Open AccessArticle

Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis

Mahmoud Abdulhadi Alabdali

^1,*

Sami A. Khan

Muhammad Zafar Yaqub

and

Mohammed Awad Alshahrani

Business Administration Department, Faculty of Economics and Administration, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Department of Human Resource Management, Faculty of Economics and Administration, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Author to whom correspondence should be addressed.

Sustainability 2024, 16(11), 4854; https://doi.org/10.3390/su16114854

Submission received: 13 April 2024 / Revised: 21 May 2024 / Accepted: 3 June 2024 / Published: 6 June 2024

(This article belongs to the Section Sustainable Management)

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Abstract

This study examines the role of using algorithmic human resource management (HRM) to make strategic decisions concerning firms’ human resource (HR) activities. This study develops a scale to measure algorithmic HRM usage in its first phase. In the second phase, it is found that algorithmic HRM usage significantly impacts strategic HR decision-making, which helps and enables firms to create a competitive advantage. Utilizing the authors’ LinkedIn profiles, 234 participants were included in the fieldwork. Collected data were analyzed by applying partial least squares structure equation modeling (PLS-SEM). The mediating roles of HR strategic decision-making and HR digital maturity as moderators in enabling the impact of algorithmic HRM on the firm’s competitive advantage have been corroborated. This study finds a strong relationship between algorithmic HRM usage and competitive advantage, a significant relationship between algorithmic HRM usage and strategic HR decision-making, and a significant relationship between strategic HR decision-making and competitive advantage. The moderating role of HR digital maturity was insignificant in this research, paving the way for future research. This research, the model, and its findings contribute to the theory and implicate the practicality of algorithmic HRM. It is one of few papers addressing algorithmic HRM usage in a transitioning economy like Saudi Arabia.

Keywords:

algorithmic HRM; algorithmic HRM usage; HR strategy; digital HRM; e-HRM; competitive advantage; HR digital maturity; resource-based view; dynamic capability; scale development; EFA; PLS-SEM

1. Introduction

In the present-day business environment, the success of any company relies heavily on having a business strategy that is both flexible and adaptable. This is particularly important in a post-COVID business environment where digital strategy and digital transformation have become paramount. Several studies [1,2,3,4,5] reinforce this view. Although certain techniques may seem compelling initially, their actual implementation often characterizes impediments and deficiencies, underscoring the importance of developing both the strategy and its execution simultaneously [6]. Moreover, successfully implementing a company’s strategy dramatically depends on reliable and exact data [7,8]. Our study examines the crucial role of algorithmic human resource management (algorithmic HRM) in improving strategic HR decision-making. This allows companies to gain a lasting competitive edge by executing data-driven strategies with great attention to detail.

Making decisions effectively depends on comprehensive knowledge, understanding the bigger picture, and connecting choices to the overarching strategic goal [9]. To optimize decision-making, firms require human and machine capabilities facilitated by technological advancements and digitalization [10,11]. Making informed and sound choices is widely acknowledged to provide a competitive advantage to a company over its rivals, whether in the short or long term [12,13].

To effectively implement strategic HR decision-making, it is essential to have access to pertinent and precise data, comprehend the broader operational context, and evaluate specific details that are in line with the firm’s strategic objectives [9,14]. Integrating advanced digital technologies in human resource management, specifically through algorithmic HRM, improves the clarity and speed of decision-making processes [5]. This, in turn, enhances a firm’s ability to maintain a competitive advantage over its competitors [12,13,15]. The adaptability of company strategies, particularly those that are digitally enabled, is crucial in a dynamically competitive market environment [1]. Therefore, it is essential for any company that wants to use algorithmic HRM to gain a competitive advantage and establish and implement a digital strategy that is in line with the latest advancements in digital technology [2,3,5,15]. This alignment is crucial for improving the strategic decision-making abilities that lead to a competitive edge in today’s corporate environment.

In the current business landscape, firms must leverage modern technologies [16] while managing their human resources effectively. The field of human resource management (HRM), often referred to as digital HRM, is undergoing digital transformation with the integration of knowledge and information from emerging technologies such as artificial intelligence (AI), blockchain technology (BCT), augmented reality (AR), big data (BD), machine learning (ML), and the Internet of Things (IoT) [17,18]. These technologies enable firms to decipher insights that may not be achievable manually within a reasonable time frame and with reduced efforts while maintaining high accuracy [19,20]. The discourse on HRM has undergone a rapid transformation with the emergence of technology, progressing from HR Information Systems (HRIS) to e-HRM [21], digital HRM [18,22], and, more recently, algorithmic HRM [23]. Algorithmic HRM refers to the formulation of rules and algorithms within computer systems to collect, analyze, and process big data from various sources, generating insights for HRM decision-making [18,19].

This approach facilitates HRM decisions and actions related to recruitment, pay increases, potential turnover, workforce planning, and the alignment of people management strategies with business strategies, reward systems, employee engagement, and satisfaction [17,18,24,25]. Advocates argue that applying algorithmic HRM enables firms to make more informed decisions directly impacting their business strategies [13,18,26]. An algorithm can learn, understand, and apply historical data; it learns from the errors or mistakes and builds a sustainable knowledge center, keeping it modified and updated with the help of big data from several sources [2,27] and then processing it in a way that decisions and intelligence insights are fused to include but are not limited to AI and ML [26].

Algorithmic HRM plays a significant role in various HR functions, including forecasting performance appraisal results, conducting interviews, hiring, and recommending suitable candidates [18,28,29]. Moreover, it aids in managing turnover by gathering data from multiple sources, such as voice recognition, people behavior, and customer services, achieving unprecedented levels of accuracy in forecasting.

The implementation of algorithmic HRM can eliminate biases [24] and promote justice and fairness in organizational practices [19]. Scholars emphasize the significance of algorithmic HRM in making accurate and unbiased judgments, reducing biases, and enhancing reliance on systematic control [13,30,31].

Research Gap

Limited research has focused on the comprehensive impact and scope of algorithmic HRM, highlighting the urgency for further investigation into its efficacy, its role in HR strategic decision-making, and its contribution to the organizational competitive advantage [9,17,18]. Adopting algorithmic HRM and its conceptualization heavily relies on the organizational context and the stakeholder readiness. Moreover, the organization’s HR competency and maturity are critical factors in leveraging technological input to achieve algorithmic HRM objectives.

The maturity, capabilities, and readiness of HR professionals play a critical role in the effective execution of algorithmic HRM within a firm [32]. Failing to consider ethical and trust-related concerns during algorithmic HRM adoption can lead to potential harm and derailment of the integration process [25]. The current study explores algorithmic HRM dynamics and its impact on leveraging the firm’s competitive advantage for organizational success. It seeks to answer the following research questions:

RQ1.

To what extent does the implementation of algorithmic HRM contribute to the strategic HR decision-making capability and its impact on a firm’s competitive advantage?

RQ2.

Is the maturity of HR competence a critical factor in the realization of HR decision-making and in developing a competitive advantage?

The research aims to explore the value addition of algorithmic HRM in enhancing the strategic HR decision-making process, its influence on the firm’s competitive advantage, and the significance of the maturity of HR competence in this process.

2. Literature Review

The present research endeavors to undertake a literature review to examine the function of algorithmic HRM in augmenting competitive advantage. It will explore the theoretical foundations of HRM and its effects on different HR decisions, activities, and transactions. It also examines the potential constraints and difficulties linked to adopting HR digital maturity (see Figure 1).

