How Leaders Generate Meanings For Monetary Rewards

Scant research has focused on how to increase the value of monetary rewards when they are delivered by leaders to employees. Drawing upon the perspectives of sensegiving and sensemaking, this study explores how leaders generate meanings of monetary rewards perceived by employee recipients in organizational settings. Using a qualitative method design and analyzing qualitative data from 291 incidents, we found that in the distribution process of monetary rewards, sensemaking of employees included strong and weak instrumental meanings as well as symbolic meanings. The results show that leaders adopted a set of sensegiving strategies in distributing monetary rewards including emphasizing money gain/loss and utility, providing feedback, valuing employees, orienting toward the future, guiding values, and publicizing. In the presence of leader’s sensegiving, employee recipients endorsed more positive symbolic meanings of monetary rewards (i.e., recognition and respect). Our research offers a richer view of the role of leader’s sensegiving in making monetary rewards gain more value through employees’ sensemaking, and enriches understanding of monetary rewards, leadership, sensegiving and sensemaking

Transfer learning for batch process optimal control using LV-PTM and adaptive control strategy

In this study, we investigate a data-driven optimal control for a new batch process. Existing data-driven optimal control methods often ignore an important problem, namely, because of the short operation time of the new batch process, the modeling data in the initial stage can be insufficient. To address this issue, we introduce the idea of transfer learning, i.e., a latent variable process transfer model (LV-PTM) is adopted to transfer sufficient data and process information from similar processes to a new one to assist its modeling and quality optimization control. However, due to fluctuations in raw materials, equipment, etc., differences between similar batch processes are always inevitable, which lead to the serious and complicated mismatch of the necessary condition of optimality (NCO) between the new batch process and the LV-PTM-based optimization problem. In this work, we propose an LV-PTM-based batch-to-batch adaptive optimal control strategy, which consists of three stages, to ensure the best optimization performance during the whole operation lifetime of the new batch process. This adaptive control strategy includes model updating, data removal, and modifier-adaptation methodology using final quality measurements in response. Finally, the feasibility of the proposed method is demonstrated by simulations