The Impact of AI Exposure on Job Insecurity, Employee Morale, and the Moderating Role of Emotional Intelligence ()
1. Introduction
The integration of artificial intelligence (AI) and job automation into modern organizational operations has fundamentally changed the nature of work by completely altering the processes and methodologies that companies have worked with since the earliest times [1]. Though these technologies guarantee remarkable levels of efficiency and productivity, they also raise serious concerns among employees regarding the possibility of job loss and vulnerability of employment [2]. As AI systems expand their capabilities to perform complicated cognitive and manual tasks, anxiety regarding the elimination of certain jobs has reached an all-time high [3]. With many employees feeling ill-prepared to respond to the quickly changing dynamics driven by technology, concerns regarding their desirability in the labor market have become paramount [4].
The onset of the COVID-19 pandemic accelerated these concerns by forcing organizations to embrace digital and automated solutions at an unprecedented speed [5]. As companies scrambled to adjust to lockdowns and remote-working mandates, they adopted technologies that enabled virtual teamwork, automated service delivery, and AI-empowered decision making [6]. This period of sudden and dramatic digital transformation brought to light the effects automation has on the psyche of an employee as they struggled with the uncertainty and increased performance expectations arising from unprecedented working conditions. Therefore, making sense of how technological developments shape the emotional and psychological experiences of employees at work has never been more urgent than before.
It is well established that individuals exhibiting high EI levels are more adaptable, resilient, and satisfied in the workplace [7]. In particular, employees with high emotional intelligence are better able to cope with stress, ambiguity and foster strong interpersonal relationships [8]. These are significant characteristics for success in organizational environments that are undergoing continuous transformation. Based on these facts, it is reasonable to argue that EI might play an important role in understanding the ways individual employees face the pressure of AI integration, such as the fear of job loss and emotional stress.
In view of these emerging issues and the opportunities they hold, the current research focuses on the psychological impacts of AI exposure on the workers in the organizations with a central emphasis on job insecurity, employee morale, and the buffering effects of EI. The core objective of the study is to investigate the interplay between these variables while employing a quantitative research design to bring forth new insights in the fields of organizational psychology and HRM.
The first important question this study aims to answer deals with the negative impacts of AI on job security in individuals exposed to artificial intelligence in the workplace. Whether technological advancement through AI applications leads to employee perceptions of job insecurity as the technology keeps evolving must be established. This understanding is crucial for both human resource managers and those in leadership positions about employing AI technology.
The second question emphasizes and examines the emotional aspect of the above relationship: to what extent does the emotional quotient moderate the effect of AI exposure on job insecurity. The study aims to determine whether employees with high EI can adequately handle the changes, disruptions, and challenges that come with AI implementation and whether such people can help to mitigate some of the negative consequences of automation on their emotional and psychological state. It hopes to expand on the effects of emotional intelligence by identifying all the coping tools of people with high EI to find out if there is a way to control their emotions in a way that can help them overcome the fears and frustrations caused by AI.
Lastly, the research aims to unravel the question: how does emotional intelligence influence the relationship between job insecurity and employee morale. It is dire that organizations realize how the relationship between emotional intelligence, job insecurity, and employee morale, if rightly perceived, can transform employee management strategies. The way employees with high levels of EIs can self-regulate and understand their emotions is evidenced by this relationship between job insecurity and employee morale amongst OEs employees. Similarly, through the enhancement of morale and the provision of emotional support to other employees, high EI employees benefit the organization as a whole since they are poised to be committed and loyal. This research will help devise ways of making the best use of high EI employees, or training and employing measures to foster EI in employees, to enhance employee motivation.
The research questions raised highlight the practical applications of the research outcomes for organizations that introduce AI technologies in workplaces. In line with these aims and objectives, the study will not only provide a fruitful and dynamic interplay between emotions and technology in the workplace but also provide evidence-based answers to how employers can design emotionally intelligent AI integration strategies.
