What determines tourist adoption of smartphone apps? An analysis based on the UTAUT-2 framework

Anil Gupta (School of Hospitality and Tourism Management, University of Jammu, Jammu, India)
Nikita Dogra (School of Hospitality and Tourism Management, University of Jammu, Jammu, India)
Babu George (Robbins College of Business and Entrepreneurship, Fort Hays State University, Hays, Kansas, USA)

Journal of Hospitality and Tourism Technology

ISSN: 1757-9880

Publication date: 12 March 2018



This study aims to identify factors affecting tourists’ intention of using travel apps installed in their smartphones.


A questionnaire was developed largely based on the available scales in the published literature. A total of 389 participants responded to the survey, out of which 343 valid responses were obtained for statistical analysis.


Significant predictors of smartphone app usage intention included performance expectancy, social influence, price saving, perceived risk, perceived trust and prior usage habits. Usage behavior was largely mediated by usage intention, except in the case of habits. Contrary to the expectation, factors such as hedonistic motivation, facilitating conditions or effort expectancy did not impact usage intention or behavior.

Practical implications

The study gives app developers vital cues on tourist expectations from the apps. Oftentimes, developers tend to focus entirely on the material utility of their apps, neglecting every other factor influencing use. One particular implication is that despite tourism being a hedonistic activity, travel app usage behavior is not a hedonistic activity.


This is one of the few studies to examine adoption of smartphone travel apps in an emerging economy context by using extended unified theory of acceptance and use of technology framework with additional constructs.













Gupta, A., Dogra, N. and George, B. (2018), "What determines tourist adoption of smartphone apps?", Journal of Hospitality and Tourism Technology, Vol. 9 No. 1, pp. 50-64. https://doi.org/10.1108/JHTT-02-2017-0013

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

1. Introduction

Smartphones usage, especially mobile applications, has not only affected everyday life but also has a significant influence on the tourism industry and travel behavior of people. Modern travelers enhance their travel experience by using smart technology (Karanasios et al., 2012), and to enhance it, a wide variety of smartphone apps are available across spectrum of travel services, including transport planning (Uber and Skyscanner), travel planning (TripIT and Tripadvisor), accommodation planning (Booking.com and Expedia), tour guide (DETOUR and NY Travel guides) and directional services (Google maps). Tourism marketers are positioning their strategy by developing smartphone travel apps around their value proposition (Samy, 2012), as they provide interactive consumer experience (Yu, 2013) and also eliminate the spatial and temporal restrictions.

With growing investment in travel-related apps, user adoption and acceptance have become imperative to ensure successful implementation. Despite the effectiveness and competencies of travel apps, the adoption of these among the consumers is still in its nascent stage (Lu et al., 2015). Recent studies have reported that 30 per cent of people use mobile apps to find hotel deals, 29 per cent use mobile apps to find flight deals, 8.1 per cent use mobile apps to buy tickets and only 15 per cent users specifically download travel apps to plan a trip ahead (How mobile app benefits travel and tourism industry, 2015). It is thus pertinent to study the factors that affect the adoption and usage of travel apps among the consumers. Better understanding of such factors will go a long way for increasing the adoption of smartphone travel apps.

Extant research examines the impact of smartphone apps on tourist experiences (Kramer et al., 2007; Wang et al., 2012), use of smartphones in travel domain (Wang et al., 2016), smartphone adoption during leisure-based tourism (O’Regan and Chang, 2015) and adoption of smartphone apps while traveling to rural sites (Lu et al., 2015) However, there is still a paucity of published literature on understanding the travelers’ intentions to adopt smartphone apps for making travel purchase. In this context, the present study aims to identify factors influencing travelers’ adoption and usage of smartphone apps for travel purchase. To understand this, we use the extended unified theory of acceptance and use of technology (UTAUT2) and integrate perceived risk and perceived trust to the base model.

