doi: 10.56294/dm2024.362

ORIGINAL

Factors Influencing the Intention to Use Human Resource Information Systems Among Employees of SMEs in Iraq

Factores que influyen en la intención de utilizar sistemas de información de recursos humanos entre los empleados de las PYME en Irak

Al Qaysi Hamid Hazim Majid¹ *, Noor Fareen Abdul Rahim¹ *, Ai Ping Teoh¹ *, Alhamzah Alnoor² *

¹Graduate School of Business, Universiti Sains Malaysia, Penang, Malaysia.

²Basra Management Technical College (BMTC), Southern Technical University, Iraq.

Cite as: Hazim Majid AQH, Abdul Rahim NF, Ping Teoh A, Alnoor A. Factors Influencing the Intention to Use Human Resource Information Systems Among Employees of SMEs in Iraq. Data and Metadata. 2024; 3:.362. https://doi.org/10.56294/dm2024.362

Submitted: 22-01-2024                   Revised: 05-05-2024                   Accepted: 06-09-2024                Published: 07-09-2024

Editor: Adrián Alejandro Vitón-Castillo

Corresponding author: Al Qaysi Hamid Hazim Majid *

ABSTRACT

Introduction: in light of technological development and digital transformation, today’s Small and Medium-Sized Enterprises (SMEs) rely heavily on their ability to use technology to succeed. Employees’ acceptance or rejection of modern technology and the factors affecting it are crucial topics for SMEs.

Method: this study investigates the moderating roles of Technology Readiness (TR), Experience (EX), Trust, and Voluntariness of Use (VU) on the relationship between Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), Task-Technology Fit (TTF), Facilitating Condition (FC), and the Intention to Use (ITO) Human Resources Information Systems (HRIS) among employees of SMEs in Iraq. Data from 304 employees of Iraqi SMEs will be collected. Statistical analysis will be performed using SPSS and Partial Least Squares (PLS).

Results: this research provides insight into the reasons behind employees’ resistance to adopting HRIS, supporting the organization’s policy of developing employee skills and training them in information technology systems.

Discussion: additionally, evaluating the acceptance of information technology systems can develop the framework for technical services in companies, including human resource units. Furthermore, defining the model’s architecture will update stakeholder knowledge and enhance human resource management services in Iraq.

Keywords: UTAUT; Trust; Task-Technology Fit; Human Resources Information Systems; Small and Medium-Sized Enterprises; Iraq.

RESUMEN

Introducción: en vista del desarrollo tecnológico y la transformación digital, las pequeñas y medianas empresas (PYME) de hoy dependen en gran medida de su capacidad para utilizar la tecnología para tener éxito. La aceptación o rechazo de la tecnología moderna por parte de los empleados y los factores que la afectan son temas cruciales para las PYME.

Método: Este estudio investiga los roles moderadores de la preparación tecnológica (TR), la experiencia (EX), la confianza y la voluntariedad de uso (VU) en la relación entre la expectativa de esfuerzo (EE), la expectativa de rendimiento (PE), la influencia social (SI), el ajuste entre tarea y tecnología (TTF), la condición facilitadora (FC) y la intención de uso (ITO) de los sistemas de información de recursos humanos (HRIS) entre los empleados de las PYME en Irak. Se recopilarán datos de 304 empleados de PYME iraquíes. Se realizará un análisis estadístico utilizando SPSS y mínimos cuadrados parciales (PLS).

Resultados: esta investigación proporciona información sobre las razones detrás de la resistencia de los empleados a adoptar HRIS, lo que respalda la política de la organización de desarrollar las habilidades de los empleados y capacitarlos en sistemas de tecnología de la información.

Discusión: además, la evaluación de la aceptación de los sistemas de tecnología de la información puede ayudar a desarrollar el marco para los servicios técnicos en las empresas, incluidas las unidades de recursos humanos. Además, la definición de la arquitectura del modelo actualizará los conocimientos de las partes interesadas y mejorará los servicios de gestión de recursos humanos en Irak.

Palabras clave: UTAUT; Confianza; Ajuste entre Tareas y Tecnología; Sistemas de Información de Recursos Humanos; Pequeñas y Medianas Empresas; Irak.

