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My research can be divided into four main areas.

Investor Behavior

In my dissertation monograph I study fund manager behaviors, specifically related to information seeking 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, in a subsequent project we test the effect of unbundling commissions on fund costs and performance (available here). In a related project, we found that accounting format had an effect on analysts' judgments of company valuation.

Gender Equality and Pay Expectations

Using a rich and novel dataset, we study (a) beliefs updating based on labor market progress and (b) whether beliefs drive the gap in realized pay taking concurrent labor market pay (available here). In a preregistered study intervention study, we test the effect of 'status momentum' and 'last place aversion', as motivated by the 'meaningful standard' on intended pay negotiations (available here). This research is funded by Jan Wallanders och Tom Hedelius stiftelse and Tore Browaldhs stiftelse. 

Consumer Behavior

In several research projects we use consumer data, 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, we find that the online shopping channel promotes purchases of products with environmental labels, especially for functional products, and that the in-store shopping channel promotes purchases of social labels, especially for experiential products. This research is funded by Hakon Swenson stiftelsen.

Academic Management

In several ongoing projects (e.g., available here ) we attempt to model quality in research and higher education. 

My teaching is mainly on quantitative methods, e.g., how to use data 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.


Orcid: https://orcid.org/0000-0002-5652-5733

OSF: https://osf.io/frbz2/


Github: https://github.com/emeliefroberg