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Resource management in Big Data initiatives: processes and dynamic capabilities
Authors
Agarwal
Amit
+68 more
Argote
Ashley Braganza
Barney
Barney
Barney
Barney
Brumfiel
Buchananl
Caloghirou
Camisón
Cepeda
Choo
Cisco Visual Networking Index
Cohen
Commission of the European Communities
Conner
Daniel Nepelski
Davenport
Davenport
Davenport
De Prato
De Prato
Delmonte
Dierickx
Donate
Eisenhardt
Eisenhardt
Erevelles
Filippini
Grant
Greenwood
Guellec
Hammer
Hill
Hofer
Irani
Joint Research Centre-European Commission
Kim
Kogut
Lam
Laurence Brooks
Liao
Lin
Maged Ali
Mahoney
Mangematin
Mata
Mayer-Schönberger
Morash
Nelson
Nonaka
Pezeshkan
Popper
Power
Raceanu
Ray
Rollins
Russ Moro
Schutz
Teece
Viia
Wamba
Wernerfelt
Wohlgemuth
Xu
Zahra
Zhang
Zollo
Publication date
1 March 2016
Publisher
'Elsevier BV'
Doi
Abstract
© 2016 The Authors. Effective management of organizational resources in big data initiatives is of growing importance. Although academic and popular literatures contain many examples of big data initiatives, very few are repeated in the same organization. This suggests either big data delivers benefits once only per organization or senior managers are reluctant to commit resources to big data on a sustained basis. This paper makes three contributions to the Special Issue’s theme of enhancing organizational resource management. One is to establish an archetype business process for big data initiatives. The second contribution directs attention to creating a dynamic capability with big data initiatives. The third identifies drawbacks of resource base theory (RBT) and it’s underpinning assumptions in the context of big data. The paper discusses lessons learnt from the case study and draws out implications for practice and business research. The paper’s intellectual and practical contributions are based on an in-depth case study of the European Poles of Excellence (EIPE) big data initiative and evidence from the extant literature
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