INTRODUCTION

Given the rapid advancements in modern technology, computer applications, and communication sciences, coupled with substantial investments in information systems, there has been a profound digital revolution in the operational dynamics of business organizations.^{(1,2,3,4,5,6)} However, one important issue facing HRIS implementation is the long time required to obtain its benefits.^(7,8) Despite spending millions on HRIS in SMEs in Arab countries,^(9,10) the requirements for applying modern technology and complex organizational requirements remain low or non-existent.

In the private sector, the role of modern technology in developing companies is widely acknowledged, but applying information systems is problematic due to employee resistance.⁽¹¹⁾ The neglect or weakness in the intention to use (ITO) HRIS is a primary cause of its lack of adoption and popularity. This neglect contributes to SMEs’ inability to compete with larger companies, affecting human resources in terms of production and continuity.⁽¹²⁾ In Iraq, SMEs face issues with employees’ lack of ITO for HRIS. They rely on traditional, paper-based methods, which increase errors in data collection and storage, negatively affecting human resource management. Reports show that 70 % of employees do not use an information system, impacting both individual and company performance.⁽¹³⁾

To address this, there are recommendations to adopt HRIS to improve employee performance through better incentives and promotions and to enhance human resources management, helping companies compete and avoid mistakes.⁽¹⁴⁾ Employee resistance is a significant obstacle to the success of modern technology applications.^(15,16,17) In Iraqi SMEs, many employees fear that modern technology will replace them, leading to resistance. Employees need to be convinced that technology will help improve their performance and reduce strain.⁽¹⁸⁾

Another reason for resistance is the lack of employee practice and training, leading to errors and inefficiencies in human resources management.⁽¹⁹⁾ The benefits of HRIS, such as promotions, incentives, reduced effort, and increased productivity, are often misunderstood.⁽²⁰⁾ Trust in technology is also a factor; without confidence in the system, employees resist its adoption.^(7,21,22,23) An important issue in Iraqi companies’ human resources departments is that not all employees need high-level technology for their tasks. When technology is seen as unnecessary or complex, it is rejected.

This issue must be addressed when implementing HRIS.⁽²⁴⁾ Users of modern technology need support and guidance from specialists. Sufficient institutional support can increase employees’ intention to use modern technology, knowing they will receive help in case of difficulties.⁽²⁵⁾ Hence, addressing issues with the intention to use modern technology among employees in SMEs is crucial.⁽¹⁴⁾ Many theories have been proposed to identify the obstacles to using information systems. Employee organization and actions are critical to the success of these systems. Companies that fail to consider employees’ intentions when implementing HRIS negatively affect the system’s application.⁽²⁶⁾

One of the most important theories in explaining obstacles to users’ intentions is The Unified Theory of Acceptance and Use of Technology (UTAUT). Researchers have adopted it to predict users’ intentions toward adopting modern technology, and it is considered one of the most effective models for illustrating the incorporation of contemporary technology in multiple contexts.⁽²⁷⁾ The UTAUT theory has been utilized in many studies, but results vary or the model lacks integration. Some studies show that certain variables have an effect, while others show that all variables have an effect, indicating a gap in previous research.^(11,28,29) The appropriateness of technology to tasks immensely affects HRIS adoption because rejections arise out of divergent task-technology fit.⁽³⁰⁾

Therefore, the study helps to fill the gap in understanding how these factors interplay to influence the intention of employees to use HRIS in SMEs in Iraq. In view of the specificity of the SME context in developing countries like Iraq, this research shall therefore be of useful meaning in a context where technological infrastructure, trust in systems, and fit with the task differ.

This research is important for practical and theoretical reasons. First, identifying the main factors that impact HRIS adoption for SMEs within Iraq will provide valuable insights to be applied in the field of work to increase employee acceptance and usage for better human resources processes and operational efficiency, as well as decision-making. This will also help policymakers guide technological development, which holds the key to economic growth for SMEs that play a vital role in the economy of Iraq.

The theoretical contribution of this research is in the extension of the UTAUT model with trust, task-technology fit, and experience for a more comprehensive understanding of technology adoption in a developing country. The research also answers the digital divide between groupings of larger corporations as opposed to SMEs; therefore, it puts forward the need to ensure that smaller businesses are not left behind in digital transformation. Moreover, this underlines the cultural and regional factors peculiar to Iraq and makes contributions that have broader implications for similar regions globally.

