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Tapanainen et al-2018-the_electronic_journal_of_information_systems_in_developing_countries

Investigating adoption factors of 3G services

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Tapanainen et al-2018-the_electronic_journal_of_information_systems_in_developing_countries

  1. 1. R E S E A R C H A R T I C L E Investigating adoption factors of 3G services: An empirical study in Vietnam Tommi J. Tapanainen1 | Kien T. Dao2 | Hai T. T. Nguyen3 1 Department of Global Studies, Pusan National University, Busandaehak‐ro 63, Beon‐gil 2, Geumjeong‐gu, Busan 46241, Republic of Korea 2 School of Economics and Management, Hanoi University of Science and Technology, No 01 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam 3 Department of Information Studies, Abo Akademi University, Fänriksgatan 3 B, 2nd floor, Åbo FI‐20500, Finland Correspondence Tommi J. Tapanainen, Department of Global Studies, Pusan National University, Busandaehak‐ro 63, Beon‐gil 2, Geumjeong‐ gu, Busan 46241, Republic of Korea. Email: Abstract This study aimed to examine the factors that affect the 3G mobile services adoption. The technology acceptance model was used, and structural equation modeling was applied to the data sample obtained from a survey in Hanoi, the capital of Vietnam. The research found that perceived usefulness has the strongest effect on intention and attitude to adopt. Social influence does not affect 3G services adoption. The result also underlines the variable of attitude that it should not be eliminated from technology acceptance models. Based on the findings, this research proposes practical recommendations to 3G providers, such as enhancing the service usefulness and focusing on features developed via collaborations between telecommunication carriers and content developers. Furthermore, 3G providers are recommended to focus on understanding their customers' needs better rather than relying on peer and social advertising in promoting 3G services. Finally, future research is proposed to study the effect of different combinations of perceived enjoyment, usefulness, and ease of use, and the relationship between perceived cost, social influence, and intention on 3G services adoption. KEYWORDS 3G services, technology adoption model (TAM), consumer behavior, structural equation modeling, Vietnam, developing countries 1 | INTRODUCTION “3G” is the third generation of mobile communication technology. It is a generic term for a group of telecommunication standards that offer similar functionalities, in principle, increasing the speed and capacity of communications over mobile networks. By adopting 3G, users can access the Internet and experience new applications and business models such as mobile banking, online games, and social networks more easily from their portable devices (Chong, Ooi, Lin, & Bao, 2012; Du, Zhu, Zhao, & Lu, 2012). 3G also enables services over the Internet for devices that are difficult to connect with fixed‐line communications, for example, personal health devices and security monitoring devices. On the other hand, it can be expensive to install fixed data cables in some locations, and 3G networks can in such cases replace these fixed cables for certain applications. In particular, in many emerging economies, 3G can promote the use of the Internet because of its advantages compared to the cable services (Ministry of Information and Communication, 2014). People who are unable to use home Internet for their e‐banking can still use e‐banking over 3G networks. In this way, 3G‐enabled wireless communication networks can enable services for people who would otherwise find themselves excluded from the information society. These possibilities are reflected in the subscriber numbers to mobile communication networks; the number of people subscribing to mobile Internet was in 2009 already at almost parity with the number of those using fixed‐line Internet (Xie, Zhang, & Zeng, 2009). Worldwide, 3G mobile networks were launched in countries such as Japan, Korea, many European countries, and the United States, from the year 2000 and their adoption has been rapid. Although many developing countries such as the Southeast Asian countries are late entrants to 3G, this rapid pace of adoption can be expected to occur also in these countries. In Vietnam, 3G was introduced as late as 2009, but the existing base of mobile subscribers in Vietnam was very high (Figure 1). This would suggest significant potential for take‐up of 3G; however, the actual adoption rate has not matched these expectations. The unexpectedly slow rate of adoption might be due to the lack of interest shown by Vietnamese consumers toward certain popular services. A comparison between services used in the United States and Vietnam (Figure 2) might provide some insight regarding this. Many Vietnamese DOI: 10.1002/isd2.12022 E J Info Sys Dev Countries. 2018;12022. © 2018 John Wiley & Sons 1 of 15
  2. 2. customers knew that 3G phones were capable of mobile TV viewing, video calling, and browsing the Internet but did not use other services that were popular in the United States, such as e‐mail. This can indicate that the services available might not have been useful to the customers or that there were problems with the quality of service. From the viewpoint of the mobile operators, there is considerable potential for 3G services uptake in Vietnam as well as other emerging markets. To encourage customers to adopt 3G, it is important that mobile carriers identify the factors that impact significantly on the consumers' decisions to adopt 3G. Based on that, they can formulate appropriate business strategies and models to improve their performance and to create loyal customers. Mobile services adoption is relatively new in terms of an object of study in research. The review of Ovcjak, Hericko, and Polanci (2015) finds that most papers on the topic have been published between the years 2009 and 2014. In fact, the emergence of 3G seems to have launched the global interest toward studying the adoption of mobile technologies. In addition, Vietnam is an interesting country to study 3G services adoption due to several reasons. One is the surprisingly slow subscription rate of 3G as mentioned above. Second, research on the adoption of IT in general has focused on the United States and East Asian countries (China, Hong Kong, Taiwan, and South Korea) rather than Southeast Asian countries such as Vietnam (Shaikh & Karjaluoto, 2015). Past studies have used different theories such as technology acceptance model (TAM), theory of planned behavior (TPB), theory of reasoned action (TRA), innovation diffusion theory (IDT) and the unified theory of acceptance and use of technology, diffusion of innovation theory, task technology fit, and other similar models to explore behavioral patterns of mobile services users (Ovcjak et al., 2015). However, most of this research corpus has applied TAM or TAM‐derived models (Ovcjak et al., 2015; Shaikh & Karjaluoto, 2015), and TAM has also been claimed to be better than TPB in the context of m‐commerce and m‐services usage (Lin & Wang, 2005; Wang et al., 2006). Previous studies on 3G adoption (eg, Agarwal, Wang, Xu, & Poo, 2007; Chong et al., 2012; Chong, Darmawan, Ooi, & Lin, 2010; Du et al., 2012; Liao, Tsou, & Huang, 2007; and Teng, Lu, & Yu, 2009) were also conducted based onTAM. While the model has been found to be robust by many prior studies, limitations have also been iden- tified (Benbasat & Barki, 2007; Chuttur, 2009) that continue to require improvements in the model. In addition, the use of TAM to examine 3G adoption has so far mostly focused on developed countries, not emerging economies such as Vietnam. Therefore, this research has two objectives: (1) to investigate the antecedents of Vietnamese users' 3G mobile services adoption intentions and (2) to develop a model that can provide insight into why consumers adopt 3G. The new model comprises the original TAM (TAM1) variables (Davis, Bagozzi, & Warshaw, 1989) and incorporates additional variables, which can affect the 3G adoption decision such as IT self‐efficiency, social influ- ence (SOI), and service quality. As many other developing countries have yet to implement 3G networks, and future mobile network generations can be expected to face similar issues as Vietnam, lessons learned in this study can be practically useful and informative for these countries. The literature review on TAM, hypotheses development, and research model are presented in the next section. Section 3 introduces the research methodology, followed by data analysis and results in Section 4. Finally, Sections 5, 6, and 7 present the discussion, contributions, and limitations as well as future research possibilities. FIGURE 1 Penetration of mobile and 3G in different countries. Source: The Nielsen Company, 2010a FIGURE 2 Use of 3G applications in Vietnam and the United States. Source: The Nielsen Company, 2010a 2 of 15 TAPANAINEN ET AL.
