Keywords

1 Introduction

Organizations encounter persistent challenges and possibilities in today's fast-paced and unpredictable business world as a result of technical breakthroughs, changing market dynamics, and evolving client expectations (Al-Abbadi et al. 2021; Al-Alwan et al. 2022 , 2022; Aityassine et al. 2021). To flourish in such a volatile climate, organizations should have employees that are adaptive, flexible, and eager to learn from volatile circumstances (Alhalalmeh et al. 2022; AlHamad et al. 2022; Al-Hawary 2013). Workforce agility has evolved as a vital notion for organizations to maintain competitiveness (Al-Hawary and Abu-Laimon 2013; AlTaweel and Al-Hawary 2021), innovate (Al-Hawary and Harahsheh 2014; Al-Hawary and Nusair 2017), and achieve long-term success (Al-Hawary et al. 2021; Al-Hawary and Al-Syasneh 2020). Workforce agility can be described as an ability to change work methods, jobs, and skills to suit changing needs and promote organizational success. Al-Hawary and Al-Rasheedy (2021) stated that a highly agile workforce anticipates changes, navigates uncertainty, and seizes novel opportunities in an evolving organisational context. Thus, in an era of digital transformation and rapid change, organizations that prioritize worker agility gain a competitive edge sooner than those that rely on regular labour and antiquated organizational structures (Al-khawaldah et al. 2022; Al-Shormana et al. 2021).

As technology continues to shape and redefine various aspects of business operations, human resource management has undergone a transformation with the rise of electronic human resource management, which refers to the incorporation of technology and digital solutions into human resource processes and practices in order to streamline and improve their functions (Al-Hawary and Al-Namlan 2018; Metabis and Al-Hawary 2013). Electronic human resource management can be used to improve the efficiency and effectiveness of human capital management within organizations, where it could improve self-service options, continuously updating of performance evaluations, and allow human resource managers to focus on strategic goals rather than routine work (Mohammad et al. 2020). However, the adoption of electronic HRM necessitates careful planning of investments in technological infrastructure, as well as the resultant issues related to data security and privacy considerations (Tariq et al. 2022; Boudlaie et al. 2022; Mukhlis et al. 2022). Therefore, organizations must ensure that electronic human resource management systems are easy to use, adaptable, and consistent with their specific human resource requirements and organizational culture (Al-Hawary et al. 2022; Mohammad 2020; Al-Rwaidan et al. 2023).

2 Literature Review and Hypotheses Development

2.1 Electronic Human Resource Management

Electronic human resource management enables greater adaptability to changing organizations’ requirements and economic conditions. The theory of social exchange suggested that the relationship between individuals and organizations is founded on mutual advantages and reciprocity (Al-Hawary et al. 2023; Pallathadka et al. 2023). Thus, electronic human resource management could encourage beneficial relationships between employees and human resource departments through self-service portals, interactive platforms, and transparent communication channels (MajdyAmiruddin et al. 2023). Employee empowerment, engagement, and happiness are increased as a result of these electronic activities, resulting in a positive dynamic interchange inside the organization.

Electronic human resource management is described as the integration of information technology with traditional human resources methods to simplify and improve the outcomes of operations inside organizations (Al-Alwan et al. 2022). Electronic human resource management involves the use of digital technologies, software programs, and online platforms to handle human resource tasks like recruiting, employee data management, training, performance appraisal, and employee engagement (AlHamad et al. 2022). Several dimensions arising from electronic human resource management contribute substantially to the successful conduct of human resource management operations utilizing digital technology. (a) E-recruitment is a method of investing technological resources which facilitates the acquisition of qualified persons for a job opportunity. (b) E-training, also known as “e-learning”, is the adoption of digital technology to provide and promote employees’ learning experiences, including formal and informal learning activities, which are frequently aided by online platforms and resources accessible through computers or cell phones (Alshuqairat et al. 2023). (c) Electronic communication refers to taking advantage of digital technology and online networks to facilitate and enhance interaction among human resources departments and employees, as well as amongst employees themselves (Al-Hawary and Al-Namlan 2018). (d) Electronic performance appraisal indicates the implementation of electronic means to automate and improve the employee performance evaluation procedure within organizations.

2.2 Workforce Agility

Workforce agility defines as an organization's capability to adjust rapidly and effectively to modify strategic situations and trends through its workforce which leads to meeting customers’ requirements. It means possessing flexible and adaptable employees that can immediately modify and adapt their skills, capabilities, and organizational resources to meet evolving demands and gain new possibilities (Al-Khasswneh et al. 2023). Workforce agility represents an organization's capacity to predict changes in the outside context and adapt appropriately to them by using its human abilities, skills, and experience (Ashura et al. 2023). Al-Hawajrehet et al. (2023) Considered that it is based on creating a dynamic workforce capable of swiftly pivoting and embracing new technologies, aligning its efforts toward strategic goals, and therefore driving organizational success and competitive advantage (Al-hawajreh et al. 2023). Additionally, workforce agility assists with advancing organizational resilience theory, which indicates an organization's capabilities to rebound from adversity and adapt to business environment troubles. Thus, the workforce's agility allows an organization to swiftly rearrange its roles, responsibilities, and competencies in order to deal with unanticipated problems and sustain organizational performance excellence (Al-Husban et al. 2023). Al-Armeti et al. (2023) mentioned that dynamic leadership style, flexibility, adaptation, collaboration, and empowerment are the core characteristics of workforce agility.

