Determinants of Continuous Usages of E-HRM: An Empirical Evidence from Bangladesh

Purpose: Technological evaluation and e-HRM gained momentum during the COVID-19 pandemic. In order to fully understand their influence on users’ intention to continuous using e-HRM systems, this study integrated IS success model and technology continuity theory (TCT), examining how attitudes and satisfaction moderate the impact on the continuous usage of e-HRM post-COVID-19. Method: The study is based on the integrated IS success model and the TCT model, a survey data’s set of 260 samples. The study deployed structural equation model (SEM) and an importance-performance map analysis (IPMA) to find the determinants of continuous usage intention of e-HRM. Results: The study found that information quality (IQ) was positively associated with perceived ease of use (PEU), satisfaction and continuous use of e-HRM, whereas system quality (SQ) was insignificantly associated with PEU but did not support attitude or continued use of e-HRM. According to IPMA, management needs to pay extra attention to perceived ease of use (PEU), satisfaction (SA), and information quality (IQ) to keep employee using e-HRM. This research has proposed and validated an integrated model by incorporating IS success model and TCT theory. Conclusions: E-HRM is the use of information technology to support human resource management (HRM) activities, resulting in improved efficiency, effectiveness and employee satisfaction. Therefore, this research has added new value for determining professional E-HRM continuous intention to use in developing countries like Bangladesh.

• Information Quality
• System Quality
• Satisfaction
• Attitude and Continuous Intention to Use

1. Introduction

The COVID-19 pandemic accelerated the shift from traditional HR practices to e-HRM, revolutionizing HR practices through online systems that support remote work and virtual teams. Electronic Human Resources Management (e-HRM) is an innovative HRM approach that enables organizations to manage human resources more efficiently and effectively ( Roul, Mohapatra, Pradhan, & Kamesh, 2024 ). E-HRM automates tasks, reduces administrative burdens, and improves accuracy. It is widely used in healthcare, finance, and technology to boost efficiency, compliance, and organizational performance ( Mollel & Rutenge, 2024 ).

Globally, e-HRM is increasingly utilizing artificial intelligence (AI), machine learning, and big data analytics to perform Bangladesh a South Asian growing nation ( Islam, Hassan, & Hossen, 2020 ). Recent advancements in e-HRM systems have increasingly integrated AI technologies, including intelligent recruitment tools, such as automated resume screening and AI-powered Chabot’s ( Upadhyay & Khandelwal, 2018 ). In Bangladesh, e-HRM is increasingly being adopted, although many organizations still rely on traditional HR practices ( Rahman, Mordi, & Nwagbara, 2018 ). Nevertheless, there is a noticeable shift towards digital HR solutions, including the use of HR software, self-service portals, data analytics and social media for recruitment etc. Challenges remain in Bangladesh, primarily due to a lack of technical expertise and a shortage of skilled HR professionals. Using the Technology Acceptance Model highlights factors influencing e-HRM adoption like system quality and user satisfaction. A similar study connects the continued use of platforms like Alipay to their perceived benefits. These studies stress improving user attitudes to enhance e-HRM adoption ( Khayer & Baoin, 2019 ).

In Bangladesh, e-HRM adoption relies on its integration with traditional HR systems and practical usefulness, underpinned by the Task-Technology Fit (TCT) and Information Systems (IS) models ( Singh & Koneru, 2022 ). These models, which highlight the importance of information and system quality for user satisfaction, have been applied in literature on digital platforms like Alipay and e-banking services. However, there’s a research gap on how these factors affect long-term e-HRM continuous intention to use in Bangladesh. Therefore, this research integrates IS theory with the technology continuance theory (TCT) to understand the continuance intention of e-HRM. This study aims to address this by exploring how information quality, system quality, and ease of use influence user satisfaction and attitudes, key to sustained e-HRM use in developing countries like Bangladesh.

The study was carried out in a planned way. In the next following sections, we will go into more depth about the theoretical background and literature review. The study’s methods are then talked about in more detail in Section 3. Section 4, discusses data analysis, including investigating hypotheses and ensuring the model fits. Finally, there is then a full discussion of the results, their theoretical and practical implications, directions for future study and the conclusions.

