IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v281y2020i3p673-686.html
   My bibliography  Save this article

Understanding the impact of business analytics on innovation

Author

Listed:
  • Duan, Yanqing
  • Cao, Guangming
  • Edwards, John S.

Abstract

Advances in Business Analytics in the era of Big Data have provided unprecedented opportunities for organizations to innovate. With insights gained from Business Analytics, companies are able to develop new or improved products/services. However, few studies have investigated the mechanism through which Business Analytics contributes to a firm's innovation success. This research aims to address this gap by theoretically and empirically investigating the relationship between Business Analytics and innovation. To achieve this aim, absorptive capacity theory is used as a theoretical lens to inform the development of a research model. Absorptive capacity theory refers to a firm's ability to recognize the value of new, external information, assimilate it and apply it to commercial ends. The research model covers the use of Business Analytics, environmental scanning, data-driven culture, innovation (new product newness and meaningfulness), and competitive advantage. The research model is tested through a questionnaire survey of 218 UK businesses. The results suggest that Business Analytics directly improves environmental scanning which in turn helps to enhance a company's innovation. Business Analytics also directly enhances data-driven culture that in turn impacts on environmental scanning. Data-driven culture plays another important role by moderating the effect of environmental scanning on new product meaningfulness. The findings demonstrate the positive impact of business analytics on innovation and the pivotal roles of environmental scanning and data-driven culture. Organizations wishing to realize the potential of Business Analytics thus need changes in both their external and internal focus.

