Introduction
Technology offers a significant advantage in enhancing processes and achieving strategic goals across various organizations, including the healthcare sector. This industry has undergone a rapid transformation as traditional manual systems have been replaced by digital healthcare technologies [1], such as personal health records, electronic prescriptions, smart health devices, wearable technologies [2], artificial intelligence-driven patient relationship management, and telemedicine [2, 3]. These advancements substantially benefit hospitals, enabling them to adopt new technologies for improved service delivery, including diagnostics and remote care for patients who may struggle to access appropriate medical treatment [4, 5]. Nurses operate in complex and demanding hospital environments, ensuring patient safety and minimizing the risk of errors, such as mistakes in drug administration that could have serious health consequences. Extended work hours, high-stress levels, and challenging circumstances, compounded by personal issues and colleagues’ anxiety, significantly impact the mental well-being of medical staff, which in turn affects their performance [6]. Psychosomatic symptoms such as physical pain, extreme fatigue, anxiety, severe headaches, nausea, skin rashes, insomnia, and stomach ulcers directly diminish staff performance due to overwhelming workloads and a challenging work environment [7].
Modern technology has become essential for enhancing the performance of paramedical staff in the healthcare sector [8]. The healthcare system is widely regarded as a complex phenomenon on a global scale. The high cost of healthcare significantly impacts nearly all major economies worldwide, as national and regional governments actively participate in developing health policies. Many countries have undergone considerable transformations, both governmental and private [9], where employees have embraced information technology to improve public services, research indicates that the adoption of technology-based applications positively influences performance [10, 11]. The Health Management Information System (HMIS) is an information technology system designed to manage data related to hospital information and awareness. This system enhances patient care, encourages collaboration among service providers, and supports informed decision-making [12]. HMIS can improve online communication between patients and healthcare professionals, including doctors and nurses, reduce medication errors, increase the efficiency of healthcare delivery, and save both time and costs [13].
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The Health Information Management System is a mobile app-based service that enables the general public to access various medical services easily. These services include expert evaluations, personalized self-management, tracking of health metrics, disease management, and online health promotion [14]. Rasmi, Alazzam [15] HIMS is linked to numerous positive outcomes and helps individuals embrace technology to manage their health. In the wake of the COVID-19 pandemic, many organizations, particularly in the healthcare sector, adopted this technology to provide services to the public [16]. The healthcare sector has faced significant strain, leading to numerous physical and psychological challenges for its employees. Nurses, in particular, carry the heaviest burden as they primarily work in direct patient care and are more accessible to patients. These factors affect nurses’ job performance, learning approaches, and overall quality of life. To address these challenges, several countries are supporting nurses in integrating information systems and adopting new technologies to improve job performance and establish effective nursing health management systems [17, 18]. Unlike other sectors, the success of these systems largely depends on user preferences [16]. The true effectiveness of the hospital information system hinges on the staff’s willingness to embrace the technology. Ultimately, the practical impact and success of these systems rely on nurses’ intention to use advanced technology [19].
The Theory of Reasoned Action (TRA) was revised by Venkatesh [20] into the Technology Acceptance Model (TAM), which includes the concepts of perceived usefulness (PU) and perceived ease of use (PEU). Wadie [21] conducted a study to examine technology acceptance based on these two predictors, along with the intention to use. Venkatesh and Davis [22], later developed TAM2, an updated version of TAM1. The extended purpose of TAM2 was to investigate additional variables and explore how these factors influenced users’ attitudes toward technology. Building on this, Venkatesh and Bala [23] and Venkatesh [20], along with Venkatesh, integrated TAM2 and proposed TAM3, which added new elements such as perceived enjoyment, objective usability, computer anxiety, computer playfulness, computer self-efficacy, and perceptions of external control.
The primary goal of the original Technology Acceptance Model was to investigate how individuals’ psychological processes and beliefs influence their attitudes, intentions, and behaviors regarding technology. A study utilized the TAM with additional components like product factors, product innovation, subjective norms, and behavioral intention to evaluate the willingness of HIV/AIDS patients in Henan province to engage with mobile information follow-up [24]. His study aimed to identify significant variables influencing patients’ willingness to accept follow-up care, investigate the underlying mechanisms of mobile services, and establish a theoretical foundation for the future development of mobile follow-up.
Kim and Park [25] enhanced the explanatory power of the Technology Acceptance Model and its relevance to health consumer intentions by incorporating additional antecedents and mediating variables. study DJ, P [26] a study utilizing TAM alongside external variables related to product characteristics was conducted to explore how perceived usefulness (PU) and perceived ease of use (PEU) of oral dialogue systems affect clinical practitioners’ willingness to use these systems for recording clinical observation outcomes during endoscopy. Similarly, Wu, Wang [27] extended the Technology Acceptance Model to assess the performance of medical professionals, specifically doctors, by incorporating factors such as compatibility, self-efficacy in mobile healthcare settings, technical support, and training to gauge the intention to use mobile healthcare systems. In contrast, Fennelly, Cunningham [28] found that nurses view Electronic Health Record (EHR) systems as beneficial for improving patient care, enhancing documentation efficiency, and facilitating communication among healthcare providers. Additionally, according to Strudwick [29] noted that the TAM model effectively boosts employee performance while promoting technology acceptance. However, unlike other medical professions, the TAM is not applied to evaluate nurses’ work performance.
