Operationalizing Competitive Intelligence Capability In Higher Education: A Structural Model of Institutional Sensing, Skill Scaffolding, and Academic Performance
PDF

Keywords

Competitive Intelligence
Higher Education
Intelligence Governance
Academic Performance
Institutional Sensing
Evidence-Based Decision Making

How to Cite

Wang, L., Lajuma, S. B., & Yu, S. (2026). Operationalizing Competitive Intelligence Capability In Higher Education: A Structural Model of Institutional Sensing, Skill Scaffolding, and Academic Performance. Journal of Sustainable Competitive Intelligence , 16, e0508. https://doi.org/10.37497/eagleSustainable.v16i.508

Abstract

Purpose: This study examines how Competitive Intelligence Capability (CICAP) can be operationalized in higher education as an organizational capability that transforms internal academic signals into strategic intelligence for institutional decision-making. It investigates the direct and indirect effects of CICAP on academic performance through skill-scaffolding mechanisms.

Methodology/Approach: A quantitative observational design was conducted using survey data from 399 students enrolled at 20 public and private universities in Bangladesh. Data were analyzed through descriptive statistics, reliability analysis, multiple linear regression, ANOVA, mediation analysis, and machine-learning algorithms (Random Forest and Gradient Boosting), within an organizational framework grounded in the Competitive Intelligence cycle.

Originality/Relevance: The study reconceptualizes lecturer-support indicators as internal organizational intelligence signals, integrating the Competitive Intelligence cycle, intelligence governance mechanisms, and dynamic capabilities into the higher education context, thereby bridging educational analytics and Competitive Intelligence literature.

Key Findings: Lecturer support showed a significant positive relationship with academic performance. Competitive Intelligence Capability demonstrated both direct and indirect effects on academic performance, with skill scaffolding partially mediating this relationship, highlighting the role of organizational intelligence in supporting evidence-based strategic decisions within universities.

Theoretical/Methodological Contributions: The study proposes an organizational framework for operationalizing Competitive Intelligence in higher education by integrating institutional sensing, intelligence governance, dynamic capabilities, and evidence-based decision-making. It also offers a reproducible analytical model that can support future empirical research on Competitive Intelligence Capability in educational institutions.

https://doi.org/10.37497/eagleSustainable.v16i.508
PDF

References

References

[1] B. I. A. Arnout, T. S. AlQahtani, and H. A. L. Melweth, “Competitive capabilities of higher education institutions from their employees’ perspectives: A case study of King Khalid University,” PLoS ONE, vol. 19, no. 5, e0302887, 2024, doi: 10.1371/journal.pone.0302887.

[2] C. Longobardi, E. Sagone, and A. Crescentini, "Editorial: Highlights in educational psychology: teacher-student relationship," Frontiers in Psychology, vol. 15, 1529198, 2024. doi: 10.3389/fpsyg.2024.1529198

[3] K. Prananto, S. Cahyadi, F. Y. Lubis, and Z. R. Hinduan, "Perceived teacher support and student engagement among higher education students – a systematic literature review," BMC Psychology, vol. 13, 178, 2025. doi: 10.1186/s40359-025-02412-w

[4] M. Khalil, B. Prinsloo, and others, "A current overview of the use of learning analytics dashboards," Education Sciences, vol. 14, no. 1, 82, 2024. doi: 10.3390/educsci14010082

[5] J. Ranjan and C. Foropon, "Big data analytics in building the competitive intelligence of organizations," International Journal of Information Management, vol. 56, 102231, 2021. doi: 10.1016/j.ijinfomgt.2020.102231

[6] F. Ouyang, M. Wu, L. Zheng, L. Zhang, and P. Jiao, "Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course," International Journal of Educational Technology in Higher Education, vol. 20, no. 1, 4, 2023. doi: 10.1186/s41239-022-00372-4

[7] T. Susnjak, G. S. Ramaswami, and A. Mathrani, "Learning analytics dashboard: a tool for providing actionable insights to learners," International Journal of Educational Technology in Higher Education, vol. 19, 12, 2022. doi: 10.1186/s41239-021-00313-7

[8] J. Wang and Y. Yu, "Machine learning approach to student performance prediction of online learning," PLOS ONE, vol. 20, no. 1, e0299018, 2025. doi: 10.1371/journal.pone.0299018

[9] H. Meng and J. Goopy, "Early-career music teachers’ perspectives of their initial teacher education program in China," International Journal of Music Education, vol. 41, no. 4, pp. 593–610, 2024. doi: 10.1177/1321103X231157190

