Elucidating social networking apps decisions: Performance expectancy, effort expectancy and social influence

Pei Ying Chua (Taylor’s Business School, Taylor’s University, Subang Jaya, Malaysia)
Sajad Rezaei (Chair of Marketing and Innovation, University of Hamburg, Hamburg, Germany)
Man-Li Gu (Taylor’s Business School, Taylor’s University, Subang Jaya, Malaysia)
YokeMoi Oh (Taylor’s Business School, Taylor’s University, Subang Jaya, Malaysia)
Manimekalai Jambulingam (Taylor’s Business School, Taylor’s University, Subang Jaya, Malaysia)

Nankai Business Review International

ISSN: 2040-8749

Publication date: 4 June 2018

Abstract

Purpose

The purpose of this paper is to investigate the determinants of behavioural intention and use behaviour towards social networking apps. Exogenous latent constructs, namely, performance expectancy, effort expectancy and social influence are the key antecedents proposed based on the unified theory of acceptance and use of technology to predict the usage intention and behaviour of social networking apps (i.e. endogenous latent constructs). Experience as a moderator is the extended construct to explain social networking apps user’s behavioural intention.

Design/methodology/approach

To target young generation (Millennial), a cross-sectional data collection approach was conducted to collect data from the social networking apps users (i.e. Facebook, WhatsApp, WeChat, Twitter, Instagram, YouTube, Snapchat and others) whereby a total of 384 valid questionnaires were obtained from six universities in Malaysia. Statistical analysis using partial least squares path modelling approach and a variance-based structural equation modelling (VB-SEM) techniques is performed to analyse the measurement and structural relationship.

Findings

The findings indicate that performance expectancy, effort expectancy and social influence determine behavioural intention, and behavioural intention impacts social networking apps use behaviour. Moreover, the moderation analysis reveals that the relationship between effort expectancy and behavioural intention is moderated by experience, whereas the relationship between social influence and behavioural intention is not moderated by experience.

Originality/value

While the surge of social networking apps has gained tremendous popularity among Millennial as an attractive market segment, previous studies mainly have focussed on intention and behaviour of online users in general. Despite apps and related technologies which have opened a new era of effective communications in marketing, social networking apps usage intention and behaviour focussing on Millennial is not well understood in the current literature. This study contributes and sheds lights on the current issue of social networking apps usage intention and behaviour and looks into a key rising market segment, the Millennial users.

Keywords

Citation

Chua, P., Rezaei, S., Gu, M., Oh, Y. and Jambulingam, M. (2018), "Elucidating social networking apps decisions", Nankai Business Review International, Vol. 9 No. 2, pp. 118-142. https://doi.org/10.1108/NBRI-01-2017-0003

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

Social networking apps are getting more vital in consumer’s daily life which has led to the emergence of significant opportunities for development of the social media and mobile apps market. In addition, there is a huge potential for the future growth of mobile industry worldwide. Wong et al. (2015a) mentioned that understanding the predictor of consumer usage intention in the mobile industry is important. Likewise, the surge of social networking apps has gained tremendous popularity among Millennial as an attractive market segment. Despite apps and related technologies having opened a new era of communications, social networking apps usage intention and behaviour is not well understood in the current literature (Hew et al., 2015; Rivera et al., 2015; Brown et al., 2016), and it highlighted that behavioural intention is the dominant indicator of mobile technology usage. For instance, researchers have testified users’ behavioural intention to adopt mobile technologies in m-learning (Yang, 2013b), m-banking (Yu, 2012) and mobile advertising (Wong et al., 2015b) and retailing (Rezaei et al., 2016b). However, a study that focuses on the behavioural intention of individual towards social networking apps is still lacking (Kang, 2016); thus, this study attempts in understanding the social networking apps usage decisions.

In the modern area, mobile devices have become a necessary gadget resulting in exponential growth of mobile applications (apps) since the first iPhone was released into the market in 2007 (Carter and Yeo, 2016). Wireless mobile technologies offer a wealth of mobile apps which is currently expanding at a breakneck pace (Rezaei et al., 2016b, Amin et al., 2014). Social networking apps are also considered as one of the mobile commerce applications. Social networking apps are normally used by people to send instant messages; share information, photos, videos and news; and build a good interpersonal relationship with people within their social circles (Lin and Lu, 2015). Examples such as Facebook, Messenger, WhatsApp and WeChat have almost become the must-have application among young adults in their daily life (Hew et al., 2015). Mobile apps are programs or software that user can download, access and perform certain activities on mobile devices (Kang, 2016). In addition, mobile apps are carried by a variety of mobile devices, for instances, smartphone, tablets and personal digital assistant and available mainly on Apple IOS or Android platform. The trend of using mobile devices and mobile apps has hit the nail on the head. Therefore, this trend makes the study of mobile apps usage intention important (June, 2016). Bomhold (2016) suggested that people use mobile apps mainly for social and communication purpose (e.g. social networking apps), followed by search engines (e.g. Google) and entertainment (e.g. gaming and music).

Malaysia is one of the developing countries in Asia, where internet technological infrastructures are relatively advanced (Chong et al., 2012; Valaei et al., 2016). According to a recent report by Malaysia Digital Association (MDA, 2016), internet users account for 67.7 per cent of the country population. Mobile phone penetration rate is 144.8 per cent, meaning there are 144 mobile phones per 100 Malaysians. A survey conducted by Nielson indicates that Malaysia ranks right after Hong Kong and Singapore, in terms of smartphone ownership. When mobile users are asked what they do on the phones, the top reason is to stay connected on social media, followed by entertainment and games. Social network penetration rate reached 67.7 per cent in 2015, as reported by The Departments of Statistics in Malaysia. A large percentage of the social networking apps users come from the younger generation including teenagers, university students and young professionals who are technology savvy (Rezaei, 2018). Young adult between 18 and 24 years of age consists of 74 per cent of the total use for social networking apps (Beneke et al., 2016). Bowen and Pistilli (2016) have also discovered that the younger generations heavily rely on mobile devices and frequently browse through social networking apps like Facebook, Twitter and Instagram. Mobile phones have become an important part in the social life of people. Marketing researchers (Leong et al., 2013; Rezaei, 2018) have concluded that social influence has a vital role in determining the behavioural intention to use mobile entertainment apps based on data collected via questionnaires collected from mobile entertainment apps users in Malaysia. However, GlobalWebIndex (2015) revealed that social media penetration in Malaysia is 55 per cent of the total population which is around 17 million of users. Furthermore, mobile content downloaded in Malaysia is lower than the regional average. According to Malaysian Communications and Multimedia Commission, MCMC (2014), the downloading rate of social networking apps in Malaysia has declined 15.4 per cent from 2013 to 2014 because people tend to remain using same social networking apps without acquiring new social networking apps. Therefore, the focus of this study is to examine behavioural intention of young consumers in Malaysia who act as major social networking apps users.

This study provides social networking apps marketers a better understanding in improving social networking apps usage intention and behaviour in adopting effective marketing strategies to meet consumers’ needs. Young adults have become a potential market in social networking apps industry as statistic shows that 74 per cent of the users were Millennial between 18 and 24 years of age (GlobalWebIndex, 2015). From a theoretical standpoint, the study enhances our understanding of apps adoption behaviour and enriches the current literature. Unlike previous studies, most of the researchers presented mobile literature using technology acceptance model (TAM) such as and mobile learning (Huang et al., 2016) and telecommunication services (Rezaei et al., 2016a). A unified theory of acceptance and use of technology (UTAUT) model has been used to examine consumer behavioural intention in social networking apps. According to Venkatesh et al. (2012), UTAUT theory can explain 70 per cent of the variance of behavioural intention, whereas TAM model only shows the acceptance of 30 per cent. Therefore, drawn upon UTAUT model, this study provides valuable insight in understanding the usage intention of social networking apps.

The objectives of this study are as follows: to predict the behavioural intention and use behaviour of social networking apps using UTAUT as the theoretical foundation; and to investigate the moderating effect of experience on the relationship between effort expectancy, social influence and behavioural intention in using social networking apps among Malaysian Millennial. Accordingly, this study is structured in several sections. The first section provides a general introduction to the research background and brief overview of the study. Section 2 highlighted a comprehensive review of the literature. This section starts with grounded theories that are associated with new technology adoption and acceptance. Section 3 discusses the methodology of research methods which refers to methodology and approach used to conduct the research. In Section 4, the data collected are analysed and discussed. Finally, Section 5 summarises the study and provides implication and recommendation.

