Accelerating employee-related scholarship in service management

Technostress Creators and Job Performance Among Frontliners: Theorizing the Moderating Role of Self-Efficacy

Technostress is evolving as an imperative area of academic research amid the "new normal" settings of working remotely. Research has investigated the relationships between technostress and job outcomes and proposed individual- and organizational-level approaches to manage it. However, insights into the influence of dynamic personality differences on this relationship are limited. This study ties the concept of self-efficacy to the transactional model of stress and coping, and investigates to what extent computer and social self-efficacy moderate the relationships between technostress creators and frontline employee's job performance. Findings shift the focus from the negative aspects of technostress and outcomes to both positive and negative aspects. This study's contributions and implications for theory and practice are discussed.

Using AI to predict service agent stress from emotion patterns in service interactions

Purpose

A vast body of literature has documented the negative consequences of stress on employee performance and well-being. These deleterious effects are particularly pronounced for service agents who need to constantly endure and manage customer emotions. The purpose of this paper is to introduce and describe a deep learning model to predict in real-time service agent stress from emotion patterns in voice-to-voice service interactions.

Design/methodology/approach

A deep learning model was developed to identify emotion patterns in call center interactions based on 363 recorded service interactions, subdivided in 27,889 manually expert-labeled three-second audio snippets. In a second step, the deep learning model was deployed in a call center for a period of one month to be further trained by the data collected from 40 service agents in another 4,672 service interactions.

Findings

The deep learning emotion classifier reached a balanced accuracy of 68% in predicting discrete emotions in service interactions. Integrating this model in a binary classification model, it was able to predict service agent stress with a balanced accuracy of 80%.

Practical implications

Service managers can benefit from employing the deep learning model to continuously and unobtrusively monitor the stress level of their service agents with numerous practical applications, including real-time early warning systems for service agents, customized training and automatically linking stress to customer-related outcomes.

Originality/value

The present study is the first to document an artificial intelligence (AI)-based model that is able to identify emotions in natural (i.e. nonstaged) interactions. It is further a pioneer in developing a smart emotion-based stress measure for service agents. Finally, the study contributes to the literature on the role of emotions in service interactions and employee stress.

Testing the influence of real-time performance feedback on employees in digital services

Purpose

While digital media changed the nature of communication in service contexts, often allowing customers to interact instantly with service providers, the implications and opportunities for managing service employees are widely unknown. This is surprising, given that service employees are an important determinant of service firms’ success. This article examines the effects of real-time performance feedback on employees’ service performance and investigates both how and under what conditions timely feedback encourages employees’ engagement.

Design/methodology/approach

Two experiments test the conceptual model and the proposed hypotheses. A field experiment uses real customer feedback gathered after interaction with the app-chat of a large telecommunications provider. It tests the effect of feedback timing on service employees’ performance and also examines the effect of feedback timing on their engagement. A subsequent scenario-based experiment then investigates the influence of selected moderators on the feedback timing–engagement relationship.

Findings

This article finds that real-time feedback leads to greater service performance than subsequent feedback. Furthermore, real-time feedback positively affects service employee engagement through the perceived controllability of the feedback and the service situation. Finally, feedback valence, task goals, individuals’ need for closure (NCL), and gender interact with feedback timing to influence employee engagement.

Originality/value

This research investigates the potential of real-time performance feedback for service firms, combines and extends a variety of literature streams, and provides recommendations for the future management of service employees.

Engaging customers with employees in service encounters

Purpose

Keeping both employees and customers highly engaged has become a critical issue for service firms, especially for high-contact and highly customized services. Therefore, it is essential to engage employees and customers during service interactions for better service outcomes. However, past research on employee and customer engagement has primarily focused on brands and organizations. Little research has concentrated on service interactions as the objects of engagement. To fill this research gap, this study aims to clarify and define service engagement behaviors (SEBs), identify various employee and customer SEBs and develop a model to investigate the relationships between these behaviors.

Design/methodology/approach

A theoretical framework was developed based on social contagion theory and service-dominant (S-D) logic to explore the effects of employee SEBs on customer SEBs through customer perceptions of relational energy and interaction cohesion. Dyadic survey data collected from 293 customer-employee pairs in various high-contact and highly customized service industries were examined through structural equation modeling.

Findings

Results show that employee SEBs (service role involvement, customer orientation behavior and customer empowerment behavior) positively influence relational energy and interaction cohesion, which in turn affect customer SEBs (service exploration behavior and service coordination behavior).

Originality/value

This study represents pioneering research to conceptualize SEBs. Different from the extant literature on engagement, SEBs capture the proactive and collaborative engagement behaviors of employees and customers in service interactions. Various employee and customer SEBs were identified and an empirical model was proposed and tested to investigate the effect of employee SEBs on customer SEBs through relational energy and interaction cohesion.