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Article

Increasing Consumers’ Purchase Intentions for the Sustainability of Live Farming Assistance: A Group Impact Perspective

by
Guangming Li
,
Liting Chang
* and
Guiqing Zhang
Business School, Hohai University, Nanjing 211100, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(17), 12741; https://doi.org/10.3390/su151712741
Submission received: 26 June 2023 / Revised: 5 August 2023 / Accepted: 9 August 2023 / Published: 23 August 2023
Browse Figure
Versions Notes

Abstract

:
Live farming assistance, which is an important channel for emerging agricultural sales, alleviated the challenges of disrupted agricultural sales caused by the COVID-19 pandemic in past years. As the final purchasers of products, consumers are directly related to the sales conversion rate of live farming assistance. Unlocking the potential influence of consumers’ purchase intentions in live farming assistance and exploring ways to improve consumers’ purchase intentions will help the sustainable operation of live farming assistance. The hidden quality of agricultural products, the public welfare nature, and the high interactivity of live farming assistance make consumers more susceptible to the group effect during the shopping process. This paper analyzes the impact of the group effect on consumers’ purchase intentions based on reference group influence theory and emotional contagion theory. Data is obtained through questionnaires for empirical testing. Three kinds of group effects are examined: informational effect, normative effect, and emotional effect. The research results indicate that the group effect has a positive and direct impact on consumers’ purchase intentions, and experience value plays a critical mediating role in this relationship. We further predict a moderated-mediation model, whereby the indirect effect of the group effect on consumers’ purchase intentions, through experience value, is moderated by tie strength. The research findings contribute to the study of consumer buying behavior in live e-commerce, and provide insights for practitioners to improve the conversion rate of live farming assistance and promote sustainable operation of live farming assistance.

1. Introduction

In recent years, live-streaming e-commerce has been developing rapidly, and the categories of carryover have gradually penetrated from beauty, clothing, and food to agricultural products. The outbreak of the COVID-19 pandemic has widely affected the upstream of agricultural products, preventing buyers from going to the countryside to purchase and transport agricultural products, resulting in widespread stagnation in the sale of agricultural products. As an emerging sales channel for agricultural products, live farming assistance has become an important way to promote farmers’ income and rural revitalization. China has many practices in live farming assistance. In 2022, “Implementing the project of ‘developing agriculture through business’ and promoting the development of agricultural and sideline products live-streaming with goods” was written into the No.1 document of the Central Committee for the first time as a key task to comprehensively promote rural revitalization. Local governments at all levels also strongly support live farming assistance, and local government officials even participate in selling goods as anchors. Entertainment stars, well-known hosts, entrepreneurs, and other public figures have also participated in live farming assistance and used their influence to drive consumers to buy agricultural products. It can be seen that the sustainable operation of live farming assistance depends fundamentally on whether it can pull consumers to buy on the demand side. Therefore, it is necessary to study consumer purchase intention in live farming assistance to provide theoretical guidance for the practical operation of enhancing consumer purchase intention.
There is also considerable academic interest in the sustainability of live farming assistance. Scholars believe that playing emotional cards and policy cards in exchange for sales volume is an unsustainable model; the key to achieving sustainable development in live farming assistance is to rely on the market mechanism to enhance the consumer experience [1], to obtain the recognition of consumers [2], and to enhance consumer bonding and desire to buy [3]. However, scholars have not performed much research on consumer purchase intention in live farming assistance, instead, research on consumer purchase intention in live-streaming e-commerce has achieved stage-by-stage results. It has been shown that anchors with high credibility [4,5,6] and attractiveness [7,8,9], as well as effective information [10,11,12] and good interactions [13,14] in the live-streaming process are all conducive to promoting the purchase intentions of consumers. The extant literature provides a well-established theoretical foundation and practical guidance to promote consumers’ purchase intentions in live farming assistance but still has limitations in at least the following two points. On the one hand, some of the literature is based on the traditional one-way persuasion model [15] to study the influence of anchors on consumers, ignoring the interaction of consumers with each other and between consumers and anchors. On the other hand, some studies, while noting the importance of live-streaming interactions, lacked an understanding of the group effect brought about by live-streaming as a space of interaction for social e-commerce. The interactivity [16,17] and public welfare [18,19] of live farming assistance and the non-standard nature of agricultural products and the invisibility of quality characteristics [20] amplify the group effect generated by live farming assistance participants, so that consumers are always under the influence of the group in the purchasing process. However, little literature has addressed the group effect in live farming assistance and its impact on consumer purchases.
Not only that, value perception is an important predictor of consumer behavior. A study by Han and Xu (2020) verified the decisive role of consumers’ perceived value of products and services recommended by anchors in their purchase intentions [21]. As a new form of e-commerce, live farming assistance breaks through the limitations of time and space, and bridges the comprehensive communication between consumers and anchors and other consumers, so that the effect of the consumer experience is rapidly enhanced. Therefore, the value of consumers’ experience in live farming assistance also stimulates their own purchase intentions. In addition, the role of group effects on consumer attitudes and behaviors is not static, but is moderated by a number of factors. The relationship between the interacting parties in social e-commerce will affect the conversion of the transaction [22]. When group effects are in play, different judgments generated by consumers about the strength of their connection to the live farming assistance group may lead to differences in consumers’ purchase intentions. Therefore, it is necessary to distinguish between consumers with different levels of tie strength with the group in order to explore in depth the impact of group effects on consumers’ purchase intentions.
In summary, this study analyzes the group effect in live farming assistance based on reference group theory and emotional contagion theory, and introduces the experience value as the mediating variable and the tie strength as the moderating variable to study the influence of the group effect on consumers’ purchase intentions, and uses the survey method to obtain cross-sectional data for empirical testing. This paper aims to clarify the influencing factors of consumer purchase intention in live farming assistance, explore methods to enhance consumers’ purchase intentions, and provide guidance for improving the conversion rate of live farming assistance and helping the sustainable development of live farming assistance at the practical level.

2. Overview of Studies and Hypothesis Development

2.1. Live Farming Assistance

Live farming assistance is a new type of e-commerce which targets the overall interests of farmers and rural areas, takes participants in the rural development process as the main body, uses the webcasting platform as a tool, and sells agricultural products as a means. The essence of live farming assistance is live-streaming e-commerce with agricultural products as the main trading object. Compared with the general live-streaming e-commerce, live farming assistance increases farmers’ income by gathering social forces to sell agricultural products through e-commerce live-streaming to alleviate the hindered sales of agricultural products [18], which has the characteristic of public welfare. The public welfare attribute of live farming assistance attracts consumers who are concerned about public welfare, arouses their sense of social responsibility, and creates a stable common context for consumers in the live broadcast [23]. In addition, compared with traditional poverty alleviation e-commerce, live farming assistance is more interactive. In the interactive field of live farming assistance, consumers can not only watch the facial expressions and movements of the anchors and hear their greetings and the introduction of agricultural products, but also experience high-frequency and real-time interactions with the anchor and other co-watchers via tweeting, liking, and giving gifts. Compared with traditional website shopping with texts and pictures as the main form of interaction, the interactive content in live farming assistance is more personalized, the interactive forms are richer, and the interactive response is more timely.

