Annotated Bibliography so, just put the things the researches didn't cover and what I'm going to cover
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Table of Contents
Introduction 3
Research Problem 3
Importance/Research Significance 4
Human Resource Role in Time Management 4
Uber's Part-time Management 6
Research Aim 7
Goals/Research Objectives 7
Research Questions 7
Data Collection Plan 8
Conclusion 9
References 10
Introduction
Recent human resource management practices have depicted substantial interest in the use of artificial intelligence. Jatobá et al. (2019) found that human resource management (HRM) research has focused on multiple domains for the application of artificial intelligence (AI), including the application of AI in recruitment and selection, work team management, managing turnover, employee training, and managing overall management. Research has focused on diverse sources of employee efficiency in HRM except for time management efficiency (Garg, Srivastav, & Gupta, 2018). However, time is an essential resource in employee performance. Therefore, time efficiency is critical to numerous domains of work outcomes (Ogohi, Daniel, and Jiya, 2020). Attempts to optimize time utilization and management apply to diverse situations, including the recent constraints of the COVID-19 pandemic that limits the time available for productive work performance (Kshirsagar, 2021). Furthermore, employees and all other entities have finite time for their work lives and lifetimes. Therefore, innovating time management in HRM is valuable to employee-related and organizational outcomes.
Research Problem
Time management in HRM is still non-optimal despite the prevalence of technology in workplaces, organizational, and management systems. A wide range of contingencies impact time flow and the practical approaches to time management, including employee-related issues of motivation, commitment, and habituation (Kshirsagar, 2021). Artificial intelligence is an evolving area of research and practice that aims to solve complicated problems through intelligent agents. However, the uncertainty of the corresponding effects on the employees and organizations makes applying artificial intelligence complex (Schafheitle et al., 2020). As a result, both the employee and organization may experience technology-related uncertainty, with costs or benefits to time management.
Introducing intelligent agents to help employees improve time management efficiency also implies introducing and adopting sophisticated technology. Such adoption comes with adoption-related constraints and other contingencies (Agboola et al., 2019). The interaction of various constraints with time efficiency is critical to the ultimate outcomes of time management. Furthermore, the adoption of artificial intelligence also introduces structural changes to operations and performance (Schafheitle et al., 2020). Employees have to adapt to the technological environment, introducing a learning curve that varies and consumes time.
Importance/Research Significance
The study explores an impactful area of technology application in HRM by focusing on how technology could impact employees’ time management efficiency. Such efficiency applies to all performance domains (Harahsheh, 2019). Artificial intelligence interacts with both the job environment and the job performers. Therefore, the technology could influence employees' engagement or immersion in their work (Braganza et al., 2020). In addition, employees may experience a learning curve or period of adapting to the technological environment of artificial intelligence. Understanding the effects of artificial intelligence on employees' time efficiency could reveal significant issues to address in HRM and improve the practicality of intelligent agents in workplaces.
Human Resource Role in Time Management
Human resource management (HRM) is responsible for ensuring a free workforce flow in an organization (Noponen, 2019). Over the decades' organizational literature has focused on how time management can improve efficiency in an organization, and those responsibilities fall under the human resource department. One way of increasing efficiency in time management has been through the use of artificial intelligence. Artificial intelligence (AI) is essential to meet increased competition and demands for efficient services that increase the availability of goods and services. Keeping a manual track of how employees conduct themselves is time-consuming and expensive. Thus, the application of AI ensures that the HRM has a centralized system where all workforce time-related activities can be tracked and recorded (Noponen, 2019). The purpose of AI in HRM time management is to achieve a cost-effective and efficient use of time.
For example, Uber is a significant company in the transport industry, but global competition from other companies like Bolt threatens its market dominance. Thus, the first principal component in integrating AI in its time-management system is an awareness of the present, past, and future trends in business (Wajcman, 2018). Uber recognizes how drivers use their time and how that knowledge can be used to complete one's responsibilities and tasks and how that can be used to match their capabilities.
