Signaling Quality with Return Insurance: Theory and Empirical Evidence

2021 ◽  
Author(s):  
Chong Zhang ◽  
Man Yu ◽  
Jian Chen
Keyword(s):  
Private Information ◽  
Operations Management ◽  
Consumer Surplus ◽  
Third Party ◽  
Quality Uncertainty ◽  
High Quality ◽  
Data Set ◽  
Effective Signal ◽  
Information Consumers ◽  
Signaling Quality

This paper examines an innovative return policy, return insurance, emerging on various shopping platforms such as Taobao.com and JD.com. Return insurance is underwritten by an insurer and can be purchased by either a retailer or a consumer. Under such insurance, the insurer partially compensates consumers for their hassle costs associated with product return. We analyze the informational roles of return insurance when product quality is the retailer’s private information, consumers infer quality from the retailer’s price and insurance adoption, and the insurer strategically chooses insurance premiums. We show that return insurance can be an effective signal of high quality. When consumers have little confidence about high quality and expect a significant gap between high and low qualities, a high-quality retailer can be differentiated from a low-quality retailer solely through its adoption of return insurance. We confirm, both analytically and empirically with a data set consisting of more than 10,000 sellers on JD.com, that return insurance is more likely adopted by higher-quality sellers under information asymmetry. Furthermore, we find that the presence of the third party (i.e., the insurer) leads to double marginalization in signaling, which strengthens a signal’s differentiating power and sometimes renders return insurance a preferred signal, in comparison with free return, whereby retailers directly compensate for consumers’ return hassles. As an effective and costly signal of quality, return insurance may also improve consumer surplus and reduce product returns. Its profit advantage to the insurer is most pronounced under significant quality uncertainty. This paper was accepted by Vishal Gaur, operations management.

scholarly journals IMPLEMENTATION OF PRIVACY PRESERVING AND DYNAMIC SEARCHING MECHANISM WITH BIOMETRIC AUTHENTICATION IN CLOUD STORAGE

2021 ◽  
Vol 9 (2) ◽  
pp. 894-912
Author(s):  
Sarita Motghare, Et. al.
Keyword(s):  
Private Information ◽  
Cloud Storage ◽  
User Authentication ◽  
Third Party ◽  
Mining Operations ◽  
Search System ◽  
Data Set ◽  
External Data ◽  
Main Challenge ◽  
Better Than

In the recent times, cloud storage tends to be a primary storage means for external data. Cloud defense of the data against attacks is the main challenge. Private or semi-private information growth has rapidly expanded over the information network; privacy safeguards have failed to address the search mechanisms. In the field of information networks, privacy protection is an important factor in carrying out various data mining operations with encrypted data stored in different storage systems. A tolerance and protection against data corruption mechanism should be developed which is difficult to achieve. Furthermore, as there is no adequate audit mechanism, the integrity of the stored data become questionable. In addition to this, the user authentication is another challenge. The current solution provides only a remote audit mechanism. It requires data owners to always remain online so that the auditing process is manually handled, which is sometimes unworkable. In this paper, we propose a new, regenerative, public audit methodology accompanied by third-party audits. The existing data search system provides one solution that can be used to maintain the confidentiality of indexing. Documents are stored on a private server in plain word form, which compromise the protection of privacy. So that this system is improved to make the document more secure and efficient, we first store the documents in encrypted form on server, and use the Key Distribution Center (KDC). To generate keys the KDC uses the user's biometric feature. In order to improve the search experience, we also implement TF-IDF, which provides an efficient evaluation of the results. Lastly, we carry out comprehensive data set experiments to evaluate our proposed system performance. Experimental results demonstrate that in terms of safeguarding the privacy, efficient and safe search for encrypted distributed documents the proposed system is better than existing. The methodology suggested also includes an auditing mechanism by third parties to ensure data integrity.

scholarly journals 344 The Ag Guide serves as a primary standard for animal scientists and AAALAC accreditation of Ag research programs

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 71-72
Author(s):  
Janeen Salak-Johnson
Keyword(s):  
Animal Welfare ◽  
Primary Standard ◽  
Animal Research ◽  
Third Party ◽  
Best Interests ◽  
Professional Judgment ◽  
High Quality ◽  
Accreditation Program ◽  
Agricultural Animal ◽  
Animal Care

Abstract Institutions that engage in animal research and production must ensure that high standards of animal care and use meet expectations of society while being ethical stewards of the animals they use in research. In order to achieve engagement in best practices, the Ag Guide is the most appropriate standard for assessing agricultural animals used in research and teaching. The Ag Guide minimizes the potential to overuse performance standards while enhancing the ability to appropriately address specific performance-derived exceptions to situations for which they have been validated. The primary objectives of the standards established in the Ag Guide are well-aligned with the goals of the AAALAC International accreditation program. The Ag Guide provides scientifically-sound, performance-based approaches to animal care and housing, which meet the expectations of AAALAC’s accreditation program. AAALAC provides a third-party peer review of all facets of the animal care and use program that serves as an effective mechanism to ensure institutions meet the standards of the Ag Guide. The process is designed to help identify the strengths and weaknesses of the program to ensure high-quality scientific outcomes and a high level of animal welfare. AAALAC accreditation program for agricultural animal research program is built on the cornerstone of the Ag Guide standards and connects science and responsible animal care. AAALAC accreditation promotes a comprehensive, institutionally supported program with a commitment to continuous improvement, humane and ethical animal care resulting in high-quality animal welfare, and scientific validity. AAALAC takes the position that, in accredited programs, the housing and care for agricultural animals should meet the standards that prevail on a high-quality, well-managed farm and the Ag Guide serves as this foundation. Therefore, the use of the Ag Guide for agricultural animal programs ensures a review that is based on science, professional judgment, and the best interests of the animal.

