Using Analytical Information for Digital Business Transformation through DataOps: A Review and Conceptual Framework
DOI:
https://doi.org/10.3127/ajis.v28.4215Keywords:
Digital business transformation, Business analytics, Analytical information processing capability, Boundary spanning, DataOpsAbstract
Organisations are increasingly practising business analytics to generate actionable insights that can guide their digital business transformation. Transforming business digitally using business analytics is an ongoing process that requires an integrated and disciplined approach to leveraging analytics and promoting collaboration. An emerging business analytics practice, Data Operations (DataOps), provides a disciplined approach for organisations to collaborate using analytical information for digital business transformation. We propose a conceptual framework by reviewing the literature on business analytics, DataOps and organisational information processing theory (OIPT). This conceptual framework explains how organisations can employ DataOps as an integrated and disciplined approach for developing the analytical information processing capability and facilitating boundary-spanning activities required for digital business transformation. This research (a) extends current knowledge on digital transformation by linking it with business analytics from the perspective of OIPT and boundary-spanning activities, and (b) presents DataOps as a novel approach for using analytical information for digital business transformation.
References
Aldrich, H., & Herker, D. (1977). Boundary Spanning Roles and Organization Structure. Academy of Management Review, 2(2), 217-230. https://doi.org/10.5465/amr.1977.4409044
Anand, A., Sharma, R., & Kohli, R. (2020). The Effects of Operational and Financial Performance Failure on BI&A-Enabled Search Behaviors: A Theory of Performance-Driven Search. Information Systems Research, 31(4), 1144-1163.
https://doi.org/10.1287/isre.2020.0936
Ancona, D. G., & Caldwell, D. (1990). Beyond Boundary Spanning- Managing External Dependence in Product Development Teams. The Journal of High Technology Management Research, 1(2), 119-135. https://doi.org/10.1016/1047-8310(90)90001-K
Ashrafi, A., Zare Ravasan, A., Trkman, P., & Afshari, S. (2019). The Role of Business Analytics Capabilities in Bolstering Firms’ Agility and Performance. International Journal of Information Management, 47, 1-15. https://doi.org/10.1016/j.ijinfomgt.2018.12.005
Aslett, M. (2020). DataOps Unlocks the Value of Data. https://www.hitachivantara.com/en-us/pdf/analyst-content/dataops-unlocks-value-of-data-451-research.pdf. Accessed
August 8, 2023
Atwal, H. (2020). Practical DataOps: Delivering Agile Data Science at Scale. Berkeley, CA, USA: Apress. https://link.springer.com/content/pdf/10.1007/978-1-4842-5104-1.pdf. Accessed August 8, 2023
Azzouz, A., & Papadonikolaki, E. (2020). Boundary-Spanning for Managing Digital Innovation in the AEC Sector. Architectural Engineering and Design Management, 16(5), 356-373. https://doi.org/10.1080/17452007.2020.1735293
Bahaa, S., Ghalwash, A. Z., & Harb, H. (2023). DataOps Lifecycle with a Case Study in Healthcare. International Journal of Advanced Computer Science and Applications, 14(1). https://doi.org/10.14569/IJACSA.2023.0140115
Beckett, R. C. (2021). Agile Coping in a Digital World: An Expanding Need for Boundary Spanning. In Ferreira, N., Potgieter, I.L., & Coetzee, M. (Eds.) Agile Coping in the Digital Workplace (pp. 119-145). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-70228-1_7
Bergh, C., Benghiat, G., & Strod, E. (2019). The DataOps Cookbook: Methodologies and Tools That Reduce Analytics Cycle Time While Improving Quality. Cambridge, MA, USA: DataKitchen Headquarters. https://www.devopsschool.com/blog/wp-content/uploads/2021/07/
DK_dataops_book_2nd_edition.pdf
Bonnet, D., & Westerman, G. (2021). The New Elements of Digital Transformation. MIT Sloan Management Review, 62(2), 82-89. https://www.proquest.com/scholarly-journals/new-elements-digital-transformation/docview/2471816389/se-2?accountid=12372
Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The Strategic Use of Artificial Intelligence in the Digital Era: Systematic Literature Review and Future Research Directions. International Journal of Information Management, 57, 1-16. https://doi.org/10.1016/j.ijinfomgt.2020.102225
Cao, G., Duan, Y., & Cadden, T. (2019). The Link between Information Processing Capability and Competitive Advantage Mediated through Decision-Making Effectiveness. International Journal of Information Management, 44, 121-131.