2.3 Electronic Human Resource Management and Workforce Agility

The researchers believe that electronic human resource management is associated with workforce agility. Al-Armeti et al. (2023) sought to ascertain the empirical relationship between electronic human resource management and organizational performance in the presence of organizational agility. A descriptive correlational analysis was used to perform this study on a sample of 40 Sri Lankan institutions. According to the findings of the study, electronic human resource management had a considerable and beneficial impact on organizational performance, whereas organizational agility mediates the relationship between electronic human resource management practices and organizational performance. Accordingly, the current research suggested the following hypotheses:

H1: Electronic human resource management has a significant impact on workforce agility.

Figure 1 depicts the impact of electronic human resource management, i.e., e-recruiting, e-training, e-communication, and e-performance appraisal, on workforce agility that form the research hypotheses.

Fig. 1
A flowchart presents how the dimensions of e-human resource management influence workforce agility through hypotheses H 1 To H 4. E-recruiting, e-training, e-communication, and e-performance appraisal are the dimensions.

Research model and hypotheses

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3 Methodology

The primary sources were gathered from a survey sent to Jordanian commercial banks’ employees. It was not possible to use a comprehensive approach to collect data. Hence, a convenience sample was used through a Google Forms survey distributed via social media and e-mail to those employees. The study survey received 714 replies; however, 155 replies were removed from the statistical analysis as a result of recurring patterns or incomplete surveys. Accordingly, 559 replies. An internet-based survey was used to obtain data on the impact of electronic human resource management on workforce agility. The survey was divided into three sections: an introduction that stated the research's goals, a section for collecting demographic information on respondents.

The independent variable of the research was electronic human resource management, which was measured using 18 items borrowed from (AlHamad et al. 2022). This variable was a second-order construct that was divided into four first-order constructs. e-recruiting was measured using five items (ER1–ER5), e-training was measured using four items (ET1–ET4), e-communication was measured using four items (EC1–EC4), and e-performance appraisal was measured by five items (EPA1–EPA4). On the other hand, the dependent variable of the research was workforce agility, which was a first-order construct measured using seven items (WFA1–WFA7) according to AlBrakat et al. (2023).

4 Results

Structural equation modeling (SEM) is a flexible statistical method to evaluating both quantitative and qualitative data. Confirmatory factor analysis (CFA) is a type of SEM used in management research to determine the validity and reliability of research instruments (Dwijendra et al. 2023). The fundamental purpose of CFA is to establish how well latent construct measurements match the researcher's initial understanding of those components and their latent factors. CFA was utilized in this study to create the indicator values displayed in Table 1, which are used to evaluate the research instrument's validity and reliability.

Table 1 Validity and reliability of measurement model
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Table 1 results indicated that all items had loadings of more than 0.5 on their latent constructs, ranging from 0.671 to 0.811, showing that they were maintained since they surpassed the minimum limit (Zahran et al. 2023). The average variance extracted (AVE) values were above the minimum permissible value of 0.5, indicating that the measurement model had convergent validity (Al-Alwan et al. 2022). Moreover, maximum shared variance (MSV) values were lower than AVE values, and the square root of AVE values surpassed the correlation between the other latent variables. These findings indicated that the measurement model had discriminatory validity (Al-Hawary 2013). On the other hand, the composite reliability (CR) of the measurement model was examined using McDonald's omega coefficient, which was more than the minimal criterion of 0.70. Therefore, the model fit indices were derived and shown in Fig. 2.

Fig. 2
A node diagram. It has 5 constructs labeled E R, E T, E C, E P A, and W F A in ovals with curved arrows representing correlations between them. Each of these constructs is formed by 4 to 7 items, influenced by e 1 to e 25 factors. The values of D F, C F I, T L I, and R M S E A are also mentioned.

The results of confirmatory factor analysis

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According to Fig. 2, the measurement model produced adequate goodness of fit indicators. The chi-squared ratio to degrees of freedom was 1.558, which was less than the highest limit of 3 (Muda et al. 2022). The comparative fit index (CFI) and Tucker-Lewis’ index (TLI) were both 0.948 and 0.921, respectively, above the minimum allowable value of 0.90 (Harahap et al. 2022). The findings for the root mean square error of approximation (RMSEA) revealed that it did not approach the highest limit of 0.08 (Attiany et al. 2023).