  3. 3. 2 | LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT 2.1 | Major factors affecting the acceptance of 3G services In Vietnam, individual customers can freely select 3G services among 4 providers: Viettel, Mobifone, Vinaphone, and Vietnammobile. Therefore, for this study, investigating the factors that lead to the acceptance of 3G services in the voluntary context is more relevant than in the compulsory context. The acceptance of IT, and more specifically, that of mobile services, is linked to many different variables. Ovcjak et al. (2015) extracted 47 widely used factors in mobile services acceptance. Shaikh and Karjaluoto (2015) found 3 main dependent variables (ie, attitude, intention, and usage) and several independent variables in their review. They also found some of the most commonly used antecedents of the intention to adopt (INT) and actual usage are perceived usefulness (PU), perceived ease of use (PEOU), and SOIs. Nearly half of the studies (43%) used PU as a key factor and empirically established the influence of this antecedent on behavioral intention and usage. Similarly, in the review of Ovcjak et al. (2015), the INT, PU, and PEOU were the variables that were used the most often. Perceived usefulness is an individual's belief on how useful and beneficial the technology would be (Davis, 1989). Perceived usefulness is one of primary variables inTAM to be connected to performance expectancy and is found in other models with different names such as relative advan- tage in diffusion of innovation theory (Ovcjak et al., 2015). Perceived ease of use is an individual's belief on how easy it would be to use a particular technology. This construct exists not only inTAM but also in IDT with the name Ease of Use (Ovcjak et al., 2015). Perceived ease of use in turn will influence the PU of the information technology. Later in the modified version of TAM, called TAM1, Davis et al. (1989) suggested to include the INT, which was positively affected by attitude toward adopt (ATT) and PU. Having a behavioral intention means that the individual is preparing to perform a given action. According to Davis (1989), the INT is defined as the likelihood that an individual will use an information system. This is based on the TPB and the TRA, which posit that individual behavior is driven by behavioral intentions. In the voluntary context of 3G mobile services, the main relevant variables of TAM including PU, perceived ease‐of‐use, and INT have been applied by several studies (Agarwal et al., 2007; Chong et al., 2010; Chong, Darmawan, Ooi, & Lee, 2011; Kuo & Yen, 2009; Liao et al., 2007). The relationships between these core variables have often been shown to hold similarly in the 3G context as in a generic IT adoption context. In addition, other variables such as perceived enjoyment (Chong et al., 2011; Liao et al., 2007); user self‐efficacy (Agarwal et al., 2007; Chong et al., 2011); price/cost (Agarwal et al., 2007; Chong et al., 2010); SOI (Chong et al., 2010); and service quality (SEQ) (Agarwal et al., 2007; Chong et al., 2010; Kuo & Yen, 2009) have been used in relation with 3G service adoption. 2.2 | Technology acceptance model Past studies have used different theories to explore behavioral patterns of mobile services users. However, TAM, which attempts to explain users' behavioral intention by measuring the likelihood that a person will adopt technology (Davis et al., 1989), is among the most used models (King & He, 2006; Ovcjak et al., 2015; Shaikh & Karjaluoto, 2015). In 2000, Venkatesh and Davis proposed a new version, TAM2, which extended TAM1 by introducing antecedent variables (such as subjective norm, image, and job relevance) to the PU. In 2008, Venkatesh and Bala proposed yet another version, TAM3, which added determinants of the PEOU (eg, computer self‐efficacy, computer anxiety, and perceived enjoyment). Although different versions of TAM are widely used, certain scholars have also pointed out limitations, eg, in the research method- ology and constructs of the model (eg, Benbasat & Barki, 2007; Chuttur, 2009). We will address these limitations henceforth. One contested modification of TAM has been the construct of attitude, which was later eliminated by Venkatesh and Davis (1996). Yang and You (2003) have suggested that attitude may have important effects on system use, and therefore, its elimination should be reconsidered. We follow Kuo and Yen (2009) in this research by using TAM1 model with the attitude construct included. In addition, we incorporate 3 further variables, IT self‐efficacy (SEF), SEQ, and SOI, as argued in the next chapter. Additionally, many previous studies used only students as survey respondents while the research results were generalized to the general population. This led to problems because students' motivations may differ from the general population (Lee, Kozar, & Larsen, 2003; Legris, Inghan, & Collerette, 2003; Yousafzai, Foxall, & Pallister, 2007). Students are more adept at using technologies, more aware of new trends, and more easily influenced by peers and technology characteristics than the general population (Schepers & Wetzels, 2006), and therefore, research conducted on a student sample is likely to show stronger linkages between different variables than would be the case for a less age‐dependent sample (ibid). Thus, our research will collect data not only from students but also from the general population, avoiding this issue. According to Ovcjak et al. (2015)), the TAM is the most frequently used to analyze the acceptance of 3G service usage drivers, while only a limited number of published works have applied an extended model of TAM to explore 3G service usage drivers in the context of developing countries. More generally, regarding research on mobile technologies, a common approach that scholars have taken has been to rely on a model based on an existing theory (TAM, etc) and to integrate other models or variables to this foundation, depending on the needs of the application (Shaikh & Karjaluoto, 2015). Thus, we follow this established approach and attempt to introduce an extended model of TAM for predicting individual's actual use of 3G services in Vietnam. TAPANAINEN ET AL. 3 of 15
  4. 4. 2.3 | Hypotheses development and research model 2.3.1 | IT self‐efficacy Self‐efficacy is defined as the perception of an individual regarding their ability to plan for and attain objectives in given situations (Agarwal et al., 2007; Igbaria & Iivari, 1995), and its link to behavior has been empirically validated in a range of domains (Venkatesh & Davis, 1996). Self‐efficacy has also been applied in the domain of information technology (IT) (eg, Agarwal, Sambamurthy, & Stair, 2000; Agarwal et al., 2007; Igbaria & Iivari, 1995; Venkatesh & David, 1996). Following previous studies (eg, Agarwal et al., 2007; Wang et al., 2006) the concept of SEF was used in this study. IT self‐efficacy is defined in this study as users' belief in their capability to use 3G services in diverse situations. The “IT self‐efficacy” variable was not included in the original TAM; prior research and TAM3 have found that it served as an antecedent for PEOU (Agarwal et al., 2000; Agarwal et al., 2007; Igbaria & Iivari, 1995; Venkatesh & Bala, 2008; Venkatesh & Davis, 1996). It is logical that an individual's beliefs about one's capabilities to use 3G services in various situations have an effect on this individual's tendency to adopt 3G services. Thus, we have the following hypothesis. H1: Self‐efficacy will have a positive direct effect on perceived ease of use. 2.3.2 | Perceived ease of use Previous studies found that PEOU can strengthen PU (Davis et al., 1989; Klopping & Mackinney, 2004; Melas, Zampetakis, Dimopoulou, & Moustakis, 2011; Taylor & Todd, 1995; Venkatesh & Davis, 2000; Venkatesh, Morris, Davis, & Davis, 2003; Verkasalo, Nicolás, Castillo, & Bouwman, 2010) and the attitude to technology (Davis, 1993; Dishaw & Strong, 1999; Klopping & Mackinney, 2004; Melas et al., 2011; Tang & Chiang, 2009; Uroso, Soyelu, & Koufie, 2010). In the 3G context, the studies from Liao et al. (2007) and Kuo and Yen (2009) concluded that PEOU has positive effects on PU and it is an essential factor in attitudes towards using 3G services. Researchers also found that PEOU is less important than PU regarding determining the INT IT; however, it is itself linked to PU (Chong et al., 2012). Thus, the following hypotheses are proposed. H2a: Perceived ease of use will have a positive direct effect on perceived usefulness. H2b: Perceived ease of use will have a positive direct effect on attitude to adopt. 