Introduction

Information and communication technology (ICT) integration has become increasingly important across many sectors, including education, in today’s fast-changing global environment (Shahzad et al. 2024). Similarly, ICT plays a crucial and pervasive role globally, influencing various aspects of society, the economy, and individuals’ daily lives (Al-Rahmi et al. 2020). The People’s Republic of China, known for its long history and dedication to education, has embraced the digital revolution to change how education is provided to individuals. China’s education sector has started a journey of innovation and modernization by utilizing the transformative power of ICT, reinventing teaching and learning techniques, improving educational access and equity, and preparing its students for the challenges of the 21st century (Geng and Xue, 2023). Intention to use information and communication technology (ITUICT) in university students influences their future learning, experiences, and academic success. Communication technology is embraced by nations worldwide as a potent tool to improve learning and teaching in an era characterized by rapid technical breakthroughs (Bai et al. 2016). China has emerged as a prominent player, making significant strides in integrating ICT into its education sector. China’s commitment to technological innovation and its ambition to become a global leader in education have driven the widespread adoption of ICT in its schools, colleges, and universities (Yu et al. 2023). This transformative journey has not only affected the methods of instruction but has also shaped the overall learning environment for students and educators nationwide.

In China, the education sector has significantly integrated ICT tools to enhance the student learning experiences. Students used different tools, such as LMS, an open-source platform for course management, where students can access resources, submit assignments, and participate in online discussions (El Tantawi et al. 2015). Students primarily use a messaging app, WeChat, which also supports educational features, like group chats and file sharing, making it popular among students for group projects and discussions. Some schools and educational institutions are starting to integrate VR and AR technologies into their curriculum to create immersive learning experiences. This includes virtual field trips, interactive simulations, and AR-enhanced textbooks (Faqih and Jaradat, 2021). With the rapid advancement of technology, the landscape of educational technology is constantly evolving, and new tools and platforms continue to emerge to support students’ learning. Different language exchange apps where students can practice foreign languages with native speakers worldwide. Therefore, this study focuses on a different factor that motivates students to use ICT tools to improve their education.

Over the past few decades, China has experienced remarkable technological growth, affecting many fields, including education. These innovations have significantly influenced the students’ learning performances (Rafi and Najmaldeen, 2023). The advent of digital technology continues to influence different facets of society, altering their conventional methods and providing fresh perspectives. ICT implementation and integration in the education sector have several advantages, from boosting instruction and facilitating learning to promoting teamwork and strengthening operational effectiveness (Lim et al. 2020). The education industry is experiencing this disruptive wave, as ICT is reshaping how educational services are delivered, encouraging innovation, and facilitating diversity in education. It is important to understand the elements that affect ICT implementation and effective use in educational institutions (Wang et al. 2022). To ensure successful implementation and usage, integrating ICT in education is a complex process influenced by several aspects that must be carefully investigated in our study. Therefore, we will explore the various aspects of ICT integration in Chinese students, such as performance expectancy, technology self-efficacy, effort expectancy, hedonic motivation, price value, and habit. In order to convey important lessons and inspire policymakers in the global education system, authorities applied these creative techniques and strategies in classrooms. The successful implementation of ICT highlights the need to utilize technology to promote educational growth and educate students about the needs of the digital age (Bai et al. 2016).

This research intends to make several contributions to modern literature. This study contributes to practice by providing empirical proof that different factors influence students’ intention to use ICT in their education. In past studies, researchers (Faqih and Jaradat, 2021; Shen et al. 2022) focused on different virtual and argument technologies. Similarly, in the past COVID-19 studies (Al-Azawei and Alowayr, 2020; Vanduhe, 2020), most researchers focused on attitude, subjective norms, and perceived usefulness to check the individual intention toward adopting mobile learning and the TAM model to implement their findings. However, limited studies investigated the ITUICT factor in university students. Therefore, the main objectives of this study are to focus on factors such as performance expectancy, technology self-efficacy, effort expectancy, hedonic motivation, price value, and habit influencing students’ ITUICT. Additionally, this study incorporates UTAUT2 and TTF theories into a novel framework and composes many hypotheses built on prior analysis (Faqih and Jaradat, 2021; Wang et al. 2022). The UTAUT2 model’s capacity to present an individual’s impressions of technology by its most crucial determinants of performance expectancy and effort expectancy is the justification for including it in the investigation into the ITUICT. Similarly, TTF integrates the performance in assessing the compatibility among tasks and technology toward the ITUICT (Wang et al. 2022). In order to conduct this study from the perspective of Chinese culture, we used university students as survey participants.

The rest of this study is prearranged as follows: Section 2 presents the literature. Section 3 presents the methodology. Section 4 deliberates the results and analysis. Section 5 expresses the study’s key findings and discusses the research implications. Section 6 discusses the study limitations and future directions.

Theoretical background and literature review

TTF and UTAUT 2 theories

This study applies the Task-Technology Fit (TTF) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) theories to investigate the intention to use ICT in the Chinese education sector. Firstly, the TTF theory postulates that the likelihood of technology being utilized and accepted depends significantly on its ability to fulfill the tasks users need (Faqih and Jaradat, 2021). In order to ascertain whether technology sufficiently facilitates task completion when incorporating new technology, the TTF theory was developed. Due to the diverse forms, opportunities, and strategies associated with ICT adoption, educators often face challenges in fully understanding and implementing them effectively (Dang et al. 2020). As a result, for the real use of ICT in educational settings to improve learning benefits, there should be some degree of compatibility among technological and task characteristics. Therefore, it makes sense to include TTF when examining the adoption of ICT from an educational standpoint. A better perception of the changing aspects of ICT usage in education is also made possible by integrating TTF with the UTAUT2 (Dang et al. 2020).

