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Mobile Information Systems
Volume 2019, Article ID 1490617, 18 pages
https://doi.org/10.1155/2019/1490617
Research Article

Understanding Mobile Tourism Shopping in Pakistan: An Integrating Framework of Innovation Diffusion Theory and Technology Acceptance Model

1School of Management, Hefei University of Technology, Hefei 230009, Anhui, China
2Institute of Management Sciences, University of Science and Technology, Bannu, Khyber Pakhtunkhwa, Pakistan
3Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing, China

Correspondence should be addressed to Ikram Ullah Khan; moc.liamg@unb.marki

Received 25 March 2019; Revised 12 May 2019; Accepted 3 June 2019; Published 24 June 2019

Academic Editor: Yuh-Shyan Chen

Copyright © 2019 Dongxiao Gu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Consumer adoption of mobile-based tourism shopping is an emerging but overlooked area in tourism research. Given the paybacks and potential scope of this new channel, this study attempts to bridge the gap by proposing a multimediation model investigating mobile tourism shopping (MTS) in a developing country, Pakistan. In particular, we applied structural equation modeling through partial-least-squares structural equation modeling (PLS-SEM) on 396 responses collected from mobile respondents who recently purchased tourism products using a mobile device(s). It was discovered that social presence, directly and indirectly, influences tourist intentions towards MTS. The results further show that the tourists’ perception of compatibility and relative advantages of MTS have insignificant influence on their intention to accept a mobile device(s) for tourism shopping. The findings and implications of the study furnish new vistas to research discourse and managerial significance. Economically, this research contributes to knowledge that could increase income and create jobs in the host country.

1. Introduction

Mobile commerce is today one of the most critical and dynamic online businesses [1]. Over the past few decades, due to limited technological capabilities, mobile phones have been primarily used for texting and calling [2]. The sweeping advances in mobile networks, such as fourth generation networks (4G) and mobile communication (M-devices), have advanced the mobile phone platform from simple communication to a multifunction mechanism that supports collaborative communication [3]. Mobile apps have provided viable alternative for buying tourism-related products and that is why this channel presents a better choice for tourists [2]. The technology continues to advance, and numerous business opportunities arise from a mobile perspective based on mobile technology development such as online shopping [4, 5], m-banking usage [6, 7], mobile learning [8], mobile services [9], mobile advertising [10], mobile healthcare [11], and mobile payments [12]. The m-internet technology can be used for mobile shopping and availing many other similar services [13]. Consumers can open/browse any website and purchase any product using a mobile device instantly, anytime and anywhere because it is the informal and convenient source [14].

Consumers can use a mobile device to perform various actions such as searching for online products, comparing the price of products, and purchasing products [15]. Werthner and Klein [16] clarified that, due to the benefit of saving time, consumers do not have to wait in queues while buying online products, an advantage compared to real-world shopping (i.e., at “bricks-and-mortar” stores). In recent years, as the world has been adopting mobile-oriented transactions, the rapid advancement in the mobile technology has affected the tourism industry and represents enormous potential for tourists, promoting tourism products from the mobile technology viewpoint [2, 17]. Hew et al. [18] found that buying travel products was no longer limited to buying online, at real-world stores, or through personal computers but is also quickly moving to mobile devices. In the early 2016, Makki et al. [19] mentioned that the mobile technology would take over and make life more relaxed. Consumers will prefer to use their mobile device(s) for online shopping instead of going out to the physical market. Schaal [20] mentioned that over one-third of the Orbitz Hotel reservations worldwide made by using m-devices. According to Johnsen [21], 68% of users use their mobile devices to buy online and also search for store locations (62%), 58% use these devices to check and compare prices, and 50% of users use these devices to get product information. Mobile technology is more convenient and easier for purchasing tourist products as compared to the traditional methods of purchasing such products at physical stores. The Travelport Commerce platform has predicted that, in the coming next three years, more than 70% of travel transactions will arise because of mobile devices [22]. A report of the eMarketer (2017) declared that travel bookings using m-devices are estimated to reach a value of 108.75 billion US dollars by 2021, and thus, the m-devices will cover more than half of the entire amount of such sales 5 (eMarketer, 2017). These predictions indicate the highly rising trend of tourists to use mobile devices to buy tourism products/services, and thus, it justifies research queries in tourism research [23]. Therefore, we believe that the importance of the mobile device in today’s era of mobile tourism shopping (MTS) can have the potential to affect future practices and research endeavors.

Some studies have examined the use of mobile device(s) for buying tourism-related products, mainly using information technology (IT) or information systems (IS) [2, 24]. However, the use of mobile devices for shopping for tourism-related services/products in developing countries is poorly understood. Therefore, the present study adopts a unique way to examine the influencing factors affecting consumers/tourists’ intentions to purchase online tourism products using a mobile device(s) by employing an integrative model. The proposed model was founded on the integration of two theoretical models: innovation diffusion theory introduced in [25] and the technology acceptance model in [26]. The integration of two models can provide richer explanation (higher R2 value) of the MTS adoption [27]. These theories have gained considerable empirical support in describing users’ acceptance of technology in several research fields [28, 29], and such integrative models are more useful for practitioners in understanding the changing paradigms to promote technology adoption in the services sector [30]. Previous studies argued to integrate the TAM with other models to cope with the fast changes in technology adoption [31]. Moreover, the two models (TAM and IDT) are complimentary to each other as the TAM is considered as a subdivision of perceived innovation [32, 33]. After thoroughly scrutinizing the past literature, the authors believe that there is no prior study that includes both the perspective of innovation-based and technology-based adoption to investigate the use of MTS in developing economies.

1.1. Status of Mobile Tourism Shopping in Pakistan

Over the last few decades, the tourism sector has snowballed throughout the world. Tourism is still seen as an economic engine because it contributes to GDP, reduces poverty, and reduces inflation, as well as creating jobs and bringing other such benefits. Tourism is considered to be vital to sustain the economy in the path of growth as it increases per capita income, generates income taxes, creates jobs, improves national infrastructure, enhances business activity by promoting the private sector, encourages and creates foreign investment opportunities, and increases foreign reserves, as well people’s living standard [34, 35].

The present study mainly focuses on a developing country, Pakistan, to examine the motivation of the consumers’ acceptance of the mobile technology to access tourism products and services. There is little research work available to uncover the factors affecting mobile tourism acceptance in developing countries, especially in Pakistan. Research conducted in the tourism sector in Pakistan has so far been limited to certain point of views such as terrorism and tourism, tourism growth, archaeological and historical tourism, adventure tourism, and online shopping adoption [3539], but no attention has been paid specifically to MTS [40]. The increasing usage and advancement of the mobile technology in tourism have shifted the emerging channel of mobile shopping from bricks-and-mortar to clicks-and-mortar. Mobile shopping was defined by Wong et al. [41] as “any monetary transaction(s) related to purchases of goods/services through internet-enabled mobile phones or over the wireless telecommunication network.” Mahrous and Hassan [40] discovered that, in emerging markets, consumers prefer to utilize the travel agent services for a particular phase, the payment and booking phase, whereas the m-device is used only for planning the travel. Hua [42] recapitulated that mobile dimension studies lag behind industrial practice in the field of hospitality and tourism. Given the importance of m-devices, MTS is defined as “the use of mobile devices to shop for tourism products and services.” Kim et al. [13] found that buying tourism-related products using a mobile device is entirely different and easy compared to purchasing through a traditional channel. According to Raun et al. [43], the use of mobile devices for buying tourism products has been accelerating among consumers.

Addressing the abovementioned research gap, the current research puts forth various contributions to the extant literature. First of all, the study identifies the most critical factors that affect intentions to purchase tourism products using a mobile device. Second, the study employs an integrative model based on TAM and IDT, thereby providing a comprehensive view to better investigate the consumer adoption of MTS and the accompanying innovation and technology-driven dividends. TAM and IDT have received considerable empirical support in describing user’s acceptance of technology in several fields, particularly information systems (IS) and information technology (IT) and specifically in online shopping and MTS [18, 29]. Hence, this unique approach will determine not only the significant variable effecting the acceptance of mobile tourism shopping but also the ones that have the most substantial impact, enhancing our understanding of MTS. Therefore, the integration of the two theoretical models gives a unique and richer view that incorporates both the technology and innovation aspects of accepting m-devices in emerging tourism research. Third, the current study examines the mediation effect of perceived usefulness, perceived enjoyment, and perceived ease of use between social presence and tourist acceptance of MTS. The present research is the first that highlights MTS in developing Asian country’s context, using Pakistan as a representative. Pakistan’s tourism industry has excellent potential for growth, and the recent government with strong backing from the prime minister has developed the tourism industry and is adding to the proliferation of technology adoption in the tourism sector. This proposed theoretical model enriches the extant literature by advancing the understanding of MTS and the intention of users, through employing TAM and IDT. The following section presents the theoretical basis on which this study is based, combining TAM and IDT.

2. Theoretical Background

2.1. Innovation Diffusion Theory and MTS

The theoretical paradigm, innovation diffusion theory (IDT), determines why people are adopting new ideas/technology [44]. To date, the IDT received a higher support in exploring consumers’ acceptance in many disciplines, predominantly, in online/E-shopping [28], tourist behavior [29], technology adoption by seniors [45, 46], and the acceptance of social network sites (SNS) [47]. Innovation is “an idea, practice, or object that is perceived as new by an individual or another unit of adoption” [25]. Diffusion, from another point of view is “the process by which an innovation is communicated through certain channels over time among the members of a social system”. IDT is a way of a rational thinking that explains the questions (how, where, and why) of spreading the new ideas or new technology to individuals [44, 48]. The innovation in tourism conveys the message that the communication channel has been transformed to mobile devices and that can create a value for the tourism industry [49].

IDT is used to understand ethical propagation of tourists’ behavior through the population, enlightening the relationship of relatively static tourism innovation and the spread of innovation [50]. IDT was concluded as an appropriate model for understanding consumers’ intentions in the community of online travelling where its constructs were found valid in explaining consumers’ behavior [29]. Therefore, IDT argues that “potential users make decisions to adopt or reject an innovation based on beliefs that they form about the innovation” [51]. In plain words, the IDT elucidates the factors that stimulate the intention to accept new technologies, considering complexity, compatibility, trialability, visibility, and relative advantage. Within the stated factors, a relative advantage has been widely studied and it has conveyed the most consistent interpretation of consumer desire to utilize the new technology [52].