2. Theoretical background and hypotheses development

Previous studies have posited various theoretical models that focus on adoption or usage of new innovation such as diffusion of innovation (DOI), theory of planned behaviour (TPB), theory of reasoned action (TRA) (Ajzen and Fishbein, 1980), social cognitive theory (SCT) and TAM (Taylor and Todd, 1995). DOI is used to predict acceptance of technology which determined by five attributes including compatibility, complexity, observability, relative advantage and trialability (Rogers, 1995). Though, Crabbe et al. (2009) criticised that IDT is less appropriate for predicting individual adoption of technology but more preferred in measuring diffusion across national boundaries. TRA was developed by Ajzen and Fishbein (1980) indicating that a person’s action would be affected by his or her behavioural intention to perform the action, and the determinant of behavioural intention is attitude and subjective norms. Similarly, TPB extended TRA by including perceived behavioural control into the model (Ajzen, 1991), and previous researchers study consumers’ behavioural intention. Yang (2013a) used TPB and TAM to investigate consumers’ acceptance of mobile apps among college students in Southeast America. IDT was applied in past study conducted by Taylor et al. (2011) to understand the young adult mobile apps usage.

UTAUT is a theory on technology adoption drawn from the compounding factors of DOI, SCT, the TPB and the TAM that hypothesises on four key constructs influencing behavioural intention and use behaviour which are effort expectancy, performance expectancy, facilitating conditions and social influence. Table AI depicts the definitions of constructs of this study. The UTAUT incorporates the construct of self-efficacy, social influence and attitude from the TPB and SCT, compatibility from DOI and perceived ease of use from the TAM. The UTAUT extends the TAM by integrating multiple contributing factors of behavioural intention that can best explain the adoption process (Kang, 2016). The UTAUT theory is primarily used in organisational contexts (Venkatesh et al., 2012). However, studies that use UTAUT to identify mobile apps usage intention beyond the workplace are currently scarce. Hew et al. (2015) presented factors influencing consumers’ behavioural intention to adopt mobile apps by using UTAUT theory. However, facilitating conditions are not included in this proposed model because many of the researchers claimed that facilitating conditions is not a significant driver of behavioural intention when performance expectancy and effort expectancy are present (Almatari et al., 2012; Williams et al., 2015). The young generations nowadays are able to use mobile gadgets without referring heavily to the user manual. Therefore, the variable facilitating condition is not tested in this study (Figure 1).

2.1 Performance expectancy

According to Brown et al (2016), performance expectancy is the extent to which using a technology will provide benefit to consumers and lead to performance gains. Results (Al-Gahtani et al., 2007) proved that performance expectancy plays a significant role in affecting teachers’ behavioural intention to use digital learning apps, as it facilitates teachers’ job task and maximises educational effect. Chong (2013) proved that performance expectancy is the strongest determinant of behavioural intention to use mobile apps. Thus, if consumers find values and innovations from the social networking apps, they are more willing to purchase and pursue the social networking apps use. Consumers will evaluate performance expectancy of social networking apps with the respect to information exchange and communicative messages before he or she uses the apps. A research conducted by Bogart and Wichadee (2015) revealed that performance expectancy directly affects behavioural intention among LINE users in Thailand. However, the research did not consider the Malaysian market and other social networking apps such as Facebook, WhatsApp and Twitter. It may affect the accuracy of the constructs, as people in different countries have different cultural values and behaviour (Chong et al., 2012; Valaei et al., 2016).

Consumers also perceive usefulness differently towards different social networking apps (Lim et al, 2011), for instance, social networking apps must be able to provide a useful function to users in terms of information sharing, joining special interest group (Wong et al., 2014) and connection building (Lewis, 2010). Based on various supports, we can conclude that if users find that social networking apps are useful, the adoption rate of social networking apps is higher. Researchers (Wong et al, 2015a; Al-Gahtani et al., 2007) suggested that performance expectancy would significantly affect behavioural intention and use behaviour in technology adoption. Pynoo et al. (2011) used the UTAUT to identify factors influencing teachers’ usage of digital learning apps. Groß (2015) also pointed out that performance a positive impact on consumer’s usage intention and usage behaviour in a mobile shopping context. It is supported by Rivera et al. (2015) who highlighted that consumers’ decisions on whether to use mobile apps depend on whether it is useful in making a task easier to complete. Based on various supports, this study hypothesises as follows:

H1.

There is a positive relationship between performance expectancy and behavioural intention in using social networking apps.

H2.

There is a positive relationship between performance expectancy and use behaviour of social networking apps.

2.2 Effort expectancy

Effort expectancy is defined as the level of ease associated with the use of a technology (Venkatesh et al., 2012), and it is repeatedly recognised as a critical predictor of user’s behavioural intention (Wong et al., 2015a). Park and Ohm (2014) have shown that the user-friendliness of mobile apps exerts positive significant influence over the adoption of mobile apps because the lesser effort is required to use the apps. Prior studies suggested that effort expectancy plays a crucial role in determining behavioural intention to use and actual use of technology. Wu et al. (2008), however, claimed that effort expectancy poses a less significant impact on the behavioural intention of 3G mobile telecommunication user in Taiwan, as it is not adequate to attract consumers only with factors of effort expectancy alone. Zhou et al. (2010) have also pointed out the direct relationship between effort expectancy and behavioural intention by using UTAUT constructs. If a new technology requires less effort to learn and understand the way of using it, users’ adoption intention of the technology would be higher. For example, simplicity and self-efficacy of an advertising medium would determine whether an advertising firm adopts social media for advertisement (Curtis et al., 2010).

Research conducted by Im et al. (2008) shows that a simplicity-driven system with maximised efficiency is more favoured by consumers compared with technology that is complicated to use. However, the consumer may perceive differently towards effort expectancy in using social networking apps compared with m-shopping apps. However, previous researchers (Taylor and Strutton, 2010; Choi et al., 2011) argued that effort expectancy is not as critical as performance expectancy in serving as a determinant of behavioural intention, as it has a more significant effect on post-adoption usage. Empirical studies have also suggested that consumers intend to use e-learning apps if the application is easy to use (Chiu and Wang, 2008). Yang (2015) conducted a study to investigate young consumers’ behavioural intention in acquiring mobile shopping apps based on survey questionnaire collected from university students in the USA. The findings demonstrated that effort expectancy was a positive predictor of the adoption of mobile shopping apps. As the complexity of technology reduces, the intention of the individual to use the technology is likely to increase (Wong et al., 2015a). For example, Groß (2015) claims that mobile shopping apps are effortless to use if the consumer can easily obtain product information, make payment and check delivery status. However, the measuring criteria of effort expectancy in using social networking apps are different which includes the ease of reaching people and interacting with them (Lim et al., 2011). In fact, most of the empirical studies proved that ease of using social networking apps support higher intention to use. Wu et al. (2012) also revealed that use behaviour of I Pass in Taiwan MRT is positively affected by effort expectancy. Therefore, this research hypothesises as follows:

H3.

There is a positive relationship between effort expectancy and behavioural intention in using social networking apps.

H4.

There is a positive relationship between effort expectancy and use behaviour of social networking apps.

2.3 Social influence

Consumers are likely to download the same apps with reference group such as friends, family and a colleague to communicate and exchange information with them. Social influence refers to the degree to which individuals perceive that significant others, such as family and friends, believe they should use a technology (Martin and Herrero, 2012). They tend to influence the behaviour of the person to adopt or use a new system. Chong (2013) proposed that social influence plays an important role in determining users’ behavioural intention in the study of mobile-commerce. This is further supported by Chong et al. (2012) who observed that social influence critically influences apps to use intention of consumers in Malaysia especially Chinese consumers. Venkatesh et al. (2012) also described social influence as the extent to which an individual concerns about opinion and perception of others who are important to the person. Individuals who desire social acceptance likely comply with others’ expectations, and it may contribute to individual‘s behavioural intention to use the system (Gruzd et al., 2012). Sulieman et al. (2015) also validated social influence to be the stimulator of usage behaviour of electronic library services.

According to Wei et al. (2009), social influence can be classified into two categories, namely, mass media influence and interpersonal influence. A previous study (Martin and Herrero, 2012) has pointed out that individual tends to follow what reference group said and behave if those referent others have power and authority to award the desired behaviour, as well as punishing non-behaviour. For example, individual’s behaviour intention in using a technology can be affected by advertisement appeared in television, newspapers, radio and internet. These advertising mediums are categorised as mass media influence. Furthermore, Taylor et al. (2011) also testified that young adult’s intention to use mobile apps is significantly affected by peers rather than family members based one survey conducted in the US Midwest universities. As for interpersonal influence, it usually results from reference group that influence individual’s opinion, attitude and behaviour, for instance, family, friends, co-workers and much more. Moreover, social influence strongly influences consumers’ behavioural intention especially in social networking apps compared with other mobile apps (Kucukemiroglu and Kara, 2015). The young generation in Malaysia likes to use Facebook compared to many other social networking apps because their friends and family are also using it and people around think he or she should use it as well. Therefore, the following hypotheses are proposed:

H5.

There is a positive relationship between social influence and behavioural intention in using social networking apps.

H6.

There is a positive relationship between social influence and use behaviour of social networking apps.