Secondly, UTAUT2 is a theoretical framework that enlightens and foresees individuals’ acceptance and espousal of technology (Subhani et al. 2025; Seo, 2020). In the context of ITUICT in the education sector, UTAUT2 can shed light on the elements that affect students’ acceptance of this technology. The UTAUT2 theory was expanded by (Venkatesh et al. 2003) by introducing three additional factors: hedonic motivation, price value, and habit behavior. Due to this modification, the new setup (UTAUT2) is now a more potent prediction framework. UTAUT2’s relevance as a framework for technology adoption is well known (Osei et al. 2022). Evidence from studies (Faqih and Jaradat, 2021; Penney et al. 2021) suggested that UTAUT2 has been shown to be a valuable model for analyzing how new technical breakthroughs are adopted in diverse cultural settings and social contexts. The main issues during the implementation of ICT are how to ensure competence and coordination between technology and related tasks. In this study, we integrated the UTAUT2 and TTF model frameworks to examine ITUICT. This combined model demonstrates viability and significance as a productivity enhancement tool, consistent with findings from previous studies (Dang et al. 2020; Du et al. 2022; Faqih and Jaradat, 2021).

Use of ICT in the education sector

ICT in education can help students acquire critical 21st-century skills as they prepare for the digital age, promote active and engaged learning, and expand access to high-quality education (Ojokoh et al. 2013). ICT plays a significant role in education, revolutionizing how students learn and educate. ICT provides access to various educational resources, including online libraries, digital textbooks, educational websites, and multimedia content (Dixit et al. 2021). This enables students to discover various subjects, engage with communicating materials, and learn independently. During COVID-19, ICT enables e-learning, which refers to using electronic technologies to deliver educational content (Duan et al. 2010). Online courses and virtual classrooms allow students to participate in remote learning, access educational materials from anywhere, and collaborate with peers and teachers globally (Yao et al. 2022). ICT tools enhance engagement, interactivity, and the overall learning experience. Communication technology enables effective collaboration and communication among students and educational institutions in China (Yu et al. 2023). It promotes online discussion forums, video conferencing, real-time project collaboration, and file sharing.

A prior study, Chi et al. (2022) linked ICT as a bridge to the digital rift by providing information and educational resources. Students can access digital libraries, research online, and study many worldviews, breaking geographical barriers and promoting equitable education. ICT offers innovative assessment methods in education, including online quizzes, simulations, and automated grading systems. ICT tools provide students with instant feedback and facilitate their education (Kayisire and Wei, 2016). Online courses, webinars, and virtual workshops provide opportunities for students to learn new learning strategies, stay updated with educational trends, and connect with a broader community of educators (Khan et al. 2019). A past study Merhi et al. (2019) focused on how ICT simplifies a variety of administrative duties in schools, including scheduling, grading, and attendance tracking. School management systems and digital record-keeping simplify administrative processes, saving time and resources. ICT promotes lifelong learning by providing opportunities for continuing education and professional development beyond traditional classroom settings.

Task-technology fit

TTF can be described as the scope to which technology aids a person in carrying out specific tasks. The TTF theory’s basic tenet states that to increase technology use, compatibility among the features of the technology and the necessary tasks is essential (Alazab et al. 2021). According to this study, using UTAUT2 as a theoretical foundation may be an effective way to explore and decipher the driving forces behind the use of ICT applications in educational institutions in China. However, the TTF is merged with the UTAUT2 theory to produce a more potent theoretical background capable of expanding the adjustment in adopting ICT technology. This increases the success of the UTAUT2 theory in amplification the individual’s intention to use ICT technology (Alajmi and Alotaibi, 2020). Based on numerous empirical investigations, combining the UTAUT2 and TTF to examine technology adoption yields a wealth of useful information for the study (Paulo et al. 2018; Sharif et al. 2019). Several studies have found strong statistical associations among TTF and UTAUT2 variables that may help enhance adoption (Faqih and Jaradat, 2021; Paulo et al. 2018).

The prior study researchers merged TTF with UTAUT2, providing the foundation for the empirical analysis to investigate embracing mobile-based ICT (Dennis and Jayawardhena, 2010). The associations between the TTF antecedents (task characteristics, technology characteristics, and task-technology fit) and the three main constructs of UTAUT2, performance expectancy, effort expectancy, and adoption intention, have traditionally been the main focus of empirical studies (Faqih and Jaradat, 2021; Wang et al. 2022) implementing the integrated concept with our study. Handling ICT activities demands a high level of technical complexity for this study. Therefore, there should be an adequate agreement between technology and task features to integrate ICT in educational contexts to enhance learning results effectively. An “adequate agreement” between technology and task features in an educational context means that the selected technology aligns with the specific requirements of the task to enhance learning outcomes (Agyei and Keengwe, 2014). For example, a task was given as collaborative group work requiring brainstorming. To solve this task, a technology such as a shared digital whiteboard (e.g., Miro or Jamboard) allows simultaneous contributions and visual organization of ideas (Liu et al. 2023). Based on the above arguments, the following hypotheses are formulated,

H1: Task characteristics positively effect the task-technology fit.

H2: Technology characteristics of ICT positively influence the task-technology fit.

