“Information Presentation in Decision and Risk Analysis: Answered, Partly Answered, and Unanswered Questions”
Professor(s) Robin Keller
Co-author(s): YiTong Wong, PhD Alumnus
Accepted at: Risk Analysis, September 2016
For the last thirty years, researchers in risk analysis, decision analysis, and economics have consistently proven that decision makers employ different processes for evaluating and combining anticipated and actual losses, gains, delays and surprises. While rational models generally prescribe a consistent response, people’s heuristic processes will sometimes lead them to be inconsistent in the way they respond to information presented in theoretically equivalent ways. We point out several promising future research directions by listing and detailing a series of answered, partly answered, and unanswered questions.
“Mixed Planar and Network Single-Facility Location Problems”
Professor(s) Carlton Scott and John Turner
Co-author(s): Zvi Drezner
Accepted at: Networks, August 2016
We consider the problem of optimally locating a single facility anywhere in a network to serve both on-network and off-network demands. Off-network demands occur in a Euclidean plane, while on-network demands are restricted to a network embedded in the plane. On-network demand points are serviced using shortest-path distances through links of the network (e.g., on-road travel), whereas demand points located in the plane are serviced using more expensive Euclidean distances. Our base objective minimizes the total weighted distance to all demand points. We develop several extensions to our base model, including: (i) a threshold distance model where if network distance exceeds a given threshold, then service is always provided using Euclidean distance, and (ii) a minimax model that minimizes worst-case distance. We solve our formulations using the “Big Segment Small Segment” global optimization method, in conjunction with bounds tailored for each problem class. Computational experiments demonstrate the effectiveness of our solution procedures. Solution times are very fast (often under one second), making our approach a good candidate for embedding within existing heuristics that solve multi-facility problems by solving a sequence of single-facility problems.
“Markov Chain Models in Practice: A Review of Low Cost Software Options”
Professor(s) Robin Keller
Co-author(s): Jiaru Bai (PhD Student) and Cristina del Campo
Accepted at: Investigación Operacional, July 2016
Markov processes (or Markov chains) are used for modeling a phenomenon in which changes over time of a random variable comprise a sequence of values in the future, each of which depends only on the immediately preceding state, not on other past states. A Markov process (PM) is completely characterized by specifying the finite set S of possible states and the stationary probabilities (i.e. time-invariant) of transition between these states. The software most used in medical applications is produced by TreeAge, since it offers many advantages to the user. But, the cost of the TreeAge software is relatively high. Therefore in this article two software alternatives are presented: Sto Tree and the zero cost add-in program "markovchain" implemented in R. An example of a cost-effectiveness analysis of two possible treatments for advanced cervical cancer, previously conducted with the Treeage software, is re-analyzed with these two low cost software packages. This paper was also written in Spanish to facilitate communication with Spanish-speaking scholars in Cuba and elsewhere, who aim to conduct Markov cost effectiveness analyses and would benefit from low cost software alternatives.
“Group Selling, Product Durability and Consumer Behavior”
Professor(s) Shuya Yin
Co-author(s): Yuhong He (Ph.D. Alumna) and Saibal Ray
Accepted at: Production and Operations Management, May 2016
Firms producing complementary goods often strategically form groups and jointly sell their products to better coordinate their decisions. For consumer durables, decisions about such collaboration might be complicated due to two factors. Because of their durability and presence of used goods markets, such products engender “future” price competition between new and used goods. On the other hand, consumers of such products might be forward-looking and patient, both of which affect their purchasing behavior. In this paper, we study how the above product and consumer characteristics interact to affect the group selling decisions of complementary firms. We do so through a two-period model consisting of a value chain with two upstream manufacturers and a downstream retailer. When consumers are relatively impatient and reluctant to wait to buy later, group selling by manufacturers will take place only when the end product is relatively perishable, i.e., product durability is low. However, if consumers are patient, i.e., willing to wait, collaboration happens only when the end product is quite durable; for relatively perishable products the manufacturers sell their products separately. We also comment on how our results are affected by factors like manufacturers directly selling to end consumers or there being multiple opportunities to decide whether or not to use group selling strategy.
