Predicting workplace relational dynamics using an affective model of relationships. (19th June 2018)

• Record Type: Journal Article
• Authors: Lopez‐Kidwell, Virginie; Niven, Karen; Labianca, Giuseppe
• Other Names: Vantilborgh Tim guestEditor.; Hofmans Joeri guestEditor.; Judge Timothy A. guestEditor.
• Is Part Of: Journal of organizational behavior. Volume 39:Number 9(2018)
• Page Start: 1129
• Page End: 1141
• Publication Date: 2018-06-19
• Subjects: emotions -- organizational social networks -- relational affect -- relational dynamics -- workplace relationships; Industrial sociology -- Periodicals; Organizational behavior -- Periodicals; Psychology, Industrial -- Periodicals; 302.35
• DOI: 10.1002/job.2300
• Languages: English
• ISSNs: 0894-3796

Abstract

Summary: Integrating insights from the organizational social networks and workplace affect literatures, the authors propose a dynamic model of relationships, focusing on the affect experienced within dyadic work relationships to predict their trajectory over time: either improving, declining, or static. The feelings each partner typically experiences within an ongoing relationship ( trait relational affect ) can be distinguished according to their hedonic tone and activation level, and the combination of both dyadic partners' trait relational affect is predictive of the relationship trajectory. Furthermore, the emotions each partner experiences during specific interactional episodes ( state relational affect ) can alter and disrupt this relationship trajectory, either temporarily or permanently, to the extent that they diverge from the trait relational affect that is typically experienced. A given relationship trajectory over time leads to the development of different types of informal work ties (strong, negative, or weak), which are associated with a wealth of organizational consequences including effort, motivation, performance, and innovation. The model addresses criticisms that organizational social network research neglects the role of affect and views networks as static entities. The model further provides affect researchers with a novel framework that considers affect as a relational rather than individual phenomenon.