Market Characteristics, Intra-Firm Coordination, and the Choice of Human Resource Management Systems: Evidence from New Japanese Data

This paper explores theoretically and empirically potentially important yet often-neglected linkage between task coordination within the organization and the structure of organization and bundling of HRMPs (Human Resource Management Practices). In so doing, we also provide fresh insights on the interplay between the firm’s technological and output market characteristics and its choice of HRMP system. We begin with constructing a team-theoretic model and derive three task coordination modes: vertical control, horizontal coordination, and hybrid coordination. The model provides rich implications about complementarity involving task coordination modes, HRMPs, training and hiring, and management strategies, and illustrates how such complementarity is affected by the firm’s technological and output market conditions. Guided by the theoretical exploration, we analyze unique data from a new survey of Japanese firms which provide for the first time data on newer forms of HRMPs adopted by Japanese firms (such as cross-functional offline teams and self-managed online teams). One novel finding (which is consistent with the theory) is that the adoption of both self-managed online teams and cross-functional offline teams usually arises in firms with shop-floor committees while the introduction of cross-functional offline teams alone often takes place in firms with joint labor-management committees. We also confirm implications from our theory that firms in more competitive markets are more likely to adopt both types of teams while firms facing more erratic price movement tend not to adopt self-managed online teams.

Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting

We consider the distributed channel selection problem in the context of device-to-device (D2D) communication as an underlay to a cellular network. Underlaid D2D users communicate directly by utilizing the cellular spectrum but their decisions are not governed by any centralized controller. Selfish D2D users that compete for access to the resources construct a distributed system, where the transmission performance depends on channel availability and quality. This information, however, is difficult to acquire. Moreover, the adverse effects of D2D users on cellular transmissions should be minimized. In order to overcome these limitations, we propose a network-assisted distributed channel selection approach in which D2D users are only allowed to use vacant cellular channels. This scenario is modeled as a multi-player multi-armed bandit game with side information, for which a distributed algorithmic solution is proposed. The solution is a combination of no-regret learning and calibrated forecasting, and can be applied to a broad class of multi-player stochastic learning problems, in addition to the formulated channel selection problem. Analytically, it is established that this approach not only yields vanishing regret (in comparison to the global optimal solution), but also guarantees that the empirical joint frequencies of the game converge to the set of correlated equilibria.Comment: 31 pages (one column), 9 figure

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology