AI Boosts Performance but Affects Employee Emotions

AI Boosts Performance but Affects Employee Emotions

Kuo-Tai Cheng, Kirk Chang, Hsing-Wei Tai
Copyright: © 2022 |Pages: 18
DOI: 10.4018/irmj.314220
OnDemand:
(Individual Articles)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Drawing on Lazarus's appraisal theory, the current research provides an integrative review of AI (artificial intelligence) and discusses its implications for emotion. Although prior studies have praised the merits of AI-M (AI-driven management), how AI-M affects employees and their emotions is not always clear. To respond to the knowledge gap, the authors conduct new research and seek answers through the amalgamation and analysis of both theoretical viewpoints and empirical studies. Through this process, they have learnt that AI-M brings diverse triggers of negative emotions, affecting both managers and employees. For employees, AI-M may lead to job insecurity and fewer career development opportunities. For managers, AI-M may take over the ownership of decision-making and compromise their influence in the workplace. To help employees cope with negative emotions, the authors reviewed the literature on emotional intelligence (EI) and proposed three EI-enhanced strategies. They also proposed three managerial schemes, enabling managers to guide their subordinates in coping with negative emotions.
Article Preview
Top

Introduction

Artificial intelligence (AI) appears both interesting and ubiquitous in modern life. AI has been applied to marketing strategies, energy savings, weather forecasting, risk analysis, customer services, education, and business solutions (Brown, Ling & Gurdeniz, 2017; Jaiswal & Arun, 2021; Tuli, Gill, Xu et al., 2022). AI processes smart technology, assisting both entrepreneurs and organizations in delivering better-quality service and more efficient performance (Chang, Abdalla & Lasyoud, 2021; Smith & Anderson, 2014; Haefnera, Wincenta, Parida, & Gassmann, 2021). Recently, AI has started to show its influence in the field of employee management; for instance, managers have improved employee performance through AI-driven techniques such as performance-tracking and KPI-monitoring software (Ernst & Young, 2018; Vrontis, Christofi, Pereira et al., 2021). Different from the conventional approach that focuses on target achievement, AI-Driven Management (AI-M) adopts a more holistic and interactive approach, enabling both managers and subordinates to monitor the performance progress more effectively, from the initial goal-setting stage to the final completion stage (Chang, 2020; Gonzales, Capman, Oswald et al., 2019). Businesses and enterprises also adopt big data in their employee management practices with the view that AI offers better insights into how to execute and operate in performance appraisal, staff recruitment, succession planning, and performance management (Pan, Froese, Liu et al., 2021; Wang, Wang & Huang, 2017). Inspired by the aforementioned AI studies, we are intrigued to know whether AI affects employees, and if so, how.

In the current research, we focus on the emotions of employees, with the following rationale: On the one hand, emotion is a subjective and conscious experience that is characterized by psycho-physiological expressions, biological reactions, and mental states (Kleinginna & Kleinginna, 1981; Lazarus, 1991). Emotion is reciprocally influential with mood, temperament, personality, disposition, and motivation (Ortony et al., 1988; Pankseep, 2005). More specifically, negative emotion refers to an affective state that is characterized by physiological and neuro-hormonal changes arising from a challenging situation, leading to feelings of stress, anxiety, anger, loss, disappointment, and sadness (Lampert & Phelps, 2013; Lazarus, 1998). Negative emotion is contagious and detrimental, causing various behavioral and physiological outcomes (Goleman, Boyatzis, & McKee, 2002). Although different in nature, previous research has implied that emotions are crucial to individuals and affect their lives.

Complete Article List

Search this Journal:
Reset
Volume 37: 1 Issue (2024)
Volume 36: 1 Issue (2023)
Volume 35: 4 Issues (2022): 3 Released, 1 Forthcoming
Volume 34: 4 Issues (2021)
Volume 33: 4 Issues (2020)
Volume 32: 4 Issues (2019)
Volume 31: 4 Issues (2018)
Volume 30: 4 Issues (2017)
Volume 29: 4 Issues (2016)
Volume 28: 4 Issues (2015)
Volume 27: 4 Issues (2014)
Volume 26: 4 Issues (2013)
Volume 25: 4 Issues (2012)
Volume 24: 4 Issues (2011)
Volume 23: 4 Issues (2010)
Volume 22: 4 Issues (2009)
Volume 21: 4 Issues (2008)
Volume 20: 4 Issues (2007)
Volume 19: 4 Issues (2006)
Volume 18: 4 Issues (2005)
Volume 17: 4 Issues (2004)
Volume 16: 4 Issues (2003)
Volume 15: 4 Issues (2002)
Volume 14: 4 Issues (2001)
Volume 13: 4 Issues (2000)
Volume 12: 4 Issues (1999)
Volume 11: 4 Issues (1998)
Volume 10: 4 Issues (1997)
Volume 9: 4 Issues (1996)
Volume 8: 4 Issues (1995)
Volume 7: 4 Issues (1994)
Volume 6: 4 Issues (1993)
Volume 5: 4 Issues (1992)
Volume 4: 4 Issues (1991)
Volume 3: 4 Issues (1990)
Volume 2: 4 Issues (1989)
Volume 1: 1 Issue (1988)
View Complete Journal Contents Listing