Academy of Entrepreneurship Journal (Print ISSN: 1087-9595; Online ISSN: 1528-2686)

Research Article: 2021 Vol: 27 Issue: 2S

Moderating Interact of Artificial Intelligence Use in The Influences of Recruitment, Selection, and Staffing on The Organizational Performance in the UAE Manufacturing Industry

Abdelrahman Hassan Mohamed Abdulla Almansoori Asian Institute of International Affairs and Diplomacy, UUM COLGI

Muhammad Fuad bin Othman Asian Institute of International Affairs and Diplomacy, UUM COLGIS

Mohammed R A Siam Asian Institute of International Affairs and Diplomacy, UUM COLGIS

Keywords

Employee Recruitment, Employee Selection, Employee Staffing, Artificial Intelligence, Organization Performance, Manufacturing Industry, UAE

Abstract

 The aim of the study is to examine the impact of employee recruitment, employee selection, and employee staffing on the organization's performance, besides the moderating impacts of artificial intelligence in the UAE manufacturing industry. The research framework illustrates the relationships between the exogenous variables (employee recruitment, employee selection, employee staffing, and artificial intelligence as a moderator) and the endogenous variable organization performance. The target or study population chosen for this research work equates to the total number of employees working in any firm in the UAE manufacturing industries and willing to respond to the questionnaire during data collection. The actual sample size is 382 employees. The distributed survey is 524, which is distributed by using face-to-face data collection methods in a convenient sample selection technique in 2019. Overall, the model is successful because it can predict 41.3% of the organization's performance and the direct relationships for the three predictors of HRM practices are significant. The precedence for the relations based on the path coefficient value is employee recruitment (0.334), employee selection (0.297), and employee staffing (0.278). For the moderating relationships of artificial intelligence, two interactions have a significant positive interaction; employee selection (0.108) and employee recruitment (0.098); but the Employee Staffing (EST) has no significant change based on artificial intelligence.

Introduction

The success of any organization lies in the human resource personnel's ability to hire, select and staff the best employees to perform needed duties and tasks and the ability to retain the functional employees (Kerzner & Kerzner, 2017; Pfeffer & Veiga, 1999; Romiszowski, 2016). As such, one of the major issues facing any organization's human resources is recruiting the best employee(s) that can get things done effectively and efficiently.

The use of an application form and request for the potential employee curriculum vitae is one of the arcade ways of recruiting employees. Using this method, critics found that either the potential employees misrepresent their working experience by inflating the number of working years or their skills. More so, using this method, the potential employer cannot determine the employees' loyalty as such limits the high turnover rate (Armstrong & Mitchell, 2019; Stamolampros et al., 2019)..Because of this, new employees are placed on probation, whereby they are being monitored for a short period before being given permanent positions. In most cases, underperforming employees were advised to find a new job while the experts among them prefer jumping to other jobs (Staff Reporter, 2018). By doing this, the firm incurs higher expenses and wastes much time (Branham, 2012; Griffeth & Hom, 2001).

Things got a little bit more complicated in recent times for human resource personnel to recruit the needed hands because there is a surge increase in information availability; therefore, they receive tons of applications when vacancies are advertised. Not only this, the recruited employees' loyalty is at the least level because they tend to dump the current employers to their competitors who offer better pay and remuneration package, specifically in Dubai, which is the context of this research (Staff Reporter, 2018; Pyakurel, 2021).

Dubai has suffered and suffered the same fate. Statistics from earlier investigations reveal that from around 90% of foreign nationals working in Dubai, almost half of these employees had either left their current job in the last one year or might be leaving their current job to (Staff Reporter, 2018), thus having a significant effect on the performance of the organization in which they were previously hired.
To limit the cost and time used by the human resource personnel, human resource practitioners and data scientist proposed the use of artificial intelligence to predicts employees' turnover rate, performance based on the submitted resume, background checks and to be used during the interview exercise (Auer, 2018; Weaver, 2017).

