Paper title (Paper Title style)


Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 1 

Does Consumers' Intention to Purchase Travel Online 
Differ Across Generations? Empirical Evidence from 
Australia 

Shavneet Sharma 

University of the South Pacific 

Fiji 

shavneet.sharma@usp.ac.fj 

Gurmeet Singh 

Graduate School of Business 

University of the South Pacific 

Fiji 

Stephen Pratt 

School of Tourism & Hospitality Management 

University of the South Pacific 

Fiji 

Abstract 

This paper examines the differences in consumers' intention to purchase travel online across 

Millennials and Baby Boomers. The Unified Theory of Acceptance and Use of Technology 2 

(UTAUT2) is extended by including attitude, compatibility, innovativeness, perceived trust, 

and perceived risk variables. Data is collected using online questionnaires from Millennials 

(N=322) and Baby Boomers (N=318) in Australia. Overall, the empirical results revealed that 

factors affecting Australian consumers' intention to purchase travel online differ across 

generations. This study contributes to the literature by extending and testing the 

comprehensive research model to understand consumers' online travel purchase behaviour 

better. 

Keywords: Generational cohorts; Technology acceptance; Online purchase; Travel; 

Millennials; Baby Boomers; UTAUT2; Australia 

1 Introduction 

The past decade has seen a drastic increase in internet connectivity and usage, which has 

reshaped tourist purchase behaviour (Buhalis & Law, 2008). Global users of the internet have 

increased from 16 million is 1995 to approximately 4 billion users by 2018 (ParidahDaud & 

Fung, 2018). Despite this, there are gaps or differences between information and 

communication technology (ICT) usage between user groups. This 'digital divide' is 

influenced by socioeconomic variables such as gender, level of education, generation, and age 

(Friemel, 2016). While some scholars argue that the digital divide between the younger and 

older cohorts is decreasing (Gilleard et al., 2015; Nishijima et al., 2017), especially in the case 

of developed countries, the literature suggests that consumers' online behaviour may still 

differ between these cohorts (Confente & Vigolo, 2018). Age has been a determining factor in 

consumers' intentions and use of online shopping with younger consumers found to be more 

likely to shop online (Khare et al., 2012). Literature shows that market segmentation is more 

efficient through generational cohorts than by age (Schewe et al., 2000). This is because cohort 



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2020, Vol 24, Research Article Online Travel Purchases Across Generations 

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segmentation provides both the insights into factors motivating consumers, which stem from 

common beliefs and values shared by the generation from coming-of-age during a particular 

period of history (Kumar & Sadarangani, 2018).  

This study investigates consumers' intention to purchase travel online across generational 

cohorts. The study compares Millennials (also referred to as Generation Y) and Baby Boomers. 

These two cohorts were selected because these generations respectively represent both 'digital 

natives' and 'digital immigrants' (Coombes, 2009). Digital natives are more comfortable with 

using and learning technology as they have always interacted with this medium when 

compared to digital immigrants who only learned to use this technology as adults (Sait et al., 

2004). These two groups also yield considerable purchasing power (Beauchamp & Barnes, 

2015). As such, marketers see vast potential when it comes to capturing sales from these two 

important consumer groups. 

The contribution of this paper is threefold. First, the UTAUT2 technology acceptance model 

will be applied to compare Millennials and Baby Boomers' intention to purchase travel 

products online. This model has primarily been used to examine applications, devices, and 

website acceptance in the tourism industry (Gupta & Dogra, 2017; Gupta et al., 2018). The 

UTAUT2 model has not been previously used in the tourism context to shed light on 

generational differences between Millennials and Baby Boomers to purchase travel online. The 

intergenerational examination herein also answers the call by Lim (2018b) and Mazaheri et al. 

(2020) for greater research in this area in order to provide a cross-comparison update to 

concepts and theories in information systems and technology (e.g. UTAUT) that were 

conceived when digital technologies in applied settings were only emerging.  

Second, this study extends the UTAUT2 model with the inclusion of additional factors 

important in predicting online travel behaviour. Literature shows that there is little empirical 

work that simultaneously captures the positive (success) and negative (resistance) factors that 

lead consumers to adopt ICT (Alalwan et al., 2018; Hult et al., 2019; Lee, 2009; Martins et al., 

2014; Zhuang et al., 2018). The academic fraternity has always given less attention to behaviour 

deterrents (Rehman et al., 2020) and focus primarily being on the drivers (Farivar et al., 2017). 

Therefore, given the importance of risk in e-commerce (Pelaez et al., 2019; Samhan, 2018), 

perceived risk (PR) has been added to the UTAUT2 model. Also, Dwivedi et al. (2019) found 

that the addition of attitude to the original UTAUT model would increase its predictive power. 

However, since then, little research has been carried out with the addition of this construct to 

the UTAUT model. This study is one of the first to incorporate attitude to the UTAUT2 model. 

Additionally, compatibility, innovativeness, and perceived trust are also added to the 

UTAUT2 model as these factors generally differ across individuals. The addition of constructs 

to extend technology acceptance models such as UTAUT is in line with the recommendations 

by Lim (2018a) as they exemplify novel and meaningful extensions that encapsulate the 

emerging realities of user interactions with technology in underexplored settings (e.g. 

tourism). 

Third, in the tourism literature, to explore the similarities and differences amongst tourism 

behaviour across generations, the concept of cohorts has been used in segmentation studies 

(Gardiner et al., 2015; Kim et al., 2018). Also, most generational studies have focused on travel 

behaviour of a specific generation, such as Generation X or Generation Y (Chen & Chou, 2019; 

Xu & Pratt, 2018), but few have compared online behaviour between generations (e.g., Beldona 

et al., 2009; Gardiner et al., 2015). Despite this, very little research has been carried out that 



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

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differentiates generational cohorts by online shopping that focuses on cohort perceptions, 

decision making, and behaviour (Herrando et al., 2019; Lissitsa & Kol, 2019; Shulga et al., 2018). 

Despite there been a huge growth in studies relating to tourism (Athiyaman, 1995), research is 

still limited when it comes to the behaviour and impact of Millennials in the tourism industry 

(Ramsay et al., 2017). The results of this study will help scholars and practitioners better 

understand the generational differences in accepting and using online travel websites.  

2 Literature Review, Conceptual model and hypotheses 

2.1 Generational cohorts 

A generational cohort can be defined by the years of birth, extending 20–25 years in duration, 

or as long as it generally takes one birth group to be born, age, and have children of their own 

(Meredith & Schewe, 1994; Strauss & Howe, 1991). These cohorts share similar ideas, attitudes, 

beliefs, and values based on being born during the same time period and living through 

common experiences, with macro-level economic, political, and social events that occurred 

during their coming of age years (Strauss & Howe, 1991). Generational cohort theory is useful 

in identifying propensities and patterns across generational groups as a socio-cultural 

theoretical framework (Pendergast, 2010). In tourism studies, several researchers have 

examined generational cohort consumer purchase behaviour (Bakewell & Mitchell, 2003) and 

tourism behaviour (Pennington-Gray et al., 2003).  

Despite tourism research identifying characteristics that are similar and different for each 

generation, there is a call for a deeper understanding of each generational cohort and its impact 

on the industry (Bowen & Chen McCain, 2015). A fair amount of evidence grounded in social 

science has been provided that demonstrates the generational approach to understanding 

groups and society. Specifically, consumer segmentation by behaviours, attitude, and 

preferences can best be understood through generational analysis (Parment, 2013). 

Tourism research has widely recognized the importance of profiling consumers by cognitive 

processes, psychological characteristics, and behavioural patterns (Lee et al., 2004). Various 

variables, factors, and descriptors have been used to profile consumer segments in the tourism 

industry: holiday and decision-making behaviour (Bicikova, 2014), electronic word-of-mouth 

(Zhang et al., 2017), and purchase behaviour (Beldona et al., 2009). Within this line of research, 

this study seeks to understand consumer segments' intention to purchase travel online. 

2.2 Unified Theory of Acceptance and Use of Technology (UTAUT)  

Venkatesh et al. (2012) proposed an extension to the original UTAUT model which included 

three additional constructs: hedonic motivation, price value and habit to the existing four 

original contracts of performance expectancy (PE), effort expectancy (EE), social influence (SI) 

and facilitating conditions (FC). When it comes to consumer technology adoption, the 

UTAUT2 model has a better predictive power (Venkatesh et al., 2012) and has contributed 

significantly to the literature on consumers' adoption of technology (Tamilmani et al., 2019). 

This model has been successfully applied in various contexts, including mobile payments 

(Morosan & DeFranco, 2016) and tourist adoption of mapping applications (Gupta & Dogra, 

2017). 

2.3 Performance expectancy 

Extant research confirms that consumers are more likely to use a technology that brings them 

more favourable outcomes and is more useful to the users (Compeau & Higgins, 1995). It has 



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 4 

been found that performance expectancy has a strong positive association with behavioural 

intention (Escobar-Rodríguez & Carvajal-Trujillo, 2014; Smith et al., 2013), mobile-based 

communication technologies (Marriott & Williams, 2018), social media applications (Huang et 

al., 2018; Lim et al., 2019) and mobile wallets (Madan & Yadav, 2016; Slade et al., 2014). Thus, 

it is hypothesized that: 

H1a: Performance expectancy positively influences consumers' attitude to purchase travel 

online. 

H1b: Performance expectancy positively influences consumers' intention to purchase travel 

online. 

2.4 Effort Expectancy 

Individuals prefer to use technology that provides maximum benefits and is easy to 

understand (Davis, 1989). The ease and comfort in relation to the use of a system are referred 

to as effort expectancy (Venkatesh et al., 2003). Several studies have shown that effort 

expectancy is positively related to behavioural intention (Awwad & Al-Majali, 2015; 

Khalilzadeh et al., 2017; Wang et al., 2017). Therefore, it is hypothesized that: 

H2a: Effort expectancy positively influences consumers' attitude to purchase travel online. 

H2b: Effort expectancy positively influences consumers' intention to purchase travel online. 

2.5 Social Influence 

When an individual perceives that a given behaviour shall be accepted by their peer group, 

the likelihood of them engaging in the given behaviour increases (Ajzen, 1991). The extent to 

which an individual believes that others expect them to use a new system is referred to as 

social influence (Venkatesh et al., 2003). Studies carried out have found that social influence 

has a significant influence on behavioural intention (Hsu & Lin, 2016; Kim & Park, 2011; 

Ozturk et al., 2016; Wu & Chen, 2015). Davis (1989) believed that subjective norm had no 

influence on technology acceptance, while Shin (2009) has hypothesized that social influence 

has a positive effect on the intention to utilize mobile payments, although the effect of social 

influence on intention was not supported. Nakagawa and Gouvêa (2010) have found that 

social influence is not relevant to the intention to adopt internet shopping. Nevertheless, 

Venkatesh et al. (2003) believed that social influence affected consumer behaviour. Besides, 

Chen et al. (2007) have found that subjective norms toward using electronic toll collection 

positively increase the intention to use. Thus, the following hypothesis is proposed: 

H3a: Social influence positively influences consumers' attitude to purchase travel online. 