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Holden and Karsh [30] systematically reviewed studies on the Technology Acceptance Model in healthcare and found that it consistently predicts healthcare professionals’ acceptance of technology. Factors such as colleagues, supervisors, and organizational standards significantly impact nurses’ willingness to adopt advanced technology. Specifically, nurses are more likely to accept technology when they receive support from their peers or superiors [31]. Kim, Lee [32] demonstrated that adequate resources, training, and technical support are essential for nurses to utilize technological advancements effectively. Furthermore, Ho, Chang [33] emphasized the importance of organizational support and sufficient training in fostering nurses’ acceptance of new technologies. Previous literature indicates that the intention to use technology has been measured among healthcare professionals, administrative staff, and physicians.
While some studies examine perceived ease of use (PEU) and perceived usefulness (PU) concerning the intention to use technology, very few focus on a conceptual framework considering the complexities of technology use. Additionally, there is a lack of research addressing the mediating roles of PEU and PU within a single framework specifically for Pakistani nurses. This represents a gap in the existing literature. Based on their literature review, the researchers identified a need to investigate the impact of technology sophistication (TS) on the intention to use technology, integrating scholarly work on PEU and PU to better understand the adoption of technology among nurses. Therefore, it is essential to identify the factors that interact with technology with PEU and PU as they pertain to nurses’ performance. Previous research has established a significant relationship between PEU and PU in technology use [34]. Still, no study uses both constructs as mediators within a framework incorporating technology sophistication.
This study answers the following questions:
1.
In what ways does technology sophistication enhance nurses’ ease of use with technology?
2.
How does technology sophistication affect perceived ease of use (PEU) and perceived usefulness (PU)?
3.
How do perceived ease of use (PEU) and perceived usefulness (PU) influence the intention to use technology?
Technology acceptance model studies
The Technology Acceptance Model (TAM) was initially developed to evaluate how users adopt new technologies, including computer systems, internet platforms, and software programs. The model has since been applied across various sectors, such as information technology, education, retail and e-commerce, banking and finance, transportation, telecommunications, tourism and hospitality, and healthcare [35‐42]. In healthcare, TAM assesses the acceptance of technologies like wearable health devices, telemedicine, and health information systems [43]. The underlying goal of digital health is to improve the accessibility and quality of healthcare through various technological resources, including platforms, artificial intelligence, medical software, and smartphone applications. As a result, digital health has attracted the attention of numerous academic institutions, health organizations, and practitioners, aiming to broaden public access to high-quality medical care [43, 44]. Furthermore, during the COVID-19 pandemic, these technologies have been crucial in supporting paramedical staff. With technology’s assistance, staff can treat patients remotely and streamline the overall medical process for patients and healthcare providers [43, 45].
Deng, Hong [46] adapted the Technology Acceptance Model (TAM) to investigate the intention to use mobile health services based on data from China. They concluded that trust, perceived usefulness, and perceived ease of use positively correlate with adopting mHealth services. They also discovered that privacy and performance risks negatively affected patients’ trust and intention to adopt these services.
Similarly, Zhou, Zhao [47] applied an extended version of the TAM to assess the intention to use telemedicine systems among elderly patients in China. Their research indicated that medical service satisfaction, ease of use, and information quality significantly influenced the acceptance of telehealth among older adults. This acceptance, in turn, considerably impacted their behavioral intentions regarding telehealth. Likewise, Ahmad, Rasul [48] explored the intention of diabetic patients in Bangladesh to use digital health services. He found that all six constructs, perceived usefulness, perceived ease of use, perceived irreplaceability, perceived credibility, compatibility, and social influence, positively impacted the continued intention of elderly diabetic patients to use digital health wearables.
Tao, Chen [49] conducted studies on consumers’ usage of a “personal health records system” (PHRS) and discovered that relatedness and competence were significant motivational factors influencing perceived ease of use among Chinese adults with at least six months of m-health experience. They found that task–technology fit and perceived ease of use significantly affected perceived usefulness. Ma and Luo [50] explored the extended Technology Acceptance Model (TAM) concerning the intention to use medical apps among elderly Chinese users. Their research revealed that attitudes towards using apps significantly influence older adults’ intentions to use them. They identified only two factors, perceived usefulness and facilitating conditions from the UTAUT model, that significantly predicted the intention of older adults to use apps, while other factors did not. Nonetheless, perceived usefulness, ease of use, subjective norms, and facilitating conditions significantly impacted attitudes toward using apps.
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Mouloudj, Bouarar [51] integrated the Technology Acceptance Model (TAM) with the Theory of Planned Behavior (TPB) and Self-Determination Theory (SDT) to investigate the intention to use a telehealth system in Algeria. Their findings indicated that perceived usefulness, attitudes, self-efficacy, and ease of use significantly and positively predicted customers’ intentions to use digital health apps. Additionally, a study conducted in Nigeria assessed healthcare workers’ acceptance of digital health technologies and found that perceived usefulness, physical condition, technological anxiety, user innovativeness, and perceived availability significantly influenced their behavioral intentions [52].
Another study [53] utilized primary data from Canada to examine health providers’ and administrators’ perceptions of the usefulness and ease of using technology in palliative care. As an information-sharing platform, the authors argued that telehealth could enhance the coordination and collaboration among interdisciplinary providers caring for patients with palliative needs. Kowitlawakul [54] found that perceived usefulness was the most significant factor influencing nurses’ intentions to use Telemedicine Technology (eICU). The key factors affecting perceived usefulness included perceived ease of use, physician support, and years of experience in the hospital.
Hung, Tsai [55] argued that the primary health information system in primary health care significantly influences service delivery. They found that compatibility positively affects the perceived usefulness and trust in the primary health information system. Alhur [56] Also, this study examined nurses’ perceptions of using electronic medical records in clinical practice and identified factors that influence their acceptance of electronic medical record documentation. This study aimed to enhance understanding of nurses’ perspectives on electronic medical records to promote their adoption and implementation in other health facilities across Saudi Arabia. Alhur found that perceived usefulness and usability are closely linked, contributing to nurses’ acceptance of electronic medical records. A summary of previous Technology Acceptance Model studies in the healthcare sector from 2010 to 2024 can be found in Appendix 2.
