Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis
Abstract
:1. Introduction
Research Gap
2. Literature Review
2.1. HR and Technology Background
2.2. Algorithmic HRM Usage
2.3. HR Strategic Decision-Making
2.4. Organizational Competitive Advantage
2.5. HR Digital Maturity
3. Conceptual Model
3.1. Theoretical Background
3.2. Hypothesis Development
3.2.1. Algorithmic HRM Usage and HR Strategic Decisions
3.2.2. Algorithmic HRM Usage and Competitive Advantage
3.2.3. HR Strategic Decision-Making and Competitive Advantage
3.2.4. HR Strategic Decision-Making and Its Mediation Role
3.2.5. HR Digital Maturity as a Moderator
4. Research Methodology
4.1. Sample Selection
4.2. Data Collection
4.3. Measurement Items
4.3.1. Phase 1. Algorithmic HRM Usage Scale Development
4.3.2. Item Generation
4.3.3. Expert Discussion
4.3.4. Item Refinement
4.3.5. Phase 2. Establishing the Relationship between Algorithmic HRM and Competitive Advantage
5. Results
5.1. Descriptive Analysis
5.2. Common Method Bias/Variance (CMB/CMV)
5.3. Measurement Model
5.3.1. Convergent Validity
5.3.2. Discriminant Validity
5.4. Structural Model
The Moderator Effect
6. Discussion
7. Implications, Limitations, and Conclusion
7.1. Theoretical Implications
7.2. Practical Implications
7.3. Limitations and Future Research Recommendations
7.4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix A.1. Profiles of the Experts Who Participated in the Discussion
Participant Number | Position | Gender | Years of Experience | Industry |
1 | Employee Relations Specialist | Female | 6 | FMCG Local |
2 | HRIS Manager | Female | 9 | Oil and Gas |
3 | Personnel Supervisor | Male | 5 | Consultation |
4 | HR Business Partner | Female | 12 | Healthcare |
5 | HR Operations Manager | Male | 10 | FMCG |
6 | Talent Management Manager | Male | 5 | Digital Banking |
7 | HR Operational Excellence Head | Male | 18 | Holding Group |
8 | Head of HR | Male | 16 | FMCG MNC |
9 | CHRO | Male | 19 | Hospitality—F&B |
Note: FMCG = Fast Moving Consuming Goods, MNC = Multinationals Corporation, F&B = Food and Beverage. |
Appendix A.2. Elements That Were Used to Assure the Participants Are Qualified for This Study
- HRIS/MS stand-alone system in the organization.
- Web-based employees and managers’ self-services being used
- Mobile application for employees and managers’ self-services being used and supporting multiple platforms such as Android, iOS, HarmonyOS, Windows Mobile, etc.
- Use of Advanced Artificial Intelligence, robotics, machine learning in all or some HR activities.
Appendix A.3. Measurement Scale Used and the Items
Construct | Item | Source |
Algorithmic HRM Usage (A) | A1. Algorithmic HRM usage will increasingly perform HR tasks. A2. HRM is able to cope with the requirements of algorithmic HRM usage. A3. HRM has an active and leading role in organizational algorithmic implementation. A4. Algorithmic HRM usage will interact and process big data from several sources that can not be handled manually. A5. Algorithmic HRM usage will reduce the dependability on HR professionals in the organization (removed). | Developed by the authors. |
HR Strategic Decision-Making (D) | D1. Decision outcomes relevant to HR strategic activities (such as recruitment, performance, forecasting required workforce, anticipating turnover, reading the engagement indicators, etc.) will be accurate using algorithmic HRM. D2. the time to arrive at decisions is fast when using algorithmic HRM. D3. The speed of arriving at decisions is high when using algorithmic HRM D4. Decision outcomes are often correct when using algorithmic HRM. D5. Decision outcomes are often precise when using algorithmic HRM. D6. Decision outcomes are often flawless when using algorithmic HRM. | Jarupathirun et al. [87] |
