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Article

Exploring UTAUT Model in Mobile 4.5G Service: Moderating Social–Economic Effects of Gender and Awareness

by
Sara Mehrab Daniali
1,*,
Sergey Evgenievich Barykin
1,*,
Marzieh Zendehdel
2,
Olga Vladimirovna Kalinina
3,
Valeriia Vadimovna Kulibanova
4,
Tatiana Robertovna Teor
5,
Irina Anatolyevna Ilyina
5,
Natalia Sergeevna Alekseeva
3,
Anton Lisin
6,
Nikita Moiseev
7 and
Tomonobu Senjyu
8
1
Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
2
Department of Business Management, Bandar Anzali International Branch, Islamic Azad University, Bandar E Anzali 4313111111, Iran
3
Graduate School of Industrial Management, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
4
Laboratory of Integrated Research on Regions’ Spatial Development, Institute for Regional Economic Studies Russian Academy of Science, 191103 Saint Petersburg, Russia
5
Department of Public Relations, Saint Petersburg Electrotechnical University “LETI”, 197376 Saint Petersburg, Russia
6
Financial Faculty, Financial University under the Government of the Russian Federation, 124167 Moscow, Russia
7
Department of Mathematical Methods in Economics, Plekhanov Russian University of Economics, 117997 Moscow, Russia
8
Department of Electrical and Electronics Engineering, University of the Ryukyus, Nishihara 903-0213, Japan
*
Authors to whom correspondence should be addressed.
Soc. Sci. 2022, 11(5), 187; https://doi.org/10.3390/socsci11050187
Submission received: 13 February 2022 / Revised: 27 March 2022 / Accepted: 21 April 2022 / Published: 24 April 2022
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Abstract

:
The current study aims to examine how students’ intentions to use 4.5G mobile phones are affected by the social-economic factors of performance expectancy, cost, effort expectancy, and social influence. This study is based on the perspectives of the unified theory of acceptance and use of technology (UTAUT). The central assumption of this study is that when students use 4.5G mobile services to source information at university, their academic performance is likely to improve. From eight private and public universities in Malaysia, 2117 students were enrolled in this study. We investigated the effects of gender and awareness as moderators on the relationships among the variables of interest. The findings showed that social influence and performance expectancy positively affected university students’ intentions to use 4.5G mobile phones. The researchers conducted a multigroup analysis to confirm the moderating effect of gender among the underlying relationships in the model. Structural equation modeling analysis indicated that, unlike awareness, gender did not moderate social influence, effort expectancy, performance expectancy, or the cost of students’ intentions to use 4.5G mobile phones. The implications of the proposed approach, considering the digital transformation concept, could be a topic for future research.

1. Introduction

The rapid developments in modern wireless communication technology and the internet have continued to promote digital trade among enterprises and consumers (Wu and Wang 2005; Yadykin et al. 2021). Modern Internet-enabled mobile phones have gained significant acceptance due to 3G and 4.5G wireless communication technologies. The third generation of computer networks is referred to as 3G, indicating also the equipment that provide mobile consumers with high Internet access speeds, streaming capabilities, and video messaging (Kumar 2013). As a result of the innovation in mobile devices, 3G technology plays an essential role in facilitating communication among users. Using this technology, users are connected to the world anytime, anywhere, and at a low cost (Sun et al. 2010). Growth in mobile phone users has led to widespread attention being paid to 3G technologies (Guo 2015). Researchers working in information systems and management science have paid considerable attention to understanding and accepting 4.5G mobile technology as an innovation (Venkatesh et al. 2003). However, there is little knowledge about how the technology has been adopted or used among the Malaysian population, although 4.5G technology was launched in most countries at the beginning of the twenty-first century (Boakye 2015). In this regard, in the present study, we intend to explore the particular factors that influence students’ intentions to use 4.5G in private and public universities in Malaysia, based on the perspective of the UTAUT model (Guo 2015), which is shown in Figure 1.

