Relevance/originality: Unlike prior studies focusing on single companies or sectors, this research integrates perceptions from professionals across multiple industries, providing novel evidence of how competitive intelligence and innovation jointly influence performance in a global context.

Objetivo do estudo: Esta pesquisa foi desenvolvida com o propósito de analisar a relação entre inteligência competitiva, inovação e desempenho na percepção de trabalhadores ao redor do mundo, considerando a relevância desses três construtos e suas interconexões. Metodologia/abordagem: O estudo foi conduzido por meio de uma pesquisa tipo survey, com a aplicação de um questionário estruturado a uma base de dados primária. No total, 508 respostas válidas foram processadas e analisadas utilizando a técnica de modelagem de equações estruturais via PLS-SEM. Principais resultados: Os resultados indicam que a inteligência competitiva impacta significativamente os níveis de inovação; os níveis de inovação, por sua vez, impactam de forma significativa os índices de desempenho; e estes, consequentemente, influenciam de maneira significativa os níveis de inteligência competitiva. Contribuições teóricas/metodológicas: O presente estudo amplia a compreensão científica sobre a relação entre inteligência competitiva, inovação e desempenho, por meio de uma pesquisa empírica sobre temas em evolução no campo. Além disso, avança na análise da percepção de profissionais de diferentes áreas, sem focar em uma empresa ou setor específico, diferentemente de estudos anteriores. Relevância/originalidade: Este estudo se destaca ao analisar a percepção de profissionais de diversas atividades sobre inteligência competitiva, inovação e desempenho, sem se concentrar em uma empresa ou setor específico, como ocorre em pesquisas anteriores.

This paper explores how Artificial Intelligence, Strategic Foresight and Competitive Intelligence convergence can significantly contribute to enhancing Morocco’s national competitiveness, transformational growth, and consequently, placing the new development model within a new AI-driven paradigm by converging different intelligences to serve competitiveness, performance, sovereignty, resilience, and Morocco's strategic and digital transformation.To this end, we adopted a mixed approach, combining a literature review, semi-structured interviews with experts and professionals in these fields, a benchmark of best practices for converging this triptych of AI, strategic foresight and competitive intelligence, and conceptual and systemic modelling that operationalizes this convergence across the entire Moroccan foresight and intelligence ecosystem. Thus, we proposed the LOUMAR Framework as part of a holistic, inclusive and convergent-oriented approach designed to harness the largely untapped synergistic potential between artificial intelligence, strategic foresight and competitive intelligence. This strategic and integrative model is based on six key levers, primarily focusing on Leadership and strategic vision, Organizing the National augmented foresight and intelligence ecosystem, Utilizing AI-enhanced competitive intelligence, Modelling AI-driven strategic foresight, Advancing AI-powered and intelligence-driven transformation, Resilience, realignment and continuous evaluation. Furthermore, we have outlined Morocco's main achievements and strengths with regards to this tripartite convergence, the constraints and points for improvement, the opportunities to be seized and the perspectives and development paths to follow, through the implementation of a strategic and digital transformation model that drives transformational growth and boosts Morocco’s augmented competitiveness.

Digital sovereignty has emerged as a critical geopolitical concept defining national autonomy in the digital realm, while competitive intelligence serves as an essential tool for understanding technological competition dynamics. This comprehensive review examines the intersection of digital sovereignty frameworks and competitive intelligence methodologies in analyzing global technological dominance patterns. The analysis synthesizes diverse theoretical approaches, policy mechanisms, and intelligence gathering techniques across various national contexts and technological sectors. This examination identifies significant advancements in integrated assessment methodologies that combine regulatory frameworks, technological capabilities, economic indicators, and strategic intelligence systems. Key findings indicate that hybrid competitive intelligence approaches, particularly combinations of open- source intelligence (OSINT) with economic espionage detection and technological trend analysis, yield robust predictive models for technological dominance patterns with improved accuracy compared to single source approaches. Advanced technologies including artificial intelligence driven intelligence analysis, blockchain-based sovereignty verification, and quantum-resistant security systems are transforming large-scale competitive intelligence capabilities, enabling real-time strategic assessment. However, significant gaps remain in standardization of sovereignty metrics, integration of cultural factors, and ethical frameworks for intelligence gathering. This review provides evidence-based recommendations for future research directions and practical implementation strategies for policymakers and intelligence professionals seeking to navigate the complex landscape of technological competition and digital autonomy.