The AI also accords the HRM tools or directions that help in using time expeditiously. For example, the Uber HRM can set goals, make disruption lists that might delay delivery of services, and come up and group tasks. In so doing, the HRM can determine workers' behavior by having a control system that determines how they behave all the time. The HRM can also provide a structure for self-monitoring and time management to allow workers to perform tasks and limit disruptions. That component refers to behavior observation. Thus, the HRM uses the AI to monitor behaviors undermine what needs to be done. The process is done by understanding time-consuming activities, increasing efficiency and changing how time is used (Wajcman, 2018). Collection of workers' data and integrating it into the system ensures that the Uber drivers or any other company using tech to manage time can have the upper hand on what is likely to happen all the time. Data is a vital asset in business. Business insights are used to make intelligent decisions based on data collected.
Uber's Part-time Management
According to Forbes, mobile-first companies have increased in the last two decades (Koetsier, 2018). With the development of technology and innovations, there has been the introduction of AI companies. The difference between the two is vital in understanding changes over the years and why AI has been replacing mobile companies to manage time better. Uber made significant advancements in AI that have seen the company use neural networks and machine learning to control aspects of the business. The company has successfully applied AI in risk assessments, driver onboarding, and matching riders and drivers in every possible way (Koetsier, 2018). All departments rely on AI to control operations and ensure all customers are effectively served. For example, Uber serves millions of clients around the world. Some work part-time, and the time they need the automobile company services to differ. Time management is vital because a slight delay can affect the customer's productivity (SCHEIBER, 2017, April 2). Also, the company collects data from clients through its online system, where clients can give feedback on areas that they need improvement. Drivers at Uber are independent business owners who work full-time or part-time, allowing the company to cut operational costs. However, Uber uses AI to show drivers which areas have high demand and connect them to those regions' customers. That helps ensure that the employees spend the least time possible to pick the clients and drop them at designated areas. The HRM controls the systems by ensuring people employed permanently or part-time to run the Uber application understand the neural networks and easily connect riders and customers. Thus, it all starts by ensuring Uber employees have the prerequisite skills to manage the online system. Training takes time since some employees might not have the skills, but it is a long-term investment that gives the company an upper hand. According to the New York Times, the company regularly sends messages to the drivers urging them to spend more time on the road to earn more commission (SCHEIBER, 2017, April 2). The HRM is responsible for sending messages to manage their staff under contract or enjoying full employment. Therefore, time management efficiency comes in ensuring the drivers spend more time for more commission and connecting the riders and customers within the shortest time possible.
Research Aim
The study aims to determine the use of artificial intelligence in HRM and its effects on employee time efficiency.
Goals/Research Objectives
The study will target to achieve its aim by pursuing the following objectives:
· Determine whether time management efficiency in HRM differs significantly across platforms or AI types.
Research Questions
· How does time management in HRM for AI-based environments compare to non-AI environments?
· In what way do different AI technologies compare regarding employees’ time management efficiency?
· What lessons for HRM emerge from the effect of artificial intelligence on-time efficiency?
Data Collection Plan
The study will collect quantitative data through an online survey using the Time Use Efficiency Scale for AI-related work or tasks compared to other contexts of work that employees perform (non-AI related). The study population includes part-time employees in companies that use artificial intelligence or organizational units and job types that use such intelligence. The exemplary organizations will consist of Uber and Amazon for Uber drivers and Amazon delivery personnel, respectively. The employees will submit data by responding to items on a time efficiency scale.
The two primary variables for operationalizing data collection include the use of AI and employee time efficiency. The use of AI will depend on whether the job environment integrates AI into the employees' tasks, e.g., Uber and Amazon jobs that automate and learn about the environment, giving employees timely feedback. The study will compare time efficiency with other general tasks, e.g., housework or a different job. The Time Use Efficiency Scale focuses on the link between time management efficiency and performance (Asri et al., 2017; Romero, 2019). The scale includes general items that could apply flexibly to diverse performance contexts, including diverse task or work performance (Romero, 2019). The study will request responses on time management for the AI-related job versus the other environments. Therefore, the study will collect quantitative data on time use efficiency among employees working in environments where artificial intelligence is dominant.