Competitive Advantage (C) | C1. The quality of the products or services that the company offers is better than that of the competitor’s products or services. C2. The company is more capable of applying algorithmic HRM than the competitors. C3. The company has better HR digital capability than the competitors. C4. The company’s profitability is better when using algorithmic HRM. C5. The corporate image of the company is better than that of the competitors when using algorithmic HRM. C6. The competitors find it difficult to take the place of the company’s competitive advantage by using algorithmic HRM. | Chang [88] |
HR Digital Maturity (M) | M1. In comparison with other firms in our industry, digital solutions in HR department are more developed. M2. In comparison with our competitors, digital transformation in HR department is substantially more advanced. M3. HR department is a leader in digital transformation within the sector. | Irimiás and Mitev [89] |
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Item | Source |
---|---|
There has been a consensus that the use of digital HRM and algorithmic HRM in firms is a reality. Many researchers endorse that algorithmic HRM usage will improve the accuracy and efficiency of the HR processes. | Rodgers et al. [49] |
Training and new learning of HR professionals is a necessity in the new scenario, and HR professionals will require training and upskilling to effectively utilize algorithmic HRM tools in their organization. | Chowdhury et al. [11] |
Algorithmic HRM usage will enhance the quality of decision-making process in HR-related activities. | Leicht-Deobald et al. [9] |
Algorithmic HRM and its usage will increasingly perform majority of HR tasks in firms. | Meijerink et al. [18] |
HRM function has to collaborate with its IT counterparts to integrate algorithmic HRM systems to make it more credible and cohesive. | Duggan et al. [42] |
Algorithmic HRM usage will enable predictive analytics for HR planning and forecasting workforce needs. | Huang et al. [91] |
Data privacy and security are also important issues while making algorithmic HRM decisions, and HR departments will ensure data privacy and security in algorithmic HRM implementation. | Langer & König [92] |
Most of the HRM function will be able to cope with the requirements of algorithmic HRM usage, and its usage will be increased with time. | Arslan et al. [93] |
Algorithmic HRM can also tackle people management and engagement issues, and its usage will facilitate personalized employee experiences and engagement in firms. | Malik et al. [94] |
HRM function has to play an active and leading role in the implementation of algorithmic HRM in firms. | Oswald et al. [95] |
It will be imperative for HR departments to monitor and evaluate the performance and impact of algorithmic HRM systems. | Rodgers et al. [49], Cheng & Hackett [17] |
Algorithmic HRM usage will require ongoing maintenance and updates to ensure its optimal functioning. | Duggan et al. [42] |
HR department has to look into the larger issues, as well, and evaluate the ethical implications and potential biases if any are associated with algorithmic HRM usage. | Köchling & Wehner [96] |
Algorithmic HRM usage will reinforce and support strategic workforce planning and talent management initiatives in firms. | Rodgers et al. [49] |
