The innovation in terms of technology and its application in various fields of the economy of Australia lead to a huge transformation in the traditional method of Human Resource Management (Fallucchi et al. 2020). The present use of Machine Learning enhanced the potential of the management in terms of output by the creation of efficiency in different fields from recruitment to enhancing creativity and its application to get a cost-efficient managing system (Fallucchi et al. 2020). A large amount of data analysis becomes easier which is almost impossible manually to maintain huge spreadsheets (Rybinski et al. 2018). Machine learning in Australia assists the Human Resource Department to even forecast the outcome of the employees working thereby determining the trends and pattern over some time and proved to be a versatile instrument and the basis of development for the country (Rybinski et al. 2018).
There exists hardly any organization, which is not yet replaced by the machine-based management system in Australia (Auer et al. 2020). There is no doubt in the fact that the impact of the present transition of the Human Resource Management process will be clearly visible in the upcoming years or the future and will prominently affect the decision - making regarding the recruitment options and retention among the employees by improving them. This versatile tool is capable of identifying every minute changes happening with time and analyzing large data sources in a short period (Auer et al. 2020). The details of the employees' wage rate, their working tenure, job description, their current job and educational qualifications could be now summarized easily and can be accessed easily as and when required, even after some time in future.
Because of the application of machine learning, the unnoticeable changes in the pattern of inputs, scheduling process, time, employer's or employees' actions can also be detected, which might otherwise is ignored (Nagar et al. 2020). These changes in the present scenario will act as a guide in the management process during the upcoming years. For instance, if the Human Resource Manager forecast a decrease in the output level per worker in present time, he or she can solve this problem by taking measures to improve their work in the future years by finding the reason behind this and correcting it (Nagar et al. 2020). Its application is going to make the administration much more effective and the benefits will be very clearly visible in the upcoming generation of the Human Resource Self betterment services (Aurelia et al. 2020). The decision - making will be more accurate and innovative in various field of HR management as in the process of recruiting staffs, organizing them, workforce motivation and their better output (Aurelia et al. 2020).
The impact of the new technology and machine learning is expanding with time. As human resource management is directly responsible for strategic planning and smartly executing the human resource (Sajjadiani et al. 2019). The engagement of machine learning in the process creates a transition executing these more efficiently in the country. In the recruitment process, the analysis of profiles of the applicant becomes easier and thus those attributes of the candidate could be identified by the use of machine learning which is not mentioned in their resume as well. It is again useful while planning the strategic measures (Chattopadhyay, 2020).
This can be done by managing the database of the employees by observing their attitude toward different job assigned, their qualification, benefits regarding the assigning of a particular job role to any of the worker and the relative external developments (Chattopadhyay, 2020). The worker's view for any certain policy can also be examined. And based on all these observations, planning is possible in a required manner. The machines are capable of processing large data volumes and storing them, which can be reviewed while framing policies. The worthful candidates are the demand for most organizations (Malik et al. 2020).
And the most suitable one could recruit only by using machine learning by the management. All the well-known companies of Australia are found to practice this algorithm in the strategic implications of Human Resource management to grab the best-suited candidate in their job design (Malik et al. 2020). The communication process is also quite improved by this method and thus assists in getting better performance by the employees (Sinha, 2017). Both the horizontal as well as vertical communication network is in existence by the help of digital platform to improve the working performance within the organizational structure. The new - generation is entering the working environment who are skilled and have greater machine learning abilities who are bringing innovative talents in the workplace (Sinha, 2017). The next level digital platform-based workforce fits the job in this dynamic environment and thus further improves the strategic method implication (Sajjadiani et al. 2019).
Instead of being quite useful and giving a large opportunity to the HR industry, the implication of machine learning is facing some of the challenges in the country, Australia. Some of the challenges behind its practical implementation are discussed as (Carlson et al. 2020):
1. There is an undefined level of productivity for a good employee. It is not defined or have any ideal measure to figure out the level of output per unit input for the workforce by any organization enjoying machine learning (Carlson et al. 2020).
2. There always exists an element of discrimination based on the competitive taste of the market.
Asymmetric information between the employer and the employee during recruitment is another challenge to be faced (Carlson et al. 2020).
3. The implantation process can be proved to be troublesome. The already existing employees are not used to the new machine based working structure and may find it difficult to use them because of their old manual practising habits. They may take time to be comfortable to the new working environment (Стоянова et al. 2018).
4. Lack of direct interaction may be another dissatisfactory situation for the workforce (Стоянова et al. 2018). Use of digital machines and appliances can create a communication gap between them which can affect their working adversely.
5. Also, the new era of machine learning applications creates a situation of unemployment among the unskilled labour force and they are replaced by the next generation skilled workforce (Стоянова et al. 2018).
