Research

Home health services have become a critical component of health care delivery, helping reduce pressure on hospitals. Effective delivery relies on generating optimal schedules and routes for daily patient visits and making the best use of providers’ time. This is a complex, computationally challenging problem that must be solved quickly to be practical.

We developed a dynamic home healthcare routing model that incorporates cooperating service providers, urgent requests and time-dependent travel times. We also designed new algorithms to handle multiple prioritized objectives. Our case study and experiments show that provider cooperation reduces postponed visits and travel time, while our algorithms deliver high-quality solutions within reasonable run times.

We use an advanced model of the global economy to consider a set of scenarios consistent with the proposal to impose a minimum 60% tariff against Chinese imports and blanket minimum 10% tariff against all other US imports. The basis for the tariff rates is a proposal from former President Elect Donald Trump. Our central finding is that a global trade war between the United States and the rest of the world at these tariff rates would cost the US economy over $910 billion.

This research introduces a methodological framework for analyzing strategic interactions in oligopoly settings where firms have private information on costs, further enhancing our understanding of competition and market behavior. The developed framework was applied to the crude-oil market, demonstrating its practical relevance.

Role of vaccination status, socioeconomic status including insurance, and geographic location, on the geospatial spread of COVID-19 pandemic are estimated using geospatial modeling techniques. Results show that the percentage of the senior population, the vaccination rate and the uninsured percentage are the most important variables for predicting infection rates and the fatality rates, while the overall social vulnerability index has a huge impact on mortality rates and infection rates.

This study offers a comprehensive view of the network centrality–firm performance relationship through a meta-analysis. Drawing on a data set of 1,699 effect sizes retrieved from 147 studies published during 2000–2022, the authors establish a positive association between degree, closeness, betweenness, and eigenvector measures of centrality with firm performance on average. Nevertheless, these measures show significant differences in their effectiveness across various contexts. While the associations of degree, closeness, and betweenness centralities with firm performance have diminished over time, the relationship between eigenvector centrality and performance has strengthened. Moreover, the linkage between degree centrality and overall performance is more pronounced in customer-oriented and larger markets, whereas closeness centrality demonstrates a stronger relationship with overall performance in larger markets. Also, social trust amplifies the relationships of degree, closeness, and betweenness centralities with overall performance. Furthermore, the centrality–performance linkage and the moderating effects of the contingency factors depend at least partially on the type of network (i.e., board interlock vs. alliance vs. supplier–customer) and the measure of firm performance used (i.e., innovation vs. financial outcomes). Incorporating these factors into social network analyses helps managers refine their networking strategies and enables scholars to improve the generalizability of their findings.

In contrast to previous research documenting negative effects of political and policy uncertainty, we show that uncertainty has a bright side. It motivates companies to increase their investment in research in development. This is due to the growth option effect - investing in more R&D now gives firms the option to invest even more in the future if the uncertainty resolution is favorable. Additional results support our conjecture. We find that firms that are more politically sensitive and operate in high-tech industries are more likely to benefit from this growth option effect.