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  4. How and when do big data investments pay off? The role of marketing affordances and service innovation
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How and when do big data investments pay off? The role of marketing affordances and service innovation

Journal
Journal of the Academy of Marketing Science
Type
Article
Date Issued
2020-08-05
Author(s)
De Luca, Luigi M.
;
Dennis Herhausen  
;
Troilo, Gabriele
;
Rossi, Andrea
DOI
10.1007/s11747-020-00739-x
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.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Springer Science and Business Media LLC
Volume
49
Number
4
Official URL
http://www.scopus.com/inward/record.url?eid=2-s2.0-85089033499&partnerID=MN8TOARS
URL
https://alexandria.unisg.ch/handle/20.500.14171/140175
Subject(s)

business studies

Division(s)

IMC – Institute for M...

File(s)
Thumbnail Image
Name

JAMS 2021 # How and when do big data investments pay off.pdf

Size

544.84 KB

Format

Adobe PDF

Checksum (MD5)

03a28124f31d91b3d0771bc825793cbe

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