A Modification of UTAUT2 Model Applied to the Field of Chinese University EFL Students’ Adoption of Mobile Technology ‐ integrated Vocabulary Learning

: Vocabulary learning plays an important role in English as a foreign language (EFL) learning among Chinese university students. The emergence of mobile technology provides ways for EFL vocabulary learning towards student-centered learning, but the factors that influence the adoption of this learning model by EFL university learners are not clear. In this study, the author explains the current situation of EFL vocabulary learning among Chinese college students and reviews the relevant theories of the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The author aims to investigate the factors influencing Chinese EFL students’ adoption of mobile technology-integrated vocabulary learning towards student-centered learning by utilizing the potential of the UTAUT2 model. The theoretical framework was modified from the original model by incorporating the additional constructs of privacy, trust, personal innovativeness, and information quality to examine their effects along with the original UTAUT2 determinants on behavioral intentions and use behavior. By exploring the influence factors of EFL students' adoption of mobile technologies using a modification of UTAUT2 model, the author hopes to promote the integration of mobile technology and English courses and contribute to the existing literature on technology acceptance.

Now people are living in an era of global integration, English is the world's language and it plays a more and more important role in our lives.English as the main communication tool, are used in International political dialogue and trade exchanges, information and technology dissemination worldwide, education and resource sharing (Rao, 2019).Many countries in the world now attach great importance to English.In most European countries, English is a compulsory subject.In Southeast Asian countries such as India, Japan, Malaysia, and Singapore almost take English as a second language.
College English education occupies an important position in the discipline construction and personnel training of Chinese universities (Dafouz, 2018).Therefore, university course requirements for English are getting higher and it is also being clearer and more scientific.The status of English teaching in universities are also becoming more and more important.However, there are some problems in English education in China at the present stage.The exam-oriented teaching mode hinders students' interest in learning.In this learning atmosphere aimed at coping with exams, it is difficult for students to truly grasp the instrumental role of English, that is, the most practical communicative function.
In English language learning content, the importance of vocabulary learning has been widely acknowledged and well documented in the field of second language acquisition (Ardasheva et al., 2019).In addition, learning new words and phrases is one of the most important factors in becoming fluent in English language.Furthermore, it is one of the most important factors in the development of the four language abilities.According to Setiawan and Wiedarti (2020), having a good vocabulary makes it easier to use a language, and conversely, using a language more often leads to an enhancement in that person's vocabulary knowledge.According to Mahdi (2018), developing one's vocabulary is one of the most effective ways for language learners to effectively acquire the target language.Under the traditional teacher-centered teaching mode, students cannot maintain their enthusiasm and motivation to learn second language vocabulary, Therefore, student-centered learning has become a trend.
At present, mobile applications are the most common applications in the English mobile learning context.They are used for the teaching and learning of all four language skills (i.e., speaking, writing, listening, and reading) (Ozer and Kılıç, 2018), as well as vocabulary, pronunciation, and grammar structures (Berger and Klímová, 2018).Nevertheless, as Lai et al. (2022), in their study, pointed out, most of the mobile applications focus on vocabulary learning.The development of technology provides a way for the concept of "studentcentered" education.The term "mobile learning technologies" refers to various digital tools and resources that can be used for educational purposes, such as personal computers, the internet, various software applications, various multimedia resources, social networking services (SNSs), blogs, smartphones, web technologies, and so on (Chawinga, 2017;Lawrence & Tar, 2018;Ngoungouo, 2017;Razak et al., 2019).Mobile learning technologies have had an impact on every facet of people's life, including the rise in interest in using them to learn foreign languages.These technologies have been made possible by developments in Internet connectivity (Gudmundsdottir et al., 2020).Mobile learning technologies that helped education have had a significant and inescapable influence on learning settings (for example, teaching techniques, learning approaches), as a result of the development of networks technology (Tran et al., 2020).So, it is a new choice to use mobile technology to integrate vocabulary learning to realize student-centered learning.However, the factors that influence the adoption of this learning model by English as a Foreign Language (EFL) university learners are not clear.Therefore, it is necessary to investigate the factors influencing Chinese EFL university students' adoption of mobile technology-integrated vocabulary learning.
