Business Intelligence as a Strategic Asset: Enhancing Decision Quality, Effectiveness and Performance in NGOs

Abstract

The exponential growth of unstructured data has prompted organizations to adopt Business Intelligence (BI) tools. While BI adoption in profit-seeking sectors is well-studied, its use in non-governmental organizations (NGOs) remains underexplored. This study aims to fill this gap by investigating how BI technologies enhance decision-making quality, organizational effectiveness and performance in NGOs operating in Türkiye. Grounded in the Resource-Based View framework, the study posits that data quality is critical for BI to improve decision-making quality, ultimately leading to improved organizational outcomes. Survey data collected from 178 NGOs reveal findings consistent with the existing research in the for-profit sector. The results demonstrate that NGOs can leverage BI tools to optimize resource allocation and program implementation, align decision-making processes with their missions and goals, better serve their stakeholders, improve program outcomes, and enhance overall organizational effectiveness and performance. This study makes a significant contribution to the literature by highlighting the key role of BI technologies in the non-profit sector. Ultimately, this study underscores the transformative potential of BI technologies in improving decision-making and organizational outcomes in NGOs, offering a valuable foundation for future research and practical applications in the sector.

Keywords

• Business Intelligence
• Decision-Making Quality
• Organizational Performance
• Organizational Effectiveness
• Non-Governmental Organizations

References

1. Aboramadan, M., & Borgonovi, E. (2016). Strategic management practices as a key determinant of superior non-governmental organizations performance. Problems of Management in the 21st Century, 11(2), 71. https://doi.org/10.33225/pmc/16.11.71 google scholar
2. Abusweilem, M. A., & Abualoush, S. (2019). The impact of knowledge management process and business intelligence on organizational performance. Management Science Letters, 9(12), 2143–2156. https://doi.org/10.5267/j.msl.2019.6.020 google scholar
3. Adiguzel, Z. (2019). Strategic approach to business intelligence and its impacts on organizational performance. Handbook of Research on Strategic Fit and Design in Business Ecosystems, January, 289–310. IGI Global. https://doi.org/10.4018/978-1-7998-1125-1.ch012 google scholar
4. Al Shobaki, M. J., El Talla, S. A., & Al Najjar, M. T. (2023). The Level of Business Intelligence in Non-Governmental Organizations in Palestine. International Journal of Academic Multidisciplinary Research (IJAMR), 7(6), 208-220. google scholar
5. Alasiri, M. M., & Salameh, A. A. (2020). The impact of business intelligence (BI) and decision support systems (DSS): Exploratory study. International Journal of Management, 11(5), 1001–1016. https://doi.org/10.34218/IJM.11.5.2020.092 google scholar
6. Arefin, M. S., Hoque, M. R., & Bao, Y. (2015). The impact of business intelligence on organization’s effectiveness: An empirical study. Journal of Systems and Information Technology, 17(3), 263–285. https://doi.org/10.1108/JSIT-09-2014-0067 google scholar
7. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108 google scholar
8. Beniger, J. (2009). The control revolution: Technological and economic origins of the information society. Harvard university press. https://doi.org/10.7312/hayo18620-037 google scholar

9. Benjamin, L. M., & Misra, K. (2006). Doing good work: Implications of performance accountability for practice in the nonprofit sector. International Journal of Rural Management, 2(2), 147-162. https://doi.org/10.1177/097300520600200202 google scholar
10. Brown, C. V., DeHayes, D. W., Hoffer, J. A., Martin, E. W., & Perkins, W. C. (2012). Managing information technology. Prentice Hall. google scholar
11. Carman, J. G. (2007). Evaluation practice among community-based organizations: Research into the reality. American Journal of evaluation, 28(1), 60-75. https://doi.org/10.1177/1098214006296245 google scholar
