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Motives in Online Shopping through Digital Platforms in Textile: Risk Perception and Purchase Intention Cover

Motives in Online Shopping through Digital Platforms in Textile: Risk Perception and Purchase Intention

By: Gülden Turhan  
Open Access
|Dec 2022

Figures & Tables

Fig. 1

Research model
Research model

ANOVA analysis and Levene statistics

Descriptive statisticsTest of homogeneity of variancesANOVA
NMeanStd. deviationStd. errorLevene statistic Sum of squaresdfMean squareFSig.
Risk perception196−0.3200.8040.0822.211Between groups6.45523.2283.9900.019
245−0.1800.8860.132df1=2, df2=356Within groups288.0133560.809
3218−0.0140.9410.064Sig.= 0.111Total 358
Total359−0.1170.9070.048
Purchase intention1960.6500.8800.0906.610Between groups7.22823.6143.4880.032
2451.1330.8820.131df1= 2, df2= 356Within groups368.8163561.036
32180.7711.0970.074Sig.= 0.002Total376.044358
Total3590.7841.0250.054

Post-hoc analysis: Multiple comparisons via Games Howell test

Dependent variable(I)Ward method(J)Ward method(I-J)Mean differenceStd. errorSig.
Risk perceptionCluster/Segment 1Cluster/Segment 2−0.1400.1550.641
Cluster/Segment 3−0.306*0.1040.010
Cluster/Segment 2Cluster/Segment 10.1400.1550.641
Cluster/Segment 3−0.1660.1470.498
Cluster/Segment 3Cluster/Segment 10.306*0.1040.010
Cluster/Segment 20.1660.1470.498
Purchase intentionCluster/Segment 1Cluster/Segment 2−0.482*0.1590.009
Cluster/Segment 3−0.1200.1170.559
Cluster/Segment 2Cluster/Segment 10.482*0.1590.009
Cluster/Segment 30.362*0.1510.049
Cluster/Segment 3Cluster/Segment 10.1200.1170.559
Cluster/Segment 2−0.362*0.1510.049

Digital platform and products purchased

Which online website/social media/social network did you shop on last?f%What was the last textile product you bought on an online website/social media/social network?f%
Trendyol9225.7Shoes5214.3
Hepsiburada5415.0Clothing154.2
N11318.7T-Shirt133.6
Instagram113.1Sweater102.8
f: frequencyDress102.8
Others (sweatpants, shorts, shirt, coat, jersey, pants, pajamas, sweatshirt, shawl, shorts, suit, trench coat, Jumpsuit)4112

Descriptive statistics of online shopping motives in the cluster/segments identified

Online shopping motivesCluster 1 N=96Cluster 2 N=45Cluster 3 N=218
MeanSDMeanSDMeanSD
1. I use an online website/social media/social network to discover and research popular or new products.−0.391.248−0.091.3621.260.651
2. I use an online website/social media/social network to conduct price research on the product and brand.0.251.1631.310.8481.400.815
3. I use an online website/social media/social network to follow fashion.−0.381.172−0.871.1981.060.898
4. I use an online website/social media/social network to make shopping faster and easier.−0.181.3630.581.4851.211.011
5. I use an online website/social media/social network to participate in competitions (such as travel, food, and holidays) that the brand organises outside of its products.−0.641.085−1.180.9340.351.344
6. I use an online website/social media/social network to follow bloggers who promote the brand.−0.471.130−1.760.435−0.051.390
7. I use an online website/social media/social network to get information about brand campaigns (promotion, discount, etc.)0.301.185−1.400.9151.060.867
8. I use an online website /social media/social network to see alternative brands.0.621.1191.091.0831.340.871
9. I use an online website/social media/social network to read reviews about the brand or its products.0.231.1980.021.6001.200.816
10. I use an online website/social media/social network to access easily the brands I want.0.281.1661.400.6541.140.892
11. I use an online website/social media/social network because I can benefit from customer service.−0.451.085−0.461.1461.031.007
12. I use an online website/social media/social network because I can find the product I want at a more affordable price.−0.341.1430.931.0311.320.691
13. I use an online website/social media/social network because I can find a product that I cannot find anywhere else.−0.471.1680.970.8671.190.868
14. I use an online website/social media/social network because I do not have time to go shopping.−0.391.1050.321.1440.661.126

Exploratory factor analysis and Cronbach’s Alpha

Extraction Method: Principal Component Analysis, Rotation Method: Varimax. (*reverse question)Factor 1Factor 2Alpha
Risk perceptionI would not feel safe shopping on an online website/social media/social network.0.723 0.711
There is too much uncertainty associated with shopping on an online website/social media/social networks.0.838
You can face some losses when you make purchases on an online website/social media/social networks.0.813
Purchase intentionI would never consider shopping on an online website/social media/social networks again.* 0.9130.938
I would probably shop again via an online website/social media/social networks. 0.944
I cannot shop online/on social media/social networks again.* 0.945

Dendrogram using Ward Linkage for online shopping motives

In the dendrogram on the left side, it is seen that there are three separate clusters according to the values observed. According to the evaluations made about the motivation criteria in online shopping, different segments were formed by dividing consumers into three separate clusters. The fact that there are three separate clusters shows that there are three separate evaluation levels for each of the online shopping motives, and these evaluations occur in three clusters at three separate levels from highest to lowest. Regardless of the standard deviation, the highest value that it can get according to the measurement scale will be 2, the average value will be 0, and the lowest value −2. For example, for motivation criterion 1, the evaluations of the individuals who make up cluster 3 are above zero by 1.26 and have a higher value than the evaluations of the individuals in other clusters. The resulting value for cluster 2 is below zero by −.09. The value that cluster 1 receives is below zero by −.39 and is lower than the assessments of individuals in other clusters. In this way, the evaluations of the individuals who make up the three clusters that arise as shown in the dendrogram for each motivation criterion can be interpreted as follows. (See Table 3)Cluster/Segment (1): Among the motivation criteria leading to online shopping, evaluation levels of criteria 3, 5, 6, 7, 9 and 11 are observed as relatively high for individuals in cluster 1 compared with those in cluster 2, but lower than those involved in cluster 3. Although, based on these criteria mentioned. they have a relatively high level of motivation compared to those in cluster 2, all but a few criteria are below zero. Although a few of them are above zero, they cannot exceed the level of 1. Therefore, in terms of all motivation criteria, cluster 1 individuals are a segment whose motivation for online shopping is at a low level.Cluster/Segment (2): Among the motivation criteria leading to online shopping, evaluation levels of criteria 1, 2, 4, 8, 10, 12, 13 and 14 are observed as relatively high for individuals in cluster 1 compared with those in cluster 2, but lower than those involved in cluster 3 (except for criterion 10). In online shopping, some motivational criteria evaluations are close to 1 or up from 1 (2, 8, 10, 12, 13) and some are close to −1 or lower than −1 (3, 5, 6, 7). Therefore, the motives that cause people in cluster 2 to make purchases online are just some of the 14 motives studied.Cluster/Segment (3): For each of the motivation criterion examined, the motivation levels of the individuals in cluster 3 are higher than the levels of those in the other two clusters. All other motivation criteria, except criteria 5, 6 and 14, are above 1. Therefore, cluster 3 individuals are a segment whose motivation for online shopping is high.
DOI: https://doi.org/10.2478/ftee-2022-0038 | Journal eISSN: 2300-7354 | Journal ISSN: 1230-3666
Language: English
Page range: 1 - 7
Published on: Dec 22, 2022
Published by: Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2022 Gülden Turhan, published by Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.