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My research concerns numbers for business administration.
In my dissertation monograph I study how fund managers use numbers from company reports for fundamental valuation of companies traded on stock markets. Having found a greater importance of direct contact with companies and sell-side sources, where more frequent direct contact with sell-side had a negative relationship with fund performance, I currently test the effect of unbundling commissions on fund costs and performance (work in progress). In a related project, we found that accounting format had an effect on analysts' judgments of company valuation.
In a second project, the common theme is academic management and how relying on measurable numbers (e.g., citations and course evaluations) will lead to the tragedy of the artificial (knowledge) commons (to be submitted for peer-review). We propose a conceptual framework (also face-validated) to manage academic tensions or struggles among funding agencies to evaluate research (to be submitted for peer-review).
In a third project (to be submitted for peer-review), we study pay expectations. Do female students at a top business school expect lower numbers on their pay check than similar male peers? We show that even among a subset of highly competitive students at a top business school in Sweden there is a persistent sticky gender gap in career entry pay expectations, robust to a large set of employer and student characteristics. The very same sample of female students later also actualise lower career entry pay compared to the male students, as pay expectations (years in advance) fully mediate actual pay. Finally, even as similar peers gain improvements in the labor market, female students do not adjust their expectations.
In several research projects we use consumer data, converted to numbers, to explore if we can understand and predict consumer behaviors. In one study, we show that automated machine learning better predicts immediate future consumer value than manager heuristics in several different retailing sectors, and can be used to automatically prioritize consumers. In a second study, we show that there are long-term positive effects of introducing an online grocery channel, as multi-channel consumers experience an increase in convenience--without getting too planned--spend more. In a third study (to be submitted for peer-review), we argue that with goal congruency consumers should be promoted to purchase environmentally, but not socially, sustainable grocery products online, and find a robust positive difference.
My teaching covers how to use numbers to make statistical inferences and/or predictions using statistical and machine learning in a business context:
Please don't hesitate to contact me: Emelie.Froberg@hhs.se.

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