Promoting Sustainable Farmers’ E-Commerce Sales Participation in Tibet: A fsQCA Analysis of Behavioral Drivers

Promoting Sustainable Farmers’ E-Commerce Sales Participation in Tibet: A fsQCA Analysis of Behavioral Drivers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Promoting Sustainable Farmers’ E-Commerce Sales Participation in Tibet: A fsQCA Analysis of Behavioral Drivers Aiyan Xu, xiu qu, xin xin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6201308/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Rural e-commerce, as a new engine for rural development in the digital economy era, is highly expected to promote economic growth and sustainable development. Based on the reality of "high willingness but low action" in rural e-commerce sales participation in Tibet, this study integrates the intrinsic motivational drivers of behavior from the Theory of Planned Behavior with the external resource endowment constraints from the Factor Endowment Theory, constructing an analytical framework of "behavioral motivation-resource endowment" influencing factors. Utilizing survey data from the Livelihood Development in Tibetan Agricultural and Pastoral Areas (XLDR, 2023) and employing the fuzzy-set Qualitative Comparative Analysis (fsQCA) method, this study identifies the conditional configurations that influence the translation of rural e-commerce sales intentions into actual behaviors. The research findings indicate that a single condition does not constitute a necessary condition for the consistency between intentions and behaviors. In the sufficiency analysis of conditional configurations, three primary paths are identified, which can be further refined into two models: the "subjective norm-natural endowment dual-drive model" and the "capital endowment multi-drive model." This study overcomes the limitations of previous literature that only considered the impact of single variables, systematically proposes multiple causal paths of antecedent configurations for enhancing rural e-commerce sales participation, and thus provides new insights for improving rural e-commerce sales participation rates. Social science/Development studies Social science/Economics Social science/Social policy rural e-commerce fsQCA Theory of Planned Behavior Factor Endowment Theory Figures Figure 1 Figure 2 1. Introduction and Literature Review Rural e-commerce has emerged as a new engine for rural economic development in the digital economy era, holding significant potential for advancing agricultural modernization and achieving sustainable development goals. Developed countries like the United States rely on sophisticated cold chain logistics systems for rural e-commerce to achieve a loss rate of less than 2% for crops from field to table (Li X et al,2023) In developing countries, Brazil, Russia, and China, among others, view rural e-commerce as a means of promoting inclusive and sustainable economic growth (Karine H,2021). Academic research indicates that rural e-commerce can narrow the urban-rural income gap, exerting greater economic effects in remote and underdeveloped areas as well as for low-income households, thereby bridging regional development disparities to some extent (Guan X et al,2024). The inclusive growth in income facilitated by rural e-commerce is also reflected in rural women's entrepreneurship, particularly benefiting those with lower education levels (Dong S et al,2024; Li Y and Zhou L,2023). Furthermore, it possesses certain ecological value by increasing the proportion of low-carbon industries, enhancing total factor productivity in agriculture, and improving transport efficiency (Ji K et al,2023). As a development model that combines economic inclusivity and environmental friendliness, rural e-commerce is of great significance for promoting sustainable economic and social development. However, infrastructure gaps and low levels of human capital remain common challenges. India's "Digital India" initiative has significantly increased rural internet coverage, but linguistic diversity limits platform usability, and limited physical and human capital restrict digital usage among rural Indian residents (Basu K,2016). Flores (2017) conducted a cross-generational study of 16 families across three generations in U.S.-Mexico border cities and found that low education levels and lack of language skills exacerbate digital inequality (Flores M Á et al,2017). Research on China revealed that the development capacity of rural e-commerce in China significantly improved from 2011 to 2022, but there is marked polarization among provinces, with eastern and central provinces leading the way, while western and peripheral provinces lag behind (Wang L et al,2024). Regional imbalances in the development of the digital economy may weaken its inclusivity, with developed regions leveraging their economic advantages to accelerate the formation of digital technology monopolies (Yao and Liu,2024), while western regions like Tibet face a "catch-up dilemma." In 2024, Tibet's rural e-commerce achieved an online retail sales volume of RMB 4.24 billion, representing a year-on-year growth of 32.73%. However, a horizontal comparison showed that none of Tibet's counties were among the top 50 counties with the fastest growth in agricultural product sales in the "2024 Taobao Harvest Festival Report." This disparity indicates that, although Tibet's rural e-commerce has achieved stage-specific results, it is characterized by "high growth but a low base," and there is an urgent need to explore effective pathways to transition from policy-driven to endogenous growth. The development of rural e-commerce in Tibet faces not only common shortcomings such as low levels of human capital and insufficient proficiency in the national common language but also realistic challenges like high altitude, fragile ecological environments, vast territory, and sparse population. In 2020, data from a nationwide survey of rural households initiated by the Rural Development Institute of the Chinese Academy of Social Sciences showed that the deviation ratio between the willingness to sell via e-commerce and actual behavior was approximately 77%, indicating a significant gap between intention and actual action (Zhang Y and Zhang Y,2024). Similarly, a survey conducted by our research team in agricultural and pastoral areas across seven cities in Tibet (XLDR, 2023) revealed that the participation rate of rural households in e-commerce sales is generally low, with only 33% of those with high willingness converting into actual behavior. The issue of "high willingness but low action" is prominent, and Tibet urgently needs to adopt effective measures to increase the participation rate in rural e-commerce sales. Research on rural e-commerce has primarily focused on developing countries and regions. In recent years, with the rapid development of rural e-commerce in China, Chinese scholars have discussed it from various perspectives, concentrating mainly on its economic benefits, ecological benefits, and the narrowing of regional disparities. Most studies have explored the mechanisms from a macro perspective. For instance, Li et al. (2024) found that rural e-commerce directly affects farmers' revenue by promoting their market integration and indirectly by expanding affiliated industries that can foster employment growth(Li W and