The road to the Master’s Thesis
The impact of dynamic pricing when it
becomes an industry norm
Results Analysis ANOVA tests Regression
04
Table of ContentsIntroduction & Theoretical Background Dynamic Pricing Price Fairness Search Intention Purchase Intention
01
Conceptual Framework Overview of the relationsbetween the variables
02
Discussion & Limitations Evaluation of the results
05
2
03
Methodology Data Collection andIntroduction
Advantages Dynamic Pricing:- Generate customer data to target the right customers - Gives insights in the customers’ shopping behaviour - Leads to a grow in profits when implemented
properly
Disadvantages Dynamic Pricing: - Unsatisfied customers
- Price Unfairness among customers
- Negative attitude towards Dynamic Pricing
Introduction 3
Research question:
“How
do customers respond to dynamic
pricing when it becomes an
Theoretical Background 4
Dynamic pricing as an industry norm or not
•
Customers expect a certain price and value within each industry → not met → price unfairness, unsatisfied customers (Dubelaar, & Ewing, 2013)•
A new efficient development or technology can trigger a change in norms, especially when it is cost-beneficial for retailers + involvement of other competitors (Ellickson, 2001)I
ndependent Variables
Types of dynamic pricing
•
Behavioural-based: prices based on customers’ personal data (King & Jessen, 2010)•
Time-based: prices are set based on the economic conditions (Koschate-Fischer & Wüllner, 2017)Theoretical Background 5
Price Fairness
•
Pricing strategy is more accepted when it is in line with the industry norm (Dickson & Kalapurakal, 1994)•
When a pricing norm becomes familiar to a customer → more accepted (Kannan & Kopalle, 2001)•
Increase in price fairness when dynamic pricing is an industry normDependent Variables (1/2)
Search Intention
•
Amount of money saved during the search must be higher than the searching costs itself → little to no price distinctions arise between retailers (Collie, Graham, & Beverley, 2002)Theoretical Background 6
Dependent Variables (2/2)
Purchase Intention•
Customers judge the willingness to purchase by looking at circumstances and pricing strategies of other competitors (Weiner, 2000)•
Prices are set the same way → less delay in purchases (Eisen, 2006)•
Increase in purchase intentions when dynamic pricing is an industry norm Moderator: Risk-taking Behaviour•
Customer’s price knowledge and spending pattern → different prices as acceptable•
Dynamic pricing leads to price uncertainty → different reaction based on individual characteristics, such as risk-taking behaviour•
Price sensitive customers are risk averse → increase in search intentions-Methodology 8
Methodology
Four conditions•
2 x 2 between subject design•
Participants were exposed to a camera (high involvement product) similar as the study of Garbarino & Maxwell (2010).•
122 participants • 51.6% female• Average age 34,5 years
Methodology 9
Price Fairness deployed by Garbarino & Maxwell [2010].
•
Cronbach’s Alpha of 0.8Search Intentions adapted from Grewal et al [1998].
•
Cronbach’s Alpha of 0.7Purchase Intention employed by Garbarino & Maxwell [2010] and Grewal, et al [1998].
•
Cronbach’s Alpha of 0.9Risk-taken behaviour regarding purchasing new products adapted from Raju [1980].
•
Cronbach’s Alpha of 0.2 (very low!)Risk-taken behaviour regarding prices created by Slovic [1972], Cable & Judge, [1994], and Judge, et al. [1999].
•
Cronbach’s Alpha of 0.5 (low!)Methodology 10
Risk-taken behaviour regarding purchasing new products adapted from Raju [1980].
•
Cronbach’s Alpha of 0.2 (very low!)Risk-taken behaviour regarding prices adapted by Slovic [1972], Cable & Judge, [1994], and Judge, et al. [1999].
•
Cronbach’s Alpha of 0.5 (low!)Factor Analysis
•
Scree plot shows 2 components•
The two factors explain 63% of the variances which is above the required amount of 60% (Malhora, 2010)•
When combining questions regarding the components still low Cronbach’s Alpha, α =.5 & α =.2 → continue with fourResults 11
Manipulation Check: Industry Norm
•
Kruskal Wallis H test χ²(2) = 1.328, p =.249, with a mean of 4.56. Manipulation Check: Dynamic Pricing•
Kruskal Wallis H test (time-based conditions) χ²(2) = 5.947, p =.015, with a mean of 5.03•
Kruskal Wallis H test (behavioural-based conditions) χ²(2) = 4.909, p =.048, with a mean of 5.57The manipulation check of the industry norm was not significant thus not successful → continue with smaller sample size (N = 57) which passed the manipulation check.