The significance of this study is twofold. First, it contributes to the mobile app adoption literature, specifically in the context of travel and tourism industry. Second, it identifies the determinants of mobile app adoption for making travel purchase, which can be used by practitioners to increase the adoption. The results of this study contribute to the empirical evidence by identifying the role of perceived risk and perceived trust along with determinants of UTAUT2 in travel mobile apps adoption.

2. Literature review and hypotheses

The extended UTAUT-2, proposed by Venkatesh et al. (2012), an extension of UTAUT (Venkatesh et al., 2003), includes hedonic motivation, price value and habit to the original four constructs: performance expectancy, effort expectancy, social influence and facilitating conditions. UTAUT-2 includes many recent developments in consumer technology adoption literature (Satama, 2014) and has a better predictive validity in technology consumption context (Venkatesh et al., 2012).

Existing research has applied the UTAUT-2 model in several contexts, including adoption of a location-based social media service for travel planning (Chong and Ngai, 2013), online purchasing intentions for low-cost carriers (Escobar-Rodríguez and Carvajal-Trujillo, 2014), consumer adoption of access-based consumption services (Satama, 2014), mobile banking (Baptista and Oliveira, 2015), student adoption of lecture capture systems (Nair et al., 2015) and mobile payments (Oliveira et al., 2016). In the following sections, we review the related literature, identify gaps and propose hypotheses.

2.1 Performance expectancy

Extant research confirms that consumers are likely to use a technology which is more useful and shall bring favorable outcomes as expected by the users (Compeau and Higgins, 1995). Further, existing studies confirm a significant positive relationship between performance expectancy and behavioral intentions in context of online travel purchasing (Escobar-Rodríguez and Carvajal-Trujillo, 2014; Oliveira et al., 2016; Slade et al., 2015a, 2015b), mobile-based communication technologies (Engotoit et al., 2016), mobile wallets (Madan and Yadav, 2016), telebanking services (Alalwan et al., 2016) and m-banking (Tan and Lau, 2016). Thus, we hypothesize:


Performance expectancy positively influences tourists’ behavioral intention to adopt smartphone apps for making travel purchase.

2.2 Effort expectancy

Consumers prefer to use a technology which is easy to understand and can provide maximum benefits (Davis et al., 1989). Effort expectancy has proved to be a strong predictor of behavioral intentions in several contexts, including internet banking (Martins et al., 2014), m-banking (Bhatiasevi, 2015; Koksal, 2016; Tan and Lau, 2016), mobile applications (Hew et al., 2015) and m-payments (Teo et al., 2015). Thus, we hypothesize:


Effort expectancy positively influences tourists’ behavioral intention to adopt smartphone apps for making travel purchase.

2.3 Social influence

According to Ajzen (1991), if a person thinks that the given behavior shall be accepted by his/her peer group, then the person is more likely to form intentions to engage in a given behavior. Existing research confirms that social influence is a strong predictor of behavioral intentions (Chong and Ngai, 2013) across various contexts, including mobile payments (Hongxia et al., 2011; Slade et al., 2015a, 2015b; Tan et al., 2014; Yang et al., 2012), m-commerce (Chong, 2013), social commerce (Akman and Mishra, 2017), m-banking (Bhatiasevi, 2015; Tan and Lau, 2016) and mobile app usage intentions (Hew et al., 2015). Thus, we hypothesize:


Social influence positively influences tourists’ behavioral intention to adopt smartphone apps for making travel purchase.

2.4 Facilitating conditions

In our study, facilitating conditions reflect the effect of necessary resources (internet connectivity and memory in the smartphone to download an app) and required knowledge to engage in travel purchase through smartphone apps. Extant research confirms significant relationship between facilitating condition and behavioral intentions across various contexts, including 3G mobile services (Wu et al., 2008), internet banking (Foon and Fah, 2011), m-learning (Jawad and Hassan, 2015; Kang et al., 2015) and mobile wallets (Madan and Yadav, 2016). According to Venkatesh et al. (2012), both behavioral intentions and actual usage were significantly influenced by facilitating conditions. Thus, we hypothesize:


Facilitating conditions positively influence tourists’ behavioral intention to adopt smartphone apps for making travel purchase.


