Data Analytics and Knowledge Integration Mechanisms: The Role of Social Interactions in Innovation Management

In a firm, which is viewed as a distributed knowledge system, the role of knowledge integration mechanisms is critical. In the context of data analytics, data mining and statistical analysis enables firms to generate knowledge; which, however, needs to be channeled to the end user of this knowledge. In this study, based on the social capital literature we argue that social interactions between IT and marketing functional unit members facilitate knowledge sharing in intraorganizational setting, which in turn results in improved innovative performance. The theoretical arguments are supported by empirical results collected via an online survey. Theoretical and practical contributions of the study are also discussed.

[1]  Detmar W. Straub,et al.  Specifying Formative Constructs in Information Systems Research , 2007, MIS Q..

[2]  Mary J. Benner,et al.  Process Management and Technological Innovation: A Longitudinal Study of the Photography and Paint Industries , 2002 .

[3]  Wenpin Tsai.  Social capital, strategic relatedness and the formation of intraorganizational linkages , 2000 .

[4]  S. Ghoshal,et al.  Social Capital, Intellectual Capital, and the Organizational Advantage , 1998 .

[5]  Dorothy E. Leidner,et al.  Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues , 2001, MIS Q..

[6]  Jason Bennett Thatcher,et al.  Six types of IT-business strategic alignment: an investigation of the constructs and their measurement , 2015, Eur. J. Inf. Syst..

[7]  Gianmario Verona,et al.  Unbundling Dynamic Capabilities: an Exploratory Study of Continuous Product Innovation , 2003 .

[8]  K. Atuahene–Gima,et al.  Market Knowledge Dimensions and Cross-Functional Collaboration: Examining the Different Routes to Product Innovation Performance , 2007 .

[9]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[10]  H. Harman.  Modern factor analysis , 1961 .

[11]  Varun Grover,et al.  Leveraging Information Technology Infrastructure to Facilitate a Firm's Customer Agility and Competitive Activity: An Empirical Investigation , 2012, J. Manag. Inf. Syst..

[12]  R. Grant.  Toward a Knowledge-Based Theory of the Firm,” Strategic Management Journal (17), pp. , 1996 .

[13]  Kai H. Lim,et al.  User Satisfaction with Information Technology Service Delivery: A Social Capital Perspective , 2012, Inf. Syst. Res..

[14]  Gilbert A. Churchill.  A Paradigm for Developing Better Measures of Marketing Constructs , 1979 .

[15]  Rex Du,et al.  Leveraging Trends in Online Searches for Product Features in Market Response Modeling , 2015 .

[16]  H. Tsoukas.  The firm as a distributed knowledge system : A constructionist approach , 1996 .

[17]  R. Grant.  Chapter 8 – Prospering in Dynamically-Competitive Environments: Organizational Capability as Knowledge Integration , 1999 .

[18]  Alex Pentland,et al.  Big Data and Management , 2014 .

[19]  Detmar W. Straub,et al.  An Update and Extension to SEM Guidelines for Admnistrative and Social Science Research , 2011 .

[20]  S. Ghoshal,et al.  Social Capital and Value Creation: The Role of Intrafirm Networks , 1998 .

[21]  Jerry N. Luftman,et al.  Key information technology and management issues 2011–2012: an international study , 2012, J. Inf. Technol..

[22]  Robert D. Galliers,et al.  Towards an understanding of the role of business intelligence systems in organisational knowing , 2016, Inf. Syst. J..

[23]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[24]  Vijay Khatri,et al.  Business analytics: Why now and what next? , 2014 .

[25]  Sunil Erevelles,et al.  Big Data consumer analytics and the transformation of marketing , 2016 .

[26]  Qing Chang,et al.  How Low Should You Go? Low Response Rates and the Validity of Inference in IS Questionnaire Research , 2006, J. Assoc. Inf. Syst..

[27]  I. Yeoman.  Competing on analytics: The new science of winning , 2009 .

[28]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[29]  D. Dougherty.  Interpretive Barriers to Successful Product Innovation in Large Firms , 1992 .

[30]  Surajit Chaudhuri,et al.  An overview of business intelligence technology , 2011, Commun. ACM.

