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42 Novel Approach: Testing and Computing Periodicity of Continuous Time Signal

The Studies of Unified Theory of Acceptance and Use of Technology (UTAUT) in M-Commerce Context

Shoaib Imtiaz*

Department of Digital Convergence Business Yeungnam University, South Korea

Email: [email protected]

AbstractThe acceptance and use of information technology (IT) and information system (IS)

innovations have been a key apprehension for research and practice. Since the emergence of Unified Theory of Acceptance and Use of Technology (UTAUT), it has been used by a large number of studies for the adoption and acceptance of different technologies (e.g. m-commerce), and hence it seems to have turned into a prevalent theoretical choice in the field of information technology (IT)/information system (IS) adoption and diffusion. With the progress of this theory and finding the future research directions, this paper presents the critical review on the studies of the UTAUT and UTAUT2 model by focusing on the findings of basic constructs of these two models to predict Behavioral Intention (BI). Results from previous studies of UTAUT and UTAUT2 reveals that constructs of these two models contributed to Behavioral Intention to adopt and use m-commerce and its applications.

Keywords: UTAUT, UTAUT2, M-commerce

1. IntroductionUnderstanding individual acceptance and use of information technology (IT) is

considered as one of the most mature streams of research in the field of information systems (IS) (Venkatesh, Davis, & Morris, 2007). Use and adoption-related issues have been continually scrutinized because of two reasons: new technologies are developing constantly and finding their place both in society and organization, and the information system (IS) failure rate kept on being high (Dwivedi et al., 2015). Due to consistent effort to understand issues related to adoption and diffusion, numerous theories have been developed, adapted or adopted in information system (IS) literature to describe acceptance and use of technology in several contexts (Morosan, 2014; Williams, Rana, & Dwivedi, 2015). To select an appropriate theory for a new study, IS researchers face many challenges. Venkatesh et al (2003) noticed that some existing theories used similar variables with different names. Considering that (Venkatesh et al., 2003) synthesized Unified Theory of Acceptance and Use of Technology (UTAUT) by thorough evaluation and integrating variables from eight conspicuous theories/models (generally in an organizational context) used in user technology acceptance (Williams, Dwivedi, Lal, & Schwarz, 2009; Williams, Rana, & Dwivedi, 2015; Williams, Rana, Dwivedi, & Lal, 2011). The Unified Theory of Acceptance and Use of Technology was proposed and approved with a specific end goal to give a unified theoretical choice to facilitate research on information technology (IT)/information system (IS) adoption and diffusion. The theory proposes four variables: performance expectancy, effort expectancy, facilitating conditions and social influence has indirect relation to use behavior through behavioral intention. The theory also provides the four moderators namely: age, gender, experience and voluntariness of use to hypothesized the relationship among constructs (Venkatesh et al., 2003; .

Manuscript Received: 30 April 2018 / Revised: 28 May 2018 / Accepted: 30 May 2018Corresponding Author: Shoaib ImtiazAuthor’s affiliation: Department of Digital Convergence Business Yeungnam University, South KoreaE-mail: [email protected]

ISSN: 2466-0094Copyright ⓒ IJICTDC

International Journal of Information Communication Technology and Digital ConvergenceVol. 3, No. 1, June 2018, pp. 42-56

IJICTDC2018 43

Venkatesh et al (2012) extended UTAUT in the consumer’s context with the addition of three more constructs namely: price value, hedonic motivation, and habit. The extended version is known as Unified Theory of Acceptance and Use of Technology 2 (UTUAT2). The moderating variables: age, gender, and experience were included in a new version of this theory but the voluntariness of use which was included in UTAUT dropped in UTAUT2. Since the development of UTAUT, this model has been used several times with different theories and also extended with the addition of new constructs to assess a range of technologies in various contexts with the involvement of both organizational and non-organizational contexts (. In recent years, the UTAUT2 is also used in many studies in different contexts of technology adoption.