2. Theoretical Framework, Literature Review and Hypotheses Development

2.1. Proposed Theoretical Framework

An information system (IS) success model and a technology continuity theory (TCT) model were used to make the proposed framework. Some researchers think that how the model is combined or expanded relies on how information systems behave after adoption ( Talwar et al., 2020 ). The three first-level antecedents in the TCT model are confirmation, perceived value and perceived ease of use ( Rahi et al., 2121a ). E-HRM works best when people think they will use it ( Rahi et al., 2020 ). Two things are especially important in emerging countries like Bangladesh, where the system is changing: well-organized, high-quality information and an easy-to-understand system. For the purpose of providing a more comprehensive insight of e-HRM continuing usage than can be obtained by applying the TCT or IS model alone, this study integrated the IS success model with the TCT in response to current demands from the researchers.

2.2. Effect of Information Quality on Satisfaction and PEU

IQ stands for “information quality,” which means how well an online portal can give you up-to-date, correct, thorough, brief and pertinent details ( Al-Rahmi et al., 2022 ). Accurate and useful data is essential for decision-making, as it reduces the likelihood of errors. In e-HRM, the perceived quality of information significantly influences its usage and user satisfaction ( Alshibly, 2014a ). Furthermore, the quality of information greatly impacts user satisfaction, and IT usage is strongly associated with information reliability ( Lutfi et al., 2022 ). Also, Davis (1989) says that “ease of use” refers to how much work people think they would save using a certain tool. Perceived value (PU) and perceived ease of use (PEU) are believed to influence system acceptance. Perceived usefulness and ease of use are crucial for encouraging system adoption ( Zhang et al., 2019 ). Additionally, a study found that information quality is related to perceived ease of use ( Ali & Younes, 2013 ). Based on these points, we developed the following hypothesis:

H₁. Information quality has a direct positive relationship with satisfaction.

H₂. Information quality has a direct positive relationship with PEU.

2.3. Effect of PEU on Satisfaction, Attitudes and Intention to Continuous Usage of E-HRM

In many areas, perceived ease of use (PEU) is a strong predictor of behavior intention. In this study, satisfaction shows how people feel about using e-HRM. Similarly, users will be satisfied with the e-HRM system if it is of good quality. Marketing literature suggests that customer satisfaction is a key factor influencing their decision to repurchase ( Khan, Liang, & Shahzad, 2015 ). E-HRM research indicates that user satisfaction is directly linked to how well their needs are met and exceeded. Ease of use encompasses simplicity, ease of learning, and understanding ( Islami et al., 2021 ). Widianto et al.’s (2020) study shows that PEU positively affects how people feel about using it. Because of this, the study comes up with the following hypotheses:

H₃. Perceived ease of use has a direct positive relationship with satisfaction.

H₄. Perceived ease of use has a direct positive relationship with continued use of E-HRM.

H₅. Perceived ease of use has a direct positive relationship with attitudes.

2.4. Effect of System Quality on Attitudes and PEU

According to Schaupp et al. (2006) , system quality is how easy it is to use the system to get things done. This is a key part of the IS success model, focusing on technical efficiencies like ease of use, response speed, dependability, security, and adaptability ( Lutfi et al., 2023 ). Additionally, Kumar and Lata (2021) discovered an interesting and positive link between system quality and customer satisfaction in online shopping. Regarding e-HRM, system quality greatly affects how it is used and how satisfied users are with it ( Alshibly, 2014a ). Djuitaningsih and Arifiyantoro (2020) also found that the system’s quality greatly affects people’s views about using the system. Studies indicate that system quality impacts the overall level of e-HRM services. Jaafreh (2017) defines system quality as including desirable IS characteristics such as ease of use, system flexibility, reliability, ease of learning, intuitiveness, sophistication and response time. Likewise, the e-HRM system is noted for being quick to respond, easy to understand, reliable and trustworthy. This study suggests the following:

H₆. System quality has a direct positive relationship with attitudes.

H₇. System quality has a direct positive relationship with PEU.

