Retail store managers may not follow order advices generated by an automated inventory replenishment system if their incentives differ from the cost-minimization objective of the system or if they perceive the system to be suboptimal. We study the ordering behavior of retail store managers in a supermarket chain to characterize such deviations in ordering behavior, investigate their potential drivers, and thereby devise a method to improve automated replenishment systems. Using orders, shipments, and point-of-sale data for 19,417 item–store combinations over five stores, we show that (i) store managers consistently modify automated order advices by advancing orders from peak to nonpeak days, and (ii) this behavior is explained significantly by product characteristics such as case pack size relative to average demand per item, net shelf space, product variety, demand uncertainty, and seasonality error. Our regression results suggest that store managers improve upon the automated replenishment system by incorporating two ignored factors: in-store handling costs and sales improvement potential through better in-stock. Based on these results, we construct a method to modify automated order advices by learning from the behavior of store managers. Motivated by the management coefficients theory, our method is efficient to implement and outperforms store managers by achieving a more balanced handling workload with similar average days of inventory.

The use of H-1B and other work visas to hire foreign information technology (IT) professionals in the United States has attracted significant controversy and policy debates. On one hand, hiring high-skill foreign IT professionals on work visas can be advantageous for U.S. firms and the overall economy. On the other hand, high-skill immigration can adversely impact the wages of foreign and American IT professionals. This study uses data on skills and compensation of more than 50,000 IT professionals in the United States over the period 2000–2005 to study patterns in compensation of foreign and American IT professionals to inform these debates. Contrary to the popular belief that foreign workers are a cheap source of labor for U.S. firms, we find that after controlling for their human capital attributes, foreign IT professionals (those without U.S. citizenship and those with H-1B or other work visas) earn a salary premium when compared with IT professionals with U.S. citizenship. The salary premiums for non-U.S. citizens and for those on work visas fluctuate in response to supply shocks created by the annual caps on new H-1B visas. Setting lower and fully utilized annual caps results in higher salary premiums for non-U.S. citizens and those with work visas. We discuss implications of this study for crafting informed visa- and immigration-related policies by the U.S. government, for staffing practices of firms, and for human capital investments by IT professionals.

Empirical research on strategic alliances has focused on the idea that partners are selected on the basis of social capital considerations. In this paper we emphasize instead the role of complementary knowledge stocks and knowledge dynamics, which have received surprisingly limited attention relative to social capital as forces behind the formation and dynamics of innovation networks. To marshal evidence in this regard, we design a simple model of partner selection in which firms ally for the purpose of learning and innovating, and in doing so create an industry network. We abstract completely from network-based structural and strategic motives for partner selection and focus instead on the idea that firms' knowledge bases must “fit” for joint learning and innovation to be possible, and thus for an alliance to be feasible. The striking result is that, despite containing no social capital considerations, this simple model replicates the firm conduct, network structure, and contingent effects of network position on performance observed and discussed in the empirical literature.

Prior research documents performance improvements following the implementation of pay-for-performance (PFP) bonus plans. However, bonus plans typically pay for performance relative to a goal, and the manager whose performance is to be evaluated often participates in setting the goal. In these settings, PFP affects managers' incentive to influence goal levels in addition to affecting performance effort. Prior field research is silent on the effect of PFP on goals, the focus of this paper. Using sales and sales goal data from 61 stores of a U.S. retail firm over 10 quarters, we find that the introduction of a performance-based bonus plan with participative goal setting is accompanied by lower goals that are more accurate predictors of subsequent sales performance. Statistical tests indicate that increased goal accuracy is attributable to managers “meeting but not beating” goals and to new information being impounded in goals. We further investigate how differences among managers are associated with goal levels. We find significant “manager effects” but no “supervisor effects.” In additional tests we find that cross-sectional differences among managers are related to differing marginal returns to slack-building effort. Turning to the role of new information on goals, we find that prior period performance has incremental power to explain goal levels in the postplan period. Our results provide field-based evidence that PFP and participative goal setting affect the level and accuracy of goals, effects that are associated with both information exchange and with managers' incentives to influence goals.