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Journal articles on the topic 'Business intelligence systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 May 2022

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1

Tunowski, Remigiusz. "Organization Effectiveness and Business Intelligence Systems. Literature Review." Management and Business Administration. Central Europe 23, no. 4 (December 15, 2015): 55–73. http://dx.doi.org/10.7206/mba.ce.2084-3356.157.

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2

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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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 (April 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 firm. Structural equation modeling (SEM) was used for data analysis and test the relevant hypothesis. The results of this article which are derived from theoretical discussion of hypothesizes show that from nine hypothesized relationships—perceived tangible and intangible benefits, firm size, organizational readiness, strategy, industry competition and competitors absorptive capacity—affect BIS adoption in the surveyed cases.

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4

Francesco Di Tria, Ezio Lefons, and Filippo Tangorra. "Evaluation of Business Intelligence Systems." International Journal of Information Processing and Management 4, no. 3 (May 31, 2013): 222–37. http://dx.doi.org/10.4156/ijipm.vol4.issue3.22.

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5

Grünwald, Markus, and Dirk Taubner. "Business Intelligence." Informatik-Spektrum 32, no. 5 (August 27, 2009): 398–403. http://dx.doi.org/10.1007/s00287-009-0374-1.

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6

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

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7

Hawking, Paul, and Carmine Sellitto. "Business Intelligence Strategy." International Journal of Enterprise Information Systems 11, no. 1 (January 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 principles to ensure that there was a close alignment of Business Intelligence outcomes with the company's needs. The paper provides insights for researchers and practitioners on the important factors need to be considered to achieve effective Business Intelligence.

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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 (April 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 utility and through prototype creation to demonstrate the need for BISCOM.

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9

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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10

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

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11

Søilen, Klaus Solberg. "Management implementation of Business Intelligence Systems." Inteligencia y Seguridad 2010, no. 9 (February 22, 2011): 47–65. http://dx.doi.org/10.5211/iys.9.article5.

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12

Mihai, ANDRONIE. "Airline Applications of Business Intelligence Systems." INCAS BULLETIN 7, no. 3 (September 10, 2015): 153–60. http://dx.doi.org/10.13111/2066-8201.2015.7.3.14.

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13

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

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14

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

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15

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

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16

N. Montero, Javier, and Mary L. Lind. "Determining Business Intelligence Usage Success." International Journal of Computer Science and Information Technology 12, no. 6 (December 30, 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 post positivist research used the information system (IS) success model to analyze how information quality and system quality influence information use in business intelligence systems. This study was also designed to investigate the moderating effects of maturity constructs (i.e., data sources and analytical capabilities) on the relationships between quality factors and information use.

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17

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

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18

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

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19

Popovič, Aleš, Borut Puklavec, and Tiago Oliveira. "Justifying business intelligence systems adoption in SMEs." Industrial Management & Data Systems 119, no. 1 (February 4, 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 performance.Practical implicationsThe results provide instrumental insights for managers and solution providers to help them understand the influence of various determinants to more effectively conclude the post-adoption process in SMEs.Originality/valueThis study represents important progress in the authors’ theoretical understanding of the role of BIS routine and innovative usage across different BIS partial impacts on firm performance dimensions.

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20

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

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21

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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22

van Dyk, Liezl, and Pieter Conradie. "Creating business intelligence from course management systems." Campus-Wide Information Systems 24, no. 2 (April 3, 2007): 120–33. http://dx.doi.org/10.1108/10650740710742727.

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23

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 pioneers regarding this and can serve as a fine example for the rest Northwest Region and Russia on the whole.

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24

Epple, Johannes, Robert Winter, Stefan Bischoff, and Stephan Aier. "Business Intelligence is No ‘Free Lunch'." International Journal of Business Intelligence Research 9, no. 1 (January 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 into cost allocation for BI systems.

