DYNAMIC PRICING: THE IMPACT
ON PRICE FAIRNESS, TRUST AND
REPURCHASE INTENTIONS
TABLE OF CONTENTS
•
Introduction
•
Conceptual model
•
Methodology – Experiment
•
Methodology - Analyses
•
Results – Cluster Analysis
•
Results – Hypothesis testing
•
Conclusion & Discussion
INTRODUCTION
•
Increase in adoption of dynamic pricing
•
Profit maximization
•
Negative customer implications
•
Customer relationship management
METHODOLOGY
-EXPERIMENT
•
Online survey in English
•
Randomly assigned to three different
conditions
- No personalized DP - Lower baseline price DP - Discount DP
•
Fictional buying situation (Samsung phone)
•
Pre-test
METHODOLOGY
-ANALYSES
•
Dummy coding
•
Reliability analyses
- Cronbach’s alpha
•
Hierarchical Cluster analysis
- Ward’s method creates segments where variation is minimized, which makes the segments internally resemble most
- Squared Euclidian distance - Z-scores
•
Linear regressions
RESULTS - CLUSTER ANALYSIS
•
Two different clusters
•
Cluster 1: Digital innovators
- Mostly online - Above average need for cognition - Medium risk - 16 – 25 years old
•
Cluster 2: Conservatives
RESULTS – HYPOTHESIS
TESTING
•
Linear regression
•
Main effects: Personalized dynamic pricing has a negative
effect on consumers’ perceived price fairness, trust in
the company and repurchase intentions. Therefore H1a,
H1b & H1c can be accepted
•
Moderating effects: All signs are as expected. However,
the only moderating effect that is significant, is the effect
of price framing when trust in the company is the
FINDINGS
•
Main effects are as expected
- Personalized dynamic pricing has negative effects on perceived price fairness, trust in the company and repurchase intentions
- New finding: the negative effect of personalized dynamic pricing is stronger for repurchase intentions than it is for perceived price fairness and trust in the company
•
Would I advice making use of Dynamic
Pricing as a company? No
•
All signs are as expected, however, the
results are not significant
- Small sample → less statistical power → reduces the chance of detecting a significant effect
- Only small variation in answers between different
type of people in experimental groups. If everyone
thinks the same about personalized DP, it is very likely that there is a less clear distinction in main results between different type of people, which resulted in non-significant findings for moderators that are linked to people; wealthiness and customer segments
LIMITATIONS & FUTURE RESEARCH
•
Limitations:
- Main results are similar for everyone, hence non-significant findings for moderators related to people - Online survey (money-related)
- COVID-19 - Small sample
