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Article

Familiar Yet New: How Design-Driven Innovation and Brand Image Affect Green Agricultural Product Purchase Intentions in the Live Streaming Environment

by
Xuguang Zhu
,
Yihan Zhang
and
Zeyu Wu
*
School of Art and Design, Zhejiang Sci-Tech University, Hangzhou 311199, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(2), 522; https://doi.org/10.3390/su17020522
Submission received: 9 December 2024 / Revised: 29 December 2024 / Accepted: 7 January 2025 / Published: 10 January 2025
Browse Figures
Versions Notes

Abstract

:
With the rapid development of live streaming e-commerce, green agricultural products have become an important consumer category. However, sales still face challenges such as weak brand effects, content homogeneity, and the lack of professional hosts. Research shows that various factors influence consumers’ purchase intentions, with design-driven attributes and brand image playing crucial roles. However, their impact in the context of green agricultural product live streaming remains underexplored. This study, based on the S-O-R theory, investigates the factors that stimulate consumer purchase intentions for green agricultural products and reveals the influence of design-driven attributes on purchase intentions. A total of 472 valid responses were collected through a questionnaire. The results indicate that social presence and brand image have a positive impact on purchase intention, with green perceived value and emotional attitude acting as full mediators. However, design-driven attributes do not have a significant direct impact on purchase intention. Nevertheless, emotional attitude plays a significant mediating role between design-driven attributes and purchase intention. This study contributes to the research on consumer behavior and perceived value in live streaming environments, particularly emphasizing the importance of design-driven attributes, and provides insights for optimizing live streaming strategies and improving agricultural product design.

1. Introduction

With the continuous advancement of information technology, live streaming platforms have become a crucial channel for consumers’ daily shopping activities [1]. During the 2024 “Double 11 shopping carnival”, live streaming sales totaled CNY 332.5 billion, making up 22% of overall sales [2]. The real-time and interactive features of live streaming effectively stimulate consumers’ purchase intention [3]. Consumers can access detailed information about a product’s appearance, quality, and origin, while streamers quickly respond to their questions [4]. This reduces the psychological distance between sellers and customers, enhancing consumers’ trust and satisfaction. Meanwhile, sales of green agricultural products have transitioned from offline to a multi-channel e-commerce model. Green agricultural products occupy a significant position in the market. They not only meet basic needs but also provide vital income for rural economies [5]. However, factors such as trust crises and service quality imbalances have hindered traditional e-commerce from effectively promoting the consumption of green agricultural products [6]. To address these challenges, live streaming e-commerce has become a key method [7].
Live streaming has significantly increased the consumption of green agricultural products in response to policy and government initiatives. In 2023, the TikTok platform sold 4.73 billion units of green agricultural products, with 7.78 million hours of live streaming [8]. However, there are still some problems that need to be addressed. The first challenge is the homogenization of live streaming content, where repetitive marketing scripts lead to viewer fatigue [9]. The second challenge is the weak brand effect of agricultural products. Producers often focus on natural attributes while neglecting added value [10]. The third challenge is that streamers often lack professional skills. Research shows that streamers with fewer than 5000 fans struggle with marketing, making them less appealing to viewers [11]. These issues prevent consumers from effectively accessing high-quality agricultural products. Therefore, it is crucial to investigate the factors influencing consumers’ purchase intention for green agricultural products in live streaming.
In response to this issue, scholars have conducted various studies from different perspectives. Arvola et al., focusing on individual consumer characteristics, found that moral norms and emotional attitudes positively influence green purchase intentions [12]. In live streaming, some scholars argue that interactivity, entertainment, and hedonic features can evoke positive emotions, driving purchasing behavior [13]. From the perspective of product attributes, Song et al. suggest that low-carbon labeling mediates the relationship between purchase intention and green perceived value [14]. Hengboriboon et al. reveal that corporate image influences consumer attitudes and their purchase intentions for green agricultural products [15]. Hong et al. explored the purchase intention of live agricultural products in the Malaysian region based on the theory of rational behavior [16]. The above studies analyze internal and external factors, providing valuable references for this study. However, most studies overlook how design-driven innovation and brand image influence green consumption intentions. When agricultural products have small differences within the same category, the market competition is more intense. When product differentiation is limited, design-driven innovation becomes a key strategy for companies to maintain competitiveness [17]. Design-driven products offer added value, strengthening the connection between companies and consumers, thereby increasing consumer trust [18]. In agricultural markets, Katttimani et al. found that packaging attributes like materials, colors, and shapes positively affect consumer decisions, a widely recognized insight [19].
However, existing research mainly examines these attributes in traditional retail or static e-commerce settings. There is a gap in understanding their impact in dynamic, interactive live streaming environments. While brand image and design-driven attributes are widely recognized as key factors of consumer trust and purchase decisions, their specific influence on perceptions of green agricultural products in live streaming contexts remains underexplored. To address this gap, this study applies the SOR theory to systematically explain the impact of agricultural product characteristics (brand image and design-driven products) on green purchase intention in the live streaming environment (social presence). The purpose of this study is to analyze the factors related to live streaming and design, especially the impact of design-driven attributes on consumer purchase intention. Thus, the main objectives of this study are as follows:
How do social presence, brand image, and design-driven products influence consumers’ green perceived value and emotional attitude?
How do green perceived value and emotional attitude influence consumers’ purchase intention in a live streaming context?
What are the most important factors in green perceived value, emotional attitude, and purchase intention?
According to the research objectives, this study uses product characteristics and the live streaming environment as antecedent variables that influence the purchase intention of consumers, while customer perceptions are treated as mediating variables. A research model is constructed to examine the factors influencing the purchase intention of green agricultural products. This study establishes and tests research hypotheses, offering deeper insights into understanding the influence of design-driven attributes and brand image, providing a referable framework for subsequent related studies. Finally, this study provides practical recommendations for businesses and live streaming platforms on how to effectively leverage design-driven strategies for marketing green agricultural products.