H3b: Social influence positively influences consumers' intention to purchase travel online. 

2.6 Facilitating conditions 

In this study, facilitating conditions reflects the effect of required knowledge and necessary 

resources (such as internet connectivity) to engage in travel purchases online. The relationship 

between facilitating condition and behavioural intention has been confirmed in various 

contexts, including NFC mobile payments (Khalilzadeh et al., 2017), biometric technology 

adoption (Akinnuwesi et al., 2016), mHealth (Hoque & Sorwar, 2017) and internet banking 

(Tarhini et al., 2016). Therefore, this research hypothesizes that: 

H4a: Facilitating condition positively influences consumers' attitude to purchase travel online. 



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

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H4b: Facilitating condition positively influences consumers' intention to purchase travel 

online. 

2.7 Hedonic motivation 

'Hedonic motivation' is defined as the fun or pleasure derived from using technology, and it 

is an important determinant of consumers' technology acceptance and use (Tamilmani et al., 

2019). The inclusion of this factor into the UTAUT2 model shifts the focus from extrinsic 

motivation as a dominant paradigm of technology adoption research to intrinsic motivation. 

Applications of the UTAUT2 has found hedonic motivation as a strong predictor in mobile 

apps (Hew et al., 2015), eLearning intention (Filippou et al., 2018), online purchase (Escobar-

Rodríguez & Carvajal-Trujillo, 2014) and NFC technology adoption. Therefore, the following 

is hypothesized: 

H5a: Hedonic motivation positively influences consumers' attitude to purchase travel online. 

H5b: Hedonic motivation positively influences consumers' intention to purchase travel online. 

2.8 Price-saving  

Various industries, including tourism, have offered price-saving (PS) incentives to consumers 

for engaging in online purchases. Consumers look for higher perceived benefits in comparison 

to the monetary sacrifice, which makes price value a significant predictor of behavioural 

intention to use technology. Consumers who engage in online shopping also consider cost-

saving a significant factor (Jensen, 2012). Thus, price-saving orientation has been observed as 

a significant predictor of purchase intention (PI) for online bookings (Escobar-Rodríguez & 

Carvajal-Trujillo, 2014) and airline e-commerce websites (Escobar-Rodríguez & Carvajal-

Trujillo, 2013). Therefore, it is hypothesized that: 

H6a: Price saving positively influences consumers' attitude to purchase travel online. 

H6b: Price saving positively influences consumers' intention to purchase travel online. 

2.9 Habit 

Habit is defined as the extent to which an individual tends to perform behaviours 

automatically because of learning (Limayem et al., 2007). Habit reveals the outcome of past 

experiences or behaviour (Venkatesh et al., 2012). Research has shown that habit is a significant 

predictor of behavioural intention (Escobar-Rodríguez & Carvajal-Trujillo, 2014; Gupta & 

Dogra, 2017; Herrero & San Martín, 2017; Hsiao et al., 2016). Based on these findings, it is 

hypothesized that: 

H7a: Habit positively influences consumers' attitude to purchase travel online. 

H7b: Habit positively influences consumers' intention to purchase travel online. 

2.10 Perceived compatibility 

The construct of perceived compatibility is defined as "the degree to which an innovation is 

perceived as being consistent with existing values, past experiences, and needs of potential 

adopters" (Rogers, 1995, p. 15). The following definition of compatibility will be employed for 

this study as it fits online shopping context; it is the extent to which consumers believe that 

purchasing travel online matches/fits their needs, shopping preference, and lifestyle 

(Vijayasarathy, 2004). According to Bellman et al. (1999), those individuals that spend a 

considerable about of time in their personal life and job with internet and related technologies 



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

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such as e-email have a higher likelihood to purchase online. The review of the literature shows 

that individuals have found a significant and positive impact of perceived compatibility on 

customers' attitudes to purchase online (Amaro & Duarte, 2015; Gillenson & Sherrell, 2002; 

Vijayasarathy, 2004). Also, Christou and Kassianidis (2002), (Li & Buhalis, 2006) and Amaro 

and Duarte (2015) found that perceived compatibility positively influenced customers' 

intention to purchase travel online. Therefore, the following is hypothesized: 

H8: Perceived compatibility positively influences consumers' intention to purchase travel 

online. 

2.11 Customer Innovativeness  

Innovativeness has been defined by Rogers and Shoemaker (1971) as "the degree to which an 

individual is relatively early in adopting new ideas than other members of his social system." 

According to the approach proposed by Bass (1969) and Midgley and Dowling (1978), 

criticizing this temporal perspective as it ignores the communication and social processes that 

characterize the diffusion of innovation, and they affirm that this concept reflects the "degree 

to which the individual is receptive to new ideas and makes innovative decisions 

independently of the communicated experience of others."  

The concept of innovativeness has been predominantly shown in electronic commerce 

literature as a domain-specific variable (Goldsmith, 2000, 2002; Park & Jun, 2002; Varma Citrin 

et al., 2000). Particularly in the domain of information technology, Agarwal and Prasad (1998) 

proposed the concept of perceived innovativeness, which is defined as the willingness of an 

individual to try out any new information technology. Following this, different authors have 

provided empirical evidence to support the effect of a user's innovativeness in the acquisition 

of products using the internet (Donthu & Garcia, 1999; Goldsmith, 2000; Varma Citrin et al., 

2000), frequency of acquisition (Goldsmith, 2000; Goldsmith & Lafferty, 2001) and online 

purchase intention in the future (Aldás-Manzano et al., 2009; Crespo & del Bosque, 2008; 

Goldsmith, 2002; Goldsmith & Lafferty, 2001; Lu et al., 2011). Other research carried out by 

San Martín and Herrero (2012) found that users' innovativeness had a positive influence on 

online purchase intention in rural tourism. Therefore, the following hypothesis can be 

developed: 

H9: Innovativeness positively influences consumers' intention to purchase travel online. 

2.12 Perceived Risk 

Due to the separation of the buyer and the seller, the concept of perceived risk becomes more 

common for online shopping (Al-Gahtani, 2011). Perceived risk represents a detractor to the 

adoption process (Gupta et al., 2018). A study by Athiyaman (2002) found that consumers 

avoid purchasing airline tickets online because of security concerns. Several studies support 

the relationship between perceived risk and behavioural intention (Liébana-Cabanillas et al., 

2017; Lu et al., 2011; Yang et al., 2012) while others reject the relationship (Kapoor et al., 2015; 

Tan et al., 2014). Therefore, this research will be of theoretical use. Thus, the following is 

proposed: 

H10: Perceived risk negatively influences consumers' intention to purchase travel online. 

2.13 2.13 Perceived Trust 

Studies carried out previously have confirmed the positive link between perceived trust and 

intention to purchase online in the context of e-commerce (Chiu et al., 2010; Gefen et al., 2003; 



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Kim et al., 2012). In the study conducted by Wen (2010), a positive relationship was found 

between perceived consumer trust and consumers' intention to purchase travel online. 

However, Kamarulzaman (2007) did not find a direct effect of trust on consumers' adoption of 

online travel shopping. In the tourism field, this relationship between trust on online travel 

shopping adoption has been supported by Bigné et al. (2010); Escobar-Rodríguez and Carvajal-

Trujillo (2014); Kim et al. (2011); Amaro and Duarte (2015); Ponte et al. (2015) and Agag and 

El-Masry (2017). Therefore, the following hypothesis is proposed: 

H11: Perceived trust positively influences consumers' intention to purchase travel online. 

2.14 Attitude 

Prior literature has acknowledged the role of attitude in explaining individuals' acceptance of 

technology (Bobbitt & Dabholkar, 2001; Dwivedi et al., 2017; Kim et al., 2009; Taylor & Todd, 

1995; Yang & Fang, 2004). The inclusion of attitude as a construct in technology acceptance 

models is consistent with TRA (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975), TPB (Ajzen, 

1991), and DTPB (Taylor & Todd, 1995). TAM is considered a special case of the TRA with an 

attitude comprising of two beliefs. According to the TRA model, attitude plays a mediating 

role in the relationship between the types of beliefs and intentions (Taylor & Todd, 1995). TAM 

postulates that technology is considered more useful when it is easier to use, and this will lead 

to a more positive attitude and intention for an individual to use the technology (Davis et al., 

1989; Taylor & Todd, 1995). The relationship between attitude and behavioural intention, as 

represented in TAM, suggests that all other things being equal, individuals with a positive 

attitude towards a behaviour will form the intention to perform the behaviour. This 

relationship is key to the TRA and other related models that researchers have presented (e.g., 

(Triandis, 1977), (Bagozzi, 1981) and (Davis et al., 1989) while developing the TAM extension 

(TAME) model, (Jackson et al., 1997). Therefore, it is hypothesized that: 

H12: Attitude positively influences consumers' intention to purchase travel online. 

2.15 Purchase Behaviour  

Purchase behaviour is "the manifest, observable response in a given situation concerning a 

given target" (Ajzen, 1991). BI is considered an immediate consequence and indicates an 

individuals' readiness to perform a specific action. In the information technology acceptance 

studies, various studies have provided evidence of the relationship between BI and PB (e.g., 

(Baabdullah et al., 2019; Gupta et al., 2018; Sharma et al., 2020; Venkatesh et al., 2012). 

Therefore, it is hypothesized that: 

H13: BI positively influences consumers' purchase behaviour to purchase travel online. 



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

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Figure 1: Conceptual Framework 

3 Methodology 

3.1 Survey Instrument 

A questionnaire was designed to capture data to address the research hypothesis. The first 

part of the questionnaire captured demographic variables such as gender, age, education level, 

and income. The age variable was used to segment the sample into the respective generations. 

A respondent was deemed a Millennial if they were between the ages of 19-37 and a Baby 

Boomer if they were between the ages of 55-73 (Suh et al., 2017). Other demographic variables, 

such as gender, were used to assess the representativeness of the sample. The second part of 

the questionnaire captured variables used in the UTAUT2 model and was adopted from 

previous literature. These variables were all asked on a 5-point Likert scale were '1' 

represented 'Strongly Disagree’, and '5' represents 'Strongly Agree.'  

Prior to carrying out the main survey, the questionnaire was piloted with 30 consumers 

consisting of 15 Millennials and 15 Baby Boomers. This resulted in some minor improvements 

being made to the item wording to enhance the readability and flow of the questions in the 

survey. 

3.2 Data collection and sample  

An online survey was conducted in Australia between March-April 2019. A professional data 

collection firm was contracted to collect data for this research using an online survey. A similar 

approach has been used in other studies (Nam et al., 2018; Njite & Schaffer, 2017). As this study 

aims to explore factors affects consumers’ intention to purchase travel online, online data 



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collection is considered relevant. This is because respondents are expected to be comfortable 

with the use of computers and internet to purchase travel online. 