The Data-Integrated Campaign Engagement (DICE) framework marks a significant advancement in customer relationship management and revenue optimization for enterprise organizations. It integrates four critical intelligence components: Customer Intelligence, Competitive Intelligence, Market Signals, and Augmented Criteria. Together, these elements form a unified architecture that transforms traditional campaign management into intelligent, adaptive market interaction systems. Through rigorous mathematical formalization and empirical validation across telecommunications networks serving over 150 million subscribers, the DICE methodology demonstrates provable performance bounds while establishing new theoretical foundations for intelligent market interaction. Its mathematical precision, combined with practical transformative impact, positions DICE as a breakthrough in computational social science and applied market theory. Real-world implementations further validate its effectiveness. Across telecom networks with more than 150 million subscribers, the framework has delivered measurable improvements: a 247% increase in campaign response rates, a 67.3% boost in operational efficiency, and a 43.8% rise in revenue, all with statistical significance (p < 0.001). DICE addresses core challenges such as data silos, limited personalization, and slow campaign execution cycles, while laying the groundwork for intelligent product architecture and streamlined order-to-cash processes

Big data analytics has become a cornerstone of modern industries, driving advancements in business intelligence, competitive intelligence, and data-driven decision-making. This study applies Neural Topic Modeling (NTM) using the BERTopic framework and N-gram-based textual content analysis to examine job postings related to big data analytics in real-world contexts. A structured analytical process was conducted to derive meaningful insights into workforce trends and skill demands in the big data analytics domain. First, expertise roles and tasks were identified by analyzing job titles and responsibilities. Next, key competencies were categorized into analytical, technical, developer, and soft skills and mapped to corresponding roles. Workforce characteristics such as job types, education levels, and experience requirements were examined to understand hiring patterns. In addition, essential tasks, tools, and frameworks in big data analytics were identified, providing insights into critical technical proficiencies. The findings show that big data analytics requires expertise in data engineering, machine learning, cloud computing, and AI-driven automation. They also emphasize the importance of continuous learning and skill development to sustain a future-ready workforce. By connecting academia and industry, this study provides valuable implications for educators, policymakers, and corporate leaders seeking to strengthen workforce sustainability in the era of big data analytics.

The Data-Integrated Campaign Engagement (DICE) framework marks a significant advancement in customer relationship management and revenue optimization for enterprise organizations. It integrates four critical intelligence components: Customer Intelligence, Competitive Intelligence, Market Signals, and Augmented Criteria. Together, these elements form a unified architecture that transforms traditional campaign management into intelligent, adaptive market interaction systems. Through rigorous mathematical formalization and empirical validation across telecommunications networks serving over 150 million subscribers, the DICE methodology demonstrates provable performance bounds while establishing new theoretical foundations for intelligent market interaction. Its mathematical precision, combined with practical transformative impact, positions DICE as a breakthrough in computational social science and applied market theory. Real-world implementations further validate its effectiveness. Across telecom networks with more than 150 million subscribers, the framework has delivered measurable improvements: a 24.7% increase in campaign response rates, a 67.3% boost in operational efficiency, and a 43.8% rise in revenue, all with statistical significance (p < 0.001). DICE addresses core challenges such as data silos, limited personalization, and slow campaign execution cycles, while laying the groundwork for intelligent product architecture and streamlined order-to-cash processes.