2. Theoretical Background and Hypothesis Development

2.1. S-O-R Theory

The Stimulus–Organism–Response (SOR) framework, proposed by Russell and Mehrabian based on the Stimulus–Response (SR) theory [20], explains how stimuli (S) influence an individual’s psychological responses and behavior. The SOR framework is widely used to analyze consumer behavior. Eroglu et al. were the first to apply it to study the relationship between the virtual environment atmosphere in e-commerce and consumers’ cognitive responses. This model has since been widely used in online shopping research [21]. Dong et al. used this framework to explain how agricultural product information quality influences consumers’ perceived value in live streaming [22]. Moon et al. introduced perceived emotional attitudes to explain changes in consumer attitudes toward online shopping under the stimulus of utilitarian factors [1]. Han et al. utilized the SOR framework to explain the relationship between consumer confidence and green purchase intentions [23].
Moreover, the SOR framework has also been applied to analyze the influence of product attribute characteristics on purchase behavior. Anis and Yasi studied the influence of packaging symbols on consumers’ purchase intentions based on brand image and perceived quality [24]. Wang et al. reveal the positive impact of design innovation in fast-moving consumer goods on purchase intentions [25]. In the field of environmental psychology, Krishna pointed out that environmental atmospheric factors consist of physical elements perceived through visual, auditory, olfactory, gustatory, and tactile senses [26]. The atmosphere of live streaming turns both the natural and design attributes of green agricultural products into a physical stimulus [27]. Therefore, based on the SOR framework, this study considers social presence, brand image, and design-driven product factors as external stimuli (S), while green perceived value and emotional attitude are the individual’s internal cognitive state (O), aiming to explore the relationships between these factors and purchase intention (R).