He W,2024). Additionally, rural e-commerce promotes farmers' income growth by enhancing information access, reducing operational costs, and increasing financial support (Qiu H et al,2024). Rural e-commerce also enhances farmers' awareness of green production, reduces the use of fertilizers in agricultural production, and plays a significant role in promoting the green transformation of farmers and agricultural production (Wang C et al,2024). The academic exploration of rural e-commerce mechanisms provides a research direction for this study. However, existing research has not adequately addressed the reality of differentiated development in latecomer regions. The relatively low level of e-commerce development in Tibet indicates that the region is still in the initial stage of e-commerce transformation. In this development context, research on latecomer e-commerce regions such as Tibet should shift its focus to identifying the core factors that influence the participation behavior of stakeholders. This not only aligns with the practical needs of regional economic digital transformation but also fills an academic gap in the study of e-commerce development under special geographical conditions in theory. Research on the factors influencing participation in rural e-commerce predominantly employs traditional linear regression analysis, supplemented by configurational analysis. Zhang et al. (2016) used field survey data from fruit farmers in major cherry-producing areas and applied iterative GMM and ordered Probit methods to analyze the data. The findings revealed that the planting scale of farmers and the convenience of logistics positively influence farmers' participation in rural e-commerce sales (Zhang Y et al,2016). Ma et al. (2017) based on the perspectives of farmers' endowments and regional environment, utilized a binary Logistic regression model to find that different resource endowments have heterogeneous effects on farmers' participation in rural e-commerce for poverty alleviation. Education level is positively correlated with participation, while income level is negatively correlated (Ma Z et al,2017). Xiang et al. (2019), from the perspective of livelihood risk perception, constructed a multi-group structural equation model to investigate the intergenerational differences and influencing factors of farmers' participation in e-commerce for poverty alleviation. The study found that human capital has a significant impact on farmers' participation in rural e-commerce. Additionally, factors such as livelihood risk perception and rural e-commerce policies also have a positive influence (Xiang L et al,2019). In recent years, Huang et al. (2021) based on the entrepreneurial event model, conducted an online survey of rural e-commerce practitioners from Jieyang and Chaozhou in Guangdong Province, China, and used a structural equation model for their research. The results showed that professional knowledge and resource endowment have a significant positive impact on Base of the Pyramid (BoP) entrepreneurship in rural e-commerce (Huang L et al,2021). Liu et al. (2021) performed regression analysis based on rural household data collected in 2019 from Shandong, Henan, and Shaanxi provinces in China and found that education level and social capital have significant influences on farmers' adoption of e-commerce (Liu M et al,2021). Luo et al. (2022), based on the Theory of Reasoned Action and the extended Technology Acceptance Model, used a structural equation model to analyze and found that perceived usefulness and government support promote the use of e-commerce platforms (Luo S et al,2022). Ye et al. (2023) based on the Theory of Planned Behavior and the entrepreneurial event-related theory, used a structural equation model to investigate and found that entrepreneurial attitude, subjective norm, and perceived behavioral control are positively correlated with farmers' use of digital technology for inclusive entrepreneurship (Ye R and Zhou D,2023). Luo et al. (2024) using models such as Oprobit, found that digital literacy positively influences farmers' willingness to participate in e-commerce, and e-commerce cognitive literacy has a significant positive impact on the willingness of two generations of farmers to participate in e-commerce (Luo L et al,2024). In general, the aforementioned studies lack empirical analysis addressing the phenomenon of the "intention-behavior" gap. Recently, although literature has acknowledged the distinction between "intention" and "behavior," recognizing that there is a gap between the two and they cannot be treated equivalently, most research methods rely primarily on linear regression, neglecting the fundamental characteristic of "multiple concurrent causes" underlying behavior. For example, Wang et al.(2021) and Xue et al. (2023) systematically examined the factors influencing the "intention-behavior" gap in farmers' participation in agricultural product e-commerce operations by constructing Logistic regression models (Wang X et al,2021;Xue F and Xia C,2023). Zhang et al. (2024), based on data from the 2020 China Rural Revitalization Survey, used a probit model to find that digital literacy can mitigate the intention-behavior gap in farmers' participation in rural e-commerce sales (Zhang Y and Zhang Y,2024). In terms of configurational analysis, few scholars have approached the topic from a micro perspective. Some scholars, taking a macro perspective and using 30 Chinese provinces as samples, analyzed the driving paths of the rural e-commerce entrepreneurial ecosystem based on the CAS (Complex Adaptive System) theory and the TOE (Technological, Organizational, and Environmental) framework (Huang L et al,2024). Configurational analysis has advantages over traditional linear regression in handling multiple concurrent causal relationships and conditional combination effects, particularly excelling in exploring pathways in complex environments. It has been extensively applied in studies on the factors influencing entrepreneurship in rural areas, which are closely related to the present study. Kimmitt J et al. (2020), based on an fsQCA (fuzzy-set qualitative comparative analysis) of changes in life circumstances of 166 farm households in rural Kenya, explored how different combinations of conversion factors enable distinct forms of entrepreneurship in the pursuit of prosperity (Kimmitt J et al,2020). Zhang et al. (2024), from a configurational perspective, used the fsQCA method with 85 typical rural innovation and entrepreneurship demonstration counties in China as research samples to explore the impact path of the rural entrepreneurship ecosystem composed of multiple factors on entrepreneurial performance and the complex causal mechanisms behind it (Zhang X et al,2024). Huang et al. (2023), using the entrepreneurial ecosystem theory as the study framework and the fsQCA method, analyzed the shaping of entrepreneurial opportunity (EO) (Huang L et al,2023). The above studies provide relevant support for the methodological choice of this research. The objective of this study is to identify the combinations of driving factors that facilitate the transition from "intention" to "behavior" in rural e-commerce sales in Tibet, addressing the challenge of "intention without action" in rural e-commerce, and providing relevant insights for countries