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Journal articles on the topic 'Business intelligence systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

G., Mahendran. "Business Intelligence." Shanlax International Journal of Commerce 7, S1 (2019): 261–62. https://doi.org/10.5281/zenodo.3451722.

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Business Intelligence systems combine operational data with analytic tools to present complex and competitive information to planners and decision-makers. Although business intelligence systems are widely used in industry, research about them is limited. Effective business intelligence systems must account for goal-oriented behavior and decision-makers.  

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Francesco Di Tria, Ezio Lefons, and Filippo Tangorra. "Evaluation of Business Intelligence Systems." International Journal of Information Processing and Management 4, no. 3 (2013): 222–37. http://dx.doi.org/10.4156/ijipm.vol4.issue3.22.

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3

Rouhani, Saeed, Amir Ashrafi, Ahad Zare Ravasan, and Samira Afshari. "Business Intelligence Systems Adoption Model." Journal of Organizational and End User Computing 30, no. 2 (2018): 43–70. http://dx.doi.org/10.4018/joeuc.2018040103.

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Decision support and business intelligence systems have been increasingly adopted in organizations, while understanding the nature of affecting factors on such adoption decisions need receiving much academic interest. This article attempts to provide an in-depth analysis toward understanding the critical factors which affect the decision to adopt business intelligence (BI) in the context of banking and financial industry. In this regard, it examines a conceptual model that shows the impacts of different technological, organizational, and environmental factors in the decision to adopt BI by a f

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Stecyk, Adam. "Business Intelligence systems in SMEs." European Journal of Service Management 27 (2018): 409–13. http://dx.doi.org/10.18276/ejsm.2018.27/2-50.

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5

Tunowski, Remigiusz. "Organization Effectiveness and Business Intelligence Systems. Literature Review." Management and Business Administration. Central Europe 23, no. 4 (2015): 55–73. http://dx.doi.org/10.7206/mba.ce.2084-3356.157.

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6

Grünwald, Markus, and Dirk Taubner. "Business Intelligence." Informatik-Spektrum 32, no. 5 (2009): 398–403. http://dx.doi.org/10.1007/s00287-009-0374-1.

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7

Herring, Jan P. "Business Intelligence." Journal of Business Strategy 14, no. 3 (1993): 10–12. http://dx.doi.org/10.1108/eb039552.

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8

Glancy, Fletcher H., and Surya B. Yadav. "Business Intelligence Conceptual Model." International Journal of Business Intelligence Research 2, no. 2 (2011): 48–66. http://dx.doi.org/10.4018/jbir.2011040104.

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A business intelligence conceptual model (BISCOM) is proposed as a process-focused design theory for developing, understanding, and evaluating business intelligence (BI) systems. Previous work has concentrated on subsets of the BI systems, use of BI tools, and specific business functional area requirements. BISCOM provides a unified and comprehensive design theory that integrates and synthesizes existing research. It extends existing research by proposing functionality that does not currently exist in BI systems. The BISCOM is validated through descriptive methods that demonstrate the model ut

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9

Hawking, Paul, and Carmine Sellitto. "Business Intelligence Strategy." International Journal of Enterprise Information Systems 11, no. 1 (2015): 1–12. http://dx.doi.org/10.4018/ijeis.2015010101.

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Although Business Intelligence is seen as priority by many companies, the level of benefits achieved varies significantly between firms. Researchers have indicated that not having an effective Business Intelligence strategy is a significant issue in regards to trying to realize organizational benefits. This paper adopting a case study method investigates an Australian energy company's Business Intelligence adoption and the development of a Business Intelligence strategy that directly informed the firm's information needs. The important elements of this strategy included using a set of guiding

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10

N. Montero, Javier, and Mary L. Lind. "Determining Business Intelligence Usage Success." International Journal of Computer Science and Information Technology 12, no. 6 (2020): 45–67. http://dx.doi.org/10.5121/ijcsit.2020.12604.

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Business intelligence systems are highly complex systems that senior executives use to process vast amounts of information when making decisions. Business intelligence systems are rarely used to their full potential due to a poor understanding of the factors that contribute to system success. Organizations using business intelligence systems frequently find that it is not easy to evaluate the effectiveness of these systems, and researchers have noted that there is limited scholarly and practical understanding of how quality factors affect information use within these systems. This quantitative

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11

Søilen, Klaus Solberg. "Management implementation of Business Intelligence Systems." Inteligencia y Seguridad 2010, no. 9 (2011): 47–65. http://dx.doi.org/10.5211/iys.9.article5.

