De Luca, Luigi M. ORCID: https://orcid.org/0000-0002-2492-3075, Herhausen, Dennis, Troilo, Gabriele and Rossi, Andrea 2021. How and when do big data investments pay off? The role of marketing affordances and service innovation. Journal of the Academy of Marketing Science 49 , pp. 790-810. 10.1007/s11747-020-00739-x |
Preview |
PDF
- Published Version
Available under License Creative Commons Attribution. Download (602kB) | Preview |
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.
Item Type: | Article |
---|---|
Date Type: | Publication |
Status: | Published |
Schools: | Business (Including Economics) |
Additional Information: | This article is licensed under a Creative Commons Attribution 4.0 International License. |
Publisher: | SAGE Publications (UK and US) / Springer Verlag (Germany) |
ISSN: | 0092-0703 |
Date of First Compliant Deposit: | 20 July 2020 |
Date of Acceptance: | 16 July 2020 |
Last Modified: | 20 May 2023 03:21 |
URI: | https://orca.cardiff.ac.uk/id/eprint/133571 |
Citation Data
Cited 49 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
Edit Item |