“TRUE” E-LOYALTY IN THE ONLINE TRAVEL MARKET

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“TRUE”

E-LOYALTY IN

THE ONLINE

TRAVEL

MARKET

The role of knowledge, privacy

concerns, and e-trust to create loyalty

in the online travel market: A

moderated mediation model.

.

By Erika Christodoulou- S4042271

MSc Marketing Management

Master Thesis Defense

26 June 2020

AGENDA

PROBLEM AND RESEARCH

QUESTIONS

CONCEPTUAL MODEL

METHODS

RESULTS

CONCLUSIONS

_IMPLICATIONS

MANAGERIAL

_{FUTURE RESEARCH}

LIMITATIONS AND

REFERENCES

2

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PROBLEM AND RESEARCH QUESTIONS (1/3)

• 4.54 billion people around the world choose to purchase their products online

(Statista, 2020)

• By 2023: $1,134.55 billion will be spent on the online travel shopping

(MarketWatch, 2020)

• Alternatives - only one click to switch to a competitor

(Singh & Rosengren, 2020)

• Hurdle & aim: “True” loyal customers

(Luarn & Lin, 2003)

• Competitive advantage, profitability and success of an e-company

(Lacey & Morgan, 2007; McCall & McMahon, 2016)

E-trust:

• When companies make an effort to create trustworthiness in e-commerce → customers appreciate it as the only way

of interaction with the e-retailer

(Bart et al., 2005)

• High perceived e-trust is closely connected to e-loyalty

(Gabisch & Milne, 2014)

Hypothesis 1: E-trust is positively related to e-loyalty in the online travel market.

Knowledge of e-commerce:

• More knowledge → more likely to create trustworthiness

(Bart et al., 2005)

• Skills and experience have a weaker direct on e-loyalty

(e.g., Swaminathan, Anderson & Song, 2018)

• Broad nature of the construct

(Ajzen, 1991)

RQ1: To what extent does e-trust mediates the knowledge of e-commerce and e-loyalty relationship?

Hypothesis 2: The relation between knowledge of e-commerce and e-loyalty is mediated by e-trust.

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Privacy concerns:

• Negatively related with purchase intention

(e.g., Hu, Kandampully & Juwaheer, 2009)

• Positive relation between e-trust and privacy concerns

(e.g., Martin, 2008)

• Privacy paradox between concerns and disclosing information/purchasing

(Gerber, Gerber & Volkamer, 2018)

• Cost = disclose information - benefit = financial rewards

RQ2: To what extent do privacy concerns moderate the relation between e-trust and e-loyalty?

Hypothesis 3: Privacy concerns moderate the relation between e-trust and e-loyalty in such a way that they become less

important once a strong level of perceived e-trust across a travel-related website is established.

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PROBLEM AND RESEARCH QUESTIONS (3/3)

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METHODS

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• English and Greek language

• People who book flight tickets or hotel accommodation online

• 383 responses

Questionnaire

• 33 invalid responses

• 350 valid sample

Data cleaning

• 67% female and 32% male

• 26 years old (average)

• Bachelor’s and Master’s Degree

• Students and employed

Socio-demographics

RESULTS (1/3)

Multiple linear regression analysis

• Hypothesis 1: E-trust is positively related to e-loyalty in the online travel market. – SUPPORTED

Coefficients

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig.

B

Std. Error

Beta

(Constant)

-6.347E-17

0.05

0.00

1.00 Privacy

concerns

0.10

0.06

0.10

1.66

0.100 E-trust

0.34

0.06

0.34

5.70

0.00 Knowledge of

e-commerce

0.11

0.05

0.11

2.20

0.01

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RESULTS (2/3)

Mediation analysis

(Hayes, 2017)

:

• Hypothesis 2: The relation between knowledge of e-commerce and e-loyalty is mediated by e-trust. – SUPPORTED

• Cross- validation: Supported for word of mouth and repurchase intention

9 c’ = 0.10*

E-loyalty

E-trust

𝑎 = 0.21** b = 0.40**

Knowledge of

e-commerce

Knowledge of

e-commerce

E-loyalty

c = 0.19** ____ Significant effects ** significant at p<0.001 * significant at p<0.05 R2 =0.05 R2 _=0.19

RESULTS (3/3)

Moderation analysis

(Hayes, 2017)

:

• Hypothesis 3: Privacy concerns moderate the relation between e-trust and e-loyalty in such a way that they become less

important once a strong level of perceived e-trust across a travel-related website is established. – SUPPORTED

• Cross-validation: supported for word of mouth and willingness to pay

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Outcome variable: Loyalty

Model

Coefficient

se

t

p

Constant

-0.04

0.05 -0.71

0.48 E-trust

0.39

0.06

6.51

0.00 Privacy concerns

0.09

0.06

1.55

0.122 E-trust* Privacy

concerns

0.07

0.04

1.84

0.067

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1. E-trust is essential in establishing loyal relationships with customers in e-travel business

