Leveraging Data Management Capabilities for Innovation Capabilities: The Moderating Role of Cross-Functional Integration
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2023.17.3.7Keywords:
cross-functional integration, data governance, data management, innovation capabilities, PLS-SEMAbstract
In today?s dynamic and competitive business environment, data are crucial for sustaining a competitive advantage. Organizations are also constantly seeking ways to enhance their innovation capabilities in order to stay ahead of the competition. One critical factor that has been identified as influential in enabling innovation are the organization?s data management capabilities. Past studies have found that cross-functional integration may enhance the impact of data management on innovation. Hence, this study aimed to investigate the influence of data management capabilities on explorative and exploitative innovation by considering the role of cross-functional integration as a moderating variable. This study used 116 data samples from medium and large companies across different industries in Indonesia. The PLS-SEM analysis was applied to test the research hypotheses. The results indicate that data management capabilities as a third-order construct, consisting of three dimensions, namely data governance, technology, and skills, have significant direct influences on explorative and exploitative innovation. This study demonstrated that cross-functional integration still plays an important role in amplifying the relationship between data management capabilities and innovation capabilities, especially in relation to explorative innovation.
Downloads
References
Yusr, M.M., Innovation Capability and its Role in Enhancing the Relationship between TQM Practices and Innovation Performance, Journal of Open Innovation: Technology, Market and Complexity, 2(1), pp. 1-15, 2016.
Shamim, S., Zeng, J., Shafi Choksy, U. & Shariq, S.M., Connecting Big Data Management Capabilities with Employee Ambidexterity in Chinese Multinational Enterprises Through the Mediation of Big Data Value Creation at the Employee Level, International Business Review, 29(6), p. 101604, 2020.
Lillie, T. & Eybers, S., Identifying the Constructs and Agile Capabilities of Data Governance & Data Management: A Review of the Literature, in Krauss, K. Turpin, M. & Naude, F. (Eds.), Locally Relevant ICT Research in Communications in Computer & Information Science, 933, Springer, pp. 313-326, 2019.
Gupta, M. & George, J.F., Toward the Development of a Big Data Analytics Capability, Information and Management, 53(8), pp. 1049-1064, 2016.
Troy, L.C., Hirunyawipada, T. & Paswan, A.K., Cross-Functional Integration and New Product Success: An Empirical Investigation of the Findings, Journal of Marketing, 72(6), pp. 132-146, 2008.
Pez-Lu, A., Bojica, A.M. & Golapakrishnan, S., When More is Less: The Role of Cross-Functional Integration, Knowledge Complexity & Product Innovation in Firm Performance, IJOPM, 39(1), pp. 94-115, 2019.
Rubera, G., Ordanini, A., & R. Calantone, Whether to Integrate R&D and Marketing: The Effect of Firm Competence, J of Product Innov Manag, 29(5), pp. 766-783, 2012.
Majchrzak, A., More, P.H.B. & Faraj, S., Transcending Knowledge Differences in Cross-Functional Teams, Organization Science, 23(4), pp. 951-970, 2012.
Lin, Y., Wang, Y. & Kung, L., Influences of Cross-Functional Collaboration and Knowledge Creation on Technology Commercialization: Evidence from High-Tech Industries, Industrial Marketing Management, 49, pp. 128-138, 2015.
Yang, S.Y. & Tsai, K.H., Lifting the Veil on the Link between Absorptive Capacity & Innovation: The Roles of Cross-Functional Integration and Customer Orientation, Industrial Marketing Management, 82, pp. 117-130, 2019.
Foss, N.J., Laursen, K. & Pedersen, T., Linking Customer Interaction and Innovation: The Mediating Role of New Organizational Practices, Organization Science, 22(4), pp. 980-999, 2011.
Calantone, R. & Rubera, G., When Should RD & E and Marketing Collaborate? The Moderating Role of Exploration-Exploitation and Environmental Uncertainty, J of Product Innov Manag, 29(1), pp. 144-157, 2012.
Brettel, M., Heinemann, F., Engelen, A. & Neubauer, S., Cross?Functional Integration of R&D, Marketing and Manufacturing in Radical and Incremental Product Innovations and Its Effects on Project Effectiveness and Efficiency, Journal of Product Innovation Management, 28(2), pp. 251-269, 2011.