2.4 Apps behavioural intention and use behaviour

The behavioural intention has been repeatedly adopted as an endogenous variable (Leong et al., 2013; Brown et al., 2016; Rivera et al., 2015). According to Yi et al. (2016), behavioural intention is the subjective probability of performing a behaviour which leads to usage intention. Hence, motivational factors that create the intention indicate the level of willingness of people to take the effort to engage in the behaviour. Behaviour intention is defined as the willingness and intention of an individual to perform certain behaviour (Keong et al., 2012). Use behaviour can be defined as the intensity of users in using a technology (Venkatesh et al., 2003; Awwad and Al-Majali, 2015). Use behaviour was commonly measured by the actual frequencies of technology use. Venkatesh et al. (2012) have conducted several studies regarding the use of technology using the construct “use behaviour”. Ajzen and Fishbein (1980) explained behavioural intention as an evaluation about the motivation of people to act upon or complete a particular behaviour. According to Venkatesh et al. (2003), behavioural intention can be used to predict desired behaviour or actual use of a technology. Thus, to determine whether consumers would download the social networking apps, the behavioural intention of the consumer has to first be identified. Behavioural intention is an extremely significant element of the use behaviour (Awwad and Al-Majali, 2015). Review of literature by Williams et al. (2015) has shown that numerous technology adoption models are developed to explain the use behaviour of technology. It is because use behaviour can best identify consumers’ actual usage of a particular technology.

Previous studies (Yang, 2013b; Yu, 2012; Wong et al., 2015b; Rezaei, 2018) related to mobile technologies have proved the direct association between behavioural intention and usage behaviour. In addition, behavioural intention is validated to be a strong indicator of actual consumer use behaviour of mobile banking service (Chen, 2013) and act as a proxy for measuring actual use behaviour in mobile internet services market (Awwad and Al-Majali, 2015). Hew et al. (2015) explained consumer’s behavioural intention in term of mobile apps adoption as the intention of the consumer to use or download the mobile app. Kuo and Yen (2009) considered behavioural intention as the major determinant of actual use behaviour of 3G mobile value-added services. Research conducted by Jati and Laksito (2012) shows that behavioural intention has a direct effect on use behaviour of the consumer using e-learning system. A comprehensive analysis of the literature by Salim (2012) revealed that use behaviour of social media in Egypt positively affected by behavioural intention. Therefore, this study hypothesis as follows:

H7.

There is a positive relationship between behavioural intention and use behaviour of using social networking apps.

2.5 Experience

Experience refers to an opportunity for an individual to use a particular technology with the passage of time from the initial use (June, 2016). Hence, the term “experience” in this research can be described as the period of usage experience of Malaysian Millennial towards social networking apps. The finding shows that users’ experience significantly affects the relationship between acceptance of technology and its determinants. According to Bomhold (2016), the effects of effort expectancy on behavioural intention are moderated by experience. With increasing experience, users’ behaviour in using a technology would be led by more associated cues. In this research, the experience would be operated based on years of usage ranging from one year of experience to five years of experience. Oppositely, consumers that have less experience in using social networking apps would concern more about whether the social networking apps are easy to operate. In a word, the effect of effort expectancy of a technology will reduce as experience increases. A literature review by Im et al. (2008) uses UTAUT to investigate the moderating effect of experience towards users’ acceptance of the technology. According to Carter and Yeo (2016), the experience served as a moderator between effort expectancy and behavioural intention. Almatari et al. (2012) claimed that the relationship between effort expectancy and behavioural intention to use M-learning is moderated by experience. Such effect will be stronger for those who adopt M-learning at early stages of individual experience. The comprehensive analysis on the effect of experience done by Samuel (2014) indicates that users’ experience exerts a considerable amount of influence towards the effect of effort expectancy and social influence on behavioural intention.

As a result, users would pay less attention in easiness of a technology to use. Furthermore, meta-analytic review by Taylor et al. (2011) highlighted that users’ affiliation needs are higher and more likely to be influenced by the opinion of reference group when users are a lack of experience towards social networking apps. Experience has been conceptualised in numerous prior studies as a moderator. Kim and Malhotra (2016) separated individual’s experience into five different periods. In addition, empirical evidence has demonstrated that effort expectancy construct tends to be more salient in the early stages of mobile apps adoption (Carter and Yeo, 2016). Wu et al. (2012) conducted a research on users’ behavioural intention for I Pass in Taiwan. They explained user experience as individual’s experience of using I Pass in the past time. Besides, prior research suggests that experience would moderate the effect of social influence on behavioural intention. Based on the observation of Varshney and Vetter (2012), social influence appears to be important only at the early stage of experience when the consumer initially uses the technology. With the passage of time, the role of social influence will weaken and change from significant to non-significant when the levels of user experience with the technology increases. Yang (2013b) found that social influence was less important with increasing experience. This is similar to the suggestion of Groß (2015) and Rezaei (2018) in the study of how the experience would affect the social influence in mobile shopping apps. The result indicates that the longer the usage experience of m-shopping apps, the less significant the social influence would affect behaviour intention to use. Experience reduces the effect of social influence on users’ behavioural intention. Therefore, this study hypothesises as follows:

H8.

The relationship between effort expectancy and behavioural intention is moderated by experience.

H9.

The relationship between social influence and behavioural intention is moderated by experience.

3. Method

While qualitative research focuses on the subjective assessment of behaviour which concerns in generating measurable data (UWE Flick, 2011), a mono-quantitative research is used in this study, as the survey is conducted to analyse the relationship between performance expectancy, effort expectancy, social influence, behavioural intention and use behaviour. This study adopts quantitative method instead of qualitative because respondent’s inner thought and opinions are not taken into account in this research (Saunders et al., 2012). Numerical data are generated rather than subjective information and are analysed by using statistical techniques named structural equation modelling (SEM). As such, each construct (Figure 1) was measured with multiple items which adapted from previous empirical studies as shown in Table AII. A pilot test was conducted to determine potential problem and weaknesses in the questionnaires and survey approach related to questionnaire items.

3.1 Sampling plan and data collection

Probability sampling is used when the total population is identifiable, and non-probability sampling is used if the population is not known (Saunders et al., 2012). In this research, non-probability sampling is adopted, as it is difficult to access information about the exact population of young smartphone users in Malaysia. According to Henry (1990), non-probability sampling is more suitable compared to probability sampling if the sample size is more than 50. As the respondents of the study were only Millennial born during the 1980s and 2000s, purposive sampling is advantageous to use, as it could generate findings with high consistency and accuracy. Therefore, we assume it as 1,000,000. Based on Krejcie and Morgan’s (1970) table, the minimum sample size for infinite target population is 384, which is adequate to generate reliable results. Moreover, survey questionnaires were distributed to students from few different universities in Malaysia such as Sunway University, Help University, TAR University College, Segi University and Taylor’s University. University students were selected because they are in the age range of Millennial and most are smartphone users. According to Yang (2015), university students are more open to adopting new ICTs. Online questionnaires were also spread to young adults in social-networking sites, as they are the potential users of social-networking apps. The online questionnaires were also administered in social networking sites as it helps to gain responses quickly. It is beneficial to use questionnaires because of convenience, cost saving and large coverage. Questionnaires were distributed to respondents within two weeks of time in different universities. 500 set of online and offline questionnaires were distributed among Generation Yers in universities. However, five questionnaires were not returned. A total of 384 valid questionnaires were obtained from six different universities in Malaysia. We performed t-test analysis and the results show insignificant differences between two groups of respondents (online and offline). Table I shows the summary of a demographic profile of respondents. Theretofore, cross-sectional data collection approach is conducted as data collected explains the situation at only one-point time.

3.2 Structural equation modelling

Partial least squares (PLS) path modelling approach, a SEM techniques using SmartPLS, was used to analyse the data. The advancement of SEM techniques has gone far beyond traditional multivariate technique in terms of convenience and efficiency accuracy (Malhotra et al., 2016), as this technique enables researchers to test or modify theories and models (Anderson and Gerbing, 1982). The SEM technique is an influential and robust second-generation multivariate analysis method for parameter assessment, confirmatory and hypotheses testing which integrates the first-generation procedure which includes factor analysis, regression or path analysisand discriminant analysis. This statistical modelling technique is getting popularity in social and behavioural science research (Richardson et al., 2009; Igbaria et al., 2007; Liang et al., 2010). There are two well-known techniques in the modern generation multivariate analysis (SEM) which are Maximum Likelihood (ML) estimation approach (Jöreskog, 1970; Jöreskog, 1978) or covariance-based CB-SEM and PLS variance-based approach (VB-SEM). VB-SEM and CB-SEM starts with theory or a set of theories and concept (Reinartz et al., 2009; Rezaei, 2015; Hair et al., 2011; Hair et al., 2012) which sharing the same roots and functions (Hair et al., 2012) but the decision on choosing appropriate statistical analysis techniques is vital and critical for social science researchers because incorrect choice of statistical technique can cause erroneous conclusion and inaccurate results (Rezeda, 2016; Rezaei, 2015; Ramayah et al., 2014).