2.2. Group Effect in Live Farming Assistance

The interactive behaviors of group members in the live farming room, which has an impact on consumers’ perception and evaluation of things, i.e., forming a group effect, make it difficult for consumers to make purchase decisions as completely independent individuals. According to reference group influence theory and emotional contagion theory, there are three kinds of group effects on individual experience in live farming assistance: the informational effect, normative effect, and emotional effect [24,25].
Consistent with reference group influence theory, the informational effect arises from consumers’ desires to collect information to deal with uncertainty in the purchase decision process and reduce shopping risk [26]. When consumers are unfamiliar with products and services, they tend to observe buying behaviors of individuals or groups that they consider to be product experts or infer the quality of a product based on the endorsement of a person or group [26]. Unlike standardized products, whose parameters, attributes, and consumption effects are easily perceived, agricultural products sold in live farming assistance, which is typical of experiential products, are non-standardized and their quality is hidden [20]. To cope with the uncertainty in purchase outcomes, consumers rely more on information from reference groups, taking anchors and co-watchers as important sources of information. The anchor is usually involved in the selection and trial and has direct information and knowledge of the product. During the live-streaming, information on agricultural products is introduced and displayed in detail, and even the production environment and process of the agricultural products are shown in the field, which is the most important source of information for consumers to make purchase decisions on agricultural products. Co-watchers may have already purchased and consumed the agricultural products, or they may be loyal fans of the anchor. Interactive clues such as likes, comments, gifts, and orders are also important ways for consumers to obtain information. Consumers infer the quality of agricultural products and the reputation of anchors through interactive behaviors and purchasing behaviors of co-watchers, which lays the foundation for purchasing decisions.
The normative effect refers to the tendency of consumers to meet the expectations and preferences of others in the group [26]. Consumers conform to group expectations, preferences, and norms through interactive behaviors to gain the approval of group members or to avoid punishment by group members [26]. In live farming assistance, consumers’ purchasing behaviors not only meet their own needs but also support farmers [27], which has significant characteristics of public welfare [18]. Celebrities such as well-known hosts, stars, and online celebrity anchors participate in live farming assistance, using their social influence to call on fans to buy agricultural products and contribute to farmers’ income. The social benefits are much greater than the economic benefits obtained by anchors. Therefore, in the interactive process of live farming assistance, if consumers perceive that anchors and the co-watchers not only actively participate in actions of agricultural support, but also encourage others to join in, consumers will feel normative pressure from the group. In addition, consumers need self-improvement [25].
The emotional effect refers to the emotional contagion that occurs during the interaction between consumers and other participants in live farming assistance. According to emotional contagion theory, consumers unconsciously perceive and imitate others’ facial expressions, intonation, postures and movements, and other emotional clues. This imitation feedback mechanism in turn reinforces consumers’ emotional experiences, resulting in perceptible emotions for consumers as well [25]. Emotional contagion not only occurs in offline face-to-face interactive situations but can also occur in online interaction [28]. In live farming assistance, the anchor’s words and actions are delivered to consumers through a highly visual interface. Consumers perceive and unconsciously imitate the anchor’s non-verbal emotional cues such as expressions, postures, movements, intonations, etc. Through this imitation feedback mechanism, consumers also generate perceptible emotional information which is integrated into the anchor’s emotions. Consumers, infected with emotions, such as the excitement and pleasure of the anchor, will engage in real-time interactions by sending shells, emojis, likes, and other emotion-rich messages. The rich cues of interactive behaviors also convey emotions in the general audience through emotional contagion mechanisms. Furthermore, the public welfare nature of live farming assistance attracts consumers who are interested in public welfare or farming assistance to join, setting a stable common context for them. Therefore, interactive cues are more likely to awaken a sense of social responsibility in consumers and become a common emotion for live agricultural assistance [23].

2.3. Experience Value

The experience value is the relative preference that emerges during the interaction between the subject and the object [29] and it is considered one of the key factors for successful transactions. In the context of live farming assistance, the experience value comes from the interactive process of acquiring and shopping for agricultural products. Experience is relative, which means that the type and magnitude of the value depend on the situation, the reference body, and by whom the value judgment is made [29]. Therefore, the experience value in different situations has different structural dimensions. Holbrook (1999), an early researcher on consumer value, classified experience value into eight categories based on three dimensions (extrinsic/intrinsic, active/reactive, self-oriented/other-oriented): efficiency, play, excellence, aesthetics, status/political, ethics, esteem, and spirituality from an experiential perspective [29]. Based on this, Mathwick et al. (2001) argue that shopping is a self-oriented activity and, therefore, disregards other-oriented dimensions to classify the experience value under traditional online shopping into four categories: consumer return on investment, service excellence, aesthetics, and playfulness [30]. Holbrook (2006) further shows that there is no clear distinction between active and reactive dimensions, so he considered only two dimensions, namely, the extrinsic/intrinsic and self/other-oriented dimensions, and classified experience value into four categories: economic value, hedonic value, social value, and altruistic value [31]. The extrinsic dimension regards the shopping experience as a means to some further end, while the intrinsic dimension regards the shopping experience itself as a value end [31].
This study focuses on the experience value of the group effect created by the interactive field of live farming assistance, rather than the intrinsic value of the shopping experience itself as a self-contained end. Therefore, this paper analyzes only external values and classifies the experience value of live farming assistance into two categories according to self-oriented/other-oriented: economic and social value. Economic value refers to the value consumers gain by using live farming assistance to reduce cognitive burden, the cost of time and effort in searching for product information, to improve the efficiency and accuracy of purchase decisions, and obtain products that ultimately provide value for money. Social value refers to the value gained by consumers who use live farming assistance as a means to shape the reactions of others. For example, live farming assistance can help consumers to improve their status, gain a good social image, and raise their self-esteem.

2.4. The Influence of Group Effect on Purchase Intention

Purchase intention refers to the possibility of consumers carrying out purchasing behaviors to satisfy their needs [32]. According to reference group influence theory, the informational effect and normative effects of the group in live farming assistance influence consumers’ purchase intentions [26]. The informational effect influences consumers’ purchase intentions mainly through the reduction in perceived risks [26]. The non-standardization and quality concealment characteristics of assisted agricultural products increase the uncertainty in purchase outcomes [20]. On the one hand, consumers obtain information about the assisted agricultural products through the anchor’s introduction, and on the other hand, they infer the quality of the assisted agricultural products and the reputation of the anchor by observing the orders and comments of the co-watchers. Therefore, the more consumers actively obtain information from the anchor and co-watchers, the more they can improve the information asymmetry dilemma, reduce the perceived risk, and thus, increase their purchase intention [33]. The normative effect impacts consumer purchase intention mainly through group reward and punishment mechanisms [26]. Participants in live farming assistance are looking forward to giving support to farmers [27]. By responding to the expectations of members of the live farming assistance room, consumers express their identification with the group’s values and close the psychological distance between them, thus establishing good relationships and enhancing their self-image. In addition, consumers can achieve self-improvement by choosing specific behaviors that meet group expectations. Therefore, the stronger the group normative effect perceived by consumers in the live farming assistance, the higher the intentions of purchasing. The emotional effect affects consumers’ purchase intentions mainly through emotional contagion. As the “atmosphere bearer” of the live room, the anchor’s words and actions are transmitted to consumers through a highly visual interface, so that consumers imitate their expressions, postures, movements, and intonation and other non-verbal emotional cues, and through the “imitation feedback” mechanism, into the anchor’s excitement [25]. Consumers infected by the anchor’s emotions will participate in real-time interactions by sending emotionally rich pop-ups, emoticons, and likes, etc. These rich interactive behavioral cues will also be emotionally transmitted among co-watchers through the emotional infection mechanism, so that consumers in live farming assistance will experience the same emotions. The emotional experience of both pleasure and excitement enhances consumer purchase intention [8]. Therefore, based on the aforementioned discussion, this paper puts forward the following hypotheses:
H1a. 
The informational effect has a positive impact on consumer purchase intention.
H1b. 
The normative effect has a positive impact on consumer purchase intention.
H1c. 
The emotional effect has a positive impact on consumer purchase intention.

