20 Years of Scientific Study on Business Intelligence and Decision-Making Performance: A Bibliometric Analysis
Downloads
Background: Business intelligence (BI) is an area in which data analytics is applied to generate crucial information supporting business decision-making and has been a significant domain for over three decades. However, there is uncertainty regarding whether investments can effectively improve organizational outcomes.
Objective: This study aimed to provide a comprehensive overview of the knowledge generated and disseminated in previous investigations related to the intricate relationship between BI and decision-making performance (DMP) over the past 20 years.
Methods: An R-tool namely bibliometrix, which supports suggested workflow for conducting bibliometrics and includes descriptive as well as knowledge structure analysis was used on a dataset containing 1,484 English-language articles published between 2003 and 2023 and indexed in Web of Science databases.
Results: The results showed that field study has stabilized over the past three years, signaling a shift in the focus of scholars. However, only a few studies use decision theory and further investigations are required to fully understand how BI impacts DMP both inside and outside organizational boundaries.
Conclusion: Based on the results, BI studies tend to be more application-oriented and there is a need to change the emphasis from focusing only on tools to variables such as the role of effective use and competencies that might improve decision quality.
Keywords: Business Intelligence, Decision-Making Performance, Decision Quality, Bibliometrics, R-tool.
Chen, Chiang, and Storey, “Business intelligence and analytics: From big data to big impact,” MIS Q., vol. 36, no. 4, p. 1165, 2012, doi: 10.2307/41703503.
R. Sharda, D. Delen, and E. Turban, Business intelligence, analytics, and data science: a managerial perspective, Fourth edi. New York: Pearson, 2014.
W. W. Eckerson, Performance dashboards: measuring, monitoring, and managing your business, 2nd ed. New York: Wiley, 2011.
Clark, Jones, and Armstrong, “The dynamic structure of management support systems: theory development, research focus, and direction,” MIS Q., vol. 31, no. 3, p. 579, 2007, doi: 10.2307/25148808.
Ö. Işık, M. C. Jones, and A. Sidorova, “Business intelligence success: The roles of BI capabilities and decision environments,” Inf. Manag., vol. 50, no. 1, pp. 13–23, Jan. 2013, doi: 10.1016/j.im.2012.12.001.
L. L. Visinescu, M. C. Jones, and A. Sidorova, “Improving Decision Quality: The Role of Business Intelligence,” J. Comput. Inf. Syst., vol. 57, no. 1, pp. 58–66, Jan. 2017, doi: 10.1080/08874417.2016.1181494.
M. Ghasemaghaei, S. Ebrahimi, and K. Hassanein, “Data analytics competency for improving firm decision making performance,” J. Strateg. Inf. Syst., vol. 27, no. 1, pp. 101–113, Mar. 2018, doi: 10.1016/j.jsis.2017.10.001.
A. S. Divatia, J. Tikoria, and S. Lakdawala, “Dimensions influencing business intelligence and analytics maturity: a critical analysis,” Int. J. Bus. Inf. Syst., vol. 37, no. 2, p. 200, 2021, doi: 10.1504/IJBIS.2021.115375.
A. Alzghoul, A. A. Khaddam, F. Abousweilem, H. J. Irtaimeh, and Q. Alshaar, “How business intelligence capability impacts decision-making speed, comprehensiveness, and firm performance,” Inf. Dev., vol. 40, no. 2, pp. 220–233, Jun. 2024, doi: 10.1177/02666669221108438.
R. Torres and A. Sidorova, “Reconceptualizing information quality as effective use in the context of business intelligence and analytics,” Int. J. Inf. Manage., vol. 49, pp. 316–329, Dec. 2019, doi: 10.1016/j.ijinfomgt.2019.05.028.
V.-H. Trieu, “Getting value from Business Intelligence systems: A review and research agenda,” Decis. Support Syst., vol. 93, pp. 111–124, Jan. 2017, doi: 10.1016/j.dss.2016.09.019.
S. Mathrani, “Critical business intelligence practices to create meta-knowledge,” Int. J. Bus. Inf. Syst., vol. 36, no. 1, p. 1, 2021, doi: 10.1504/IJBIS.2021.112413.
