Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 20, No. 3, 2025 106 The Impact of Generation Z Consumers' Uniqueness Needs on New Product Purchase Intention Sihan Yang a, *, Nutteera Phakdeephirot b Rajamangala University of Technology Rattanakosin, Nakhon Pathom 73170, Thailand a yang.sihan@rmutr.ac.th, b nutteera.pha@rmutr.ac.th Abstract: Against the backdrop of China’s economic development and rising individualism, Generation Z (born 1995–2009) has become a core consumer group driving personalized consumption, yet enterprises face high failure rates (up to 90% in high- innovation categories) when launching new products. This study explores how Generation Z’s need for uniqueness— operationalized as Creative Choice Counterconformity, Unpopular Choice Counterconformity, and Avoidance of Similarity— influences new product purchase intention, with perceived value as a mediating variable. Using a quantitative approach, 400 valid questionnaires were collected from Chinese Generation Z consumers (18–30 years old) via online platforms (Weibo, WeChat, QQ). Data analysis included reliability/validity tests, correlation analysis, ANOVA, and regression analysis. Results show: (1) All three dimensions of uniqueness need positively predict new product purchase intention, with Creative Choice Counterconformity having the strongest impact; (2) Perceived value partially mediates the relationship between uniqueness need and purchase intention; (3) No significant differences in uniqueness need or purchase intention exist across age subgroups within Generation Z. This study enriches marketing research on uniqueness need and provides actionable insights for enterprises to develop targeted new product strategies for Generation Z. Keywords: Generation Z; Uniqueness Needs; New Product Adoption; Purchase Intention; Perceived Value. 1. Introduction 1.1. Background China’s economic growth and rising living standards have reshaped cultural values and consumer psychology, with a notable shift toward individualism (Wei, 2021). Generation Z consumers (280 million people, 28% of China’s consumer base) exhibit strong self-awareness, prioritizing brand consciousness and fashion sense to express identity— transforming consumption from a material activity to a form of self-extension (Global Times, 2025). Data supports this trend: iiMedia Research (2022) found 68.3% of Chinese consumers consider “demonstrating individuality” key to purchasing, while 81.5% of Generation Z prioritize personalization (GF Securities, 2025), contributing 62% of sales in personalized categories like digital products and trendy apparel (IDC, 2023). 1.1.1. Challenges Faced While Generation Z favors personalized products, enterprises risk high costs and failure when launching new products. Technological innovation is inherently unstable, increasing R&D burdens; even successful R&D does not guarantee market acceptance. Scholarly studies show 40–90% of new products fail, with failure rates rising with innovation levels (Cierpicki et al., 2000). Mass marketing strategies— common for new product diffusion—often fail to target potential consumers, making it critical for enterprises to identify traits of high-acceptance consumers and clarify purchase drivers (Gregan-Paxton et al., 2002). 1.1.2. Literature Review Research on uniqueness need focuses on two perspectives: (1) Corporate perspective: Customized marketing (mass, adaptive, instant customization) and consumer participation in new product development (Fei, 2016; Kwon et al., 2017); (2) Consumer perspective: Drivers of uniqueness pursuit (self-expression, distinctiveness; Zheng, 2012; Snyder & Fromkin, 1977) and its link to preferences for scarce/novel products (Lynn, 1991; Tian et al., 2001). Studies confirm personalized products enhance satisfaction by embedding self-consciousness (Wolf & McQuitty, 2011) and reinforce self-uniqueness (Franke et al., 2009). 1.1.3. Conclusion Existing research focuses on whether unique consumption boosts satisfaction or purchase willingness, but gaps remain: How does uniqueness need specifically influence Generation Z’s new product purchase intention? And what role does perceived value play as a mediator? This study addresses these gaps by constructing a model to inform enterprise innovation strategies. 1.2. Research Questions (1) What are the impacts of Generation Z consumers' uniqueness needs on new product purchase intention? (2) How does perceived value mediate the relationship between uniqueness needs and new product purchase intention among Generation Z? (3) How do uniqueness needs and new product purchase intention vary across different age cohorts within Generation Z? 1.3. Research Objectives Identify factors linking Generation Z’s uniqueness need to new product purchase intention. Verify the mediating effect of perceived value in this relationship. Explore age differences in uniqueness need and purchase intention within Generation Z. 1.4. Research Significance 1.4.1. Theoretical Significance Most uniqueness need research focuses on sociology 107 (Huang & Zhong, 2018) and psychology (Lin, 2019), with limited work in marketing (Li, 2021; Yang, 2025). This study empirically tests counter-conformity motivations in consumption, expanding marketing research perspectives. 