Sampling
Random sampling will apply for obtaining a sample of participants. The study will apply an inclusion criterion of the age of consent, working for over one year, part-time, in the AI-based environment, and confirming that they regularly attend to any other routine task environment, e.g., another job or routine housework for over two hours a day. The participants should also confirm their availability to submit written responses. The target sample is 100 participants, which will depend on response rates.
Conclusion
Human resource management (HRM) is continuing to adopt artificial intelligence (AI) in diverse aspects, and there is a need to understand whether such application impacts employee time management efficiency. The study will compare the HRM effect of AI on employee time management efficiency. The study will apply quantitative methods to survey Uber and Amazon gig workers using the Time Use Efficiency Scale. The measures will include time management efficiency for the AI-related work compared to employees' other tasks in non-AI-related environments. The analysis will determine whether HRM for AI-based job environments yields different employee time management efficiency levels than non-AI environments. Such analysis will also establish whether time efficiency in HRM differs significantly across platforms or AI types. The study will draw recommendations for HRM on the use of artificial intelligence to improve time management. AI time management is an evolutionary process that continues to disrupt and develop industries. The managers assess the impact new technology has on the industry and how it can be incorporated to increase efficiency and effectiveness. Human resource management also works with other leaders in technology and other departments to make technological changes.
References
Agboola, M., Akinbode, M., Segun-Adeniran, C., Dibia, P., Oloruntoba, A., Dinyain, T., Fabunmi, T., Funsho, T., Akinde, O., & Betek, C. (2019). Technology usage and employee behavior: Controversies, complications, and implications in the Nigerian business environment. Earth and Environmental Science, 331(012031). DOI: 10.1088/1755-1315/331/1/012031.
Asri, M., Ali, M., Danial, M., Bin, A., Khamar T. M. (2017). The relationship between environmental factors, learning style and self-management towards the academic performance of accounting students: A case of United, 10. South-East Asia Journal for Contemporary Business, Economics, and Law, 10(1).
Braganza, A., Chen, W., Canhoto, A., & Sap, S. (2020). Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement, and employee trust. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2020.08.018.
Enuoh, R. O., & Edema A. J. M. (2019). Exploring time management skills for employee performance. International Journal of Economics, Commerce, and Management.
Garg, V., Srivastav, S., & Gupta, A. (2018). Application of artificial intelligence for sustaining green human resource management. International Conference on Automation and Computational Engineering (ICACE - 2018), pp. 113-116. DOI: 10.1109/ICACE.2018.8686988.
Harahsheh, F. (2019). The effects of time management strategies on employee’s performance efficiency: Evidence from Jordanian firms. Management Science Letters, 9: 1669-1674. DOI: 10.5267/j.msl.2019.5.021.
Jatobá, M., Santos, J., Gutierriz, I., Moscon, D., Fernandes, P., & Teixeira, J. (2019). Evolution of artificial intelligence research in human resources. Procedia Computer Science, 164: 137-142. DOI: 10.1016/j.procs.2019.12.165.
Koetsier, J. (2018). Uber Might Be The First AI-First Company, Which Is Why They “Don’t Even Think About It Anymore.” Forbes. https://www.forbes.com/sites/johnkoetsier/2018/08/22/uber-might-be-the-first-ai-first-company-which-is-why-they-dont-even-think-about-it-anymore/
Kshirsagar, P. (2021). Employee association, commitment, and habituation in the time of C0VID-19: Imputation for human resource management. Psychology and Education, 58(2): 4825-4834.
Noponen, N. (2019). Impact of artificial intelligence on management. Electronic Journal of Business Ethics and Organization Studies, 24(2).
Ogohi, C., Cross, D., & Jiya, S. (2020). Effective Time Management on Employee Performance of Northern Nigeria Noodle Company Ltd. DOI: 10.5281/zenodo.3612089.
