Personal virtual assistants (PVAs) based on artificial intelligence are frequently used in private contexts but have yet to find their way into the workplace. Regardless of their potential value for organizations, the relentless implementation of PVAs at the workplace is likely to run into employee resistance. To understand what motivates such resistance, it is necessary to investigate the primary motivators of human behavior, namely emotions. This paper uncovers emotions related to organizational PVA use, primarily focusing on threat emotions. To achieve our goal, we conducted an in-depth qualitative study, collecting data from 45 employees in focus-group discussions and individual interviews. We identified and categorized emotions according to the framework for classifying emotions Beaudry and Pinsonneault (2010) designed. Our results show that loss emotions, such as dissatisfaction and frustration, as well as deterrence emotions, such as fear and worry, constitute valuable cornerstones for the boundaries of organizational PVA use.

Emotions are deeply rooted in the human mind and vital to many knowledge processes, such as knowledge creation and knowledge sharing. Nonetheless, the knowledge management (KM) discipline largely approaches KM from a rational rather than an emotional standpoint. Therefore, starting with a broad view on emotions in general as well as several discrete emotions, our paper presents a structured review of existing evidence on emotions and their role in KM research. We use a structured literature review approach to examine research on emotions as a general concept as well as several discrete emotions in KM research. We recognize and incorporate an integrative emotionsin-KM framework, dividing KM into enablers, processes, and intermediary outcomes as well as organizational performance, and connected emotions with each of these parts.After identifying 72 relevant research publications, we analyze and assign these publications to our initially developed integrative review framework. We present several research opportunities to inspire and encourage further research on emotions in KM. Our analysis reveals a strong focus on empirical approaches; we suggest future research employs further qualitative research to incorporate profound theories and models for further exploring emotions in KM. Furthermore, emotions as the intermediary outcome or during knowledge creation and knowledge use could be investigated in further research endeavors. By showing in which KM contexts and processes emotions are displayed, organizations can draw conclusions to trigger positive emotions for better KM as well as reducing barriers caused by emotions.

Although emotions play an important role in human behavior and knowledge studies, knowledge management (KM) research considers them from specific angles and, to date, has lacked a comprehensive understanding of the emotions dominating KM. To offer a holistic view, this study investigates the presence of emotions in KM publications by applying a sentiment analysis. The authors present a sentiment dictionary tailored to KM, apply it to KM publications to determine where and how emotions occur, and categorize them on an emotion scale. The considerable amount of positive and negative emotions expressed in KM studies prove their relevance to and dominance in KM. There is high term diversity but also a need to consolidate terms and emotion categories in KM. This study's results provide new insights into the relevance of emotions in KM research, while practitioners can use this method to detect emotion-laden language and successfully implement KM initiatives.

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Knowledge, being context-specific and bound to individuals, is strongly related to human emotions such as joy or fear. Although emotions play an important role to articulate knowledge in text, KM research only offers insight on emotions from specific angles, neglecting a holistic view. Applying a sentiment analysis, this study closes the aforementioned gap by investigating the occurrence of emotions in KM publications. Based on general sentiment dictionaries, we (1) develop a dictionary aligned with KM, and (2) apply it to KM publications to determine the presence of positive and negative emotions and categorize them according to an emotion scale. Our results reveal that a variety of emotions is expressed in KM studies, both positive and negative, proving its relevance for this domain. We find that there is high term diversity, but also the need for consolidation of terms as well as emotion categories in KM.