An examination of the organizational impact of business intelligence and big data based on management theory

Authors

  • Mouhib Alnoukari

DOI:

https://doi.org/10.37380/jisib.v10i3.637

Keywords:

Big data, big data analytics, business intelligence, management theory, organizational theory

Abstract

Big data and big data analytics have been considered to be a disruptive technologythat will rebuild business intelligence. The purpose of this study is to enrich the literature onthe organizational impact of business intelligence and big data based on management theory.While the majority of the organizational theories have had research dedicated to enhance theunderstanding of the impact of business intelligence and big data on organizational performanceand decision-making, the research lacks scholarly work capable of identifying the other mainorganizational outcomes. To achieve this goal, a semi-systematic literature review was carriedout to find all studies related to the research topic. Then, an analysis was conducted tounderstand the use of the organizational theory in accordance with business intelligence andbig data. Finally, a grouping was developed to assign each organizational theory the relatedimpact. The main findings of this work, after examining thirty-three related organizationaltheories, was that there are other important organizational impacts including innovation,agility, adoption, and supply-chain support.

References

Ahmad, R., & Hashim, K. F. (2016).

Innovation traits for business intelligence

successful deployment. Journal of Theoretical

and Applied Information Technology, 89(1),

–107.

Al-Qirim, N., Rouibah, K., Serhani, M. A.,

Tarhini, A., Khalil, A., Maqableh, M., &

Gergely, M. (2019). The Strategic Adoption of

Big Data in Organizations. In Sun, Z.

(Ed.), Managerial Perspectives on Intelligent

Big Data Analytics (pp. 43-54). IGI Global.

Alnoukari, M., & Hanano, A. (2017). Integration

of business intelligence with corporate

strategic management. Journal of Intelligence

Studies in Business, 7(2), 5-16.

Akter, S., & Fosso Wamba, S. (2016). Big data

analytics in E-commerce: A systematic review

and agenda for future research. Electronic

Markets, 26(2), 173–194.

Akter, S., Fosso Wamba, S. F., Gunasekaran, A.,

Dubey, R. and 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.

Bischof, C., Gabriel, M., Rabel, B., & Wilfinger, D.

(2016). Strategic Implications of BIG DATA –

A Comprehensive View. Proceedings of the

Management International Conference (MIC

(pp. 143–160). Pula, Croatia.

Braganza, A., Brooks, L., Nepelski, D., Ali, M. &

Moro, R. (2017). Resource management in big

data initiatives: Processes and dynamic

capabilities. Journal of Business Research, 70,

–337.

Calof, J., Richards, G. & Santilli, P. (2017).

Integration of business intelligence with

corporate strategic management. Journal of

Intelligence Studies in Business, 7(3), 62-73.

Calof, J., & Viviers, W. (2020). Big data analytics

and international market selection: An

exploratory study. Journal of Intelligence

Studies in Business, 10(2), 13-25.

Chang, Y. W., Hsu, P. Y., & Wu, Z. Y. (2015).

Exploring managers’ intention to use business

intelligence: The role of motivations.

Behaviour & Information Technology, 34(3),

–285.

Cheah, S., & Wang, S. (2017). Big data-driven

business model innovation by traditional

industries in the Chinese economy. Journal of

Chinese Economic and Foreign Trade Studies,

(3), 229–251.

Chen, D. Q., Preston, D. S., & Swink, M. (2015).

How the use of big data analytics affects value

creation in supply chain management.

Journal of Management Information Systems,

(4), 4–39.

Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017).

Assessing business value of big data analytics

in European firms. Journal of Business

Research, 70, 379–390.

Du, L., Huang, Q., Yeung, K., & Jian, Z. (2016).

Improving service innovation performance

through the big data management closed loop

competence in China Mobile. International

Journal of Computer Science and

Applications, 13(1), 116–132.

Dubey, R., Gunasekaran, A., & Childe, S. J.

(2018). Big data analytics capability in supply

chain agility: the moderating effect of

organizational flexibility. Management

Decision. Emerald. DOI 10.1108/MD-01-2018-

Erevelles, S., Fukawa, N., & Swayne, L. (2016).

Big Data consumer analytics and the

transformation of marketing. Journal of

Business Research, 69, Elsevier, 897-904.

Erickson, G. S., & Rothberg, H. N. (2017).

Healthcare and hospitality: Intangible

dynamics for evaluating industry sectors.

Service Industries Journal, 37, 589–606.

Esteves, J., & Curto, J. (2013). A risk and benefits

behavioral model to assess intentions to adopt

big data. Journal of Intelligence Studies in

Business, 3, 37–46.

Fan, S., Lau, R., & Zhao, J. A. (2015).

Demystifying Big Data Analytics for Business

Intelligence Through the Lens of Marketing

Mix. Big Data Research 2, 28–32.