The data collection firm collected data through a consumer panel for the final survey. Data 

firms maintain extensive data sets of different categories of respondents, which they use as 

their sample frame. To participate, respondents were required to meet the following screening 

criteria: 1) be an Australian citizen, 2) to be more than 18 years of age. In marketing literature, 

an online data collection method is quite extensively used (Arnett et al., 2018; Dwivedi et al., 

2016; McBride et al., 2020).  

The main survey received 1000 responses. Generational cohorts were measured using the birth 

year of respondents (Tsaur & Yen, 2018). A part of the quality control procedure of the data 

collection firm, the demographic characteristics such as age gender, location were verified by 

the firm. The respondents were then grouped into three categories: Baby Boomers, Generation 

Xers, and Millennials. As this study aims to assess behavioural differences between Baby 

Boomers and Millennials, all respondents that did not belong to these two categories were 

screened out. 

A total of 562 responses remained. Of this, 560 were completely filled out (Millennials N= 341; 

Baby Boomers N=309). The demographic profiles of each cohort are presented in Table 1. The 

sample for Millennials and Baby Boomers is weighted towards males as well as educated 

people who might already be interested in purchasing travel online (see Table 1 for details). 

However, we consider the profile of Millennials and Baby Boomers as representative of those 

taking online surveys (Duffy et al., 2005).  

3.3 Data Analysis Procedure 

This study used SPSS 25 for descriptive statistics and AMOS 24 for structural equation 

modelling (SEM). The measurement model was evaluated for goodness of fit (Goodness of Fit 

Index), reliability, validity, and measurement invariance. Validity was assessed using 

convergent validity (AVE), construct validity (GFI, CFI (Comparative Fit Index), RMSEA (Root 

Mean Square Error Approximation), chi-square/degree of freedom), and discriminant validity. 

To assess the convergent validity, J. Hair et al. (2006) recommended using factor loadings, 

composite reliability, and average variance extracted (AVE). Factor loadings ≥ 0.5, and 

preferably ≥ 0.70, show high convergent validity. Discriminant validity was assessed using 

latent constructs correlations matrices, where the square roots of the AVEs along the diagonals 

were indicated. Discriminant validity is realized when the diagonal elements (square roots of 

AVEs) exceed the off-diagonal elements (correlations between constructs) in the same row and 

column (Fornell & Larcker, 1981b). The criteria to assess these statistics was p ≥ 0.05, the 2 / 

df ≤2.00, [RMSEA] ≤ 0.05, CFI ≥ 0.90, GFI ≥ 0.90, adjusted goodness of fit [AGFI] ≥ 0.90, and 

root mean square residual [RMR]. 

The structural model was assessed by studying the R² determination coefficients, regression 

estimates, and statistical significance. The R² value exemplifies an amount of prognostic power 

and shows the extent of divergence, justified by its antecedent variables in the model. Chin 

(1998) considered R² values of 0.67, 0.33, and 0.19 as significant, reasonable, and weak, 

respectively. 

  



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3.4 Demographic Profile 

 
 Millennials Baby Boomers 

N 341 309 

Gender 

Female 127 183 

Male 214 124 

Rather not say - 2 

Education 

Primary school education 2 1 

Secondary School 10 70 

Diploma/Certificate 65 105 

Bachelors education 95 87 

Postgraduate education 105 45 

Rather not say 64 1 

Income 

I do not earn a fixed income 34 31 

Under $15,000 16 12 

$15,000-$29,999 46 45 

$30,000-$44,999 40 37 

$45,000-$59,999 49 50 

$60,000-$74,999 44 31 

$75,000-$89,999 28 19 

$90,000 + 48 36 

Rather not say 36 48 

Note: Aggregate percentage can be slightly more or less than 100 because of rounding off. 

Table 1: Profile of Respondents 

4 Results 

To operationalize the conceptual framework in Figure 1 and test the hypotheses proposed in 

the literature review section, we undertake a confirmatory factor analysis (CFA) using a 

structural equation model as used in previous studies of this kind (Palau-Saumell et al., 2016; 

Song et al., 2017). 

4.1 Descriptive Statistics of Items and Constructs 

The descriptive statistics of the constructs used in the proposed research model are shown in 

Table 2. On average, the means for most of the items were above 3.23 (out of 5) for the overall 

sample (N=650), above 3.51 for the Millennials' sample (NML= 341) and above 3.16 for the Baby 

Boomers' sample (NBB= 309). These results show that the majority of the respondents generally 

express positive answers to the variables used in the research model. Table 2 also shows that 

the standard deviations ranged from 0.861 to 1.211 for the overall sample, from 0.731 to 1.228 

for the Millennials' sample and from 0.916 to 1.194 for the Baby Boomers' sample. This 

indicates a narrow spread around the mean.  

  



Australasian Journal of Information Systems Sharma, Singh & Pratt 
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Single Group CFA 

 Overall Sample 
Millennials 

Sample 

Baby Boomers 

Sample 

χ2 3618.082 3333.921 3112.882 

Df  1133 1133 1133 

χ2 /df  3.193 2.943 2.747 

RMSEA  0.043 0.0381 0.0451 

RFI  0.947 0.915 0.923 

IFI 0.934 0.933 0.924 

NFI 0.893 0.896 0.904 

TLI 0.929 0.927 0.916 

CFI 0.933 0.911 0.902 

Table 2: The Results of Confirmatory Factor Analyses 

 
    SL CR SMC SL CR SMC SL CR SMC 

PE 

PE1 0.9 28.92 0.82 0.89 17.13 0.78 0.92 0.84 23.7 

PE2 0.86 26.38 0.74 0.81 15.21 0.66 0.9 0.81 22.61 

PE3 0.89 28.26 0.8 0.87 16.68 0.75 0.91 0.82 23.09 

PE4 0.84  0.7 0.79  0.62 0.87 0.76  

EE 

EE1 0.88 27.42 0.78 0.87 19.11 0.75 0.9 0.82 20.16 

EE2 0.87 26.9 0.76 0.82 17.4 0.67 0.91 0.84 20.57 

EE3 0.91 28.78 0.82 0.89 20 0.79 0.91 0.83 20.37 

EE4 0.84  0.7 0.85  0.73 0.83 0.69  

SI 

SI1 0.89 34.28 0.8 0.85 20.81 0.72 0.93 0.87 28.1 

SI2 0.93 37.59 0.86 0.91 24.51 0.83 0.94 0.88 28.66 

SI3 0.92  0.84 0.92  0.84 0.92 0.84  

FC 

FC1 0.76 21.74 0.58 0.73 14.11 0.54 0.79 0.62 16.61 

FC2 0.87 26.61 0.75 0.86 17.9 0.74 0.87 0.76 19.51 

FC3 0.85  0.73 0.85  0.72 0.86 0.73  

HM 

HM1 0.91 30.11 0.82 0.85 17.86 0.73 0.95 0.9 25.43 

HM2 0.92 30.87 0.85 0.87 18.37 0.75 0.95 0.91 25.67 

HM3 0.86  0.74 0.85  0.72 0.87 0.76  

PS 

PS1 0.85 26.84 0.72 0.78 14.79 0.61 0.9 0.82 25.78 

PS2 0.75 21.82 0.56 0.73 13.6 0.54 0.75 0.57 17.28 

PS3 0.89  0.78 0.83  0.69 0.94 0.88  

HB 

HB1 0.69 14.81 0.47 0.74 12.03 0.55 0.63 0.39 8.989 

HB2 0.89 18.3 0.8 0.9 14.49 0.8 0.89 0.79 11.33 

HB3 0.91 18.54 0.83 0.9 14.53 0.81 0.93 0.86 11.53 

HB4 0.66  0.43 0.71  0.51 0.59 0.35  

CP 

CP1 0.85  0.73 0.82  0.68 0.87 0.76  

CP2 0.91 29.84 0.83 0.88 18.24 0.77 0.93 0.86 23.93 

CP3 0.9 29.09 0.81 0.86 17.5 0.73 0.93 0.87 24.26 

IN 

IN1 0.8 27.52 0.65 0.75 16.65 0.57 0.85 0.72 22.17 

IN2 0.84 30.47 0.71 0.79 18.37 0.63 0.89 0.79 25.05 

IN3 0.91 36.79 0.83 0.89 23.25 0.79 0.93 0.86 28.4 

IN4 0.93  0.87 0.93  0.87 0.93 0.86  

PR 
PR1 0.84  0.71 0.82  0.67 0.86 0.75  

PR2 0.94 31.18 0.87 0.91 19.43 0.83 0.95 0.91 24.9 

Table 3: Confirmatory Factor Analysis Results for Refined Measurement Items 



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4.2 Measurement Model Assessment and Invariance Testing 

A preliminary single-group CFA was first conducted using the full-sample data (N =650), 

followed by two separate single-group CFA for each generation. As observed in Table 3, the 

fit indices for all the single-group CFAs were within limits recommended by J. F. Hair et al. 

(2006), suggesting that the model fits well separately for the two generations. Before 

conducting the multi-group comparison, it is imperative first to establish that the measures 

perform adequately in both sub-samples. The findings in the evaluation of the measurement 

models in the aggregate sample also apply to the generation-specific sub-samples. Indicator 

reliability was verified in both sub-samples. All loadings of the reflective measurement models 

were significant at the 0.01 level and above the recommended 0.7 threshold (Table 4) 

(Nunnally & Bernstein, 1994). The estimated indices for composite reliability and Cronbach's 

alpha demonstrate the reliability of the constructs in each sub-sample. The respective AVE 

values were all above the minimum requirement of 0.50 (Bagozzi & Yi, 1988), confirming 

convergent validity. Discriminant validity was also assured as an inspection of the indicators' 

cross-loadings. This showed that none of the indicators load higher on an opposing construct. 

An application of the Fornell and Larcker (1981a) criterion further confirmed discriminant 

validity (Tables 4, 5, and 6). 

Having established that the measures are valid for both Millennials and Baby Boomers, the 

next step was the simultaneous estimation of the unconstrained model in the two samples to 

verify configural invariance. The results show that the configural model fits the data 

adequately (χ2 = 1769.74; df=696; p < 0.01; χ2/df=2.54; RMSEA=0.044; CFI=0.9108; NFI= 0.896; 

TLI=0.925). To test for metrical invariance, we imposed equality constraints on all the factor 

loadings in the two groups. Despite the metric model fitting well (χ2=1660.45; df=799; p<0.01; 

χ2/df=2.08; RMSEA=0.051; CFI=0.928; NFI= 0.91; TLI=0.931), a comparison of the χ2 of the 

configural and the constrained models suggest significant differences (Δ χ2=107.85; Δ df =106; 

p < 0.01). 

