Research Article: 2024 Vol: 28 Issue: 3S
Sona Vikas, The NorthCap University Gurugram
Ashish Mathur, Jai Narain Vyas University, Jodhpur, Rajasthan
Sathi Jyothirmaye Reddy, Marwadi University, Morbi Highway Road, Rajkot, Gujarat
Ashish Manohar, KLEF, Vijaywada
Anil Kumar, Graphic Era Hill University Haldwani
Rahul Vishwanath Dandage, Dr. Vishwanath Karad MIT World Peace University, Pune, Maharashtra
Citation Information: Vikas, S., Mathur, A., Jyothirmaye Reddy, S., Manohar, A., Kumar, A., & Vishwanath Dandage, R. (2024). Artificial intelligence applications for talent acquisition and employee retention in human resources. Academy of Marketing Studies Journal, 28(S3), 1-10.
Purpose: AI has great potential to strengthen and motivate employees provided it is used in a responsible and ethical manner. The study aims towards exploring artificial intelligence applications talent acquisition and employee retention in human resources. Methodology: In the field of human resources, artificial intelligence (AI) has become a game-changer, altering approaches to both recruiting and retaining top personnel. This research explains how artificial intelligence (AI) helps with key HR processes. The existing study chooses employees of the companies as a subject to obtain responses designed on structured questionnaire and sample size is of 287. Findings: Issues like data privacy, algorithmic bias, and the requirement for continuous model validation must be resolved before the HR industry can fully take use of AI's potential. As a whole, the research shows that AI can improve HR operations, help managers make better choices, and make work better for employees. Practical implications: With the right approach, firms may gain a competitive edge in the war for talent in today's competitive business environment by adopting AI technology. Human resources experts and academics need to be on the lookout for novel approaches to maximising AI's benefits while minimising its dangers and biases as the technology develops. Originality: Artificial intelligence (AI) has the potential to improve HR procedures, decision-making, and the employee experience, as discussed in the literature on AI applications for talent acquisition and employee retention in HR. It also highlights the need to solve ethical and practical problems to fully realise the potential of AI in HR processes.
Applications, Artificial Intelligence, Employee Retention, Human Resources, Talent Acquisition.
Applications of AI in HR have had a major effect on methods for both recruiting and keeping good employees. Human resources (HR) procedures may benefit from the use of AI in the areas of talent acquisition and employee retention due to the potential for increased efficiency, less bias, and data-driven choices(Enholm, Papagiannidis, Mikalef, & Krogstie, 2022). AI has great potential to strengthen and motivate employees provided it is used in a responsible and ethical manner.
Some concrete examples of AI's use in various domains are as follows:
Talent Acquisition
1. Analysis of Resumes: Quickly analysing individuals' resumes based on a set of predetermined criteria including abilities, experience, and education is now possible thanks to the use of (Palanivelu, et al. 2020). AI algorithm. This facilitates a quicker and more impartial first screening.
2. Compatible Job Seekers: Artificial intelligence may use data from job postings and applicant profiles to find a good fit. Down this way, HR departments may zero in on the most qualified applicants for open positions.
3. Conversational Robots and Digital Helpers: Chatbots powered by AI are available around the clock to answer queries from job seekers about the company's culture, available positions, and the application process. They are able to do initial applicant information gathering and interview scheduling all in one place (Anute, et al. 2021; Palanivelu & Vasanthi, 2020).
4. Predictive Analytics: Using data from past hires, AI can determine who will be the most successful in a given position. Human resources may use this information to make better choices about which applicants to pursue.
5. Testing and Interviews using Videoconferencing: Video interviewing systems powered by artificial intelligence examine applicants' answers, emotions, and gestures to determine their fitness for a job. Effective applicant screening is facilitated by this.
Employee Retention
1. Taking the Pulse of Employee Morale: The results of employee surveys may be analysed by AI-powered systems to determine the degree of engagement across the board and pinpoint problem areas. Human resources might then take specific measures to improve morale.
2. Analysis of Attrition Predictability: To determine which employees are most likely to quit, AI may examine measures like performance, attendance, and employee satisfaction. Human resources may then step in with retention techniques like career advancement opportunities and perks to keep employees around (Geisel, 2018).
3. Adaptive and Individualized Instruction: Employees' abilities, hobbies, and professional aspirations may all be taken into account by AI to come up with individualised recommendations for training and development. Employees are more likely to feel appreciated and engaged in their careers as a result.