Theoretical background and hypotheses development
Technology sophistication
Information technology is a method that organizations use to enhance information-processing abilities [57]. Huber [58] described, “use of advanced IT leads to more available and more quickly retrieved information, including external information, internal information, and previously encountered information, and thus leads to increased information accessibility.” In 1986, Daft and Lengel emphasized technology sophistication as a key element in reducing uncertainty in organizations. Similarly, a study El Louadi, Galletta [59] confirmed that institutional technology sophistication directly impacts internally and externally. Also, Ismail and King [60] found a positive association between advanced information systems with ease of use and technology sophistication.
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In their study, al-Eqab and Ismail [26] defined a significant connection between advanced technology system design (usefulness, friendly users, ease of understanding) and contingency factors. Bandura [61] discussed that self-learning technology is also linked to ease of use. The ease of use of technology directly impacts job performance learning during the task compilation; therefore, several scholars consider technology usefulness and ease of use to be essential factors for technology acceptance [62‐64]. Consequently, the existing literature shows the interaction between technology sophistication and intention to use perceived usefulness and ease of use to make it successful. Therefore, we hypothesized that TS relates to the IU, PU, and PEU. The study revealed that technology sophistication helps to better performance and satisfaction in accepting and executing hospital technology systems. Thus, TS is significantly associated with IU, PU, and PEU, as we hypothesized.
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H1: TS positively relates to IU.
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H2: TS positively relates to PU
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H3: TS positively relates to PEU
The original technology acceptance model
In 1980, Davis proposed the “Technology Acceptance Model (TAM)” to predict and explain users’ intentions regarding technology Davis [65]. Building on Fishbein and Ajzen’s “Theory of Reasoned Action (TRA),” [66]. Davis [67] designed TAM to identify the determinants of computer acceptance. The model aims to explain user behavior across various end-user computing technologies and populations while remaining parsimonious and theoretically sound. TAM has been extensively evaluated across various contexts and respondent groups, establishing it as a robust and reliable framework for understanding new technology acceptance [68‐71]. The model posits that perceived usefulness and perceived ease of use (PEU) are vital variables influencing individuals’ acceptance of technology systems and their behaviors in actual usage scenarios [67, 72]. Since its introduction, TAM has evolved into a dependable model for forecasting user acceptance. Its initial limitations have been addressed, leading to the development of several improved theoretical models, all extending from the core constructs of perceived usefulness and ease of use [34, 57].
The “Unified Theory of Acceptance and Use of Technology (UTAUT)” [57], “TAM 2” [22, 57], and “TAM 3“ [23] are advanced iterations of the technology acceptance model. Various studies have evaluated TAM in the context of the health sector [64], the model has not been empirically validated to assess nurses’ acceptance of IT solutions in healthcare. This research integrates critical factors into a proposed framework to measure nurses’ intentions to use technology. Perceived usefulness is defined as “the degree to which an employee believes that using technology would enhance his or her job performance,” while perceived ease of use is described as “the degree to which an employee believes that using technology would be free of effort” [67].
Since 2010, the Technology Acceptance Model (TAM) has been extensively utilized in healthcare research to examine the factors influencing healthcare professionals’ adoption of technology. Below are some pertinent studies demonstrating TAM’s relevance and effectiveness in addressing issues related to healthcare technology adoption. TAM and its variants have been employed to evaluate various healthcare technologies, including Electronic Health Records and Clinical Decision Support Systems [30]. This review confirmed that perceived usefulness and ease of use among healthcare workers significantly influence technology acceptance, suggesting that TAM remains a strong predictor in healthcare contexts where usability and perceived patient impact are critical.
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In the study by [73], TAM was applied to assess nurses’ acceptance of electronic health records (EHRs). Their findings revealed that perceived usefulness and ease of use significantly correlate with nurses’ willingness to adopt EHRs. TAM proved effective in elucidating healthcare professionals’ acceptance of new technology, significantly when such technology impacts their daily workflows. Rahimi, Nadri [74] applied the Technology Acceptance Model (TAM) to explore the acceptance of mHealth applications among professionals in Iran. They identified two key determinants of acceptance: perceived ease of use and perceived usefulness. This study emphasized TAM’s applicability across different cultural contexts and its significance in promoting mHealth adoption in resource-limited settings. An, You [75] utilized an extended TAM to investigate healthcare professionals’ acceptance of telemedicine during the COVID-19 pandemic. Their findings indicated that the urgency of the pandemic required swift technology integration, making perceived ease of use and usefulness crucial for adoption. This research underscores the ongoing relevance of TAM in understanding healthcare professionals’ responses to urgent technological implementations.
Alanazi [76] Employing TAM, the researchers examined healthcare providers’ adoption of health information technology in Saudi Arabia. Their study confirmed that perceived usefulness, ease of use, and social influence influenced technology acceptance. The researchers further demonstrated that TAM applies and is adaptable to various cultural contexts and healthcare environments, each with unique challenges and pressures. Chen, Zhang [77] investigated physicians’ acceptance of artificial intelligence (AI) diagnostic tools through an extended Technology Acceptance Model framework. They found that perceived usefulness, ease of use, and trust in AI influence physicians’ willingness to adopt AI-based diagnostic technologies. This study illustrates the applicability of TAM for emerging technologies and intricate healthcare innovations, highlighting its relevance for current and future healthcare challenges.
