| Literature DB >> 35457759 |
Wanjing Jiang1,2, Yao Song2,3,4.
Abstract
COVID-19 has impacted economic and social conditions around the globe. In a post-pandemic world, the labor models have been shifting in favor of working from home and shopping toward online purchasing through mobile devices. The pandemic has, in addition to disrupting the world economy, triggered changes in consumer behavior that require a rethinking of marketing efforts from the consumer's perspective and a fundamental shift in branding strategies and managerial thinking. This paper expanded the understanding of the mobile consumer behavior of Generation Z consumers in China by examining the changes in their behavior in response to the pandemic. We used a structural equation model (SEM) to show that, in mobile shopping, the hedonic experience has played an essential role in signaling brand conspicuousness and product aesthetics, in turn promoting brand identity and associated behavioral reactions. The paper concludes with a discussion of the implications of these changes for branding identity and brand management.Entities:
Keywords: Chinese Generation Z; brand identity; consumer behavior; repurchase intention
Mesh:
Year: 2022 PMID: 35457759 PMCID: PMC9031833 DOI: 10.3390/ijerph19084894
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1Diagram of the research.
Theoretical Background.
| Theory | Scholar(s) | Conclusions |
|---|---|---|
| Hedonic experience | H.R. Chaudhuri | Pleasure (i.e., its multisensory, fantasy, and emotive aspects) increases brand conspicuousness and aesthetics. |
| H.R. Chaudhuri, et al. [ | ||
| Perceived aesthetics | H.T. Keh [ | Perceived aesthetics enhance the capacity to build brand identity and product attitude. |
| M. Hingle, et al. [ | ||
| Brand conspicuousness | L. Harris, et al. [ | Brand conspicuousness helps consumers to satisfy self-defined needs relating to attractiveness and meaning, product attitude, and identity. |
| H. He, et al. [ | ||
| Attitude and behavior | Ajzen [ | A positive attitude toward a particular brand and brand identity tends to strengthen the intention to repurchase. |
| P.K. Hellier, et al. [ |
Figure 2Theoretical framework and hypotheses.
Measurement items of the research.
| Measures | Measure Items | Reference |
|---|---|---|
| Brand Conspicuousness (BC) | When people use high-end brands, they are more likely to be recognized by others. | Patsiaouras and Fitchett [ |
| I think people who buy high-end brands are more likely to be socially successful. | ||
| I might envy people who buy high-end brands. | ||
| Hedonic Experience (HE) | Shopping and browsing in online stores is a pleasant pastime for me. | Chaudhuri and Majumdar [ |
| I spend lots of time researching online products because I am interested in mobile shopping. | ||
| When I shop for products, I like to browse online malls. | ||
| Perceived Aesthetics (AE) | I think the design of mobile shopping fits my aesthetic. | Hagtvedt and Patrick [ |
| I think mobile shopping is very stylish. | ||
| I think the mobile mall is very attractive. | ||
| Repurchase Intention (RI) | I have purchased online products in the past 2 years. | Wen et al. [ |
| I have a high probability of purchasing online products in the next two years. | ||
| I’m very much looking forward to continuing mobile shopping. | ||
| Product Attitudes (PA) | I think online products are good. | Wilkie and Pessemier [ |
| I think online products are desirable. | ||
| I think online products are pleasant. | ||
| Brand Identity (BI) | I think the online brands and my image are consistent. | Rather [ |
| I think the online brands and my values are in line. | ||
| I strongly agree with the online brands. | ||
| Choosing an online brand makes me feel more innovative in my life | ||
| Choosing an online brand makes me feel that I am living a healthier life. |
Reliability and validity of the research.