Successful adoption of HRIS could increase efficiency, job satisfaction, and retention, which are very critical for SMEs due to the limited number of available resources. Eventually, this research will contribute to the growth and competitiveness of SMEs by fostering wider economic development in Iraq while it seeks to diversify its economy.

Hypotheses development

Technology Acceptance Factors and Human Resource Information System

Performance Expectancy (PE) means the belief that employing HRIS will improve performance. According to Venkatesh et al. (2003),⁽²⁷⁾ PE is the primary determinant of a user’s willingness to adopt technology and directly influences behavioral intention.^(15,31) Researchers aim to ascertain PE’s effect on the intention to use HRIS among users. Studies have shown the key role of PE in ITO within a UTAUT framework.⁽³²⁾ Empirical studies have demonstrated a substantial positive impact of PE on ITO for technology systems.^(33,34) However, only a limited number of studies have used this connection to forecast how PE may affect HRIS workers’ desire to use the system. Researchers have explained the significant positive role of PE in increasing ITO for modern technology.⁽¹¹⁾ Thus, the subsequent hypothesis was suggested:

H1: Performance expectancy positively influences Intention to Use

Whenever new technologies are user-friendly, easy to use, and allow users to access human resource management resources, the likelihood that they will be accepted and embraced by users is high. Users are expected to accept HRIS when it is simple for them. Anticipated effort has been identified as a key component of the UTAUT framework by technologists, who have further discovered that their prediction model on this factor, called Effort Prediction, is highly significant in determining intention toward using ITU techniques.^(35,36) Effort expectation has been shown in studies to substantially determine one’s readiness to utilize ITU mechanisms.^(37,38,39) More research has illustrated the constructive impact of thinking about the effort on ITU with respect to HRIS.^(12,25) Conversely, other studies suggest that there might be little correlation between expected exertion and technology utilization behavior.^(11,40) Accordingly, we propose the following hypothesis:

H2: Effort expectancy positively influences Intention to Use.

Subjective norms, as defined by Ajzen and Fishbein (1980), represent the social pressures or expectations individuals perceive regarding their participation in or abstention from specific behaviors. Literature integrates social influence as a foundational construct within UTAUT to assess the acceptance of technological innovations among users.^(36,41,42) Several studies investigated the impact of SI on the intention to use HRISs, finding significant positive effects on employees’ intentions.^(11,43) However, other studies have indicated that SI does not significantly affect ITO.^(44,45,46) Therefore, the hypothesis was formulated:

H3: Social influence positively influences Intention to Use.

This research has also revealed that enabling factors were important in affecting technology system adoption.^(37,47) The facilitating conditions in the use of HRIS by end users have been explored in the literature using UTAUT as the framework.^(35,48) In this light, it is possible to conclude that supporting conditions strongly influence employee intentions toward the use and adoption of HRIS.^(49,50) This claim though has been challenged by some scholars who argue that there is no relationship between supportive conditions and employees’ embrace of modern technologies.^(33,40,44) Thus, the hypothesis on that:

H4: Facilitating conditions positively influence Intention to Use.

Task Characteristics and Task Technology Fit

The performance of the work is enhanced by optimal task technology fit because it aligns technology with task requirements and user skills thus enabling efficient task completion.⁽⁵¹⁾ Users view technology in terms of its functional efficiency and ability to support their tasks and needs.⁽⁵²⁾ Similarly, tasks involve various demands and features that the supporting technology should handle; this is called technological functionality.⁽⁵³⁾ It is for this reason that the hypotheses are:

H5: Task characteristics positively influence Task Technology Fit.

Technology Characteristics and Task Technology Fit

Technology characteristics refer to the features of tools people use to perform specific tasks. Goodhue and Thompson (1995) consider technological characteristics as reliable indicators of system quality, defining them as “the technology that people use to perform their tasks”.⁽⁵⁴⁾ Previous research has shown a significant relationship between technological attributes and the adoption of Internet technology in organizations.⁽⁵⁵⁾ According to recent studies, technological characteristics are very important, and they affect users’ intentions of technology use in order to improve work performance. They found that the application of TTF and UTAUT constructs would highly influence users’ intention for adopting technology.⁽¹¹⁾ In that case, my hypothesis developed for this particular study is:

H6: Technology characteristics positively influence Task Technology Fit.