In addition, there are indications that organizations resolve to social media platforms to access potential employees. This is because employees carelessly display their skills and work-life, emotions, and prospect on social media platforms. As such, using Artificial Intelligence (AI) algorithms, their real skill set can be effortlessly determined (Chamorro-Premuzic et al., 2016; Treem & Leonardi, 2013). However, an investigation by Van Iddekinge, et al., (2016); Stoughton, Thompson & Meade (2015) strongly oppose this proposition on the ground that employees' or potential employees' social life is different from career life.

On the other hand, Sajjadiani, et al., (2019) study emphasized using AI to predict employees' turnover rate during the stages of HR recruitment processes.

Meanwhile, prior studies present mixed evidence pertaining to the relationship between HR practices and employees' turnover (Andersén & Andersén, 2019; Cooke et al., 2019) that affects organizational performance. Outside the mixed evidence, the construct HR practices in earlier studies are either being used as a single constructor as motivational factors to enhance employees' satisfaction (Bowen & Ostroff, 2004; Tzafrir, 2005). As evidence from the studies of few authors cited, what constitutes HR practices that relate to high employee turnover is not clear. Nevertheless, with emphasis on the issues raised by Staff Reporter (2018) pertaining to the major causes of employees' high turnover rate and low performance in Dubai, these include lack of opportunities and advancement, unsupportive leadership, unconducive work environment, and desire for a more challenging work task correspond to Keegan & Hartog (2019) exploration of employees performance rating. All these points towards the advanced HR practices and not traditional HR practices (Keegan & Hartog, 2019; Noe et al., 2017).

Meanwhile, insights to earlier literature reveal that scholars gave worthy attention to the strategic human resource practices such as employees' motivation, remuneration package, satisfaction, loyalty, and retention, to name a few (Baah & Amoako, 2011; Grant & Berry, 2011; Fahim, 2018). While studies focusing on the fundamentals of human resource practices receive less attention. Those who tend to empirically investigate this do that by isolating factors such as recruitment, selection, and staffing or combining the fundamental factors with other motivational or strategic factors of HR practices such as training and development. Notably, some of the available studies also examined HR practices as a single construct that influences organizational performance (Katou, 2017; Noe et al., 2017). Thus, considering the identified issues in earlier studies, this research deemed it fit to empirically investigates the relationship between the fundamentals of HR practices (hiring, selection, and staffing) on organizational performance

As discussed earlier, artificial intelligence in recent times had been employed by HR practitioners to help read numerous employees' resume and social media profile (Chamorro-Premuzic et al., 2016; Treem & Leonardi, 2013); nevertheless, there is a strong caution and opposing view from scholars such as Van Iddekinge, et al., (2016) and Stoughton, et al., (2015). Hence, this study did not only employ artificial intelligence as a mechanism that scan through potential employees' social profile, nonetheless, but artificial intelligence is also employed to run a full background check on potential employees' past employment records, cognitive ability, attitude towards work as an individual and as a team before such employees can be perhaps hired, selected and staffed (Saddam & Abu Mansor, 2015; Oaya et al., 2017; Saviour et al., 2016; Kim & Ployhart, 2014; Pangemanan, 2015; Nasurdin et al., 2016).

The aim of the study is to examine the impact of employee recruitment, employee selection, and employee staffing on the organization's performance, besides the moderating impacts of artificial intelligence in the UAE manufacturing industry.

Literature Review

Artificial Intelligence and Human Resource Management

The effect of technology on HR practices had been so profound to the extent that hardly can the two be separated. Technologies range from the use of the internet, basic software applications to advanced applications such as applicant tracking system that is used in vetting and selecting the needed candidates from the pool of millions resumes submitted over the internet. Considering this, there have been ongoing arguments about the contributions of technology to the field of human resources (Collins & Smith, 2006; Eddleston et al., 2008; Mueller, 1996; Stone & Deadrick, 2015). These arguments can, therefore, be divided into technological pros and cons.

Meanwhile, before explaining the pros and cons of technology on HR practices, there is a need to understand the different technological types, definitions, and functions. Starting with definition, according to Grace, et al., (2018); Lemley, et al., (2017), and robotics.com, there is a difference between technology and artificial intelligence. In the overall description, technology is regarded as the overall use of machines and software to execute tasks. While on the other hand, the use of artificial intelligence encompasses the use of an automated machine to conduct tasks primarily designed for human execution with little to no involvement of human intelligence.