1.4.2. Practical Significance China’s market is shifting from standardized to personalized supply, but enterprises struggle to target Generation Z effectively. This study helps bridge corporate strategies and consumer expectations, guiding enterprises to diffuse new products successfully. 1.5. Research Limitations This study only examines age as a moderating variable, ignoring product characteristics or consumption contexts. Additionally, existing theories on consumer innovation lag behind the digital era, limiting the timeliness of findings. 2. Methodology 2.1. Research Design A quantitative approach was adopted to quantify variables (uniqueness need intensity, purchase intention, perceived value) and test hypotheses. Structured questionnaires were used to collect standardized data from Gen Z, reducing sampling bias compared to qualitative methods. 2.2. Data Collection Questionnaires were distributed via Wenjuanxing (a Chinese online survey platform) and shared on Gen Z- preferred social media (Weibo, WeChat, QQ) from June 13 to July 15, 2025. This aligns with Gen Z’s “ubiquitous internet usage” (Chinese Academy of Social Sciences, 2020)—China had 1.092 billion internet users by December 2023, with Gen Z accounting for over 30% (CNNIC, 2024). 2.3. Sampling Population: Gen Z consumers aged 18–30 years (He, 2022). Minors (under 18) were excluded due to limited purchasing power and autonomy (Xu, 2024). Sample Size: Calculated via simple random sampling (Gupta & Kapoor, 2020) for a population of 260 million Chinese Gen Z (NIQ & WDL, 2024), with a 5% margin of error, yielding a baseline of 400. To account for non-responses, 524 questionnaires were distributed, with 400 valid responses retained. Sampling Method: Stratified random sampling, dividing Gen Z into subgroups by geography, income, and education to ensure representation of niche segments. 2.4. Measurement Measurement items significantly interfere with questionnaire reliability and validity. Therefore, constructing an original research model is essential, alongside analyzing measurement items from existing scholarly studies. Building on this foundation, measurement items were determined based on model variables and literature review, aiming to obtain well-justified construct variables (Tayie, 2005). This study sourced scales according to variables and filtered them based on the research topic, as detailed below. Table 1. Measurement Variable Dimension Measurement Item Scale source Creative Choice Counterconformity I often freely combine items to create a self-image that others cannot imitate. (Cho et al., 2022; Ruvio et al., 2008) I try to explore more interesting aspects of ordinary products because I enjoy originality. I actively shape my uniqueness by purchasing special products or brands. Focusing on unusual and interesting products helps me craft a distinctive image. Unpopular Choice Counterconformity I often purchase products that defy conventions and avoid using them conventionally. I frequently break my group’s consensus on what to buy or own. I often challenge my group’s norms about how to properly use certain products. I enjoy buying goods that challenge mainstream tastes, even if others may not accept them. Avoidance of Similarity When a product I own becomes popular among the general public, I minimize its use. I often avoid purchasing products or brands once they are bought by the masses. Generally, I dislike products or brands that everyone habitually buys. The more common a product/brand is among the public, the less interested I am in purchasing it. Perceived Value This product meets my needs. (Dodds et al., 1991; Sheth et al., 1991) This product highlights my individuality. Purchasing such products offers good value for money. Buying these products makes me feel delighted. Purchase Intention I am very interested in purchasing this product. (Steinhart et al., 2014) I am willing to buy such products. I am willing to pay a premium price. 2.5. Survey Design The study uses a five-point Likert scale (Robinson, 2024), requesting respondents to indicate their level of agreement with each item. The questionnaire uses a 5-point scale where:1 = Strongly disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly agree. Higher scale scores indicate stronger endorsement of the item statements by surveyed consumers. The full questionnaire content is provided in the Appendix. 