2026-03-25
Ana Julia Dal Forno, Luiza Tagliari Brustolin, Leonardo Mejia Rincon
Textile companies in emerging markets often struggle with limited analytical tools and manual processes for monitoring competitors and defining pricing strategies, which reduces their ability to react quickly in dynamic markets. At the same time, there is a lack of empirical studies that integrate big data, competitive intelligence (CI), and decision support systems (DSS) in low-tech industries such as textiles. Addressing these gaps, this article develops and empirically validates a big data–based competitive intelligence platform, applied to the sportswear segment. The methodology consists of a practical case study, applying and testing the technological solution in a leading Brazilian company and its direct competitors. The solution uses web scraping, Apache Spark, SQL Server, and data visualization through Power BI. Validation shows that the platform significantly enhances agility in strategic decision making, providing real-time information on competitor behavior with efficiency superior to manual methods. The analysis reveals distinct competitive strategies among market participants and confirms the replicability of the model in several industrial contexts. Theoretically, the study contributes to the integration of big data, CI, and strategic pricing, while from a managerial perspective it proposes a scalable framework for digital transformation and increased analytical maturity in traditionally less digitized sectors.
2026-03-25
Avner Barnea
The massive number of publications on AI appears to be building an alternative to Competitive Intelligence (CI) analysts. Suddenly, the world has found a better alternative to intelligent analysts, and some wonder if human analysts will still be needed. The rapid advancement of AI in competitive intelligence (CI) has introduced new tools and capabilities for data collection and analysis. As organizations face increasingly complex markets and rapid shifts in competitive dynamics, combining AI-powered tools is becoming more crucial. The demand for better intelligence is growing, and executives are confident that CI can be more effective with the new AI tools they acquire. These expectations by senior executives are pressuring CI directors. Let us investigate this matter to examine AI's tremendous progress and evaluate the added value provided by human analysts. While human inference by analysts will continue to be needed, the input from AI tools is growing. The limitations of AI tools are well understood and anticipated. The focus of the added value of human analysts will be on delivering Anticipatory Intelligence (ANTINT). Analysts will have to be familiar with the ANTINT methodology and its interface with AI.
2026-03-25
Raed Momani, Mamdouh Abdel Latif Al Mawahreh, Ayman Hindieh, Tariq Samarah, Andrejs Cekuls, Bader Ismaeel
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.
2026-03-25
Andrejs Cekuls
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.
2025-04-27
Safa Al Olimat, Mifleh Ali Abu-Oliem, Shaker Jaralla Alkshali
This study sought to assess the impact of business intelligence with its dimensions (data warehouse, data mining, online analytical processing, report preparation, and business performance management) on organizational flexibility at Jordan Customs. The study’s population consisted of (544) managers at Jordan Customs Department. A simple random sample of (224) employee who hold a managerial position was taken from the study population. The questionnaire was distributed electronically to the managers in the study sample, and (210) questionnaires valid for statistical analysis were retrieved. Several statistical methods were utilized to analyze the study data and obtain the results via (Smart PLS4-SEM). The study revealed several key findings. Notably, the results indicated high levels of study variables, represented by business intelligence and organizational flexibility at Jordan Customs. Furthermore, the study’s findings revealed the presence of statistically significant impact of business intelligence with its dimensions on organizational flexibility at Jordan Customs Department. In light of the findings, the study proposed the managements of Jordan Customs to seek assistance from information technology companies to develop its systems and train its employees on how to identify data sources and how to acquire, store, analyze, and preserve them. In addition, it can develop its relationship with the sources from which it obtains the data it needs, which gives it an advantage in obtaining data; achieving the required flexibility for the department.