 



Australasian Journal of Information Systems Sharma, Singh & Pratt 
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 α CR AVE MSV 
MaxR 

(H) 
CI AT PE EE SI FC HM PS HB PR PT CP PI PB 

CI 0.926 0.93 0.76 0.32 0.94 0.874              

AT 0.946 0.95 0.78 0.72 0.946 0.409*** 0.882             

PE 0.927 0.93 0.76 0.72 0.931 0.405*** 0.846*** 0.873            

EE 0.928 0.93 0.77 0.76 0.931 0.361*** 0.663*** 0.762*** 0.875           

SI 0.937 0.94 0.83 0.47 0.939 0.518*** 0.615*** 0.641*** 0.471*** 0.913          

FC 0.865 0.87 0.69 0.76 0.875 0.326*** 0.713*** 0.801*** 0.872*** 0.488*** 0.828         

HM 0.922 0.92 0.8 0.6 0.928 0.483*** 0.758*** 0.771*** 0.641*** 0.688*** 0.650*** 0.895        

PS 0.861 0.87 0.69 0.72 0.882 0.337*** 0.791*** 0.848*** 0.696*** 0.587*** 0.759*** 0.727*** 0.829       

HB 0.877 0.87 0.63 0.36 0.914 0.564*** 0.425*** 0.363*** 0.260*** 0.597*** 0.163*** 0.556*** 0.318*** 0.796      

PR 0.935 0.94 0.83 0.15 0.952 0.153*** -0.247*** -0.296*** -0.283*** 0.067 -0.391*** -0.105* -0.314*** 0.258*** 0.912     

PT 0.899 0.9 0.69 0.52 0.903 0.316*** 0.661*** 0.676*** 0.570*** 0.511*** 0.627*** 0.682*** 0.715*** 0.331*** -0.196*** 0.831    

CP 0.916 0.92 0.79 0.71 0.921 0.403*** 0.694*** 0.747*** 0.635*** 0.589*** 0.690*** 0.678*** 0.728*** 0.465*** -0.283*** 0.720*** 0.887   

PI 0.939 0.94 0.75 0.71 0.942 0.335*** 0.727*** 0.784*** 0.663*** 0.559*** 0.730*** 0.659*** 0.766*** 0.343*** -0.321*** 0.697*** 0.843*** 0.869  

PB 0.804 0.82 0.6 0.35 0.844 0.465*** 0.528*** 0.518*** 0.396*** 0.592*** 0.401*** 0.507*** 0.443*** 0.533*** 0.112* 0.440*** 0.535*** 0.552*** 0.77 

Table 4: Discriminant Validity Analysis from Confirmatory Factor Analysis - Overall Sample 

 α CR AVE MSV 
MaxR 

(H) 
CI AT PE EE SI FC HM PS HB PR PT CP PI PB 

CI 0.904 0.91 0.71 0.41 0.93 0.844              

AT 0.925 0.93 0.72 0.68 0.929 0.525*** 0.846             

PE 0.902 0.9 0.7 0.72 0.911 0.475*** 0.827*** 0.838            

EE 0.916 0.92 0.74 0.72 0.921 0.325*** 0.703*** 0.808*** 0.858           

SI 0.921 0.92 0.8 0.54 0.927 0.642*** 0.621*** 0.635*** 0.498*** 0.893          

FC 0.852 0.86 0.67 0.72 0.868 0.357*** 0.709*** 0.801*** 0.849*** 0.465*** 0.816         

HM 0.890 0.89 0.73 0.59 0.891 0.545*** 0.771*** 0.759*** 0.671*** 0.733*** 0.625*** 0.855        

PS 0.819 0.82 0.61 0.72 0.83 0.445*** 0.804*** 0.850*** 0.739*** 0.622*** 0.750*** 0.750*** 0.781       

HB 0.889 0.89 0.67 0.43 0.913 0.564*** 0.493*** 0.449*** 0.284*** 0.620*** 0.150* 0.657*** 0.383*** 0.816      

PR 0.923 0.93 0.81 0.16 0.949 0.232*** -0.076 -0.067 -0.169** 0.232*** -0.273*** 0.079 -0.160* 0.400*** 0.898     

PT 0.884 0.89 0.66 0.6 0.891 0.402*** 0.682*** 0.692*** 0.601*** 0.599*** 0.612*** 0.716*** 0.772*** 0.395*** -0.051 0.812    

CP 0.887 0.89 0.73 0.78 0.892 0.512*** 0.734*** 0.813*** 0.720*** 0.638*** 0.698*** 0.723*** 0.797*** 0.544*** -0.08 0.760*** 0.853   

PI 0.919 0.92 0.7 0.78 0.921 0.364*** 0.705*** 0.792*** 0.677*** 0.564*** 0.689*** 0.641*** 0.774*** 0.403*** -0.133* 0.745*** 0.882*** 0.834  

PB 0.811 0.82 0.61 0.48 0.851 0.512*** 0.522*** 0.579*** 0.377*** 0.691*** 0.368*** 0.587*** 0.463*** 0.622*** 0.249*** 0.521*** 0.594*** 0.580*** 0.78 

Table 5: Discriminant Validity Analysis from Confirmatory Factor Analysis - ML Sample 

  



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 α CR AVE MSV 
MaxR 

(H) 
CI AT PE EE SI FC HM PS HB PR PT CP PI PB 

CI 0.858 0.941 0.81 0.31 0.949 0.898              

AT 0.960 0.960 0.83 0.74 0.962 0.308*** 0.91             

PE 0.944 0.945 0.81 0.74 0.945 0.343*** 0.860*** 0.899            

EE 0.937 0.937 0.79 0.78 0.943 0.371*** 0.632*** 0.723*** 0.89           

SI 0.949 0.946 0.86 0.42 0.951 0.395*** 0.607*** 0.640*** 0.439*** 0.929          

FC 0.874 0.876 0.7 0.78 0.882 0.285*** 0.713*** 0.793*** 0.885*** 0.502*** 0.839         

HM 0.945 0.948 0.86 0.61 0.956 0.423*** 0.754*** 0.778*** 0.613*** 0.651*** 0.661*** 0.925        

PS 0.894 0.898 0.75 0.72 0.928 0.241*** 0.770*** 0.846*** 0.666*** 0.548*** 0.763*** 0.703*** 0.868       

HB 0.862 0.847 0.6 0.32 0.919 0.554*** 0.357*** 0.272*** 0.214** 0.563*** 0.152* 0.471*** 0.247*** 0.774      

PR 0.946 0.947 0.86 0.26 0.96 0.056 -0.412*** -0.500*** -0.405*** -0.114† -0.510*** -0.278*** -0.465*** 0.094 0.926     

PT 0.913 0.913 0.73 0.48 0.916 0.232*** 0.638*** 0.666*** 0.543*** 0.418*** 0.637*** 0.655*** 0.672*** 0.246*** -0.359*** 0.852    

CP 0.936 0.938 0.83 0.66 0.942 0.310*** 0.664*** 0.701*** 0.574*** 0.546*** 0.678*** 0.647*** 0.682*** 0.386*** -0.465*** 0.693*** 0.912   

PI 0.951 0.950 0.79 0.66 0.962 0.286*** 0.727*** 0.770*** 0.642*** 0.537*** 0.748*** 0.661*** 0.758*** 0.259*** -0.495*** 0.657*** 0.809*** 0.89  

PB 0.767 0.784 0.56 0.28 0.837 0.378*** 0.526*** 0.464*** 0.393*** 0.463*** 0.424*** 0.427*** 0.427*** 0.392*** -0.081 0.327*** 0.486*** 0.513*** 0.8 

Table 6: Discriminant Validity Analysis from Confirmatory Factor Analysis - BB Sample 

 



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4.3 Structural Relationships  

After the measurement model was successfully evaluated for goodness of fit, reliability, 

validity, and measurement invariance, a structural equation model using the maximum 

likelihood method of estimation was used to test the hypotheses. 

First, for the Millennials, the model was adequate as the chi-square value was statistically 

significant (χ2=5224.321; df=1202; p<0.01; χ2/df=4.346; RMSEA=0.0943; CFI=0.926; NFI= 0.901; 

TLI=0.919). Of the 20 estimate path coefficients, 14 were statistically significant at the .05, .01, 

or .001 level. The significant relationships were found on the paths for PE to AT (β=0. 0.664, p 

< 0.001), FC to AT (β=0. 187, p < 0.001), HM to AT (β=0. 278, p < 0.001), PS to AT (β=0.361, p < 

0.001), HB to AT (β=0.142, p < 0.05), PE to PI (β=0. 346, p < 0.001), SI to PI (β=0.131, p < 0.05), 

HM to PI (β=0.121, p < 0.05), HB to PI (β=0.086, p < 0.05), AT to PI (β=0.121, p < 0.05), CP to PI 

(β=0.659, p < 0.001), IN to PI (β=0.122, p < 0.01), PT to PI (β=0.283, p < 0.001), and PI to PB 

(β=0.561, p < 0.001). However, H2a, H2b, H3a, H4b, H6b and H10 were not supported for 

Millennials. 

Second, for the Baby Boomers, the model was adequate as the chi-square value was statistically 

significant (χ2 = 5006.065; df=1202; p < 0.01; χ2/df= 4.165; RMSEA= 0.0953; CFI=0.921; NFI= 

0.879; TLI=0.906). Of the 20 estimate path coefficients, 12 were statistically significant at the 

.05, .01, or .001 level. The significant relationships were found on the paths for PE to AT 

(β=0.382, p < 0.001), FC to AT (β=0.121, p < 0.001), HM to AT (β=0.238, p < 0.001), PS to AT 

(β=0.221, p < 0.001), HB to AT (β=0. 0798, p < 0.05), PE to PI (β=0.115, p < 0.05), SI to PI (β=0.076, 

p < 0.05), FC to PI (β=0.184, p < 0.001), PS to PI (β=0.231, p < 0.001), CP to PI (β=0.608, p < 0.001), 

PR to PI (β=-0.088, p < 0.05), and PI to PB (β=0.394, p < 0.001). However, H2a, H2b, H3a, H5b, 

H7b, H8, H11, and H12 were not supported for Baby Boomers. 

To better understand the moderating effects of the generational cohort, we conducted further 

analyses to identify the structural relationships that were non-invariant. The significance and 

magnitude of path coefficients in the inner model were also compared to ascertain whether 

the strength and directionality of the structural relationships differ across the two samples. 

Table 8 presents the generational-specific MGA results for the Millennials and Baby Boomers. 

The fit indices suggest that the structural model is adequate in the Millennial and Baby Boomer 

samples (χ2 = 5142.52; df =1129; p < 0.01; χ2 /df=4.555; RMSEA=0.057; CFI=0.916; NFI= 0.891; 

TLI=0.917).  