Á. Szukits and P. Móricz, “Towards data-driven decision making: the role of analytical culture and centralization efforts,” Rev. Manag. Sci., vol. 18, no. 10, pp. 2849–2887, Oct. 2024, doi: 10.1007/s11846-023-00694-1.
L. Hurbean, F. Militaru, M. Muntean, and D. Danaiata, “The impact of business intelligence and analytics adoption on decision making effectiveness and managerial work performance,” Sci. Ann. Econ. Bus., vol. 70, no. SI, pp. 43–54, Feb. 2023, doi: 10.47743/saeb-2023-0012.
T. D. Oesterreich, E. Anton, and F. Teuteberg, “What translates big data into business value? A meta-analysis of the impacts of business analytics on firm performance,” Inf. Manag., vol. 59, no. 6, p. 103685, Sep. 2022, doi: 10.1016/j.im.2022.103685.
M. P. Valadares de Oliveira, K. P. McCormack, M. Bronzo, and P. Trkman, “The effect of individual analytical orientation and capabilities on decision quality and regret,” Int. J. Bus. Anal., vol. 9, no. 1, pp. 1–19, Oct. 2021, doi: 10.4018/IJBAN.288510.
M. Ghasemaghaei, “Does data analytics use improve firm decision making quality? The role of knowledge sharing and data analytics competency,” Decis. Support Syst., vol. 120, pp. 14–24, May 2019, doi: 10.1016/j.dss.2019.03.004.
T.-P. Liang and Y.-H. Liu, “Research landscape of business intelligence and big data analytics: A bibliometrics study,” Expert Syst. Appl., vol. 111, pp. 2–10, Nov. 2018, doi: 10.1016/j.eswa.2018.05.018.
V.-H. Trieu, “Towards an understanding of actual business intelligence technology use: an individual user perspective,” Inf. Technol. People, vol. 36, no. 1, pp. 409–432, Jan. 2023, doi: 10.1108/ITP-11-2020-0786.
V.-H. Trieu, A. Burton-Jones, P. Green, and S. Cockcroft, “Applying and extending the theory of effective use in a business intelligence context,” MIS Q., vol. 46, no. 1, pp. 645–678, Feb. 2022, doi: 10.25300/MISQ/2022/14880.
S. Shamim, J. Zeng, Z. Khan, and N. U. Zia, “Big data analytics capability and decision making performance in emerging market firms: The role of contractual and relational governance mechanisms,” Technol. Forecast. Soc. Change, vol. 161, p. 120315, Dec. 2020, doi: 10.1016/j.techfore.2020.120315.
S. Jarupathirun and F. “Mariam” Zahedi, “Exploring the influence of perceptual factors in the success of web-based spatial DSS,” Decis. Support Syst., vol. 43, no. 3, pp. 933–951, Apr. 2007, doi: 10.1016/j.dss.2005.05.024.
G. P. Huber, “A theory of the effects of advanced information technologies on organizational design, intelligence, and decision making,” Acad. Manag. Rev., vol. 15, no. 1, p. 47, Jan. 1990, doi: 10.2307/258105.
T. Ramakrishnan, M. C. Jones, and A. Sidorova, “Factors influencing business intelligence (BI) data collection strategies: An empirical investigation,” Decis. Support Syst., vol. 52, no. 2, pp. 486–496, Jan. 2012, doi: 10.1016/j.dss.2011.10.009.
A. N. H. Zaied, G. S. Hussein, and M. O. Grida, “Critical success factors framework for implementing and adapting BIS on organisational performance,” Int. J. Bus. Inf. Syst., vol. 38, no. 1, p. 107, 2021, doi: 10.1504/IJBIS.2021.118632.
M. Ranjbarfard and Z. Hatami, “Critical success factors for implementing business intelligence projects (a BI implementation methodology perspective),” Interdiscip. J. Information, Knowledge, Manag., vol. 15, pp. 175–202, 2020, doi: 10.28945/4607.
G. Phillips-Wren, M. Daly, and F. Burstein, “Reconciling business intelligence, analytics and decision support systems: More data, deeper insight,” Decis. Support Syst., vol. 146, p. 113560, Jul. 2021, doi: 10.1016/j.dss.2021.113560.
U. B. Chaudhry and M. A. Chaudhry, “Harnessing big data for business innovation and effective business decision making,” in Cybersecurity in the Age of Smart Societies, Springer, Cham, 2023, pp. 47–60. doi: 10.1007/978-3-031-20160-8_4.
