• Understand the strategic value of quantitative methods that help to solve business problems using data.
• Be able to select appropriate tools and methods for data collection and analysis to answer specific managerial questions.
• Be able to translate results from quantitative analysis into business insights.
• Be able to communicate insights from quantitative analysis clearly and persuasively to a business audience in written and verbal form.

Strategic use of data can help organizations to generate business insights that provide objective advice relating to the strategy, structure, management and operations of an organization. The digitalization of industries has given firms easy access to large amounts of data that can be used to derive valuable business insights. Especially, small and medium sized firms can benefit from the increased access to relevant data to generate valuable business insights without significant investment. For instances, large scale online surveys provide detailed and comprehensive insights on consumers’ and citizens’ perceptions. E-commerce leaders are conducting a plethora of experiments to find the best performing design of their mobile app and website. Music streaming providers use data on our listening behavior to accurately predict our music preferences. For companies, the question is how to collect and analyze relevant data to gain the valuable business insights.

When analyzed rigorously, data offers tremendous business potential. This is why firms are looking for university graduates possessing the skills to collect, analyze, and interpret quantitative data. Especially employees at the intersection of business and analytics can provide significant value to companies. These employees possess fundamental knowledge of both business and analytics that helps firms to steer their analytics efforts into strategically valuable directions. The aim of this course is to understand the business value of data and become able to unlock this value for businesses.

This course will enable you to learn how to apply different methods to collect and analyze data to inform business decisions. Deploying rigorous methods is the cornerstone to answer important business questions. We will use statistical analysis software and interpret the business implications of our empirical findings. We will train you in how to ask research questions and how to answer them using quantitative research methodologies. To achieve our learning objectives, you will work on interesting hands-on projects in a digital context applying survey methodology, experimentation, and predictive analysis.

Importantly, this course does not require any pre-experience in statistics but rather your motivation and interest to acquire these valuable skills. This course can also help you to conduct quantitative research for your Master thesis project.

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy any material. Here are some examples.

*Almenberg, J., & Dreber, A. (2015). Gender, stock market participation and financial literacy. Economics Letters, 137, 140-142.

*Bleier, A., and Eisenbeiss, M. 2015. The Importance of Trust for Personalized Online Advertising. Journal of Retailing, 91(3), pp. 390–409.

*Cox. (2017). Exploratory Data Analysis: What Data Do I Have? In Translating Statistics to Make Decisions (pp. 47–74). Apress. https:/​/​doi.org/​10.1007/​978-1-4842-2256-0_3

*Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. (2014). Embracing digital technology: A new strategic imperative. MIT sloan management review, 55(2), pp. 1-12.

*Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1, pp.104-121.

*Hoehle, H., Aloysius, J.A., Goodarzi, S., and Venkatesh,V. (2019). A nomological network of customers’ privacy perceptions: linking artifact design to shopping efficiency, European Journal of Information Systems, 28:1, pp. 91-113

*Jung, E. Y., Baek, C., & Lee, J. D. (2012). Product survival analysis for the App Store. Marketing Letters, 23(4), 929-941.

*Lai, Y. L., Lin, F. J., & Lin, Y. H. (2015). Factors affecting firm's R&D investment decisions. Journal of Business Research, 68(4), 840-844.

*List, J.A., 2011. Why Economists Should Conduct Field Experiments and 14 Tips for Pulling One Off. Journal of Economic Perspectives, 25(3), pp.3-16.

*Prentice, C., Nguyen, M. (2021). Robotic service quality – Scale development and validation. Journal of Retailing and Consumer Services, Vol: 62, pp.1-7.

*Schuetz, S.W., Sykes, T.A. and Venkatesh, V. (2021). Combating COVID-19 fake news on social media through fact checking: antecedents and consequences, European Journal of Information Systems, 30:4, pp. 376-388.

*Shmueli, G., & Koppius, O. R. 2011. Predictive analytics in information systems research. MIS Quarterly, 35(3), pp.553-572.

*Tangi L., Janssen M., Benedetti M., Noci G. (2020). Barriers and Drivers of Digital Transformation in Public Organizations: Results from a Survey in the Netherlands. In: *Viale Pereira G. et al. (eds) Electronic Government, pp. 42-56.