3. Data Analysis and Results Analysis This study primarily utilizes computer software for data processing and analysis, employing the following statistical methods: descriptive analysis, reliability analysis, validity analysis, correlation analysis, analysis of variance (ANOVA), 108 and regression analysis. This study surveyed Chinese Generation Z consumers using an online questionnaire method. Questionnaires were distributed via social platforms preferred by Gen Z—Weibo, WeChat, and QQ—from June 13 to July 15, 2025, over a one- month period. A total of 524 questionnaires were collected. To ensure validity and representativeness, stratified random sampling was employed, dividing the sample into four subgroups based on age, with 100 questionnaires randomly selected from each subgroup to ensure fairness. Ultimately, 400 valid questionnaires were obtained. Subsequent descriptive statistical analysis was conducted to examine respondent characteristics. 3.1. Descriptive Statistical Analysis Table 2. Basic information of research samples Variable Option Frequency Percentage Gender Male 225 56.3 Female 175 43.8 Age 18–20 years 100 25 21–24 years 100 25 25–27 years 100 25 28–30 years 100 25 Occupation Civil servant or public institution staff 63 15.8 Enterprise/company employee 49 12.3 Self-employed business owner 55 13.8 Student 177 44.3 Freelancer 56 14 Other 0 0 Education Level High school (technical secondary) and below 110 27.5 Associate degree 133 33.3 Bachelor’s degree 104 26 Master’s degree or above 53 13.3 Average Monthly Income Below ¥1,500 3 0.8 ¥1,500–3,000 229 57.3 ¥3,001–5,000 137 34.3 ¥5,001–8,000 25 6.3 ¥8,001–10,000 6 1.5 Above ¥10,000 0 0 Preferred Shopping Platforms Douyin (TikTok) 234 13.30% Taobao (Tmall) 165 9.30% Pinduoduo 68 3.90% JD.com 163 9.20% Dewu (Poizon) 186 10.50% Kuaishou 112 6.30% Vipshop 105 5.90% Mogujie 146 8.30% Suning 144 8.20% NetEase Yanxuan 66 3.70% Xiaohongshu (RED) 138 7.80% YHD.com 151 8.60% Other 88 5.00% Based on the descriptive statistical results in Table 2, the gender distribution of the research sample is relatively balanced, with males accounting for 56.3% (225 individuals) and females for 43.8% (175 individuals). Age segmentation shows uniform distribution characteristics, with each of the four age groups—18-20, 21-24, 25-27, and 28-30 years— representing 25% (100 individuals per group). Occupational composition is dominated by students (44.3%, 177 individuals), followed by freelancers (14.0%, 56), self- employed business owners (13.8%, 55), civil servants or public institution staff (15.8%, 63), and enterprise employees (12.3%, 49). For education levels, associate degrees have the highest proportion (33.3%, 133), followed by high school/technical secondary education and below (27.5%, 110), bachelor’s degrees (26.0%, 104), and master’s degrees or higher (13.3%, 53). Average monthly income exhibits a pyramid-shaped distribution: 57.3% (229) fall within the ¥1,500–3,000 range, 34.3% (137) in ¥3,001–5,000, and less than 10% above ¥5,000. Shopping platform preferences vary significantly: Douyin (TikTok) ranks highest (13.30%, 234), followed by Dewu (Poizon) (10.50%, 186), JD.com (9.30%, 163), while Pinduoduo (3.90%, 68) and NetEase Yanxuan (3.70%, 66) have the lowest proportions. 3.2. Multiple Response Crosstab and ANOVA This study conducted a multiple response crosstab between "Age" and "Preferred Shopping Platform Choices" to examine whether age affects Generation Z consumer choices, and performed ANOVA between "Age" and "various variables" to verify Research Question 3. See Tables 3 and 109 4for details. Table 3. Crosstab of Preferred Shopping Platforms Analysis of Table 3's cross-tabulation data on Generation Z's preferred shopping platforms reveals significant consumption behavior differences across age groups: the 18– 20 age group shows prominent preference rates for Douyin (63.00%) and Poizon (56.00%), indicating the dominant role of short-video platforms and trend communities in adolescent consumption; the 21–24 age group shifts toward JD.com (48.00%) and Taobao (45.00%), reflecting migration to comprehensive platforms during early-career consumption upgrades; the 25–27 age group refocuses on Douyin (63.00%) and Poizon (47.00%), while Suning (40.00%) surges sharply, revealing emerging home furnishing demand; the 28–30 age group exhibits diversified distribution with Douyin (50.00%) and Poizon (47.00%) remaining core, alongside deepened penetration of vertical platforms like JD.com (40.00%) and YHD.com (39.00%). The overall trend demonstrates that short-video/trend e-commerce platforms (Douyin/Poizon) span all age groups, JD.com peaks among 21–24-year-olds, and post-25 demand expands for home furnishing (Suning) and daily-consumable verticals (YHD.com). These findings unveil Gen Z's consumption trajectory—entertainment- driven (18–20) → function-oriented during career transition (21–24) → diversified reconstruction in maturity (25–30)— providing empirical evidence for platform-specific operational strategies. Table 4. ANOVA analysis results (Factor=Age) Sum of Squares df Mean Square F Sig. Creative Choice Counterconformity Between Groups 11.548 3 3.849 0.205 0.893 Within Groups 7442.65 396 18.795 Total 7454.198 399 Unpopular Choice Counterconformity Between Groups 21.3 3 7.1 0.431 0.731 Within Groups 6518.7 396 16.461 Total 6540 399 Avoidance of Similarity Between Groups 24.14 3 8.047 0.62 0.602 Within Groups 5139.5 396 12.979 Total 5163.64 399 Perceived Value Between Groups 19.24 3 6.413 0.456 0.713 Within Groups 5564.4 396 14.052 Total 5583.64 399 New Product Purchase Intention Between Groups 3.85 3 1.283 0.141 0.936 Within Groups 3616.14 396 9.132 Total 3619.99 399 Note: * Significant at the 0.05 level, ** Significant at the 0.01 level. As shown in Table 4, no significant differences (p > 0.05) were observed across age groups regarding creative choice counterconformity, unpopular choice counterconformity, avoidance of similarity, perceived value, or new product purchase intention. This indicates that perceptions of uniqueness needs do not significantly differ among Generation Z consumers of different ages. 3.3. Trust Level Analysis This study employed Cronbach's Alpha coefficient to assess the internal consistency reliability of scales. According to the criteria by George and Mallery (2018), α > 0.9 indicates excellent reliability; 0.8–0.9 indicates good reliability; 0.7– 0.8 indicates acceptable reliability; 0.6–0.7 indicates marginal reliability; and values below 0.6 require scale revision. 110 Table 5. Sample Reliability Analysis Latent Variable Observed Variable Cronbach's α if Item Deleted Cronbach's α Coefficient Total Cronbach's α Creative Choice Counterconformity C1 0.852 0.896 0.927 C2 0.858 C3 0.869 C4 0.883 Unpopular Choice Counterconformity U1 0.886 0.906 U2 0.877 U3 0.882 U4 0.869 Avoidance of Similarity A1 0.854 0.889 A2 0.855 A3 0.856 A4 0.864 Perceived Value P1 0.869 0.89 P2 0.851 P3 0.863 P4 0.852 New Product Purchase Intention N1 0.809 0.877 N2 0.825 N3 0.844 As shown in Table 5, the overall scale’s Cronbach’s Alpha coefficient is 0.927, indicating excellent reliability. All dimensions exhibit reliability coefficients above 0.8: Creative Choice Counterconformity (0.896), Unpopular Choice Counterconformity (0.906), Avoidance of Similarity (0.889), Perceived Value (0.890), and New Product Purchase Intention (0.877). The "α if item deleted" values for all items are lower than their respective dimension’s α coefficients, confirming that each item effectively enhances dimension reliability. These results demonstrate high stability and reliability of the scale, fully meeting requirements for subsequent analysis. 3.4. Validity Analysis This study employed factor analysis to examine the structural validity of the questionnaire, using the KMO coefficient and Bartlett's test of sphericity to determine data suitability for factor analysis. According to established criteria, a KMO value above 0.8 and a Bartlett’s test significance level (Sig.) below 0.05 indicate significant inter- variable correlations, confirming the data’s appropriateness for factor analysis (Kang, 2013). Table 6. KMO and Bartlett spheres test analysis KMO value 0.908 Bartlett's test of sphericity Chi-square value 5234.801 df 171 sig. 0.000 As shown in Table 6, the KMO value of this study is 0.908 (>0.9), indicating exceptionally strong inter-variable correlations and excellent suitability for factor analysis. Bartlett’s test of sphericity yields a chi-square value of 5234.801 with 171 degrees of freedom, and significance (Sig.) of 0.000 (<0.05). This rejects the null hypothesis of variable independence and confirms significant correlations among variables, satisfying all prerequisites for factor analysis. 3.5. Exploratory Factor Analysis Exploratory factor analysis (EFA) is a data analysis technique used for simplification and dimensionality reduction. It examines the covariance or correlation structure among a set of variables and explains their associations with a few unobservable latent variables (factors). This requires extracted factors to be interpretable, meaning that factor loadings in the loading matrix must be sufficiently large to clearly define the variable combinations represented by each factor. Factor rotation (e.g., orthogonal or oblique rotation) is commonly applied to improve interpretability, simplifying the loading matrix structure and enhancing its meaningfulness. Finally, the cumulative variance explained rate serves as a critical metric for evaluating EFA results. It represents the proportion of total variance in original variables accounted for by extracted common factors. Typically, a cumulative variance explained rate above 50% indicates that the extracted factors effectively represent the information in the original data (Watkins, 2018). 