[31]  Rajeev Sharma,et al.  Transforming Decision-Making Processes Transforming decision-making processes : a research agenda for understanding the impact of business analytics on organizations , 2017 .

[32]  Jan vom Brocke,et al.  Utilizing big data analytics for information systems research: challenges, promises and guidelines , 2016, Eur. J. Inf. Syst..

[33]  Morten T. Hansen,et al.  Knowledge Transfer in Intraorganizational Networks : Effects of Network Position and Absorptive Capacity on Business Unit Innovation and Performance , 2007 .

[34]  Kevin Zheng Zhou,et al.  How knowledge affects radical innovation: Knowledge base, market knowledge acquisition, and internal knowledge sharing , 2012 .

[35]  Jinghua Xiao,et al.  Resource Structuring or Capability Building? An Empirical Study of the Business Value of Information Technology , 2012, J. Manag. Inf. Syst..

[36]  Vasant Dhar,et al.  Data science and prediction , 2012, CACM.

[37]  Aviv Nevo,et al.  Why Do Manufacturers Issue Coupons? An Empirical Analysis of Breakfast Cereals , 2002 .

[38]  M. Tushman,et al.  Ambidexterity as a Dynamic Capability: Resolving the Innovator's Dilemma , 2007 .

[39]  G. Tellis,et al.  Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation , 2014 .

[40]  Mary J. Benner,et al.  Exploitation, Exploration, and Process Management: The Productivity Dilemma Revisited , 2003 .

[41]  V. Grover,et al.  A process-oriented perspective on differential business value creation by information technology: An empirical investigation , 2008 .

[42]  Jerry N. Luftman,et al.  Key information technology and management issues 2012–2013: an international study , 2011, J. Inf. Technol..

[43]  Vernon J. Richardson,et al.  Returns on Investments in Information Technology: A Research Synthesis , 2002, J. Inf. Syst..

[44]  Varun Grover,et al.  Types of Information Technology Capabilities and Their Role in Competitive Advantage: An Empirical Study , 2005, J. Manag. Inf. Syst..

[45]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[46]  Gianmario Verona.  A Resource-Based View of Product Development , 1999 .

[47]  Kalle Lyytinen,et al.  Research Commentary - The New Organizing Logic of Digital Innovation: An Agenda for Information Systems Research , 2010, Inf. Syst. Res..

[48]  Saggi Nevo,et al.  The Formation and Value of IT-Enabled Resources: Antecedents and Consequences of Synergistic Relationships , 2010, MIS Q..

[49]  Kenneth L. Kraemer,et al.  Review: Information Technology and Organizational Performance: An Integrative Model of IT Business Value , 2004, MIS Q..

[50]  Youngjin Yoo,et al.  It is not about size: a further thought on big data , 2015, J. Inf. Technol..

[51]  Paulo B. Góes,et al.  Editor's comments: big data and IS research , 2014 .

[52]  P. M. Podsakoff,et al.  Self-Reports in Organizational Research: Problems and Prospects , 1986 .

[53]  A. Bharadwaj,et al.  IT Capabilities : Theoretical Perspectives and Empirical Operationalization , 2013 .

[54]  Gary L. Frankwick,et al.  Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective , 2016 .

[55]  Daniel A. Levinthal,et al.  Exploration and Exploitation in Organizational Learning , 2007 .

[56]  Saggi Nevo,et al.  An examination of the trade-off between internal and external IT capabilities , 2007, J. Strateg. Inf. Syst..

[57]  Prasanna Tambe.  Big Data Investment, Skills, and Firm Value , 2014, Manag. Sci..

[58]  Thomas H. Davenport,et al.  Analytics at Work: Smarter Decisions, Better Results , 2010 .

[59]  Vasant Dhar,et al.  Editorial - Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research , 2014, Inf. Syst. Res..

[60]  J. Hair.  Multivariate data analysis , 1972 .

[61]  Henk W. Volberda,et al.  Exploratory Innovation, Exploitative Innovation and Peformance: Effects of Organizational Antecedents and Environmental Moderators , 2006, Manag. Sci..

[62]  Robert W. Zmud,et al.  Research Commentary: The Organizing Logic for an Enterprise's IT Activities in the Digital Era - A Prognosis of Practice and a Call for Research , 2000, Inf. Syst. Res..