and regions facing similar issues, thereby promoting rural economic growth and sustainable development. In summary, the innovations of this study are as follows: Firstly, it focuses on the mechanism of sustainable behavior transformation in plateau border regions, filling the research gap in the "intention-behavior" domain. Existing research predominantly concentrates on central and eastern China, neglecting the unique decision-making processes of farmers in ethnic border areas. This study targets Tibetan farmers and herdsmen, utilizing survey data on the livelihood development in Tibetan agricultural and pastoral areas (XLDR, 2023), to address the paradox of "high intention-low behavior" in rural e-commerce sales, thereby bridging the geographical gap in sustainable e-commerce behavior research. Secondly, it reveals sustainable driving pathways through the synergy of multiple factors, innovating the application scenarios of methodology. Current literature on the micro-level factors influencing smallholders' participation in rural e-commerce typically employs linear regression to quantify the "net effect" of individual factors, making it difficult to capture the complex causal mechanisms in unevenly developed regions. This study introduces fuzzy-set qualitative comparative analysis (fsQCA) to analyze the influencing factors of smallholders' participation in rural e-commerce sales from a configurational perspective, identifying equivalent sustainable transformation pathways and promoting a paradigm shift in rural e-commerce research from "whether it works" to "how it works." Thirdly, it constructs a dual-drive framework of "behavioral motivation-resource endowment" by integrating the Theory of Planned Behavior and the Factor Endowment Theory, transcending the traditional one-dimensional analysis paradigm. This framework not only focuses on the internal drivers of behavior generation but also considers external constraints, enriching the theoretical application of factors influencing rural e-commerce participation behavior. The rest of this paper is organized as follows. The second section introduces the research analytical framework. The three section discusses the fsQCA research method. The four section presents the fsQCA analysis process and empirical results. Finally, the five section provides conclusions, limitations, and directions for future research. 2. Framework for analysis In the existing analytical framework for examining the factors influencing the "intention-behavior gap," most scholars have centered their research around the Theory of Planned Behavior (Wang X et al,2021;Cao H et al,2024;Chen H and Mu Y,2024). However, this study posits that there is room for expansion. The Theory of Planned Behavior, which evolved from the Rational Behavior Theory proposed by Fishbein (1963), emphasizes the central role of behavioral intention in behavioral decision-making (Duan W et al,2008). According to this theory, behavioral attitude, subjective norm, and perceived behavioral control constitute a three-dimensional driving mechanism for behavioral intention. These components are both independent and interrelated, sharing a common belief basis, and form a classic framework for explaining individual behavior. It is noteworthy that Sheppard's (1988) meta-analysis indicated that the Theory of Planned Behavior has limited explanatory power for actual behavior, suggesting the need to introduce external contextual variables to enhance the theory's explanatory power (Sheppard B H et al,1988). Loch (1996) also argued that relying solely on the Theory of Planned Behavior to explain individual behavior is inadequate (Loch K D and Conger S,1996). This theoretical gap provides a logical foundation for the introduction of the Factor Endowment Theory. Originally developed by Heckscher and Ohlin in the field of international trade to explain the reasons for trade between two countries, the Factor Endowment Theory posits that differences in factor endowments lead to variations in productivity among countries (Tsai W and Ghoshal S,1998). It represents an extension and development of comparative advantage and is predominantly studied at the macro level. Some scholars, based on this theory, have categorized factor endowments into natural capital, physical capital, human capital, economic capital, and social capital for micro-level research (Liu Ke et al, 2019 ; Zhang Y et al,2015;Zhang T et al,2017; Zhang F et al,2020).The relationship between factor endowments and behavior can be summarized as follows: differences in factor endowments directly influence farmers' production behavior (Zhang T et al,2017; Jamison D T and Gaag J V D,1987; Qiu H et al,2012) and factor endowments can serve as external incentives, driving individuals to act or make specific choices in particular directions (Liu J et al,2024). Farmers may choose to abandon behaviors if their own endowments do not meet the requirements for engaging in those behaviors (Liu K et al,2019). Variations in factor endowments also affect farmers' production attitudes and, consequently, their agricultural production behavior (He Yue,2019). The availability of resources such as individual economic and human capital reflects behavioral control (Ajzen I,1991). Therefore, it can be seen that there is an interactive relationship between the Theory of Planned Behavior and the Factor Endowment Theory, satisfying the presupposition that the influencing factors in QCA should be interrelated. By integrating the intrinsic behavioral motivation driving conditions of the Theory of Planned Behavior with the extrinsic resource endowment constraints of the Factor Endowment Theory, this study constructs a configurational analysis framework of "behavioral motivation-factor endowment" influencing factors to explain smallholders' participation in e-commerce sales. The specific framework is illustrated in Fig. 1 below. 2.1. Theory of Planned Behavior Specifically, the three core dimensions of the Theory of Planned Behavior exhibit unique connotations in the context of e-commerce participation. Behavioral attitudes refer to an individual's tendency to perform a specific behavior, representing the actor's mental disposition towards engaging in a particular action. It is generally regarded as a positive factor and the most effective variable for predicting individual behavior. The more positive an actor's attitude towards a certain action, the stronger their intention to take action. Generally speaking, if farmers have a positive evaluation of e-commerce, their willingness to participate in it will be stronger; conversely, if farmers perceive risks and uncertainties associated with e-commerce, their willingness to participate will diminish. Subjective norms refers to the extent to which others, outside of the actor, influence the actor's behavior, specifically referring to the impact of others' expectations on the actor (Patrick S,1991). Due to the strong connectivity of rural social networks, the policy mobilization by village officials, the demonstration effect of neighbors, and the opinion tendencies of relatives form a composite pressure field. This "ripple effect" of group cognition significantly influences individuals' conformity tendencies in decision-making, and subjective norm has an amplifying effect in rural social contexts. Perceived behavioural control is equivalent to self-efficacy. When an individual is performing a certain behavior, their perception of the ease or difficulty of the behavior directly affects whether the behavior occurs. It reflects the individual's judgment, based on past experiences, of the resources they can control to engage in the behavior. In the context of e-commerce participation, perceived behavioral control is a self-assessment of the relevant knowledge and skills required for e-commerce behavior. The stronger farmers' perceived usefulness and ease of use of e-commerce, the stronger their willingness to participate in e-commerce sales, and they are more willing to take action, leading to a greater consistency between intention and behavior (Wang X et al,2021). 