Session 12: Behavioral Operations 2

Parallel Sessions

Session 12: Behavioral Operations 2 – Chair: Eva Demerouti

Jelle de Vries (Vrije Universiteit Amsterdam) – Worth the wait? How waiting influences customer behavior and revenues
In many service industries customers have to wait for service. When customers have a choice, this waiting may influence their service experience, sojourn time, and ultimately spending, reneging, and returning behavior. Not much is known however, about the precise impact of waiting on customer behavior and resulting revenue. In this paper we empirically investigate this by analyzing data obtained from 94,404 customers visiting a popular restaurant during a 12 month period. The results show that a longer waiting time relates to reneging behavior, a longer time until a customer returns, and a shorter dining duration. To find-out the long-term impact of the consequences of waiting time, we use the empirical findings and data collected in a simulation experiment. This experiment shows that, without waiting, the total revenue generated by the restaurant would increase by nearly 15% compared to the current situation. Furthermore, the results of simulation experiments suggest that, within the boundaries of the current capacity, revenue could be increased by a maximum of 7.5% if more flexible rules were used to allocate customers to tables. Alternatively, by increasing the existing seating capacity by 20%, revenue could be boosted by 7.7% without the need to attract additional customers. Our findings extend the knowledge on the consequences of customer waiting, and enable service providers to better understand the long-term financial and operational impact of waiting-related decisions in service settings.

Alexandros Pasparakis (Erasmus University Rotterdam) – In the driver’s seat: the role of leadership in safe and productive cargo transport
Road accidents are common. Therefore, truck drivers are exposed to high levels of physical risk (BLS, 2012). Around the globe, more than 1.2 million lives are lost annually in road traffic crashes (WHO, 2015). In addition, truck accidents lead to subsequent costs and supply chain disruptions (Tatikonda & Frohlich, 2013). The vast majority of truck accidents are caused by driver-related factors (FMCSA, 2006). The need to diminish risk factors in truck driving is obvious, and focusing on the driver to achieve that is therefore mandatory.Within the context of a company, a driver is expected to follow the instructions provided by his direct manager. As a consequence, a driver’s perception of the leadership behaviour of his manager could influence his decisions and behaviour on the road. In truck driving, drivers do not maintain constant contact with their manager and are trusted to execute their tasks effectively and efficiently without constant supervision. Still, dispatcher leadership has been linked to safety performance in truck driving, mediated by safety climate perceptions (Zohar et al., 2014). We focus on Safety-Specific Transformational Leadership (SSTL) that has been a well-established predictor of occupational accidents (De Koster et al., 2011; De Vries et al., 2016). Even in settings where the leader can be geographically distant from the follower (Howell & Hall-Merenda, 1999), SSTL leads to similar results. Also in the context of long-haul truck driving, individual driver’s personality characteristics have been linked to driving safety and productivity (De Vries et al., 2017). In explaining the mechanism through which SSTL relates to safety outcomes, Barling et al., (2002) established that safety consciousness (SC) plays a mediating role, as the transformational leader affects the safety identity/awareness of the follower. Based on this, we hypothesize that SSTL relates positively to driver safety behaviour and negatively to productivity, mediated by the effects of the driver’s safety consciousness. To investigate these relationships, we collect data from major truck transport companies in the India, including route data from the ERP systems and detailed GPS trip data, as well as psychometric data from surveys conducted among the truck drivers. We operationalize driving safety as the inverse of risky driving behaviour by using objective indicators such as speed violations and driving over extended periods of time without stoppage. We operationalize driving productivity as the advance on the estimated transit lead time, by comparing the time driven to the estimated trip duration based on Google maps. We use a regression/mixed effects model and test our hypotheses to establish SSTL’s impact on safety and driver productivity. This study aims to deepen our understanding in the effects that leadership has on truck driving performance outcomes. We expect to contribute to procedures by which transport companies select supervisors, and training and supervision practices.

Lijia Tan (Eindhoven University of Technology) – Leak or not leak? A behavioral investigation of information leakage
We experimentally investigate an information leakage problem in supply chains where two retailers competing in a linear demand consumer market are scouring from the same supplier. The demand of market has two states: either high or low. One retailer has full information about the realized state and also is the first mover of making an order from the supplier. The other retailer and the supplier only know a distribution of market state. In this setting, a moral hazard problem is raised on the supplier’s side who may leak the first retailer’s high quantity order which infers high demand to the second retailer for stimulating more orders.  A revenue-sharing contract is considered to avoid such information leakage by sharing market revenue with the supplier. We design two between-subjects treatments named Wholesale price (W) treatment and Revenue-Sharing (RS) treatment corresponding to two kinds of contract, wholesale price and revenue-sharing contracts. We found the supply chain’s surplus is not significantly different in two treatments, which is not consistent with theory predicts a higher surplus in the revenue-sharing contract. Moreover, we found the proportion of suppliers leaking high order quantity information is as high as 79.38% deviating theory predicts 0% in the revenue-sharing contract. To understand the supplier’s leaking decision deeper, we also design another four treatments with computerized retailers only implementing the optimal order quantity. Even though two retailers are all computerized, 5 out of 18 suppliers choose to leak high order quality with a proportion higher than 70%. We adopt a behavioral model to explain why the supplier still leaks in a revenue-sharing contract and provide managerial insights on contract selection incorporating in behavioral considerations.