The Problem’s Background

Although many studies in the literature have implemented different consumer adoption models, e.g., the technology acceptance model, UTAUT was developed according to eight other theories (Alghazi et al. 2021) to present a comprehensive approach that surpasses previous models. The Venkatesh theory is the most widely used in technology acceptance research. UTAUT has attempted to clarify users’ intentions concerning adopting a new information system (IS) and their further usage of the system. Authors have considered the influence of the following key concepts: effort expectancy (EE), performance expectancy (PE), social influence (SI), and facilitating conditions (FCs) (Venkatesh et al. 2003). Hence, in this study, we implement the UTAUT model to investigate the influence of the four abovementioned critical concepts on students’ intentions to use the 4.5G mobile technology. It also provides the basis for investigating the moderating role of gender and awareness in affecting consumers’ intentions of using/adopting information technology (Dong and Zhang 2011; Faqih and Jaradat 2015). Hitherto, an extant literature review has shown that there has not been much focus on how the construct of awareness moderates relationships. However, awareness as a construct has been examined in previous m-commerce studies (Abubakar and Ahmad 2013; Omar 2011). This study aims to enrich the literature on 4.5G adoption, particularly among developing countries. The findings will contribute to the contemporary literature related to the moderating effects of awareness. In addition, the study will be helpful for testing technology acceptance theories, as it provides insights into how individuals’ use of 4.5G mobiles is affected by gender and awareness.

2. Materials and Methods

The central assumption of this study is that when students use 4.5G mobile services to source information in the university, their academic performances are likely to improve (Hossain et al. 2019); hence, their intention to use the technology will increase. We thus developed the following hypothesis:
Hypothesis 1.
PE directly influences the intention to use 4.5G mobile service.
Additionally, this study supposes that, concerning EE, the user perceives that using the 4.5G mobile will be easy, requiring little effort. Students have this perception. This construct is one of the central determinants of 4.5G mobile acceptance. Investigating the role of effort expectancy in influencing peoples’ intention to accept/use the 4.5G technology is fundamental since this technology has not yet been adequately adopted in developing countries (Zhou et al. 2008). Accordingly, it is assumed that the acceptance and use of 4.5G mobiles will be related to the extent to which students presume that the 4.5G technology will be easy to use (Ajzen and Fishbein 1980). Therefore, we developed the following hypothesis:
Hypothesis 2.
EE directly influences the intention to use 4.5G mobile service.
Social influence is another vital construct that plays a significant role in determining how people accept technology. For the purpose of this study, social influence refers to the extent to which students recognize that people who are important to them would expect them to obtain and use a 4.5G mobile phone (Venkatesh et al. 2003). In e-mobile commerce, social influence is essential in explaining the intention to use mobile technologies (Chang 2013). This study shows that social influence significantly affects the intention to use 4.5G mobile technology. Moreover, social influence assumes that students are more likely to comply with the adoption of 4.5G technology when they receive a recommendation to use it by those important to them. The literature has shown that students’ intentions to use technology have often been influenced by their peers, instructors, parents, and others who are important to them (Ho et al. 2013; Chang 2013). Similarly, social influence was found to be one of the essential determinants for accepting technology (Liu et al. 2009). This finding was consistent with (Nysveen et al. 2005), who found that the substantial segmentation of mobile usage was relatively young and was affected mainly by social influence factors concerning how users adopt technology (Leong et al. 2013). Therefore, the role of social influence factors, such as family, colleagues, friends, and peers, in persuading students to use 4.5G mobile technology was essential. Hence, the mobile industry should use advertising media to promote their products and services, such as television and social networking websites. Based on the preceding observations, we developed the following hypothesis:
Hypothesis 3.
SI influence the acceptance and intention to use 4.5G mobile service.
Among the core variables in the UTAUT model, facilitating conditions refer to people’s perceptions of resources and support in performing a specified behavior. The self-reported experiences of students concerning their perceptions of the availability of 4.5G technology for their use are examined in this study. The literature has shown that more experienced technology users are less likely to depend on external support (Venkatesh et al. 2003). Furthermore, easy-to-learn and free-to-use functionalities that require little time and effort to operate are offered by 4.5G technologies. These features enable 4.5G users to manage their mobiles without requiring additional support for learning. Hence, facilitating conditions were excluded from our research model because we assume that their influence would be marginal in the context of the current research.

