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Industry 4.0, based on increasingly progressive digitalization, is a global phenomenon that affects every part of our work. The Internet of Things (IoT) is pushing the process of automation, culminating in the total autonomy of cyber-physical systems. This process is accompanied by a massive amount of data, information, and new dimensions of flexibility. As the amount of available data increases, their specific timeliness decreases. Mastering Industry 4.0 requires humans to master the new dimensions of information and to adapt to relevant ongoing changes. Intentional forgetting can make a difference in this context, as it discards nonprevailing information and actions in favor of prevailing ones. Intentional forgetting is the basis of any adaptation to change, as it ensures that nonprevailing memory items are not retrieved while prevailing ones are retained. This study presents a novel experimental approach that was introduced in a learning factory (the Research and Application Center Industry 4.0) to investigate intentional forgetting as it applies to production routines. In the first experiment (N = 18), in which the participants collectively performed 3046 routine related actions (t1 = 1402, t2 = 1644), the results showed that highly proceduralized actions were more difficult to forget than actions that were less well-learned. Additionally, we found that the quality of cues that trigger the execution of routine actions had no effect on the extent of intentional forgetting.
Social networking sites (SNSs) are central to social interaction and information sharing in the digital age. However, consuming social information on SNSs invites social upward comparisons with highly socially desirable profile representations, which easily elicits envy in users and leads to unfavorable behaviors on SNSs. This in turn can erode the subjective well-being of users and the sustainability of the SNS platform. Therefore, this paper seeks to develop a better theoretical understanding of how users respond to envy on SNSs. We review literature on envy in offline interactions to derive three behavioral strategies to reduce envy, which we then transfer to the SNS context (self-enhancement, gossiping, and discontinuous intention). Further, we propose a research model and examine how culture, specifically individualism-collectivism, affects the relationship between envy on an SNS and the three strategies. We empirically test the variance-based structural equation model through survey data collected of Facebook users from Germany and Hong Kong. Our findings provide first insights into the link between envy on SNSs, related behavioral strategies and the moderating role of individualism for self-enhancement.