The Influence of Artificial Intelligence (AI) On Job Insecurity

With the declining labor force participation and increased labor costs, the prospect of the conventional manufacturing industry and the overall job market depending on workforce development may not be realistic or optimistic. Artificial intelligence (AI) is taking over much of the work in organizations, posing a threat to job security in many sectors. This paper is a proposed study on the influence of AI on job security. The proposal is divided into four major sections, including an introduction, providing an overview of the topic and a literature review, study methods, an overview of the study conducted, and the results and discussions of expected findings.

The Influence of Artificial Intelligence (AI) On Job Insecurity

The labor force participation is declining, and labor cost is rising. The participation rate is expected to fall by 1.6% points at the global level between 2015 and 2030, with a decline in excess of 2% anticipated in most regions. According to the Labor Department, the employment cost index jumped by 1% in the last quarter of 2021, after increasing by 1.3% between July and September. The labor costs included 4% on a year-to-year basis, the highest since 2001, after rising by 3.7% in the third quarter (Koh & Yu, 2021). With the declining labor force participation and increased labor costs, the prospect of the conventional manufacturing industry and the overall job market depending on workforce development may not be realistic or optimistic. Artificial intelligence (AI) technologies, including computer vision, cloud computing, natural language understanding, intelligent homes, and many more, are increasingly extensively adopted in the manufacturing industry (Liu & Zhan, 2020). However, the rise and implementation of (AI) in the workplace influences employee perceptions of job security, with many workforces perceiving AI as a threat to their job security AI’s impact on increasing productivity.

Braganza et al. (2021) studied the influence of AI implementation on psychological contracts, employee trust, and job engagement relative to organizational productivity. The researchers noted that AI is a globally challenging foundation of the corporate world, altering how people work. It impacts work responsibilities and tasks while potentially enhancing organizational productivity and efficiency. The authors further gave manifestation of AI already applied in the business environments, including chatbots and machine learning. With current technological advancements, the growth of the AI industry is expected to hit $47 billion by 2020. Implementing AI in the workplace facilitates automation of tasks and is anticipated to increase productivity with an augmented workforce, potentially more significant quality output, and increased demand for highly customized products and services. Organizations’ rapid implementation of AI is expected to accelerate the growing pattern of progressively autonomous work cultures. However, Braganza et al. (2021) further argued that some work, primarily those prone to automation, will begin to disappear. They asserted that it is likely that AI will be deployed and replace workers in jobs that demand analytical and mechanical intelligence but will limited potential in job tasks that demand empathetic or intuitive intelligence. According to the researchers, mechanical intelligence is the ability to perform repeated tasks, while analytical intelligence denotes the ability to process problem-solving-related information and learn from the same. There is intuitive intelligence, which requires thinking creatively and adjusting effectively to novel situations, and empathetic intelligence, which entails understanding people’s emotions, responding accordingly emotionally, and influencing others. Al automation technologies can effectively pick data, translates information, and control processes or make decisions. Hence, potentially can replace humans in all four intelligence. Nevertheless, Braganza et al. (2021) maintain that despite AI improving organizational productivity and efficiency, it is likely to reduce employee engagement and trust and weakens the relational part of psychological contracts. AI implementation leads to significant uncertainties for workers about the security of their jobs as the whole job or selected tasks may be automated out of existence. The workforce that perceives the potential adverse impact of AI is likely disengaged and loses trust with the organization and must be supported and guaranteed some certainty concerning their future.

On the same note, Lingmont and Alexiou (2020), while studying the contingent influence of work automating technology on perceived job insecurity, noted that an AI-aware workforce is likely to experience declined organizational commitment and low job satisfaction alongside high turnover intention rates. According to the researchers, the development of Robotic and AI technologies is likely to cause further revolutions in manufacturing and production, customer service, and forecasting and have potential organizational and social implications. The concern among the economists, policymakers and the public is how STARA (Smart Technology, AI, Robotics and Algorithms) technologies and their central automation could affect industries and jobs, wealth distribution, and unemployment rate (Lingmont and Alexiou 2020). The researchers further noted that about 47% of the present work occupations in the United Kingdom risk job automation. About 60% of the present jobs are approximately one-third capable of full automation (Lingmont and Alexiou 2020). The anticipated organizational restructuring caused by job automation is a precursor of job insecurity. As technology, including AI and Robotics, is projected to revolutionize job tasks across industries, employees aware of these technologies are likely to experience a threat to their jobs in the face of their implementation in the organizations (Lingmont and Alexiou 2020). The researcher further established that a STARA-aware workforce is likely to experience declined organizational commitment and low job satisfaction alongside high turnover intention rates and depression. About 65% of US citizens anticipate robots to take up most of the current jobs in 50 years, while 18% believe that their present occupations or professions will likely cease to exist in the 50 years (Lingmont and Alexiou 2020) even though the findings are precursors of job insecurity and potential extended losses soon.

Liu and Zhan (2020), while studying the impact of AI on job security, argued that an AI-aware workforce is likely to develop anxiety, an emotion that the researchers defined as job insecurity, the incapability of the workforce to sustain the status quo when they feel threatened at work. The researcher asserted that AI’s influence on the employment of a compatible emerging technology would be more profound than in previous technology revolutions. AI will directly replace approximately 13% of the current occupations, including financially rewarding and brain-intensive jobs such as senior management, accounting, and finance. AI is perceived as the external organizational factor which will restructure the workplace, threatening the stability of the workforce’s working state. The researcher argued that organizations must make timely, necessary adjustments to avoid the adverse impact of anxiety among AI-aware employees. At the same time, explaining the development of job insecurity among the workforce, the researchers Hobfoll’s (1989) resource protection model explains their point. The model postulates that when the workforce is confronted with external factors for organizational change, they are likely to strive to protect and maintain their status quo. However, when the employee perceives an inability to protect resources or uncertainty in accessing resources, they tend to develop personal stress as the ultimate response to the environmental change, which translates into job insecurity.

Research Hypotheses

H1. Implementation of AI increases employees’ perception of job insecurity.

H2. AI awareness increases employees’ perception of job insecurity.

The hypotheses lead us to the proposed conceptual model, as shown in figure 1.

AI Awareness

Perception of Job Insecurity

AI Implementation

Figure 1: Conceptual Model

                                            

Methods

Research Design

This study explores the impact of AI on employee job insecurity. IA implementation and AI awareness are the studies (IVs), and perception of job insecurity is the dependent variable (DV). The whole sample population will be divided into two major groups and four subgroups. The groups include (1) the subjects working in forms that have implemented AI, (2) subjects working in firms that have not implemented AI, (3) subjects who are aware of AI, and (4) subjects who are not aware of the AI. That forms a factorial design making the study a 2 (AI implementation: has implemented, has not implemented) x2 (AI awareness: a participant is aware, the participant is not aware) between-groups factorial. Regarding AI implementation, the perception of job security will be measured in the first subgroup and measured against the second subgroup. Similarly, the perception of AI on job security will be measured against the AI awareness of the subject by comparing the third and fourth subgroups.

Specifically, AI implementation will be measured using the utilization scale for industrial robots created by Wang Cai (2019) for both subgroups 1 and 2. The researcher will measure the outcomes of the perceived job insecurity (DV) using a four-item job insecurity scale adapted from de Witte (2000) for each group and tabulate the data for analysis. Similarly, the researcher will measure AI awareness in subgroups 3 and 4 using the STARA awareness scale of Brougham and Haar (2018). The outcomes of the perceived job insecurity in the two subgroups will be measured using the four-item job insecurity scale adapted from de Witte (2000). Lastly, all the mean results on the four subgroups are compared to find the impact of AI implementation and AI awareness on the perception of job security in the overall sample population.

 Notably, the AI implementation variable can be randomized across the levels of AI awareness to ensure the study from selection and accidental bias. That will allow a conscious manipulation of the variable. The researcher can find when the implementation of AI, along the lifecycle of AI awareness, impacts the perception of job security and the chronological changes, if any, from positive to negative perception of job security. For instance, the researcher may decide to control the group working in organizations with no AI implementation 1(has not implemented AI) X2 (is aware of AI, is not aware of AI) and measure the perception of job security in such organizations.

Participants

The researcher targets a sample study population of 500 individuals working in either of the following industries: construction, repair and maintenance, transportation and logistics, manufacturing and agriculture, and sales, as they are considered the category of the workforce whose jobs are at higher risk of AI automation.

Materials

A 5-point Likert scale will be adopted to measure all the study’s primary construct. The Likert scale will range from 5 (strongly agree) to 1 (strongly disagree). Each scale will Cronbach’s alpha. Independent variables (IVs) will be measured using the following scales: a) AI implementation will be measured using the utilization scale for industrial robots, created by Wang Cai (2019); b) AI awareness will be measured using a four-item scale for IA awareness (α = 0.85), the same metric adopted by Brougham and Haar (2018) to measure STARA awareness.

A four-item job insecurity scale adapted from de Witte (2000) will be used to measure the dependent variable (DV), perceived job insecurity (α = 0.85). Some of the items include: “There are chances I may lose my job soon.”

Procedure

The researcher will develop an online survey questionnaire to collect data for this research. The survey instrument will target employees across California and Texas, the US, which are the dominating US states in applying AI industries. The survey data was collected by requesting the targeted research participants scan QR codes using their smartphones to link to our online survey questionnaire. The questionnaire will comprise two sections. The first section will comprise basic information, including age, sex, educational level, and occupation. The second section will comprise of questionnaire of investigation and study variables. The researcher will ensure a completely ethical approach before collecting data for the primary survey. A pilot study will be conducted to assess and improve the study questionnaire as well as test the robustness of, reliability, and validity of measurement items.