2.1. HR and Technology Background

The HRM landscape has undergone significant transformation due to technological advancements, encompassing various technologies such as IT, cloud computing, and AI. E-HRM, or electronic human resource management, is a widely adopted interface that facilitates interactions and transactions between HR activities, from pre-onboarding to post-offboarding, and technologies. Its purpose is to create, (re)configure, deliver, and enhance value-added services for employees, managers, HR practitioners, and other stakeholders [33]. The implementation and utilization of e-HRM are often the result of managerial decision-making and strategic planning for HRM service delivery in many firms. While the outcomes are not primarily aimed at strengthening the strategic aspect of HR jobs, they are intended to enhance the efficiency and effectiveness of HRM services provided [34].

The term e-HRM refers to the digitization of HR functions that were previously conducted manually. The rapid evolution of information technology (IT) has played a crucial role as a facilitator in achieving the desired automation of human resource management functions, leading to increased efficiency and productivity in firms [17,34]. This technological shift has compelled HR activities to become increasingly digital, prompting new thinking and a change in attitude to leverage cutting-edge technology for HR deliverables. As a critical component of digitalization, automation focuses on analysis, innovation, and invention rather than solely delivering services. Its successful implementation requires the involvement of HR professionals to develop solutions, optimize processes, and appropriately streamline operations, aligned and coherent with organizational goals [18,35].

Over time, various technological advancements, such as enterprise resource planning (ERP), Human Resource Information System/Management System (HRIS/MS), e-HR, digital HR, and HR analytics, have brought significant value to the HR architecture [36]. However, technology experts are still pondering how to ensure emerging technologies truly enhance HR management and provide business value [7]. Additionally, there needs to be more clarity regarding the distinctions between e-HRM, digital HR, and algorithmic HRM within the emerging technology landscape. Many HR professionals grapple with the effectiveness and comprehensive understanding of algorithmic HRM, highlighting the need for further exploration.

2.2. Algorithmic HRM Usage

Algorithmic HRM refers to intelligent rules and formulas [17] that utilize a vast amount of data [18,24], information, and knowledge from diverse sources and technologies [24], including augmented reality (AR), machine learning (ML), artificial intelligence (AI), blockchain (BC), big data (BD), and the Internet of Things (IoT) [37]. It aims to learn, analyze, and make HR-related decisions with minimal human intervention [18,38]. This concept involves embracing technology and adopting a digital mindset to facilitate change management, build capabilities, and create readiness for technological integration [39,40,41]. Algorithmic HRM encompasses elements of both digital and electronic HRM. Still, it stands out as it can learn autonomously and make decisions independently without direct inputs from individuals (see Figure 2).

Applying and using algorithmic HRM helps to make the right decisions that directly impact the business strategy [13,18,26]. A strategy executed with appropriate and accurate decisions helps the firm create an advanced, sustainable competitive advantage [14].

Algorithmic HRM can learn, understand, and apply without human intervention. It builds on the history data, learns from the errors or mistakes, forecasts, and builds on the sustainable knowledge center that keeps modified, updated big data and information from various sources [2,27] and processes them in terms of decisions and intelligence insights that are not limited to AI and ML [26,42]. Algorithmic HRM has revolutionized forecasting accuracy, enabling precise predictions in various HR activities [29,43].

With its implementation, firms can forecast performance appraisal results, interview outcomes, and future hiring needs and identify suitable job applicants [28]. At the same time, algorithms have historically been used in various fields like mathematics, science, and programming languages [44], where their application to business and management has significantly transformed decision-making processes [45]. Firms can now access big data to analyze the benefits of services, products, and customer behavior [46]. Algorithmic HRM processes data using systems without direct human intervention to make decisions relevant to HR activities [18]. Integrating humans and AI in algorithmic HRM can lead to mutual benefits, with increased data access and collaboration between humans and AI resulting in improved performance. However, this integration poses challenges from the human capital management perspective [42]. Implementing algorithmic HRM has resulted in significant change and transformation, prompting HR professionals, regulators, firms, and researchers to address the impact of eliminating emotions in HR processes [47].

2.3. HR Strategic Decision-Making

The increasing recognition of big data’s potential benefits for businesses has led to the development of algorithms to leverage these data for decision-making [9]. Algorithmically driven decision-making tools enable the consolidation of data from various sources, proving to be more effective in certain contexts compared to human decision-making [19,48,49], such as in selecting high-performing job candidates [50].

Both lines of research are relevant to top executives’ decisions, as fully realized AI-based decision-making systems are likely to incorporate elements from big data analysis and algorithmic decision-making. In the past, it has been suggested that de-individualizing the source of management decision-making could moderate intra-organizational conflicts when the consequences of such decisions are unfavorable to some firm members [13]. In the HR IT industry, firms like Oracle, IBM, and SAP are leaders that offer integrated talent management software packages capable of extracting information from diverse sources [19,51].

As digitalization advances, AI-based decision systems in the workplace will likely assign less weight to subjective and non-computable criteria, instead focusing on objective and computable factors, such as quantitative targets and qualitative values. These systems can autonomously “learn and evolve” beyond the initial stage of human instruction [9,52].

2.4. Organizational Competitive Advantage

The concept of dynamic capabilities and their potential for sustained competitive advantage in firms has been a subject of academic debate. An integrative approach has emerged to reconcile conflicting perspectives on this matter. HR activities have started to leverage AI platforms for automation, such as AI-based recruitment processes [28], and algorithms are being utilized to facilitate remote work [53]. The future of work is expected to be closely associated with AI, with decisions being supported by AI systems [12,19,26].

Algorithmic HRM helps optimize and lead remote teams [9]. The formulae and rules used in algorithmic HRM are considered a competitive advantage and kept secure [17,54]. The development of algorithmic HRM suggests that not only can HRM operational activities and transactions be improved through digital technology, but they can also be delegated to digital platforms, robots, or computers that learn and improve autonomously through machine learning, artificial intelligence, the Internet of Things, and more [17,42].

2.5. HR Digital Maturity

The ability of firms to adapt quickly has been hindered by constraints on HR’s ability to act on data [55]. Apart from technical and data-related obstacles, the main barrier to analytics’ maturity in some firms is the management’s incapacity to act on the data and analysis provided by HR [11]. The maturity level of HR professionals and their maturity level in digital capabilities and readiness are critical factors in effectively executing algorithmic HRM [32].

Algorithmic HRM can potentially harm workers, particularly in the gig economy [56]. Addressing the concerns of fairness and ethics in designing algorithms that align with HR decision-making values is crucial [25]. Research has found a correlation between the level of HR digital maturity and the number of applications and tools it uses, the number of processes it has integrated, and the number of goals connected to AI [26,30].

The potential of digital and algorithmic HRM is evident in the transformative impact that machine learning can have on various aspects of human resource management, including recruitment, selection, performance management, training and development, and employee engagement [57]. By integrating machine learning algorithms into digital HRM, decision-making processes can be enhanced, resulting in improved efficiency and effectiveness of HR operations [26,58]. Machine learning algorithms can also provide valuable insights into employee performance and engagement, empowering HR professionals to make data-driven decisions that enhance employee satisfaction and retention. This emphasizes the growing importance of digital and algorithmic HRM in modern work environments [56].

3. Conceptual Model

3.1. Theoretical Background

This study focuses on the impact of using algorithmic HRM to make strategic decisions in HR activities and suggests that the resource-based theory (RBV) provides a strong theoretical foundation for this analysis. The resource-based view proposes that firms can gain a competitive advantage by effectively leveraging their intangible resources [59]. Algorithmic HRM is considered an intangible resource for firms, and the design of algorithms itself can be a key factor in achieving a competitive advantage [11,19].

