Abstract
Big data technologies and analytics enable new digital services and are often associated with superior performance. However, firms investing in big data often fail to attain those advantages. To answer the questions of how and when big data pay off, marketing scholars need new theoretical approaches and empirical tools that account for the digitized world. Building on affordance theory, the authors develop a novel, conceptually rigorous, and practice-oriented framework of the impact of big data investments on service innovation and performance. Affordances represent action possibilities, namely what individuals or organizations with certain goals and capabilities can do with a technology. The authors conceptualize and operationalize three important big data marketing affordances: customer behavior pattern spotting, real-time market responsiveness, and data-driven market ambidexterity. The empirical analysis establishes construct validity and offers a preliminary nomological test of direct, indirect, and conditional effects of big data marketing affordances on perceived big data performance.
Highlights
Big data technologies and analytics enable new digital services and are often associated with superior performance
Results from Study 2 provide a further validation of the three scales and generally support our expectations regarding the chain of relationships connecting big data investments to perceived big data performance, via big data marketing affordances and service innovation
We show that service innovation mediates between real-time market responsiveness and data-driven market ambidexterity and perceived big data performance; in contrast, customer behavior pattern spotting is directly related to perceived big data performance
Summary
Big data technologies and analytics enable new digital services and are often associated with superior performance. Firms are investing significant resources into big data technologies and analytics (BDTA), following the assumption that they may drive superior performance (Lambrecht and Tucker 2015), enable business transformation (Davenport and Bean 2019), and facilitate disruptive business model innovations (Sorescu 2017) This is evident in the service industry, where BDTA are changing the nature of the customer–firm connection, thereby disrupting existing value propositions (Huang and Rust 2017). We conceptualize and operationalize three distinct big data marketing affordances, defined as specific marketing actions enabled by investments into BDTA These are customer behavior pattern spotting, real-time market responsiveness, and data-driven market ambidexterity. We add to research on the relationship between big data investments and performance by simultaneously accounting for big data marketing affordances and service innovation, against previous literature that focused on the direct effects of big data investments (Wamba et al 2017), and identify industry digitalization as a boundary condition for the effect of big data investments on perceived big data performance
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