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12

Jason, Gary, Paul Harmon, and David King. "Expert Systems: Artificial Intelligence in Business." Technology and Culture 28, no. 4 (1987): 894. http://dx.doi.org/10.2307/3105218.

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13

Stecyk, Adam. "A development of Business Intelligence Systems." European Journal of Service Management 28 (2018): 305–11. http://dx.doi.org/10.18276/ejsm.2018.28/1-38.

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14

Mihai, ANDRONIE. "Airline Applications of Business Intelligence Systems." INCAS BULLETIN 7, no. 3 (2015): 153–60. http://dx.doi.org/10.13111/2066-8201.2015.7.3.14.

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15

Allen, R. H. "Expert systems: Artificial intelligence in business." Artificial Intelligence in Engineering 2, no. 2 (1987): 125. http://dx.doi.org/10.1016/0954-1810(87)90187-7.

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16

Gilad, Benny, and Tamar Gilad. "A systems approach to business intelligence." Business Horizons 28, no. 5 (1985): 65–70. http://dx.doi.org/10.1016/0007-6813(85)90070-9.

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17

Hampton, Clark, and Theophanis C. Stratopoulos. "Business Intelligence Systems and Fraud Opportunity." Academy of Management Proceedings 2012, no. 1 (2012): 11087. http://dx.doi.org/10.5465/ambpp.2012.11087abstract.

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18

Anurag Reddy Basani, Maria, and Anudeep Kandi. "Data-Driven Decision Making: Advanced Database Systems for Business Intelligence." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 844–50. http://dx.doi.org/10.21275/sr241114034006.

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19

Thaduri, Upendar Rao. "Business Security Threat Overview Using IT and Business Intelligence." Global Disclosure of Economics and Business 6, no. 2 (2017): 123–32. http://dx.doi.org/10.18034/gdeb.v6i2.703.

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Through a critical reflection on how information systems (IS) are currently defined in the IS literature, this study aims to contribute to a more profound knowledge of information systems (IS). The research identifies a number of definitions of IS that may be found in the literature by employing the hermeneutic method for literature reviews. The examination of these definitions allows for the differentiation of four distinct perspectives on information systems (IS): a technology view, which emphasizes the technological aspects of IS; a social theory, which highlights the sociocultural factors

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20

Denić, Nebojša, Kostadinka Stojanović, and Miloš Popović. "Intelligent systems in business function." Ekonomski signali 18, no. 1 (2023): 1–10. http://dx.doi.org/10.5937/ekosing2301001d.

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The goal of this studious analysis is to determine the extent to which sophisticated technology can be used to improve business operations. Today's time brings daily new and rapid changes in the business environment to which small and medium-sized enterprises in Serbia should adapt as soon as possible, often and on the fly. The defined objectives of the research refer to the assumption that business intelligence with its methods, techniques and tools can improve existing business processes in the company, make them more efficient while reducing overall business costs. Research shows that there

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21

Ebule, Amejuma Emmanuel. "Leveraging Artificial Intelligence in Business Intelligence Systems for Predictive Analytics." International Journal of Scientific Research and Management (IJSRM) 13, no. 01 (2025): 1862–79. https://doi.org/10.18535/ijsrm/v13i01.ec02.

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Artificial Intelligence (AI) and Business Intelligence (BI) are rapidly emerging as the next big things for organizations to analyze data and gain insights. As this article will go on to examine, the concept of using AI for BI is one that has significant implications about the possible integration of AI into various Business Intelligence systems examined in this article will focus on the application of AI for BI in the use of predicting analytics. When integrating Machine learning, natural language processing, and intelligent automation, these AI-Advanced BI systems assist organizations to go

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22

Epple, Johannes, Robert Winter, Stefan Bischoff, and Stephan Aier. "Business Intelligence is No ‘Free Lunch'." International Journal of Business Intelligence Research 9, no. 1 (2018): 1–15. http://dx.doi.org/10.4018/ijbir.2018010101.

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Cost allocations for business intelligence (BI) costs create cost awareness, enhance cost transparency, and support the management of BI systems. Although BI cost allocation is highly relevant in practice, the field is widely uncharted in current scholarly research. In this article, the state of the art in scientific literature is analyzed. The review is comprised of three iterations. It shows that certain general approaches for information systems cost allocation are suitable candidates if being combined and tailored to BI systems. Based on synthesis, an agenda is derived for future research

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23

Yeoh, William, Andy Koronios, and Jing Gao. "Managing the Implementation of Business Intelligence Systems." International Journal of Enterprise Information Systems 4, no. 3 (2008): 79–94. http://dx.doi.org/10.4018/jeis.2008070106.