2.2. Technology Acceptance Technology and MTS

The technology acceptance model is the most widely employed theory in information system research, exploring individual use of networks or adoption of any technology. The model introduces critical factors influencing users’ intentions to adopt any new system or technology [5356]. TAM is a modified form of the theory of reasoned action (TRA) that was initially established by Fishbein and Ajzen [57], explaining behavior related to the acceptance of computer usage. TAM defines the attitude of users and also identifies the role of ease of use and usefulness to clarify the acceptance of any information system [58, 59].

Although considerable research has adopted TAM, its nature has been criticized for not fully reflecting consumer adoption. In our study, TAM provides a connection between tourist behavior and the adoption of a specific technology. Some studies [6062] have expanded the TAM framework with an additional antecedent to get the best explanatory power. The TAM theory is based on the idea that individual EOU and PU are two determining factors that define the adoption of any system/technology. Perceived usefulness (PU) describes “the extent to which a person believes that mobile shopping service is useful for improving online shopping performance” [63]. Perceived ease of use (PEOU) is referred to the degree that consumers can use technology or products easily and effortlessly [26]. In the context of tourism and hospitality, many researchers have applied and extended TAM to explore adoption of new technology, such as in hotel front office systems [64], consumer intention to purchase online travel [28], and biometric system adoption in hotels [65]. The results of these studies show that PU and PEOU both are the most dynamic and critical factors of consumer acceptance of new technology. Therefore, TAM was considered as a suitable model for achieving the objective of this research study. While applying to different contexts, the TAM got various extension and modifications, such as Venkatesh and Davis [59] added subjective norm as well as image to the existing constructs of TAM and this new version was called TAM-2. Similarly, the model (TAM-2) was more modified with addition of perceived enjoyment and known as TAM-3 [66]. These extensions helped cover the different limitations of TAM like lack of actual guidance [67] and unsuitability in certain situations [68].

3. Development of Hypotheses

3.1. Perceived Relative Advantage

Relative advantage (RA) is one of the essential elements introduced by IDT. RA is the “extent to which innovation is considered higher than its current practice” [69]. People tend to adopt innovation when they think that it is more useful and is likely to increase their performance and efficiency [70]. Perceived relative advantage may be thought of as a better choice of mobile tourism shopping as compared to physical shopping. In this study, relative advantage may be construed as the extent of using a mobile device for tourism products, an advantage that ultimately provides benefits to tourists such as convenience, time-saving, and ease. Previously published studies have shown that the relative advantages have a positive and substantial relationship with the user’s intent to accept any technology such as mobile commerce [71] and mobile payment [72]. Consumers who perceive the relative advantage of using a mobile device for purchasing tourism products are more likely to adopt the system. Therefore, this study suggests that consumers’ perceived relative advantage and the attribute of IDT predict the consumers’ intentions of MTS. Thus, this study proposes the following:H1: perceived relative advantage has a positive and significant effect on consumer intentions to use a mobile device for purchasing tourism products

3.2. Compatibility

Rogers [25] explained that compatibility is “the degree to which an innovation is perceived as being consistent with existing values, past experiences, and needs of potential adopters.” Moreover, previously published studies have stated that compatibility is one of the active drivers for new technology acceptance [73, 74]. The previous research conducted in the setting of online shopping has supported the significant and positive relationship between attitude and online shopping behavior [28, 29, 75, 76]. The present study suggests that the attribute of ID, i.e., compatibility, predicts the tourist adoption of MTS. Therefore, the study posits the following:H2: compatibility significantly influences consumer intentions to use MTS

3.3. Social Presence

Taking the explanation of Qiu and Li [77], social presence (SP) can be defined as “the extent to which a medium is perceived as sociable, warm, sensitive, personal, or intimate when it is used to interact with other people.” Social presence is one of the vital concepts because the contemporary technologies (like social networking sites) offer and develop this role of being socially present [78]. Social presence is a significant construct in the area of computer-mediated communications [79]. Social presence is the extent to which communication channels facilitate the awareness of communication partners and interpersonal relationships during interactions [80]. According to the communication theory of social presence, the channel falls along one continuum “social presence” [81]. Previous studies have approached the social presence taking various viewpoints: (1) it was thought of as an inherent quality in the channels of communication [81]; (2) ability to send information on face expressions, postures, and nonverbal gestures [81]; and (3) relationships with wealth of information and interactivity [82, 83].

Social presence is a significant concept because of its magnificent role in the development of technology and its effectiveness in online selling that carries the idea of a human touch [8486]. The theory of social presence [81] gave birth to the construct of social presence, which is rightly construed as a primary element in the field of online social networking. According to Wei et al. [87], the theory of social presence advocates that if an intermediary has a socially suitable level of job attendance, the connection will be more effective. Recently, social presence has been developed as a remarkable concept in mobile technology and online networks [78, 88]. Apart from the increasing use of online social networks [8991], the social presence factor has been found to be one of the positive determinants affecting PEOU, perceived enjoyment, and PU in the electronic environment and in electronic shopping [9296]. The current paper conceptualizes that social presence motivates consumers towards MTS intention, which is represented by PEOU, PU, and perceived enjoyment. Consistent with the above arguments, we propose the following hypotheses:H3: there is a positive relationship between social presence and perceived enjoymentH4: there is a positive relationship between social presence and perceived ease of useH5: there is a positive relationship between social presence and perceived usefulnessH6: social presence has a significant influence on consumer intention to use MTS

3.4. Perceived Enjoyment (PEJ)

Davis et al. [97] explained PEJ as “the extent to which the activity of using a system is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated.” Davis et al. [97] elucidated that PEJ is the intrinsic stimuli coming from a particular activity. Enjoyment empowers users who perceive difficulty to focus on the use of any technology, which further leads to a comprehensive enjoyment process [59]. In previous studies, PEJ was endorsed as a significant precursor of behavioral intention in different mobile studies [98, 99]. Jeng [100] found that consumers remain happy when they are searching for online tourism products. Young Im and Hancer [101] found that consumers using mobile devices while looking for information on travel sites are often concurrently connected with friends, connections which potentially give them pleasure and enjoyment.

Additionally, Scholl-Grissemann and Schnurr [102] found that customizing travel products leads to pleasant consumer experiences. Ramification strategies have been further proposed by Ozturk et al. [103], and PEJ was considered an essential factor in the development of mobile bookings for a hotel. Thus, the following hypothesis is suggested:H7: there is a positive relationship between perceived enjoyment and tourist intention to use a mobile device for shopping tourism products

3.5. Perceived Ease of Use (PEOU)

Davis [104] defined PEOU as “the degree to which a person believes that using a particular system would be free of effort.” In this research, we define PEOU as the degree to which tourists/consumers believe that the MTS will be an effortless and easy job for them. When using the system takes little effort and it is easy to learn and understand, consumers are more likely to accept the system [105]. Several previous studies have used the construct of PEOU to research the adoption of information technology and found a positive relationship between PEOU and adoption of technology such as online shopping [106, 107], mobile technologies [108110], e-learning [111], and online games [112]. Therefore, it can be concluded that purchasing tourism products using a mobile device will be more beneficial if it is perceived to be easy in use. Hence, we posit the hypothesis:H8: perceived ease of use positively impacts consumer intention to use MTS

3.6. Perceived Usefulness (PU)

By the definition of Davis [104], PU is “the degree to which a person believes that using a particular system would enhance his or her job performance.” In the current study, perceived usefulness refers to the extent where the consumer believes that using a mobile device for tourism shopping will improve his/her performance. Adams et al. [113] found that PU is one of the crucial factors identified by TAM that predicts consumer intentions and performance. Liao et al. [114] suggested in motivation theory that people will be more influential and accept new technology if they realize that the activity leads to positive performance. Previous research extensively considered PU in different contexts such as social networking [115118]. There is a lack of literature clarifying the consumers’ beliefs that using MTS could result in a positive outcome and how those beliefs affect intentions. Therefore, we suggest the following hypothesis:H9: perceived usefulness positively affects tourist intention to use MTS

3.7. Mediating Role of PEOU, PU, and PEJ

According to TAM, PEOU, and PU, envisage the actual acceptance of individuals to use a system, with behavioral intentions as a mediator between the given predictors and the actual use of the system [104]. Jiang and Xu [119] confirmed a substantial effect of satisfaction and perceived usefulness on the continuation intention of e-government in China. Similarly, a study conducted (Hu et al. [120]) revealed that PU is an essential indicator of the continued usage of e-tax service in Hong Kong. Similarly, Hsu and Lu [112] conducted a study on online games where they determined the positive impact of PEOU on generating the experience with immersion. Moreover, Chitungo and Munongo [121] found a positive relationship between PEOU and usage intention in m-mobile usage. With the increasing importance of hedonistic features of the cellular system, information system research has verified the significant role of PEJ [122]. In simple words, the predictor value and mediating role of PEJ have been suggested in extensive literature while explaining the adoption of new technologies [123]. According to Kawaf and Tagg [124], using the stimulus-organism-response paradigm, both PU and PEOU, as well as the response (PEJ), are presumed as mediators between external stimuli (i.e., social presence, perceived mobility, and the system) and the provision of quality services. Based on the different studies (e.g., [125128]), the current study supposes that PEJ, PU, and PEOU will mediate the relationships between social presence and consumers’ actual usage of MTS. Based on vast literature search, the authors believe that no prior study has tested the mediation in the relationship between social presence and actual usage in the perspective of mobile tourism shopping. Thus, we hypothesize the following:H10: perceived enjoyment significantly mediates the relationship between social presence and tourist MTS intentionH11: perceived ease of use positively mediates the relationship between social presence and tourist MTS intentionH12: perceived usefulness positively mediates the relationship between social presence and tourist usage of MTS

To sum up, it is expected that the exogenous variables can directly and positively influence the consumers’ intentions toward mobile tourism shopping. Also, it is likely that the relationship between social presence and usage intention toward MTS is mediated by PEJ, PEOU, and PU. To this end, based on rigorous literature on the relative advantages, compatibility, and social presence in the context of MTS, we propose a multimediation model, as demonstrated in Figure 1.

Figure 1: Conceptual model.

4. Methodology and Measurement Development

4.1. Data Collection and Sampling Method

The data collection process was carried out from August to October 2018 in Karachi, Pakistan. Hard copies of the questionnaire were distributed among the participants, and the questionnaire was filled out through face-to-face interaction with the respondents [129]. The research survey was conducted in four different malls in Karachi, namely, Dolman Mall Clifton, LuckyOne Mall Karachi, Dolmen Mall Tariq Road, and Port Grand. According to Kasim and Alfandi [130], the shopping centers approach for data collection is the best way to collect data from marketers/buyers. Other researchers also elaborated that the mall-intercept method is a more unbiased and fair data collection method because of the anonymity and random educated responses [131, 132]. Also, the approach constitutes an appropriate sampling [133]. In the context of mobile tourism, the same approach has been recommended by various researchers in Malaysia [2, 134]. Therefore, the authors decided to choose Karachi, a metropolitan city of Pakistan, as an ideal city for potential tourists and MTS. Four research assistants were also hired to assist in the data collection, and thus a total of 450 survey questionnaires were distributed among the consumers during the different time intervals. The consumers were contacted and were politely asked if they were using a mobile device for purchasing tourism products or have had experience with MTS shopping; their responses were welcomed, and they were thanked for their willingness to complete the survey. The data collection process was carried out in three consecutive months. A total of 422 completed questionnaires were received. Subsequent analyses revealed that only 396 responses were useable for statistical analysis after removing outliers and responses with missing items.