2.2. Research Hypothesis

2.2.1. Impact of Social Presence on Green Perceived Value, Emotional Attitude, and Purchase Intention

Social presence refers to the psychological perception of others’ presence in media and interactions, an inherent attribute of communication media [20]. Previous studies have confirmed that social presence significantly influences consumer behavior on online shopping platforms by enhancing emotional connection with products and brands, thereby increasing purchase intention [7]. In this study, social presence refers to the sense of involvement that individuals experience when interacting with others through live streaming media. Moreover, social presence provides consumers with extra information that can influence their product evaluations. When consumers watch the process of harvesting, digging, and processing agricultural products on live streaming platforms, their green perceived value of the products increases [22].
Consumers prioritize healthiness, safety, and environmental sustainability when choosing green agricultural products compared to other goods [28]. The atmosphere of live streaming created by rural elements can enhance consumers’ green perceived value [29]. A high level of social presence fosters positive emotional attitudes. When a streamer presents the farmers’ background stories, ecological farming methods, and environmental concepts, viewers feel a sense of anticipation or emotional resonance, which motivates them to purchase green agricultural products [30]. Thus, social presence positively influences consumers’ green perceived value and emotional attitude, ultimately enhancing their purchase intention. Based on this, this study proposes the following hypotheses:
H1a. 
Social presence in the live streaming of green agricultural products positively influences green perceived value.
H1b. 
Social presence in the live streaming of green agricultural products positively influences purchase intention.
H1c. 
Social presence in the live streaming of green agricultural products positively influences emotional attitude.

2.2.2. Impact of Brand Image on Green Perceived Value, Emotional Attitude, and Purchase Intention

Brand image refers to the perception that consumers develop about a brand through associations [31]. In the absence of prior product knowledge, consumers often rely on brand image to guide their purchasing decisions [32]. Studies have highlighted the role of brand image in shaping consumer attitudes and behavior. For instance, Kong et al. demonstrated that emotional marketing and enhanced experiences can strengthen brand image, thereby positively influencing consumers’ emotional attitudes toward agricultural products [33]. Similarly, Yang et al. identified a positive correlation between brand image and perceived value for green agricultural products [34]. Live streaming amplifies brand image effects through dynamic interactivity. When streamers emphasize a brand’s green attributes (e.g., environmental certifications or pollution-free production), consumers perceive the products as having higher environmental value [35]. A strong brand image also alleviates concerns about product quality, boosting security and satisfaction. Therefore, this study posits the following hypotheses:
H2a. 
Brand image positively influences green perceived value.
H2b. 
Brand image positively influences consumers’ purchase intention.
H2c. 
Brand image positively influences consumers’ emotional attitude.

2.2.3. Impact of Design-Driven Attributes on Green Perceived Value, Emotional Attitude, and Purchase Intention

Design-driven attributes refer to unique characteristics shaped by design, influencing users’ perceptions, emotions, and behaviors through innovation, aesthetics, and enhanced experiences [36,37]. These attributes also convey a company’s philosophy or lifestyle values, fostering consumer recognition [38]. Research indicates a demand for design-driven attributes in green agricultural products. Ragaert et al. found that different packaging materials affect consumers’ perceptions and choices of green agricultural products [39]. Pollard further highlighted that consumers prefer green agricultural products packaged with environmentally friendly materials [40]. Ampuero and Vila explored the positive impact of packaging elements, such as color, typography, and graphic shapes, on consumer perception [41]. In offline retail scenarios, these attributes often become less noticeable due to environmental distractions and the prominence of their natural attributes [42]. However, live streaming’s interactivity allows consumers to engage directly with design-driven attributes, such as biodegradable packaging or recycling concepts, enhancing their green perceived value. Moreover, visually appealing packaging can also evoke positive emotional attitudes toward green agricultural products. Therefore, this study posits the following:
H3a. 
Design-driven attributes positively influence green perceived value.
H3b. 
Design-driven attributes positively influence consumers’ purchase intentions.
H3c. 
Design-driven attributes positively influence consumers’ emotional attitudes.

2.2.4. Green Perceived Value, Emotional Attitude, and Purchase Intention

Perceived value refers to the subjective evaluation of a product or service, which can differ based on various usage scenarios and product characteristics [43]. It reflects individuals’ attitudes toward products’ quality or performance [44]. In the context of green agricultural products, Chen defines green perceived value as the comprehensive assessment consumers make after evaluating the benefits and costs of a product or service based on their environmental needs, sustainability expectations, and green demands [45]. A high level of green perceived value can encourage customers to make repeat purchases of green agricultural products [46]. Furthermore, green perceived value encompasses several dimensions, including green value, environmental value, emotional value, and social value [47]. Some scholars have suggested that consumers prefer products with moral and social responsibility value, viewing such purchases as positive and optimistic actions [48]. Green perceived value serves as a moderating factor between live streaming marketing and purchase intentions for green agricultural products [49]. Meanwhile, a positive emotional attitude can also facilitate purchase decisions [50]. Based on these observations, this study proposes the following hypotheses:
H4. 
Green perceived value positively influences consumers’ purchase intentions during live streaming of green agricultural products.
H5. 
Emotional attitude positively influences consumers’ purchase intentions during live streaming of green agricultural products.
In summary, this study establishes a structural equation model based on the SOR framework, hypothesizing that social presence, brand image, and design-driven attributes influence green perceived value and emotional attitude, thereby affecting consumers’ purchase intention (Figure 1).

