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Business value of in-memory technology – multiple-case study insights

Rieke Bärenfänger (Institute of Information Management, University of St Gallen, St Gallen, Switzerland)
Boris Otto (Audi-Endowed Chair of Supply Net Order Management, TU Dortmund University, Dortmund, Germany)
Hubert Österle (Institute of Information Management, University of St Gallen, St Gallen, Switzerland)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 7 October 2014

1171

Abstract

Purpose

The purpose of this paper is to assess the business value of in-memory computing (IMC) technology by analyzing its organizational impact in different application scenarios.

Design/methodology/approach

This research applies a multiple-case study methodology analyzing five cases of IMC application scenarios in five large European industrial and service-sector companies.

Findings

Results show that IMC can deliver business value in various applications ranging from advanced analytic insights to support of real-time processes. This enables higher-level organizational advantages like data-driven decision making, superior transparency of operations, and experience with Big Data technology. The findings are summarized in a business value generation model which captures the business benefits along with preceding enabling changes in the organizational environment.

Practical implications

Results aid managers in identifying different application scenarios where IMC technology may generate value for their organizations from business and IT management perspectives. The research also sheds light on the socio-technical factors that influence the likelihood of success or failure of IMC initiatives.

Originality/value

This research is among the first to model the business value creation process of in-memory technology based on insights from multiple implemented applications in different industries.

Keywords

Citation

Bärenfänger, R., Otto, B. and Österle, H. (2014), "Business value of in-memory technology – multiple-case study insights", Industrial Management & Data Systems, Vol. 114 No. 9, pp. 1396-1414. https://doi.org/10.1108/IMDS-07-2014-0212

Publisher

:

Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited

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