In algorithmic HRM usage, there is likelihood to interact and process big data from several sources that can not be handled manually. | Hamilton & Sodeman [97] |
HRM function will leverage machine learning algorithms to automate candidate screening and selection in their firms. | Garg et al. [57] |
Algorithmic HRM usage will enable HR professionals to focus on strategic initiatives and value-added tasks. | Nankervis et al. [98] |
HRM functionaries will collaborate with internal stakeholders to align algorithmic HRM practices with organizational goals. | Langer & König [92] |
Algorithmic HRM usage will reduce the dependability on the HR professionals in the organization. | Köchling & Wehner [96] |
HR professionals has to update their skill base, and to be more responsive and in times to come, the algorithmic HRM usage will ultimately enhance the HR agility and responsiveness to the changing business needs. | Chowdhury et al. [11] |
Code | Item |
---|---|
A1 | Algorithmic HRM will be increasingly used in performing the HR tasks. |
A2 | HRM function is able to cope with the requirements of algorithmic HRM and its usage. |
A3 | HRM function has an active and leading role in organizational algorithmic implementation. |
A4 | Algorithmic HRM and its usage will interact and process big data from several sources that can not be handled manually. |
A5 | Algorithmic HRM and its usage will reduce the dependability on the HR professionals in the organization. |
Code | Item | Loading |
---|---|---|
A1 | Algorithmic HRM usage will increasingly perform HR tasks. | 0.792 |
A2 | HRM is able to cope with the requirements of algorithmic HRM usage. | 0.715 |
A3 | HRM has an active and leading role in organizational algorithmic implementation. | 0.782 |
A4 | Algorithmic HRM usage will interact and process big data from several sources that can not be handled manually. | 0.796 |
A5 | Algorithmic HRM usage will reduce the dependability on the HR professionals in the organization. | 0.274 (removed) |
Construct Measured | Scale Used | Internal Consistency | AVE |
---|---|---|---|
Algorithmic HRM Usage (A) | Constructed during the exploratory phase. | α = 0.786 | 0.534 |
HR Digital Maturity (M) | Irimiás and Mitev (2020) [89] | α = 0.878 | 0.804 |
HR Strategic Decision (D) | Jarupathirun et al. (2007) [87] | α = 0.885 | 0.640 |
Competitive Advantage (C) | Chang (2011) [88] | α = 0.877 | 0.620 |
Frequency | % | Frequency | % | ||
---|---|---|---|---|---|
Region | Occupational Level | ||||
Saudi Arabia | 221 | 94% | Entry | 12 | 5% |
Other | 13 | 6% | Specialist/Supervisor | 66 | 30% |
Gender | Manager/Sr Manager | 77 | 35% | ||
Male | 142 | 61% | Director | 52 | 24% |
Female | 92 | 39% | Leadership | 14 | 6% |
Age (years) | HR Specialty | ||||
20–30 | 51 | 22% | Relations and Services | 92 | 39% |
31–40 | 127 | 54% | HRIS | 6 | 3% |
41–60 | 56 | 24% | Talent Acquisition | 47 | 20% |
Years of Experience (years) | T&D | 18 | 8% | ||
2–5 | 42 | 18% | Performance Management | 23 | 10% |
6–10 | 55 | 24% | Rewards/OD | 48 | 21% |
11–15 | 71 | 30% | Firm Size | ||
16–20 | 45 | 19% | <100 | 33 | 14% |
21+ | 21 | 9% | 100–500 | 55 | 24% |
Education Level | 501–1000 | 29 | 12% | ||
Diploma | 8 | 3% | 1001–5000 | 79 | 34% |
Bachelor | 112 | 48% | >5000+ | 38 | 16% |
Master | 99 | 42% | |||
Ph.D. | 15 | 6% |
R2 Without Marker Variable | R2 With Marker Variable | |
---|---|---|
Competitive Advantage (C) | 0.450 | 0.450 |
HR Strategic Decision-Making (D) | 0.448 | 0.448 |
Construct | Item | Loading | VIF | CA | rho_A | CR | AVE |