2. Literature Review
2.1. Theoretical Context
2.1.1. AI and Automation in the Workplace
Artificial intelligence (AI) and automation technologies have become the capital of all industrial activities, and this has brought about various changes to the labor ecosystems [9]. Machine learning, robotic process automation, and advanced analytics are part of these technologies that help businesses execute their tasks successfully and on time, on a massive scale, and with tenacity never known before [10]. On the other hand, the broad application of AI gives rise to significant challenges, particularly in the job market. Researchers have concluded that AI poses a real danger to jobs that do not engage in manual, cognitive, or routine tasks such as those found in manufacturing, clerical support, and customer care [11]. The economic effect of this shift is also intensely psychological, directed by a growing allure of automation in their day-to-day business processes, several employees would find it difficult to believe that they are vital, to strategize their future likelihood for continued employment, and to guarantee stability in their positions. The changes in the workplace environment can cause resistance to technological changes, reduced productivity, and the fear that the employee will be active in the workplace for a shorter period [12]. Hence, although advanced systems such as AI, as well as other forms of modernization, have formally placed employee capabilities at a much higher level, the existing traditional contract is also compelled to redefine itself as a result of the same technology [13]. In turn, this brings up some important questions on how workers can see and approach change and how they would emotionally cope with it in the current professional and labor environment.
2.1.2. Job Insecurity and Employee Morale
Job insecurity has been increasingly recognized as an issue that needs to be addressed in employability and personal fulfilment [14]. Mainly, this is set against the backdrop of technological advancements in the workplace at a rapid pace [15]. In empirical studies, it was noted that a person who faces uncertainty regarding the preservation of his job can zone down on a high stress level, anxiety, and depressive disorders along with low job satisfaction, poor job performance, and reduced organizational commitment [16]. The fear of a possible threat to job and career prospects has been further fuelled by the introduction of AI technologies, leading to the idea that employees’ positions can be changed or merged with machines at any time [17]. In this regard, even a mere fear of change without any possibility of an immediate change can lead to emotional conflicts and a loss of commitment. This perilous pattern of behavior is further impacted by declining morale, which is an employee’s overall experience of feeling good, passion, and being part of the company’s workforce, and leads to absenteeism, desire to quit a job, and bad morale itself. The first thing that any organization should improve in today’s fast world is employee well-being, morale, as the major determinant of employment productivity and investment profitability [18]. With close and ongoing development, an organization with a depressed employee interest may suffer a loss of capability and changing dynamics, leading to a decline in public participation and damage to the reputation of reputation [19]. Hence, the interplay between job insecurity and morale is of the utmost importance for any organization striving to successfully steer workforce transformation sustainably in a technological environment, where hostile sentiments impact the sense of shared responsibility and drive towards excellence and success.
2.1.3. Emotional Intelligence
Emotional intelligence (EI) refers to an individual’s ability not only to perceive effectively but also to appraise and utilize emotions in two spheres of personal and group dealings [20]. In the work environment, emotional intelligence has continued to gain importance as one of the main skill sets needed to cultivate resilience, readapt appropriately to shifting situations, and enhance collaboration amongst employees during change and in uncertain times when things seem not to be working out according to plan [21]. There are studies indicating that workplaces where EI levels are high are less likely to suffer from issues such as ineffective stress management, poor interpersonal relations among employees, or low productivity [22]. Furthermore, those employees who have acquired high emotional intelligence are better suited for high-pressure situations, are better able to view changes in tasks as opportunities to develop rather than threats to one’s self-worth, and are better equipped to tackle and solve problems than the rest of the workers [23].
As the ever-warming spectre of automation hovers over the labor market, emotional intelligence becomes even more crucial for successful engagements within the workplace due to the heightened stress levels and limited job opportunities [24]. A Golemanian conceptualization of emotional intelligence as a “real-time” approach to sanctioning the consequences of job-related strain demonstrates that employees with higher levels of this ability can constructively interact with their organizations and job roles instead of experiencing dread and cynicism [25]. Indeed, emotional intelligence can play a vital role in the process of equipping and training employees to positively and accurately perceive, intervene, and control their as well as the feelings and emotional states of people around them has to be a severely protective factor that would help in lessening the adverse effects of the changes related to artificial intelligence on the quality of work life [26].