AlNuaimi, B.K., Khan, M. & Ajmal, M.M., The Role of Big Data Analytics Capabilities in Greening E-Procurement: A Higher Order PLS-SEM Analysis, Technological Forecasting & Social Change, 169, 120808, 2021.
Sarstedt, M., Hair, J.F., Cheah, J.H., Becker, J.M. & Ringle, C.M., How to Specify, Estimate and Validate Higher-Order Constructs in PLS-SEM, Australasian Marketing Journal, 27(3), pp. 197-211, 2019.
Nisar, Q.A., Nasir, N., Jamshed, S., Naz, S., Ali, M. & Ali, S., Big Data Management and Environmental Performance: Role of Big Data Decision-Making Capabilities and Decision-Making Quality, JEIM, 34(4), pp. 1061-1096, 2021.
McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D. & Barton, D., Big Data: The Management Revolution, Harvard Business Review, 90(10), pp. 60-68, 2012.
Janssen, M., Van Der Voort, H. & Wahyudi, A., Factors Influencing Big Data Decision-Making Quality, Journal of Business Research, 70, pp. 338-345, 2017.
Shams, S.M.R. & Solima, L., Big Data Management: Implications of Dynamic Capabilities and Data Incubator, MD, 57(8), pp. 2113-2123, 2019.
Hartman, T., Kennedy, H., Steedman, R. & Jones, R., Public Perceptions of Good Data Management: Findings from a UK-Based Survey, Big Data & Society, 7(1), 205395172093561, 2020.
Briney, K., Coates, H. & Goben, A., Foundational Practices of Research Data Management, RIO, 6, e56508, 2020.
Wang, X., Williams, C., Liu, Z.H. & Croghan, J., Big Data Management Challenges In Health Research a Literature Review, Briefings in Bioinformatics, 20(1), pp. 156-167, 2019.
Liu, G., Zotoo, I.K. & Su, W., Research Data Management Policies in USA, UK and Australia Universities: An Online Survey, Malaysian Journal of Library & Information Science, 25(2), pp. 21-42, 2020.
Baro, E., Degoul, S., Beuscart, R. & Chazard, E., Toward a Literature-Driven Definition of Big Data in Healthcare, BioMed Research International, pp. 1-9, 2015.
Brous, P., Janssen, M. & Krans, R., Data Governance as Success Factor for Data Science, in Hattingh, M., Matthee, M., Smuts, H., Pappas, I., Dwivedi, Y.K. & Mtymi, M., Eds., Responsible Design, Implementation & Use of Information and Communication Technology, in Lecture Notes in Computer Science, 12066, pp. 431-442, 2020.
Barney, J.B., The Resource based View of Strategy: Origins, Implications, and Prospects, Journal of management, 17(1), pp. 97-211, 1991.
Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R. & Childe, S.J., How to Improve Firm Performance using Big Data Analytics Capability and Business Strategy Alignment?, International Journal of Production Economics, 182, pp. 113-131, 2016.
Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J., Dubey, R. & Childe, S.J., Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities, Journal of Business Research, 70, pp. 356-365, 2017.
Khatri, V. & Brown, C.V., Designing Data Governance, Commun. ACM, 53(1), pp. 148-152, 2010.
Otto, B., Organizing Data Governance: Findings from the Telecommunications Industry and Consequences for Large Service Providers, Communications of the Association for Information Systems, 29(1), 3, 2011.
Kroll, J.A., Data Science Data Governance [AI Ethics], IEEE Secur. Privacy, 16(6), pp. 61-70, 2018
Weber, K., Otto, B. & terle, H., One Size Does Not Fit All-a Contingency Approach to Data Governance, J. Data and Information Quality, 1(1), pp. 1-27, 2009.
Minbaeva, D.B., Building Credible Human Capital Analytics for Organizational Competitive Advantage, Human Resource Management, 57(3), pp. 701-713, 2018.
Brinch, M., Gunasekaran, A. & Fosso Wamba, S., Firm-Level Capabilities Towards Big Data Value Creation, Journal of Business Research, 131, pp. 539-548, 2021.
Cosic, R., Shanks, G. & Maynard, S.B., A Business Analytics Capability Framework, AJIS, 19, pp. S5-S19, 2015.
