« Don’t judge a book by its cover» How Big Data changes decision processes of marketing managers. Global Marketing Conference 2018 Tokyo, 28 th July 2018 Christoph Wortmann/Peter M. Fischer/Sven Reinecke
Agenda of today’s presentation. 1 Problem definition 2 Theoretical background 3 Conceptual framework & hypotheses 4 Empirical findings 5 Contribution & Further research Page 2 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Big Data: A crucial issue in practice. Availability of new data sources (social media data or sensory data) improving decision making and firm performance (360 ° customer view) individual customer targeting (Barton & Court 2012). Recent research has found great potential for generating insights and better decision making (LaValle et al. 2011; McAfee & Brynjolfsson 2012) especially in stable environments with relatively little uncertainty (Gigerenzer 2014). Besides, it seems that the implementation of Big Data solutions positively affects firm performance (Mueller, Fay & vom Brocke 2018). Application of Big Data Important decisions are increasingly based on data insights 23% 35% 2016 40 40 18% 38 2015 37 24% 0 50 100 Big Data in use Big Data in planning fully agree rather agree Discussion of Big Data No Big Data source: Bitkom 2016 Page 3 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Literature on Big Data. 15 13 10 6 5 5 3 2 2 2 0 1 1 1 1 1 1 1 0 2015 2016 2017 Marketing Science Journal of Business Research Management Science Journal of Marketing Journal of Retailing source: www.scopus.com No substantial contribution in the four top-tier marketing outlets (JM, JMR, JCR and MS); exception: Marketing Science but no focus on managerial decision-making Page 4 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Agenda of today’s presentation. 1 Problem definition 2 Theoretical background 3 Conceptual framework & hypotheses 4 Empirical findings 5 Contribution & Further research Page 5 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Decision-making in marketing: Three different options. The Subjective Marketing (Traditional) Marketing Automated Marketing Decision Modeling Decision Modeling Decision Modeling Approach Approach Approach Management (Marketing) problem Management (Marketing) problem Management (Marketing) problem Model Model Managerial Judgment Managerial Judgment Decision Decision Decision BIG DATA ? e.g. Wübben & v. Wangenheim 2008 e.g. McAfee & Brynjolfsson 2012; Algorithm aversion Müller, Fay, & vom Brocke 2018 (Dietvorst, Simmons, & Massey 2014) vs. algorithm appreciation (Logg, Minson, & Moore 2018) Source: Lilien 2011 Page 6 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Decision-making properties depend on hierarchy level. Top-Management Lower-level Management • • Due to the postulated high superiority Lower level managers might perceive of Big Data , top managers might be facts and figures generated by Big Data inclined to use it defensive Analytics as an identity threat (Dalton motifs /playing safe/justification & Huang 2014) (Ashforth & Lee 1990) • Lower level managers have more time • Top managers have less time and and resources to critically investigate resources to critically investigate Big Big Data (Barton & Court 2012; Stone 2014) questioning of the “buzz word” Data (Barton & Court 2012; Stone 2014) Big Data Page 7 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Why do top-managers rely on Big Data? Two different and competing approaches … but the same outcome Big Data (existence in company) Prevention focus (Higgins 1997) Promotion focus (Higgins 1997) e.g. need for security; fulfilment of duties e.g. maximizing success; risk acceptance Defensive decision-making Non-defensive decision-making • • Defensive-decision making is In contrast to this, non-defensive characterized by risk aversion and decision-making is characterized joint decision-making (Ashforth & by egocentric behavior and risk Lee 1990) affinity Big Data: “playing safe/scapegoat” Big Data: Feeling invincible Reliance on Big Data Page 8 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Agenda of today’s presentation. 1 Problem definition 2 Theoretical background 3 Conceptual framework & hypotheses 4 Empirical findings 5 Contribution & Further research Page 9 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Study 1: Conceptual framework & hypotheses. Information source Practical experience Agreement with derived Market Research recommendations for action Big Data + + Hierarchy Hypotheses: H 1a/1b Marketing managers have a greater (lower) tendency to accept recommendations for action derived from Big Data compared to recommendations derived from market research or practical experience. H 2 Top-executives in marketing have a greater tendency to accept recommendations for action derived from Big Data than lower-level managers. Page 10 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Study 2: Conceptual framework & hypotheses. Perceived maturity of Situational Promotion Decision behavior Big Data in own + - Focus (degree of defensive and cautious organization decision-making) (Pham & Avnet 2004) (Germann et al. 2014) Only for top-level executives (CMO, CEO, Head of Sales) Hypotheses: H 3a Top-managers resort to Big Data, as it activates their prevention focus, thus making them more defensive and cautious in decision-making. H 3b Top-managers resort to Big Data, as it activates their promotion focus, thus making them less defensive and cautious in decision-making. Page 11 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Study 2: Conceptual framework & hypotheses. Perceived maturity of Situational Promotion Decision behavior Big Data in own + - Focus (degree of defensive and cautious organization decision-making) (Pham & Avnet 2004) (Germann et al. 2014) Only for top-level executives (CMO, CEO, Head of Sales) Hypotheses: H 3a Top-managers resort to Big Data, as it activates their prevention focus, thus making them more defensive and cautious in decision-making. H 3b Top-managers resort to Big Data, as it activates their promotion focus, thus making them less defensive and cautious in decision-making. Page 12 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Study 3: Conceptual framework & hypotheses. Big Data vs. Market Defensive and cautious - Research decision-making - Prevention focus activation Replication of the results found in Study 2 through experimentation and moderation Hypotheses: H 3a Top-managers resort to Big Data, as it activates their prevention focus, thus making them more defensive and cautious in decision-making. H 3b Top-managers resort to Big Data, as it activates their promotion focus, thus making them less defensive and cautious in decision-making. Page 13 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Study 4: Conceptual framework & hypotheses. Deactivation of lay belief - Situational Promotion Big Data vs. Defensive & cautious Focus Market Research decision-making + - (Pham & Avnet 2004) Hypothesis: H 4 Top-managers resort to Big Data, as it activates their prevention focus, thus making them more defensive and cautious in decision-making. Page 14 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Study 4: Conceptual framework & hypotheses. Deactivation of lay belief - Situational Promotion Big Data vs. Defensive & cautious Focus Market Research decision-making + - (Pham & Avnet 2004) Hypothesis: H 4 Top-managers resort to Big Data, as it activates their prevention focus, thus making them more defensive and cautious in decision-making. Page 15 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
Agenda of today’s presentation. 1 Problem definition 2 Theoretical background 3 Conceptual framework & hypotheses 4 Empirical findings 5 Contribution & Further research Page 16 Christoph Wortmann/Peter M. Fischer/Sven Reinecke | GMC 2018 | 28.07.2018
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