Task Technology Fit and Intention to Use

The Task-Technology Fit (TTF) concept was introduced by Goodhue and Thompson (1995) which measured user acceptance of new technology through three dimensions namely task requirements, technological features, and individual skills. This concept argues that users are more likely to accept technology if the task requirement matches the technical capability (Goodhue & Thompson, 1995).⁽⁵⁴⁾ On the other hand, if a task becomes more complex but no corresponding technology is available to support it, this reduces TTF ⁽⁵⁶⁾ (Ratna et al., 2020). Based on this explanation then my hypothesis will be:

H7: Task Technology Fit positively influences Intention to Use.

Moderating Role of Technology Trust

Trust denotes the readiness of one party to depend on or rest on the actions of another.⁽⁵⁷⁾ Another view is that trust is the total of a customer’s perception of goodness, competency, and integrity which in turn can increase the user’s readiness to embrace and use a system for meeting work requirements securely and without apprehension.⁽⁴¹⁾ Trust between individuals or within a system is crucial for users’ confidence in technology.⁽⁵⁸⁾ As per Taselaar, 2020,⁽⁵⁷⁾ technological features such as anonymity underscore why trust matters when deciding whether to adopt technology across systems. Regarding personal information, however, users must have faith in technology before they can take it up and use it for transactions and records keeping such as their contact addresses and phone numbers are concerned.⁽⁵⁹⁾ In addition, when adopting modern technology for the first time; not trusting it enough over uncertainties may lead to its rejection ⁽⁶⁰⁾ and diminished intention to utilize. Conversely, by enhancing the ease of use through reducing complexity high user confidence fosters the adoption of technology hence increasing the intention to use.⁽⁶¹⁾ Accordingly, this hypothesis was developed by us in our study as follows:

H8: Technology trust moderates the relationship between performance expectancy, effort expectancy, social influence, facilitating conditions, and intention to use.

Moderating Role of Experience and Voluntariness of Use

According to studies, an increased IT users’ Experience (EX) greatly affects their technology adoption.⁽⁶²⁾ It is maintained by some researchers generally agree on the influence of previous EX on technology adoption. A study conducted by ⁽⁶³⁾ showed that prior personal EX may predict acceptance of computers and related modern technology skills in IT reliably. Users have sometimes to go through training and professional EX to gain the necessary IT skills. High levels of EX make the use of the IT system for enhancing user experience more comfortable and enjoyable; therefore, it is expected that such enhanced usage intention would correlate positively with system usability since EX has been shown to have a positive relationship with system usability.^(64,65) Hence, the hypothesis is:

H9: Experience moderates the relationship between effort expectancy, social influence, facilitating condition, and intention to use.

Technology adoption research has proven the importance of voluntary use (VU) for systems in this regard.⁽⁶⁶⁾ If end users see it as being optional, they are more likely to voluntarily embrace it.⁽⁶⁶⁾ Additionally, the decision to adopt new technologies at work is influenced by organizational policies that guide whether or not this technology should be compulsory.⁽⁶⁷⁾ In this kind of setting, the choice to adopt and use information technology is left open to users according to diffusion theory in innovation studies. This was the hypothesis of the study:

H10: Voluntariness of use moderates the relationship between social influence and intention to use.

Moderating Role of Technology Readiness

The adoption of new information technology is significantly influenced by technological advancement and staff opposition.⁽⁶⁸⁾ Depending on attitudes and beliefs, users may accept or refuse innovations. Readiness for technology encompasses both the infrastructure and human elements that might either facilitate or impede technology acceptance within companies.^(69,70) Therefore, the technology readiness hypothesis is:

H11: Technology readiness moderates the relationship between task-technology fit and intention to use.

METHOD

The research philosophy that informs this study is a positivist viewpoint. This philosophy has stronger features of objectivity, quantification, and employment of scientific methods in finding the relationships between variables.⁽⁸⁾ In addition, Akour et al. (2023) indicated that it presupposes an objective reality, observed or measured.⁽⁷¹⁾ The intention of using HRIS among employees of SMEs in Iraq will be monitored as a measurable phenomenon that can be studied through the collection and analysis of empirical data.