2.5. The Mediating Role of Experience Value

According to experience value theory, experience value is relative, which means the reference body influences the value assessment of consumers [29]. Group effect refers to the influence of group members on consumers’ cognition and evaluation of objects [34]. The group effect affects consumers’ perceptions of experience value. First of all, the informational effect enhances the value of consumer experience mainly through the efficiency of purchase decisions and perceived behaviors. The group interaction in live farming assistance helps consumers effectively filter products and information in the vast amount of information available on the internet, reducing the cognitive burden of consumers and saving their time and energy, thus enhancing the economic value perceived by consumers. Moreover, consumers’ participation in helping farmers, such as sending barrage and placing orders, are mentioned by anchors in the live-streaming room, which enhances group members’ perceptions of the farming behaviors of consumers, and thus, contributes to consumers’ perceptions of social value. Secondly, the normative effect enhances the consumer experience value mainly through trust. The stronger the normative effect perceived by consumers, the stronger their trust in the group [35], which will greatly reduce the time and cognitive costs of consumers in the process of purchasing decisions, improve decision efficiency, and enhance consumers’ perceptions of economic value. On the other hand, consumers are influenced by group norms, and their behaviors of supporting farmers meet group members’ expectations, which is conducive to improving their self-esteem and social image, gaining recognition from group members, and thus, obtaining higher social value. Finally, the emotional effect enhances the value of the consumer experience through the equivalent mechanism of emotional information. According to the information equivalence model, consumers may not adopt the evaluation strategy that requires cognitive effort, but an emotionally heuristic simplified evaluation strategy that judges things based on consumers’ emotional experience [36]. Participants in live farming assistance have a stable shared context and shared emotions. Emotional contagion brings consumers emotional experiences such as enthusiasm and excitement for helping farmers, which promotes the adoption of emotional heuristic evaluation strategies, simplifies the purchase decision process, and enhances the perception of economic value. Moreover, the emotional experience shared with group members also pulls into the psychological distance between consumers and group members, enhancing consumers’ social identity and self-esteem, which in turn enhance the perception of social value. Moreover, studies have shown that experience value has a positive impact on consumer purchase intention. Chiang (2018) proved that in the context of O2O, consumer experience value significantly positively influences consumer purchase intention through their satisfaction with the process [37]. Hwang et al.’s (2020) empirical research on the effect of background music in e-commerce demonstrated that experience value has a significant positive correlation with consumer purchase intention [38]. Based on the above reasoning, this paper puts forward the following hypotheses:
H2a. 
Economic value mediates the relationship between group effect and consumer purchase intention.
H2b. 
Social value mediates the relationship between group effect and consumer purchase intention.

2.6. The Moderating Role of Tie Strength

Tie strength refers to the contact frequency and intimacy between individuals and group members [39]. In live farming assistance, some consumers often participate in live activities and interact with the anchor and other fans, while others may be new entrants. Therefore, their tie strength with other members in the live-streaming room is different. The tie strength determines the difference in information, knowledge, and familiarity held by both parties [40]. A high tie strength means consumers have repetitive knowledge information acquired in live farming assistance and are familiar with the interactive field experience of live farming assistance. According to perceptual adaptation theory, consumers with higher tie strength typically participate in live farming shopping frequently, and they become increasingly familiar with the group interaction experience of live farming assistance and, therefore, become less responsive to the group interaction experience [41]. Therefore, the higher the tie strength, the weaker the influence of group effect on consumers. Accordingly, this paper puts forward the following hypotheses:
H3a. 
Tie strength moderates the relationship between informational influence and purchase intention, such that this relationship is stronger when tie strength is low as opposed to high.
H3b. 
Tie strength moderates the relationship between normative influence and purchase intention, such that this relationship is stronger when tie strength is low as opposed to high.
H3c. 
Tie strength moderates the relationship between emotional influence and purchase intention, such that this relationship is stronger when tie strength is low as opposed to high.
According to the above discussion, the research model of this paper is shown in Figure 1.

3. Methodology

3.1. Research Design and Measurement

The study was conducted using a representative sample of consumers who have watched live farming assistance in China, where there is a large userbase because live e-commerce has flourished for many years and has been practiced extensively in the field of live farming assistance [18,19]. The participants were selected by setting the question “Have you watched the live farming assistance”. The questionnaire consisted of two parts.
The first part contained basic information about the respondents including gender, age, monthly income, shopping frequency of live farming assistance, and live online shopping experience according to previous studies to avoid bias due to variability in personal characteristics and shopping experience characteristics and to improve the robustness of empirical evidence [42,43,44]. In addition, by setting questions of “When was the last time you watched the live farming assistance” and “What type of anchor did you last watch in the live farming assistance”, respondents were guided to recall the last time they watched live farming assistance, which was conducive to their providing true answers in the follow-up questionnaire. The second part contained the measurement of core variables in the research model. A textual reminder of “Please indicate your agreement with each question in the form of a score based on your real experience and feelings of watching the live farming assistance last time” was given to participants. The variable measures draw on well-established scales from the previous literature. Participants assessed informational and normative effects using Bearden et al.’s (1989) [24] scale. The emotional effect was measured using Doherty et al.’s (1995) scale [45]. The economic value was measured using Rintamäki et al.’s (2006) scale [46]. The social value was measured using Sweeney and Soutar’s (2001) scale [47]. The purchase intention was measured using Dodds et al.’s (1991) scale [32], and tie strength was measured using Bansal and Voyer’s (2000) scale [48]. The items elicited responses along a 7-point agree–disagree Likert scale: 1 (strongly disagree) to 7 (strongly agree).

3.2. Pre-Investigation and Questionnaire Revision

The scale was partially modified in the Chinese context. To ensure the reliability of the scale, a pre-investigation was conducted, and 111 valid questionnaires were returned. The results of the pre-investigation showed the KMO was 0.817, and Bartlett’s spherical test was significant (p < 0.001), so exploratory factor analysis was further performed on the sample data in SPSS 24.0. The results showed four items retained in informational effect, seven items retained in normative effect, three items retained in purchase intention, and four items retained in tie strength, with a cumulative explained variance of 73.774%. The Cronbach’s a for each of the seven variables was greater than 0.800, indicating a good level of reliability. Therefore, the scale could be used in the formal survey.

3.3. Formal Survey and Sample Description

In the formal survey, 800 questionnaires were collected online and offline. Questionnaires that were too short to fill out, never watched live farming assistance, and had obvious wrong information (e.g., reverse items were the same as forward items, all options were the same, options were obviously regular, etc.) were eliminated, and 716 valid questionnaires were finally obtained. As shown in Table 1, the sample was relatively evenly distributed, and it was in line with the structural characteristics of the population watching online live-streaming presented in the 2021 China Online Live-Streaming Industry Development Research Report 1, i.e., mainly female and middle-aged people, which was representative.

3.4. Analytical Approach

Before testing the hypotheses, it was necessary to conduct a descriptive statistical analysis of the 716 valid sample data. Consistent with previous studies on consumers’ purchase intentions in live e-commerce [8,9,49], a reliability test to ensure that the measurement model had a good fit, discriminant validity, and convergent validity were conducted. In addition, a homogeneity error test was performed to prevent possible common method variance when data for multiple variables are all provided by the same subjects. The structural equation model (SEM) was estimated using the maximum likelihood estimation method to initially reveal the relationship among variables in the research model as a whole. This model validation method has been applied to scenarios of purchase intention in live e-commerce [50], environmental investment behavior of farmers [27], and willingness to purchase agricultural products online [51]. Finally, all hypotheses in the model were further analyzed using the causal steps method. This approach was used in previous studies of live weblebrity qualities [52] and live scene atmosphere cues [53].