2.2. Current Studies and Gaps
Despite the reality that there has been an increased number of empirical studies on automation and the impact of this phenomenon on job security, the understanding of the cognitive aspects of how workers’ behavior is affected by AI integration is still at an elementary level [27]. Most existing studies examine macro-level trends like unemployment rates, sectoral movement, and reorganization of companies, and give little consideration to how personal competencies, especially emotional intelligence, can mitigate these effects [28]. Although a lot of literature has already been published on the positive impact of EI on reducing stress and improving resilience in the workplace, more empirical studies that can focus on the specific influence of emotional intelligence on the kind of changes that reorganization due to applying AI technologies leads to need to be conducted. In addition, the possible interaction of emotional intelligence and job insecurity in situations where the technology is changing rapidly has not been adequately conceptualized. Besides, there is a significant lack of research that effectively maps the indirect flow of AI-related stressors through feelings of job security and EI. In addressing this area, it becomes hard for the organizations to comprehend the subtle emotional challenges that their employees encounter while implementing changes in the workplace. Filling this gap has the potential to enhance not only the level of academic knowledge but also the available tools for helping businesses in the cultivation of talented and contented personnel. That is why this research is intended to explore the topic of EI and its relation to AI integration and workplace adaptation, with the end view of extending technological advancements to a higher level of improved and people-centered change management practices.
2.3. Conceptual Framework
2.3.1. AI Exposure → Job Insecurity → Employee Morale
This section presents the foundation of the conceptual framework for this research, bringing together the various relevant theories and concepts that underpin the study. The conceptual framework is divided into three primary components: AI exposure, job insecurity, and employee morale. The first component, AI exposure, refers to the extent to which employees are exposed to and required to use AI-based technologies in their work environments [29]. The second component, job insecurity, pertains to the employees’ perception of the likelihood of job loss or negative changes in their employment conditions due to AI implementation [30]. The third component, employee morale, encapsulates the overall level of employees’ motivation, enthusiasm, and satisfaction concerning their work and organization [31]. The framework depicts that AI exposure can create a sense of job insecurity in employees, leading to the reduction of overall employee morale. In this case, employees may feel that their jobs are threatened due to the increased use of AI in the workplace, causing anxiety, stress, demotivation, and disengagement. It is also important.
The conceptual framework developed in this study elucidates the mediational of the impact of exposure to artificial intelligence tools on job insecurity and employee morale. As artificial intelligence has been integrated into many organizations’ work practices and processes, employees have been exposed to systems intended to change or carry out the duties previously done by humans [32]. This growing concern can increase the level of job insecurity as employees start to feel uncertain about the duration and significance of their current positions [33]. Job insecurity, in turn, can hurt workers, such as low job satisfaction, decreased attachment towards an organization, and lack of emotional involvement at work [34]. The sequential effect described above shows how technological disruption can profoundly affect workers psychologically. Understanding the pathway of this relationship is crucial for researchers and practitioners alike, as it indicates that the influence of AI goes beyond operational efficiency by also addressing employees’ well-being and the culture within organizations. Job insecurity, as a mediating variable, provides a crucial insight into how AI indirectly affects workplace outcomes and highlights the roadmap for interventions that can be made to address not only how technology can be well introduced but also how people feel about changes in their organizations.
2.3.2. Moderating Role of Emotional Intelligence
The conceptual framework incorporates emotional intelligence as a potential moderation mechanism that can either increase or influence the strength and direction of the three relationships: AI exposure, job insecurity, and employee morale. It is believed that individuals with high emotional intelligence skills have better abilities to manage their feelings, view the threat differently, and react to problems positively [35]. In the case of AI integration, this means that employees who have a better level of emotional intelligence see automation not as some incipient monster waiting to engulf them but as a field where they should adjust and build their usefulness to the organization [36]. The implication of this is that individuals who are highly emotionally intelligent might have minimized the effect of exposure to AI on their perception of job insecurity [37]. When one has higher levels of emotional intelligence, they might also be less adversely affected by job insecurities, as they are likely to become emotionally resilient, optimistic, and proactively involved with organizational change processes [38]. Hence, the moderating role of emotional intelligence can be understood as an important asset for both employees as well as organizations that strive to address the human side of digital transformation. The inclusion of Emotional Intelligence in the model enables a more elaborate picture of how attitudes and psychological characteristics affect employees’ adaptability to technological disruption, while at the same time demonstrating the importance of enhancement in emotional competence in contrast to an emphasis only on technical skills.
3. Methodology
3.1. Research Design
3.1.1. Quantitative Approach and Rationale
As it effectively analyzes the correlation among emotional intelligence, job insecurity, employee morale, and AI exposure, this study utilizes a quantitative research approach [39]. A quantitative design is considered ideal, as it enables the systematic collection and analysis of numerical data, which can be used to reveal trends, associations, and possibly causal interconnections between the different variables [40]. As such, this objective method yields data that robustly analyzes the response of the workforce under AI integration as well as the degree of emotional intelligence’s moderation in certain relationships. Given these attributes, it can, therefore, be said that incorporating a quantitative framework will ideally inform the research process and findings.