PLS path modelling is a method for complex causal model, as it can simultaneously assess multiple cause-and-effect relationships among the constructs (Sarstedt, 2008) and does not need strong assumptions such as distributions, normality and sample size (Henseler et al., 2009; Sarstedt, 2008; Henseler, 2010). It has been broadly adopted by various researchers (Levin et al., 2012; Ringle et al., 2014; Henseler, 2010; Valaei et al., 2016), ML focuses on factor analysis which is often used for theory testing known as covariance-based SEM (CB-SEM). Oppositely, PLS approach is a best practice for component analysis which appropriates for explaining the complex relationship between variables but less suitable for confirmatory testing (Hair et al., 2011; Rezaei, 2018). As such, as there are a number of interactions between variables in this research, PLS is used in this study because it is capable for testing complex path models with latent variables (Chin et al., 2015). The two-step approach is used for SEM analysis to test behavioural intention and use behaviour of the consumer. The focus of measurement model assessment is to evaluate causal relations between indicators/items and validation of theoretical construct while structural model evaluates the causal relations between theoretical constructs (Anderson and Gerbing, 1982).

3.3 Common method bias

Common method bias or sometimes called common method variance (CMV) can occur in a number of different ways. For example, it may arise when a single survey method is used to collect data from target responses or when data were collected from a single source (MacKenzie and Podsakoff, 2012; Podsakoff et al., 2003; Rezaei, 2015). As such, CMV affects item reliabilities and the covariation between latent constructs (MacKenzie and Podsakoff, 2012), which in turn, influences structural relationship (Kline et al., 2000). CMV could be problematic in behaviour studies because it introduces systematic variance brought by the measurement method instead of the construct itself (Podsakoff et al., 2003; Rezaei, 2015). According to Reio, there are two ways to reduce CMV in research, namely, procedural design and statistical control. This study addressed CMV with using guideline recommend by Podsakoff et al. (2003). We avoid common scale anchors in minimising common rate effects, acquiescence biases, item characteristic effects, common scale formats, item priming effects and scale length during designing the questionnaire followed by a previous study (Rezaei, 2018). As for data analysis, three statistical techniques including Harman’s one-factor test, the partial correlation procedures and the structural model marker-variable technique were performed and the results reveal that the CMV is not a concern in this study.

4. Results

4.1 Measurement model

The reliability and validity of measurement model (The first stage) are examined using the SmartPLS software (Ringle et al., 2005) before conducting a test for structural models. The first step includes construct validity testing and discriminant validity testing for measurement model and the second step involves hypothesis testing for structural model. To examine the measurement model, a construct validity test was conducted including the assessment of outer loadings, average variance extracted (AVE), composite reliability (CR) and Cronbach’s alpha (Levin et al., 2012; Rezaei, 2015; Valaei et al., 2016). Table II reported that outer loadings of all measurement items are well above the minimum threshold value of 0.7 except SI1 and SI5 whose outer loadings are 0.695 and 0.670, respectively. As for composite reliability, CR values for all construct exceeds 0.6. It indicates the high level of internal consistency reliability of construct. As depicted in Table II, AVE values are reported to be greater than 0.5 as recommended by Rezaei (2015) and Leong et al. (2011), thus demonstrating convergent validity. According to Hair et al. (2012) reflective indicators of Cronbach’s alpha is 0.7 or greater. As the Cronbach’s alpha values for all constructs are well above 0.7, it shows that the scales were reliable.

Discriminant validity was also tested by evaluating Fornell and Larcker’s (1981) criterion and cross-loading criterion. Based on Table III, the off-diagonals values represent correlations between latent constructs. Squared correlation refers to shared values between constructs. The loadings and cross-loadings of exploratory factor analysis as reported in Table IV shows that the loadings between own construct are higher than cross-loadings. Therefore, discriminant validity is achieved.

4.2 Structural model

PLS-SEM algorithm was used to assess the significance of the structural model relationship, and PLS-SEM bootstrapping procedure (option) was used to assess the statistical significance among constructs. As shown in Table V, the path coefficients values represent the hypothesised relationship between the reflective constructs. The level and significance of the path coefficient were obtained through bootstrapping 1,000 resample and the findings of hypothesis testing are reported accordingly. Based on Table V, the standardised path coefficients, t-values and R2 values, are presented to assess the predictability of behavioural intention and use behaviour. The results reveal that 64.2 per cent of the total variation in behavioural intention to use social networking apps can be explained by using variation in performance expectancy, effort expectancy and social influence. On the other hand, there are 63.4 per cent of variation accounted for use behaviour of social networking apps which explained by performance expectancy, effort expectancy, social influence and behavioural intention. Consequently, in line with a previous study (Hew et al., 2015), it proved that the UTAUT model is applicable in determining user’s usage intention and use behaviour of social networking apps.

When the behavioural intention of social networking apps was predicted, it was found that performance expectancy (Beta = 0.279, p < 0.01), effort expectancy (Beta = 0.295, p < 0.01) and social influence (Beta = 0.349, p < 0.01) were significant predictors. Use behaviour was also significantly affected by performance expectancy (Beta = 0.116, p < 0.05), effort expectancy (Beta = 0.193, p < 0.01), social influence (Beta = 0.162, p < 0.01) and behavioural intention (Beta = 0.432, p < 0.01). Among all the exogenous latent constructs, social influence appears to be the strongest determinant of behavioural intention; the behavioural intention is the most influential factor of use behaviour because of greater beta values. All the direct structural relationships are statistically significant to predict behavioural intention and use behaviour (Table V). Hence, H1, H2, H3, H4, H5, H6 and H7 are supported.

Next, the moderating effect of experience towards effort expectancy–behavioural intention relationship and social influence–behavioural intention relationship was tested. The variance in behavioural intention explained by performance expectancy, effort expectancy and social influence increased from 64.2 per cent to 66 per cent after including the moderating effect, a beta value of effort expectancy*EXP and social influence*EXP are also evaluated to predict behavioural intention. The higher the proportion of explained variation in a model, the better the model fits the data; thus, the result in this study yields variance explained of over 60 per cent in behavioural intention. H8, which proposes that experience moderates the effect of effort expectancy on behavioural intention, was not supported. It is because the p-value is not less than 0.05 which indicates that the moderating effect of experience on effort expectancy–behavioural intention was not significant. Furthermore, H9, which predicted that experience moderate the effect of social influence on behavioural intention, is supported. It is because p-value is less than 0.01 and beta value shows that low level of experience enhances the effect of social influence on behavioural intention (Beta = 0.760; p < 0.01), which is corresponding to H9.

5. Discussion

In this study, UTAUT model was used as an underlying framework to assess the relationship between consumer’s behavioural intention in using social networking apps and its key constructs including performance expectancy, effort expectancy and social influence to predict the actual use behaviour. Furthermore, the experience was incorporated in this study as a moderator to examine whether there is a moderating effect on the relationship between effort expectancy and behavioural intention and behavioural intention. Based on the results presented in the previous section, all relationships were testified to have a significant direct relationship with behavioural intention and use behaviour. Among all the three exogenous latent constructs, social influence was found to be the most important attributes of behavioural intention. The finding was inconsistent with of the past research studies (Wong et al., 2015a, Salim, 2012; Sulieman et al., 2015) which suggested that social influence had no significant effect on behavioural intention because what other people think is considered as an external factor that is less important compared to internal product factor of usefulness and user-friendliness. However, the significance of social influence on behavioural intention was similar to findings of Taylor et al. (2011) who also conducted research on young adult’s mobile apps adoption. Chong et al (2012) also similarly suggest that social influence plays a significant role in m-commerce adoption. A possible explanation for this is that young adults rely more on the opinion of the reference group, especially the peers, to decide in adopting mobile apps, as both the studies focus on the Millennial. Age group of 16 to 26 is the most likely to be influenced stage in terms of buying behaviour compared other age group. It is because they have strong desire to adopt norms and behaviour of other who they aspire to associate with. In addition, Malaysia is considered to be a collectivist culture, where members of the society are more inter-dependent and relationship-oriented (Valaei et al., 2016). This implies a strong tendency to conform to group norm or social influence (Hofstede and Hofstede, 1997). Therefore, the younger generation tends to consider the opinion of important others first when downloading an app rather than focussing on the complexity of use and usefulness of the app.

Effort expectancy was rated as the second important determinant of behavioural intention. It is supported by Tan et al. (2012) who claimed that effort expectancy has a strong impact on behavioural intention because apps that require significant efforts to use would discourage consumers from adopting it. Besides, performance expectancy was discovered as the least influencing factors that affect Millennial intention to use social networking apps. It is opposing to result of Almatari et al. (2012) who confirmed that performance expectancy has the strongest predictability towards behavioural intention compared to effort expectancy and social influence. Wong et al. (2015a) argued that consumer would not adopt a technology that is deemed not useful, no matter how user-friendliness the technology is and how strong it is recommended by others. On the other hand, the behavioural intention was rated as the dominant predictor driving consumers’ use behaviour of social networking apps. It is closely aligned with most of the previous studies (Wu et al., 2012; Williams et al., 2015; Salim, 2012). For instances, empirical studies have concluded that behavioural intention of users acts as a definite indicator of actual usage for electronic tickets (Wu et al., 2012), electronic library services and electronic learning platform (Samuel, 2014). Results also indicated that performance expectancy, effort expectancy and social influence exert a positive significant influence towards use behaviour of the young consumer for social networking apps.