H3: Task-technology fit positively effects the performance expectancy of ICT.

H4: Task-technology fit positively effects the intention to use ICT in individuals.

Performance expectancy

Performance expectation is the extent to which people think utilizing a specific technology will improve their ability to do their jobs or make tasks simpler and more effective (Gellerstedt et al. 2018). In education, ICT refers to using digital tools and resources for learning, and administrative purposes. Performance expectancy leads to enhanced students’ learning experiences and outcomes (Sewandono et al. 2023). Educators and administrative staff realize that using ICT can save time, increase efficiency, and reduce administrative burdens; they are more inclined to adopt it (Al-Rahmi et al. 2020). In a past study Gupta et al. (2008), researchers explained that ICT provides access to digital materials such as online libraries, educational websites, and updated multimedia content. Various learning styles and abilities can be met by utilizing these resources, which provide a wide range of learning opportunities. Students can receive personalized instruction tailored to their needs, pace, and learning preferences through these tools. Individuals perceive that ICT can support differentiated instruction and cater to the unique requirements of each student. They are more likely to embrace its adoption (Mensah, 2019).

By using ICT in education, educators can certify that students are outfitted with the necessary digital literacy skills and are prepared for the demands of the modern workforce (Nikolopoulou et al. 2021). A prior study Alshmrany and Wilkinson (2017) highlighted performance expectancy positively influences ICT usage in the education sector by enhancing learning experiences and increasing efficiency and productivity. Performance expectancy provides access to rich learning resources, facilitating personalized learning, improving communication and collaboration, and keeping pace with technological advancements. Students perceive that using ICT will result in these benefits and are more likely to embrace its integration into their educational practices (Abbad, 2021). Based on the above discussion, it hypothesized,

H5: Performance expectancy is significantly related to the intention to use ICT in university students.

Effort expectancy

Educators demonstrate a high level of effort expectancy, making them more inclined to adopt ICT in the education sector (Shahzad et al. 2024). Factors such as ease of use, time-saving benefits, accessibility of resources, effective training support, and positive user experiences significantly enhance the perception of ICT. These advantages collectively contribute to increased utilization of ICT in educational settings (Hunde et al. 2023). The concept of effort expectancy describes how easy it is to use and how much work it takes to embrace and use ICT in the educational field. Communication technology tools and platforms have intuitive interfaces and clear instructions and require minimal technical expertise, and educators feel more comfortable incorporating them into their learning methods. ICT solutions can streamline administrative tasks, lesson preparation, and content delivery; educators perceive them as time-saving tools (Mosunmola et al. 2018). Intention to use ICT becomes more appealing by reducing the effort required for routine tasks, such as grading, organizing materials, or creating engaging learning activities.

Effort expectancy is positively influenced when ICT enables educators to access relevant educational resources and tools conveniently. Similarly, the ICT platforms provide a wide range of digital content, such as e-books and different online learning modules; students can easily incorporate them into learning without requiring extensive effort or resource gathering (Hunde et al. 2023). Adequate training and technical support are vital in increasing effort expectancy. Students receiving comprehensive training on ICT tools and platforms feel more confident integrating them into their learning practices. Positive experiences and feedback from early adopters can significantly influence the perception of effort expectancy (Gellerstedt et al. 2018). Educators observe colleagues successfully implementing ICT in their classrooms and witness positive outcomes, such as improved student engagement, enhanced learning outcomes, or increased efficiency. They are likelier to perceive the effort required as worthwhile (Tiwari, 2020). Based on the above arguments, it hypothesized,

H6: Effort expectancy is significantly related to the intention to use ICT in university students.

Technology self-efficacy

Feeling confident in using technology increases students’ eagerness to try ICT tools by reducing fear and resistance. This confidence fosters curiosity, encouraging exploration and skill development in digital learning (Bubou and Job, 2022). By nurturing technology self-efficacy among educators, universities can create an environment conducive to successfully integrating and utilizing ICT tools and resources for effective teaching and learning. Individuals with higher degrees of technological self-efficacy are more self-assured when using it efficiently (Zhang et al. 2023). This assurance translates into a willingness to investigate and test ICT services, boosting adoption. Students and administrators who believe in technological capabilities are likelier to embrace and incorporate ICT into their education practices. Education professionals are less likely to view technology as a difficult or burdensome task when they feel confident and at ease using it (Robertson and Al-Zahrani, 2012).

Technology self-efficacy empowers educators to leverage ICT tools to enhance their instructional practices. Students are more expected to incorporate technology into their learning methods when they believe in its potential (Awofala et al. 2019). They can explore innovative approaches, such as online collaborative platforms and educational apps, to create engaging and interactive learning experiences. Technology self-efficacy promotes a proactive approach to learning and developing digital skills. Educators with higher levels of self-efficacy are more likely to seek professional development opportunities and training programs to enhance their technological competencies (Merhi et al. 2019). Educators with high technology self-efficacy serve as positive role models for students. Students witness their teachers confidently using technology, and they are likelier to develop a positive attitude toward ICT (Papastergiou et al. 2011). This can create a ripple effect, where students become motivated to adopt and utilize ICT tools for their learning, further promoting ICT use in the education sector. Therefore, it is hypothesized that,

H7: Technology self-efficacy is significantly related to the intention to use ICT in university students.

