Implementation of Business Intelligence and Analytics in Human Resource Management as a Core Competency for Sustainability †
1
Roza Academy, Maltepe, 34846 Istanbul, Türkiye
2
College of Business, Australian University-Kuwait, West Mishref, Safat 13015, Kuwait
*
Author to whom correspondence should be addressed.
†
Presented at the International Scientific Conference on Digitalization, Innovations & Sustainable Development: Trends and Business Perspectives, West Mishref, Kuwait, 29 November & 14 December 2023.
Proceedings 2024, 101(1), 22; https://doi.org/10.3390/proceedings2024101022
Published: 12 June 2024
(This article belongs to the Proceedings of International Scientific Conference on Digitalization, Innovations & Sustainable Development: Trends and Business Perspectives 2023)
1. Objectives
Business intelligence and analytics have been important and very critical tools for organizations along with a significant increase in the usage of data in almost all HR functions [1,2,3]. As seven of the UN sustainability goals can be related to the successful management of human resources, business analytics has proven to be a crucial tool for sustainability in all organizations. This study aims to summarize what data analytics is and how it can be utilized in HR to support sustainability goals [4,5].
2. Methodology
We followed a qualitative research methodology for this research. First, we summarized the theories, different models, and processes of business intelligence and data analytics in HR. We also carried out a literature review to find out how HR analytics can contribute to sustainability goals in the organizations. By providing examples, including the COVID-19 pandemic, we intend to present how this tool can be utilized for promoting sustainability and how it can be crucial for managers in the decision-making process.
3. Results
In the systematic review, we provide a detailed definition of business intelligence and data analytics in HR and their differences from pure statistics. We also explained different phases of HR analytics step by step by providing detailed examples and explanations. Different analytics models are also introduced. Examples from different cases and industries showed that HR analytics can enable managers to plan proactively and significantly contribute to sustainability goals.
4. Implications
Technologies, such as databases, artificial intelligence, big data, cloud computing, machine learning, and mobile technologies and devices, have offered organizations new tools and ways to collect, store, assess, and analyze employee-related data to establish a reliable basis for decision-making. HR Analytics, by using these data and processing them through several phases of data analytics, can provide vital information for HR managers to take proactive actions that contribute to sustainability.
5. Originality Value
Our study is one of the pioneer studies that systematically explore and summarize the usage of HR analytics for reaching sustainability goals. HR managers and decision makers cannot ignore the data science perspective anymore. The increasing usage of data and artificial intelligence in almost every field has provided managers with smart tools to diagnose the situation and predict the future status [6,7,8]. For that purpose, our study provides significantly valuable information to better understand the current trends and techniques.
6. Contribution
In almost every industry, business intelligence and data analytics have been used to improve all types of functions [6,7]. HRM is a field dealing with all HR functions, as well as human attitudes and behaviors [8,9,10], which are difficult to measure and analyze. Furthermore, ethical considerations must also be taken into consideration in HR activities, such as diversity and well-being. Hence, our study introduced HR analytics, a crucial methodology and tool, that might shed light for managers and decision makers by helping them significantly contribute to sustainability.
Author Contributions
Conceptualization, all authors; methodology, A.E. and M.S.; software, R.A. and A.E.; validation, M.S. and F.A.; formal analysis, A.E. and M.S.; investigation, R.A. and F.A.; resources, R.A., A.E. and M.S.; data curation, A.E. and M.S.; writing—original draft preparation, all authors; writing—review and editing, all authors; visualization, A.E.; supervision, R.A. and A.E.; project administration, A.E.; funding acquisition, M.S. and F.A. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Ethical review and approval were waived for this study due to the fact that this study does not involve any medical application and thus does not pose any kind of threat to the health or well-being of the participants.
Informed Consent Statement
Informed consent will be obtained from all participants involved in the interviews and study.
Data Availability Statement
The study is at the initial phase, thus there is no data to be shared. Moreover, qualitative data that will be obtained from the interviews in the following phases will be kept confidential and cannot be shared without the written consent of the participants due to the privacy reasons.
Conflicts of Interest
The authors declare no conflict of interest.
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MDPI and ACS Style
Alkhayyat, R.; Erturk, A.; Safi, M.; Abdallah, F. Implementation of Business Intelligence and Analytics in Human Resource Management as a Core Competency for Sustainability. Proceedings 2024, 101, 22. https://doi.org/10.3390/proceedings2024101022
AMA Style
Alkhayyat R, Erturk A, Safi M, Abdallah F. Implementation of Business Intelligence and Analytics in Human Resource Management as a Core Competency for Sustainability. Proceedings. 2024; 101(1):22. https://doi.org/10.3390/proceedings2024101022
Chicago/Turabian StyleAlkhayyat, Razan, Alper Erturk, Mirna Safi, and Farid Abdallah. 2024. "Implementation of Business Intelligence and Analytics in Human Resource Management as a Core Competency for Sustainability" Proceedings 101, no. 1: 22. https://doi.org/10.3390/proceedings2024101022
