Master Thesis Defense
Weixiang Wang
Msc Marketing Intelligence
S2509652
wwx_steven@163.com
Differential impacts of recommendations via online
social networks and provider systems in e-commerce:
Agenda
›
Motivation of research
›
Main findings
›
Theoretical framework
›
Research methodology
›
Results
›
Conclusion
Motivation of research
Importance and surge of recommendations in e-commerce
Limited evidence with regards to exploring relative impacts of
recommendations through different sources
Previous research overlook potential interaction between
recommendations via systems and online social networks
Important for online retailers
Main findings
•
Positive OSN recommendation is more effective than
RS in driving purchase intention in e-commerce
•
There is a complementary relationship between the
impact of OSN recommendation and the impact of RS
on purchase intention
Theoretical framework
Online recommendation via systems: Internet-based software, based on users’ needs and profile to suggest ideal products (Maes et al, 1999).
Research methodology
›
Recommendation manipulation
›
Pilot tests
›
Online questionnaire
›
120 participants
- 108 valid
›
Regression model
- 3 conditions with 2 dummy
variables
5.51 3.79 2.97 4.69 6.08 1.03 1.13 0.67 1.79 0.75 0 1 2 3 4 5 6 7Pen Toothbrush Coffee
machine purifier Water Smart phone
Results
Sample description
-male (42%) -Age 18-43 (85%)
-College education and above (95%) -Medium to high income (74%)
Main effects
-
relative impacts of RS vs OSN recommendation-
interaction effect between RS and OSN recommendation Main model TRS*OSN as reference Sub-model TRS as reference TRS -5.726* - TOSN -3.556* 2.170* TRS*OSN - 5.726* P-value <0.001 R2 84.3%Hypotheses main effects Overall support
H1 : Impact of RS < impact of OSN recommendation
Supported
H2 : Impact of RS and impact of OSN recommendation have a complementary relationship → purchase intention
Results
Moderation effects
-
moderation effect of product knowledge on impact of RS
-
moderation effect of product knowledgeon impact of OSN recommendation Hypotheses main effects Overall
support
H3 : product knowledge * RS → purchase intention (+)
product knowledge * OSN recommendation→ purchase intention (-)
Partially Supported
Moderation effects coefficient p-value
TRS*PK 1.003 0.017
Extension for differential impacts of
recommendations from different sources
in e-commerce
- new insight in online social networks
Relationship between effectiveness of
recommendations via personal source
and impersonal source
- valid combination of recommendations
from different sources
Conclusion
Theoretical implications
Managerial implications
More attention and innovation on OSN
-exploit social ‘hubs’
- Link web-page to social networks
Launch loyalty programs
Improve the post-purchase service and
platform design
Thank you for your attention !
References
Maes. P., Guttman R. H. and Moukas A. G. 1999. Agents that buy and sell. Communicitions of the ACM 42(3), 81-91