Finally, the results suggest that consumers experience moderates the relationship between social influence and behavioural intention but app users experience do not moderate the relationship between effort expectancy and behavioural intention. Consumer’s level of experience in using social networking apps exerts a moderating effect between social influence and behavioural intention. It is supported by Taylor et al. (2011) who obtained a similar result and proved that users who are a lack of experience in using apps tend to be influenced more by the opinion of others on the decision to acquire apps compared with experienced users. However, the moderating effect of experience on effort expectancy–behavioural intention relationship is consistent with past study of Venkatesh et al. (2012). The effect of effort expectancy on behavioural intention is not moderated by experience, such that the effect is supposed to be stronger for those with limited experience. It indicated that the more experienced the users are, the more attention they pay to the simplicity of use. While it has been argued that when consumers start using social networking apps, they are experienced and knowledgeable in evaluating the user-friendliness of apps and seek apps that are convenient to use, but the results of this study show that the adopted and experienced consumers are less influenced by ease of use. Therefore, Millennial experience is an important moderator that influence social factors and behavioural intention relationships.

5.1 Implications

From a theoretical standpoint, this study enriches the existing research gap of UTAUT model by extending behavioural intention to the use behaviour and integrating experience as a moderator to better explain user’s adoption of social networking apps among Millennial. Overall, the outcome of the result was consistent with what is suggested in UTAUT model, as performance expectancy, effort expectancy and social influence is validated to have a significant relationship with behavioural intention and use. Consumer’s use behaviour is also significantly affected by behavioural intention, performance expectancy, effort expectancy and social influence. Functional and contextual factors are incorporated in the UTAUT to improve the explanatory power in user’s technology adoption. Thus, it is reasonable to assume UTAUT model to be superior compared with prior theoretical models in explaining technology acceptance and use (Martin and Herrero, 2012). Besides, Persaud and Azhar (2012) presented factors influencing behavioural intention of consumers to adopt mobile apps in Canada using TRA. Additionally, UTAUT is proposed by Venkatesh et al. (2003) describing intention to adopt a technology. Experience is confirmed to be a significant moderator, as it moderates the effect of social influence on behavioural intention. This could provide valuable insights towards current literature by serving as an extended construct of former UTAUT model. As there is a dearth of relevant research, this study set a foundation for future investigations. The new integrated model is believed to contribute to the knowledge bank and narrow down the research gap in investigating factors affecting social networking apps.

Practically, understanding young adult use behaviour of social networking apps in Malaysia and other emerging economy is essential. Malaysian government aspires to develop and further strengthen its digital economy. As documented by the Malaysian Communications and Multimedia Commission (MCMC, 2014), smartphone users in the country are dominated by young consumers, who heavily rely on social media to stay connected with their peers. The findings of the study help to inform industry manufacturers to further develop social networking apps to meet consumers’ needs and strengthen their competitive positions in the industry. As the behavioural intention is the strongest determinant of consumer’s use behaviour, app developers and app marketers should focus on improving elements that drive behaviour intention which includes performance expectancy, effort expectancy, and social influence to enhance the actual usage of social networking apps. Thus, marketing managers should be aware of elements that encourage a young adult to adopt social networking apps. Moreover, based on findings of the current study, social influence has been confirmed as the most significant driver among young Malaysians. Therefore, app developers should always consider the element of social needs or group norm and allocate greatest amount resources to improve the social influence of social networking apps. Marketing managers could create more social influence by advertising online and spread it to the social media to stimulate e-WOM (Rivera et al., 2015). According to Gruzd et al. (2012), attractive words and meaningful messages delivered from advertisement would likely to encourage people to share it with their friends surrounding. As experience significantly moderates the effect of social influence on behavioural intention, apps developers and marketing managers are advised to increase social influence mainly on first time user. It is because they lack experience in using the apps and more likely to be influenced by words and suggestions of the reference group. For instances, developers can offer some benefits to existing users by giving extra points and marks when they invite their friends who have never used the apps to download it. Thus, it can help to attract more new users with limited experience when the apps are recommended by their friends.

In addition, effort expectancy was regarded as the second important direct predictor of behavioural intention in using social networking apps. Therefore, the effort expectancy aspect should be taken seriously to stimulate the growth of consumers’ behavioural intention and use behaviour. Although young users are seen as relatively more competent in technology adoption, developers should continue to enhance the user-friendliness of social networking apps by providing simple guidelines for beginner to use when the apps are first download. Marketing managers and apps developers are also recommended to design a functional button of apps and place close to user’s finger movement range to ensure convenience of user interface (Hew et al., 2015). For example, developers can do an observation on consumers’ daily use of apps to investigate what functions can be integrated to enable users to make specific responses based on a different situation such as aeroplane mode, silent mode, reject calls, voice record and much more. Finally, in terms of performance expectancy, managers should also put emphasis on increasing it, as result proved that performance expectancy would stimulate consumer’s behavioural intention; thus, managers are suggested to create more apps features that are useful for consumers.

5.2 Limitations and future research direction

The chosen research methodology consists of several limitations. Because of time constraint and budget limitation, cross-sectional study was conducted instead of longitudinal study. However, the cross-sectional study failed to capture the change of consumers’ behaviour across time and environment. Therefore, future research should consider longitudinal method; thus, changing behaviour would be considered because of technological change, trends and various factors. In addition, this research focussed on the Millennial segment which might be inadequate to generalise the whole population of social networking apps user in Malaysia. Future study is suggested to cover a wider group of respondent including different age group. Finally, the quantitative study might ignore the context of a phenomenon. Qualitative research provides detailed and rich information that covers subjective feelings; however, results generated from quantitative research can be generalised to the population. Thus, future research can be improved by including both quantitative and qualitative research approach, for instance, both survey and interview can be conducted to enhance validity and reliability of findings.

Figures

Theoretical research framework

Figure 1.

Theoretical research framework

Summary of demographic profile of respondents

Measure Items Frequency (%)
Gender Male 183 47.7
Female 201 52.3
Age 18-21 316 82.3
22-25 56 14.6
26-29 7 1.8
30-35 5 1.3
Education level SPM 28 7.3
Foundation 57 14.8
Diploma 61 15.9
Degree 227 59.1
Master 4 1
Others 7 1.8
University Taylor’s University 101 26.3
Sunway University 56 14.6
Monash University 58 15.1
Inti University 64 16.7
SEGI University 31 8.1
TAR University College 21 5.5
Others 53 13.8
Income level < RM 1000 250 65.1
RM 1,001-RM 2,000 76 19.8
RM 2,001-RM 3,000 25 6.5
RM 3,001-RM 4,000 20 5.2
RM 4,001-RM 5,000 7 1.8
> RM 5,000 6 1.6
Experience Less than 1 year 34 8.9
1-3 years 111 28.9
4-6 years 102 26.6
> 6 years 137 35.7
Daily usage Less than 1 h 6 1.6
1-3 h 123 32
4-6 h 134 34.9
7-9 h 69 18
> 9 h 52 13.5
Social network apps Facebook 146 38
WhatsApp 142 37
WeChat 20 5.2
Twitter 11 2.9
Instagram 13 3.4
YouTube 28 7.3
Snapchat 17 4.4
Others 7 1.8

Construct validity

Construct Items Item loadings AVE* Composite reliability (CR)** Cronbach’s alpha
Performance expectancy (PE) PE1 0.740 0.576 0.871 0.815
PE2 0.765      
  PE3 0.817      
  PE4 0.747      
  PE5 0.721      
Effort expectancy (EE) EE1 0.735 0.559 0.864 0.803
EE2 0.747      
  EE3 0.747      
  EE4 0.729      
  EE5 0.781      
Social influence (SI) SI1 0.695 0.534 0.851 0.781
SI2 0.790      
  SI3 0.739      
  SI4 0.755      
  SI5 0.670      
Behavioural intention (BI) BI1 0.731 0.594 0.854 0.772
BI2 0.795      
  BI3 0.784      
  BI4 0.772      
Use behaviour (UB) UB1 0.767 0.643 0.843 0.721
UB2 0.848      
  UB3 0.788      
Notes:
*

Average variance extracted (AVE) = (summation of the square of the factor loadings)/{(summation of the square of the factor loadings) + (summation of the error variances)};

**

Composite reliability (CR) = (square of the summation of the factor loadings)/{(square of the square of the factor loadings) + (summation of the error variances)}