Singh and Jaiswal [78] applied TAM to evaluate healthcare providers’ adoption of wearable health devices in India. They noted that perceived usefulness and ease of use were critical factors for healthcare workers in accepting wearable devices, increasingly recognized as patient monitoring tools. This research indicates that TAM is well-suited to various healthcare contexts and underscores the importance of user-centered design in enhancing technology acceptance. TAM has proven robust and flexible in studying various healthcare technologies, such as EHRs, telemedicine, AI, and wearable devices. It identifies the key factors influencing healthcare professionals’ decisions to adopt technology, focusing on perceived usefulness and ease of use. This evidence establishes TAM’s relevance and widespread acceptance in addressing healthcare technology adoption challenges from 2010 to 2023.
According to Kamal, Shafiq [79] the study identified several key factors influencing the intention to use telemedicine services, including perceived usefulness, social influence, trust, ease of use, and facilitating conditions. The literature suggests that the effectiveness of nurses’ services in enhancing hospital performance relies heavily on their perceptions of the technology system’s usefulness and ease of use. The study found that perceived usefulness (PU) and perceived ease of use (PEU) are critical in fostering intention to use (IU) hospital technology. Therefore, we hypothesized that PU and PEU are positively related to IU:
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H4: PU positively relates to IU
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H5: PEU positively relates to IU.
The effect of PU and PEU on IU
As previously established by the Technology Acceptance Model (TAM), individuals’ intentions to use a system are influenced by two fundamental beliefs: perceived usefulness (PU) and perceived ease of use (PEU). These intentions, in turn, shape attitudes toward using the system, ultimately leading to its adoption. Research by Winata, Permana [80], Asiri [81], and Muslichah [82] demonstrated that both PU and PEU significantly affect behavioral intentions. Additionally, Chen and Tseng [62] found that PU is the primary factor directly influencing intention. At the same time, Mallat, Rossi [83] argued that the impact of PU on intention to use (IU) depends on specific instances of system usage. Nevertheless, PEU and PU remain the main predictors of attitudes toward usage intentions.
Furthermore, Wu and Wang [84] identified that both PU and PEU play essential roles in technology adoption. Rezvani, Heidari [85] highlighted the role of perceived ease of use as a mediator between system quality and user satisfaction. Research by Daud, Farida [86] examined satisfaction with PU as a mediator between computer anxiety, system acceptance, and satisfaction, as shown by Igbaria, Schiffman [87].
The existing literature suggests that PU and PEU mediate the relationship between technology systems and intentions to use, contributing to their overall success. Consequently, we hypothesized that PU and PEU correlate with technology systems and intentions to use. Our study found that PU and PEU significantly enhance nurses’ intentions to accept and implement hospital management systems. Therefore, PU and PEU are significantly essential factors in this context.
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H6: PU mediates the relationship between TS and IU
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H7: PEU mediates the relationship between TS and IU
Conceptual model
Figure 1 presents the study’s research model, which defines the independent variable (TS) and dependent variable (PU, PEU, and IU).
Fig. 1
Conceptual model
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Research methods
Context selection
The adapted questionnaires were distributed to nurses using random sampling from public hospitals between February and June 2023. This survey approach, commonly used in the social sciences, was employed to gather empirical data for analyzing the research hypotheses. The study was conducted in public hospitals across five major districts: Multan, Lodhran, Bahawalpur, Bahawalnagar, and Rahimyar Khan in Punjab, Pakistan, during working days from Monday to Saturday. The questionnaire consisted of two sections: one for demographic details and the other for information on technology sophistication, perceived usefulness (PU), perceived ease of use (PEU), and intention to use (IU). We targeted 850 respondents, following the recommendations of as Saunders, Lewis [88], and Krejcie and Morgan [89] recommended. Ultimately, we achieved an 81% response rate, receiving 687 completed questionnaires.
Constructs development and research instruments
All items adapted from the previous studies, such as technology sophistication, perceived ease of use, perceived usefulness, and intention to use. Technology sophistication was measured through 6 items adapted from Angst and Agarwal [90] and Boadu, Lamptey [91] with the sample item “Overall, I enjoyed using the technology with sophistication”. Perceived usefulness accessed by five items adapted from Davis [67], Davis, Bagozzi [92], Alfuqaha, Rabay’ah [93] with the sample item “The degree to which a nurse believes that the use of technology would enhance his or her job performance.” Perceived ease of use was measured through 4 constructs adapted from the studies of Davis [67] and Davis, Bagozzi [92] with the sample item “The degree to which a nurse believes that the use of technology would be free of effort.” Intention to use evaluated from 2 items developed by Taylor and Todd [68], Hung, Tsai [55] with the sample item “Given that I have access to the health information, I predict that I would use it.” All constructs can be seen in Appendix 1. The respondents’ demographic characteristics included gender, education, and age, as seen in Table 1. A 5-point Likert scale was used to measure all questions, with 1 representing strongly disagree, 2nd disagree Agree, 3rd neutral, 4th Agree, and 5th representing strongly agree.
Establishing the reliability of the questionnaire
A pilot study was conducted before the primary research to assess the reliability of the questionnaire. A sample of approximately 75 participants was chosen, representing about 10.91% of the total population. Internal consistency was evaluated using Cronbach’s alpha (CA) test in SPSS. A Cronbach’s Alpha coefficient of 0.7 is generally considered a good indicator of reliability, as supported by multiple sources [94‐97] and [98]. This standard is widely accepted in social science research. The results in Table 1 demonstrate a level of reliability that meets this established criterion. Table 1 lists the constructs and their respective CA coefficients, indicating that respondents understood the items well.