The study participants will comprise only individuals whose job occupations are at higher risk of automation by AI. The industries considered at higher risk of AI automation include construction, repair and maintenance, transportation and logistics, manufacturing and agriculture, and sales. The respondents will only participate in the survey to work in any mentioned industries. This will allow the researcher to effectively develop a sample that represents the study population. A sample size of 450 respondents will be a target for the analysis. 

Expected Results

I generally expect to find a relationship between AI implementation and AI awareness with the perception of job security. So far, I have theorized that AI implementation and AI awareness would negatively influence the employee perception of job insecurity by escalating the experience. However, I will conduct an ANOVA to nullify my hypothesis H1. Implementation of AI increases employees’ perception of job insecurity and H2. AI awareness increases employees’ perception of job insecurity. This research has 2 (AI implementation: has implemented, has not implemented) x2 (AI awareness: the participant is aware, the participant is not aware) between-groups factorial design.

I expect the results to indicate that implementation of AI and awareness of AI will negatively affect the perception of job security, in that participants will have a high level of concern about their job security. For instance, participants who work in a firm that has implemented AI are expected to have, on average, higher job security concern scores (M = 4.0) than those who watch reality TV (M = 1.0; p < .05). The more the organization implements AI in different sections or tasks, the higher the employees will experience perceived job insecurity. In the same way, participants who are aware of AI are expected, on average, they will have higher job security concern scores (M = 3.5) than those who watch reality TV (M = 1.5; p < .05). The more knowledgeable or aware the workforce is about AI, the higher their perceived job insecurity level.

Concerning the combined influence of AI adoption and awareness on job engagement and commitment, job trust, and job satisfaction, it is anticipated that AI adoption and AI awareness will adversely impact job engagement and commitment, job trust, and job satisfaction (p < 0.01). This confirms the literature findings that AI adoption and AI awareness increase employee perception of job insecurity and decreases job engagement and commitment, job trust, and job satisfaction because of uncertainties, as pointed out in the literature review.

Discussion

AI implementation has the potential to increase the employee’s perception of job insecurity. When implemented in the workplace, AI technologies can lead to the automation of some tasks or the entire job, potentially leading to job losses. Employees who feel that AI is taking up their jobs will develop a high level of perceived job insecurity, impacting their overall productivity. AI challenges sustainable development goals set out by the UN, which advocates for decent jobs to promote economic development. AI can potentially reduce or remove the workforce already in decent employment, full-time permanent occupations, as many of such jobs can be replaced by short-term ad hoc or temporary gig work. Subsequently, AI is likely to replace decent employment with contingency work, substituting the certainty of regular income with income volatility, where one has decent income at one time to no income in other periods. Income volatility has been shown to cause vulnerability in the workforce, where the employees lack control of their lives, and their families and social cohesion are disrupted (Lingmont & Alexiou, 2020). Such disruptions cause the employees to develop a high perception of job insecurity and the powerlessness to sustain the continuity in the threatened job. Adopting AI also causes perceived role ambiguity due to anticipated changes, contributing to job insecurity. Anticipated changes refer to any event perceived as a threat to the present organization, including new technologies, while perceived role ambiguity denotes a lack of clarity concerning job requirements (Lingmont & Alexiou, 2020).

AI awareness also raises the perceived job insecurity among the workforce. With increased awareness of technological developments, it is logical to expect changes in the job space as a result of automation. This study argues that the more knowledgeable the workforce is about AI technologies, the higher their perceived job insecurity. Organizations must be aware that the workforce could become more AI aware over time, resulting in more job insecurity. In the process, the workforce’s work attitude, well-being, and behavior are impacted by the constant worry about job insecurity, leading to a declined performance (Braganza et al., 2021). Managers should consider strategies such as retraining if the employee perceives a threat to their job security to introduce a new AI system or are aware of the organization’s plan to implement AI, or have a general knowledge concerning AI’s role in the workplace. Retraining reduces perceived job insecurity. AI awareness is also linked to a decline in job commitment and engagement, job trust, and job satisfaction, precursors of job insecurity. A positive derivative of high commitment and engagement is that the workforce is happier with their job, spends more time at work, and is less likely to exit the reorganization (Lingmont & Alexiou, 2020). However, perceived job insecurity dilutes such attitudes, hence a low committed and engaged workforce. Subjective and objective threat to the job also triggers loss of trust and low satisfaction with the job. When the workforce feels that their job is threatened by AI adoption or any other technology, they will likely experience withdrawal from the job, followed by a loss of trust and a decline in job satisfaction.

The reaction to job insecurity is a factor of several variables, including employment market features, employability, and individual characteristics. An employment market characterized by the availability of alternative jobs will trigger a lower perception of job insecurity with the implementation of awareness of AI as the workforce is sure of getting an alternative job. Employability is the employee’s ability to gain and maintain new employment and factors skills levels, education achievement, and experience. An employee with a combination of the three factors will experience less perceived job security with the implementation of awareness of AI, which less-skilled low education and less experienced employees are likely to experience higher levels of job insecurities. Regarding individual characteristics, adults with more responsibilities, such as family to care for, are more likely to experience job insecurity with AI adoption than young newcomers in the job market.