ENTER2020 eTourism Conference presentation

1. Exploring the Ways
to Improve Personalisation:
The Influence of Tourist Context on
Service Perceptions
Katerina Volcheka, Haiyan Songa, and Dimitrios Buhalisb and Rob Lawa
A School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hong Kong SAR
B Faculty of Management, Bournemouth University, UK

2. PERSONALISATION
IN TOURISM CONTEXT
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In the next 5 years personalisation will become
a key driver of business success (McKinsey,
2019)
Travel businesses won’t survive without
personalisation (Skift, 2019)
Personalisation should become the main
direction for investment (Amadeus, 2019)
Inaccurate recognition of tourist
context and interpretation of their
needs prevents wide acceptance of
personalisation technologies
(Skift, 2018)

3. RESEARCH GAP
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Existing research in tourism:
• Confirms the importance of personalisation (e.g. Choi,
Ryu, & Kim, 2019; Neuhofer, Buhalis, & Ladkin, 2015)
• Explores ways to improve personalisation methods by
increasing the relevance of provided services (e.g.
Glatzer, Neidhardt, & Werthner, 2018; Grün, Neidhardt,
& Werthner, 2017; Massimo & Ricci, 2019; Piccoli, Lui,
& Grün, 2017).
Research Gap:
Lack of understanding of the
influence that contextual
factors have on tourist needs
and service perceptions with
the view on improving
personalisation

4. PERSONALISATION
IN TOURISM CONTEXT
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A strategy of automatic adaptation of a service parameters
• according to the individual tourist needs, wants,
desired, problems and restrictions,
• identified in real-time context in which the service
is used

5. CONCEPTUAL BACKGROUND
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A
STANDARDISED
SERVICE
PERSONALISED
SERVICE
PERSONALISED
SERVICE
PERSONALISED
SERVICE
Individual Perceptions Individual Perceptions

6. CONCEPTUAL BACKGROUND
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A
STANDARDISED
SERVICE
PERSONALISED
SERVICE
PERSONALISED
SERVICE
PERSONALISED
SERVICE
Individual Perceptions Individual Perceptions

7. RESEARCH AIM AND OBJECTIVES
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To investigate the effects of observable factors of
internal and external contexts on tourist
perceptions of personalised information services
• To explore the presence of differences in
different tourist perceptions on the
performance of the personalised service
• To explore the presence of differences in
the way how different tourist expectations
towards
CO-CREATED
SERVICE
PERFORMANCE
EXPECTATIONS
CO-CREATED
VALUE
SATISFACTION
LOYALTY

8. METHODOLOGY
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Target Population:
• Hong Kong permanent
residents:
• Above 18 ye
• Who travelled abroad
• Who used Google Trips
app to plan their vacation
Personal Context:
• Place of birth
• Gender
• Age
• Completed education
• Family Status
Travel Context:
• Travel Experience
• Type of Destination
• Social environment
Research Context:
• Online panel (N = 244)
• Non-probability sampling (online panel)
Technical Context:
• Awareness of Personalisation
• Awareness of Data being
tracked
• Previous experience with
travel planners
• Operating System used for
survey completion
• Device used for survey
completion
Collected Data:

9. METHODOLOGY
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PLS SEM:
•Outer and
inner model
Assessment
Validate
Model
3-step
MICOM:
measurement
invariance of
the latent
constructs
Test
Equivalence
of
Perceptions
PLS-MGA/
OTG:
differences in
path
coefficients
Compare
the effects
of the
factors

10. FINDINGS
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Reflective Constructs:
• l >0.70
• AVE > 0.50
• 0.60 < CR < 0.90
• HTMT ≠ 1
Formative Constructs:
• VIF < 3
• W > 0.20
• l > 0.50
• redundancy analysis
β > 0.70; R2 > 0.60
Predictive Power and Relevance:
SRMRSat = 0.045
SRMREst= 0.045
NFISat = 0.881
NFIEst = 0.88
Q²incl > 0