Romero, M. (2019). Time management in Mediated Project Activities from a Distance. The Autonomous University of Barcelona.
Schafheitle, Simon & Weibel, Antoinette & Ebert, Isabel & Kasper, Gabriel & Schank, Christoph & Leicht-Deobald, Ulrich. (2020). No stone left unturned? Towards a framework for the impact of datafication technologies on organizational control. Academy of Management Discoveries, 6(3). DOI: 10.5465/amd.2019.0002.
SCHEIBER, N. (2017, April 2). How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons. The New York Times. https://www.nytimes.com/interactive/2017/04/02/technology/uber-drivers-psychological-tricks.html
Wajcman, J. (2018). The Digital Architecture of Time Management. Science, Technology, & Human Values, 44(2), 315–337. https://doi.org/10.1177/0162243918795041
Annotated Bibliography
Cavaliere, L. P. L., Nath, K., Wisetsri, W., Villalba-Condori, K. O., Arias-Chavez, D., Setiawan, R., ... & Regin, R. (2021). The Impact of E-Recruitment and Artificial Intelligence (AI) Tools on HR Effectiveness: The Case of High Schools (Doctoral dissertation, Petra Christian University).
The authors Cavaliere et al. published the article, “the impact of e-recruitment and Artificial Intelligence tools on HR effectiveness,” to illustrate and show how AI has changed the hiring process within companies, primarily through E-recruitment. According to the authors, the e-recruitment method has realized the massive success of organizational performance. Unlike traditional forms of hiring employees that were time-consuming and entailed a great deal of paperwork for recruiters, the use of AI has ensured a faster and more competitive hiring process, free of bias and no paperwork involved. The authors published the article in 2021. However, the participants selected for this research has been the main subject of criticism. The research used high school students as the respondents to the interviews and questionnaires. Most critics maintained that these respondents had no significant knowledge of the study subject.
Jia, Q., Guo, Y., Li, R., Li, Y., & Chen, Y. (2018, June). A conceptual artificial intelligence application framework in human resource management. In Proceedings of the international conference on electronic business (pp. 106-114).
“A conceptual artificial intelligence application Framework in Human Resource Management,” an article by Qiong Jia, Yue Guo, Rong Li, Yurong Li, and Yuwei Chen, was published in 2018. The study proposed a conceptual framework of artificial intelligence (AI) technology application for human resource management (HRM). The focus of the study was on six basic dimensions of HRM, including recruitment, training and development processes, human resource strategy and planning, performance management, salary evaluation, and employee management. The research established that the efficiency of the employees could be ensured by the human resource managers through the use of Ai through online training and recruitment analysis before finally hiring the employees. According to the authors, AI technology is an effective tool to help orient employees to produce the desired results. Besides, due to its association with Big Data analytics, HR can get the selected employees with the desired skills and expertise. AI ensures a fast and effective recruitment process and can also train employees depending on the organisation's needs. However, one critic of the research is that it has not explicitly illustrated the various risks involved in adopting AI by HRM to enhance employee efficiency within organizations. Instead, it has only focused on the benefits.
Zahidi, F., Imam, Y., Hashmi, A. U., & Baig, M. M. (2020). Impact of Artificial Intelligence on HR Management–A.
The “impact of artificial intelligence on HR Management-A review” is a research journal by Farah Zahidi, Yasar Iman, Ahmad Umair Hashimi, and Mirza Mashokoor published in 2020. In this journal, the researchers aim to establish the revolutionary changes in how things are done within the human resource management departments to ensure the efficiency of the employees. According to the authors, the efficiency of the employees within the HRM has been significantly enhanced through the adoption of AI. The authors explain that AI has created a more robust and competent way of satisfying the different HR functions. Besides, the authors have indicated both the positive and negative consequences of using AI in HRM to ensure employee efficiency in time management, hence a practical resource for the research. However, it has failed to establish the most effective solutions to the emerging problems of adopting AI in the HRM field.
Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2021) Impact of artificial intelligence on employees working in industry 4.0 led organizations. ResearchGate. https://www.researchgate.net/publication/351700523_Impact_of_Artificial_Intelligence_on_Employees_working_in_Industry_40_Led_Organizations
In their work, Impact of artificial intelligence on employees working in industry 4.0 led organizations (2021), Malik et al. stated that employees have positive experiences due to AI adoption and the creation of technostress. An interview carried out by 32 professionals with average experience across nine industries shows that embracing AI ease work for the management and save time for an organization at large. Malik et al., for example, state that during recruitment, AI allows cross-reference checks, and utilization of psychometrics such as Holland occupational codes saves a considerable amount of time for both employees and the management. Even though Malik et al. have met their purpose in this article, the impacts of AI on employees` experiences have not been explored.
Sari, R. E., Min, s., & Furinto, a. (2020). (PDF) Artificial intelligence for better employee engagement. ResearchGate. https://www.researchgate.net/publication/350180136_Artificial_Intelligence_for_a_Better_Employee_Engagement
Artificial intelligence for better employee engagement, by Sari et al. (2020), aims at seeing whether AI-based technology assists management discovers intangible things, for example, worker involvement levels. The research study was conducted on 39 SML workers, and results showed that AI-based software such as performance management software offered precise records on the amount of time a worker used working on a task. However, this work does not show employees perceptions of the AI-based system. This research provides practical insight and chances for firm’s management to utilize AI in managing well limited time.
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. Human Resource Management, 62(4), np. https://doi.org/10.1177/0008125619867910
Tambe`s et al. (2019) journal Artificial Intelligence in Human Resources Management indicates a significant gap between the promise and reality of AI in HRM. It identifies the importance of data science and AI for HRM tasks. This study proposes practical responses to these benefits based on casual reasoning, experiments and randomization, and workers' socially appropriate and economically efficient engagement for using AI in employee management. Leveraged accurately, AI abilities have a powerful impact on workers` performance and organisational time management. However, this research has exhausted the benefits of AI towards workers` productive time management, and it does not talk about the damaging allegations that AI has on the firm and workers` experience.
Mirbabaie, M., Brünker, F., Möllmann, N., & Stieglitz, S. (2021, October 5). The rise of artificial intelligence – understanding the AI identity threat at the workplace. SpringerLink. https://link.springer.com/article/10.1007/s12525-021-00496-x
Mirbabaie`s et al. article, the rise of artificial intelligence – understanding the AI identity threat at the workplace, (2021) indicates that AI in firm management is increasingly integrated today and has assisted in ensuring proper job allocation and competent time management, among other factors in companies. This research study shows that paperwork, apart from different dynamics, has been reduced in most companies. As a result, retrieving documents, for instance, has been made more efficiently, thus saving time. With all these aims achieved, the paper has failed to show how this contributes to HRM. The article would otherwise explain how AI fosters the relationship between employees and management.
Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Wiley Online Library. https://onlinelibrary.wiley.com/doi/10.1002/smj.3322
Tong et al., in their article, The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance (2021), shows that most companies use AI to provide employees feedback on performance by tracking their behaviour at work. However, it states that this application has provoked much controversy as employees might negatively perceive AI data analysis. As a result, the findings of this research indicate strong evidence that employees` time management in most firms is pretty excellent. However, they fear losing jobs with the increasing adoption of AI. The results provide crucial implications for management theory, public policies, and practice but fail to explore how AI affects workers` viewpoints and output. Everything is monitored, and the employees` freedom is squeezed.
Laker, B. (2021). Embedding Artificial Intelligence at Work: From Efficiency Gains to Leadership Expertise. Forbes. https://www.forbes.com/sites/benjaminlaker/2021/11/14/embedding-artificial-intelligence-at-work-from-efficiency-gains-to-employee-experience/?sh=36b189dc6d6f
With the increasing application of AI in company management, Laker, 2021 in his article, Embedding Artificial Intelligence at Work: From Efficiency Gains to Leadership Expertise, notes that employees' discipline, especially on proper time management, is doing well. Lakers and Prof. Malik examined massive impacts of AI on time management and in studying and practicing HRM. The research findings showed that using biometrics and AI-based technology, apart from ensuring efficient time management, has increased workers' output in most firms. However, besides achieving the study purpose, the research has failed to account for the improvement in workers` work.