Facilitating conditions positively influences tourists’ use behavior of smartphone apps for making travel purchase.

2.5 Hedonic motivation

Based on the literature in the consumer context (Brown and Venkatesh, 2005) and information system research (Van der Heijden, 2004), hedonic motivation (an intrinsic motivation) has been considered as an important predictor of technology acceptance and use (Venkatesh et al., 2012). While applying UTAUT-2 framework, hedonic motivation has been found to be strong predictor of adoption of mobile banking (Alalwan et al., 2017; Baptista and Oliveira, 2015), social networking sites (Herrero and San Martín, 2017), e-learning systems (El-Masri and Tarhini, 2017), NFC mobile payments (Morosan and DeFranco, 2016; Slade et al., 2015a, 2015b), online purchase (Escobar-Rodríguez and Carvajal-Trujillo, 2014) and mobile apps (Hew et al., 2015). Thus, we hypothesize:


Hedonic motivation positively influences tourists’ intention to adopt smartphone apps for making travel purchase.

2.6 Price-saving orientation

Various mobile apps across various sectors, including hospitality and tourism, have introduced innovative pricing strategies and provide value by offering cash back offers and price savings. Venkatesh et al. (2012), while developing the UTAUT-2 framework, argued that price value is a significant predictor of behavior intention to use a technology as consumers look for higher perceived benefits in comparison to the monetary sacrifice. Consumers while shopping online look for significant cost savings (Jensen, 2012), and therefore, this price-saving orientation has been observed as a significant predictor of purchase intention (Lien et al., 2015), especially in context of airline e-commerce websites (Escobar-Rodríguez and Carvajal-Trujillo, 2013) and also online booking of low-cost carriers (Escobar-Rodríguez and Carvajal-Trujillo, 2014). Thus, we hypothesize:


Price-saving orientation positively influences tourists’ behavioral intention to adopt smartphone apps for making travel purchase.

2.7 Habit

Extant research on sharing travel experience on social networking sites (Herrero and San Martín, 2017) observed habit as a significant predictor of behavioral intention. Existing studies have also highlighted the significant effects of consumer habit on behavioral intentions and actual usage (Chen et al., 2015; Escobar-Rodríguez and Carvajal-Trujillo, 2014; Hsiao et al., 2016; Ohtonen and Karjaluoto, 2016). Based on these findings, we hypothesize:


Habit positively influences tourists’ behavioral intention to adopt smartphone travel apps.


Habit positively influences tourists’ usage behavior of smartphone travel apps.

2.8 Perceived risk

Perceived risk can be defined as customers’ perception of uncertainty and negative consequences or outcomes associated with the specific behavior (Bauer, 1960; Mandrik and Bao, 2005). Researchers have suggested that perceived risk is more common in online shopping because of the spatial separation between sellers and consumers (Al-Gahtani, 2011). Extant research in e-commerce (Aghekyan-Simonian et al., 2012; Chang and Wu, 2012; Wu and Ke, 2016), m-payments (Slade et al., 2015a, 2015b), m-banking (Mortimer et al., 2015; Tan and Lau, 2016; Yuan et al., 2016) and online travel purchase (Amaro and Duarte, 2015) establish the inverse relationship between consumers’ perceived risk and behavioral intentions. Based on this, we hypothesize:


Perceived risk negatively influences tourists’ behavioral intention to adopt smartphone apps for making travel purchase.