4.2. Demographic Characteristics of the Sample

The demographic features of respondents are presented in Table 1. Male respondents accounted for 230 (58.08%) responses in the sample, while 166 were from females (41.92%). A large majority of respondents (70.4%) were aged between 20 and 39 years (17.6%, aged 20 to 24 years; 25.2%, aged 25 to 29 years; 16.5%, aged 30 to 34 years; and 11.2%, aged 35 to 39 years). In terms of education, the largest group of respondents was those with at most a high school education, numbering 166 (41.91%). Moreover, in terms of experience using a mobile device, 36.4% had less than three years’ experience, 32.8% had 3 to 5 years’ experience, and 30.8% had more than five years’ experience using a mobile device to shop for tourism products. Table 1 presents the demographic profile of the final sample.

Table 1: Demographic profile of respondents
4.3. Survey Instrument

The measurement items of the current study were adapted from previous studies and modified according to the perspective of MTS. All the indicators were measured on a seven-point Likert scale ranging from “1, strongly disagree” to “7, strongly agree.”

4.4. Common Method Bias (CMB)

The study collected the data using a single source for both dependent and independent factors, and so to check for problems of the possible common method bias, we used Harman’s single-factor test [135]. Statistically, if the result of Harman’s single-factor test accounts 40% or above, then there may be a CMB problem in the data. In the present study, all factors were uploaded with a single factor. The study found 37.2% variance in data, which is below the cut-off value of 40%. Moreover, the construct correlation matrix (Table 2) also indicates that each value of interconstruct correlations is less than 0.76 as CMB may be an issue when correlations are greater than 0.9 [136, 137]. Therefore, it is concluded that no issue of CMB exists in the data.

Table 2: Correlations and interconstruct reliability.

5. Results and Discussion

For testing the different paths in the proposed model, we applied partial-least-squares structural equation modeling (PLS-SEM). PLS-SEM is a comprehensive modeling approach that helps researchers measure the relationships between constructs as well the reliability and validity of any research model [138]. In the context of tourism literature, the PLS-SEM has received important recognition among researchers [139]. Moreover, PLS-SEM is a powerful technique and can predict a complicated model without any need of distribution assumptions, and it can also handle nonnormal distributions of data [140]. Given the advantages of PLS, the current research examined the factors that affect the consumer’s acceptance of mobile tourism shopping through PLS, which is considered suitable to evaluate the relationships in any structure model, specifically in the IS context [141]. We used the software Smart PLS 3 to do so.

5.1. Measurement Model

We examined the measurement model by using CFA [138]. Notably, we checked content validity, convergent validity, and discriminant validity. After a critical review of past literature and pilot testing, we measured content validity. Assessing convergent validity, we evaluated the values of factor loadings, Cronbach’s alpha (CA), composite reliability (CR), and average variance extracted (AVE). The CFA results show that the factor loadings of all items were more significant than 0.70 except for SP3.5, PU 6.1, and PEOU which had 7.3; these three items were removed subsequently from the final analysis [142]. As indicated in Table 3, the CFA results fulfill the recommended standard levels of CA, CR, and AVE which were higher than 0.7, 0.7, and 0.5, respectively, thus showing good convergent validity [138, 142, 143].

Table 3: Results of the measurement model.

Discriminant validity, which indicates that the measures of one variable are different from the others, is evaluated by three methods [144]. As argued by Fornell and Larcker [143], first we compared the associations among the correlations between variables and AVE of all the hypotheses. Table 2 shows that, for all constructs, the AVE square root is above the correlation values, showing acceptable discriminant validity. Second, we compared items loadings and cross loadings, and as indicated in Table 4, we find that the items loadings are higher than the cross loadings of other latent variables, which show good discriminant validity [145]. Third, using the heterotrait-monotrait ratio (HTMT) method with the complete bootstrapping technique of 2000 samples, we assessed discriminant validity. Table 5 indicates that the maximum value in the table is 0.83, which is below the cut-off value of 0.85 and the confidence interval ratio of all variables is below 1, thus showing sound discriminant validity.

Table 4: s Cross loadings.
Table 5: HTMT results.
5.2. Structural Model Results

The hypothesized relationships among the constructs were examined using standardized path examination. The direct and indirect effects of dependent variables on the independent construct were examined and provide practitioners with possible results concerning cause and effect relationships. The results are presented in Table 6. We estimated the path significance levels by a bootstrap method with resampling 2000 times [146], with zero change selection, which achieves the most conventional results [147]. All the 12 hypotheses were tested; two hypotheses were found to be insignificant, and the remaining hypotheses are significant at the level. The results indicated in Table 6 show that social presence positively influences consumer PEJ (β = 0.506, ), PU (β = 0.371, ), PEOU (β = 0.441, ), and the MTS intention (β = 0.341, ). These findings indicate that H3, H4, H5, and H6 all hold for MTS adoption, so these four hypotheses are supported. Similarly, PEJ (β = 0.148, ), PEOU (β = 0.124, ), and PU (β = 0.456, ) did significantly affect MTS shopping intention; therefore, these results provide statistical support for hypotheses H7, H8, and H9. Moreover, we found insignificant correlations of RA (β = −0.052, ) and COM (β = −0.045, ) with MTS intention; hence, H1 and H2 are unsupported.

Table 6: Results of the hypothesized structural model.

Second, the main goal of PLS-PM is to evaluate the predictive power of a proposed model as well the key constructs; therefore, it is important to assess the structure model by evaluating the coefficient value, R2, of the constructs as reported by Hair et al. [147], indicating the variance in the constructs of the proposed research model. As shown in Figure 2, the results state that PEJ (R2 = 0.193), PEOU (R2 = 0.202), and PU (R2 = 0.144) meet the requirements, thereby proving an acceptable level of predictability. Overall variance in the multimediation model was measured at 69% in MTS shopping (R2 = 0.694), showing that the variance explained by the independent variables represents an excellent explanatory power. Lastly, we used the blindfolding procedure as suggested by Hair et al. [147], to generate the crossvalidated redundancy measure Q2. Hair et al. [140] advocated using Q2 to ensure the predictive capability of any research model. If the value of Q2 for endogenous construct is positive (more than zero), it demonstrates that the model predictability is relevant and acceptable [148]. The value of Q2 > 0 represents the model’s predictive relevance for the respective relationships of PEJ, PEOU, and PU with MTS intention.

Figure 2: Model with results. Relationships are highly significant. ns: hypotheses are not significant.
5.3. Mediation Analysis

To test the multiple mediation effect, we followed the method suggested by Hair et al. [147] and Zhao et al. [149] instead of using the proposal of Baron and Kenny [150]. The later papers described three types of the mediation process and two types of nonmediation procedures. If both the direct and indirect mediation effects on the relationships between dependent and independent variables are insignificant, then the specific path has a nonmediation effect. If, however, the direct effect on the dependent variable is significant, then the path has only one nonmediation effect, but if the direct effect is insignificant, then the researcher will have to evaluate the significance of indirect effects to further differentiate between complementary partial, full, and competitive partial mediation. Similarly, complementary partial mediation takes place when both the direct effect and indirect effects are moving in the similar direction. Second, competitive partial mediation takes place when there is a positive direct effect, but the movement is at the opposite track. Finally, full mediation will occur if there is an insignificant direct effect. Hypotheses H11 and H13 posit that PEOU and PU partially mediate between the social presence and MTS adoption, and H12 posits that PEJ has no mediation impact on the connection between social presence and MTS adoption. Following Ringle et al. [146], we adopted the bootstrapping method for testing mediation effects. The results suggest that social presence has significant indirect effects (β = 0.299, ) on MTS adoption, while the direct effect of social presence is also significant. Thus, we conclude that PEOU and PU have a partial mediating role in the current study, thus supporting H11 and H12, and that PEJ has no mediation effect, so H10 is unsupported (Table 7).

Table 7: Multiple mediation analysis.
5.4. Discussion

This study presents a novel and integrated model examining key factors that influence tourists’ intention to use mobile device(s) for online shopping of tourism products and services. In the proposed model, social presence, relative advantage, and compatibility act as antecedents of consumer intentions towards mobile tourism shopping. Also, perceived enjoyment, perceived ease of use, and perceived usefulness are mediators between the relationship of social presence and adoption of MTS. In this respect, as foreseen by hypotheses associated with the direct effect of relative advantage, the results show that the relationship between relative advantage and consumer intention toward MTS is insignificant, consistent with the results of the previous studies (e.g., [151, 152]). Our results are contrary to Ainin et al. [153] who found a significant association between social media and relative advantage among Malaysian SMEs. One of the possible reasons for the insignificant relationship between relative advantage and mobile tourism shopping adoption is that the mobile tourism technology is new in Pakistan and hence the respondents are relatively unfamiliar with this new technology. This may enhance their ability to use effectively, eliminating its significance of relative advantages in decisive behavioral intentions. Therefore, this inconsistency does not mean that the technology provider thinks accepting MTS has no technical advantage over other alternatives. Amaro and Duarte [28] found a significant relationship between consumers attitude toward participating in the online travel community. Hung et al. [154] studied the adoption of the CRM system in hospital and found a significant relationship between relative advantage and CRM adoption system. Mallat and Tuunainen [155] explored a positive relationship relative advantage and adopter view on the new payment technology system. Lin [156] found that relative advantage had a positive and significant influence on consumer adoption of mobile banking. Prior research also found a significant association between relative advantage and (B2B) e-commerce adoption by Egyptian manufacturer in SMEs [157]. Hussein et al. [158] investigated and successfully identified that relative advantage plays a significant role in using B-to-B e-commerce among the Jordanian manufacturing SMEs.