Robert, L., Pierce, C., Morris, L., Kim, S., & Alahmad, R. (2020). Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda. arXiv.org e-Print archive. https://arxiv.org/ftp/arxiv/papers/2002/2002.09054.pdf
In their article, Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda, they claim that the deployment of AI in handling managerial work is, to some point, unfair to the employees. This paper approaches the issue of AI impacts on workers in three significant ways. It introduces the organizational justice theory, focuses on AI fairness, and proposes a designed plan. The general result of this study shows an increase in employees` turnover and a decrease in workers` effort. The article has only stated that an AI-based system upturns employees` throughput while a reduction of their attempts. It fails to explain how these happen.
Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda✰. ScienceDirect.com | Science, health and medical journals, full-text articles and books. https://www.sciencedirect.com/science/article/pii/S004016252031218X
Artificial intelligence and innovation management: A review, framework, and research agenda by Haefner et al., 2021, states that AI has reshaped firms and how tasks are carried out. It reviews and explores how AI has impacted workers and their workshop environments. By using ideas from the Carnegie School and the behavioral theory of the organization, the results show that AI has relatively increased productivity in most companies through the easy allocation of jobs and time-saving. However, the study does not show how employees find the experience of embracing AI in workplaces.
Yawalkar, M. V. V. (2019). a Study of Artificial Intelligence and its role in Human Resource Management. International Journal of Research and Analytical Reviews (IJRAR), 6(1), 20-24.
A study of artificial intelligence and its role in Human Resource Management by Mr Vivek V. Yawalkar was published in 2019 and aimed to establish the advantages of adopting AI by the HRM in enhancing the competence and efficiency of employees. The study established that AI has transformed the functions of HRM and can now perform hiring, recruitment, and analysis of data that has helped reduce the workplace workload and enrich workplace efficiency. The research has utilized secondary data collection approach to gather information, especially from survey reports, HR blogs, websites, publications, and compared their results, an element that makes the research results valid. However, one critique of this source was its limited coverage.
Damioli, G., Roy, V. V., & Vertesy, D. (2021). The impact of artificial intelligence on labour productivity. SpringerLink. https://link.springer.com/article/10.1007/s40821-020-00172-8
According to Damioli`s et al., The impact of artificial intelligence on labour productivity, (2021), there has been an increase in AI patenting events in the current years, proposing that solutions based on AI technologies may have started to exert effects on the economy. This hypothesis has been tested using a global sample of approximately 5257 companies that have filed at least a patent related to the field of AI between 2000 and 2016. The result analysis indicates that once controlling for other patenting activities, AI patent applications generate an extra-positive effect on companies` labour productivity. The impacts concentrate on SMEs and services industries, suggesting that quickly readjusting and introducing AI-based applications in the production process is an essential determinant of AI's impact. Even though the article has achieved its purpose, it entirely relies on AI patent applications thus does not encompass inventions protected by other formal and informal intellectual rights.
Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I. (2021). Artificial intelligence and human workers interaction at the team level: A conceptual assessment of the challenges and potential HRM strategies. Discover Journals, Books & Case Studies | Emerald Insight. https://www.emerald.com/insight/content/doi/10.1108/IJM-01-2021-0052/full/html
The article, Artificial intelligence and human workers interaction at the team level: A conceptual assessment of the challenges and potential HRM strategies by Arslan et al. (2021) focuses on HRM's challenges in contemporary firms to close interaction between AI and human workers. The article further explores significant potential strategies that might be useful in overcoming these challenges based on a conceptual review of extant research. The research found that interaction between human workers and robots is visible in various organizational functions, where both are working as teammates. This challenges HRM functions when they need to address workers` fear of working with AI. This, as a result, affects employees' concentration in fear of job loss, thus low output. However, this research has not determined how AI affects time consumption in firms.
