We live in a rapidly changing business environment where change has become the norm for organizations to maintain competitiveness. Although both scholars and practitioners agree that organizational change communication is important to help employees adjust to change, little is known about how negative informal information before the change affects employees’ reaction to the change and occurrence of possible within-person dynamics of resistance intention over time. Based on the construal-level theory, we used SPSS 22, AMOS 20, and HLM 6.0 as tools to explore how negative informal information affects individual performance. We used a multilevel approach to probe within-person processes among 215 MBA students in China. The results show that (1) negative informal information provided before the organizational change is positively related to the resistance intention, (2) resistance intention decreases significantly over time, and (3) negative informal information is negatively related to individual performance during the organizational change. The results from this study extend the literature on informal communication before the change and provide a dynamic perspective on the occurrence of possible within-person dynamics of resistance intention over time.
Drawing from social exchange theory, we developed a dual-path model of employees’ reactions to episodic help received from colleagues. Through a diary study, using data collected from 127 full-time employees working in a large Chinese bank, we tested this model, revealing that receiving episodic help from colleagues is positively related to the help receivers’ gratitude and ego depletion. Through these two ambivalent psychological states, help receivers were found to simultaneously engage in more organizational citizenship behaviors and deviance behaviors on a daily basis. These empirical findings contribute to research that adopts a target-centric perspective in examining the consequences of helping behavior in the workplace.
Purpose Microsurgical Aneurysm Clipping Surgery (MACS) carries a high risk for intraoperative aneurysm rupture. Automated recognition of instances when the aneurysm is exposed in the surgical video would be a valuable reference point for neuronavigation, indicating phase transitioning and more importantly designating moments of high risk for rupture. This article introduces the MACS dataset containing 16 surgical videos with frame-level expert annotations and proposes a learning methodology for surgical scene understanding identifying video frames with the aneurysm present in the operating microscope’s field-of-view. Methods Despite the dataset imbalance (80% no presence, 20% presence) and developed without explicit annotations, we demonstrate the applicability of Transformer-based deep learning architectures (MACSSwin-T, vidMACSSwin-T) to detect the aneurysm and classify MACS frames accordingly. We evaluate the proposed models in multiple-fold cross-validation experiments with independent sets and in an unseen set of 15 images against 10 human experts (neurosurgeons). Results Average (across folds) accuracy of 80.8% (range 78.5–82.4%) and 87.1% (range 85.1–91.3%) is obtained for the image- and video-level approach, respectively, demonstrating that the models effectively learn the classification task. Qualitative evaluation of the models’ class activation maps shows these to be localized on the aneurysm’s actual location. Depending on the decision threshold, MACSWin-T achieves 66.7–86.7% accuracy in the unseen images, compared to 82% of human raters, with moderate to strong correlation. Conclusions Proposed architectures show robust performance and with an adjusted threshold promoting detection of the underrepresented (aneurysm presence) class, comparable to human expert accuracy. Our work represents the first step towards landmark detection in MACS with the aim to inform surgical teams to attend to high-risk moments, taking precautionary measures to avoid rupturing.
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