11. FINDINGS: MODERATING EFFECT
β (F) β (M) β difference
Co-Created Service Performance -> Co-Created Value 0.744*** 0.589*** 0.154
Co-Created Service Performance -> Satisfaction 0.021 0.282*** 0.260**
Co-Created Value -> Loyalty 0.396*** 0.404*** 0.008
Co-Created Value -> Satisfaction 0.822*** 0.616*** 0.206
Expectations -> Co-Created Service Performance 0.240*** 0.607 0.367**
Expectations -> Co-Created Value 0.201** 0.308 0.107
Expectations -> Satisfaction -0.136 -0.030 0.106
Satisfaction -> Loyalty 0.500*** 0.453* 0.047
Gender: Male vs Female
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12. FINDINGS: MODERATING EFFECT
β (Gen X) β (Gen BB) β (Gen BB-Gen X)
Co-Created Service Performance -> Co-Created Value 0.542*** 0.720*** 0.185
Co-Created Service Performance -> Satisfaction 0.067 0.431*** 0.372**
Co-Created Value -> Loyalty 0.513*** 0.482*** 0.05
Co-Created Value -> Satisfaction 0.683*** 0.520*** 0.174
Expectations -> Co-Created Service Performance 0.516*** 0.530*** 0.015
Expectations -> Co-Created Value 0.364*** 0.187 0.186
Expectations -> Satisfaction 0.127 -0.148 0.272**
Satisfaction -> Loyalty 0.333** 0.423*** 0.113
Age: Generation X vs Baby Boomers
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13. FINDINGS: MODERATING EFFECT
β (LH) β (SH) Β (SH- LH)
difference
Co-Created Service Performance -> Co-Created Value 0.765*** 0.690*** 0.076
Co-Created Service Performance -> Satisfaction 0.122 0.161** 0.039
Co-Created Value -> Loyalty 0.503*** 0.373*** 0.130
Co-Created Value -> Satisfaction 0.748*** 0.705*** 0.043
Expectations -> Co-Created Service Performance 0.483*** 0.427*** 0.055
Expectations -> Co-Created Value 0.010 0.247*** 0.237*
Expectations -> Satisfaction -0.141 -0.051 0.090
Satisfaction -> Loyalty 0.349* 0.511*** 0.162
Tourist Type: Short Haul vs Long Haul
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14. FINDINGS: MODERATING EFFECT
β (Fam) β (Fr) β (Fr-Fam)difference
Co-Created Service Performance -> Co-Created Value 0.579*** 0.812*** 0.233
Co-Created Service Performance -> Satisfaction 0.375** 0.25 0.124
Co-Created Value -> Loyalty 0.757*** 0.626*** 0.131
Co-Created Value -> Satisfaction 0.416* 0.608*** 0.192
Expectations -> Co-Created Service Performance 0.489*** 0.052 0.437*
Expectations -> Co-Created Value 0.403** 0.256* 0.146
Expectations -> Satisfaction 0.096 -0.014 0.109
Satisfaction -> Loyalty 0.065 0.3 0.234
Social Environment: with Family Members vs with Friends
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Social Environment: with Friends vs with a Spouse
β (Fr) β (Sp) β (Fr-Sp) difference
Co-Created Service Performance -> Co-Created Value 0.812*** 0.675*** 0.137
Co-Created Service Performance -> Satisfaction 0.250 0.081 0.170
Co-Created Value -> Loyalty 0.626*** 0.339*** 0.286
Co-Created Value -> Satisfaction 0.608*** 0.812*** 0.204
Expectations -> Co-Created Service Performance 0.052 0.570*** 0.519**
Expectations -> Co-Created Value 0.256** 0.177** 0.079
Expectations -> Satisfaction -0.014 -0.109 0.095