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25

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

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26

Chen, Xiaofeng, and Keng Siau. "Business Analytics/Business Intelligence and IT Infrastructure." Journal of Organizational and End User Computing 32, no. 4 (October 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 a more flexible IT infrastructure. However, the data does not reveal the proposed interaction between the two drivers of organizational agility.

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27

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 (July 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, data were collected through a questionnaire survey from 331 business users working in a variety of industries in Jordan. Data were analysed using structural equation modeling (SEM) techniques. The results demonstrated that the three quality factors– information quality, system quality and analysis quality – are key antecedents of perceived usefulness and ease of use, which in turn were found to be strong predictors of users' intention to use SSBI tools. The findings of this study provide several implications for research and practice, and thus should help in the design and deployment of more user-accepted SSBI tools.

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28

Bell, David, and Sara Robaty Shirzad. "Social Media Business Intelligence." International Journal of Sociotechnology and Knowledge Development 5, no. 3 (July 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 pharmaceutical industry. The authors use their own methodology to uncover market participants, linkages and prominent issues that may help new firms to position themselves effectively within a new marketplace. SMNs provide a sizable source of information and new approaches are required to fully leverage their considerable value. This paper explores how SMNs can be used as an effective source of business intelligence by utilising two popular SMN platforms.

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29

Chikohora, Teressa Tjwakinna, and Bukohwo Michael Esiefarienrhe. "A Hybrid Framework for The Implementation of Business Intelligence Systems in Small Scale Enterprises." Journal of Information Systems and Informatics 4, no. 1 (March 15, 2022): 40–54. http://dx.doi.org/10.51519/journalisi.v4i1.221.

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Small scale enterprises can improve their operations by implementing business intelligence systems. The business intelligence systems are complex and require expertise to ensure successful implementation, hence the need for small scale enterprises to determine their readiness before undertaking the project. To improve chances for successful implementation, this study proposed a framework to guide small scale enterprises on the requirements for business intelligence systems. The design steps defined by Edwards and Goodrich & Tamassia were followed to design the framework. The framework components were informed by the Diffusion of Innovation and Technology Organization and Environment theories, the Information Evaluation Model, and the critical success factors for BIS implementation. A small business may evaluate its resources against the framework components to determine whether to implement a business intelligence system. In future, the framework may be extended to include weights and other criteria to calculate a business’s status.

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30

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

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31

Grublješič, Tanja, and Jurij Jaklič. "Three Dimensions of Business Intelligence Systems Use Behavior." International Journal of Enterprise Information Systems 10, no. 3 (July 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 determine convergent and discriminant validities proving construct validities, based on survey data gathered from BIS users in medium and large sized organizations. The results of our study provide sound evidence for apprehending these three constructs as different conceptualizations. This work adds to the literature by emphasizing the role of post-adoptive behavior that can lead to greater and effective utilization in the studied BIS environment, and potentially to a better organizational performance.

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32

Gaardboe, Rikke, Tom Nyvang, and Niels Sandalgaard. "Business Intelligence Success applied to Healthcare Information Systems." Procedia Computer Science 121 (2017): 483–90. http://dx.doi.org/10.1016/j.procs.2017.11.065.

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33

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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34

Kabakchieva, Dorina. "Business Intelligence Systems for Analyzing University Students Data." Cybernetics and Information Technologies 15, no. 1 (March 1, 2015): 104–15. http://dx.doi.org/10.1515/cait-2015-0009.

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Abstract : Globalization and ICT rapid development have led to strong competition between educational institutions. Advanced analytical technologies, including Business Intelligence (BI) tools, are implemented at universities worldwide for analyzing data and getting profound knowledge of the students, their individual learning characteristics and specific educational needs. This paper presents an example of BI implementation for student data analysis.

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35

YANIK, AKAN. "BIG DATA SYSTEMS, BUSINESS INTELLIGENCE AND PUBLIC RELATIONS." Journal of International Social Research 8, no. 40 (October 20, 2015): 822. http://dx.doi.org/10.17719/jisr.20154013962.

