The Impact of Decision Types on Revenue Management Decisions: An Experimental Study

Abstract

In the standard two‐class revenue management model, the decision maker allocates a fixed resource between two customer classes with hierarchical prices and uncertain demand. The normative (i.e., expected revenue‐maximizing) allocation is given by Littlewood's Rule, but little is known about how decision makers actually form these decisions. We report results of an experimental study that investigates revenue management decision‐making. We find that subjects' behavior is influenced by the decision type. In particular, our subjects reserve more units for the high‐end segment when they are asked to set the protection level (the number of units to set aside for the higher priced class) compared to when they set the booking limit (the number of units available for the lower priced class). We propose that this behavioral pattern can be explained by our subjects’ different valuations of revenues from the high‐ and low‐end sales. We also observe that when there is a change in segment prices, although decision makers adjust allocations in the direction suggested by normative theory, the magnitude of adjustments is greater (and hence closer to the normative level) when the source of the price change matches the class whose allocation they determine.

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

“…Managers' behavioral biases in revenue management have been studied mostly in theoretical or laboratory papers; see Özer and Zheng (2012) for a review. Some of the topics these experimental papers study are adjustment to inventory levels (Bearden et al 2008); framing effects (Kocabıyıkoglu et al 2018); managers' physiological conditions (Bendoly 2011(Bendoly , 2013; and how pricing interacts with other considerations, such as inventory (Kocabıyıkoglu et al 2015), production quantity (Ramachandran et al 2018), or buyer/seller role (Mak et al 2018).…”

Decision biases in revenue management revisited: Dynamic decision‐making under stationary and nonstationary demand

“…The authors use this setting to compare the RM decision to a normatively equivalent version of the newsvendor problem. Kocabiyikoglu et al (2018) rely on the same static setting to investigate different RM decision tasks. Their design asks participants to set static booking limits or protection levels, given stationary demand for two fare classes.…”

“…Kocabiyikoglu et al. (2018) rely on the same static setting to investigate different RM decision tasks. Their design asks participants to set static booking limits or protection levels, given stationary demand for two fare classes.…”

Revenue management systems as symbiotic analytics systems: insights from a field study

“…An emerging strain of research focuses on behavioral perspective of human decision making when facing the RM problem without emphasizing the role of an automated system [compare Bearden et al (2008), Bendoly (2011Bendoly ( , 2013, Kocabiyikoglu et al (2015Kocabiyikoglu et al ( , 2018, Schütze and Cleophas (2019)]. These studies focus on individual behavior in a revenue management setting.…”

“…Main findings in this stream of research are that participants use advanced decision policies (Bearden et al 2008) but the number of simultaneous tasks increases stress and faulty decisions (Bendoly 2011). Furthermore, participants reserve more units of capacity for high-value customers when they set protection levels instead of bookings levels (Kocabiyikoglu et al 2018), and they anchor their decisions on non-stationary information like the willingness to pay of customers (Schütze and Cleophas 2019). At most, these studies consider a forecast as given to support human decision making.…”