Perhaps the description given once can simply argue the difference between technology and artificial intelligence. For example, the use of the internet, applications like MS Excel, MS office, cloud services that store data, and telephone service enhance HR services' effective execution can be argued to be technology adoption. The use of software such as applicant tracking system to sort applicants resume which on a good day, such task supposed to be executed by the human, can be categorized under artificial intelligence. However, for simplicity reasons, the two will be treated as one in this research context.

The contribution of technology, on the other hand, had been heavily criticized among human resource scholars by scholars such as Asma (2018), Eddleston, et al., (2008); Li, et al., (2006), and Sivathanu & Pillai (2019) to name few scholars who argue in favor of technological adoption in the field of human resource. On the other hand, there is heavy criticism from several earlier studies arguing that technology has contributed to a high rate of unemployment because humans have been displaced by machine factors (Ginzberg, 1982; Korinek & Stiglitz, 2017; Rotman, 2013). Similarly, a blog post by Asma (2018) of HRinasia attests to the ongoing arguments saying that technology is virtually employed in every work sphere ranging from selection, recruiting, managing, data management, performance measurement, employer branding, and even outsourcing; however, this adoption negatively affects human intelligence.

On the positive side, Asma (2018) argues that the recruitment process has been cost-effective with the adoption of technology. This is because instead of posting jobs via print media, less amount is spent on a similar advertisement that covers a wider geographical location. More so, the study of Sivathanu & Pillai (2019) argues that the adoption of technology into HR practices specifically in managing talent contributes to talent analytics that leads to high-performing talent pool development via strategic human resource development and as well contributes to overall performance. From the arguments of Asma (2018), the functions of HRM include securing competitive advantage via activities like recruiting, effective managerial role, succession, evaluation, planning, among others, and as such, adopting technologies make the accomplishments of these various tasks to be achieved.

Does the adoption of technologies create innovative capacities for firms that adopt them? Eddleston, et al., (2008) argue that an enabling technological environment as well creates an innovative opportunity that overall has a positive influence on the firms' performance. Meanwhile, for the technology adoption to be a success, enthusiastic employee training and motivation must be put in place (Li et al., 2006).

Conversely, the findings from the study of Rotman (2013) were that although technology creates a new job and makes the execution of the task done efficiently and effectively, it was argued that these technologies were destroying more jobs than they create. Therefore, contributing to the middle income's economic stagnation and the widely observed income inequality, specifically, in the United States (US). Supporting the claims of Rotman (2013), the study of Acemoglu & Restrepo (2018) argues in a similar path. On account of these authors, there exist both sides in the sense that with the advent of technology, there is a drop in production cost; however, in the long run, wages of employees are relatively low because automation (technology) displaces workers in many workforces. An earlier note by Martin and Reddington (2009) was that in the near future, technology would not only take the role of 'back-office, but it is likely to displace humans in their HR functions.

Employee Recruitment

The act of employment involves obtaining and evaluating resumes to fill up an empty position in the company. The term recruitment indicates the act of finding and reviewing candidates to fill up a position at your company (Shammot, 2014). Recruiting includes continuous analysis to locate the finest employees for your business. It's all about constructing your firm's recognition amongst talented professionals and enticing all of them that your organization is actually the ideal fit for their goals and capability set (Gberevbie, 2010; Shammot, 2014). Moreover, a company can inform the public of an open position by advertising on platforms such as LinkedIn or other websites.

The recruitment process's main goal is to find the most suitable candidates with the knowledge and expertise that best match the open position in the company and, as a result, reach the company's objectives. As the standard oppressively systems continuously become less relevant, and the more associations use multidisciplinary teams to remain in the competitive market, the necessity for calculated and a transparent system becomes vital. Nowadays, more companies are moving away for the traditional concept of jobs that focus only on tasks to jobs that are self-directed and are supportive of team-work and that hire individuals who in addition to being knowledgeable, they are also independent and belong to the diverse background. Thus, companies need employees who are more capable of adjusting, depending on the business market's change and the customer's needs (Ashraf, 2017).