2.3.3 | Perceived usefulness Previous studies (eg, Davis, 1993; Davis et al., 1989; Klopping & Mackinney, 2004; Uroso et al., 2010; Yu, Yu, & Cheng, 2012) confirmed that PU impacts the attitude of customers. In the IT domain, PU has been shown to be positively related to user adoption of IT (Carlsson, Hyvönen, Repo, & Walden, 2005; Dishaw & Strong, 1999; Klopping & Mackinney, 2004; Lin et al., 2005; Liu & Li, 2011; Park, 2009; Punnoose, 2012; Rigopoulos, Psarras, & Askounis, 2008; Shih & Huang, 2009; Tang & Chiang, 2009; Wei, Marthandan, Chong, Ooi, & Arumugam, 2009). Also in the 3G services context, prior research has demonstrated the link between PU and adoption attitudes and intentions (Chong et al., 2010; Kuo & Yen, 2009; Liao et al., 2007; Pagani, 2004). Essentially, 3G enables already existing mobile GSM networks to carry more data faster, which can create a value proposition for customers if they see this increased speed and data as beneficial. Thus, this research sets the following hypotheses. H3a: Perceived usefulness will have a positive direct effect on attitude to adopt. H3b: Perceived usefulness will have a positive direct effect on intention to adopt. 2.3.4 | Attitude to adopt Attitude to adopt (ATT) is defined as the degree to which an individual wishes to adopt and uses a given technology (Fishbein & Ajzen, 1975). Prior literature has confirmed the relationship between a favorable attitude toward the adoption of technology and the behavior to adopt the technology (Davis et al., 1989; Kulviwat, Bruner, Kumar, Nasco, & Clark, 2007; Kuo & Yen, 2008; Liu, Liao, & Peng, 2005; Malhotra & Galletta, 1999; Melas et al., 2011; Moon & Kim, 2001; O'Cass & Fenech, 2003; Park, 2009; Venkatesh et al., 2003; Vijayasarathy, 2004; Shroff, Deneen, & Ng Eugenia, 2011). In the domain of 3G networks, Liao et al. (2007) has used ATT as the degree to which potential adopters of 3G services are disposed toward the technology. Accordingly, we hypothesize that H4: Attitude will have a positive direct effect on the intention to adopt. 2.3.5 | Social influence Social influence is defined as the importance assigned by an individual to the opinions of other people regarding this individual's use of a given tech- nology (Venkatesh et al., 2003). Individuals receive messages not only from their friends and family but also from mass media, which can shape their beliefs about the opinions of other people (Wei et al., 2009) and consequently influence their adoption behavior. The individual may then view the technology as a desirable item that increases his or her standing in the reference groups he/she participates in. Social influence is one of the direct determinants of behavioral intention in the unified theory of acceptance and use of technology model proposed by Venkatesh et al. (2003). It was also called as subjective norm inTAM2, TRA and TPB or image in IDT. In the area of mobile technology, SOI has been found to increase the explan- atory power of TAM in consumers' adoption decisions (Zhang, Zhu, & Liu, 2012; Zhou, Lu, & Wang, 2010), and this SOI was seen to be particularly important when examining adoption behavior in Malaysian 3G networks (Chong et al., 2010). This research therefore hypothesizes that 4 of 15 TAPANAINEN ET AL.
  5. 5. H5: Social influence will have a positive direct effect on intention to adopt. 2.3.6 | Service quality While DeLone and McLean (1992) define SEQ as “the degree of general performance of an information system and related services,” in the context of mobile services, Kuo, Wu, and Deng (2009) evaluated SEQ into a range of dimensions, including content quality, navigation and visual design, customer service and system reliability, and connection speed. The evaluation of SEQ in the case of 3G services is similar, with network‐related variables, software‐related variables, and handset‐related (hardware) variables having been proposed. For instance, Agarwal et al. (2007)) and Chong et al. (2010)) used the variables of network reliability, availability, and accessibility. They included, eg, the items of network coverage, band- width, network transfer speed, and response time in their construct of network performance. On the software and handset side, Teng et al. (2009) and Pagani (2004) used battery life, display, speed, and functionalities provided in their research designs. Service quality has been found to have a significant positive effect on intention to use 3G (Pagani, 2004; Teng et al., 2009; Xin, 2004). Thus, in this study, SEQ is also assumed to have a positive impact on the INT of 3G customers. Table 1 lists the constructs used in this research and the related studies. H6: Service quality will have a positive direct effect on intention to adopt. 2.4 | Research model Based on the literature review and hypotheses, the research model is presented as shown in Figure 3. 3 | RESEARCH METHODOLOGY 3.1 | Sampling and data collection The survey method was selected for this research because of its suitability to collect data for the purpose of verifying hypotheses (Pinsonneaults & Kraemer, 1993). Surveys are also commonly used as a method in the field of mobile service acceptance (Ovcjak et al., 2015). The data collection was conducted in Hanoi, Vietnam, from January to April 2015. The questionnaire was translated from English to Vietnamese and vice versa to ensure that the intended meanings were conveyed to respondents. A pretest of the questionnaire was completed with 10 Vietnamese individuals who had experience in using 3G because of the relative lack of prior reports in studying the adoption of 3G in developing countries. Statistics reports (The Nielsen Company, 2010b) show that approximately half of Vietnamese young people (between ages 15 and 24) use mobile services. They are much more likely than the general population to use services benefiting from 3G, such as mobile Internet and multimedia services (Figure 4). Therefore, the adopters of 3G are likely to come from this group. To reflect this, the survey was divided into two parts. One part of the survey used paper forms and was conducted at a university where there is a high proportion of young mobile users. The second part of the survey was conducted as an e‐mail survey with no specific age bias. The university survey was realized from January to February 2015, with 200 questionnaires distributed at the National University in Hanoi, a large national university in Vietnam. We received 130 answers, making the response rate 65.00%. It resulted in 118 fully completed, valid responses. The e‐mail survey was realized after this, between March and April 2015, with respondents from the general population. The question- naires were e‐mailed to 350 people who did not participate in the first survey and who were found via the snowball sampling method. The number TABLE 1 Construct definitions and related studies Construct Conceptual Definition Related Research IT self‐efficacy (SEF) Users' belief in their capability to use 3G services for their purpose in diverse situations Compeau & Higgins, 1995; Venkatesh, 2000; Agarwal et al., 2007 Perceived ease of use (PEOU) The degree to which a person believes that using 3G services would be free of effort Fishbein & Ajzen, 1975; Davis, 1993; Taylor and Todd (1995); Venkatesh & Davis, 2000 Perceived usefulness (PU) The degree to which a person believes that using 3G services would enhance his or her job performance Fishbein & Ajzen, 1975; Davis, 1993; Taylor & Todd, 1995; Venkatesh, 2000; Venkatesh et al., 2003; Klopping & Mackinney, 2004 Attitude to adopt (ATT) The degree to which an individual feels good about adopting and using 3G services Davis, 1989; Davis, 1993; Taylor & Todd, 1995; Venkatesh et al., 2003 Service quality (SEQ) The user's perceptions on the service provider's performance The degree of general performance of an information system and related services (DeLone & McLean, 1992) Smith & Kumar 2004; Agarwal et al., 2007; Kuo et al., 2009 Social influence (SOI) The weight assigned by an individual to the opinions of other people regarding this individual's use of 3G services Fishbein & Ajzen, 1975; Thompson et al., 1991; Moore & Benbasat, 1991; Taylor & Todd, 1995; Venkatesh et al. 2000; Venkatesh et al., 2003 Intention to adopt (INT) Subjective probability that an individual will use 3G services Davis, 1989; Davis, 1993; Venkatesh, 2000 TAPANAINEN ET AL. 5 of 15