Lu, J., Cairns, L. & Smith, L., Data Science in the Business Environment: Customer Analytics Case Studies in SMEs, JM2, 16(2), pp. 689-713, 2021.
Xu, L., Zhang, J., Ding, Y., Sun, G., Zhang, W., Philbin, S. P., & Guo, B. H., Assessing the Impact of Digital Education and the Role of the Big Data Analytics Course to Enhance the Skills and Employability of Engineering Students, Front. Psychol., 13, 974574, 2022.
Kurtzke S. & Setkute, J., Analytics Capability in Marketing Education: A Practice-Informed Model, Journal of Marketing Education, 43(3), pp. 298-316, 2021.
Korsten, G., Aysolmaz, B., Turetken, O., Edel, D. & Ozkan, B., ADA-CMM: A Capability Maturity Model for Advanced Data Analytics, presented at the Hawaii International Conference on System Sciences, 2022.
Hornick, M., A Data Science Maturity Model for Enterprise Assessment, USA: Oracle, 2020.
Limpeeticharoenchot, S., Cooharojananone, N., Chavarnakul, T., Charoenruk, N. & Atchariyachanvanich, K., Adaptive Big Data Maturity Model Using Latent Class Analysis for Small and Medium Businesses in Thailand, Expert Systems with Applications, 206, 117965, 2022.
Hassan, C.A.U., Irfan, R. & Shah, M.A., Integrated Architecture of Data Warehouse with Business Intelligence Technologies, presented at the 2018 24th International Conference on Automation & Computing (ICAC), IEEE, 2018, pp. 1-6, 2018.
Ghasemaghaei, M., Ebrahimi, S. & Hassanein, K., Data Analytics Competency for Improving Firm Decision Making Performance, The Journal of Strategic Information Systems, 27(1), pp. 101-113, 2018.
Galp, M.O., Galp, E., Kayabay, K., Koi?it, A. & Eren, P.E., Data-Driven Manufacturing: An Assessment Model for Data Science Maturity, Journal of Manufacturing Systems, 60, pp. 527-546, 2021.
Halaweh, M. & Massry, A.E., Conceptual Model for Successful Implementation of Big Data in Organizations, Journal of International Technology & Information Management, 24(2), 2, 2015.
Davenport, T., DELTA Plus Model and Five Stages of Analytics Maturity: A Primer, Portland, United States: International Institute for Analytics, 2018.
Bryan Jean, R.J., Kim, D. Chiou, J.S. & Calantone, R., Strategic Orientations, Joint Learning and Innovation Generation in International Customer-Supplier Relationships, International Business Review, 27(4), pp. 838-851, 2018.
Hogan, S.J., Soutar, G.N., McColl-Kennedy, J.R. & Sweeney, J.C., Reconceptualizing Professional Service Firm Innovation Capability: Scale Development, Industrial Marketing Management, 40(8), pp. 1264-1273, 2011.
Levinthal D.A. & March, J.G., The Myopia of Learning, Strategic Management Journal, 14, pp. 95-112, 1993.
Sinha, S., Managing an Ambidextrous Organization: Balancing Innovation and Efficiency, Strategic Direction, 32(1), pp. 35-37, 2016.
Jansen, J.J.P., Van Den Bosch, F.A.J. & Volberda, H.W., Exploratory Innovation, Exploitative Innovation and Performance: Effects of Organizational Antecedents & Environmental Moderators, Management Science, 52(11), pp. 1661-1674, 2006.
Rosemann, M., Proposals for Future BPM Research Directions, in Asia Pacific Business Process Management, pp. 1-15, 2014.
Pavlou, P.A. & El Sawy, O.A., Understanding the Elusive Black Box of Dynamic Capabilities, Decision Sciences, 42(1), pp. 239-273, 2011.
Khazanchi D. & Yadav, S.B., Data Management a New Approach to Problem Definition using Information Objects, Information Systems Management, 12(2), pp. 21-26, 1995.
Zotoo, I.K., Lu, Z. & Liu, G., Big Data Management Capabilities and Librarians? Innovative Performance: The Role of Value Perception using The Theory of Knowledge-Based Dynamic Capability, The Journal of Academic Librarianship, 47(2), 102272, 2021.
Wu, L., Lou, B. & Hitt, L., Data Analytics Supports Decentralized Innovation, Management Science, 65(1), pp. 4863-4877, 2019.