Our results indicate that relative advantage is not necessary when it comes to using innovation. It may due to the specific characteristics of the respondents that they prefer face-to-face shopping and do not consider the advantages of mobile shopping or the respondents lack familiarity with the mobile-based tourism as a viable alternative to the physical store shopping. Another hypothesis about the effect of compatibility with the consumer using m-devices is also unsupported, showing an insignificant relationship with MTS. Insignificant result of compatibility was also reported in [157] in the context of e-commerce adoption. The result is also in line with the results of Ahmad et al. [159], who found an insignificant relationship between compatibility and social media acceptance. Meanwhile, the effect of compatibility on MTS acceptance is not significant. This insignificance can be explained by noting that most consumers do not use mobile device(s) to do online shopping in Pakistan or it may relate to the specificity of the data collected. Agag and El-Masry [29] found a significant association of consumers’ attitude and online (tourism) shopping. Qazi et al. [151] studied the acceptance of e-book reading among higher education students and found that compatibility does influence consumers’ (students’) intentions and has a significant impact on consumer’s attitudes toward adopting e-books. Previous research also found a significant association between compatibility and consumer’s acceptance and adoption of mobile ticketing services [160]. Lu et al. [52] found that relative advantage and compatibility had a significant and positive impact on consumer adoption of innovation. Thus, relative advantage and compatibility of mobile tourism shopping loses its effect in such cases.

As predicted by H3, H4, H5, and H6, our findings regarding social presence express the significant influence of perceived usefulness, perceived ease of use, and perceived enjoyment, on tourists’ intention toward MTS. The results reveal that the social connectivity or being socially present is an important feature of mobile-based tourism shopping. Additionally, the users’ ability to connect through mobile devices and shop online adds to the significance of social presence. Therefore, hypotheses H3, H4, H5, and H6 are supported. These results of our study also correspond to those in the previous literature (i.e., [161, 162]). Also, perceived ease of use directly affects MTS intention and mediates the effect of social presence on MTS adoption. These findings are in line with the study of mobile social network sites by Leong et al. [163] who found that perceived ease of use has a considerable influence on consumer intention and argued that the result is due to the favorable experience of mobile usage for social networking among consumers, which guides them to use SNS easily for learning and shopping activities. Similarly, Nunkoo and Ramkissoon [164] acknowledged that whenever a tourist has a comprehensive knowledge of online tourism shopping, they are more focused on utilizing the usefulness of online tourism shopping. Our result explains and supports the significant role of perceived usefulness in MTS adoption. In our study, perceived enjoyment was another predictor of tourist intention to use a mobile device to purchase tourism products, which agree with past studies which describe the significant role of PEJ in technology acceptance [123, 165]. Ha and Stoel [166] found that perceived enjoyment (PEJ) has a substantial and direct effect on consumer intention toward MTS intention. Our study’s conclusion about PEJ is similar and supports the predictive ability of PEJ. Hence, it is concluded that as long as users think that the MTS involves fun and pleasure, they intend to accept MTS.

Our results confirm the applicability of TAM in predicting tourists’ intention towards MTS [26]. Ye et al. [162] found that the impacts of social presence on perceived usefulness, perceived ease of use, and perceived enjoyment are positive, and they concluded that the more the website is enjoyed, the more it is valuable and easy to use. Our results support the multiple mediation analysis, showing that PU, PEOU, and PEJ each mediates in the association between social presence and MTS adoption. These results are also consistent with previous studies [126, 162, 167, 168]. Overall, the adoption of the multimediation model has been well established in the perspective of MTS.

6. Implications and Conclusion

6.1. Theoretical Implications

Given the increasing importance of mobile technology in tourism and hospitality, this research contributes to academic research in the context of developing countries. In the current research work, we employed two theories (TAM and IDT) to examine consumer intention towards the adoption of MTS. This study has rich theoretical contributions to tourism research and mobile applications in the services sectors. The research combines two known theories to understand the adoption more comprehensively. The first, IDT, elucidates how innovation spreads among people [169], while TAM has been extensively used in tourism and hospitality industry to examine that how consumers adopt new technology [60, 61]. Past studies show that integrated theoretical models are more applicable and representative in tourism research. Also, this research has contributed to the development of tourism, marketing, and hospitality literature in developing economies. The use of the integrated model represents a new avenue for further research in the IT/IS domain in many other contexts. We used PLS-SEM, a comprehensive statistical technique, which adds to the theoretical contribution in the area of tourism.

Moreover, our results complement previous studies which tested the mediating roles of PEJ, PU, and PEOU in online technology acceptance as well as studies investigating the consumer behavioral intention and actual usages. The relationship of social presence and the adoption of MTS with the mediation of PU, PEOU, and PU is a new addition in the MTS perspective. This will have substantial theoretical implications for the IDT and TAM models, just as the mediation results in the new context add to the existing literature on tourism, IDT, and TAM research. Theoretically, this research opens new ways to investigate the use of mobile technology in tourism industry, and the integrative model helps academician in forming their future research models in more solid ways.

6.2. Managerial Implications

This study is based on the premise of a research gap that previous research ignored the factors that cause tourists to accept MTS, especially in developing countries. Hence, we aimed for a robust empirical investigation to analyze the determinants of tourist intentions to participate in MTS. Besides the theoretical implications, the findings of the current research provide a set of important implications for practitioners, managers, tourists, and regulators. With a sound understanding of the intention of mobile shoppers and the mediating effects of PU, PEOU, and PEJ, various stakeholders (airlines, mobile device designers, software developers, travel organizations, and tourism-related organizations) can further strengthen their marketing and financing policies for tourism products by considering the significant factors identified in this paper. The results might be helpful to design strategies to motivate more consumers for MTS, and thus, it can add value to the tourism industry as well as other relevant organizations. The government, especially the Pakistan Tourism Development Corporation, can utilize the findings in a better way to promote mobile tourism shopping in Pakistan. This will not only create job opportunities but also help the government in documenting the shopping channel. Moreover, the focus of this study is restricted to Pakistan tourism shopping through mobile use; further research can work on the crosscultural study to confirm and validate our findings. We recommend testing the research model in different phases of shopping like before and after as well as during the travel to determine how well such relationships exist in other economies. It can also be a worthy pursuit in the future to focus on shopping places and time (like office, home, travelling sites, or working day or weekend). The prospective researchers can concentrate on multifactor analysis groups that can be helpful in exploring the differences among various tourism services/products, like hotels, airline companies, restaurants, and rental-car services. The heterogeneous nature of MTS adoption across different industries can help the marketing managers to devise their strategies according to the nature of various segments, like airlines and hotels. This segmentation will be highly promising to the consumers’ wellbeing and satisfaction.

6.3. Social Implications

This study also grants social implications by exploring the tourists’ intentions and highlighting the dynamics of using mobile phones for tourism shopping. The mobile usage for such purposes can be thought of as a social value, which might influence society in this regard. Such usage also affects other individuals, as it promotes the social judgment of other individuals towards the MTS adoption. Thus, the increased understanding can also spur the arrival of tourists to Pakistan, which is timely since Pakistan has a great potential for tourism, and the present government has initiated moves to develop the tourism industry. This knowledge will also add to the proliferation of technology adoption in the tourism sector. Indirectly, MTS can be proved to be an economic engine and an agent of social change because of its contribution to GDP, decreasing of unemployment, increasing of country income, and creation of opportunities for tourism-related jobs.

7. Conclusion, Limitations, and Future Research Directions

Mobile tourism shopping is not only considered to be a revolutionary movement in the tourism perspective but also a fashionable pattern where the tourists use mobile devices for buying tourism-related products. Because accepting the m-gadgets for tourism is the trending research area, the current research proposed an integrated framework to examine the tourists’ intention to accept MTS. The present research contributes theoretically, managerially, and also has social implications, thereby benefiting both the literature and practice.

While the proposed model has been developed on a rich theoretical basis, the current research has various inescapable limitations that need to be covered and focused in future research. First of all, this study used a convenience sampling method; therefore, future studies may use random sampling from general other users or groups of users. Second, the scope of our study is restricted to Pakistan; therefore, the findings, though likely generalizable to some other developing countries, may not be applicable in settings with significantly different social, religious, or political features. Addressing the problem in other countries and contexts is worthwhile. Third, this study does not consider crosscultural issues because the use of ICT usage behavior is more credible in a single national culture [170], so it is advisable to focus on multicultural backgrounds or using cultural dimensions such as those as suggested in [171]. Fourth, data were collected cross-sectionally and were analyzed through PLS-equation modeling. Scholars assume that the data are homogenous, and this assumption can be unrealistic [172]. Therefore, the authors suggest that future research may consider longitudinal data with multigroup analysis. In smart PLS, this can be handled through the FIMIX-PLS tool. Fifth, the current study aimed to research MTS from the tourists’ perspective. The spread of any technological innovation also depends on organizations, merchants, and other sellers; this aspect could be covered in follow-up research studies. The extended models of TAM (like TAM-2 and TAM-3) are also worth investigating in different contexts. Finally, new variables relevant to mobile tourism, such as social, religious, or political factors, can be included, which will be helpful to researchers in their future research endeavors.

Data Availability

This research is part of a series of research studies conducted by a group of researchers, and few parallel studies are still in progress; the data used to support the findings of this study are currently under embargo, while the research findings are commercialized. Requests for data after 12 months of publication of this article will be considered by the corresponding author with mutual consultation of related group members and researchers.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research work was partially supported by the National Natural Science Foundation of China under grant nos. 71771077 and 71601061.