The competitive advantage of a company can be easily enhanced if the company adapted an effective hiring system that consists of hiring employees quickly and cost-effectively (Delery & Roumpi, 2017). Among the damaging effects of bad employment is the issue of employees' turnover of employees who are working at the top-level in the company. Enticing and selecting competent, skilled, and expert people is crucial for companies to guarantee that their market values and strategy are actually obtained (Centre for Development & Enterprise, 2007).

Kaufman, et al., (2021) highlighted that it is important to make sure that the recruitment strategy is in line with the company's objectives. Crane & Hartwell (2019) mentioned that before any company can utilize their talented employees, they need to recruit them first; therefore, companies need to employ people with comparatively important knowledge, capabilities, and perspectives. This is considering that wrong employment selection is a lot more costly because it means that the company will go through the process more than once. Moreover, Singh & Finn (2003) indicated that a company's ability to attract and maintain competent employees is one of the essential components of organizational effectiveness. The reason for that is because recruitment is an important factor in improving organizational survival and effectiveness in a stable or unstable working environment.

Employee Selection

Selection is commonly described as the techniques focused on choosing from a list of applicants that have ideal understanding, skill-sets, and capability to perform the work properly (Campion et al., 2019). It is the procedure that assists in separating the candidates' depending on their qualifications and then choosing those that are more likely to be suitable to complete the task. Basak & Khanna (2017) highlighted that the process of choosing the best candidate from a list of possible candidates to work in a certain position is called selection. Moreover, Noel, et al., (2017) considered selection as the method through which firms decide on an individual that is going to be permitted to be part of the company or not. Michael (2019) explained that selection is actually the process of gathering and assessing details regarding the individual to expand the employment offer, they added that the process is executed under legal restrictions, and generally, it clearly shows the interest of the association and of the individual. Bako & Aladelusi (2017) showed that the process of selection of employees is not only to replace employees who left or to increase the number of employees in a company but also to upgrade to employees with better knowledge and skills and who has a higher level of commitment towards the company.

Acikgoz (2019) commented that the selection procedure is concerned along with selecting trained applicants to fill up current vacant positions in the company. Villeda, et al., (2019) explained that selection is a method of choosing applicants with pertinent credentials to work in the company; that it is actually the method of deciding on the individual(s) from a group/list of possible applicants that comply with the need of the opportunities determined in the organization Van Esch, et al., (2019) asserted that the objective of the selection process is to choose the best candidates that meet the task and organizations' requirements effectively. They also highlighted the importance of increasing the use of technology in the recruitment and selection process. Pain & Lu (2018) mentioned that a good employee selection system could easily include efficiency in the company's total performance. Makarius (2017) emphasized the value of selecting methods by highlighting that when an employee does not match correctly with the requirements needed for the position, this will negatively impact the company. Eva (2018) highlighted that by choosing the required employees to fill in the empty positions in particular departments then the supervisors can easily assist in accomplishing a far better fit among the task and the job

Employee Staffing

Staffing is considered part of the employee recruitment, evaluation, and selection process conducted within an organization to occupy a job position opening. The staffing department in an organization handles tasks such as retention, termination, and training (Pahos & Galanaki, 2019). This function is occasionally handled outside of an organization by utilizing additional servicers; Small institutions might handle staffing one case at a time, while bigger organizations may have multiple staffing processes in the year. Organizations of any kind of dimension can use staffing in order to gain temporary or permanent employees (Choi et al., 2021; Pahos & Galanaki, 2019).

According to Ployhart (2006, p. 868), "staffing is broadly described as the procedure of attracting, deciding on, and maintaining proficient people to accomplish organizational objectives." In addition, Parnes (1984) explains that "the most effective way for an organization to improve their employees is by whom they hire in the first place." Dyck & Neubert (2009) describe staffing as "the HRM procedure of determining, recruitment and retaining individuals with the needed knowledge, skill-sets, and abilities to fulfill the accountabilities of potential and present jobs in the organization." Staffing is actually the way where companies sponsor and select candidates along with better and general human resources (Pahos & Galanaki, 2019). In this particular option, recruitment and selection can be considered as part of the staffing process.