Hedonic motivation

Hedonic motivation is people’s desire for pleasure, enjoyment, and self-gratification when participating in activities. In education, ICT can provide interactive and multimedia-rich content, gamification elements, virtual reality experiences, and other engaging tools that make learning enjoyable and entertaining (Oluwajana et al. 2019). Students are more likely to use ICT in education when it provides pleasurable and stimulating experiences, motivating them to participate and explore educational materials actively. ICT enables personalized and self-directed learning experiences by providing access to various educational resources and tools (Chun et al. 2012). With interactive information tailored to each student’s tastes and needs, students can study areas of interest at their own speed. Students may be motivated to embrace ICT in education by their autonomy and freedom to choose their educational route (Shahzad et al. 2024). Students’ autonomy and freedom in choosing their educational path can motivate them to embrace ICT tools like gamified learning platforms (e.g., Kahoot, Duolingo) and interactive simulations (e.g., PhET simulations), which enhance hedonic motivation by making learning engaging, enjoyable, and immersive (Dah et al. 2023).

Learners can interact with peers, share ideas, and work on group projects using collaborative platforms, including online discussion boards and video conferencing. These interpersonal and group activities can develop a sense of satisfaction, encouraging favorable views about using ICT in education (Muangmee et al. 2021). Innovations in technology and the use of ICT in education can spark interest and novelty. The novelty factor associated with ICT use can motivate students to explore and embrace these technologies in education (Al-Azawei and Alowayr, 2020). Hedonic motivation positively influences the adoption of ICT in the education sector by providing engaging learning experiences and enabling personalized and self-directed learning (Li et al. 2021). Hedonic motivation facilitates social interaction and collaboration, offering immediate feedback and recognition and leveraging the novelty and technological appeal of ICT tools. These motivational factors enhance students’ interest, involvement, and acceptance of ICT in education, leading to its wider adoption (Al-Rahmi et al. 2020). Based on the above arguments, it is hypothesized,

H8: Hedonic motivation is significantly related to the intention to use ICT in university students.

Price value

Price value, or affordability, plays a vital role in embracing ICT in the education sector. Affordable devices ensure a broader reach, enabling more individuals to benefit from digital learning opportunities (Lee et al. 2011). ICT tools with reasonable prices encourage educational institutions to spend money on the required infrastructure, like computer labs, high-speed internet connectivity, and software licenses (Ali et al. 2016). It is more practical for schools and colleges to build and maintain ICT infrastructure due to lower costs. Affordable ICT tools also lead to the distribution of digital educational content and resources. Lower prices facilitate the development and broadcasting of e-books, online courses, educational software, and multimedia materials, which can enhance the learning experience (X. Gu et al. 2015). It is simpler for educational institutions to offer training and active development opportunities for teachers and employees to use ICT tools readily. Lower costs allow schools and colleges to offer workshops, webinars, and online courses that improve students’ digital literacy and ICT integration (Lu and Price, 2018).

Accessible ICT resources can increase student enthusiasm and involvement in the educational process. This engagement can improve academic performance and positively impact learning outcomes (Masserini et al. 2018). The price value is particularly important in bridging the digital divide and promoting equity in education. Lower prices for ICT tools ensure that students from diverse socio-economic backgrounds have equal opportunities to access digital resources and participate in technology-enhanced learning. Affordable ICT use helps reduce disparities in educational outcomes (Fincher and Katsinas, 2017). Affordable prices for ICT tools provide for the wider adoption and integration of technology in the education sector. They increase accessibility, promote infrastructure development, facilitate digital content creation, support training initiatives, enhance student engagement, and foster educational equity (Chen et al. 2021). Therefore, it is hypothesized that,

H9: Price value is significantly related to the intention to use ICT in university students.

Habit

Habits are developed and fostered among educators, students, and other stakeholders, and they can have a transformative impact on the incorporation and use of ICT tools in education. Habitually using technology in education helps create a comfort zone and familiarity with digital tools (Elsalem et al. 2020). Students consistently incorporate ICT into their daily routines, which becomes a natural part of their learning process. Habits encourage individuals to invest time and effort in acquiring and improving digital skills (Ali et al. 2022). For example, regularly checking and responding to emails each morning or using a project management app daily to track tasks are habitual ICT uses that enhance time management and digital skills over time (Hermsen et al. 2016). Regular engagement with ICT tools cultivates proficiency, allowing educators to use technology in their teaching methods successfully and students to enhance their digital literacy. The habit of embracing lifelong learning contributes to embracing ICT in education. Educators committed to staying updated on technological advancements and educational strategies will likely leverage ICT effectively and keep pace with evolving digital trends (Dah et al. 2023).

Positive habits around collaboration and communication fostered through ICT facilitate knowledge sharing and interaction among educators, students, and the broader educational community (Danner and Pessu, 2013). Using digital platforms and tools, stakeholders can connect, collaborate, and exchange ideas, enhancing learning experiences. Developing habits of using technology to streamline administrative tasks, automate processes, and enhance productivity can free up educators’ time, allowing them to focus more on instructional activities (Wang et al. 2022). This efficiency can promote ICT use as it demonstrates technology’s tangible benefits and impact on educational workflows. Habits that promote personalized learning experiences through ICT can help cater to student’s diverse needs and preferences. By leveraging adaptive learning platforms, online resources, and digital assessments, educators can tailor instruction, monitor progress, and provide targeted feedback, improving learning outcomes (Nikolopoulou et al. 2021). Therefore, positive habits regarding integrating and using ICT in the education sector contribute to a culture of technological adoption and open up new possibilities for effective learning and collaboration. Therefore, it is hypothesized that,

H10: Habit is significantly related to the intention to use ICT in university students.