  



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Path 

Name 
ML Beta 

BB 

Beta 

Difference 

in Betas 

P-Value 

for 

Difference 

Interpretation 

PE → 

AT.  
0.664*** 0.382*** 0.282 0.019 

The positive relationship 

between PE and AT is stronger 

for ML.  

EE → 

AT.  
0.0718 -0.0645 0.1363 0.251 There is no difference  

SI → 

AT.  
0.0796 0.049 0.0306 0.85 There is no difference 

FC → 

AT.  
0.187*** 0.121** 0.066 0.858 There is no difference.  

HM → 

AT.  
0.278*** 0.238*** 0.04 0.975 There is no difference.  

PS → 

AT.  
0.361*** 0.221*** 0.14 0.418 There is no difference.  

HB → 

AT.  
0.142** 0.0798† 0.0622 0.964 There is no difference.  

PE → 

PI.  
0.346*** 0.115† 0.321 0.067 

The positive relationship 

between PE and PI is stronger 

for ML.  

EE → 

PI.  
-0.017 0.008 -0.025 0.874 There is no difference  

SI → 

PI.  
0.131** 0.076† 0.055 0.61 There is no difference.  

FC → 

PI.  
0.034 0.184*** -0.15 0.155 

The positive relationship 

between FC and PI is only 

significant for BB.  

HM → 

PI.  
0.121** 0.022 0.099 0.169 

The positive relationship 

between HM and PI is only 

significant for ML.  

PS → 

PI.  
0.0226 0.231*** -0.2084 0.07 

The positive relationship 

between PS and PI is stronger 

for BB.  

HB → 

PI.  
0.086† 0.038 0.048 0.132 

The positive relationship 

between HB and PI is only 

significant for ML.  

AT → 

PI.  
0.121† 0.0622 0.0588 0.581 

The positive relationship 

between AT and PI is only 

significant for ML.  

CP → 

PI.  
0.659*** 0.608*** 0.051 0.544 There is no difference.  

IN → 

PI.  
0.122** 0.051 0.071 0.021 

The positive relationship 

between CI and PI is stronger for 

ML.  

PR → 

PI.  
-0.061 -0.088 0.027 0.549 

The negative relationship 

between PI and PR is only 

significant for BB.  

PT → 

PI.  
0.283*** 0.041 0.232 0.023 

The positive relationship 

between PI and PT is stronger 

for ML.  

PI → 

PB.  
0.561*** 0.394*** 0.167 0.017 

The positive relationship 

between PB and PI is stronger 

for ML.  

Table 7: Structural Model Relationships 



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5 Discussion  

To evaluate the predictive power of the endogenous constructs, the explained variance (R²) of 

the endogenous constructs was examined (Chin, 2010). This explains the amount of variance 

in the construct that is explained by the model. Using the full set of data, the R² value of the 

original UTAUT2 model is 33 percent. With the addition of attitude to the model, the R² value 

increases to 38 percent. Furthermore, adding compatibility, innovativeness, perceived risk, 

and perceived trust increases the R² value to 64 percent. The three categories that can be used 

to classify the R² values of endogenous latent variables are: weak (19 percent), moderate (33 

percent), and substantial (67 percent) (Chin et al., 2008). This increase in R² highlights the 

importance of constructs in predicting consumers' online purchase intention. The study 

conducted by Escobar-Rodríguez and Carvajal-Trujillo (2014) derived a model with a R² value 

of 57.9%. The study conducted by San Martín and Herrero (2012) had an R² value of 40 percent, 

while the study by Agag and El-Masry (2017) had an R² value of 59%. 

5.1 Theoretical Contributions 

This study has provided further insights into online travel purchase behaviour by extending 

the UTAUT2 model. The inclusion of attitude, compatibility, innovativeness, perceived risk, 

and perceived trust was able to increase the predictive power of the model. This allows for 

empirical evidence pertaining to new relationships to be tested, which provides greater 

insights into consumer behaviour. This also reiterates the importance of applying the 

technology acceptance model to consumer context to understand behaviour. This study 

highlights the importance of generational cohort theory in online travel purchase intention. It 

demonstrates how factors vary between Millennials and Baby Boomers. Much of the studies 

carried out previously have looked at a single generation (Chen & Chou, 2019; Ladhari et al., 

2019). This study is the first to apply the UTAUT2 model with the generational cohort theory 

to understanding online travel purchase behaviour.  

5.2 Practical Implications 

At a time when internet use and online travel shopping are more prevalent, it is important to 

understand the factors that impact consumers' intention to purchase travel online, and how 

this outcome varies across generational cohorts. It is particularly important in the tourism 

industry to understand the different consumer segments to develop strategies to increase 

online purchases. 

Empirical evidence from this study shows the influence of performance expectancy on 

behavioural intention is stronger for millennials. This can be attributed to millennial 

consumers having relatively higher technological innovativeness compared to Baby Boomers 

(Church & Iyer, 2017). Despite Baby Boomers being open-minded and active, along with a 

keen sense of interest towards new technology (Kumar & Lim, 2008), they are unable to 

perceive the usefulness of new technology in the absence of ease of use (Hur et al., 2017). 

Therefore, online travel providers need to give greater emphasis on efficient content 

development and design so that the consumers find information on the website accurate, 

reliable, and useful (Gupta et al., 2018). This will enhance perceived utility, which will help 

increase adoption. 

FC was also found as a variable that positively influences BB online travel purchase behaviour. 

This highlights the importance of resources and support services to be available to more 

mature customers when engaging in online purchases. As such, businesses need to ensure that 



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

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their websites are informative about the support services available to customers should they 

encounter problems when purchasing online. This can include a list of frequently asked 

questions or online chat services to resolve customer confusion or doubts. The reason this 

relationship was only found significant for BB could be that they are less used to the internet 

compared to ML. 

Looking at the results of the relationship between HM and PI, according to literature, only one 

study by Baptista and Oliveira (2017) found age as a significant moderator. Two other studies 

by Ramantoko et al. (2016) and Ramírez-Correa et al. (2014) did not find a significant 

relationship. ML is more likely to feel positive emotions while browsing when the travel 

website has a high quality of security and is well-designed (Éthier et al., 2006). To increase 

hedonic shopping experiences on e-commerce websites, online retailers can incorporate 

exclusivity (e.g., deals and items that are web-exclusive) and concepts of game mechanics (i.e., 

gamification) (Insley & Nunan, 2014). To encourage the adoption of online travel shopping, 

web designers need to improve the hedonic features of the websites. 

The importance of price-saving has been highlighted in this study. As such, online travel 

providers need to provide customers cost-saving deals, best offers, and promotions as 

incentives to purchase travel online (Ladhari et al., 2019). For instance, customers that 

purchase travel online will receive an additional discount. Based on the empirical results of 

this study, this factor is only critical to BB. This may mean if price saving is a benefit of 

engaging in online buying, BB would be more likely to engage in this behaviour. 

Results from this study have shown that habit is a significant factor impacting consumers' 

intention to purchase travel online in the ML sample. It is therefore suggested that businesses 

in Australia should formulate strategies of marketing communication that create the habit of 

consumers' online travel purchase intention that will lead to an increase in purchase intention, 

thus, generating more online purchase behaviour. The findings from this study imply that in 

order for businesses to increase sales via online travel websites, businesses need to advertise 

using a combination of traditional media and the internet about how their websites can be 

used on different occasions and contexts to purchase travel. 

Innovativeness is a personality trait that manifests in the ways that tourists that are more 

innovative are attracted more by new services that are an improvement to the existing options. 

This implies that marketing managers need to monitor levels of perceived value and benefits 

for ML constantly so that improved and innovative service options can be made available to 

consumers (Kindström & Kowalkowski, 2014; Nicolau & Santa-María, 2013; Sandvik et al., 

2014). 

The results of this study support the crucial role that perceived risk plays on consumers' 

intention to purchase travel online. Thus, it is important that online travel providers come up 

with strategies to reduce consumers' perceived risk to increase the adoption of online 

purchases. While results demonstrate that perceived risk negatively impact online shopping 

both age groups, the relationship is stronger for Baby Boomers. Research carried out by 

Chakraborty et al. (2016) and Pascual-Miguel et al. (2015) found similar results. The practical 

implication of this is that online travel providers need to convince their mature consumers 

about the security measures in place that help mitigate the risk. Providing consumer 

information about their rights and protection, communicating high-security standards, having 

security approval symbols (e.g., VeriSign), offering money-back guarantees as well as clear 



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 19 

information privacy policies can be some of the risk-reducing strategies employed (Amaro & 

Duarte, 2015). 

Website designers and service providers can increase their visibility and trust by making the 

process and outcomes associated with purchasing travel online more easily accessible to 

consumers (e.g., with the use of personas and first-person-view videos that consumers are able 

to associate themselves with) with an explanation of the not so apparent support services (i.e., 

backend) will help enhance the observability. Showcasing the positive outcomes to 

demonstrate the outcome of using online travel websites can also help businesses in increasing 

the adoption rate, for example, by highlighting testimonials and positive reviews about 

purchasing travel online. This will also help increase consumers' trust perception of online 

travel websites and increase adoption. This study has evidenced the importance of trust in 

consumer decision making to purchase online. Therefore, travel providers should offer 

refunds, assure customers about their policies to ensure that their private and confidential 

information is not shared, provide formal guarantees of products and services, inform 

consumers about the availability of the product or service at the time of purchase and also 

allows comments and feedback to be posted by consumers. Explaining the reliability 

measures, not including hidden costs, using the latest encryption technology, not disclosing 

credit card details, explaining how the information collected from consumers will be used, and 

having a well-designed website can help increase trust in online travel shopping (Teubner & 

Klein, 1998). 

5.3 Limitation and future research 

Just like any other study, there are limitations to this study that provide good directions for 

future research. The results from this study may be difficult to generalize to other national 

markets because these are based on the unique experiences of consumers in Australia. Future 

research could better explore how online travel purchase behaviour varies across generations 

in different countries.  

Another limitation of this research is in relation to the definition of an online travel purchase. 

The definition used in this research can be considered broad as it includes the purchase of all 

types of tourism products and services such as hotel reservations, holiday packages, cruises, 

and airline tickets. Therefore, future research can examine the motivations to purchase travel 

products online while considering distinct product and service categories. Studies (e.g., 

(Bogdanovych et al., 2006; Kamarulzaman, 2007) have found that consumers are less likely to 

purchase complex travel products online as they prefer to use travel agents to book complex 

travel. As such, the results of this study may differ if applied to products of high and low 

complexity.  