To comprehend the mobile commerce (m-commerce), UTAUT is prevalent model commonly used by researchers. M-commerce is defined as the selling and buying of products and services via wireless mobile devices (Qingfei, Shaobo, & Gang, 2008). Consumer’s acceptance is the base for the success of m-commerce. Unlike e-commerce, m-commerce customers utilize mobile information systems (IS) and related applications. According to (Hino, 2015) the UTAUT approach is an adaptable technique to conceptualize mobile commerce acceptance, as it considers the joining of extra factors into the conceptual framework of technology acceptance. Various theories have been applied to m-commerce in various studies but there were no comprehensive review of UTAUT for m-commerce. This paper will provide a critical review of the previous studies of UTAUT in the context of m-commerce.

Given the above discussions, the paper is organized as follows. Section 2 will provide an overview of the UTAUT and an overview of its extended version UTUAT2. Section 3 will discuss the previous studies of UTAUT for m-commerce. Section 4 will present the previous studies of UTAUT2 for m-commerce and conclusion in Section 5.

2. Unified Theory of Acceptance and Use of Technology (UTAUT)2.1 Overview of UTAUT

Both practitioners and academicians are keen on understanding the fundamental factors of technology acceptance behavior. Venkatesh et al (2003) developed UTAUT model after a far-reaching evaluation of eight conspicuous models used in user technology acceptance.

Theories/Models AuthorsThe theory of reasoned action (TRA) (Fishbein & Ajzen, 1975)

The technology acceptance model (TAM) (Davis, 1989)

Social cognitive theory (SCT) (Bandura, 1986)

Innovation diffusion theory (IDT) (Moore & Benbasat, 1991)

The theory of planned behavior (TPB) (Ajzen, 1991)

The model of PC utilization (MPCU) (Thompson, Higgins, & Howell, 1991)

The motivational model (MM) (Davis, Bagozzi, & Warshaw, 1992)

A model combining TAM and TPB (C-TAM-TPB)

(Taylor & Todd, 1995a) and (Taylor & Todd, 1995b)

Table 1. Theories and models used in user acceptance model

These theories were tried on the adoption and diffusion of new products and services. In addition, few of these model specifically emphasis on adoption of ICTs. Several studies effectively used these theories and models for the adoption and diffusion of technology within both the IS (information system) field and other categories like social psychology, management, and marketing. The inspiration to characterize and approve


the UTAUT depended on the contention that a large number of the constructs of existing theories are comparative in nature; in this way, it was logical to integrate them to make the incorporated theoretical base (Venkatesh et al., 2003). Therefore, the developers of UTAUT expected that future investigations would require not to seek, gather and coordinate constructs from various diverse models however rather could simply apply the UTAUT to understand several issues identified with IS/IT adoption and diffusion.

The UTAUT approach is especially useful for two reasons. First, trying to describe the user acceptance of IT, the UTAUT approach combines a variety of hypothetically and empirically pertinent variables from different models (Venkatesh et al., 2003). Second, the UTAUT approach is additionally an adaptable technique to conceptualize mobile commerce acceptance, as it considers the joining of extra factors into the conceptual framework of technology acceptance (Hino, 2015). Venkatesh et al (2003) proposed a new model shown in Figure 1 named as “Unified Theory of Acceptance and Use of Technology (UTAUT)” after a thorough evaluation of eight models particularly emphasizing on ICTs.

Figure 1. UTAUT model (Venkatesh et al., 2003)

The main constructs of this model were effort expectancy (EE), performance expectancy (PE), facilitating conditions (FC) and social influence (SI). These four variables (EE, PE, FC, and SI) has a direct relation with intention and other variables like gender, age, being voluntary and experience has a direct effect on use (Mutlu & Der, 2017). The basic constructs of UTAUT model act as determinants of intention and usage behavior. The definitions of these constructs are given in Table 2.

Constructs DefinitionsPerformance Expectancy “The degree to which an individual believes that using the system

will help him/her to attain gains in job performance” (Venkatesh et al., 2003).

