A Multidisciplinary Overview of “Business Intelligence Systems” Concept and Maturity Criteria: A Study in the Logistics and Transportation Sector

Öz

The aim of this study is to reveal the concept of business intelligence and the extent of business intelligence systems and to determine the maturity of business intelligence systems. So, an intensive literature review was conducted and the maturity criteria of business intelligence systems were determined. It has been seen that all information systems with strategic importance within the organization's thinking mechanism can be called a business intelligence system. Moreover, it has been concluded that business intelligence systems maturity can be evaluated by the success factors of information systems (IS Success), the strategic aligned between IT and the organizations (IT Strategic Alignment), and the contribution of these two factors to organizational intelligence. According to these criteria; transportation and logistics organizations in Turkey were reached. Because transportation and logistics organizations use information systems intensively in organizational mechanisms. As a result; it has been understood that information systems, which are sufficient in terms of information system infrastructure, can contribute to corporate intelligence factors, especially leadership practices and organizational positive thinking, and thus it can provide business intelligence support to organizations. Also, it has been understood that the strategic alignment between IT and the organizations is important for business intelligence support to organizations. In addition, it has been determined that the versatile strategic alignment between IT and the organizations will make the business intelligence support to be obtained from information systems more strategically distinct. This work; it is extremely important in terms of its contribution to the literature and suggestions for future studies.

Anahtar Kelimeler

• Business Intelligence
• Business Intelligence Systems
• Business Intelligence Maturity
• IT-Strategic Alignment
• Logistics
• Transportation

Kaynakça

1. Abubakre, M., Zhou, Y., & Zhou, Z. (2020). The impact of information technology culture and personal innovativeness in information technology on digital entrepreneurship success. Information Technology & People; https://doi.org/10.1108/ITP-01-2020-0002. google scholar
2. Acito, F., & Khatri, V. (2014). Business analytics: Why now and what next?. Business Horizons, 57, 565-570. google scholar
3. Ain, N., Vaia, G., Delone, W. H., & Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success – a systematic literature review. Decision Support Systems, 125, 1-13. google scholar
4. Albrecht, K. (2003). The power of minds at work: Organizational intelligence in action. New York: AMACOM. google scholar
5. Alkan, Ö., Oktay, E., Ünver, Ş., & Gerni, E. (2020). Determination of factors affecting the financial literacy of university students in Eastern Anatolia using ordered regression models. Asian Economic and Financial Review, 10(5), 536-546. google scholar
6. Alpar, R. (2018). Spor, sağlık ve eğitim bilimlerinden örneklerle uygulamalı istatistik ve geçerlik-güvenirlik (5. edition) [Applied statistics and validity-reliability with examples from sports, health, and educational sciences]. Turkey: Detay Yayıncılık. google scholar
7. Antoniadis, I., Tsiakiris, T., & Tsopogloy, S. (2015). Business intelligence during times of crisis: Adoption and usage of ERP systems by SMEs. Procedia - Social And Behavioral Sciences, 175, 299 – 307. google scholar
8. Arnott, D., Lizama, F., & Song, Y. (2017). Patterns of business intelligence systems use in organizations. Decision Support Systems, 97, 58–68. google scholar

9. Balaban, I., Mu, E., & Divjak, B. (2013). Development of an electronic portfolio system success model: An information systems approach. Computers & Education, 60, 396–411. google scholar
10. Baransel, A. E., & Baransel, C. (2012). Architecturing business intelligence for SMEs. IEEE 36th International Conference on Computer Software and Applications. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6340198. google scholar