Figure 1 shows the conceptual framework of the intention to use ICT in university students.

Fig. 1
figure 1

Conceptual framework.

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Research methodology

The essential elements of an empirical study that attempted to verify and test the proposed research model are discussed in the following sections.

Research instruments

This study investigated how the students felt about the elements that affected the acceptance of ICT applications in the education sector. A research model was created, and an empirical investigation was carried out to achieve this goal. The empirical study for this subject was carried out in the Chinese environment. A total of 31 items, ranging from 1 (strongly disagree) to 5 (strongly agree), were evaluated using the Likert scale. The UTAUT2 and TTF models and earlier studies in this field, including those by (Faqih and Jaradat, 2021; Lee et al. 2019; Venkatesh et al. 2003; Wang et al. 2022), served as the foundation for the measurement scale and items, which were then customized for the unique context of this study.

Task-technology fit (TTF) three items were adopted from a previous study (Faqih and Jaradat, 2021). Performance expectancy (PE) was calculated by three items from a past study (Wang et al. 2022). Technology self-efficacy (TSE), three items were derived from an earlier research (Govender, 2009). Technology characteristics (TC) were assessed by three items measured from a past study (Faqih and Jaradat, 2021). Effort expectancy (EE) was evaluated by three items from the past study (Wang et al. 2022). Hedonic motivation (HM) three items were evaluated from a prior study (Al-Azawei and Alowayr, 2020). Price value (PV) was assessed by three items taken from (Shen et al. 2022). Habit (HAB) three items were assessed from a study (Wang et al. 2022). Task characteristics (TCH) three items were adopted from a previous study (Faqih and Jaradat, 2021). Intention to use ICT (ITUICT) three items were assessed by a prior study (Shahzad et al. 2024). During the pilot test, a reliability analysis was done to evaluate the internal consistency of each measurement used in this study. Items’ dimensions detailed are described in Table 2.

Data collection and procedure

Due to the geographical distance, the research team used online approaches to gather the data. Online questionnaires were distributed through Wenjuanxing platforms (https://www.wjx.cn) in China’s most commonly used (Shahzad et al. 2024). The questionnaire was divided into two sections: (i) demographics and (ii) all items of each construct. The research questionnaire was initially created in both Chinese and English. A pilot test involving ten students was carried out. This pilot study was conducted to evaluate the questionnaire’s quality. The pilot testing was conducted after the researcher revised the item’s phrasing in light of the findings. To ensure that all of the questions on the original questionnaire were correctly and precisely formulated, participants were requested to complete it. After that, the questions were revised appropriately. This study used a convenience sampling method along with a non-probability sampling strategy. The method used for the unit of analysis is the students from different universities. The convenience sampling technique is used as the population from the education sector is unknown. Convenience sampling involves selecting cases at random that are the simplest to obtain for the sample; this process continues until the necessary number of examples is obtained (Farrukh et al. 2023).

The information was gathered from March to May 2023 from 714 students. All questionnaires were examined for any missing information. 601 questionnaires were discovered to be useful for future investigation. All of the responders were Chinese students who were found through class announcements, posts on social media sites, and the help of peers from other universities. Students from the Beijing University of Technology in Hebei Province, China, comprised the majority of participants (66%), with the remaining students hailing from six other universities and provinces. All participants were chosen willingly. Self-administered (online) primary data collection was used. In the past, researchers have employed a similar approach on the same topic, such as (Al-Azawei and Alowayr, 2020; Shen et al. 2022). According to Shen et al. (2022), PLS-SEM recommends a sample size ten times the number of indicators of the construct with the greatest number of indicators. Our sample size was 601, which was greater than the minimum needed volume according to the benchmark in the past study (Faqih and Jaradat, 2021). Recommendations stating that empirical research should include more than 30 but less than 500 individuals are consistent with this sample size (Roscoe et al. 1975). Participants ranged in age from 20 to 31, with women (45%) and men (55%). Furthermore, the participant’s profile can be seen in Table 1.

Table 1 Profile of sample (n = 601).
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Analysis and results

PLS-SEM was used in this study to test the suggested model. According to Nunkoo et al. (2013), the SEM approach has gained popularity and is commonly used in research in the education sector. Moreover, statistical tools have widely incorporated PLS-SEM statistical techniques for estimating the dysfunctional interactions between independent, mediating, moderating, and dependent variables (Faqih and Jaradat, 2021). PLS-SEM, in particular, combines two modeling steps to identify the measurement and structural modeling. Therefore, this study was carried out using the SmartPLS software in two steps: estimating the measurement model and evaluating the structural model (Henseler et al. 2016).

Common method bias

In a survey-based research methodology where data was acquired from a single source, common method variance (CMB) may be a significant feature to consider (Podsakoff et al. 2003). The respondents were particularly chosen based on their knowledge of ICT usage in their education. As a result, careless responses were less likely as the survey questions were well constructed, and respondents responded to them according to their perspectives. This study used Harman’s one-factor test, as used by (Farrukh et al. 2023), to demonstrate the absence of common method bias. Our study results revealed that single factor variance was 34.4%, below the 50% threshold, in accordance with the findings from the exploratory factor analysis performed on all variables’ items, ruling out the possibility of a CBM issue with the survey data. Furthermore, the Bagozzi and Edwards (1998) approach was also used to establish robustness in detecting CBM, which states that a higher correlation among the constructs would show the presence of CBM. In this study, we measured inter-construct correlations using Smart-PLS, but there is no evidence of CBM. Next, we used a contemporary approach to analyze this study’s variance inflation factor (VIF), as proposed by (Farrukh et al. 2023). All measured VIF values were placed in the range of less than 5.

