Ashish Kalra, Omar S. Itani and Sijie Sun
This study examines the contextual variables that can curb the negative effects of role conflict on job satisfaction and enhance the positive effect of job satisfaction on…
Abstract
Purpose
This study examines the contextual variables that can curb the negative effects of role conflict on job satisfaction and enhance the positive effect of job satisfaction on creativity and service performance. More specifically, adopting the job demands-resources theory, the authors explore the interactive effect of frontline employee (FLE) self-monitoring and FLE-manager trust on the relationship between role conflict and job satisfaction. Extending this line of inquiry, the authors adopt social identity theory and analyze the moderating effect of FLE-manager identification on the relationship between job satisfaction and creativity and between job satisfaction and service performance.
Design/methodology/approach
Dyadic data utilizing 122 responses from FLEs and their managers were obtained from FLEs working with a major financial services firm in India. Structural equation modeling and PLS were used to assess the hypothesized relationships.
Findings
The negative relationship between role conflict and job satisfaction is reduced at higher levels of FLE self-monitoring and FLE-manager trust. Furthermore, FLE manager identification accentuates the effect of job satisfaction on creativity and service performance.
Practical implications
Organizations should invest in developing FLEs' personal and job-related resources to reduce the deleterious effects of role conflicts on FLEs' job outcomes. Specifically, managers should hire FLEs who are high in self-monitoring while enhancing FLE-manager trust and FLE-manager identification.
Originality/value
Role conflict is inevitable in a service job and can have serious negative downstream consequences. Hence, the study explores the important contextual factors that can help an organization develop policies to reduce the negative effects of role conflict.
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Keywords
Sijie Tong, Qingchen Liu, Qichao Ma and Jiahu Qin
This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential…
Abstract
Purpose
This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.
Design/methodology/approach
The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.
Findings
Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.
Originality/value
This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.
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Keywords
Qian Zhou, Shuxiang Wang, Liya Wang and Wei Xu
Open innovation platform has become an effective field through which enterprises can acquire valuable knowledge for incremental and breakthrough innovation. However, as more…
Abstract
Purpose
Open innovation platform has become an effective field through which enterprises can acquire valuable knowledge for incremental and breakthrough innovation. However, as more entities join the innovation platform, the knowledge activities in the platform ecosystem are now facing higher complexity and vulnerability due to the differences in the knowledge demands as well as conflicting interest claims of participants. The lack of mature governance mechanisms has caused opportunistic behaviors like knowledge infringement, leakage and hiding, which seriously hinder the in-depth knowledge sharing and effective utilization. What’s more, the enthusiasm for collaborative innovation also reduced among multi-subjects. Therefore, the purpose of this study is to improve platform participants’ innovation ambidexterity under the guidance of scientific design of platform knowledge governance mechanisms through improved knowledge transformation processes.
Design/methodology/approach
Therefore, based on knowledge governance theory and knowledge transformation model (SECI, socialization-externalization-combination-internalization), the study explored the influence of relationship and contractual knowledge governance on the innovation ambidexterity of platform participants through the mediation effect of knowledge transformation. To better analyze complex causal relationships among variables and the chain multiple mediation effect, structural equation modeling is used, coupled with bootstrap analysis verification.
Findings
Platform contractual governance and relationship governance can positively influence the innovation ambidexterity of participants through knowledge trading and reuse, as well as through knowledge sharing and creation. The findings not only contribute to optimizing the effectiveness of knowledge activities on digital platforms but also provide empirical evidence and practical insights to support enterprises’ incremental and breakthrough innovation according to their own knowledge bases.
Practical implications
The findings offer valuable insights for providing decision-making guidance not only for platform-leading enterprises but also for individual and enterprise users on effectively using open innovation platforms to conduct knowledge seeking, trading or sharing and knowledge reuse or creation to enlarge the incremental innovation value and to trigger breakthrough innovation value in their product and technology developments.
Social implications
Through diverse knowledge governance mechanisms, platform-leading enterprises do not only act as “economic agents” with private attributes to reduce knowledge asymmetry in the public trading market, diffuse knowledge broadly and mitigate cooperation costs to increase economic value; they also serve as “social actors” for multilateral participants to increase the cohesion of knowledge sharing and creation to provide sustainable knowledge fuel for the higher level of breakthrough innovation. Overall, knowledge arrangement efficiency can be optimized, and breakthrough innovation value can be activated in a well-governed platform, gradually escaping the diminishing marginal benefits of exploitative innovation.
Originality/value
This study has extended the views of the knowledge transformation model under the platform context and proposed dualistic knowledge transformation pathways, named “tacit knowledge socialization” and “explicit knowledge combination,” respectively. Besides, it discovered that under the contractual and relationship knowledge governance mechanisms’ guiding, participants in open innovation platforms may choose different knowledge searching and exchange ways according to their knowledge needs and thus trigger the different knowledge transform process. Then, “tacit knowledge socialization” transformation can show larger positive impact on breakthrough innovation, while “explicit knowledge combination” transformation makes larger impact on incremental innovation.