3.1.2. Statistical Analysis
To ensure that the data collected is accountable, consistent, and comparable among various respondents, structured surveys are utilized as the primary tool for data collection [41]. Such methodology enables large-scale data collection, which, when processed through analysis, will favourably yield accurate statistical analysis [42]. In analyzing the data collected, sophisticated statistical methods will be used to test assumptions and validate the theoretical model that was proposed for the study.
3.2. Variables and Measurements
3.2.1. AI Exposure
In this study, AI exposure is defined as the measure of how much employees would be exposed to AI tools, AI systems, or AI processes in their jobs [43]. According to this research, AI exposure was investigated through a questionnaire that assessed information concerning the frequency of AI usage at the workplace, the type of AI tools used, and their perceived effects on job performance, task automation, and significant decision-making processes [44]. By adopting this approach, it will be possible to obtain a detailed understanding of the diverse ways in which Artificial Intelligence may have been signifyingly integrated into the contemporary working environment for this study.
3.2.2. Job Insecurity
The dependent variable, job insecurity, in the current study, entails how the workforce perceives their job security as per the current trend of AI invasion. To fulfill the aims of the current research, Job Insecurity Scale (JIS), a well-acknowledged Likert-type questionnaire, will be utilized to analyze how individuals report fears over job futures and whether or not they still feel secure in their line of employment [45]. Reference to such data from extensive inquiries and diverse subject samples will strengthen the reliability of the research findings and allow researchers to assess the effect of inferior job security on the organization’s collective morale about the progressive implementation.
3.2.3. Employee Morale
Employee morale serves as an important metric for employees’ emotional and psychological well-being in the professional setting. Employee morale will be measured by the use of the Likert-type Workers’ Breed Morale Scale to evaluate aspects of job satisfaction, emotional engagement, organizational commitment, and workplace climate [46]. Accordingly, [47] have thoroughly examined this aspect, translated into measurable components of adherence in organizations since they set the tone of employee behavior as well as attitudes in an organization.
3.2.4. Emotional Intelligence
Emotional intelligence is also commonly referred to as the emotional quotient and is a compilation of competencies that an individual should possess and be able to use in gauging emotional signs in self and others in an organizational context enhancing productivity motivation as well as retention rates [48]. This study offers the perspective that emotional intelligence should not be understood as a generic dimension but rather as a multi-dimensional structure, which has different components such as self-awareness, empathy, emotion regulation, stress control, and social skills. These elements collectively help determine a person’s emotional proficiency and adaptability in organizations. Emotional intelligence entails the ability to actively determine one’s feelings, acknowledge them as well as their effect on others, and have the capability of wondering whether others feel the same way effectively. About an organization’s outer context, Emotional Intelligence refers to down and inward feelings as these contain the focus adjustments together with management and the personal and professional growth, especially during times of hardship.
It is imperative to make sure that emotional intelligence measurement is theoretically grounded and supported in practice in terms of its validity. The study conducted by [49] made use of an emotional intelligence questionnaire that has been widely used and proved to be effective in evaluating the separate yet integrated dimensions of emotional intelligence that exist in the organizational environment. By recognizing the multi-dimensional nature of emotional intelligence, this study attempted to examine the possibility of emotional intelligence moderating the relationship between job insecurity and employee morale more precisely and accurately than was done previously, allowing for finer approximations and analytic scrutiny. The research represents an initial step in developing and adapting the emotional intelligence construct further and provides additional evidence for the strong and well-founded theoretical underpinnings of that construct. Consequently, effective measures should be put in place to endorse the existence of emotional intelligence in employees and teach them to respond positively to the various aspects associated with job insecurity.
3.3. Data Collection
3.3.1. Sampling Strategy
The research conducted uses a method known as stratified random sampling, which is seen as being suitable for guaranteeing sufficient representation of the various employees in the different sectors and positions in the labor market [50]. Through the stratified sampling techniques, participants were selected from the sample as on their different levels of AI exposure and demographic diversity in the industries that were in the scope of the study. This method was mainly used to prevent the exclusion of any vital segment of the population from the sample, as well as to ensure that the analysis covered a broad section of employees in line with the advice of [51]. It is important to find out just how effective AI can be in human resource and performance management in general, and therefore, for the greatest part of the research to represent every employee in practice.