For comparative analysis, it would be interesting to replicate this study in different countries 

with different generational cohorts. It is important to highlight that the effect the extended 

UTAUT2 model together is likely to vary from one context to another. Specifically, the effect 

of facilitating condition and social influence may vary in situations where users' risk 

perception regarding online purchase is higher (e.g., in countries with lower levels of legal 

security and technology). To add on, effort expectancy and innovativeness of users can be 

more significant in cases where more involvement is demanded from consumers, and the 

purchasing process is more complex (Limayem et al., 2000). Other recommendations for future 

research can be to adopt a different methodological approach that distinguishes between 

"individuals that have purchased online" and "those that have not" to study in greater detail 



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

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psychological factors influencing consumers' online purchase intention. Also, when making 

multimodel comparisons, future research must remain cognizant of hypothesis testing errors 

emerging from type one and type two errors and the ways to mitigate those errors (see Lim 

and Mandrinos (2020)). 

6 Conclusion 

This study was carried out with the aim of understanding consumers' intention to purchase 

travel online by extending the UTAUT2 model. Attitude, compatibility, innovativeness, 

perceived trust, and perceived risk were added to the UTAUT2 model to increase its predictive 

power. Empirical results from the study showed that with the addition of these factors to the 

UTAUT2 model, its predictive power was significantly increased. Additionally, the analysis 

of results across Millennials and Baby Boomers showed that factors affecting consumers' 

decision to travel online vary across the two generations. This study contributes theoretically 

and practically to better understanding of motivators and barriers of online purchase 

behaviour.  

References 

Agag, G. M., & El-Masry, A. A. (2017). Why do consumers trust online travel websites? Drivers 

and outcomes of consumer trust toward online travel websites. Journal of Travel Research, 

56(3), 347-369.  

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal 

innovativeness in the domain of information technology. Information Systems Research, 

9(2), 204-215.  

Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human 

Decision Processes, 50, 179-211. De Young, 50(2), 509-526.  

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Prentice 

Hall. 

Akinnuwesi, B. A., Uzoka, F.-M. E., Okwundu, O. S., & Fashoto, G. (2016). Exploring biometric 

technology adoption in a developing country context using the modified UTAUT. 

International Journal of Business Information Systems, 23(4), 482-521.  

Al-Gahtani, S. S. (2011). Modeling the electronic transactions acceptance using an extended 

technology acceptance model. Applied Computing and Informatics, 9(1), 47-77.  

Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018). Examining factors 

influencing Jordanian customers’ intentions and adoption of internet banking: 

Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125-138. 

doi:10.1016/j.jretconser.2017.08.026 

Aldás-Manzano, J., Lassala-Navarré, C., Ruiz-Mafé, C., & Sanz-Blas, S. (2009). The role of 

consumer innovativeness and perceived risk in online banking usage. International 

Journal of Bank Marketing, 27(1), 53-75.  

Amaro, S., & Duarte, P. (2015). An integrative model of consumers' intentions to purchase 

travel online. Tourism Management, 46, 64-79.  

Arnett, D. B., Sandvik, I. L., & Sandvik, K. (2018). Two paths to organizational effectiveness–

Product advantage and life-cycle flexibility. Journal of Business Research, 84, 285-292.  



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 21 

Athiyaman, A. (1995). The interface of tourism and strategy research: an analysis. Tourism 

Management, 16(6), 447-453.  

Athiyaman, A. (2002). Internet users' intention to purchase air travel online: an empirical 

investigation. Marketing Intelligence & Planning, 20(4), 234-242.  

Awwad, M. S., & Al-Majali, S. M. (2015). Electronic library services acceptance and use: an 

empirical validation of unified theory of acceptance and use of technology. The Electronic 

Library, 33(6), 1100-1120.  

Baabdullah, A. M., Rana, N. P., Alalwan, A. A., Islam, R., Patil, P., & Dwivedi, Y. K. (2019). 

Consumer Adoption of Self-Service Technologies in the Context of the Jordanian 

Banking Industry: Examining the Moderating Role of Channel Types. Information 

Systems Management, 36(4), 286-305.  

Bagozzi, R. P. (1981). Attitudes, intentions, and behavior: A test of some key hypotheses. 

Journal of Personality and Social Psychology, 41(4), 607.  

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the 

Academy of Marketing Science, 16(1), 74-94. doi:10.1007/bf02723327 

Bakewell, C., & Mitchell, V.-W. (2003). Generation Y female consumer decision-making styles. 

International Journal of Retail & Distribution Management, 31(2), 95-106.  

Baptista, G., & Oliveira, T. (2017). Why so serious? Gamification impact in the acceptance of 

mobile banking services. Internet Research, 27(1), 118-139.  

Beauchamp, M. B., & Barnes, D. C. (2015). Delighting baby boomers and millennials: factors 

that matter most. Journal of Marketing Theory and Practice, 23(3), 338-350.  

Beldona, S., Nusair, K., & Demicco, F. (2009). Online travel purchase behavior of generational 

cohorts: a longitudinal study. Journal of Hospitality Marketing & Management, 18(4), 406-

420.  

Bellman, S., Lohse, G. L., & Johnson, E. J. (1999). Predictors of online buying behavior. 

Communications of the ACM, 42(12), 32-38.  

Bicikova, K. (2014). Understanding student travel behavior: A segmentation analysis of British 

university students. Journal of Travel & Tourism Marketing, 31(7), 854-867.  

Bigné, E., Sanz, S., Ruiz, C., & Aldás, J. (2010). Why some Internet users don’t buy air tickets online: 

Springer. 

Bobbitt, L. M., & Dabholkar, P. A. (2001). Integrating attitudinal theories to understand and 

predict use of technology-based self-service: the internet as an illustration. International 

Journal of Service Industry Management, 12(5), 423-450.  

Bogdanovych, A., Berger, H., Simoff, S., & Sierra, C. (2006). Travel agents vs. online booking: 

Tackling the shortcomings of nowadays online tourism portals. Information and 

Communication Technologies in Tourism, 418-428.  

Bowen, J. T., & Chen McCain, S.-L. (2015). Transitioning loyalty programs: A commentary on 

“the relationship between customer loyalty and customer satisfaction”. International 

Journal of Contemporary Hospitality Management, 27(3), 415-430.  



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 22 

Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 

20 years on and 10 years after the Internet—The state of eTourism research. Tourism 

Management, 29(4), 609-623.  

Chakraborty, R., Lee, J., Bagchi-Sen, S., Upadhyaya, S., & Rao, H. R. (2016). Online shopping 

intention in the context of data breach in online retail stores: An examination of older 

and younger adults. Decision Support Systems, 83, 47-56.  

Chen, C.-D., Fan, Y.-W., & Farn, C.-K. (2007). Predicting electronic toll collection service 

adoption: An integration of the technology acceptance model and the theory of planned 

behavior. Transportation Research Part C: Emerging Technologies, 15(5), 300-311.  

Chen, C.-F., & Chou, S.-H. (2019). Antecedents and consequences of perceived coolness for 

Generation Y in the context of creative tourism-A case study of the Pier 2 Art Center in 

Taiwan. Tourism Management, 72, 121-129.  

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern 

Methods for Business Research, 295(2), 295-336.  

Chin, W. W. (2010). How to write up and report PLS analyses in the Handbook of Partial Least 

Squares (655-690): Springer. 

Chin, W. W., Peterson, R. A., & Brown, S. P. (2008). Structural equation modeling in marketing: 

Some practical reminders. Journal of Marketing Theory and Practice, 16(4), 287-298.  

Chiu, C.-M., Huang, H.-Y., & Yen, C.-H. (2010). Antecedents of trust in online auctions. 

Electronic Commerce Research and Applications, 9(2), 148-159.  

Christou, E., & Kassianidis, P. (2002). Consumer's perceptions and adoption of online buying 

for travel products. Journal of Travel & Tourism Marketing, 12(4), 93-107.  

Church, E. M., & Iyer, L. (2017). “When Is Short, Sweet?” Selection Uncertainty and Online 

Review Presentations. Journal of Computer Information Systems, 57(2), 179-189.  

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure 

and initial test. MIS Quarterly, 19(2), 189-211.  

Confente, I., & Vigolo, V. (2018). Online travel behaviour across cohorts: The impact of social 

influences and attitude on hotel booking intention. International Journal of Tourism 

Research, 20(5), 660-670.  

Coombes, B. (2009). Generation Y: Are they really digital natives or more like digital refugees. 

Synergy, 7(1), 31-40.  

Crespo, Á. H., & del Bosque, I. R. (2008). The effect of innovativeness on the adoption of B2C 

e-commerce: A model based on the Theory of Planned Behaviour. Computers in Human 

Behavior, 24(6), 2830-2847.  

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of 

information technology. MIS Quarterly, 13(3), 319-340.  

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: 

a comparison of two theoretical models. Management Science, 35(8), 982-1003.  

Donthu, N., & Garcia, A. (1999). The internet shopper. Journal of Advertising Research, 39(3), 52-

52.  



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 23 

Duffy, B., Smith, K., Terhanian, G., & Bremer, J. (2005). Comparing data from online and face-

to-face surveys. International Journal of Market Research, 47(6), 615-639.  

Dwivedi, A., Wilkie, D., Johnson, L., & Weerawardena, J. (2016). Establishing measures and 

drivers of consumer brand engagement behaviours. Journal of Brand Management, 23(5), 

41-69.  

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017). Re-examining 

the unified theory of acceptance and use of technology (UTAUT): Towards a revised 

theoretical model. Information Systems Frontiers, 1-16.  

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining 

the unified theory of acceptance and use of technology (UTAUT): Towards a revised 

theoretical model. Information Systems Frontiers, 21(3), 719-734.  

Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2013). Online drivers of consumer purchase of 

website airline tickets. Journal of Air Transport Management, 32, 58-64.  

Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014). Online purchasing tickets for low cost 

carriers: An application of the unified theory of acceptance and use of technology 

(UTAUT) model. Tourism Management, 43, 70-88.  

Éthier, J., Hadaya, P., Talbot, J., & Cadieux, J. (2006). B2C web site quality and emotions during 

online shopping episodes: An empirical study. Information & Management, 43(5), 627-639.  

Farivar, S., Turel, O., & Yuan, Y. (2017). A trust-risk perspective on social commerce use: an 

examination of the biasing role of habit. Internet Research, 27(3), 586-607.  

Filippou, J., Cheong, C., & Cheong, F. (2018). A model to investigate preference for use of 

gamification in a learning activity. Australasian Journal of Information Systems, 22. 

doi:10.3127/ajis.v22i0.1397  

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behaviour: An Introduction to 

theory and research. Addison-Wesley Pub. Co. (Reading, Mass).  

Fornell, C., & Larcker, D. F. (1981a). Evaluating structural equation models with unobservable 

variables and measurement error. Journal of Marketing Research, 18(1), 39-50.  

Fornell, C., & Larcker, D. F. (1981b). Structural equation models with unobservable variables 

and measurement error: Algebra and statistics. Journal of Marketing Research, 382-388.  

Friemel, T. N. (2016). The digital divide has grown old: Determinants of a digital divide among 

seniors. New Media & Society, 18(2), 313-331.  