  6. 6. of complete and valid responses was 227, which shows a response rate of 64.85%. Two surveys were comprised to create only one sample of 345, which is greater than the recommended sample size of 300 or more (Comrey & Lee, 1992), to run the hypothesis tests for this study. 3.2 | Variable measurement The variables used in this study were adapted from Agarwal et al., 2007; Compeau and Higgins (1995) for SEF with 6 items; Davis (1993), Taylor and Todd (1995), and Venkatesh and Davis (2000) for PEOU with 5 items; Davis (1993), Klopping and Mackinney (2004), Taylor and Todd (1995), and Venkatesh (2000, 2003) for PU with 6 items; Davis (1993), Taylor and Todd (1995), and Venkatesh (2003) for ATT with 4 items; Taylor & Todd (1995) and Venkatesh (2000) for SOI with 4 items; SEQ with 4 items from Smith and Kumar (2004), Agarwal et al. (2007), and Kuo et al. (2009); SOI with 4 items from Taylor and Todd (1995) and Venkatesh (2000); and finally, Davis (1993) and Venkatesh (2000) for INT with 4 items. A 5‐point Likert scale from 1 (totally disagree) to 5 (totally agree) was used to measure the variables (see Appendix A). 4 | DATA ANALYSIS AND RESULTS The multivariate analysis was used to analyze the collected data. A two‐step approach (Anderson & Gerbing, 1988; Chong et al., 2012; Mohammadi, 2015) was applied by firstly testing the reliability, validity of the measurement model and the model fitness, then using structural equation modelling (SEM) analysis to examine the hypotheses. 4.1 | Sample characteristics The demographic characteristics of the respondents were summarized in Table 2. The number of female respondents was 206, accounted for 59.70% and 139 participants (40.30%) were male. There were 146 respondents (58.70%), who were below 25 years old and 199 participants (41.30%) who were above 25 years old. Regarding the education level, most (94.20%) of the respondents had at least bachelor or master degrees. This could be due to the survey being conducted in the capital, Hanoi, where many large universities are located. 4.2 | Reliability, validity, and model fitness analysis Construct reliability was accessed via Cronbach's alpha. According to Hair, Black, Babin, Anderson, and Tatham (2006) and Nunnally (1978), construct reliability is confirmed if the Cronbach's alpha values are all greater than 0.70 (Table 3). FIGURE 4 Use of two different 3G services in 2009 and 2010: a comparison between young users and average other ages. Source: The Nielsen Company, 2010b FIGURE 3 Research model 6 of 15 TAPANAINEN ET AL.
  7. 7. For the exploratory factor analysis, the principal component analysis, with varimax rotation and eigenvalue greater than 1, was used (Kaiser, 1958). To make the intercorrelation matrix contain sufficient common variance, a KMO (Kaiser–Meyer–Olkin) value of more than 0.50 and the total variance of more than 50% were also used to ensure that. Finally, the corrected item‐total correlation coefficient should be more than 0.30 and factor loadings greater than 0.5 (Table 3). Confirmatory factor analysis was used to evaluate convergent and discriminant validity of the instrument. Convergent validity measures whether items in the same factor are related, and it was evaluated by checking the item loadings (which are greater than 0.5), the composite reli- ability (which is greater than 0.7), and the average variance extraction (which is greater than 0.5). These numbers establish convergent validity (Bagozzi & Yi, 1988; Choi & Choi, 2009; Joreskog, 1971; Kline, 2011; Wu & Chuang, 2010). As seen in Table 3, all the constructs met the require- ments, which confirmed that convergent validity was achieved (Steenkamp & van Trijp, 1991). Discriminant validity, on the other hand, measures whether separate factors are unrelated. Discriminant validity was evaluated (Fornell & Larcker, 1981; Gefen et al., 2000) and is shown on Table 4. The discriminant validity of all constructs is satisfied if the numbers in bold, which are the squared roots of the average variance extractions, are greater than the vertical numbers, which are the factor correlation coefficients. As listed in Table 4, constructs used in this research satisfy the discriminant validity criteria. To assess the measurement model's goodness of fit, multiple fit indices including the chi‐square/df value and the associated df, comparative fit index (CFI), Tucker and Lewis Index (TLI), Incremental Fit Index (IFI), and root mean square error of approximation (RMSEA) were measured. Table 5 shows that chi‐square/df = 2.538 < 3; CFI, TLI, and IFI were all over 0.9 (ie, CFI = 0.928, TLI = 0.917; IFI = 0.929) and RMSEA = 0.063, which satisfy the criteria recommended by Hair et al. (2006). Thus, the measurement model possesses a sufficiently good model fit. TABLE 2 Sample characteristics Content Frequency Percentage Gender Male 139 40.3 Female 206 59.7 Age <25 146 42.3 >25 199 57.7 Education University 325 94.2 Others 20 5.8 Total 345 100 TABLE 4 Assessment of discriminant validity SEF PEOU PU SOI ATT SEQ INT SEF 0.789 PEOU 0.757 0.847 PU 0.391 0.539 0.752 SOI 0.221 0.247 0.609 0.731 ATT 0.300 0.387 0.741 0.644 0.788 SEQ 0.159 0.242 0.569 0.393 0.629 0.811 INT 0.242 0.383 0.718 0.522 0.735 0.74 0.749 Abbreviations: ATT, attitude toward adopt; INT, intention to adopt; PEOU, perceived ease of use; PU, perceived usefulness; SEF, IT self‐efficacy; SEQ, service quality; SOI, social influence. TABLE 3 Assessment of reliability and convergent validity Constructs (No. of Items) Cronbach's Alpha Minimum Item Correlation KMO Minimum Factor Loadings (EFA) Total Variance Explained (TVE), % Minimum Factor Loading (CFA) Composite Reliability (CR) Average Variance Extracted (AVE), % SEF (3) 0.796 0.513 0.703 0.837 74.413 0.738 0.831 62.311 PEOU (5) 0.895 0.663 0.855 0.816 77.176 0.753 0.927 71.682 PU (6) 0.875 0.611 0.862 0.769 64.659 0.721 0.886 56.565 ATT (4) 0.778 0.498 0.803 0.723 66.616 0.747 0.831 62.166 SEQ (4) 0.830 0.515 0.828 0.805 74.254 0.735 0.884 65.708 SOI (4) 0.865 0.650 0.738 0.775 64.838 0.687 0.820 53.393 INT (4) 0.794 0.500 0.776 0.809 68.072 0.704 0.836 56.085 Abbreviations: ATT, attitude toward adopt; CFA, confirmatory factor analysis; EFA, exploratory factor analysis; INT, intention to adopt; KMO, Kaiser– Meyer–Olkin; PEOU, perceived ease of use; PU, perceived usefulness; SEF, IT self‐efficacy; SEQ, service quality; SOI, social influence. TAPANAINEN ET AL. 7 of 15