Effort Expectancy “The degree of ease associated with the use of system” (Venkatesh et al., 2003).

Social Influence “The degree to which an individual perceives that important others believe they should use the new system” (Venkatesh et al., 2003).

Facilitating Conditions “The degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system”

(Venkatesh et al., 2003).

Table 2. Definitions of constructs in UTAUT

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2.2 Overview of UTAUT2

Although UTAUT gives a decent and detailed explanation for the acceptance and use of technology but this model has some limitations (Negahban & Chung, 2014). In this manner (Venkatesh et al., 2012) proposed Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) in 2012 shown in Figure 2. It is the broadened type of UTAUT. UTAUT2 added the new factors to the model; its investigation unit mainly emphases on user acceptance and use. This model has seven variables (i) performance expectancy (PE), (ii) social influence (SI), (iii) effort expectancy (EE), (iv) facilitating conditions (FC), (v) price value (PV), (vi) hedonic motivation (HM), (vii) habit (HB) (Venkatesh et al., 2012). Three new variables were added as Hedonic motivation (HM) because of its consideration as a key indicator in substantially prior research and their significance detailed therein (Venkatesh et al., 2003). Price value (PV) because the price is an important factor in consumer context and for the services use, Habit (HB) discussed in past studies that demonstrated it to be a basic factor in the context of technology use (Kim & Malhotra, 2005; Limayem, Hirt, & Cheung, 2007).

Figure 2. UTAUT2 model (Venkatesh et al., 2012)

As compare to UTUAT, UTAUT2 have shown a considerable change in variance. As variance explained in BI improved from 56% to 74% and variance explained in technology use improved from 40% to 52% (Chang, 2012). Age, gender, and experience included as a moderating variable that has an influence on other variables but voluntariness which was included in UTAUT dropped in this model. This model has a direct relationship of facilitating condition on behavioral intention and habit also has a direct relation on behavioral intention and use behavior. Apart from these changes, the impact of behavioral intention on use behavior is additionally moderated by experience. Definitions of new constructs in the UTAUT2 model are given in Table 3.

Constructs DefinitionsHedonic

Motivation“The pleasure or enjoyment derived from using a technology” (Dodds, Monroe,

and Grewal, 1991)

Price Value “Consumers cognitive tradeoff between perceived benefits of the applications and the monetary cost of using them” (Dodds, Monroe, and Grewal, 1991).

Habit “The extent to which people tend to perform behaviors automatically because of learning” (Limayem, Hirt, & Cheung, 2007).

Table 3. Definitions of constructs in UTUAT2


The designer of UTAUT-2 stated that Model-2 is better than Model-1, as the level of difference is clarified in both use and intention. Few studies found on the use of UTAUT2 as compare to UTAUT because UTAUT has seen many times in literature for the acceptance of technologies. Venkatesh et al (2012) proposed that future research should apply UTAUT2 in various nations, crosswise over various age gatherings, and on various technologies. It was likewise prescribed that future research should endeavor to recognize other pertinent elements to extend UTAUT2.

The UTAUT2 model has turned into the benchmark in technology acceptance (AlAwadhi & Morris, 2008).

3. Previous study of UTAUT for M-commerceSeveral studies on m-commerce by various authors, were based on Unified Theory

of Acceptance and Use of Technology (UTAUT) model. Blaise, Halloran, & Muchnick (2018) made a study to predict m-commerce purchase intention based on UTAUT model. Findings indicate that FC, SI, EE, and PE were significant for the prediction of m-commerce purchase intention. Qingfei, Shaobo, & Gang (2008) depicted a theoretical framework to understand an acceptance and usage of m-commerce in China based on Unified Theory of Acceptance and Use of Technology.

Chong (2013) examined the study on predicting the factors of m-commerce adoption by using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. All the factors like FC, EE, PE, and SI were found significant for m-commerce adoption. Chou, Li, & Ho (2018) examined a study to identify the factors having a significant effect on intention to adopt m-commerce in Taiwan. Findings show that EE, SI, PE, and trust had a significant relation on behavioral intention to adopt m-commerce.