2.5. Effect of Satisfaction on Continuous Intention to Use and Attitudes

The researcher in the earlier study said that user satisfaction is usually based on past usage experience, which can greatly impact how information systems are used ( Bhattacherjee, 2001 ). Several studies have demonstrated that user satisfaction increases the likelihood of continued use of mobile phone-related services. Satisfaction is a short-lived emotion linked to a specific experience, whereas attitude is more general and enduring ( Taylor & Todd, 1995 ). Attitudes significantly influence feelings towards a service or product ( Venkatesh & Davis, 2000 ). This research shows how satisfied workers are with their jobs and how they feel about the e-HRM system in Bangladesh. A previous study ( Iranmanesh et al., 2017 ; Yang et al., 2017 ) said that satisfaction positively affects how people feel about using technology. So, the following hypotheses were put forward by this study:

H₈. The user’s satisfaction has a direct positive relationship with their attitude towards e-HRM.

H₉. Satisfaction has a direct positive relationship with the continuous intention to use e-HRM.

2.6. Effect of Attitudes on Continuous Intention to Use

According to Al Amin et al. (2020) , attitudes are the general positive or negative response of the customer to engaging in a certain behavior with regard to a specific device and technology usage. Increasing research shows that attitude strength predicts purchase behavior, with the direction of the behavior varying as a function of whether attitude valence is strongly positive or strongly negative. There is a positive relationship between attitude and continuous usage. Furthermore, Yusliza and Ramayah (2011) investigated HR professionals’ self-reported intention to use e-HRM technology and found that attitude has a positive impact on the intention to use e-HRM. Furthermore, Giri et al. (2019) concluded that attitude is the most important factor influencing e-HRM use intention. Therefore, we propose the following hypothesis:

H₁₀. Attitude has direct positive relationship towards continuous intention to use of e-HRM.

2.7. Mediating Role of Satisfaction

In various digital service contexts, there is an interconnection between satisfaction, information quality and continuous use intention. Several researchers say satisfaction mediates the relationship between information quality and the intention to continue using e-HRM ( Obeidat, 2016 ). Huang and Liao (2015) found that user satisfaction significantly mediates this relationship, while Wang et al. (2014) demonstrated that information quality correlates with continued e-HRM use, heavily dependent on user satisfaction. This hypothesis suggests that perceived information quality in e-HRM systems directly influences user satisfaction, which in turn affects their long-term intention to use the systems. In e-government services, perceived ease of use did not directly influence continuous intentions but did so through satisfaction ( Mirkovski et al., 2023 ). All of these results demonstrate the possibility that increasing perceived ease of use may increase user satisfaction and, in turn, improve long-term intention to use e -HRM. Therefore, this study proposes the following hypothesis:

H₁₁a. Satisfaction mediates between information quality and continuous intention to use of e-HRM.

H₁₁b. Satisfaction mediates between PEU and continuous intention to use of e-HRM.

2.8. Mediating Role of Attitude

Attitudes play a crucial role in shaping human behavior and decision-making processes. Several scholars have investigated the theory that attitudes mediate the relationship between ongoing interest in using e-HRM and system quality. Venkatesh and Davis (2000) found that system quality significantly influences users’ perceptions of technology, which in turn affects their intentions to continue using it. According to Mohamad et al. (2023 ); and Fahmi et al. (2021 ) attitudes do not directly mediate between system quality as well as PEU and continuous intention to use e-HRM. Bhattacherjee (2001) further supported the mediating role of attitudes in the relationship between system quality and the intention to use technology. Many studies ( Venkatesh & Davis, 2000 ; Bhattacherjee, 2001 and Liu et al., 2020 ) have focused on the concept that attitudes mediate between ongoing intention to use e-HRM and PEU. They specifically highlight the role of attitudes as a critical mediator between continuous intention and perceived ease of use, as well as between system quality and the continuous intention to use e-HRM. Therefore, this study proposes the following:

H₁₂a. Attitudes mediates between system quality and continuous intention to use of e-HRM.

H₁₂b. Attitudes mediates between PEU and continuous intention to use of e-HRM.

In summary, IQ impacts SA and PEU while SA directly connects to the output variable, CU. On the other hand, PEU impacts SA and AT and AT connects to the output variable, CU. Additionally, SQ impact PEU and AT. The theoretical framework of the research is demonstrated in Figure 1 below:

Figure 1

Figure 1. Theoretical framework.

3. Research Methods

To analyze the data, the authors apply SPSS 23.0 and SMART PLS (Version 4.00).