References

  1. T. Chi, “Understanding Chinese consumer adoption of apparel mobile commerce: an extended TAM approach,” Journal of Retailing and Consumer Services, vol. 44, pp. 274–284, 2018. View at Publisher · View at Google Scholar · View at Scopus
  2. G. W.-H. Tan, V. H. Lee, B. Lin, and K.-B. Ooi, “Mobile applications in tourism: the future of the tourism industry?” Industrial Management & Data Systems, vol. 117, no. 3, pp. 560–581, 2017. View at Publisher · View at Google Scholar · View at Scopus
  3. H. R. Marriott, M. D. Williams, and Y. K. Dwivedi, “What do we know about consumer m-shopping behaviour?” International Journal of Retail & Distribution Management, vol. 45, no. 6, pp. 568–586, 2017. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Mahapatra, “Mobile shopping among young consumers: an empirical study in an emerging market,” International Journal of Retail & Distribution Management, vol. 45, no. 9, pp. 930–949, 2017. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Shang and W. Wu, “Understanding mobile shopping consumers’ continuance intention,” Industrial Management & Data Systems, vol. 117, no. 1, pp. 213–227, 2017. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Singh and R. K. Srivastava, “Predicting the intention to use mobile banking in India,” International Journal of Bank Marketing, vol. 36, no. 2, pp. 357–378, 2018. View at Publisher · View at Google Scholar · View at Scopus
  7. A. A. Alalwan, Y. K. Dwivedi, N. P. P. Rana, and M. D. Williams, “Consumer adoption of mobile banking in Jordan,” Journal of Enterprise Information Management, vol. 29, no. 1, pp. 118–139, 2016. View at Publisher · View at Google Scholar · View at Scopus
  8. F. Martin and J. Ertzberger, “Here and now mobile learning: an experimental study on the use of mobile technology,” Computers & Education, vol. 68, pp. 76–85, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Nikou and J. Mezei, “Evaluation of mobile services and substantial adoption factors with analytic hierarchy process (AHP),” Telecommunications Policy, vol. 37, no. 10, pp. 915–929, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Grewal, Y. Bart, M. Spann, and P. P. Zubcsek, “Mobile advertising: a framework and research agenda,” Journal of Interactive Marketing, vol. 34, pp. 3–14, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. R. Meagher and E. Kousvelari, “Mobile oral heath technologies based on saliva,” Oral Diseases, vol. 24, no. 1-2, pp. 194–197, 2018. View at Publisher · View at Google Scholar · View at Scopus
  12. T. Oliveira, M. Thomas, G. Baptista, and F. Campos, “Mobile payment: understanding the determinants of customer adoption and intention to recommend the technology,” Computers in Human Behavior, vol. 61, pp. 404–414, 2016. View at Publisher · View at Google Scholar · View at Scopus
  13. M. J. Kim, N. Chung, C.-K. Lee, and M. W. Preis, “Motivations and use context in mobile tourism shopping: applying contingency and task-technology fit theories,” International Journal of Tourism Research, vol. 17, no. 1, pp. 13–24, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Hoehle and V. Venkatesh, “Mobile application usability: conceptualization and instrument development,” MIS Quarterly, vol. 39, no. 2, pp. 435–472, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. A. R. D. M. Neves, Á. M. G. Carvalho, and C. G. Ralha, “Agent-based architecture for context-aware and personalized event recommendation,” Expert Systems with Applications, vol. 41, no. 2, pp. 563–573, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Werthner and S. Klein, Information Technology and Tourism: A Challenging Relationship, Springer-Verlag Wien, Vienna, Austria, 1999.
  17. J. J. Hew, V. H. Lee, L. Y. Leong, T. S. Hew, and K. B. Ooi, “The dawning of mobile tourism: what contributes to its system success?” International Journal of Mobile Communications, vol. 14, no. 2, pp. 170–201, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. J.-J. Hew, L.-Y. Leong, G. W.-H. Tan, V.-H. Lee, and K.-B. Ooi, “Mobile social tourism shopping: a dual-stage analysis of a multi-mediation model,” Tourism Management, vol. 66, pp. 121–139, 2018. View at Publisher · View at Google Scholar · View at Scopus
  19. A. M. Makki, D. Singh, and A. B. Ozturk, “HotelTonight usage and hotel profitability,” Journal of Hospitality and Tourism Technology, vol. 7, no. 3, pp. 313–327, 2016. View at Publisher · View at Google Scholar · View at Scopus
  20. D. Schaal, The State of Mobile Booking 2015, 2014, http://cdn2.hubspot.net/hub/449646/file-2570942564-pdf/29-SkiftReport-State-ofMobile-Booking-20151.pdf.
  21. M. Johnsen, Smartphones, Social Media and In-Store Customer Service to Play Big Roles in Holiday Shopping Behavior, 2012, https://www.drugstorenews.com/news/smartphones-social-media-and-store-customer-service-play-big-roles-holiday-shopping-behavior/.
  22. A. Loureiro, “How technology is successfully transforming travel to better serve the ever-connected digital consumer,” Worldwide Hospitality and Tourism Themes, vol. 9, no. 6, pp. 675–678, 2017. View at Publisher · View at Google Scholar · View at Scopus
  23. N. Liu and R. Yu, “Identifying design feature factors critical to acceptance and usage behavior of smartphones,” Computers in Human Behavior, vol. 70, pp. 131–142, 2017. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Morosan, “Toward an integrated model of adoption of mobile phones for purchasing ancillary services in air travel,” International Journal of Contemporary Hospitality Management, vol. 26, no. 2, pp. 246–271, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. E. Rogers, Diffusion of Innovations, The Free Press, New York, USA, 4th edition, 1995.
  26. F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User acceptance of computer technology: a comparison of two theoretical models,” Management Science, vol. 35, no. 8, pp. 982–1003, 1989. View at Publisher · View at Google Scholar
  27. Y. Sun, N. Wang, X. Guo, and Z. Peng, “Understanding the acceptance of mobile health services: a comparison and integration of alternative models,” Journal of Electronic Commerce Research, vol. 14, no. 2, pp. 183–200, 2013. View at Google Scholar
  28. S. Amaro and P. Duarte, “An integrative model of consumers’ intentions to purchase travel online,” Tourism Management, vol. 46, pp. 64–79, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. G. Agag and A. A. El-Masry, “Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: an integration of innovation diffusion theory and TAM with trust,” Computers in Human Behavior, vol. 60, pp. 97–111, 2016. View at Publisher · View at Google Scholar · View at Scopus
  30. M. Y. Yi, J. D. Jackson, J. S. Park, and J. C. Probst, “Understanding information technology acceptance by individual professionals: toward an integrative view,” Information & Management, vol. 43, no. 3, pp. 350–363, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. Y.-H. Lee, Y.-C. Hsieh, and C.-N. Hsu, “Adding innovation diffusion theory to the technology acceptance model: supporting employees’ intentions to use e-learning systems,” Journal of Educational Technology & Society, vol. 14, no. 4, pp. 124–137, 2011. View at Google Scholar
  32. J.-H. Wu and S.-C. Wang, “What drives mobile commerce?” Information & Management, vol. 42, no. 5, pp. 719–729, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. A. N. Giovanis, S. Binioris, and G. Polychronopoulos, “An extension of TAM model with IDT and security/privacy risk in the adoption of internet banking services in Greece,” EuroMed Journal of Business, vol. 7, no. 1, pp. 24–53, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. F. Habibi, “The determinants of inbound tourism to Malaysia: a panel data analysis,” Current Issues in Tourism, vol. 20, no. 9, pp. 909–930, 2017. View at Publisher · View at Google Scholar · View at Scopus
  35. A. Jalil, T. Mahmood, and M. Idrees, “Tourism-growth nexus in Pakistan: evidence from ARDL bounds tests,” Economic Modelling, vol. 35, pp. 185–191, 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. M. I. Arshad, M. A. Iqbal, and M. Shahbaz, “Pakistan tourism industry and challenges: a review,” Asia Pacific Journal of Tourism Research, vol. 23, no. 2, pp. 121–132, 2018. View at Publisher · View at Google Scholar · View at Scopus
  37. A. H. Fakhar, Factors Affecting Tourism, Tourism Potential and Strategies for Development as an Industry in Pakistan, University of Gävle, Gävle, Sweden, 2010.
  38. S. Khalil, M. K. Kakar, and A. Malik, “Role of tourism in economic growth: empirical evidence from Pakistan economy,” The Pakistan Development Review, vol. 46, no. 4II, pp. 985–995, 2007. View at Publisher · View at Google Scholar
  39. A. R. Ashraf, N. Thongpapanl, and S. Auh, “The application of the technology acceptance model under different cultural contexts: the case of online shopping adoption,” Journal of International Marketing, vol. 22, no. 3, pp. 68–93, 2014. View at Publisher · View at Google Scholar · View at Scopus
  40. A. A. Mahrous and S. S. Hassan, “Achieving superior customer experience: an investigation of multichannel choices in the travel and tourism industry of an emerging market,” Journal of Travel Research, vol. 56, no. 8, pp. 1049–1064, 2017. View at Publisher · View at Google Scholar · View at Scopus
  41. C. H. Wong, H. S. Lee, Y. H. Lim, B. H. Chua, B. B. H. Chai, and G. W. H. Tan, “Predicting the consumers’ intention to adopt mobile shopping: an emerging market perspective,” International Journal of Network and Mobile Technologies, vol. 3, no. 3, pp. 24–39, 2012. View at Google Scholar
  42. N. Hua, “E-commerce performance in hospitality and tourism,” International Journal of Contemporary Hospitality Management, vol. 28, no. 9, pp. 2052–2079, 2016. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Raun, R. Ahas, and M. Tiru, “Measuring tourism destinations using mobile tracking data,” Tourism Management, vol. 57, pp. 202–212, 2016. View at Publisher · View at Google Scholar · View at Scopus
  44. E. M. Rogers and D. Williams, Diffusion of Innovations, The Free Press, Glencoe, IL, USA, 1983.
  45. M. J. Kim, C.-K. Lee, and N. S. Contractor, “Seniors’ usage of mobile social network sites: applying theories of innovation diffusion and uses and gratifications,” Computers in Human Behavior, vol. 90, pp. 60–73, 2019. View at Publisher · View at Google Scholar · View at Scopus
  46. P. Sugarhood, J. Wherton, R. Procter, S. Hinder, and T. Greenhalgh, “Technology as system innovation: a key informant interview study of the application of the diffusion of innovation model to telecare,” Disability and Rehabilitation: Assistive Technology, vol. 9, no. 1, pp. 79–87, 2014. View at Publisher · View at Google Scholar · View at Scopus
