Latest Seminars

“Un”Fair Machine Learning Algorithms
Dr. Manmohan ASERI, Visiting Assistant Professor, Tepper School of Business, Carnegie Mellon University, USA

Date 15.11.2019
Time 10:30 am - 12:00 pm
Venue Case Room G001, G/F, LSK Business Building

Blockchain Adoption for Combating Deceptive Counterfeits
Dr Hubert Pun, Professor and PhD Program Coordinator, Ivey Business School, Western University

In this paper, we study combating deceptive counterfeiting using blockchain technology. When a product is tagged with a non-duplicable unique identifier customers know whether a product is authentic or fake. However, customers have privacy concerns while using blockchain. We consider a market with a manufacturer and a deceptive counterfeiter. The manufacturer can either use blockchain or signal through pricing to validate product authenticity. The government can incentivize blockchain adoption by providing subsidy to the manufacturer while optimizing social welfare. We find that without government subsidy, blockchain should be used only when the counterfeit quality is intermediate or when customers have intermediate distrust about products in the market. When customers have serious distrust about products, differential pricing strategy is more effective than blockchain. With subsidy, however, we show that differential pricing strategy should never be used. Blockchain can be more effective than differential pricing strategy in eliminating post-purchase regret, and our result advocates that government should participate in the manufacturer’s blockchain adoption decision because this benefits customers and society.

Key words: blockchain, customer privacy, deceptive counterfeit, government subsidy

Date 11.11.2019
Time 11:00 am - 12:15 pm
Venue Room G001, LSK Business Building

Bug Bounty Programs, Security Investment and Law Enforcement: A Security Game Perspective
Mr. Jiali Zhou, PhD student, ISOM

Date 01.11.2019
Time 10:30 am - 12:00 pm
Venue ISOM Conference Room 4047, LSK Business Building

Assigning Priorities (or not) in Service Systems with Nonlinear Waiting Costs
Dr Huiyin Ouyang, Assistant Professor, School of Business and Economics, The University of Hong Kong

For a queueing system with multiple types of customers differing in service time distributions and costs for waiting, it is well known that the rule is optimal if costs for waiting are incurred linearly with time. It is also known that when costs are convex increasing functions, the generalized rule, which is dependent on queued customers' already experienced waiting times, is asymptotically optimal under heavy traffic. In this paper, we seek to identify policies that minimize the long-run average cost under possibly nonlinear waiting cost functions and arbitrary traffic conditions but within the set of static policies that require information only on the type identities of jobs and their order of arrival. For a single-server queueing system with two types of customers, our main result gives conditions under which type-based priority policies and the first-come-first-serve (FCFS) policy can be ordered for non-decreasing cost functions that are first-order differentiable. We then apply this result to polynomial cost functions and obtain useful insights into when prioritization should be preferred over FCFS and when it should be avoided. For example, unlike in the linear-cost case, it could be better not to give priority to a certain type at all and employ FCFS under quadratic cost functions, especially when the traffic intensity is high. Finally, by means of a numerical study, we test how the best static policy compares with the generalized rule, which requires information on the current waiting times of customers and precise structure for the cost functions. In these experiments with quadratic waiting costs, we find that using the best static policy performs comparably with (sometimes even better than) the generalized rule except when the traffic intensity is high and there is not a clearly more “important” type in regards to dominance with respect to rates of cost and service.

Date 01.11.2019
Time 11:00 am - 12:15 pm
Venue Room 1001, LSK Business Building

Financial News Credibility Measurement and Analysis in Fintech Applications
Mr. Ka Chung NG, Boris, PhD student, ISOM

Date 25.10.2019
Time 10:30 am - 12:00 pm
Venue ISOM Conference Room 4047, LSK Business Building

Misreporting Demand Information
Dr Xi Li, Assistant Professor, Department of Marketing, The City University of Hong Kong

In this study, we investigate information sharing in a distribution channel in which the retailer possesses superior information about market demand. Departing from the existing literature on information sharing which assumes that information sharing must be truthful, we allow the retailer to manipulate and misreport its demand information for its benefit at an information manipulation cost. We find that the retailer's ability to manipulate information has substantial effects on the equilibrium outcome: when the cost of manipulation is low, the retailer cannot help but to deflate its demand forecast (even if the actual demand is high) to convince the manufacturer to offer it a low wholesale price. When the cost of manipulation is moderate, the retailer, in the case of high demand, randomizes between misreporting and truthful reporting. Finally, when the cost of manipulation is high, the retailer never misreports its demand information. While the manufacturer's profit increases with the manipulation cost, the retailer's profit is nonmonotone with this cost. At first, it decreases but only up to a certain point, after which the effect is reversed. Within a certain parameter space, the retailer's ability to manipulate information hurts both the manufacturer's and retailer's profits, thereby creating a lose-lose situation. Collectively, these results underscore the significant effects of information manipulation in distribution channel management.