[10] L. Zhao, W. Huang, B. Han, and X. Wu, "Classroom climate dimensions, self-efficacy, and music aesthetic literacy: a mediation study with Chinese non-music major college students," Frontiers in Psychology, vol. 16, 1716470, 2025. doi: 10.3389/fpsyg.2025.1716470

[11] J. Zhang, "Music engagement, metacognitions, and performance outcomes: an empirical investigation among Chinese advanced music students," Frontiers in Psychology, vol. 15, 1712501, 2025. doi: 10.3389/fpsyg.2025.1712501

[12] B. Mallik, "Teacher–student relationship and its influence on college student engagement and academic achievement," Anatolian Journal of Education, vol. 8, no. 1, pp. 93–112, 2023. doi: 10.29333/aje.2023.817a

[13] B. Mallik, "Factors affecting college teacher–student relationship: A case study of a Govt College in Bangladesh," Anatolian Journal of Education, vol. 8, no. 2, pp. 161–180, 2023. doi: 10.29333/aje.2023.8211a

[14] M. J. A. Sarker, M. Hasan, A. Kabir, and A. Haque, "Leveraging artificial intelligence to assess the impact of COVID-19 on the teacher-student relationship in higher education," PLOS ONE, vol. 20, no. 3, e0317567, 2025. doi: 10.1371/journal.pone.0317567

[15] C. de las Heras-Rosas and J. Herrera, "Innovation and competitive intelligence in business: A bibliometric analysis," International Journal of Financial Studies, vol. 9, no. 2, 31, 2021. doi: 10.3390/ijfs9020031

[16] M. Yağcı, "Educational data mining: Prediction of students’ academic performance using machine learning algorithms," Smart Learning Environments, vol. 9, no. 11, 2022. doi: 10.1186/s40561-022-00192-z

[17] N. Sanz-Angulo and others, "Identifying the determinants of academic success: A machine learning approach in Spanish higher education," Systems, vol. 12, no. 10, 425, 2024. doi: 10.3390/systems12100425

[18] J. Liu, "Cognitive returns to having better educated teachers: Evidence from the China Education Panel Survey," Journal of Intelligence, vol. 9, no. 4, 60, 2021. doi: 10.3390/jintelligence9040060

[19] N. Abuzinadah, M. Umer, A. Ishaq, A. Al Hejaili, S. Alsubai, A. A. Eshmawi, A. Mohamed, and I. Ashraf, "Role of convolutional features and machine learning for predicting student academic performance from MOODLE data," PLOS ONE, vol. 18, no. 11, e0293061, 2023. doi: 10.1371/journal.pone.0293061

[20] Adefemi, K. O., & Mutanga, M. B. “A robust hybrid CNN–LSTM model for predicting student academic performance,” Digital, vol. 5, no. 2, p. 16, 2025, doi: 10.3390/digital5020016.

[21] X. Liu, "Effect of teacher–student relationship on academic engagement: the mediating roles of perceived social support and academic pressure," Frontiers in Psychology, vol. 15, 1331667, 2024. doi: 10.3389/fpsyg.2024.1331667

[22] L. Zhou, Y. Gao, J. Hu, X. Tu, and X. Zhang, "Effects of perceived teacher support on motivation and engagement amongst Chinese college students: Need satisfaction as the mediator," Frontiers in Psychology, vol. 13, 949495, 2022. doi: 10.3389/fpsyg.2022.949495

[23] M. Carmona-Halty, K. Alarcón-Castillo, C. Semir-González, G. Sepúlveda-Páez, P. Mena-Chamorro, F. Barrueto-Opazo, and M. Salanova, "How study-related positive emotions and academic psychological capital mediate between teacher-student relationship and academic performance: a four-wave study among high school students," Frontiers in Psychology, vol. 15, 1419045, 2024. doi: 10.3389/fpsyg.2024.1419045

[24] X.-C. Wang, M. Zhang, and J.-X. Wang, "The effect of university students’ academic self-efficacy on academic burnout: The chain mediating role of intrinsic motivation and learning engagement," Journal of Psychoeducational Assessment, vol. 42, no. 7, pp. 798–812, 2024. doi: 10.1177/07342829241252863

[25] S. Shen, T. Tang, L. Pu, Y. Mao, Z. Wang, and S. Wang, "Teacher emotional support facilitates academic engagement through positive academic emotions and mastery-approach goals among college students," SAGE Open, vol. 14, no. 2, 2024. doi: 10.1177/21582440241245369