According to Bradley (2009) and Sultana (2020), UTAUT 2 is a reliable model for explaining the usage and adoption of various technologies.This is due to the model's greater explanatory power of behavioral use technology, which is widely applicable in a variety of settings and capacities, and its high explanatory power of behavioral use technology.The rarity of the UTAUT2 model within the English curriculum raises pertinent questions about its explicit inclusion and its potential to unravel the intricacies of technology adoption and use (Venkatesh et al., 2012).By incorporating the UTAUT2 model, educators and researchers can delve into uncharted territories, unraveling the nuances of technology integration in EFL contexts (Wong et al., 2015).Its rarity underscores an opportunity for educational researchers to uncover insights that can reshape pedagogical practices, inform curriculum design, and enhance language learning experiences.This study aims to explore this gap in research by utilizing the potential of the UTAUT 2 model in investigating EFL students' adoption of mobile technology-integrated vocabulary.

The Extended Unified Theory of Acceptance and Use of Technology Model (UTAUT2)
Investigating learner engagement with the mobile technology-integrated vocabulary should make use of various theoretical perspectives and conceptual frameworks.This is an important additional point to take into consideration.In the past, the Technology Acceptance Model (TAM) has often been used for the purpose of conducting research on the students' acceptance elements about the mobile technologyintegrated vocabulary and vital characteristics (Kui et al., 2019).To this day, only a small amount of study has been carried out with the conventional method model, also known as the expanded Unified Theory of Acceptance and Use of Technology (UTAUT 2, hereafter).The UTAUT 2 takes into account a number of different factors, including performance expectation, effort expectancy, social influence, hedonic motivation, enabling circumstances, price value, and routine, which is shown in figure 1.At University Utara in Malaysia, Raman and Don (2013) investigated whether or not students liked and used a Learning Management System (LMS) for their classes.In addition, the UTAUT2 model was used to examine the impacts of use behavior and behavioral intention on factors such as effort and performance expectations, hedonic motivation, social influences, habit, and enabling situations.For this reason, researchers omitted a "price value" component from their suggested model because the institution gave the Moodle LMS free of charge.To gather data from 288 students, researchers used online surveys, which found that enabling circumstances and hedonic motivation were the most important predictors of behavioral intention, whereas habit had no discernible influence.There was a 29.5% variance in the participants' intentions to utilize the Moodle LMS, indicating that students were more likely to use technology when it was freely available and offered results that made them believe they were improving and succeeding in their studies.

Privacy
Privacy According to Lester et al. (2017), ethical and privacy issues should be seen as "underlying all other domains" (Lester et al., 2017), and they should have an influence on all organizational sectors in the framework.The authors highlight the ethical and privacy difficulties that arise for an institution when it acts as both a student and a provider of data for Baicizhan APPs.They advise that vendors should constantly be addressing the privacy demands of their customers.The authors describe the primary categories of individual concerns about privacy and ethics as the personal contexts of permission and agency, as well as trust and prejudice.They imply that students who provide data to LA systems are resigned to or are unaware of consent given to have these data harvested, and that the tradeoff for the individual and the institution alike must be a significant benefit to student performance as a result of this exchange.They say this because they suggest that students who provide data to LA systems are resigned to or are unaware of consent given to have these data harvested.Because of this, the authors describe students as being in "constant negotiation" (Lester et al., 2017) between the potential benefits of the surrender of their data for use in systems and the choices they make to trust in the LA system to provide personal benefit.This is because of the potential benefits of the surrender of their data for use in systems (s).In the current research, ethics and privacy were investigated by investigating the degree to which students thought that mobile learning took private issues into account in its use of Baicizhan.