scholarly journals Text Mining of Hazard and Operability Analysis Reports Based on Active Learning

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1178
Author(s):  
Zhenhua Wang ◽  
Beike Zhang ◽  
Dong Gao
Keyword(s):  
Active Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Chemical System ◽  
Chemical Safety ◽  
High Quality ◽  
Data Set ◽  
Final Model ◽  
Named Entity ◽  
Sampling Algorithms

In the field of chemical safety, a named entity recognition (NER) model based on deep learning can mine valuable information from hazard and operability analysis (HAZOP) text, which can guide experts to carry out a new round of HAZOP analysis, help practitioners optimize the hidden dangers in the system, and be of great significance to improve the safety of the whole chemical system. However, due to the standardization and professionalism of chemical safety analysis text, it is difficult to improve the performance of traditional models. To solve this problem, in this study, an improved method based on active learning is proposed, and three novel sampling algorithms are designed, Variation of Token Entropy (VTE), HAZOP Confusion Entropy (HCE) and Amplification of Least Confidence (ALC), which improve the ability of the model to understand HAZOP text. In this method, a part of data is used to establish the initial model. The sampling algorithm is then used to select high-quality samples from the data set. Finally, these high-quality samples are used to retrain the whole model to obtain the final model. The experimental results show that the performance of the VTE, HCE, and ALC algorithms are better than that of random sampling algorithms. In addition, compared with other methods, the performance of the traditional model is improved effectively by the method proposed in this paper, which proves that the method is reliable and advanced.

scholarly journals Is Access Enough? Interrogating the Influence of Money and Power in Shaping Information

2020 ◽  
Vol 4 (1) ◽  
pp. 29-38
Author(s):  
Heather Brodie Perry
Keyword(s):  
Open Access ◽  
Critical Evaluation ◽  
Access To Information ◽  
Power Structures ◽  
High Quality ◽  
Information Power ◽  
Complex Information ◽  
Community Information ◽  
Truth Claims ◽  
Information Consumers

AbstractAccess to information encourages innovation and leads to participation in society of individuals. The emergence of Open Access supports the inclusion of all, including the voices of the traditionally marginalized, yet access alone is insufficient to enable consumers to effectively use information. Power structures can influence the information available and silence opposing viewpoints. Industry disinformation can influence viewpoints and shape policy in ways that can be detrimental to individuals and the community. Information consumers may not possess the competence required to navigate the complex information ecosystem to find the accurate, high-quality, resources required to meet their need. Libraries have a role in assisting consumers develop the critical evaluation capabilities essential to the exercise of informed skepticism when evaluating truth claims. Access is essential; however, without the knowledge to determine the quality and validity of information, a consumer can be misled in ways that can cause harm to themselves and society.

Value of High-Quality Logistics: Evidence from a Clash Between SF Express and Alibaba

2020 ◽  
Vol 66 (9) ◽  
pp. 3879-3902 ◽  
Author(s):  
Ruomeng Cui ◽  
Meng Li ◽  
Qiang Li
Keyword(s):  
Operations Management ◽  
Economic Value ◽  
Product Variety ◽  
Star Products ◽  
High Quality ◽  
Long Tail ◽  
Online Retail ◽  
Delivery Service ◽  
Product Delivery ◽  
Identification Strategy

Consumers regard product delivery as an important service component that influences their shopping decisions on online retail platforms. Delivering products to customers in a timely and reliable manner enhances customer experience and companies’ profitability. In this research, we explore the extent to which customers value a high-quality delivery experience when shopping online. Our identification strategy exploits a natural experiment: a clash between SF Express and Alibaba, the largest private logistics service provider with the highest reputation in delivery quality in China and the largest online retail platform in China, respectively. The clash resulted in Alibaba unexpectedly removing SF Express as a shipping option from Alibaba’s retail platform for 42 hours in June 2017. Using a difference-in-differences design, we analyze the market performance of 129,448 representative stock-keeping units on Alibaba to quantify the economic value of a high-quality delivery service to sales, product variety, and logistics rating. We find that the removal of the high-quality delivery option from Alibaba’s retail platform reduced sales by 14.56% during the clash, increased the contribution of long-tail to total sales—sales dispersion—by 3%, but did not impact the variety and logistics rating of sold products. Furthermore, we also identify product characteristics that attenuate the value of high-quality logistics and find that the removal of SF Express is more obstructive for (1) star products as compared with long-tail products because the same star products are likely to be supplied by competing retail platforms that customers can easily switch to, (2) expensive products because customers need a reliable delivery service to protect their valuable items from damage or loss, and (3) less-discounted products because customers are more willing to sacrifice the service quality over a price markdown. This paper was accepted by Victor Martínez-de-Albéniz, operations management.