4. Results

4.1. Reliability and Validity Test

The confirmatory factor analysis (CFA) was conducted in AMOS 24.0 to examine the distinctiveness of seven variables. The CFA result showed the hypothesized seven-factor model (χ2 = 1192.734, df = 474, χ2/df = 2.516, RMSEA = 0.046, GFI = 0.903, AGFI = 0.885, NFI = 0.938, CFI = 0.962) was statistically significant. Additionally, the Cronbach’s a for each of the seven variables was greater than 0.850, indicating a good level of reliability (as shown in Table 2). The standardized factor loadings of all items were greater than 0.6 and the t-values were significant, indicating that the correspondence between the items and the factors was correct. The average variance extraction (AVE) of each variable calculated from the standardized load coefficient and measurement errors of items were all over 0.6, and each combined reliability (CR) was over 0.8, indicating acceptable convergence validity (as shown in Table 2). The square root of AVE was greater than the correlation coefficient of that factor with other factors, indicating good discriminant validity (see Table 3).

4.2. Common Method Variance Test

Data on multiple variables were self-reported, which may lead to common method variance. Therefore, this study was statistically tested for homoscedasticity error through the Harman single-factor test. An exploratory factor analysis of all items showed that the first factor accounted for 46.066% of all explanatory variables, which was less than 50% of the critical value standard. Therefore, the common method was well-controlled in this study.

4.3. Hypothesis Test

The SEM was conducted using the maximum likelihood estimation in AMOS 24.0. The number of observed variables in this study was 33, and the effective sample size was 716, which met the requirement that the ratio of observed variables to sample size was at least 1:10. The fit indicators of the structural equation model met the requirements (χ2 = 967.066, df = 363, χ2/df = 2.662, RMSEA = 0.048, GFI = 0.911, AGFI = 0.894, NFI = 0.943, CFI = 0.963). The results of the conceptual model estimation are shown in Table 4.
The results of the analysis provided a preliminary understanding of the relationships between variables in the research model. Next, the research hypotheses were tested in SPSS 24.0 using the causal step method. Controls of consumers’ gender, age, monthly income, shopping frequency of live farming assistance, and live online shopping experience were included in the model to improve the reliability of results. Before conducting the regression, the variables were tested for multicollinearity and serial correlation. The results showed that the maximum value of the VIF indicator was 2.285 and the maximum value of DW was 2.054, indicating that there was no serious multicollinearity or serial correlation, and regression analysis could be performed. The results show that gender has a non-significant effect on consumer purchase intention (β = −0.031, p > 0.05), age (β = −0.121, p < 0.01) and monthly income (β = −0.120, p < 0.01) have a significant negative effect on consumer purchase intention, and the shopping frequency of live farming assistance (β = 0.088, p < 0.05) and live online shopping experience (β = 0.100, p < 0.01) have a significant positive effect on consumer purchase intention. Comparing M1 and M2 (as shown in Table 5), the explanatory power of the model was significantly improved after three dimensions of group effect were added to the model (ΔR2= 0.455, p < 0.001). Therefore, the three dimensions of group effect had significant positive impacts on consumer purchase intention (β1 = 0.337, p < 0.001; β2 = 0.238, p < 0.001; β3 = 0.245, p < 0.001), so hypotheses H1a, H1b, and H1c were all verified.
The mediating effects were conducted in three steps: ⅰ. whether the independent variables can significantly predict the dependent variables; ⅱ. Whether the independent variables can significantly predict the intervening variables; and ⅲ. after including the intervening variables, can the independent variables significantly predict the dependent variables. At this point, if the influence of the independent variables on the dependent variables is no longer significant, it is a full mediation; if the coefficient of the independent variable’s influence on the dependent variable is obviously smaller, but it is still significant, it is a partial mediation. By this causal steps approach, the mediating effect of experience value was tested (Table 5). Consistent with our hypotheses H1a, H1b, and H1c, the informational effect (β1 = 0.337, p < 0.001), normative effect (β2 = 0.238, p < 0.001), and emotional effect (β3 = 0.245, p < 0.001) were positively related to consumer purchase intention. M9 and M10 showed that the informational effect, normative effect, and emotional effect had significant positive effects on economic value and social value, respectively, satisfying step ⅱ. Comparing M2 and M3, it was found that after including the economic value, the three dimensions of group effect had a smaller but still significant effects on purchase intention (β decreased from 0.337, 0.238, and 0.245 to 0.276, 0.155, and 0.211, respectively). Similarly, comparing M2 and M4, it was found that after including the social value, the three dimensions of group effect had a smaller but significant effect on purchase intention (β decreased from 0.337, 0.238, and 0.245 to 0.322, 0.155, and 0.222 respectively). According to step iii, it was concluded that either economic value or social value played a partial intervening role in the relationship between group effect and purchase intention. Thus, H2a and H2b were verified.
The causal steps approach was also applied to test the moderating effect of tie strength (see Table 5). Based on M2, the explanatory power of the model was significantly improved by including the tie strength as an independent variable to form M5 (ΔR2 = 0.004, p < 0.05). Based on M5, the interaction item of informational effect and tie strength after centering was added to form M6, which further improved the explanatory power of the model (ΔR2 = 0.01, p < 0.001), and as expected, the relationship between informational effect and consumer purchase intention was stronger when tie strength was low (β = −0.115, p < 0.001). Based on M5, the interaction item of normative effect and tie strength after centering was added to form M7, which significantly improved the explanatory power of the model (ΔR2 = 0.009, p < 0.001), and tie strength was proved to moderate the relationship between normative effect and purchase intention (β = −0.106, p < 0.001). Based on M5, the interaction item of emotional effect and tie strength after centering was added to form M8, which significantly improved the explanatory power of the model (ΔR2 = 0.028, p < 0.001), and tie strength significantly negatively moderated the relationship between emotional effect and purchase intention (β = −0.190, p < 0.001). Thus, H3a, H3b, and H3c were supported.

