Explore the vast range of titles available.

Background Non-suicidal self-injury (NSSI) behaviors pose a significant threat to the physical and psychological well-being of adolescents. Recent research suggests that persistent, uncontrollable and repetitive NSSI can be conceptualized as a behavioral addiction. The addictive feature of NSSI behavior can be assessed using Ottawa self-injury inventory (OSI), the higher addiction score indicates the more serious NSSI behavior. This study aims to explore the relationship of impulsivity and decision-making on the addictive features of NSSI in adolescents with depressive disorder, to explore the influencing factors of behavioral addictive features of NSSI and to predict the addictive features of NSSI. Methods Using a cross-sectional design, a total of 126 adolescent outpatients and inpatients with a mean age of 15.49 years old ( M  = 15.49, SD  = 1.56), male students ( n  = 28, 22.2%) and female students ( n  = 98, 77.8%) diagnosed with depressive disorders were recruited according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), and clinical interviews were completed by two psychiatrists. NSSI addictive features according to the OSI’s addictive features items. The final group was categorized into three groups: depression without NSSI ( n  = 42), depression with NSSI without addictive features ( n  = 44), and depression with NSSI and addictive features ( n  = 40). The present study employed the Hamilton Depression Scale (HAMD-24), Chinese Revised Barratt Impulsiveness Scale Version 11 (BIS-11), OSI, and the Adolescent Non-Suicidal Self-Injury Questionnaire (ANSSIQ). Cognitive decision-making abilities were assessed using the Iowa Gambling Task (IGT). Results The depression with NSSI addictive features group had significantly lower total net scores and net scores of block3, block4, and block5 in the IGT than the depression without NSSI group, whereas there was no statistically significant difference between the two in net scores of block1 and block2. Lower scores mean more unfavorable decisions and strategy adjustments. The addictive features of NSSI behaviors were significantly and positively correlated with the severity of NSSI behaviors, depression, and cognitive impulsiveness, and significantly and negatively correlated with the total net score of the IGT. The severity of NSSI behaviors, severity of depression, cognitive impulsiveness positively predicts the addictive features of NSSI behaviors, the total net score of the IGT negatively predicted the addictive features of NSSI behaviors. Conclusion Adolescents with depressive disorders with NSSI behavioral addictive features had higher severity of depression, exhibited higher cognitive impulsivity, and made more unfavorable decisions when making choices.

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.

Recent research has found widespread discrimination by hosts against guests of certain races in online marketplaces. In this paper, we explore ways to reduce such discrimination using online reputation systems. We conducted four randomized field experiments among 1,801 hosts on Airbnb by creating fictitious guest accounts and sending accommodation requests to them. We find that requests from guests with African American–sounding names are 19.2 percentage points less likely to be accepted than those with white-sounding names. However, a positive review posted on a guest’s page significantly reduces discrimination: when guest accounts receive a positive review, the acceptance rates of guest accounts with white- and African American–sounding names are statistically indistinguishable. We further show that a nonpositive review and a blank review without any content can also help attenuate discrimination, but self-claimed information on tidiness and friendliness cannot reduce discrimination, which indicates the importance of encouraging credible peer-generated reviews. Our results offer direct and clear guidance for sharing-economy platforms to reduce discrimination. This paper was accepted by Vishal Gaur, operations management.

In the United States, 42% of public infrastructure projects report delays or cost overruns. To mitigate this problem, regulators scrutinize project operations. We study the effect of oversight on delays and overruns with 262,857 projects spanning 71 federal agencies and 54,739 contractors. We identify our results using a federal bylaw: if the project’s budget is above a cutoff, procurement officers actively oversee the contractor’s operations; otherwise, most operational checks are waived. We find that oversight increases delays by 6.1%–13.8% and overruns by 1.4%–1.6%. We also show that oversight is most obstructive when the contractor has no experience in public projects, is paid with a fixed-fee contract with performance-based incentives, or performs a labor-intensive task. Oversight is least obstructive—or even beneficial—when the contractor is experienced, paid with a time-and-materials contract, or conducts a machine-intensive task. This paper was accepted by Serguei Netessine, operations management.