15. FINDINGS:
COMPOSITIONAL VARIANCE
Mean value difference Gen Y - Gen Baby
Boomers
Gen Y - Gen X Gen Z-Gen Y
Co-Created Service Performance -0.490** -0.258 0.298
Co-Created Value -0.369* -0.114 0.310
Expectations -0.363* -0.195 -0.083
Loyalty -0.454** -0.439*** 0.505**
Satisfaction -0.317 -0.103 0.426
Larger age gaps: do not confirm partial invariance = attribute different meanings to the constructs
Age and Generations
18–24 years (Gen Z)
25–34 years (Gen Y)
35–54 years (Gen X)
55-64 years (Baby Boomer)
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16. FINDINGS:
COMPOSITIONAL VARIANCE
Mean value difference Non Aware - Aware
Co-Created Service Performance 0.989***
Co-Created Value 0.964***
Expectations 0.886***
Loyalty 0.816***
Satisfaction 0.791***
Awareness about Personalisation
Awareness about Personal Data being Applied: do not confirm partial invariance = attribute different
meanings to the constructs
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17. FINDINGS:
COMPOSITIONAL VARIANCE
Mean value difference No Experience - Experience
Co-Created Service Performance 0.715**
Co-Created Value 0.806*
Expectations 0.285
Loyalty 0.829*
Satisfaction 0.955*
Other Planners vs No Experience: do not confirm partial invariance = attribute different
meanings to the constructs
Previous application of personalised travel planners
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18. FINDINGS:
COMPOSITIONAL VARIANCE
Mean value difference Regular - Frequent Infrequent - Frequent Infrequent -
Regular
Co-Created Service Performance 0.407** 0.717*** 0.544***
Co-Created Value 0.383** 0.695** 0.541***
Expectations 0.280 0.545* 0.355**
Loyalty 0.484*** 0.764*** 0.450***
Satisfaction 0.307 0.703** 0.507***
Travel Experience (Frequency)
Frequent traveller (>3 trips per year)
Regular Traveller (2–3 trips per year)
Infrequent traveller (once a year or less)
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19. FINDINGS:
COMPOSITIONAL VARIANCE
Mean value difference Single - Married
Co-Created Service Performance 0.257*
Co-Created Value 0.130**
Expectations 0.370***
Loyalty 0.232**
Satisfaction 0.069
Family Status
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20. FINDINGS:
COMPOSITIONAL VARIANCE
Mean value difference Windows - Other OS Mac - Other OS
Co-Created Service Performance 0.008 0.050
Co-Created Value 0.461*** 0.486**
Expectations 0.244 0.015
Loyalty/ Use Intentions 0.439** 0.329
Satisfaction 0.479*** 0.413*
Operating System Used to Complete the Survey
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21. CONCLUSION
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moderates
the structural relationships
between tourist expectations and
perceptions
triggers
distinct perceptions and
interpretations
of experienced interactions
Travel
Context
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22. 20 January 2020 ENTER2020 – Research Track Page 22
• An evidence that a more comprehensive service design
strategy is required to maximise co-created value and
satisfaction and to motivate tourists to use the service
again.
CONTRIBUTION AND LIMITATIONS
• A comparative method to assess
personalisation
• New insights about tourist perceptions of personalised
information service
• Potential bias due to the
sample size
• Complex context is not
considered

23. 20 January 2020 ENTER2020 – Research Track Page 23
REFERENCES
Choi, I. Y., Ryu, Y. U., & Kim, J. K. (2019). A recommender system based on personal constraints for smart tourism city *.
Asia Pacific Journal of Tourism Research.
Glatzer, L., Neidhardt, J., & Werthner, H. (2018). Automated Assignment of Hotel Descriptions to Travel Behavioural
Patterns. In Information and Communication Technologies in Tourism 2018 (pp. 409-421): Springer.
Grün, C., Neidhardt, J., & Werthner, H. (2017). Ontology-Based Matchmaking to Provide Personalized Recommendations
for Tourists. In Information and Communication Technologies in Tourism 2017 (pp. 3-16): Springer.
Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural
equation modeling: SAGE Publications.
Massimo, D., & Ricci, F. (2019). Clustering Users’ POIs Visit Trajectories for Next-POI Recommendation. In Information and
Communication Technologies in Tourism 2019 (pp. 3-14): Springer.
Neuhofer, B., Buhalis, D., & Ladkin, A. (2015). Technology as a Catalyst of Change: Enablers and Barriers of the Tourist
Experience and Their Consequences. Information & Communication Technologies in Tourism 2015, 789.
Volchek, K., Law, R., Buhalis, D., & Song, H. (2019). The Good, the bad, and the ugly: Tourist perceptions on interactions
with personalised content. E-review of Tourism Research, 16(2-3), 62-72.