Measurement model

In the measurement model, two categories of validity are evaluated: convergent and discriminant. The confirmatory factor analysis (CFA) outcomes demonstrate strong convergent validity. According to (Hair et al. 2020), reliability and validity measurements that are frequently utilized include Cronbach’s alpha (α), composite reliability (CR), and average variance extracted (AVE). The reliability of a scale or construct is shown by a measure of internal consistency called Cronbach’s alpha. It assesses the scale’s reliability by calculating the average correlation among the items. Composite reliability is another measure of reliability that evaluates the internal consistency of a construct. Composite reliability is calculated using the standardized factor loadings and item error variances. Convergent validity is measured by AVE, which shows how much variance is accounted for by the construct in relation to measurement error (Shahzad and Xu, 2024; Voorhees et al. 2016). All item factor loadings above 0.70 are in Table 2. As a result, all elements lie within range and prove the validity of the results.

Table 2 Confirmatory factor analysis.
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According to Fornell and Larcker (1981), discriminant validity was assessed using three techniques: the Heterotrait-Monotrait ratio (HTMT), comparison among item loadings and cross-loadings, and correlations between variables and AVE using Fornell–Larcker. These findings of discriminant validity of this study demonstrated the valid relationship between the variables. One of the important methods to test discriminant validity is the HTMT ratio. The information indicates that the HTMT ratio is below 0.90 (Voorhees et al. 2016). Table 3 demonstrates valid discriminant validity of all variables.

Table 3 Discriminant validity.
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Structural model

The connections between the conceptual model’s latent variables are represented by the structural model. To obtain both direct and indirect correlations between the constructs, this study makes use of PLS-SEM. PLS-SEM primary objective is to evaluate the constructs’ coefficient value R2 to assess the structural model (Fassott, 2010; Shahzad et al. 2025). Values of R2 imitate how well the IVs can explain the DVs. In our study, R2 values of ITUICT = 0.824, TTF = 0.038 and PE = 0.012. The R2 values of constructs were also used to calculate the goodness of fit (GoF) index (Shahzad et al. 2024). The values SRMR = 0.147, Chi-square = 9877.191, and NFI = 0.607 represent GoF in Table 4.

Table 4 Model-fit.
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Furthermore, in order to investigate the relevance of the structural path coefficients, a resampling was done using the bootstrapping method, with 5000 resamples and a 95% bias correction (Voorhees et al. 2016). Table 5 presents the model’s bootstrapping result, demonstrating that every variable exhibits meaningful correlations. According to the results, all hypotheses H1, H2, H3, H4, H6, H7, H8, H9, and H10 were significant at the p < 0.05 level shown in Table 5. Figure 2 shows the structural model of ITUICT.

Table 5 Hypothesis testing.
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Fig. 2
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Significance of inner model (*p < 0.05; **p < 0.01; ***p < 0.001).

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Discussion and conclusion

Information and Communication Technology (ICT) has significantly changed many aspects of our daily lives, including the way we work, communicate, and learn. In education, ICT has become a powerful tool for transformation, offering new methods to enhance students’ learning experiences (Shahzad et al. 2024). The vast magnitude and complexity of China’s educational system, which has millions of learners and educators dispersed over a variety of sociocultural and geographical settings, stands remarkable. ICT integration in this setting is a strategic necessity for encouraging creativity, advocating equitable access to top-notch education, and creating a workforce that is competitive on the world market (Gellerstedt et al. 2018). Therefore, based on TTF and UTAUT2, the current research study examines the impact of task-technology fit, performance expectancy, technology self-efficacy, effort expectancy, hedonic motivation, price value, and habit on intention to use ICT in university students. 601 university students in China completed an online survey to assess the theoretical framework. The study’s findings showed that all ten hypotheses were supported.

Regarding H1, it proposed that task characteristics are positively linked with task technology fit with values (β = 0.170, t = 4.671, p < 0.05). Hence, H1 is supported. This finding established the suggestions by (Faqih and Jaradat, 2021; Wang et al. 2022). When ICT is introduced into these tasks, it is crucial to consider how well the technology aligns with these characteristics (Bai et al. 2016). However, as tasks are redesigned or reimagined to be more technology-friendly, the positive link suggests a potential for enhancing educational outcomes (Dang et al. 2020).

The outcome study analysis H2 indicated that technology characteristics are significantly associated with task-technology fit with values (β = 0.111, t = 3.100, p < 0.05). Hence, results are supported by H2. These findings align with fresh and past literature (Alazab et al. 2021; Faqih and Jaradat, 2021) as technology characteristics are significantly linked with task-technology fit. A past study, Faqih and Jaradat (2021) highlighted task-technology fit, which implies that the technology used aligns well with the educational tasks or goals, making it more likely to be adopted and used effectively by students. Selecting the ICT technology considers not only the features and functionalities of the technology but also how well these align with the specific educational tasks and goals they aim to achieve. Students can enhance their study learning with technologies that align well with their tasks (Bai et al. 2016).

