The effects of subsidiary’s leadership and entrepreneurship on international marketing knowledge transfer and new product development

This study investigates the role of a strong subsidiary leadership and entrepreneurial culture in the promotion of marketing knowledge inflows. We further examine their consequences on the subsidiary’s ability to develop new products when moderated by the tacitness of knowledge. The data were collected from 202 Portuguese subsidiaries of multinational corporations and were analyzed using partial least squares structural equation modelling to test the hypotheses. The results indicate that subsidiaries’ strong leadership support and entrepreneurial culture are fundamental mechanisms that foster marketing knowledge inflows from both the headquarters and peer subsidiaries. Moreover, marketing knowledge inflows enhance the focal subsidiary’s innovation abilities. We also find that tacit knowledge exerts contradictory moderating effects on the transfers of marketing knowledge, carrying distinct implications for a subsidiary’s knowledge management. The results expand our understanding of the effectiveness of transferring marketing knowledge among multinational corporations’ (MNCs) subsidiaries.     

教练型领导-下属认知匹配对下属工作幸福感的影响

作为新型领导风格，教练型领导颇受理论界和实践界的关注，现有研究强调教练型领导的积极价值，然而忽视了在领导管理过程中，是领导与下属双方共同塑造了教练型领导所产生的影响。本研究采取身份建构理论，从领导与下属匹配视角，探讨教练型领导和员工对教练型领导力的感知的一致性对工作幸福感的影响以及员工心理资本的中介作用。采用多元回归及响应面分析方法，通过对117个团队865份配对问卷调查数据分析结果表明，领导与下属的教练领导力评价在"高-高"一致时，员工工作幸福感更高；当领导-下属对教练行为的评价存在不一致时，领导低-下属高的情形下对工作幸福感的正向影响更强；下属的心理资本在一致性与工作幸福感的关系之间起部分中介作用。团队领导成员关系质量调节了一致性对心理资本的效应。本研究从身份建构理论视角，发现教练型领导与员工认知协同的重要意义，揭示了其对员工工作幸福感的作用机制，为教练型领导相关研究提供了更加细致的理论揭示和实证支持。     

How to “improve” prediction using behavior modification

Many internet platforms that collect behavioral big data use it to predict user behavior for internal purposes and for their business customers (e.g., advertisers, insurers, security forces, governments, political consulting firms) who utilize the predictions for personalization, targeting, and other decision-making. Improving predictive accuracy is therefore extremely valuable. Data science researchers design algorithms, models, and approaches to improve prediction. Prediction is also improved with larger and richer data. Beyond improving algorithms and data, platforms can stealthily achieve better prediction accuracy by pushing users’ behaviors towards their predicted values, using behavior modification techniques, thereby demonstrating more certain predictions. Such apparent “improved” prediction can result from employing reinforcement learning algorithms that combine prediction and behavior modification. This strategy is absent from the machine learning and statistics literature. Investigating its properties requires integrating causal with predictive notation. To this end, we incorporate Pearl’s causal $do(.)$ operator into the predictive vocabulary. We then decompose the expected prediction error given behavior modification and identify the components impacting predictive power. Our derivation elucidates implications of such behavior modification to data scientists, platforms, their customers, and the humans whose behavior is manipulated. Behavior modification can make users’ behavior more predictable and even more homogeneous; yet this apparent predictability might not generalize when business customers use predictions in practice. Outcomes pushed towards their predictions can be at odds with customers’ intentions, and harmful to manipulated users.     

Inclusive leadership via empathic communication

In today’s rapidly changing society, leaders have to constantly deal with multiple urgent demands interacting with others through various communication modalities - that can happen face to face, via video conferencing, phone calls, etc. - and guide diverse groups of individuals. Having empathic conversations is key for inclusive leadership. Leaders need to understand their emotions and others’ emotional state to build bonds for effective collaboration at work. Yet the reality is that it is challenging for leaders to be aware of their own empathic or non-empathic approach, especially given that hybrid communication is required in their daily lives. This paper introduces communication approaches for empathic conversations considering various modalities including face-to-face, video, or voice-only communication. We provide step-by-step instructions and guided questions to showcase how to develop empathic communication skills. With these strategies, leaders can practice their empathy ‘muscle’ considering various communication modalities with a focus on non-verbal and verbal cues.     

Crossing the bridge from network training to development: A guide to move trainees from classroom insights to effective networks

Professional networks help employees accomplish work tasks, progress in their careers, and thrive personally. Decades of research suggest that achieving these outcomes requires more than simply amassing 'more' network contacts. Instead, networks with certain characteristics (e.g., networks that are open, diverse, and deep) enhance effectiveness. Network training teaches trainees about effective networks and helps trainees identify their network development needs by providing feedback on their current network. Once back on the job, trainees are assumed to take appropriate actions to develop their networks. However, our research and experience training MBA students, executives, and employees at all levels suggest that trainees often struggle to develop their networks after training. We studied 119 trainees engaged in network training and development to understand why. Our investigation revealed that many trainees fail to set network development goals that match their personalized feedback, identify strategies that match their goals, and take actions to develop networks that match their strategies. These mismatches create gaps in the bridge that trainees build to take themselves from network training to development. Further, even after building strong bridges by aligning their feedback, goals, strategies, and actions, many trainees encounter on-the-job barriers that prevent them from improving the effectiveness of their network. We offer a guide to help address translational gaps and mitigate on-the-job barriers, thereby enhancing the translation of network training insights into network development.