Determinants of Consumer Attitudes
Results
Reliability and validity
An exploratory factor analysis was performed using SPSS 25, and the results are summarized in Table 3. The indicator validity was checked with all factor loadings exceeding a recommended threshold of .70. Internal consistency was confirmed with all constructs' Cronbach's alpha values, and composite reliability values exceeding .70. Convergent validity was established, as the average variances extracted (AVE) were all greater than the acceptable threshold of .5. Finally, discriminant validity was confirmed by comparing the square roots of the AVE values with the corresponding estimates of the correlation values (Table 4). Overall, the measurement items fulfilled the reliability and validity requirements for further analysis.
Table 4 Correlation matrix
| Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Co-creation | 5.168 | 1.016 | .811 | ||||||||
| Cost-effectiveness | 5.676 | .940 | .556 | .920 | |||||||
| Website attractiveness | 5.462 | .993 | .677 | .582 | .847 | ||||||
| Sustainability | 5.102 | 1.038 | .635 | .434 | .557 | .850 | |||||
| Brand uniqueness | 5.378 | 1.191 | .609 | .543 | .650 | .606 | .937 | ||||
| Social media engagement | 4.873 | 1.557 | .542 | .474 | .678 | .475 | .572 | .907 | |||
| Innovativeness | 5.582 | .936 | .634 | .546 | .717 | .620 | .765 | .632 | .824 | ||
| Attitude | 5.554 | 1.094 | .587 | .656 | .646 | .479 | .513 | .595 | .593 | .871 | |
| Repurchase intention | 5.708 | .987 | .577 | .640 | .636 | .522 | .614 | .517 | .594 | .779 | .917 |
- The lower triangle of the matrix represents the correlation coefficients between constructs
- The diagonal values (italics values) represent the square root of the average variance extracted of each construct
Measurement model
The data was analyzed through the partial least squares path modeling technique (PLS-SEM), using SmartPLS 2.0 software. As a component-based modelling approach, PLS is often preferred to covariance-based approaches such as structural equation modelling (CB-SEM), and multiple regression when estimating a complex path model with. Our sample size of 210 satisfied the criterion for PLS-SEM with 10 times the largest number of structural paths directed at a particular construct in the structural model, as the sample size threshold for our model would have been 90. Moreover, the use of PLS modeling is recommended when the research model is exploratory in nature, rather than confirmatory. A nonparametric bootstrapping procedure was conducted to test the significance of path coefficients.
The results of the analysis are summarized in Fig. 1 and Table 5. The analysis reveals that the following variables significantly influenced the consumers' attitudes toward DTC brands: co-creation (β = .115, p < .05), cost-effectiveness (β = .480, p < .001), website attractiveness (β = .303, p < .001), brand uniqueness (β = .138, p < .01), social media engagement (β = .300, p < .001), and innovativeness (β = .139, p < .01). Hence, H1a, H2a, H3a, H5a, H6a, and H7a were supported. Sustainability was the only variable that did not have a significant effect on attitude, rejecting H4a. On the other hand, the determinants that significantly influenced consumers' re-purchase intentions include brand uniqueness (β = .331, p < .001), social media engagement (β = .157, p < .01), and innovativeness (β = .115, p < .01), supporting H5b, H6b, and H7b. The variable, co-creation (H1b), cost-effectiveness (H2b), website attractiveness (H3b), and sustainability (H4b), did not significantly affect re-purchase intentions. Additionally, the indirect effects of the independent variables on re-purchase intentions through attitudes were analyzed (see Table 6). While most findings did not differ significantly from the results of the direct effects, it was discovered that the cost-effectiveness variable had a significant indirect influence on re-purchase intentions through attitude (β = .335, p < .001). Finally, consumers' attitudes toward DTC brands had a positive, and significant influence on their re-purchase intentions (β = .700, p < .001), which suggests a strong correlation between attitude and behavioral intentions (H8 supported).
Fig. 1 PLS results of the conceptual model
Table 5 Results of the Hypothesis Testing
| Hypothesis | Beta | Support |
|---|---|---|
| H1a. Co-creation → Attitude toward DTC brand | .115* | Yes |
| H1b. Co-creation → Re-purchase intention | − .025 | No |
| H2a. Cost-effectiveness → Attitude toward DTC brand | .480*** | Yes |
| H2b. Cost-effectiveness → Re-purchase intention | .114 | No |
| H3a. Website attractiveness → Attitude toward DTC brand | .303*** | Yes |
| H3b. Website attractiveness → Re-purchase intention | .082 | No |
| H4a. Sustainability → Attitude toward DTC Brand | .072 | No |
| H4b. Sustainability → Re-purchase intention | .096 | No |
| H5a. Brand uniqueness → Attitude toward DTC brand | .138** | Yes |
| H5b. Brand uniqueness → Re-purchase intention | .331*** | Yes |
| H6a. Social media engagement → Attitude toward DTC brand | .300*** | Yes |
| H6b. Social media engagement → Re-purchase intention | .157** | Yes |
| H7a. Innovativeness → Attitude toward DTC brand | .139* | Yes |
| H7b. Innovativeness → Re-purchase intention | .115* | Yes |
| H8. Attitude toward DTC brand → Re-purchase intention | .700*** | Yes |
- *** p < .001; ** p < .01; * p < .05
Table 6 Results of the indirect effects
| Path | Beta | Support |
|---|---|---|
| Co-creation → Attitude → Intention | .077 | No |
| Cost-effectiveness → Attitude → Intention | .335*** | Yes |
| Website attractiveness → Attitude → Intention | .100 | No |
| Sustainability → Attitude → Intention | .027 | No |
| Brand uniqueness → Attitude → Intention | .231*** | Yes |
| Social media engagement → Attitude → Intention | .147** | Yes |
| Innovativeness → Attitude → Intention | .118* | Yes |
- *** p < .001; ** p < .01; * p < .05
The percentage of variance explained by the predictors for the endogenous variable of attitude toward DTC brand was 58.0% (R2 = .580). The predictors for re-purchase intentions accounted for 68.7% of the variance (R2 = .687). These R2 values suggest that a high percentage of variance of the endogenous variables was explained, showing support for the conceptualized model.