Further, a cross-sectional design is followed where the data is collected at one point in time from a given population.⁽⁷²⁾ In this regard, it is relevant to test how the relationships among variables, i.e., UTAUT, trust, task-technology fit, and experience, are linked to the use of HRIS within a defined period. This cross-sectional design will let one measure how these factors impact the adoption of HRIS at this present moment.

The quantitative research methodology was harnessed under the guidance of a positivist philosophy. In this respect, quantitative data is collected through questionnaires prepared to measure the principal variables indicated in this study. These variables were operationalized by scales derived from existing validated instruments in literature based on a five-point Likert scale. UTAUT factors,⁽⁴⁷⁾ Trust,⁽⁴⁸⁾ Task-Technology Fit,⁽⁵¹⁾ Experience,⁽⁶²⁾ and HRIS adoption intention.⁽³⁸⁾

The sample population consists of employees working in SMEs in Iraq. To ensure rigorous analysis (Cohen, 1988) recommend calculating the sample size before performing the study. Using G*Power software, with an effect size (f2) of 0,15, a type I error probability (α) of 0,05, a type II error probability (β) of 0,95, and 18 predictors or indicators, the estimated minimum sample size (N) is 213, ensuring sufficient statistical power to effectively evaluate the research model. Surveys were distributed via email to targeted participants in Iraqi SMEs, yielding 304 responses from lower-level employees in the human resources department.

Data analysis was conducted using Smart-PLS, a software tool developed for partial least squares structural equation modeling. PLS-SEM is particularly suitable where there is exploratory research or in cases when the research model is complex, and latent variables are involved, as in this case.^(73,74) This study assessed the measurement model for reliability and validity to ensure that the constructs have been measured correctly. This would consist of testing for construct reliability using Composite Reliability, convergent validity using Average Variance Extracted, and discriminant validity using Fornell-Larcker Criteria.

RESULTS

Indicator reliability, or factor loading, is crucial in assessing a measurement model as it measures how much an indicator’s variance is explained by its corresponding latent variable.⁽⁷⁵⁾ In this study, SmartPLS was employed to calculate Factor ⁽⁷⁵⁾ Loadings (FL) for all 51 indicators, detailed in Table 1. A threshold of 0,70 for factor loadings was applied to determine their significance, as recommended in relevant guidelines.

Internal reliability assesses how well observable variables align with the concepts they represent.⁽⁷⁵⁾ Composite reliability (CR) has supplanted Cronbach’s alpha in recent research, especially in Partial Least Squares Structural Equation Modeling (PLS-SEM), as it accounts for varying factor loadings of indicators.⁽⁷³⁾ CR values above 0,70 are typically considered adequate for evaluating internal consistency.⁽⁷³⁾ This study used correlation analysis, setting a threshold value of ≥ 0,70. SmartPLS analysis revealed CR values ranging from 0,739 for the TTF construct to 0,927 for the TR construct (see Table 1), indicating strong internal consistency across all constructs.

Convergent validity assesses the extent to which two measurements of a concept, theoretically related, are indeed associated.⁽⁷³⁾ (Fornell & Larcker, 1981) ⁽⁷⁶⁾ proposed using Average Variance Extracted (AVE) to measure this, which quantifies how much variance a construct captures relative to measurement error. AVE values above 0,50 are generally considered acceptable.⁽⁷³⁾ In this study, AVE was used to assess convergent validity, with all values exceeding the recommended threshold (see table 1). For example, AVE values for TR ranged from 0,535 to 0,759, indicating that each latent variable met the criterion for convergent validity.