---|---|---|---|---|---|---|---|
Algorithmic HRM Usage (A) | 0.777 | 0.786 | 0.850 | 0.534 | |||
A1 | 0.745 | 1.699 | |||||
A2 | 0.627 | 1.580 | |||||
A3 | 0.664 | 1.751 | |||||
A4 | 0.748 | 1.232 | |||||
Competitive Advantage (C) | 0.877 | 0.884 | 0.907 | 0.620 | |||
C1 | 0.705 | 1.698 | |||||
C2 | 0.736 | 2.656 | |||||
C3 | 0.678 | 2.062 | |||||
C4 | 0.894 | 2.144 | |||||
C5 | 0.738 | 2.018 | |||||
C6 | 0.654 | 1.680 | |||||
HR Digital Maturity (M) | 0.879 | 0.886 | 0.925 | 0.804 | |||
M1 | 0.735 | 2.852 | |||||
M2 | 0.897 | 2.995 | |||||
M3 | 0.881 | 1.990 | |||||
HR Strategic Decision-Making (D) | 0.887 | 0.893 | 0.914 | 0.640 | |||
D1 | 0.667 | 1.931 | |||||
D2 | 0.794 | 2.129 | |||||
D3 | 0.876 | 2.696 | |||||
D4 | 0.750 | 2.803 | |||||
D5 | 0.769 | 2.506 | |||||
D6 | 0.644 | 1.696 |
Algorithmic HRM (A) | Competitive Advantage (C) | HR Digital Maturity (M) | HR Strategic Decision-Making (D) | |
---|---|---|---|---|
Algorithmic HRM (A) | 0.730 | 0.723 | 0.563 | 0.783 |
Competitive Advantage (C) | 0.601 | 0.788 | 0.766 | 0.693 |
HR Digital Maturity (M) | 0.463 | 0.673 | 0.897 | 0.455 |
HR Strategic Decision-Making (D) | 0.657 | 0.620 | 0.407 | 0.800 |
Algorithmic HRM (A) | Competitive Advantage (C) | HR Digital Maturity (M) | HR Strategic Decision-Making (D) | |
---|---|---|---|---|
A1 | 0.791 | 0.443 | 0.378 | 0.560 |
A2 | 0.724 | 0.465 | 0.442 | 0.384 |
A3 | 0.766 | 0.428 | 0.231 | 0.468 |
A4 | 0.579 | 0.356 | 0.299 | 0.452 |
C1 | 0.461 | 0.738 | 0.425 | 0.458 |
C2 | 0.498 | 0.847 | 0.643 | 0.452 |
C3 | 0.467 | 0.776 | 0.530 | 0.410 |
C4 | 0.543 | 0.830 | 0.539 | 0.612 |
C5 | 0.488 | 0.800 | 0.531 | 0.493 |
C6 | 0.362 | 0.726 | 0.515 | 0.474 |
D1 | 0.502 | 0.414 | 0.182 | 0.773 |
D2 | 0.568 | 0.508 | 0.332 | 0.850 |
D3 | 0.634 | 0.558 | 0.350 | 0.808 |
D4 | 0.512 | 0.484 | 0.404 | 0.844 |
D5 | 0.504 | 0.530 | 0.308 | 0.816 |
D6 | 0.400 | 0.461 | 0.362 | 0.698 |
M1 | 0.414 | 0.576 | 0.896 | 0.318 |
M2 | 0.423 | 0.612 | 0.920 | 0.387 |
M3 | 0.409 | 0.620 | 0.874 | 0.381 |
Competitive Advantage (C) | HR Strategic Decision-Making (D) | |
---|---|---|
Algorithmic HRM (A) | 1.759 | 1.324 |
HR Strategic Decision-Making (D) | 1.759 | |
HR Digital Maturity (M) | 1.293 |
Hypothesis | Relationship | Std Beta | Std Error | f2 | |t-Value| | p-Value | Decision |
---|---|---|---|---|---|---|---|
H1 | A → C | 0.400 | 0.104 | 0.165 | 3.824 | 0.000 *** | Supported |
H2 | A → D | 0.662 | 0.083 | 0.690 | 7.978 | 0.000 *** | Supported |
H3 | D → C | 0.403 | 0.107 | 0.168 | 3.760 | 0.000 *** | Supported |
H4 | A → D → C | 0.267 | 0.068 | 0.027 | 3.917 | 0.000 *** | Supported |
H5 | Moderation | −0.104 | 0.070 | 0.025 | 1.495 | 0.065 | Rejected |
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Alabdali, M.A.; Khan, S.A.; Yaqub, M.Z.; Alshahrani, M.A. Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis. Sustainability 2024, 16, 4854. https://doi.org/10.3390/su16114854
Alabdali MA, Khan SA, Yaqub MZ, Alshahrani MA. Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis. Sustainability. 2024; 16(11):4854. https://doi.org/10.3390/su16114854
Chicago/Turabian StyleAlabdali, Mahmoud Abdulhadi, Sami A. Khan, Muhammad Zafar Yaqub, and Mohammed Awad Alshahrani. 2024. "Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis" Sustainability 16, no. 11: 4854. https://doi.org/10.3390/su16114854
APA StyleAlabdali, M. A., Khan, S. A., Yaqub, M. Z., & Alshahrani, M. A. (2024). Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis. Sustainability, 16(11), 4854. https://doi.org/10.3390/su16114854