Discriminant validity – Fornell–Larcker criterion

Research constructs PE EE SI BI UB
PE 0.576*
EE 0.228 0.559
SI 0.306 0.254 0.534
BI 0.384 0.340 0.223 0.594
UB 0.312 0.212 0.250 0.373 0.643
Notes:

The off-diagonal values in the above matrix are the square correlations between the latent constructs and diagonal are AVEs;

Performance expectancy (PE); Effort expectancy (EE); Social influence (SI); Behavioural intention (BI); Use behaviour (UB)

Discriminant validity – loading and cross-loading criterion

Items PE EE SI BI UB
PE1 0.740 0.503 0.516 0.526 0.490
PE2 0.765 0.433 0.531 0.528 0.483
PE3 0.817 0.462 0.570 0.558 0.538
PE4 0.747 0.354 0.529 0.507 0.436
PE5 0.721 0.413 0.549 0.518 0.481
EE1 0.378 0.735 0.440 0.464 0.468
EE2 0.429 0.747 0.457 0.469 0.482
EE3 0.443 0.747 0.419 0.492 0.484
EE4 0.500 0.729 0.497 0.549 0.488
EE5 0.383 0.781 0.407 0.496 0.475
SI1 0.512 0.439 0.695 0.499 0.461
SI2 0.561 0.462 0.790 0.575 0.509
SI3 0.507 0.423 0.739 0.503 0.502
SI4 0.546 0.468 0.755 0.542 0.531
SI5 0.468 0.378 0.670 0.520 0.444
BI1 0.530 0.551 0.531 0.731 0.502
BI2 0.591 0.534 0.587 0.795 0.597
BI3 0.520 0.466 0.562 0.784 0.582
BI4 0.499 0.487 0.546 0.772 0.552
UB1 0.505 0.507 0.550 0.575 0.767
UB2 0.566 0.546 0.573 0.448 0.848
UB3 0.468 0.489 0.489 0.596 0.788
Notes:

Bold values are loadings for each item that are above the recommended value of 0.5; and an item’s loadings in its own variable are higher than all of its cross-loadings with other variable

Result of hypothesis testing and structural relationships

Hypothesis Path Path
coefficient
SD Standard
error
t-statistics Decision
H1 PE → BI 0.279 0.059 0.059 4.688** Supported
H2 PE → UB 0.116 0.053 0.053 2.200* Supported
H3 EE → BI 0.295 0.050 0.050 5.866** Supported
H4 EE → UB 0.193 0.049 0.049 3.906** Supported
H5 SI → BI 0.349 0.057 0.057 6.099** Supported
H6 SI → UB 0.162 0.061 0.061 2.626** Supported
H7 BI →UB 0.432 0.064 0.064 6.731** Supported
H8 EE → EXP → BI 0.536 0.313 0.313 1.713 Not supported
H9 SI → EXP → BI 0.760 0.287 0.287 2.652** Supported
Notes:

t = 1.96;

*

p < 0.05; t = 2.58;

**

p < 0.01 (Hair et al., 2011);

Performance expectancy (PE); Effort expectancy (EE); Social influence (SI); Behavioural intention (BI); Use behaviour (UB)

Definitions of the UTAUT constructs

Elements Type of variable Definition of variable Source
Performance expectancy (PE) Exogenous latent construct The degree to which using a technology will provide benefits to consumers in performing certain activities Venkatesh et al. (2012)
The extent to which an individual perceives that using a system will enhance his or her productivity, and thus lead to performance gains Brown et al. (2016)
Effort expectancy (EE) Exogenous latent construct The degree of ease associated with consumers’ use of technology Venkatesh et al. (2012)
Social influence (SI) Exogenous latent construct The extent to which consumers perceive that important others (e.g. family and friends) believe they should use a particular technology
Behavioural intention (BI) Endogenous and exogenous latent construct An individual subject probability of performing a behaviour Yi et al. (2016)
A user group willing to use information technologies for their tasks Keong et al. (2012)
Use behaviour (UB) Endogenous latent construct The intensity of users in using a technology Venkatesh et al. (2003)

Measurement of exogenous and endogenous latent constructs

Construct Measurement items Sources
Performance expectancy PE1: I find social networking apps useful in my daily life (Al-Gahtani et al., 2007)
PE2: Using social networking apps helps me accomplish things more quickly
PE3: Using social networking apps increases my productivity
PE4: Using social networking apps enhances my effectiveness on the task
Effort expectancy EE1: Learning how to use social networking apps is easy for me
EE2: My interaction with social networking apps is clear and understandable (Hew et al., 2015)
EE3: I find social networking apps easy to use
EE4: It is easy for me to become skilful at using social networking apps
EE5: Using the social networking apps is simple to me
Social influence SI1: People who are important to me think that I should use social networking apps (Martin and Herrero, 2012)
SI2: People who influence my behaviour think that I should use social networking apps
SI3: People whose opinions that I value prefer that I use social networking apps
SI4: People around me consider it is appropriate to use social networking apps
Apps behavioural intention BI1: I intend to continue using social networking apps in the future (Venkatesh et al., 2012)
BI2: I will always try to use social networking apps in my daily life
BI3: I plan to continue to use social networking apps frequently
BI4: I will continue to use social networking apps on a regular basis
Apps usage behaviour UB1: I have used social networking apps a lot in the past one month (Awwad and Al-Majali, 2015)
UB2: I have been using social networking apps regularly to communicate with people
UB3: I have been using social networking apps in my daily life
Note:
*

All items are measured using 5-point Likert scale from “1 = Strongly disagree” to “5 = Strongly agree”

Appendix

Table AI

Table AII

References

Ajzen, I. (1991), “The theory of planned behaviour”, Organisational Behaviour and Human Decision Processes, Vol. 50 No. 2, pp. 179-211.

Ajzen, I. and Fishbein, M. (1980), Understanding Attitudes and Predicting Social Behaviour, Prentice-Hall, Englewood Cliffs, NJ.

Al-Gahtani, S.S., Hubona, G.S. and Wang, J. (2007), “Information technology (IT) in Saudi Arabia: culture and the acceptance and use of IT”, Information and Management [Online], Vol. 44 No. 8, pp. 681-691.

Almatari, A.Y., Iahad, N.A. and Balaid, A.S. (2012), “Factors influencing students’ intention to use M-learning”, Journal of Information Systems Research and Innovation, Vol. 7 No. 5, pp. 515-543.

Amin, M., Rezaei, S. and Abolghasemi, M. (2014), “User satisfaction with mobile websites: the impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust”, Nankai Business Review International, Vol. 5 No. 3, pp. 258 -274.

Anderson, J.C. and Gerbing, D.W. (1982), “Some methods for respecifying measurement models to obtain unidimensional construct measurement”, Journal of Marketing Research, Vol. 19 No. 4, pp. 453-460.

Awwad, M.S. and Al-Majali, S.M. (2015), “Electronic library services acceptance and use”, The Electronic Library [Online], Vol. 33 No. 6, pp. 1100-1120.

Beneke, J., Scheffer, M.K. and Du, W. (2016), “Beyond price – an exploration into the factors that drive young adults to purchase online”, International Journal of Marketing Studies, Vol. 2 No. 2, pp. 212-222.

Bogart, W.V.D. and Wichadee, S. (2015), “Exploring students’ intention to use LINE for academic purposes based on technology acceptance model”, The International Review of Research in Open and Distributed Learning, Vol. 16 No. 3, pp. 65-85.

Bomhold, C.R. (2016), “Educational use of smart phone technology”, Electronic Library and Information Systems, Vol. 47 No. 4, pp. 424-436.

Bowen, K. and Pistilli, M.D. (2016), Student Preferences for Mobile App Usage, EDUCAUSE Center for Analysis and Research, Louisville, CO [online].

Brown, S.A., Dennis, A.R. and Venkatesh, V. (2016), “Predicting collaboration technology use: integrating technology adoption and collaboration research”, Journal of Management Information Systems, Vol. 27 No. 2, pp. 9-53.

Carter, S. and Yeo, C.M. (2016), “Mobile apps usage by Malaysian business undergraduates and postgraduates: implications for consumer behaviour theory and marketing practice”, Internet Research, Vol. 26 No. 3, pp. 733-757.

Chen, C.S. (2013), “Perceived risk, usage frequency of mobile banking services”, Managing Service Quality: An International Journal, Vol. 23 No. 5, pp. 410-436.

Chin, W.W., Marcolin, B.L. and Newsted, P.R. (2015), “Partial least squares latent variable modelling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study”, Information Systems Research, Vol. 14 No. 2, pp. 189-217.

Chiu, M. and Wang, E.T.G. (2008), “Understanding web-based learning continuance intention: the role of subjective task value”, Information and Management, Vol. 45 No. 3, pp. 194-201.

Choi, H., Kim, Y.C. and Kim, J.W. (2011), “Driving factors of post adoption behavior in mobile data services”, Journal of Business Research, Vol. 64 No. 11, pp. 1212-1217.