Z. Jaradat, A. Al-Dmour, H. Alshurafat, H. Al-Hazaima, and M. O. Al Shbail, “Factors influencing business intelligence adoption: evidence from Jordan,” J. Decis. Syst., vol. 33, no. 2, pp. 242–262, Apr. 2024, doi: 10.1080/12460125.2022.2094531.
A. Kumar, B. Krishnamoorthy, D. Kamath, C. D’, and N. A. Lima, “Business analytics adoption in firms,” Int. J. Bus. Inf. Syst., vol. 40, no. 4, p. 464, 2022, doi: 10.1504/IJBIS.2022.124928.
N. Ain, G. Vaia, W. H. DeLone, and M. Waheed, “Two decades of research on business intelligence system adoption, utilization and success – A systematic literature review,” Decis. Support Syst., vol. 125, p. 113113, Oct. 2019, doi: 10.1016/j.dss.2019.113113.
A. Di Vaio, R. Hassan, and C. Alavoine, “Data intelligence and analytics: A bibliometric analysis of human–artificial intelligence in public sector decision-making effectiveness,” Technol. Forecast. Soc. Change, vol. 174, p. 121201, Jan. 2022, doi: 10.1016/j.techfore.2021.121201.
J. R. López-Robles, J. R. Otegi-Olaso, I. Porto Gómez, and M. J. Cobo, “30 years of intelligence models in management and business: A bibliometric review,” Int. J. Inf. Manage., vol. 48, pp. 22–38, Oct. 2019, doi: 10.1016/j.ijinfomgt.2019.01.013.
M. Aria and C. Cuccurullo, “bibliometrix: An R-tool for comprehensive science mapping analysis,” J. Informetr., vol. 11, no. 4, pp. 959–975, 2017, doi: 10.1016/j.joi.2017.08.007.
C.-L. Hsu and C.-H. Chiang, “Electronic commerce research profiles: Comparing e-commerce and information systems journals,” Pacific Asia J. Assoc. Inf. Syst., pp. 49–66, 2017, doi: 10.17705/1pais.09303.
N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. Marc, “How to conduct a bibliometric analysis : An overview and guidelines,” J. Bus. Res., vol. 133, no. March, pp. 285–296, 2021, doi: 10.1016/j.jbusres.2021.04.070.
A. T. Guler, C. J. F. Waaijer, and M. Palmblad, “Scientific workflows for bibliometrics,” Scientometrics, vol. 107, no. 2, pp. 385–398, May 2016, doi: 10.1007/s11192-016-1885-6.
B. Kitchenham, “Procedures for performing systematic reviews,” Keele Univ. Tech. Rep., vol. 33, no. 2004, pp. 1–26, 2014, [Online]. Available: https://www.researchgate.net/publication/228756057
N. R. Haddaway, M. J. Page, C. C. Pritchard, and L. A. McGuinness, “PRISMA2020 : An R package and Shiny app for producing PRISMA 2020‐compliant flow diagrams, with interactivity for optimised digital transparency and open synthesis,” Campbell Syst. Rev., vol. 18, no. 2, Jun. 2022, doi: 10.1002/cl2.1230.
M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, Mar. 2021, doi: 10.1136/bmj.n71.
C. A. Tavera Romero, J. H. Ortiz, O. I. Khalaf, and A. Ríos Prado, “Business intelligence: Business evolution after industry 4.0,” Sustainability, vol. 13, no. 18, p. 10026, Sep. 2021, doi: 10.3390/su131810026.
J. R. Turner and R. Baker, “Collaborative research: Techniques for conducting collaborative research from the science of team science (SciTS),” Adv. Dev. Hum. Resour., vol. 22, no. 1, pp. 72–86, Feb. 2020, doi: 10.1177/1523422319886300.
A. Popovič, R. Hackney, P. S. Coelho, and J. Jaklič, “Towards business intelligence systems success: Effects of maturity and culture on analytical decision making,” Decis. Support Syst., vol. 54, no. 1, pp. 729–739, Dec. 2012, doi: 10.1016/j.dss.2012.08.017.
