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We test for three-way complementarities among information technology (IT), performance pay, and HR analytics practices. We develop a principal-agent model examining how these practices work together as an incentive system that produces a larger productivity premium when the practices are implemented in concert rather than separately. We assess our model by combining fine-grained data on Human Capital Management (HCM) software adoption over 11 years with detailed survey data on incentive systems and HR analytics practices for 189 firms. We find that the adoption of HCM software is greatest in firms that have also adopted performance pay and HR analytics practices. Furthermore, HCM adoption is associated with a large productivity premium when it is implemented as a system of organizational incentives, but has less benefit when adopted in isolation. The system of three-way complements produces disproportionately greater benefits than pairwise interactions, highlighting the importance of including all three complements. Productivity increases significantly when the HCM systems "go live" but not when they are purchased, which can be years earlier. This helps rule out reverse causality as an explanation for our findings.

By studying the change in employees' 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. ABSTRACTBy studying the changes in employees' networks and performance before and after the introduction of a social networking tool, I find that a structurally diverse network (low in cohesion and rich in structural holes) has a positive effect on work performance. The size of the effect is smaller than traditional estimates, suggesting that omitted individual characteristics may bias the estimated network effect. I consider two intermediate mechanisms by which a structurally diverse network is theorized to improve work performance: information diversity (instrumental) and social communication (expressive) and quantify their effects on two types of work outcomes: billable revenue and layoffs. Analysis shows that the information diversity derived from a structurally diverse network is more correlated with generating billable revenue than is social communication. However, the opposite is true for layoffs. Friendship, as approximated by social communications, is more correlated with reduced layoff risks than is information diversity. Field interviews suggest that friends can serve as advocates in critical situations, ensuring that favorable information is distributed to decision makers. This, in turn, suggests that having a structurally diverse network can drive both work performance and job security, but that there is a tradeoff between either mobilizing friendship or gathering diverse information. Furthermore, it is important to examine the mechanisms by which social communications reduce the risks of being laid off. If social communications promote team effectiveness, delegating decisions rights to managers is optimal. However, if managers choose to optimize their own power at the expense of the firm, the positive impact of social communications on layoffs is evidence that ...

Abstract-Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce 1) feeder failure rankings, 2) cable, joint, terminator and transformer rankings, 3) feeder MTBF (Mean Time Between Failure) estimates and 4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or real-time, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The "rawness" of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City's electrical grid.Index Terms-applications of machine learning, electrical grid, smart grid, knowledge discovery, supervised ranking, computational sustainability, reliability !

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