  8. 8. 4.3 | Structural model 4.3.1 | Path coefficient In the second step, structural equations modeling was applied to test the hypotheses. The results are shown in Table 6. Based onTable 6, PEOU has the negative direct effect (because of negative Beta, β = −.114)) while the relationship between PEOU and ATT is positive (because of the positive discriminant validity, r = 0.387). This could be caused by the negative suppression and the relationship between POEU and ATT should be reconsidered (Maassen et al., 2001). In addition, POEU has a high correlation with PU (r = 0.387) and PU has a high cor- relation with ATT (r = 0.387). Thus, only the effect of POEU on ATT via PU should be included in the study. Table 7 shows the new results after redoing SEM analysis without H2b, among the factors influencing INT, SEQ (β = 0.527, P < .001) showed the greatest effects, followed by PU (β = 0.425, P < .001) and ATT (β = 0.283, P < .05) had significant paths as well. However, the SOI showed no significant effect on intention to use (β = 0.022, P = 0.621 > .05). The influence of PU on the ATT is also positive (β = 0.856, P value < .001), thus supporting Hypothesis H3a. Perceived ease of use (β = 0.521, P < .001) is found to have a significant influence on PU, thus supporting Hypothe- sis H2a. Finally, SEF (β = 0.761, P < .05) is found to have a significant and direct relationship with PEOU, thus supporting Hypothesis H1. In sum- mary, Hypotheses H1, H2a, H3a, H3b, H4 and H6 are supported but not H5. Intention to adopt is well forecasted (79.5%) by SEQ, PU, and ATT. In addition, significant variance of attitude (73.3%) is predicted by PU while PEOU explained only 27.2% variance of PU. The degree of variance in PU explained by PEOU is small enough to conclude that other factors are responsible for most of the variance in this variable. Finally, the 58.0% variance of PEOU is explained by self‐efficient. Path coefficients and their significances are shown in Figure 5. 4.3.2 | Path analysis As seen in Figure 5, intention to use of 3G customers is influenced not only directly by PU, ATT, and SEQ but also indirectly by PEOU and SEF. Thus, we used bootstrapping with 1000 replications to estimate the direct, indirect, and total impact. Based on the results shown in Table 8, the highest impact comes from PU (λ = 0.667), followed by SEQ (λ = 0.527), PEOU (λ = 0.348), ATT (λ = 0.283), and finally, self‐efficacy (λ = 0.265). Table 8 also indicates that PU had the strongest effect on INT and ATT. This result reinforces the findings of Davis (1989) and Davis et al. TABLE 6 Path coefficients and significances Hypotheses Path Beta (P Value) Supported or Not H1 IT self‐efficacy → Perceived ease of use 0.761 (<.001) Yes H2a Perceived ease of use → Perceived usefulness 0.541 (<.001) Yes H2b Perceived ease of use → Attitude to adopt −0.114 (.030) No H3a Perceived usefulness → Attitude to adopt 0.927 (<.001) Yes H3b Perceived usefulness → Intention to adopt 0.426 (<.001) Yes H4 Attitude to adopt → Intention to adopt 0.281 (.017) Yes H5 Social influence → Intention to adopt 0.022 (.621) No H6 Service Quality → Intention to adopt 0.528 (<.001) Yes TABLE 7 The second path coefficients and significances Hypotheses Path Beta (P Value) Supported or Not H1 IT Self‐efficacy → Perceived ease of use 0.761 (<.001) Yes H2a Perceived ease of use → Perceived usefulness 0.521 (<.001) Yes H3a Perceived usefulness → Attitude to adopt 0.856 (<.001) Yes H3b Perceived usefulness → Intention to adopt 0.425 (<.001) Yes H4 Attitude to adopt → Intention to adopt 0.283 (.014) Yes H5 Social influence → Intention to adopt 0.022 (.621) No H6 Service Quality → Intention to adopt 0.527 (<.001) Yes TABLE 5 The values of fit indices Goodness of Fit Measures Chi‐square/df Comparative Fit Index (CFI) Tucker and Lewis Index (TLI) Incremental Fit Index (IFI) Root Mean Square Error of Approximation (RMSEA) Recommended value <3 >0.9 >0.9 >0.9 <0.08 CFA model 2.538 0.927 0.917 0.929 0.063 Structural model 2.788 0.920 0.910 0.921 0.072 Abbreviation: CFA, confirmatory factor analysis. 8 of 15 TAPANAINEN ET AL.
  9. 9. (1989) that PU is the main factor in the ATT. Moreover, the result is in‐line with Liao et al. (2007) and Kuo and Yen (2009) who investigated 3G mobile services in Taiwan. 5 | DISCUSSION This study derived its conceptual model from the original TAM, one of the most cited models for understanding users' decisions to adopt IT (Ovcjak et al., 2015; Shaikh & Karjaluoto, 2015; Benbasat & Barki, 2007; King & He, 2006) and aimed at understanding the determinants of intention to use of 3G services in Vietnam. We discuss the main findings in this section, starting from the dependent variable and variables on the right side of Figure 5 (Hypotheses H4, H3b, and H6), then discussing SOI (Hypothesis H5), and finally, moving left in the figure to the other variables (Hypoth- eses H2a, H3a, and H1). First, we found that PU has a significant effect on behavioral intention (Hypothesis H3b). This result is consistent with the hypothesis of TAM and other previous studies. For example, the review of Ovcjak et al. (2015) found that the relationship between PU and adoption intention was significant in 52 studies of 61 (85% of studies) where this was investigated. In addition, attitude had a significantly positive effect on behavioral intention (Hypothesis H4). This result is consistent with the findings of previous studies, such as Davis et al. (1989), Liao et al. (2007), Moon and Kim (2001), and Kuo and Yen (2009). Indeed, this relationship was also found to be significant in all the studies reviewed by Ovcjak et al. (2015). Moreover, our study found that SEQ was an important determinant of the INT 3G services (Hypothesis H6). This is reasonable because services that are of poor quality (eg, slow speed of connection or unstable Internet access) would be unlikely to be adopted by many users. The finding is also reinforced by prior studies (eg, Agarwal et al., 2007). On the other hand, it is surprising to find that SOI did not have influence on the INT 3G services (Hypothesis H5). Considering the notion of culture as expressed by Hofstede (1991), one could expect that consumers in Vietnam would be highly collectivistic and therefore subject to peer influence in their adoption decisions. Such a supposition regarding Asian consumers was actually found by Chong et al. (2012) in their study of Chi- nese 3G services users. However, there are also findings showing that Western consumers are more affected by SOI than non‐Western ones (Schepers & Wetzels, 2007). De Matos, Ferreira, and Krackhardt (2014) explained in their research that the weak link between peer influence and 3G adoption, which they found in one country in Europe, could result from the high cost of handsets that are 3G enabled. In our survey, around 80% of participants had an income of below 400 dollars per month and 3G services was quite expensive for them. Thus, similar to the study of De TABLE 8 Effects of variables on intention to adopt Dependent Variables Effects SEQ SEF PEOU PU ATT PEOU Direct effects 0.000 0.761 0.000 0.000 0.000 Indirect effects 0.000 0.000 0.000 0.000 0.000 Total effects 0.000 0.761 0.000 0.000 0.000 PU Direct effects 0.000 0.000 0.521 0.000 0.000 Indirect effects 0.000 0.397 0.000 0.000 0.000 Total effects 0.000 0.397 0.521 0.000 0.000 ATT Direct effects 0.000 0.000 0.000 0.856 0.000 Indirect effects 0.000 0.340 0.446 0.000 0.000 Total effects 0.000 0.340 0.446 0.856 0.000 INT Direct effects 0.527 0.000 0.000 0.425 0.283 Indirect effects 0.000 0.265 0.348 0.242 0.000 Total effects 0.527 0.265 0.348 0.667 0.283 Abbreviations: ATT, attitude toward adopt; INT, intention to adopt; PEOU, perceived ease of use; PU, perceived usefulness; SEF, IT self‐efficacy; SEQ, service quality. FIGURE 5 The result of structure model TAPANAINEN ET AL. 9 of 15
  10. 10. Matos, due to the financial influence, Vietnamese consumers might have tended to make decisions based on their own situation, rather than relying on references from peers and friends. Other way to explain the lack of SOI found in this research could be that the participants of the sample were early adopters of 3G who were themselves dominant influencers in their peer relationships. At the time of the study, the penetration rate of 3G services was still relatively low in Vietnam, supporting the argument that Vietnam was in the early diffusion phase of 3G services. Another reason could be that the participants in the surveys had high education level, more technology savvy, and more aware of the current technologies available. Thus, they are quite confident in selecting the technology that serves their demand the most. Finally, according to Venkatesh and Davis (2000), voluntary use is not connected with SOI. Furthermore, the relationships between PEOU, PU, and ATT are considered. Consistent withTAM, we found that PEOU influences PU of 3G, which in turn influences the attitude to adoption of 3G services. This is consistent with the foundations of TAM (Davis, 1989; Davis et al., 1989) and holds true in the 3G mobile services adoption research (Kuo & Yen, 2009; Liao et al., 2007). In addition, our study echoes the findings of pre- vious studies (eg, Chong et al., 2012; Jeyaraj, Rottman, & Lacity, 2006; Wei et al., 2009), which emphasizes the role of PU over PEOU in adoption. This might be due to existing experiences in using other mobile technologies (Wei et al., 2009), but it has also been noted that present mobile technologies are, in general, relatively easy to use (Chong et al., 2012) and therefore PEOU might be viewed as less important than PU. Finally, similar to the result of Agarwal et al. (2007) and Chong et al. (2011), SEF variable has the positive impact on PEOU and explained almost 60% of the variance of PEOU, suggesting that a majority of the sample were ready to take the initiative to learn how to use the 3G services. 