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36

Owusu, Acheampong, Abbas Ghanbari-Baghestan, and Abdolhossein Kalantari. "Investigating the Factors Affecting Business Intelligence Systems Adoption." International Journal of Technology Diffusion 8, no. 2 (April 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, Malaysia which were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that Absorptive Capacity, Competitive Pressure, Complexity, IT Infrastructure, Presence of Champion, Top Management Support, and Vendor Selection, are the factors influencing BIS adoption in the universities. The results also indicate that most private universities in Malaysia are currently involved in level 2 of BIS adoption. Other implications are also discussed.

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37

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 (June 25, 2015): 14–15. http://dx.doi.org/10.14445/22492593/ijcot-v21p304.

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38

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 (May 2020): 1638–48. http://dx.doi.org/10.1109/tsmc.2017.2710161.

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39

Arnott, David, Felix Lizama, and Yutong Song. "Patterns of business intelligence systems use in organizations." Decision Support Systems 97 (May 2017): 58–68. http://dx.doi.org/10.1016/j.dss.2017.03.005.

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40

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

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41

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 (April 30, 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 organize data, and process them with specific procedures established according to the workflow mechanism in the organization, in order to obtain the final outputs. It is noteworthy that information systems are completely different from information technology, as information systems use information technology techniques which were created to serve its based business.

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42

Firmansyah, Denny. "Business Intelligence (BI) Implementation - COBIT 4.1." Journal of Applied Information, Communication and Technology 5, no. 1 (April 25, 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 implementation and management capability.

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43

Rostek, Katarzyna. "Business Intelligence for Insurance Companies." Foundations of Management 1, no. 1 (January 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 implementations in selected insurance companies.

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44

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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45

Milolidakis, Giannis, Demosthenes Akoumianakis, and Chris Kimble. "Digital traces for business intelligence." Journal of Enterprise Information Management 27, no. 1 (February 4, 2014): 66–98. http://dx.doi.org/10.1108/jeim-09-2012-0061.

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Purpose – Data from social media (SM) has grown exponentially and created new opportunities for businesses to supplement their business intelligence (BI). However, there are many different platforms all of which are in a constant state of evolution. The purpose of this paper is to describe a generic methodology for the gathering of data from SM and transforming it into valuable BI. Design/methodology/approach – The approach taken is termed virtual excavation and builds on the similarities between the manipulation of technological artefacts virtual communities using various forms of SM and the excavation and analysis of physical artefacts found in archaeological settlements. Findings – The paper reports on a case study using this technique that looks at the Facebook fan pages of three mobile telecommunications service providers in Greece. The paper identifies many of the standard BI indicators as well as demonstrating that additional information relating to cross-page use can be collected by looking at how users manipulate artefact such as the “like” button in Facebook. Research limitations/implications – Although the methodology is widely applicable, the paper only reports on the analysis of one platform, Facebook, and is heavily reliant on visualization tools. Future work will examine different platforms and different tools for analysis. Practical implications – The paper discusses some of the ways in which this approach could be used and suggests some areas in which it might be applied. Originality/value – The approach of using virtual excavations to extract BI from virtual communities in online SM offers a systematic approach for dealing with a variety of information from a variety of different media that is not found in techniques based on information systems or management science.

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46

Cronin, Blaise. "Strategic intelligence and networked business." Journal of Information Science 26, no. 3 (June 2000): 133–38. http://dx.doi.org/10.1177/016555150002600302.

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47

Davenport, Thomas H. "Business Intelligence and Organizational Decisions." International Journal of Business Intelligence Research 1, no. 1 (January 2010): 1–12. http://dx.doi.org/10.4018/jbir.2010071701.