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24

Popovič, Aleš, Borut Puklavec, and Tiago Oliveira. "Justifying business intelligence systems adoption in SMEs." Industrial Management & Data Systems 119, no. 1 (2019): 210–28. http://dx.doi.org/10.1108/imds-02-2018-0085.

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PurposeThe purpose of this paper is to provide a better understanding of how post-adoption use of business intelligence system (BIS) affects firm performance. It develops and empirically tests a conceptual model for assessing the impact of BIS routine and innovative usage on firm performance the context of small and medium enterprises (SMEs).Design/methodology/approachDrawing on the data collected from 181 SMEs we analyzed how BIS usage influences firm performance using the PLS-SEM method.FindingsThe paper provides empirical insights about how BIS routine and innovative usage affect firm perfo

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25

Petkovski, Dj B., and D. Kukolj. "Distributed Intelligence Systems and Complex Business Organizations." IFAC Proceedings Volumes 24, no. 5 (1991): 239–44. http://dx.doi.org/10.1016/s1474-6670(17)51254-9.

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26

Duan, Lian, and Li Da Xu. "Business Intelligence for Enterprise Systems: A Survey." IEEE Transactions on Industrial Informatics 8, no. 3 (2012): 679–87. http://dx.doi.org/10.1109/tii.2012.2188804.

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27

van Dyk, Liezl, and Pieter Conradie. "Creating business intelligence from course management systems." Campus-Wide Information Systems 24, no. 2 (2007): 120–33. http://dx.doi.org/10.1108/10650740710742727.

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28

Ahmadzai, Abdul Shabir, Qurban Yazdani, and Adamu Abubakar. "Analysis of Business Intelligence Systems Transmission Session." Journal of Science and Technology 27, no. 1 (2022): 16–28. http://dx.doi.org/10.20428/jst.v27i1.1984.

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Information and Communication Technology (ICT) is the central part of Intelligent systems. Business Intelligence (BI) utilizes ICT resources and capabilities to enhance decision-making from the available information. One of the core components of ICT resources is the network. Hypertext Transfer Protocol (HTTP) is an application layer protocol in a network model responsible for handling "requests" in a transmission session. Transmission Control Protocol (TCP) establishes connections session and keeps track of the state of the communication session. Unfortunately, a request from a critical onlin

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29

Tyson, Kirk W., and Kathryn Swanson. "Executive information systems approach for business intelligence." Competitive Intelligence Review 3, no. 1 (1992): 16–20. http://dx.doi.org/10.1002/cir.3880030107.

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30

Yeoh, William, and Andy Koronios. "Critical Success Factors for Business Intelligence Systems." Journal of Computer Information Systems 50, no. 3 (2010): 23–32. https://doi.org/10.1080/08874417.2010.11645404.

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31

Chen, Xiaofeng, and Keng Siau. "Business Analytics/Business Intelligence and IT Infrastructure." Journal of Organizational and End User Computing 32, no. 4 (2020): 138–61. http://dx.doi.org/10.4018/joeuc.2020100107.

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This is an empirical research investigating the impact of business analytics (BA) and business intelligence (BI) use, IT infrastructure flexibility, and their interactions on organizational agility. Synthesizing the systems theory and awareness-motivation-capability framework, the authors propose that BA-Use, IT infrastructure flexibility, and their interactions significantly influence organizational agility. The results show the significant association of BA use and IT infrastructure flexibility with organizational agility. The results also suggest that BA use may demand corporations to build

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32

Peshkova, Galina Yu, and Anna Yu Samarina. "PROSPECTS OF BUSINESS INTELLIGENCE TECHNOLOGIES USE IN SAINT-PETERSBURG." Economy of the North-West: problems and prospects of development 1, no. 64 (2021): 69–74. http://dx.doi.org/10.52897/2411-4588-2021-1-69-74.

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The present article concentrates on revealing prospects of Business Intelligence systems use for efficient management. Terms studied include information technologies, management, Business Intelligence, vendor, dashboard, business analytics. Digital transformation influences all the spheres and management is no exception. Data and related processes are considered to be one of the most significant aspects of doing business. Public administration is also exploring prospects of information technologies in general and Business Intelligence systems in particular. Saint-Petersburg is one of the pione

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33

Lim, Ee-Peng, Hsinchun Chen, and Guoqing Chen. "Business Intelligence and Analytics." ACM Transactions on Management Information Systems 3, no. 4 (2013): 1–10. http://dx.doi.org/10.1145/2407740.2407741.

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34

Daradkeh, Mohammad, and Radwan Moh'd Al-Dwairi. "Self-Service Business Intelligence Adoption in Business Enterprises." International Journal of Enterprise Information Systems 13, no. 3 (2017): 65–85. http://dx.doi.org/10.4018/ijeis.2017070105.