3.1. Population, Sample and Sampling Method

Convenience, judgmental, and quota sampling were used in the study to survey 260 respondents from Bangladesh’s HR sector using a non-probability sample method. Prior to data collection, McQuitty (2004) emphasized the need of finding out the minimum number of samples needed to attain an appropriate level of statistical power for a particular model. Schreiber et al. (2006) pointed out that a generally acknowledged guideline is to have 10 participants for each estimated scale, while the required sample size might vary depending on factors including data normality and the estimating technique used. Garver and Mentzer (1999) and Hoelter (1983) suggested that a “critical sample size” of 240 is enough for Structural Equation Modeling (SEM), although there is no clear consensus on the optimal sample size ( Sivo et al., 2006 ). Consequently, it is generally accepted that a sample size greater than 240 offers sufficient statistical power for data interpretation.

3.2. Data Use and Collection Techniques

The study recruited 260 Bangladeshi e-HRM users through active face-to-face recruitment and individual administration from January to March 2024. Direct communication ensured a high response rate, with verbal consent obtained before administering the survey. Participants received an explanation of the study’s purpose and data protection measures. After sending out 290 surveys, 275 responses were received, and following data cleansing, 260 surveys were used for analysis. Demographically, 56.86% were male, 43.14% female, with 48.58% holding a bachelor’s degree and 47% aged between 31 and 39.

3.3. Data Collection Instrument

The study collected primary data through a questionnaire consisting of two sections: Part-A gathered demographic information. In part B, continuous intention to use e-HRM was measured using a 24-item scale developed from interviews with HRM professionals. To enhance the content validity, all measurement instruments were adapted from existing literature (Appendix 1). Items were rated on a 5-point scale, ranging from strong disagreement to strong agreement.

3.4. Data Analysis

The questionnaire was meticulously crafted to prioritize structured and semi-structured questions, ensuring clarity, relevance and sensitivity to respondents’ experiences. Following Hair et al. (2006) , data analysis began with descriptive statistics, followed by statistical tests to validate hypotheses. Structural model evaluation, including the moderation effect of organizational types, utilized structural equation modeling (SEM), specifically Partial Least Squares (PLS), due to its suitability and popularity ( Hair et al., 2019 ). The hierarchical examination of the structural model employed a two-step approach ( Henseler et al., 2012 ), assessing validity and reliability of the measurement model before testing structural pathways via multiple regression. Additionally, the moderating effect of organizational types was analyzed through multiple group analysis (MGA) ( Hair et al., 2006 ). At the last stage, we have conducted IPMA to recognize the critical factors that have higher importance but lower performance in explaining the continuance intention of e-HRM.

4. Findings

4.1. Common Method Bias and Multicollinearity Test

The study addressed common method bias (CMB) in cross-sectional survey data using Harman’s single-factor test, following Podsakoff et al. (2003) and Khayer et al. (2020) . Principal axis factor analysis (PAF) revealed that one component explained only 35.85% of the overall variance, contrary to the suggested 50%. Additionally, variance inflation factor (VIF) values obtained from collinearity tests, were below the standard threshold of 3.5, indicating no concern for CMB. Furthermore, VIF values well below the threshold of 10 suggest no multicollinearity issue in the study ( Table 1 ).

Table 1

Table 1. Variance Inflation Factor (VIF).

Constructs	AT	CU	PEU	SA
AT		1.528
CU
IQ	3.493		2.412	2.894
PEU	2.377	1.541		2.373
SA	2.484	1.791
SQ	3.046		2.412	2.717

4.2. Measurement Model

Hair et al. (2006) developed the measurement model by combining the research instrument’s convergent validity, discriminant validity and reliability. The study evaluated convergent validity using composite reliability (CR), average variance extracted (AVE) and factor loadings, following Hair et al. (2006) and Henseler et al. (2016) . AVE values for all variables exceeded 0.50, indicating convergent validity, while reliability coefficients surpassed the recommended 0.70 standard ( Table 2 ). The study also ensured internal reliability and convergent validity by confirming composite reliability above 0.70 and factor loadings above 0.50. Additionally, the square roots of AVE values exceeded relevant cross-correlations, demonstrating discriminant validity ( Table 2 ). Heterotrait-Monotrait (HTMT) ratio values were below 0.90, further supporting discriminant validity as per Henseler et al. (2014) , with all constructs scoring below the threshold for discriminant validity ( Table 3 ).