However, there comes a lot of opportunities with the machine-based management of human capital with creative and more innovative strategic measures for both the employers and the employees (Garg et al. 2021). Some of them are mentioned as:
1. The learning capacity of the individual is enhancing, leading to their overall growth and development.
2. The objective determined during the planning process is achieved more efficiently by using the element of technological methods in the strategic framework and the administration process by the management (Garg et al. 2021).
3. Predictions regarding the capacity, performances and the time required to complete a certain project can be made and thus the decision - making is done on this basis, enhancing the overall productivity (Garg et al. 2021).
4. The time - to - time updates of the work done by the employees could be done by the supervisors (Garg et al. 2021), which could assist the supervisor in further planning.
5. Proper records of the employees can be maintained by using a digital spreadsheet.
6. The already existing database analysis, based on machine learning can help the management to determine the demand and helps them to plan the respective human resource-based activities (Punnoose et al. 2019).
Machine learning has occupied a vast space in Human Resource Management and is going to flourish the department with much more creative human capital in the coming years in Australia (Punnoose et al. 2019). The workload is divided among the workers based on their skills by creating various department for each kind of job leading to specialization and greater output. With the advent of technological advancement, the employees can get more time for focusing on their job and serves the country a better future (Punnoose et al. 2019).
Auer, E. M., & Landers, R. N. (2020). Creating Data-Driven HR Insights: Data Science in HRM. In Encyclopedia of Electronic HRM (pp. 270-275). De Gruyter Oldenbourg.
Aurelia, S., & Momin, M. M. (2020). Global reverberation and prediction for HRM amid and after COVID-19: A technological viewpoint. Materials Today: Proceedings.
Carlson, D. A., Kou, W., Rooney, K. P., Baumann, A. J., Donnan, E., Triggs, J. R., Teitelbaum, E. N., Holmstrom, A., Hungness, E., Sethi, S., Kahrilas, P.J., & Pandolfino, J. E. (2020). Achalasia subtypes can be identified with functional luminal imaging probe (FLIP) panometry using a supervised machine learning process. Neurogastroenterology & Motility, 33(3), e13932.
Chattopadhyay, P. (2020). A Study on Various Applications of Artificial Intelligence (AI) in the field of Human Resource Management (HRM). International Journal of Advanced Research in Science, Communication and Technology, 11 (2), 63-67.
Fallucchi, F., Coladangelo, M., Giuliano, R., & William De Luca, E. (2020). Predicting Employee Attrition Using Machine Learning Techniques. Computers, 9(4), 86.
Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2021). A review of machine learning applications in human resource management. International Journal of Productivity and Management. Retrieved from https://doi-org.ezproxy.cqu.edu.au/10.1108/IJPPM-08-2020-0427
Malik, A., Srikanth, N. R., & Budhwar, P. (2020). Digitisation, artificial intelligence (AI) and HRM. Human Resource Management: Strategic and International Perspectives, 88.
Nagar, M., & Saxena, T. (2020). HRM in the era of Artificial Intelligence. Innovation in Global Business and Technology: Trends, Goals and Strategies, 1.
Punnoose, R. & Ajit, P. (2016). Prediction of Employee Turnover in Organizations using Machine Learning Algorithms A case for Extreme Gradient Boosting. International Journal of Advanced Research in Artificial Intelligence, 5 (9), 22-26.
Rybinski, K., & Tsay, V. (2018). The Application of Machine Learning in Faculty Assessment: A Case Study of Narxoz University. Human Resource Management/Zarzadzanie Zasobami Ludzkimi.
Sajjadiani, S., Sojourner, A. J., Kammeyer- Mueller, J. D., & Mykerezi, E. (2019, March 25). Using machine learning to translate applicant work history into predictors of performance and turnover. Journal of Applied Psychology, 104 (10), 1207 – 1225.
Sinha, V. (2017). Artificial Intelligence and machine learning in hrm: study the impacts on employees and organizations. ICRBS-2017, 2017.
Стоянова, О., & Бакотин, В. (2018). Text processing and machine learning methods in HRM applications: opportunities and features. Litres.
Remember, at the center of any academic work, lies clarity and evidence. Should you need further assistance, do look up to our Human Resource Management Assignment Help
5 Stars to their Experts for my Assignment Assistance.
There experts have good understanding and knowledge of university guidelines. So, its better if you take their Assistance rather than doing the assignments on your own.
What you will benefit from their service -
I saved my Time (which I utilized for my exam studies) & Money, and my grades were HD (better than my last assignments done by me)
What you will lose using this service -
Unfortunately, i had only 36 hours to complete my assignment when I realized that it's better to focus on exams and pass this to some experts, and then I came across this website.
Kudos Guys!Jacob "
Proofreading and Editing$9.00Per Page
Consultation with Expert$35.00Per Hour
Live Session 1-on-1$40.00Per 30 min.
Doing your Assignment with our resources is simple, take Expert assistance to ensure HD Grades. Here you Go....