Fiorini, P.C., Seles, B.M.R.P., Jabbour, C.J.C.,

Mariano, E.B., Jabbour, A.B.L.S. (2018).

Management theory and big data literature:

From a review to a research agenda.

International Journal of Information

Management, 43, 112–129.

Fosso Wamba, S., 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.

Fosso Wamba, S., Gunasekaran, A., Akter, S.,

Ren, S. Ji-fan., Dubey, R. & Childe, S. J.

(2017). Big data analytics and firm

performance: effects of dynamic capabilities.

Journal of Business Research, 70, 356-365.

Fosso Wamba, S., 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. doi:10.1016/j.ijpe.2014.12.031.

Fu, Y. G., & Zhu, J. M. (2017). The credit game on

network supplier and customer based on big

data. Electronic Commerce Research, 17, 1–23.

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.

Gupta, M., & George, J. F. (2016). Toward the

development of a big data analytics capability.

Information & Management, 53, Elsevier,

-1064.

Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-

Farmer, L. A. (2014). Data quality for data

science, predictive analytics, and big data in

supply chain management: An introduction to

the problem and suggestions for research and

applications. International Journal of

Production Economics, 154, 72–80.

Hazen, B. T., Skipper, J. B., Ezell, J. D., & Boone,

C. A. (2016). Big Data and predictive analytics

for supply chain sustainability: A theorydriven

research agenda. Computers &

Industrial Engineering, 101, 592–598.

Herden, T.T., (2020). Explaining the competitive

advantage generated from Analytics with the

knowledge-based view: the example of

Logistics and Supply Chain Management.

Business Research, 13, 163–214

Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R.,

Ravald, A., Sarantopoulos, P., Villarroel-

Ordenes, F., & Zaki, M. (2020). Customer

Experience Management in the Age of Big

Data Analytics: A Strategic Framework.

Journal of Business Research.

https://doi.org/10.1016/j.jbusres.2020.01.022

Jin, D.H., & Kim, H. J. (2018). Integrated

Understanding of Big Data, Big Data

Analysis, and Business Intelligence: A Case

Study of Logistics. Sustainability, 10, 3778;

doi:10.3390/su10103778.

Kowalczyk, M., & Buxmann, P. (2014). Big data

and information processing in organisational

decision processes. Business & Information

Systems Engineering, 6, 267–278.

Kwon, O., Lee, N., & Shin, B. (2014). Data quality

management, data usage experience and

acquisition intention of big data analytics.

International Journal of Information

Management, 34(3), 387–394.

Lee, N., Li, S., Shin, B., & Kwon, O. (2016). Social

comparison, goal contagion, and adoption of

innovative information technology. Journal of

Computer Information Systems, 56(2), 127–

Lin, C. & Kunnathur, A. (2019). Strategic

orientations, developmental culture, and big

data capability. Journal of Business Research,

, 49-60.

Liu, F., Dedehayir, O., & Katzy, B. (2015).

Coalition formation during technology

adoption. Behaviour & Information

Technology, 34, 1186–1199.

Liu, S., Shao, B., Gao, Y., Hu, S., Li, Y., & Zhou,

W. (2017). Game theoretic approach of a novel

decision policy for customers based on big

data. Electronic Commerce Research, 17, 1–16.

Liu, P., & Yi, S. P. (2017). Pricing policies of green

supply chain considering targeted advertising

and product green degree in the big data

environment. Journal of Cleaner Production,

, 1614–1622.

Llave, M.R. (2018). Data lakes in business

intelligence: reporting from the trenches.

Procedia Computer Science 138, 516–524.

Mazzei, M. J., & Noble, D. (2020). Big Data and

Strategy: Theoretical Foundations and New

Opportunities.In Orlando B., Strategy and

Behaviors in the Digital Economy,

IntechOpen, DOI: 10.5772/intechopen.84819.

Accessed April 20, 2020,

https://www.intechopen.com/books/strategyand-

behaviors-in-the-digital-economy/bigdata-

and-strategy-theoretical-foundationsand-

new-opportunities

Mazzei, M. J., & Noble, D. (2017). Big data

dreams: A framework for corporate strategy.

BUSHOR-1369, ScienceDirect, Elsevier, No. of

Pages. 10.

Mikalef, P., Pappas, O. I., Krogstie, J. & Pavlou,

P. (2020). Big data and business analytics: A

research agenda for realizing business value.

Information & Management, 57(1). 103237.

Mikalef, P., Krogstie, J., Wetering, R. V., Pappas,

O. I., & Giannakos, M. N. (2018). Information

Governance in the Big Data Era: Aligning

Organizational Capabilities. Proceedings of

the 51st Hawaii International Conference on

System Sciences (HICSS) (pp. 4911-4920),

Hawaii.