12. Chae, B. K., Yang, C., Olson, D., & Sheu, C. (2014). The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective. Decision Support Systems, 59, 119-126. https://doi.org/10.1016/j.dss.2013. 10.012 google scholar
13. Chatfield, A. T., & Bjørn-Andersen, N. (1997). The impact of IOS-enabled business process change on business outcomes: Transformation of the value chain of Japan Airlines. Journal of Management Information Systems, 14(1), 13-40. https://doi.org/10.1080/07421222. 1997.11518152 google scholar
14. Cheng,C., Zhong,H., & Cao,L. (2020). Facilitating speed of internationalization: The roles of business intelligence and organizational agility. Journal of Business Research, 110, 95–103. https://doi.org/10.1016/j.jbusres.2020.01.003 google scholar
15. Clark Jr, T. D., Jones, M. C., & Armstrong, C. P. (2007). The dynamic structure of management support systems: theory development, research focus, and direction. MIS Quarterly, 579-615. https://doi.org/10.2307/25148808 google scholar
16. Cosgrave, J. (1996). Decision making in emergencies. Disaster Prevention and Management: An International Journal, 5(4), 28-35. https:// doi.org/10.1108/09653569610127424 google scholar
17. Daft, R. (2007). Organization Theory and Design. 9th ed. Cincinnati, OH: South-Western google scholar
18. Daneshvar Kakhki, M., & Palvia, P. (2016). Effect of business intelligence and analytics on business performance. Twenty-second Americas Conference on Information Systems, San Diego, USA. google scholar
19. Dubey, R., Gunasekaran, A., & Childe, S. J. (2019). Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility. Management Decision, 57(8), 2092-2112. https://doi.org/10.1108/md-01-2018-0119 google scholar
20. Farzaneh, M., Isaai, M. T., Arasti, M. R., & Mehralian, G. (2018). A framework for developing business intelligence systems: a knowledge perspective. Management Research Review, 41(12), 1358-1374. https://doi.org/10.1108/mrr-01-2018-0007 google scholar
21. Felder, C. (2012). The potential role of business intelligence in church organizations (Doctoral dissertation, Walden University). google scholar
22. Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), 1923-1936. https://doi.org/10.1108/md-07-2018-0825 google scholar
23. Gauzelin, S., & Bentz, H. (2017). An examination of the impact of business intelligence systems on organizational decision making and performance: The case of France. Journal of Intelligence Studies in Business, 7(2), 40–50. https://doi.org/10.37380/jisib.v7i2.238 google scholar
24. Giovinazzo, W. (2009). BI: Only as good as its data quality. Information Management Special Reports, 1, 2009-2. google scholar
25. Guarda, T., Santos, M., Pinto, F., Augusto, M., & Silva, C. (2013). Business intelligence as a competitive advantage for SMEs. International Journal of Trade, Economics and Finance, 4(4), 187. https://doi.org/10.7763/ijtef.2013.v4.283 google scholar
26. Haghighat M., &. Mayani, M. (2012). Reviewing the effect of content dimensions of organization according to BI effectiveness according to role of management—Case Study: Saman Bank. (Researcher) Management Journal, 4(27), 65-84. google scholar
27. Hayles, C. S. (2010). An examination of decision making in post disaster housing reconstruction. International Journal of Disaster Resilience in the Built Environment, 1(1), 103-122. https://doi.org/10.1108/17595901011026508 google scholar
28. Heyse, L. (2013). Tragic choices in humanitarian aid: a framework of organizational determinants of NGO decision making. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 24, 68-92. https://doi.org/10.1007/s11266-012-9292-y google scholar
29. Inmyxai, S., & Takahashi, Y. (2010). Performance Contrast and Its Determinants between Male and Female Headed Firms in Lao MSMEs. google scholar
30. International Journal of Business and Management, 5(4). https://doi.org/10.5539/ijbm.v5n4p37 google scholar
31. Işık, Ö., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information and Management, 50(1), 13–23. https://doi.org/10.1016/j.im.2012.12.001 google scholar
32. Janssen, M., Van Der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345. https://doi.org/10.1016/j.jbusres.2016.08.007 google scholar
