The Impact of IOS Use and Interpersonal Ties on Digital Innovation: Insights from Boundary Spanning and Institutional Theories

Drawing upon the boundary spanning and institutional theories, this study investigates the influence of interorganizational systems (IOS) use and interpersonal ties between a firm and its suppliers on a firm’s digital innovation and how such effects are moderated by institutional distance between the firm and its suppliers. Based on a pilot test of 123 Chinese firms, our results find that a firm’s use of IOS significantly improves its digital innovation, while interpersonal ties between the firm and its suppliers do not significantly improve the firm’s digital innovation. Further, we find that institutional distance between the firm and its suppliers differentially moderates the influences of IOS use and interpersonal ties on digital innovation. Specifically, institutional distance negatively moderates the impact of IOS use on digital innovation yet positively moderates the impact of interpersonal ties on digital innovation. We further discuss the theoretical contributions and managerial implications of the current study

Research on the Moderating Role of Authorized Leadership in the Relationship Between Mental Capital and Innovative Performance of Knowledge-Oriented Employees

In this paper, through the collection and processing of 206 questionnaires of knowledge-oriented employees and their supervisors to match the effective data, empirical research on the impact of the psychological capital of knowledge-oriented staff on innovative performance through the intermediary mechanism, and the moderating role of authorized leadership in the intermediary mechanism. The results show that the mental capital of knowledge-based staff can influence the intrinsic mechanism of innovation performance through work effort, and authorized leaders can positively adjust the influence of psychological capital on work effort. Meanwhile, authorized leaders can adjust the influence of knowledge workers ' work on their innovation performance. On this basis, this paper proposes that managers can influence the relationship between mental capital of knowledge-based employees and their work and innovation performance through empowering leadership style and give full play to the positive role of knowledge workers in enterprise innovation. Keywords: Empowering Leadership, Knowledge workers, Innovative behavior, Trust mechanis

Research on the Affective Mechanism of Authorized Leaders' Influence on the Innovation Performance of Knowledge Workers

In this paper, we have collected and processed 233 effective data of paired survey questionnaires for knowledge workers and their supervisors, and empirically studied the affective mechanism of authorized leaders' influence on the innovative performance of knowledge workers. The results show that authorized leadership can positively influence the positive emotion of knowledge workers; it has negative influence on the knowledge worker's negative emotion, and ultimately affects the knowledge worker's innovation performance. Meanwhile, the Leader-Member eXchange (LMX) plays a positive role in the positive relationship between the authorized leadership and the knowledge worker. It plays a negative moderating role in the negative affective relationship between the authorized leadership and the knowledge worker. On this basis, this paper posits that leaders need to manage the innovative behavior of knowledge workers by implementing the authoritative leadership style; meanwhile, the authorized leaders should have more and better communication and interaction with the knowledge workers, which will effectively promote the positive emotion of the knowledge workers by authorized leadership style. And also, to further on dissolve their negative emotion, and finally improve the knowledge-oriented staff's innovative performance, in order to give full play to the knowledge of staff in the role of enterprise innovation. Keywords: Authorized leaders; Knowledge workers; Innovative performance; Trust mechanism

Radial Angular Momentum Transfer and Magnetic Barrier for Short-Type Gamma-Ray Burst Central Engine Activity

Soft extended emission (EE) following initial hard spikes up to 100 seconds was observed with {\em Swift}/BAT for about half of short-type gamma-ray bursts (SGRBs). This challenges the conversional central engine models of SGRBs, i.e., compact star merger models. In the framework of the black hole-neutron star merger models, we study the roles of the radial angular momentum transfer in the disk and the magnetic barrier around the black hole for the activity of SGRB central engines. We show that the radial angular momentum transfer may significantly prolong the lifetime of the accretion process and multiple episodes may be switched by the magnetic barrier. Our numerical calculations based on the models of the neutrino-dominated accretion flows suggest that the disk mass is critical for producing the observed EE. In case of the mass being $\sim 0.8M_{\odot}$, our model can reproduce the observed timescale and luminosity of both the main and EE episodes in a reasonable parameter set. The predicted luminosity of the EE component is lower than the observed EE with about one order of magnitude and the timescale is shorter than 20 seconds if the disk mass being $\sim 0.2M_{\odot}$. {\em Swift}/BAT-like instruments may be not sensitive enough to detect the EE component in this case. We argue that the EE component would be a probe for merger process and disk formation for compact star mergers.Comment: 9 pages, 3 figures, accepted for publication in Ap

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.Comment: Accepted by AAAI 2023, appendix included. 9 pages + 5 pages appendix, 12 figures, in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23), Feb 202

2-(o-Tol­yloxy)benzoic acid

In the crystal structure of the title compound, C14H12O3, mol­ecules are linked via inter­molecular O—H⋯O hydrogen bonds, resulting in dimer formation. The dihedral angle between the two phenyl rings is 76.2 (2)°