In the process of data collection, 140 questionnaires were given to the target respondents, and out of these, 134 responses were received back. However, following the cleaning of data, 125 valid responses were considered for the final analysis. Stratified sampling is a research technique aimed at enhancing and increasing the representativeness of research results. On the contrary, it has been observed that the sample size of 125 respondents may limit the generalizability of the results obtained, especially considering the wide spread of all the sectors that are likely to be affected by the integration of AI. Additionally, the determination of the problems of the research may call for the sincere analysis of the data collected, and it becomes evident that future studies may yield narrower and more credible results if further study is undertaken featuring more concentrated characteristics of the study population. The respondents are characterized in terms of job title and industry they work in, but that incredible detail has not been revealed through the findings of the research. Therefore, it is intended that future research should focus on obtaining a larger sample size and as such give explicit descriptions of the participants while undertaking a similar research to enhance the results; as such the outcomes of the research can be acknowledged in a wider range of settings and that is bound to several organizations.
3.3.2. Survey Distribution Method
To effectively gather significant information and insights from all participants, an online survey was formulated and carefully prepared before its conduct, mainly through email using a secure web-based platform, to guarantee that the participants would find it easy to fill it out and that their data would be protected [52]. Actually, at no time was participation in the survey assumed as mandatory on the part of the respondents; rather, it was made clear that the decision was upon the respondent as they were expected to give their informed consent before the process of data collection and administration started. An extensive description of the study and its purpose, including clarity about the benefits of participation in the process, along with the confidentiality measures undertaken by the researchers, was clearly and adequately communicated to the participants [53]. Although this type of survey is a well-known method of data collection and is believed to be highly efficient and competitive, the researcher provided the non-respondents with friendly reminders in a time of intervals of greater than two weeks during which the data was being collected for the study, to increase response rates.
While online surveys are generally regarded as efficient and scalable methods of data collection, the study also acknowledges the inherent potential limitations associated with self-reported data, particularly when it concerns subjective constructs such as Emotional Intelligence (EI) and job insecurity, these measures are subject to undue influences from social desirability or perceptual biases that could lead to unreliable information being collected. A strategy to minimize these possible biases was the use of well-established instruments in the questionnaire that have been validated and shown to have solid reliability and construct validity. One of the critical aspects to note is that despite the rigorous reliability and objectivity of the measures used, the inherent dependence on self-reporting methodology still represents a significant limitation with the potential to affect the accuracy and objectivity of the results obtained. Further research employing other methodologies such as observational studies or qualitative interviews could provide a more holistic and nuanced understanding of emotional intelligence and its effect on job insecurity.
3.3.3. Validation of Models and Hypotheses Testing
The reliability and validity of measurement instruments in the current realm of social science research were undertaken using Cronbach’s alpha and confirmatory factor analysis, which have been statistically established as crucial tools in ensuring that research instruments are capable of measuring the constructs they were intended to measure [54]. Structural equation modeling SEM was employed to test the stated hypothesis concerning the relationships among variables such as AI exposure, job insecurity, employee morale, and emotional intelligence which all seem to have important implications on the current state of the workforce and how people are coping with change in the current era in which creation of AI has brought about profound implications [55].
3.4. Validation of Models and Hypotheses Testing
3.4.1. Informed Consent and Participant Confidentiality
As a consideration, participants are informed of the study’s objectives, the processes involved, and their rights before providing the information on this matter so that they can meet research ethics well [56]. Participants’ reporting procedures were made explicit and clear, and the issue of anonymity of participants’ confidentiality in the process was taken into account during the research process, as well as the reporting of results [57]. In any published report, there will be no identification or naming of individual participants; instead, general reports will be based on both aggregate data and the complete sample [58]. This will enable adherence to ethical principles of voluntary participation and maintaining the integrity of the research process.
3.4.2. Data Storage and Handling Protocols
All data were securely stored in encrypted digital formats with access restricted to the research team [59]. Data handling complies with institutional and ethical guidelines to ensure participant protection and integrity of research findings.