Findings related to H3 proposed that task technology fit positively influences the ITUICT (β = 0.072, t = 4.768, p < 0.05). This result supported H3. The study outcomes align with the literature (Faqih and Jaradat, 2021; Wang et al. 2022) on relationship task technology fit positively impacts ITUICT in university students. A past study Shahzad et al. (2024) highlighted that Chinese universities have always valued education and ICT technology to achieve progress and success. Universities provide facilities to students to see the relevance and utility of ICT in their academic and personal development, thereby increasing their intention to use it. Students perceive ICT tools and platforms as effective and efficient in helping them achieve their academic goals; they are more likely to have a positive intention to use them (Geng and Xue, 2023).

Regarding H4, task technology fit positively effects performance expectancy with values (β = 0.109, t = 2.943, p < 0.05). This study’s results validated H4. The study aligns with the literature (Dang et al. 2020; D. Gu et al. 2021) on relationship task technology that positively impacts performance expectancy in university students. The TTF factor assesses how well the technology aligns with the specific needs and requirements of educational tasks in China. Consider evaluating the ICT systems in terms of their compatibility, complexity, and flexibility to determine their fit for educational tasks (Vanduhe, 2020). Enhancing performance expectancy towards ICT use can lead to increased adoption and more effective utilization of ICT in the educational sector. Students have higher expectations of the ICT tools; they are more likely to learn how to use them effectively (Shahzad et al. 2024). With high-performance expectancy, educational institutions can provide training and professional development opportunities for students to enhance their ICT skills and confidence (Faqih and Jaradat, 2021).

In discussing the low explanatory power of Task-Technology Fit (TTF) for the intention to use ICT, it’s important to acknowledge that while TTF is a useful framework for understanding ICT adoption, it may not capture all the factors influencing an individual’s intention to use technology. Additionally, users might prioritize system usability and external incentives over the alignment between tasks and technology. Variations in personal preferences or organizational culture can also dilute TTF’s impact. Measurement issues, like insufficiently capturing TTF constructs, could further reduce its explanatory power.

Similarly, going forward, this study formulated H5, performance expectancy positively linked with ITUICT (β = 0.643, t = 22.388, p < 0.05), as shown in Table 5 validated H5. The study findings are linked with past studies (Faqih and Jaradat, 2021; Wang et al. 2022), as performance expectancy is positively linked with ITUICT in university students. Performance expectancy examines the perceived benefits of using ICT in education, such as improved learning outcomes, enhanced learning effectiveness, and increased efficiency (Afari et al. 2023). Students who believe that ICT can enhance learning outcomes are more likely to adopt ICT into their educational practices (Yu et al. 2023). Performance expectancy is often associated with the belief that using ICT will lead to better educational outcomes. Institutions prioritize investing in ICT infrastructure, training, and resources if they recognize the potential benefits in terms of educational quality (Gellerstedt et al. 2018).

This study developed the H6 hypothesis: effort expectancy positively correlates with ITUICT with values (β = 0.323, t = 6.405, p < 0.05). This study’s results validated H6. The study results align with the past literature (El-Masri and Tarhini, 2017; Madan and Yadav, 2016) on relationship effort expectancy positively associated with ITUICT in university students. Effort expectancy analyzes the ease of use and the effort required to incorporate ICT into educational tasks. User-friendly interfaces, intuitive design, and adequate training can positively impact effort expectancy (Gellerstedt et al. 2018). A past study, Yu et al. (2023) suggested that students familiar with ICT tools and platforms as easy to use are more likely to adopt them for various educational purposes. Students’ efforts to enhance the ease of use of ICT tools can lead to increased adoption rates and better utilization in Chinese universities. Institutions that provide user-friendly interfaces and offer ongoing support can potentially boost the ITUICT among students (Shahzad et al. 2024).

This study established the H7 hypothesis: technology self-efficacy is positively associated with ITUICT with values (β = 0.265, t = 7.105, p < 0.05). This study’s results supported H7. The study results align with the past literature (Sharifi Fard et al. 2016; Zainab et al. 2017) on relationship effort expectancy positively associated with ITUICT in university students. Technology self-efficacy assesses the individual’s confidence in using ICT in educational settings (Waheed et al. 2015). A prior study Zhang et al. (2023) confirmed that students with higher levels of technology self-efficacy are more inclined to use ICT in their learning practices. As technology continues to evolve and play a significant role in various aspects of society, it becomes crucial for students to adapt and incorporate ICT tools and platforms into their education. Universities provide access to digital resources, and fostering a culture of technological innovation can help create an environment where students feel supported and encouraged to use ICT (Shahzad et al. 2024).

Similarly, going forward, this study formulated H8, hedonic motivation positively linked with ITUICT (β = 0.076, t = 3.317, p < 0.05), as shown in Table 5 validated H8. The findings are linked with past studies (El-Masri and Tarhini, 2017; Meet et al. 2022), as hedonic motivation is positively related to using ICT in university students. Hedonic motivation explores the potential enjoyment, satisfaction, or pleasure of using ICT in education. Features like gamification, interactive elements, and multimedia content can enhance the hedonic motivation to use ICT (Oluwajana et al. 2019). Hedonic motivation is pivotal in shaping individuals’ attitudes and behaviors toward using ICT (Faqih and Jaradat, 2021). When individuals perceive ICT usage as enjoyable, entertaining, or personally rewarding, they are more likely to develop a positive ITUICT. In Chinese universities, hedonic motivation can manifest in various forms, such as enjoying online learning platforms, interactive educational games, social media interactions related to academic topics, or even using virtual reality and augmented reality tools for immersive learning experiences (Shahzad et al. 2023).