The Interplay Between Online Reviews and Physician Demand: An Empirical Investigation

2021 ◽  
Author(s):  
Yuqian Xu ◽  
Mor Armony ◽  
Anindya Ghose
Keyword(s):  
Service Quality ◽  
Operations Management ◽  
Choice Model ◽  
Healthcare Delivery ◽  
Online Reviews ◽  
The United States ◽  
Patient Choice ◽  
Data Set ◽  
Textual Information ◽  
Time Service

Social media platforms for healthcare services are changing how patients choose physicians. The digitization of healthcare reviews has been providing additional information to patients when choosing their physicians. On the other hand, the growing online information introduces more uncertainty among providers regarding the expected future demand and how different service features can affect patient decisions. In this paper, we derive various service-quality proxies from online reviews and show that leveraging textual information can derive useful operational measures to better understand patient choices. To do so, we study a unique data set from one of the leading appointment-booking websites in the United States. We derive from the text reviews the seven most frequently mentioned topics among patients, namely, bedside manner, diagnosis accuracy, waiting time, service time, insurance process, physician knowledge, and office environment, and then incorporate these service features into a random-coefficient choice model to quantify the economic values of these service-quality proxies. By introducing quality proxies from text reviews, we find the predictive power of patient choice increases significantly, for example, a 6%–12% improvement measured by mean squared error for both in-sample and out-of-sample tests. In addition, our estimation results indicate that contextual description may better characterize users’ perceived quality than numerical ratings on the same service feature. Broadly speaking, this paper shows how to incorporate textual information into an econometric model to understand patient choice in healthcare delivery. Our interdisciplinary approach provides a framework that combines machine learning and structural modeling techniques to advance the literature in empirical operations management, information systems, and marketing. This paper was accepted by David Simchi-Levi, operations management.

RiRiShun Logistics: Home Appliance Delivery Data for the 2021 Manufacturing & Service Operations Management Data-Driven Research Challenge

2021 ◽  
Author(s):  
Xiaolong Guo ◽  
Yugang Yu ◽  
Gad Allon ◽  
Meiyan Wang ◽  
Zhentai Zhang
Keyword(s):  
Operations Management ◽  
Service Operations ◽  
Optimization Techniques ◽  
Data Driven ◽  
Analytical Models ◽  
Data Set ◽  
Service Operations Management ◽  
Level Data ◽  
Home Appliance ◽  
Research Challenge

To support the 2021 Manufacturing & Service Operations Management (MSOM) Data-Driven Research Challenge, RiRiShun Logistics (a Haier group subsidiary focusing on logistics service for home appliances) provides MSOM members with logistics operational-level data for data-driven research. This paper provides a detailed description of the data associated with over 14 million orders from 149 clients (the consigners) associated with 4.2 million end consumers (the recipients and end users of the appliances) in China, involving 18,000 stock keeping units operated at 103 warehouses. Researchers are welcomed to develop econometric models, data-driven optimization techniques, analytical models, and algorithm designs by using this data set to address questions suggested by company managers.

Does Water Scarcity Affect Environmental Performance? Evidence from Manufacturing Facilities in Texas

2021 ◽  
Author(s):  
Suresh Muthulingam ◽  
Suvrat Dhanorkar ◽  
Charles J. Corbett
Keyword(s):  
Water Scarcity ◽  
Operations Management ◽  
Environmental Performance ◽  
Data Set ◽  
Full Sample ◽  
Toxic Releases ◽  
Manufacturing Operations ◽  
Toxic Emissions ◽  
Manufacturing Facilities ◽  
The Impact

It is well known that manufacturing operations can affect the environment, but hardly any research explores whether the natural environment shapes manufacturing operations. Specifically, we investigate whether water scarcity, which results from environmental conditions, influences manufacturing firms to lower their toxic releases to the environment. We created a data set that spans 2000–2016 and includes details on the toxic emissions of 3,092 manufacturing facilities in Texas. Additionally, our data set includes measures of the water scarcity experienced by these facilities. Our econometric analysis shows that manufacturing facilities reduce their toxic releases into the environment when they have experienced drought conditions in the previous year. We examine facilities that release toxics to water as well as facilities with no toxic releases to water. We find that the reduction in total releases (to all media) is driven mainly by those facilities that release toxic chemicals to water. Further investigation at a more granular level indicates that water scarcity compels manufacturing facilities to lower their toxic releases into media other than water (i.e., land or air). The impact of water scarcity on toxic releases to water is more nuanced. A full-sample analysis fails to link water scarcity to lower toxic releases to water, but a further breakdown shows that manufacturing facilities in counties with a higher incidence of drought do lower their toxic releases to water. We also find that facilities that release toxics to water undertake more technical and input modifications to their manufacturing processes when they face water scarcity. This paper was accepted by David Simchi-Levi, operations management.

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