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Tackling Lack of Motivation in Aspirational Analytics Companies: SME Examples from the Manufacturing Industry
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Informationssystem (IS), Information Systems)ORCID iD: 0000-0001-5435-9535
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Informationssystem (IS), Information Systems)ORCID iD: 0000-0002-7858-9471
Department of Economics and Informatics, University West, Trollhättan, Sweden.ORCID iD: 0000-0003-1134-1938
2019 (English)In: International Journal of Business Intelligence Research, ISSN 1947-3591, E-ISSN 1947-3605, Vol. 10, no 1Article in journal (Refereed) Published
Abstract [en]

Establishing business intelligence analytics (BIA) in small- and medium-sized manufacturing enterprises is a pervasive problem. SME’s - the majority of businesses - play an important role in creating jobs, but research is primarily focused on large corporations. The authors worked with small manufacturing companies at the aspirational capability level but found that their motivation to introduce BIA was low. They had many business challenges but perceived the obstacles (primarily cost and effort) as too great, and their priorities were with operational issues. A two-phase approach based on a well-known analytics maturity model was devised to help raise company motivation. The article describes three studies in different companies using variations of the approach. Comparative analysis of the cases shows that demonstrating a clear path to improved functional efficiency is key to improving motivation, and that simple, easy to learn tools can provide these insights at little cost.

Place, publisher, year, edition, pages
IGI Global, 2019. Vol. 10, no 1
Keywords [en]
Business Intelligence, Information systems, Manufacturing, Maturity model, SME
National Category
Information Systems
Research subject
Information Systems
Identifiers
URN: urn:nbn:se:his:diva-16702DOI: 10.4018/IJBIR.2019010101Scopus ID: 2-s2.0-85071252646OAI: oai:DiVA.org:his-16702DiVA, id: diva2:1296420
Projects
BISONMM2
Funder
Knowledge Foundation
Note

EISBN13: 9781522566922

Available from: 2019-03-15 Created: 2019-03-15 Last updated: 2019-10-28Bibliographically approved
In thesis
1. Towards facilitating BI adoption in small and medium sized manufacturing companies
Open this publication in new window or tab >>Towards facilitating BI adoption in small and medium sized manufacturing companies
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This work concerns how to support Small and Medium sized Manufacturing Enterprises(SMMEs) with their Business Intelligence (BI) adoption, with the long term aim of supporting them in making better use of their BI investments and becoming (more)data-driven in their decision-making processes. Current BI research focuses primarily on larger enterprises, despite the fact that the majority of businesses are small or mediumsized. Therefore, this research focuses on the body of knowledge concerning how SMMEs can be more intelligent about their business, and better adopt BI to improve decision-making. Accordingly, the overall research aim is to create an artefact that can support SMMEs to facilitate BI adoption. An understanding of the current situation of BI adoption within SMMEs needs to be attained to achieve this, which is the focus for the first research question: What is the current state-of-practice in relation to BI adoption in SMMEs? The research question adds to current knowledge on how SMMEs are taking advantage of BI and highlights which functions within companies are currently supported by BI. Research question two identifies the main challenges that SMMEs are facing in this context: What are the main challenges for BI adoption in SMMEs? This question adds to knowledge regarding some of the barriers and hindrances SMMEs face in BI adoption. Finally, the third research question addresses how SMMEs can address the challenges in successfully adopting BI: How can the main challenges be addressed? The research question is answered by providing descriptions of work in four participating companies addressing different types of problems. Many of the challenges from literature (and from empirical data from the participating companies) regarding BI adoption are met. The outcome adds to the literature a hands-on approach for companies to address chosen problems in their settings, and addressing many of the factors previously found in the BI adoption literature. An action design research (ADR) method is used to fulfill the overall research aim. The ADR method is used to guide the development of a framework artefact based on previousliterature, and on empirical findings from working with participating companies. Theoretical background was obtained through a literature review of BI adoption and usage. Empirical material was gathered both through interviews and by reviewing documents from the companies. The work that was done in participating companies was supported by previous literature in several ways: through the use of an elicitation activity, through the core concepts of BI, and by focusing on categories presented in a BI maturity model. The principal contribution of the research is in the form of a framework: the Business Intelligence Facilitation Framework (BIFF), which includes four phases. All phases contain activities that support companies in addressing BI adoption challenges from the literature and empirical data, in order to achieve the overall research aim. This research contributes both to research and practice. From a research point of view, the framework provides a way to address many of the factors previously identified in literature that need to be in place to increase the likelihood of successful BI adoption. From a practice perspective, the framework supports practitioners offering guidance in how to improve their BI adoption, providing activities for them to take, and guidance in how to carry out the activities.

Place, publisher, year, edition, pages
Skövde: University of Skovde, 2019. p. 126
Series
Dissertation Series ; 30 (2019)
National Category
Information Systems
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-17819 (URN)978-91-984918-2-1 (ISBN)
Public defence
2019-11-22, G110, University of Skövde, Skövde, 13:00 (English)
Opponent
Supervisors
Available from: 2019-10-30 Created: 2019-10-28 Last updated: 2019-11-08Bibliographically approved

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Gudfinnsson, KristensRose, JeremyAggestam, Lena

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