The current research includes as one of its goals the determination of the degree to which students believe that ethical considerations and privacy concerns have been taken into account in mobile learning's use of Baicizhan.Several studies have asked students about their expectations, experiences, and perspectives on the usage of such data.This is because Baicizhan Apps rely on student-specific data being gathered, held, and evaluated.It is essential to this study that past research on students' concerns about ethics and privacy in Baicizhan Apps, as well as the link between those issues and the usage of Baicizhan Apps, be understood.This part investigates people's attitudes of access and ethics in relation to the use of student-specific data in Baicizhan Apps, in addition to addressing issues regarding privacy and security.
Outside of the students' direct experiences with mobile learning, a number of studies have solicited answers from students on who has access to their data and the choices that are made using it.Likewise, what is discussed was something that students were concerned about ( 2018) discovered that student worries regarding who had access to data were mostly caused by not knowing who had access.This finding suggests that the co-creation of tools by employing students might aid in assisting in reducing such issues.Ifenthaler and Schumacher (2016) conducted a survey with 330 students enrolled in bachelor's and master's programs and found that there was a desire to exchange enrollment data, test results on learning techniques, and test results on motivation.However, the responses indicated that the vast majority of them were unwilling to provide the LA system with their medical records (92%), information about their income (91%), data generated outside the institution using resources such as social media 90%, and information about their marital status (87%).Ifenthaler and Schumacher (2016) found that beyond a general willingness to share, when students were presented with three different Baicizhan Apps, they chose the one in which they had the easiest amount of control over what information of theirs was shared and what was kept private.This was the case regardless of whether or not the students were willing to share their information in general.These two studies are among those that are highlighted by Whitelock-Wainwright (2020) as incentives for the authors to design the instrument that the current study draws upon, which is the Student Expectations from Learning Analytics Questionnaire (SELAQ).Whitelock-Wainwright et al. (2020) surveyed students at three different international universities to determine students' expectations of ethics and privacy in LA.They discovered that, across all institutions, the belief that institutions had a responsibility to keep their data private was the item that generated the strongest response.
In studies of students' perceptions on mobile learning, the concept of privacy is often and directly tied to ethical considerations (Lester et al., 2017;Jones et al., 2019;Slade et al., 2019).In order to determine the extent to which students believe that Johnson County College has considered ethics and privacy in its use of Baicizhan's and the SELAQ survey tool it adapts, the current study has also grouped these concerns into a single category.This was done so that the findings can be more easily analyzed and interpreted.
According to the findings of Slade et al. (2019), even though 75% of respondents trusted their institution with the information they supplied in Baicizhan systems, 48% of respondents still considered privacy as the most critical feature of learning analytics (the other two selections were safety, which ranked 32%).Concerns about consent and information sharing were raised as a result of the discovery made by Fisher (2014) that all nine participants had faith in their institution's ability to protect their data but were hesitant to grant their institutions permission to use the information they provided.The focus group transcripts from Roberts et al. (2016) revealed, within the identified theme of wondering where data collection will stop, concerns of privacy invasion as well as a voicing a need for informed consent for participation in systems.These concerns were highlighted within the context of the theme of wondering where data collection will stop.These are consistent with the results of the Whitelock-Wainwright (2020) research, which said that all institutions discovered that the top-rated expectation in ethics and privacy is that institutions would secure their data.These are in accordance with the findings of the study.Only one study (Vu et al., 2020), a quantitative survey of student perspectives of institutional data tracking, came to the conclusion that students were unlikely to care that they were being tracked, despite indicating awareness of their activity, performance, and behavior surveillance.The study was able to draw this conclusion because students indicated that they were aware of their own activity, performance, and behavior surveillance.
