BENEFITS OF AI IN TIME AND ATTENDANCE

Authors

  • Ankur Saini Author

Keywords:

Compliance, Predictive Analytics, Real-Time Data, Fraud Prevention, Artificial Intelligence (AI), Time And Attendance, Workforce Management, Accuracy, Productivity, Automation,

Abstract

Using advanced technologies like Artificial Intelligence (AI) in time and attendance systems is revolutionizing workforce management due to accuracy, productivity, and cutting costs. This research answers numerous advantages in this specific domain of AI that minimize inefficiencies and inexactitudes arising from conventional tracking methods. Implementing AI in the timekeeping mechanisms eradicates manual overhead and time theft, thus enhancing appropriate compliance with labor laws and improving human and resource management management. In addition, the real-time analysis of information and the ability to make predictions provide managers with vital tools that offer them a glimpse of future attendance trends, which in turn assists them in making purposeful decisions about human resource management plans and policies. With the integration of AI, sustainability is ascertained concerning its impacts on reducing dependence on paper systems and supporting environmentalism. Moreover, integrating AI with other HR systems expands operational effectiveness, enhances the simplified actions related to the payroll system, and helps avoid fraud by using biometric authentication. This study asserts that AI is essential for today's organizations striving to improve workforce management, boost employee satisfaction, and secure their competitiveness within a growing digital landscape. Using case studies and relevant examples, the paper shows how AI transforms time and attendance management, making a convincing argument for its broad industry adoption. The results illustrate the critical function of AI in developing a more effective, reliable, and sustainable way to oversee time and attendance in today's workplaces.

References

American Payroll Association. (2018). Case studies in payroll accuracy: The impact of AI on retail timekeeping. APA Research Publications.

Anderson, M. (2020). AI in Compliance: A Case Study of Walmart's Approach. Journal of Business Law, 37(3), 456-472.

Anderson, P. (2016). Managing workforce time: The inefficiencies of manual systems. Journal of Human Resources, 12(3), 120-135.

Armstrong, M., & Taylor, S. (2021). Armstrong's Handbook of Human Resource Management Practice (15th ed.). Kogan Page.

Barton, D., Bynghall, S., & Westerman, G. (2020). Workforce management in the AI era: Strategies for success. Journal of Business Strategy, 41(4), 10-17.

Beheshti, H. M. (2018). Improving productivity and profitability in a small company: the role of computer-based information systems. Journal of Business & Industrial Marketing, 33(2), 234-245.

Berente, N., Seidel, S., & Safadi, H. (2019). Data-driven workforce management: Predictive analytics in action. Information Systems Journal, 29(2), 215-239.

Bessen, J. E. (2019). AI and Jobs: The Role of Demand. NBER Working Paper Series. https://doi.org/10.3386/w24235

Bies, R. J., & Moag, J. F. (1986). Interactional justice: Communication criteria of fairness. In R. J. Lewicki, B. H. Sheppard, & M. H. Bazerman (Eds.), Research on Negotiation in Organizations (Vol. 1, pp. 43-55). JAI Press.

Bredeson, D. (2019). Global Workforce Compliance: Navigating Multinational Labor Laws. HR Compliance Review, 22(1), 34-46.

Brougham, D., & Haar, J. M. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257.

Brown, T., & Johnson, A. (2020). AI and workforce management: Transforming HR with predictive analytics. Journal of Business Analytics, 12(3), 205-221.

Brynjolfsson, E., & McAfee, A. (2017). Harnessing AI for business: The path forward. MIT Sloan Management Review, 58(2), 32-40.

Caruso, S., Bruccoleri, M., Pietrosi, A., & Scaccianoce, A. (2023). Artificial intelligence to counteract “KPI overload” in business process monitoring: the case of anti-corruption in public organizations. Business Process Management Journal, 29(4), 1227-1248.