4. Data Analysis, Discussion, and Conclusions
The purpose of this section is to present in detail the findings that are obtained from the conducted survey, along with the detailed statistical analysis performed and the interpretation of the results. A descriptive survey was used to collect data from participants, and it was analyzed using several statistical analyses, including descriptive statistics, reliability testing, correlation analysis, regression modelling, and moderation analysis, to test the research hypotheses formulated for this research. Again, the recorded findings are discussed in the context of the existing literature, theories related to the study topic, and the conclusions are provided based on the statistical results of the analysis. A lot of time and effort was spent on the analysis of the data to present the most complete and accurate explanation of the study results.
4.1. Descriptive Statistics
Table 1 presents the demographic profile of respondents, highlighting gender, age group, and years of experience. Among the 125 respondents, 52% are male and 48% female. The age distribution shows that 40% are aged 26 - 35, while 20% are 18 - 25, 24% are 36 - 45, and 16% are 46 and above. In terms of experience, 32% have less than 5 years, 36% have 5 - 10 years, 20% have 11 - 15 years, and 12% have more than 15 years of experience.
Table 1. Demographic profile of respondents.
Variable |
Frequency |
Percentage (%) |
Gender |
|
|
Male |
65 |
52.0 |
Female |
60 |
48.0 |
Age group |
|
|
18 - 25 |
25 |
20.0 |
26 - 35 |
50 |
40.0 |
36 - 45 |
30 |
24.0 |
46 and above |
20 |
16.0 |
Years of experience |
|
|
<5 years |
40 |
32.0 |
5 - 10 years |
45 |
36.0 |
11 - 15 years |
25 |
20.0 |
>15 years |
15 |
12.0 |
4.2. Reliability Analysis
Table 2 displays the reliability analysis results, measured by Cronbach’s Alpha for various constructs. AI Exposure has a reliability score of 0.78, indicating acceptable internal consistency. Job Insecurity scores higher at 0.84, suggesting good reliability. Employee Morale shows the highest reliability with a score of 0.88, reflecting strong internal consistency. Emotional Intelligence also demonstrates good reliability at 0.81.
Table 2. Reliability analysis.
Construct |
Cronbach’s Alpha |
AI exposure |
0.78 |
Job insecurity |
0.84 |
Employee morale |
0.88 |
Emotional intelligence |
0.81 |
Interpretation: All scales demonstrated acceptable reliability (α > 0.70).
4.3. Correlation Analysis
Table 3 presents the correlation analysis among four variables: AI Exposure, Job Insecurity, Employee Morale, and Emotional Intelligence. AI Exposure correlates positively with Job Insecurity (0.42), suggesting that higher AI exposure may be associated with increased job insecurity. Both AI Exposure and Job Insecurity negatively correlate with Employee Morale (−0.38 and −0.50, respectively), indicating that as job insecurity rises, employee morale tends to decline. Emotional Intelligence shows weak negative correlations with AI Exposure (−0.15) and Job Insecurity (−0.32), but has a moderate positive correlation with Employee Morale (0.45), indicating that greater emotional intelligence is linked to higher morale.
Table 3. Correlation analysis.
Variables |
1 |
2 |
3 |
4 |
1. AI exposure |
1 |
|
|
|
2. Job insecurity |
0.42 |
1 |
|
|
3. Employee morale |
−0.38 |
−0.50 |
1 |
|
4. Emotional intelligence |
−0.15 |
−0.32 |
0.45 |
1 |
Note: p < 0.01.
4.4. Regression Analysis
Table 4 outlines Model 1, which predicts Job Insecurity based on AI Exposure. The regression analysis shows that AI Exposure has a significant positive effect on Job Insecurity, with a coefficient (B) of 0.56 and a beta value of 0.42. The model explains 18% of the variance in Job Insecurity, indicated by R2 = 0.18 and a significant F-statistic (21.8, p < 0.001).
Table 4. Model 1 Predicting Job Insecurity from AI Exposure.
Predictor |
B |
SE |
Beta |
t |
p |
AI exposure |
0.56 |
0.12 |
0.42 |
4.67 |
<0.001 |
R2 = 0.18, F (1, 123) = 21.8, p < 0.001 |
|
|
|
|
|
Interpretation: AI exposure significantly increases perceptions of job insecurity.