4. Management via Observation and Commentary: Systems driven by AI may deliver continuous improvement and goal monitoring feedback in real time to both workers and supervisors. They are able to pinpoint weak spots in one's skill set and provide suggestions on how to improve.
5. Employee Appreciation and Benefits: Using employee performance data and accomplishments, AI may assist HR in implementing individualised recognition and incentive programmes (Mishra & Tripathi, 2021). Employee morale is boosted and they are inspired to do their best work.
6. Strategy for the Future: In the future, AI may recommend people with leadership or crucial job potential. A seamless transition is ensured in the event of a significant staff departure.
7. Despite the numerous benefits of AI in talent acquisition and staff retention, the technology is not without its drawbacks, such as the need for continual data validation and maintenance and worries about data privacy and algorithmic bias. It is also crucial to make sure that AI-driven procedures are consistent with the ethos of the business.
There is a growing corpus of research and practical insights into how AI is revolutionising HR practises, as shown by a study of the literature on artificial intelligence (AI) applications for talent acquisition and employee retention. Here is some of the most important results and developments in the literature:
Talent Acquisition
Research shows that AI can successfully automate the processes of resume screening and applicant matching (Madhavi, 2021). By removing subjectivity and concentrating on objective criteria, these AI systems may greatly cut down on the time spent on preliminary screening by HR personnel.
Chatbots and virtual assistants powered by artificial intelligence are being lauded by researchers for their potential to improve the job-seeker experience. More applicants are engaged and satisfied with the application process because of the efficiency with which questions are answered, interviews are scheduled, and data is collected using these technologies.
Predictive analytics' potential in the HR field has been spotlighted recently. Algorithms powered by AI look at past hiring data to make predictions about how well an applicant would do in a certain position(Sahoo, et.al., 2023).). This may lead to more informed recruiting choices and less employee turnover.
Employee Retention
Personalized feedback, growth opportunities, and recognition have all been shown to increase employee engagement, so it stands to reason (Afework, et al. 2020) that AI may have a similar effect. HR may use AI-powered tools like surveys and sentiment analysis to learn what influences employee satisfaction (Leitch, 2021); Bunod et al. (2022).
The ability of AI to foresee employee turnover has been extensively discussed in the literature. Machine learning algorithms may examine a wide variety of data sources to predict which workers are most likely to leave, so HR can take preventative action.
The potential of AI to provide individualised training and improvement plans has been well accepted. Employees gain from individualised training programmes because they help them fill up skill gaps and advance in their careers.
It is well acknowledged that AI-powered, always-on feedback systems may do wonders for productivity and morale in the workplace. Better performance management may result from continuous feedback and data-driven assessments of employee contributions(Chalmers, MacKenzie, & Carter, 2021).
The healthy work culture may be reinforced with the help of AI-supported recognition and incentives systems. Improve morale with personalised rewards based on performance metrics.
Research Gap
Concerns about data privacy, ethical issues related to algorithmic bias, and the need for continuous training and validation of AI models to ensure accuracy and fairness are just a few of the hurdles that have been highlighted in the literature as barriers to the widespread use of AI in human resources. Artificial intelligence (AI) has the potential to improve HR procedures, decision-making, and the employee experience, as discussed in the literature on AI applications for talent acquisition and employee retention in HR. It also highlights the need to solve ethical and practical problems to fully realise the potential of AI in HR processes.
Objectives of the Study
• To quantitatively analyse artificial intelligence applications for talent acquisition in human resources.
• To explore artificial intelligence applications for employee retention in human resources.
Hypothesis of the Study
H01: There is no significant relationship among artificial intelligence applications and talent acquisition in human resources
Ha1: There is significant relationship among artificial intelligence applications and talent acquisition in human resources.
H02: There is no significant relationship among artificial intelligence applications and employee retention in human resources.
Ha2: There is significant relationship among artificial intelligence applications and employee retention in human resources.