2.2. Factor Endowment Theory Resource endowment, comprising resources and capabilities innate or acquired by family members, is the most direct factor constraining individuals' behavioral decisions (Zhang C et al,2016). Agriculture is a sector highly dependent on natural conditions, and superior natural capital provides favorable conditions for large-scale operations. Factors such as land size and geographical location determine the e-commerce suitability of product supply. For example, the spatial agglomeration advantage of specialty agricultural product production areas can foster e-commerce economies of scale, enhance farmers' long-term expectations, and make them more likely to choose rural e-commerce for online sales (Zhang N et al,2023). Physical capital primarily refers to the infrastructure on which farmers rely to participate in e-commerce. Road facilities and network coverage are the foundations for farmers' engagement in rural e-commerce, and mobile phone ownership is a necessary element for conducting e-commerce (Lin H et al,2019). The continuous improvement of rural transportation infrastructure enables rural logistics to provide a range of services including packaging, warehousing, and delivery (Zhou D et al,2019). Internet infrastructure development is a crucial driver of rural e-commerce growth, offering farmers more possibilities for internet use. Rao (2008) argues that government investment in network infrastructure significantly supports e-commerce development (Rao S S,2008). Human capital mainly encompasses the quantity and quality of human capital (Schultz T W,1961). The number of family labor represents the quantity of human capital (Zhang T et al,2017), while the quality of human capital refers to farmers' knowledge reserves and abilities. Educational experience within human capital has a direct impact on entrepreneurial success (Wang S et al,2010). Farmers with higher education levels have broader perspectives and are more receptive to new technologies. Proficiency in Chinese is also crucial for farmers' participation in rural e-commerce, as language skills are an important form of human capital. For ethnic minority practitioners, a lack of language skills can hinder entry into the mainstream market (Pisani M J et al,2017). Generally, health status is an important aspect of measuring human capital quality; however, e-commerce employment opportunities break the physical limitations for people with disabilities and represent a new direction for their future employment (Liao J,2015). Therefore, in this study, human capital quality does not include health status but is measured primarily by the number of family labor, education level, and proficiency in listening, speaking, reading, and writing the national language. Economic capital primarily refers to the economic capacity required for farmers to participate in rural e-commerce, reflecting farmers' financial strength, economic status, and ability to invest in new technologies and cope with new risks (Chen Y and Zhao M ,2024). The higher the income proportion, the greater the likelihood of farmers participating in industrial projects (Tian Y and Zhang W,2018). Farmers with higher economic capital have lower trial-and-error costs and stronger financial support to promote e-commerce operations, making them more inclined to participate in rural e-commerce. Social capital refers to farmers' relationship networks and mobilizable resources (Zhu Q et al,2020). As the first recipients of policies, village officials have a high degree of identification with and expectation for policies and closely follow national policy guidance to internalize rural e-commerce into actual production and sales. The social networks and reciprocal norms formed between small farmers and other social entities (Yang Y and Shi Z,2012), facilitate agricultural production cooperation through organized diffusion, reducing farmers' transaction costs for technology adoption and enabling resource integration to empower small farmers to connect with the e-commerce market. Cooperatives have strong negotiation capabilities, coordinate market service dilemmas in the process of e-commerce development, and help small farmers address the blindness and disorder in connecting with the e-commerce market on a small scale (Zhang J and Xi Y,2019;Gan Y,2022). Therefore, the higher the social capital endowment, the easier it is to participate in rural e-commerce. 3. Research methodology 3.1. Methodology: fsQCA Research on influencing factors often employs a single-factor analysis approach. However, in reality, the transition from intention to behavior in rural e-commerce sales is typically the result of collaborative evolution, interaction, and interlinked development among multiple factors influencing each other. Fuzzy Set Qualitative Comparative Analysis (fsQCA) combines quantitative and qualitative research methods, offering advantages in analyzing pathways to enhance rural e-commerce participation behavior. It comprehensively considers the relationships between multiple antecedent conditions and outcome variables, making it suitable for exploring how various factors jointly influence the occurrence of behavior. As the range of sample data gradually increases, fsQCA is widely applied in small and medium-sized sample studies. Moreover, it can convert any data into a 0–1 membership score, thereby imposing lower requirements on data (Chi M et al,2021). Based on this, the present study adopts the fsQCA research method and utilizes survey data on the livelihood development in Tibetan agricultural and pastoral areas (XLDR, 2023) to identify the configurational conditions that influence the transformation of rural e-commerce sales intentions into actual behaviors. 3.2. Variable measurement and data sources 3.2.1. Outcome variable This study aims to explore the pathways for enhancing rural e-commerce sales participation behavior, specifically by improving the consistency between farmers' "intention to participate" and their "actual behavior" in e-commerce. Drawing on relevant research (Chang Q et al,2021;Yan B et al,2024), define inconsistency in rural e-commerce participation intention and behavior as the scenario where "farmers have the intention to participate in rural e-commerce but do not engage in actual behavior." Based on this, samples from farmers without strong participation intentions were excluded, retaining only those who responded with "strongly agree" to the question, "If circumstances permit, would you engage in online sales?" The rationale behind this is that the focus of this study is on enhancing the e-commerce participation behavior of farmers with strong intentions to engage in rural e-commerce sales. The issue with farmers lacking strong participation intentions lies in cultivating their willingness, which is not the core scope of this study. According to the sample data, 68 samples demonstrated consistency between rural e-commerce sales behavior and intention, accounting for only 33% of the total research sample. Among these, Chamda had the highest proportion at 16.43%, while Lhoka had the lowest. The specific details are shown in Fig. 2 below. Therefore, it is necessary to explore pathways for enhancing the consistency between intention and behavior. 