2.1. Cost

Another critical factor that has an essential influence on users’ intentions to use technology is perceived cost. Therefore, this study suggests that companies should implement creative promotional and pricing strategies, such as cost reductions, to attract more price-conscious customers. In the context of this perception, cost pertains to the degree to which one perceives that the 4.5G mobile service would be a costly technology to adopt (Ajzen 2006). A study by Nayak et al. (2014) revealed that perceived cost negatively correlates with accepting mobile banking services (Nayak et al. 2014). In this regard, our analysis assumes that the cost incurred in securing 4.5G mobile technologies negatively affects the intention to use 4.5G mobile services. Hitherto, consumers have been very reluctant to adopt mobile 4.5G services despite the numerous benefits derived from using 4.5G technologies. The fact that many potential customers have become comfortable with low-cost services online and rarely seek something new at a higher cost is among the identified causes of this reluctance (Riquelme and Rios 2010; Velmurugan and Velmurugan 2014). Hence, in our proposed study model, cost is one of the fundamental constructs to be investigated. We assume that the higher the price, the less intention to adopt the technology. We thus offered the following hypothesis:
Hypothesis 4.
Cost negatively affects the intention to use 4.5G mobile service.
Based on the empirical evidence, we observed that awareness is one of the main determinants of users’ acceptance behavior, associated with becoming acquainted with a specific product. However, awareness as a construct is still emerging, especially as it applies to new technologies such as 4.5G. Because most consumers lack knowledge about a phone’s design, interface, contents, navigation, and uses, they may hesitate to use 4.5G technologies. A study by Velmurugan and Velmurugan (2014) has shown that the absence of awareness was an obstacle barring users’ intentions to use mobile phones (Velmurugan and Velmurugan 2013). In addition, Kuo and Yen (2009) have revealed that the rate of consumer usage of current 4.5G value-added services has remained low in telecommunication services due to the lack of adequate awareness (Kuo and Yen 2009). Hence, improving the adoption of 4.5G mobile gadgets requires further research. Usually, consumers hesitate to access or use mobile phones because they are less familiar with new mobile technologies (López-Nicolás et al. 2008). Studies have shown that many consumers are not conversant with 4.5G mobile technologies (Cruz et al. 2010; Devi et al. 2011). Thus, one of the main barriers to the success of mobile buying and selling is absence of awareness because consumers cannot patronize the product or service unless they are aware of it (Cruz et al. 2010; Devi et al. 2011). In the few studies investigating the association between cognition and behavioral intention, the findings have shown that attention significantly affects the intention to use technology (Wan et al. 2012; Velmurugan and Velmurugan 2014).

