Self-Service Business Analytics and the Path to Insights : Integrating Resources for Generating Insights
Summary, in English
This study consists of a collection of five papers, whose findings provide answers to two research questions: RQ1— How do organisations enable an SSBA environment? And RQ2—How do users integrate resources during an analytical task in SSBA? In line with the research questions and the study’s aim, Service Dominant Logic was used as a theoretical lens. This dissertation employs an interpretive case study design to investigate SSBA. Three sources of empirical evidence have been used (semi-structured interviews, observations, and documents) to collect data from the top digital marketplace in Norway – Finn.no.
From a theoretical perspective, by portraying Self-Service Business Analytics as an approach to data analytics enabled through the presence of different analytical services such as tools, technologies, and support to assist the user in achieving independence, this dissertation emphasises the central idea of a service environment and move beyond the classic description of BA and DSS. It also provides a showcase through empirical evidence on how to use S-D logic in IS research and how it could be employed as an analytical lens. Finally, this thesis contributes to both BA and S-D logic literature by theorising the resource integration patterns, modes of engagement and the self-service environment in business analytics. From a practical perspective, this thesis relates to the industry by highlighting five major points of interest in relation to information authorship, the criticality of the setup phase in SSBA, steps to solve an analytical problem, and the competencies involved.
Lund Studies in Informatics
Printed in Sweden by Media-Tryck, Lund University
- Information Systems, Social aspects
- Electrical Engineering, Electronic Engineering, Information Engineering
- Self-Service Business Analytics
- Business Analytics
- Resource integration
- organizational environment
- service dominant logic
- ISSN: 1651-1816
- ISBN: 978-91-981550-5-1
18 February 2020