3. Method

3.1. Measurement Variables and Questionnaires

The variables included in this study are based on established scales from the existing literature on live streaming e-commerce and design-driven products. To ensure that the scales accurately capture green consumption behavior, the items were adjusted to reflect the characteristics of live streaming platforms and green consumer preferences. Descriptive text was used to clarify product positioning and mitigate the influence of extraneous factors. Consequently, six dimensions were adopted: social presence, design-driven attributes, brand image, green perceived value, emotional attitude, and purchase intention (Table 1). A preliminary test with 10 consumers was conducted to assess the clarity and comprehensibility of the questionnaire. Based on the feedback, two ambiguous questions were revised to eliminate misunderstanding. The finalized questionnaire consists of two sections. The first section collects participants’ demographic information, including gender, age, education level, and income. The second section focuses on investigating consumers’ purchase intentions for design-driven green products in live streaming contexts. The questionnaire is composed of six subscales with a total of 23 items. A standardized 5-point Likert scale was employed, where 1 indicates “strongly disagree” and 5 indicates “strongly agree”.

3.2. Data Collection and Sample Description

To ensure the reliability of the survey, a total of 472 questionnaires were distributed in Hangzhou, China, from September to October 2024, using a combination of online platforms and offline methods. Verifying respondents’ participation in live-stream shopping was essential for the study’s relevance. Invalid responses, including those with duplicate answers or excessively short completion times, were excluded, resulting in 432 valid samples and an effective response rate of 91% (Table 2). Of the respondents, 235 were female, representing 54.4% of the total. In terms of age distribution, 130 participants were aged 18–24, 181 were aged 25–34, 98 were aged 35–44, 17 were aged 45–54, and 6 were aged 55 and above, accounting for 30.1%, 41.9%, 22.7%, 3.9%, and 1.3% of the sample, respectively. As the primary consumer group for live-stream shopping, youth aged 18–34 comprised 72% of the sample, meeting the criteria set for this study. The age distribution of the sample is consistent with descriptions of live-stream consumer demographics reported in authoritative surveys. The sampling mainly focused on Chinese participants to reflect the unique cultural and market dynamics of China. This approach ensures the study captures insights from the most representative market [3]. Therefore, the data collected in this study can reliably support the accuracy and validity of the research conclusions.

3.3. Data Analyze

SPSS20.0 and AMOS24.0 were employed for statistical data analysis. First, the measurement model was tested. Confirmatory factor analysis (CFA) was utilized to test the reliability and validity of the questionnaire, and the coefficient of Cronbach’s α was calculated to evaluate the consistency of each scale. Lastly, SEM was applied to conduct the path analysis to test the research hypotheses.

4. Results

4.1. Testing of Validity and Reliability

This research adopted CFA to evaluate the reliability and validity of the measurement model (Table 3). In this study, the Cronbach’s α coefficients for all observed indicators are above 0.8, indicating that the scale has high reliability, with a strong correlation between the items for each variable. The AVE (Average Variance Extracted) of the six variables was greater than 0.5, and the CR (composite reliability) was greater than 0.7. According to the research of Fornell and Larcker, the structure of the scale has a good degree of convergence [55]. In addition, the AVE of each variable is greater than the correlation coefficients between that variable and other variables, demonstrating good discriminant validity (Table 4).

4.2. Model Fit Test

The goodness of fit of the measurement model was evaluated (Table 5). The results show that X2/DF = 1.495, GFI = 0.941, TLI = 0.976, CFI = 0.979, and RMSEA = 0.034. A comprehensive examination of these indices indicates that the research model exhibits good fit.