Table 1. Indicators Reliability Results
Construct	Items	Facto Loading	CR≥0,70	AVE≥0,50
Intention to Use	ITU1	0,917	0,840	0,759
	ITU2	0,890
	ITU3	0,803
Performance Expectancy	PE1	0,886	0,814	0,643
	PE2	0,837
	PE3	0,831
	PE4	0,835
Effort Expectancy	EE1	0,777	0,754	0,577
	EE2	0,829
	EE3	0,757
	EE4	0,761
Social Influence	SI1	0,764	0,784	0,577
	SI2	0,721
	SI3	0,771
	SI4	0,847
Facilitating Condition	FC1	0,806	0,793	0,617
	FC2	0,761
	FC3	0,810
	FC4	0,762
Task Technology Fit	TTF1	0,756	0,739	0,656
	TTF2	0,852
	TTF3	0,819
Task Characteristics	TC1	0,871	0,788	0,702
	TC2	0,806
	TC3	0,836
Technology Characteristics	TTC1	0,886	0,826	0,741
	TTC2	0,845
	TTC3	0,851
Technology readiness	TR1	0,731	0,927	0,535
	TR2	0,669
	TR3	0,798
	TR4	0,725
	TR5	0,748
	TR6	0,703
	TR7	0,726
	TR8	0,786
	TR9	0,783
	TR10	0,699
	TR11	0,690
	TR12	0,738
	TR13	0,698
Technology Trust	TT1	0,883	0,827	0,743
	TT2	0,838
	TT3	0,863
Voluntariness of use	VU1	0,845	0,816	0,731
	VU2	0,859
	VU3	0,860
Experience	EX1	0,799	0,817	0,645
	EX2	0,730
	EX3	0,841
	EX4	0,836

Table 2 presents the discriminant validity analysis of study variables (ITO, PE, EE, SI, FC, TTF, TC, TTC, TR, TT, VU, and EX), confirming strong discriminant validity according to Fornell and Larcker’s criteria. The findings demonstrate that each construct is sufficiently distinct from the others in the study.

Table 2. Results of Fornell and Larcker Criteria
Construct	ITO	EE	EX	FC	PE	SI	TC	TR	TT	TTC	TTF	VU
ITU	0,871
EE	0,533	0,759
EX	0,409	0,621	0,803
FC	0,413	0,638	0,511	0,785
PE	0,586	0,639	0,496	0,503	0,802
SI	0,473	0,637	0,550	0,683	0,576	0,777
TC	0,430	0,607	0,542	0,620	0,479	0,558	0,838
TR	0,540	0,692	0,692	0,654	0,634	0,674	0,654	0,731
TT	0,465	0,564	0,618	0,542	0,497	0,552	0,568	0,673	0,862
TTC	0,481	0,559	0,590	0,570	0,604	0,534	0,597	0,737	0,550	0,861
TTF	0,392	0,557	0,558	0,656	0,437	0,601	0,579	0,594	0,519	0,522	0,810
VU	0,383	0,528	0,715	0,468	0,452	0,482	0,527	0,676	0,575	0,519	0,516	0,855

Table 3. Result of Hypothesis Testing
No.	Hypothesis	β-value	T- value	P- value	Decision
H1	EE -> ITU	0,032	1,081	0,280	Not Supported
H2	FC -> ITU	0,311	3,218	0,001	Supported
H3	PE -> ITU	0,102	3,234	0,001	Supported
H4	SI -> ITU	0,578	8,372	0,000	Supported
H5	TC -> TTF	0,222	3,460	0,001	Supported
H6	TTC -> TTF	0,212	3,043	0,002	Supported
H7	TTF -> ITU	0,428	6,587	0,000	Supported
H8	EX x FC -> ITU	0,057	1,539	0,124	Not Supported
H9	EX x SI -> ITU	0,052	1,453	0,146	Not Supported
H10	TT x FC -> ITU	0,197	3,290	0,001	Supported
H11	TT x SI -> ITU	0,177	4,661	0,000	Supported
H12	TR x TTF -> ITU	0,047	2,402	0,016	Supported
H13	TT x PE -> ITU	0,123	3,299	0,001	Supported
H14	EX x EE -> ITU	0,026	1,513	0,130	Not Supported
H15	VU x SI -> ITU	0,001	0,098	0,922	Not Supported
H16	TT x EE -> ITU	0,052	4,661	0,003	Supported

Figure 1. Final Structural Model

You must match all results provided with a detailed explanation of the processing of these results in the method.

DISCUSSION

The aim of this study is to investigate the factors influencing the intention to use Human Resource Information Systems (HRIS) among employees of SMEs in Iraq, specifically focusing on the Unified Theory of Acceptance and Use of Technology (UTAUT) factors, Trust, Task-Technology Fit (TTF), and Experience.

