I am CEO of HEGERS HOME, our fourth-generation family business specializing in custom-made kitchens, tables, and staircases.
Before my role in the family business, I was an Assistant Professor at Vrije Universiteit Amsterdam, specializing in IT & Accounting. My research uses survey and archival data to contribute to our understanding of data analytics, forecasting, and the quality of financial and non-financial information.
Eddy Cardinaels, Oliver Hegers, and Anil Kshatriya
Accounting, Organizations and Society (accepted, VHB-Ranking A+, Financial Times 50)
Abstract. Business unit (BU) controllers often face role conflicts between their fiduciary duty to report accurately and role expectations of their superiors or colleagues that put pressure on controllers to engage in misreporting. We develop theory that the positive relation between role conflict and misreporting is strengthened for business controllers with a growth mindset. While a growth mindset can be beneficial for learning and overcoming challenges, it can also lead to a flexible attitude toward rule-breaking, which might be dysfunctional in environments where a BU controller’s fiduciary duties clash with role expectations of their business unit. Through a survey of BU controllers and an experiment, in which we manipulate mindset, we find that role conflict leads to more misreporting. We further find that a growth mindset relative to a fixed mindset strengthens the positive relation between role conflict and misreporting. Our results are relevant for practice: encouraging a growth mindset in business unit controllers can backfire and can lead to more misreporting, particularly when role conflict increases.
Oliver Hegers, Matthias D. Mahlendorf, and Fan Wu
Accounting, Organizations and Society (VHB-Ranking A+, Financial Times 50)
Abstract. Crowd-based ratings have become one of the most widely used forms of performance measurement. They inform customers, job seekers, and investors, provide firms with diagnostic feedback, and are increasingly tied to employee evaluation and pay. Accounting research, however, has paid little attention to how the design of these measurement systems shapes their quality. We examine a change in Amazon’s rating system that removed a small participation friction (“sludge”), the requirement to write a text review when submitting a star rating. Using 77 million product-week observations, we find that removing this friction broadened participation by previously silent, authentic customers and diluted the influence of paid raters, making rating manipulation less effective. Composite ratings became more informative. Their alignment with independent expert quality assessments strengthened, and the sensitivity of future sales to ratings nearly doubled. Our findings suggest that crowd-based performance measures are not passive aggregators of dispersed information; their informativeness depends critically on design choices that govern who participates in the measurement process.
Marie Dutordoir, Oliver Hegers, Joao Quariguasi Frota Neto, and Frank Verbeeten
European Accounting Review (VHB-Ranking A)
Abstract. We evaluate the usefulness of third-party brand value estimates (BVE) in forecasting corporate cash flows. Our sample consists of BVE for publicly-listed firms published by brand consultancy firms Interbrand, Brand Finance and BrandZ between 2006 and 2021. We find a positive incremental impact of Interbrand and BrandZ BVE on one-year-ahead operating cash flows in in-sample regressions. However, out-of-sample predictions based on linear regressions and Machine Learning methods show that BVE have no incremental cash flow forecasting power over standard accounting information. Analyses of long-term stock returns on zero-cost brand-owner portfolios and immediate stock price reactions to BVE announcements support the non-relevance of third-party BVE for predicting operating cash flows. Our findings extend to free cash flow and earnings forecasts and hold under alternative methodological specifications.
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.