The previous literary works have revealed that implementing an effective staffing process is positively correlated with organizational performance (Delery & Doty, 1996). An organized selection system examines a candidate's potential for a specific position and decreases the organization's amount of uncertainty when it comes to hiring external employees (Lado & Wilson, 1994). A strict recruitment and selection system likewise gives those chosen employees a feeling of elitism, imparts stronger desires of efficiency, and conveys information of the significance of individuals to the organization (Pfeffer, 1998). Having employees who are incompatible with the institution can restrain them from achieving a certain level of performance (Lado & Wilson, 1994). Moreover, implementing a good staffing process can offer the institution employees that match the current organization employees' abilities and fit into the existing social framework, all with reducing the training expenses.

Organizational Performance

IGI Global (2019) described that firm performance exceeds operating in effective and efficient manners, which might involve financial and non-financial performance. It encompasses recognition in the market in which they operate. Similarly, Taouab & Issor (2019) described an organization's performance as the ability to do things faster, better, and with the needed quality than the competitors, adapt, and overcome the foreseen and emergency turbulent within the business environment. Meanwhile, the study of Verboncu & Zalman (2005) described organization performance as the ability of the firm to compete in the market and operate efficiently and effectively to secure a competitive edge.

From the various definitions given above, it can easily be argued that any firm's performance can be measured through its competitive strategies and how effectively and efficiently they were able to use their available resources to create values and dominate the market they serve. Concerning this, scholars had identified numerous factors that contribute to organizational performance. These are not limited to the strategic plan, organizational leadership, and commitment to achieve quality influences organization performance (Sergio et al., 2017).

Meanwhile, an earlier study by Joiner (2007) argues a strong relationship between total quality management and organizational performance, which is strongly influenced and executed by human factors. On a similar note, the study of Ericksen & Dyer (2005) concludes the critical influence of human relation in executing the organizational strategic plan in a bid to ensure values that translate to performance.
Still, on identifying the factors or determinants of firm performance, using a subjective model, Selvam, et al., (2016) conclude the significance of firms' profitability performance, market-share value, employee and customer satisfaction, growth (expansion), corporate governance, environmental audit, and social performance.

Contrary to the recent arguments, and an earlier study by Nkomo (1987) argues no difference in organization performance between firms that formally engage human resources and those that do not. Considering some of the significant firm strategic decisions, product diversification (that is, enhancing product offering portfolio), Forrester & Drexler (1999) argued that high product diversification that relates to expansion has a significant negative contribution to firm performance. However, further findings by these authors support the role of human resource practices arguing that collectivist human resource practices that are human resource practices that involve all employees and society rather than designated functions contribute to organizational performance.

Insight to earlier investigations, on the other hand, presents mixed evidence of human resource practices and organizational performance with scholars such as Selvam, et al., (2016); Sergio, et al., (2017); Singh & Finn (2003); Sun & Pan (2011) argues a significant effect in the context of India and China. However, Sun & Pan (2011) made this remark considering the moderating effect of employees' commitment. At the same time, studies such as Forrester and Drexler (1999) & Nkomo (1987) are of a different opinion.

Considering these studies, it is clearly evident that there are extensive investigations on the link between human resources and organization or firm performance. However, from these works of literature, there is no mention employee selection process. To fill up the identified literature lapses, the researcher thus employs the use of technology in employee selection as a moderator between human resource practices and organizational performance.

Conceptual Framework

The research framework in Figure 1 illustrates the relationships between the exogenous variables (employee recruitment, employee selection, employee staffing, and artificial intelligence as a moderator) and the endogenous variable organization performance. The proposed hypotheses are the following:

•H1: Employee recruitment has a significant positive relationship to organizational performance in the UAE manufacturing industry.

• H2: Employee selection has a significant positive relationship to organizational performance in the UAE manufacturing industry.

• H3: Employee staffing has a significant positive relationship to organizational performance in the UAE manufacturing industry.