2.2. Moderating Effects of Awareness and Gender

From another perspective, a few studies have examined the moderating effect of awareness on the relationship between intention and intention to use and found that awareness moderates the relationship between the variables (Abubakar and Ahmad 2013; Omar 2011). The higher the attention, the higher the diffusion of the technology and vice versa. To understand the critical role of awareness in moderating relationships among the UTAUT, the current study has examined the role of the variable in mediating the relationships among PE, social influence, EE, cost, and behavioral intentions to use 4.5G mobile technologies.
Additionally, the moderating role of gender in 4.5G mobile adoption has not been well investigated. However, a few studies have indicated that males tend to adopt technology more than females and are more likely to be positive about m-commerce (mobile commerce) than females (Venkatesh et al. 2003). Male users experience more confidence in using new technologies and exposure to technology at work. Women have displayed lower rates of adaptation and utilization of new technologies than men due to lower confidence in their ability (Kimbrough et al. 2013). However, contrary to expectations, an exploratory study by Yang (2005) revealed the effect of gender regarding perceived usefulness and ease of use (Megdadi and Nusair 2011). The term “ease of use” can be defined as how a particular system would affect one’s mindset regarding improving job performance.
The influence of gender on the acceptance of information communication technologies (ICTs) has attracted much attention in the literature. Its role in moderating technology acceptance behavior has also drawn the attention of researchers from IS research. Hence, UTAUT theorized that gender has a significant moderating effect on technology acceptance and use. Accordingly, Venkatesh et al. (2003) posited that gender plays a vital role in moderating the relationships between the constructive psychological elements of UTAUT and the intention to use technology. Hence, individuals develop different values based on gender differences, causing them to differ in their ethical and value preferences (Yang 2005; Barba and Iraizoz 2020). The extant research has shown that gender has a moderating effect on the relationships among the constructs, including PE, EE, social influence, and behavioral intention (Okazaki and Mendez 2013; Cheng et al. 2011). Among these relationships, men have shown the most potent effects associated with performance expectancy, whereas women have shown the most substantial impact regarding effort expectancy and social influence (Chong et al. 2012; Al-Dalahmeh et al. 2021; Prasanna et al. 2021). However, empirical evidence has shown that the effect of gender on technology adoption and acceptance is diminishing significantly, owing to the impact of technology diffusion (Khechine et al. 2014). For instance, Bigné et al. (2007) revealed no significant difference between men and women in their mobile technology usage behaviors for shopping (Bigné et al. 2007). Hence, it has been suggested (Yol et al. 2006) that male and female users of m-commerce services show the same patterns in their perceptions and behavioral outcomes of customer satisfaction. In another study, gender was also been found to have no significant influence on the m-commerce adoption of SMEs (Lip-Sam and Hock-Eam 2011). As posited in Lip-Sam and Hock-Eam (2011), awareness is an essential prerequisite for the growth of ethical standards (Lip-Sam and Hock-Eam 2011). Unfortunately, there is little awareness in developing countries concerning e-mobile services (Wan et al. 2012). For instance, it has been suggested that the slow adoption of 4.5G technology was due to lack of awareness about its advantages, and the need to improve that awareness has become crucial (Yaqub et al. 2013). However, it should be noted that in this instance, the researchers’ belief was not empirically tested, especially regarding how awareness moderates the relationships between the UTAUT constructive factors and behavioral intention. Hence, this study examines awareness as a moderator among performance expectancy, effort expectancy, social influence, cost, and behavioral intentions to use 4.5G. The following hypotheses were thus proposed to achieve the objectives of the study:
Hypothesis 5.1.
Gender moderates the effect of PE on the intention to use 4.5G mobile service.
Hypothesis 5.2.
Gender moderates the EE effect on the intention to use 4.5G mobile service.
Hypothesis 5.3.
Gender moderates the social influence effect on the intention to use 4.5G mobile service.
Hypothesis 5.4.
Gender moderates the cost effect on the intention to use 4.5G mobile service.
Hypothesis 6.1.
Awareness moderates the effect of PE on the intention to use 4.5G mobile service.
Hypothesis 6.2.
Awareness moderates the EE effect on the intention to use 4.5G mobile service.
Hypothesis 6.3.
Awareness moderates the social influence effect on the intention to use 4.5G mobile service.
Hypothesis 6.4.
Awareness moderates the cost effect on the intention to use 4.5G mobile service.