Table 1
Establishing the reliability of the study
Variables | CA coefficient |
|---|---|
TS | 0.852 |
PU | 0.829 |
PEU | 0.867 |
IU | 0.873 |
The demographic details of 687 respondents can be seen in Table 2. Furthermore, the demographic description of the respondents includes 687 nurses who were only females. The 319 participants (46.43%) belong to the age of 20–29, 168 participants (24.46%) belong to the age of 30–39, 132 nurses (19.21%) belong to the age of 40–49 and 68 nurses (9.90%) above the age of 50 years. The participants’ ages ranged from 20 to 29 (21.64%), 30–39 (19.84%), 40–49 (20.65%), and above 50 (37.87%). The educational level of the participants specified that 72.05% had a graduation education, while 27.95% had a master’s education.
Table 2
Demographic details
Description | No. | Percentage |
|---|---|---|
Gender | ||
Female (Only) | 687 | 100.00 |
Age | ||
20–29 | 319 | 46.43 |
30–39 | 168 | 24.46 |
40–49 | 132 | 19.21 |
≥50 | 68 | 9.90 |
Education | ||
Graduate | 495 | 72.05 |
Master | 192 | 27.95 |
Analysis methods
The Social Science Statistical Package (SPSS), analysis of moment structures (AMOS), and structural equation modeling were used to analyze the data for reliability, validity, correlations, and descriptive findings. We performed confirmatory factor analysis (CFA) and structural equation modeling (SEM) tests to examine our hypothesized model. The SEM was employed to validate the study hypotheses, which are widely used for the studies of healthcare [99‐102]. The consistency analysis evaluated all items’ reliability and authenticity [103]. Additionally, we created a measuring model that considered the relationships between the investigated factors and their items. We employed a three-stage approach to implement Qing’s [26] suggested SEM approach. Firstly, the research employed a measurement model to evaluate component scores for all items. Secondly, discriminant validity was determined using CFA, and thirdly, the casual model was accessed through the SEM technique [104]. These measures were taken to ensure the causal model’s validity and reliability. The analyses of mediating impact were employed through AMOS 21.0, SPSS.
Results
Descriptive statistics and correlations
The descriptive statistics included correlations, mean, and standard deviation of the variable demographic details exhibited in Table 3. TS shows a significant correlation to PU (r = 0.36, p < 0.01), PEU (r = 0.45, p < 0.01), and IU (r = 0.47, p < 0.01). A significant correlation was found between PU and IU (r = 0.29, p < 0.01) and between PEU and IU (r = 0.38, p < 0.01). Moreover, the measurement model factor loadings for the current research ranged from 0.713 to 0.898, which revealed a strong association between underlying items of the variable. The validity confirmed all constructs with Cronbach’s alpha; all values are shown in Table 4. Furthermore, confirmatory factor analyses were run before testing the hypotheses to verify the most suitable measurement model [105]. The proposed model’s result showed excellent model fit, χ2 = 941.25, χ2 /df = 1.957, CFI = 0.97, TLI = 0.96, and RMSEA = 0.03, compared to another possible model. Also, all indicators’ values were greater than 0.5, and factor loadings were loaded significantly. The proposed model has adequate discriminant validity, as shown in Table 5.
Table 3
Descriptive statistics
AVE | Mean | SD | Correlations | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | ||||
1. Age | - | 3.56 | 1.85 | - | ||||||
2. Gender | - | 1.93 | 0.64 | 0.8 | - | |||||
3. Education | - | 2.08 | 1.14 | 0.19* | 0.21** | - | ||||
4. TS | 0.68 | 3.31 | 0.98 | 0.15* | 0.09 | 0.23** | - | |||
5. PU | 0.74 | 2.98 | 1.07 | 0.06 | 0.11 | 0.09 | 0.36** | - | ||
6. PEU | 0.69 | 3.04 | 1.47 | 0.10 | 0.01 | 0.01 | 0.45** | 0.39** | - | |
7. IU | 0.60 | 2.89 | 1.39 | 0.04 | 0.03 | 0.06 | 0.47** | 0.29** | 0.38** | - |
Table 4
Confirmatory factor analysis CFA
Factor | Items | Loadings | S.E. | T | C.R. | Α |
|---|---|---|---|---|---|---|
TS | TS1 | 0.732 | - | - | 0.88 | 0.91 |
TS2 | 0.721 | 0.049 | 14.714** | |||
TS3 | 0.850 | 0.056 | 15.179** | |||
TS4 | 0.805 | 0.054 | 14.907** | |||
TS5 | 0.713 | 0.050 | 14.260** | |||
TS6 | 0.898 | 0.047 | 19.106** | |||
PU | PU1 | 0.745 | - | - | 0.89 | 0.90 |
PU2 | 0.771 | 0.051 | 15.118** | |||
PU3 | 0.826 | 0.047 | 17.574** | |||
PU4 | 0.803 | 0.063 | 12.746** | |||
PU5 | 0.840 | 0.052 | 16.154** | |||
PEU | PEU1 | 0.864 | - | - | 0.85 | 0.84 |
PEU2 | 0.731 | 0.045 | 16.244** | |||
PEU3 | 0.728 | 0.048 | 15.167** | |||
PEU4 | 0.759 | 0.059 | 12.864** | |||
IU | IU1 | 0.811 | - | - | 0.86 | 0.88 |
IU2 | 0.876 | 0.053 | 16.528** |
Table 5
Measurement model
Model | χ2 | Df | χ2 /df | CFI | TLI | RMSEA | SRMR |
|---|---|---|---|---|---|---|---|
4-factor model (hypothesized model) | 941.25 | 481 | 1.957 | 0.97 | 0.96 | 0.03 | 0.04 |
3-factor model (TS & PU combined) | 1489.81 | 483 | 3.084 | 0.81 | 0.81 | 0.09 | 0.10 |
3-factor model (TS & PEU combined) | 2061.75 | 485 | 4.251 | 0.70 | 0.69 | 0.17 | 0.21 |
1-factor model | 2435.65 | 486 | 5.012 | 0.62 | 0.62 | 0.19 | 0.24 |
Direct and mediating effect of PU
The study’s findings were as per the instructions of Preacher and Hayes [106] and Baron and Kenny [107]. Table 6 describes a significant connection between the TS and IU (β = 0.23, p < 0.001). According to Baron and Kenny [107], the first mediation condition is covered. Next, TS and PU were positively significant (β = 0.44, p < 0.001). Hence, the result of the study supported the second condition of mediation and the (H2). Next, TS and PEU linked significantly (β = 0.47, p < 0.001). This relation is also supported by (H3). The PU and IU are positive (β = 0.31, p < 0.001). So, this study links support to (H4) and fulfills the mediation conditions. According to the directions of Preacher and Hayes [106], mediation was tested.