Many online retailers provide real-time inventory availability information. Customers can learn from the inventory level and update their beliefs about the product. Thus, consumer purchasing behavior may be impacted by the availability information. Based on a unique setting from Amazon lightning deals, which displays the percentage of inventory consumed in real time, we explore whether and how consumers learn from inventory availability information. Identifying the effect of learning on consumer decisions has been a notoriously difficult empirical question because of endogeneity concerns. We address this issue by running two randomized field experiments on Amazon in which we create exogenous shocks on the inventory availability information for a random subset of Amazon lightning deals. In addition, we track the dynamic purchasing behavior and inventory information for 23,665 lightning deals offered by Amazon and use their panel structure to further explore the relative effect of learning. We find evidence of consumers learning from inventory information: a decrease in product availability causally attracts more sales in the future; in particular, a 10% increase in past claims leads to a 2.08% increase in cart add-ins in the next hour. Moreover, we show that buyers use observable product characteristics to moderate their inferences when learning from others; a deep discount weakens the learning momentum, whereas a good product rating amplifies the learning momentum. This paper was accepted by Serguei Netessine, operations management.

This paper studies the strategy of sharing inventory information for a firm that sells two vertically differentiated products. The seller has private information on the aggregate inventory level and the inventory composition of two product variants. The seller credibly and discretionarily discloses inventory information to customers either fully or partially, i.e., disclosing the exact inventory of each product variant, the aggregate inventory level, or no information to customers. Customers form expectations of future availability and make rational purchasing decisions accordingly. In the disclosure literature, discretion usually leads to an unraveling result: sellers who learn favorable market information opt to disclose it, making full disclosure the equilibrium. This paper shows that aggregate inventory disclosure, i.e., partial disclosure, can be instead sustained as an ex post equilibrium. We demonstrate that inventory information aggregation arises when there is an ex post desire to reduce supply–demand mismatches in all inventory scenarios. Specifically, when customers’ preferred products are more likely to stock out, the seller could entice more incoming consumers who hope that their desired products are in stock by withholding product composition but disclosing the aggregate inventory level. If customers’ desired products are sold out, the seller can benefit from upselling or cross-selling customers’ less preferred products. Alternatively, when the seller stocks more desired products, aggregate disclosure can dampen the flow of incoming customers and reduce the shortage penalty cost. This result is robust under various settings: risk-averse customers, heterogeneous customers, and horizontally differentiated products. This paper was accepted by Serguei Netessine, operations management.

We provide an empirical and theoretical assessment of the value of information sharing in a two-stage supply chain. The value of downstream sales information to the upstream firm stems from improving upstream order fulfillment forecast accuracy. Such an improvement can lead to lower safety stock and better service. Based on the data collected from a consumer packaged goods company, we empirically show that, if the company includes the downstream sales data to forecast orders, the improvement in the mean squared forecast error ranges from 7.1% to 81.1% across all studied products. Theoretical models in the literature, however, suggest that the value of information sharing should be zero for over half of our studied products. To reconcile the gap between the literature and the empirical observations, we develop a new theoretical model. Whereas the literature assumes that the decision maker strictly adheres to a given inventory policy, our model allows him to deviate, accounting for private information held by the decision maker, yet unobservable to the econometrician. This turns out to reconcile our empirical findings with the literature. These “decision deviations” lead to information losses in the order process, resulting in a strictly positive value of downstream information sharing. Furthermore, we empirically quantify and show the significance of the value of operations knowledge—the value of knowing the downstream replenishment policy. This paper was accepted by Serguei Netessine, operations management.