 

Date 11.10.2019
Time 11:00 am - 12:15 pm
Venue Room 1001, LSK Business Building

Effect of Consumer Awareness on Corporate Social Responsibility Under Asymmetric Information
Dr Xiaomeng Guo, Assistant Professor, Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University

This paper studies the interaction between a firm and consumers under the consideration of corporate social responsibility. The firm can be either socially responsible or socially irresponsible; however, the consumers cannot observe the firm's exact type, which is private information. The firm can try to signal its type through pricing and other information-disclosure mechanisms (e.g., issue sustainability reports or third-party certifications). We find that due to the existence of asymmetric information, increasing consumer awareness of corporate social responsibility may not necessarily benefit a firm with socially responsible behaviors. More specifically, when a larger fraction of consumers become socially concerned or when the consumers have stronger willingness to reward (punish) the responsible (irresponsible) firm, the responsible firm could be worse off whereas the irresponsible firm could be better off. This is because the seemingly favorable trend in consumer behavior will affect the responsible firm's signaling cost as well as its equilibrium strategy (separating vs. pooling). In addition, we find that improving the accuracy of the deployed signaling mechanism will always benefit the responsible firm but may or may not hurt the irresponsible firm. Our results suggest that addressing the information asymmetry issue is the key to align consumers' goodwill with firms' responsible corporate behaviors. In particular, concerned parties should first exert efforts to create transparency in firms' sustainability practices before making investments to educate consumers and influence their purchasing behaviors. We further provide several model discussions to both confirm the robustness of our results and derive additional managerial insights.

Date 04.10.2019
Time 11:00 am - 12:15 pm
Venue Room 3003, LSK Business Building

Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test
Prof. Ke-Wei Huang, Department of Information Systems and Analytics, National University of Singapore

Date 20.09.2019
Time 10:30 am - 12:00 pm
Venue ISOM Conference Room 4047, LSK Business Building

Data-driven Consumer Debt Collection via Machine Learning
Dr Qingchen Wang, Innovation and Information Group, Faculty of Business and Economics, The University of Hong Kong

This paper develops and tests a data-driven framework for the scheduling of outbound calls made by debt collectors. We determine on a daily basis which debtors should be called to maximize the amount of debt recovered in the long term, under the constraint that only a limited number of phone calls can be made each day. Our approach is inspired by Markov decision processes, but given the intractability arising from having an extensive state space, we approximate the value function based on detailed historical data through the use of machine learning. Specifically, we predict the likelihood with which a debtor in a particular state is going to settle his debt and use this as a proxy for the value function. Based on this approximation, we compute for each debtor the marginal value of making a call, and prescribe phone calls by prioritizing debtors in states that have the highest marginal value. This approach is flexible, and is able to exploit all information available in the data regardless of complexity. We validate our methodology using a controlled field experiment conducted with 921 real debtors in partnership with a mid-sized debt collection agency. The results show that our data-driven policy substantially outperforms the incumbent calling policy that has been used in business practice for many years—collecting more debt while using substantially fewer resources. To conclude, the performance uplift from the experiment suggests that our framework is able to learn from the data and improve calling decisions.

Date 06.09.2019
Time 11:00 am - 12:15 pm
Venue Room 1001, LSK Business Building

Achieving High Individual Service-Levels without Safety Stock? Optimal Rationing Policy of Pooled Resources
Professor Jiawei Zhang, Professor of Technology, Operations, and Statistics and Robert Stansky Research Faculty Fellow, Stern School of Business, New York University

Resource pooling is a fundamental concept that has many applications in Operations Management for reducing and hedging uncertainty. An important problem in resource pooling is to decide (1) the capacity level of pooled resources in anticipation of random demand of multiple customers and (2) how the capacity should be allocated to fulfill customer demands after demand realization. In this paper, we present a general framework to study this two-stage problem when customers require individual and possibly different service-levels. Our modeling framework generalizes and unifies many existing models in the literature.