33. Jess, D., & Member, C. (2019). Business intelligence systems input : effects on organizational by Sherylene Shamma Naidoo MOUSSA BERETE , DBA , Faculty Mentor and Chair WILLIAM J . McKIBBIN , PhD , Committee Member Tonia Teasley , JD , ( Interim Dean ) School of Business and Technology. March. google scholar
34. Juran, J.M.; Godfrey, A.B. (1999) Juran’s Quality Handbook, 5th ed.; McGraw-Hill Companies: New York, NY, USA, google scholar
35. Keller, K. L., & Staelin, R. (1987). Effects of quality and quantity of information on decision effectiveness. Journal of Consumer Research, 14(2), 200-213. https://doi.org/10.1086/209106 google scholar
36. Knox Clarke, P., & Campbell, L. (2020). Decision-making at the sharp end: a survey of literature related to decision-making in humanitarian contexts. Journal of International Humanitarian Action, 5(1), 1-14. https://doi.org/10.1186/s41018-020-00068-2 google scholar
37. Kruesi, M. A., & Bazelmans, L. (2023). Resources, capabilities and competencies: a review of empirical hospitality and tourism research founded on the resource-based view of the firm. Journal of Hospitality and Tourism Insights, 6(2), 549-574. https://doi.org/10. 1108/jhti-10-2021-0270 google scholar
38. Kulkarni, U., Robles-Flores, J. A., & Popovič, A. (2017). Business intelligence capability: The effect of top management and the mediating roles of user participation and analytical decision making orientation. Journal of the Association for Information Systems, 18(7), 516–541. https://doi.org/10.17705/1jais.00462 google scholar
39. Lange, M. (2021). Multidimensional poverty in Kolkata’s slums: towards data driven decision making in a medium-sized NGO. Journal of Poverty and Social Justice, 29(1), 121-130. https://doi.org/10.1332/175982720x16034770581665 google scholar
40. Llave, M. R. (2019). A review of business intelligence and analytics in small and medium-sized enterprises. International Journal of Business Intelligence Research, 10(1), 19–41. https://doi.org/10.4018/IJBIR.2019010102 google scholar
41. Llave, M. R., Hustad, E., & Olsen, D. H. (2018). Creating value from business intelligence and analytics in SMEs: Insights from experts. Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018, January 2019. google scholar
42. Mata, F. J., Fuerst, W. L., & Barney, J. B. (1995). Information technology and sustained competitive advantage: A resource-based analysis. MIS Quarterly, 487-505. https://doi.org/10.2307/249630 google scholar
43. Miller, H. (1996). The multiple dimensions of information quality. Information Systems Management, 13(2), 79–82. https://doi.org/10.1080/10580539608906992 google scholar
44. Mohammad, H. A. (2012). The Impact of Business Intelligence and Decision Support on the Quality of Decision Making: An Empirical Study on Five Stars Hotels in Amman Capital. Master’s Thesis, Middle East University. google scholar
45. Mwai, G. (2018). Influence of Strategy Implementation on Organizational Effectiveness of Non-Governmental Organizations in Kenya (Doctoral dissertation, United States International University-Africa). google scholar
46. Naidoo, S.S. (2019). Business intelligence systems input: effects on organizational decision-making. PhD. Dissertation, Capella University google scholar
47. Nunnally J. C. (1978), Psychometric Theory, 2nd ed., New- York, McGraw-Hill google scholar
48. Obeidat, B. Y., Abdallah, A. B., Aqqad, N. O., Akhoershiedah, A. H. O. M., & Maqableh, M. (2016). The effect of intellectual capital on organizational performance: The mediating role of knowledge sharing. Communications and Network, 9(1), 1-27. https://doi.org/ 10.4236/cn.2017.91001 google scholar
49. Olaru, C. (2014). Business intelligence in telecommunications industry. International Journal of Economic Practices and Theories, 4(1), 89-100. google scholar
50. Olszak, C. M. (2016). Toward Better Understanding and Use of Business Intelligence in Organizations. Information Systems Management, 33(2), 105–123. https://doi.org/10.1080/10580530.2016.1155946 google scholar
51. O'Reilly, C. A. (1982). Variations in decision makers’ use of information sources: The impact of quality and accessibility of information. Academy of Management Journal, 25(4), 756-771. https://doi.org/10.5465/256097 google scholar
