Investigating the factors influencing Business Intelligence adoption: A case of Saudi Arabia
Literature Review
Business intelligence
Business Intelligence can be defined as a term that covers different themes including applications, tools, infrastructure, and best practices that are being used for data and information analysis (Lönnqvist&Pirttimäki, 2006). More specifically, in a BIsystem the data of operations are combined with the tools of analysis to present the complex data as the unique information set to the decision makers. It delivers critical information on the time of making decisions (Lloyd, 2011).
Factors influencing BI adaptation
Different studies have already been done about the factors that influence the adoption of business intelligence. The study has found some factors of BI in context with the case study of Pakistan. It identified the drivers such as market competition, demands of consumers (James, 2011), technology and innovation trends, government regulations, Overload information, accountability demand, etc. (Khan, Amin &Lambrou, 2009; Yee, 2013). These factors are found as impacting positively on the BI adoption.
Like these factors, there are many other factors have been found in different studies in the implementation and adoption of Business Intelligence that impact negatively. The study has found some challenges such as resistance from the employees to adopt thetechnology, lack of knowledge of IT among the majority of staff. Also,the costs of the business process, errors in the data due to IT mistakes and trust level of stakeholders.There are some other factors found in the pool of literature (Khan, Amin & Lambrou, 2009; Van Den Berg, 2014; Yee, 2013).
Research Problems
The business environment and process is becoming complex because of the growing globalization and technology. Data is considered as one of the most important tools to make decisions for the organizations and to address the business process challenges. In this context, the companies are involved in collecting extensive data to understand the nature of customers, market needs and changes. However, the dealing with that large data is itself is a complex process. Therefore, the importance of business intelligence is increasing in the context of the organization's to deal with a large amount of data because of the increasing complexity of business and competition (Rabuzin, Skvorc&Klicek, 2015).
Although, business intelligence is a significant approach for the organisations, however, the public sectors and educational institutes of Saudi Arabia have not adopted the business intelligence in their business and organisational process. The public sector in Saudi Arabia is facing the challenges in the strategic actions because of the poor quality of information and data. However, these challenges can be solved with the help of using business intelligence (Smith & Abouammoh, 2013).
Business intelligence helps the organization to link its different departments in terms of sharing data to improve the business process and performance (Puklavex, Oliveira &Popovic, 2014). The use of Business Intelligence is increased because of its benefits. The major benefits include collecting and analysing data, online data reporting and processing, data analyzing, predicting and forecasting, help in improving business performance and decision making (Rud, 2009; Olszak&Ziemba, 2007). Based on the complexity of data and many benefits of business intelligence, it becomes significant for the organisation to adopt business intelligence. For this purpose, the research is going to analyse different factors that help the organisations to adopt business intelligence, same as the factors that develop challenges in adopting BI. For more clear and specific results, the research will use a case of Saudi Arabia, mainly its organization that will be influenced by the following facts in adopting BI.
Research Gap and Contributions
The existing and previous studies conducted on the similar topic will contribute greatlyin the following research. The studies will provide some clear route and track to lead the research to achieve the aim and objectives. Moreover, they will also help to understand the background of the research problem to justify the research study (Boonsiritomachai, McGrath & Burgess, 2014).
The studies will also contribute to understanding the business intelligence importance in developing countries such as Oman and Pakistan. Despite this importance, the current studies are lacking the focus on Saudi Arabia. Some studies have been doneon technology adoption in higher education in Saudi Arabia (Tashkandi& Al-Jabri, 2015). No study has been done to determine mainly the BI adoption in Saudi Arabia and the drivers and barriers of adopting BI. It is difficult to state here, that why yet the researchers have not focused on the business intelligence adoption in Saudi Arabia, however, it can be said that the lack of research and study might be the cause of lack of business intelligence adoption in the country. Therefore, it is important to discuss the factors that can attract the organisations in Saudi Arabia to take on BI while on the same side what factors can develop barriers in the organisations of Saudi Arabia.