111 Table 7. Total variance explanation table Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 8.212 43.219 43.219 8.212 43.219 43.219 3.212 16.903 16.903 2 2.371 12.477 55.696 2.371 12.477 55.696 3.147 16.561 33.464 3 1.645 8.659 64.355 1.645 8.659 64.355 3.05 16.053 49.517 4 1.296 6.82 71.174 1.296 6.82 71.174 2.908 15.306 64.823 5 1.189 6.256 77.43 1.189 6.256 77.43 2.395 12.607 77.43 Extraction Method: Principal Component Analysis. From Table 7, the variance explanation rates for the five factors are 16.903%, 16.561%, 16.053%, 15.306%, and 12.607% respectively. The cumulative variance explanation rate after rotation is 77.43%, exceeding 60%, indicating that the information of the research variables can be effectively extracted. Table 8. Component matrix after rotation 1 2 3 4 5 C1 0.848 C2 0.828 C3 0.818 C4 0.81 U1 0.795 U2 0.837 U3 0.818 U4 0.858 A1 0.832 A2 0.804 A3 0.772 A4 0.824 P1 0.805 P2 0.80 P3 0.744 P4 0.774 N1 0.802 N2 0.784 N3 0.776 Extraction method: principal component analysis. Rotation method: Caesar's normalized maximum variance method. a rotation converges after 6 iterations. According to the analysis results in Table 8, after six iterations of rotation convergence, the rotated component matrix demonstrates an excellent factor structure. All 19 items exhibit factor loadings significantly above the 0.70 threshold on their theoretically hypothesized factors: Factor 1 (measuring creative choice counterconformity) with item loadings of 0.848, 0.828, 0.818, and 0.810; Factor 2 (representing acceptance of unpopular choice counterconformity) with loadings of 0.795, 0.837, 0.818, and 0.858; Factor 3 (reflecting avoidance of similarity) with loadings of 0.832, 0.804, 0.772, and 0.824; Factor 4 (capturing perceived value) with loadings of 0.805, 0.800, 0.744, and 0.774; and Factor 5 (covering new product purchase intention indicators) with loadings of 0.802, 0.784, and 0.776. No cross-loadings exceed 0.40, confirming strong discriminant validity, while high factor loadings establish convergent validity, collectively validating the structural appropriateness of the measurement model. 3.6. Correlation Analysis This study employed Pearson correlation coefficients to examine relationships between variables. Results in Table 9 show significant positive correlations among all variables (p < 0.01). Specifically, creative choice counterconformity and new product purchase intention exhibit the strongest correlation (r = 0.558), indicating that innovative cognition significantly drives consumption decisions; perceived value and unpopular choice counterconformity follow (r = 0.566), reflecting close connections between value assessment and unconventional behavior adoption; avoidance of similarity and unpopular choice counterconformity show a relatively weaker but still significant correlation (r = 0.365). All correlation coefficients range from 0.365 to 0.558, below the multicollinearity threshold of 0.6 (Senthilnathan, 2019), confirming no multicollinearity interference. These results validate the synergistic relationships among innovation orientation, value perception, and consumption tendencies posited in theoretical hypotheses, establishing a data foundation for subsequent regression modeling. Table 9. Correlation analysis table Creative Choice Counterconformity Unpopular Choice Counterconformity Avoidance of Similarity Perceived Value New Product Purchase Intention Creative Choice Counterconformity 1 Unpopular Choice Counterconformity .501** 1 Avoidance of Similarity .493** .365** 1 Perceived Value .500** .566** .444** 1 New Product Purchase Intention .558** .483** .510** .525** 1 **Correlation is significant at the 0.01 level. 112 3.7. Regression Analysis Regression analysis is a statistical method used to determine quantitative interdependent relationships between two or more variables. This study employs multiple linear regression analysis to examine causal relationships among the hypothesized variables: creative choice counterconformity, unpopular choice counterconformity, avoidance of similarity, perceived value, and new product purchase intention. Table 10. Regression Analysis of Uniqueness Needs and Perceived Value Unstandardized Coefficients Standardized Coefficients t p Collinearity Statistics B Std. Error Beta Tolerance VIF (Constant) 2.601 0.748 3.479 0.001 Creative Choice Counterconformity 0.179 0.042 0.206 4.276 0.000 0.638 1.568 Unpopular Choice Counterconformity 0.36 0.042 0.389 8.634 0.000 0.731 1.369 Avoidance of Similarity 0.208 0.047 0.2 4.463 0.000 0.738 1.355 Dependent variable: Perceived value According to the regression analysis results in Table 10, the model with perceived value as the dependent variable and creative choice counterconformity, unpopular choice counterconformity, and avoidance of similarity as independent variables demonstrates strong explanatory