Enhancing college students’ proficiency in advanced analytics software skills: challenges, motivation and factors

Purpose - Companies increasingly emphasize the importance of analytics software, including R programming, Tableau and advanced Excel skills. They expect college graduates to be proficient in these analytical tools. However, most colleges primarily teach basic Excel skills in lower-level courses, leaving advanced skillsets underexplored across different subject areas. This study investigates college students’ challenges and motivations related to learning advanced software skills. Design/methodology/approach - Recognizing the fact that this study requires collection of perceptual data about various psychometric measures, we used survey instruments to measure participants’ perception. The unit of analysis is a college/graduate student who has taken an analytics course at the college. Data analysis was done through structural equation modeling, first through measurement model, and then structural model. Findings - Our findings reveal that while college students recognize the demand for advanced analytics skills, they often lack full proficiency. Learning and using analytical tools can be daunting, leading to anxiety. Drawing on Dweck’s self-theories, our study explores factors influencing college students’ intentions to learn and use analytics software. We identify eight variables that impact this intention. This research contributes to data analytics education by highlighting potential gaps between students and employers regarding the demand for advanced analytics software skills. By understanding these factors, educators and institutions can better support students in developing proficiency in advanced analytics. Research limitations/implications - First, our participants were exposed only to advanced Excel (spreadsheet) and Tableau (visual analytics). Given the variety of advanced analytics software, it would be a good idea for future researchers to examine students with analytics programming skills like R or Python. Second, we recruited survey participants only from business schools. Although there were some MBA students with different backgrounds, examining students who have to study advanced software in their specific disciplines will offer a richer ground for expanding this model. Practical implications - This study highlights the challenges college students face in mastering data analytics skills. By leveraging self-theories and growth mindset theory, we identified key factors such as perceived usefulness and innovativeness that significantly influence students’ intentions to learn and use analytics software. Encouraging a growth mindset is essential for enhancing software adoption and utilization, with strategies focusing on effort, learning and improvement proving effective. Moreover, addressing software fatigue through addressing computer anxiety, adequate training and resource availability is crucial, thereby fostering a more conducive learning environment. Social implications - In summary, this research informs educational practices, workforce readiness and the alignment of student skills with industry needs. It emphasizes the importance of fostering a growth-oriented mindset in students as they navigate the complexities of analytics software. Originality/value - Given the highly demanded analytics skillset by employers, there is a paucity of research on how college students prepare for data analytics skillsets through advanced software. In this study we developed a model of college students’ use of advanced analytics software. Using the self-theories and the growth mindset theory, we theoretically investigate factors affecting college students’ behavioral intentions to learn and continue to use analytics software, in comparison with other general educational software skills. In our model eight variables are identified along with the three information processing dimensions, which explains 50.6% of the variance in using advanced analytics software.