11. I. A. Bashmakov, S. A. Braginskii, E. Y. Faddeeva, & M. I. Malyshev, "A technological management concept in digital logistics," 2021 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED), Moscow, Russian Federation, 2021, 1-5. doi: 10.1109/TIRVED53476.2021.9639214. google scholar
12. Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the Logistics Performance Index. The Asian Journal of Shipping and Logistics, 36, 34–42. google scholar
13. Bhatiasevi, V., & Naglis, M. (2020). Elucidating the determinants of business intelligence adoption and organizational performance. Information Development, 36(1), 78–96. google scholar
14. Bidgoli, (2014). MIS management information systems 5 (5th edition). Cengage Learning. google scholar
15. Bimonte, S., Ren, L., & Koueya, N. (2020). A linear programming-based framework for handling missing data in multi-granular data warehouses. Data & Knowledge Engineering. Retrieved from https://www.sciencedirect.com/science/article/pii/S0169023X19301016. google scholar
16. Bourbonnais, P., & Morency, C. (2018). A robust datawarehouse as a requirement to the increasing quantity and complexity of travel survey data. Transportation Research Procedia, 32, 436–447. google scholar
17. Bozic, K., & Dimovski, V. (2019). Business intelligence and analytics for value creation: The role of absorptive capacity. International Journal of Information Management, 46, 93-103. google scholar
18. Brichni, M., Dupuy-Chessa, S., Gzara, L., Mandran, N., & Jeannet, C. (2017). Bi4bi: A continuous evaluation system for business intelligence systems. Expert Systems with Applications, 76, 97–112. google scholar
19. Brooks, P., El-Gayar, O., & Sarnikar, S. (2015). A framework for developing a domain specific business intelligence maturity model: Application to healthcare. International Journal of Information Management, 35, 337–345. google scholar
20. Chamakiotis, P., Panteli, N., & Davison, R. M. (2021). Reimagining e-leadership for reconfigured virtual teams due to covid-19. International Journal of Information Management, 60. https://www.sciencedirect.com/science/article/pii/S0268401221000748. google scholar
21. Chan, L., & Lau, P. (2018). Investigating the impact of system quality on service-oriented business intelligence architecture. SAGE Open, 1–14. google scholar
22. Chan, Y. E., Huff, S. L., & Copeland, D. G. (1998). Assessing realized information systems strategy. Journal of Strategic Information Systems, 6, 273-298. google scholar
23. Chatterjee, S., Moody, G., Lowry, P. B., Chakraborty, S., & Hardin, A. (2020). Information technology and organizational innovation: Harmonious information technology affordance and courage-based actualization. Journal of Strategic Information Systems, 29, 1-23. google scholar
24. Chee, T., Chan, L., Chuah, M., Tan, C., Wong, S., & Yeoh, W. (2009). Business intelligence systems: State-of-the-art review and contemporary applications. Symposium on Progress in Information & Communication Technology. 96-101. [suspicious link removed], google scholar
25. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intellıgence and analytıcs: From big data to big impact. MIS Quarterly, 36 (4), 1165-1188. google scholar
26. Chen, Y., & Lin, Z. (2020). Business intelligence capabilities and firm performance: A study in China. International Journal of Information Management. Retrieved from https://Doi. Org/10.1016/J.Ijinfomgt.2020.102232. google scholar
27. Chen, X., & Siau, K. (2020). Business analytics/business intelligence and IT infrastructure: Impact on organizational agility. Journal of Organizational and End User Computing, 32 (4). doi: 10.4018/JOEUC.2020100107. google scholar
28. Chuah, M., & Wong, K. (2014). Web based enterprise business intelligence maturity (EBI2M) assessment tool. Retrieved from https://ieeexplore.ieee.org/document/7021827. google scholar
29. Chung, W., Chen, H., & Nunamaker Jr., J. F. (2002). Business intelligence explorer: A knowledge map framework for discovering business intelligence on the web. Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS’03). Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1173649. google scholar
30. Corte-Real, N., Ruivo, P., & Oliveira, T. (2014). The diffusion stages of business intelligence & analytics (BI&A): A systematic mapping study. Procedia Technology, 16, 172 – 179. google scholar