Table 5 presents Model 2, which predicts Employee Morale based on Job Insecurity. The analysis reveals a significant negative effect of Job Insecurity on Employee Morale, with a coefficient (B) of −0.71 and a beta value of −0.50. The model accounts for 25% of the variance in Employee Morale, indicated by R2 = 0.25 and a significant F-statistic (25.7, p < 0.001).
Table 5. Model 2 predicting employee morale from job insecurity.
Predictor |
B |
SE |
Beta |
t |
p |
Job insecurity |
−0.71 |
0.14 |
−0.50 |
−5.07 |
<0.001 |
R2 = 0.25, F (1, 123) = 25.7, p < 0.001 |
|
|
|
|
|
Interpretation: Job insecurity significantly reduces employee morale.
Table 6 examines the moderation effect of Emotional Intelligence on the relationship between Job Insecurity and Employee Morale. Job Insecurity significantly negatively impacts morale (B = −0.65). Emotional Intelligence positively influences morale (B = 0.40). The interaction term (Job Insecurity × Emotional Intelligence) is significant (B = 0.28), suggesting that higher Emotional Intelligence enhances morale even in the presence of Job Insecurity.
Table 6. Model 3 Moderation Effect of Emotional Intelligence 3a. Moderating Job Insecurity → Morale.
Predictor |
B |
SE |
Beta |
t |
p |
Job insecurity |
−0.65 |
0.16 |
−0.46 |
−4.06 |
<0.001 |
Emotional intelligence |
0.40 |
0.11 |
0.38 |
3.64 |
<0.001 |
Job insecurity × EI (interaction) |
0.28 |
0.09 |
0.26 |
3.11 |
0.002 |
Interpretation: Emotional intelligence significantly moderates the relationship between job insecurity and morale. High EI buffers the negative effect.
4.5. Hypothesis Testing Summary
Table 7 summarizes the hypothesis testing results. Hypothesis 1 (H1) is supported, indicating that higher AI exposure is positively associated with job insecurity. Hypothesis 2 (H2) is also supported, showing that lower job insecurity correlates positively with employee morale. However, Hypothesis 3 (H3), which posited that Emotional Intelligence (EI) moderates the relationship between AI exposure and job insecurity, was not significant. In contrast, Hypothesis 4 (H4) is supported, suggesting that EI does moderate the relationship between job insecurity and employee morale, indicating that individuals with higher emotional intelligence maintain better morale despite job insecurity.
Table 7. Hypothesis testing summary.
Hypothesis |
Statement |
Result |
H1 |
Higher AI exposure is positively associated with job insecurity. |
Supported |
H2 |
Lower job insecurity is positively associated with employee morale. |
Supported |
H3 |
EI moderates the AI exposure–job insecurity relationship. |
Not significant |
H4 |
EI moderates the job insecurity–morale relationship. |
Supported |
4.6. Discussion
The findings of this study are expounded, signifying that increasing mechanization with the usage of AI technologies causes people, the employees in any workplace, to have their emotions go high. Employees having such tendencies of being afraid or worried about being laid off or job insecurity are most likely not to be happy in their jobs and manage not to bring out their best proposals in the jobs. Consequently, this research agreed with previous studies on how AI machines and other automated machines lead to pressure and tension for the employees and as a result affect productivity negatively.
I’ll find out at this point that those employees who have high emotional intelligence self-control in the face of job insecurity are not ones to be easily given to the emotional pressures, but have a good capacity to do the jobs as expected as well as maintaining a level of morale that is required for doing the jobs as required. Employees who possess high emotional intelligence have been proven to achieve high levels of organizational innovativeness despite the high amounts of change in job security. On the other hand, the absence of any moderating effect of Emotional Intelligence between exposure to AI and Job insecurity calls for further research. This would be because the level of AI for Employee insecurity is initially based on organizational factors like communication and role clarity as opposed to the personality traits of the employees.
4.7. Conclusions
This thesis confirms that AI exposure leads to Job insecurity and employees’ low morale through various direct and indirect pathways, as depicted by the selected mediation model. It affirms that Job insecurity and depressive outcomes of the employees are not only brought about by the challenges of technology on employee systems, but also UI based hostile transitions are led by other psychological processes such as emotional intelligence. The study thus recommends social actors’ and practitioners’ attention to the underlying role played by Emotional Intelligence in buffering the negative consequences of technology-related job insecurity and low morale.
Conflicts of Interest
The author declares no conflicts of interest.