2025-04-27
Andrejs Cekuls
Competitive Intelligence (CI) refers to the systematic collection, analysis, and dissemination of information about a business, its external environment, and the overall business context to support strategic decision-making. As the environment becomes increasingly complex and dynamic, the need for CI becomes more pronounced. In recent years, the field of CI has undergone significant transformation, driven by technological innovations, the demand for real-time information, and a rise in interdisciplinary approaches. These developments are reflected in recent academic publications, which increasingly focus on topics such as the integration of artificial intelligence (AI), business intelligence tools, innovation support, the role of education, global collaboration and competition, and the evolution of interdisciplinary work. Emerging trends include the integration of AI and big data analytics, which are fundamentally changing how organizations collect and process information. AI-powered systems facilitate real-time analysis of large datasets, uncovering patterns and trends that would be difficult to identify manually. This transformation enhances decision-making by delivering timely and actionable insights. For instance, predictive analytics—enabled by machine learning algorithms—allow businesses to anticipate market shifts, identify emerging competitors, and optimize strategic actions (Sun et al., 2021; Chen et al., 2021). The use of AI in CI is expected to continue growing. Advanced systems not only automate routine data collection tasks but also support more sophisticated analyses, offering deeper insights and more accurate forecasts. AI is also increasingly recognized as a catalyst for innovation within organizations, fostering the development of new products and services. This connection between AI and innovation underscores the importance of cultivating an organizational culture that values knowledge acquisition and environmental awareness (de las Heras-Rosas & Herrera, 2021). As CI tools and methodologies evolve, the demand for professionals with both technical and analytical skills is rising. In response, educational institutions are updating curricula to include data science, business analytics, and information management. Beyond technical competencies, CI professionals must also develop soft skills such as critical thinking, adaptability, and ethical decision-making (Freyn & Hoffman, 2023; Calof & Cekuls, 2023). Lifelong learning and continuous professional development are essential to keep pace with new tools and practices. Many academic programs now emphasize simulations and case-based learning to better prepare future CI analysts for real-world challenges. Given the growing complexity of CI tasks, interdisciplinary expertise—combining knowledge from business, technology, psychology, and communication—is increasingly vital. Collaboration across disciplines and industries will further accelerate the advancement of CI methodologies, ensuring that intelligence practices remain relevant in a rapidly changing business landscape. This synthesis of knowledge and practice will help CI become a more integrated and strategic function that underpins organizational success. As organizations continue to navigate uncertainty and complexity, CI will play a critical role in enabling proactive and informed decision-making. Looking ahead, improved data integration and interdisciplinary collaboration will be key drivers of CI’s evolution, ensuring that organizations remain agile, innovative, and competitive. Accordingly, the Journal of Intelligence Studies in Business (JISIB) is receiving a growing number of submissions on contemporary CI applications. These publications aim to connect scholars and professionals in the CI field, fostering ongoing dialogue and development. I would like to express my gratitude to all contributors to this issue. On behalf of the Editorial Board, Sincerely Yours, Prof. Dr. Andrejs Cekuls University of Latvia, Latvia
2025-04-27
Bernhard Erös, Christoph Gritsch, Andrea Tick, Philipp Rosenberger
This paper addresses the multilingual language understanding of ChatGPT‒3.5 and 4 to investigate their performance with respect to languages with different degrees of prevalence on the internet. ChatGPT’s training data mostly consists of website content. As the language distribution is unevenly allocated and a low number of languages is used on websites this should impact performance. Both ChatGPT versions should rate reviews between 1 to 5 stars based solely on the product description and the review texts. Therefore, 500 e‒commerce reviews are collected for each of five languages: English, German, Dutch, Korean and Hindi, which are evenly distributed at 100 reviews per star rating. The evaluation methods and metrics used in this study include t‒tests, confusion matrices, macro F1 values and a defined cumulative star deviation. The results indicate a significant correlation between the degree of dissemination and the accuracy of the ChatGPT‒3.5 evaluation. In direct comparison, ChatGPT‒4 shows superior accuracy in all languages studied, while maintaining acceptable performance in less represented languages. The hypothesis that ChatGPT‒4 scoring accuracy increases with an increase in the number of words in reviews in less represented languages could not be confirmed. These findings illustrate the influence of the selected language on the interaction with ChatGPT and its language comprehension, which suggests that multilingualism should be given greater consideration in the future development and optimization of large language models.