  47. H.-S. Chiang, “Continuous usage of social networking sites,” Online Information Review, vol. 37, no. 6, pp. 851–871, 2013. View at Publisher · View at Google Scholar · View at Scopus
  48. T. S. Robertson, “The process of innovation and the diffusion of innovation,” Journal of Marketing, vol. 31, no. 1, pp. 14–19, 1967. View at Publisher · View at Google Scholar
  49. S. Dabphet, N. Scott, and L. Ruhanen, “Applying diffusion theory to destination stakeholder understanding of sustainable tourism development: a case from Thailand,” Journal of Sustainable Tourism, vol. 20, no. 8, pp. 1107–1124, 2012. View at Publisher · View at Google Scholar · View at Scopus
  50. A. Ganglmair-Wooliscroft and B. Wooliscroft, “Diffusion of innovation: the case of ethical tourism behavior,” Journal of Business Research, vol. 69, no. 8, pp. 2711–2720, 2016. View at Publisher · View at Google Scholar · View at Scopus
  51. R. Agarwal, “Individual acceptance of information technologies,” Educational Technology Research and Development, vol. 40, pp. 90–102, 2000. View at Google Scholar
  52. Y. Lu, S. Yang, P. Y. K. Chau, and Y. Cao, “Dynamics between the trust transfer process and intention to use mobile payment services: a cross-environment perspective,” Information & Management, vol. 48, no. 8, pp. 393–403, 2011. View at Publisher · View at Google Scholar · View at Scopus
  53. S.-I. Cheng, S.-C. Chen, and D. C. Yen, “Continuance intention of E-portfolio system: a confirmatory and multigroup invariance analysis of technology acceptance model,” Computer Standards & Interfaces, vol. 42, pp. 17–23, 2015. View at Publisher · View at Google Scholar · View at Scopus
  54. R. Scherer, F. Siddiq, and J. Tondeur, “The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education,” Computers & Education, vol. 128, pp. 13–35, 2019. View at Publisher · View at Google Scholar · View at Scopus
  55. N. Park, M. Rhoads, J. Hou, and K. M. Lee, “Understanding the acceptance of teleconferencing systems among employees: an extension of the technology acceptance model,” Computers in Human Behavior, vol. 39, pp. 118–127, 2014. View at Publisher · View at Google Scholar · View at Scopus
  56. A. Shukla and S. K. Sharma, “Evaluating consumers’ adoption of mobile technology for grocery shopping: an application of technology acceptance model,” Vision: The Journal of Business Perspective, vol. 22, no. 2, pp. 185–198, 2018. View at Publisher · View at Google Scholar · View at Scopus
  57. M. Fishbein and I. Ajzen, Understanding Attitudes and Predicting Social Behavior, Prentice Hall, Upper Saddle River, NJ, USA, 1980.
  58. S. Taylor and P. Todd, “Assessing IT usage: the role of prior experience,” MIS Quarterly, vol. 19, no. 4, pp. 561–570, 1995. View at Publisher · View at Google Scholar
  59. V. Venkatesh and F. D. Davis, “A theoretical extension of the technology acceptance model: four longitudinal field studies,” Management Science, vol. 46, no. 2, pp. 186–204, 2000. View at Publisher · View at Google Scholar · View at Scopus
  60. J. Kim, “An extended technology acceptance model in behavioral intention toward hotel tablet apps with moderating effects of gender and age,” International Journal of Contemporary Hospitality Management, vol. 28, no. 8, pp. 1535–1553, 2016. View at Publisher · View at Google Scholar · View at Scopus
  61. C. Morosan and A. DeFranco, “When tradition meets the new technology: an examination of the antecedents of attitudes and intentions to use mobile devices in private clubs,” International Journal of Hospitality Management, vol. 42, pp. 126–136, 2014. View at Publisher · View at Google Scholar · View at Scopus
  62. K. T. Manis and D. Choi, “The virtual reality hardware acceptance model (VR-HAM): extending and individuating the technology acceptance model (TAM) for virtual reality hardware,” Journal of Business Research, vol. 100, pp. 503–513, 2019. View at Publisher · View at Google Scholar · View at Scopus
  63. H. P. Lu and P. Y.-J. Su, “Factors affecting purchase intention on mobile shopping web sites,” Internet Research, vol. 19, no. 4, pp. 442–458, 2009. View at Publisher · View at Google Scholar · View at Scopus
  64. D. J. Kim, D. L. Ferrin, and H. R. Rao, “A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents,” Decision Support Systems, vol. 44, no. 2, pp. 544–564, 2008. View at Publisher · View at Google Scholar · View at Scopus
  65. C. Morosan, “Theoretical and empirical considerations of guests’ perceptions of biometric systems in hotels,” Journal of Hospitality & Tourism Research, vol. 36, no. 1, pp. 52–84, 2012. View at Publisher · View at Google Scholar · View at Scopus
  66. V. Venkatesh and H. Bala, “Technology acceptance model 3 and a research agenda on interventions,” Decision Sciences, vol. 39, no. 2, pp. 273–315, 2008. View at Publisher · View at Google Scholar · View at Scopus
  67. Y. Lee, K. A. Kozar, and K. R. Larsen, “The technology acceptance model: past, present, and future,” Communications of the Association for Information Systems, vol. 12, no. 1, p. 50, 2003. View at Publisher · View at Google Scholar
  68. P. Esmaeilzadeh, M. Sambasivan, and H. Nezakati, “The limitations of using the existing tam in adoption of clinical decision support system in hospitals: an empirical study in Malaysia,” International Journal of Research in Business and Social Science, vol. 3, no. 2, pp. 56–68, 2014. View at Publisher · View at Google Scholar
  69. E. M. Rogers and F. F. Shoemaker, “Communication of innovations: a cross-cultural approach,” American Anthropologist, vol. 74, no. 6, pp. 1375-1376, 1972. View at Publisher · View at Google Scholar
  70. A. Lin and N.-C. Chen, “Cloud computing as an innovation: percepetion, attitude, and adoption,” International Journal of Information Management, vol. 32, no. 6, pp. 533–540, 2012. View at Publisher · View at Google Scholar · View at Scopus
  71. K.-C. Chung, “Gender, culture and determinants of behavioural intents to adopt mobile commerce among the Y Generation in transition economies: evidence from Kazakhstan,” Behaviour & Information Technology, vol. 33, no. 7, pp. 743–756, 2014. View at Publisher · View at Google Scholar · View at Scopus
  72. A. Duane, P. O’Reilly, and P. Andreev, “Realising M-Payments: modelling consumers’ willingness to M-pay using Smart Phones,” Behaviour & Information Technology, vol. 33, no. 4, pp. 318–334, 2014. View at Publisher · View at Google Scholar · View at Scopus
  73. K. Zhu, K. L. Kraemer, and S. Xu, “The process of innovation assimilation by firms in different countries: a technology diffusion perspective on E-business,” Management Science, vol. 52, no. 10, pp. 1557–1576, 2006. View at Publisher · View at Google Scholar · View at Scopus
  74. N. Zhang, X. Guo, and G. Chen, “IDT-TAM integrated model for IT adoption,” Tsinghua Science and Technology, vol. 13, no. 3, pp. 306–311, 2008. View at Publisher · View at Google Scholar · View at Scopus
  75. L. R. Vijayasarathy, “Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model,” Information & Management, vol. 41, no. 6, pp. 747–762, 2004. View at Publisher · View at Google Scholar · View at Scopus
  76. L.-D. Chen, M. L. Gillenson, and D. L. Sherrell, “Enticing online consumers: an extended technology acceptance perspective,” Information & Management, vol. 39, no. 8, pp. 705–719, 2002. View at Publisher · View at Google Scholar · View at Scopus
  77. L. Qiu and D. Li, “Applying TAM in B2C E-commerce research: an extended model,” Tsinghua Science and Technology, vol. 13, no. 3, pp. 265–272, 2008. View at Publisher · View at Google Scholar · View at Scopus
  78. S. Han, J. Min, and H. Lee, “Antecedents of social presence and gratification of social connection needs in SNS: a study of Twitter users and their mobile and non-mobile usage,” International Journal of Information Management, vol. 35, no. 4, pp. 459–471, 2015. View at Publisher · View at Google Scholar · View at Scopus
  79. F. Biocca, T. Kim, and M. R. Levy, “The vision of virtual reality,” in Communication in the Age of Virtual Reality, pp. 3–14, Lawrence Erlbaum Associates, Mahwah, NJ, USA, 1995. View at Google Scholar
  80. J. Fulk, C. W. Steinfield, J. Schmitz, and J. G. Power, “A social information processing model of media use in organizations,” Communication Research, vol. 14, no. 5, pp. 529–552, 1987. View at Publisher · View at Google Scholar · View at Scopus
  81. E. B. Parker, J. Short, E. Williams, and B. Christie, “The social psychology of telecommunications,” Contemporary Sociology, vol. 7, no. 1, p. 32, 1978. View at Publisher · View at Google Scholar
  82. A. P. Massey and M. M. Montoya-Weiss, “Unraveling the temporal fabric of knowledge conversion: a model of media selection and use,” MIS Quarterly, vol. 30, no. 1, pp. 99–114, 2006. View at Publisher · View at Google Scholar
  83. R. E. Rice, D. Hughes, and G. Love, “Usage and outcomes of electronic messaging at an R&D organization: situational constraints, job level, and media awareness,” Office Technology and People, vol. 5, no. 2, pp. 141–161, 1989. View at Publisher · View at Google Scholar
  84. C.-H. Tu and M. McIsaac, “The relationship of social presence and interaction in online classes,” American Journal of Distance Education, vol. 16, no. 3, pp. 131–150, 2002. View at Publisher · View at Google Scholar
  85. P. A. Pavlou, H. Liang, and Y. Xue, “Understanding and mitigating uncertainty in online exchange relationships: a principal-agent perspective,” MIS Quarterly, vol. 31, no. 1, pp. 105–136, 2007. View at Publisher · View at Google Scholar
  86. A. M. Kaplan and M. Haenlein, “Users of the world, unite! the challenges and opportunities of Social Media,” Business Horizons, vol. 53, no. 1, pp. 59–68, 2010. View at Publisher · View at Google Scholar · View at Scopus
  87. J. Wei, S. Seedorf, P. B. Lowry, C. Thum, and T. Schulze, “How increased social presence through co-browsing influences user engagement in collaborative online shopping,” Electronic Commerce Research and Applications, vol. 24, pp. 84–99, 2017. View at Publisher · View at Google Scholar · View at Scopus
  88. S. O. Ogara, C. E. Koh, and V. R. Prybutok, “Investigating factors affecting social presence and user satisfaction with mobile instant messaging,” Computers in Human Behavior, vol. 36, pp. 453–459, 2014. View at Publisher · View at Google Scholar · View at Scopus
  89. D. Lee, J. Yejean Park, J. Kim, J. Kim, and J. Moon, “Understanding music sharing behaviour on social network services,” Online Information Review, vol. 35, no. 5, pp. 716–733, 2011. View at Publisher · View at Google Scholar · View at Scopus