In the field of human resources, artificial intelligence (AI) has become a game-changer, altering approaches to both recruiting and retaining top personnel. This research explains how artificial intelligence (AI) helps with key HR processes. The existing study chooses employees of the companies as a subject to obtain responses designed on structured questionnaire and sample size is of 287. The variables understudy was as follows Tables 1 & 2:
| Table 1 Talent Acquisition | ||
| S.No. | Description of variables understudy | References |
| 1. | Analysis of Resumes | (Soni, Sharma, Singh, & Kapoor, 2019) |
| 2. | Compatible Job Seekers | (Soni, Sharma, Singh, & Kapoor, 2020) |
| 3. | Conversational Robots and Digital Helpers | (Soni et al., 2020) |
| 4. | Predictive Analytics | (Sadiku, Fagbohungbe, & Musa, 2020) |
| 5. | Testing and Interviews using Videoconferencing | (Supriyanto., et.al., 2018) |
| Table 2 Employee Retention | ||
| S.No. | Description of variables understudy | References |
| 1. | Taking the Pulse of Employee Morale | (Roundy, 2022) |
| 2. | Analysis of Attrition Predictability | (Enholm et al., 2022) |
| 3. | Adaptive and Individualized Instruction | (Mishra & Tripathi, 2021) |
| 4. | Management via Observation and Commentary | (Soni et al., 2020) |
| 5. | Employee Appreciation and Benefits | (Sadiku et al., 2020) |
| 6. | Strategy for the Future | (Obschonka & Audretsch, 2019) |
Table 3 analysed the demographic statistics and stated that majority of respondents are female having age of less than 20 years, having unmarried status, having graduation qualification and earning Rs.10000-Rs.15000.
| Table 3 Demographic Analysis | |||
| Demographic Analysis | |||
| Gender | Frequency | Percent | |
| Female | 187 | 65.15% | |
| Male | 100 | 34.84% | |
| Age | Less than 20 | 83 | 28.91% |
| 20-25 | 56 | 19.51% | |
| 25-30 | 43 | 14.98% | |
| 30-35 | 27 | 9.40% | |
| 35 and above | 78 | 27.17% | |
| Marital Status | Married | 98 | 34.14% |
| Unmarried | 189 | 65.85% | |
| Education Level | Below Graduation | 73 | 25.43% |
| Graduation | 96 | 33.44% | |
| Post-Graduation | 47 | 16.37% | |
| Others | 71 | 24.73% | |
| Income Level | Less than Rs. 10000 | 56 | 19.51% |
| Rs. 10000- Rs.15000 | 92 | 32.05% | |
| Rs. 15000- Rs. 20000 | 86 | 29.96% | |
| Rs. 20000 and above | 53 | 18.46% | |
Talent Acquisition:
Table 4 depicted the analysis of reliability statistics and documented that findings of Cronbach Alpha test is 0.886 (N=5) which is greater than the acceptable threshold limit of 0.60. Therefore, internal consistency among the variables under study significantly exist and further statistical test can be performend to conduct indepth analysis.
| Table 4 Reliability Statistics | |
| Reliability Statistics | |
| Cronbach's Alpha | N of Items |
| 0.886 | 5 |
Table 5 analysed the descriptive statistics and analysed the artificial intelligence applications towards talent acquisition in human resource industry and stated that “Predictive Analytics” (Mean=4.46 and Standard deviation=.661) used the most by respondents followed by “Conversational Robots and Digital Helpers” (Mean=4.45 and Standard deviation=.756). “Testing and Interviews using Videoconferencing” (Mean=4.10 and Standard deviation=.973) least used Artificial Intelligence application by the respondents.
| Table 5 Descriptive Statistics | |||||
| N | Minimum | Maximum | Mean | Std. Deviation | |
| Analysis of Resumes | 287 | 1 | 5 | 4.36 | 0.797 |
| Compatible Job Seekers | 287 | 1 | 5 | 4.41 | 0.658 |
| Conversational Robots and Digital Helpers | 287 | 1 | 5 | 4.45 | 0.756 |
| Predictive Analytics | 287 | 1 | 5 | 4.46 | 0.661 |
| Testing and Interviews using Videoconferencing | 287 | 1 | 5 | 4.10 | 0.973 |
Table 6 analysed the correlation analysis and documented that in all the variables the estimated value of significance is .000 which is less than the acceptable threshold limit of 0.005. Therefore, the variables are having positive correlations with each other.