• H4: Artificial intelligence has a significant moderating impact on the relationship between employee recruitment and organizational performance in the UAE manufacturing industry.

• H5: Artificial intelligence has a significant moderating impact on the relationship between employee selection and organizational performance in the UAE manufacturing industry.

• H6: Artificial intelligence has a significant moderating impact on the relationship between employee staffing and organizational performance in the UAE manufacturing industry.

Figure 1: Conceptual Framework

Methodology

The study assumed that organizational performance, employee recruitment, employee selection, and employee staffing could be measured in numbers, and prediction can be acquired from the analysis. Therefore, the study belongs to the positivism philosophy, deduction approach, quantitative methodology, empirical survey passed study, used cross-sectional data, and original data.

The target or study population chosen for this research work equates to the total number of employees working in any firm in the UAE manufacturing industries and willing to respond to the questionnaire during data collection. The actual sample size is 382 employees. The distributed survey is 524, which is distributed by using face-to-face data collection methods in a conventional sample selection technique in 2019.

The tool used for data collection is a well-structured survey that is adapted from previous. The survey was organized to ask questions in Likert-5 format. Likert 5 questionnaire style has been used in social science studies for a long time and proved to be a suitable style for measuring human perceptions. Structural Equation Modelling (SEM) techniques are used for statistical data analysis via the Smart PLS software package, which is used in management and social science studies such as (Salem & Alanadoly, 2020; Salem & Salem, 2018).

Findings

The distributed questionnaires were 524; the collected samples were 414; uncompleted cases were 18, initial cases for analysis were 396, unengaged screening was 7, univariate screening was 5, multivariate screening was 2, and the cleaned cases for analysis 382 cases.

Descriptive Statistics

As seen in Table 1, Employee Recruitment (ER) shows a positive satisfying level with a mean value of 3.55, which reflects a positive perception by respondents. Employee Staffing (EST) shows a positive satisfying level with a mean value of 3.48, which reflects a positive perception by respondents. Employee Selection (ESE) shows a positive satisfying level with a mean value of 3.34, which also reflects a positive perception by respondents. Completive Advantage (CA) shows a positive satisfying level with a mean value of 3.67, which also reflects a positive perception by respondents. Organizational Performance (OP) shows a positive satisfying level with a mean value of 3.57, which also reflects a positive perception by respondents. Table 1 shows the details of the constructs.

Table 1
Descriptive Statistics of Research Constructs
  Min Max Mean Std. D
Employee Recruitment (ER) 1 5 3.55 0.799
Employee Selection (ESE) 1 5 3.34 0.862
Employee Staffing (EST) 1.3 5 3.48 1.12
Organization Performance (EP) 1.76 5 3.57 0.876
Artificial Intelligence (AI) 1.55 4.33 3.21 0.786

Validity and Reliability of Constructs

Validity and reliability tests must be conducted before proceeding to the relationship findings. In Smart PLS, there are a series of tests to assure internal consistency, convergent validity, and divergent validity (Hair et al., 2016; Sekaran & Bougie, 2016). Table 2 shows the findings of two main tests of reliability and validity. Cronbach's Alpha measures composite reliability and all values are above the cut-off value of 0.70. Results of the four constructs have valued above 0.7; therefore, the reliability of the measurement model is achieved. The Average Variance Extracted (AVE) values are above 0.5; therefore, convergent validity is achieved. All other test shows an adequate level of validity and reliability. Table 3 shows the Fornell & Larcker criterion matrix to test the divergent validity; the results show that the variables have enough divergence between each other.