RBV and dynamic capabilities (DC) theories are credited with understanding the sources of competitive advantage by examining their direct and indirect effects on a firm’s performance [60]. In the RBV framework, “resources” refer to tangible or intangible assets owned or acquired by an organization, while “capabilities” refer to the ability to use these resources to carry out tasks [61,62].

Technologically based resources and capabilities, such as exceptional access to specialized data, information, and knowledge, significantly drive algorithmic HRM’s effectiveness [11,27]. This leads to superior decision-making without significant human interference and helps sustain a competitive advantage among firms [14,63,64]. Therefore, firms possessing valuable, rare, inimitable, and non-substitutable resources are better positioned to implement value-creating strategies that are difficult for competitors to replicate [59,65].

The dynamic capabilities (DC) theory highlights the necessity for firms to possess the ability to develop and acquire the essential skills, knowledge, and capabilities to effectively respond to a rapidly changing environment [8]. DC theory explores the relationships among organizational resources and emphasizes the importance of adapting internal resources and capabilities to external environmental changes. This adaptation is achieved by integrating new technologies, which necessitates dynamic capabilities [11,66].

Creating a machine learning application requires diverse skills in data collection, algorithm creation, and overseeing the training process. To succeed in this rapidly evolving field, firms must engage diverse, talented individuals to build organizational expertise in ML and AI [67,68,69]. This study extends and integrates the resource-based view (RBV) and dynamic capabilities (DC) theories to explore the internal relationships between decision-making, internal resources, and competitive advantage. It also considers the implications of algorithmic HRM and HR strategic decision-making on the competitive advantage through the organization’s mature HR capabilities, examining advanced technologies like ML, AI, BD, BCT, IoT, and algorithms [49,67,68,69]. The proposed model contributes to theoretical implications by connecting algorithmic HRM with strategic decisions in HR activities, considering its impact on the organizational competitive advantage (see Figure 3).

3.2. Hypothesis Development

3.2.1. Algorithmic HRM Usage and HR Strategic Decisions

Using algorithms in human resource management (HRM) is increasingly significant for making informed decisions. Algorithms are considered key decision-making elements in various firms, businesses, and regulations. Regression-based forecasting approaches operated by predictive algorithms enable managers to predict employee attrition and estimate the future performance of job candidates [9,17].

Machine learning and data mining are techniques used in predictive HRM to identify patterns in data that may not be apparent to humans. The integration of algorithmic approaches allows for automated and augmented decision-making processes in HRM [18,35,70]. These algorithmic HRM advancements can impact HR decision-making and organizational outcomes significantly. Based on this premise, the first hypothesis is as follows:

H1.

Algorithmic HRM usage positively impacts the HR strategic decision-making process.

3.2.2. Algorithmic HRM Usage and Competitive Advantage

Decisions made by a computer based on a set of rules or goals are said to be algorithmic [64]. Part of the digital transformation adds value to the competitive advantage [39], using algorithmic HRM as part of the transformation agenda [4]. Besides that, we are examining the assumptions underlying algorithmic decision-making to obtain a better sense of what it includes and how it can affect enterprises [32]. This study analyzes and interprets how the machine represents the pinnacle of algorithmic decision-making and contributes to leveraging the firm’s competitive advantage with an integrated algorithmic HRM arrangement [21,42].

Using algorithmic HRM has gained significant traction as a prevalent method for administering human resources within firms [53]. Algorithmic HRM leverages sophisticated algorithms, AI, and machine learning methodologies [12,19,26] to furnish HR practitioners with valuable insights and recommendations for making informed decisions about recruitment [28], selection, performance management, working remotely, and employee engagement [9]. Algorithmic HRM is commonly perceived as a means for firms to gain a competitive edge through its implementation and efficient utilization [53]. Nevertheless, the assertion lacks empirical substantiation.

To verify this supposition, it is imperative to conduct a study that scrutinizes the correlation between the utilization of algorithmic HRM and the attainment of competitive advantage [4], employing suitable data-gathering and analysis techniques. The findings of this study hold significant implications for human resource practitioners and organizational executives, as they can inform their strategic choices regarding the integration and execution of algorithmic HRM to achieve a competitive edge [49]. Thus, it is possible to formulate the research hypothesis in the following manner:

H2.

Algorithmic HRM usage contributes positively to the organizational competitive advantage.

3.2.3. HR Strategic Decision-Making and Competitive Advantage

Human resources must start using evidence to back up their decisions instead of depending on hunches or speculation [9]. The HR function is hampered by the fact that most HR practitioners come from the social sciences and need more arithmetical and analytical abilities [47]. Rodgers et al. [49] argue that measuring the human elements of a business is crucial for increasing employee productivity. Businesses now need to realize that gaining a competitive edge and becoming strategic partners requires them to employ predictive modeling and HR analytics [68].

Therefore, it is important to retrain workers to have the necessary analytical skills. A firm of any size can benefit greatly from using analytical tools because of the high direct value they produce [71]. In the realm of human resources, the automation of decision-making and execution in areas like selection is most popular with online labor platforms like Uber, Upwork, and Deliveroo [18].

A common assumption is that algorithm-based propositions will be impartial since they will not be influenced by stereotyping or cultural bias [72]. U.S. technologists share the viewpoint and rank human reasoning abilities below those of ever-evolving computers. As a result, the more nuanced the choice, the more appealing it is to trust the efficacy of an algorithm. After all, citing the firm’s highly complex and expensive algorithm-based HR decision-making tool for use in such situations is a great way to reassure skeptical coworkers, higher management, or stockholders that the risky choice is well-reasoned. Algorithmic decision-making can impose a monolatry and automation bias on humans, although some may accept this to avoid responsibility for their mistakes [9].

H3.

HR strategic decision-making positively contributes to creating the organizational competitive advantage.

3.2.4. HR Strategic Decision-Making and Its Mediation Role

While the relationship between HRM and sustainable competitive advantage (SCA) is extensively documented in the literature, the nature of the relationship is inconsistent [73]. Moreover, there is limited research focusing on the connection between HR strategic decisions and SCA [32]. HR strategies are essential for the success of firms since they shape the relationship between HRM and organizational competitive advantage [74]. HR strategies assist the firm in surviving in the market by recruiting, training, motivating, and retaining the workforce. In the modern business setting, using modern technological tools is the key.

Algorithmic HRM is used to perform repetitive HR tasks, create predictive models, and assist managers in making informed decisions. Since strategic decision-making requires new thinking [75], which is made more accessible by algorithmic outputs, it helps firms become more competitive. HR strategies are pivotal in implementing an effective algorithmic HRM and organizational competitive advantage. Furthermore, algorithmic HRM supports HRM decision-making by providing information and automation, and algorithms develop outputs that help human decision-makers make informed decisions [18]. Moreover, HRM algorithms can augment human decision-making by predicting how a strategic decision could impact future outcomes [49]. Therefore, the above discussions lead to the proposed hypothesis as follows:

H4.

HR strategic decision-making mediates the relationship between algorithmic HRM usage and competitive advantage.

3.2.5. HR Digital Maturity as a Moderator

An organization’s HRM competence level can be measured by looking at how well it manages and cultivates its employees. Developing effective HRM digital capabilities and its digital maturity can be seen as an internal business process [76,77]. The level of HRM digital maturity can serve as either a boon or a bane to the process of implementing digital HR [32] and algorithmic HRM. HRM digital capability in the HRM architecture serves as the foundation for the HRM digitalization practices in the firm [78,79]. Human resource management system (HRMS) maturity is also necessary in that regard, and it refers to the degree to which an organization’s HRM procedures and systems are integrated and evolving [80,81].