Lai & Lai (2013) developed a study on acceptance of m-commerce in Macau using UTAUT model and results showed that effort expectancy was not significant. Teo et al (2015) discussed m-payment while using the UTAUT model. Findings show that FC and EE had a significant effect on behavioral intention. Perceived transaction convenience (PTC) had a positive relationship with EE and PE. Furthermore, perceived transaction speed (PTS) had a positive relationship with EE and BI (behavioral intention).

Carlsson et al (2006) examined the study on adoption of mobile devices/services by testing the applicability of UTAUT model. Results reveal that PE and EE had a substantial effect on BI but SI and FC had no effect to adopt m-services. Moreover, attitudes toward m-services influenced BI. Koivumäki, Ristola, & Kesti (2008) developed a study for exploring the perception concerning mobile devices by testing UTAUT model.

Wang & Yi (2012) developed a study for the acceptance of mobile payment by using the UTAUT model. Findings reveal that PE and EE found as a key factor to adopt m-payment. SI and PR (perceived risk) had no significant relation on BI. Moreover, FC had no relation to user behavior. Park, Yang, & Lehto (2007) explored a study on adoption of mobile phone technologies based on UTAUT.

Escobar-Rodríguez & Carvajal-Trujillo (2014) conducted a study for buying tickets online based on UTAUT model. All the variables like performance and effort expectancies, trust, cost saving, habit, hedonic motivation, ease of use and social factors found as a key factor in purchasing. Among these variables, Ease of use and habit found as most important factors to online purchase intentions. Shin (2009) explored a study on the acceptance of mobile wallet using the UTAUT. Findings show that trust and perceive security found as a key factor to influence user’s attitude and intention towards mobile wallet acceptance.

Tai (2013) conducted a study on using mobile stock trading based on UTAUT model. The analysis shows that social influence, performance expectancy and effort expectancy found as positive determinants, but functional risk, economic risk, and security risk

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found as negative determinants on intention to adopt mobile stock trading. Zhou, Lu & Wang (2010) examined study based on UTAUT for the adoption of mobile banking services. Findings reveal that facilitating conditions, task-technology fit, performance expectancy and social influence had a substantial effect on user adoption. Moreover, task-technology fit also had a positive relationship on performance expectancy. A summary of the previous study for m-commerce based on Unified Theory of Acceptance and Use of Technology (UTAUT) is provided in Table 4.

Author(s) and Year Study Objectives Affecting FactorsBlaise, Halloran, &

Muchnick (2018)Predicting m-commerce

purchase intention· Facilitating conditions· Social Influence· Effort expectancy· Performance expectancy

Qingfei, Shaobo, & Gang (2008)

Understanding an acceptance and usage of

m-commerce

· Trust and privacy· Utility expectancy· Effort expectancy· Social factors· Convenience and cost

Chong (2013) Predicting the factors of m-commerce adoption

· Facilitating conditions· Effort expectancy· Performance expectancy· Social influence

Chou, Li, & Ho (2018) Identifying the factors affecting the intention to

adopt m-commerce

· Effort expectancy· Social influence· Performance Expectancy· Trust

Lai & Lai (2013) Understanding the acceptance of m-commerce

· Performance expectancy· Social influence· Facilitating conditions· Privacy concern· Effort expectancy

Teo et al (2015) Understanding the acceptance of m-payment

· Facilitating conditions· Effort expectancy

Carlsson et al (2006) Identify factors affecting the adoption of mobile

devices/services

· Performance expectancy· Effort expectancy· Social influence· Facilitating conditions

Koivumäki, Ristola, & Kesti (2008)

Exploring the perception concerning mobile devices

· Performance expectancy· Effort expectancy· Social influence· Facilitating conditions

Wang & Yi (2012) Investigating the acceptance of mobile

payment

· Performance expectancy· Effort expectancy· Social influence· Perceived Risk

Park, Yang, & Lehto (2007)

To examine the adoption of mobile phone

technologies

· Performance Expectation · Effort Expectation · Social Influence · Facilitating Condition · Attitude on using Mobile Technology

Table 4. Summary of the previous study of UTAUT for M-commerce


Many researchers examined the studies emphasizing UTAUT in the context of the customer, involving the adoption of given below ICTs by users in Table 5.