As suggested by Baron and Kenny [107], the authors evaluated the significant indirect impacts by bootstrapping the sampling distribution. The results demonstrated that the indirect effect of technology sophistication on intention to use is also found to be substantial (β = 0.136, p < 0.001), (S.E = 0.061), and (t = 2.230). The bootstrap results on a 95% level of confidence for all confidence intervals did not contain zero (“Lower levels of confidence interval” (LLCI) = 0.293, “Upper levels of confidence interval” (ULCI) = 0.392). Therefore, these findings are also supported (H6). Table 6 also defines the indirect effects values. PEU and IU also connected significantly (β = 0.35, p < 0.001) and also assisted the (H5). The indirect effect of TS on IU also discovers a significant association (β = 0.164, p < 0.001), (S.E = 0.059) and (t = 2.779). The bootstrap result on a 95% confidence level for all confidence intervals did not contain zero (LLCI = 0.315, ULCI = 0.537). Therefore, these findings are also supported (H7).
Table 6
Direct, Indirect and total B coefficients (H1- H7)
Hypotheses | Paths | β | S.E. | t | Confidence Interval (95%) |
|---|---|---|---|---|---|
Direct effects | |||||
H:1 | TS → IU | 0.23 | 0.062 | 3.710 | (0.270, 0.326) |
H:2 | TS → PU | 0.44 | 0.063 | 6.984 | (0.199, 0.293) |
H:3 | TS → PEU | 0.47 | 0.062 | 7.581 | (0.135, 0.449) |
H:4 | PU → IU | 0.31 | 0.065 | 4.769 | (0.256, 0.341) |
H:5 | PEU → IU | 0.35 | 0.063 | 5.556 | (0.390, 0.582) |
Indirect effects | |||||
H:6 | TS → PU → IU | 0.136 | 0.061 | 2.230 | (0.293, 0.392 |
H:7 | TS → PEU → IU | 0.164 | 0.059 | 2.779 | (0.315, 0.537) |
Total effect | 0.300 | 0.076 | 3.947 | (0.138, 0.253) | |
Discussion
The study findings indicate that technology sophistication positively influences perceived usefulness, perceived ease of use, and the intention to use technology among nurses in public sector hospitals. Additionally, the results reveal that perceived ease of use and perceived usefulness significantly mediate the relationship between technology sophistication and intention to use. Research by Abugabah and Sanzogni [108] and Chirchir, Aruasa [109] demonstrates that technology is positively associated with usage intention for improved performance, which aligns with our findings. Goodhue and Thompson [61] regard technology as a crucial factor for enhancing system and workforce performance. With the aid of sophisticated medical technologies and online access to patient data, nurses can now operate on par with medical professionals when addressing patient health concerns [110]. The healthcare sector, which is continually evolving to improve care quality and reduce costs through the adoption of new technologies, stands to benefit significantly from advancements in internet sophistication [111]. Jokonya [112] highlights that technological sophistication is essential for organizational success, while Isaac and Abdullah [63] illustrate a positive correlation between technology and hospital effectiveness. They also point out that institutions can engage effectively with staff and patients through technological sophistication, ultimately enhancing hospital services. Therefore, this study underscores the importance of technology in empowering nurses to navigate the challenges of modern healthcare.
Perceived ease of use positively correlates with nurses’ intentions to use technology. A study by Prastiawan, Aisjah [113] on student learning and technology acceptance found that the ease of use of digital systems significantly increases respondents’ intentions to adopt technology. Similarly, research indicates a strong positive relationship between perceived ease of use and citizens’ intentions to utilize technology for information gathering [114]. Users’ willingness to accept technology systems is greatly influenced by how easily they use them [115]. The authors of one study concluded that in China’s technology sector, perceived ease of use and usefulness significantly enhance customer satisfaction, trust, and loyalty intentions [116]. According to Dalle, Raisinghani [44] the ease of use of technology directly motivates user acceptance; when technology is user-friendly, the likelihood of adaptation increases. Several scholars have identified perceived ease of use as a crucial factor in technology adoption among healthcare professionals [117, 118]. The simplicity of medical apps has improved treatment quality, safety, time efficiency, cost reduction, and patient satisfaction. Medical apps are becoming increasingly popular among healthcare workers to support their duties [119, 120]. Stoumpos, Kitsios [121] noted that user-friendly technology enables nurses to collaborate more effectively with other medical staff, share vital patient information, and coordinate treatment. The advancement of technology enhances communication among medical professionals, fostering well-informed and coordinated healthcare teams, ultimately leading to improved patient outcomes.