| Factors | Cronbach’s Alpha | Variable | Standardized | SMC | AVE | Composite | |
|---|---|---|---|---|---|---|---|
| Factor Loading | Reliability | ||||||
| Brand Conspicuousness (BC) | 0.862 | BC1 | 0.851 | - | 0.724 | 0.683 | 0.865 |
| BC2 | 0.869 | 16.176 | 0.755 | ||||
| BC3 | 0.755 | 14.151 | 0.57 | ||||
| Hedonic Experience (HE) | 0.768 | HE1 | 0.711 | - | 0.506 | 0.525 | 0.768 |
| HE2 | 0.754 | 9.963 | 0.568 | ||||
| HE3 | 0.693 | 10.334 | 0.48 | ||||
| Perceived Aesthetics (AE) | 0.926 | AE1 | 0.905 | - | 0.817 | 0.808 | 0.927 |
| AE2 | 0.956 | 22.738 | 0.818 | ||||
| AE3 | 0.901 | 22.752 | 0.791 | ||||
| Repurchase Intention (RI) | 0.924 | RI1 | 0.831 | - | 0.69 | 0.818 | 0.931 |
| RI2 | 0.915 | 20.593 | 0.837 | ||||
| RI3 | 0.961 | 22.172 | 0.923 | ||||
| Product Attitudes (PA) | 0.918 | PA1 | 0.897 | - | 0.805 | 0.792 | 0.919 |
| PA2 | 0.869 | 21.684 | 0.755 | ||||
| PA3 | 0.898 | 23.407 | 0.807 | ||||
| Brand Identity (BI) | 0.951 | BI1 | 0.859 | - | 0.738 | 0.952 | 0.799 |
| BI2 | 0.873 | 28.407 | 0.762 | ||||
| BI3 | 0.932 | 24.718 | 0.868 | ||||
| BI4 | 0.918 | 23.383 | 0.842 | ||||
| BI5 | 0.877 | 24.424 | 0.769 |
Note: Statistical theoretical values are considered according to Gârdan et al.’s work [75].
Correlation and discriminant validity of the constructs.
| CR | AVE | MSV | ASV | RI | BC | HE | AE | BI | PA | |
|---|---|---|---|---|---|---|---|---|---|---|
| RI | 0.931 | 0.819 | 0.677 | 0.429 | 0.905 | |||||
| BC | 0.865 | 0.682 | 0.268 | 0.220 | 0.454 *** | 0.826 | ||||
| HE | 0.768 | 0.525 | 0.394 | 0.262 | 0.417 *** | 0.518 *** | 0.725 | |||
| AE | 0.927 | 0.808 | 0.615 | 0.439 | 0.642 *** | 0.432 *** | 0.628 *** | 0.899 | ||
| BI | 0.952 | 0.799 | 0.863 | 0.523 | 0.822 *** | 0.481 *** | 0.512 *** | 0.765 *** | 0.894 | |
| PA | 0.919 | 0.792 | 0.863 | 0.514 | 0.823 *** | 0.453 *** | 0.459 *** | 0.784 *** | 0.929 *** | 0.890 |
Note: *** p < 0.01.
The goodness of fit for the model.
| Category | Measure | Acceptable Values | Value |
|---|---|---|---|
| Absolute fit indices | Chi-square | 353.893 | |
| d.f. | 161 | ||
| Chi-square/d.f. | 1–5 | 2.198 | |
| GFI | ≥0.80 | 0.886 | |
| AGFI | ≥0.90 | 0.852 | |
| RMSEA | 0.05–0.08 | 0.064 | |
| Incremental fit indices | NFI | ≥0.90 | 0.938 |
| IFI | ≥0.90 | 0.965 | |
| TLI | ≥0.90 | 0.956 | |
| CFI | ≥0.90 | 0.965 |
Figure 3Path analysis for the structural equation model (SEM). Note: *** p < 0.01.
Results of the path analysis and testing of the hypotheses.
| Path Direction | Standardized Coefficient | Standard Error | C.R. ( | Result | |
|---|---|---|---|---|---|
| H1a | HE > BC | 0.554 *** | 0.109 | 7.317 | Accepted |
| H1b | HE > PA | 0.643 *** | 0.105 | 8.840 | Accepted |
| H2a | BC > PA | −0.006 | 0.038 | 0.180 | Rejected |
| H2b | BC > BI | 0.197 *** | 0.043 | 4.106 | Accepted |
| H2c | AE > PA | 0.167 ** | 0.055 | 3.353 | Accepted |
| H2d | AE > BI | 0.867 *** | 0.050 | 12.734 | Accepted |
| H3a | BI > PA | 0.805 *** | 0.074 | 13.183 | Accepted |
| H3b | BI > RI | 0.440 *** | 0.199 | 3.267 | Accepted |
| H3c | PA > RI | 0.410 *** | 0.168 | 3.026 | Accepted |
Note: ** p < 0.05, *** p < 0.01.
Demographic information for the participants.
| Attributes | Value | Frequency | Percentage (%) |
|---|---|---|---|
| Gender | Male | 96 | 32.76% |
| Female | 197 | 67.24% | |
| Age | 0~18 | 1 | 0.34% |
| 18~25 | 157 | 53.58% | |
| 25~40 | 120 | 40.96% | |
| 40+ | 15 | 5.12% | |
| Education | High School | 10 | 3.41% |
| Bachelor | 189 | 64.51% | |
| Master | 63 | 21.50% | |
| Ph.D. | 31 | 10.58% |