[26] J. Liu, H. Du, and X. Lu, "Teacher support, academic self-efficacy, student engagement, and academic achievement in emergency online learning," Behavioral Sciences, vol. 13, no. 9, 704, 2023. doi: 10.3390/bs13090704

[27] L. Sun, "Context matters: adaptation of student-centred education in China school music classrooms," Music Education Research, vol. 25, no. 5, pp. 543–556, 2023. doi: 10.1080/14613808.2023.2230587

[28] C. Song, "Harmonising minds: How AI-powered learning tools shape music education students’ cognitive load, well-being and academic success," European Journal of Education, vol. 60, e70122, 2025. doi: 10.1111/ejed.70122

[29] Y. Wang, W. H. Tan, Q. Ye, and T. Gu, "Effects of game-based learning on piano music knowledge among elementary school pupils: Pretest-posttest quasi-experimental study," JMIR Serious Games, 2026. doi: 10.2196/80766

[30] G. Sala and F. Gobet, "Cognitive and academic benefits of music training with children: A multilevel meta-analysis," Memory & Cognition, vol. 48, pp. 1429–1441, 2020. doi: 10.3758/s13421-020-01060-2

[31] M. O. Johansen and L. M. Jeno, "The bright and dark side of autonomy: How autonomy support and thwarting relate to student motivation and academic functioning," Frontiers in Education, vol. 8, 1153647, 2023. doi: 10.3389/feduc.2023.1153647

[32] C. Mgweba, V. P. Rawjee, and P. Naidoo, "The use of competitive intelligence as a strategic tool for student recruitment in public universities," International Journal of Business Ecosystem & Strategy, vol. 6, no. 3, pp. 196–203, 2024. doi: 10.36096/ijbes.v6i3.523

[33] Adewusi, A. O., Okoli, U. I., Adaga, E., Olorunsogo, T., Asuzu, O. F., & Daraojimba, D. O. (2024). Business intelligence in the era of big data: A review of analytical tools and competitive advantage. Computer Science & IT Research Journal, 5(2), 415–431. https://doi.org/10.51594/csitrj.v5i2.791

[34] L. Bennett and S. Folley, "Four design principles for learner dashboards that support student agency and empowerment," Journal of Applied Research in Higher Education, vol. 12, no. 1, pp. 15–26, 2020. doi: 10.1108/JARHE-11-2018-0251

[35] Paulsen L, Lindsay E. Learning analytics dashboards are increasingly becoming about learning and not just analytics-A systematic review. Education and Information Technologies. 2024 Aug;29(11):14279-308. doi: 10.1007/s10639-023-12401-4

[36] U. Kalim and S. Bibi, "Understanding data-driven decision-making approach in Chinese higher education through the lens of Bakers model," International Journal of Chinese Education, vol. 12, no. 1, 2023. doi: 10.1177/2212585X231162120

[37] J. L. Rastrollo-Guerrero, J. A. Gómez-Pulido, and A. Durán-Domínguez, "Analyzing and predicting students’ performance by means of machine learning: a review," Applied Sciences, vol. 10, no. 3, 1042, 2020. doi: 10.3390/app10031042

[38] A. Angeioplastis, A. Tsimpiris, D. Varsamis, M. G. Pirina, and R. Solinas, "Predicting student performance and enhancing learning outcomes: A data-driven approach using educational data mining techniques," Computers, vol. 14, no. 3, 83, 2025. doi: 10.3390/computers14030083

[39] M. Chen, X. Zhang, and J. Li, "Predicting performance of students by optimizing tree components of random forest using genetic algorithm," Heliyon, vol. 10, no. 12, e32570, 2024. doi: 10.1016/j.heliyon.2024.e32570

[40] Jawad K, Shah MA, Tahir M. Students’ academic performance and engagement prediction in a virtual learning environment using random forest with data balancing. Sustainability. 2022 Nov 9;14(22):14795. doi: 10.3390/su142214795

[41] T. Le Quy, T. H. Nguyen, G. Friege, and E. Ntoutsi, "Evaluation of group fairness measures in student performance prediction problems," Lecture Notes in Computer Science, vol. 13715, pp. 119–136, 2022. doi: 10.1007/978-3-031-23618-1_8

[42] S. Wang, J. Bao, Y. Liu, and D. Zhang, "The impact of online learning engagement on college students’ academic performance: The serial mediating effect of inquiry learning and reflective learning," Innovations in Education and Teaching International, vol. 61, no. 6, pp. 1416–1430, 2024. doi: 10.1080/14703297.2023.2236085

Downloads

Download data is not yet available.