According to the findings of Dollinger and Lodge (2018), the privacy concerns of students may be mitigated if they are included in the Baicizhan design process.This identifies the role that students play and the necessity of include them as stakeholders.Ifenthaler and Schumacher (2016) conducted a survey with 330 students and found that a large number of students were unwilling to share their personal data.They suggest that "the relationship between the acceptance and use of learning analytics systems and privacy principles, that is, control over data and sharing of data, highlights the need to actively involve students and other stakeholders".
In addition, research that was carried out in 2017 by Schumacher and Ifenthaler verified the notion that there is an inverse association between the degree to which a student is concerned about their privacy and the advantages that come from using mobile learning system.These results show that there may be a link between a student's apparent regard for ethics and privacy in Baicizhan and that student's propensity to employ advice from Baicizhan in a community college setting.

Trust
Trust plays a crucial role in the UTAUT (Unified Theory of Acceptance and Use of Technology) extended model, particularly in the context of mobile learning for university students.It influences individuals' perceptions, attitudes, and behaviors towards mobile learning technologies.Understanding the importance of trust and its impact on behavioral intentions and use behavior can inform the design and implementation of effective mobile learning interventions.This section will explore the importance of trust in the UTAUT extended model and the reasons for including it as an individual variable (Patil et al.,2020).
Trust significantly shapes individuals' perceptions of the usefulness of mobile learning technologies.When individuals trust the technology and believe in its capabilities, they are more likely to perceive mobile devices and applications as valuable tools for learning.This perception of usefulness positively influences their behavioral intentions and actual use behavior.Trust helps individuals recognize the potential benefits and advantages associated with mobile learning, such as improved academic performance and enhanced learning experiences.In addition, the use of mobile learning technologies entails various risks, including concerns related to privacy, security, and potential negative consequences.Trust acts as a mitigating factor, helping to alleviate individuals' concerns and reduce their perceived risks.When individuals trust the technology and the institutions or platforms supporting it, they are more likely to perceive a lower level of risk associated with using mobile learning technologies.This reduction in perceived risks positively influences their intentions and behaviors towards mobile learning (Antoniadis et al.,2022).Furthermore, Trust is closely linked to social influence within the UTAUT extended model.When individuals trust their peers, instructors, or educational institutions endorsing the use of mobile learning technologies, they are more likely to be influenced by their recommendations and adopt these technologies.Trust in the recommendations and opinions of others fosters a positive social influence, which can lead to higher behavioral intentions and actual use behavior.Peers and instructors who are trusted by students can play a significant role in encouraging adoption and creating a supportive learning environment.Trust contributes to the development of a positive user-technology relationship in the context of mobile learning.Individuals who trust the technology and have positive experiences with it are more likely to establish a long-term relationship with mobile learning technologies.This relationship is characterized by reliability, dependability, and a sense of confidence in the technology's ability to support their learning needs.Trust promotes the establishment of a positive user experience, which in turn leads to sustained use and continued engagement with mobile learning.
User-Technology Interaction: Trust captures individuals' subjective perceptions and beliefs towards technology.Including trust as an individual variable in the UTAUT extended model recognizes the importance of the usertechnology interaction.It acknowledges that individuals' trust in the technology can significantly influence their adoption and use behavior.By considering trust as a distinct construct, researchers and practitioners can better understand how users' trust perceptions impact their acceptance and use of mobile learning technologies (Chao, 2019).In addition, User-Centric Perspective: Including trust as an individual variable in the UTAUT extended model reflects a user-centric perspective.It recognizes that users' perceptions of trust are essential in understanding their intentions and behaviors towards mobile learning technologies.Trust is subjective and varies among individuals.Acknowledging the role of trust in the model emphasizes the significance of the user's perspective and individual beliefs in shaping technology acceptance and use.Furthermore, Trust has been consistently shown to have a direct influence on individuals' behavioral intentions and actual use behavior.It influences their decision-making process, confidence in the technology, and willingness to engage with mobile learning.Including trust as a variable allows researchers and practitioners to examine its role in mediating the relationship between other constructs in the model, such as performance expectancy, effort expectancy, and social influence.This comprehensive understanding helps identify the specific mechanisms through which trust impacts behavioral intention and use behavior.Finally, Recognizing the importance of trust in the UTAUT extended model has practical implications for the design and implementation of mobile learning interventions.Educational institutions can focus on building trust with students by providing transparent information, ensuring the reliability and security of mobile learning technologies, and addressing any trust-related concerns.Establishing trust is crucial for creating a supportive and conducive learning environment where students feel confident in using mobile technologies for learning purposes.Furthermore, understanding the role of trust can guide the development of interventions that promote trust-building strategies, enhance user experience, and address perceived risks, ultimately fostering greater acceptance and use of mobile learning technologies (Jaradat et al.,2020).