https://doi.org/10.1016/j.ijinfomgt.2018.10.003
Capizzi, A., Distefano, S., & Mazzara, M. (2020). From DevOps to DevDataOps: Data Management in DevOps Processes. In Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment: Second International Workshop, DevOps 2019 (Vol. 12055). Cham, Switzerland: Springer.
https://doi.org/10.1007/978-3-030-39306-9_4
Christensen, C. M. (2013). The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail. Harward University, MA, USA: Harvard Business Review Press.
Conboy, K., Mikalef, P., Dennehy, D., & Krogstie, J. (2020). Using Business Analytics to Enhance Dynamic Capabilities in Operations Research: A Case Analysis and Research Agenda. European Journal of Operational Research, 281(3), 656-672. https://doi.org/10.1016/j.ejor.2019.06.051
Cronin, M. A., & George, E. (2023). The Why and How of the Integrative Review. Organizational Research Methods, 26(1), 168-192. https://doi.org/10.1177/1094428120935507
Daft, R. L., & Lengel, R. H. (1986). Organizational Information Requirements, Media Richness and Structural Design. Management Science, 32(5), 554-571.
https://doi.org/https://doi.org/10.1287/mnsc.32.5.554
Davenport, T. H., & Harris, J. (2017). Competing on Analytics: Updated, with a New Introduction: The New Science of Winning. Harward University, MA, USA: Harvard Business Press.
Davenport, T. H., & Westerman, G. (2018). Why So Many High-Profile Digital Transformations Fail. Harvard Business Review, 9, 1-5. https://www.nutanix.com/content/dam/nutanix-cxo/pdf/Why%20So%20Many%20High-Profile%20Digital%20Transformations%20
Fail.pdf
Dremel, C., Wulf, J., Herterich, M. M., Waizmann, J. C., & Brenner, W., 16(2). (2017). How Audi AG Established Big Data Analytics in Its Digital Transformation. MIS Quarterly Executive, 16(2), 81-100. https://aisel.aisnet.org/misqe/vol16/iss2/3
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., Le Meunier-FitzHugh, K., Le Meunier-FitzHugh, L. C., Misra, S., Mogaji, E., Sharma, S. K., Singh, J. B., Raghavan, V., Raman, R., Rana, N. P., Samothrakis, S., Spencer, J., Tamilmani, K., Tubadji, A., Walton, P., & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy. International Journal of Information Management, 57, 1-47.
https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Eckerson, W. (2019a). Trends in DataOps: Bringing Scale and Rigor to Data and Analytics. https://www.eckerson.com/register?content=trends-in-dataops-bringing-scale-and-rigor-to-data-and-analytics. Accessed August 8, 2023
Eckerson, W. (2019b). The Ultimate Guide to DataOps: Production Evaluation and Selection Criteria. https://www.eckerson.com/register?content=the-ultimate-guide-to-dataops. Accessed
August 8, 2023
Ereth, J. (2018). DataOps-Towards a Definition. In Proceedings of the Conference Lernen, Wissen, Daten, Analysen (German for Learning, Knowledge, Data, Analysis) (LWDA) 2018, Mannheim, Germany, August 22-24, 2018 (pp. 104-112). https://ceur-ws.org/Vol-2191/paper13.pdf
Ereth, J., & Eckerson, W. (2018). DataOps: Industrializing Data and Analytics Strategies for Streamlining the Delivery of Insights.