52. Owda, R. O., Owda, M., Abed, M. N., Abdalmenem, S. A., Abu-Naser, S. S., & Al Shobaki, M. J. (2019a). The Reality of Decision Making in NGOs in Gaza Strip. International Journal of Academic Multidisciplinary Research (IJAISR), 3(8), 1-10. google scholar
53. Owda, R. O., Owda, M. O., Abed, M. N., Abdalmenem, S. A., Abu-Naser, S. S., & Al Shobaki, M. J. (2019b). Design Thinking and Its Impact on Decision Making In NGOs. International Journal of Academic Multidisciplinary Research (IJAMR), 3(7): 41-52. google scholar
54. Popovič, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729-739. https://doi.org/10.1016/j.dss.2012.08.017 google scholar
55. Popovič, A., Puklavec, B., & Oliveira, T. (2019). Justifying business intelligence systems adoption in SMEs: Impact of systems use on firm performance. Industrial Management and Data Systems, 119(1), 210–228. https://doi.org/10.1108/IMDS-02-2018-0085 google scholar
56. Power, D. J., & Sharda, R. (2007). Model-driven decision support systems: Concepts and research directions. Decision Support Systems, 43(3), 1044–1061. https://doi.org/10.1016/j.dss.2005.05.030 google scholar
57. Pranjić, G. (2018). Decision Making Process in the Business Intelligence 3.0 Context. Ekonomska Misao i Praksa, 2, 603–619. google scholar
58. Raghunathan, S. (1999). Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis. Decision Support Systems, 26(4), 275-286. https://doi.org/10.1016/s0167-9236(99)00060-3 google scholar
59. Redman, T. C. (1998). The impact of poor data quality on the typical enterprise. Communications of the ACM, 41(2), 79-82. https://doi. org/10.1145/269012.269025 google scholar
60. Kelidbari, H. R. R., & Rayat, M. (2017). The Effects of Business Intelligence on the Effectiveness of the Organization (Case Study: Airline Companies in Iran). Review of European Studies, 9(3), 176. https://doi.org/10.5539/res.v9n3p176 google scholar
61. Ross, J. W., Beath, C. M., & Goodhue, D. L. (1996). Develop long-term competitiveness through IT assets. MIT Sloan Management Review, 38(1), 31-42. google scholar
62. Sabanovic, A. (2008). Business Intelligence Software Customers’ Understanding, Expectations and Needs. University of Kristianstad. Thesis for the Master’s Degree in Business Administration. google scholar
63. Sanz de Acedo Lizarraga, M. L., Sanz de Acedo Baquedano, M. T., Soria Oliver, M., & Closas, A. (2009). Development and validation of a decision-making questionnaire. British Journal of Guidance & Counselling, 37(3), 357-373. https://doi.org/10.1080/03069880902956959 google scholar
64. Saucedo-Martínez, J. A., Pérez-Lara, M., Marmolejo-Saucedo, J. A., Salais-Fierro, T. E., & Vasant, P. (2018). Industry 4.0 framework for management and operations: a review. Journal of Ambient Intelligence and Humanized Computing, 9, 789-801. google scholar
65. Scholtz, B., Calitz, A., & Haupt, R. (2018). A business intelligence framework for sustainability information management in higher education. International Journal of Sustainability in Higher Education, 19(2), 266-290. https://doi.org/10.1108/ijshe-06-2016-0118 google scholar
66. Schulze, W.S. (1992). The two Resource-Based Models of the firm: Definitions and implications for research. Academy of Management Best Papers Proceedings, 37-41. https://doi.org/10.5465/ambpp.1992.4976714 google scholar
67. Sharda, R., Delen, D., & Turban, E. (2014). Business intelligence and analytics: systems for decision support. Pearson. google scholar
68. Shollo, A. D. (2013). The Role of Business Intelligence in Organizational Decision-making. PhD Thesis, Copenhagen Business School. google scholar
69. Solberg Søilen, K. (2015). A place for intelligence studies as a scientific discipline. Journal of Intelligence Studies in Business, 5(3), 35-46. https://doi.org/10.37380/jisib.v5i3.136 google scholar
70. Soltani, Z. K., & Siadati, S. (2019). The impact of business intelligence on increasing the efficiency and health quality of surgical centers. Journal of Economic and Social Research, 18(5), 895–904. google scholar
