Abstract
With the continuous progress and penetration of automated data collection technology, enterprises and organizations are facing the problem of information overload. The demand for expertise in data mining and analysis is increasing. Self-efficacy is a pivotal construct that is significantly related to willingness and ability to perform a particular task. Thus, the objective of this study is to develop an instrument for assessing self-efficacy in data mining and analysis. An initial measurement list was developed based on the skills and abilities about executing data mining and analysis, and expert recommendations. A useful sample of 103 university students completed the online survey questionnaire. A 19-item four-factor model was extracted by exploratory factor analysis. Using the partial least squares-structural equation modeling technique (PLS-SEM), the model was cross-examined. The instrument showed satisfactory reliability and validity. The proposed instrument will be of value to researchers and practitioners in evaluating an individual’s abilities and readiness in executing data mining and analysis.
Highlights
With the penetration and advent of data storage technologies and automatic data collection techniques, the big data age is coming
Before conducting the exploratory factor analysis (EFA), three tests were performed to check the adequacy of the survey data for EFA
The results demonstrated a satisfactory suitability of the data for factor analysis (χ2 = 3387.31, p < 0.001)
Summary
With the penetration and advent of data storage technologies and automatic data collection techniques, the big data age is coming. These technologies bring rich and diverse digital data to organizations, they can cause serious information overload. At present instruments to properly and accurately measure individual abilities in data mining and analysis remain lacking. This study addresses this gap in research and practice. Data mining technology is an indispensable technology in the era of big data analysis. Hand et al (2001) define data mining as the analysis of data sets (usually a large number of data sets) to identify unexpected relationships and summarize the data in novel patterns, and provide useful information. Jain and Srivastava (2013) observed that data mining algorithms are divided into two functional types, predictive and descriptive, and eight application types, classification, estimation, forecasting, correlation analysis, sequence, time series, description, and visualization (Dunham, 2003)
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