2. Synergy: knowledge of e-commerce and e-trust

• Knowledge of e-commerce = broad term affecting e-loyalty in an indirect way

(Ajzen, 1991)

• More knowledgeable customers are more likely to trust such websites = loyalists by referrals and repeated

purchases

3. Privacy paradox:

• Theoretical background → cost – benefit

• This study →feeling – effect

• Privacy concerned customers are more likely to create referrals and purchase more for services once a strong e-trust

is established

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CONCLUSIONS

MANAGERIAL IMPLICATIONS

Encourage confidence and create a safe online environment

→

increases e-trust → increases e-loyalty

Provide customers with positive experience and opportunities to enhance technical skills on e-commerce

→

drives e-trust → increases e-loyalty

Follow strategies which provide enough safeguards, encryption and policies approving the claims of an e-company

→

increases e-trust → make potential “violating privacy techniques” less important → increases e-loyalty

“E-trust rules the internet”

(Reichheld & Schefter, 2000)

Establishing a high perceived trustworthiness pays off by creating “true” loyal customers

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LIMITATIONS AND FUTURE RESEARCH

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Limitations

Future research

Cross-sectional study: No cause and effect analysis

Replicate the study using multiple scenarios: how

results differ under causal relationships

Only attitudinal perspective of e-loyalty

Research on both attitudinal and behavioral

perspectives

No support for:

• Mediate effect of e-trust in the relation between

knowledge of e-commerce and willingness to pay

• Moderate effect of privacy concerns between e-trust

and repurchase intention

Replicate study trying to understand how “true”

e-loyalty can be expressed by:

• Willingness to pay for knowledgeable customers

• Repurchase intention for privacy concerned

customers

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THANK YOU FOR YOUR ATTENTION!

Are there are any further questions?

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REFERENCES

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. Retrieved from https://doi.org/10.1016/0749-5978(91)90020-T

• Bart, Y., Shankar, V., Sultan, F., & Urban, G. L. (2005). Are the drivers and role of online trust the same for all web sites and consumers? A large-scale exploratory empirical study. Journal of Marketing, 69(4), 133-152. Retrieved from https://doi.org/10.1509/jmkg.2005.69.4.133

• Gabisch, J. A., & Milne, G. R. (2014). The impact of compensation on information ownership and privacy control. Journal of Consumer Marketing, 31(1), 13–26. Retrieved from https://doi.org/10.1108/JCM-10-2013-0737 • Gerber, N., Gerber, P., & Volkamer, M. (2018). Explaining the privacy paradox: a systematic review of literature investigating privacy attitude and behavior. Computers & Security, 77, 226–261. Retrieved from

https://doi.org/10.1016/j.cose.2018.04.002

• Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis, second edition: A regression-based approach. Retrieved from https://ebookcentral.proquest.com/lib/rug • Lacey, R., & Morgan, R. (2007). Committed customers as strategic marketing resources. Journal of Relationship Marketing, 6(2), 51–65. Retrieved from https://doi-org.proxy-ub.rug.nl/10.1300/J366v06n02_05 • Luarn, P., & Lin, H. H. (2003). A customer loyalty model for e-service context. Journal of Electronic Commerce Research., 4(4), 156-167. Retrieved from

https://pdfs.semanticscholar.org/c524/089e59615f90f36e3f89aeb4485441cd7c06.pdf

• MarketWatch (27 April 2020). Global Online Travel Market Share, Industry Trends, Revenue, Demand and Forecast to 2023. Retrieved from

https://www.marketwatch.com/press-release/global-online-travel-marketshare-industry-trendsrevenue-demand-and-forecast-to-2023-2020-04-27?mod=mw_quote_news

• Martin, K. (2018). The penalty for privacy violations: how privacy violations impact trust online. Journal of Business Research, 82, 103–116. Retrieved from https://doi.org/10.1016/j.jbusres.2017.08.034 • McCall, M., & McMahon, D. (2016). Customer loyalty program management: What matters to the consumer. Cornell Hospitality Quarterly, 57(1), 111–115. Retrieved from https://doi.org/10.1177/1938965515614099 • Reichheld, F. F., & Schefter, P. (2000). E-loyalty: your secret weapon on the web. Harvard Business Review, 78, 105–113. Retrieved from http://web.b.ebscohost.com.proxy-ub.rug.nl/ehost/pdfviewer/pdfviewer • Singh, R., & Rosengren, S. (2020). Why do online grocery shoppers switch? An empirical investigation of drivers of switching in online grocery. Journal of Retailing and Consumer Services, 53. Retrieved from

https://doi.org/10.1016/j.jretconser.2019.101962

• Statista (2020). Global digital population as of January 2020. Retrieved from, https://www.statista.com/statistics/617136/digital-population-worldwide/

• Swaminathan, S., Anderson, R., & Song, L. (2018). Building loyalty in e-commerce: impact of business and customer characteristics. Journal of Marketing Channels, 25(1-2), 22–35. Retrieved from https://doi.org/10.1080/1046669X.2019.1646184

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