https://www.eckerson.com/register?content=dataops-industrializing-data-and-analytics. Accessed August 8, 2023
Fleischer, J., & Carstens, N. (2021). Policy Labs as Arenas for Boundary Spanning: Inside the Digital Transformation in Germany. Public Management Review, 24(8), 1208-1225. https://doi.org/10.1080/14719037.2021.1893803
Flynn, B. B., & Flynn, E. J. (1999). Information‐Processing Alternatives for Coping with Manufacturing Environment Complexity. Decision Sciences, 30(4), 1021-1052. https://doi.org/10.1111/j.1540-5915.1999.tb00917.x
Fox, N. J. (2011). Boundary Objects, Social Meanings and the Success of New Technologies. Sociology, 45(1), 70-85. https://doi.org/10.1177/0038038510387196
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 Technologies: Implementation Patterns in Manufacturing Companies. International Journal of Production Economics, 210, 15-26. https://doi.org/10.1016/j.ijpe.2019.01.004
Friedman, T., & Heudecker, N. (2019). Introducing DataOps into Your Data Management Discipline. https://www.gartner.com/document/3970916?ref=solrAll&refval=246181733. Accessed August 8, 2023
Galbraith, J. R. (1974). Organization Design: An Information Processing View. Interfaces, 4(3), 28-36. https://doi.org/10.1287/inte.4.3.28
Gartner. (2021). 6 Key Takeaways from the Gartner Board of Directors Survey. https://www.gartner.com/en/articles/6-key-takeaways-from-the-gartner-board-of-directors-survey . Accessed August 8, 2023
Glaser, L., Fourné, S. P. L., & Elfring, T. (2015). Achieving Strategic Renewal: The Multi-Level Influences of Top and Middle Managers’ Boundary-Spanning. Small Business Economics, 45(2), 305-327. https://doi.org/10.1007/s11187-015-9633-5
Grover, V., Chiang, R. H. L., Liang, T.-P., & Zhang, D. (2018). Creating Strategic Business Value from Big Data Analytics: A Research Framework. Journal of Management Information Systems, 35(2), 388-423. https://doi.org/10.1080/07421222.2018.1451951
Guinan, P. J., Parise, S., & Langowitz, N. (2019). Creating an Innovative Digital Project Team: Levers to Enable Digital Transformation. Business Horizons, 62(6), 717-727. https://doi.org/10.1016/j.bushor.2019.07.005
Gupta, G., & Bose, I. (2022). Digital Transformation in Entrepreneurial Firms through Information Exchange with Operating Environment. Information & Management, 59(3). https://doi.org/10.1016/j.im.2019.103243
Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T., & Potapov, D. (2020). Digital Analytics: Modeling for Insights and New Methods. Journal of Interactive Marketing, 51(1), 26-43. https://doi.org/10.1016/j.intmar.2020.04.003
Gur, I., Moller, F., Hupperz, M., Uzun, D., & Otto, B. (2022). Requirements for DataOps to Foster Dynamic Capabilities in Organizations - a Mixed Methods Approach. In 2022 IEEE 24th Conference on Business Informatics (CBI), Amsterdam, Netherlands, June 15-17, 2022 (pp. 166-175). https://doi.org/10.1109/CBI54897.2022.00025
Harter, L., & Krone, K. (2001). The Boundary-Spanning Role of a Cooperative Support Organization: Managing the Paradox of Stability and Change in Non-Traditional Organizations. Journal of Applied Communication Research, 29(3), 248-277. https://doi.org/10.1080/00909880128111
Hess, T., Matt, C., Benlian, A., & Wiesböck, F. (2016). Options for Formulating a Digital Transformation Strategy. MIS Quarterly Executive, 15(2), 123-139.