Large language models (LLMs) have emerged as transformative technologies in financial services, demonstrating unprecedented capabilities in extracting structured knowledge from unstructured financial documents, generating analytical insights, and supporting strategic corporate planning decisions. This review paper examines the comprehensive applications of LLMs including GPT-4, Claude, PaLM, and domain-specific financial models in automating knowledge extraction from diverse sources including earnings calls, financial reports, regulatory filings, and market commentary. We analyze how transformer-based architectures (TA) leverage attention mechanisms and contextual embeddings to understand complex financial terminology, temporal relationships, and causal connections in financial narratives. The paper explores advanced techniques including prompt engineering, few-shot learning, retrieval-augmented generation (RAG), and fine-tuning strategies that adapt general-purpose LLMs to specialized financial tasks. We examine applications in sentiment analysis of financial texts, automatic summarization of lengthy reports, entity recognition for companies and products, relationship extraction between financial events, and question-answering systems for financial queries. The review investigates how LLMs generate analytical insights through scenario analysis, trend identification, risk assessment, and competitive intelligence synthesis. We analyze corporate planning support applications including strategic initiative identification, market opportunity analysis, resource allocation recommendations, and investment thesis generation. Furthermore, we discuss integration architectures combining LLMs with structured databases, time-series models, and visualization tools to create comprehensive decision support systems. The paper addresses critical challenges including hallucination mitigation, accuracy verification, regulatory compliance, data privacy concerns, and the need for human oversight in high-stakes financial decisions. We examine evaluation methodologies for financial LLM applications, including domain-specific benchmarks, expert assessment protocols, and real-world performance metrics. Through synthesis of current research and deployed systems, we identify limitations including computational costs, update frequency challenges, bias in training data, and difficulties in explaining model reasoning. The review concludes by outlining promising research directions including multimodal financial analysis, real-time information integration, federated learning for privacy-preserving collaboration, and neuro-symbolic approaches combining neural language understanding with formal financial reasoning.

Is it possible for businesses to establish accounting procedures to make the most of big data and gain a competitive intelligence advantage in order to endure and thrive in a COVID-19 environment? This paper employed a desk research or systematic literature review approach to explore the difficulties and potential benefits of big data analytics for businesses during and after the COVID-19 pandemic. The study reveals that the most effective way to manage fluctuating consumer demand and potential losses is to have a plan in place for handling critical situations. Again, big data is viewed as an intangible asset that can be utilised to plan for and implement important contingencies that will increase a company's chances of surviving a pandemic. This study clearly demonstrates that incorporating big data into accounting methods, such as recording, measuring, valuation, risk assessment, materiality, intelligence, audit, and reporting, can enhance competitive intelligence, leading to new forms of trust and responsibility, and ultimately, effective internal governance. This study contributes to accounting research by presenting new and important insights into how accountants perceive the applications, opportunities, and challenges of big data beyond the COVID-19 pandemic. It provides an in-depth examination of the predictive analytics of big data as an intangible asset from an accounting perspective. This research includes policy and expert recommendations for developing new accounting perspectives on big data analytics to ensure organisations' financial viability throughout and beyond the COVID-19 situation.

With the growing dominance of Online Travel Agent (OTA) platforms in the digital tourism industry, the data presented therein has become a crucial asset for market analysis, competitive intelligence, and academic research. However, automatic data extraction (web scraping) from these modern platforms faces significant challenges due to the use of JavaScript frameworks that generate dynamic HTML structures with non-semantic and frequently changing CSS class names. This study proposes and validates a robust web scraping methodology to address these issues. Using Traveloka's website as a case study, this research leverages the data-testid attribute—a stable marker deliberately implemented by developers for automated testing purposes—as the primary selector for data extraction. The implementation was carried out using the Python programming language, with Selenium for browser automation and Beautiful Soup for HTML parsing. The results demonstrate that this method successfully and consistently extracts accommodation data, including hotel names, locations, rating scores, number of reviews, and pricing structures. The study concludes that utilizing the data-testid attribute offers a superior and more stable alternative to traditional scraping techniques that rely on CSS selectors or DOM structures, thereby providing an effective blueprint for data acquisition from contemporary dynamic web applications.

In an increasingly networked global context, commercial counterintelligence units and competitive intelligence experts must deal with sophisticated social engineering threats that exploit human psychology rather than technological shortcomings. This article highlights the importance of counterintelligence training and robust security measures while analyzing the psychological manipulation tactics employed by adversaries to lower these risks. The article examines social engineering strategies such as scarcity, authority, reciprocity, fear, and trust qualitatively to emphasize the significance of behavioral defenses and organizational awareness. The methodology, which evaluates institutional responses and psychological exploitation strategies, incorporates a review of the literature and expert comments. The paper’s conclusion recommends a multi-layered approach that incorporates organizational cultural reforms, technical defenses, and psychological awareness to safeguard sensitive data from insider threats and social engineering.