2.3. Methodology

From eight public and private universities located in Selangor, Malaysia, 2117 students were enrolled in this study. Data were collected from students through a survey. Each constructive variable in the proposed model was estimated with multiple items derived from the extant literature to improve the content validity. For this research, the indicators for the instruments were adapted from the original approach developed by Venkatesh et al. (2003) and the m-commerce literature. Five constructs were included in the hypothesized research model. The indicators measuring social influence, PE, and EE, were adapted from Venkatesh et al.’s (2003) study on intentions toward 4.5G mobile phone usage, adapted from two items. The four indicators developed by Ajzen (2006) were applied to measure cost. To measure awareness, three items were adapted. A questionnaire was used and distributed among students with the help of some research assistants. This questionnaire consisted of closed-ended questions based on a five-point Likert scale. To identify the public and private universities, cluster sampling was applied. The population of the Klang Valley consists of a diversity of demographic backgrounds and cultures, which reflects the actual population of Malaysia. The sample reflects the general population of Malaysia. Therefore, it can be presumed that a sample drawn from this would have more external validity in generalizing the study’s findings. As 4.5G mobile use expands, the users’ willingness to engage with advanced wireless technology and their commitment to participate in actions using 4.5G-supported systems increase (Hossain et al. 2019), offering more opportunities than other wireless services. The dependent variable in this study is behavioral intentions toward 4.5G adoption. Several researchers have affirmed that behavioral intentions are a central determining factor of user behavior (Khalid et al. 2021). Hence, Zhou et al. (2008) maintained that the intention of the consumer to use the technology is the most critical factor governing user acceptance and use of technology (Zhou et al. 2008). Although behavioral intention has been broadly researched, there is a need for further research on the construct, especially regarding the viewpoints of developing economies such as Malaysia. In other words, extending existing technology acceptance models to diverse settings and scenarios will add to the predictive strength of such models beyond their original stipulations (Venkatesh et al. 2003).
The unified theory aims to explore technology acceptance behavior by considering four main variables (performance expectancy, effort expectancy, social influence, and facilitating conditions). Furthermore, the model posits that demography variables and voluntariness have a moderating role in the relationships among the four core variables. Various studies confirm the assumptions of UTAUT. They show that PE positively influences users’ intention and behavioral performance (Ajzen and Fishbein 1980; Zhou et al. 2008). Venkatesh et al. (2003) asserted that PE has the most vital effect on intentions to use technology. In the 4.5G mobile context, PE is defined as the degree to which students consider that 4.5G mobile technology will increase their performance and help them to achieve a better lifestyle (Venkatesh and Morris 2000). Accordingly, by strengthening this belief among students, it is predicted that their intention to accept and use 4.5G mobile will significantly improve. As a construct, PE is derived from perceived usefulness, as stated in the theory of acceptance model (TAM) and the theory of reasoned action (TRA). Venkatesh et al. (2003) said that PE reflects consumers’ perceptions of performance improvements, such as convenience, service efficiency, and fast responses.

3. Results

To analyze the data collected to achieve the objectives of this study, structural equation modeling (SEM) was utilized using Python. Because SEM has a high potential to reduce measurement errors and multiple relationships can be assessed simultaneously among several variables in a model, SEM was suitable for this study. Confirmatory factor analysis (CFA) was performed as the first step for the individual constructs. An overall measurement model was assessed and confirmed for all the items in the survey instrument. Furthermore, the structural model was specified and assessed, and the specific criteria for model fit indices were all tested to be satisfactory. Good fit indices were obtained for the model, as can be seen in Table 1: chi-squared value = 1818, degrees of freedom = 286, relative chi-squared value = 6.360, TLI = 0.945, NFI = 0.943, RMSEA = 0.050, and CFI = 0.952. As indicated above, the measurement model of this study has satisfied the goodness-of-fit criteria (Murtagh and Heck 2012).
Convergent and divergent validity tests were conducted with all values meeting the recommended indices, and valid construct reliability was obtained for all the constructs (CR > 0.7). According to the literature, all the factor loadings and average variance extracted (AVE) values were adequate, at ≥0.5 (Hair 2009; Fornell and Larcker 1981; An et al. 2021; Mikhaylov 2021; Murtagh and Heck 2012). The results are summarized in Table 2. Similarly, the indices used in the measurement model indicated that the interrelationships among the variables were validly explained. Hence, after the measurement model test, all constructs were tested for discriminant validity through a correlation analysis (Table 3). As Kline’s (2015) recommendation was less than 0.85, the estimated correlations between the constructs were not excessively high, and the constructs’ discriminant validity was established (Kline 2015). If the value is higher than this threshold, it can be concluded that there is a lack of discriminant validity.
The structural model was evaluated to test the hypotheses of the study based on the results of the analyses of the measurement model. To examine the fitness of the structural model, measures of the goodness-of-fit test were applied, and all the recommended values were obtained, as can be seen in Table 4. The valid fit indices indicate that the model satisfied the fit criteria and is an acceptable structural model. As presented in Figure 1 and Table 5, the results summarize the relationships between the different constructs investigated in the model and the first p values shows no evidence of difference.
The researchers conducted a multigroup analysis to confirm the moderating effect of gender among the underlying relationships in the model. A median splitting test was performed based on the scores obtained for awareness on the two subjects’ subsamples with low awareness (sample size = 933) and high attention (sample size = 1184). First, the model was estimated based on the two subgroups to provide a fit for any group. A multigroup analysis was then performed by relating the two groups at different levels of awareness. A constrained model was compared, not allowing the structural limits to vary across the two subgroups of subjects in the unconstrained model and the two subgroups. The moderation analysis was conducted after setting the awareness levels (high level and low level). Then, the model fit indices ( x 2 (CMIN), df and p) were compared between the unconstrained and measurement residuals. Both models were found to have significant p values (p < 0.05). However, the unconstrained residual had a better x 2 value, which was smaller than the measurement residuals model in the study. Hence, we examined the significance of the x 2 difference and conducted the model comparisons. Having found significant differences (p < α) in the relationships, the authors conclude that awareness has some moderating effect on the overall model’s x 2 value. Furthermore, the moderating effect of gender was tested within the relationships in the model, and the results are presented in Table 6 and Table 7. Overall, gender did not exhibit any significant interactions with any of the predictor’s latent variables, indicating that both female and male students had approximately the same features in terms of their familiarity with using 4.5G mobile technologies and in terms of the consideration that they gave to the ideas of their peers. However, this result contradicts the UTAUT model’s (Venkatesh et al. 2003).
All in all, the result of the structural model is presented in Figure 2 according to Table 6 and Table 7 to illustrate the effect of gender and awareness on intention towards 4.5G services.