2023-12-23
Luis Madureira, Iuliia Sergeenko, Sergei Zaimeko
This study delves into the pivotal role of Competitive Intelligence (CI) in shaping International Business Development (IBD) strategies for multinational enterprises (MNEs) operating in the fast-food industry amidst the geopolitical turbulence of the Russia-Ukraine conflict. It addresses a critical gap in existing research by examining how CI influences strategic decision-making in conflict-affected zones. The research is anchored on the premise that traditional IBD frameworks exhibit limitations when applied to unstable geopolitical contexts, necessitating a nuanced understanding of the interplay between CI and IBD in such environments. Employing a mixed-methods approach, the study integrates a comprehensive literature review with case studies and empirical data analysis. It particularly leverages the Competitive Intelligence Funnel framework to assess both external and internal business factors that influence strategic decisions. This methodology facilitates a holistic examination of the strategic manoeuvres of prominent fast-food corporations, including McDonald’s, YUM! Brands, and Subway, in response to the conflict. The findings reveal that these MNEs employed adaptive strategies in various domains such as marketing, supply chain management, corporate social responsibility, and investment decisions. Notably, the study uncovers a significant reliance on real-time geopolitical analysis and ethical considerations in strategy formulation, underscoring the limitations of conventional IBD models in conflict scenarios. Conclusively, the research posits that existing IBD frameworks require integration with real-time geopolitical insights and ethical considerations to be effective in conflict zones. This study contributes to the academic discourse by highlighting the indispensability of CI in the strategic planning of MNEs in volatile environments. It provides a novel perspective on the dynamic relationship between CI and IBD strategies, offering valuable insights for both scholars and practitioners in the realms of international business and strategic management.
2023-03-08
Shereen Aly
The purpose of this study is to examine the effect of marketing intelligence (MI) adoption on enhancing the profitability indicators of banks adopting MI and listed in the Egyptian stock exchange. A statistical analysis was carried based on data collected, using a questionnaire instrument to measure the efficiency of adopting MI among 12 banks adopting MI and listed in the Egyptian stock exchange. The study focuses on using 2 measures of profitability indicators; return on equity (ROE) and return on assets (ROA).The profitability indicators (ROE, ROA) of 12 central banks adopting MI and listed in the Egyptian stock exchange were measured during the period (2012–2021). Then, statistical analysis was conducted based on data collected using the simple linear regression model. The results of the study indicated a significant effect of MI adoption on enhancing the profitability indicators of 12 banks adopting MI and listed in the Egyptian stock exchange.
2023-02-22
Nouha Taifi
This study is about big data and its relationships with business intelligence and real time analysis. Few studies have studied this relation and fewer the parameters and variables of the characteristics and relations. In this study, this is presented in the literature review then for the research method, it is a questionnaire to finance sector leaders managers –unit of analysis with Lickert scale, yes and no questions and comments about the characteristics and relations of big data with real time analysis and business intelligence. The analysis uses SPSS for windows and NVIVO 12 for the quantitative and qualitative analysis. The results of the analysis present concrete and concise models in which big data is in relation to real time analysis and business intelligence. It also provides a thematic analysis leading to the development of a new framework model that lead to the definition of the characteristics and relationships. There are various theoretical and managerial implications for the big data management and possible finance sector. The future research is to scale the questionnaire to a survey basis, to modify the origins of the questions to a complete Lickert scale and to elaborate on new links with big data using the new conceptual framework.
2012-12-26
Stéphane Goria
This research paper investigates some fundamental principles of marketing warfare to see specifically what kinds of maneuvers can be used to defend or take control of a certain market. We present military war-games and its history to ease the understanding of the fundamentals in this area of study. Since we did not find a visual business wargame solution for our problem in the literature, we decided to develop one, based on the French market of game consoles between Nintendo and Sony in the period between 1994 and 2010. Our experiment confirmed the value of war-gaming. It showed that a parallel could be made between tactical maneuvers on the map and the statistics of sales for this market during the time interval