Applications AuthorsInternet banking (AbuShanab & Pearson, 2007; Im, Hong & Kang, 2011;

Riffai, Grant & Edgar, 2012; Al-Qeisi et al., 2014; Martins, Oliveira & Popovič, 2014)

Mobile banking (Zhou, Lu & Wang, 2010; Baptista & Oliveira, 2015)

Question answer services (Deng, Liu, & Qi, 2011)

Location-based services (Xu & Gupta, 2009)

Online ticket Escobar-Rodríguez & Carvajal-Trujillo, (2014)

Mobile payment (Abrahão, Moriguchi, & Andrade, 2016)

Online purchase intention in regard to rural tourism

(San Martín & Herrero, 2012)

Table 5. Applications of UTAUT in the consumer context

There are further more studies based on UTAUT model for the adoption of technologies in organizational context such as computer use based on UTAUT (Al-Gahtani, Hubona, & Wang, 2007); e-government (Gupta, Dasgupta, & Gupta, 2008); Health information systems (Alapetite, Andersen, & Hertzum, 2009; Kijsanayotin, Pannarunothai, & Speedie, 2009; Duyck et al., 2010); organizational social networks (Curtis et al., 2010; Sykes, Vankatesh, & Gosain, 2009); human resource databases (Eckhardt, Laumer, & Weitzel, 2009) and virtual learning (van Raaij & Schepers, 2008; .

4. Previous study of UTAUT2 for M-CommerceAs we have seen that lot of studies have done based on UTAUT for the adoption

of technologies like m-commerce. After UTAUT, various studies are also done by

Author(s) and Year Study Objectives Affecting FactorsEscobar-Rodríguez &

Carvajal-Trujillo (2014)Exploring the study for

buying tickets online· Performance expectancy· Effort expectancy· Trust· Cost saving· Habit· Hedonic motivation· Ease of use · Social factors

Shin (2009) Examining the study on the acceptance of mobile

wallet

· Trust· Perceived Security

Tai (2013) Investigating the study of using mobile stock trading

· Social influence· Performance expectancy· Effort expectancy· Functional risk· Economic risk · Security risk

Zhou, Lu & Wang (2010) Exploring the study on adoption of mobile

banking services

· Facilitating conditions· Task-technology fit· Performance expectancy· Social influence

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various authors based on Extended Unified Theory of Acceptance and Use of Technology (UTAUT2).

Verkijika (2018) explored the factors affecting the adoption of mobile commerce applications in Cameroon based on revised Unified Theory of Acceptance and Use of Technology (UTAUT2). Findings reveal that hedonic motivations, facilitating conditions, perceived trust, social influence and perceived risk had a significant effect on intention to use mobile commerce applications. Hew, Lee, Ooi, & Wei (2015) found that PE, FC, EE, HB, and HM were strongly related to the intention to use mobile applications based on adapted UTAUT2.

Mousa Jaradat & Al Rababaa (2013) used modified UTAUT to investigate determinants affecting the intention to accept m-commerce by Jordanian consumers. The study showed that PE and EE had a significant effect on acceptance but SI had no significant effect on the acceptance of m-commerce. Slade, Williams, & Dwivdei (2013) developed one study on mobile payment made to investigate the consumer adoption of m-payments based on UTAUT2 by adding two new constructs like trust and perceived risk to increase the pertinence of UTAUT2 in the context of mobile payment.