D. Arnott and G. Pervan, “A critical analysis of decision support systems research revisited: The rise of design science,” in Enacting Research Methods in Information Systems, Cham: Springer International Publishing, 2016, pp. 43–103. doi: 10.1007/978-3-319-29272-4_3.
D. Arnott and G. Pervan, “A critical analysis of decision support systems research,” J. Inf. Technol., vol. 20, no. 2, pp. 67–87, Jun. 2005, doi: 10.1057/palgrave.jit.2000035.
S. Chaudhuri, U. Dayal, and V. Narasayya, “An overview of business intelligence technology,” Commun. ACM, vol. 54, no. 8, pp. 88–98, Aug. 2011, doi: 10.1145/1978542.1978562.
M. Ghasemaghaei, K. Hassanein, and O. Turel, “Increasing firm agility through the use of data analytics: The role of fit,” Decis. Support Syst., vol. 101, pp. 95–105, Sep. 2017, doi: 10.1016/j.dss.2017.06.004.
K. ElMalah and M. Nasr, “Cloud business intelligence,” Int. J. Adv. Netw. Appl., vol. 10, no. 06, pp. 4120–4124, 2019, doi: 10.35444/IJANA.2019.100612.
N. J. van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, Aug. 2010, doi: 10.1007/s11192-009-0146-3.
A. McAfee and E. Brynjolfsson, “Big data: The management revolution,” Harvard Business Review. [Online]. Available: https://hbr.org/2012/10/big-data-the-management-revolution
A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” Int. J. Inf. Manage., vol. 35, no. 2, pp. 137–144, Apr. 2015, doi: 10.1016/j.ijinfomgt.2014.10.007.
W. Glänzel and A. Schubert, “Analysing scientific networks through co-authorship,” in Handbook of Quantitative Science and Technology Research, Dordrecht: Kluwer Academic Publishers, 2005, pp. 257–276. doi: 10.1007/1-4020-2755-9_12.
A. Schubert and G. Schubert, “Internationality at university level,” Scientometrics, vol. 123, no. 3, pp. 1341–1364, Jun. 2020, doi: 10.1007/s11192-020-03443-3.
F. Wang, M. S. Raisinghani, M. Mora, and J. Forrest, “Effective decision support in the big data era,” Int. J. Decis. Support Syst. Technol., vol. 14, no. 1, pp. 1–16, Mar. 2022, doi: 10.4018/IJDSST.286683.
M. R. Arizmendi and L. Stapleton, “Failure factors in the control of large-scale business intelligence systems development projects,” IFAC-PapersOnLine, vol. 52, no. 25, pp. 579–584, 2019, doi: 10.1016/j.ifacol.2019.12.609.
C. M. Olszak, “Toward better understanding and use of business intelligence in organizations,” Inf. Syst. Manag., vol. 33, no. 2, pp. 105–123, Apr. 2016, doi: 10.1080/10580530.2016.1155946.
M. Purdy and A. M. Williams, “How AI can help leaders make better decisions under pressure,” Harvard Business Review. Accessed: Jun. 03, 2024. [Online]. Available: https://hbr.org/2023/10/how-ai-can-help-leaders-make-better-decisions-under-pressure
S. Jabraoui and A. Charkaoui, “Analysis of Gartner’s BI&A capabilities reports over a decade: A systematic review approach,” in 2023 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD), IEEE, Nov. 2023, pp. 1–7. doi: 10.1109/ICTMOD59086.2023.10438134.
P. Alpar and M. Schulz, “Self-Service Business Intelligence,” Bus. Inf. Syst. Eng., vol. 58, no. 2, pp. 151–155, Apr. 2016, doi: 10.1007/s12599-016-0424-6.
J. Passlick, N. Guhr, B. Lebek, and M. H. Breitner, “Encouraging the use of self-service business intelligence – an examination of employee-related influencing factors,” J. Decis. Syst., vol. 29, no. 1, pp. 1–26, Jan. 2020, doi: 10.1080/12460125.2020.1739884.
V. B. Vukšić, M. P. Bach, and A. Popovič, “Supporting performance management with business process management and business intelligence: A case analysis of integration and orchestration,” Int. J. Inf. Manage., vol. 33, no. 4, pp. 613–619, Aug. 2013, doi: 10.1016/j.ijinfomgt.2013.03.008.
Copyright (c) 2024 The Authors. Published by Universitas Airlangga.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