Human resource management (HRM) capability and its digital capacity maturity influence digital HRM activities [32,61], both in terms of how effective they are within the firm and with personnel. It is proposed that digital HRM practices facilitate the provision of efficient services that better suit the needs of line managers when HRM digital maturity is high [32]. Based on this connotation, our proposed hypothesis is as follows:

H5.

HR digital maturity moderates the relationship between algorithmic HRM use and HR strategic decision-making.

4. Research Methodology

Given that the research is quantitative and positivism is the guiding philosophy, the methodology applied was deductive. This meant that the provisional idea was developed in line with the theoretical basis where RBV and DC theories were applied utilizing current literature and then coming up with propositions that could be tested and controlled. This research uses a survey as its technique is tied to the deductive approach, which highly serves the present management research [82]. A questionnaire method is used for the data collection following the survey approach, and respondents were supposed to answer questions such as “what”, “who”, “where”, “why”, “when”, and “how”. Cross-sectional studies, which generally match the survey approach and examine the phenomena in particular periods, often in less than six months, as described in the timeline portion of this paper, have been selected as the time horizon for this study [83]. Thus, the data were collected between December 2022 and February 2023.

4.1. Sample Selection

In this study, the participants were selected using the convenience sampling method, which forms the most basic approach to data collection [84]. Brief information about algorithmic HRM was included in web-administrated questionnaires to give a basic idea about the proposed research and their suitability to know at least what they have already experienced within their firms using HR digital/technology and capture their perceptions about the usage of algorithmic HRM (see Appendix A.2).

4.2. Data Collection

Taking advantage of one of the authors who has been an HR practitioner for more than 15 years, has a vast HR network, and using his LinkedIn profile, around 30,000 network active members were utilized to reach the desired HR constituency and achieve better efficacy of the sample selection for the desired research. In the first part of the research, a sample size of 85 was reached, and 70 samples were actively completed to develop the algorithmic HRM scale. In this study’s second phase, a larger sample size of 350 was approached, a different set of samples from the first sample. For the data collection purpose, a message to the relevant connections was sent using a personalized message with their first name to give a high level of importance to the sent electronic questionnaire and to increase the possibility of participation and facilitation of its completion. Web questionnaires eased and accelerated the procedure, saving time and effort [85,86]. In the second phase of the research, 273 responses (out of 350 approached) were received after the scale development and its validation. In comparison, 39 responses were excluded due to their unsuitability regarding the required filter for the usage of algorithmic HRM in their firms. The remaining 234 participants were finally considered as the sample for the second part of the research.

4.3. Measurement Items

Algorithmic HRM is an emergent and nascent topic. In the absence of any scale for assessing the impact of algorithmic HRM, an attempt has been made to develop a scale for algorithmic HRM usage (A). The authors, appreciating the earlier work carried out on the algorithmic HRM by Cheng and Hackett [17], Meijerink et al. [18], and Meijerink and Bondarouk [43], developed the scale for the usage of algorithmic HRM and validated it. It was used in the subsequent research to evaluate the relationship between strategic HR decision-making, competitive advantage, and the digital HR maturity of the firm. The other measurements were adopted from the previous research, i.e., HR strategic decision (D) scale was adopted and amended by Jarupathirun et al. [87], while competitive advantage (C) was adopted from the study by Chang [88], and HR digital maturity (M) adopted from the study by Irimiás and Mitev [89] (see Appendix A.3 for the details of the scales).

4.3.1. Phase 1. Algorithmic HRM Usage Scale Development

Scale development is crucial to research, especially in the social sciences, as it allows researchers to measure abstract constructs and variables quantitatively. A scale is a set of items or statements to assess individuals’ attitudes, perceptions, behaviors, or other psychological or behavioral characteristics. Developing a reliable and valid scale is essential because it gives researchers a standardized and systematic way to measure variables, ensuring consistency and comparability across different studies. A well-constructed scale enhances the rigor and validity of research findings, enabling researchers to draw more accurate conclusions and make informed decisions. It also helps in theory testing, hypothesis formulation, and establishment of the causal relationships between variables.

One of the main contributions of this research was to develop a measurement scale for the algorithmic HRM utilization construct. The scale development for the algorithmic HRM construct was inspired by the work carried out by Cheng and Hackett [17], Meijerink et al. [18], and Meijerink and Bondarouk [43]. To attain the stated goal, the present study employed Churchill’s [90] prescribed methodology, which involves a systematic process beginning with a clear definition of the construct. Subsequently, a roster of preliminary items that accurately represent the construct is produced. This roster is further improved by eliminating redundancies and extraneous or unclear items and incorporating items encompassing diverse facets of the construct. The wording of the items will be formulated in a manner that is both lucid and succinct, ensuring comprehensibility among the intended audience.

4.3.2. Item Generation

Based on the literature, a long list of potential measurement items was generated—the items aimed to capture the various aspects of algorithmic HRM usage in firms. In the absence of any given scale, it took much work for us to adapt it to our context, and we preferred to look into the contextual realities of algorithmic HRM practices rather than the nuts and bolts of algorithmic HRM per se. The items were further analyzed considering the usage of algorithmic HRM and its challenges and circumstances. The exhaustive references of the literature used to capture the essence of algorithmic HRM realities, and its context for its usage are listed in Table 1.

4.3.3. Expert Discussion

To ensure the suitability of the generated items from the literature, a panel discussion of nine experts comprising HR professionals, researchers, and IT business analyst practitioners was undertaken [99]. The expert discussion sessions aimed to gather diverse perspectives and insights on the dimensions and indicators of algorithmic HRM and its use in the organization. It took much work to ascertain the items of algorithmic HRM per se, so we were focused on the usage of algorithmic HRM and what challenges and opportunities it brings. Seeing the complexity of algorithmic HR arrangements, the experts, through consensus, supported the relevance of its usage, and their experiences, knowledge, and opinions were critical in refining our topic. As mentioned, the discussion played an essential role in identifying key themes. Appendix A.1 showcases the demographic details of the experts who participated in this discussion. The diverse participants, backgrounds, and experiences added value to reaching the concluded keywords [100]. The keywords of the discussion were focused on usage and raising concepts such as task [49], abilities [11], role of HRM [54], utilization [1], and dependability [101]. These keywords are seconded in the literature. This led to the refined items discussed in the following subsection.

4.3.4. Item Refinement

From the literature review, expert discussion, and outcome analysis, as Hinkin [102] recommended, the items were deductively assessed, refined, and summarized, as shown in Table 2. The researchers reach the following shortlisted items applicable for further analysis and development, which are elaborated on in forthcoming subsections.

The scale for measuring algorithmic HRM usage was developed by the authors and was inspired by the work undertaken by Cheng and Hackett [17], Meijerink et al. [18], and Meijerink and Bondarouk [43]. This scale measures algorithmic HRM usage and its ability to cope with the system inadequacy or expedite processes, which is crucial for understanding how firms can effectively cope with the challenges of algorithmic HRM usage. The scale design should also consider how HRM departments can effectively equip themselves with the necessary technological resources and actively employ and use algorithms. Moreover, the scale should measure the ability of firms to effectively deal with large data sets and reduce dependency on HR professionals. Accurately measuring these constructs is necessary for understanding the relationship between algorithmic HRM usage and competitive advantage, which can help inform organizational decision-making processes.

Moreover, this paper deliberates on suitable response alternatives for the measurement instrument, ensuring that adequate response options encompass a diverse spectrum of responses. Upon devising the scale, the preliminary trial was conducted with a limited cohort of HR senior professionals invited to participate in the data collection for the scale development. The scale’s dependability and accuracy assessment were facilitated by analyzing the responses of the 70 participants in this stage, and the sample size seems sufficient [102].