  90. B.-W. Park and K. C. Lee, “Effects of knowledge sharing and social presence on the intention to continuously use social networking sites: the case of twitter in Korea,” in Proceedings of the International Conference on U-and E-Service, Science and Technology, Springer, Jeju Island, Korea, December 2010.
  91. C. Xu, S. Ryan, V. Prybutok, and C. Wen, “It is not for fun: an examination of social network site usage,” Information & Management, vol. 49, no. 5, pp. 210–217, 2012. View at Publisher · View at Google Scholar · View at Scopus
  92. D. Cyr, K. Hassanein, M. Head, and A. Ivanov, “The role of social presence in establishing loyalty in E-service environments,” Interacting with Computers, vol. 19, no. 1, pp. 43–56, 2007. View at Publisher · View at Google Scholar · View at Scopus
  93. K. Hassanein and M. Head, “The impact of infusing social presence in the web interface: an investigation across product types,” International Journal of Electronic Commerce, vol. 10, no. 2, pp. 31–55, 2005. View at Publisher · View at Google Scholar · View at Scopus
  94. K. Hassanein and M. Head, “Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping,” International Journal of Human-Computer Studies, vol. 65, no. 8, pp. 689–708, 2007. View at Publisher · View at Google Scholar · View at Scopus
  95. J. Shen, “Social comparison, social presence, and enjoyment in the acceptance of social shopping websites,” Journal of Electronic Commerce Research, vol. 13, no. 3, pp. 198–212, 2012. View at Google Scholar
  96. J. A. Smith and S. A. Sivo, “Predicting continued use of online teacher professional development and the influence of social presence and sociability,” British Journal of Educational Technology, vol. 43, no. 6, pp. 871–882, 2012. View at Publisher · View at Google Scholar · View at Scopus
  97. F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “Extrinsic and intrinsic motivation to use computers in the Workplace1,” Journal of Applied Social Psychology, vol. 22, no. 14, pp. 1111–1132, 1992. View at Publisher · View at Google Scholar · View at Scopus
  98. Y. Liu and H. Li, “Mobile internet diffusion in China: an empirical study,” Industrial Management & Data Systems, vol. 110, no. 3, pp. 309–324, 2010. View at Publisher · View at Google Scholar · View at Scopus
  99. D. C. Karaiskos, D. A. Drossos, A. S. Tsiaousis, G. M. Giaglis, and K. G. Fouskas, “Affective and social determinants of mobile data services adoption,” Behaviour & Information Technology, vol. 31, no. 3, pp. 209–219, 2012. View at Publisher · View at Google Scholar · View at Scopus
  100. S.-P. Jeng, “Online gift-searching:gift-giving orientations and perceived benefits of searching,” Online Information Review, vol. 37, no. 5, pp. 771–786, 2013. View at Publisher · View at Google Scholar · View at Scopus
  101. J. Young Im and M. Hancer, “Shaping travelers’ attitude toward travel mobile applications,” Journal of Hospitality and Tourism Technology, vol. 5, no. 2, pp. 177–193, 2014. View at Publisher · View at Google Scholar · View at Scopus
  102. U. Scholl-Grissemann and B. Schnurr, “Room with a view: how hedonic and utilitarian choice options of online travel agencies affect consumers’ booking intentions,” International Journal of Culture, Tourism and Hospitality Research, vol. 10, no. 4, pp. 361–376, 2016. View at Publisher · View at Google Scholar · View at Scopus
  103. A. B. Ozturk, K. Nusair, F. Okumus, and N. Hua, “The role of utilitarian and hedonic values on users’ continued usage intention in a mobile hotel booking environment,” International Journal of Hospitality Management, vol. 57, pp. 106–115, 2016. View at Publisher · View at Google Scholar · View at Scopus
  104. F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly, vol. 13, no. 3, pp. 319–340, 1989. View at Publisher · View at Google Scholar
  105. T. Pikkarainen, K. Pikkarainen, H. Karjaluoto, and S. Pahnila, “Consumer acceptance of online banking: an extension of the technology acceptance model,” Internet Research, vol. 14, no. 3, pp. 224–235, 2004. View at Publisher · View at Google Scholar · View at Scopus
  106. D. H. Zhu and Y. P. Chang, “Investigating consumer attitude and intention toward free trials of technology-based services,” Computers in Human Behavior, vol. 30, pp. 328–334, 2014. View at Publisher · View at Google Scholar · View at Scopus
  107. J. K. Ayeh, “Travellers’ acceptance of consumer-generated media: an integrated model of technology acceptance and source credibility theories,” Computers in Human Behavior, vol. 48, pp. 173–180, 2015. View at Publisher · View at Google Scholar · View at Scopus
  108. Y. K. Choi and J. W. Totten, “Self-construal’s role in mobile TV acceptance: extension of TAM across cultures,” Journal of Business Research, vol. 65, no. 11, pp. 1525–1533, 2012. View at Publisher · View at Google Scholar · View at Scopus
  109. V. Dutot, “Factors influencing near field communication (NFC) adoption: an extended TAM approach,” Journal of High Technology Management Research, vol. 26, no. 1, pp. 45–57, 2015. View at Publisher · View at Google Scholar · View at Scopus
  110. J.-J. Sim, G. W.-H. Tan, J. C. J. Wong, K.-B. Ooi, and T.-S. Hew, “Understanding and predicting the motivators of mobile music acceptance—a multi-stage MRA-artificial neural network approach,” Telematics and Informatics, vol. 31, no. 4, pp. 569–584, 2014. View at Publisher · View at Google Scholar · View at Scopus
  111. Y. C. Lee, “An empirical investigation into factors influencing the adoption of an e-learning system,” Online Information Review, vol. 30, no. 5, pp. 517–541, 2006. View at Publisher · View at Google Scholar · View at Scopus
  112. C.-L. Hsu and H.-P. Lu, “Why do people play on-line games? An extended TAM with social influences and flow experience,” Information & Management, vol. 41, no. 7, pp. 853–868, 2004. View at Publisher · View at Google Scholar · View at Scopus
  113. D. A. Adams, R. R. Nelson, and P. A. Todd, “Perceived usefulness, ease of use, and usage of information technology: a replication,” MIS Quarterly, vol. 16, no. 2, pp. 227–247, 1992. View at Publisher · View at Google Scholar
  114. C.-H. Liao, C.-W. Tsou, and Y.-C. Shu, “The roles of perceived enjoyment and price perception in determining acceptance of multimedia-on-demand,” International Journal of Business and Information, vol. 3, no. 1, pp. 27–52, 2008. View at Google Scholar
  115. C.-C. Chang, S.-W. Hung, M.-J. Cheng, and C.-Y. Wu, “Exploring the intention to continue using social networking sites: the case of Facebook,” Technological Forecasting and Social Change, vol. 95, pp. 48–56, 2015. View at Publisher · View at Google Scholar · View at Scopus
  116. F. Liébana-Cabanillas, J. Sánchez-Fernández, and F. Muñoz-Leiva, “The moderating effect of experience in the adoption of mobile payment tools in virtual social networks: the m-Payment acceptance model in virtual social networks (MPAM-VSN),” International Journal of Information Management, vol. 34, no. 2, pp. 151–166, 2014. View at Publisher · View at Google Scholar · View at Scopus
  117. S. Nikou and H. Bouwman, “Ubiquitous use of mobile social network services,” Telematics and Informatics, vol. 31, no. 3, pp. 422–433, 2014. View at Publisher · View at Google Scholar · View at Scopus
  118. R. Rauniar, G. Rawski, J. Yang, and B. Johnson, “Technology acceptance model (TAM) and social media usage: an empirical study on Facebook,” Journal of Enterprise Information Management, vol. 27, no. 1, pp. 6–30, 2014. View at Publisher · View at Google Scholar · View at Scopus
  119. M. Jiang and H. Xu, “Exploring online structures on Chinese government portals: citizen political participation and government legitimation,” Social Science Computer Review, vol. 27, no. 2, pp. 174–195, 2009. View at Publisher · View at Google Scholar · View at Scopus
  120. P. J.-H. Hu, S. A. Brown, J. Y. L. Thong, F. K. Y. Chan, and K. Y. Tam, “Determinants of service quality and continuance intention of online services: the case of eTax,” Journal of the American Society for Information Science and Technology, vol. 60, no. 2, pp. 292–306, 2009. View at Publisher · View at Google Scholar · View at Scopus
  121. S. K. Chitungo and S. Munongo, “Extending the technology acceptance model to mobile banking adoption in rural Zimbabwe,” Journal of Business Administration and Education, vol. 3, no. 1, pp. 51–79, 2013. View at Google Scholar
  122. N. Sanakulov and H. Karjaluoto, “Consumer adoption of mobile technologies: a literature review,” International Journal of Mobile Communications, vol. 13, no. 3, p. 244, 2015. View at Publisher · View at Google Scholar · View at Scopus
  123. S. Yang, B. Wang, and Y. Lu, “Exploring the dual outcomes of mobile social networking service enjoyment: the roles of social self-efficacy and habit,” Computers in Human Behavior, vol. 64, pp. 486–496, 2016. View at Publisher · View at Google Scholar · View at Scopus
  124. F. Kawaf and S. Tagg, “Online shopping environments in fashion shopping: an S-O-R based review,” The Marketing Review, vol. 12, no. 2, pp. 161–180, 2012. View at Publisher · View at Google Scholar
  125. P. Guriting and N. Oly Ndubisi, “Borneo online banking: evaluating customer perceptions and behavioural intention,” Management Research News, vol. 29, no. 1-2, pp. 6–15, 2006. View at Publisher · View at Google Scholar · View at Scopus
  126. J.-H. Wu, S.-C. Wang, and L.-M. Lin, “Mobile computing acceptance factors in the healthcare industry: a structural equation model,” International Journal of Medical Informatics, vol. 76, no. 1, pp. 66–77, 2007. View at Publisher · View at Google Scholar · View at Scopus
  127. T. Ramayah and M. C. Lo, “Impact of shared beliefs on “perceived usefulness” and “ease of use” in the implementation of an enterprise resource planning system,” Management Research News, vol. 30, no. 6, pp. 420–431, 2007. View at Publisher · View at Google Scholar · View at Scopus
  128. C.-L. Hsu, K.-C. Chang, and M.-C. Chen, “The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators,” Information Systems and e-Business Management, vol. 10, no. 4, pp. 549–570, 2012. View at Publisher · View at Google Scholar · View at Scopus
  129. S. D. Hunt, R. D. Sparkman Jr., and J. B. Wilcox, “The pretest in survey research: issues and preliminary findings,” Journal of Marketing Research, vol. 19, no. 2, pp. 269–273, 1982. View at Publisher · View at Google Scholar
  130. A. Kasim and A. Alfandi, “Managing destination image for potential gulf countries tourists via communication effects assessment: the case of Malaysia,” International Journal of Business & Society, vol. 15, no. 3, 2014. View at Google Scholar