4.3. Path Analysis and Hypothesis Testing

The fit of the structural equation model was first evaluated to test the hypotheses in the study. The results showed that X2/DF = 2.220, GFI = 0.910, TLI = 0.940, CFI = 0.948, and RMSEA = 0.053, indicating that the structural equation model is acceptable. The path analysis results (Table 6) indicate that design-driven attributes do not have a significant positive effect on purchase intention, and hypothesis H3b was not supported. The remaining hypotheses were all significant, suggesting that they are valid (Figure 2).
This study used the Bootstrap method to test the mediating effects in the research model [56]. All hypothesis tests employed the bias-corrected percentile method for error correction in evaluating mediating effects. When the confidence interval is not 0, it indicates that there is a mediating effect. Hypothesis testing results show that there are significant mediating effects in all paths, and the hypotheses are valid (Table 7). In order to explore the relationship between green perceived value, emotional attitude, and purchase intention, further analysis is required to determine whether the mediating effect is full or partial. The data in Table 6 indicate that there is no direct effect in the “design-driven–purchase intention” path, suggesting that green perceived value and emotional attitude play a full mediating effect. Therefore, it can be concluded that hypotheses H6a, H6b, H7a, and H7b partially hold, and hypotheses H8a and H8b hold.

5. Discussion

This research explores the crucial factors influencing consumers’ green purchase intention in live streaming. Part of the research outcomes verified the hypotheses and other parts presented new insights. The results are as follows:
(1)
Social presence has a significantly positive effect on green perceived value (β = 0.225; p < 0.001). This indicates that hypothesis H1a can be verified, which is consistent with the findings of previous research [46]. The social and interactive atmosphere of an environment can enhance consumers’ green perceived value. During live-stream interactions, consumers can genuinely perceive the environmental and health value of green agricultural products. Social presence significantly influences emotional attitude (β = 0.172; p < 0.001), supporting hypothesis H1c. Immersive live streaming environments enhance consumers’ engagement, which boosts their positive emotional response.
(2)
Brand image significantly enhances green perceived value (β = 0.448; p < 0.001) and emotional attitude (β = 0.233; p < 0.001). Therefore, hypotheses H2a and H2c are supported. These findings further enhance previous research [51]. In the live streaming context, the brand image of green agricultural products shapes consumers’ expectations of their value. Consumers often consider the origin of the product, health benefits, and the production environment as determinants of green agricultural product quality. When a brand establishes the credibility through green marketing, its loyal consumers are more likely to view its products as a high-value and environmentally friendly option, thus enhancing their green perceived value. Furthermore, a strong brand image fosters emotional connections with consumers, boosting trust and identification, which in turn enhances emotional attitude.
(3)
Design-driven attributes have a significantly positive impact on green perceived value (β = 0.170; p < 0.001) and emotional attitude (β = 0.406; p < 0.001); thus, hypotheses H3a and H3c are supported. When the design of green agricultural products aligns with environmental values, consumers perceive that the products not only meet their needs but also reduce pollution, thereby improving their green perceived value. Furthermore, high-quality design can evoke consumers’ emotional resonance, and products with creativity and aesthetic value are more likely to gain emotional recognition from consumers.
(4)
Green perceived value (β = 0.280; p < 0.001) and emotional attitude (β = 0.280; p < 0.001) both have a positive influence on purchase intention, so hypotheses H4 and H5 are supported. According to the SOR theory, the information obtained during live streaming serves as an external stimulus that inevitably influences consumers’ internal perceptions and attitudes [42]. When consumers are effectively stimulated by environmental value, it triggers their evaluation, which ultimately impacts their purchase decisions. Green perceived value provides a rational basis for consumers, while emotional attitude enhances purchase intention through emotional resonance. In the highly interactive context of live streaming, the combined effects of theses factors become pronounced.
(5)
It is worth noting that social presence (β = 0.255; p < 0.001) and brand image (β = 0.204; p < 0.001) both have a positive influence on purchase intention, so hypotheses H1b and H2b are verified. Perceived green value and emotional attitude both serve as mediators between brand image and purchase intention, as well as between social presence and purchase intention. These mediating effects indicate that consumers are more likely to develop purchase intentions when they perceive the product’s value and form a positive emotional connection. This finding aligns with the value dimension orientation principle in consumer value theory, emphasizing the critical role of perceived value in shaping consumer behavior [57]. However, design-driven attributes do not have a significant effect on purchase intention (β = 0.105; p > 0.05); thus, hypothesis H3b is not supported. This indicates that design-driven attributes do not directly influence green purchase intention on live streaming platforms. This may be attributed to the inherent characteristics of green agricultural products. While excellent design can stimulate consumer preferences, it does not provide green products with a significant advantage over similar offering [58]. Moreover, the context of live streaming shopping could amplify this effect, as consumers tend to prioritize information clarity and trustworthiness when purchasing agricultural products online. Thus, design features may lack the direct impact needed to trigger purchase intentions.
(6)
Another important finding is that the impact of design-driven attributes on emotional attitude is considerably higher than that of brand image and social presence in live streaming. This may be because in the live streaming context, consumers can immediately visualize design details such as packaging, color, and convenience, directly influencing their feelings. Moreover, the added value created by design is often regarded as an expression of consumers’ self-identity and values. Agricultural products with green design can make consumers feel that they are contributing to environmental protection, thereby fostering a positive emotional attitude [59].