Tak & Panwar (2017) conducted a study using the UTAUT2 model to understand the factors of mobile application based shopping with special context to India. This study deals with the intention and usage behavior of consumers by selecting the revised Unified theory of acceptance and use of technology (UTAUT2) to describe mobile app-based shopping. Slade, Williams, & Dwivedi (2014) conducted another study using UTAUT2 for the adoption of mobile payments and extended this theory with the addition of some new constructs like innovativeness, trust, self-efficacy and perceived risk.

Vinnik (2017) made a study on adoption of mobile applications. He found that PE, PV, and HB had a significant effect on intention to adopt mobile applications based on UTAUT2 model. Tang et al (2014) made a study to explore the key factors for the adoption of the mobile wallet in Malaysia by using the UTAUT2 model. Results showed that FC, HB, HM, EE, and PE had a significant relationship on intention but SI and PV had no significant relation on intention to adopt mobile wallet in Malaysia.

Alkhunaizan & Love (2012) explored a study on determinants affecting the acceptance of mobile commerce in Saudi Arabia by using UTAUT2 model. Findings showed that all the variables were significant to intention and use. Morosan & DeFranco (2016) developed a study for NFC mobile payments in hotels based on UTAUT2 model, PE had a strong relationship on intention but HM, HB and SI had a weak relation on intention to use NFC mobile payments in hotels.

Megadewandanu, Suyoto, & Pranowo (2016) conducted research for mobile wallet adoption in Indonesia by applying UTAUT2 and habit found as an important factor affecting the adoption of the mobile wallet. Bendary & Al-Sahouly (2018) developed a study to enhance the perception of m-commerce users by examining the factors dealing with UTAUT2 extension. Three factors like social influence, hedonic motivation and convenience were highly important and had a strong effect.

Kit, Ni, Badri, & Yee (2014) applied UTAUT2 model to find the key factors affecting the intention to adopt mobile applications in Malaysia and found HB, EE, PE and HM significant and FC, PV and SI found insignificant to adopt mobile applications. Li & Yang (2016) conducted a study to find the determinants affecting the tourist adoption behavior towards mobile e-commerce while traveling. Results showed among all variables, habit, facilitating conditions, engagement intention and expectancy variable were considered significant.

Mahfuz, Khanam, & Mutharasu (2016) developed a study by extending the UTAUT2 model with website quality and cultural dimensions to know the factors affecting the mobile banking services adoption in Bangladesh and found FC, PV and PE had significant influence but EE had no significant influence on adoption. A summary of


the previous study for m-commerce based on Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) is provided in Table 6.

Author(s) and Year Study Objectives Affecting FactorsVerkijika (2018) To explore the factors affecting the

adoption of mobile commerce applications· Hedonic motivation· Facilitating conditions· Perceived trust · Social influence · Perceived risk

Hew, Lee, Ooi, & Wei (2015)

To find the factors affecting the intention to use mobile applications

· Performance expectancy· Facilitating conditions· Effort expectancy · Habit · Hedonic motivation

Mousa Jaradat & Al Rababaa (2013)

To investigate determinants affecting the intention to accept m-commerce

· Performance expectancy· Effort expectancy· Social influence

Slade, Williams, & Dwivdei (2013)

To investigate the consumer adoption of m-payments

· Perceived risk · Trust

Tak & Panwar (2017) To understand the factors of mobile application based shopping

· Performance expectancy· Facilitating conditions· Effort expectancy · Habit · Hedonic motivation· Social influence· Price value

Slade, Williams, & Dwivedi (2014)

Exploring determinants affecting the adoption of mobile payments

· Perceived risk · Trust · Trialability · Innovativeness · Self-efficacy · Price value · Social influence · Effort expectancy · Performance expectancy· Hedonic motivation · Facilitating conditions · Habit

Vinnik (2017) Investigating the study on intention to adopt mobile applications

· Performance expectancy· Price value· Habit

Tang et al (2014) To explore the key factors for the adoption of mobile wallet

· Facilitating conditions· Habit· Hedonic motivation· Effort expectancy· Performance expectancy· Social influence· Price value

Table 6. Summary of the previous study of UTAUT2 for M-commerce

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As UTAUT2 developed by (Venkatesh et al., 2012) UTAUT2 is more appropriate because this model has been experimentally tried and asserted to have a more noteworthy explanatory power in anticipating behavioral intention than the previous technology adoption models toward innovative advancements (Venkatesh et al., 2012; Zhou, 2012).