Personal Innovativeness
Rogers released a paper in 1962 with the title "Diffusion of Innovations," which was a synthesis of the information that was collected from over 508 different diffusion experiments (Rogers, 1962).The objective of the theory known as the diffusion of innovation is to provide an explanation for why, how, and how rapidly novel concepts and technology diffuse across society.According to Rogers' theory, the process of idea transmission is strongly dependent on human capital, and there are four primary factors that drive the spread of a novel concept.The invention itself, time, communication channels, and a social structure are the four components that make up this equation.According to Rogers, every new invention has to be broadly embraced in order for it to be sustainable, and although the pace of adoption is increasing, there comes a moment at which the critical mass of user acceptance of the innovation is reached.
Because e-learning is seen as a relatively recent advancement in technical practice, the diffusion of Innovation Theory is pertinent to this research.E-learning was first used in the early 1960s, expanded its scope to include greater interactivity in the 1970s, and has been growing popularity over the years because of the quick development and increasing cost-effectiveness of ICT (Hubackova, 2015).Because the students who took part in this poll attend online schools that make use of asynchronous communication channels, it can be deduced that two-way communication is an essential component of the system for the purpose of fostering cooperation.In light of this, it can be concluded that human capital is an essential component of the educational process in the examined institutions.Online schools that provide asynchronous communication tools and encourage social networking among other students develop an internal social structure that grows throughout the course of attendance (Tang & Hew, 2017).
According to their level of technological advancement, Rogers classified technology users into five distinct groups (Porter & Graham, 2016).The categories comprised (a) innovators, (b) early adopters, (c) early majority, (d) late majority, and (e) laggards, and they were based on how rapidly consumers accepted new technologies.In addition to this, Rogers proposed that there are five distinct stages of adoption that people go through when they start using new technology.Knowledge (a), persuasion (b), choice (d), execution (e), and confirmation (e) were the five steps that comprised this process.The five phases, when seen from a marketing perspective, depict the actions that lead up to the decision to purchase, as well as implementation (i.e., the usage of the system) and confirmation of what the customer anticipated from the product or service (Porter & Graham, 2016).
Because of the Diffusion of Innovation Theory's adaptability, it has been used in a wide variety of distinct technologies (Song et al., 2017).Within the context of the UTAUT, complexity was a consideration that was relevant to effort expectancy.Nevertheless, the complexity scale was not included in this research; rather, the quality scales utilized for information quality in the UTAUT are comparable to one another.In the process of making decisions on innovations, the Diffusion of Innovation Theory takes into account the aspects of implementation and confirmation as they relate to acceptance (that is, the usage of the system) and desire to adopt.The ideas of technology adoption from the Diffusion of Innovation Theory are applicable to this research since online students make the choice to use a mobile technologyintegrated vocabulary learning, then actually utilize the system, and then validate their expectations, which ultimately leads to some amount of satisfaction with the experience.The perceived qualities, the phases of the decision-making process, and the sorts of choices that are taken are all related to the four components of diffusion, which are innovation, communication channel, time, and social system.