This study’s first phase entails developing a new scale to measure the algorithmic HRM utilization construct. Upon completion of the scale finalization process, an exploratory factor analysis (EFA) statistical technique was employed to assess the reliability and validity of the developed scale [103] using SPSS 25 software. EFA detects latent factors or dimensions within the variables and involves analyzing observed variables to identify correlation patterns among them [103,104]. According to Watkins [105], one of the main objectives of operating EFA is to identify the minimum number of underlying factors that account for the observed variance in the data. Thus, an EFA was operated using principal component analysis and varimax rotation. The minimum factor loading criteria was set to 0.50. To ensure acceptable levels of explanation, the commonality of the scale should be assessed, which reveals the amount of variance in each dimension [106]. The results show that all commonalities were over 0.50 except A5, which was removed due to low loading, i.e., 0.274.

A significant step involved weighing the overall significance of the correlation matrix through Bartlett’s test of sphericity, which measures the statistical probability that the correlation matrix has significant correlations among some of its components [107]. The results were significant, x² (n = 70) = 87.230 (p < 0.000), which indicates its suitability for factor analysis.

Furthermore, the Kaiser–Meyer–Olkin Measure of Sampling Adequacy (MSA), which indicates the appropriateness of the data for factor analysis, was applied [108]. As recommended by Kaiser [109], a Kaiser–Meyer–Olkin (KMO) value of 0.5 is barely accepted, values between 0.7 and 0.8 are acceptable, and values above 0.9 are excellent. For this study, the result was 0.768.

Nonetheless, in this initial EFA, one item (i.e., “A5. Algorithmic HRM usage will reduce the dependability on the HR professionals in the organization”) failed to load on the dimension significantly. The authors repeated the EFA without incorporating this item. This new analysis confirmed the four-item structure theoretically defined in the research, where the KMO MSA was 0.780. In this regard, data with MSA values were near 0.8, which is considered acceptable for factor analysis [109]. Bartlett’s sphericity test proved significant, and commonalities were over the required value of 0.500. The four items identified as part of this EFA aligned with the theoretical proposition in this research (see Table 3).

4.3.5. Phase 2. Establishing the Relationship between Algorithmic HRM and Competitive Advantage

In addition to the algorithmic HRM usage (A) that was developed in its first phase, there are three additional variables, namely, HR digital maturity (M), HR strategic decision-making (D), and competitive advantage (C), used in this research, as mentioned earlier. For HR digital maturity, the scale given by Irimiás and Mitev [89] was used, having three items, whereas the HR strategic decision (D) scale developed by Jarupathirun et al. [87] that was used has six items. The competitive advantages (C) scale by Chang [88] that we used is widely popular and has six items (refer to Appendix A.3). Table 4 presents the primary attributes of the assessment instruments utilized in this study. Each construct’s reliability and internal consistency were measured using Cronbach’s alpha, while the convergent validity was assessed using average variance extracted (AVE). Cronbach’s alpha is the coefficient used to measure internal consistency. The threshold level for reference value is established by Nunnally [110] at 0.70. According to Hair et al. [111] and Sarstedt et al. [112], the AVE must exceed 0.50 to ascertain convergent validity.

All of the constructs were graded using a seven-point Likert scale [113], with one point denoting “strongly disagree” and seven denoting “strongly agree”. This scale is superior to other measures [114,115,116]. Back translation has been performed on the instruments to ensure no variation in the language used, which should be appropriate for the context and fulfill the requirements of the culture and society [117]. Web-based surveys available in both Arabic and English were used to obtain a greater level of comprehension.

5. Results

5.1. Descriptive Analysis

The demographic items answered by the participants and the respondents’ profiles were analyzed and are described in Table 5. Women constitute 39% of the sample size. The majority of the participants are in their mid-career, as 76% of the participants are less than 40 years old, and 95% of respondents are at specialist level or above, with 96% having bachelor’s degrees or above. These demographic details validate the profiles of the participants as qualified to participate and respond in terms of their experience and maturity in applying algorithmic HRM arrangements in their firms.

5.2. Common Method Bias/Variance (CMB/CMV)

It was shown that the measured latent marker variable (MLMV) technique might be utilized to discover issues with common method variance (CMV) in PLS-SEM [118]. A years of experience variable was not part of the study model, as it is measured and does not fall within the same domain as the other variables. Table 6 shows that the R² value of dependent variables is not different after adding years of experience, which suggests the model of this study was found to be free of CMB/CMV problems by applying MLMV [118,119]. Random variables were also included in the data set, and the variance inflation factor (VIF) was checked to see if it was still less than 3.3 for both inner and outer models, which was confirmed in this study and found in line with Kock [120]. The coefficient of determination R² results were 0.450 and 0.448 for C and D, respectively (see Table 6). Both met the cutoff moderate range [111,120].

5.3. Measurement Model

The applied model of this study had reflective constructs. Convergent and discriminant validity were examined in this study to investigate whether the items in the questionnaire explain what they are supposed to measure.

5.3.1. Convergent Validity

Convergent validity was used to evaluate whether the factors included in the questionnaire were associated and relevant to measuring the construct. All factors’ loading was found to be 0.579, which indicates acceptable reliability and explains more than 50% of the item’s variance [111]. CA, rho_A, and CR were found to be more than the requisite threshold of >0.5, confirming the data’s internal reliability [111]. Moreover, all AVE results exceeded the threshold value of 0.50 [111,112] (see Table 7).

5.3.2. Discriminant Validity

Unlike convergent validity, discriminant validity investigates that the items of each construct should not be similar to those of other constructs. It is recommended to report only the heterotrait–monotrait ratio (HTMT) criterion, which is found to be consistent with the research, and it meets the threshold, which is supposed to be less than 0.9 [111,121,122] (see Table 8). Moreover, discriminant validity could be achieved by the square root of AVE, which should be more than the correlations of the latent variables, according to the Fornell–Larcker Criterion [113] and Hair et al. [111]. The diagonals are the square root of AVE and indicate the highest in the column and its left row (see Table 8).

Additionally, all cross-loading results were found to be more than the cutoff of 0.5 (see Table 9). This is per the rule of thumb defined by Hair et al. [111]. Thus, the construct measurements in this study were reliable and valid; the next step lies in evaluating the structural model postulations.

To assess potential collinearity, the variance inflation factor (VIF) was used in the inner and outer reflective model and considered as not problematic because the value of VIF is <3.3 [111,123]. Table 10 shows that the VIF value was less than 3 in all constructs, confirming that no collinearity issues exist in the model.

5.4. Structural Model

Structural model assessment substantiated the measurement model assessment at the second evaluation step, and the hypothetical assumptions were analyzed systematically. First, the direct effects of algorithmic HRM usage (A) were examined in HR strategic decision-making (D). Second, the direct effects of algorithmic HRM usage (A) were investigated with competitive advantage (C) (see Figure 4). Furthermore, to ascertain the significance of direct paths and estimate standard errors, the bootstrap resampling method with 10,000 resamples [124] was applied.

Table 11 represents the results of hypothetical assumptions for direct and indirect relationships. The hypothesis tests revealed a significant positive effect of algorithmic HRM usage on HR strategic decision-making (Hl: β = 0.400, t = 3.824, p = 0.000 ***) and competitive advantage (H2: β = 0.662, t = 7.978, p = 0.000 ***). Results also show a significant impact of HR strategic decision-making on the competitive advantage (H3: β = 0.403, t = 3.760, p = 0.000 ***). Therefore, H1, H2, and H3 were established in this study.