Chong, A.Y.L. (2013), “Predicting m-commerce adoption determinants: a neural network approach”, Expert Systems with Applications, Vol. 40 No. 2, pp. 523-530.

Chong, A.Y.L., Chan, F.T.S. and Ooi, K.B. (2012), “Predicting consumer decisions to adopt mobile commerce: cross country empirical examination between China and Malaysia”, Decision Support Systems, Vol. 53 No. 1, pp. 34-43.

Crabbe, M., Standing, C., Standing, S. and Karjaluoto, H. (2009), “An adoption model for mobile banking in Ghana”, International Journal of Mobile Communications, Vol. 7 No. 5, pp. 515-543.

Curtis, L., Edwards, C., Fraser, K.L., Gudelsky, S., Holmquist, J., Thornton, K. and Sweetser, K.D. (2010), “Adoption of social media for public relations by nonprofit organizations”, Public Relations Review, Vol. 36 No. 1, pp. 90-92.

Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.

GlobalWebIndex (2015), “Malaysia social media statistics 2015”, available at: http://blog.malaysia-asia.my/2015/03/malaysia-social-media-statistics-2015.html (accessed 18 June 2016).

Groß, M. (2015), “Mobile shopping: a classification framework and literature review”, International Journal of Retail & Distribution Management, Vol. 43 No. 3, pp. 221-241.

Gruzd, A., Staves, K. and Wilk, A. (2012), “Connected scholars: examining the role of social media in research practices of faculty using the UTAUT model”, Computers in Human Behavior, Vol. 28 No. 6, pp. 2340-2350.

Hair, J.F., Ringle, C.M. and Sarstedt, M. (2011), “PLS-SEM: indeed a silver bullet”, Journal of Marketing Theory and Practice, Vol. 19 No. 2, pp. 139-151.

Hair, J.F., Sarstedt, M., Ringle, C.M. and Mena, J.A. (2012), “An assessment of the use of partial least squares structural equation modelling in marketing research”, Journal of the Academy of Marketing Science, Vol. 40 No. 3, pp. 414-433.

Henry, G.T. (1990), Practical Sampling, Sage, Newbury Park, CA.

Henseler, J. (2010), “On the convergence of the partial least squares path modelling”, Computational Statistics, Vol. 28 No. 2, pp. 565-580.

Henseler, J., Ringle, C.M. and Sinkovics, R.R. (2009), “The use of partial least squares path modeling in international marketing”, in Rudolf, R.S. and Pervez, N.G. (Eds), New Challenges to International Marketing (Advances in International Marketing), Emerald Group Publishing Limited, Vol. 20, pp. 277-319.

Hew, J.J., Lee, V.H., Ooi, K.B. and Wei, J. (2015), “What catalyses mobile apps usage intention: an empirical analysis”, Industrial Management & Data Systems, Vol. 115 No. 7, pp. 1269-1291.

Hofstede, G. and Hofstede, G.J. (1997), Cultures and Organizations: Software of the Mind, McGraw-Hill, London.

Huang, J.H., Yu, R.L. and Shu, T.C. (2016), “Elucidating user behavior of mobile learning: a perspective of the extended technology acceptance model”, The Electronic Library [Online], Vol. 25 No. 5, pp. 585-598.

Igbaria, M., Zinatelli, N., Cragg, P. and Cavaye, A.L.M. (2007), “Personal computing acceptance factors in small firms: a structural equation model”, MIS Quarterly, Vol. 21 No. 3, pp. 279-305.

Im, I., Kim, Y. and Han, Y.J. (2008), “The effects of perceived risk and technology type on users’ acceptance of technologies”, Information and Management, Vol. 45 No. 1, pp. 1-9.

Jati, N.J. and Laksito, H. (2012), “Analisis faktor-faktor yang mempengaruhi minat pemanfaatan dan penggunaan sistem E-learning”, Media Riset Akuntansi, Auditing Dan Informasi, Vol. 12 No. 2, pp. 22-56.

Jöreskog, K.G. (1970), “A general method for analysis of covariance structures”, Biometrika, Vol. 57 No. 2, pp. 239-251.

Jöreskog, K.G. (1978), “Structural analysis of covariance and correlation matrices”, Psychometrika, Vol. 43 No. 4, pp. 443-477.

June, L. (2016), “Are personal innovativeness and social influence critical to continue with mobile commerce?”, Internet Research, Vol. 24 No. 2, pp. 134-159.

Kang, S. (2016), “Factors influencing intention of mobile application use”, International Journal of Mobile Communications, Vol. 12 No. 4.

Keong, M.L., Thurasamy, R., Sherah, K. and Chiun, L.M. (2012), “Explaining intention to use an enterprise resource planning (ERP) system: an extention of the UTAUT model”, Business Strategy Series, Vol. 13 No. 4, pp. 108-120.

Kim, S.S. and Malhotra, N.K. (2016), “A longitudinal model of continued IS use: an integrative view of four mechanisms underlying post-adoption phenomena”, Management Science, Vol. 51 No. 5, pp. 741-755.

Kline, T.J.B., Sulsky, L.M. and Rever-Moriyama, S.D. (2000), “Common method variance and specification errors: a practical approach to detection”, The Journal of Psychology, Vol. 134 No. 4, pp. 401-421.

Krejcie, R.V. and Morgan, D.W. (1970), “Determining sample size for research activities”, Educational and Psychological Measurement, Vol. 30 No. 3, pp. 607-610.

Kucukemiroglu, S. and Kara, A. (2015), “Online word-of-mouth communication on social networking sites”, International Journal of Commerce and Management, Vol. 25 No. 1, pp. 2-20.

Kuo, Y.F. and Yen, S.N. (2009), “Towards an understanding of the behavioral intention to use 3G mobile value-added services”, Computers in Human Behavior, Vol. 25 No. 1, pp. 103-110.

Leong, L.Y., Ooi, K.B., Chong, A.Y.L. and Lin, B. (2011), “Influence of individual characteristics, perceived usefulness and ease of use on mobile entertainment adoption”, International Journal of Mobile Communications, Vol. 9 No. 4, pp. 359-382.

Leong, L.Y., Ooi, K.B., Chong, A.Y.L. and Lin, B. (2013), “Modelling the stimulators of the behavioral intention to use mobile entertainment: does gender really matter?”, Computers in Human Behaviour, Vol. 29 No. 5, pp. 2109-2121.

Levin, M.A., Hansen, J.M. and Laverie, D.A. (2012), “Toward understanding new sales employees participation in marketing-related technology: motivation, voluntariness, and past performance”, Journal of Personal Selling and Sales Management, Vol. 32 No. 3, pp. 379-393.

Lewis, B.K. (2010), “Social media and strategic communication: attitudes and perceptions among college students”, Public Relations Journal, Vol. 4 No. 3, pp. 1-23.

Liang, H., Saraf, N., Hu, Q. and Xue, Y. (2010), “Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management”, MIS Quarterly, Vol. 31 No. 1, pp. 59-87.

Lim, S., Xue, L., Yen, C.C., Chang, L., Chan, H.C., Tai, B.C., Duh, H.B. and Choolani, M. (2011), “A study on Singaporean women’s acceptance of using mobile phones to seek health information”, International Journal of Medical Informatics, Vol. 80 No. 12, pp. 189-202.

Lin, K.Y. and Lu, H.P. (2015), “Predicting mobile social network acceptance based on mobile value and social influence”, Internet Research, Vol. 25 No. 1, pp. 107-130.

MacKenzie, S.B. and Podsakoff, P.M. (2012), “Common method bias in marketing: causes, mechanisms, and procedural remedies”, Journal of Retailing, Vol. 88 No. 4, pp. 542-555.

Malhotra, N.K., Kim, S.S. and Patil, A. (2016), “Common method variance in IS research: a comparison of alternative approaches and a reanalysis of past research”, Management Science, Vol. 52 No. 12, pp. 1865-1883.

Martin, H.S. and Herrero, A. (2012), “Influence of the user’s psychological factors on the online purchase intention in rural tourism: integrating innovativeness to the UTAUT framework”, Tourism Management, [online], Vol. 33 No. 2, pp. 341-350.

MCMC (2014), “Hand Phone Users Survey 2014”, available at: www.skmm.gov.my/skmmgovmy/media/General/pdf/MCMC-Hand-Phone-User19112015.pdf (accessed 18 June 2016).

MDA (2016), “2016 Malaysia digital landscape: exploring the digital landscape in Malaysia – boosting growth for a digital economy”, available at www.malaysiandigitalassociation.org.my/wp-content/uploads/2016/08/Malaysia-Digital-Landscape-August-2016.pdf (accessed 27 April 2017).

Park, E. and Ohm, J. (2014), “Factors influencing users’ employment of mobile map services”, Telematics and Informatics, Vol. 31 No. 2, pp. 253-265.

Persaud, A. and Azhar, I. (2012), “Innovative mobile marketing via smartphones: are consumers ready?”, Marketing Intelligence & Planning, Vol. 30 No. 4, pp. 418-443.