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Despite the growing popularity of self-service business intelligence (SSBI) tools, empirical research that investigates their acceptance by business professionals is still scarce. This paper presents and tests an integrated model of the antecedents of users' acceptance of SSBI tools in business enterprises. The proposed model is developed based on the technology acceptance model (TAM) and incorporating information and system quality from DeLone and McLean IS success model. It also includes an important factor from the business intelligence literature called analysis quality. To test the model,

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35

Kalashnikov, A. A., and D. S. Tyurbeeva. "Application of artificial intelligence systems for business decision making." Вестник Северо-Кавказского федерального университета, no. 3 (96) (2023): 74–82. http://dx.doi.org/10.37493/2307-907x.2023.3.8.

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In the context of digital transformation, due to help managers and organizations increase the efficiency and competitiveness of operations, the use of artificial intelligence methods or technologies for decision-making becomes more relevant. Thanks to the work of artificial intelligence, business gets a real basis for making correct and operational decisions. And this allows us to improve and develop business processes, increase profitability. Goal. Highlighting the problems and prospects of artificial intelligence, its impact on the economy and business in general. The study is based on the a

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36

LIEBOWITZ, JAY. "EXPERT SYSTEMS FOR BUSINESS APPLICATIONS." Applied Artificial Intelligence 1, no. 4 (1987): 307–13. http://dx.doi.org/10.1080/08839518708927977.

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37

Bell, David, and Sara Robaty Shirzad. "Social Media Business Intelligence." International Journal of Sociotechnology and Knowledge Development 5, no. 3 (2013): 51–73. http://dx.doi.org/10.4018/ijskd.2013070104.

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Social media tools are increasingly used for relationships management among marketplace actors (e.g. organisations, suppliers and individuals). As markets become ever more global and dynamic, new entrants find themselves struggling to fully understand the marketplace, companies operating with it and changes that occur. The authors discuss Social Media Network (SMN) tools and outline a methodology and procedure that supports the identification of domain specific networks within particular global business-to-business environments. Research is carried out using SMN data about firms in the pharmac

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38

Firmansyah, Denny. "Business Intelligence (BI) Implementation - COBIT 4.1." Journal of Applied Information, Communication and Technology 5, no. 1 (2018): 1–9. http://dx.doi.org/10.33555/ejaict.v5i1.5.

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The present paper aims to propose a general procedure to sizing and improve the Information Technology in an organization. The use of information systems has led to the recognition of the importance of quality management in the competitive environment. However, only few companies have taken actions to measure and enhance the quality of information. Hidden costs and poor quality of information may adversely measuring the performance of Business Intelligence systems based on COBIT 4.1 [1]. The value of the study is the ability to use business intelligence for the purpose of implement

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Siskos, Evangelos, Konstantia Darvidou, N. P. Lytvynenko, and M. P. Khmara. "INTEGRATION OF ARTIFICIAL INTELLIGENCE INTO BUSINESS PROCESSES." Actual Problems of International Relations, no. 147 (2021): 65–73. http://dx.doi.org/10.17721/apmv.2021.147.1.65-73.

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AI performs many tasks, including - creating and working in symbolic computing systems, creating and working in systems with fuzzy logic, cognitive psychology, understanding natural language, creating expert systems, computer linguistics, automation of various vocabulary and lexical cards, behavior planning (search and proposal of the most optimal way to achieve the goal, based on this situation), machine translation, creation and management of intelligent robots and groups of mobile robots, etc.

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40

Rostek, Katarzyna. "Business Intelligence for Insurance Companies." Foundations of Management 1, no. 1 (2009): 65–82. http://dx.doi.org/10.2478/v10238-012-0005-z.

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Business Intelligence for Insurance CompaniesThe article presents current status of IT implementation in Polish insurance companies. Afterwards the man issues connected with information management and proposal of dealing with these issues, due to the implementation of Business Intelligence system, is presented. Definition, structure and scope of application as well as the Business Intelligence system implementation methodology, with a particular consideration for the insurance market character, are presented. Article is summed up with examples of effective Business Intelligence systems impleme

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41

Nasir, Intedhar Shakir, Ayad Hameed Mousa, and Ihab L. Hussein Alsammak. "SMUPI-BIS: a synthesis model for users' perceived impact of business intelligence systems." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (2021): 1856–67. https://doi.org/10.11591/ijeecs.v21.i3.pp1856-1867.