5. Discussion

Consistent with reference group influence theory and emotional contagion theory, the current paper analyzes and tests the influence of group effect on consumer purchase intention in live farming assistance through empirical methods. The research findings confirm all the hypotheses formulated in the paper (see Table 6).
First, the group effect in live farming assistance includes three categories: informational effect, normative effect, and emotional effect. The personalized interactive content, rich interactive forms, and instant interactive responses in live farming assistance make it possible for consumers to obtain information on farming assistance in group interactions, while their interactive behaviors are also influenced by group norms. Consumers in the live farming assistance room can form common emotions with group members via emotional infection. This suggests that some features of live e-commerce summarized by previous research can be reflected in the context of live farming assistance, such as live-streaming not only provides rich information to help consumers make purchase decisions [10,11] but also constructs a social arena for virtual coexistence and real interaction with others [16,17], creating a shared context and shared emotions between live-streams [23,54].
Second, the group effect has a direct and positive effect on consumers’ willingness to purchase assisted agricultural products. Among the three dimensions of the group effect, the informational effect plays the most important role, and it is a good answer to the question of how to overcome the uncertainty caused by the non-standard and hidden quality of agricultural products in the purchasing process [20]. In addition, the influence of group emotional effect on purchase intention should not be underestimated. This finding is a further extension of the influence of online celebrities on consumer emotions in live e-commerce [54], demonstrating emotional contagion and its applicability in the live farming assistance scenario [25], and corroborating the importance that e-commerce live businesses place on the cultivation of anchor discourse and atmosphere in their actual operations. Finally, the normative effect has a positive impact on consumers’ willingness to purchase. This finding extends the group normative effect from traditional online shopping [51] to the interactive social domain of live-streaming.
Third, the group effect impacts consumers’ willingness to purchase assisted agricultural products through the mediating role of experience value. Group effect not only helps consumers reduce the time, energy, and cognition costs in the purchase decision process but also improves the efficiency of the purchase decision. It can also help consumers enhance group identity, shape a good social image, etc., so that consumers can gain economic and social value in the process of live shopping, and thus, increase their willingness to purchase assisted agricultural products. This finding is of academic significance as it provides an empirical test of the economic and social benefits of live farming [55], which is still at a theoretical level at this stage.
Fourth, the positive relationship between group effect and consumer purchase intention is moderated by tie strength. When the consumer holds a higher tie strength with the group within the live-streaming room, the consumer feels more familiar with the group interactive experience of live farming assistance, and the response to the group interactive experience gradually diminishes. This finding validates the adaptive theory of perception [41].
Finally, age and monthly income have a significant negative effect on consumer purchase intention, and the shopping frequency of live farming assistance and the live online shopping experience have a significant positive effect on consumer purchase intention. Social cognitive theory suggests that behavior is determined by the individual, the stimulus, and the interaction between the two [56]. Therefore, consumers with different characteristics will show different purchase intentions in live farming assistance. On the one hand, one of the major advantages of live farming assistance is the provision of low-priced agricultural products, and consumers who are younger and have lower monthly incomes are less economically capable and more price-sensitive, and will have a stronger purchase intention. On the other hand, consumers who are experienced in live online shopping and have a high shopping frequency of live farming assistance have a long time of contact with e-commerce and are more likely to reach an understanding of and form trust in live farming assistance, and, therefore, may show a higher purchase intention.

6. Conclusions

6.1. Theoretical Contribution

The existing research on live farming assistance mainly analyzes its value and optimization paths from the perspective of “sellers” and mostly stays at the theoretical level. From the perspective of the “purchaser”, this paper takes consumers, who play a decisive role in the conversion rate of live farming assistance, as the research object, combines the characteristics of live farming assistance, and analyzes the role of “the reference group effect” in consumers’ purchase intentions with the empirical method. The experience value and tie strength are introduced in the research model to better explore the underlying influence mechanism of group effect on consumer purchase intention and deepen the research content of live farming assistance.
In addition, current research on live e-commerce either ignores the interaction between consumers and between consumers and anchors, or fails to really pay attention to the group effect brought about by live broadcasting as an interactive field of social e-commerce. This paper provides a new research perspective of group effect for the study of live broadcast e-commerce, which broadens the breadth of existing research.
Ultimately, in studying the influence of others in the group in the consumer purchase decision process, this paper focuses on the phenomenon of emotional infection within the group and proposes that the influence of group effect should include informational, normative effects, and emotional effects. This paper further compares the concepts and formation mechanisms of the three while constructing a model of the role of reference group effects on consumers’ purchase intentions in live farming assistance to study the spillover effect of the three. The emotional effect proposed in this paper in conjunction with emotional contagion theory is a refinement of the reference group influence theory [24,25], which compensates to a certain extent for the limitation of the reference group theory at the level of unconscious influence.

6.2. Research Enlightenment

This study has the following implications for e-commerce enterprises to achieve sustainable good operations in live farming assistance.
First, continuously innovate interactions to help deliver group information, norms, and emotions. Enterprises should not only cultivate the persuasive power of anchors but also their ability to shape the group atmosphere through interactive strategies. Specifically, the anchor, as the dominant person in the live broadcast room, should lead the live-streaming team to stimulate consumers to participate in the interaction via material rewards and spiritual incentives, and to enhance the personalization and interactive responsiveness of the interactive content by answering questions and mentioning customers’ nicknames in a timely and targeted manner during the interaction process. The anchors are also suggested to create the group interaction scene and atmosphere of live farming assistance through interactive strategies, such as multi-angle shooting and scene shaping to enrich the form of interaction. Furthermore, they should continuously improve the social functions of the live room with timed draws, red packets, coupons, and other activities to fully mobilize consumers to participate in the interactive enthusiasm, create a relaxed and pleasant atmosphere in the live room, improve the sense of consumer participation and sense of presence, and increase stickiness.
Secondly, satisfy customers’ economic demands, assume corporate social responsibility, and improve consumer experience value. Operators of live farming assistance should provide agricultural products that are rich in variety, traceable in origin, of good quality and taste, and relatively inexpensive, with beautiful live scenarios and distinctive live content, in order to improve the efficiency with which consumers deal with that purchasing decision and to enable them to experience rewards in terms of economic utility in terms of money, time, and effort. In addition, efforts should be made to expand the scope of social responsibility activities and improve the quality of social responsibility as far as possible. At the same time, with the advantage of social media, the special events should be emphasized, so that the image of live farming assistance can be deeply rooted in people’s minds, further deepening the degree of consumer recognition of live broadcasting in effectively promoting the sale of agricultural products and helping farmers to increase their income, and enhancing consumers’ goodwill towards live farming assistance.
Third, identify consumer needs and characteristics and carry out differentiated marketing. On the one hand, the preliminary prediction of the tie strength between consumers and anchors and co-watchers, fully grasp the needs and preferences of different consumer groups, targeted use of the influence of the reference group, and effectively increase the consumer purchase intention in live farming assistance. On the other hand, continuously innovate the interaction mode in the live room to reduce the perceived adaptability of old customers to group interaction. Just as offline retail stores have to enhance consumers’ attention through the rearrangement of goods, the novel interaction method of the live-streaming room helps to enhance the involvement of regular customers in the live interaction process and increase the group effect on regular customers’ purchase intentions due to the adaptation.

6.3. Limitations and Future Research

Although the current research makes multiple contributions to the literature, it is not without limitations. First, different types of anchors may affect the group effect of the interaction field. This study is also limited in not considering the differential effects of different types of anchors, such as celebrities, farmers, and government officials, on the group effect generated by the interaction, which can be further explored in the future. Second, the different susceptibility of consumers to group effects may lead to variability in the impact of group effects on consumer purchase intention, which can be further explored in the future by incorporating consumer susceptibility into the research model.

Author Contributions

G.L.: writing—review and editing, project administration, funding acquisition; L.C.: conceptualization, formal analysis, data curation, writing—original draft preparation, analyzed and interpreted the data; G.Z.: supervision, paper modification. All authors have read and agreed to the published version of the manuscript.

Funding

General Projects of the National Social Science Foundation of China “Research on the Path and Countermeasures of Digital Technology Empowering Enterprise Urban Poverty Alleviation Innovation” (20BGL008).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the privacy of the interviewees.