31. Dadkhah, M., Lagzian, M., Rahimnia, F., & Kimiafar, K. (2019). The potential of business intelligence tools for expert finding. Journal of Intelligence Studies in Business, 9(2), 82-95. google scholar
32. Dahiya, D., & Mathew, S. K. (2017). IT infrastructure capability and e-government system performance: An empirical study. Transforming Government: People, Process and Policy, 12 (1), 16-38. google scholar
33. Dai, Q., Kauffman, R. J., & March, S. T. (2007). Valuing information technology infrastructures: A growth options approach. Information Technology and Management, 8 (1), 1-17. google scholar
34. Dalal, N., & Pauleen, D. J. (2018). The wisdom nexus: Guiding information systems research, practice, and education. Info Systems J, 29, 224-244. google scholar
35. De Winnaar, K., & Scholtz, F. (2020). Entrepreneurial decision-making: New conceptual perspectives. Management Decision, 58 (7), 1283-1300. google scholar
36. De Winter, J. C. F., Dodou, D., & Wieringa, P. A. (2009). Exploratory factor analysis with small sample sizes. Multivariate Behavioral Research, 44:2, 147-181. doi: 10.1080/00273170902794206. google scholar
37. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information System, 19 (4), 9-30. google scholar
38. Deng, C., Wang, T., Teo, T. S. H., & Song, Q. (2021). Organizational agility through outsourcing: roles of it alignment, cloud computing and knowledge transfer. International Journal of Information Management, 60. https://www.sciencedirect.com/science/article/pii/S0268401221000785. google scholar
39. Dent, B. (2016). The power of a leadership philosophy. Nurse Leader. Retrieved from http://dx.doi.org/10.1016/j.mnl.2016.09.003. google scholar
40. Diop, M., Camara, M. S., Bah, A., & Fall, I. (2019). Prior management of temporal data quality in a data mining process: An implementation architecture process: an implementation architecture. Procedia Computer Science, 148, 273-282. google scholar
41. Doz, Y. (2020). Fostering strategic agility: How individual executives and human resource practices contribute. Human Resource Management Review, 30. https://doi.org/10.1016/j.hrmr.2019.100693. google scholar
42. Dyk, L V., & Conradie, P. (2007). Creating business intelligence from course management systems. Campus-Wide Information Systems, 24 (2), 120-133. google scholar
43. Erçetin, Ş. Ş. (2001). Örgütsel zekâ (1th. edition) [Organizational intelligence]. Ankara: Nobel Yayın Dağıtım. google scholar
44. Erçetin, Ş. Ş., Potas, N., & Koç, İ. (2016). Organizational intelligence scale for business organizations in chaotic situations. In Ş. Ş. Erçetin & H. Bağcı (Eds.), Handbook of research on chaos and complexity theory in the social sciences (pp. 133–152). New York, NY: IGI Global. google scholar
45. Erçetin, S., Çetin, B., & Potas, N. (2007). Multi-Dimensional organizational intelligence scale (Muldimorins). World Applied Sciences Journal, 2(3), 151-157. google scholar
46. Erçetin, Ş.Ş. (2004). The abilities related to the organizational intelligence and their action dimensions at schools. Res. Educ. Reform, 9(3), 3-18. google scholar
47. Erkuş, A. (2012). Psikolojide ölçme ve ölçek geliştirme [Measurement and scale development in psychology]. Ankara: Pegem Akademi. google scholar
48. Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education. New York: McGraw Hill. google scholar
49. Francia, M., Golfarelli, M., & Rizzi, S. (2019). Augmented business intelligence. Workshop Proceedings of the EDBT/ICDT 2019 Joint Conference. Retrieved from http://ceur-ws.org/Vol-2324/Paper02-MGolfarelli.pdf. google scholar
50. Francia, M., Golfarelli, M., & Rizzi, S. (2020). A-BI+: A framework for augmented business intelligence. Information Systems, 92. https://doi.org/10.1016/j.is.2020.101520. google scholar
51. Francois, M. D. (2020). An assessment of the impact of logistics and related infrastructure on the economy: A comparative analysis of the Visegrad Countries. Polısh Journal of Management Studies, 22 (1), 295-309. doi: 10.17512/pjms.2020.22.1.19. google scholar
52. Gambetti, E., & Giusberti, F. (2019). Personality, decision-making styles and investments. Journal of Behavioral and Experimental Economics, 80, 14-24. google scholar