6. Conclusions and Future Directions

6.1. Conclusions and Implication

The main contribution of this study is analyzing the factors influencing consumer willingness to purchase green agricultural products in live streaming contexts. The findings reveal that social presence and brand image significantly and positively affect purchase intention, while design-driven attributes indirectly influence purchase intention. Emotional attitude and green perceived value are identified as full mediators between design-driven attributes and purchase intention, and as partial mediators for other factors. By integrating design-driven attributes and brand image into the SOR model, this study provides novel empirical evidence. The findings suggest that designers should shift their strategies to focus more on shaping corporate brand image, creating agricultural products that better align with consumer preferences on live streaming platforms.
The primary theoretical contribution of this study is the development of a theoretical model to explore the mechanisms through which product characteristics and live streaming platforms influence consumers’ purchase intentions of green agricultural products. The study elaborates on how social presence, brand image, and design-driven attributes impact purchase intention through green perceived value and emotional attitude; the mediating effects within these relationships are also verified. Furthermore, the study extends the application of the SOR model to the green agricultural product industry. This study provides novel insights into the interactions between platform characteristics and green purchasing behaviors with theoretical advancement value.
In terms of managerial practice, the design-driven strategy remains effective. The results recommended that companies consider the impact of design innovation on consumers’ emotional attitudes and green perceived value when developing products, in order to meet consumers’ latent needs and enhance the competitiveness of green agricultural products. Specifically, companies could develop packaging and branding that visually emphasize environmental benefits, making green attributes immediately recognizable to consumers. Administrators should unify brand image and quality services through design-driven strategies, establish corporate image, meet customers’ ecological expectations, and increase the added value of green agricultural products. For example, targeted campaigns featuring relatable stories about sustainable farming or eco-friendly practices could foster deeper consumer connections and drive purchase behavior. Additionally, through live streaming platform marketing, managers should clearly communicate which green products are environmentally friendly. Furthermore, managers should leverage various marketing communication channels, including mass media and social media, to emphasize the importance of a green and healthy lifestyle.

6.2. Research Limitations and Prospects

This study has several limitations. First, the respondents were mainly from Chinese live streaming platforms, because China has a unique culture and market dynamics for live streaming shopping. While this focus provides valuable insights into a highly advanced market, it may not fully capture the perspectives of consumers from regions where live-stream shopping is less developed or culturally distinct. Therefore, future research should expand to include consumers from different cultural backgrounds to confirm the results of this study. Second, while this study focused on the impact of design-driven attributes and live streaming features on purchase intention, it did not integrate external variables such as price, income, and product quality. The impact of design-driven attributes may not be applicable when confronted with agricultural products that are primarily characterized by price and quality, and needs to be further explored. Future studies should incorporate these variables into the research framework to provide a more comprehensive understanding of consumer behavior in the context of live streaming green agricultural products. Therefore, subsequent research should consider other factors within live streaming green agricultural product contexts, based on the related literature and theories.