3.2.2. Conditional Variables (1) Behavioural attitudes: individuals showing positive or negative evaluations of a certain behaviour affects the positive and negative feelings of individuals when they participate in the action (Song Y et al., 2023). When farmers hold a positive evaluation of rural e-commerce, the stronger the active participation behaviour. ‘Safety and Reliability Approval’ and ‘Improvement in Quality of Life Approval’ were chosen to characterize behavioural attitudes. (2) Subjective norms: Individuals tend to adopt the norms and behavioural patterns of the group in order to maintain consistency with the group, and subjective norms are measured by the degree of social support (Chen M et al., 2024). The questions ‘Is there an e-commerce service station in this village to help support villagers to sell online’ and ‘Do you agree with the statement “Many people around you are selling online”’ were used to characterise the degree of subjective norms. (3) Perceived behavioural control: High self-efficacy improves individuals' persistence in the face of challenges and makes them more likely to overcome obstacles and complete tasks successfully (Deng Z et al., 2022). Perceived behavioural control is characterized by ‘Do you think online selling is easy’ and ‘Recognition of training related to participation in online selling’. (4) Natural endowment: As an indispensable resource element for farmers‘ production, natural capital is the basis for farmers’ survival, and ‘the area of agricultural land (including pasture, arable land, and forest land)’ was chosen to measure natural capital. (5) Physical capital: Internet infrastructure and mobile terminal equipment are the most basic hardware facilities for e-commerce development, providing a platform for farmers to participate in rural e-commerce, providing comprehensive and high-quality information, narrowing the information gap, and cracking the ‘access gap’ (Tian L et al., 2024). Road infrastructure is the foundation of efficient and fast logistics system in rural areas, based on this, this study originally set ‘network infrastructure’, ‘road infrastructure’, ‘with or without communication equipment’ to characterise the physical capital. Physical capital, but in reality, the sample farmers have network infrastructure and communication devices (mobile phones), so only ‘road infrastructure’ is left to characterise physical capital. (6) Human capital: The level of human capital mainly consists of quantity and quality, with the quantity of human capital mainly referring to the number of labourers in the household, and the quality of human capital is generally characterised by the level of education (Chen M et al., 2024), and the national common language, as a widely-used linguistic tool, plays an important role in the rural areas and ethnic minority-populated regions. The human capital is characterised by the education level of farmers, the number of labourers in the household, and the level of proficiency in listening, speaking, reading and writing in the national language. (7) Economic capital: economic capital mainly refers to the economic capacity needed in the production and management of farming households (Zhang T et al., 2017), and is characterised by ‘the level of household income (including agricultural and animal husbandry income, non-agricultural and animal husbandry, and subsidy income)’. (8) Social capital: social capital is an individual's possession of social resources with livelihood value (Cao T and Zou W, 2022), using ‘whether there is a co-operative in the village’ and ‘whether he/she is a village cadre or holds a position in a township or a higher-level administrative unit’. Finally, the scores of each component were measured using the entropy method, and the definitions of the variables and descriptive statistics are shown in Table 1 . Table 1 Variable definitions and descriptive statistics Variable Type Variable Name Variable Description Variable Assignment Mean Standard Deviation Outcome variable ‘Willingness-behaviour’ agreement 1 = no bias (willingness and actual participation in rural e-commerce) 0 = Bias exists (willing but not involved in rural e-commerce) Do you have any experience in online selling? Yes = 1; No = 2 If conditions permit, would you sell online? Strongly disagree = 1; Quite disagree = 2. Average = 3; More agree = 4; Strongly agree = 5 0.3285 0.4708 Conditional variable Behavioural attitudes Safety and Reliability Approval Do you agree with the statement "Rural e-commerce is safe and reliable"? Strongly disagree = 1; Quite disagree = 2. Average = 3; More agree = 4; Strongly agree = 5 4.0386 0.9646 Improvement in Quality of Life Approval Do you agree with the statement "Participating in rural e-commerce can improve the quality of life"? Strongly disagree = 1; Quite disagree = 2. Average = 3; More agree = 4; Strongly agree = 5 3.7005 1.1563 Subjective norms Degree of social support Is there an e-commerce service station in this village to help support villagers to sell online? Yes = 2; No = 1 1.1932 0.3958 Degree of online sales by people around you Do you agree with the statement ‘Many people around you are selling online’? Strongly disagree = 1; Quite disagree = 2. Average = 3; More agree = 4; Strongly agree = 5 3.9420 1.1768 Perceived behavioural control Internet selling is easy Approval Do you think online selling is simple? Strongly disagree = 1; Quite disagree = 2. Average = 3; More agree = 4; Strongly agree = 5 3.8986 1.2045 Degree of participation in training related to online sales Would you like to participate in training related to online sales? Strongly disagree = 1; Quite disagree = 2. Average = 3; More agree = 4; Strongly agree = 5 3.8357 1.2433 Natural endowment Agricultural land area Area of pasture/cropland/woodland ________ acres. 