SC Johnson (United States)

Consumers curate collections of items for various reasons and categorize them into subsets or categories based on different criteria as their collections grow. The items in a collection reflect a consumer's preferences, and the categories provide insights into the different contexts in which items are consumed. The authors develop a novel deep generative modeling framework that captures the network structure of consumer collections using multiple interlocked hypergraphs. This model employs message-passing variational autoencoders that leverage hypergraph structures and entity-specific covariates to generate probabilistic deep embeddings for consumers, items, and item categories. Applying this framework to digital music collections and playlists of music consumers, the authors demonstrate that the model outperforms several sophisticated benchmarks in predicting linkages within these collections. They then illustrate how this approach enables firms to generate novel personalized product bundles, recommend relevant items and bundles, and dynamically expand existing bundles with new items. Beyond the music application, this method is broadly applicable to other consumer collections, such as food recipes and content collections on social curation platforms like Pinterest.

How do venture capital investors react to social movements, including those that relate to historical underrepresentation in funding? We use image and name algorithms combined with clerical review to classify race for 150,000 founders and 30,000 investors. These data allow us to assess the impact of George Floyd’s murder on VC funding of Black entrepreneurs and identify which VCs were most responsive. Although VCs responded swiftly, investment in Black-founded startups reverted to prior levels within two years. This temporary reaction was concentrated among those who had never previously invested in any Black entrepreneur. Moreover, the investors who responded were less likely to invest in more than one Black-founded startup and were less inclined to engage deeply by taking a board seat. Finally, it appears that the best Black entrepreneurs may have anticipated this “token” response because they did not match with investors who had no experience funding Black startups. This paper was accepted by Toby Stuart, entrepreneurship and innovation. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06410 .

Goal setting is only somewhat more common than the failure to follow through on one’s goals. Recognizing the challenge of long-term behavior change, we asked what best predicts long-term goal adherence: extrinsic motivation (the extent to which goal pursuit is experienced as a means to an end) or intrinsic motivation (the extent to which the same goal pursuit is experienced as an end in itself). In a year-long longitudinal study, U.S. adults set extrinsic New Year’s resolutions, but intrinsic motivation predicted adherence to these goals more than extrinsic motivation (Study 1). These findings emerged among adults in China (Study 2) and when measuring goal adherence objectively using the number of steps U.S. adults walked over 2 weeks (Study 3). Understanding how intrinsic motivation affects long-term persistence critically informs interventions that promote goal pursuit. Indeed, increasing intrinsic (vs. extrinsic) motivation increased U.S. adults’ goal adherence (Study 4). Overall, intrinsic motivation both predicted and causally increased goal adherence.

We report structures of the Mycobacterium tuberculosis isoprenyl diphosphate synthase Rv2173 in three forms: apo and two substrate-bound forms [isoprenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP)]. The protein possesses a canonical all-α-helical trans-isoprenyl diphosphate synthase fold that is dimeric in each form. There are some differences between the structures: the IPP-bound form shows IPP bound in the DMAPP/allylic substrate-binding site with three divalent metal ions bound around the IPP and the complete C-terminus closing around the active site, while the apo and DMAPP-bound forms are more open, with some of the C-terminal region disordered, supporting suggestions that the C-terminus is important in substrate entry/product exit. In the DMAPP form DMAPP occupies the expected allylic substrate site, but only two metal ions are associated with the binding, with the DMAPP diphosphates adopting a slightly different binding pose compared with IPP in the same site, and the third metal-binding site is unoccupied. In no case is the IPP binding site occupied by IPP. There has been some uncertainty regarding product length for Rv2173, with variable lengths being reported. In the structures reported here, the `capping' residue at the bottom of the binding cavity is tryptophan and comparison with other IPP synthases suggests that the structure of Rv2173 is most consistent with a C10-C15 final product size.

Following the onset of COVID-19, demand for US veterinary services rapidly increased, similarly driving up demand in upstream veterinary labor markets. This increasing demand starkly contrasted with the "excess capacity" period following the Great Recession, marked by fewer opportunities and low wages for veterinarians. Better models and forecasts of veterinary labor market conditions improve information for decision-making by veterinarians, relevant business owners, professional associations, and educational institutions. Well-performing approaches will adapt to new data as they become available and avoid the assumption that short-term market dynamics will persist permanently. In this paper, we modify an approach often used to identify the forecasting model best suited to the data. Then, using the best forecasting model, we generate forecasts and prediction intervals around them to meet the available data and realities of veterinary labor markets to 2035. This approach removes subjectivity, accommodates model refinements, and acknowledges that the future is unknown. We find that the supply and demand indicators for veterinary services both indicate continued growth. We do not find statistically significant or consistent differences in either supply or demand growth, which would be consistent with a competitive market for veterinary services and labor. The data do not support an expectation of a continued shortage in the US veterinarian labor markets, as suggested in previous studies. We identify several areas where improved data resources and further methodological advancements could shed more light on the economic conditions facing veterinarians.