Aim and Objectives
Based on the above title, the aim of this study is to explore the factors that influence the adoption of Business Intelligence (BI) among large organisations in the context ofSaud Arabia. The research will investigate the factors that help the organisation to adopt the particular technology in contrast with those difficulties and challenges that come in the way to implement BI technology. The study of BI adoption and the factors influencing the adoption will be revolved around the case of Saudi Arabia as a developing country. In context with the above-mentioned aim and purpose, the objectives of the research are as follows:
Research Questions
The research questions of the research study are as follows:
What is the process of adoption of BI in Saudi Arabia?
Is there any correlation of BI with the influencing factors?
What are the positive factors that support the adoption of BI in Saudi organisations?
What are the negative factors that influence the process of adopting BI in Saudi organisations?
Theoretical Framework and Hypothesis
Based on the previous studies, the factors influencing the adoption of business intelligence can be classified as external factors, technological factors and internal factors. Some of them will be found as barriers while, some of them will drive the adoption. Although, different studies have been done to find the factors influencing the adoption and implementation of BI, however, these factors have not been studied in the context ofSaudi Arabia. Therefore, being a primary research of BI adoption in Saudi Arabia, the different factors will be discussed under these three categories include internal factors, external factors and technological factors (Boonsiritomachai, McGrath & Burgess, 2014).
Competition, demand and government regulations are some of the external factors found in the literature. Relaibility, complexity and observabiliy are considered as some of the technological factors where, internal factors might include organisational size, culture, management and leadership, resistance from employees, innovativeness of managers and owners (Boonsiritomachai, McGrath & Burgess, 2014; Boonsiritomachai, 2014; Twigt, 2013), etc.
Based on the multiple studies and frameworks such as technological adoption framework of Yee (2013), BI adoption of Small and Medium-Sized enterprises framework of Boonsiritomachai, McGrath & Burgess (2014), Context of technological innovation in the firm model of Boonsiritomachai (2014) and Cloud BI adoption in small and medium size enterprises of Tiwgt (2013), the three category business intelligence adoption model is developed as follows:
As the research is going to study different organizations in Saudi Arabia, therefore, the five-level models are used for to study the impacts of organizational size (Boonsiritomachai, McGrath & Burgess, 2014; Van den berg, 2014).
Based on the business intelligence adoption model, following hypotheses are developed:
H1: Market Competition effects the adoption of BI in the organisations of Saudi Arabia (Waarts et al, 2002; Alshawi et al, 2011).
H2: Demand effects the adoption of BI in the organisations of Saudi Arabia (Wixom et al, 2011).
H3: Government regulations effect the adoption of BI in the organisations of Saudi Arabia (Madrid-Guijarro et al, 2009; Chen, 2006; Esselaar et al, 2007).
H4: Reliability in the organisations effect the adoption of BI in Saudi Arabia (Benlian & Hess, 2011, Bhattacherjee & Park, 2013, Abadi, 2009, Faasen, 2013).H5: Complexity effects the adoption of BI in the organisations of Saudi Arabia (Sahey & Ranjan, 2008)
H6: Observability effects the adoption of BI in the organisations of Saudi Arabia (Roger, 1995; Lundblad, 2003).
H7: Organisational Size effects the adoption of BI in the organisations of Saudi Arabia (Jang et al, 2009; Ramamurthy et al, 2008).
H8: Organisational Culture effects the adoption of BI in the organisations of Saudi Arabia (Romm et al 1991).
H9: Cost benefits effects the adoption of BI in the organisations of Saudi Arabia (Sahandi et al., 2012, Weindhardt et al., 2009, Benlian & Hess, 2011, Bhattacherjee & Park, 2013)
H10: Employees’ resistance effects the adoption of BI in the organisations of Saudi Arabia (The Economist Intelligence Unit, 2007).
H11: innovativeness of owners and managers effects the adoption of BI in the organisations of Saudi Arabia (Hung et al, 2011; Ghobakhloo et al, 2011; Nguyen & Waring, 2013; Chang & Tsia, 2006; Wejnert, 2002).