1344.4720 2820.5097 Physical capital Road infrastructure What is the type of road connecting this village to the township? No road connectivity = 1; levelled unpaved gravel/dirt road = 2; concrete road = 3; tarmac road = 4 3.4396 0.6932 Human capital Number of labour force Obtained by summing the number of household workers 3.0290 1.9827 Highest level of education Other = 0; No schooling or temple study = 1; No schooling but temple study = 2; Primary school = 3; Junior high school = 4; Ordinary high school = 5; Secondary school, vocational high school, technical school = 6; University college = 7; Undergraduate college = 8 2.3623 1.3435 Listening and speaking skills in the national common language Degree of mastery of listening and speaking skills in national common language Very unskilled = 1; Quite unskilled = 2; Fair = 3; Quite skilled = 4; Very skilled = 5 2.5942 1.3328 Literacy in national languages Degree of proficiency in reading and writing the national common language Very unskilled = 1; Quite unskilled = 2; Fair = 3; Quite skilled = 4; Very skilled = 5 2.2947 1.2248 Economic capital Income level Sum of farming, non-farming and subsidised income 54856.7600 45404.1121 Social capital Whether a village cadre Have you served as a village cadre or in a township or higher level administrative unit? Never served = 1; Served in the past, not now = 2; Current village cadre = 3 1.4300 0.7398 Existence of co-operative societies Is there a co-operative in this village? Yes = 2; No = 1 1.7101 0.4548 3.2.3. Data source The data of this study comes from the survey data of livelihood development in Xizangan agricultural and pastoral areas (XLDR, 2023), the group conducted a face-to-face household questionnaire survey on Xizangan farmers and herdsmen from December 2023 to January 2024, and the data samples cover 20 villages in 7 prefectures and municipalities in the whole region, including 8 agricultural areas, 7 pastoral areas, and 5 semi-agricultural and semi-pastoral areas. The questionnaire content mainly includes supervisory questionnaire and household questionnaire, in order to ensure the questionnaire's scientific and rationality, firstly, pre-survey was conducted in the villages around Lhasa city, and then the questionnaire was modified and perfected in response to the pre-survey situation, and due to the difference in minority languages, Tibetan students from the same region were recruited in the whole school for training, and after eliminating the samples that do not meet the focus of the present study, 207 valid questionnaires were obtained. 3.2.4. Data calibration Combined with previous studies, this study calibrated the data, and the condition variables were calibrated using the direct calibration method, which set the upper quartile, mean, and lower quartile of the sample data to three anchors, namely, not at all affiliated, intersection, and fully affiliated, respectively, and the outcome variable was a 0–1 variable, which did not need to be calibrated. 4. fsQCA analysis process 4.1. Necessary conditions analysis Before conducting the fsQCA (fuzzy set qualitative comparative analysis) configuration analysis, it is necessary to perform a necessity analysis on individual conditions to identify whether a single condition is a necessary condition for the occurrence of the outcome. If the consistency level is above 0.9, then a particular condition variable is considered a necessary condition for the occurrence of the outcome variable (Schneider C Q and Wagemann C,2012). The necessity analysis reveals that the consistency levels of all condition variables are below 0.9, as shown in Table 2 . This indicates that the condition variables do not constitute necessary conditions for the outcome when considered individually. Therefore, a configuration analysis is required. Table 2 Necessity analysis Coherence of will and behaviour Consistency Coverage Behavioural attitude 0.478489 0.253951 ~ Behavioural attitude 0.52163 0.449944 Subjective norms 0.259391 0.450056 ~ Subjective norms 0.740641 0.450056 Perceived Behavioural Control 0.39777 0.211292 ~ Perceived Behavioural Control 0.602321 0.518822 Natural endowment 0.695209 0.333713 ~ Natural endowment 0.304995 0.317622 Physical capital 0.164762 0.376296 ~ Physical capital 0.835327 0.320584 Human capital 0.448597 0.364831 ~ Human capital 0.551562 0.304076 Economic capital 0.278109 0.35696 ~ Economic capital 0.721968 0.318836 Social capital 0.365377 0.436283 ~ Social capital 0.63478 0.287749 4.2 Sufficiency analysis of conditional configurations Conditional grouping sufficiency analysis is a core part of QCA analysis by analysing different combinations of conditional variables affecting the outcome generation. The judgement criterion is that the consistency level of sufficiency is greater than or equal to 0.75. Based on previous studies and the specifics of this study, in constructing the truth table, the consistency threshold was chosen to be 0.8, the PRI threshold was 0.7, and given that the sample was a larger one, the frequency of cases was set to 2 (Zeng F and Chen Y,2024). The study covered 207 case samples, and due to inter-individual differences, it was not clear how the antecedent condition acted on the outcome variable, so the direction was not preset in the counterfactual operation step, and the conditional variable was set to ‘presence or absence’(Zhang F,2023). The complex solution does not simplify the result, the parsimonious solution contains only the core conditions, and the intermediate solution contains both the core conditions and presents the edge conditions. Therefore, the study searched for appropriate grouping paths with the intermediate solution as the main one and the parsimonious solution as the secondary one, and the results are shown in Table 3 .The overall consistency of the three groupings is 0.81, which is higher than 0.75, indicating that the overall consistency has a high explanatory strength, and that the three groupings are a sufficient condition for the generation of congruence between willingness and behaviours. Further to verify the robustness of the results, the consistency threshold was increased to 0.85 and the PRI threshold was increased to 0.75, which produced the three groupings of groupings of states that were basically consistent, and therefore this study has good robustness, and the results are shown in Table 3 . Table 3 shows that natural endowment exists in each of the three enhancement paths of rural e-commerce willingness and behavioural consistency, indicating that natural capital endowment is the basic condition for the development of rural e-commerce, an important factor in the formation of brand advantages, and an important support for promoting the sustainable development of rural e-commerce. Rural e-commerce is dominated by agricultural e-commerce, which is highly dependent on the natural environment. According to the basic law of agricultural development, the scale of agricultural operation is positively correlated with agricultural economic efficiency, that is, through the scale effect, reduce production costs and improve production efficiency (Zhu T and Xia Y,2022), good natural capital endowment is conducive to enhancing the long-term expectations of farmers, so that farmers use more powerful agricultural production and management methods. (1) The dual-drive model combining subjective norms and natural endowments presents two configurations where subjective norms serve as the core condition, while natural capital functions as a peripheral condition. These configurations enhance the consistency between willingness and actual participation in e-commerce sales. This model suggests that when subjective norms reach a high level, further enhancing natural capital endowments can boost participation in e-commerce sales behaviors.Rural areas are static societies characterized by collectivism, where individual behaviors are easily influenced by others' thoughts and attitudes. Farmers' participation in e-commerce is more susceptible to the influence of other farmers in the village community, as well as incentives or constraints from government and social forces (Wang M and Cao X,2008). When the external