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http://hdl.handle.net/11375/25322
Title: | Understanding Users’ Action On Data Analytics Recommendations |
Authors: | Eslami, Seyed Pouyan |
Advisor: | Hassanein, Khaled |
Department: | Business Administration |
Publication Date: | 2019 |
Abstract: | Current trends indicate that many organizations are making significant investments in Data Analytics (DA) to leverage big data. However, recent studies also indicate that a large percentage of these investments are unsuccessful and that a majority of users do not act upon a data analytics tool’s recommendations. This research draws upon the S-O-R framework and the Theory of Planned Behaviour to develop and empirically validate a theoretical model of the factors that influence/hinder a user’s concordance with and actions taken with respect to a DA tool’s recommendations. The model reflects the nuances of DA tool use within organizations including: (i) technological characteristics, (ii) individual characteristics, (iii) situational characteristics, and (iv) task-related characteristics. In addition, this study investigates the factors that shape a user’s perception of the quality of a DA tool’s recommendations, while trying to understand how, and to what extent, this perception influences a user’s concordance with, and the actions taken in regards to, a DA tool’s recommendations. The results of this research confirm that personal concordance and recommendation actionability are positively associated with user action on a DA tool’s recommendations. Moreover, perceived risk of action was found to be negatively associated with user actions taken with respect to a DA tool’s recommendations. It was also found that DA tool recommendation quality is shaped from intrinsic data quality, contextual data quality, DA tool quality, DA tool recommendation understandability, and analyst competency. |
URI: | http://hdl.handle.net/11375/25322 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Eslami_Seyed Pouyan_201906_PhD.pdf | 1.29 MB | Adobe PDF | View/Open |
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