This study developed the H9 hypothesis, as price value is positively associated with the ITUICT with values (β = 0.156, t = 4.916, p < 0.05). This study’s results validated H9. The study results align with the past literature (Chen et al. 2021; Dajani and Abu Hegleh, 2019) on relationship price value positively associated with ICT usage in university students. Price value evaluates the cost-effectiveness and value proposition of using ICT in education (Fincher and Katsinas, 2017). A past study, Ali et al. (2016) highlighted a positive association between price value and the use of ICT, suggesting that cost-effective solutions or pricing strategies can effectively promote ICT adoption. This finding is particularly relevant for university administrators responsible for budget allocations and ICT procurement decisions. Therefore, universities should invest in training programs, technical support, infrastructure, and cost-effective pricing strategies to ensure that users can effectively utilize and benefit from ICT tools (Nikolopoulou et al. 2021).

This study established the H10 hypothesis, as habit positively associated with ITUICT with values (β = 0.171, t = 5.388, p < 0.05). This study’s results supported H10. The study results align with the past literature (Wang et al. 2022; Zacharis and Nikolopoulou, 2022) on relationship habits positively associated with intention to use ICT in university students. Prior experience, existing usage patterns, and the availability of alternative ICT solutions can affect the formation of habits and subsequent adoption (Wang et al. 2022). The positive association between habit and the ITUICT aligns with previous research emphasizing the role of habit in shaping technology adoption and usage (Nikolopoulou et al. 2021). In the context of ICT, forming a habit around using technology can lead to increased frequency and duration of usage and a greater proficiency in utilizing different ICT tools. Institutions can leverage this knowledge to promote the development of positive ICT habits among students in their learning activities and create a technology-rich learning environment (Ali et al. 2016).

Strength and implication of the study

This study has potential benefits in the education sector. Firstly, this study uses the UTAUT2 and TTF theories to emphasize the importance of aligning technology with the tasks it is intended to support. Secondly, performance and effort expectancy suggest that when students perceive ICT in education as beneficial and capable of enhancing their academic performance, they are more likely to use it. Effort expectancy implications suggest that students’ perceptions of the effort required to use ICT in education play a crucial role in their adoption and usage. Thirdly, technology self-efficacy emphasizes the significance of students’ beliefs in their ability to use ICT effectively in their education. Educators should focus on students’ confidence and competence in using technology to enhance their learning experiences. Fourthly, hedonic motivation indicates that students’ enjoyment and satisfaction from using ICT in education can significantly influence their adoption and continued usage. Fifthly, price value highlights the importance of students’ perceptions of the value they receive from ICT, considering its cost. This finding underscores the need for affordable and accessible ICT solutions in the education sector. Lastly, the habitual use of ICT in education increases the usage of ICT in individuals. As students develop a habit of using ICT tools for learning, it becomes an ingrained behavior requiring less conscious effort. Encouraging regular and consistent ICT use can contribute to the establishment of positive habits among students.

This study has some practical contributions. Firstly, educational institutions should offer training and support programs that assist students in acquiring the skills required to use ICT tools effectively if they want to increase students’ technological self-efficacy. This can include workshops, tutorials, and resources that build students’ confidence in utilizing technology. Secondly, the fun and satisfaction of students can be increased by including gamification, interactivity, and personalization components in ICT solutions. Teachers can encourage students’ motivation and active engagement by designing captivating and immersive learning experiences. Thirdly, priority should be given to ICT solutions’ accessibility and cost. Collaboration among educational institutions, policymakers, and technology suppliers is necessary to guarantee that all students can access ICT tools and resources regardless of socioeconomic status. It is possible to promote the routine and habitual use of ICT in education by integrating it into routine learning activities and offering rewards for consistent use. Similarly, with positive usage habits, students are more likely to reap the benefits of ICT in their educational journey. Lastly, monitoring and evaluating the impact of ICT implementation in the education sector is crucial. Usage patterns and academic outcomes can provide insights into the effectiveness of ICT interventions, enabling policymakers to make knowledgeable decisions and refine their strategies. By considering these theoretical and practical implications, stakeholders in the education sector can effectively harness the potential of ICT to enhance students’ learning experiences in China.

Limitations and future directions

This study has some limitations and is important for determining the direction of future research. First, this study intends to use ICT as an outcome variable. However, using real behavior as an endogenous variable might result in some intriguing findings for future research. Second, because the study only included students or learners, expanding it to examine educators’ perspectives on using ICT from a country like China’s perspective would require an extraordinary amount of work. Future research will be on different countries or cultures. Third, this study was conducted on students from Chinese universities. Future research should broaden its scope to incorporate additional academic institutions and consider students from different educational backgrounds. Fourth, the TTF and UTAUT2 theories were primarily developed in Western contexts, and their applicability to the Chinese cultural context may be limited. For future studies, it is important to use cultural factors, such as collectivism, hierarchy, and Confucian values, which may influence the ITUICT in education differently in China. Fifth, this study gathered the data through a questionnaire (cross-sectional) in a limited time frame. Future studies will use a longitudinal approach to collect data that can provide a more comprehensive implementation of ICT in the education sector of China. Sixth, in this study, researchers used the influences of different constructs on ICT usage in educational settings in the literature. Future research should examine this topic from the perspective of mobile devices using the mediation moderation model. Finally, computer-based technologies were used to implement the current study. Examining the use of mobile augmented reality, particularly in developing nations, may produce interesting information and suggestions.