The Q² investigates the model to possess the predictive power of the data; the results were 0.383 and 0.426 for competitive advantage (C) and HR strategic decision-making (D), respectively, which were above 0 [124,125].

In line with the recommendation of Hair et al. [124], this study applied bootstrapping 10,000 to correct the path coefficient and confidence interval bias, including the model fit criterion. It was further tested for the indirect relations of HR strategic decision-making (D) mediation role. The standardized root mean square residual (SRMR) value was reported as 0.076, less than the threshold of 0.08 [122,126]. This confirms the model fit accordingly.

This study postulates five relationship hypotheses, and barring the moderation relationship of HR digital maturity, the other four hypotheses were supported with a high significance value where the p-value was 0.000 *** (see Table 11), and all paths’ coefficients were significant at the 99.999% confidence interval. This confirmed the strong positive relationships between algorithmic HRM and HR strategic decision-making, a stronger association of HR strategic decision-making with the competitive advantage, and the positive algorithmic HRM relationship to the competitive advantage.

The results show that HR strategic decision-making mediates the relationship between algorithmic HRM and competitive advantage. This relationship was supported significantly and can be seen as a contribution of this study. As the H1, H2, and H3 were supported and found to be positively significant, HR strategic decision-making partially mediates the relationship between algorithmic HRM and competitive advantage (see Table 11).

The Moderator Effect

Based on the standardized beta coefficient (Std Beta) of −0.104, it was found that there was a weak negative relationship between HR digital maturity and algorithmic HRM. This means that as HR digital maturity increases, the use of algorithmic HRM decreases slightly.

However, since the p-value (0.065) was greater than the conventional threshold of 0.05, it is difficult to conclude that this relationship was statistically significant. This means that the relationship observed could have occurred by chance and not because of a genuine relationship between HR digital maturity and algorithmic HRM. Therefore, the moderator analysis suggests a weak relationship between HR digital maturity and algorithmic HRM, and it was not statistically significant. Further research is required to evaluate the dynamics of this relationship (see Figure 5).

6. Discussion

This study aimed to understand the role of algorithmic HRM usage in firms to leverage HR strategic decisions in reinforcing organizational competitive advantage. Based on the RBV and DC theories, this study examined the interconnections between algorithmic HRM and competitive advantage, between HR decision-making and competitive advantage, and between algorithmic HRM and HR strategic decision-making and evaluated the moderating role of HR digital maturity and the mediating role of HR strategic decision-making between algorithmic HRM usage and firm competitive advantage.

The first hypothesis proposed in this study was that algorithmic HRM positively impacts HR strategic decision-making. The findings suggest that algorithmic HRM positively impacted HR strategic decisions with a p-value of 0.000 ***. According to Cheng and Hackett [17], HRM algorithms are either predictive or descriptive, so they differ from empirically tested theories in that context, and these algorithms cannot be used independently to inform the decision-making process directly. However, algorithmic HRM has the potential to automate decision-making fully or partially in HRM [127].

For example, converting paper-based records into electronic records is one element of digitization that can help capture workers’ behavior, actions, performance, and other domains of HRM [128]. Algorithmic output could be in the form of descriptive statistics that offer additional insights into the workforce [129]. Predictive algorithms in HR can include machine learning and data mining to explore patterns in data that humans could not have uncovered using their subconscious minds [32].

In this context, an argument can be made that an algorithm uses two underlying logics to understand the relationships between input and output: deterministic and probabilistic [17]. In HRM research, both deterministic and probabilistic relationships are salient. Deterministic causal relationships help optimize operational management in a firm, whereas probabilistic algorithmic relationships are found helpful in traditional HRM practices such as turnover, selection, and recruitment [64].

For instance, firms use regression-based forecasting, in which algorithms assist managers in predicting employees’ likelihood to quit and making predictions of their future performances [70]. The application of these predictive modeling or data mining algorithms to forecast new or future occurrences in a firm forms the pinnacle of decision-making in HRM these days [35]. Therefore, algorithmic HRM influenced HR strategic decisions positively, and H1 was supported.

The current study also incurs that algorithmic HRM usage helps create a competitive advantage (H2). The results of the analysis endorse this view with a p-value of 0.000. The use of information from algorithm outputs assists HR in making a more informed decision [128], and evidence suggests that many tech firms use HR strategic decisions that are backed up by sophisticated algorithms to run their firms, which is also referred to as the transformation agenda [4]. These algorithms minimize the number of repetitive tasks a human performs in HRM, thus allowing them to be more productive in other qualitative tasks [43].

In this way, these firms stay ahead of their competitors, who might lag in adopting algorithmic HRM. Furthermore, Kellogg et al. [129] note that algorithms encourage interaction, meaning that employers can engage with and monitor chat channels. HRM practices are critical to business success because they shape relationships between firms and employees. Software algorithms influence people’s work in many fields in the contemporary business environment.

It is well-accepted that digital strategies, including investment in information technology and the Internet of Things, are critical domains of the overall business strategy and help firms differentiate from their competitors [2,64]. HRM algorithm usage is part of the transformation agenda that builds competitive advantage. The present study reinforces this and confirms that algorithmic HRM usage helps create an organizational competitive advantage.

The third hypothesis (H3) proposed in this study relates to the impact of HR strategic decision-making on the organizational competitive advantage. The analysis results reveal that HR strategic decision-making strengthens the organizational competitive advantage, showing a p-value of 0.000. The RBV, in the context of human resources, depicts that human capabilities are a critical source of competitive advantage. According to Ren et al. [130] and Chowdhury et al. [11], these capabilities contribute to better performance realization when used for strategic decisions and problem-solving. Strategic decision-making and agility are critical in achieving market sustainability when firms compete in an unstable environment [75].

Furthermore, developing new resources through strategic decision-making is critical to achieving a sustainable competitive advantage [131,132]. Alabdali and Salam [38] similarly emphasize the need for strategic agility and decisions to achieve competitive advantage. Also, digital tools help firms adapt to a fast-paced environment to improve employee performance, improve HR strategy development [79], and create a competitive advantage [32]. The study findings confirm that strategic HR decision-making in the firms is crucial and contributes positively to creating organizational competitive advantage.

Based on the existing theoretical background, the current study also proposed that HR strategic decisions mediate the relationship between the algorithmic HRM adopted in a firm and competitive advantage. The study results find that the HR strategic decisions partially mediate the relationship between the algorithmic HRM and competitive advantage with a p-value of 0.000. Battour et al. [75] concur that while the relationship between HRM algorithm usage and competitive advantage exists and is well-documented in the literature, the strengths and nature of this relationship still need to be consistent.

Van Giffen et al. [133] also prescribe an indirect effect on the competitive advantage through the strategic decision as a variable. This notion means that the HRM algorithm has a causal relationship with the firm’s sustainable competitive advantage through HR strategic decisions [64]. However, the HRM algorithm was also correlated directly with the competitive advantage. HR strategic decision-making partially mediates between the HRM algorithm and competitive advantage. Studies such as [51,134] support this hypothesis, showing that firms using algorithmic HRM to create a competitive advantage benefit from HR strategic decisions.

The fifth hypothesis (H5) proposed that HR digital maturity moderates the relationship between algorithmic HRM and HR strategic decision-making. The results indicate that the HR digital maturity does not moderate the relationship between algorithmic HRM and HR strategic decision-making with a p-value of 0.065. HR digital maturity is recognized as an internal HRM capability that can either boost or derail the implementation of digital HR in a firm. Wang et al. [32] employed the adaptive structuration theory (AST) to explore the moderating effect of capability maturity on digital HRM and HR decision-making process.