Author Contributions

Conceptualization, Z.W. and Y.Z.; methodology, Z.W.; software, Z.W.; validation, X.Z., Y.Z. and Z.W.; formal analysis, Z.W.; investigation, Y.Z.; resources, X.Z.; data curation, Z.W.; writing—original draft preparation, Y.Z.; writing—review and editing, X.Z.; visualization, Z.W.; supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Fund Arts Project of 2023 “Research on Promoting Rural Value Chain Reconstruction and Enhancement through Fusion Design Thinking” Project Number: 23BG145.

Institutional Review Board Statement

Our study did not require further ethics committee approval as it did not involve animal or human clinical trials and was not unethical. In accordance with the ethical principles outlined in the Declaration of Helsinki, all participants provided informed consent before participating in the study. The anonymity and confidentiality of the participants were guaranteed, and participation was completely voluntary.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are not available due to privacy or ethical restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 00522 g001
Figure 1. Proposed research model.
Figure 1. Proposed research model.
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Figure 2. Results of structural model testing. The *** in the figure caption denotes that the hypotheses tested have results with very high statistical significance (p < 0.001).
Figure 2. Results of structural model testing. The *** in the figure caption denotes that the hypotheses tested have results with very high statistical significance (p < 0.001).
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Table 1. Measurement scale.
Table 1. Measurement scale.
ConstructItemsReference
Social Presence (SP)While watching the green agricultural product live stream, I was totally immersed in the world that the live stream created.[22]
While watching the green agricultural product live stream, I can get a more comprehensive understanding of product information.
While watching the green agricultural product live stream, I feel the atmosphere is warm and friendly in the live stream.
While watching the green agricultural product live stream, there is a sense of face-to-face interaction communication with the streamer.
Brand Image (BI)I prefer branded green agricultural products in the live streaming.[34,51]
I believe that branded green agricultural products in the live streaming come with assured quality.
I hold the view that using branded green agricultural products in the live streaming is safe.
The brand of green agricultural products in the live streaming is highly recognized in the market.
Design-driven Attribute (DA)The green agricultural product presents a new and different design in the live streaming.[25]
Consumers can tell at a glance that the design of green agricultural products “makes sense” in the live streaming.
The design innovation makes the green agricultural products very distinctive in the live streaming.
The design innovation presents a new solution of existing problems of green agricultural products in the live streaming.
Green Perceived Value (GPV)Using green agricultural product offers value for money.[52,53]
Using green agricultural product helps me cultivate a positive and healthy image.
Using green agricultural product can alleviate my concerns about food safety.
Using green agricultural products have higher nutrition, good freshness and better quality.
Using green agricultural product is environmentally friendly and helps to improve the ecological environment.
Emotional Attitude (EA)The green agricultural products displayed in live streaming make me feel more intimate with the brand.[25]
The green agricultural products displayed in live streaming make me feel more intimate with the brand.
The green agricultural products displayed in live streaming have increased my trust on the brand.
Purchase Intention (PI)I am willing to purchase these green agricultural products through live streaming.[54]
If I need to buy green agricultural products, I would prefer to choose the ones recommended by the live streaming.
I would like to advise friends and acquaintances to buy green agricultural products through live streaming.
