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This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.

This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.

Product recommendations can benefit consumers' online product search via multiple underlying mechanisms, such as showing products that offer them high value, facilitating navigation on the website, or exposing more product information. However, it is unclear ex ante which is the primary underlying mechanism that drives the benefits of product recommendations to consumers. We conducted a randomized field experiment to estimate the benefits of an item-based collaborative filtering (CF) recommendation system to consumers. We collect unique data on the affinity scores computed by an item-based CF algorithm to develop measures of a product's net value and horizontal (taste) fit for consumers. Our results indicate that product recommendations help consumers search for higher-value products that are lower priced, fit their tastes better, or both. Besides that, we find that the ability to find higher-value products (rather than easy navigation or exposure to more product information) is the primary driver for consumers' higher purchase probabilities under recommendations. We further find a higher benefit of recommendations in product categories with higher price dispersion and heterogeneity in consumers' tastes, providing additional evidence for the lower price and better horizontal fit mechanisms. Finally, we find that when made available, consumers substitute their usage of other search tools on the website with product recommendations. Our findings have important implications for online retailers, policymakers, regulators, and item-based CF recommendation system design. This paper was accepted by D. J. Wu, information systems. Funding: This work was supported by the Public Utility Research Center of the University of Florida, the Hi! PARIS Fellowship, the HEC Foundation, and the Leavey School of Business at Santa Clara University [Grant 102720]. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4951. [ABSTRACT FROM AUTHOR]

Target values for 25-hydroxy vitamin D and 1,25(OH)2D or 1,25-dihydroxy vitamin D remain a topic of debate among clinicians. We analysed data collected from December 2012 to April 2020 from two cohorts. Cohort A, comprising 455,062 subjects, was used to investigate the relationship between inflammatory indicators (white blood cell [WBC] count and C-reactive protein [CRP]) and 25(OH)D/1,25(OH)2D. Cohort B, including 47,778 subjects, was used to investigate the connection between 25(OH)D/1,25(OH)2D and mineral metabolism markers (phosphate, calcium, and intact parathyroid hormone [iPTH]). Quadratic models fit best for all tested correlations, revealing U-shaped relationships between inflammatory indicators and 25(OH)D and 1,25(OH)2D. Minimal CRP and WBC counts were observed at 1,25(OH)2D levels of 60 pg/mL and at 25(OH)D levels of 32 ng/mL, as well as of 42 ng/mL, respectively. iPTH correlated inversely with both 1,25(OH)2D and 25(OH)D, while phosphate as well as calcium levels positively correlated with both vitamin D forms. Calcium-phosphate product increased sharply when 25(OH)D was more than 50 ng/mL, indicating a possible risk for vascular calcification. Multiple regression analyses confirmed that these correlations were independent of confounders. This study suggests target values for 25(OH)D between 30–50 ng/mL and for 1,25(OH)2D between 50–70 pg/mL, based particularly on their associations with inflammation but also with mineral metabolism markers. These findings contribute to the ongoing discussion around ideal levels of vitamin D but require support from independent studies with data on clinical endpoints. [ABSTRACT FROM AUTHOR]

We propose a theoretical model based on the judge-advisor system (JAS) and empirically examine how algorithmic advice, compared to identical advice from humans, influences human judgment. This effect is contingent on the level of transparency, which varies with whether and how the prediction performance of the advice source is presented. In a series of five controlled behavioral experiments, we show that individuals largely exhibit algorithm appreciation; that is, they follow algorithmic advice to a greater extent than identical human advice due to a higher trust in an algorithmic than human advisor. Interestingly, neither the extent of higher trust in algorithmic advisors nor the level of algorithm appreciation decreases when individuals are informed of the algorithm's prediction errors (i.e., upon presenting prediction performance in an aggregated format). By contrast, algorithm appreciation declines when the transparency of the advice source's prediction performance further increases through an elaborated format. This is plausibly because the greater cognitive load imposed by the elaborated format impedes advice taking. Finally, we identify a boundary condition: algorithm appreciation is reduced for individuals with a lower dispositional need for cognition. Our findings provide key implications for research and managerial practice. [ABSTRACT FROM AUTHOR]

We examine how social influence interacts with other information sources to affect user behaviors in the context of medical crowdfunding. We conduct a large-scale randomized field experiment on a leading medical crowdfunding platform, showing friends' donation information to donors in the treatment group and not showing such information in the control group, and examine how the likelihood to donate differs. In addition, we conduct a survey on Amazon Mechanical Turk to evaluate the informational value of different case attributes in conveying the patients' need for help to donors. We find that for cases containing attributes with high informational value (e.g., minor patient, severe conditions), social influence is insignificant. In contrast, for cases lacking attributes with high informational value, social influence significantly increases donors' likelihood to donate. Overall, our results show that the impact of social influence depends on the informational value of other information sources, suggesting that the social influence in our context is primarily informational. Our findings indicate that rather than generating an entrenchment effect, where cases with attributes of high informational value attract disproportionate benefits, social influence can increase donation likelihood to cases that lack such attributes, promoting more equal access to resources overall. History: Olivia Liu Sheng, Senior Editor; Yuliang (Oliver) Yao, Associate Editor. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1189. [ABSTRACT FROM AUTHOR]