A/B Testing in HR Technology: Improving User Experience and Decision-Making with Data-Driven Metrics
Abstract
Based on the analysis, A/B testing is essential for enhancing usability and is the most important decision-making tool in the field of HR technology. When a feature or a product is implemented in two different ways, it is possible to determine the goal standards and objectively establish efficiency and user satisfaction enhancements within an organization. Identifying how these methods can be translated between various applications of HR technology, such as internal mobility platforms, onboarding processes, and tools that are implemented for and by employees, is the focus of this paper. By implementing A/B testing, the HR teams will enhance engagement and efficiency and make much better decisions for the overall good of the employees and the company. For example, A/B testing applies to improving the selection aids, the tailored experience of onboarding new employees, or enhancing the intranet and internal communication technologies. Through frequent assessment of the feedback provided by users concerning multiple versions, organizations can optimize the usage of HR technologies to enhance the experiences of the employees with whom they interact. Finally, A/B testing positively affects business operations and reduces costs by improving the effectiveness of HR technology implementation.
References
Kohavi R, Tang D, Xu Y. Trustworthy online controlled experiments: A practical guide to a/b testing. Cambridg Univ Press; 2020 Feb 14.
Feldman LH. Jew and Gentile in the ancient world: attitudes and interactions from Alexander to Justinian. Prince Univ Press; 2021 Aug 10.
Brynjolfsson E, Jin W, McElheran K. The power of prediction: predictive analytics, workplace complements, and business performance. Busi Eco. 2021 Oct;56:217–39.
Gable GG. Information systems research strategy. J Stratgic Info Sys. 2020 Jun 1;29(2):101620.
Gill A. Developing a real-time electronic funds transfer system for credit unions. IntlJ Adv Res Eng Tech. 2018;9(1):162–84.
Cascio WF, Boudreau JW. The search for global competence: From international HR to talent management. J Wld Busi. 2016 Jan 1;51(1):103–14.
Cowgill B. Bias and productivity in humans and algorithms: Theory and evidence from resume screening. Colum Busi Schl, Columbia Univ. 2020 Mar 21;29.
Nyati S. Revolutionizing LTL Carrier Operations: A Comprehensive Analysis of an Algorithm-Driven Pickup and Delivery Dispatching Solution. Intl J Sci Res (IJSR). 2018 Feb;7(2):1659–66.
Brawley AM, Pury CL. Work experiences on MTurk: Job satisfaction, turnover, and information sharing. Compu Human Behav. 2016 Jan 1;54:531–46.
Crayne MP, Brawley Newlin AM. Driven to succeed, or to leave? The variable impact of self-leadership in rideshare gig work. IntlJ Human Resourc Mgmt. 2024 Jan 2;35(1):98–120.
Sammut R, Griscti O, Norman IJ. Strategies to improve response rates to web surveys: a literature review. Intl J Nurs Std. 2021 Nov 1;123:104058.
Moran K, Linares-Vásquez M, Bernal-Cárdenas C, Poshyvanyk D. Auto-completing bug reports for android applications. In: Proceedings of the 2015 10th joint meeting on foundations of software engineering. 2015 Aug 30;p.67386.
Davidov G, Alon-Shenker P. The ABC Test: A new Model for employment status Determination? Industl Law J. 2022 Jun 1;51(2):235–76.
Kurnat-Thoma E, Ganger M, Peterson K, Channell L. Reducing annual hospital and registered nurse staff turnover—A 10-element onboarding program intervention. SAGE Open Nurs. 2017 Mar; 3:2377960817697712.
Sammut R, Griscti O, Norman IJ. Strategies to improve response rates to web surveys: a literature review. Intl J Nurs Std. 2021 Nov 1;123:104058.