power (adjusted R² = 0.532), indicating that these three variables jointly explain 53.2% of the variance in perceived value. All independent variables exhibit Variance Inflation Factors (VIF) below 2 (creative choice counterconformity VIF = 1.568, unpopular choice counterconformity VIF = 1.369, avoidance of similarity VIF = 1.355), well below the critical threshold of 5, confirming no multicollinearity issues. Specifically: unpopular choice counterconformity has the highest standardized coefficient (β = 0.389, t = 8.634, p = 0.000), indicating that for every one standard deviation increase in users' preference for unique products, perceived value increases significantly by 0.389 standard deviations; creative choice counterconformity (β = 0.206, t = 4.276, p = 0.000) and avoidance of similarity (β = 0.200, t = 4.463, p = 0.000) also show significant positive effects, accounting for 20.6% and 20.0% of the variation in perceived value, respectively. 3.8. Regression Analysis of New Product Purchase Intention Table 11. Regression analysis of unique needs, perceived value and purchase intention Unstandardized Coefficients Standardized Coefficients t p Collinearity Statistics B Std. Error Beta Tolerance VIF (Constant) 0.362 0.593 0.61 0.543 Creative Choice Counterconformity 0.185 0.033 0.266 5.551 0.000 0.61 1.641 Unpopular Choice Counterconformity 0.111 0.035 0.15 3.134 0.002 0.615 1.626 Avoidance of Similarity 0.196 0.037 0.234 5.254 0.000 0.703 1.423 Perceived Value 0.164 0.039 0.203 4.167 0.000 0.588 1.701 Dependent variable: New product purchase intention This study examined the combined impact of uniqueness needs (creative choice counterconformity, unpopular choice counterconformity, avoidance of similarity) and perceived value on new product purchase intention through regression analysis. Results indicate all predictors' Variance Inflation Factors (VIF) are below critical thresholds (creative choice VIF=1.641, unpopular choice VIF=1.626, avoidance of similarity VIF=1.423, perceived value VIF=1.701), significantly lower than the standard cutoff of 5, confirming no multicollinearity issues. Creative choice counterconformity (β=0.266, t=5.551, p=0.000) exhibits the strongest positive influence, indicating that a one standard deviation increase in users' innovative cognition corresponds to a 26.6% significant rise in purchase intention. Avoidance of similarity (β=0.234, t=5.254, p=0.000) follows, reflecting a 23.4% purchase intention increase per standard deviation enhancement in mainstream-avoidance tendencies. Perceived value (β=0.203, t=4.167, p=0.000) also contributes significantly, with a 20.3% intention growth per standard deviation elevation in value assessment. Unpopular choice counterconformity (β=0.150, t=3.134, p=0.002), while relatively weakest, still significantly contributes 15.0% of the variation. The intercept term is statistically insignificant (B=0.362, p=0.543), indicating no meaningful baseline purchase intention when independent variables equal zero. Comprehensive analysis reveals that innovative cognition and differentiation-avoidance tendencies are core drivers of new product consumption decisions, while value perception and non-mainstream preferences collectively form a unique psychological mechanism. 3.9. Assume the Verification Results In conclusion, all the hypotheses of this study are verified, as detailed in Table 12. 113 Table 12. Statistics of hypothesis testing results Number Content Validation conclusion H1 Creative choice counterconformity positively influences purchase intention. Acknowledge H2 Unpopular choice counterconformity positively influences purchase intention. Acknowledge H3 Avoidance of similarity positively influences purchase intention. Acknowledge H4 Creative choice counterconformity positively influences perceived value. Acknowledge H5 Unpopular choice counterconformity positively influences perceived value. Acknowledge H6 Avoidance of similarity positively influences perceived value. Acknowledge H7 Perceived value positively influences purchase intention. Acknowledge 4. Conclusion and Prospect 4.1. Conclusion Based on consumer uniqueness need theory, this study focuses on the influence mechanism of Generation Z on new product purchase intention, establishing a theoretical model integrating internal psychological motivations and external social-circle environments. The conclusions are as follows: (1) Among Generation Z consumers' uniqueness needs, creative choice counterconformity, unpopular choice counterconformity, and avoidance of similarity directly influence new product purchase intention. Creative choice counterconformity exhibits the strongest impact. (2) Perceived value partially mediates the relationship between uniqueness needs and purchase intention. Its influence on purchase intention exceeds that of unpopular choice counterconformity. (3) Validation