Is Expert Input Valuable? The Case of Predicting Surgery Duration

Ibrahim, Roub; Kim, Song Hee

doi:10.35152/snusjb.2019.25.2.001

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…When humans have private information, their insight combined with the algorithm's can outperform the algorithm alone. Examples of such an approach are Ball and Ghysels (2017), Blattberg and Hoch (1990), Flicker (2018), Hoch and Schkade (1996), Ibrahim and Kim (2019), Ibrahim et al (2021), Oh andOliva (2020), andvan der Staak et al (2020). Arvan et al (2019) reviews the literature on human-algorithm forecast integration, noting that all the work on adherence is theoretical or in the laboratory.…”

Section: Human-algorithm Interactionmentioning

confidence: 99%

Believing in Analytics: Managers’ Adherence to Price Recommendations from a DSS

Caro

Cuenca

2023

M&SOM

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Problem definition: We study the adherence to the recommendations of a decision support system (DSS) for clearance markdowns at Zara, the Spanish fast fashion retailer. Our focus is on behavioral drivers of the decision to deviate from the recommendation, and the magnitude of the deviation when it occurs. Academic/practical relevance: A major obstacle in the implementation of prescriptive analytics is users’ lack of trust in the tool, which leads to status quo bias. Understanding the behavioral aspects of managers’ usage of these tools, as well as the specific biases that affect managers in revenue management contexts, is paramount for a successful rollout. Methodology: We use data collected by Zara during seven clearance sales campaigns to analyze the drivers of managers’ adherence to the DSS. Results: Adherence to the DSS’s recommendations was higher, and deviations were smaller, when the products were predicted to run out before the end of the campaign, consistent with the fact that inventory and sales were more salient to managers than revenue. When there was a higher number of prices to set, managers of Zara’s own stores were more likely to deviate from the DSS’s recommendations, whereas franchise managers did the opposite and showed a weak tendency to adhere more often instead. Two interventions aimed at shifting salience from inventory and sales to revenue helped increase adherence and overall revenue. Managerial implications: Our findings provide insights on how to increase voluntary adherence that can be used in any context in which a company wants an analytical tool to be adopted organically by its users. We also shed light on two common biases that can affect managers in a revenue management context, namely salience of inventory and sales, and cognitive workload. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1166 .

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Section: Human-algorithm Interactionmentioning

confidence: 99%

Believing in Analytics: Managers’ Adherence to Price Recommendations from a DSS

Caro

Cuenca

2023

M&SOM

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Applied Machine Learning in Operations Management

Bastani

Zhang

2021

Springer Series in Supply Chain Management

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Eliciting Human Judgment for Prediction Algorithms

2021

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Even when human point forecasts are less accurate than data-based algorithm predictions, they can still help boost performance by being used as algorithm inputs. Assuming one uses human judgment indirectly in this manner, we propose changing the elicitation question from the traditional direct forecast (DF) to what we call the private information adjustment (PIA): how much the human thinks the algorithm should adjust its forecast to account for information the human has that is unused by the algorithm. Using stylized models with and without random error, we theoretically prove that human random error makes eliciting the PIA lead to more accurate predictions than eliciting the DF. However, this DF-PIA gap does not exist for perfectly consistent forecasters. The DF-PIA gap is increasing in the random error that people make while incorporating public information (data that the algorithm uses) but is decreasing in the random error that people make while incorporating private information (data that only the human can use). In controlled experiments with students and Amazon Mechanical Turk workers, we find support for these hypotheses. This paper was accepted by Charles Corbett, operations management.

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Is Expert Input Valuable? The Case of Predicting Surgery Duration

Cited by 6 publications

References 28 publications

Believing in Analytics: Managers’ Adherence to Price Recommendations from a DSS

Believing in Analytics: Managers’ Adherence to Price Recommendations from a DSS

Applied Machine Learning in Operations Management

Eliciting Human Judgment for Prediction Algorithms

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