Results 12
Dynamic Pricing → Price Fairness
•
Whole sample: F (1,120) = 0.441, p =.508•
Smaller sample: F (1,55) = 0.700, p =.406No support for increasing price fairness when dynamic pricing is an industry norm Dynamic Pricing → Search Intentions
•
Whole sample: F (1,120) = 0.096, p =.757•
Smaller sample: χ²(2) = 1.464, p =.226, with a mean of 17.6No support for decreasing search intention when dynamic pricing is an industry norm Dynamic Pricing → Purchase Intentions
•
Whole sample: F (1,120) = 0.428, p =.514•
Smaller sample: χ²(2) = 2.156, p =.142, with a mean of 23.3No support for increasing purchase intention when dynamic pricing is an industry norm
Results – ANOVA / Kruskal Wallis H Test
Why ANOVA?
+ Allows for comparison between two means of groups.
Results 13
Why Linear Regression for risk items?
✓
The understanding and interpretation of each variable can be given according to a coefficient.✓
Suitable for dependent variables with a seven-point Likert scale.✓
Helps understanding relationships between variables and moderators. Why not groups based on risk preference?•
In general, risk preference can be divided into three groups: risk averse, risk neutral and risk taken customers.•
Not suitable for this dataset → unequal group sizes (around 5 participants risk averse, 75 participants risk neutral and 42 participants risk taken). Therefore, the results will lead to invalid results.Results 14
Risk negatively influence Price Fairness
•
First table shows whole sample• Two risk items shows a significant negative moderating effect.
•
Second table shows smaller sample size • Three risk items shows a significant negativemoderating effect
Results – Regression
Why Regression?
+ Allows for comparison between two means of groups.
+ Controls for overall type I error rate (rejection of a true null hypothesis) + Parametric test so more powerful, if normality assumptions hold
Estimates Sig.
Intercept -.236 .727
Interaction effects
I would rather stick with a brand I normally purchase than try something I am not very sure of * industry norm
-1.134 .086* To hold out for the best price on something, even if it means waiting a long
time * industry norm
-1.269 .083* Choose a product with fluctuating price over a product with a steady price
* industry norm
1.048 .135
I never purchase something I do not know about at the risk of making a mistake * industry norm
-.544 .433
Intercept 20.388 .000***
Interaction effects
I would rather stick with a brand I normally purchase than try something I am not very sure of * industry norm
-1.511 .095*
To hold out for the best price on something, even if it means waiting a long time * industry norm
-2.454 .044**
Choose a product with fluctuating price over a product with a steady price * industry norm
1.355 .180
I never purchase something I do not know about at the risk of making a mistake * industry norm
-2.937 .002*** Note: Graph shows risk item “To hold out for the best price
Results 15
Search Intentions
•
Whole sample: All interaction effects show insignificant values.•
Smaller sample: All interaction effects show insignificant values. No support for moderating risk effect.Purchase Intentions
•
Whole sample: All interaction effects show insignificant values.•
Smaller sample: Interaction effect risk item “I never purchase something I do not know about at the risk of making a mistake” and industry norm is significant (β =3.847, p <.05).• When risk increases with one during the period that dynamic pricing is an industry norm, the purchase intention increases by 7.54.
Results 16
Price Fairness
•
ANOVA: F (1,120) = 4.131, p =.044.•
Means differ between groups•
Behavioural-based condition experiences the prices on average 1,897 fairer than the average fairness of the time-based condition. Support for different price fairness perceptions when dynamic pricing is an industry norm or not.Search Intentions
•
ANOVA: F (1,120) = 0.282, p =.597No support for different search intentions when dynamic pricing is an industry norm or not.
Results 17
Purchase Intentions
•
ANOVA: F (3,118) = 5.614, p <.01•
Means differ between groups•
Behavioural-based pricing as an industry norm has a negative influence on the purchase intention compared to time-based pricing strategy as an industry norm.•
Higher purchase intention when time-based pricing is an industry norm than when it is not an industry norm.Support for different purchase intentions when dynamic pricing is an industry norm or not.
The more risktaken, the higher the perceptions of price unfairness when dynamic pricing is an industry norm.
Customers who are risk-taken and focus more on prices of others, evaluate prices as unfairer because they keep comparing prices of others to prices that they paid (Mandel, 2003).
Discussion 18
1
2
3
Behavioural-based pricing is considered on average twice as fairer than the time-based pricing.
Behavioural-based pricing has benefits for customers such as personal offers which are higher than benefits of time-based pricing (Pazgal & Soberman, 2008).
Higher purchase intention when time-based pricing is an industry norm compared to when behavioural based pricing is an industry norm.
Retailers are less differentiated, so customers get benefits from every retailer therefore, lower purchase intention at same retailer (Acquisti & Varian, 2005). Time-based prices are more accepted among customers (Xia, Monroe, & Cox, 2004).
Limitations & Recommendations
19
Limitations & Recommendations
Low internal consistencies between risk items.
• Include more risk constructs for fuller picture.
Manipulation check not successful
• Results regarding dynamic pricing as an industry norm might not be generalizable for
the whole population.
Sample size from Dutch population
• The effect of an industry norm might be different in
other countries due to the different social norms
Only scenario about a purchase of a camera
Does anyone have any questions?