4.3. Structural Model

This section delves into the empirical examination of the continuous usage model of e-HRM services. The results indicate that the model explains 34.9% of the variance in attitude, 57.9% in perceived ease of use, 73.1% in continuous usage and 59.7% in satisfaction.

According to the paths of the results, the following hypotheses were statistically significant: H₁ (IQ -> S, β = 0.062, t = 11.152), H₂ (IQ -> PEU, β = 0.075, t = 5.701), H₃ (PEU -> S, β = 0.072, t = 1.010), H₄ (PEU -> CU, β = 0.050, t = 10.861), H₅ (PEU -> A, β = 0.098, t = 2.565), H₆ (SQ -> A, β = 0.085, t = 0.253), H₇ (SQ -> PEU, β = 0.074, t = 5.237), H₈ (S -> CU, β = 0.060, t = 7.962), H₉ (S -> A, β = 0.075, t = 6.702), H₁₀ (A -> CU, β = 0.056, t = 1.693), H_11(a) (IQ -> S -> CU, β = 0.055, t = 5.903), H_11(b) (PEU -> S -> CU, β = 0.034, t = 1.029), H_12(a) (SQ -> A -> CU, β = 0.010, t = 0.212) and H_12(b) (PEU -> A -> CU, β = 0.022, t = 1.099) were not statistically significant in relation to the hypotheses. As a result, among the hypotheses H₃, H₆, H₁₀, H_11(b), H_12(a) and H_12(b) were not supported but H₁, H₂, H₄, H₅, H₇, H₈, H₉ and H_11(a) were supported ( Table 4 ).

Table 2

Table 2. Measurement model evaluation.

Constructs	Items	Loading	Cronbach’s alpha	Composite reliability (rho_a)	Composite reliability (rho_c)	Average variance extracted (AVE)
AT	A1	0.805	0.806	0.809	0.873	0.633
	A2	0.766
	A3	0.832
	A4	0.777
CU	CU1	0.729	0.865	0.873	0.909	0.716
	CU2	0.890
	CU3	0.882
	CU4	0.873
IQ	IQ1	0.885	0.859	0.861	0.905	0.704
	IQ2	0.845
	IQ3	0.812
	IQ4	0.811
PEU	PEU1	0.825	0.858	0.860	0.904	0.701
	PEU2	0.867
	PEU3	0.848
	PEU4	0.808
SA	SA1	0.780	0.813	0.817	0.877	0.641
	SA2	0.808
	SA3	0.768
	SA4	0.845
SQ	SQ1	0.821	0.840	0.841	0.893	0.675
	SQ2	0.804
	SQ3	0.833
	SQ4	0.829

Source: Smart PLS, Compiled by Author. Note: AT = Attitude, CU = Continues to Use, IQ = Information quality, PEU = Perceived Ease of Use, SA = Satisfaction, SQ = System Quality.

Table 3

Table 3. Heterotrait-Monotrait (HTMT) ratio test.

	AT	CU	IQ	PEU	SA	SQ
A
CU	0.51
IQ	0.534	0.986
PEU	0.544	0.891	0.842
SA	0.697	0.881	0.881	0.681
SQ	0.539	0.962	0.900	0.828	0.854

Source: Smart PLS, Compiled by Author; Note: AT = Attitude, CU = Continues to Use, IQ = Information quality, PEU = Perceived Ease of Use, SA = Satisfaction, SQ = System Quality.

Table 4

Table 4. Structural model.

Hypotheses	Paths	Coefficients (β)	T-Statistics	P-Value	Comments
H1	IQ -> S	0.062	11.152	0.000	Supported
H2	IQ -> PEU	0.075	5.701	0.000	Supported
H3	PEU -> S	0.072	1.010	0.312	Not Supported
H4	PEU -> CU	0.050	10.861	0.000	Supported
H5	PEU -> A	0.098	2.565	0.010	Supported
H6	SQ -> A	0.085	0.253	0.800	Not Supported
H7	SQ -> PEU	0.074	5.237	0.000	Supported
H8	S -> CU	0.060	7.962	0.000	Supported
H9	S -> A	0.075	6.702	0.000	Supported
H10	A -> CU	0.056	1.693	0.091	Not supported
H11(a)	IQ -> S -> CU	0.055	5.903	0.000	Supported
H11(b)	PEU -> S -> CU	0.034	1.029	0.303	Not Supported
H12 (a)	SQ -> A -> CU	0.010	0.212	0.832	Not Supported
H12 (b)	PEU -> A -> CU	0.022	1.099	0.272	Not Supported

Note(s): AT = Attitude, CU = Continues to Use, IQ = Information quality, PEU = Perceived Ease of Use, SA = Satisfaction, SQ = System Quality.

