The Effect of Artificial Intelligence Awareness on Job Performance: Gender as Moderator and Experience as Mediator

1. Introduction

Nowadays, the world faces many serious challenges (e.g., war, disease, poverty, gender inequality, climate change, etc.). Therefore, many researchers and practitioners have tried to find tools to help address these challenges and open up new opportunities. Among these tools were artificial intelligence (AI) technologies, which came back into vogue in the 2010s (Duan et al., 2019). In fact, AI tools have been used in various industries, such as tourism, hospitality, manufacturing, finance, education, healthcare, etc. (Buhalis & Leung, 2018; García-Madurga & Grilló-Méndez, 2023; Kim, 2011; Prentice et al., 2020; Toumia & Zouari, 2024a, 2024b; Yetgin and Toumia, 2023; Yu & Schwartz, 2006; Zeba et al., 2021; Zouari, 2019). These tools improve and increase the operational efficiency of companies (Russell, 2016; Younus, 2021). Indeed, it is well documented that artificial intelligence has an impact on all departments of a company, such as marketing, human resources, production, etc. (Kumar et al., 2019; Mohammadi & Minaei, 2019; Vrontis et al., 2022). Despite the benefits of AI, many contributions have highlighted that there are potential drawbacks to using AI (Cellan-Jones, 2014; Duan et al., 2019; Rawlinson, 2015). Stephen Hawking announced that “the development of full artificial intelligence could spell the end of the human race” (Cellan-Jones, 2014). For instance, artificial intelligence is now able to beat humans in complicated games (e.g., the AlphaGo system and IBM’s Watson system), interpret sounds and language, solve problems, improve prediction, provide a deeper level of transparency, create a more rewarding workplace, use facial recognition tools, diagnose medical cases, drive a car on the road, play games like chess, and imitate the impressionistic imagery in Van Gogh's paintings (Agrawal et al., 2019; Gabbatt, 2011; Jarrahi, 2018; Kaplan & Haenlein, 2019; Koch, 2016; Tuomi, 2019).

While acknowledging the impact of AI on improving the efficiency of businesses (Abusalma, 2021; Wijayati et al., 2022), it is important to investigate the relationship between artificial intelligence and job performance. Indeed, many studies have used sociodemographic mediating variables (e.g., gender, age, and tenure) to investigate the relationship between job performance (see Shirom et al., 2008) and other factors such as work-related stressors, work-family conflict, and organizational commitment. However, to our knowledge, no previous quantitative research dealt with the effect of artificial intelligence awareness on job performance in the context of Tunisian employees by using gender as the moderator variable and experience as the mediating variable. Our present research aims to contribute to filling this gap.

In order to test our model, a questionnaire was created with a dataset of 350 employees from various industries. IBM SPSS.26 and IBM SPSS AMOS.26 were used to evaluate the generated data. Indeed, we performed the explanatory factor analysis (EFA), the confirmatory factor analysis (CFA), and the structural equation modeling technique (i.e., it is employed to conduct a path analysis for the relationships among variables in our model).

This chapter is divided into five main sections. The first section presents the introduction. The second section provides a theoretical background for our research. The third section describes the methodology used to solve our research question. The fourth section introduces and discusses the main findings. The five section summarizes the findings and presents the contributions, limitations, and future research.