2.9 Perceived trust

Trust is the degree to which a consumer believes in the trustee and feels secured about making any transaction with that particular service provider (Komiak and Benbasat, 2004). Trust is a significant predictor of adoption of e-shopping (Grabosky, 2001; Ha and Stoel, 2009), social networking sites (Sledgianowski and Kulviwat, 2009), mobile shopping and mobile payments (Chong, 2013; Wang and Lin, 2016) and influences online purchase intentions (Ponte et al., 2015; Wen, 2009; Xie et al., 2015) and repeat purchase (Chiu et al., 2010). Based on this, we hypothesize:


Trust positively influences tourists’ behavioral intention to adopt smartphone apps for making travel purchase/bookings.

2.10 Behavioral intentions

According to Fishbein and Ajzen (1975), the behavioral intention is considered as the best predictor of behavior, which is also well established in consumer research literature (Im, et al. 2011; Martins et al 2014). Extant research in the area of m-banking, internet banking, online travel purchase behavior and use of mobile services (Arenas-Gaitan et al., 2015; Baptista and Oliveira, 2015; Escobar-Rodríguez and Carvajal-Trujillo, 2014; Mafe et al., 2010) has established the relationship between behavioral intentions and actual use. Thus, we hypothesize:


Behavioral intention to adopt smartphone apps for travel purchase/booking positively influences tourists’ usage behavior of these apps.

3. Methodology

3.1 Measurement

The survey instrument was developed using existing scales from studies by Baptista and Oliveira (2015), Escobar-Rodríguez and Carvajal-Trujillo (2014) and San Martín and Herrero (2012), items for perceived risk were adopted from studies by Kesharwani and Singh Bisht (2012), Mcleod et al. (2009) and Slade et al. (2015a, 2015b) and items for perceived trust were based on the studies of Escobar-Rodríguez and Carvajal-Trujillo (2014), Harris et al. (2016) and Zhou (2012). All items were measured on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Using the recommendation of Davis et al. (1989) and Ajzen and Fishbein (1980), we measured how often the actual behavior is performed by asking “how often do they use mobile apps for travel bookings,” which was measured on a five-point Likert scale, with 1 = never and 5 = always.

3.2 Sample and data collection

With over 300 million smartphone users, 450 million internet users and six billion mobile app downloads, India is the second largest market for smartphones in the world. For travel companies, such as Cleartrip, Makemytrip, Yatra and Goibibo with more than ten million downloads, India is one of the most lucrative market. The target population of the study, thus selected, was travelers in India. An email invitation with link to the survey was sent to 2,687 Indian domestic tourists served by various travel agencies during April-June 2016, out of which 389 responded to the survey. Of these, only the respondents who had used mobile apps to make travel-related bookings in the past six months were retained in the analysis. A total of 343 valid responses were obtained, providing a response rate of 12.76 per cent. Table I indicates the respondents’ profile

4. Data analysis and results

To test the research hypothesis, we used partial least squares (PLS) using SmartPLS software, Version 3.0 (Ringle et al., 2015). PLS is one of the most popular and powerful statistical technique, because of its ability to calculate the path estimates and the model parameters under conditions of non-normality (Hulland, 1999), and it is also suitable for small-to-medium-sized samples.

4.1 Measurement model

The first stage examined the convergent and discriminant validity, as well as reliability, of all the constructs. Table II shows factor loadings of each item, indicating that all were statistically significant and were above the minimum acceptable value of 0.70 (Fornell and Larcker, 1981). Similarly, the average variance extracted (AVE) scores of all the constructs are indicated in Table II, which shows that all the values are above the desirable threshold of 0.50 (Fornell and Larcker, 1981).

The reliability of the indicators was also verified using composite reliability coefficient (Werts et al, 1974) and Cronbach α (Nunnally and Bernstein, 1994). As the composite reliabilities and Cronbach α coefficients, as presented in Table II, are above the minimum acceptable levels of 0.70 (Churchill, 1979; Gefen et al., 2000), the researchers decided to perform the confirmatory analysis.

The discriminant validity established as the square root of AVE is greater than the correlation between the constructs (Fornell and Larcker, 1981), as presented in Table III.

