Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications

Promise versus reality: a systematic review of the ongoing debates in people analytics

“…Additionally, the lack of organization and centralized storage location for data has also lead to several issues, including data duplication, incorrect and inaccurate entries and missing data (King, 2016; Boudreau and Cascio, 2017; Levenson and Fink, 2017; Minbaeva, 2018; Shet et al , 2021). According to Andersen (2017), this lack of data quality in HR can be attributed to the lack of a coherent data strategy, not understanding the strategic importance of data, poor data management and a lack of critical data sources.…”

“…For example, HRIS providers are offering advanced modules featuring BI and data analytics capabilities to enable HR professionals to form predictions and to make more informed data-driven decisions through the use of online analytical processing (OLAP), data mining techniques, perform advanced statistical analysis and the development of analytical models for forecasting and engaging in predictive analytics (Kapoor and Sherif, 2012). Similarly, those at the highest level of people analytics maturity are also modeling their workforce data using AI algorithms in open-source statistical platforms, such as R and Python, to make predictions about their workforce (Gelbard et al , 2018; Margherita, 2020; Shet et al , 2021; Tursunbayeva et al , 2021). Overall, given the advancements in people analytics technology, we suggest that current HRIS capabilities coupled with the flexibility offered by open-source statistical platforms have improved the deficit faced by HR technology platforms of the past.…”

“…These new forms of collecting employee data presents HR professionals and organizations with an emerging challenge concerning how to ensure the privacy and security of employee data while also navigating how data are being used and analyzed ethically (Leonardi and Contractor, 2019; Falletta and Combs, 2020; Jeske and Calvard, 2020; Chatterjee et al , 2021; Shet et al , 2021).…”

“…To alleviate these concerns and standardize how employee data are used, countries have begun to implement regulations, such as the General Data Protection Regulation (GDPR) in Europe, to help mitigate the misuse of employee personal information. Furthermore, organizations have also started appointing roles such as Chief Data Officers and data governance committees responsible for the overall governance and implementation of ethical standards concerning organizational-wide data (Nie et al , 2019; Janssen et al , 2020; Shet et al , 2021). These efforts are directly related to ensuring employees are comfortable with how their data are being tracked and used for making decisions.…”

Construction and Empirical Study of Show Analysis Model of Enterprise Mankind Resource Management Based on Deep Learning

“…Each key indicator needs to give several quantifiable indicators [25]. Shet et al and Liu used the principal component analysis method to analyze many factors in the employee show evaluation system in the article "Principal Component Analysis of Employee show Evaluation System" and simplified many factors affecting employee show into a few comprehensive components, that is, using principal component analysis to determine the key indicators of show evaluation [26,27]. On the basis of the above-mentioned related research, this paper determines the positive role of DL in the field of enterprise mankind resource management show analysis, constructs a show analysis model combining various algorithms, makes deep analysis and research on the acquired and collected data by using DL and algorithms, makes more effective use of the data, and mines valuable information hidden behind the data, so as to simplify and make the management more efficient.…”