Chen, X., Zhang, L., & Wang, Y. (2022). Artificial Intelligence in Workforce Management: Applications and Future Trends. Journal of Business Technology, 23(1), 45-62.

Coleman, J., & Mahaffey, S. (2020). The Hidden Costs of Time Theft. Journal of Business Ethics, 163(3), 513-529. https://doi.org/10.1007/s10551-019-04326-5

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.

Davis, R., & Clark, S. (2022). The future of work: How AI is shaping employee management. International Journal of Human Resource Management, 33(7), 1442-1458.

Deloitte. (2021). AI in Workforce Management: Enhancing Compliance and Efficiency. Deloitte Insights. Retrieved from https://www.deloitte.com/insights/

Ernst & Young. (2015). Human error and its impact on payroll accuracy. Ernst & Young Publications.

Fountain, J. E., Deutch, S., & Bergman, E. (2021). Biometrics in Workforce Management: Benefits and Challenges. Information Systems Journal, 31(2), 205-223. https://doi.org/10.1111/isj.12267

García, M., Fernández, R., & Morales, S. (2020). Challenges in Compliance Management: The Role of Human Error in Non-Compliance with Labor Laws. International Journal of Human Resources, 18(3), 200-215.

Gartner. (2022). The Role of AI in Ensuring Compliance with Labor Laws: A Study of Unilever. Gartner Research Reports.

Haghani, M., Coughlan, M., Crabb, B., Dierickx, A., Feliciani, C., van Gelder, R., ... & Wilson, A. (2023). A roadmap for the future of crowd safety research and practice: Introducing the Swiss Cheese Model of Crowd Safety and the imperative of a Vision Zero target. Safety science, 168, 106292.

Harrison, R., & Greenfield, S. (2022). Digital Transformation and Environmental Sustainability in the Workplace. Routledge.

Hays, J., & McDonough, T. (2021). The Environmental Impact of Industrial Paper Production. Journal of Environmental Studies, 45(3), 221-234.

Healthcare Management Review. (2017). The benefits of AI-driven timekeeping in healthcare settings. Healthcare Management Review, 34(4), 78-89.

International Journal of Production Research. (2018). Improving labor law compliance through AI in manufacturing. International Journal of Production Research, 56(7), 210-225.

Johnson, P. (2021). Manual Processes and Compliance Risks in Workforce Management. HR Management Journal, 29(4), 237-252.

Jones, P. (2019). Artificial Intelligence in Human Resources: A Path to Sustainable Practices. Cambridge University Press.

Kakavand, H., & Kost, B. (2019). The Economic Impact of AI on Payroll Systems. Journal of Labor Research, 40(1), 86-101. https://doi.org/10.1007/s12122-019-09279-0

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.

Kavanagh, M. J., & Johnson, R. D. (2017). Human resource information systems: Basics, applications, and future directions. Sage Publications.

Kavanagh, M. J., Thite, M., & Johnson, R. D. (2018). Human resource information systems: Basics, applications, and future directions (4th ed.). Sage Publications.

Kelley, B. J. (2023). Wage Against the Machine: Artificial Intelligence and the Fair Labor Standards Act. Stan. L. & Pol'y Rev., 34, 261.

Krakowski, S., Luger, J., & Raisch, S. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425-1452.

Kumar, S., & Verma, P. (2022). AI-Driven Audits: Enhancing Compliance in the Modern Workplace. Journal of Regulatory Affairs, 12(4), 89-103.

Lee, S. (2021). Regional Compliance and AI: Customizing Workforce Management Systems. International Journal of Human Resources, 15(2), 78-93.

Lee, S., & Park, J. (2022). The Future of AI in Compliance: Predictive Analytics and Beyond. Technology and Society, 29(2), 73-89.

Leung, J., Chan, R., & Chong, Y. (2019). The Influence of AI-Driven Attendance Systems on Employee Trust and Engagement. Journal of Organizational Behavior, 40(3), 345-360.

