Social Network Effects on Productivity and Job Security: Evidence From the Adoption of a Social Networking Tool

By studying the change in employees\u27 network positions before and after the introduction of a social networking tool, I find that information-rich networks (low in cohesion and rich in structural holes), enabled by social media, have a positive effect on various work outcomes. Contrary to the notion that network positions are difficult to alter, I show that social media can induce a change in network structure, one from which individuals can derive economic benefits. In addition, I consider two intermediate mechanisms by which an information-rich network is theorized to improve work performance—information diversity and social communication—and quantify their effects on productivity and job security. Analysis shows that productivity, as measured by billable revenue, is more associated with information diversity than with social communication. However, the opposite is true for job security. Social communication is more correlated with reduced layoff risks than with information diversity. This, in turn, suggests that information-rich networks enabled through the use of social media can drive both work performance and job security, but that there is a trade-off between engaging in social communication and gathering diverse information

Artificial Intelligence and Drug Innovation

We study how artificial intelligence (AI) can influence the drug development process in the global pharmaceutical industry. Despite considerable effort made in developing drugs, pharmaceutical firms experience declines in novelty for drugs they produced. As AI becomes an important general purpose technology (GPT), it could be used to address some known challenges in the drug development process. Using two large-scale datasets that contain detailed historical records of global drug development and patents, we identify AI-related patents to approximate firms’ AI capabilities and construct a relatively new similarity-based metric to measure drug novelty based on their chemical structure. We find that AI can primarily affect the earliest stage in drug discovery when tasks are heavily dependent on automatic data processing and reasoning. However, it may not necessarily help with the more expensive and risky clinical trial stages that require substantial human engagements and interventions. Additionally, AI can facilitate the development for drugs at the medium level of chemical novelty more than at the extreme ends of the spectrum. Our study sheds light on the understanding of the roles and limitations modern technology can have on drug development, one of the most complex innovation processes in the world

How Do Data Skills Affect Firm Productivity: Evidence from Process-driven vs. Innovation-driven Practices

As digitization of human behaviors become more prevalent, we examine whether data-analysis capabilities can help with process- and innovation-oriented firm practices. Using firm level data on employee data analysis capabilities combined with a survey of organizational practices for 330 large firms, we find that while neither data skills nor process-related practices affect productivity directly, they have a substantial positive interaction. Specifically, firms with process-related practices receive a greater marginal benefit for the presence of or acquisition of data-related skills in their workforce. However, we do not find the same complementarities between data-related skills and innovation-oriented practices and at times the interaction can even be negative. These results are also unique to data-related skills and not IT skills generally. Overall these results highlight the potential tradeoffs of using data analytics at firm, similar to the tradeoffs between exploitation and exploration

The Future of Prediction: How Google Searches Foreshadow Housing Prices and Quantities

To make effective decisions, consumers, executives and policymakers must make predictions. However, most data sources, whether from the government or businesses, are available only after a substantial lag, at a high level of aggregation, and for a small set of variables that were defined in advance. This hampers real-time prediction. A critical advance in IT research has been the development of powerful search engines and the underlying Internet infrastructure. We demonstrate a highly accurate but simple way to predict future business activities by using data from such search engines. Applying our methodology to predict housing trends, we find that our index of housing search terms can predict future quantities and prices in the housing market. During our sample period, each percentage rise in our housing search index predicts sales of 121,400 additional houses in the next quarter. This approach can be applied to other markets, transforming the future of prediction

THEOREMS OF KIGURADZE-TYPE AND BELOHOREC-TYPE REVISITED ON TIME SCALES

This article concerns the oscillation of second-order nonlinear dynamic equations. By using generalized Riccati transformations, Kiguradzetype and Belohorec-type oscillation theorems are obtained on an arbitrary time scale. Our results cover those for differential equations and difference equations, and provide new oscillation criteria for irregular time scales. Some examples are given to illustrate our results

Artificial Intelligence, CEO Turnover, and Directional Change in Firm Innovation

We examine the role of artificial intelligence (AI) in facilitating a change in innovation directions after a leadership change. Using patent data for firms that have gone through a CEO turnover, we find that firms with greater AI investment are more successful in changing their innovation directions. The effect of AI is driven principally by the continued development of innovation in areas that are modestly different from the past. Further analyses show that this effect is likely due to firms with AI investment that can enable strategic change in cultivating culture of exploring frontiers in innovation and managing R&D. A new CEO can direct more resources to the acquisition of employees with greater technological capabilities such as AI skills to facilitate the innovation change. Overall, our study sheds light on the value of AI in fostering the change in innovation directions during uncertain and turbulent times

OSCILLATION OF SOLUTION TO SECOND-ORDER HALF-LINEAR DELAY DYNAMIC EQUATIONS ON TIME SCALES

This article concerns the oscillation of solutions to second-order half-linear dynamic equations with a variable delay. By using integral averaging techniques and generalized Riccati transformations, new oscillation criteria are obtained. Our results extend Kamenev-type, Philos-type and Li-type oscillation criteria. Several examples are given to illustrate our results

Positron emission tomography imaging of endometrial cancer using engineered anti-EMP2 antibody fragments.

PurposeAs imaging of the cell surface tetraspan protein epithelial membrane protein-2 (EMP2) expression in malignant tumors may provide important prognostic and predictive diagnostic information, the goal of this study is to determine if antibody fragments to EMP2 may be useful for imaging EMP2 positive tumors.ProceduresThe normal tissue distribution of EMP2 protein expression was evaluated by immunohistochemistry and found to be discretely expressed in both mouse and human tissues. To detect EMP2 in tumors, a recombinant human anti-EMP2 minibody (scFv-hinge-C(H)3 dimer; 80 kDa) was designed to recognize a common epitope in mice and humans and characterized. In human tumor cell lines, the antibody binding induced EMP2 internalization and degradation, prompting the need for a residualizing imaging strategy. Following conjugation to DOTA (1,4,7,10-tetraazacyclododecane-N,N',N',N'″-tetraacetic acid), the minibody was radiolabeled with (64)Cu (t (1/2) = 12.7 h) and evaluated in mice as a positron emission tomography (PET) imaging agent for human EMP2-expressing endometrial tumor xenografts.ResultsThe residualizing agent, (64)Cu-DOTA anti-EMP2 minibody, achieved high uptake in endometrial cancer xenografts overexpressing EMP2 (10.2 ± 2.6, percent injected dose per gram (%ID/g) ± SD) with moderate uptake in wild-type HEC1A tumors (6.0 ± 0.1). In both cases, precise tumor delineation was observed from the PET images. In contrast, low uptake was observed with anti-EMP2 minibodies in EMP2-negative tumors (1.9 ± 0.5).ConclusionsThis new immune-PET agent may be useful for preclinical assessment of anti-EMP2 targeting in vivo. It may also have value for imaging of tumor localization and therapeutic response in patients with EMP2-positive malignancies

Physical and Psychological Aggression in At-Risk Young Couples: Stability and Change in Young Adulthood

Physical and psychological aggression was examined over a 2 1/2-year period for at-risk young couples. It was predicted, first, that there would be persistence in any physical aggression across time in the group of couples who stayed together; second, that stability in levels of aggression toward a partner would be higher for men who remained with the same partner compared to men who repartnered; third, that increases in levels of aggression would occur over time for couples with the same partners; and fourth, that changes in aggression over time would be concordant for couples. Measures of aggression included reports of aggression and observed aggression. Findings indicated considerable stability in aggression for the same-, but not for the different-, partner group