Business Intelligence as a Source of Competitive Advantage in SMEs: A Systematic Review
DBS Business Review Volume 2
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Keywords

Business Intelligence
Competitive Advantage
SME
Capabilities
Key Performance

Abstract

Competitive advantage is the ‘Holy Grail’ in strategic management theory. What makes a company more successful than its rivals has dominated scholarship in this area for more than 20 years. There have been two main theories proposed to attempt to identify the important resources and capabilities that configure to build competitive advantage; the Resource-based View and Dynamic Capability View. There is a growing literature stream in the area of Business Intelligence (BI) and Big Data Analytics with regard to both the computer technology and business management constructs. However, the literature is silent of the affordances of BI for Small to Medium Enterprises (SMEs), and so a significant gap in the literature remains. This discussion aims to signal the need to fill that gap and to build awareness of BI as a potentially significant contributor to sustained competitive advantage in SMEs underpinned by the iniquitousness of cloud applications previously the domain of Multinational Corporations.

https://doi.org/10.22375/dbr.v2i0.23
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References

Achtenhagen, L., Melin, L., and Naldi, L. (2013) ‘Dynamics of business models - strategizing, critical capabilities and activities for sustained value creation’, Long Range Planning, 46(6), pp. 427-442.

Adair, J. (2010) ‘Leadership for innovation: How to organize team creativity and harvest ideas’, Human Resource Management International Digest, 18(6) pp.185

Adner, R., and Helfat, C. (2003) ‘Corporate Effects and Dynamic Managerial Capabilities’, Strategic Management Journal, 24(10), pp. 1011-1025.

Agarwal, R., Echambadi, R., Franco, A. M., and Sarkar, M. B. (2004) ‘Knowledge transfer through inheritance: spin-out generation, development, and survival’, Academy of Management Journal, 47(4), pp. 501-522.

Ahlemeyer-Stubbe, A. (2014) A practical guide to data mining for business and industry. Chichester, West Sussex, United Kingdom: Wiley.

Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016) ‘How to improve firm performance using big data analytics capability and business strategy alignment?’, International Journal of Production Economics, 182, pp. 113-131.

Ahenkora, K., and Adjei, E. (2012). ‘A Dynamic Capabilities Perspective on the Strategic Management of an Industry Organisation’, Journal of Management and Strategy, 3(3), pp. 21.

Antoniadis, I., Tsiakiris, T. and Tsopogloy, S. (2015) ‘Business Intelligence During Times of Crisis: Adoption and Usage of ERP Systems by SMEs’, Procedia - Social and Behavioral Sciences, 175, pp. 299–307.

Appelbaum, D., Kogan, A., Vasarhelyi, M., and Yan, Z. (2017) ‘Impact of business analytics and enterprise systems on managerial accounting’, International Journal of Accounting Information Systems, 25, pp. 29-44.

Argote, L., and Ingram, P. (2000) ‘Knowledge transfer: A basis for competitive advantage in firms’, Organizational behavior and human decision processes, 82(1), pp. 150-169.

Augier, M., and Teece, D. (2009) ‘Dynamic capabilities and the role of managers in business strategy and economic performance’, Organization Science, 20(2), pp. 410-421.

Babar, M., and Arif, F. (2017) ‘Smart urban planning using Big Data analytics to contend with the interoperability in Internet of Things’, Future Generation Computer Systems, 77, pp. 65-76.

Barney, J. (1991). ‘Firm resources and sustained competitive advantage’, Journal of Management, 17(1), p. 99-120.

Barney, J. B. and Clark, D. N. (2007) Resource-based theory: creating and sustaining competitive advantage. Oxford: Oxford University Press.

Bayrak, T. (2015) ‘A review of business analytics: A business enabler or another passing fad’, Procedia - Social and Behavioral Sciences, 195, pp. 230–239.

Beach, L. R., and Mitchell, T. R. (1996) ‘Image theory, the unifying perspective’, in Beach L. R. (ed.) Decision making in the workplace: A unified perspective. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 1-20.

Beck, J. B., and Wiersema, M. F. (2013) ‘Executive decision making: Linking dynamic managerial capabilities to the resource portfolio and strategic outcomes’, Journal of Leadership & Organizational Studies, 20(4), pp. 408-419.

Becker, H. S. (1982) ‘Culture: A sociological view’, Yale Review, 71(4), 513-527.

Chae, B. K., Yang, C., Olson, D., and 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, pp.119-126.

Chang, C. C. (2012) ‘Exploring IT entrepreneurs' dynamic capabilities using Q-technique’, Industrial Management & Data Systems, 112(8), pp. 1201-1216.

Chen, H., Chiang, R. H., and Storey, V. C. (2012) ‘Business intelligence and analytics: From big data to big impact’, MIS Quarterly, 36(4), pp. 1165-1188.

Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort-Martorell, X., and Reis, M. S. (2016) ‘How can SMEs benefit from big data? Challenges and a path forward’ Quality and Reliability Engineering International, 32(6), pp. 2151-2164.

Demchenko, Y., Grosso, P., De Laat, C., and Membrey, P. (2013) ‘Addressing big data issues in Scientific Data Infrastructure’, in 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA: IEEE, pp. 48–55.

Eisenhardt, K., Furr, N., and Bingham, C. (2010) ‘CROSSROADS—Microfoundations of performance: Balancing efficiency and flexibility in dynamic environments’, Organization Science, 21(6), pp. 1263-1273.

Eisenhardt, K., and Martin, J. (2000) ‘Dynamic capabilities: what are they?’, Strategic Management Journal, 21(10-11), pp. 1105-1121.

Elbashir, M. Z., Collier, P. A., and Davern, M. J. (2008) ‘Measuring the effects of business intelligence systems: The relationship between business process and organizational performance’, International Journal of Accounting Information Systems, 9(3), pp. 135-153.

Elberse, A. and Ferguson, A. (2013) ‘Ferguson’s Formula’, Harvard Business Review, 91(10), pp. 116–125.

Erevelles, S., Fukawa, N., and Swayne, L. (2016) ‘Big Data consumer analytics and the transformation of marketing’, Journal of Business Research, 69(2), pp. 897-904.

e-skills UK (2013). ‘Big Data Analytics, Adoption and Employment Trends, 2012- 2017’. SAS Available at: https://www.thetechpartnership.com/globalassets/pdfs/research-2013/bigdataanalytics_report_nov2013.pdf (Accessed 17 July 2018).

George, G., Haas, M. R., and Pentland, A. (2014) ‘Big data and management’, Academy of Management Journal, 57(2), pp. 321-326.

Ghobadian, A., and Gallear, D. N. (1996) ‘Total quality management in SMEs’, Omega, 24(1), pp. 83-106.

Grant, R. M. (2005) Contemporary strategy analysis. 5th ed. Malden, MA: Blackwell Pub.

Guarda, T., Santos, M., Pinto, F., Augusto, M., and Silva, C. (2013) ‘Business intelligence as a competitive advantage for SMEs’, International Journal of Trade, Economics and Finance, 4(4), p. 187-190.

Gupta, M., and George, J. F. (2016) ‘Toward the development of a big data analytics capability’, Information & Management, 53(8), pp. 1049-1064.

Helfat, C., and Peteraf, M. (2003) ‘The dynamic resource-based view: Capability lifecycles’, Strategic Management Journal, 24(10), pp. 997-1010.

Haug, A., and Arlbjørn, J. (2011) ‘Barriers to master data quality’, Journal of Enterprise Information Management, 24(3), pp. 288-303.

Hoopes, D. G., and Postrel, S. (1999) ‘Shared knowledge," glitches," and product development performance’, Strategic Management Journal, 20(9), pp. 837-865.

Johnson, B. D. (2012) ‘The Secret Life of Data In the Year 2020’, The Futurist, 46(4), pp. 20–23.

Kaplan, R. S. and Norton, D. P. (2004) Strategy maps: converting intangible assets into tangible outcomes. Boston: Harvard Business School Press.

Kaplan, R. S. and Norton, D. P. (2001) The strategy-focused organization: how balanced scorecard companies thrive in the new business environment. Boston: Harvard Business School Press.

Keller, P. A. (2011) Statistical process control demystified. New York: McGraw-Hill

Kiron, D., Prentice, P. K., and Ferguson, R. B. (2012). ‘Innovating with analytics’ MIT Sloan Management Review, 54(1), p. 47-51.

Kiron, D., and Shockley, R. (2011). ‘Creating business value with analytics’ MIT Sloan Management Review, 53(1), p .57.

Kitchenham, B. (2004). ‘Procedures for performing systematic reviews’ Available at: http://www.inf.ufsc.br/~aldo.vw/kitchenham.pdf (Accessed 14 November 2018).

Kraaijenbrink, J., Spender, J. C., and Groen, A. J. (2010) ‘The resource-based view: a review and assessment of its critiques’, Journal of Management, 36(1), pp. 349-372.

Kumar, M., Antony, J., Madu, C. N., Montgomery, D. C., and Park, S. H. (2008). ‘Common myths of Six Sigma demystified’ International Journal of Quality & Reliability Management, 25(8), pp. 878-895.

Lamba, H. S. and Dubey, S. K. (2015) ‘Analysis of requirements for Big Data Adoption to maximize IT Business Value - IEEE Conference Publication’, 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) , Noida, Uttar Pradesh, India, 2-4 September. IEEE, pp. 284–290.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., and Kruschwitz, N. (2011) ‘Big data, analytics and the path from insights to value’, MIT Sloan Management Review, 52(2), p. 20-32.