4. Discussion

In the current study, we examined the intention of students to use 4.5G mobile technology in Malaysia. PE was the most vital factor influencing students’ intentions to use 4.5G mobile technology. This finding was in line with the extant literature (Mikhaylov 2021). This result implies that students in higher learning institutions in emerging countries perceive that 4.5G mobile technology is helpful and allows them to implement their academic activities more efficiently and more quickly than before. The result of the structural model analysis also showed that social influence significantly contributes to students’ adoption of 4.5G technologies. However, this construct had the lowest significance level among all the four constructs investigated. These results further support the hypothesis that students perceive that their classmates and friends influence their acceptance and use of 4.5G mobile technologies. Hence, it may be rationally concluded that students’ family and friends strongly influence their intention to use 4.5G mobile technology. Invariably, users of 4.5G technologies are likely to persuade their classmates and friends to accept and use such technologies (Venkatesh and Morris 2000).
Accordingly, the results have revealed that EE strongly affects students’ intentions to use 4.5G mobile technologies. This suggests that undergraduates perceive that they need less effort to use 4.5G mobile technologies because they are clear and understandable and can be used easily (Venkatesh and Morris 2000). The most astonishing finding was that the apparent cost of using 4.5G services, which was hitherto considered to be the main reason why people in Malaysia hesitate to use other technologies, was not found to be significantly related to the acceptance of 4.5G mobile services in this study. Nonetheless, our finding is consistent with (Mikhaylov 2021). However, this might have been caused by the fact that the respondents taking part in this study are university students, who depend on either their parents or guarantors to survive, and do not bear the burden of what they spend. Furthermore, as strengthened by the affirmative connection between image and intentions towards 3G acceptance (Yaqub et al. 2013; Hair 2009; Fornell and Larcker 1981; Mikhaylov 2021), financial status might be linked with positions and subsequent intentions to use 4.5G technologies. For example, students might feel that using costly 4.5G technologies will elevate their jobs or the positions of their parents or sponsors and create a unique image for themselves, influencing their increased intentions to use such technologies.
We also found that gender had no confirmed moderating effect on the relationships within the study model, with males and females expressing non-significant differences regarding their intentions to use 4.5G technologies. Contrary to our expectations, the test of the influence of gender on the relationships among EE, PE, cost, social influence, and students’ intentions to use 4.5G mobile technology was not significant. This also contradicts the findings of several studies that included gender differences as part of the socio-cultural differences inherent in technology adoption (Venkatesh et al. 2003; Chong et al. 2012). However, the absence of gender differences in the present study may be attributed to the nature of the sample size of the study, which comprised male and female students whose backgrounds included related scholastic opportunities in similar university settings, and in which attempts were made to offer both genders equal social, political, and economic status.
The test of the moderating effect of awareness was significant among the identified relationships in the constructive variables of the model, which means that the construct has a significant moderating effect on both the overall model and the paths that represent relationships between social influences and intentions. However, significant moderating effects were found for other constructs in the model. Although the connection between social impact and intentions to use 4.5G technologies was stronger among customers with a high level of awareness than among customers with a low level of understanding, gender was not found to moderate any relationship in the proposed model.
We could discuss the implication of the proposed approach taking into account the digital transformation. The use of a digital twin could be considered an instrument for the implication of the social development concept. We observed interesting and differing approaches to implementing digital twins in logistics and trade networks (Barykin et al. 2021a). In this regard, the impact of digitalization on the various social and economic aspects could be a topic for future research. Furthermore, the context of the complexity of communication processes with Customers 4.0 (Wereda and Woźniak 2019; Barykin et al. 2021b) can be considered in future research.
A limitation of this study was the focus of the research on university students, rather than covering all educational levels, and the failure to include other moderating factors in the study. Future research may also consider participants with broader generalizability potential who could reflect variables such as culture and trust.