71. Sorensen, C. (2020). Business Intelligence vs. Business Analytics. Retrived from https://us.hitachi-solutions.com/blog/business-intelligence-vs-business-analytics/ google scholar
72. Sousa, M. J., & Dias, I. (2020). Business intelligence for human capital management. International Journal of Business Intelligence Research, 11(1), 38–49. https://doi.org/10.4018/IJBIR.2020010103 google scholar
73. Southekal, P. (2023). Data Quality: Empowering Businesses with Analytics and AI. John Wiley & Sons. google scholar
74. Strong, D. M., Lee, Y. W., & Wang, R. Y. (1997). Data quality in context. Communications of the ACM, 40(5), 103–110. google scholar
75. Tayi, G. K., & Ballou, D. P. (1998). Examining data quality. Communications of the ACM, 41(2), 54-57. https://doi.org/10.1145/269012.269021 google scholar
76. Teegen, H., Doh, J. P., & Vachani, S. (2004). The importance of nongovernmental organizations (NGOs) in global governance and value creation: An international business research agenda. Journal of International Business Studies, 35, 463-483. https://doi.org/10. 1057/palgrave.jibs.8400112 google scholar
77. Teoh, A. P., Rajendran, M., & Lim, E. K. (2014). Predictors and Outcome of Business Intelligence System Implementation: A Perspective of Manufacturers in Malaysia. Research Journal of Applied Sciences, Engineering and Technology, 8(18), 1980-1993. https://doi.org/ 10.19026/rjaset.8.1190 google scholar
78. Torres, R., Sidorova, A., & Jones, M. C. (2018). Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective. Information and Management, 55(7), 822–839. https://doi.org/10.1016/j.im.2018.03.010 google scholar
79. Turban, E., Sharda, R., Aronson, J. E., & King, D. (2008). Business intelligence: A managerial approach (pp. 58-59). Upper Saddle River, NJ: Pearson Prentice Hall. google scholar
80. Visinescu, L. L., Jones, M. C., & Sidorova, A. (2017). Improving decision quality: The role of business intelligence. Journal of Computer Information Systems, 57(1), 58–66. https://doi.org/10.1080/08874417.2016.1181494 google scholar
81. Vukovic, N. (2020). Aligning BI with Corporate Strategy in SME: A case study based on the BISC Framework. Master’s Thesis. Uppsala University google scholar
82. Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. https://doi.org/10.1016/j.ijpe. 2014.12.031 google scholar
83. Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365. https://doi.org/10.1016/j.jbusres.2016.08.009 google scholar
84. Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33. https://doi.org/10.1080/07421222.1996.11518099 google scholar
85. Warth, J., Kaiser, G., & Kügler, M. (2011). The impact of data quality and analytical capabilities on planning performance: insights from the automotive industry. Proceedings of the 10th International Conference on Wirtschaftsinformatik, WI 2.011, Volume 1, 322-331. google scholar
86. Wixom, B., & Watson, H. (2010). The BI-based organization. International Journal of Business Intelligence Research (IJBIR), 1(1), 13-28. https://doi.org/10.4018/978-1-4666-0279-3.ch014 google scholar
87. Watts, S., Shankaranarayanan, G., & Even, A. (2009). Data quality assessment in context: A cognitive perspective. Decision Support Systems, 48(1), 202-211. https://doi.org/10.1016/j.dss.2009.07.012 google scholar
88. Wieder, B., & Ossimitz, M. L. (2015). The impact of Business Intelligence on the quality of decision making–a mediation model. Procedia Computer Science, 64, 1163-1171. https://doi.org/10.1016/j.procs.2015.08.599 google scholar
89. Wieder, B., Ossimitz, M., & Chamoni, P. (2012). The impact of business intelligence tools on performance: a user satisfaction paradox?. International Journal of Economic Sciences and Applied Research, 5(3), 7-32. google scholar
90. Yeoh, W., & Popovič, A. (2016). Extending the understanding of critical success factors for implementing business intelligence systems. Journal of the Association for Information Science and Technology, 67(1), 134–147. https://doi.org/10.1002/asi.23366 google scholar
91. Yu, L. and Lai, K. K. (2011). A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support. Decision Support Systems, 51(2), 307-315. https://doi.org/10.1016/j.dss.2010.11.024 google scholar