6 | IMPLICATION AND CONCLUSION 6.1 | Theoretical contribution Mobile services have become more important as a field of study in recent years due to their increased role in society, focusing attention on the acceptance of mobile services therefore (Ovcjak et al., 2015). This research has several theoretical contributions to the field of IT adoption and human behavior, particularly the adoption of 3G services and TAM. These contributions extend TAM and deepen our understanding regarding 3G service adoption. First, the adoption of 3G services among the Vietnamese mobile users is still relatively low (around 17%). At the juncture in which advanced countries are already moving to newer generations of mobile technologies, it is interesting to see how consumers in Vietnam respond to this generation of cellular technology. Researchers have been concerned with the applicability of models that can be used to explain the adoption trend of 3G in the Vietnamese market. While prior research on mobile technology adoption has been performed in China and Taiwan (Chang et al., 2012; Kuo & Yen, 2009; Liao et al., 2007), this paper extends the research to Vietnam, a country with few prior studies on the topic. It has filled the research gap, and our results are in general consistent with other 3G adoptions studies. Therefore, it is possible to develop a 3G service acceptance model, which can be applicable for general context. This research found that TAM can be applied to explain the acceptance behavior of Vietnamese 3G service users, even though Straub, Keil, and Brenner (1997) and McCoy, Everard, and Jones (2005) concluded that TAM did not fit non‐Western cultural attitudes. The analysis confirms the significantly positive relations of PU and INT, and attitude and INT, which are most frequently used in prior studies and are the core relationships in TAM. Therefore, this research added to evidence showing the applicability of TAM for Vietnamese 3G mobile services adoption. Our research also underlines the variable of attitude. Similar to what was stated by Ovcjak et al. (2015), we found that the relationship between attitude and behavioral intention is significant, and we concur with Yang and You (2003) in that the variable of attitude should not be eliminated from TAMs. Our study also found that PEOU has indirectly influence on attitude positively via PU. Even though some previous studies (eg, Mao, Srite, Thatcher, & Yaprak, 2005; Schepers & Wetzels, 2007) concluded that PU seemed more relevant in Western attitudes, and PEOU was more impor- tant in non‐Western cultures, this study underlined PU in Vietnam. It might indicate a shift in which PU is becoming more important also in non‐ Western cultures. Lastly, one surprising finding of our research was the lack of relationship between SOI and adoption, even though subjective norm, a variable similar to SOI was found in some studies to have a stronger effect on behaviors in non‐Western cultures than in Western cultures (eg, Chong et al., 2012; Zhang et al., 2012). Because of the contradictions between our results and prior literature regarding these variables, we are cautious in interpreting our results and prefer to set the challenge for future research to create new models in light of which they can be understood with more confidence. 6.2 | Practical contribution As there is still much potential for mobile operators in expanding their services in Vietnam as well as in other emerging markets, this research can offer practical information for operators which they can use in positioning their 3G services. Table 8 indicates that PU had the strongest effect on intention to use and attitude, which suggests that operators should concentrate on the usefulness aspect of 3G services rather than the ease‐of‐use aspect of 3G services to attract clients. Managers should also be aware that as mobile usage is as common as it is today, usability might now be less important in shaping attitude, intentions, and behavior as it has been in the past. Thus, at least in the case of young consumers in the big cities in Vietnam, operators should focus on the value proposition for services that these consumers are interested in. 10 of 15 TAPANAINEN ET AL.
  11. 11. Moreover, although in this study the adoption of 3G is not effected directly by PEOU, an easier application will be perceived as being more useful, which then leads to adoption. Thus, mobile carriers and application developers should also attempt to improve the ease of use of 3G applications. Findings from this research show that SEF plays an important role in increasing PEOU. As such, 3G providers should plan to create public awareness of 3G by providing information, eg, what 3G is and how to use it in different channels. In this way, customers could access and learn about the services at their most convenient time. However, as we learn from this study, the concept of ease‐of‐use may not be as straightforward as it sounds. It is possible that some situations require an optimal level of “easiness”; in other words, “as easy as possible” may not always be desirable. For instance, if applications are too easy, users might lose their interest, and negative attitudes result, whereas if they are too difficult, individuals will not be able to use them. This means that it can be important to investigate and identify the optimal level of ease before developing the applications. Furthermore, most 3G users in Vietnam appear to be using basic services such as telephone calls, the Internet, games, and chatting programs (Figure 2), which can work with older technologies as well. Therefore, to show clear value for 3G services, operators should advertise services that use the increased speed and capacity of 3G technologies but are also attractive to consumers. In addition, mobile carriers should diversify the 3G value‐added services. Viettel, one of the Vietnamese operators, established the content and software development centers to provide technical supports and more services to the firm's customers. As suggested in Chong et al. (2012) and Keong (2009), one possibility to encourage the uptake of 3G could be the creation of incentives for software developers to produce applications that appeal to consumers. Finally, we suggest that peer/social marketing approaches are unlikely to bring significant benefits for operators. Vietnam is a low‐income country: Based on the descriptive statistics of our survey, around 80% of participants had an income of below 400 dollars per month. As other mobile technologies were offered at lower prices compared to 3G, it was considered as an expensive service for many Vietnamese. Because Vietnamese consumers were able to make price comparisons and had a limited budget, they might have tended to make decisions based on their own situation, rather than relying on references from peers and friends. Therefore, Vietnamese carriers can be better off by conducting mass marketing or improving the price/value‐ratio of the 3G services instead of relying on social marketing. 