4.4. Predictive Relevance

The section utilizes Stone-Geisser’s Q² to assess the predictive significance of endogenous constructs. Cohen & Cohen (2013) categorizes Q² values of 0.02, 0.15, and 0.35 as indicating low, moderate, and significant predictive relevance. Reveals positive Q² values exceeding 0.15 for all endogenous constructs, suggesting significant predictive importance. Specifically, attitude, continuous use, perceived ease of use (PEU), and satisfaction with e-HRM display Q² values of 0.339, 0.728, 0.575, and 0.593, respectively, indicating substantial predictive significance. Additionally, R² values, indicating predictive capability, are discussed ( Table 5 ). Hair et al. (2019) and Henseler et al. (2009) define R² values of 0.75, 0.50, and 0.25 as moderate, weak, and significant, respectively.

Table 5

Table 5. Predictive relevance.

Endogenous latent variables	R2	Q2
Attitude	0.349	0.339
Continuous to use	0.731	0.728
Perceived Ease of Use	0.579	0.575
Satisfaction	0.597	0.593

Source: Smart PLs, Compiled by Author.

4.5. Importance Performance Map Analysis (IPMA)

We performed an importance-performance map analysis (IPMA) in PLS. IPMA in PLS-SEM identifies variables with high path coefficients (importance) but poor overall performance. It utilizes average latent variable scores for performance and marginal effects for influence. Perceived ease of use, satisfaction, and information quality are crucial for e-HRM usage, which has high path coefficients. However, despite lower importance, attitude and system quality exhibit better performance.

Table 6

Table 6. Importance-Performance Map Analysis (IPMA) for continues to use.

Latent constructs	Total effect of the Continues to use	Latent constructs
Attitude	−0.096	59.961
Information Quality	0.305	61.678
Perceived Ease of Use	0.553	60.702
Satisfaction	0.430	59.099
System Quality	−0.001	58.347

Source: Smart PLs, Compiled by Author.

The IPMA says that management needs to pay extra attention to perceived ease of use (PEU), satisfaction (SA), and information quality (IQ) to keep employees using e-HRM. This is because perceived ease of use is more important than satisfaction, performance and information quality. However, managers should not overemphasize the importance of attitude (AT) and system quality (SQ) characteristics to improve performance. Instead, they should strive to increase their significance, given that these characteristics are not as important as other variables, but their performance is still superior. In conclusion, managers should invest more funds to improve IQ, PEU and SA. The researcher also creates priority maps ( Figure 2 ). These maps show that, in comparison to other elements, PEU (61.678), satisfaction (59.099), and information quality (61.678) have comparatively high-performance ratings. However, their path coefficients are also high (high significance scores). However, attitude (59.961) and system quality (58.347) perform better than their importance. Managers do not need to focus more on its performance but rather on its importance. In depth, the importance of satisfaction (0.430) is high and performance is low comparatively with information quality because its performance is high but importance is low that is 0.305. So, managers should place more priority on satisfaction than information quality ( Table 6 ).

Figure 2

Figure 2. IPMA (Priority Map) for performance impact on Continues to use (Source: Smart PLs, Compiled by Author).