The study reveals that PE significantly influences technology adoption propensity. Users’ perceptions of technology’s potential to enhance work efficiency strongly shape their adoption attitudes. Specifically, a robust positive link between PE and HRIS adoption intention was found (β = -0,102, t = 3,234, p > 0,001), underscoring PE’s greater impact compared to SI or enabling factors. This points out that users are more likely to take up technology when they see it as advancing their professional or personal goals. The interaction between PE and HRIS adoption intention is moderated by TT, which is supported by previous research.^(69,77) Trust strengthens such beliefs by increasing faith in technological performance, minimizing doubts about its acceptance. In addition, trust increases system dependability and content validity needed for HRIS adoption specifically in dealing with sensitive employee data thereby reinforcing the connection between PE and tech acceptance. Furthermore, FC represents the perception of organization and technological infrastructure support to system use. FC impacts user’s technology adoption intentions according to their level of expertise. Nevertheless, knowledgeable users who can overcome barriers and utilize available resources require less than ideal FC while accepting any new technology-based products. On the other hand, novices depend heavily on supportive FC, which if not present may reduce their user intention to adopt it. This comprehension will assist in customizing methodologies for deploying technologies, which would better serve the different levels of user experiences allowing technology adoptions to be more successful. Technological readiness is important in determining how users accept and use new technologies, this is brought out by users’ preparedness and beliefs about technology’s performance benefits. Hopeful creative persons embrace technology efficiently whereas those who are disturbed or shy may not appreciate the need to adopt it. Knowledge of technological readiness helps to create programs that motivate workers to take on and use available technologies amongst employees.

Implications of the study

The research paper about the small and medium enterprises of Iraq is highly essential since it explores HRIS adoption by examining TR, TT, EX, and voluntariness and thus makes a theoretical and practical contribution. Research other than those of HRIS adoption in SMEs has been given less attention as to show the recentness or innovation of this study. For Iraqi SMEs, TTF is integrated with IT/ IS acceptance theories to uncover paramount precursors for HRIS adoption. This helps in affirming UTAUT’s universality beyond Western cultures hence making it more applicable. This research also emphasizes on trust factor because its role is vital in promoting HRIS adoption. The uncertainty surrounding new technologies can be mitigated thus facilitating their acceptance and use within an organization. Therefore, a reliable system has to be established that will support sensitive employee data management within the context of HRIS. In addition to academic implications, these results could provide meaningful suggestions for interested SMEs who want to improve their HRIS adoption. Building trust and being technologically ready could help to reduce the opposition of employees leading SMEs to HRIS implementation; nevertheless, it increases their chances. To sum it up, this enhances organizational competitiveness even against larger companies plus enhancing worker performance. Smaller organizations such as multinational corporations have a higher value in terms of the employee life cycle. On the whole, here this research provides an exhaustive mechanism of predicting behaviors on HRIS adoption so it shows those factors that lower resistance among SME workers hence impacting how they use these systems in their firms for greater acceptance levels.

CONCLUSION

This contribution is concluded with a hypothetical model delivered and examined for evaluating the factors that impact employees’ willingness to adopt HRIS among Iraqi SMEs. It provides evidence, through quantitative analysis, for some of the existent theories such as UTAUT, TTF, etc. By following a positivist philosophy, the research methodology followed a cross-sectional approach. Data were collected with the help of structured questionnaires from a sample of 213 employees as determined by the G*Power software. The data is analyzed, considering key variables like PE, SI, TTF, FC, TT, and TR, using Smart-PLS.

These factors were found to be relevant in predicting the adoption of HRIS at an organizational level and thus their importance in surmounting barriers or incentives to its use. This means that any research currently being done should have practical implications as it develops a theory on the evaluation of HRIS investments by decision-makers. Development in technology and increasing awareness among workers will alter strategies to implement HRIS, so there must be constant theoretical developments in the area together with practical ones.

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FINANCING

The authors received no financial support for the research, authorship, and/or publication of this article.

CONFLICT OF INTEREST

The authors declare the absence of any conflict of interest regarding the research, authorship and/or publication of this article.

AUTHORSHIP CONTRIBUTION

Conceptualization: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Data curation: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Formal analysis: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Research: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Methodology: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Project management: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Resources: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Software: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Supervision: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Validation: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Display: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Drafting - original draft: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.

Writing - proofreading and editing: Al Qaysi Hamid Hazim Majid, Noor Fareen Abdul Rahim, Ai Ping Teoh, Alhamzah Alnoor.