Barriers to the Adoption of Big Data Analytics in the Automotive Sector

Big data analytics as source of competitive advantages is pivoting the automotive sector. Today’s original equipment manufacturers, however, are confronted by several barriers to the successful adoption of big data analytics. First, the cross-disciplinary nature of big data analytics requires (1) sufficient and skilled resources, (2) the collaboration of different business departments, supported by (3) appropriate organizational structures, (4) a data-driven culture, and (5) a defined business value, and (6) access to relevant data pools to achieve commitment and relevance within the organization. Drawing on the results of a revelatory case study as well as on the socio-technical systems theory I have identified six barriers to the adoption of big data analytics. From an academic perspective, these barriers contribute to the current body of knowledge of the adoption of big data analytics. For practice, they provide guidance for firms in the automotive sector as well as other traditionally goods-dominant industries which barriers need to be tackled to leverage the business value of big data analytics.

[1]  Ken Kennedy,et al.  Automotive big data: Applications, workloads and infrastructures , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[2]  A. Strauss,et al.  Grounded theory , 2017 .

[3]  Benjamin T. Hazen,et al.  Evaluating Adoption of Emerging IT for Corporate IT Strategy: Developing a Model Using a Qualitative Method , 2013, Inf. Syst. Manag..

[4]  Kecheng Liu,et al.  Big Data Architecture for Pervasive Healthcare: A Literature Review , 2015, ECIS.

[5]  Stephanie L Woerner,et al.  Big data: extending the business strategy toolbox , 2015, J. Inf. Technol..

[6]  Marlena J. Gaul.  Big Data at Work: Dispelling the Myths, Uncovering the Opportunities , 2014 .

[7]  Tina Blegind Jensen,et al.  Reframing Open Big Data , 2013, ECIS.

[8]  Sebastian Olbrich,et al.  Madness of the Crowd - How Big Data Creates Emotional Markets and What can be Done to Control Behavioural Risk , 2014, ECIS.

[9]  Dawei Wang,et al.  Whose Talk is Walked? IT Decentralizability, Vendor versus Adopter Discourse, and the Diffusion of Social Media versus Big Data , 2015, ICIS.

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

[11]  Morten Fjeld,et al.  Volvo Single View of Vehicle: Building a Big Data Service from Scratch in the Automotive Industry , 2015, CHI Extended Abstracts.

[12]  Michael D. Myers,et al.  A Set of Principles for Conducting and Evaluating Interpretive Field Studies in Information Systems , 1999, MIS Q..

[13]  M. Porter,et al.  How Smart, Connected Products Are Transforming Companies , 2015 .

[14]  Lars Mathiassen,et al.  Embracing Digital Innovation in Incumbent Firms: How Volvo Cars Managed Competing Concerns , 2017, MIS Q..

[15]  Jochen Wulf,et al.  Towards a Capability Model for Big Data Analytics , 2017, Wirtschaftsinformatik.

[16]  Robert P. Bostrom,et al.  A Meta-Theory for Understanding Information Systems Within Sociotechnical Systems , 2009, J. Manag. Inf. Syst..

[17]  Izak Benbasat,et al.  The Case Research Strategy in Studies of Information Systems , 1987, MIS Q..

[18]  Roger H. L. Chiang,et al.  Big Data Research in Information Systems: Toward an Inclusive Research Agenda , 2016, J. Assoc. Inf. Syst..

[19]  Kalyan Agrawal,et al.  Investigating the determinants of Big Data Analytics (BDA) adoption in Asian emerging economies , 2015, AMCIS.

[20]  K. Eisenhardt.  Building theories from case study research , 1989, STUDI ORGANIZZATIVI.

[21]  Anne Fleur van Veenstra,et al.  Modes of Governance in Inter-Organizational Data Collaborations , 2015, ECIS.

[22]  Wil M.P. van der Aalst,et al.  "Mine your own business" : using process mining to turn big data into real value , 2013, ECIS 2013.

[23]  Sebastian Olbrich,et al.  Increasing the Level of Customer Orientation - A Big Data Case Study from Insurance Industry , 2015, ECIS.

[24]  Kalle Lyytinen,et al.  Explaining information systems change: a punctuated socio-technical change model , 2008, Eur. J. Inf. Syst..

[25]  M. Patton.  Qualitative Research & Evaluation Methods: Integrating Theory and Practice , 2014 .

[26]  M. Taisch,et al.  The value of Big Data in servitization , 2015 .

[27]  Guangming Cao,et al.  Understanding the Impact of Business Analytics on Innovation , 2020, ECIS.

[28]  Ioanna D. Constantiou,et al.  New games, new rules: big data and the changing context of strategy , 2015, J. Inf. Technol..

[29]  Michel Avital,et al.  Designing interviews to generate rich data for information systems research , 2011, Inf. Organ..

[30]  Lech J. Janczewski,et al.  SEC-TOE Framework: Exploring Security Determinants in Big Data Solutions Adoption , 2015, PACIS.

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

[32]  Suprateek Sarker,et al.  Guest editorial: qualitative studies in information systems: a critical review and some guiding principles , 2013 .

[33]  Ling Liu,et al.  Adoption of Big Data Analytics in Healthcare: The Efficiency and Privacy , 2015, PACIS.

[34]  William H. Dutton,et al.  Clouds, big data, and smart assets: Ten tech-enabled business trends to watch , 2010 .

[35]  Adam Jacobs,et al.  The pathologies of big data , 2009, Commun. ACM.

[36]  Bart Baesens,et al.  Call for Papers MISQ Special Issue on Transformational Issues of Big Data and Analytics in Networked Business , 2014 .

[37]  Wanda J. Orlikowski,et al.  Research Commentary: Desperately Seeking the "IT" in IT Research - A Call to Theorizing the IT Artifact , 2001, Inf. Syst. Res..

[38]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[39]  Robert P. Bostrom,et al.  Mis problems and failures: a socio-technical perspective , 1977 .

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

[41]  Rick Kazman,et al.  Demystifying Big Data Adoption: Beyond IT Fashion and Relative Advantage , 2015 .

[42]  Christopher G. Reddick,et al.  Capability Challenges in Transforming Government through Open and Big Data: Tales of Two Cities , 2015, ICIS.

[43]  Nicolaus Henke,et al.  The age of analytics: competing in a data-driven world , 2016 .

[44]  Jan vom Brocke,et al.  Comparing Business Intelligence and Big Data Skills , 2014, Business & Information Systems Engineering.

[45]  Jeffrey S. Saltz,et al.  The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[46]  Liisa von Hellens,et al.  Qualitative Research in Information Systems , 2007, Australas. J. Inf. Syst..

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

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

[49]  Thi Trieu,et al.  Extending the Theory of Effective Use: the Impact of Enterprise Architecture Maturity stages on the Effective Use of Business Intelligence Systems , 2013, ICIS.

[50]  Michael J. Shaw,et al.  The Impact of Business Analytics Strategy on Social, Mobile, and Cloud Computing Adoption , 2014, ICIS.

[51]  Mathias Johanson,et al.  Big Automotive Data: Leveraging large volumes of data for knowledge-driven product development , 2014, 2014 IEEE International Conference on Big Data (Big Data).