environment supports e-commerce participation, it has a positive impact on farmers' involvement in e-commerce. The No. 1 Central Document of 2020 explicitly emphasizes strengthening the construction of village-level e-commerce service stations to facilitate the two-way flow of products between urban and rural areas. The Tibet Autonomous Region Department of Commerce issued the "Work Guidelines for Comprehensive Demonstration of E-commerce in Rural Tibet," stating that "the construction of rural e-commerce service stations must cover more than 30% of the total administrative villages in demonstration counties." The government encourages farmers to participate in rural e-commerce and provides them with a high-quality service environment.A typical example of this configuration is found in Markam County, Changdu City. Markam County is abundant in natural capital, with diverse products such as Sodoxi chili sauce, Yanjing wine, and wild mushrooms. The richness of natural capital contributes to the diversity of rural e-commerce products. In 2020, Tibet Post established a rural e-commerce service center in Markam County, allowing farmers and herdsmen to enjoy high-quality e-commerce services within the village. The incentive support from the external environment enhances rural farmers' participation in e-commerce sales.In summary, this dual-drive model highlights the importance of both subjective norms and natural endowments in promoting farmers' participation in e-commerce. When these two conditions are met, especially with strong external support, farmers are more likely to engage in e-commerce activities, thereby contributing to the development of rural e-commerce. (2) Capital endowment diversification driven.The multi-drive model based on diverse capital endowments is represented by Configuration 3, which indicates that a combination of high human capital and economic capital, complemented by natural and social capital as peripheral conditions, can enhance the consistency between willingness and actual participation in rural e-commerce. This configuration suggests that individuals with high levels of human and economic capital, when supported by natural and social capital, are more likely to engage in rural e-commerce activities.High-capital-endowed groups are more capable of converting digital technology into their own benefits (Chen H and Xie K,2024). Households with abundant human capital are more likely to improve the efficiency of e-commerce collaboration and possess stronger risk resistance. The quality of human capital directly affects farmers' adoption of digital technology, with farmers leveraging the internet and mobile payments to create a driving effect for rural e-commerce (Su Q and Xing H,2025). Human capital also influences farmers' subjective initiative; high human capital enhances farmers' vigilance towards e-commerce entrepreneurship, increases the probability of opportunity recognition, and reduces the risk of blindly participating in e-commerce startups (Zeng Y et al,2019). In ethnic minority areas, human capital also includes proficiency in the national common language. Groups with high proficiency in foreign languages are more likely to obtain information from official institutions or other formal channels (Underhill E et al,2019), while farmers with poor national common language skills struggle to learn relevant skills from online social media, reducing the effectiveness of information (Zhang W et al,2022).Economic capital, such as startup funds, is a critical factor affecting farmers' entrepreneurial activities, including rural e-commerce. It provides financial security for farmers in the early stages of rural e-commerce. The more abundant the economic capital, the wider the budget line, the greater the decision-making space for investment and consumption, and the more generous the startup funds willing to be invested in rural e-commerce. Social capital serves as a primary channel for accessing external information and resources, significantly improving the availability of entrepreneurial resources and facilitating successful entrepreneurial activities (Zhang Q et al,2022).Taking Nyemo County in Lhasa as an example, Nyemo County is a county dominated by agriculture with a combination of agriculture and animal husbandry. It is rich in agricultural and animal husbandry resources and is one of the important agricultural production areas in Lhasa. In recent years, Nyemo County has held multiple special conferences on increasing farmers' and herdsmen's income to effectively improve their economic capital. According to statistical bulletins, the per capita disposable income of farmers and herdsmen in 2023 was 22,403 yuan, far exceeding the regional average. Nyemo County has leveraged cooperatives to actively organize rural e-commerce training, enhancing farmers' and herdsmen's human capital. Training has been conducted for different groups, including rural e-commerce practitioners, entrepreneurs, staff of agriculture-related enterprises, and farmers and herdsmen, covering e-commerce policies, theories, operations, and practical skills, with a cumulative impact on employment and entrepreneurship for about 30 people. It is evident that Nyemo County has adopted a multi-pronged approach to enhance participation in rural e-commerce sales through multiple dimensions. 5. Conclusions and policy recommendations The digital economy, as a catalyst for sustainable development, offers innovative pathways for economic growth in underdeveloped regions. In Tibet, the digital divide intertwines with the constraints of high-altitude environments. E-commerce platforms not only bridge the market access for small-scale farmers but also further narrow the digital divide. This study further responds to the call for research on how digital tools advance Sustainable Development Goals (SDGs) in underdeveloped regions. The primary research content involves utilizing survey data on people's livelihood development in Tibet to empirically analyze, from the micro-perspective of small-scale farmers, the pathways for enhancing farmers' e-commerce sales behavior and facilitating the transition from "behavioral intention" to "behavioral realization." By integrating the internal behavioral motivation drivers from the Theory of Planned Behavior (TPB) with the external resource endowment constraints from the Factor Endowment Theory, this study constructs an analytical framework for the configuration of "behavioral motivation-resource endowment" influencing factors. The research indicator system is built based on the three elements of the TPB and the five elements of resource endowment. Empirical research is conducted using the fsQCA (fuzzy-set Qualitative Comparative Analysis) method, and the robustness of the research results is demonstrated through threshold-raising tests. The findings are as follows: Firstly, the necessity analysis of fsQCA shows that among the eight antecedent conditions encompassed by the TPB and the Factor Endowment Theory, no single variable constitutes a necessary condition for the consistency between intention and behavior. This conclusion illustrates the complex causal mechanism of participation in rural e-commerce sales behavior in the context of the digital economy, where a single policy stimulus is unlikely to produce systematic effects. Secondly, through