Acknowledgments

The authors would like to thank the National Social Science Foundation of China for the financial support of the general project “Digital Technology Enabling Enterprise Innovation for Urban Poverty Alleviation and Countermeasures”.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Guo, H.D.; Qu, J. A study on the sustainability of direct broadcasting with goods to help farmers. People’s Trib. 2020, 20, 74–76. [Google Scholar]
  2. Li, X.X.; Zhao, X.F. Live farming assistance: A new model of rural e-commerce for the integrated development of rural revitalization and network poverty alleviation. J. Commer. Econ. 2020, 19, 131–134. [Google Scholar]
  3. Dai, J.P.; Luo, W.P. The key path to enhance user stickiness in the context of agricultural live broadcasting. China Bus. Mark. 2022, 36, 30–41. [Google Scholar]
  4. Park, H.J.; Lin, L.M. The Effects of Match-ups on the Consumer Attitudes toward Internet Celebrities and Their Live Streaming Contents in the Context of Product Endorsement. J. Retail. Consum. Serv. 2020, 52, 101934. [Google Scholar] [CrossRef]
  5. Jia, K. The Influence of Perceived Trust and Time Moderator on the Purchase Intention of Consumers in the Context of E-Commerce Livestreaming. In Proceedings of the WHICEB 2021 Proceedings, Wuhan, China, 5–7 August 2021; Available online: https://aisel.aisnet.org/whiceb2021/53 (accessed on 25 June 2023).
  6. He, Y.; Li, W.; Xue, J. What and how driving consumer engagement and purchase intention in officer live streaming? A two-factor theory perspective. Electron. Commer. Res. Appl. 2022, 56, 101223. [Google Scholar] [CrossRef]
  7. Xu, X.; Wu, J.; Li, Q. What Drives Consumer Shopping Behavior in Live Streaming Commerce? J. Electron. Commer. Res. 2020, 21, 144–167. [Google Scholar]
  8. Sun, K.; Liu, L.C.; Liu, C.L. Impulsive Purchase Intention of Live Streaming E-commerce Consumers from the Perspective of Emotion. China Bus. Mark. 2022, 36, 33–42. [Google Scholar] [CrossRef]
  9. Fan, Y.J.; Liu, Q. Bullet Screen Interaction, Online Commodity Display and Consumers’ Impulsive Purchasing Behavior-mediated by Presence and Flow Experience. J. Harbin Univ. Commer. (Soc. Sci. Ed.) 2022, 3, 78–89. [Google Scholar]
  10. Fei, M.; Tan, H.; Peng, X.; Wang, Q.; Wang, L. Promoting or Attenuating? An Eye-tracking Study on the Role of Social Cues in E-commerce Livestreaming. Decis. Support Syst. 2021, 142, 113466. [Google Scholar] [CrossRef]
  11. Zhang, M.; Sun, L.; Qin, F.; Wang, G.A. E-service quality on live streaming platforms: Swift guanxi perspective. J. Serv. Mark. 2020, 35, 312–324. [Google Scholar] [CrossRef]
  12. Gong, X.; Jiang, X. Understanding Consumer Impulse Buying in Livestreaming Commerce: The Product Involvement Perspective. Front. Psychol. 2023, 14, 1030. [Google Scholar] [CrossRef] [PubMed]
  13. Lyu, B. How is the Purchase Intention of Consumers Affected in the Environment of E-commerce Live Streaming? In Proceedings of the 2021 International Conference on Financial Management and Economic Transition (FMET 2021), Guangzhou, China, 27–29 August 2021; Atlantis Press: Amsterdam, The Netherlands, 2021; pp. 50–59. [Google Scholar]
  14. Liang, L.; Yuan, L.Y.; Xie, L.H. Empirical Evidence of Purchase Behavior Mechanism of Live-streaming Bandwagon Users Based on ABC Attitude Theory. Soft Sci. 2022, 12, 118–126. [Google Scholar] [CrossRef]
  15. Hovland, C.I.; Weiss, W. The Influence of Source Credibility on Communication Effectiveness. Public Opin. Q. 1951, 15, 635–650. [Google Scholar] [CrossRef]
  16. Sun, Y.; Shao, X.; Li, X.T. How Live Streaming Influences Purchase Intentions in Social Commerce: An IT Affordance Perspective. Electron. Commer. Res. Appl. 2019, 37, 100886. [Google Scholar] [CrossRef]
  17. Yu, C.; Cheah, J.-H.; Liu, Y. To Stream or not to Stream? Exploring Factors Influencing Impulsive Consumption through Gastronomy Livestreaming. Int. J. Contemp. Hosp. Manag. 2022, 34, 3394–3416. [Google Scholar] [CrossRef]
  18. Zeng, Y.W.; Ma, C.J.; Li, L.L. Livestreaming E-commerce and Agricultural Product upward Value Reconstruction: Mechanism and Realization Path. Issues Agric. Econ. 2022, 2, 108–117. [Google Scholar] [CrossRef]
  19. Xiong, X.; Zhu, C.X.; Zhu, H.B. Formation Mechanism of Consumer Trust in Livestreaming of Agricultural E-commerce: From the Perspective of Intermediary Capability. J. Nanjing Agric. Univ. (Soc. Sci. Ed.) 2021, 21, 142–154. [Google Scholar] [CrossRef]
  20. Liang, W.Z.; Hou, Y.X.; Ge, R. Analysis on Characteristics of Online Shopping Agricultural Products in China. Issues Agric. Econ. 2012, 33, 40–43. [Google Scholar]
  21. Han, X.Y.; Xu, Z.L. The influence of e-commerce anchor attributes on consumers’ willingness to purchase online—A study based on the rooting theory approach. Foreign Econ. Manag. 2020, 10, 62–75. [Google Scholar]
  22. Zhu, G.W.; Hou, M.L.; Zhou, L. An investigation of the impact of social e-commerce interaction styles on consumer advertising attitudes. Soft Sci. 2020, 34, 122–127. [Google Scholar]
  23. Sun, Z.H.; Zhu, R.N. Convergent Communication and Interactive Ceremony: CCTV news Live with Goods Mode Exploration. TV Res. 2020, 10, 52–54. [Google Scholar]
  24. Bearden, W.O.; Netemeyer, R.G.; Teel, J.E. Measurement of Consumer Susceptibility to Interpersonal Influence. J. Consum. Res. 1989, 15, 473–481. [Google Scholar] [CrossRef]
  25. Hatfield, E.; Cacioppo, J.T.; Rapson, R.L. Emotional Contagion. Curr. Dir. Psychol. Sci. 1993, 2, 96–100. [Google Scholar] [CrossRef]
  26. Bearden, W.O.; Etzel, M.J. Reference Group Influence on Product and Brand Purchase Decision. J. Consum. Res. 1982, 9, 183–194. [Google Scholar] [CrossRef]
  27. Li, X.Y. Practical Path and Value Analysis of Public Welfare Livestreaming to Help Farmers—A Case Study of Three Central Media Cooperating with Taobao Livestreaming to Bring Goods for Public Welfare. Media 2021, 5, 37–38+40. [Google Scholar]
  28. Shi, M.; Liu, C.L.; Shi, K.; Liu, J.Z. A Study of Emotional Infection Pathways Influencing Online Collective Behavioral Intentions-Based on an Emotion-Information Theoretical Perspective. J. Intell. 2018, 37, 103–109+121. [Google Scholar]
  29. Holbrook, M.B. Introduction to Consumer Value, Consumer Value: A Framework for Analysis and Research; Psychology Press: London, UK, 1999; pp. 1–28. [Google Scholar]
  30. Mathwick, C.; Malhotra, N.; Rigdon, E. Experiential Value: Conceptualization, Measurement and Application in the Catalog and Internet Shopping Environment. J. Retail. 2001, 77, 39–56. [Google Scholar] [CrossRef]
  31. Holbrook, M.B. Consumption Experience, Customer Value, and Subjective Personal Introspection: An Illustrative Photographic Essay. J. Bus. Res. 2006, 59, 714–725. [Google Scholar] [CrossRef]
  32. Dodds, W.B.; Monroe, K.B.; Grewal, D. Effects of Price, Brand, and Store Information on Buyers’ Product Evaluations. J. Mark. Res. 1991, 28, 307–319. [Google Scholar] [CrossRef]
  33. Loebnitz, N.; Grunert, K.G. The Impact of Abnormally Shaped Vegetables on Consumers’ Risk Perception. Food Qual. Prefer. 2018, 63, 80–87. [Google Scholar] [CrossRef]