4.2 Structural model

The structural model and the hypothesized relationships were tested using PLS analysis. The statistical significance and the path coefficients were examined by performing bootstrapping procedure with 5,000 iterations, and the results of the same are summarized in Figure 1 and Table IV.

Habit (β = 0.137, p < 0.05), performance expectancy (β = 0.183, p < 0.05), price-saving orientations (β = 0.181, p < 0.05), social influence (β = 0.170, p < 0.05), perceived risk (β = −0.133, p < 0.05) and perceived trust (β = 0.142, p < 0.05) were found to be statistically significant in explaining behavioral intentions. Further, we observed that behavioral intention (β = 0.272, p < 0.05) is a significant predictor of use behavior, explaining 42.6 per cent of its variance. Surprisingly, effort expectancy (β = 0.047, p > 0.05), facilitating conditions (β = 0.041, p > 0.05) and hedonic motivations (β = 0.074, p > 0.05) were statistically insignificant and had no influence on behavioral intentions to make travel-related purchase through mobile apps.

Habit was the only significant direct antecedent of actual usage behavior in addition to consumers’ behavioral intentions. Overall, the factors under study together explained a variation of 58.1 per cent in behavioral intention and 42.6 per cent in use behavior.

5. Discussion and conclusion

The main objective of this study was to extend the UTAUT-2 framework by integrating perceived risk and perceived trust constructs to investigate the factors that affect the consumers’ intention to use smartphone apps for travel bookings. Our findings support theoretically and empirically the ability of UTAUT-2 to predict the adoption of smartphone apps in travel context. The findings indicate that consumers’ adoption of smartphone apps was significantly affected by price-saving orientation, performance expectancy, social influence, perceived risk, perceived trust and habit. The findings of this study are consistent with those of previous research (Antunes and Amaro, 2016; Arenas-Gaitan et al., 2015; Baptista and Oliveira, 2015; Escobar-Rodríguez and Carvajal-Trujillo, 2014; Hahn et al., 2014; Morosan and DeFranco, 2016; Oliveira et al., 2014). Among these constructs, this study found that performance expectancy is the strongest determinant, followed by price-saving orientation and social influence. Further, the findings of this study indicate that effort expectancy, facilitating conditions, or hedonistic motivation does not significantly predict tourists’ behavioral intentions. Overall, the proposed model achieves acceptable fit and explains 58.1 per cent variation in behavioral intention and 42.6 per cent in use behavior.

5.1 Theoretical implications

The findings of this study contribute to travel technology adoption literature in the emerging market context. While technology adoption has been studied somewhat extensively, specific nuances associated with the adoption of smart phone apps have not received sufficient scholarly attention. It also extends the theorized technology adoption model propounded by Venkatesh et al. (2012) by integrating new constructs, i.e. perceived risk and perceived trust, which are crucial to examine the case of online transactions. Finally, this study contributes to the scarce literature on understanding the adoption and use behavior of smartphone app in context of travel bookings.

5.2 Practical implications

In emerging market context, our study confirms that performance expectancy, price-saving orientation and social influence are top three significant predictors of intention to purchase through mobile apps. Smartphone app developers should take these as important cues. Online marketers should focus on offering various cost saving deals (such as “earn by referring others” or offering extra discounts on using mobile apps for making travel bookings) to add more perceived value and benefits in terms of saving money. App developers should lay emphasis on efficient content development and management. The travel apps providing useful, reliable and accurate information will lead to enhanced perceived utility among the consumers and will ultimately increase the adoption of mobile apps among the tourists. Considering the important role of social influence, it is recommended that the marketers and developers should focus on relationship marketing approach, which enables them to connect with the customers. They must strive to have a good rapport with existing clients to have a favorable opinion, which further would help in spreading positive word-of-mouth. For example, app developers should make a provision of collecting regular feedback from the customers to improve the app performance. Service providers should concentrate on reducing the overall risk (such as financial, product performance, and time/convenience) by ensuring good services, secured transactions and maximum value.