According to the AST, the effectiveness of advanced information technology depends on the structure of the technology and other structures, such as internal context [18]. While HRM digital maturity makes the relationship between HR and line managers more efficient, in low-maturity firms, more relevant knowledge and skills and better communication issues can lead to significant workflow conflicts between their departments. Moreover, Zare et al. [76] found that if an organization’s HRM digital maturity fails to match the capability of a digital HRM system, the line managers lack effective channels to provide feedback to HR in real time, leading to inappropriate performance appraisals. In line with the above proposition, H5 was not validated, though it can be a subject of inquiry in future research.

This study investigates the impact of algorithmic HRM on strategic HR decision-making and its effect on promoting organizational competitive advantage. It focuses on the RBV and DC theories and examines how algorithmic processes can improve HR decision-making. The results confirm that algorithmic HRM significantly improves HR strategic decision-making, as HR algorithms offer a structured framework for decision-making. This organized approach facilitates a methodical and evidence-based decision-making process, crucial in the complex digital environments of contemporary businesses today.

Algorithmic HRM also plays a significant role in establishing a competitive edge, as it enhances understanding of worker behaviors and performance indicators by transforming traditional records into digital formats. This enables predictive analytics to predict important factors like employee turnover and performance trends. This is especially evident in technology-oriented companies where digital strategies, such as IoT and advanced IT solutions, play a crucial role.

According to the resource-based view theory, algorithmic HRM plays a crucial role in improving strategic decision-making abilities, which in turn helps maximize the competitive advantage gained from human capital. This study supports previous claims that adaptability and timely decisions are essential in unpredictable market conditions. Organizations can enhance their competitive advantage by promptly adjusting and implementing strategic decisions using real-time data analytics.

The mediation study demonstrated that HR strategic decisions are pivotal in connecting algorithmic HRM with competitive advantages, aligning with Van Giffen et al.’s [133] perspective on the indirect impact of strategic decisions on organizational success. However, the moderation analysis revealed no significant impact of HR digital maturity on the association between algorithmic HRM usage and strategic decision-making, suggesting that digital maturity may be intricate and potentially affected by other organizational elements like culture and current technology infrastructure. The findings provide a strong foundation for future research, particularly investigating the ethical implications and potential prejudices linked to algorithmic decision-making systems in HRM, ensuring that implementation aligns with overall business values and societal standards.

7. Implications, Limitations, and Conclusion

7.1. Theoretical Implications

This research makes use of the resource-based view (RBV) theory of Barney [59,65] and the dynamic capabilities (DC) theory developed by Teece [8], and in addition to that, it considers the work undertaken by Barney [65] and Marler and Parry [34]. Creating a measurement scale for algorithmic HRM usage is a noteworthy advancement in HR, as it offers several significant contributions to the field. The scale presents a dependable and sound assessment of the utilization of algorithmic HRM, a domain of research that has gained significant prominence in contemporary times. By assessing algorithmic HRM utilization, entities can assess the efficacy of their HRM methodologies and arrive at well-informed determinations concerning adopting and integrating HRM technologies. The present research analyzes the moderating influence of HR digital maturity to determine the readiness of using algorithmic HRM for HR decision-making. This relationship was not established, but HR’s digital capability and maturity in the firm play a critical role in implementing essential HR decisions.

Within the field of algorithmic HRM, this research will undoubtedly benefit policymakers, business professionals, HR practitioners, and regulators. This study elucidates the factors and relationships between the algorithmic HRM dispensation, HR strategic decision-making process, and resultant competitive advantage. The role of HR digital maturity is one area that warrants further inquiry, and a mixed method of inquiry will reveal a much more profound understanding of this issue.

This study has several theoretical implications. First, it enhances the learning derived from the resource-based view and dynamic capabilities theories of HR and their relevance in the realization of the competitive advantage of the firm. Very few studies have attempted to evaluate the mediating role of HR strategic decision-making in establishing the relationship between algorithmic HRM and competitive advantage. Scholars have listed maturity, the mindset of implementers [40], organizational characteristics and type, and competencies as four key reasons algorithmic HRM is still unpopular [13]. The maturity focus is hardly studied; in that background, the present study has attempted to open a pathway for exploring HR digital maturity and other related vital domains further to understand the correlation between algorithmic HRM and its implementation to harness the competitive advantage in a firm.

7.2. Practical Implications

This study also holds significant practical implications. The study findings are essential for policymakers, business professionals, HR practitioners, and organizational leaders. They can help them utilize the knowledge gained through this study to gain the required competitive advantage, for example, by enhancing their HR strategic decision-making capability and employing the HRM algorithm. The current body of evidence suggests that algorithmic HRM has advantages for employees and businesses, as demonstrated by Kellogg et al. [129] and Duggan et al. [43].

Firms may include predictive data from algorithmic HRM in their planning processes to make complete HR strategic decisions. This guarantees the ability to adjust to market fluctuations and maintain strength in the face of operational challenges. Developing organizational capability necessitates continuous training for HR professionals in the practical and strategic utilization of algorithmic technologies. To ensure a robust data infrastructure, conducting frequent audits, complying with data protection regulations, and implementing advanced cybersecurity protocols are necessary. It is crucial to have cross-functional collaboration, which involves combining HR data with insights from finance, operations, and marketing. Ensuring ethical governance is essential for mitigating potential biases in algorithmic systems. Regular monitoring and assessment using key performance indicators are crucial for measuring the impact of strategy decisions and making necessary adjustments. Robust leadership and proactive stakeholder involvement are crucial for promoting modern HR technologies and cultivating a culture prioritizing innovation and data-driven decision-making. This method improves the decision-making process and promotes the organization’s ability to outperform its competitors.

The strategic application of algorithmic HRM in conjunction with sustainability goes beyond conventional business practices, enabling firms to cultivate a flexible and resilient workforce that is efficient and morally aware of the long-term objectives of social, environmental, and economic stewardship. Companies can improve resource management and operational efficiencies by utilizing sophisticated data analytics and AI-driven insights in HR practices. Additionally, minimize waste through precise workforce planning and foster the continuous growth and well-being of the employees, contributing to sustainable development. Integrating algorithmic HRM with sustainability principles enhances a company’s competitive advantage. It demonstrates its dedication to the practices that generate value for the corporation and society for advanced and sustainable competitive advantage.

This study builds upon previous research and emphasizes the significance of HR strategic decisions in facilitating the connection between algorithmic HRM and competitive advantage. Business practitioners and other stakeholders must recognize the crucial mediating roles HR strategic decisions play in this relationship. When the HR digital maturity level is high, algorithmic HRM is more likely to have positive impacts such as improved internal consistency, etc. [32]. In order to realize the effectiveness of algorithmic HRM and achieve a competitive advantage, business experts and HR specialists need to improve HR strategic decisions by building a workforce that supports strategic business approaches. In addition, a competitive advantage analysis must be conducted before implementing any algorithmic HRM strategy to see its pre- and post-effect.

7.3. Limitations and Future Research Recommendations

This research, like previous studies, has several limitations that suggest directions for future research. First, one of the areas in this study pertains to the mediating role played by HR strategic decision-making. However, other factors could mediate the relationship between algorithmic HRM and competitive advantage. Future research needs to expand the scope of research to integrate other relevant variables to enrich the HRM algorithm discourse. Second, this study does not focus on a specific industry or use a type of industry as a control variable; this can be attempted by focusing on various industry types and documenting the efficacy of algorithmic HRM in that context.