| Table 6 Correlation Analysis | ||||||
| Analysis of Resumes | Compatible Job Seekers | Conversational Robots and Digital Helpers | Predictive Analytics | Testing and Interviews using Videoconferencing | ||
| Analysis of Resumes | Pearson Correlation | 1 | 0.468** | 0.395** | 0.422** | 0.501** |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | ||
| N | 287 | 287 | 287 | 287 | 287 | |
| Compatible Job Seekers | Pearson Correlation | 0.468** | 1 | 0.664** | 0.568** | 0.480** |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | ||
| N | 287 | 287 | 287 | 287 | 287 | |
| Conversational Robots and Digital Helpers | Pearson Correlation | .395** | .664** | 1 | .531** | .540** |
| Sig.(2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | ||
| N | 287 | 287 | 287 | 287 | 287 | |
| Predictive Analytics | Pearson Correlation | 0.422** | 0.568** | 0.531** | 1 | .287** |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | ||
| N | 287 | 287 | 287 | 287 | 287 | |
| Testing and Interviews using Videoconferencing | Pearson Correlation | .501** | .480** | .540** | .287** | 1 |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | ||
| N | 287 | 287 | 287 | 287 | 287 | |
Table 7 analysed the Regression analysis and documented that r square and adjusted r square value is close to each other and also significance F value is .000. Moreover, r square value is greater than 30%. Hence dependent variable “Talent Acquisition” is impacting the independent variables, namely, Analysis of Resumes, Compatible Job Seekers, Conversational Robots and Digital Helpers, Predictive Analytics, Testing and Interviews using Videoconferencing.
| Table 7 Model Summary | |||||||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | ||||
| R Square Change | F Change | df1 | df2 | Sig. F Change | |||||
| 1 | 0.595a | 0.354 | 0.342 | 0.648 | 0.354 | 29.548 | 9 | 485 | 0.000 |
Table 8 analysed the ANOVA analysis and documented that significance value is 0.000. Hence dependent variable “Talent Acquisition” is impacting the independent variables, namely, Analysis of Resumes, Compatible Job Seekers, Conversational Robots and Digital Helpers, Predictive Analytics, Testing and Interviews using Videoconferencing.
| Table 8 Anovaa | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 111.496 | 9 | 12.388 | 29.548 | .000b |
| Residual | 203.341 | 485 | .419 | |||
| Total | 314.836 | 494 | ||||
Employee Retention
Table 9 depicted the analysis of reliability statistics and documented that findings of Cronbach Alpha test is 0.873 (N=6) which is greater than the acceptable threshold limit of 0.60. Therefore, internal consistency among the variables under study significantly exist and further statistical test can be performend to conduct indepth analysis.
| Table 9 Reliability Statistics | |
| Cronbach's Alpha | N of Items |
| 0.873 | 6 |
Table 10 analysed the descriptive statistics and analysed the artificial intelligence applications towards employee retention in human resource industry and stated that “Management via Observation and Commentary” (Mean=4.45 and Standard deviation=.661) used the most by respondents followed by “Taking the Pulse of Employee Morale” (Mean=4.42 and Standard deviation=.639). “Employee Appreciation and Benefits” (Mean=4.15 and Standard deviation=.937) least used Artificial Intelligence application by the respondents.
| Table 10 Descriptive Statistics | |||||
| N | Minimum | Maximum | Mean | Std. Deviation | |
| Taking the Pulse of Employee Morale | 287 | 1 | 5 | 4.42 | .639 |
| Analysis of Attrition Predictability | 287 | 1 | 5 | 4.37 | .787 |
| Adaptive and Individualized Instruction | 287 | 1 | 5 | 4.38 | .813 |
| Management via Observation and Commentary | 287 | 1 | 5 | 4.45 | .661 |
| Employee Appreciation and Benefits | 287 | 1 | 5 | 4.15 | .937 |
| Strategy for the Future | 287 | 1 | 5 | 4.38 | .762 |
Table 11 analysed the correlation analysis and documented that in all the variables the estimated value of significance is .000 which is less than the acceptable threshold limit of .005. Therefore, the variables are having positive correlations with each other.