https://aisel.aisnet.org/misqe/vol15/iss2/6
Heudecker, N., Friedman, T., & Dayley, A. (2020). Innovation Insight for DataOps. https://www.gartner.com/document/3896766?ref=solrAll&refval=232501050&qid=. Accessed Agust 8, 2023
Ismail, M. H., Khater, M., & Zaki, M. (2017). Digital Business Transformation and Strategy: What Do We Know So Far. Cambridge Service Alliance, 10(1), 1-36. https://cambridgeservicealliance.eng.cam.ac.uk/system/files/documents/2017NovPaper_Mariam.pdf
Jonsson, K., Holmström, J., & Lyytinen, K. (2009). Turn to the Material: Remote Diagnostics Systems and New Forms of Boundary-Spanning. Information and Organization, 19(4), 233-252. https://doi.org/10.1016/j.infoandorg.2009.07.001
Karippur, N. K., & Balaramachandran, P. R. (2022). Antecedents of Effective Digital Leadership of Enterprises in Asia Pacific. Australasian Journal of Information Systems, 26, 1-35. https://doi.org/ttps://doi.org/10.3127/ajis.v26i0.2525
Khuntia, J., Saldanha, T., Kathuria, A., & Tanniru, M. R. (2022). Digital Service Flexibility: A Conceptual Framework and Roadmap for Digital Business Transformation. European Journal of Information Systems, 1-19. https://doi.org/10.1080/0960085x.2022.2115410
Leifer, R., & Delbecq, A. (1978). Organizational:Environmental Interchange: A Model of Boundary Spanning Activity. Academy of Management Review, 3(1), 40-50. https://doi.org/10.5465/amr.1978.4296354
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive Analytics: Literature Review and Research Challenges. International Journal of Information Management, 50, 57-70. https://doi.org/10.1016/j.ijinfomgt.2019.04.003
Levina, N., & Vaast, E. (2014). Turning a Community into a Market: A Practice Perspective on Information Technology Use in Boundary Spanning. Journal of Management Information Systems, 22(4), 13-37. https://doi.org/10.2753/mis0742-1222220402
Li, H., Wu, Y., Cao, D., & Wang, Y. (2021). Organizational Mindfulness Towards Digital Transformation as a Prerequisite of Information Processing Capability to Achieve Market Agility. Journal of Business Research, 122, 700-712.
https://doi.org/10.1016/j.jbusres.2019.10.036
Loebbecke, C., & Picot, A. (2015). Reflections on Societal and Business Model Transformation Arising from Digitization and Big Data Analytics: A Research Agenda. The Journal of Strategic Information Systems, 24(3), 149-157. https://doi.org/10.1016/j.jsis.2015.08.002
Mainali, K., Ehrlinger, L., Matskin, M., & Himmelbauer, J. (2021). Discovering DataOps: A Comprehensive Review of Definitions, Use Cases, and Tools. In Data Analytics 2021 : The 10th International Conference on Data Analytics, Barcelona, Spain, October 3-7, 2021 (pp. 61-69). https://www.researchgate.net/profile/Kiran_Mainali2/publication/
_Discovering_DataOps_A_Comprehensive_Review_of_Definitions_Use_Cases_and_Tools/links/615dd703fbd5153f47e938a1/Discovering-DataOps-A-Comprehensive-Review-of-Definitions-Use-Cases-and-Tools.pdf
Marrone, J. A. (2010). Team Boundary Spanning: A Multilevel Review of Past Research and Proposals for the Future. Journal of Management, 36(4), 911-940.
https://doi.org/10.1177/0149206309353945
Matt, C., Hess, T., & Benlian, A. (2015). Digital Transformation Strategies. Business & Information Systems Engineering, 57(5), 339-343. https://doi.org/10.1007/s12599-015-0401-5
Müller, O., Junglas, I., Debortoli, S., & vom Brocke, J. (2016). Using Text Analytics to Derive Customer Service Management Benefits from Unstructured Data. MIS Quarterly Executive, 15(4), 243-258. https://doi.org/10.25300/MISQ/2018/13239
Munappy, A. R., Bosch, J., & Olsson, H. H. (2020). Data Pipeline Management in Practice: Challenges and Opportunities. In Proceedings of the Product-Focused Software Process Improvement Conference, Turin, Italy, November 25-27, 2020 (pp. 168-184). https://doi.org/10.1007/978-3-030-64148-1_11
Munappy, A. R., Mattos, D. I., Bosch, J., Olsson, H. H., & Dakkak, A. (2020). From Ad-Hoc Data Analytics to DataOps. In Proceedings of the International Conference on Software and System Processes, Seoul, Republic of Korea, June 26-28, 2019 (pp. 165-174). https://doi.org/10.1145/3379177.3388909
Nambisan, S., Wright, M., & Feldman, M. (2019). The Digital Transformation of Innovation and Entrepreneurship: Progress, Challenges and Key Themes. Research Policy, 48(8), 1-9. https://doi.org/10.1016/j.respol.2019.03.018
Naseer, H., Maynard, S. B., & Desouza, K. C. (2021). Demystifying Analytical Information Processing Capability: The Case of Cybersecurity Incident Response. Decision Support Systems, 143, 1-11. https://doi.org/10.1016/j.dss.2020.113476
Naseer, H., Maynard, S. B., & Xu, J. (2020). Modernizing Business Analytics Capability with DataOps: A Decision-Making Agility Perspective. In Proceedings of the European Conference on Information Systems (ECIS 2020), Marrakech, Morocco, June 15-17, 2020 (pp. 1-11). https://aisel.aisnet.org/ecis2020_rip/36
Neirotti, P., Pesce, D., & Battaglia, D. (2021). Algorithms for Operational Decision-Making: An Absorptive Capacity Perspective on the Process of Converting Data into Relevant Knowledge. Technological Forecasting and Social Change, 173.