Prentice, C., Dominique Lopes, S., & Wang, X. (2020). Emotional intelligence or artificial intelligence–an employee perspective. Journal of Hospitality Marketing & Management, 29(4), 377-403.
The emotional intelligence or artificial intelligence-employee perspective journal by Prentice et al., published in 2020, explores how emotional and artificial intelligence influences the retention and performance of employees with a focus on service employees within the hotel industry. According to the authors, HRM has used AI and emotional intelligence to retain employees and enhance their performances. The technology has effectively boosted employee productivity and has significantly reduced time wastage as it collects data from various ranking hotels that the HR managers can use in the decision making and directing the employees on the relevant tasks to be undertaken. The paper is suitable for the research since it concludes the research process with a conclusion and recommendations on the practical usage of AI by the HRM to improve employee performance.
Berhil, S., Benlahmar, H., & Labani, N. (2020). A review paper on artificial intelligence at the service of human resources management. Indonesian Journal of Electrical Engineering and Computer Science, 18(1), 32-40.
In their journal, “artificial intelligence at the service of human resources management”, Berhil et al., published in 2020, explore the focus of the human resource managers in influencing employees or human resources who are the primary influencers of the company success and development. According to the authors, the adoption of AI within companies impacts enhancing the businesses' profitability by influencing employees’ productivity. The article is effective for the research as it seeks to determine the problems the HR managers face when handling the employees and how these problems can be solved by adopting AI. However, the study has a limitation in that it does explicitly illustrate the methodology used in the research.
Artificial intelligence has seen massive applications and had significant impacts on human resource management. The journal the application of artificial intelligence in human resource management by Bora et al. (2020) investigates the various ways in which HRM have used AI to influence the productivity, timing, and efficiency of the employees. According to the authors, AI has produced numerous hiring managers solutions that incorporate intermediate applications, advanced Ai solutions, and essential recruiting tools. The practice has enabled the HRM to hire competent and more efficient employees who have ensured high performance within the society. However, one drawback of this research is that the methodology was limited, and interviews were only performed amongst the organizations using the AI, excluding those willing to implement it in the future.
Dashora, J., & Saxena, K. (2022). Effect of Artificial Intelligence on Human Resource Profession: A Paradigm Shift. Impact of Artificial Intelligence on Organizational Transformation, 57-71.
Jyoti Dashora and Karunesh Saxena, in their book, ‘effect of artificial intelligence on Human Resource Profession, published in 2022, attempt to illustrate the importance of human resources to business. According to the authors, HRM has seen a massive paradigm shift. The developments have significantly influenced the various activities in the organization in information technology. Based on the research, the authors have established that chatbots can help HR managers carry out monotonous and routine administrative tasks that consume much time when handled with humans. However, despite the benefits of AI, the authors have concluded that AI should not be used in organizations to replace human professionals as they lack emotional intelligence. However, the benefits of AI in this research are more than the drawbacks established; hence, adopting AI by HRM would be appropriate, contrary to the study result.
Hemalatha, A., Kumari, P. B., Nawaz, N., & Gajenderan, V. (2021, March). IEEE. Impact of Artificial Intelligence on Recruitment and Selection of Information Technology Companies. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) (pp. 60-66).
Hemalatha et al. (2020) strive to establish the impacts of artificial intelligence on the recruitment and selection process by the HRM. The primary aim of the research was to critically analyze the impact of Artificial intelligence on HR practices, especially during the recruitment and selection processes. In the study, the researchers collected primary data through online surveys. The research established that AI technologies such as machine vision, automation, and augmentation have had positive impacts during the recruitment and selection process especially in terms of accuracy, time, cost saving, and enhanced efficiency. Also, AI effectively eliminated bias during the recruitment process, which is vital in acquiring highly efficient and competitive employees. According to the authors, AI has been effective in workforce management.