5. Implications and Conclusions

This study has identified some specific factors determining students’ intentions to use 4.5G mobile technologies in a developing country based on the assumptions of the UTAUT model. EE, PE, and social influence were obvious and significant determinants of the adoption and use of 4.5G technologies among university students in Malaysia and, implicitly, among most students in developing countries. The study has provided valuable insights regarding people’s underlying intentions towards the use of 4.5G technologies in developing countries. However, students’ use as the subjects of this study may restrict the extent to which the study’s findings can be generalized. In essence, students’ use of 4.5G technologies was significantly predicted by the critical variables of interest in the study, namely, EE, PE, social influence, cost, and the moderating role of gender and awareness.
The study model has established a foundation for further research into the adoption of 4.5G technologies in developing countries. Our findings imply that, for people to use and adopt 4.5G mobile technologies, these technologies must be perceived as applicable to the intended users. They must be efficient and worthy of the users’ needs. Hence, widespread awareness campaigns should target potential 4.5G technology users to inform them about the benefits of such technologies, including low costs, time-saving potential, and ease of use. Accordingly, marketers should reinforce their awareness campaigns and training programs for customers to appreciate the critical application areas of 4.5G mobile phones. It is also suggested that mobile phone companies offer more comprehensive support programs, including customer care through an online FAQ page, chat and e-mails to boost awareness of the capabilities of 4.5G mobile technologies among potential users. In addition, students should endeavor to explore the multifunctional features of 4.5G mobile technologies and utilize them adequately for their academic purposes. These features may include voice and video calls, text messaging, a camera, an alarm, a calendar, an address book, and browsing services, as well as high-speed data connectivity, high-speed streaming multimedia applications, and high-speed Internet bandwidth, which facilitate movie-watching, interactive games, and data downloads. Overall, this study has contributed to laying the foundation for investigations of the moderating role of awareness on the relationship between social influence and behavioral intentions. The study can be replicated among several other cultures/samples and for different types of technology.

Author Contributions

Conceptualization, S.M.D. and S.E.B.; methodology, S.E.B.; software, M.Z.; validation, A.L., N.M.; formal analysis, T.S.; investigation, O.V.K.; resources, O.V.K. and N.S.A.; data curation, S.M.D.; writing—original draft preparation, V.V.K. and T.R.T.; visualization, I.A.I. All authors have read and agreed to the published version of the manuscript.

Funding

The research by S.M.D., S.E.B., N.S.A., and O.V.K. is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program ‘Priority 2030’ (Agreement 075-15-2021-1333 dated 30 September 2021).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.

