- Management Accounting
- Financial Accounting
- Data Science
- Survey and Archival Research
Does a growth mindset increase or decrease financial misreporting?
by Oliver Hegers
Abstract. I study the relation between a business unit (BU) controller’s mindset, role conflict, and financial misreporting. Mindset is based on implicit person theory and ranges from the deeply held belief on whether, in general, people can learn, develop and change throughout their lives (growth mindset), or whether, for example, one’s abilities and character are inherited and thus unmalleable (fixed mindset). While having a growth mindset is beneficial for learning and overcoming challenges (e.g., facing competing responsibilities), it also has a ‘dark side.’ A growth mindset reduces the psychological cost of harming other interest groups or breaking rules. I argue that a growth mindset lowers the BU controller’s perceived level of role conflict, which is associated with financial misreporting. However, it may also increase misreporting, as rule breaking behavior is less psychologically costly. Survey data from 180 BU controllers support these predictions and suggest that a growth mindset is positively associated with financial misreporting when role conflict is high.
The effect of forecast disaggregation and environmental uncertainty on internal financial
by Oliver Hegers, Frank Verbeeten, and Klaus Möller
Abstract. Prior literature suggests the existence of two opposing effects in disaggregated internal financial forecasts (IFFs). While disaggregated random errors (mistakes) offset each other, disaggregated non-random errors (biases) accumulate when they are combined. We argue that environmental uncertainty interacts with the level of disaggregation. Uncertainty may reduce non-random errors (as it is more difficult to consistently bias results) yet may also increase random errors in the forecast (due to unpredictability of results). Using survey data from 167 controllers, we theoretically predict and empirically show that forecast disaggregation increases (reduces) forecast accuracy under high (low) environmental uncertainty. Moreover, our findings suggest that the joint effect of disaggregation and uncertainty on forecast accuracy disappears when the ability to manipulate earnings is high. Our results imply that investments in more sophisticated forecasting tools may not provide the expected benefits when non-random errors in forecasting or a weak internal control environment are a key concern in firms.
Does lowering barriers to rate improve the informativeness of the rating consensus on online platforms?
by Oliver Hegers and Matthias D. Mahlendorf
Abstract. Platform providers such as Amazon, Google, and Glassdoor have to design policies for submitting ratings. We investigate how lowering the barriers to rate affects the informativeness of the rating consensus. In 2020, a major platform introduced a new one-tap rating system, whereas before a written text review was required to rate a product. Anecdotal evidence suggests that the goal of this change was to reduce the relative influence of paid ratings by substantially increasing the number of authentic ratings. We use a diff-in-diff approach and compare the rating consensus of the same books beteen different platforms. Our analyses show that after the policy change, the average rating increases and the standard deviation across products decreases. Thus, the rating consensus become less informative for platform users to discriminate between products. A potential explanation is that lowering the barriers makes it cheaper to provide paid ratings which may outweigh a potential increase of authentic ratings. The results of several additional analyses are consistent with this explanation.
Work in Progress
About right or absolutely wrong? The predictive ability of brand value estimates
by Marie Dutordoir, Oliver Hegers, Joao Quariguasi Frota Neto, and Frank Verbeeten
The future of management accounting research: Which path should we take?
by Oliver Hegers and Klaus Möller