Cambridge Judge Business School (CJBS) runs a number of seminar series, including those organised by the individual subject groups and research centres. Seminars are posted here as and when they are arranged, and tend to only take place in term-time. Sign up to our seminars mailing list to receive invitations.
Onur Boyabatli, Associate Professor, Lee Kong Chian School of Business, Singapore Management University
In practice, manufacturing firms face a number of uncertainties while choosing their capacity investment levels. Besides the uncertainty in product demand, capacity investment may also be subject to uncertainty in the availability of production resources (used together with the capacity invested) and these resources may become constraining in the production stage. The production resource can be a financial resource such as operating budget, and its shortage can be attributed to the worsened external financing conditions (eg, 2008 financial crisis). The production resource can also be a physical resource such as a component and its shortage can be attributed to a variety of factors including health and safety issues in supplier’s premises (eg, COVID-19 pandemic) and industry-wide shortage (eg, shortage in semiconductor components in the automotive industry). Motivated by these observations, this paper studies a manufacturing firm’s capacity investment decision under demand and production resource uncertainties. To this end, we consider a firm who produces and sells a single product in a single selling season to maximise its expected profit. We formulate a two-stage stochastic model. In the first stage, the firm chooses the capacity investment level in the presence of demand and production resource uncertainties. In the second stage, after both uncertainties are realised, the firm then decides on the optimal production quantity constrained by the available capacity and production resource. We conduct sensitivity analyses to examine the impact of production resource variability and its correlation with demand. We find that the firm always benefits from a higher correlation. For the effect of production resource variability, we identify the critical roles played by the correlation and the capacity investment cost. In particular, we find that the firm benefits from a lower production resource variability when the capacity investment cost is sufficiently high or the correlation is sufficiently low. In other cases, the firm benefits from a lower production resource variability only when this variability is sufficiently high; otherwise a higher production resource variability increases profitability. These results have important managerial implications on how a local versus global supply chain disruption affects the firm where correlation is weak (or zero) for the former and it is large in absolute value for the latter. (more…)
Panos Markou, Assistant Professor, UVA Darden School of Business
How should an expert committee vote to reach a collective recommendation on a complex problem? We examine how sequential versus simultaneous voting schemes shape information gathering, discussion and deliveration, voting patterns, and ultimately the decision quality of a committee’s recommendation. Combining multiple data sets based on US Food and Drug Administration (FDA) Advisory Committees’ evaluations of new drugs and medical devices, we leverage a 2007 change from sequential to simultaneous voting to establish three key insights. We show that, relative to a sequential voting protocol, the adoption of a simultaneous voting mechanism by FDA Advisory Committees led to (1) an increase in the breadth of information deliberated by the committee and a change in the linguistic characteristics used by members during discussions, (2) a reduction in unanimous voting outcomes, and (3) a reduction in the risk of an approved drug being withdrawn from the market (ie, an increase in decision quality). This suggests that simple changes in committee protocols can have large impacts on information-gathering and decision-making. (more…)
Professor Mike Peng, University of Texas at Dallas
A country’s economic system has a significant impact on the behaviour and performance of firms operating in that country. Yet, management and organisational scholars have grappled with the nature of China’s economic system. Is it capitalist? If so, what “variety” of capitalism is it? We argue that China and other countries led by communist parties follow a unique, yet inadequately understood, variety of capitalism: communist capitalism. This distinct form of capitalism has unique implications for three main types of organisational form: state-owned, privately-owned, and foreign-owned firms. Enriching the varieties of capitalism literature, we argue that it is important to distinguish communist capitalism as a unique economic system. Leveraging the scholarly struggle to come to make sense of the contradiction of “communist capitalism,” we call for novel theory building that enhances the diversity and pluralism of management and organisational research.
Yingshuai Zhao, Assistant Professor, University of Cologne
Supply chain collaboration often involves lies, particularly in the digital era where the cost of lying is low. While researchers have proposed several approaches to this problem such as mechanism design, contract design, and reputation building, these approaches are not effective in detecting lies before transactions, especially if the lies can be denied. To address this issue, we employ process data associated with decisions to detect lies. Process data refer to the information generated during the process of decisions, such as response times and mouse trajectories. By analysing process data, we can gain insights into the intra-choice dynamics that underlie the decision-making process. We set up an experiment to test the approach, in which a retailer has private demand information and a supplier makes production decisions based on the retailer’s self-reported demand. In this scenario, the retailer has the incentive to inflate demand information to maximise profits. (more…)
Professor Agni Orfanoudaki, Saïd Business School of Oxford University
There is a growing amount of evidence that machine learning (ML) algorithms can be used to develop accurate clinical risk scores for a wide range of medical conditions. However, the degree to which such algorithms can affect clinical decision-making is not well understood. Our work attempts to address this problem, investigating the effect of algorithmic predictions on human expert judgment. Leveraging an online survey of medical providers and data from a leading US hospital, we develop an ML algorithm and compare its performance with that of medical experts in the task of predicting 30-day readmissions after solid-organ transplantation. We find that our algorithm is not only more accurate in predicting clinical risk but can also positively influence human judgment. However, its potential impact is mediated by the users’ degree of algorithm aversion and trust. We show that, while our ML algorithm establishes non-linear associations between patient characteristics and the outcome of interest, human experts mostly attribute risk in a linear fashion. To capture potential synergies between human experts and the algorithm, we propose a human-algorithm “centaur” model. We show that it can outperform human experts and the best ML algorithm by systematically enhancing algorithmic performance with human-based intuition. Our results suggest that implementing the centaur model could reduce the average patient readmission rate by 26.4%, yielding up to a $770,000 reduction in annual expenditure at our partner hospital and up to $67 million savings in overall U.S. healthcare expenditures. (more…)