53. Gartner (2025, October 15). Business intelligence services. Retrieved from https://www.gartner.com/en/information-technology/glossary/business-intelligence-bi-services#:~:text=Business%20intelligence%20(BI)%20services%20are,related%20technology%20applications%20and%20platforms. google scholar
54. George, D., & Mallery, M. (2010). Spss for windows step by step: a simple guide and reference, 17.0 Update (10a ed.) Boston: Pearson. google scholar
55. Gerow, J. E., Thatcher, J. B., & Grover, V. (2015). Six types of it-business strategic alignment: An investigation of the constructs and their measurement. European Journal of Information Systems, 24 (5), 465-491. google scholar
56. Gastaldi, L., Pietrosi, A., Lessanibahri, S., Paparella, M., Scaccianoce, A., Provenzale, G., Corso, M., & Gridelli, B. (2018). Measuring the maturity of business intelligence in healthcare: Supporting the development of a roadmap toward precision medicine within ISMETT hospital. Technological Forecasting and Social Change, 128, 84–103. https://doi.org/10.1016/j.techfore.2017.10.023. google scholar
57. Gilbert, F. J. (2020). Ten lessons of leadership: Reflections of a female academic. Clinical Radiology, 75, 799-803. google scholar
58. Gottfried, A. Hartmann, C., & Yates, D. (2021). Mining open government data for business intelligence using data visualization: A two-industry case study. Journal of Theoretical and Application Electronic Commerce Research, 16, 1042–1065. google scholar
59. Grossman, R. L. (2018). A framework for evaluating the analytic maturity of an organization. International Journal of Information Management, 38, 45-51. google scholar
60. Grzesik, K. (2019). The determinants influencing decision making in organizational settings - an integral approach. 2nd International conference on Decision making for Small and Medium-Sized Enterprises (DEMSME), May 16-17, Czech Republic. google scholar
61. Haag, S., & Cummings, M. (2015). Management information systems for the information age. McGraw-Hill Higher Education. google scholar
62. Hahn, M. H., Lee, K. C., & Lee, D. S. (2015). Network structure, organizational learning culture, and employee creativity in system integration companies: The mediating effects of exploitation and exploration.Computers in Human Behavior, 42, 167–175. google scholar
63. Halpern, N., Mwesiumo, D., Suau-Sanchez, P., Budd, T., & Brathen, S. (2021). Ready for digital transformation? The effect of organisational readiness, innovation, airport size and ownership on digital change at airports. Journal of Air Transport Management, 90, 1-11. google scholar
64. Hamidinava, F., Ebrahimy, A., Samiee, R., & Didehkhani, H. (2021). A model of business intelligence on cloud for managing SMEs in Covid-19 pandemic (Case: Iranian SMEs). Kybernetes, 52, 207-234. google scholar
65. Henderson, J. C., & Venkatraman, N. (1999). Strategic alignment: Leveraging information technology for transforming organizations. IBM Systems Journal, 38 (2&3), 472-484. google scholar
66. Holsapple, C., Lee-post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64, 130–141. google scholar
67. Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modeling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60. google scholar
68. Hu, L., & Bentler, P. M. (1999). Cut off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. google scholar
69. IBM (2025, October 15). What is business intelligence? Retrieved from https://www.ibm.com/topics/business-intelligence. google scholar
70. Jackson, D. L. (2001). Sample size and number of parameter estimates in maximum likelihood confirmatory factor analysis: A Monte Carlo investigation. Structural Equation Modeling, 8 (2). DOI: 10.1207/S15328007SEM0802_3. google scholar
71. Jain, A., & Ranjan, S. (2020). Implications of emerging technologies on the future of work. IIMB Management Review, 000, 1-7. https:// doi.org/10.1016/j.iimb.2020.11.004. google scholar
72. Jeyaraj, A. (2020). Variation in the effect of system usage and individual impact: A meta- regression of empirical findings. Information & Management, 57. https://doi.org/10.1016/j.im.2019.103242. google scholar
73. Jewer, J., & Compeau, D. R. (2022). Understanding information systems success: A hybrid view. European Journal of Information Systems, 31 (5), 577-596. doi: 10.1080/0960085X.2021.1890529. google scholar