The literature analysis on end users' adoption of mobile learning in an educational context revealed that human dynamics and innovativeness were two of the most prevalent themes.The Diffusion of Innovation Theory and TAM frameworks found in the research literature made predictions about the human behavior dynamics involved in end users' adoption of innovations.The Diffusion of Innovation Theory framework recognizes the importance of users' individual inventiveness as a critical component in the adoption of mobile learning (Tang & Hew, 2017).The Diffusion of Innovation Theory hypothesis asserts that social systems are the sites where communication channels ultimately give rise to innovative practices (Song et al., 2017).The degree of perceived ease of use is strongly correlated with a company's level of innovation, training, and education.Arpaci, 2019 explored the ways in which the perceived ease of use (PEOU) component of Rogers's (1962) The Diffusion of Innovation Theory drove end user perception of difficulty, which in turn affected the degree and pace of adoption by end users.Arpaci discovered that there was no causal relationship between end users' expectations regarding knowledge application and perceived usefulness.This means that end users were not more motivated or less motivated to adopt mobile learning resources based on the possibility that they could learn something new.It is largely up to the person to decide whether or not to accept a newly developed technology or innovative practice.According to Rogers (2003), adoption may be defined as "complete use of an invention as the best available course of action".Rogers defined rejection as the decision "not to embrace an invention" on the part of an individual.When making choices about adoption, the structure of a social system comprised of adopters is a crucial consideration (Rogers, 2003).According to Rogers' definition, the patterned groupings of units inside a system constitute a social system's structure.

Information Quality
During the period of development, the content consists of all of the instructional elements such learning objects, introductory lessons, tutorials, and simulations (Ruiz et al., 2006).When it comes to the construction of e-learning, information quality serves as the most important foundation.This is because the instructional designer is tasked with the responsibility of aligning the material with the learner's goals and their overall intended result (Wang et al., 2007).This will help in the process of developing a suitable learning module by taking the preparatory steps in examining the survey findings of essential trainings in a company.Next, the training materials contain adequate management, which guarantees that the system and service quality are audited and meet the requirements for the students (Wang et al., 2007).It is important to give careful consideration to the user interface, also known as the industrial design of human-to-computer interaction, in order to distribute e-learning resources in a way that is both straightforward and efficient (Lee & Kim, 2015).E-learning modules also include the following services: online support, instructor-peer collaborations, aid from information technology personnel, and general availability of consultations (Dominici & Palumbo, 2013).After the instructional designer has validated the creation and management stages, the overall deliverability may either be synchronous or asynchronous (Ruiz et al., 2006) ).In most cases, this is handled via the performance of pilot tests, the receipt of feedback, the administration of focus groups, and the examination of survey findings.This paves the way for the creation of e-learning for subsequent learning groups, enabling the quality of its material to be adjusted appropriately as required.It is possible that having a high-quality system will have a favorable impact on the user's learning experience; nevertheless, a number of academics believe that the content structure has a substantially more significant impact on the value received through e-learning (Luke K et al., 2016).The content of the program is the primary focus in terms of achieving one's goal of developing the most effective learning programs; nevertheless, the e-learning tools themselves are very important to the overall adaptability of the user (Navimipour & Zareie, 2015).The information quality (IQ) of e-learning modules refers to how helpful, clear, and adequate the material is (Wang et al., 2007).When evaluating the quality of information, many factors are taken into consideration.These include the quality of the content, the amount of the material, the work relevance, and the maintenance of the content.-The Quality of the Material: A Significant Factor of User Satisfaction The quality of the elearning content is an important indicator (Almarashdeh, 2016).The content quality of e-learning programs must be considerably superior to that of conventional in-person courses if students are going to choose them over the more traditional option of studying via on-site sessions.In order to prevent the learner from experiencing cognitive overload and to accommodate a wide range of viewers by eliminating ambiguity, it is essential that the content of the e-learning platform have elements that encourage interaction (Jia et al., 2011).-Amount of Content: According to the findings of study conducted by behavior therapists, the amount of material presented does not automatically result in increased learning (Granpeesheh et al., 2010).To obtain more appropriate learning outcomes, administrators, instructional designers, and facilitators need to think about how to strike a balance between the amount and quality of learning opportunities.-Applicability to the Learner's Job: In order to provide a beneficial educational experience, the e-learning material must be relevant to the learner's current position (Navimipour & Zareie, 2015).Research on instructional design has revealed that students have greater prospects for learning when they remain collaborative and explore the work usefulness of learning modules (Malik, 2009).Therefore, effective e-learning modules provide their users with knowledge that can be directly applied to the tasks they do.-Content Upkeep: The control of the content of the e-learning courses is an essential component in the production of a beneficial experience for the students (Malik, 2009).The kinds of work that are expected from workers of an organization, as well as the kinds of work that are demanded of those employees, may vary as an organization adjusts to the dynamic changes in its environment (Jia et al., 2011).