“Managing a Closed-Loop Supply System with Random Returns and a Cyclic Delivery Schedule”
Professor(s) Rick So
Co-author(s): Candice Huyhn (Ph.D. Alumna) and Haresh Gurnani
Accepted at: European Journal of Operational Research, May 2016
Motivated by an industry example, we develop a mathematical framework to address the inventory replenishment and capacity planning problem for a closed-loop supply system with random returns. The provider needs to deliver new or refurbished products to a group of clients under a fixed cyclic schedule, and also collects back a random portion of the used products in the subsequent delivery cycle for refurbishment. We first address the product replenishment strategy, in which only a random portion of the delivered products will be returned for refurbishment and the supplier must regularly purchase new products to replace the lost units. We then analyze the capacity decision problem where the provider uses his facility to refurbish the returned products for reuse, and the provider could incur extra refurbishing cost to handle the returned product at the end of each cycle due to insufficient capacity. Our models provide a simple decision support tool for making effective replenishment and capacity decisions in managing such a closed-loop supply system.
“The Value of Demand Forecast Updates in Managing Component Procurements for Assembly Systems”
Professor(s) Rick So
Co-author(s): James Cao (Ph.D. Alumnus)
Accepted at: IIE Transactions, May 2016
This paper examines the value of demand forecast updates in an assembly system where a single assembler must order components from independent suppliers with different lead-times. By staggering each ordering time, the assembler can utilize the latest market information, as it is developed, to form a better forecast over time. The updated forecast can subsequently be used to decide the following procurement decision. The objective of this research is to understand the specific operating environment under which demand forecast updates are most beneficial. Using a uniform demand adjustment model, we are able to derive analytical results that allow us to quantify the impact of demand forecast updates. We show that forecast updates can drastically improve profitability by reducing the mismatch cost caused by demand uncertainty.
“Thinking Styles Affect Reactions to Brand Crisis Apologies”
Professor(s) Robin Keller
Co-author(s): Shijian Wang , Liangyan Wang (Ph.D. Alumna) and Jie Li
Accepted at: European Journal of Marketing, April 2016
Purpose – This article examines how a person’s thinking style, specifically holistic versus analytic, and a firm’s crisis apology with the remedial solution framed in “why” (vs. “how”) terms can interactively impact consumers’ perceived efficacy of the firm to respond to the crisis and their impression or evaluation of the brand. Design/methodology/approach – Hypotheses were tested through three experimental studies involving 308 participants recruited in China. Participants answered survey questions investigating the interactive effects from consumers’ thinking style (culture as a proxy in study 1, measured in study 2 or primed in study 3) and a brand’s crisis apology with the remedial solution framed in “why” (vs. “how”) terms on consumers’ perceived efficacy and evaluation of the firm. Findings – The frame of the remedial solution resulting in a higher evaluation improvement depended on a consumer’s thinking style. For holistic thinkers, a “why” (vs. “how”) framed remedial solution resulted in a higher evaluation improvement; however, for analytic thinkers, a “how” (vs. “why”) framed remedial solution resulted in a higher evaluation improvement. Additionally, the results showed that a consumer’s perceived efficacy of the brand being able to successfully respond to the crisis mediated the interactive effects of the remedial solution framing and thinking styles on the evaluation improvement. Research limitations/implications – Different ways of framing the remedial solution in a firm’s apology will have different impacts on people with different thinking styles. Participants in studies 2 and 3 were recruited from samples on campus in China. Additionally, the automobile brand used in this study is fictional to avoid prior brand name or brand commitment impact. Practical implications – Our findings provide evidence that framing of the remedial solution can be leveraged as a tool to reduce negative impact resulting from a brand crisis. Specifically, our results suggest that companies may do well to employ a “why” framed remedial solution, particularly in cases where consumers are likely to process information holistically. Conversely, a “how” framed remedial solution may be effective in situations where consumers are likely to process information analytically. Originality/value – This research contributes to the literature, being among the first to consider how the remedial solution framing in a firm’s apology can enhance people’s evaluation of the brand and decrease the perceived negative impact resulting from the brand crisis.
“Trust Antecedents, Trust and Online Microsourcing Adoption: An Empirical Study from the Resource Perspective”
Professor(s) Robin Keller
Co-author(s): Baozhou Lu, Tao Zhang, and Liangyan Wang (Ph.D. Alumna)
Accepted at: Decision Support Systems, April 2016
The online microsourcing marketplace is a new form of outsourcing that is organized over online platforms for the performance of relatively small service tasks. Microsourcing offers a more flexible way to hire contract workers or to outsource. Prior research indicates the importance of individual-level trust when choosing providers in online sourcing marketplaces. We argue that institution-based trust is also crucial for online microsourcing adoptions. Drawing on a trust framework adapted from prior literature, this paper uncovers the trust-building mechanisms in online microsourcing marketplaces, as well as the marketplace-related attributes for online microsourcing adoption. The proposed research model is tested with a data set collected from the clients of a typical marketplace in China – zhubajie.com. The findings suggest that perceptions of resource-based attributes of a marketplace, together with the perceived effectiveness of its intermediary role, can help to build trust towards the marketplace, enhancing trust towards the community of providers and driving the intent to adopt online microsourcing. Thus, this paper confirms the roles of online marketplaces as both the resource pool and the transaction intermediary from the perspective of clients. Finally, this paper not only indicates the relevance of resource theories in understanding this new trend in outsourcing, but also suggests the importance of trusted relational governance in governing online microsourcing transactions.