Gardiner, S., Grace, D., & King, C. (2015). Is the Australian domestic holiday a thing of the 

past? Understanding baby boomer, Generation X and Generation Y perceptions and 

attitude to domestic and international holidays. Journal of Vacation Marketing, 21(4), 336-

350.  

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An 

integrated model. MIS Quarterly, 27(1), 51-90.  

Gilleard, C., Jones, I., & Higgs, P. (2015). Connectivity in later life: The declining age divide in 

mobile cell phone ownership. Sociological Research Online, 20(2), 1-13.  



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 24 

Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: an extended technology 

acceptance perspective. Information & Management, 39(8), 705-719.  

Goldsmith, R. E. (2000). How innovativeness differentiates online buyers. Quarterly Journal of 

Electronic Commerce, 1(4), 323-333.  

Goldsmith, R. E. (2002). Explaining and predicting consumer intention to purchase over the 

internet: an exploratory study. Journal of Marketing Theory and Practice, 10(2), 22-28.  

Goldsmith, R. E., & Lafferty, B. A. (2001). Innovative online buyers. Proceedings of the Society 

for Marketing Advances, 275-278.  

Gupta, A., & Dogra, N. (2017). Tourist adoption of mapping apps: a UTAUT2 perspective of 

smart travellers. Tourism and Hospitality Management, 23(2), 145-161.  

Gupta, A., Dogra, N., & George, B. (2018). What determines tourist adoption of smartphone 

apps? An analysis based on the UTAUT-2 framework. Journal of Hospitality and Tourism 

Technology, 9(1), 50-64.  

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis. New 

Jersey: Pearson Educational: Inc. 

Herrando, C., Jimenez-Martinez, J., & Martin-De Hoyos, M. (2019). Tell me your age and I tell 

you what you trust: the moderating effect of generations. Internet Research, 29(4), 799-

817.  

Herrero, Á., & San Martín, H. (2017). Explaining the adoption of social networks sites for 

sharing user-generated content: A revision of the UTAUT2. Computers in Human 

Behavior, 71, 209-217.  

Hew, J.-J., Lee, V.-H., Ooi, K.-B., & Wei, J. (2015). What catalyses mobile apps usage intention: 

an empirical analysis. Industrial Management & Data Systems, 115(7), 1269-1291.  

Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth 

by the elderly: an extension of the UTAUT model. International Journal of Medical 

Informatics, 101, 75-84.  

Hsiao, C.-H., Chang, J.-J., & Tang, K.-Y. (2016). Exploring the influential factors in continuance 

usage of mobile social Apps: Satisfaction, habit, and customer value perspectives. 

Telematics and Informatics, 33(2), 342-355.  

Hsu, C.-L., & Lin, J. C.-C. (2016). Effect of perceived value and social influences on mobile app 

stickiness and in-app purchase intention. Technological Forecasting and Social Change, 108, 

42-53.  

Huang, S.-Y., Yang, M. M., & Chen, C.-H. (2018). When Do Motivational Factors Lead to 

Negative User Experience on Social Networking Applications? Australasian Journal of 

Information Systems, 22. doi:10.3127/ajis.v22i0.1533 

Hult, G. T. M., Sharma, P. N., Morgeson III, F. V., & Zhang, Y. (2019). Antecedents and 

Consequences of Customer Satisfaction: Do They Differ Across Online and Offline 

Purchases? Journal of Retailing, 95(1), 10-23.  

Hur, H. J., Lee, H. K., & Choo, H. J. (2017). Understanding usage intention in innovative mobile 

app service: Comparison between millennial and mature consumers. Computers in 

Human Behavior, 73, 353-361.  



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 25 

Insley, V., & Nunan, D. (2014). Gamification and the online retail experience. International 

Journal of Retail & Distribution Management, 42(5), 340-351.  

Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an understanding of the behavioral 

intention to use an information system. Decision Sciences, 28(2), 357-389.  

Jensen, J. M. (2012). Shopping orientation and online travel shopping: the role of travel 

experience. International Journal of Tourism Research, 14(1), 56-70.  

Kamarulzaman, Y. (2007). Adoption of travel e-shopping in the UK. International Journal of 

Retail & Distribution Management, 35(9), 703-719.  

Kapoor, K. K., Dwivedi, Y. K., & Williams, M. D. (2015). Examining the role of three sets of 

innovation attributes for determining adoption of the interbank mobile payment service. 

Information Systems Frontiers, 17(5), 1039-1056.  

Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended 

UTAUT model for NFC based mobile payment in the restaurant industry. Computers in 

Human Behavior, 70, 460-474.  

Khare, A., Khare, A., & Singh, S. (2012). Attracting shoppers to shop online—Challenges and 

opportunities for the Indian retail sector. Journal of Internet Commerce, 11(2), 161-185.  

Kim, H.-W., Xu, Y., & Gupta, S. (2012). Which is more important in Internet shopping, 

perceived price or trust? Electronic Commerce Research and Applications, 11(3), 241-252.  

Kim, M.-J., Chung, N., & Lee, C.-K. (2011). The effect of perceived trust on electronic 

commerce: Shopping online for tourism products and services in South Korea. Tourism 

Management, 32(2), 256-265.  

Kim, S.-H., & Park, H. J. (2011). Effects of social influence on consumers' voluntary adoption 

of innovations prompted by others. Journal of Business Research, 64(11), 1190-1194.  

Kim, S., Choe, J. Y., & Lee, S. (2018). How are food value video clips effective in promoting 

food tourism? Generation Y versus non–Generation Y. Journal of Travel & Tourism 

Marketing, 35(3), 377-393.  

Kim, Y. J., Chun, J. U., & Song, J. (2009). Investigating the role of attitude in technology 

acceptance from an attitude strength perspective. International Journal of Information 

Management, 29(1), 67-77.  

Kindström, D., & Kowalkowski, C. (2014). Service innovation in product-centric firms: A 

multidimensional business model perspective. Journal of Business & Industrial Marketing, 

29(2), 96-111.  

Kumar, A., & Lim, H. (2008). Age differences in mobile service perceptions: comparison of 

Generation Y and baby boomers. Journal of Services Marketing, 22(7), 568-577.  

Kumar, S., & Sadarangani, P. (2018). An Empirical Study on Shopping Motivation among 

Generation Y Indian. Global Business Review, 0972150918807085.  

Ladhari, R., Gonthier, J., & Lajante, M. (2019). Generation Y and online fashion shopping: 

Orientations and profiles. Journal of Retailing and Consumer Services, 48, 113-121.  

Lee, C.-K., Lee, Y.-K., & Wicks, B. E. (2004). Segmentation of festival motivation by nationality 

and satisfaction. Tourism Management, 25(1), 61-70.  



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 26 

Lee, M.-C. (2009). Factors influencing the adoption of internet banking: An integration of TAM 

and TPB with perceived risk and perceived benefit. Electronic Commerce Research and 

Applications, 8(3), 130-141. doi:10.1177/0972150918807085 

Li, L., & Buhalis, D. (2006). E-Commerce in China: The case of travel. International Journal of 

Information Management, 26(2), 153-166.  

Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017). A SEM-neural network approach 

for predicting antecedents of m-commerce acceptance. International Journal of Information 

Management, 37(2), 14-24.  

Lim, W. M. (2018a). Dialectic antidotes to critics of the technology acceptance model: 

Conceptual, methodological, and replication treatments for behavioural modelling in 

technology-mediated environments. Australasian Journal of Information Systems, 22. 

doi:10.3127/ajis.v22i0.1651 

Lim, W. M. (2018b). Revisiting concepts and theories in information systems and technology. 

Australasian Journal of Information Systems, 22. doi:10.3127/ajis.v22i0.1733 

Lim, W. M., Lim, A. L., & Phang, C. S. C. (2019). Toward a conceptual framework for social 

media adoption by non-urban communities for non-profit activities: Insights from an 

integration of grand theories of technology acceptance. Australasian Journal of Information 

Systems, 23. doi: 10.3127/ajis.v23i0.1835 

Lim, W. M., & Mandrinos, S. (2020). Incidental findings on multimodel comparison, 

hypothesis testing error, and its solution. Australasian Journal of Information Systems, 24. 

doi.org/10.3127/ajis.v24i0.2555 

Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of 

intention: The case of information systems continuance. MIS Quarterly, 705-737.  

Limayem, M., Khalifa, M., & Frini, A. (2000). What makes consumers buy from Internet? A 

longitudinal study of online shopping. IEEE Transactions on Systems, Man, and 

Cybernetics-Part A: Systems and Humans, 30(4), 421-432.  

Lissitsa, S., & Kol, O. (2019). Four generational cohorts and hedonic m-shopping: association 

between personality traits and purchase intention. Electronic Commerce Research, 1-26.  

Lu, Y., Cao, Y., Wang, B., & Yang, S. (2011). A study on factors that affect users’ behavioral 

intention to transfer usage from the offline to the online channel. Computers in Human 

Behavior, 27(1), 355-364.  

Madan, K., & Yadav, R. (2016). Behavioural intention to adopt mobile wallet: a developing 

country perspective. Journal of Indian Business Research, 8(3), 227-244.  

Marriott, H. R., & Williams, M. D. (2018). Exploring consumers perceived risk and trust for 

mobile shopping: A theoretical framework and empirical study. Journal of Retailing and 

Consumer Services, 42, 133-146.  

Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the Internet banking adoption: 

A unified theory of acceptance and use of technology and perceived risk application. 

International Journal of Information Management, 34(1), 1-13.  



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 27 

Mazaheri, E., Lagzian, M., & Hemmat, Z. (2020). Research Directions in Information Systems 

Field, Current Status and Future Trends: A literature analysis of AIS Basket of Top 

Journals. Australasian Journal of Information Systems, 24. doi:10.3127/ajis.v24i0.2045 

McBride, M., Carter, L., & Phillips, B. (2020). Integrating the theory of planned behavior and 

behavioral attitudes to explore texting among young drivers in the US. International 

Journal of Information Management, 50, 365-374.  

Meredith, G., & Schewe, C. (1994). The power of cohorts. American Demographics, 16(12), 22-28.  

Morosan, C., & DeFranco, A. (2016). It's about time: Revisiting UTAUT2 to examine 

consumers’ intentions to use NFC mobile payments in hotels. International Journal of 

Hospitality Management, 53, 17-29.  

Nakagawa, S. S. Y., & Gouvêa, M. A. (2010). Identification of influent factors on products purchase 

through internet. Paper presented at the POMS 21st Annual Conference, Vancouver, 

Canada. 

Nam, K., Baker, J., Ahmad, N., & Goo, J. (2018). Dissatisfaction, disconfirmation, and distrust: 

An empirical examination of value co-destruction through negative electronic word-of-

mouth (eWOM). Information Systems Frontiers, 22, 113-130.  

Nicolau, J. L., & Santa-María, M. J. (2013). The effect of innovation on hotel market value. 