  131. R. Bhukya and S. Singh, “The effect of perceived risk dimensions on purchase intention,” American Journal of Business, vol. 30, no. 4, pp. 218–230, 2015. View at Publisher · View at Google Scholar
  132. A. M. Elbedweihy, C. Jayawardhena, M. H. Elsharnouby, and T. H. Elsharnouby, “Customer relationship building: the role of brand attractiveness and consumer-brand identification,” Journal of Business Research, vol. 69, no. 8, pp. 2901–2910, 2016. View at Publisher · View at Google Scholar · View at Scopus
  133. J.-J. Hew, M. N. B. A. Badaruddin, and M. K. Moorthy, “Crafting a smartphone repurchase decision making process: do brand attachment and gender matter?” Telematics and Informatics, vol. 34, no. 4, pp. 34–56, 2017. View at Publisher · View at Google Scholar · View at Scopus
  134. J.-J. Hew, G. W.-H. Tan, B. Lin, and K.-B. Ooi, “Generating travel-related contents through mobile social tourism: does privacy paradox persist?” Telematics and Informatics, vol. 34, no. 7, pp. 914–935, 2017. View at Publisher · View at Google Scholar · View at Scopus
  135. P. M. Podsakoff, S. B. MacKenzie, J.-Y. Lee, and N. P. Podsakoff, “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, 2003. View at Publisher · View at Google Scholar · View at Scopus
  136. M. Falahat, G. Knight, and I. Alon, “Orientations and capabilities of born global firms from emerging markets,” International Marketing Review, vol. 35, no. 6, pp. 936–957, 2018. View at Publisher · View at Google Scholar · View at Scopus
  137. R. P. Bagozzi and Y. Yi, “On the evaluation of structural equation models,” Journal of the Academy of Marketing Science, vol. 16, no. 1, pp. 74–94, 1988. View at Publisher · View at Google Scholar · View at Scopus
  138. J. F. Hair, R. E. Anderson, R. L. Tatham, and C. William, Multivariate Data Analysis, Prentice-Hall International, Upper Saddle River, NJ, USA, 1998.
  139. M. J. Kim, C.-K. Lee, J. F. Petrick, and S. S. Hahn, “Factors affecting international event visitors’ behavioral intentions: the moderating role of attachment avoidance,” Journal of Travel & Tourism Marketing, vol. 35, no. 8, pp. 1027–1042, 2018. View at Publisher · View at Google Scholar · View at Scopus
  140. J. F. Hair Jr., M. Sarstedt, L. Hopkins, and V. G. Kuppelwieser, “Partial least squares structural equation modeling (PLS-SEM),” European Business Review, vol. 26, no. 2, pp. 106–121, 2014. View at Publisher · View at Google Scholar · View at Scopus
  141. I. U. Khan, Z. Hameed, Y. Yu, T. Islam, Z. Sheikh, and S. U. Khan, “Predicting the acceptance of MOOCs in a developing country: application of task-technology fit model, social motivation, and self-determination theory,” Telematics and Informatics, vol. 35, no. 4, pp. 964–978, 2018. View at Publisher · View at Google Scholar · View at Scopus
  142. J. Nunnally and I. Bernstein, Psychometric Theory, McGraw-Hill, New York, USA, 1978.
  143. C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” Journal of Marketing Research, vol. 18, no. 1, pp. 39–50, 1981. View at Publisher · View at Google Scholar
  144. J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” Journal of the Academy of Marketing Science, vol. 43, no. 1, pp. 115–135, 2015. View at Publisher · View at Google Scholar · View at Scopus
  145. C.-L. Hsu and J. C.-C. Lin, “An empirical examination of consumer adoption of Internet of Things services: network externalities and concern for information privacy perspectives,” Computers in Human Behavior, vol. 62, pp. 516–527, 2016. View at Publisher · View at Google Scholar · View at Scopus
  146. C. M. Ringle, S. Wende, and J.-M. Becker, SmartPLS 3, SmartPLS GmbH, Boenningstedt, Germany, 2015, http://www.smartpls.com.
  147. J. F. Hair Jr., G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage Publications, Thousand Oaks, CA, USA, 2016.
  148. J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, and K. O. Thiele, “Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods,” Journal of the Academy of Marketing Science, vol. 45, no. 5, pp. 616–632, 2017. View at Publisher · View at Google Scholar · View at Scopus
  149. X. Zhao, J. G. Lynch Jr., and Q. Chen, “Reconsidering baron and kenny: myths and truths about mediation analysis,” Journal of Consumer Research, vol. 37, no. 2, pp. 197–206, 2010. View at Publisher · View at Google Scholar · View at Scopus
  150. R. M. Baron and D. A. Kenny, “The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations,” Journal of Personality and Social Psychology, vol. 51, no. 6, pp. 1173–1182, 1986. View at Publisher · View at Google Scholar · View at Scopus
  151. W. Qazi, S. A. Raza, and N. Shah, “Acceptance of e-book reading among higher education students in a developing country: the modified diffusion innovation theory,” International Journal of Business Information Systems, vol. 27, no. 2, pp. 222–245, 2018. View at Publisher · View at Google Scholar
  152. I. Sahin, “Detailed review of Rogers’ diffusion of innovations theory and educational technology-related studies based on Rogers’ theory,” Turkish Online Journal of Educational Technology (TOJET), vol. 5, no. 2, pp. 14–23, 2006. View at Google Scholar
  153. S. Ainin, F. Parveen, S. Moghavvemi, N. I. Jaafar, and N. L. Mohd Shuib, “Factors influencing the use of social media by SMEs and its performance outcomes,” Industrial Management & Data Systems, vol. 115, no. 3, pp. 570–588, 2015. View at Publisher · View at Google Scholar · View at Scopus
  154. S.-Y. Hung, W.-H. Hung, C.-A. Tsai, and S.-C. Jiang, “Critical factors of hospital adoption on CRM system: organizational and information system perspectives,” Decision Support Systems, vol. 48, no. 4, pp. 592–603, 2010. View at Publisher · View at Google Scholar · View at Scopus
  155. N. Mallat and V. K. Tuunainen, “Exploring merchant adoption of mobile payment systems: an empirical study,” e-Service Journal, vol. 6, no. 2, pp. 24–57, 2008. View at Publisher · View at Google Scholar
  156. H.-F. Lin, “An empirical investigation of mobile banking adoption: the effect of innovation attributes and knowledge-based trust,” International Journal of Information Management, vol. 31, no. 3, pp. 252–260, 2011. View at Publisher · View at Google Scholar · View at Scopus
  157. H. Hamad, I. Elbeltagi, and H. El-Gohary, “An empirical investigation of business-to-business e-commerce adoption and its impact on SMEs competitive advantage: the case of Egyptian manufacturing SMEs,” Strategic Change, vol. 27, no. 3, pp. 209–229, 2018. View at Publisher · View at Google Scholar · View at Scopus
  158. L. A. Hussein, A. S. Baharudin, K. Jayaraman, and S. Kiumarsi, “B2B e-commerce technology factors with mediating effect perceived usefulness in Jordanian manufacturing SMES,” Journal of Engineering Science and Technology, vol. 14, no. 1, pp. 411–429, 2019. View at Google Scholar
  159. S. Z. Ahmad, A. R. Abu Bakar, and N. Ahmad, “Social media adoption and its impact on firm performance: the case of the UAE,” International Journal of Entrepreneurial Behavior & Research, vol. 25, no. 1, pp. 84–111, 2019. View at Publisher · View at Google Scholar · View at Scopus
  160. N. Mallat, M. Rossi, V. K. Tuunainen, and A. Öörni, “The impact of use context on mobile services acceptance: the case of mobile ticketing,” Information & Management, vol. 46, no. 3, pp. 190–195, 2009. View at Publisher · View at Google Scholar · View at Scopus
  161. I. P. Tussyadiah, “An exploratory study on drivers and deterrents of collaborative consumption in travel,” in Information and Communication Technologies in Tourism 2015, pp. 817–830, Springer, Berlin, Germany, 2015. View at Google Scholar
  162. S. Ye, T. Ying, L. Zhou, and T. Wang, “Enhancing customer trust in peer-to-peer accommodation: a “soft” strategy via social presence,” International Journal of Hospitality Management, vol. 79, pp. 1–10, 2019. View at Publisher · View at Google Scholar · View at Scopus
  163. L. W. Leong, O. Ibrahim, M. Dalvi-Esfahani, H. Shahbazi, and M. Nilashi, “The moderating effect of experience on the intention to adopt mobile social network sites for pedagogical purposes: an extension of the technology acceptance model,” Education and Information Technologies, vol. 23, no. 6, pp. 2477–2498, 2018. View at Publisher · View at Google Scholar · View at Scopus
  164. R. Nunkoo and H. Ramkissoon, “Travelers’ E-purchase intent of tourism products and services,” Journal of Hospitality Marketing & Management, vol. 22, no. 5, pp. 505–529, 2013. View at Publisher · View at Google Scholar · View at Scopus
  165. A. A. Alalwan, A. M. Baabdullah, N. P. Rana, K. Tamilmani, and Y. K. Dwivedi, “Examining adoption of mobile internet in Saudi Arabia: extending TAM with perceived enjoyment, innovativeness and trust,” Technology in Society, vol. 55, pp. 100–110, 2018. View at Publisher · View at Google Scholar · View at Scopus
  166. S. Ha and L. Stoel, “Consumer e-shopping acceptance: antecedents in a technology acceptance model,” Journal of Business Research, vol. 62, no. 5, pp. 565–571, 2009. View at Publisher · View at Google Scholar · View at Scopus
  167. H. Van der Heijden, “Factors influencing the usage of websites: the case of a generic portal in Netherlands,” Information & Management, vol. 40, no. 6, pp. 541–549, 2003. View at Publisher · View at Google Scholar · View at Scopus
  168. R. Henderson and M. J. Divett, “Perceived usefulness, ease of use and electronic supermarket use,” International Journal of Human-Computer Studies, vol. 59, no. 3, pp. 383–395, 2003. View at Publisher · View at Google Scholar · View at Scopus
  169. Y.-S. Wang, S.-C. Wu, H.-H. Lin, Y.-M. Wang, and T.-R. He, “Determinants of user adoption of web “automatic teller machines”: an integrated model of “transaction cost theory” and “innovation diffusion theory”,” Service Industries Journal, vol. 32, no. 9, pp. 1505–1525, 2012. View at Publisher · View at Google Scholar · View at Scopus
  170. F. O. Bankole and O. O. Bankole, “The effects of cultural dimension on ICT innovation: empirical analysis of mobile phone services,” Telematics and Informatics, vol. 34, no. 2, pp. 490–505, 2017. View at Publisher · View at Google Scholar · View at Scopus
  171. I. U. Khan, Z. Hameed, and S. U. Khan, “Understanding online banking adoption in a developing country,” Journal of Global Information Management, vol. 25, no. 1, pp. 43–65, 2017. View at Publisher · View at Google Scholar · View at Scopus
  172. C. M. Ringle, M. Sarstedt, and R. Schlittgen, “Finite mixture and genetic algorithm segmentation in partial least squares path modeling: identification of multiple segments in complex path models,” in Advances in Data Analysis, Data Handling and Business Intelligence, pp. 167–176, Springer, Berlin, Germany, 2009. View at Google Scholar