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Socsci 11 00187 g001 550
Figure 1. A research model for m-commerce based on the UTAUT model.
Figure 1. A research model for m-commerce based on the UTAUT model.
Socsci 11 00187 g001
Socsci 11 00187 g002 550
Figure 2. The structural model with the results as conceptual model.
Figure 2. The structural model with the results as conceptual model.
Socsci 11 00187 g002
Table
Table 1. Model fit indices.
Table 1. Model fit indices.
NFICFIIFITLIRMSEANPARCMINDFpCMIN/DF
0.9430.95200.9100.9450.050911818.8372860.0006.360
Table
Table 2. Construct validity of study instruments.
Table 2. Construct validity of study instruments.
ConstructNo ItemAVECR
Performance expectancy40.620.87
Effort expectancy50.700.92
Social influence50.580.87
Cost30.540.78
Intention70.590.92
Table
Table 3. Correlation.
Table 3. Correlation.
ConstructEffort ExpectancySocial InfluenceCostIntentionPerformance
Effort expectancy1
Social influence0.4081
Cost0.3760.4251
Intention 0.0100.4320.2351
Performance expectancy 0.1170.4320.3470.7961
Table
Table 4. Structural model fit.
Table 4. Structural model fit.
MODELCMINDFCMIN/DFTLICFIRMSEANFI
Default model1986.0742876.9209390.9470.0530.938
Table
Table 5. Results of hypotheses testing.
Table 5. Results of hypotheses testing.
Unstandardized
Estimated		SECRStandardized
Estimated		pR2
BI4G<---PE0.7320.02826.4620.772***0.64
BI4G<---EF−0.0840.013−5.492−0.109***
BI4G<---SI0.0660.0183.6360.086***
BI4G<---CST−0.2540.269−0.947−0.0230.344
Note: *** shows no evidence of difference.
Table
Table 6. Moderation test of the effect of gender on the relationship between intention and construct variables.
Table 6. Moderation test of the effect of gender on the relationship between intention and construct variables.
ConstructBBetapCR Differences
Social influence
Female0.0640.0810.0082.662
Male0.070.090.0132.471
Performance expectancy
Female0.7560.765***17.925
Male0.7560.778***17.576
Effort expectancy
Female−0.072−0.094***−3.775
Male−0.093−0.131***−4.120
Cost
Female−0.364−0.0190.578−0.556
Male−0.208−0.0250.489−0.691
Note: *** shows no evidence of difference.
Table
Table 7. Moderation test of the effect of awareness on the relationship between intention and construct variables.
Table 7. Moderation test of the effect of awareness on the relationship between intention and construct variables.
ConstructBBetapCR for Difference
Social influence
High level0.0930.13***4.175
Low level0.0050.0060.8720.161
Performance expectancy
High level0.6710.72***17.925
Low level0.8090.76***16.379
Effort expectancy
High level−0.088−0.162***−5.590
Low level−0.065−0.0690.025−2.236
Cost
High level−0.763−0.0270.593−0.535
Low level−4.332−0.0640.811−0.239
Note: *** shows no evidence of difference.
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Daniali, S.M.; Barykin, S.E.; Zendehdel, M.; Kalinina, O.V.; Kulibanova, V.V.; Teor, T.R.; Ilyina, I.A.; Alekseeva, N.S.; Lisin, A.; Moiseev, N.; et al. Exploring UTAUT Model in Mobile 4.5G Service: Moderating Social–Economic Effects of Gender and Awareness. Soc. Sci. 2022, 11, 187. https://doi.org/10.3390/socsci11050187

AMA Style

Daniali SM, Barykin SE, Zendehdel M, Kalinina OV, Kulibanova VV, Teor TR, Ilyina IA, Alekseeva NS, Lisin A, Moiseev N, et al. Exploring UTAUT Model in Mobile 4.5G Service: Moderating Social–Economic Effects of Gender and Awareness. Social Sciences. 2022; 11(5):187. https://doi.org/10.3390/socsci11050187

Chicago/Turabian Style

Daniali, Sara Mehrab, Sergey Evgenievich Barykin, Marzieh Zendehdel, Olga Vladimirovna Kalinina, Valeriia Vadimovna Kulibanova, Tatiana Robertovna Teor, Irina Anatolyevna Ilyina, Natalia Sergeevna Alekseeva, Anton Lisin, Nikita Moiseev, and et al. 2022. "Exploring UTAUT Model in Mobile 4.5G Service: Moderating Social–Economic Effects of Gender and Awareness" Social Sciences 11, no. 5: 187. https://doi.org/10.3390/socsci11050187

APA Style

Daniali, S. M., Barykin, S. E., Zendehdel, M., Kalinina, O. V., Kulibanova, V. V., Teor, T. R., Ilyina, I. A., Alekseeva, N. S., Lisin, A., Moiseev, N., & Senjyu, T. (2022). Exploring UTAUT Model in Mobile 4.5G Service: Moderating Social–Economic Effects of Gender and Awareness. Social Sciences, 11(5), 187. https://doi.org/10.3390/socsci11050187

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