Suggested Citation

  • Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    • Handle: RePEc:eee:ejores:v:281:y:2020:i:3:p:673-686
    DOI: 10.1016/j.ejor.2018.06.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221718305447
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2018.06.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dahlander, Linus & Gann, David M., 2010. "How open is innovation?," Research Policy, Elsevier, vol. 39(6), pages 699-709, July.
    2. Donald C. Hambrick, 1982. "Environmental scanning and organizational strategy," Strategic Management Journal, Wiley Blackwell, vol. 3(2), pages 159-174, April.
    3. Curtis P. Armstrong & V. Sambamurthy, 1999. "Information Technology Assimilation in Firms: The Influence of Senior Leadership and IT Infrastructures," Information Systems Research, INFORMS, vol. 10(4), pages 304-327, December.
    4. Herrera, Maria Elena Baltazar, 2015. "Creating competitive advantage by institutionalizing corporate social innovation," Journal of Business Research, Elsevier, vol. 68(7), pages 1468-1474.
    5. Danny Miller & Peter H. Friesen, 1982. "Innovation in conservative and entrepreneurial firms: Two models of strategic momentum," Strategic Management Journal, Wiley Blackwell, vol. 3(1), pages 1-25, January.
    6. Ranyard, J.C. & Fildes, R. & Hu, Tun-I, 2015. "Reassessing the scope of OR practice: The Influences of Problem Structuring Methods and the Analytics Movement," European Journal of Operational Research, Elsevier, vol. 245(1), pages 1-13.
    7. Choi, Kanghwa & Narasimhan, Ram & Kim, Soo Wook, 2016. "Opening the technological innovation black box: The case of the electronics industry in Korea," European Journal of Operational Research, Elsevier, vol. 250(1), pages 192-203.
    8. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G., 2016. "Predictive analytics model for healthcare planning and scheduling," European Journal of Operational Research, Elsevier, vol. 253(1), pages 121-131.
    9. Stock, Ruth & Zacharias, Nicolas, 2013. "Two Sides of the Same Coin: How Do Different Dimensions of Product Program Innovativeness Affect Customer Loyalty?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 63414, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    10. Pape, Tom, 2016. "Prioritising data items for business analytics: Framework and application to human resources," European Journal of Operational Research, Elsevier, vol. 252(2), pages 687-698.
    11. Matthew J. Liberatore & Wenhong Luo, 2010. "The Analytics Movement: Implications for Operations Research," Interfaces, INFORMS, vol. 40(4), pages 313-324, August.
    12. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    13. Armstrong, J. Scott & Overton, Terry S., 1977. "Estimating Nonresponse Bias in Mail Surveys," MPRA Paper 81694, University Library of Munich, Germany.
    14. Vidgen, Richard & Shaw, Sarah & Grant, David B., 2017. "Management challenges in creating value from business analytics," European Journal of Operational Research, Elsevier, vol. 261(2), pages 626-639.
    15. G Royston, 2013. "Operational Research for the Real World: big questions from a small island☆," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(6), pages 793-804, June.
    16. Cuellar, Michael J. & Gallivan, Michael J., 2006. "A framework for ex ante project risk assessment based on absorptive capacity," European Journal of Operational Research, Elsevier, vol. 173(3), pages 1123-1138, September.
    17. Toby E. Stuart, 2000. "Interorganizational alliances and the performance of firms: a study of growth and innovation rates in a high‐technology industry," Strategic Management Journal, Wiley Blackwell, vol. 21(8), pages 791-811, August.
    18. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    19. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    20. Pan, Xiaojun & Li, Shoude, 2016. "Dynamic optimal control of process–product innovation with learning by doing," European Journal of Operational Research, Elsevier, vol. 248(1), pages 136-145.
    21. Dutta, Debprotim & Bose, Indranil, 2015. "Managing a Big Data project: The case of Ramco Cements Limited," International Journal of Production Economics, Elsevier, vol. 165(C), pages 293-306.
    22. Atuahene-Gima, Kwaku, 1996. "Market orientation and innovation," Journal of Business Research, Elsevier, vol. 35(2), pages 93-103, February.
    23. Leal-Rodríguez, Antonio L. & Ariza-Montes, José A. & Roldán, José L. & Leal-Millán, Antonio G., 2014. "Absorptive capacity, innovation and cultural barriers: A conditional mediation model," Journal of Business Research, Elsevier, vol. 67(5), pages 763-768.
    24. Willis, Graham & Cave, Siôn & Kunc, Martin, 2018. "Strategic workforce planning in healthcare: A multi-methodology approach," European Journal of Operational Research, Elsevier, vol. 267(1), pages 250-263.
    25. Shaker A. Zahra & Harry J. Sapienza & Per Davidsson, 2006. "Entrepreneurship and Dynamic Capabilities: A Review, Model and Research Agenda," Journal of Management Studies, Wiley Blackwell, vol. 43(4), pages 917-955, June.
    26. Stock, Ruth & Zacharias, Nicolas, 2013. "Two Sides of the Same Coin: How Do Different Dimensions of Product Program Innovativeness Affect Customer Loyalty?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 61663, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    27. K. D. Joshi & Lei Chi & Avimanyu Datta & Shu Han, 2010. "Changing the Competitive Landscape: Continuous Innovation Through IT-Enabled Knowledge Capabilities," Information Systems Research, INFORMS, vol. 21(3), pages 472-495, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ashrafi, Amir & Zareravasan, Ahad, 2022. "An ambidextrous approach on the business analytics-competitive advantage relationship: Exploring the moderating role of business analytics strategy," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    2. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    3. Radicic, Dragana & Petković, Saša, 2023. "Impact of digitalization on technological innovations in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    4. Osama Musa Ali Al-Darras & Cem Tanova, 2022. "From Big Data Analytics to Organizational Agility: What Is the Mechanism?," SAGE Open, , vol. 12(2), pages 21582440221, June.
    5. Lin, Shunzhi & Lin, Jiabao, 2023. "How organizations leverage digital technology to develop customization and enhance customer relationship performance: An empirical investigation," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    6. Yao-Chin Lin & Ching-Chuan Yeh & Wei-Hung Chen & Wei-Chun Liu & Jyun-Jie Wang, 2020. "The Use of Big Data for Sustainable Development in Motor Production Line Issues," Sustainability, MDPI, vol. 12(13), pages 1-24, July.