We propose a simple randomized rationing policy for any fixed feasible capacity level. Our main result is the optimality of this policy for very general service-level constraints, including Type-I and Type-II constraints and beyond. The result follows from a semi-infinite linear programming formulation of the problem and its dual. We also prove optimality of index policies for a large class of problems when the set of feasible fulfilled demands is a polymatroid.

Date 29.07.2019
Time 11:00 am - 12:15 pm
Venue Room 3003, LSK Business Building

A Primal-Dual Approach to Analyzing ATO Systems
Prof Yehua Wei, Fuqua School of Business, Duke University

We study assemble-to-order (ATO) problems from the literature. ATO problems with general structure and integrality constraints are well known to be difficult to solve, and we provide new insight into these issues by establishing worst-case approximation guarantees through various primal-dual analyses and LP rounding. First, we relax the one-period ATO problem using a natural newsvendor decomposition and use the dual solution for the relaxation to derive a lower bound on optimal cost, providing a tight approximation guarantee that grows with the maximum product size in the system. Then, we present LP rounding algorithms that achieve both asymptotic optimality as demand grows large, and a 1.8 approximation factor for any problem instance. Finally, we demonstrate that our one-period LP rounding results can be extended to analyze dynamic ATO problems. Specifically, we use our rounding scheme to develop an asymptotically optimal integral policy for dynamic ATO problems with backlogging and identical component lead-times.

Date 04.07.2019
Time 11:00 am - 12:15 pm
Venue Room 3003, LSK Business Building

The Overlooked Benefit of Consumer Showrooming for a Physical Retailer
Professor Lin Hao, Department of Information Systems and Operations Management, University of Washington

Date 27.06.2019
Time 10:30 am - 12:00 noon
Venue ISOM Conference Room 4047, LSK Business Building

Proactively Managing the Early Stages of an IS Academic Career
Professor Arun Rai, Regents’ Professor of the University System of Georgia, Robinson and Harkins Chairs of Information Systems, Robinson College of Business, Georgia State University

Date 25.06.2019
Time 10:30 am - 12:00 noon
Venue ISOM Conference Room 4047, LSK Business Building

To Brush or Not to Brush: Product Rankings, Customer Search, and Fake Orders
Prof Luyi Yang, Carey Business School, Johns Hopkins University

“Brushing”---the practice of online merchants placing fake orders of their own products to artificially inflate sales on e-commerce platforms---has recently received widespread public attention. On the one hand, brushing enables merchants to boost their rankings in search results, because products with higher sales volume are often ranked higher. On the other hand, rankings matter because search frictions faced by customers narrow their attention to only the few products that show up at the top. Thus, fake orders from brushing may affect customer choice. We build a stylized model to understand merchants’ strategic brushing behavior and its welfare implications. We consider two competing merchants selling substitutable products (one of high quality, the other of low quality) in an evolutionary sales-based ranking system that assigns a higher ranking to a product with higher sales. In principle, such an adaptive system improves customer welfare relative to a case in which products are randomly ranked, but it also triggers brushing as an unintended consequence. Since the high-quality merchant receives a favorable bias in the sales-based ranking, he mainly has a defensive brushing incentive, whereas the low-quality merchant mostly has an offensive brushing incentive. As a result, brushing is a double-edged sword for customers. It may lead customer welfare to be even lower than what it would be in a random-ranking system, but in some other cases, it can surprisingly improve customer welfare. If brushing is more difficult for merchants (e.g., due to tougher regulations), it may make customers worse off as it attenuates brushing by the high-quality merchant but induces the low-quality one to brush more aggressively. If search is easier for customers (e.g., due to improved search technologies), it can actually hurt them as it may disproportionately discourage the high-quality merchant from brushing.

Date 14.06.2019
Time 11:00 am - 12:15 pm
Venue Room 1003, LSK Business Building

Digital Multisided Platforms and Women's Health: An Empirical Analysis of Peer-to-Peer Lending and Abortion Rates
Professor Anandasivam Gopal, Dean's Professor of Information Systems, Robert H. Smith School of Business, University of Maryland

Date 10.06.2019
Time 10:30 am - 12:00 noon
Venue Room 1003, LSK Business Building