Podsakoff, P.M., MacKenzie, S.B., Jeong-Yeon, L. and Podsakoff, N.P. (2003), “Common method biases in behavioral research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903.

Pynoo, B., Devolder, P., Tondeur, J., van Braak, J., Duyck, W. and Duyck, P. (2011), “Predicting secondary school teachers’ acceptance and use of a digital learning environment: a crosssectional study”, Computers in Human Behavior, Vol. 27 No. 1, pp. 568-575.

Ramayah, T., Ignatius, J., Leen, J.Y.A. and Chiun, L.M. (2014), Discriminant analysis, Probability. Statistics: Adidact Introduction. p. 384.

Reinartz, W.J., Haenlein, M. and Henseler, J. (2009), “An empirical comparison of the efficacy of covariance-based and variance-based SEM”, International Journal of Research in Marketing, Vol. 26 No. 4, pp. 332-344.

Rezaei, S. (2015), “Segmenting consumer decision-making styles (CDMS) toward marketing practice: a Partial Least Squares (PLS) path modeling approach”, Journal of Retailing and Consumer Services, Vol. 22, pp. 1-15.

Rezaei, S. (2018), “Dragging market mavens to promote apps repatronage intention: the forgotten market segment”, Journal of Promotion Management, Vol. 24 No. 4 pp. 511-532.

Rezaei, S., Amin, M., Moghaddam, M. and Mohamed, N. (2016a), “3G post adoption users experience with telecommunications services”, Nankai Business Review International, Vol. 7 No. 3, pp. 361-394.

Rezaei, S., Shahijan, M.K., Amin, M. and Ismail, W.K.W. (2016b), “Determinants of app stores continuance behavior: a PLS path modelling approach”, Journal of Internet Commerce, Vol. 15 No. 4, pp. 408-440.

Rezeda, M. (2016), “Selection of appropriate statistical methods for research results processing”, Mathematics Education, Vol. 11 No. 1, pp. 303-315.

Richardson, H.A., Simmering, M.J. and Sturman, M.C. (2009), “A tale of three perspectives: examining post hoc statistical techniques for detection and correction of common method variance”, Organizational Research Methods, Vol. 12 No. 4, pp. 762-800.

Ringle, C.M., Wende, S. and Becker, J.-M. (2014), SmartPLS 3, SmartPLS, Hamburg.

Ringle, C.M., Wende, S. and Will, A. (2005), SmartPLS, 2.0 (Beta) Ed, SmartPLS.de, Hamburg.

Rivera, M., Gregory, A. and Cobos, L. (2015), “Mobile application for the timeshare industry: the influence of technology experience, usefulness, and attitude on behavioral intentions”, Journal of Hospitality and Tourism Technology, Vol. 6 No. 3, pp. 242-257.

Rogers, E.M. (1995), Diffusion of Innovations, 4 ed., The Free Press, New York, NY.

Salim, B. (2012), “An application of UTAUT model for acceptance of social media in Egypt: a statistical study”, International Journal of Information Science, Vol. 2 No. 6, pp. 92-105.

Samuel, N.B. (2014), “Using the UTAUT model to analyze students’ ICT adoption”, International Journal of Education and Development Using Information and Communication Technology (IJEDICT), Vol. 10 No. 3, pp. 75-86.

Sarstedt, M. (2008), “A review of recent approaches for capturing heterogeneity in partial least squares path modelling”, Journal of Modelling in Management, Vol. 3 No. 2, pp. 140-161.

Saunders, M., Lewis, P. and Thornhill, A. (2012), Research Methods for Business Students, 5 ed., Pearson Education Limited, Harlow.

Sulieman, M., Sawsan, A. and Al-Majali, M. (2015), “Electronic library services acceptance and use”, The Electronic Library, Vol. 33 No. 6, pp. 1100-1120.

Tan, G.W.H., Sim, J.J., Ooi, K.B. and Phusavat, K. (2012), “Determinants of mobile learning adoption: an empirical analysis”, Journal of Computer Information Systems, pp. 82-91.

Taylor, D.G. and Strutton, D. (2010), “Has e-marketing come of age? Modelling historical influences on post-adoption era internet consumer behaviors”, Journal of Business Research, Vol. 63 Nos 9/10, pp. 950-956.

Taylor, D.G., Voelker, T.A. and Pentina, I. (2011), “Mobile application adoption by young adults: a social network perspective”, International Journal of Mobile Marketing, Vol. 6 No. 2, pp. 60-70.

Taylor, S. and Todd, P.A. (1995), “Understanding information technology usage: a test of competing models”, Information Systems Research, Vol. 6 No. 2, pp. 144-176.

UWE Flick (2011), Introducing Research Methodology, SAGE Publications, Germany.

Valaei, N., Rezaei, S., Ismail, W.K.W. and Oh, Y.M. (2016), “The effect of culture on attitude towards online advertising and online brands: applying Hofstede’s cultural factors to internet marketing”, International Journal of Internet Marketing and Advertising, Vol. 10 No. 4, pp. 270-301.

Varshney, U. and Vetter, R. (2012), “Mobile commerce: framework, applications and networking support”, Mobile Networks and Applications, Vol. 7 No. 3, pp. 185-198.

Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003), “User acceptance of information technology: toward a unified view”, MIS Quarterly, Vol. 27 No. 3, pp. 425-478.

Venkatesh, V., Thong, J.Y.L. and Xu, X. (2012), “Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology”, MIS Quarterly, Vol. 36 No. 1, pp. 157-178.

Vinzi, V.E., Trinchera, L. and Amato, S. (2010), “PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement”, Handbook of Partial Least Squares, Springer, pp. 47-82.

Wei, T.T., Marthandan, G., Chong, A.Y.L., Ooi, K.B. and Arumugam, S. (2009), “What drives Malaysian m-commerce adoption? An empirical analysis”, Industrial Management & Data Systems, Vol. 109 No. 3, pp. 370-388.

Williams, M.D., Rana, N.P. and Dwivedi, Y.K. (2015), “The unified theory of acceptance and use of technology (UTAUT): a literature review”, Journal of Enterprise Information Management, Vol. 28 No. 3, pp. 443-488.

Wong, C.H., Tan, G.W.H., Loke, S.P. and Ooi, K.B. (2014), “Mobile TV: a new form of entertainment?”, Industrial Management & Data Systems, Vol. 114 No. 7, pp. 1050-1067.

Wong, C.H., Tan, G.W.H., Loke, S.P. and Ooi, K.B. (2015a), “Adoption of mobile social networking sites for learning”, Online Information Review, Vol. 39 No. 6, pp. 762-778.

Wong, C.H., Tan, G.W.H., Tan, B.I. and Ooi, K.B. (2015b), “Mobile advertising: the changing landscape of the advertising industry”, Telematics and Informatics, Vol. 32 No. 4, pp. 720-734.

Wu, M.Y., Yu, P.Y. and Weng, Y.C. (2012), “A study on user behavior for I pass by UTAUT: using Taiwan’s MRT as an example”, Asia Pacific Management Review, Vol. 17 No. 1, pp. 91-111.

Wu, Y.L., Tao, Y.H. and Yang, P.C. (2008), “The use of unified theory of acceptance and use of technology to confer the behavioural model of 3G mobile telecommunication users”, Journal of Statistics and Management Systems, Vol. 11 No. 5, pp. 919-949.

Yang, H. (2013a), “Bon appétit for apps: young American consumers’ acceptance of mobile applications”, Journal of Computer Information Systems, Vol. 53 No. 3, pp. 85-96.

Yang, K. (2015), “Determinants of US consumer mobile shopping services adoption: implications for designing mobile shopping services”, Journal of Consumer Marketing, Vol. 27 No. 3, pp. 262-270.

Yang, S. (2013b), “Understanding undergraduate students’ adoption of mobile learning model: a perspective of the extended UTAUT2”, Journal of Convergence Information Technology, Vol. 8 No. 10, pp. 969-979.

Yi, M., Jackson, J., Park, J. and Probst, J. (2016), “Understanding information technology acceptance by individual professionals: toward an integrative view”, Information and Management, Vol. 43 No. 3, pp. 350-363.

Yu, C.S. (2012), “Factors affecting individuals to adopt mobile banking: empirical evidence from the UTAUT model”, Journal of Electronic Commerce Research, Vol. 13 No. 2, pp. 104-121.

Zhou, T., Lu, Y. and Wang, B. (2010), “Integrating TTF and UTAUT to explain mobile banking user adoption”, Computers in Human Behaviour, Vol. 26 No. 4, pp. 760-767.

Further reading

Cooper, D.R. (2008), Business Research Methods, 10th ed., McGraw-Hill Higher Education, London.

Davis, F.D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, Vol. 13 No. 3, pp. 319-340.

Supplementary materials

NBRI_9_2.pdf (12.2 MB)

Corresponding author

Sajad Rezaei can be contacted at: mmg.sajad@gmail.com