Mikalef, P., Pappas, O. I., Giannakos, N. M.,

Krogstie, J. & Lekakos, G. (2016). Big Data

and Strategy: A Research Framework. Tenth

Mediterranean Conference on Information

Systems (MCIS) (pp. 1-9), Paphos, Cyprus.

Nocker, M. & Sena, V. (2019). Big Data and

Human Resources Management: The Rise of

Talent Analytics. Social Sciences, 8, 273.

Passlick, J., Lebek, B., & Breitner, M. H. (2017).

A Self-Service Supporting Business

Intelligence and Big Data Analytics

Architecture, in Leimeister, J.M.; Brenner, W.

(Hrsg.): Proceedings der 13. Internationalen

Tagung Wirtschaftsinformatik (WI 2017), St.

Gallen, S. 1126-1140.

Prasad, S., Zakaria, R., & Altay, N. (2016). Big

data in humanitarian supply chain networks:

A resource dependence perspective. Annals of

Operations Research, 1–31.

Prescott, M. E. (2014). Big data and competitive

advantage at Nielsen. Management Decision,

(3), 573–601.

Rialti, R., Zollo, L., Ferraris, A., Alon, I. (2019).

Big data analytics capabilities and

performance: Evidence from a moderated

multi-mediation model. Technological

Forecasting & Social Change, 149, 1-10.

Sadovskyi, O., Engel, T., Heininger, R., Böhm, M.

& Krcmar, H. (2014). Analysis of Big Data

enabled Business Models using a Value Chain

Perspective. Proceedings of Multikonferenz

Wirtschaftsinformatik (MKWI 2014) (pp.

–1137).

Sarkis, J., Zhu, Q., & Lai, K. H. (2011). An

organizational theoretic review of green

supp

ly chain management literature.

International Journal of Production

Economics, 130(1), 1–15.

Silva, D. D. (2015). Addressing the complexities

of big data analytics in healthcare: The

diabetes screening case. Australasian Journal

of Information Systems, 19, 99–115.

Shams, S. and Solima, L. (2019). Big data

management: implications of dynamic

capabilities and data incubator. Management

Decision, 57(8), 2113-2123.

S

data analysis adaptation and enterprises’

competitive advantages: the perspective of

dynamic capability and resource-based

theories. Technology Analysis & Strategic

Management, DOI:

1080/09537325.2018.1516866.

Shin, D. H. (2016). Demystifying big data:

Anatomy of big data developmental process.

Telecommunications Policy, 40, 837–854.

Snyder, H. (2019). Literature review as a

research methodology: An overview and

guidelines. Journal of Business, 104, 333–339.

Soon, K. W. K., Lee, C. A., & Boursier, P. (2016).

A study of the determinants affecting adoption

of big data using integrated technology

acceptance model (TAM) and diffusion of

innovation (DOI) in Malaysia. International

Journal of Applied Business and Economic

Research, 14, 17–47.

Sun, Z., Zou, H., & Strang, K. (2015). Big Data

Analytics as a Service for Business

Intelligence. 14th Conference on e-Business, e-

Services and e-Society (I3E). Delft,

Netherlands. pp.200-211.

Suoniemi,, S., Meyer-Waarden, L., & Munzel, A.

(2017). Big Data Resources, Marketing

Capabilities, and Firm Performance. 2017

Winter AMA Conference, Orlando, USA.

Waller, M. A., & Fawcett, S. E. (2013). Data

science, predictive analytics, and big data: A

revolution that will transform supply chain

design and management. Journal of Business

Logistics, 34(2), 77–84.

Walls, C. & Barnard, B. (2020). Success Factors

of Big Data to Achieve Organisational

Performance: Theoretical Perspectives. Expert

Journal of Business and Management, 8(1), 1-

Wang, Y., Kung, L. & Byrd, T. A. (2018). Big data

analytics: Understanding its capabilities and

potential benefits for healthcare

organizations. Technological Forecasting and

Social Change, 126, 3–13.

Wang, Y., Kung, L., Wang, W.Y.C., & Cegielski,

C.G. (2018). An integrated big data analyticsenabled

transformation model: Application to

health care. Information & Management,

(1). 64-79.

Wilburn, K. M., & Wilburn, H. R. (2016). Asking

“What Else?” To identify unintended negative

consequences. Business Horizons, 59(2), 213–

Xie, K., Wu, Y., Xiao, J., & Hu, Q. (2016). Value

co-creation between firms and customers: The

role of big data-based cooperative assets.

Information & Management, 53(8), 1034–

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Published

2020-12-10

Issue

Vol. 10 No. 3 (2020): Journal of Intelligence Studies in Business Vol 10 Nr 3 2020

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