The bottled water market in China is substantial, with consumers spending approximately 200 billion yuan in 2019. Manufacturers employ differentiation strategies, focusing on packaging and functionality to attract customers. Nielsen data highlights packaging's crucial role in promoting new product purchases, surpassing advertising's impact. While research exists on the influence of packaging color, bottle shape, and sustainable materials on consumer purchase intentions, a comprehensive analysis of various product knowledge factors (sensory experience, brand image, and design-driven attributes) and their influence on purchase intentions remains limited. This study addresses this gap by employing the S-O-R (Stimuli-Organism-Response) model to systematically investigate the influence of bottled water design characteristics on consumer purchase intentions. The study aims to analyze the effects of product knowledge, particularly design-driven attributes, on purchase intentions, and to examine the mediating roles of perceived value and emotional attitude in this process.

The S-O-R model, building upon the S-R model, posits that environmental stimuli influence consumers' internal states (emotions) and subsequently their behavior. Existing research using this model has explored various stimuli (advertisements, community features, online content) and their impact on consumer behaviors, particularly impulse buying. Studies have also highlighted the importance of visual cues in retail settings and the role of sensory attributes in creating brand experiences. Consumers use search, experience, and credence attributes when assessing products, with sensory attributes playing a vital role in generating emotional responses and influencing purchase intentions. This study extends prior work by focusing on the comprehensive influence of product knowledge (sensory experience, brand image, and design-driven attributes) on consumer purchase intentions within the context of bottled water, a significant FMCG product.

The study employed a quantitative research design using a questionnaire survey conducted in Nanjing, China from September to October 2022. A total of 350 questionnaires were distributed, combining field distribution and an online platform. Convenience sampling targeted consumers who had previously purchased design-driven FMCG products. After removing invalid questionnaires (incomplete, short completion time, repetitive answers), 322 valid responses were analyzed (92% validity rate). The sample comprised 52.5% female respondents, with the majority (67.1%) aged 18-30 years. The questionnaire included six latent variables (sensory experience, brand image, design-driven attributes, perceived value, emotional attitude, and purchase intention) measured using a 5-point Likert scale. Reliability and validity tests (Cronbach's alpha, KMO, Bartlett's test, CR, AVE) were performed to ensure the quality of the data. Structural equation modeling (PLS-SEM) was used to test the hypotheses, with the maximum likelihood method employed to estimate path coefficients and assess causal relationships. A bootstrap test was utilized to assess mediating effects. The study also checked for multicollinearity using the Variance Inflation Factor (VIF) method.

The results of the path analysis supported several hypotheses: Brand image significantly and positively influences both perceived value (β = 0.243, p < 0.001) and emotional attitude (β = 0.209, p < 0.001). Design-driven attributes significantly and positively influence perceived value (β = 0.239, p < 0.001), but not emotional attitude. Sensory experience significantly and positively influences emotional attitude (β = 0.171, p < 0.01), but not perceived value or purchase intention. Brand image (β = 0.161, p < 0.05) and design-driven attributes (β = 0.138, p < 0.05) significantly and positively influence purchase intention. Emotional attitude significantly and positively influences purchase intention (β = 0.171, p < 0.05), while perceived value does not. The bootstrap test revealed significant mediating effects: Perceived value partially mediates the relationship between brand image and purchase intention, while emotional attitude fully mediates the relationship between sensory experience and purchase intention.

The findings support the importance of brand image and design-driven attributes in influencing consumer purchase intentions in the FMCG context. The mediating roles of perceived value and emotional attitude highlight the complexity of the consumer decision-making process. The lack of a direct effect of sensory experience on purchase intention suggests that while sensory experience influences emotional attitude, this attitude needs to be further translated into perceived value to significantly influence purchase decisions. The strong influence of brand image on both perceived value and emotional attitude underscores the importance of building strong brand equity. The positive effect of design-driven attributes on perceived value emphasizes the need for innovative and functional designs to enhance product attractiveness and consumer perception.

This study contributes to the understanding of consumer behavior in the FMCG sector, particularly regarding the impact of design-driven products. The results demonstrate the significant role of brand image and design-driven attributes in influencing purchase intentions through the mediating effects of perceived value and emotional attitude. Future research could explore the influence of price and sustainability concerns on purchase intentions, expanding the model to include a wider range of FMCG products and consumer segments. Furthermore, investigating the interaction effects between different product knowledge dimensions and exploring the roles of other mediating variables would enhance the understanding of this complex consumer decision-making process.

The study's limitations include the focus on a single FMCG product (bottled water) and a specific geographical location (Nanjing, China). The convenience sampling method may limit the generalizability of the findings to other contexts. The model employed could also be refined by incorporating additional variables, such as price, sustainability considerations, and cultural factors, which could further influence consumer purchase intentions. The cross-sectional nature of the data limits the ability to draw causal inferences.