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Unleashing the inner geek: Thinking about the link between math anxiety and the anxiety we encounter teaching BA

The need for quantitative and analytical skills in the modern business environment is growing as companies attempt to leverage data, analytics and AI to improve everything, from customer relationship management and marketing to human resource management, planning and finance (Bag & Wood, 2022). Data driven organisations are the future of business and employees need to be able to foster data driven decision making and the tools that enable it (Phelps & Szabat, 2017).

The problem is that many students and employees still find themselves avoiding mathematics and find new software learning onerous and highly time consuming. Integrating analytics into workflows often needs to happen in an organic way where the will and desire of the employee to engage is as important as the tools and the data (Phillips, 2021). Even at a low-level employees need to be able to work with business analysts and understand the pitfalls of data management (Vidgen et al., 2020).

In business schools we have the opportunity and expertise to facilitate this journey but first we need to understand some of the drivers. Mathematical anxiety is a pernicious driver as it is a self-reinforcing phenomenon, where any failure leads to further anxiety making further failure more likely (Howard & Warwick, 2016). Thus, we need to 

  • be empathic and accentuate the wins while minimising the mistakes
  • understand this can be a deeply personal challenge
  • foster a growth mindset
  • mark on process and not on right or wrong
  • emphasise process and minimize the performance orientation (Howard & Warwick, 2016)
  • avoid demanding performance above the students current learning
  • foster a culture of risk taking and show it is OK to make a mistake  (Howard & Warwick, 2016)

Studies of the teaching of analytics have come mainly from those based in statistics departments at universities. Even here though they recognise the need to be teaching far more than just basic statistics skills. There is a need for

·         soft skills such as communicating results and eliciting needs (Phelps & Szabat, 2017)

·         design and visualisation skills to display and present results in compelling ways

·         telling stories with data that speak to employees, suppliers and customers

·         critical thinking and applied analysis skills (Howard & Warwick, 2016)

The other problem is that students have often come from a school environment where they have learned to ‘switch off’ when learning mathematics as they consider it dry and boring  (Howard & Warwick, 2016) and do not see the need for learning it. So, we should

  • ·         start early – as soon as the student comes into the business school environment
  • ·         use data and industry examples to show the use and business advantage afforded by using             data and analytics

In summary, it is possible for us to make the journey into quantitative skills for business more enjoyable and relevant, but we need to

  • ·         Begin early
  • ·         Use real world examples as motivation

o   Guest speakers

o   Data stories

  • ·         Have fun by using open learning environments which foster risk taking and a growth mindset
  • ·         Utilise the other important ‘soft’ skills where those weak in quantitative skills can excel,               such as visualisation, storytelling, communication, and design
  • ·         Teach concepts first to build familiarity with critical thinking and analysis skills
  • ·         Be empathic, math anxiety is real and a highly debilitating for many students

Original draft by Christina J Phillips (@DrArtAnalytics) with ideas input from Dr J Brown, Dr M A  Alaoui and Prof T Wall, following the World Cafe event at LJMU, Liverpool Business School. 

References

Bag, S., & Wood, L. C. (2022). Guest editorial: Human resource development in the digital age: recent issues and future research directions. International Journal of Manpower, 43(2), 253–262. https://doi.org/10.1108/ijm-05-2022-561

Howard, A., & Warwick, J. (2016). The Prevalence of Mathematical Anxiety in a Business School : A Comparative Study across Subject Areas. International Journal of Social Sciences & Educational Studies, 3(2), 4–25.

Phelps, A. L., & Szabat, K. A. (2017). The Current Landscape of Teaching Analytics to Business Students at Institutions of Higher Education: Who is Teaching What? American Statistician, 71(2), 155–161. https://doi.org/10.1080/00031305.2016.1277160

Phillips, C. J. (2021). When Simulation Becomes Human Centric Analytics. In M. Fakhimi, D. Robertson, & T. Boness (Eds.), Proceedings of the Operational Research Society Simulation Workshop 2021 (SW21) (Vol. 1, pp. 164–167). Operational Research Society. https://doi.org/10.36819/sw21.017

Vidgen, R., Hindle, G., & Randolph, I. (2020). Exploring the ethical implications of business analytics with a business ethics canvas. European Journal of Operational Research, 281(3), 491–501. https://doi.org/10.1016/j.ejor.2019.04.036