5. ConclusionMany studies referred to the originating article of UTAUT. This gives off an

impression of increasing trend of using external factors and external theories along with UTAUT. UTAUT gives a valuable technique by which to assess the potential for accomplishment of new technology development and helps to explore the factors liable to impact the adoption of technology. This paper provides the previous studies on UTAUT and UTAUT2 for m-commerce and for the m-commerce services adoption like location-based services, mobile applications, mobile payments, and mobile banking by different researchers. UTAUT2 is a broad theory, a large number of studies used external theories together with UTAUT2 in their research. There has been a

Author(s) and Year Study Objectives Affecting FactorsAlkhunaizan & Love

(2012)Examining a study on determinants affecting the acceptance of mobile

commerce

· Trust · Cost · Performance expectancy · Effort expectancy· Social influence · Facilitating conditions

Morosan & DeFranco (2016)

Exploring a study for NFC mobile payments in hotels

· Performance expectancy· Hedonic motivation· Habit· Social influence

Megadewandanu, Suyoto, & Pranowo (2016)

Investigating a study on mobile wallet adoption

· Performance expectancy· Facilitating conditions· Effort expectancy · Price value· Hedonic motivation· Social influence· Habit

Bendary & Al-Sahouly (2018)

To enhance the perception of m-commerce users by examining the

factors

· Social influence · Hedonic motivation · Convenience

Kit, Ni, Badri, & Yee (2014)

To find the key factors affecting the intention to adopt mobile applications

· Habit· Effort expectancy· Performance expectancy · Hedonic motivation· Facilitating conditions· Price value· Social influence

Li & Yang (2016) to find the determinants affecting the tourist adoption behavior towards mobile

e-commerce

· Habit· Facilitating conditions· Engagement intention · Expectancy variable

Mahfuz, Khanam, & Mutharasu (2016)

To know the factors affecting the mobile banking services adoption

· Facilitating conditions· Price value· Performance expectancy · Effort expectancy


proportionate increase in Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) theory usage among articles which referred it in contrast with UTAUT. In the last few years, many studies significantly increased in utilizing UTAUT2 in a different context of technology adoption. This increasing number of studies is an evidence to the fact that this theory is prevalent among researchers particularly to examine customer centered issues. UTAUT2 is picking up a momentum in terms of its utilization for the issues related to scrutinize IS/IT adoption and diffusion. Variety of studies have done on m-commerce adoption based on a variety of theories. UTAUT and UTAUT2 have been used many times for m-commerce by researchers. Both UTAUT and UTAUT2 have tried and connected to various technology contexts which give a vigorous platform to additionally predict and investigate the intention to adopt M-commerce. Some researchers used the same constructs used in UTAUT and UTAUT2 but some researchers added few new constructs in models to predict the intention to adopt m-commerce and its applications.

This study analyzed works which utilized UTAUT and UTAUT2 by concentrating on the findings of the basic constructs of UTAUT and UTAUT2 to predict the Behavioral Intentions (BI). Findings of the previous studies show that the constructs of the UTAUT and UTAUT2 contributed to BI to accept and use m-commerce and its applications. Performance Expectancy (PE) appeared to be the most significant factor among all other factors. The immediate implications are for researchers who wish to examine behavioral intentions to adopt m-commerce using UTAUT or UTAUT2 models. They will be able to consider what factors to examine and future research to conduct and what theoretical models to use for their research. This study has some limitations, one of them is the number of papers examined. It would be more accurate to increase the number of paper examined. The other limitation is the ability to draw a statistical conclusion each research examined.

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