7 | LIMITATION AND FUTURE RESEARCH Even though this study reached its objectives, it had certain limitations. First, the survey was conducted in an urban area and excluded the vast rural areas in Vietnam, where 70% of the population is living. However, this might not have significant impact on the study result because most of the customers who adopt advanced technology and services mainly live in cities. Second, the sample size is relatively small compared to around 20 million customers in Vietnam, which can reduce the representation/generalization of the study. The sample size can be increased in the further research, particularly research on the future service such as 4G. The future research possibilities arising from this work are outlined as follows. First, Kuo and Yen (2009) mentioned perceived cost and adop- tion, and found that perceived cost has a negative influence on attitudes to adopt 3G in the sample fromTaiwan. No relationship between cost and adoption intentions for 3G was observed by Chong et al. (2012). They, however, found a strong link between SOI and PU. Another statement regarding SOI was that it is not strong enough to promote adoption behavior (De Matos et al., 2014). Clearly, there are conflicting reports testifying to the power of SOI and perceived cost in 3G services adoption. The relationship among perceived cost, SOI, and adoption is controversial and complex. Thus, we recommend studying the relationship between perceived cost, SOI, and adoption further. Second, Ovcjak et al. (2015) observed that the enjoyment variable plays a significant role in mobile services acceptance research; however, the number of studies on this factor is still limited and the results are contradictory. For example, Liao et al. (2007) found that there is a direct relation- ship between PEOU and perceived enjoyment, and then INT 3G while the finding of other researchers (eg, Chong et al., 2011) is different. Adding with our finding that ease of use might lead to negative ATT, we suggest to study further the relationship between perceived enjoyment, PEOU, PU, and attitude to adopt. REFERENCES Agarwal, N. K., Wang, Z., Xu, Y., & Poo, D. C. C. (2007). Factors affecting 3G adoption: An empirical study. In Proceedings of the 11th Pacific‐Asia Conference on Information Systems. Agarwal, R., Sambamurthy, V., & Stair, R. M. (2000). The evolving relationship between general and specific computer self‐efficacy—An empirical assessment. Information Systems Research, 11(4), 418–430. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two‐step approach. Psychological Bulletin, 103(3), 411–423. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74–94. Benbasat, I., & Barki, H. (2007). Quo vadis TAM? Journal of the Association for Information Systems, 8(4), 211–218. Carlsson, C., Hyvönen, K., Repo, P., & Walden, P. (2005). Adoption of mobile services across different technologies. In Proceedings of the 38th annual Hawaii international conference on system sciences. Choi, J., & Choi, Y. (2009). Behavioral dimensions of public relations leadership in organizations. Journal of Communication Management, 13, 292–309. Chong, A. Y. L., Darmawan, N., Ooi, K. B., & Lee, V. H. (2011). Determinants of 3G adoption in Malaysia: A structural analysis. The Journal of Computer Information Systems, 51, 71–80. TAPANAINEN ET AL. 11 of 15
  12. 12. Chong, A. Y. L., Darmawan, N., Ooi, K. B., & Lin, B. (2010). Adoption of 3G services among Malaysian consumers: An empirical analysis. International Journal of Mobile Communications, 8, 129–149. Chong, A. Y. L., Ooi, K. B., Lin, B., & Bao, H. (2012). An empirical analysis of the determinants of 3G adoption in China. Computers in Human Behavior, 28(2), 360–369. Chuttur, M. Y. (2009). Overview of the technology acceptance model: Origins developments and future directions. Working Papers on Information Systems, 9(37), 9–37. Compeau, D. R., & Higgins, C. A. (1995). Computer self‐efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis. Erlbaun, NY: Hilsdale. Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. Davis, F. D. (1993). User acceptance of computer technology: System characteristics user perceptions and behavior characteristics. International Man‐Machine studies, 38, 475–487. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. De Matos, M. G., Ferreira, P., & Krackhardt, D. (2014). Peer influence in the diffusion of iPhone 3G over a large social network. MISQuarterly, 38(4), 1103–1133. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task technology fitt constructs. Information Management, 36, 9–21. Du, H. Y., Zhu, G., Zhao, L. M., & Lu, T. J. (2012). An empirical study of consumer adoption on 3G value‐added services in China. Nankai Business Review, 3(3), 257–283. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. MA: Addison‐Wesley, Reading. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6nd ed.). Upper Saddle River NJ: Prentice –Hall. Hofstede, G. (1991). Cultures and organizations: Software of the mind. London: Mc‐Graw Hill. Igbaria, M., & Iivari, J. (1995). The effects of self‐efficacy on computer usage. International Journal of Management Science, 23(6), 587–605. Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21, 1–23. Joreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36, 409–426. Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187–200. Keong, L. M. (2009). Funding boost for Malaysia's mobile developers. ZDNet Asia Websites accessed 8 May 2016‐ boost‐for‐malaysias‐mobile‐developers/. King, W. R., & He, J. (2006). A meta‐analysis of the technology acceptance model. Information Management, 43, 740–755. Kline, R. B. (2011). Principles and practice of structural equation modeling (3nd ed.). Toronto: Guilford Press. Klopping, I. M., & Mackinney, E. (2004). Extending the technology acceptance model and the task–technology fit model to consumer e‐commerce. Information Technology, Learning, and Performance Journal, 22(1), 35–48. Kulviwat, S., Bruner, G. C., Kumar, A., Nasco, S. A., & Clark, T. (2007). Toward a unified theory of consumer acceptance technology. Psychonogy and Marketing, 24(12), 1059–1084. Kuo, Y. F., & Yen, S. N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value‐added services. Computers in Human Behavior, 25, 103–110. Kuo, Y.‐F., Wu, C.‐M., & Deng, W.‐J. (2009). The relationships among service quality, perceived value, customer satisfaction, and post‐purchase intention in mobile value‐added services. Computers in Human Behavior, 25, 887–896. Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: Past, present, and future. Communication of the AIS, 12(50), 752–780. Legris, P., Inghan, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information Management, 40, 191–204. Liao, C. H., Tsou, C. W., & Huang, M. F. (2007). Factors influencing the usage of 3G mobile services in Taiwan. Online Information Review, 31, 759–774. Liu, S. H., Liao, H. L., & Peng, C. J. (2005). Applying the technology acceptance model and flow theory to online e‐learning users' acceptance behavior. Information Systems, 6(2), 175–181. Liu, Y., & Li, H. (2011). Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China. Computers in Human Behavior, 27, 890–898. Maassen, G. H., & Bakker, A. B. (2001). Suppressor variables in path models: Definitions and interpretations. Sociological Methods & Research, 30(2), 241–270. Malhotra, Y., & Galletta, D. F. (1999). Extending the technology acceptance model to account for social influence: Theoretical bases and empirical validation. In Proceedings of the 32nd Hawaii International Conference on System Sciences. U.S. Mao, E., Srite, M., Thatcher, J. B., & Yaprak, O. (2005). A research model for mobile phone service behaviors: Empirical validation in the U.S. and Turkey. Journal of Global Information Technology Management, 8(4), 7–28. McCoy, S., Everard, A., & Jones, B. M. (2005). An examination of the technology acceptance model in Uruguay and the US: A focus on culture. Journal of Global Information Technology Management, 8(2), 27–45. Melas, C. D., Zampetakis, L. A., Dimopoulou, A., & Moustakis, V. (2011). Modelling the acceptance of clinical information systems among hospital medical staff: An extended TAM model. Journal of Biomedical Informatics, 44, 553–564. Ministry of Information and Communication (MIC) (2014). Information and data on information and communication technology: Vietnam 2014. Hanoi: Information and Communication Publishing House. 12 of 15 TAPANAINEN ET AL.