74. Kalish, Y., & Luria, G. (2021). Traits and time in leadership emergence: A longitudinal study. The Leadership Quarterly, 32. https://doi.org/10.1016/j.leaqua.2020.101443. google scholar
75. Kassim, E. S., Jailani, S. F. A. K., Hairuddin, H., & Zamzuri, N. H. (2012). Information system acceptance and user satisfaction: The mediating role of trust. Procedia - Social and Behavioral Sciences, 57, 412 – 418. google scholar
76. Kawtar, I., Karim, D., & Salah, B. (2019). Proposal model of change for business IT alignment. Procedia Computer Science, 164, 96–104. google scholar
77. Kearns, G. S., & Sabherwal, R. (2007). Strategic alignment between business and information technology: A knowledge-based view of behaviors, outcome, and consequences. Journal of Management Information Systems, 23 (3), 129-162. google scholar
78. Khaddam, A. A., Alzghoul, A., Abusweilem, M. A., & Abousweilem, F. (2021). Business intelligence and firm performance: a moderated-mediated model. The Service Industries Journal, 43(13–14), 923–939. https://doi.org/10.1080/02642069.2021.1969367. google scholar
79. Khan, A., Ehsan, N. Mirza, E., & Sarwar, S. Z. (2012). Integration between customer relationship management (CRM) and data warehousing. Procedia Technology, 1, 239-249. google scholar
80. Kim, M., Kim, A. C. H., Newman, J. I., Ferris, G. R., & Perrewe, P. L. (2019). The antecedents and consequences of positive organizational behavior: The role of psychological capital for promoting employee well-being in sport organizations. Sport Management Review, 22, 108–125. google scholar
81. Kitsios, F., & Kapetaneas, N. (2022). Digital transformation in healthcare 4.0: Critical factors for business intelligence systems. Information, 13. https://doi.org/10.3390/info13050247. google scholar
82. Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: The Guilford Press. google scholar
83. Koliadenko, S., Golubkova, I., Babachenko, M., Levinska, T., & Burmaka, L. (2020). Development and use of IT solutıons in logistics.Фінансово-кредитна діяльність: проблеми теорії та практики : зб. наук. пр. Україна: Харків, 3 (34), 230-236. google scholar
84. Koohang, A., Nowak, A., Paliszkiewicz, J., & Nord, J. H. (2020). Information security policy compliance: Leadership, trust, role values, and awareness. Journal of Computer Information Systems, 60 (1), 1–8. google scholar
85. Nadj, M., Maedche, A., & Schieder, C. (2020). The effect of interactive analytical dashboard features on situation awareness and task performance. Decision Support Systems, 135. https://doi.org/10.1016/j.dss.2020.113322. google scholar
86. Nakhal A, A. J., Patriarca, R., Gravio, G. D., Antonioni, G., & Paltrinieri, N. (2021). Investigating occupational and operational industrial safety data through business intelligence and machine learning. Journal of Loss Prevention in the Process Industries, 73. https://doi.org/10.1016/j.jlp.2021.104608. google scholar
87. Naveed, Q. N., Alam, M. M., Qahmash, A. I., & Quadri, K. M. (2021). Exploring the determinants of service quality of cloud e-learning system for active system usage. Applied Science, 11 (4176), 1-18. google scholar
88. Neyişci, N., & Erçetin, Ş. Ş. (2020). The effect of social network interactions on development of organizational intelligence. Hacettepe University Journal of Education, 35(2), 354-374. doi: 10.16986/huje.2019052439. google scholar
89. Ngo, J., Hwang, B., & Zhang, C. (2020). Factor-based big data and predictive analytics capability assessment tool for the construction industry. Automation in Construction, 110. https://doi.org/10.1016/j.autcon.2019.103042. google scholar
90. Nino, H. A. C., Nino, J. P. C., & Ortega, R. M. (2020). Business intelligence governance framework in a university: Universidad de la costa case study. International Journal of Information Management, 50, 405-412. google scholar
91. Niu, Y., Ying, L., Yang, J., Bao, M., &Sivaparthipan, C. B. (2021). Organizational business intelligence and decision making using big data analytics. Information Processing and Management, 58. https://doi.org/10.1016/j.ipm.2021.102725. google scholar
92. Njanka, S. Q., Sandula, G., & Colomo-Palacios, R. (2021). IT-business alignment: A systematic literature review. Procedia Computer Science, 181, 333–340. google scholar
