In this issue, I have compiled articles that focus on analytics. One misconception that students have about learning analytics is that it can become quite mathematical. Their past experiences in calculus or trigonometry classes have skewed their perception of “numbers.” An example that I often use in class when preparing students for quantitative topics is that as marketing managers, we do not need to know intricate details of how a car functions. However, we do need to know how to drive from point A to point B. In other words, analytics is a tool that can be used to make recommendations and decisions. Knowing how the engine of a car functions is certainly an added bonus! This shift in perspective can be useful in reframing students’ minds and their approach to “numbers.”
This issue begins by examining the misconception that students have about quantitative courses. In the work by He, Alexander, Nikolov, and Chen, they investigate students’ lack of interest in enrolling in marketing analytics courses. This misconception stems from the perception of marketing as a “soft skill” field, causing students to reassess their expectations about the discipline. The authors discuss the importance of self-efficacy and intervention approaches to address this challenge. Furthermore, Elhajjar and Borna’s research seeks to identify the perspectives of students and educators regarding Big Data courses. Their findings contribute to a list of recommendations for teaching big data in marketing programs.
Relatedly, the work by Veeck, Quareshi, O’Reilly, Mumuni, MacMillan, Luqmani, Luqmani, and Xie investigates intervention strategies to assess and address the self-efficacy of marketing students in acquiring analytical skills. They identify three types of confidence: natural ability, ability to learn, and current skills. The authors demonstrate that intervention strategies should differ depending on the level and type of confidence, suggesting that faculty should devise multiple approaches to increase students’ self-efficacy when teaching analytics. Additionally, Teimourzadeh, Kakavand, and Kakavand provide guidelines on how Python, as a programming language, can help students explore large datasets and improve their data analysis and data visualization skills.
Lastly, Iqbal examines the importance of curriculum design in relation to career outcomes. As the marketing discipline continues to evolve, the author draws on six best practices from the marketing education literature to demonstrate the impact of curriculum design on career outcomes, such as post-graduation income and long-term career satisfaction.
In conclusion, it is evident that more efforts are needed to address the misconceptions students have about the marketing discipline, particularly regarding quantitative courses, and to foster self-efficacy in order to promote student interest and engagement in analytics courses within marketing education. By considering these factors, educators can create an environment that nurtures students’ interest and proficiency in analytics, ultimately equipping them with the necessary skills to excel in their careers.
Disclosure statement
No potential conflict of interest was reported by the author(s).