5.3 Limitations and future research

Considering the limitations of the study, the following suggestions are proposed for future research; first, the proposed model predicted just less than half of all the variance in purchase intention or behavior. Thus, it is suggested that future studies can focus on examining the effects of other variables on consumers’ intentions to adopt smartphone apps for making travel purchase/bookings. The other variables would include self-efficacy, personal innovativeness, anxiety to use new technology, perceived credibility, privacy concerns, attitude and perceived security. As the effect of moderating variables included in UTAUT2 was not tested, future studies could also examine the moderating effects of age, gender and experience on the variables influencing behavioral intentions. The studies can also analyze factors behind the resistance to use mobile apps for making travel-related bookings. Finally, the sample to undertake this research was drawn from Indian domestic tourists; therefore, the findings of the study cannot be generalized to users beyond India. It is possible that users from other countries and regions may have different perception and outlook toward mobile app adoption. Thus, future studies can explore the possible variation in consumer needs across different groups and cultures.


Result of the structural model

Figure 1.

Result of the structural model

Gender and age characteristics of the respondents

Characteristics Frequency (%)
Male 198 57.72
Female 145 42.27
Total 343 100
Age (years)
Below 20 50 18.9
20-30 133 38.7
30-40 89 25.9
40-50 71 20.69
Total 343 100

Item loadings, composite reliability, AVE and Cronbach α coefficients

Construct Scale item Loadings Composite reliability AVE Cronbach α
Performance expectancy (PE) PE1 0.732 0.798 0.498 0.666
PE2 0.746
PE3 0.718
PE4 0.603
Effort expectancy (EE) EE1 0.635 0.836 0.563 0.757
EE2 0.797
EE3 0.812
EE4 0.717
Social influence (SI) SI1 0.859 0.917 0.782 0.861
SI2 0.932
SI3 0.854
Facilitating conditions (FC) FC1 0.829 0.844 0.732 0.631
FC2 0.821
Hedonic motivation (HM) HM1 0.853 0.886 0.721 0.807
HM2 0.864
HM3 0.824
Price-saving orientation (PO) PO1 0.789 0.875 0.701 0.789
PO2 0.847
PO3 0.858
Habit (HT) HT1 0.883 0.888 0.726 0.812
HT2 0.894
HT3 0.768
Perceived trust (PT) PT1 0.828 0.847 0.585 0.769
PT2 0.861
PT3 0.625
PT4 0.69
Perceived risk (PR) PR1 0.723 0.875 0.638 0.815
PR2 0.765
PR3 0.841
PR4 0.73
Behavioral intentions (BI) BI1 0.799 0.875 0.700 0.786
BI2 0.877
BI3 0.831
Use behavior (AU) AU1 1.000 1.000 1.000 1.000

Discriminant validity of constructs

AU 1.000
BI 0.405 0.837
EE 0.125 0.42 0.75
FC 0.158 0.301 0.573 0.733
HM 0.242 0.416 0.455 0.322 0.849
HT 0.405 0.455 0.247 0.163 0.425 0.852
PE 0.295 0.465 0.447 0.39 0.266 0.326 0.706

Summary of test results for the structural model

Hypothesis Path Standardized path coefficient p-value Supported? Construct R2
H1 PE-BI 0.183 0.019 Yes Behavioral intention 0.581
H2 EE-BI 0.047 0.615 No
H3 SI-BI 0.170 0.030 Yes
H4a FC-BI 0.041 0.657 No
H5 HM-BI 0.074 0.331 No
H6 PO-BI 0.181 0.009 Yes
H7a HT-BI 0.137 0.036 Yes
H9 PR-BI −0.133 0.024 Yes
H10 PT-BI 0.142 0.019 Yes
H4b FC-AU 0.035 0.721 No Use behavior 0.426
H7b HT-AU 0.278 0.000 Yes
H8 BI-AU 0.272 0.001 Yes


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Corresponding author

Babu George can be contacted at: bpgeorge@fhsu.edu