A significant part of supply chain management research has been devoted to understanding the role of information sharing in achieving the best performance. The modern technology, especially the internet, is creating new channels that facilitate interactions and communications between different parties in supply chains. Motivated by various companies that share information with their buyer and suppliers, we study the value of information sharing in supply chains. The dissertation consists of three self-contained papers. Motivated by our interaction with a leading consumer packaged goods company in the beverage industry, in Chapter 1, we provide an empirical and theoretical assessment of the value of information sharing in a two-stage supply chain. The value of downstream sales information to the upstream firm stems from improving upstream order fulfillment forecast accuracy. Such an improvement can lead to lower safety stock and better service. Based on the data collected from the CPG company, we empirically show that, if the company includes the downstream sales data to forecast orders, the improvement in the mean squared forecast error ranges from 7.1% to 81.1% across all studied products. Theoretical models in the literature, however, suggest that the value of information sharing should be zero for over half of our studied products. To reconcile the gap between the literature and the empirical observations, we develop a new theoretical model. While the literature assumes that the decision maker strictly adheres to a given inventory policy, our model allows him to deviate, accounting for private information held by the decision maker, yet unobservable to the econometrician. This turns out to reconcile our empirical findings with the literature. These \"decision deviations\" lead to information losses in the order process, resulting in a strictly positive value of downstream information sharing. Furthermore, we empirically quantify and show the significance of the value of knowing the downstream replenishment policy. Sellers could use operations information disclosure to affect consumer behavior and benefit the sellers. Chapter 2 studies the inventory information sharing behavior of a firm that sells vertically differentiated products. The seller credibly and discretionarily discloses inventory information to customers either fully or partially, i.e., disclosing only the aggregate inventory level. In the disclosure literature, discretion usually leads to the unraveling results: full disclosure is the equilibrium even when it is not optimal for the seller. Instead, this paper shows that aggregate inventory disclosure can sustain as an ex post equilibrium, which is also ex ante optimal for the seller. We explore when and why it is optimal to do so. Chapter 3 studies an inventory replenishment policy that attempts to keep a constant amount of days of inventory, which we refer to as ConDOI policy. This practice is widely used in practice, including the CPG company that we worked with and from which we receive the data set in our first paper. This policy is easy to implement and free from heavy computational burdens, because it requires only one parameter (targeted days of inventory) to manage inventory. While its performance is equivalent to that of a constant base stock policy under stationary demand, its most attractive feature is the adaptability to a non-stationary (e.g. seasonal) environment. In this paper, we consider a dynamic forecast-inventory model with forecast updates under the MMFE demand. Customers participate in the discussions of companies' products and services. Customers' voice is embedded in the social media content. Chapter 4 empirically explores how much social media information helps improve sales forecasting. Using (1) daily sales data from an online apparel startup company that primarily advertises on Facebook, and (2) publicly available Facebook posts and the users' comments and likes data, we find a statistically significant improvement in sales forecast accuracy. We analyze the underlying mechanism—the endorsement effect and the attention effect. We show that sales from new customers are driven by the endorsement effect and sales from repeated customers are driven by the attention effect. Since new customers have never purchased the product before, they are less familiar with the products and thus have difficulties evaluating the quality of products. Attention might not be enough to drive sales. A purchasing decision of a new customer might rely on the endorsement from established social relationships. On the other hand, repeated customers who already have purchasing experience are less likely to learn the product quality from others. A reminder of the brand's promotions or simply the brand's name might lead to a potential purchase from repeated customers. (Abstract shortened by UMI.)

We study the disproportionate impact of the lockdown as a result of the COVID-19 outbreak on female and male academics' research productivity in social science. The lockdown has caused substantial disruptions to academic activities, requiring people to work from home. How this disruption affects productivity and the related gender equity is an important operations and societal question. We collect data from the largest open-access preprint repository for social science on 41,858 research preprints in 18 disciplines produced by 76,832 authors across 25 countries over a span of two years. We use a difference-in-differences approach leveraging the exogenous pandemic shock. Our results indicate that, in the 10 weeks after the lockdown in the United States, although the total research productivity increased by 35%, female academics' productivity dropped by 13.9% relative to that of male academics. We also show that several disciplines drive such gender inequality. Finally, we find that this intensified productivity gap is more pronounced for academics in top-ranked universities, and the effect exists in six other countries. Our work points out the fairness issue in productivity caused by the lockdown, a finding that universities will find helpful when evaluating faculty productivity. It also helps organizations realize the potential unintended consequences that can arise from telecommuting.

We study the disproportionate impact of the lockdown as a result of the COVID-19 outbreak on female and male academics' research productivity in social science. The lockdown has caused substantial disruptions to academic activities, requiring people to work from home. How this disruption affects productivity and the related gender equity is an important operations and societal question. We collect data from the largest open-access preprint repository for social science on 41,858 research preprints in 18 disciplines produced by 76,832 authors across 25 countries over a span of two years. We use a difference-in-differences approach leveraging the exogenous pandemic shock. Our results indicate that, in the 10 weeks after the lockdown in the United States, although the total research productivity increased by 35%, female academics' productivity dropped by 13.9% relative to that of male academics. We also show that several disciplines drive such gender inequality. Finally, we find that this intensified productivity gap is more pronounced for academics in top-ranked universities, and the effect exists in six other countries. Our work points out the fairness issue in productivity caused by the lockdown, a finding that universities will find helpful when evaluating faculty productivity. It also helps organizations realize the potential unintended consequences that can arise from telecommuting.