  13. 13. Mohammadi, H. (2015). Investigating users' perspectives on e‐learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374. Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world‐wide‐web context. Information Management, 38(4), 217–230. Moore, G., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2, 192–222. Nunnally, J. C. (1978). Psychometric theory. NY: McGraw‐Hill. O'Cass, A., & Fenech, T. (2003). Web retailing adoption: Exploring the nature of Internet users web retailing behaviour. Journal of Retailing and Consumer Services, 10, 81–94. Ovcjak, B., Hericko, M., & Polanci, G. (2015). Factors impacting the acceptance of mobile data services—A systematic literature review. Computers in Human Behavior, 53, 24–47. Pagani, M. (2004). Determinants of adoption of third generation mobile multimedia services. Journal of Interactive Marketing, 18, 46–59. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e‐learning. Educational Technology & Society, 12(3), 150–162. Pinsonneaults, A., & Kraemer, K. L. (1993). Survey research methodology in management information systems: An assessment. Journal of Management Information Systems, 10(2), 75–105. Punnoose, A. C. (2012). Determinants of intention to use e‐learning based on the technology acceptance model. Journal of Information Technology Education Research., 11, 301–337. Rigopoulos, G., Psarras, J., & Askounis, D. T. (2008). A TAM model to evaluate user`s attitude towards adoption of decision support systems. Journal of Applied Sciences, 8(5), 899–902. Schepers, J., & Wetzels, M. (2007). A meta‐analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information Management, 44, 90–103. Shaikh, A. A., & Karjaluoto, H. (2015). Making the most of information technology & systems usage: A literature review, framework and future research agenda. Computers in Human Behavior, 49, 541–566. Shih, Y. Y., & Huang, S. S. (2009). The actual usage of ERP systems: An extended technology acceptance perspective. Journal of Research and Practice in Information Technology, 41(3), 263–276. Shroff, R. H., Deneen, C. C., & Ng Eugenia, M. W. (2011). Analysis of the technology acceptance model in examining students' behavioural intention to use an e‐portfolio system. Australasian Journal of Educational Technology, 27(4), 600–618. Steenkamp, J. B. E. M., & van Trijp, H. C. M. (1991). The use LISREL in validating marketing construct. International Journal of Research in Marketing, 8(4), 283–299. Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information Management, 33(1), 1–11. Tang, J. T. E., & Chiang, C. (2009). Towards an understanding of the behavioral intention to use mobile knowledge management. Wseas Transactions on Information Science, 9(6), 1601–1613. Taylor, S., & Todd, P. (1995). An integrated model of waste management behaviour: A test of household recycling and composting intentions. Environment and Behavior, 27, 603–630. Teng, W., Lu, H.‐P., & Yu, H. (2009). Exploring the mass adoption of third‐generation (3G) mobile phones inTaiwan. Telecommunications Policy, 33, 628–641. The Nielsen Company (2010a). A year of 3G in Vietnam how attracting the young can accelerate growth, websites, accessed 7 May 2016: http%3A%2F% 3Futm_source%3Dfeedburner%26amp%3Butm_medium%3Dfeed%26amp%3Butm_campaign%3DFeed%3A%2BNielsenWire%2B%2528Nielsen% 2BWire%2529 The Nielsen Company (2010b). Age matters mobile youth in Vietnam, websites, accessed 7 May 2016: 2Finsights%2Fnews%2F2010%2Fage-matters-mobile-youth-in-vietnam.html%3Futm_source%3Dfeedburner%26amp%3Butm_medium%3Dfeed% 26amp%3Butm_campaign%3DFeed%3A%2BNielsenWire%2B%2528Nielsen%2BWire%2529 Uroso, A., Soyelu, S., & Koufie, M. (2010). Task technology fit and technology acceptance models applicability to e‐tourism. Journal of Economic Development, Management, IT Finance and Marketing, 2(1), 1–32. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. Verkasalo, H., Nicolás, C. L., Castillo, F. J. M., & Bouwman, H. (2010). Analysis of users and non‐users of smartphone applications. Telematics and Informatics, 27, 242–255. Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on‐line shopping: The case for an augmented technology acceptance model. Information Management, 41(6), 747–762. Wei, T. T., Marthandan, G., Chong, A. Y. L., Ooi, K. B., & Arumugam, S. (2009). What drives Malaysian m‐commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109, 370–388. Wu, I. L., & Chuang, C. H. (2010). Examining the diffusion of electronic supply chain management with external antecedents and firm performance: A multi‐stage analysis. Decision Support Systems, 50, 103–115. TAPANAINEN ET AL. 13 of 15
  14. 14. Xie, M., Zhang, J., & Zeng, J. (2009). M‐commerce in the Period of 3G. In Proceedings of the International Conference on Management and Service Science, 2009. MASS'09 (pp. 1–4). Yang, H. D., & You, Y. (2003). It's all about attitude: Revisiting the technology acceptance model. Decision Support Systems, 38(1), 19–31. Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: A meta‐analysis of the TAM: Part 1. Journal of Modelling in Management, 2(3), 251–280. Yu, C., Yu, T. F., & Cheng, Y. S. (2012). The investigation under technology acceptance model for the factors of using e‐book among employees of Taiwanese financial and insurance company. International Journal of Digital Content Technology and Its Application, 6(3), 244–253. Zhang, L., Zhu, J., & Liu, Q. (2012). A meta‐analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28, 1902–1911. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26, 760–767. How to cite this article: TapanainenTJ, Dao KT, Nguyen HTT. Investigating adoption factors of 3G services: An empirical study in Vietnam. E J Info Sys Dev Countries. 2018;e12022. APPENDIX A THE RESEARCH INSTRUMENT Construct Code Questions References Factor Loading IT self‐efficacy SEF Compeau & Higgins, 1995; Agarwal et al., 2007) SEF1 I could learn how to use 3G services if there was no one around to tell me what to do 0.744 SEF2 I could learn how to use 3G services if I had never used something like it before 0.879 SEF3 I could learn how to use 3G services even if I had only the online manuals for reference 0.738 Perceived ease of use PEOU Davis (1993); Taylor and Todd (1995); Venkatesh and Davis (2000) PEOU1 I find 3G services to be easy to use 0.848 PEOU2 I find it easy to get 3G services to do what I want it to do 0.754 PEOU3 Using 3G services does not require a lot of my mental effort. 0.916 PEOU4 I find using 3G services is clear and understandable. 0.841 PEOU5 I find learning how to use 3G services is not too difficult 0.867 Perceived usefulness PU Davis (1993); Taylor and Todd (1995); Venkatesh (2000, 2003); Klopping & Mckinney (2004) PU1 Using 3G services improves my Internet use 0.736 PU2 3G services increases the effectiveness of my Internet use 0.746 PU3 I found 3G services valuable for me. 0.818 PU4 I found the content provided via 3G services useful for me. 0.750 PU5 I found 3G services to be convenient for people 0.737 PU6 Overall, I find 3G services useful. 0.718 Attitude to adopt ATT Davis (1993); Taylor and Todd (1995); Venkatesh (2003) ATT1 Using 3G services is a good idea. 0.823 ATT2 I like the idea of using 3G services to replace the traditional services e.g. ADSL. 0.579 ATT3 I find using 3G services is an interesting experience. 0.796 ATT4 I think using 3G services is modern. 0.760 Service quality SEQ SEQ1 3G services provider gives me prompt services Smith & Kumar (2004); Agarwal et al., 2007; Kuo et al., 2009 0.735 SEQ2 3G services provider provides services as promised 0.829 SEQ3 3G services of provider are interesting, especially the core services 0.829 SEQ4 In general, I believe provider provides 3G services as expected 0.846 (Continues) 14 of 15 TAPANAINEN ET AL.
  15. 15. (Continued) Construct Code Questions References Factor Loading Social influence SOC Taylor and Todd (1995); Venkatesh (2000) SOC1 People who influence my behavior think that I should use 3G services 0.687 SOC2 People who are important to me think that I should use 3G services 0.730 SOC3 I think using 3G services is a way to be friend with surrounding people 0.799 SOC4 I think not using 3G services is out of fashion 0.700 Intention to adopt INT Davis (1993); Venkatesh (2000) INT1 I will use 3G services when I have need for them 0.692 INT2 Assuming I use 3G services, I intend to use 3G services provided by my current operator 0.702 INT3 Given that people have access to 3G services, I predict that people would use them more 0.808 INT4 I will recommend 3G services to other people 0.780 TAPANAINEN ET AL. 15 of 15