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Original Articles

Does Knowledge Sharing Belief of Data Analysts Impact Their Behavior?

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Pages 460-478 | Published online: 10 Jun 2022
 

ABSTRACT

If data analysts’ do not share the insights they obtain through the use of data analytics tools, firms may not be able to enhance the quality of their decisions. Therefore, it is critical to understand the factors that encourage data analysts to share their knowledge. In this study, we utilize the theory of planned behavior to understand whether the data analysts’ knowledge sharing beliefs form their behavior. We also utilize contingency and task-technology fit theories to understand the factors that influence the impact of data analysts’ knowledge sharing beliefs on their behaviors. Survey data collected from 226 data analysts U.S.-based companies and the findings showed that: data analysts’ knowledge sharing belief is formed by subjective norm and perception of control; task interdependency does not moderate the effect of data analysts’ knowledge sharing belief on their knowledge sharing behavior; analytical skills positively moderate this association; and tool comprehensiveness negatively moderates it.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

a In firms that employ data analytics, data analysts who use various analytical tools to generate insights (i.e., descriptive insight, predictive insight, and prescriptive insight) are a good source of knowledge. Therefore, data analysts are critical firm resources that have knowledge about market and business and, thus, they know if, in general, the decisions made in their firm fit with the insights they obtain from data. Thus, data analysts were considered as proper participants to answer questions regarding different variables in the research model.

b The survey was anonymous, so we could not verify the number of responses from unique firms. Future studies could add such checks to assess the number of unique firms and the frequency of each.

Additional information

Funding

This work was supported by the Social Sciences and Humanities Research Council (SSHRC) [20006922].

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