By Anton Ivanov, Abhijeet Ghoshal, and Akhil Kumar📧
In Production and Operations Management, 2024. https://doi.org/10.1177/10591478241282327
Online retail platforms are increasingly challenged by the proliferation of low-quality products, which may damage their reputation and sales. To address this problem, we propose a system architecture to proactively identify products that are likely to go “out of favor”. Our approach uses historical data to extract useful information from customer ratings and textual reviews. Available data are fed into a state-of-the-art deep learning sequence model to forecast future ratings. We then analyze rating trends, extracting hyperparameters that a binary classifier uses to label products as “out of favor” or not. We tested this system on an Amazon dataset comprising nearly 800,000 observations across 2,826 electronics products. Our results show that the Long Short-Term Memory (LSTM) model excels in forecasting future product ratings compared to other benchmarks. Ablation analysis shows sentiment-related features significantly improve rating forecasts by up to 40%, with review topics adding 10% and other review characteristics 4%. Counterintuitively, topic extraction from reviews does not provide substantial benefits, despite the heavy computational resources it requires. Finally, the two-stage classification process, which leverages time-series data and rating trends, offers a more stable and robust performance than conventional single-stage methods. We provide considerations for system architecture development through robustness checks ensuring its resilience to stressors. Our experiments indicate that rating trends can change in subtle ways over time, leading a promising “star” product to turn into a liability (“dog”). E-commerce platforms can use the proposed system architecture proactively to identify and remove potentially dubious products instead of waiting to take reactive action.
Although online peer-production systems have proven to be effective in producing high-quality content, their open call for participation makes them susceptible to ongoing quality problems. A key concern is that the problems should be addressed quickly to prevent low-quality content from remaining in place for extended periods. We examine the impacts of two control mechanisms, bots and policy citations, and the number of contributors, with and without prior experience in editing an article, on the cleanup time of 4,473 quality problem events in Wikipedia. We define cleanup time as the time it takes to resolve a quality problem once it has been detected in an article. Using an accelerated failure time model, we find that the number of bots editing an article during a quality problem event has no effect on cleanup time; that citing policies to justify edits during the event is associated with a longer cleanup time; and that more contributors, with or without prior experience in editing the article, are associated with a shorter cleanup time. We also find important interactions between each of the two control mechanisms and the number of different types of contributors. There is a marginal increase in cleanup time that is larger when an increase in the number of contributors is accompanied by fewer bots editing the article during a quality problem event. This interaction effect is more pronounced when increasing the number of contributors without prior experience in editing the article. Further, there is a marginal decrease in cleanup time that is larger when an increase in the number of contributors, with or without prior experience in editing the article, is accompanied by fewer policy citations. Taken together, our results show that the use of bots and policy citations as control mechanisms must be considered in conjunction with the number of contributors with and without prior experience in editing an article. Accordingly, the number of contributors and their experience alone may not explain important outcomes in peer production; it is also important to find an appropriate mix of different control mechanisms and types of contributors to address quality problems quickly.
This study investigates the factors influencing consumers’ adoption of parcel lockers for last-mile deliveries. Attitudinal data on parcel locker adoption were collected from individuals in the United States, and structural equation models were estimated to assess consumers’ intention to use the lockers as a delivery method. The study is grounded in the Technology Acceptance Model, evaluating perceived ease of use, usefulness, risks, and location preferences. Consumer attitudes indicate that parcel lockers are perceived as easy to use and useful, offering efficient and fast delivery times. Moreover, consumers generally regard these facilities as safe. However, perceived risks include concerns about privacy and the potential loss of packages. The results show that urban residents are less likely to use parcel lockers than suburban residents. Additionally, consumers show a preference for parcel lockers located near their workplaces. The modeling results provide critical implications for service providers, highlighting the features that users value when considering this delivery alternative, thereby aiding in evaluating potential parcel locker implementations.
The case study has two primary objectives: incorporating fairness considerations in managerial decision making and introducing task allocation using an optimization model. The case focuses on a call center that faces the issue of daily task allocation. The current self-selection-based allocation protocol has created friction among employees. To enable employee satisfaction and boost team morale, the call center needs to develop an allocation model that is perceived to be fair by employees. The case tends to elicit enthusiastic participation from students, especially on the theme of workplace fairness and the role of gender and individual constraints on employees’ ability to excel at the workplace. Overall, students believed the case was challenging and that it provided them skills to combine (i) optimization modeling and (ii) qualitative considerations for workplace fairness that they studied in management and leadership courses.
The bioeconomy is a complex system involving a plethora of bio-products and several groups of stakeholders—such as government institutions, industry, environmental organizations, and civil society—across the bioeconomy supply chain. Successful bioeconomy activities hinge on the collective efforts and coordinated development across all involved. This study seeks to understand different stakeholder groups’ perceptions, expectations, needs, issues, goals, and constraints as related to the development of the U.S. bioeconomy, with biochar as a bioproduct of focus. Focus groups were held with a representative sample of stakeholders involved in the bioeconomy. Results show encouraging trends in increased interests and awareness, and carbon market development; while regulatory structure, production capacity and commercialization, and developing industry standards present key areas of challenges. Going forward, large-scale real-world research, commercial viability, and education are perceived to be imperative.
When stakeholders are unaware of information that is of relevance to them and when this information is costly to obtain (due to costs associated with information awareness, acquisition, and integration), those stakeholders suffer from information asymmetry vis-à-vis the producer and owner of this information. To overcome those conditions, mandated disclosure has been used in a number of settings, requiring the owner of information such as a chemical manufacturer to disclose the types and level of toxic releases it produces. However, without the ease of information acquisition and subsequent dissemination, such disclosure’s value remains limited. In this paper, we study broadband penetration in the United States as an enabler of the public to be better informed about manufacturers’ pollution and its implications on curbing toxic releases. Because some marginalized communities have been documented to suffer more from such toxic releases, we also study the disproportionate impact of broadband on Black communities.