This research indicates that perceived usefulness positively influences nurses’ intentions to use technology. Several scholars [20, 122‐124] have highlighted the strong impact of perceived usefulness on the acceptance of technology among healthcare service providers. A study by Han and Sa [125] found that a user-friendly interface in an application enhances user intention to adopt the technology system. Additionally, training in technology use is essential, particularly for senior staff who may be less familiar with these systems, leading to their reluctance to embrace technology. Characteristics of digital systems, such as being understandable, user-friendly, and valuable, gradually motivate users to adopt and accept technology, as noted by Peng, Yin [126].
Van Der Steen, Toscani [127] indicate that paramedical staff recognize the necessity and significance of technology in their practice, Saadatzi, Logsdon [128], highlight that this recognition influences job performance and the healthcare sector. The improvement of the healthcare industry is linked to technological elements such as technology sophistication, perceived usefulness, perceived ease of use, and intention to use. Research has shown that PU and PEU are crucial factors that affect system usage and mediate the relationship between technology sophistication and IU. By digitizing patient data, nurses gain immediate access to health indicators, diagnoses, and treatment plans, which empowers them to provide enhanced care [43]. With this wealth of information at their disposal, nurses can better assess patients’ conditions, plan for their needs, and deliver personalized, effective care [129]. Furthermore, AlQudah et al. [65] and Rajak & Shaw [130, 131] demonstrate that technology is essential for service delivery, as many hospitals have already adopted and integrated technology into the healthcare sector. Our study shows that perceived usefulness is a crucial factor in technology acceptance and is directly associated with the intention to use.
To maximize the benefits of hospital information systems, it is essential to provide training and organizational support. This ensures that users fully understand the advantages of the technology and that the information technology systems can be easily adapted to meet their needs. However, nurses may face challenges learning about new technologies and staying current with best practices due to a shortage of continuing education and training opportunities. When nurses lack the necessary technical skills and training, their ability to deliver high-quality care is compromised, leading to feelings of intimidation or burden from technological advancements. Portable devices allow nurses to easily monitor patients, even during busy shifts, recording vital signs such as heart rate, oxygen saturation, and respiration rate [56]. These devices enable quick responses when patients require immediate care, significantly improving response times. Based on this discussion, this research suggests that an advanced health technology system can provide quality information with minimal errors and effectively address ongoing issues.
Clinical significance and implications
This study aims to connect nurses with advanced techniques to enhance their job performance. It also seeks to alleviate the routine stress that negatively affects their work, personal lives, learning ability, and even the safety of their patients. There is an urgent need for a robust system to reduce stress among nurses, improving their skills and performance while supporting their daily lives. The administration could implement a leave program lasting one to two months to increase nurses’ life satisfaction and reduce their psychological distress. Pappa, Ntella [132] found that nurses experience higher levels of anxiety and depression compared to doctors, likely due to the unique demands of their profession, including more frequent face-to-face interactions with patients and extended hospital shifts.
Frontline healthcare workers are experiencing unprecedented levels of stress, burnout, and moral injury due to witnessing the pain and loss of patients and coworkers, navigating emotionally challenging interactions with patients and their families, balancing personal and professional responsibilities, and confronting tough ethical dilemmas. The COVID-19 pandemic significantly heightened rates of burnout and psychological issues among health practitioners, underscoring the need for close monitoring and prompt treatment of these conditions [133]. Pakistani nurses encountered similar challenges during and after the pandemic, prompting the government to implement counseling programs to address this issue. Additionally, educating nurses about the consequences of COVID-19 can help them develop adaptive coping strategies at both individual and institutional levels. The findings from this study on frontline nurses highlight the critical role of timely support systems during crises and demonstrate how the Technology Acceptance Model underscores the impact of external support and organizational preparedness on technology adoption in healthcare. Notably, perceived usefulness and ease of use significantly influence users’ decisions to adopt new technologies, as outlined by TAM. By establishing robust psychological support systems and crisis training, healthcare institutions can enhance nurses’ perceptions of technology for crisis management and mental health support. When nurses view these tools as integral to their support system, they often find them more valuable during crises, which enables them to embrace technologies that improve productivity, communication, and stress management.
Moreover, TAM suggests that organizational readiness and external conditions affect user acceptance. This insight allows authorities and researchers to integrate digital resources, such as mobile health apps and virtual support platforms, into reformed emergency psychological support systems. These technologies can be designed to help nurses respond more effectively to unexpected crises, improving their efficiency in high-stress environments. Implementing such preparatory measures ensures that nurses perceive new technologies not as additional burdens but as beneficial resources, aligning with TAM principles by enhancing perceptions of usefulness and ease of use. Ultimately, this approach can lead to higher technology adoption rates and better preparedness for future epidemic events.
Contribution
Theoretical contribution
The theoretical contribution of examining the mediating effects of perceived usefulness and perceived ease of use of advanced technology on nurses’ intention to adopt such technology is significant both in Pakistan and globally. This paper suggests that the Technology Acceptance Model can be enhanced by applying it within the nursing sector, particularly in healthcare environments characterized by resource limitations and the unique cultural context of Pakistan. As healthcare technology evolves rapidly, understanding the subtle variables influencing nurses’ willingness to use this technology is increasingly vital. The findings are particularly relevant for Pakistan, where healthcare delivery often suffers due to overcrowded facilities, constrained budgets, and a shortage of specialized health professionals [134]. They underscore the importance of perceived usefulness and ease of use in promoting technology adoption. Many frontline nurses face barriers such as outdated infrastructure and low digital literacy, which can undermine the perceived value of new technologies [135]. This research illustrates how technology can mediate TAM’s general applicability by emphasizing the roles of perceived usefulness and perceived ease of use in making technology appealing to nurses in resource-constrained settings.