  34. Rothenbusch, S.; Zettler, I.; Voss, T.; Lösch, T.; Trautwein, U. Exploring reference group effects on teachers’ nominations of gifted students. J. Educ. Psychol. 2016, 108, 883. [Google Scholar] [CrossRef]
  35. Hwang, Y.; Lee, K.C. Investigating the Moderating Role of Uncertainty Avoidance Cultural Values on Multidimensional Online Trust. Inf. Manag. 2012, 49, 171–176. [Google Scholar] [CrossRef]
  36. Schwarz, N.; Clore, G.L. Mood, Misattribution, and Judgments of Well-being: Informative and Directive Functions of Affective States. J. Personal. Soc. Psychol. 1983, 45, 513. [Google Scholar] [CrossRef]
  37. Chiang, C.H. Experiential Value Affects Purchase Intentions for Online-to-offline Goods: Consumer Feedback as a Mediator. J. Mark. Manag. 2018, 6, 10–24. [Google Scholar] [CrossRef]
  38. Hwang, A.H.C.; Oh, J.; Scheinbaum, A.C. Interactive Music for Multisensory E-commerce: The Moderating Role of Online Consumer Involvement in Experiential Value, Cognitive Value, and Purchase Intention. Psychol. Mark. 2020, 37, 1031–1056. [Google Scholar] [CrossRef]
  39. Ketelaar, P.E.; Janssen, L.; Vergeer, M.; van Reijmersdal, E.A.; Crutzen, R.; van’t Riet, J. The success of viral ads: Social and attitudinal predictors of consumer pass-on behavior on social network sites. J. Bus. Res. 2016, 69, 2603–2613. [Google Scholar] [CrossRef]
  40. Perry-Smith, J.E. Social Network Ties beyond Nonredundancy: An Experimental Investigation of the Effect of Knowledge Content and Tie Strength on Creativity. J. Appl. Psychol. 2014, 99, 831–846. [Google Scholar] [CrossRef]
  41. Solomon, M.R. Consumer Behavior, Buying, Having, and Being; China Renmin University Press: Beijing, China, 2018; p. 66. [Google Scholar]
  42. Xu, Y.; Chen, M.H. Study on the factors influencing the purchase of agricultural products in the consumer online channel. Rural Econ. 2019, 10, 122–128. [Google Scholar]
  43. Han, D.; Mu, J.; Song, L. Research on the factors influencing consumers’ willingness to purchase fresh produce online—An empirical analysis based on the UTAUT model. Dongyue Trib. 2018, 4, 91–101. [Google Scholar] [CrossRef]
  44. Xu, Y.; Zheng, F.Y.; Chen, W.P. The effects of technology availability and anchor characteristics on consumers’ willingness to purchase agricultural products. Rural Econ. 2021, 11, 104–113. [Google Scholar]
  45. Doherty, R.W.; Orimoto, L.; Singelis, T.M.; Hatfield, E.; Hebb, J. Emotional contagion: Gender and occupational differences. Psychol. Women Q. 1995, 19, 355–371. [Google Scholar] [CrossRef]
  46. Rintamäki, T.; Kanto, A.; Kuusela, H.; Spence, M.T. Decomposing the value of department store shopping into utilitarian, hedonic and social dimensions: Evidence from Finland. Int. J. Retail Distrib. Manag. 2006, 34, 6–24. [Google Scholar] [CrossRef]
  47. Sweeney, J.C.; Soutar, G.N. Consumer Perceived Value: The Development of a Multiple Item Scale. J. Retail. 2001, 77, 203–220. [Google Scholar] [CrossRef]
  48. Bansal, H.S.; Voyer, P.A. Word-of-mouth Processes within a Services Purchase Decision Context. J. Serv. Res. 2000, 3, 166–177. [Google Scholar] [CrossRef]
  49. Liu, F.J.; Meng, L.; Chen, S.Y.; Duan, K. A study on the influence of Netflix live streaming on consumers’ purchase intention and its mechanism. Chin. J. Manag. 2020, 17, 94–104. [Google Scholar]
  50. Zhou, Y.S.; Tang, S.H.; Xiao, J. A study of consumers’ purchase intention on live e-commerce platform—Based on social presence perspective. Contemp. Econ. Manag. 2021, 1, 40–47. [Google Scholar] [CrossRef]
  51. Zhao, X.F.; Gao, Q.A. Research on the factors influencing the willingness to purchase agricultural products online and the mechanism of their effect—Analysis based on the perspective of reference effect. J. Beijing Technol. Bus. Univ. (Soc. Sci.) 2016, 3, 42–53. [Google Scholar] [CrossRef]
  52. Chen, H.Q.; Zhang, Y.; Guo, W.Q. Influence of Netflix Traits on Fans’ Purchase Intentions in Live Streaming Platforms. China Bus. Mark. 2020, 10, 28–37. [Google Scholar] [CrossRef]
  53. Gong, X.X.; Ye, Z.L.; Wu, Y.P.; Liu, J.Y. Study on the mechanism of influence of live scene atmosphere cues on consumers’ intention to consume impulsively. Chin. J. Manag. 2019, 6, 875–882. [Google Scholar]
  54. Meng, L.M.; Duan, S.; Zhao, Y.; Lü, K.; Chen, S. The impact of online celebrity in livestreaming E-commerce on purchase intention from the perspective of emotional contagion. J. Retail. Consum. Serv. 2021, 63, 102733. [Google Scholar] [CrossRef]
  55. Zan, M.Y.; Wang, Z.B. Live agricultural e-commerce: A new model of e-commerce to alleviate poverty. Issues Agric. Econ. 2020, 11, 77–86. [Google Scholar] [CrossRef]
  56. Bandura, A. Self–efficacy: Toward a unifying theory of behavioral change. Psychol. Rev. 1977, 84, 191–215. [Google Scholar] [CrossRef] [PubMed]
Sustainability 15 12741 g001
Figure 1. Research model.
Figure 1. Research model.
Sustainability 15 12741 g001
Table 1. Descriptive statistics of table samples (N = 716).
Table 1. Descriptive statistics of table samples (N = 716).
Demographic VariablesClassifyPercentage
GenderMan44.70%
Woman55.30%
Monthly incomeCNY 5000 and below16.50%
CNY 501–10,00030.70%
CNY 10,001–15,00026.10%
CNY 1001–20,00014.10%
More than CNY 200012.60%
Shopping frequency of live farming assistanceLess11.60%
3–4 times a year35.90%
3–4 times a month31.00%
3–4 times a week13.40%
More8.10%
AgeUnder 18 years old3.50%
18–26 years old12.70%
27–39 years old50.00%
40–49 years old20.70%
50–59 years old8.10%
60 years old and above5.00%
Live online shopping experienceLess than 1 year (inclusive)12.70%
1–2 years (excluding 2 years)28.90%
2–3 years (excluding 3 years)21.90%
3–4 years (excluding 4 years)15.10%
4–5 years (excluding 5 years)14.20%
More than 5 years (inclusive)7.20%
Table 2. Results of reliability and validity test.
Table 2. Results of reliability and validity test.
VariableMeasurement ItemStandardized LoadAVECR
Informational
effect
a = 0.878		If I’m not familiar with a certain product, I’ll ask for information about the product in the live farming assistance.0.7920.6460.879
When I want to buy the best product from similar products, I will ask in the live farming assistance.0.835
Before buying the product, I will learn about all aspects of the product in the live farming assistance.0.825
In order to ensure that I can buy the products I want, I will observe what the people in the live farming assistance have bought.0.760
Normative
effect
a = 0.932		People in the live farming assistance can see whether I make a purchase or not through the screen, so I will buy the product.0.8790.6690.934
It’s important for me that people in the live farming assistance like me to buy products in the live farming assistance.0.866
People in the live farming assistance recognize the purchase of agricultural products, so I will buy products in the live farming assistance.0.831
I want to know if others have a good impression of the people who buy products in the live farming assistance.0.784
I want to get a sense of belonging by buying products in the live farming assistance like the people in the live farming assistance.0.754
