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Mar 13, 2024

Research on perception of analytics information receives Best Paper Award

Clara Chen, the Lillian and Morrie Moss Distinguished Professor in Accountancy; Bill Wright, Zimmerman Scholar in Gies; and their coauthor Ryan Hudgins (an Gies PhD alumnus now an Assistant professor and Director of accounting major at Earlham College) recently received the 2023 Journal of Management Accounting Research (JMAR) Best Paper Award for their article "The Effect of Advice Valence on the Perceived Credibility of Data Analytics.”Their work highlights the difficulties that can arise in bringing new technologies into the area of managerial decision-making.

In their paper, these researchers examine the degree to which people will accept information derived from data analytics when making forecasting decisions. In their experiment, the researchers investigated the degree to which advice about estimated demand was perceived as credible depending on whether the information was derived from data analytics versus coming from a human expert. They predicted that – if the information was bad news – individuals would tend to find the data analytics results as less credible than human experts.

In the experiment, participants were presented with a scenario in which they were the president of the Innovative Toy Company, which was introducing a new product. They received an opinion first from a sales manager, which suggested sales would be 60,000 units. Participants then received additional information from a second source. The second source was represented as coming from either a human expert or a data analytics algorithm. The second source would suggest demand would be 70,000 units (good news) or 50,000 units (bad news). Participants then rated how credible they found this second source of information to be.

The researchers found that participants rated bad news information that was presented as coming from data analytics as less credible than the bad news information from a human source. There was no difference in the perception of credibility for good news regardless of whether it is from data analytics or human. So, participants viewed a human expert as more competent and more credible than a data analytic algorithm – but only if the advice presents bad news.

Chen explained, “In this article, we show that the psychological phenomenon of motivated reasoning – that is, an individual's tendency to seek out and use information that confirms their existing beliefs or preferred conclusions and ignore or dismiss information that contradicts their preferred conclusions – affects decision-making involving data analytics.”

New technologies like data analytics could improve decision-making in business operations, but firms are not taking full advantage of these resources. “Despite the increased sophistication of data analytics, the human element is critical to effective use of data analytics,” Chen said.

The experiment provides insights into the use of analytics, which is increasingly prevalent and important for making informed managerial decisions. “Our results are important because companies are increasingly using more advanced techniques to make operational decisions,” Chen said. “Data analytics have great potential for analyzing a large volume of data in real-time, so firms that take advantage of data analytics will have a competitive advantage. However, our results suggest that employees will perceive data analytics to be less competent and less credible when it suggests bad news, and in turn, will be less likely to use the advice when making decisions. Furthermore, given that ‘bad news’ implies that firms are performing worse than expected, our results suggest that individuals may find data analytics less credible precisely when they are most in need of data-driven predictive models. By providing evidence on a potential impediment to the use of data analytics, our study can help firms transition to a data-driven culture for decision-making.”

The Best Paper Award is a distinction that recognizes an outstanding paper published in JMAR over the prior three-year period. For Chen, the lead author, this work reflects her focus on the effect of management accounting systems (such as budgeting and forecasting, performance measurement, and incentives) on decision-making within organizations. “My research in this area highlights the importance of incorporating social motives and psychological factors into economics-based models,” she said. “In particular, my studies suggest that people’s motivation is driven by factors beyond money, including reciprocity, fairness, social norms, the feeling of solidarity with group members, and the meaning of work. Therefore, organizations need to take these factors into consideration when they design management accounting systems.”