Table 2. Demographics of the respondents.
Table 2. Demographics of the respondents.
MeasureItemsNumberPercent (%)
GenderMale19745.6%
Female23554.4%
Age18–2413030.1%
25–3418141.9%
35–449822.7%
45–54173.9%
55 and above61.3%
EducationHigh school and below17139.6%
Undergraduate/specialized20146.5%
Bachelor’s degree4710.9%
PhD and above133.0%
IncomeCNY 2500 and below10724.8%
CNY 2500–450017841.2%
CNY 4500–65008920.6%
CNY 6500–8500327.4%
CNY 8500 and above266.0%
Table 3. Validity and reliability analysis.
Table 3. Validity and reliability analysis.
ConstructsItemsMeanS.D.LoadingsαCRAVE
Social PresenceSP13.4490.9260.8090.8560.8580.602
SP2 0.819
SP3 0.708
SP4 0.762
Brand ImageBI13.5550.9000.8300.8620.8660.618
BI2 0.816
BI3 0.758
BI4 0.735
Design-driven ProductsDA13.6900.8810.7500.8630.8660.618
DA2 0.785
DA3 0.844
DA4 0.762
Green Perceived ValueGPV13.9120.7750.7580.8740.8760.587
GPV2 0.769
GPV3 0.814
GPV4 0.744
GPV5 0.743
Emotional AttitudeEA13.7160.9120.8090.8320.8410.639
EA2 0.731
EA3 0.853
Purchase IntentionPI13.7980.8690.8020.8100.8130.592
PI2 0.739
PI3 0.766
Table 4. Discriminate validity of the research model.
Table 4. Discriminate validity of the research model.
ConstructsSPBIDAGPVEAPI
SP0.775
BI0.371 **0.786
DA0.457 **0.367 **0.786
GPV0.372 **0.470 **0.353 **0.766
EA0.364 **0.352 **0.464 **0.331 **0.799
PI0.485 **0.479 **0.443 **0.504 **0.487 **0.769
In the context of discriminant validity, the “**” in the table typically indicates statistical significance (usually at a certain confidence level, such as 0.01 or 0.05) for the correlation between the constructs.
Table 5. The goodness-of-fit indices for the measurement model and research model.
Table 5. The goodness-of-fit indices for the measurement model and research model.
ModelX2X2/DFGFITLICFIRMSEA
Measurement model321.4501.4950.9410.9760.9790.034
Research model483.9502.2200.9100.9400.9480.053
Recommended criteria>0.05<3>0.9>0.9>0.9<0.08
Table 6. The results of the hypothesis test.
Table 6. The results of the hypothesis test.
HypothesesHypothesized PathBβS.E.TResult
H1aSP → GPV0.1950.2250.0454.324 ***Supported
H1bSP → PI0.2230.2550.0464.816 ***Supported
H1cSP → EA0.1790.1720.0543.283 ***Supported
H2aBI → GPV0.4120.4480.0537.827 ***Supported
H2bBI → PI0.1890.2040.0553.448 ***Supported
H2cBI → EA0.2580.2330.0594.398 ***Supported
H3aDP → GPV0.1450.1700.0443.328 ***Supported
H3bDP → PI0.0910.1050.0481.891Rejected
H3cDP → EA0.4180.4060.0587.234 ***Supported
H4GPV → PI0.2840.2800.0624.549 ***Supported
H5EA → PI0.2560.3040.0515.014 ***Supported
The *** indicates that the result is statistically significant with a p-value less than 0.001.
Table 7. Results of mediating effect.
Table 7. Results of mediating effect.
PathEstimateS.E.pBias-Corrected 95% CISignificance (p < 0.5)
Lower Upper
H6a SP → GPV → PI0.0620.0240.0010.0220.118Yes
H6b SP → EA → PI0.0510.0240.0090.0140.110Yes
H7a BI → GPV → PI0.1190.0300.0000.0670.185Yes
H7b BI → EA → PI0.0700.0260.0000.0290.136Yes
H8a DA → GPV → PI0.0460.0220.0080.0110.101Yes
H8b DA → EA → PI0.1220.0340.0000.0660.203Yes
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Zhu, X.; Zhang, Y.; Wu, Z. Familiar Yet New: How Design-Driven Innovation and Brand Image Affect Green Agricultural Product Purchase Intentions in the Live Streaming Environment. Sustainability 2025, 17, 522. https://doi.org/10.3390/su17020522

AMA Style

Zhu X, Zhang Y, Wu Z. Familiar Yet New: How Design-Driven Innovation and Brand Image Affect Green Agricultural Product Purchase Intentions in the Live Streaming Environment. Sustainability. 2025; 17(2):522. https://doi.org/10.3390/su17020522

Chicago/Turabian Style

Zhu, Xuguang, Yihan Zhang, and Zeyu Wu. 2025. "Familiar Yet New: How Design-Driven Innovation and Brand Image Affect Green Agricultural Product Purchase Intentions in the Live Streaming Environment" Sustainability 17, no. 2: 522. https://doi.org/10.3390/su17020522

APA Style

Zhu, X., Zhang, Y., & Wu, Z. (2025). Familiar Yet New: How Design-Driven Innovation and Brand Image Affect Green Agricultural Product Purchase Intentions in the Live Streaming Environment. Sustainability, 17(2), 522. https://doi.org/10.3390/su17020522

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