If I want to be the same person as the people in the live farming assistance room, I will try to buy agricultural products through live farming assistance.0.795
I want to identify myself with others by purchasing products in the live farming assistance.0.807
Emotional
effect
a = 0.936		When the anchor smiles warmly at me, I will smile back and feel warm inside.0.8670.7460.936
When I am in the live farming assistance in a cheerful atmosphere, my heart will be filled with happiness.0.845
I also feel sad when the anchor talks about the tragic experience of farmers.0.871
If the anchor cries, I’ll have tears in my eyes.0.871
I can very sensitively capture the emotional changes of the anchor and other audiences.0.864
Economic value
a = 0.907		It can save some money to buy agricultural products in the live farming assistance.0.8020.6230.908
I can buy cheap agricultural products in the live farming assistance.0.803
The agricultural products in the live farming assistance are cheaper than those in other places.0.686
I can buy the agricultural products I need in the live farming assistance.0.827
There will be no chaotic queues or other delays in the live farming assistance.0.779
It is very convenient to buy agricultural products in the live farming assistance room.0.828
Social value
a = 0.926		Live farming assistance makes me feel that I will be accepted.0.8760.7580.926
Live farming assistance will improve others’ perception of me.0.886
Live farming assistance will help me make a good impression on others.0.873
Live farming assistance will make me recognized by the society.0.847
Purchase intention
a = 0.873		I am willing to buy agricultural products in the live farming assistance.0.8600.7020.876
I have a good chance to buy agricultural products in the live farming assistance.0.858
I will recommend the live farming assistance to others.0.794
Tie strength
a = 0.900		I have a close connection with the people in the live farming assistance.0.8280.6960.901
I may share my secret with the people in the live farming assistance.0.847
I may provide daily help to the people in the live farming assistance.0.812
It’s possible for me to spend my free time with the people in the live farming assistance.0.849
Note: a means the Cronbach’s a coefficient.
Table 3. Discriminant validity results.
Table 3. Discriminant validity results.
VariableInformational
Effect		Normative EffectEmotional EffectEconomic ValueSocial ValuePurchase IntentionTie
Strength
Informational effect0.804
Normative effect0.574 ***0.818
Emotional effect0.642 ***0.537 ***0.864
Economic value0.641 ***0.651 ***0.562 ***0.789
Social value0.493 ***0.699 ***0.489 ***0.664 ***0.871
Purchase intention0.705 ***0.601 ***0.632 ***0.663 ***0.556 ***0.838
Tie strength0.393 ***0.650 ***0.411 ***0.596 ***0.679 ***0.433 ***0.834
Note: *** means p < 0.001. The diagonal is the square root of the mean variance extraction (AVE), and the font is bold.
Table 4. Conceptual model estimation results.
Table 4. Conceptual model estimation results.
PathEstimateS.E.C.R.
Economic value←Informational effect0.326 ***0.0496.944
Social value←Informational effect0.091 *0.0572.003
Economic value←Normative effect0.398 ***0.0389.667
Social value←Normative effect0.593 ***0.04913.656
Economic value←Emotional effect0.141 ***0.0363.344
Social value←Emotional effect0.114 **0.0432.702
Purchase intention←Informational effect0.344 ***0.0526.967
Purchase intention←Normative effect0.109 *0.0492.125
Purchase intention←Emotional effect0.199 ***0.0364.738
Purchase intention←Economic value0.210 ***0.0484.442
Purchase intention←Social value0.076 +0.0381.708
Note: * means p < 0.05, ** means p < 0.01, *** means p < 0.001, and + means significant at the 0.1 level.
Table 5. Results of regression analysis.
Table 5. Results of regression analysis.
VariablePurchase IntentionEconomic ValueSocial Value
M1M2M3M4M5M6M7M8M9M10
Gender−0.031−0.017−0.022−0.015−0.013−0.021−0.019−0.0260.024−0.011
Age−0.121 **−0.058 *−0.070 **−0.065 *−0.070 *−0.065 *−0.068 *−0.073 **0.0510.041
Monthly income−0.120 **−0.033−0.028−0.043−0.034−0.032−0.031−0.028−0.020.060 *
Shopping frequency of live farming assistance0.088 *0.0170.0160.0080.0030.0170.0180.0230.0040.053
Live online shopping experience0.100 **0.105 ***0.094 ***0.099 ***0.108 ***0.111 ***0.110 ***0.115 ***0.0510.040
Informational effect 0.337 ***0.276 ***0.322 ***0.334 ***0.300 ***0.325 ***0.273 ***0.280 ***0.098 **
Normative effect 0.238 ***0.155 ***0.155 ***0.199 ***0.191 ***0.154 ***0.193 ***0.379 ***0.521 ***
Emotional effect 0.245 ***0.211 ***0.222 ***0.236 ***0.211 ***0.235 ***0.209 ***0.157 ***0.144 ***
Economic value 0.218 ***
Social value 0.159 ***
Tie strength 0.082 *0.083 *0.079 *0.068 *
Informational effect × Tie strength −0.115 ***
Normative effect × Tie strength −0.106 ***
Emotional effect × Tie strength −0.190 ***
R20.0480.5030.5280.5160.5060.5160.5150.5340.4740.463
ΔR20.0480.4550.4800.4680.0040.0100.0090.0280.4500.425
∆F7.136 ***215.476 ***179.327 ***170.851 ***5.547 *14.243 ***12.583 ***41.758 ***201.681 ***186.300 ***
Note: the coefficient is standardized, and R is an unadjusted value. * means p < 0.05, ** means p < 0.01, *** means p < 0.001.
Table 6. Summary of hypothesis test results.
Table 6. Summary of hypothesis test results.
NumberHypothesisResults
H1aThe informational effect has a positive impact on consumer purchase intention.Supported
H1bThe normative effect has a positive impact on consumer purchase intention.Supported
H1cThe emotional effect has a positive impact on consumer purchase intention.Supported
H2aEconomic value mediates the relationship between group effect and consumer purchase intention.Supported
H2bSocial value mediates the relationship between group effect and consumer purchase intention.Supported
H3aTie strength moderates the relationship between informational influence and purchase intention, such that this relationship is stronger when tie strength is low as opposed to high.Supported
H3bTie strength moderates the relationship between normative influence and purchase intention, such that this relationship is stronger when tie strength is low as opposed to high.Supported
H3cTie strength moderates the relationship between emotional influence and purchase intention, such that this relationship is stronger when tie strength is low as opposed to high.Supported
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Li, G.; Chang, L.; Zhang, G. Increasing Consumers’ Purchase Intentions for the Sustainability of Live Farming Assistance: A Group Impact Perspective. Sustainability 2023, 15, 12741. https://doi.org/10.3390/su151712741

AMA Style

Li G, Chang L, Zhang G. Increasing Consumers’ Purchase Intentions for the Sustainability of Live Farming Assistance: A Group Impact Perspective. Sustainability. 2023; 15(17):12741. https://doi.org/10.3390/su151712741

Chicago/Turabian Style

Li, Guangming, Liting Chang, and Guiqing Zhang. 2023. "Increasing Consumers’ Purchase Intentions for the Sustainability of Live Farming Assistance: A Group Impact Perspective" Sustainability 15, no. 17: 12741. https://doi.org/10.3390/su151712741

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