Table 2
Constructs Reliability and Validity
Construct AVE Cronbach's Alpha
Employee recruitment (ER) 0.701 0.901
Employee Selection (ESE) 0.533 0.844
Employee Staffing (EST) 0.621 0.832
Organization Performance (EP) 0.561 0.787
Artificial Intelligence  (AI) 0.562 0.749
Table 3
Fornell & Larcker Criterion Matrix
Construct ER ESE EST EP AI
Employee Recruitment (ER) 0.837
Employee Selection (ESE) 0.334 0.73
Employee Staffing (EST) 0.456 0.345 0.788
Organization Performance (EP) 0.523 0.453 0.337 0.749
Artificial Intelligence  (AI) 0.224 0.39 0.456 0.56 0.75

Relationships Examinations and Discussions

For the purpose of assessing the power of the model construct in predicting the outcome variables, predictive power R2 and predictive relevance were used (Hair et al., 2016). As seen in Table 4, the main dependent variable, organization performance (OP), illustrates a satisfactory predictive power and a medium predictive relevance. As seen in the table, the related R square value is 0.413 (power of 41.3%), and the related Q square is 0.313 (relevance of 31.3%). Employee Recruitment (ER), Employee Staffing (EST), and Employee Selection (ESE) can explain more than 41.3% of the Organization's Performance (OP) variance. Table 5 shows the path coefficient assessment with T Statistics' values and Beta values for the end result variable Organization Performance (OP). The three antecedents have significant relation, in which the p-value scores are above 0.05, and the t statistics scores are above 1.65. The precedence for the relations based on the path coefficient value is ER (0.334), ESE (0.297), and EST (0.278).

Table 4
Predictive Power and Predictive Relevance of Proposed Model
Predictive Power Predictive Relevance
R Square Status Q Square Status
Organization performance 0.413 Satisfactory 0.313 Medium
Table 5
Direct Path Coefficient Assessment
Hypothesis Path Coefficient Standard Deviation T Statistics P Value (one tailed) Status
H1 ER à OP 0.334 0.038 9.82 0 Significant
H2 ESE à OP 0.297 0.04 8.3 0 Significant
H3 EST à OP 0.278 0.056 7.901 0 Significant

Table 5 shows the path coefficient assessment with the values of T Statistics values for the outcome variable Artificial Intelligence (AI) as a moderator(Table 6). Respectively, the Path Coefficient for the interaction with Employee Staffing (EST) variable is 0.013, the T statistics is 0.558, and also the P-value is 0.380 as non-significant, the Path Coefficient for the interaction with Employee Recruitment (ER) variable is 0.037, T- statistics is 1.974. The P-value is 0.098 significant, the path coefficient for the interaction Employee Selection (ESE) variable is 0.108, T statistics is 2.123. Also, the P-value is 0.024 as significant.

Table 6
  Moderating Impact of Artificial Intelligence
Hypothesis   Path Coefficient Standard Deviation T Statistics P-Value (one-tailed) Status
H4 AI * ER à OP 0.098 0.029 1.974 0.037 Significant
H5 AI * ESE à OP 0.108 0.046 2.123 0.024 Significant
H6 AI * EST à OP 0.013 0.055 0.558 0.38 Non-Significant

Conclusion

Overall, the model is successful because it can predict 41.3% of the organization's performance and the direct relationships for the three predictors of HRM practices are significant. The precedence for the relations based on the path coefficient value is employee recruitment (0.334), employee selection (0.297), and employee staffing (0.278). For the moderating relationships of artificial intelligence, two interactions have a significant positive interaction; employee selection (0.108) and employee recruitment (0.098); but the Employee Staffing (EST) has no significant change based on artificial intelligence.

The study contributes to the knowledge of artificial intelligence use in employees' hiring practices to have a better organizational performance in the UAE manufacturing industry. The proposed combination of variables and the inclusion of artificial intelligence as a moderator is another theoretical contribution, especially when applied in the manufacturing industry. The study also adds knowledge of the AI practices in human resource management in the UAE context.

Managers and decision-makers in HRM in the UAE manufacturing industry should emphasize the role of artificial intelligence in recruitment and selection as both are the most contributors to organizational performance. Policymakers should create new policies to ensure the most use of artificial intelligence in recruitment, selection, and HRM practices.

This study is limited to the empirical examination of the UAE manufacturing industry; however, replicating the same design with the same research design but in different countries will provide extra knowledge to generalize the proposed relations. The interception of artificial intelligence in the relationship from employee staffing is found to be no significant; additional work is needed to reveal the reason and explain this non logical relation. The model can also explain up to 41.3% of the organizational performance variance, and scholars are welcome to investigate more HRM practices to increase the model power.

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