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Business intelligence is a collection of methodologies, methods, architectures, and technologies that convert raw data into significant and useful information used by organizations to enable more effective strategic, tactical, and operational insights and decision-making. In spite of several studies have examined the critical success factors and development of business intelligence system, but few relevant studies have investigated perceptions of end-user’s business intelligence systems. Furthermore, none of those studies was performed in a higher education sector in Iraq. Consequently,

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42

RTAL, Mohamed, and Mostafa HANOUNE. "Strategic Information Systems and Artificial Intelligence in Business." International Journal of Information Technology and Applied Sciences (IJITAS) 3, no. 2 (2021): 78–83. http://dx.doi.org/10.52502/ijitas.v3i2.28.

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Information systems are defined as systems that consist of a group of people, data records, and some manual and non-manual operations. These systems generally handle data and information related to each system, and it can also be defined as a set of elements that overlap with each other to collect, process, store and distribute information on a specific topic. Systematically, in order to support the organization, control it, and the analysis, and form a clear current and future vision of the topic in question. Information systems are a group of programs that are used to archive, manage and org

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M. Olszak, Celina, and Ewa Ziemba. "Approach to Building and Implementing Business Intelligence Systems." Interdisciplinary Journal of Information, Knowledge, and Management 2 (2007): 135–48. http://dx.doi.org/10.28945/105.

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Grublješič, Tanja, and Jurij Jaklič. "Three Dimensions of Business Intelligence Systems Use Behavior." International Journal of Enterprise Information Systems 10, no. 3 (2014): 62–76. http://dx.doi.org/10.4018/ijeis.2014070105.

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In the Business Intelligence Systems (BIS) use context, the well-researched dimension of intensity of use is not broad enough to capture its meaningful use. The authors therefore investigate the whole spectrum of its use that encompasses effective utilization. The post-adoption research of Information System (IS) use is still under-researched; the study therefore brings novel insights to this area. The authors have conceptualized three dimensions of BIS use that capture the intensity of BIS use, the extent of BIS use, and BIS embeddedness. Confirmatory Factor Analysis (CFA) was used to determi

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45

Goede, Roelien. "Sustainable business intelligence systems: Modelling for the future." Systems Research and Behavioral Science 38, no. 5 (2021): 685–95. http://dx.doi.org/10.1002/sres.2813.

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46

Olszak, Celina M. "Business Intelligence Systems for Innovative Development of Organizations." Procedia Computer Science 207 (2022): 1754–62. http://dx.doi.org/10.1016/j.procs.2022.09.233.

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Cao, Jianping, Senzhang Wang, Benxian Li, Xiao Wang, Zhaoyun Ding, and Fei-Yue Wang. "Integrating Multisourced Texts in Online Business Intelligence Systems." IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, no. 5 (2020): 1638–48. http://dx.doi.org/10.1109/tsmc.2017.2710161.

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48

Owusu, Acheampong, Abbas Ghanbari-Baghestan, and Abdolhossein Kalantari. "Investigating the Factors Affecting Business Intelligence Systems Adoption." International Journal of Technology Diffusion 8, no. 2 (2017): 1–25. http://dx.doi.org/10.4018/ijtd.2017040101.

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This study explores the factors influencing the adoption of Business Intelligence Systems (BIS) in Higher Educational Institutions (HEIs), and also evaluate the extent of adoption in the universities. A research framework was developed based on the Technology-Organization-Environment (TOE) framework and the Diffusion of Innovations (DOI) theory, comprising of ten factors which were hypothesized and tested for the adoption of BIS in HEIs. Data was collected through a paper survey questionnaire from a sample of 120 managers and academicians in twelve private universities in Selangor State, Malay

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Khalid, Muhammad, Zahid Javed, Tariq Shahzad, Muniza Iqbal, and Rukhsana Safdar. "Challenges of Dimensional Modeling in Business Intelligence Systems." International Journal of Computer & Organization Trends 21, no. 1 (2015): 14–15. http://dx.doi.org/10.14445/22492593/ijcot-v21p304.

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Adimi, Amira, Mokhtar Mohammed Ghilan, and Walid Yousef. "Business Intelligence Systems Adoption: A Systematic Literature Review." مجلة جامعة صنعاء للعلوم التطبيقية والتكنولوجيا 2, no. 6 (2024): 527–37. https://doi.org/10.59628/jast.v2i6.1242.

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Even though business intelligence (BI) systems are widely used by organizations across all industries, they frequently fail to yield the expected benefits because the critical success factors (CSFs) that guarantee effective adoption are not considered. Giving this field more scholarly and practical attention is essential. The purpose of this study is to identify and rank the CSFs that influence the adoption of BI. This research employed the systematic literature review methodology. The study analyzes and synthesizes 64 relevant studies to address the CSFs influencing BI adoption. The findings

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