Khan, Z., Rao-Nicholson, R. & Tarba, S.Y., Global Networks as a Mode of Balance for Exploratory Innovations in a Late Liberalizing Economy, Journal of World Business, 53(3), pp. 392-402, 2018.
Su, Z., Chen, J. & Wang, D., Organisational Structure and Managerial Innovation: The Mediating Effect of Cross-Functional Integration, Technology Analysis & Strategic Management, 31(3), pp. 253-265, 2019.
Hirunyawipada, T., Beyerlein, M. & Blankson, C., Cross-Functional Integration as a Knowledge Transformation Mechanism: Implications for New Product Development, Industrial Marketing Management, 39(4), pp. 650-660, 2010.
Argote L. & Miron-Spektor, E., Organizational Learning: from Experience to Knowledge, Organization Science, 22(5), pp. 1123-1137, 2011.
Ernst, H., Hoyer, W.D. & Rsaamen, C., Sales, Marketing, and Research and Development Cooperation Across New Product Development Stages: Implications for Success, Journal of Marketing, 74(5), pp. 80-92, 2010.
Liao, S., Hu, Q. & Wei, J., How to Leverage Big Data Analytic Capabilities for Innovation Ambidexterity: A Mediated Moderation Model, Sustainability, 15(5), 3948, 2023.
Paul, M., Maglaras, L., Ferrag, M.A. & Almomani, I., Digitization of Healthcare Sector: A Study on Privacy and Security Concerns, ICT Express, p. S2405959523000243, 2023.
Wang, R.Y. & Strong, D.M., Beyond Accuracy: What Data Quality Means to Data Consumers, Journal of Management Information Systems, 12(4), pp. 5-33, 1996.
Matin, H.Z., Jandaghi, G., Karimi, F.H. & Hamidizadeh, A. (2010). Relationship between Interpersonal Communication Skills and Organizational Commitment (Case Study: Jahad Keshavarzi and University of Qom, Iran), European Journal of Social Sciences, 13(3), pp. 387-398.
Power, D.J., Data Science: Supporting Decision-Making, Journal of Decision Systems, 25(4), pp. 345-356, 2016.
Tippins M.J. & Sohi, R.S., IT Competency and Firm Performance: Is Organizational Learning a Missing Link?, Strat. Mgmt. J., 24(8), pp. 745-761, 2003.
Huang, S.M., Ou, C.S., Chen, C.M. & Lin, B., An Empirical Study of Relationship Between IT Investment and Firm Performance: A Resource-Based Perspective, European Journal of Operational Research, 173(3), pp. 984-999, 2006.
Hair, J.F., Ringle, C.M. & Sarstedt, M., PLS-SEM: Indeed a Silver Bullet, Journal of Marketing Theory & Practice, 19(2), pp. 139-152, 2011.
Chin, W.W., In the Partial Least Squares Approach for Structural Equation Modeling, Lawrence Erlbaum Associates, 1998.
Malhorta N.K. & Birks, D.F., Marketing Research: An Applied Approach, Harlow: Prentice Hall, 2006.
Hair, J.F., Risher, J.J., Sarstedt, M. & Ringle, C.M., When to Use and How to Report the Results of PLS-SEM, EBR, 31(1), pp. 2-24, 2019.
Henseler, J., Ringle, C.M. & Sarstedt, M., A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling, J. of the Acad. Mark. Sci., 43(1), pp. 115-135, 2015.
Gomes, J.F., de Weerd-Nederhof, P.C., Pearson, A.W. & Cunha, M.P., Is More Always Better? An Exploration of the Differential Effects of Functional Integration on Performance in New Product Development, Technovation, 23(3), pp. 185-191, 2003.
De Visser, M., de Weerd-Nederhof, P., Faems, D., Song, M., Van Looy, B. & Visscher, K., Structural Ambidexterity in NPD Processes: A Firm-Level Assessment of the Impact of Differentiated Structures on Innovation Performance, Technovation, 30(5-6), pp. 291-299, 2010.
Jugend, D., Araujo, T.R.D., Pimenta, M.L., Gobbo, J.A. & Hilletofth, P., The Role of Cross-Functional Integration in New Product Development: Differences between Incremental and Radical Innovation Projects, Innovation, 20(1) pp. 42-60, 2018.
 
						