“A Queueing Model for Managing Small Projects under Uncertainties”
Professor(s) Rick So and Ph.D. Student Jiaru Bai
Co-author(s): Chris Tang
Accepted at: European Journal of Operation Research, March 2016
We consider a situation in which a home improvement project contractor has a team of regular crew members who receive compensation even when they are idle. Because both projects arrivals and the completion time of each project are uncertain, the contractor needs to manage the utilization of his crews carefully. One common approach adopted by many home improvement contractors is to accept multiple projects to keep his crew members busy working on projects to generate positive cash flows. However, this approach has a major drawback because it causes "intentional" (or foreseeable) project delays. Intentional project delays can inflict explicit and implicit costs on the contractor when frustrating customers abandon their projects and/or file complaints or lawsuits. In this paper, we present a queueing model to capture uncertain customer (or project) arrivals and departures, along with the possibility of customer abandonment. Also, associated with each admission policy (i.e., the maximum number of projects that the contractor will accept), we model the underlying tradeoff between accepting too many projects (that can increase customer dissatisfaction) and accepting too few projects (that can reduce crew utilization). We examine this tradeoff analytically so as to determine the optimal admission policy and the optimal number of crew members. We further apply our model to analyze other issues including worker productivity and project pricing. Finally, our model can be extended to allow for multiple classes of projects with different types of crew members.
“Intention-Behavior Discrepancy of Foreign versus Domestic Brands in Emerging Markets: The Relevance of Consumer Prior Knowledge”
Professor(s) Robin Keller
Co-author(s): Luping Sun, Xiaona Zheng, and Meng Su
Accepted at: Journal of International Marketing, November 2016
Most research on the performance of foreign versus domestic brands in emerging markets examines measures of product evaluation or purchase intention. However, consumers intending to buy a product may switch to competing brands, displaying an intention-behavior discrepancy (IBD). Drawing upon literature on country associations and dual process theory, we examine the difference in IBD of foreign versus domestic brands in emerging markets and the moderating role of prior knowledge. We conducted an intention survey followed by a post-purchase survey in the Chinese automobile and smartphone industries. We found that foreign brands have an advantage on IBD relative to domestic brands, indicating that they have the dual advantage of higher evaluations and lower IBDs. Furthermore, foreign brands’ advantage on IBD is smaller for consumers with inaccurate prior knowledge, as they are more likely to systematically reprocess information and discount foreign brands’ favorable country associations. For these consumers, overestimating the product reduces foreign brands’ advantage to a smaller degree than underestimating it due to confirmation bias. These findings provide implications for brands in emerging markets.
“A Unified Framework for the Scheduling of Guaranteed Targeted Display Advertising under Reach and Frequency Requirements”
Professor(s) John Turner
Co-author(s): Ali Hojjat (Ph.D. Alumnus), Suleyman Cetintas, Jian Yang
Accepted at: Operations Research, October 2016
Motivated by recent trends in online advertising and advancements made by online publishers, we consider a new form of contract which allows advertisers to specify the number of unique individuals that should see their ad (reach), and the minimum number of times each individual should be exposed (frequency ). We develop an optimization framework that aims for minimal under-delivery and proper spread of each campaign over its targeted demographics. As well, we introduce a pattern-based delivery mechanism which allows us to integrate a variety of interesting features into a website’s ad allocation optimization problem which have not been possible before. For example, our approach allows publishers to implement any desired pacing of ads over time at the user level or control the number of competing brands seen by each individual. We develop a two-phase algorithm that employs column generation in a hierarchical scheme with three parallelizable components. Numerical tests with real industry data show that our algorithm produces high-quality solutions and has promising run-time and scalability. Several extensions of the model are presented, e.g., to account for multiple ad positions on the webpage, or randomness in the website visitors’ arrival process.