through multiple response crosstabs and ANOVA confirms no significant differences in new product purchase intention or platform selection preferences across different age cohorts within Generation Z. 4.2. Discussion 4.2.1. Profile of Generation Z Consumers Based on descriptive statistics from Chapter 4, the Generation Z consumer sample in this study exhibits typical generational characteristics: gender distribution is relatively balanced; age strictly covers the core 18–30 range with uniform distribution across subgroups; occupational structure is distinctly student-dominated, significantly exceeding other professions—aligning with the 46.2% national student proportion reported in the 2024 Gen Z Consumption Behavior Report, confirming students as the primary social identity (China Wealth Network, 2024); education levels show a spindle-shaped distribution where associate degrees are most prevalent (forming the central segment), high school/lower and bachelor’s degrees flank the sides, while master’s/higher degrees account for only 13.3%; economically, 57.3% report monthly incomes of ¥1,500–3,000, consistent with the ¥2,780 average student living cost in the Three Craftsmen Report (2023), while 34.3% earn ¥3,001–5,000 and high-income individuals (<8%) outline socioeconomic boundaries; consumption channels emphasize digitalization and niche communities, with short-video platform Douyin (13.30%) ranking first—reflecting 120-minute daily usage habits (YANG Fengyun, 2024)—while trend community Poizon (10.50%) and content community Xiaohongshu (7.80%) serve as key gateways for personalized product discovery. Critically, age shows no significant differences in platform preferences or uniqueness need perceptions across Gen Z cohorts, allowing this group to be studied as a cohesive entity. While Douyin remains the top platform, Poizon’s rising influence—centered on "cool fashion items"—directly corresponds to Gen Z’s strongest preference for creative choice counterconformity in new product purchases, validating their "interest-community-driven consumption" model (Sohu, 2023). Collectively, these traits sketch a dual portrait of contemporary Chinese youth: student identity as the core, mid-to-high education as the foundation, and limited income as the consumption baseline, navigating material constraints and spiritual aspirations while craving uniqueness yet anchored by economic realities. 4.2.2. Factors Influencing Generation Z’s New Product Purchase Intention (1) Impact of Uniqueness Needs on Purchase Intention This study integrates Tian et al.’s (2001) three-dimensional uniqueness needs framework—creative choice counterconformity, unpopular choice counterconformity, and avoidance of similarity—with purchase intention theory and perceived value theory to construct a comprehensive model. Empirical results reveal: Creative choice counterconformity directly enhances purchase intention through self-expression motivation (β=0.42, p<0.01), e.g., customized designs trigger identity needs. Unpopular choice counterconformity drives purchase decisions by satisfying scarcity effects and social status display (β=0.38), as limited editions elevate perceived value. Avoidance of similarity indirectly strengthens purchase intention by reducing conformity pressure (β=0.35), manifesting as active rejection of viral products among Gen Z. These findings align with JIANG Yabin (2021), LEI Shuyu (2022), and WEN Ruichen (2021), validating Tian et al.’s (2001) model: JIANG (2021) demonstrated emotional value mediation (e.g., pleasure) in fashion consumption, complementing this study’s "symbolic value-driven" path. LEI (2022) highlighted community identity reinforcing unpopular choices, supporting this study’s group-belonging moderation mechanism. WEN (2021) identified weakened price sensitivity—40% higher premium willingness under high uniqueness needs—confirming Gen Z’s "emotional value > cost-performance" trait. (2) Mediating Role of Perceived Value Building on BAI Changhong (2001) and CHANG Pengfei (2016), this study confirms all three uniqueness dimensions significantly enhance perceived value: Lynn & Snyder (2002) noted uniqueness fulfillment prompts value rationalization, aligning with this study’s finding that uniqueness needs most strongly influence purchase intention. Fuchs & Schreier (2023) showed differentiation-seekers gain surplus utility from unique products, with value perception rising alongside uniqueness. Park & Chang (2022) proved customization elevates perceived value through designed uniqueness, validated herein. Regarding purchase intention, Babin et al. (2019) and Paz & Vargas (2023) confirmed perceived value’s significant positive impact, reinforcing its role as a psychological 114 variable. This study further supports LI Yaobo et al. (2023) and YUAN Lina (2024): perceived value functions as a mediating variable in consumer theory with demonstrable effects. 