5. Discussion

This study verified that attitudes on the intention to continue using e-HRM are influenced by perceived ease of use and satisfaction. On the other hand, it was found that the attitude was indirectly impacted by information quality. The degree of satisfaction with the use of e-HRM and the degree to which users may find it useful and beneficial can be utilized for evaluating an individual’s attitude. This study asserted in line with previous research ( Slade et al., 2015 ; Liébana-Cabanillas et al., 2014 ) that consumers’ attitudes toward the adoption of technological systems are influenced by their usefulness and benefits. System quality was found insignificant on users’ attitudes because, while using e-HRM for business operations, users primarily concentrate on the benefits they see. In the same manner, attitudes and perceived ease of use correlate to system quality, but not directly to satisfaction. The association between attitudes and continuous usage is substantially correlated with perceived ease of use, while the relationship between continuous usage and information quality is mediated by satisfaction. Perceived ease of use, however, does not substantially affect satisfaction. Lastly, attitude does not act as a mediator between perceived ease of use or system quality and continuing user intention, which may account for users’ resistance to transitioning from complex, obsolete systems to easier alternatives. These results suggest that online services systems like e-HRM can offer various services (such hiring, recruitment, and training) depending on the location, time, environment, etc. of users. According to Alshibly (2014b) , the perceived quality of information in e-HRM has significant effects on how users use it and how satisfied they are with it. Additionally, IT use is directly related to information security, and the quality of the information significantly impacts user satisfaction ( Lutfi et al., 2022 ). Furthermore, this study demonstrated that attitude, particularly in the context of Bangladesh, has a significant and powerful effect in a decision to continue using e-HRM. Users may plan to use e-HRM continuously based on their attitude strength, as indicated by the strong and significant impact of the mediating construct attitude on continuance intention. This study also discovered that the quality of the information determines e-HRM users’ level of satisfaction. These results suggest that a wide range of e-HRM functional benefits directly impact user satisfaction.

Additionally, the degree to which customer satisfaction first expectations are met influences their level of satisfaction with e-HRM. Furthermore, the intention to continue is highly influenced by the user’s satisfaction. Previous studies carried out in different circumstances have confirmed these results ( Bhattacherjee, 2001 ; Nascimento et al., 2018 ). This result is expected because the intention to continue using e-HRM is largely dependent on how well users get this service. Consistent with certain previous studies, Information Quality System Quality has an impact on perceived Ease of Use. This suggests customers adjust their impressions of the advantages of e-HRM based on their actual experience using it. The level of system quality and information quality regarding users’ initial expectations of using e-HRM can be associated to how well the system functions and improves perceived ease of use. In contrast, inaccurate information and inadequate system quality result in lower levels of ease of use. This result emphasizes the importance of giving users access to relevant real-time information.

Finally, this study identifies several critical factors that have relatively higher total effects but lower performance compared to other factors in explaining the continuance usage intention of e-HRM. According to the results of IPMA, it is apparent that perceived ease of use (PEU), satisfaction (SA), and information quality (IQ) are the most critical factors. As these factors have higher total effects but lower performance scores, special managerial actions must be taken to improve the performance of these factors.

6. Limitations and Future Research Scope

The paper highlights practical and theoretical implications along with certain limitations. Firstly, while the proposed model integrates TCT and IS, it’s noted that future studies should expand the model to include additional predictors like trust, security and motivation, considering contextual relevance. Secondly, the study’s reliance on cross-sectional data from HR professionals suggests the need for multi-country, longitudinal, and case-based studies.

Thirdly, we collected data from a diverse range of organizations including both public and private, and small and large institutions. We conduct a subgroup or comparative analysis based on organizational type in the future study.

7. Conclusions

In conclusion, it can be concluded that this combined model of TCT and IS contributes to the existing body of knowledge that users’ continued use of e-HRM is substantially important. This study proved that information availability as per necessity, system quality, and ease of use is mostly important for the continued use of e-HRM in Bangladesh. Also, satisfaction with the process significantly influences the service, information and ease of use of the e-HRM system. However, the attitude of the professionals does not differ for service quality, but influences information and ease of use. Though Bangladesh has many limitations when using e-HRM, professionals prefer to use it based on the information, system quality and ease of use.

7.1. Theoretical Contributions

The study contributes significantly to the theoretical knowledge of extended technology use by combining the Technology Continuance Theory (TCT) with the Information Systems (IS) Success Model. By combining these models, the study demonstrates how users’ cognitive and emotive responses (from TCT) and system success factors (from the IS Success Model), such as system quality, information quality, and service quality, affect continuous usage intentions. This dual-framework approach contributes to the literature by providing a comprehensive approach that considers the e-HRM system’s technical performance as well as the psychological factors that affect users’ decisions to remain with the system over time. As the model explained 73.1% of the variance in continuance usage intention, 34.9% of the variance in attitude, 69.7% of the variance in satisfaction, and 57.9% of the variance in perceived ease of use, the study’s findings demonstrate that the suggested model has a strong predictive capacity. This study offers generalizability as well as the combining of theories and models in the context of e-HRM; as a result, this model can be used to evaluate the intention of other innovative technologies to continue. Analyses were conducted on the scales’ validity and reliability as part of the survey instrument.

