Older Tourists: An Exploratory Study on Online Behaviour

ENTER 2014 Research Track Slide Number 1

Older tourists: an exploratory study on online behaviour

Vania Vigolo and Ilenia Confente

Department of Business AdministrationUniversity of Verona, Italy


Agenda


Introduction

An ageing world

Population 60+,% of total


Introduction

The European context What opportunities

for the tourism industry?


Introduction

Older touristshealthierhealthier

wealthierwealthier

more activemore active


Introduction

Older tourists and online behaviour

OUR OBJECTIVE:To investigate the online behaviour of older

tourists in an increasingly ageing context

The Internet has reshaped the way consumers can search for and purchase tourism products.

Does it apply also to older tourists?


Introduction

The study context: ItalyAge• In 2012, 40% of the population aged 50+• By 2030, 25% of the population will be 65+ (European

Commission, 2012)

Internet usage• Only 19.5% of the 55+ population uses the internet

(www.audiweb.it)


Literature overview

Extant literature on older tourists• travel motivations (Jang, Bai, Hu, & Wu, 2009; Le Serre &

Chevalier, 2012; Le Serre, Legohérel, & Weber, 2013)

• psychological factors (Jang et al., 2009)

• experience characteristics (Hunter-Jones & Blackburn, 2007; Batra, 2009)

• service needs and perceptions (Wang, Ma, Hsu, Jao, & Lin, 2013; Chen, Liu, and Chang, 2013).


Literature overview

Older tourist and online behaviour • Most published studies on online behaviour deal with the “Y generation” (Nusair et al., 2012)

• Little research exists on the role of Internet in the senior travel market (Le Serre & Chevalier, 2012; Kohlbacher, 2012)

GAP


Research questions

• (1) What are the determinants of older consumers’ intention to buy tourism products online?

• (2) Are there any differences between seniors and prospective-seniors in the propensity to buy tourism products online?


offline WOMoffline WOM

e-WOMe-WOM

online info searchonline info search

educationeducation

prior online purchase

prior online purchase

prior online travel purchase

prior online travel purchase

prior online travel purchase (other)

prior online travel purchase (other)

Online travel

purchase intention

Model


Method• Questionnaire design:- Predictors (dummy variables)- Dependent variable (1-5 Likert type)- self-administered, pre-tested among 20 older tourists • Sampling process- snowballing (Lee, Huang, & Yeh, 2010)

- age-quota: 50+ years, prospective seniors (50-64) and seniors (65+) (Chen et al., 2013)

• SPSS for analysis of results• N= 205, aged 50-81 years (average age 59.5)


Results

Sample profile: demographics

Age group Gender Education

Prospective seniors (78.9)

Male (48.3) Primary (30.7)

Seniors(21.1)

Female (51.2) High school/university (68.8)


Results

Sample profile: travel behaviour and online experience

Percentage

Travelled in the last two years 88.8

Prior travel choice based mainly on e-WOM 22.0

Online purchase experience 45.4

Online travel purchase experience 38.5

Online travel purchase experience (other people) 29.3

Online information search before travel purchase 74.1

Wrote an online travel review 8.8


online info searchonline info search

prior online purchaseprior online purchase

offline WOMoffline WOM

e-WOM**e-WOM**

education*education*

prior online travel purchase**

prior online travel purchase**

prior online travel purchase (other)**prior online travel purchase (other)**

Online travel purchase intention

51.7 % of variance explained (adj. R2)** p< 0.01; *p< 0.05

Results (1) Predictors of online travel

purchase intention


Results

(1) Predictors of online travel purchase intention

Determinants β StdError Std. β t F VIF

Constant 1.074 0.232 4.621** 31.828

e-WOM 0.665 0.212 0.166 3.136** 1.166

Prior online travel purchase 1.549 0.246 0.454 6.297** 2.168

Prior online travel purchase (other) 0.475 0.184 0.131 2.585** 1.069

Education 0.455 0.194 0.126 2.341* 1.217

Offline WOM 0.217 0.185 0.058 1.168 1.044

Previous online purchase 0.413 0.263 0.124 1.570 2.589

Online information search 0.160 0.213 0.042 0.753 1.310

R2 = 0.533; Adjusted R2 = 0.517

*p < 0.05; **p < 0.01


Results

(2) Differences between prospective seniors and seniors

Prospective seniors (50-64)……express higher intention:

to purchase online (t= 3.415, p< 0.01) to purchase tourism products online (t= 2.884, p< 0.01)

…are more likely: to make travel decisions based on e-WOM (χ²= 10.929, p< 0.01),to search travel information online (χ²= 36.003, p< 0.01) to write an online review about their travel experience (χ²= 3.975, p < 0.05)


Discussion• Context-specific online experience rather than generic

(non tourism-related) online experience can enhance the likelihood to buy tourist services online.

• Online information search was not a significant predictor online commercial/promotional information is perceived as less reliable than UGC (Bruwer & Thach, 2013)

• Highly-educated older tourists are more likely to make online travel purchases (Porter & Donthu, 2006; Lee et al., 2007).


Managerial implicationsIn the next years, older tourists will be more sensitive to e-

WOM and will increase their propensity towards online travel purchase.

Implications for tourism managers: To rethink communication and distribution strategies to

reach this target To concentrate on peer-to-peer communication (UGC)

rather than on commercial communicationTo stimulate passive readers to become active senders, thus

enhancing the role of e-WOM as an influential source for other older tourists


Limitations and further research

• Sample selection • Sample size• This study focused on past behaviour and online

experience. Other variables not included in this study (e.g. perceived risk, perceived price convenience etc.) could play a significant role in determining purchase intention.

• Personal features may influence older tourists’ purchase behaviour.


THANK YOU!THANK YOU!

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Older Tourists: An Exploratory Study on Online Behaviour

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