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- All Subjects: Interpersonal communication
- Creators: Ayer, Steven K
Description
ABSTRACT
This study examines the ways in which employees experience moral emotions that violate employee treatment and how employees co-construct moral emotions and subsequent expressions of dissent. This qualitative study consisted of 123 full-time employees and utilized open-coding, content analysis, constant comparison analysis, and concept mapping. The analysis revealed that employees expressed dissent laterally as a series of sensemaking processes, such as validation of feelings, moral assessments, and assessing the fear of moral transgressions. Employees also expressed dissent as a series of risk assessments that overlapped with the ways in which employees made sense of the perceived infraction. Employees' lateral dissent expression manifested as a form of social support which occasionally led to co-rumination. Employees expressed dissent upwardly when seeking a desired action or change. Circumvention was utilized as a direct reflection to the type and degree of moral transgression related to the person responsible for the mistreatment. Results indicated that experiencing moral emotions that led to expressing dissent with a designated audience was determined by where employees were situated in the cyclical model of communicating moral emotions and in relation to the co-construction of both the infraction related to employee mistreatment and the experience of moral emotions. Results contribute to the existing body of literature on dissent and emotions. A discussion synthesizing the findings and analysis is presented, in addition to the implications for future research.
KEYWORDS: Emotion, Dissent, Moral Emotions, Sensemaking, Risk-Assessment, Social Support, Co-Rumination
This study examines the ways in which employees experience moral emotions that violate employee treatment and how employees co-construct moral emotions and subsequent expressions of dissent. This qualitative study consisted of 123 full-time employees and utilized open-coding, content analysis, constant comparison analysis, and concept mapping. The analysis revealed that employees expressed dissent laterally as a series of sensemaking processes, such as validation of feelings, moral assessments, and assessing the fear of moral transgressions. Employees also expressed dissent as a series of risk assessments that overlapped with the ways in which employees made sense of the perceived infraction. Employees' lateral dissent expression manifested as a form of social support which occasionally led to co-rumination. Employees expressed dissent upwardly when seeking a desired action or change. Circumvention was utilized as a direct reflection to the type and degree of moral transgression related to the person responsible for the mistreatment. Results indicated that experiencing moral emotions that led to expressing dissent with a designated audience was determined by where employees were situated in the cyclical model of communicating moral emotions and in relation to the co-construction of both the infraction related to employee mistreatment and the experience of moral emotions. Results contribute to the existing body of literature on dissent and emotions. A discussion synthesizing the findings and analysis is presented, in addition to the implications for future research.
KEYWORDS: Emotion, Dissent, Moral Emotions, Sensemaking, Risk-Assessment, Social Support, Co-Rumination
ContributorsKamrath, Jessica K (Author) / Kassing, Jeffrey W. (Thesis advisor) / Waldron, Vincent R. (Committee member) / Meân, Lindsey J. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Interpersonal communications during civil infrastructure systems operation and maintenance (CIS O&M) are processes for CIS O&M participants to exchange critical information. Poor communications that provide misleading information can jeopardize CIS O&M safety and efficiency. Previous studies suggest that communication contexts and features could be indicators of communication errors and relevant CIS O&M risks. However, challenges remain for reliable prediction of communication errors to ensure CIS O&M safety and efficiency. For example, existing studies lack a systematic summarization of risky contexts and features of communication processes for predicting communication errors. Limited studies examined quantitative methods for incorporating expert opinions as constraints for reliable communication error prediction. How to examine mitigation strategies (e.g., adjustments of communication protocols) for reducing communication-related CIS O&M risks is also challenging. The main reason is the lack of causal analysis about how various factors influence the occurrences and impacts of communication errors so that engineers lack the basis for intervention.
This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.
This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.
This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.
This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.
ContributorsSun, Zhe (Author) / Tang, Pingbo (Thesis advisor) / Ayer, Steven K (Committee member) / Cooke, Nancy J. (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2020