Methodology
The researcher will use the quantitative research strategy to collect data. The quantitative approach is significant to collect statistical data for more appropriate outcomes and results. As the research is aimed to study the case of Saudi Arabia, therefore, the population for research will be based on large organisations in Saudi Arabia.
For conducting quantitative research, online survey technique will be used to collect data. From the total population of all the organisations of Saudi Arabia, three of the largest organisations are selected to be used as the participants in the research. These three organisations include the following:
(SIDF) Saudi Industrial Development Fund.
(SAMA) Saudi Arabian Monetary Agency, which is the central bank.
ELM Company, which is a joint-stock company owned by the Public Investment Fund (PIF).
Reference
Abadi, D.J. (2009). Data management in the Cloud: Limitations and Opportunities. IEEE Computer society technical committee on Data Engineering.
Alshawi, S., Missi, F. & Irani, Z. (2011). Organisational, technical and Data Quality Factors in CRM adoption00SMEs perspective,. Industrial Marketing Management, Vol. 40, 376-383.
Benlian, A., & Hess, T. (2011). Opportunities and risks of software-as-a-service: Findings from a survey of IT executives. Decisioin Support Systems, Vol. 52, pp. 232-246.
Bhattacherjee, A., & Park, S.C. (2013). Why end-users move to the cloud: a migration-theoretic analysis. European Journal of Information Systems 2013, pp. 1-16.
Boonsiritomachai, W. (2014). Enablers affecting the adoption of business intelligence: a study of Thai small and medium-sized enterprises (Doctoral dissertation, Victoria University).
Boonsiritomachai, W., McGrath, M. & Burgess, S. (2014). A research framework for the adoption of Business Intelligence by Small and Medium-sized enterprises.
Chang, S.I., & Tsia, Y.F. (2006). Critical Factors of E-SCM adoption on small and medium sized enterprises performance: an empirical study. International Journal of Electronic Business, vol. 4, no. 2, pp. 159-72.
Chen, J. (2006). Development of Chinese small and medium-Sized enterprises. Journal of Small business and Enterprise Development, Vol. 13, pp. 140-147.
Esselaar, S., Storck, C., Ndiwalana, A., & Deen-Swarray, M. (2007). ICT usage and Its impact of profitability of SMEs in 13 African Countries. Informational technologies and International Development, Vol. 4.
Ghobakhloo, M., Arias-Aranda, D. and Benitez-Amado, J. (2011). Adoption of e-commerce applications in SMEs. Industrial Management & Data Systems, Vol. 111, pp. 1238-1269.
Hung, S.Y., Hung, W.H., Tsai, C.A. & Jiang, S.C. (2010). Critical factors of hospital adoption on CRM system: organisational and information system perspectives. Decision Support Systems, vol. 48, no. 4, pp. 592-603.
James, L. (2011). Business Intelligence: Drivers, challenges, benefits and ROI. Available from https://www.yellowfinbi.com/YFCommunityNews-Business-Intelligence-Drivers-challenges-benefits-and-ROI-103783 [Accessed 11 December 2016]
Jang, W. Y., Lin, C. I. & Pan, M. J. (2009). Business Strategies and the adoption of ERP: Evidence from Taiwan’s communications industry. Journal of Manufacturing Technology Management, Vol. 20, pp. 1084-1098.
Khan, A.M., Amin, N., &Lambrou, N. (2009). Drivers And Barriers To Business Intelligence Adoption: A Case Of Pakistan.
Lloyd, J. (2011). Identifying Key Components of Business Intelligence Systems and Their Role in Managerial Decision making (Doctoral dissertation, Intel Corporation).
Lönnqvist, A., &Pirttimäki, V. (2006). The measurement of business intelligence. Information Systems Management, vol.23, no.1, pp. 32.
Lundblad, J. P. (2003). A review and critiques of Roger’s diffusion of innovation theory as it applies to organisations. Organisational Development Journal, Vol. 21, pp. 50-64.