93. Oracle (2025, October 15). Business intelligence defined. Retrieved from https://www.oracle.com/business-analytics/business-intelligence. google scholar
94. Özdemir, D. (2010). Strategic choice for Istanbul: A domestic or international orientation for logistics?. Cities, 27, 154-163. google scholar
95. Paliszkiewicz, J. (2019). Information security policy compliance: Leadership and trust. Journal of Computer Information Systems, 59 (3), 211-217. doi:10.1080/08874417.2019.1571459. google scholar
96. Pare, G., Guillemette, M. G., & Raymond, L. (2020). IT centrality, IT management model, and contribution of the IT function to organizational performance: A study in Canadian Hospitals. Information & Management, 57. https://doi.org/10.1016/j.im.2019.103198. google scholar
97. Patil, A. D., & Gangadhar, N. D. (2016). OLaaS: OLAP as a service. IEEE International Conference on Cloud Computing in Emerging Markets. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7819682. google scholar
98. Pennetti, C. A., Sreekumar, S., Hollenback, K., Fontaine, M. D., & Lambert, J. H. (2020). Quantifying operational disruptions as measured by transportation network reliability. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 6 (4). google scholar
99. Peter, M. K., & Jarratt, D. G. (2015). The practice of foresight in long-term planning. Technological Forecasting & Social Change, 101, 49–61. google scholar
100. Petrini, M., & Pozzebon, M. (2009). Managing sustainability with the support of business intelligence: Integrating socio-environmental indicators and organisational context. Journal of Strategic Information Systems, 18, 178–191. google scholar
101. Petter, S., & Fruhling, A. (2011). Evaluating the success of an emergency response medical information system. International Journal of Medical Informatics, 80, 480–489. google scholar
102. Phillips-Wren, G., Daly, M., & Burstein, F. (2021). Reconciling business intelligence, analytics and decision support systems: More data, deeper insight. Decision Support Systems, 146. https://doi.org/10.1016/j.dss.2021.113560. google scholar
103. Porfírio, J. A., Carrilho, T., Felicio, J. A., & Jardim, J. (2021). Leadership characteristics and digital transformation. Journal of Business Research, 124, 610–619. google scholar
104. Potas, N., & Akçil Ok, M. (2020). Örnekleme yöntemleri [Sampling methods]. In Ş. Ş. Erçetin (Ed.), Araştırma teknikleri [Research techniques] (pp. 143–163). Ankara: Nobel Yayıncılık. google scholar
105. Potas, N., Erçetin, Ş. Ş., & Koçak, S. (2010). Multi-dimensional organizational intelligence measurements for determining the institutional and managerial capacity of girl’s technical education institution (Diyarbakır, Şanlıurfa, Konya/Turkey). African Journal of Business Management, 4(8), 1644-1651. google scholar
106. Presidency of the Republic of Turkey, Presidency of Strategy and Budget. (2019). Eleventh development plan (2019–2023). Ankara: Presidency of the Republic of Turkey. google scholar
107. Pustokhina, I. V., Pustokhin, D. A., Aswathy RH, Jayasankar, T., Jeyalakshmi, C., Díaz, V. G., & Shankar, K. (2021). Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms. Information Processing and Management, 58. https://doi.org/10.1016/j.ipm.2021.102706. google scholar
108. Qlik (2025, October 15). What is business intelligence? Retrieved from https://www.qlik.com/us/business-intelligence. google scholar
109. Wu, J., & Wang, Y. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information & Management, 43, 728–739. google scholar
110. Xiang, X., Zhongliang, G., Xiaoliang, X., & Jiashi, L. (2013). Synergic relationship and synergic degree between an information system and corporate strategy. Cybernetics and Information Technologies, 13, 110-121. google scholar
111. Yavaş, V., & Özkan-Özen, Y. D. (2020). Logistics centers in the new industrial era: A proposed framework for logistics center 4.0. Transportation Research Part E, 135. https://doi.org/10.1016/j.tre.2020.101864. google scholar
112. Zelenka, M., & Podaras, A. (2021). Increasing the effectivity of business intelligence tools via amplified data knowledge. Studies in Informatics and Control, 30 (2), 67-77. google scholar