https://doi.org/10.1016/j.techfore.2021.121088
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hrobjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021, Apr). The Prisma 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. International Journal of Surgery, 88, 105906. https://doi.org/10.1016/j.ijsu.2021.105906
Palmer, A. (2015). From DevOps to DataOps. In C. Shannon (Ed.), Getting Data Right: Tackling the Challenges of Big Data Volume and Variety (pp. 49-57). Sebastopol, CA, USA: O’Reilly. https://cdn2.hubspot.net/hubfs/2631050/CCO%20USA/Getting_Data_Right_Tamr.pdf
Papanagnou, C., Seiler, A., Spanaki, K., Papadopoulos, T., & Bourlakis, M. (2022). Data-Driven Digital Transformation for Emergency Situations: The Case of the Uk Retail Sector. International Journal of Production Economics, 250.
https://doi.org/10.1016/j.ijpe.2022.108628
Pappas, I. O., Mikalef, P., Giannakos, M. N., Krogstie, J., & Lekakos, G. (2018). Big Data and Business Analytics Ecosystems: Paving the Way Towards Digital Transformation and Sustainable Societies. Information Systems and e-Business Management, 16(3), 479-491. https://doi.org/10.1007/s10257-018-0377-z
Pershina, R., Soppe, B., & Thune, T. M. (2019). Bridging Analog and Digital Expertise: Cross-Domain Collaboration and Boundary-Spanning Tools in the Creation of Digital Innovation. Research Policy, 48(9), 1-13. https://doi.org/10.1016/j.respol.2019.103819
Premkumar, G., Ramamurthy, K., & Saunders, C. S. (2005). Information Processing View of Organizations: An Exploratory Examination of Fit in the Context of Interorganizational Relationships. Journal of Management Information Systems, 22(1), 257-294. https://doi.org/10.1080/07421222.2003.11045841
Ranjan, J., & Foropon, C. (2021). Big Data Analytics in Building the Competitive Intelligence of Organizations. International Journal of Information Management, 56, 1-13. https://doi.org/10.1016/j.ijinfomgt.2020.102231
Richardson, G. (2020). To Build or Buy? Effective DataOps in an Era of Rapid Change. Database and Network Journal, 50(1), 3-5.
https://link.gale.com/apps/doc/A619013513/AONE?u=unimelb&sid=googleScholar&xid=fb06b7f3
Rodriguez, M., De Araújo, L. J. P., & Mazzara, M. (2020). Good Practices for the Adoption of DataOps in the Software Industry. In Journal of Physics: Conference Series (Vol. 1694, pp. 012-032). Innopolis, Russia: IOP Publishing.
https://doi.org/10.1088/1742-6596/1694/1/012032
Sahoo, P. R., & Premchand, A. (2019). DataOps in Manufacturing and Utilities Industries. International Journal of Applied Information Systems, 12(23), 1-6.
https://doi.org/10.5120/ijais2019451814
Saldanha, T. J. V. V., Mithas, S., & Krishnan, M. S. (2017). Leveraging Customer Involvement for Fueling Innovation: The Role of Relational and Analytical Information Processing Capabilities. MIS Quarterly, 41(1), 267-286. https://www.jstor.org/stable/26629647
Schallmo, D., Williams, C. A., & Boardman, L. (2017). Digital Transformation of Business Models — Best Practice, Enablers, and Roadmap. International Journal of Innovation Management, 21(08), 1-17. https://doi.org/10.1142/s136391961740014x
Schotter, A. P. J., Mudambi, R., Doz, Y. L., & Gaur, A. (2017). Boundary Spanning in Global Organizations. Journal of Management Studies, 54(4), 403-421.
