Title: Impulse Buying: Designing for Self-Control with E-commerce Open Access Deposited
|Related items in Deep Blue Documents|
(2020). Impulse Buying: Designing for Self-Control with E-commerce [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/xj8r-sy16
- This work is not a member of any user collections.
Files (Count: 15; Size: 13.8 MB)
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|README_ImpulseBuying_Mar18.txt||2020-03-18||2020-03-18||4.6 KB||Open Access||
|Variable_Code_Book_Mar10.xlsx||2020-03-10||2020-03-10||18.9 KB||Open Access||
|ImpulseBuying_SurveyInstruments_...0.pdf||2020-03-10||2020-03-10||12.8 MB||Open Access||
|Study3_Data_Public_Mar10.csv||2020-03-10||2020-03-10||27.5 KB||Open Access||
|Study3_Data_Public_Mar10.sav||2020-03-10||2020-03-10||34.5 KB||Open Access||
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|Study3_Syntax_Public_Mar10.sps||2020-03-10||2020-03-10||7.38 KB||Open Access||
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|Study4_Syntax_Public_Mar10.rtf||2020-03-18||2020-03-18||50.2 KB||Open Access||
|Study4_Syntax_Public_Mar10.sps||2020-03-10||2020-03-10||10.9 KB||Open Access||
|Study5_Data_Public_Mar10.csv||2020-03-10||2020-03-10||328 KB||Open Access||
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|Study5_Syntax_Public_Mar10.rtf||2020-03-18||2020-03-18||59.1 KB||Open Access||
|Study5_Syntax_Public_Mar10.sps||2020-03-10||2020-03-10||18.2 KB||Open Access||
Date: 18 March, 2020
Dataset Title: Impulse Buying: Designing for Self-Control with E-commerce [Data set]
Dataset Creators: Moser, C., Schoenebeck, S., Resnick, P
Dataset Contact: Carol Moser (email@example.com)
- This work is part of a dissertation that includes 5 studies. Only studies 3-5 are represented in this data set.
- Study 3: We compare the felt urge to buy and purchase intent before versus after a ~25 hour delay.
- Study 4: We compare the number of impulse purchases, the number of dollars spent impulsively, and the percentage of adds-to-cart that are purchased between participants who have their Amazon purchases delayed for 10 minutes versus those who have no delay.
- Study 5: We compare the felt urge to buy and the purchase intent between three conditions: Reflection (where participants provide reasons for and against buying), Distraction (where participants complete a distracting counting exercise), and Control.
Impulse buying is a common but potentially problematic behavior that can leave consumers with financial hardship and feelings of regret. The goal of this dissertation is to understand how to support consumers who wish to gain greater control of their online impulse buying. We designed and tested postponement, reflection, and distraction interventions to encourage self-control with e-commerce (Studies 3-5). Through an online experiment, we show that a 25-hour delay is effective at lowering consumer’s felt urge to buy impulsively and also at lowering purchase intent (Study 3). Conversely, an in-lab experiment testing a 10-minute delay on Amazon purchases failed to show a statistically significant decline in the number of impulse products purchased or dollars spent impulsively (Study 4). We highlight that 100% of participants continued to shop during their 10-minute delay to help explain the lack of an effect. Finally, through an online experiment, we show that prompting consumers to spend approximately 3 ½ minutes listing reasons for and against buying a product or engaging in a distracting task reduces the felt urge to buy impulsively and purchase intent. We conclude by asserting that postponement is an effective self-control strategy if (a) the delay is long enough to allow for the natural distractions of life to cool the impulse to buy or (b) is short but focused on either reflecting on the product or focused on something distracting, but not focused on browsing for additional impulse purchases.
The data come from online and in-lab experiments.
Instrument and/or Software specifications: The data are available in both .csv and .sav (SPSS). Syntax is provided in SPSS's .sps format. SPSS version 126.96.36.199 was used.
Note about null-values: The data include some null values. Null values represent the following cases: (1) the question was optional and not provided by the participant (e.g., self-describing gender), (2) the question provided checkmarks to "select all that apply" and the participant did not select that option (e.g., employment: full-time; part-time; retired; etc), (3) the question was only presented to a subset of participants (e.g., questions unique to the treatment condition not shown to, and therefore not answered by, the control group), (4) the participant did not complete the study (which is indicated in each dataset with a variables (e.g., Study 3: Part2_Complete), see each data set's codebook for details, or (5) the variable was calculated with SPSS syntax and was not applicable to that participant (e.g., % of cart adds that were purchased calculated for someone who did not add any products to their cart is set as null). Any other special cases for missing values are defined in each data set's codebook.
Files contained here:
Moser, C. (2020). Impulse Buying: Designing for Self-Control with E-commerce (Doctoral Dissertation). Forthcoming.
Use and Access:
This data set is made available under a Creative Commons with Attribution License (CC BY 4.0).
To Cite Data:
Moser, C., Schoenebeck, S., Resnick, P. Impulse Buying: Designing for Self-Control with E-commerce [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/xj8r-sy16