Madrid-Guijarro, A., Garcia, D. & Van Auken, H. (2009). Barriers to innovation among Spanish Manufacturing SMEs. Journal of Small Business Management. Vol. 47, pp. 465-488.
Nguyen, T.H., & Waring, T.S. (2013). 'The adoption of customer relationship management (crm) technology in SMEs: an empirical study', Journal of Small Business and Enterprise Development, vol. 20, no. 4, pp. 824-48.
Olszak, C.M. and Ziemba, E., (2007). Approach to building and implementing business intelligence systems. Interdisciplinary Journal of Information, Knowledge, and Management, Vol. 2, pp.134-148.
Puklavec, B., Oliveira, T. and Popovič, A., (2014). Unpacking Business Intelligence Systems Adoption Determinants: An Exploratory Study of Small and Medium Enterprises. Economic and Business Review, Vol. 16, No. 2, pp.185-213.
Rabuzin, K., Skvorc, D. and Klicek, B., (2015). Implementing Business Intelligence in Textile Industry-Are Managers Satisfied?. International Journal of Information Processing and Management, Vol. 6, No. 1, p.1.
Ramamurthy, K., Sen, A. & Sinha, A. P. (2008). An Empirical investigation of Key Determinants of data warehouse adoption. Decision Support Systems, Vol. 44, pp. 817-841.
Rogers, E. M. (1995). Diffusion of Innovations, New York, Press.
Romm, T., Pliskin, N., Weber, Y., Lee, A.S. (1991). Identifying organizational culture clash in MIS implementation. Information & Management, Vol. 21, pp. 99-109.
Romm, T., Pliskin, N., Weber, Y., Lee, A.S. (1991). Identifying organizational culture clash in MIS implementation. Information & Management, Vol. 21, pp. 99-109.
Rud, O. P. (2009). Business intelligence success factors: tools for aligning your business in the global economy. John Wiley & Sons.
Sahandi, R., Alkhalil, A., Opara-Martins, J. (2012). SMEs Perception of Cloud computing: Potential and security. IFIP AICT 380 pp. 186-195.
Sahay, B. S. & Ranjan, J. (2008). Real time business intelligence in supply chain analytics. Information Management & Computer Security, Vol. 16, pp. 28-48.
Smith, L., & Abouammoh, A. (2013). Higher Education in Saudi Arabia. Springer.
Tashkandi, A.N. and Al-Jabri, I.M., (2015). Cloud computing adoption by higher education institutions in Saudi Arabia: an exploratory study. Cluster Computing, Vol. 18, No. 4, pp. 1527-1537.
The Economist Intelligence Guide. (2009). Drivers of Business Intelligecne [BI]. Available from http://www.thebusinessintelligenceguide.com/bi_strategy/Drivers_Of_BI.php [Accessed on 23 January 2016]
Twigt, A. (2013). Drivers and barriers of Cloud Business Intelligence: An investigation into the adoption factors for Small and Medium-sized Enterprises. Available from https://arietwigt.files.wordpress.com/2013/07/ba-thesis-arie-twigt-2516460-final-version-5-juli-20130705.pdf [Accessed 22 January 2016]
Van den Berg, R., (2014). The influence of data management, organisation culture and organisation motivation on the likeliness of Business Intelligence adoption.
Waarts, E., Everdingen, Y. M. and Hillegersberg, J. (2002). The dynamics of factors affecting the adoption of innovations. Journal of Product Innovation Management, Vol. 19, pp. 412-423.
Weinhardt, C., Anandasivam, W.A., Borissov, N., Meinl, T., Michalk, W.W., Stosser, J. (2009). Cloud computing: A classification, Business Models and research directions. Business and information systems engineering. No. 5, 2009.
Wejnert, B. (2002). Integrating models of diffusion of innovations: a conceptual framework. Annual review of sociology, vol. 28, pp. 297-326.
Wixom, B, Ariyachandra, T, Goul, M, Gray, P, Kulkarni, U & Phillips-Wren, G. (2011). The current state of business intelligence in academia. Communications of the Association for Information System, vol. 29, no. 16, pp. 299-312.