Lister, K., & Harnish, T. (2017). The state of telework in the US: How individuals, businesses, and government benefit. Global Workplace Analytics.

Malik, A., & Singh, R. (2020). Adapting AI to Regional Labor Laws: A Case Study of Multinational Companies. Journal of International Business Studies, 41(3), 56-72.

Marr, B. (2018). How AI is transforming workforce management. Forbes. Retrieved from https://www.forbes.com/sites/bernardmarr/2018/08/13/how-ai-is-transforming-workforce-management/

Martinez, C., & Schmalz, M. (2020). AI Adoption in Small and Medium Enterprises: A Cost-Benefit Analysis. International Journal of Production Research, 58(6), 1827-1840. https://doi.org/10.1080/00207543.2019.1675917

Mathis, R. L., Jackson, J. H., Valentine, S. R., & Meglich, P. A. (2020). Human Resource Management (16th ed.). Cengage Learning.

McKinsey & Company. (2020). The role of AI in reducing time theft in organizations. McKinsey Reports.

Miller, J. (2019). AI in workforce management: Enhancing accuracy and efficiency. AI and Business Review, 21(2), 45-60.

Nankervis, A. R., & Cameron, R. (2023). Capabilities and competencies for digitised human resource management: Perspectives from Australian HR professionals. Asia Pacific Journal of Human Resources, 61(1), 232-251.

Noe, R. A., Hollenbeck, J. R., Gerhart, B., & Wright, P. M. (2019). Fundamentals of Human Resource Management (8th ed.). McGraw-Hill Education.

Noyes, J. M., & Tunstall, L. (2018). The Financial Impact of AI in the Manufacturing Sector. Technology and Society, 54, 78-88. https://doi.org/10.1016/j.techsoc.2018.03.002

PWC. (2021). Managing fraud risk: A guide for employers. Retrieved from [website].

Retail Technology Review. (2019). Enhancing accuracy in retail timekeeping through AI. Retail Technology Review, 27(5), 98-113.

Sarker, S. (2022). Artificial intelligence and its application in workforce management. Journal of Human Resources and Technology, 18(3), 45-58.

Sharda, R., Delen, D., & Turban, E. (2021). Business Intelligence, Analytics, and Data Science: A Managerial Perspective (5th ed.). Pearson.

Sharma, R., & Kumar, N. (2021). AI and biometric integration in attendance systems: A case study. Journal of Business and Technology, 12(4), 67-82.

Smith, A., & Triplett, J. (2020). Automating HR: The Financial Benefits of AI. Human Resource Management Journal, 30(4), 489-506. https://doi.org/10.1111/1748-8583.12266

Smith, J., & Watson, H. (2019). Legal Transparency Through AI: Building a Robust Compliance Framework. Business Ethics Quarterly, 30(1), 19-35.

Smith, J., Lee, M., & Zhang, Q. (2021). Harnessing predictive analytics for workforce optimization. Human Resource Management Review, 31(4), 567-583.

Smith, L., & Patterson, J. (2020). Sustainable Workforce Management: The Role of AI in Reducing Environmental Footprints. Journal of Business Ethics, 57(2), 113-126.

Smith, R. (2020). Compliance in the Modern Workplace: The Role of Technology. Journal of Employment Law, 42(2), 123-139.

Vasarhelyi, M. A., Kogan, A., & Tuttle, B. (2018). AI and the future of business decision making. Journal of Emerging Technologies in Accounting, 15(1), 71-83.

Williams, P., Adams, K., & Roberts, L. (2019). Proactive management strategies in the age of AI. Journal of Organizational Behavior, 40(2), 345-360

Downloads

Published

2023-02-27

How to Cite

Ankur Saini. (2023). BENEFITS OF AI IN TIME AND ATTENDANCE. INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET), 14(2), 30-52. https://lib-index.com/index.php/IJARET/article/view/IJARET_14_02_003