Furthermore, IPMA in PLS has been used in this work to produce more insights into the factors influencing the intention to continue using the product. In this research environment, the purpose of IPMA was to investigate the predictors’ performance (average latent variable scores) and relevance (total effects) in influencing target constructs and continuance intention. Such analysis was carried out in a relatively small amount of research, especially when it came to e-services.

7.2. Practical Implications

The study provides significant practical insights for businesses by combining the theories of information systems (IS) and technology continuous Theory (TCT). The report addresses the significance of user satisfaction, ease of use, and information quality. It recommends that firms allocate resources towards user-friendly e-HRM platforms, comprehensive training initiatives, and accurate security procedures in order to maintain an excellent user experience and establish credibility. By making a major impact on the willingness of the employees to remain with the system, these initiatives can ensure its continuous acceptance and use. Since continuation intention is correlated with a positive attitude, it stands to reason that e-HRM service providers should benefit from helping clients generate positive attitudes. To maintain a good attitude toward the use of e-HRM, they must concentrate on enhancing the functional benefits that meet up to users’ expectations and raising the level of satisfaction. To make sure that e-HRM services have all the features and functionalities that users need, managers have to allocate more funds to improving their R&D activities.

In order to improve information quality and perceived ease of use, managers ought to consider about incorporating customers in the design of services and applications. This will ultimately increase user happiness. Furthermore, the e-HRM system’s usefulness and relevance inside the company can be increased by integrating user feedback to make continual improvements ( Bhattacherjee, 2001 ; Venkatesh & Davis, 2000 ; Pavlou, 2003 ). With significant practical implications, this study recommends managers and legislators to continue using E-HRM for efficient organization administration.

Appendix: Questionnaire

Construct	Items	Source/Author(s)
Information Quality (IQ)	IQ1: People have received sufficient information for monitoring E-HRM.	Xu et al. (2013) ; Wu and Tian, (2021) ; Duy Phuong and Trang (2018)
	IQ2: Authority provides reliable and necessary information for E-HRM services.
	IQ3: Information is available for further analysis.
	IQ4: The information provided by E-HRM is up-to-date.
System Quality (SQ)	SQ1: Usefulness and easy to use are important for E-HRM.	Wu and Tian (2021) ; Duy Phuong and Trang (2018)
	SQ2: The ESN provides excellent service quality on the whole beyond my expectations.
	SQ3: Information, interactivity, trust, response time, website design, intuitiveness, flow, inattentiveness, integration, communication, business process, and sustainability are major E-HRM quality instruments.
	SQ4: The responsible service personnel are always highly willing to help whenever I need support with the E-HRM services.
Perceived ease of use (PEU)	PEU1: It is easy for me to remember how to perform tasks using E-HRM and I find easy to recover the error encountered while using E-HRM.	Rawashdeh et al. (2021) ; Fathima and Muthumani (2019)
	PEU2: It is easy to become proficient in using E-HRM.
	PEU3: My interaction with E-HRM would be clear and understandable.
	PEU4: It would be easy for me to become skillful with E-HRM I would find E-HRM easy to use.
Satisfaction (SA)	SA1: I am satisfied with the functions provided by the E-HRM system and very contended with my overall E-HRM used.	Rawashdeh et al. (2021) ; Sareen (2015)
	SA2: I am absolutely delighted with the overall use of my E-HRM.
	SA3: Interaction with E-HRM does not require a lot of my mental effort.
	SA4:I find it easy to get the Internet banking to do what I want it to do.
Attitudes (AT)	AT1: Using E-HRM for learning would be a wise idea.	Voermans and Van Veldhoven (2007) ; Rahi et al. (2021b)
	AT2: I like working with E-HRM.
	AT3: It is desirable to use the E-HRM.
	AT4: Using E-HRM is a pleasant experience.
Intention to continue use (CU)	CU1: I intend to continue using E-HRM rather than discontinue its use.	Rawashdeh et al. (2021) ; Noerman et al. (2021)
	CU2: My intentions are to continue using E-HRM income tax rather than manual processing or other alternative means.
	CU3: I intend to continue using E-HRM to save time and cost.
	CU4: I will recommend other people to use E-HRM.

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