https://doi.org/10.1111/joms.12256
Schwade, F. (2021). Measuring and Visualising Boundary Spanningin Enterprise Collanoration Systems. In Proceedings of the European Conference on Information Systems (ECIS 2021), Marrakech, Morocco, June 14-16, 2021. (pp. 1-16).
https://aisel.aisnet.org/ecis2021_rp/63
Sebastian, I., Ross, J., Beath, C., Mocker, M., Moloney, K., & Fonstad, N. (2017). How Big Old Companies Navigate Digital Transformation. MIS Quaterly Executive, 16(3), 197-213. https://aisel.aisnet.org/misqe/vol16/iss3/6
Setia, P., Setia, P., Venkatesh, & Joglekar, S. (2014). Leveraging Digital Technologies: How Information Quality Leads to Localized Capabilities and Customer Service Performance. MIS Quarterly, 13(2), 565-590. https://www.jstor.org/stable/43825923
Sia, S. K., Soh, C., & Weill, P. (2016). How DBS Bank Pursued a Digital Business Strategy. MIS Quarterly Executive, 15(2), 105-121. https://aisel.aisnet.org/misqe/vol15/iss2/4
Singh, A., Klarner, P., & Hess, T. (2020). How Do Chief Digital Officers Pursue Digital Transformation Activities? The Role of Organization Design Parameters. Long Range Planning, 53(3), 1-14. https://doi.org/10.1016/j.lrp.2019.07.001
Snyder, H. (2019). Literature Review as a Research Methodology: An Overview and Guidelines. Journal of Business Research, 104, 333-339.
https://doi.org/10.1016/j.jbusres.2019.07.039
Someh, I. A., & Shanks, G. (2013). The Role of Synergy in Achieving Value from Business Analytics Systems. In Proceedings of the International Conference on Information Systems (ICIS), Milan, Italy, December 15-18, 2013 (pp. 1-15). https://aisel.aisnet.org/icis2013/proceedings/KnowledgeManagement/7
Song, H., Li, M., & Yu, K. (2021). Big Data Analytics in Digital Platforms: How Do Financial Service Providers Customise Supply Chain Finance? International Journal of Operations & Production Management, 41(4), 410-435. https://doi.org/10.1108/ijopm-07-2020-0485
Srinivasan, R., & Swink, M. (2018). An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective. Production and Operations Management, 27(10), 1849-1867. https://doi.org/10.1111/poms.12746
Subramaniam, M. (2021). The 4 Tiers of Digital Transformation. Harvard Business Review. https://hbr.org/2021/09/the-4-tiers-of-digital-transformation. Accessed May 14, 2023
Suseno, Y., Laurell, C., & Sick, N. (2018). Assessing Value Creation in Digital Innovation Ecosystems: A Social Media Analytics Approach. The Journal of Strategic Information Systems, 27(4), 335-349. https://doi.org/10.1016/j.jsis.2018.09.004
Tan, F. T. C., Ondrus, J., Tan, B., & Oh, J. (2020). Digital Transformation of Business Ecosystems: Evidence from the Korean Pop Industry. Information Systems Journal, 30(5), 866-898. https://doi.org/10.1111/isj.12285
Thusoo, A., & Sarma, J. (2017). Creating a Data-Driven Enterprise with DataOps. Sebastopol, CA, USA: O’Reilly.