24. 20 January 2020 ENTER2020 – Research Track Page 24
Thank you very much
for your attention!

25. FINDINGS: MODERATING EFFECT

26. FINDINGS: MODERATING EFFECT
No Completed Degree vs with
Degree
β (Deg) β (NoD) p-Values
(Deg)
p-Values
(NoD)
β
difference
p-Value (Deg-
NoD)
Co-Created Service Performance ->
Co-Created Value
0.637 0.736 0.000 0.000 0.100 0.420
Co-Created Service Performance ->
Satisfaction
0.107 0.171 0.101 0.117 0.064 0.596
Co-Created Value -> Loyalty/ Use
Intentions
0.361 0.451 0.000 0.000 0.091 0.510
Co-Created Value -> Satisfaction 0.714 0.761 0.000 0.000 0.047 0.737
Expectations -> Co-Created Service
Performance
0.434 0.456 0.000 0.013 0.021 0.905
Expectations -> Co-Created Value 0.249 0.200 0.002 0.038 0.049 0.694
Expectations -> Satisfaction -0.050 -0.127 0.590 0.123 0.076 0.552
Satisfaction -> Loyalty/ Use
Intentions
0.475 0.471 0.000 0.000 0.004 0.976
Education

27. FINDINGS: MODERATING EFFECT
Google Trips VS Other Travel Planners β (GT) β (Other) p-Values (GT) p-Values
(Other)
β
difference
p-Value (GT-
Other)
Co-Created Service Performance ->
Co-Created Value
0.671 0.534 0.000 0.000 0.137 0.161
Co-Created Service Performance ->
Satisfaction
0.190 0.145 0.005 0.125 0.045 0.346
Co-Created Value -> Loyalty/ Use
Intentions
0.399 0.317 0.000 0.004 0.082 0.269
Co-Created Value -> Satisfaction 0.672 0.767 0.000 0.000 0.096 0.753
Expectations -> Co-Created Service
Performance
0.445 0.505 0.000 0.000 0.060 0.652
Expectations -> Co-Created Value 0.228 0.328 0.006 0.005 0.100 0.755
Expectations -> Satisfaction -0.083 -0.210 0.299 0.127 0.127 0.202
Satisfaction -> Loyalty/ Use Intentions 0.461 0.525 0.000 0.000 0.004 0.976
Previous application of personalised travel planners

28. FINDINGS: MODERATING EFFECT
Desktop vs Mobile β (D) β (M) p-Values (D) p-Values (M) β difference p-Value (PLS-
MGA)
Co-Created Service Performance -> Co-
Created Value
0.699 0.686 0.000 0.000 0.013 0.443
Co-Created Service Performance ->
Satisfaction
0.217 0.072 0.001 0.571 0.146 0.153
Co-Created Value -> Loyalty/ Use
Intentions
0.388 0.425 0.000 0.000 0.038 0.610
Co-Created Value -> Satisfaction 0.637 0.838 0.000 0.000 0.201 0.915
Expectations -> Co-Created Service
Performance
0.401 0.471 0.001 0.016 0.070 0.644
Expectations -> Co-Created Value 0.212 0.250 0.009 0.012 0.038 0.622
Expectations -> Satisfaction -0.009 -0.194 0.874 0.132 0.186 0.101
Satisfaction -> Loyalty/ Use Intentions 0.467 0.497 0.000 0.000 0.030 0.609
Device Used to Complete the Survey