| Table 11 Correlation Analysis | |||||||
| Taking the Pulse of Employee Morale | Analysis of Attrition Predictability | Adaptive and Individualized Instruction | Management via Observation and Commentary | Employee Appreciation and Benefits | Strategy for the Future | ||
| Taking the Pulse of Employee Morale | Pearson Correlation | 1 | .454** | .422** | .534** | .392** | .397** |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| N | 287 | 287 | 287 | 287 | 287 | 287 | |
| Analysis of Attrition Predictability | Pearson Correlation | .454** | 1 | .504** | .421** | .463** | .349** |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| N | 287 | 287 | 287 | 287 | 287 | 287 | |
| Adaptive and Individualized Instruction | Pearson Correlation | .422** | .504** | 1 | .461** | .530** | .405** |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| N | 287 | 287 | 287 | 287 | 287 | 287 | |
| Management via Observation and Commentary | Pearson Correlation | .534** | .421** | .461** | 1 | .449** | .413** |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| N | 287 | 287 | 287 | 287 | 287 | 287 | |
| Employee Appreciation and Benefits | Pearson Correlation | .392** | .463** | .530** | .449** | 1 | .463** |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| N | 287 | 287 | 287 | 287 | 287 | 287 | |
| Strategy for the Future | Pearson Correlation | .397** | .349** | .405** | .413** | .463** | 1 |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| N | 287 | 287 | 287 | 287 | 287 | 287 | |
Table 12 analysed the Regression analysis and documented that r square and adjusted r square value is close to each other and also significance F value is .000. Moreover, r square value is greater than 30%. Hence dependent variable “Employee Retention” is impacting the independent variables namely, Taking the Pulse of Employee Morale, Analysis of Attrition Predictability, Adaptive and Individualized Instruction, Management via Observation and Commentary, Employee Appreciation and Benefits, Strategy for the Future.
| Table 12 Model Summary | |||||||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | ||||
| R Square Change | F Change | df1 | df2 | Sig. F Change | |||||
| 1 | 0.658a | 0.433 | 0.423 | 0.486 | 0.433 | 41.166 | 9 | 485 | 0.000 |
Table 13 analysed the ANOVA analysis and documented that significance value is 0.000. Hence dependent variable “Employee Retention” is impacting the independent variables namely, “Taking the Pulse of Employee Morale, Analysis of Attrition Predictability, Adaptive and Individualized Instruction, Management via Observation and Commentary, Employee Appreciation and Benefits, Strategy for the Future.
| Table 13 Anovaa | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 87.599 | 9 | 9.733 | 41.166 | .000b |
| Residual | 114.672 | 485 | .236 | |||
| Total | 202.271 | 494 | ||||
Hypothesis Testing
After application of statistical tools, namely, correlation analysis and regression analysis the findings of the study stated that null hypothesis (There is no significant relationship among artificial intelligence applications and talent acquisition in human resources and there is no significant relationship among artificial intelligence applications and employee retention in human resources) are rejected and alternative hypothesis (There is significant relationship among artificial intelligence applications and talent acquisition in human resources and there is significant relationship among artificial intelligence applications and employee retention in human resources) are accepted.
Finally, the literature on AI's use in talent acquisition and employee retention in HR demonstrates AI's potential to significantly alter the HR industry. The following conclusions and patterns have been highlighted by this review:
Talent Acquisition
1. The first phases of the hiring process benefit greatly from the use of AI for things like resume screening and applicant matching.
2. Candidates have a better experience with chatbots and virtual assistants since they get prompt replies and help at every step of the application process.
3. Data-driven decisions made possible by predictive analytics contribute to improved hiring results.
Employee Retention
1. By providing constructive criticism, learning opportunities, and public acknowledgment, AI systems can boost employee enthusiasm.
2. Using data from predictive attrition analyses, HR can better anticipate who could be departing and take preventative measures to keep them.
3. Job satisfaction and skill acquisition both benefit from personalised learning and development strategies.
4. Improvements may be made in real time thanks to technologies that provide constant feedback and monitor performance.
5. Programs that use AI to recognise and reward employees improve morale and foster productive cultures in the workplace.
6. However, issues like data privacy, algorithmic bias, and the requirement for continuous model validation must be resolved before the HR industry can fully take use of AI's potential. As a whole, the research shows that AI can improve HR operations, help managers make better choices, and make work better for employees. With the right approach, firms may gain a competitive edge in the war for talent in today's competitive business environment by adopting AI technology. Human resources experts and academics need to be on the lookout for novel approaches to maximising AI's benefits while minimising its dangers and biases as the technology develops.
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Received: 16-Sep-2023, Manuscript No. AMSJ-23-14016; Editor assigned: 18-Sep-2023, PreQC No. AMSJ-23-14016(PQ); Reviewed: 27-Oct-2023, QC No. AMSJ-23-14016; Revised: 03-Jan-2024, Manuscript No. AMSJ-23-14016(R); Published: 06-Feb-2024