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The focus on transactional systems in the earlier decades of information management is beginning to shift toward decisions. In order to study the relationship between information and decisions, the author interviewed 32 managers in 27 organizations where an attempt to use information to support decision-making had been made. A framework involving three different relationships between information and decisions is introduced: loosely-coupled, structured human, and automated. It is suggested that loosely-coupled information and decision environments, while productive for information providers, may require too much knowledge on the part of information users to be effective. A four-step process for bringing information and decisions in closer alignment is also advanced.

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48

Ozigbo, Nathaniel. "BUSINESS INTELLIGENCE SYSTEMS SUPPORTING SUSTAINABILITY ON FIRM’S DECISION-MAKING PROCESSES AND PERFORMANCE." International Journal of Advanced Research in Statistics, Management and Finance 8, no. 1 (January 5, 2021): 64–75. http://dx.doi.org/10.48028/iiprds/ijarsmf.v8.i1.05.

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In today’s competitive business environment, there exist the need to investigate the adequacy of infrastructural flexibility for Business Intelligence Systems to support Sustainability on Firm’s Decision-making processes and performance. The Business Intelligence Systems consist of unique functions that are intrinsic aimed at supporting better quality decision-making within the organization. It is critical to the smooth operations of every organization. The intention of this study is not to discuss the technical details of implementing Business Intelligence Systems but to explore how to gain maximum benefits from the applications and usage of Business Intelligence Systems. An intensive literature review that highlighted the impact of Business Intelligence on firm performance and decision-making processes were initiated. The focus was on how to apply the Business Intelligence Systems to gain competitive advantage and to create more value from information by aligning it with the needs of employees who are engaged in complex business decision-making in today’s business environment. The study contributed in both academic and industrial operations by providing first time evidence of Business Intelligence solutions with particular reference to Nigerian firms located in Lagos State, Nigeria. The study employed the partial least square analysis with a sample size of 985 respondents randomly selected from five manufacturing companies. The findings suggested that the more effective use of Business Intelligence implementation, the more effective the decision-making processes and firm performance. The study offers a number of implications for theory and practice, noting that Business Intelligence System is in effect allowing firms to shift their structure to more carefully and thoughtfully align with the needs of customers and partners.

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49

Prem, Mary Jeyanthi, and M. Karnan. "Business Intelligence–Hybrid Metaheuristics Techniques." International Journal of Business Intelligence Research 5, no. 1 (January 2014): 64–70. http://dx.doi.org/10.4018/ijbir.2014010105.

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Business Intelligence (BI) is about getting the right information, to the right decision makers, at the right time. A business intelligence environment offers decision makers information and knowledge derived from data processing, through the application of mathematical models and algorithms. BI systems tend to promote a scientific and rational approach to managing enterprises and complex organizations. Soft computing is a collection of new techniques in artificial intelligence, which exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The purpose of this article is to provide an overview of soft computing techniques for the optimal and dynamic decision making system in the current business world.

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Shakir Nasir, Intedhar, 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 (March 10, 2021): 1856. http://dx.doi.org/10.11591/ijeecs.v21.i3.pp1856-1867.

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Abstract:

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-users Business Intelligence Systems. Furthermore, none of those studies was performed in a Higher Education Sector in Iraq. Consequently, the study aims to determine the business intelligence system features influencing perceived impact end users’ and of using business intelligence systems in Iraqi educational institutes. A technology acceptance model and technology organization environment framework were syntheses as a basis to develop a research model for business intelligence users' perceived impact and adopt of business intelligence systems named (SMUPI-BIS). Later, an online instrument (questionnaire) was designed to gather data from the business intelligence system users in five Iraqi universities. Twenty-one hypotheses were proposed and later tested. Then, for data analysis, the authors used several methods such as hierarchical regression, one-way ANOVA, descriptive statistics as well as structural equation modeling (SEM). The main outcomes of this study suggest that decision support, information quality, and real-time reporting are the most significant system characteristics influencing end users' perceived impact and their usage of business intelligence systems.

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