The Conceptual Framework
In this study, the seven basic components of the UTAUT2 model are taken into consideration: Performance expectation (PE), Effort expectancy (EE), Social influence (SI), Facilitating Condition (FC), Hedonic Motivation (HM), Price Value (PV), and Habit (HT).The model has also been changed to take into account the nature of mobile technologyintegrated vocabulary learning by including four new factors, including privacy, trust, personal innovativeness, and information quality to the original seven variables.Therefore, 11 criteria are considered in this study, and the research model is constructed based on these aspects.The researcher aims to explore the relationship between these 11 exogenous variables the endogenous variables of behavioral intention and use behavior.The result is a more comprehensive conceptual framework, which is shown in figure 2.

Conclusion
Based on the review of the literature about mobile technology-integrated vocabulary learning among Chinese university EFL students and theories about the Unified Theory of Acceptance and Use of Technology Model, the author modified the UTAUT2 model by incorporating additional constructs of privacy, trust, personal innovativeness and information quality to form a more complex conceptual model.The conceptual model hypothesized relationships between the exogenous variables of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, privacy concerns, trust, personal innovativeness and information quality with the endogenous variables of behavioral intention and use behavior.The author hopes to explore the influencing factors of English students' adoption of mobile technology by applying the modified UTAUT2 model to the field of Chinese university EFL students' adoption of mobile technology, thus enrich the EFL learning knowledge system and contribute to the existing theories of acceptance and use of technology.
Lewis et al. (2013) used the UTAUT2 model to examine faculty members at South-Eastern University in the United States' acceptance and adoption of new and existing classroom technology.The sample size of 46 participants represented 51% of the student population.This study's findings reveal how student attitudes toward and use of technology in class are influenced by their expectations for effort and performance, habit, as well as their social milieus and relationships outside of the classroom.

Figure 1 .
Figure 1.The Extend Unified Theory of Acceptance and Use of Technology The UTAUT2 model (Venkatesh et al., 2012) comprises seven independent factors that influence the dependent factors behavioral intention and use behavior, including performance expectancy, effort expectancy, social influence, facilitating condition, hedonic motivation, price value, and habit.In order to examine the factors influencing EFL college students' behavioral intention to adopt mobile technology, the author incorporates four new variables to the model based on the following literature review.

Figure 2 .
Figure 2. The Conceptual Framework Dollinger & Lodge, 2019; Ifenthaler & Schumacher, 2016; Jones et al., 2019; Klein et al., 2019; Roberts et al., 2016; Slade et al., 2019).Dollinger and Lodge ( . The term "synchronous delivery" refers to live-time training sessions in which the teacher immediately connects with all of the students in a way that is synchronized.Webcam lectures, teleconferences, and instant messaging are a few examples of this kind of communication.On the other hand, asynchronous delivery does not entail the engagement of several parties at the same time.Learners are accountable for setting their own pace of study while maintaining open lines of contact with