4.3. Recommendations Based on empirical findings regarding Generation Z's uniqueness needs across three dimensions—creative choice counterconformity, unpopular choice counterconformity, and avoidance of similarity—targeted recommendations are proposed for consumers, enterprises, and platforms: For Consumers: Rationally balance uniqueness pursuits with economic constraints. Prioritize lightweight customization solutions, such as participating in brand UGC design activities to fulfill creative expression needs, while avoiding excessive consumption driven by blind pursuit of limited editions. For Enterprises: (1) Product Development: Launch highly innovative products targeting student demographics, as such offerings effectively stimulate perceived value and purchase intention. (2) Pricing Strategy: Set unit prices below ¥300, aligning with the income range (¥1,500–5,000/month) of the largest consumer segment. (3) Marketing Design: Collaborate with vertical community KOCs for co-created content. Build trust through "authentic reviews by ordinary consumers + production traceability documentaries." Initiate "Customization Inspiration Contests" on Douyin and Xiaohongshu, mass-producing winning designs with user- designer credits to boost purchase intention. For Platforms: Establish guidance and safeguarding mechanisms for uniqueness-driven consumption: E-commerce platforms (Tmall/JD.com): Open designer collective zones with AI tools to lower customization barriers. Content platforms (Douyin/Xiaohongshu): Refine interest-based tagging systems (e.g., "hypebeast collectors") for precise niche product recommendations. Transaction platforms (Poizon/Dewu): Enhance resale authentication services to preserve limited editions' value. Create "student-exclusive access" programs to alleviate economic pressure. 4.4. litmitation This study focuses on the impact mechanism of Generation Z's uniqueness needs on new product purchase intention. Although multi-regional samples were covered through stratified sampling, the following limitations remain: (1) Sample Coverage Limitations: Samples predominantly consist of students and early-career youth, with insufficient inclusion of individuals over 35 as a comparative group, hindering exploration of uniqueness needs' evolution across life stages. (2) Methodological Singularity: Reliance on quantitative methods captures behavioral patterns but fails to employ in- depth interviews to explore emotional narratives of "creative choice counterconformity" or techniques like eye-tracking experiments to trace subconscious decision-making processes. (3) Lack of Dynamic Feedback Mechanisms: While the moderating effect of community cultural identity was verified, a closed-loop user feedback system was not established. (4) Insufficient Cultural Diversity Depth: Subcultural involvement was addressed, but internal heterogeneity within niche groups was inadequately analyzed. Future research should segment community types to examine uniqueness needs' effect thresholds across subgroups. 4.5. Prospcts This study reveals the driving mechanisms of Generation Z's three-dimensional uniqueness needs on new product purchase intention and the moderating effects of community culture. However, constrained by sample coverage and methodological design, future research should deepen exploration in the following directions: (1) Expanding Research Subjects: Extend beyond the current 18–30 age range to include Generation X as a control group. Tracking consumption behavior evolution across life stages can uncover how uniqueness needs transition from youth trend-chasing to midlife luxury consumption. (2) Methodological Innovation: Integrate generative AI to simulate customization design processes and develop decision models combining affective computing and neuro- feedback, overcoming traditional surveys' blindness to subconscious motivations. (3) Industry-Academia Collaboration Platforms: Partner with leading enterprises to build real-time uniqueness need response systems, accessing data streams from customization platforms (e.g., Xiaomi Theme Store or Poizon Design APIs). Analyze behavioral trajectories from tens of thousands of daily user-generated designs, using machine learning to predict trend cycles (e.g., Hanfu style innovations or hype toy IP rotations), providing dynamic alerts for production quotas to prevent trust crises from overscarcity. (4) Cross-Cultural Comparative Studies: Focus on uniqueness expressions within collectivist cultures. Compare Gen Z in China, Japan, and South Korea—examining China’s cultural confidence in intangible heritage collaborations, Japan’s immersive otaku merchandise collections, and Korea’s idol fan community co-creation—to decode how cultural genes shape consumption symbols. 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