International Journal of Hospitality Management, 32, 71-79.  

Nishijima, M., Ivanauskas, T. M., & Sarti, F. M. (2017). Evolution and determinants of digital 

divide in Brazil (2005–2013). Telecommunications policy, 41(1), 12-24.  

Njite, D., & Schaffer, J. (2017). Revisiting attributes: how important is green in the consumer 

selection of hotel rooms? International Journal of Hospitality & Tourism Administration, 

18(2), 219-244.  

Nunnally, J. C., & Bernstein, I. (1994). Psychometric Theory (McGraw-Hill Series in Psychology) 

(Vol. 3): McGraw-Hill New York. 

Ozturk, A. B., Nusair, K., Okumus, F., & Hua, N. (2016). The role of utilitarian and hedonic 

values on users’ continued usage intention in a mobile hotel booking environment. 

International Journal of Hospitality Management, 57, 106-115.  

Palau-Saumell, R., Forgas-Coll, S., Amaya-Molinar, C. M., & Sánchez-García, J. (2016). 

Examining how country image influences destination image in a behavioral intentions 

model: The cases of Lloret De Mar (Spain) and Cancun (Mexico). Journal of Travel & 

Tourism Marketing, 33(7), 949-965.  

ParidahDaud, S. N. B. A., & Fung, K. I. (2018). The influence of consumer perceived risks on 

online consumer’s repurchase behavior in Malaysia online shopping: the mediating role 

of consumer attitude. Asian Journal of Science and Technology, 9(12), 9143-9150.  

Park, C., & Jun, J. (2002). A cross-cultural comparison of online buying intention: effects of internet 

usage, perceived risks, and innovativeness. Paper presented at the The AusWeb02: Eighth 

Australian World Wide Web Conference. 

Parment, A. (2013). Generation Y vs. Baby Boomers: Shopping behavior, buyer involvement 

and implications for retailing. Journal of Retailing and Consumer Services, 20(2), 189-199.  



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 28 

Pascual-Miguel, F. J., Agudo-Peregrina, Á. F., & Chaparro-Peláez, J. (2015). Influences of 

gender and product type on online purchasing. Journal of Business Research, 68(7), 1550-

1556.  

Pelaez, A., Chen, C.-W., & Chen, Y. X. (2019). Effects of perceived risk on intention to purchase: 

A meta-analysis. Journal of Computer Information Systems, 59(1), 73-84.  

Pendergast, D. (2010). Getting to know the Y generation. Tourism and generation Y, 1, 1-15.  

Pennington-Gray, L., Fridgen, J. D., & Stynes, D. (2003). Cohort segmentation: An application 

to tourism. Leisure Sciences, 25(4), 341-361.  

Ponte, E. B., Carvajal-Trujillo, E., & Escobar-Rodríguez, T. (2015). Influence of trust and 

perceived value on the intention to purchase travel online: Integrating the effects of 

assurance on trust antecedents. Tourism Management, 47, 286-302.  

Ramantoko, G., Putra, G., Ariyanti, M., & Sianturi, N. V. (2016). Early Adoption Characteristic of 

Consumers' Behavioral Intention to Use Home Digital Services in Indonesia. Paper presented 

at the 3rd International Seminar and Conference on Learning Organization. 

Ramírez-Correa, P., Rondán-Cataluña, F. J., & Arenas-Gaitán, J. (2014). An empirical analysis 

of mobile Internet acceptance in Chile. Information Research, 19(3), 19-13.  

Ramsay, G., Dodds, R., Furtado, D., Mykhayletska, Y., Kirichenko, A., & Majedian, M. (2017). 

The Barriers to Millennials Visiting Rouge Urban National Park. Sustainability, 9(6), 904.  

Rehman, Z. U., Baharun, R., & Salleh, N. Z. M. (2020). Antecedents, consequences, and 

reducers of perceived risk in social media: A systematic literature review and directions 

for further research. Psychology & Marketing, 37(1), 74-86.  

Rogers, E. (1995). Diffusion of Innovations (4th Eds.) ACM The Free Press (Sept. 2001). New 

York, 15-23.  

Rogers, E. M., & Shoemaker, F. F. (1971). Communication of Innovations; A Cross-Cultural 

Approach. New York: The Free Press 

Sait, S., Al-Tawil, K., & Hussain, S. (2004). E-commerce in Saudi Arabia: Adoption and 

perspectives. Australasian Journal of Information Systems, 12(1). doi: 10.3127/ajis.v12i1.105 

Samhan, B. (2018). Revisiting Technology Resistance: Current Insights and Future Directions. 

Australasian Journal of Information Systems, 22. doi: 10.3127/ajis.v22i0.1655 

San Martín, H., & Herrero, Á. (2012). Influence of the user’s psychological factors on the online 

purchase intention in rural tourism: Integrating innovativeness to the UTAUT 

framework. Tourism Management, 33(2), 341-350.  

Sandvik, I. L., Duhan, D. F., & Sandvik, K. (2014). Innovativeness and profitability: An 

empirical investigation in the Norwegian hotel industry. Cornell Hospitality Quarterly, 

55(2), 165-185.  

Schewe, C. D., Meredith, G. E., & Noble, S. M. (2000). Defining moments: Segmenting by 

cohorts. Marketing Management, 9(3), 48-53.  

Sharma, R., Singh, G., & Sharma, S. (2020). Modelling internet banking adoption in Fiji: A 

developing country perspective. International Journal of Information Management, 53, 

102116.  



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 29 

Shin, D.-H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. 

Computers in Human Behavior, 25(6), 1343-1354.  

Shulga, L. V., Busser, J. A., & Kim, H. (2018). Generational profiles in value Co-creation 

interactions. Journal of Hospitality Marketing & Management, 27(2), 196-217.  

Slade, E. L., Williams, M. D., & Dwivedi, Y. K. (2014). Devising a research model to examine 

adoption of mobile payments: An extension of UTAUT2. The Marketing Review, 14(3), 

310-335.  

Smith, R., Deitz, G., Royne, M. B., Hansen, J. D., Grünhagen, M., & Witte, C. (2013). Cross-

cultural examination of online shopping behavior: A comparison of Norway, Germany, 

and the United States. Journal of Business Research, 66(3), 328-335.  

Song, H., Lee, C.-K., Reisinger, Y., & Xu, H.-L. (2017). The role of visa exemption in Chinese 

tourists’ decision-making: a model of goal-directed behavior. Journal of Travel & Tourism 

Marketing, 34(5), 666-679.  

Strauss, W., & Howe, N. (1991). Generations: The History of America’s Future, 1584 to 2069. New 

York, NY: William Morrow and Company: Inc. 

Suh, E., Alhaery, M., Abarbanel, B., & McKenna, A. (2017). Examining millennials’ online 

gambling behavior: a comparison of generational differences. Journal of Hospitality and 

Tourism Technology, 8(3), 314-336.  

Tamilmani, K., Rana, N. P., Prakasam, N., & Dwivedi, Y. K. (2019). The battle of brain vs. heart: 

a literature review and meta-analysis of “hedonic motivation” use in UTAUT2. 

International Journal of Information Management, 46, 222-235.  

Tan, G. W.-H., Ooi, K.-B., Chong, S.-C., & Hew, T.-S. (2014). NFC mobile credit card: the next 

frontier of mobile payment? Telematics and Informatics, 31(2), 292-307.  

Tarhini, A., El-Masri, M., Ali, M., & Serrano, A. (2016). Extending the UTAUT model to 

understand the customers’ acceptance and use of internet banking in Lebanon: A 

structural equation modeling approach. Information Technology & People, 29(4), 830-849.  

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of 

competing models. Information Systems Research, 6(2), 144-176.  

Teubner, R., & Klein, S. (1998). Planning and Designing Web-Based Electronic Commerce: a 

case study in the insurance industry. Australasian Journal of Information Systems, 6(1). 

https://doi.org/10.3127/ajis.v6i1.331 

Triandis, H. (1977). Interpersonal behavior (p. 329). Monterey, CA: Brooks: Cole Publishing 

Company. 

Tsaur, S.-H., & Yen, C.-H. (2018). Work–leisure conflict and its consequences: Do generational 

differences matter? Tourism Management, 69, 121-131.  

Varma Citrin, A., Sprott, D. E., Silverman, S. N., & Stem Jr, D. E. (2000). Adoption of Internet 

shopping: the role of consumer innovativeness. Industrial Management & Data Systems, 

100(7), 294-300.  

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information 

technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.  



Australasian Journal of Information Systems Sharma, Singh & Pratt 
2020, Vol 24, Research Article Online Travel Purchases Across Generations 

 30 

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information 

technology: extending the unified theory of acceptance and use of technology. MIS 

Quarterly, 36(1), 157-178.  

Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: the case 

for an augmented technology acceptance model. Information & Management, 41(6), 747-

762.  

Wang, X., Weeger, A., & Gewald, H. (2017). Factors driving employee participation in 

corporate BYOD programs: A cross-national comparison from the perspective of future 

employees. Australasian Journal of Information Systems, 21. 

https://doi.org/10.3127/ajis.v21i0.1488 

Wen, I. (2010). Online travelers' decision makings: a new equation model to evaluate impacts 

of website, search intention, and trust. Information Technology & Tourism, 12(2), 153-173.  

Wu, C.-H., & Chen, S.-C. (2015). Understanding the relationships of critical factors to Facebook 

educational usage intention. Internet Research, 25(2), 262-278.  

Xu, X., & Pratt, S. (2018). Social media influencers as endorsers to promote travel destinations: 

an application of self-congruence theory to the Chinese Generation Y. Journal of Travel & 

Tourism Marketing, 35(7), 958-972. doi:10.1080/10548408.2018.1468851 

Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption 

across time: An empirical study of the effects of behavioral beliefs, social influences, and 

personal traits. Computers in Human Behavior, 28(1), 129-142.  

Yang, Z., & Fang, X. (2004). Online service quality dimensions and their relationships with 

satisfaction: A content analysis of customer reviews of securities brokerage services. 

International Journal of Service Industry Management, 15(3), 302-326.  

Zhang, T., Abound Omran, B., & Cobanoglu, C. (2017). Generation Y’s positive and negative 

eWOM: use of social media and mobile technology. International Journal of Contemporary 

Hospitality Management, 29(2), 732-761.  

Zhuang, H., Leszczyc, P. T. P., & Lin, Y. (2018). Why is Price Dispersion Higher Online than 

Offline? The Impact of Retailer Type and Shopping Risk on Price Dispersion. Journal of 

Retailing. 94(2), 136-153. 

 

 

Copyright: © 2020 Sharma, Singh & Pratt. This is an open-access article distributed under the 

terms of the Creative Commons Attribution-NonCommercial 3.0 Australia License, which 

permits non-commercial use, distribution, and reproduction in any medium, provided the 

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