Mahoney, J. T., and Pandian, J. R. (1992) ‘The resource-based view within the conversation of strategic management’, Strategic Management Journal, 13(5), pp. 363-380.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. (2011). ‘Big data: The next frontier for innovation, competition, and productivity’,

McKinsey Global Institute. Available at: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Big%20data%20The%20next%20frontier%20for%20innovation/MGI_big_data_exec_summary.ashx (Accessed 14 November 2018).

Marr, B. (2016) Big data in practice: how 45 successful companies used big data analytics to deliver extraordinary results. Chichester, West Sussex: Wiley.

McAfee, A., Brynjolfsson, E., and Davenport, T. H. (2012) ‘Big data: the management revolution’, Harvard business review, 90(10), pp. 60-68.

Mikalef, P., Pappas, I. O., Krogstie, J., and Giannakos, M. (2018) ‘Big data analytics capabilities: a systematic literature review and research agenda’, Information Systems and e-Business Management, 16(3) pp. 547-578.

Nerur, S., Mahapatra, R., and Mangalaraj, G. (2005) ‘Challenges of migrating to agile methodologies’, Communications of the ACM, 48(5), pp. 72-78.

Pierce, J. L., Boerner, C. S., and Teece, D. (2002). ‘Dynamic capabilities, competence and the behavioral theory of the firm’, in Augier, M. (ed.) The Economics of Choice, Change and Organization. Essays in Memory of Richard M. Cyert. Northampton: Edward Elgar, pp.81-95.

Porter, M. E. (1985) Competitive advantage: creating and

sustaining superior performance. New York: Free Press.

Priem, R., and Butler, J. (2001) ‘Is the resource-based “view” a useful perspective for strategic management research?’, Academy of Management Review, 26(1), pp.22-40.

Reeves, M., and Deimler, M. S. (2009) ‘Strategies for winning in the current and post-recession environment’, Strategy & Leadership, 37(6), pp.10-17.

Roy, K., and Khokhle, P. W. (2011) ‘Integrating resource-based and rational contingency views: understanding the design of dynamic capabilities of organizations’, Vikalpa: The Journal for Decision Makers, 36(4), pp.67-75.

Seddon, J. J., and Currie, W. L. (2017) ‘A model for unpacking big data analytics in high-frequency trading’, Journal of Business Research, 70, pp.300-307.

Sharda, R., Delen, D., Turban, E., Aronson, J., and Liang, T. P. (2014) Business Intelligence and Analytics: Systems for Decision Support. London: Prentice Hall.

Sun, E. W., Chen, Y.-T., and Yu, M.-T. (2015). ‘Generalized optimal wavelet decomposing algorithm for big financial data’ International Journal of Production Economics, 165, pp.194-214.

Tallon, P. P., Ramirez, R. V., and Short, J. E. (2013). ‘The information artifact in IT governance: toward a theory of information governance’ Journal of Management Information Systems, 30(3), pp.141-178.

Tan, W., Blake, M. B., Saleh, I., and Dustdar, S. (2013). ‘Social-network-sourced big data analytics’ IEEE Internet Computing, 17(5), pp. 62-69.

Teece, D. (2018) ‘Business models and dynamic capabilities’, Long Range Planning, 51(1), pp. 40–49.

Teece, D. (2012). ‘Dynamic capabilities: Routines versus entrepreneurial action’. Journal of Management Studies, 49(8), p. 1395 –1401.

Teece, D. (2000). ‘Strategies for Managing Knowledge Assets: the Role of Firm Structure and Industrial Context’ Long Range Planning, 33(1), pp. 35-54.

Teece, D., and Linden, G. (2017). Business models, value capture, and the digital enterprise. Journal of Organization Design, 6(1), p. 1-14.

Teece, D., and Pisano, G. (1994). ‘The dynamic capabilities of firms: an introduction’ Industrial and Corporate Change, 3(3), pp. 537–556.

Treacy, M., and Wiersema, F. (1993). Customer intimacy and other value disciplines. Harvard Business Review, 71(1), pp. 84-93.

Turban, E., Sharda, R., Aronson, J. E., and King, D. (2008). Business intelligence: A managerial approach. Upper Saddle River, NJ: Pearson Prentice Hall.

Vidgen, R., Shaw, S., and Grant, D. B. (2017). Management challenges in creating value from business analytics. European

Journal of Operational Research, 261(2), pp. 626-639.

Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), pp. 171-180.

Winter, S. (2000). The satisficing principle in capability learning. Strategic Management Journal, 21(10), p. 981-996.

Wixom, B., and Watson, H. (2012). ‘‘The BI-based organization’ in Herschel, R. (ed.) Organizational Applications of Business Intelligence Management: Emerging Trends, Hershey PA: IGI Global, pp.193-209.

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