The corporate glass ceiling continues to be a challenge for many organizations. However, women executives may be facing a second pane of obstructionan expatriate glass ceiling -that prevents them from receiving the foreign management assignments and experience that is becoming increasing critical for promotion to upper management. The responsibility to break the expatriate glass ceiling lies with both female managers and the multinational corporations that utilize expatriates. In this paper, we propose pre-assignment, on-assignment, and post-assignment strategies for breaking the expatriate glass ceiling.
To better understand how neurodiversity (i.e., neurobiological/brain-related differences) is related to entrepreneurial cognition, this study draws on prior research from entrepreneurship and neuroscience to empirically examine the relationship between attention-deficit/hyperactivity disorder (ADHD) and the entrepreneurial mindset. We examine differences between entrepreneurs with and without ADHD in cognitive style, entrepreneurial alertness, metacognition, and resource-induced coping heuristic (RICH). Our results suggest neurodiversity from ADHD is meaningfully related to aspects of an entrepreneurial mindset. Our results suggest entrepreneurs with ADHD employ a more intuitive cognitive style and demonstrate higher levels of entrepreneurial alertness and RICH, while no significant differences in metacognition were found.
Researchers have linked tacit knowledge to improved organizational performance, but research on how to measure tacit knowledge is scarce. In the present study, the authors proposed and empirically tested a model of tacit knowledge and an accompanying measurement scale of academic tacit knowledge. They present 6 hypotheses that support the proposed tacit knowledge model regarding the role of cognitive (self-motivation, self-organization); technical (individual task, institutional task); and social (task-related, general) skills. The authors tested these hypotheses with 542 responses to the Academic Tacit Knowledge Scale, which included the respondents' grade point average-the performance variable. All 6 hypotheses were supported.
Hospitality venues traditionally use historical data from customers for their customer relationship management systems, but now they can also collect real-time data and automated procedures to make dynamic decisions and predictions about customer behavior. Machine learning is an example of automated processes that create insights into cocreation of value through dynamic customer engagement. To show the merits of automation, machine learning was implemented at a major hospitality venue and compared with traditional methods to identify what customers value in a loyalty program. The results show that machine learning processes are superior in identifying customers who find value in specific promotions. This research deepens practical and theoretical understanding of machine learning in the customer engagement-to-value loyalty chain and in the customer engagement construct that uses a dynamic customer engagement model.
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