https://www.oreilly.com/library/view/creating-a-data-driven/9781492049227/. Accessed August 8, 2023
Tim, Y., Hallikainen, P., Pan, S. L., & Tamm, T. (2020). Actualizing Business Analytics for Organizational Transformation: A Case Study of Rovio Entertainment. European Journal of Operational Research, 281(3), 642-655. https://doi.org/10.1016/j.ejor.2018.11.074
Torraco, R. J. (2016). Writing Integrative Literature Reviews. Human Resource Development Review, 15(4), 404-428. https://doi.org/10.1177/1534484316671606
Tushman, M. L., & Nadler, D. A. (1978). Information Processing as an Integrating Concept in Organizational Design. Academy of Management Review, 3(3), 613-624. https://doi.org/10.5465/amr.1978.4305791
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein, M. (2021). Digital Transformation: A Multidisciplinary Reflection and Research Agenda. Journal of Business Research, 122, 889-901. https://doi.org/10.1016/j.jbusres.2019.09.022
Vial, G. (2019). Understanding Digital Transformation: A Review and a Research Agenda. The Journal of Strategic Information Systems, 28(2), 118-144.
https://doi.org/10.1016/j.jsis.2019.01.003
Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management Challenges in Creating Value from Business Analytics. European Journal of Operational Research, 261(2), 626-639. https://doi.org/10.1016/j.ejor.2017.02.023
Vilvovsky, S. (2009). Internal and External Boundary Spanning in Outsourced IS Development Projects: Opening the Black Box. In Proceedings of the Americas Conference on Information Systems (AMCIS), AMCIS 2009 Doctoral Consortium, San Francisco, CA, USA (pp. 1-11). https://aisel.aisnet.org/amcis2009_dc/4
vom Brocke, J., Simons, A., Riemer, K., Niehaves, B., Plattfaut, R., & Cleven, A. (2015). Standing on the Shoulders of Giants: Challenges and Recommendations of Literature Search in Information Systems Research. Communications of the Association for Information Systems, 37. https://doi.org/10.17705/1cais.03709
Walsh, B. (2023). AI Best Practice and DataOps. In C. J. Suresh, L. Berendson, & M. Powers (Eds.), Productionizing AI (pp. 41-74). Apress, Berkley, CA, USA. https://doi.org/10.1007/978-1-4842-8817-7_2
Warner, K. S. R., & Wäger, M. (2019). Building Dynamic Capabilities for Digital Transformation: An Ongoing Process of Strategic Renewal. Long Range Planning, 52(3), 326-349. https://doi.org/10.1016/j.lrp.2018.12.001
Watson, R. T., & Webster, J. (2020). Analysing the Past to Prepare for the Future: Writing a Literature Review a Roadmap for Release 2.0. Journal of Decision Systems, 29(3), 129-147. https://doi.org/10.1080/12460125.2020.1798591
Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 26(2), xiii-xxiii. https://www.jstor.org/stable/4132319
Wee, M., Scheepers, H., & Tian, X. (2022). Understanding the Processes of How Small and Medium Enterprises Derive Value from Business Intelligence and Analytics. Australasian Journal of Information Systems, 26, 1-26. https://doi.org/10.3127/ajis.v26i0.2969
Wells, D. (2019). Intelligent Data Operations: The Next Wave in Smart Data Ecosystems. https://www.eckerson.com/register?content=intelligent-data-operations-the-next-wave-in-smart-data-ecosystems . Accessed August 8, 2023
Wixom, B. H., Yen, B., & Relich, M. (2013). Maximizing Value from Business Analytics. MIS Quarterly Executive, 12(2), 111-123. https://aisel.aisnet.org/misqe/vol12/iss2/6
Xue, F., Zhao, X., Tan, Y., & Xie, L. (2022). Digital Transformation of Manufacturing Enterprises: An Empirical Study on the Relationships between Digital Transformation, Boundary Spanning, and Sustainable Competitive Advantage. Discrete Dynamics in Nature and Society, 2022, 1-16. https://doi.org/10.1155/2022/4104314
Yang, M., Wang, J., & Zhang, X. (2021). Boundary-Spanning Search and Sustainable Competitive Advantage: The Mediating Roles of Exploratory and Exploitative Innovations. Journal of Business Research, 127, 290-299.
https://doi.org/10.1016/j.jbusres.2021.01.032
Yoo, Y. (2010). Computing in Everyday Life: A Call for Research on Experiential Computing. MIS Quarterly, 34(2), 213-231. https://doi.org/10.2307/20721425
Zahid, H., Mahmood, T., & Ikram, N. (2018). Enhancing Dependability in Big Data Analytics Enterprise Pipelines. In International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage (pp. 272-281). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-05345-1_23
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