The Configurational Impact of Top Management Team Characteristics on Marketing Analytics Use in SME: A Fuzzy-Set Qualitative Comparative Analysis

Top management team might make primary usage decisions related to marketing analytics. To date, extant research has mostly focused on investigating the impact of marketing analytics on firm performance; limited research exists to examine the conditions of utilizing marketing analytics. Furthermore, little is known about how the combinations of conditions affect marketing analytics use. Drawing on upper echelons and configuration theories, this study proposes that small and medium-sized enterprises (SMEs) have alternative pathways to utilizing marketing analytics. Based on a sample of 187 managers from UK SMEs and employing fuzzy set qualitative comparative analysis (fsQCA), this study confirms that (1) configurations of antecedents exist to provide alternative pathways to utilizing marketing analytics, and (2) configurations for small firms are distinctively different from those for medium-sized firms. This study contributes to upper echelon theory and configuration theory by highlighting different pathways to marketing analytics use. This study also helps a firm to improve its analytics practice by choosing the configuration that fits best with its organizational context.

[1]  Morgan Swink,et al.  How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management , 2015, J. Manag. Inf. Syst..

[2]  A. Rangaswamy,et al.  Performance implications of deploying marketing analytics , 2013 .

[3]  Abdul A. Rasheed,et al.  Configuration Research in Strategic Management: Key Issues and Suggestions , 1993 .

[4]  Van-Hau Trieu,et al.  Getting value from Business Intelligence systems: A review and research agenda , 2017, Decis. Support Syst..

[5]  A. Woodside Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory , 2013 .

[6]  Omar El Sawy,et al.  The Role of Business Intelligence and Communication Technologies in Organizational Agility: A Configurational Approach , 2017, Journal of the Association for Information Systems.

[7]  Tiago Oliveira,et al.  Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors , 2014, Inf. Manag..

[8]  Petri Hallikainen,et al.  Enterprise size matters: objectives and constraints of ERP adoption , 2007, J. Enterp. Inf. Manag..

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

[10]  Rajiv Sabherwal,et al.  Antecedents and Consequences of Information Systems Planning Integration , 2007, IEEE Transactions on Engineering Management.

[11]  A. Parasuraman,et al.  When the Recipe Is More Important Than the Ingredients , 2014 .

[12]  Clyde W. Holsapple,et al.  A unified foundation for business analytics , 2014, Decis. Support Syst..

[13]  Emilio Paolucci,et al.  Is it all about size? Comparing organisational and environmental antecedents of IT assimilation in small and medium-sized enterprises , 2013, Int. J. Technol. Manag..

[14]  Qing Hu,et al.  Assimilation of Enterprise Systems: The Effect of Institutional Pressures and the Mediating Role of Top Management , 2007, MIS Q..

[15]  Chee-Sing Yap,et al.  Top Management Support, External Expertise and Information Systems Implementation in Small Businesses , 1996, Inf. Syst. Res..

[16]  Paul Blackwell,et al.  An effective decision-support framework for implementing enterprise information systems within SMEs , 2006 .

[17]  James Y. L. Thong,et al.  An Integrated Model of Information Systems Adoption in Small Businesses , 1999, J. Manag. Inf. Syst..

[18]  Scott B. MacKenzie,et al.  Working memory: theories, models, and controversies. , 2012, Annual review of psychology.

[19]  Benjamin T. Hazen,et al.  Big data and predictive analytics for supply chain and organizational performance , 2017 .

[20]  M. Wedel,et al.  Marketing Analytics for Data-Rich Environments , 2016 .

[21]  D. Hambrick Upper Echelons Theory: An Update , 2007 .

[22]  D. Hambrick,et al.  Upper Echelons: The Organization as a Reflection of Its Top Managers , 1984 .

[23]  Vassilis Kostakos,et al.  Applying configurational analysis to IS behavioural research: a methodological alternative for modelling combinatorial complexities , 2017, Inf. Syst. J..

[24]  Franz W. Kellermanns,et al.  The longitudinal impact of enterprise system users’ pre-adoption expectations and organizational support on post-adoption proficient usage , 2014, EJIS.

[25]  Peer C. Fiss A set-theoretic approach to organizational configurations , 2007 .

[26]  Vallabh Sambamurthy,et al.  Sources of Influence on Beliefs about Information Technolgoy Use: An Empirical Study of Knowledge Workers , 2003, MIS Q..

[27]  Izak Benbasat,et al.  Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology , 1995, MIS Q..

[28]  Joey F. George,et al.  Toward the development of a big data analytics capability , 2016, Inf. Manag..

[29]  S. Henneberg,et al.  Different recipes for success in business relationships , 2017 .

[30]  Sunil Mithas,et al.  Business Analytics: Radical Shift or Incremental Change? , 2012, Commun. Assoc. Inf. Syst..