This study enriches TAM by demonstrating its adaptability and reconfiguration for resource-sensitive organizations, such as nursing, as technology increasingly integrates into global healthcare practices. The relevance of TAM’s perceived usefulness and perceived ease of use has grown even more critical in light of emerging challenges like the COVID-19 pandemic and the rising demand for remote and digital health solutions [136]. For example, there was an urgent need for telehealth and electronic health record systems during the pandemic. However, despite widespread adoption, technology was sometimes perceived as complicated or insufficiently beneficial. This study extends TAM by proposing that these mediators are crucial in promoting nurses’ adoption of new healthcare technology. This suggests that the realization or perception of these mediators can be a crucial determinant of successful adoption. Concentrating on these mediators, this research provides theoretical insights on how tailored approaches within the TAM framework can bridge the gap between healthcare technology design and actual use, offering a valid roadmap for enhancing healthcare technology integration in resource-limited and developed contexts worldwide.
Practical contribution
This research provides a detailed practical contribution by highlighting that perceived usefulness and ease of use are crucial factors influencing nurses’ adoption of advanced technology in healthcare settings in Pakistan and internationally. Healthcare facilities in Pakistan face challenges such as limited resources, high nurse-to-patient ratios, and inadequate technology training [98]. By recognizing perceived usefulness and ease of use as mediators in technology adoption among nurses, healthcare administrators can develop targeted interventions, including hands-on training, user-friendly interfaces, and demonstrations of technology benefits. These initiatives can enhance nurses’ comfort, interest, willingness, and ability to adopt advanced tools. Implementing these strategies could reduce operational inefficiencies, streamline patient care, and improve effectiveness among nurses utilizing existing resources. The findings carry significant implications for technology deployment in the global healthcare sector and offer practical guidance for enhancing the end-user experience. Governments and administrators can boost technology adoption rates across various healthcare contexts by focusing technology efforts on features that align with nurses’ needs, such as the technology’s utility in clinical tasks and its compatibility with existing workflows. This approach enhances patient care and operational efficiency and facilitates a global transition to patient-centered, technology-driven healthcare systems that empower nurses as primary caregivers.
The study contributes to the field by empirically analyzing the relationships between technological sophistication, perceived usefulness, perceived ease of use, intention to use medical applications, and their effects on nurses’ performance and hospital systems. This research expands the existing literature by employing well-established theoretical frameworks such as the TAM. The significance of the study lies in its potential to provide valuable insights for implementing specific measures and making informed decisions that enhance nurses’ efficiency and improve hospital systems through the strategic adoption of new technologies. The conclusions drawn have various practical implications. By highlighting the findings’ relevance for healthcare policymakers, the study effectively bridges theoretical understanding and practical execution. It offers a framework for policymakers to successfully leverage advanced technology systems to achieve organizational objectives and enhance nursing performance, thereby contributing to the ongoing discourse on technology-driven healthcare innovation. Decision-makers can prioritize acquiring technologically advanced tools that demonstrate clear benefits regarding perceived usefulness and ease of use, informed by the strong connection between technology sophistication and nurses’ intention to utilize medical applications.
Conclusion
The study investigated the mediating roles of Perceived Usefulness (PU) and Perceived Ease of Use (PEU) in the relationship between technology sophistication and the intention to use technology among nurses in Pakistan’s healthcare sector. Consistent with previous research, the findings indicate that the intention to use technology improves when users perceive the system as more straightforward and beneficial, enhancing performance and saving time. Historical studies on technology systems’ impact on users have shown that characteristics such as sophistication, usefulness, and ease of use are crucial factors influencing the benefits derived from their use. Moreover, increased technology acceptance leads to improved performance levels, more staff, reduced daily user stress, and an enhanced public image. This study adds to the literature on the nursing community’s healthcare practices by providing empirical evidence that sheds light on nurses’ technology use behaviors in Pakistan and how hospitals’ performance can be enhanced by implementing advanced technology systems. Historically, the healthcare sector in Pakistan has received limited attention. Therefore, this study advocates for implementing more user-friendly hospital information systems for nurses, alleviating pain, ulcers, fatigue, migraines, stomach problems, skin eruptions, and insomnia. The Ministry of Information Technology should prioritize the healthcare sector as a critical area for implementing sophisticated technology systems in hospitals. These systems have become vital to the country, particularly in the pre and post-COVID era, as they are associated with improved service delivery, job performance, and patient satisfaction. Consequently, hospitals must be empowered to make decisions that facilitate the adoption of new systems for both their staff and the public.
Limitations and future research avenues
This study has several limitations. It focused on public hospitals in five districts of Southern Punjab, Pakistan: Multan, Bahawalpur, Lodhran, Bahawalnagar, and Rahim Yar Khan. Data were collected from nurses. Countries encounter various healthcare service challenges, including infrastructure and technological advancement issues. The current model could be enhanced by incorporating additional variables and applied to other sectors. Increasing the respondents would lead to a deeper and more comprehensive understanding. Furthermore, this research is limited to a single developing nation. Future studies should consider other developing low- and middle-income countries. The data were obtained using a cross-sectional study design, but a longitudinal design could yield valuable insights and clarify causal relationships. Although there has been a noticeable shift in the mood of nurses since the COVID-19 outbreak, further research is needed to evaluate their levels of depression, stress, and anxiety following the pandemic, as these findings may align with our study. Additionally, compliance with preventative public health policies is known to influence disease transmission, and this compliance is often connected to public perceptions of vulnerability to disease.
Acknowledgements
We would like to thank all nurses who participated in this study.
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Committee of Islamia University, Bahawalpur, Pakistan (No: 871 HREC/2023), following the principles outlined in the Helsinki Declaration. Participants were informed about the study’s objectives and were free to withdraw at any time. All collected data were anonymized and handled with strict confidentiality, adhering to ethical guidelines. Informed consent was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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