    7. Hayajneh, Jamal Abdelrahman .M. & Elayan, Malek Bakheet Haroun & Abdellatif, Mamdouh Abdallah Mohamed & Abubakar, A. Mohammed, 2022. "Impact of business analytics and π-shaped skills on innovative performance: Findings from PLS-SEM and fsQCA," Technology in Society, Elsevier, vol. 68(C).
    8. Niu, Wenju & Shen, Houcai, 2022. "Investment in process innovation in supply chains with knowledge spillovers under innovation uncertainty," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1128-1141.
    9. Akter, Shahriar & Hossain, Md Afnan & Sajib, Shahriar & Sultana, Saida & Rahman, Mahfuzur & Vrontis, Demetris & McCarthy, Grace, 2023. "A framework for AI-powered service innovation capability: Review and agenda for future research," Technovation, Elsevier, vol. 125(C).
    10. Zhu, Xiumei & Li, Yue, 2023. "The use of data-driven insight in ambidextrous digital transformation: How do resource orchestration, organizational strategic decision-making, and organizational agility matter?," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    11. Liang, Xiaoning & Li, Guoxin & Zhang, Hao & Nolan, Eimear & Chen, Fadong, 2022. "Firm performance and marketing analytics in the Chinese context: A contingency model," Journal of Business Research, Elsevier, vol. 141(C), pages 589-599.
    12. Maya Vachkova & Arsalan Ghouri & Haidy Ashour & Normalisa Binti Md Isa & Gregory Barnes, 2023. "Big data and predictive analytics and Malaysian micro-, small and medium businesses," SN Business & Economics, Springer, vol. 3(8), pages 1-28, August.
    13. Mihai BOGDAN & Anca BORZA, 2020. "Big Data Analytics And Firm Performance: A Text Mining Approach," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(1), pages 549-560, November.
    14. Sultana, Saida & Akter, Shahriar & Kyriazis, Elias, 2022. "How data-driven innovation capability is shaping the future of market agility and competitive performance?," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    15. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    16. Mudassar Ali & Li Zhang & Zhenduo Zhang & Muhammad Zada & Abida Begum & Heesup Han & Antonio Ariza-Montes & Alejandro Vega-Muñoz, 2021. "Can Leaders’ Humility Enhance Project Management Effectiveness? Interactive Effect of Top Management Support," Sustainability, MDPI, vol. 13(17), pages 1-17, August.
    17. Awan, Usama & Shamim, Saqib & Khan, Zaheer & Zia, Najam Ul & Shariq, Syed Muhammad & Khan, Muhammad Naveed, 2021. "Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance," Technological Forecasting and Social Change, Elsevier, vol. 168(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    2. Burger, Katharina & White, Leroy & Yearworth, Mike, 2019. "Developing a smart operational research with hybrid practice theories," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1137-1150.
    3. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
    4. Osman, Ibrahim H. & Anouze, Abdel Latef & Irani, Zahir & Lee, Habin & Medeni, Tunç D. & Weerakkody, Vishanth, 2019. "A cognitive analytics management framework for the transformation of electronic government services from users’ perspective to create sustainable shared values," European Journal of Operational Research, Elsevier, vol. 278(2), pages 514-532.
    5. Hindle, Giles A. & Vidgen, Richard, 2018. "Developing a business analytics methodology: A case study in the foodbank sector," European Journal of Operational Research, Elsevier, vol. 268(3), pages 836-851.
    6. Ipsmiller, Edith & Dikova, Desislava & Brouthers, Keith D., 2022. "Digital Internationalization of Traditional Firms: Virtual Presence and Entrepreneurial Orientation," Journal of International Management, Elsevier, vol. 28(4).
    7. Michael Sheppard, 2020. "The relationship between discretionary slack and growth in small firms," International Entrepreneurship and Management Journal, Springer, vol. 16(1), pages 195-219, March.
    8. Richard Arend, 2013. "Ethics-focused dynamic capabilities: a small business perspective," Small Business Economics, Springer, vol. 41(1), pages 1-24, June.
    9. Chen, Yen-Chun & Lin, Ya-Hui & Li, Po-Chien & Chen, Chung-Jen, 2022. "Understanding the interplay between competitor and alliance orientations in product innovativeness: An integrative framework," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    10. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    11. Richard Arend, 2014. "Entrepreneurship and dynamic capabilities: how firm age and size affect the ‘capability enhancement–SME performance’ relationship," Small Business Economics, Springer, vol. 42(1), pages 33-57, January.
    12. Ni, Ji & Chen, Bowei & Allinson, Nigel M. & Ye, Xujiong, 2020. "A hybrid model for predicting human physical activity status from lifelogging data," European Journal of Operational Research, Elsevier, vol. 281(3), pages 532-542.
    13. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    14. Salunke, Sandeep & Weerawardena, Jay & McColl-Kennedy, Janet R., 2013. "Competing through service innovation: The role of bricolage and entrepreneurship in project-oriented firms," Journal of Business Research, Elsevier, vol. 66(8), pages 1085-1097.
    15. Buccieri, Dominic & Javalgi, Raj G. & Cavusgil, Erin, 2020. "International new venture performance: Role of international entrepreneurial culture, ambidextrous innovation, and dynamic marketing capabilities," International Business Review, Elsevier, vol. 29(2).
    16. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    17. Marijana Srećković, 2018. "The performance effect of network and managerial capabilities of entrepreneurial firms," Small Business Economics, Springer, vol. 50(4), pages 807-824, April.
    18. Ranyard, J.C. & Fildes, R. & Hu, Tun-I, 2015. "Reassessing the scope of OR practice: The Influences of Problem Structuring Methods and the Analytics Movement," European Journal of Operational Research, Elsevier, vol. 245(1), pages 1-13.
    19. Vidgen, Richard & Shaw, Sarah & Grant, David B., 2017. "Management challenges in creating value from business analytics," European Journal of Operational Research, Elsevier, vol. 261(2), pages 626-639.
    20. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:281:y:2020:i:3:p:673-686. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.