Future researchers may also apply what they have learned from this study to multiple contexts, such as in SMEs. Lastly, this was quantitative research, a mixed method that will give better insights into future research, especially on the moderating effect of HR digital maturity in evaluating the relationship of HRM algorithm on HR decision-making. The present research has limitations as it needs to cover some important issues regarding the usage of algorithmic HRM, such as the processes, the challenge of implementing the HRM algorithm, issues regarding the ethical usage of algorithmic HRM, and the erosion of trust issue of employees. While algorithmic HRM offers numerous benefits, on the flip side, the concerns about ethics, privacy, conditionality, worker exploitation, higher work intensity, lower perceptions of justice and fairness, and the lack of objectivity need to be addressed for its broader appeal and usage in time to come [1,19,56,135]. To overcome these issues, it is also essential to consider governance, legal, and regulatory perspectives [136]. By eliminating biases and promoting justice and fair practices, algorithmic HRM can achieve positive outcomes [19,24]. As mentioned, there is substantial criticism concerning algorithmic HRM, particularly regarding issues of privacy, conditionality, and ethics [137], and addressing these concerns is crucial for the responsible use of algorithmic HRM in times to come, and we recommend this issue to be analyzed in the future research [56,135,136].

7.4. Conclusions

There has been an upsurge in the usage of algorithmic HRM in corporations. It is becoming common in firms that digital transformation technology is emerging as an essential component for achieving commercial success [61]. Significant opportunities and challenges mark the development of digital technologies like algorithmic HRM. The data and information gathered, shared, and analyzed by algorithmic HRM significantly influence the processes and operations in firms. It is necessary to make HR decisions; the most fundamental use is for working together and exchanging information and activities, which are crucial to a business’s process.

The present study found that algorithmic HRM directly influences competitive advantage. At the same time, HR strategic decision-making partially mediates in realizing the relationship between algorithmic HRM and competitive advantage. On the other hand, HR digital maturity was found to be short of playing a moderating role in the relationship between algorithmic HRM usage and HR strategic decisions. This study aimed to investigate the effectiveness of algorithmic HRM and its contextual factors and the status and utilization of this approach in the post-COVID-19 environment. The scale developed for algorithmic HRM usage is a vital result of this study, and it contributes to the extant literature on measurement scale development for creating a novel measurement instrument for algorithmic HRM. The implementation of EFA underscores the significance of utilizing suitable statistical methodology in scale development as a preliminary step in scale development and its validation. The research aimed to investigate how algorithmic HRM can contribute to improving competitive advantage through the implementation of automation, augmentation, and control within its operational sphere. This was achieved by incorporating the mediating role of HR strategic decisions and the moderating role of HR digital maturity.

This study provides a deep understanding of how algorithmic HRM might bring about significant changes within the strategic structure of contemporary businesses. As organizations shift towards digital integration after COVID-19, algorithmic HRM’s importance is highlighted as a technology improvement and a critical strategic asset that can significantly impact competitive dynamics. The direct influence of algorithmic HRM on improving strategic decision-making and thus strengthening competitive advantage establishes a clear path for utilizing digital technology in human resource management practices.

Nevertheless, the intricate function of HR digital maturity, which did not arise as a substantial moderator, indicates a highly relevant subject for further investigation. This element could have varied effects in organizations at different maturity levels, suggesting that a one-size-fits-all approach may not work. Gaining insight into the precise circumstances in which HR digital maturity affects algorithmic HRM could result in more customized and efficient implementation strategies that align with each firm’s unique requirements and capacities. Thus, from the divergence approach of HRM, it becomes highly pertinent, and adaptability and context become highly relevant, which will be worth investigating in future research.

Moreover, the research has successfully created a validated scale to measure the usage of algorithmic HRM. This scale represents substantial progress and aims to serve as an essential tool for academics and practitioners who want to assess the integration and efficacy of algorithmic HRM in their businesses. In essence, this study has established a basic understanding of the strategic implications of algorithmic HRM. As mentioned earlier, with the growing popularity of the Internet of Things and big data analytics in business, algorithmic HRM will be the central issue for the business digitalization process. Extensive big data opportunities emerging in businesses have forced firms to use and take advantage of algorithmic HRM to gain a competitive advantage for organizational excellence and survival. This study, therefore, contributes to setting up a clear boundary for the effectiveness of algorithmic HRM implementation in a firm by examining the interactions between algorithmic HRM, HR strategic decision, HR digital maturity level, and its resultant competitive advantage.

Author Contributions

Conceptualization M.A.A. (Mahmoud Abdulhadi Alabdali); Methodology M.A.A. (Mahmoud Abdulhadi Alabdali), S.A.K.; Software M.A.A. (Mahmoud Abdulhadi Alabdali) and M.A.A. (Mohammed Awad Alshahrani); Validation M.A.A. (Mahmoud Abdulhadi Alabdali), S.A.K. and M.A.A. (Mohammed Awad Alshahrani); Formal analysis M.A.A. (Mahmoud Abdulhadi Alabdali) and M.A.A. (Mohammed Awad Alshahrani); Investigation M.A.A. (Mahmoud Abdulhadi Alabdali), S.A.K.; Resources M.A.A. (Mahmoud Abdulhadi Alabdali); Data curation M.A.A. (Mahmoud Abdulhadi Alabdali); Writing—original draft M.A.A. (Mahmoud Abdulhadi Alabdali), S.A.K. and M.A.A. (Mohammed Awad Alshahrani); Writing—review & editing M.A.A. (Mahmoud Abdulhadi Alabdali), S.A.K. and M.Z.Y.; Visualization M.A.A. (Mahmoud Abdulhadi Alabdali), S.A.K. and M.Z.Y.; Project administration M.A.A. (Mahmoud Abdulhadi Alabdali); Supervision S.A.K. and M.Z.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflict 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

Based on the definition provided, I clearly understand the differences between e-HRM, digital HR, and Algorithmic HRM. (Yes/No).

My employer already applied and used at least one of the following (select as many as applicable):

The HR function in my organization is known as the best-fit professional practices.

HR module within ERP such as Oracle, SAP, Microsoft Dynamics. etc. are being used.

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.

My employer willing to invest in the HR advanced technologies. (Yes/No).

My employer has a clear digital transformation agenda. (Yes/No).

Based on the definition provided, I exactly understand what the Algorithmic HRM is all about. (Yes/No).

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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Figure 1. The construct illustration. Source: authors’ own work.

Figure 2. Evolution of digital and technological HRM. Summarized and designed by the authors.

Figure 3. The conceptual model based on Barny [59,65], Marler and Parry [34], and Teece [8]. The dotted line represents the indirect relationship. Source: authors’ own work.

Figure 4. Structural model result (source: analyzed by the authors and extracted from SmartPLS4).

Figure 5. The slope graph of moderation (source: extracted from SmartPLS 4 application).

Table 1. Items generated and sources.

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]

Table 2. Algorithmic HRM usage: the refined items.

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.

Table 3. EFA results of the algorithmic HRM usage (A) (source: analyzed by the authors using SPSS software).

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)

Table 4. Construct validity.

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

Table 5. Analysis of the respondents’ profiles and their demography (n = 234).

	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%

Table 6. Common method bias/variance (CMB/CMV).

	R² Without Marker Variable	R² With Marker Variable
Competitive Advantage (C)	0.450	0.450
HR Strategic Decision-Making (D)	0.448	0.448

Table 7. Measurement model. CA = Cronbach’s alpha; CR = Composite Reliability; AVE = Average Variance Estimation.

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

Table 8. Discriminant validity (Fornell–Larcker Criterion) and heterotrait–monotrait ratio (HTMT).

	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

Table 9. Cross-loading.

	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

Table 10. Collinearity assessment, VIF inner model.

	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

Table 11. Structural model.

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

Notes: *** p < 0.001

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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

AMA Style

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 Style

Alabdali, 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

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