Structural equation modeling (SEM) was used to test the researcher's hypotheses. Researchers could apply SEM to examine the appropriateness of their models and analyse associations between observable variables and latent variables with the maximum likelihood approach (Rahman et al. 2023). The structural model used to investigate the influence of electronic human resource management on workforce agility is illustrated in Fig. 3.

Fig. 3
A node diagram explains how the E R, E T, E C, and E P A dimensions of electronic human resource management impact W F A with path coefficient values. Dimensions are formed by 18 items influenced by e 1 to e 18 factors. The values of D F, C F I, T L I, and R M S E A are also mentioned.

SEM for testing the impact of E-HRM on WFA

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According to the results of Fig. 3, the structural model obtained appropriate values of the goodness of fit. The chi-squared ratio to degrees of freedom is 1.302, therefore it was lower than the upper threshold of 3 (Mohammad 2019). The comparative fit index (CFI) and the Tucker–Lewis’s index (TLI) were 0.962 and 0.955, respectively, thus they exceeded the minimum allowed value of 0.90 (Mohammad et al. 2022; Al-Awamleh et al. 2022). As for the root mean square error of approximation (RMSEA), the results showed that it did not reach the upper value of 0.08 (AL-Zyadat et al. 2022; Alolayyan et al. 2022). Accordingly, path coefficients for the impact of electronic human resource management on workforce agility were extracted and included in Table 3. This table includes five columns, each representing the relationship of anelectronic human resource management practice to workforce agility.

Table 3 Path coefficient for the impact of E-HRM on WFA
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According to the findings in Table 3, the four paths of electronic human resource management had a statistically significant favourable impact on workforce agility. Path coefficients (B) vary from 0.718 to 0.812, with e-recruiting having the highest coefficient, suggesting that it had the most positive impact on workforce agility. The standard regression coefficients (β) were in the (0.648–0.733) range, indicating that raising the standard deviation in any combination of electronic human resource management had a significant impact on workforce agility. The t values were substantially greater than the crucial value of 2.05 (at a 0.05 significance level). Furthermore, all of the paths had a p-value of less than 0.05, indicating that the relationships between electronic human resource management dimensions and workforce agility were statistically significant.

5 Discussion

The findings revealed that electronic human resource management has significant effects on worker agility. Therefore, electronic human resource management enables employees’ self-service selections and online platforms through which they may access and update personal information, manage their work schedules, and seek time off or other benefits. According to many studies, allowing workers to handle duties connected to their human resources, such as electronic human resource management, increases employee adaptability and minimizes administrative requirements on human resources staff. Employees may make timely changes to their work arrangements, allowing the firm to adapt rapidly to changing demands.

Furthermore, cooperation and communication are vital for an agile workforce, where electronic human resource management is essential in this context. Employees may collaborate on projects, exchange information and expertise, and work together regardless of physical location using digital platforms and tools including, including intranets, collaboration software, and social media. These findings are congruent with (Kumar and Kumar 2022), which found that immediate interaction through features like instant messaging, video conferencing, and discussion boards allows for seamless cooperation and information sharing across departments. Although electronic human resource management provides major advantages for workforce agility, it is necessary to evaluate possible issues and constraints. Setiawan and Gunawan (2023) suggested that organizations should have data security and privacy measures to safeguard key employee information. Furthermore, strong change management techniques and training programs are required for the successful deployment of e-HRM to ensure employee acceptance and engagement in the transformation process.

In conclusion, the effects of electronic human resource management on workforce agility matter for organizations. Major results to consider include increased employee autonomy, empowerment, improved performance management, more interaction and organizational cooperation, and the requirement for adaptive human resource management practices. Organizations may improve workforce agility, react to changing business environments, and maintain a competitive advantage in today's dynamic world by efficiently deploying electronic human resource management systems. To fully realize the promise of electronic HRM for workforce agility, organizations should also address the issues of synchronizing human resource strategy, data security, and privacy.

6 Implications

The research revealed multiple implications associated with the positive impact of electronic human resource management on workforce agility. First, emphasizing on excellent change management and smooth electronic human resource management integration to optimize the impact of electronic human resource management on worker agility. thus, Organizations should provide thorough training programs and address any concerns or resistance to change by proactively managing the transition process. Second, continuous learning and growth are advantages of electronic human resource management. Therefore, organizations should use electronic human resource management to provide tailored learning paths, online training modules, and access to educational resources, resulting in the development of an agile workforce capable of adapting to changing demands. Finally, an electronic human resource management strategy could improve employee engagement by allowing self-service, real-time feedback and evaluation, and open communication. Hence, organizations ought to utilize electronic human resource management to generate an engaging employee experience, stimulate collaboration and knowledge sharing, and develop a feeling of belonging and purpose, because engaged people are more adaptive, inventive, and agile at work.