configuration analysis, three equivalent pathways are identified, which can be aggregated into two typical driving modes. One is the "norm-endowment dual-drive mode," with subjective norms as the core condition and natural capital as the auxiliary condition, indicating that when the demonstration effect and regional resource advantages are coupled, the behavioral transformation threshold can be effectively crossed. The other is the "capital endowment multi-drive mode," with human capital (including proficiency in listening, speaking, reading, and writing the national common language) and economic capital as the core, and natural capital and social capital as marginal conditions, which can enhance the transformation of rural e-commerce sales behavior. Based on the above conclusions, this paper proposes the following policy recommendations: 1. Enhance the drive for natural endowment development. Given the ubiquity of natural endowments across all configurations, it is recommended to implement the "Digitalization Project for Distinctive Resources of the Qinghai-Tibet Plateau". Firstly, strengthen investments to improve agricultural infrastructure such as irrigation systems, roads, and drainage facilities to enhance agricultural production conditions; promote land circulation and strengthen the provision of agricultural socialized services to achieve large-scale agricultural operations. Secondly, establish a special fund for geographical indication certification of distinctive agricultural products in Tibetan areas and build a blockchain traceability system for products such as cordyceps sinensis and Tibetan medicine. Finally, introduce the "enclave economy" model, leveraging the East-West collaboration mechanism to deeply integrate data resources from coastal e-commerce platforms with physical resources in Tibetan areas. 2. Strengthen the hierarchical cultivation mechanism for human capital. Firstly, implement a "three-tier" e-commerce training program, with the basic tier focusing on national common language and mobile payment skills, potentially through the development of an AI assistant for Tibetan-Chinese bilingual e-commerce platforms to reduce language barriers. The advanced tier should train participants in short video marketing and live streaming sales techniques, while the elite tier should cultivate data analysis and supply chain management capabilities. Secondly, establish a "digital entrepreneur" certification system and incorporate e-commerce skills training into the certification standards for new-type professional farmers. Finally, promote a "village official + local influencer" pairing mechanism, with publicly selected e-commerce mentors stationed in villages for guidance each year. 3. Innovate social capital activation models. To overcome the marginalization of social capital, a trinity development model of "cooperative + platform + finance" can be established, along with the implementation of the "Thousand Villages, Thousand Influencers" plan, to incubate Tibetan e-commerce IPs with cultural identity and leverage platforms such as Douyin and Kuaishou to create "Roof of the World Live Streaming Rooms". Finally, establish an e-commerce credit community in Tibetan areas to amplify the financial empowerment effect of social networks. 4. Improve the economic capital supply system. Addressing the capital constraints faced by capital endowment-driven pathways, a "Snowy Land E-commerce Growth Enterprise Market" can be created to provide interest-free loans to startup projects with appropriately extended repayment periods. Additionally, an East-West e-commerce revenue-sharing mechanism can be established to guide eastern enterprises to enjoy tax incentives for the Western Development Strategy when setting up "cloud warehousing" centers in Tibetan areas. Finally, this study also has two limitations that urgently need to be addressed: Firstly, the cross-sectional data characteristics limit the dynamic testing of causality. With the long-term tracking survey of Tibetan agricultural and pastoral areas conducted by the research team, dynamic QCA methods will be adopted in the future to capture the evolution of configurations. Secondly, digital technology is reconstructing social network forms, and future research will continuously incorporate virtual community influence indices to construct a subjective norm measurement system that integrates online and offline dimensions. Declarations Disclosure Statement: No potential conflict of interest was reported by the author(s). Foundation Support: The Investigation and Research On the Current Situation of Consolidating the Results of Poverty Alleviation and Rural Revitalisation in Agricultural and Pastoral Areas of Tibet under the Project of the National Social Science Foundation[22BMZ126]; Cultivation Plan for Postgraduate Students' Scientific Research Ability in Chinese Minority Economics of School of Economics and Management, Tibet University; Postgraduate High-level Talent Cultivation Plan of Tibet University [2022-GSP-B001]. Data availability The original contributions presented in the study are included in the article/Supplementary Material , further inquiries can be directed to the corresponding author. Competing interests The authors declare no competing interests. Ethical approval The Tibet University human research ethics committee approved this study(XLDR,2023), under the condition that it be conducted with integrity, respect for life, and adherence to human rights. This study has been performed in accordance with the Declaration of Helsinki. Informed consent During the survey period from December 2023 to January 2024, written informed consent forms were obtained from all participants using on-site questionnaires. Participation is entirely voluntary and without any form of compensation. Participants agree to publish or display research articles and/or share anonymous data with other researchers. Author Contribution Data curation, A.X. (Aiyan Xu); investigation, A.X. (Aiyan Xu) X.Q. (Xiu Qu) and X.X.(Xin Xin); writing—original draft preparation, X.Q. (Xiu Qu); writing—review and editing, A.X. (Aiyan Xu) and X.X.(Xin Xin). All authors have read and agreed to the published version of the manuscript. References Li X, Jiang J, Cifuentes-Faura J. The impact of logistic environment and spatial spillover on agricultural economic growth: An empirical study based on east, central and west China[J]. PLoS One, 2023, 18(7): e0287307. Karine H. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6201308","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":435656183,"identity":"45ce48cd-61ae-4853-9efb-a069cea660b2","order_by":0,"name":"Aiyan Xu","email":"","orcid":"","institution":"Xizang University","correspondingAuthor":false,"prefix":"","firstName":"Aiyan","middleName":"","lastName":"Xu","suffix":""},{"id":435656184,"identity":"995b4a41-9b3b-49b1-a0be-f658379530ad","order_by":1,"name":"xiu qu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBAC++PNBx98qJCQsz/eQKyeM8eSDWecsTBmOHOAWC03csykOdsqEhluJBCpg3FGjrExY5tEAuPMxxtvMNTYRBPUwszzrPBxwTmJPGbptGILhmNpuQ2EtLCxJ282nlEmUcwmnWMmwdhwmLAWHoYEM2keNonEHskzRGqR4EgBammTSJwhwUOkFgMecCBLGBvwAP2SQIxfDNjBUVknZ8B+eOONDzU2hLWgaJdIIEU5RAupOkbBKBgFo2BkAABg1T+d7Ufa5wAAAABJRU5ErkJggg==","orcid":"","institution":"Xizang University","correspondingAuthor":true,"prefix":"","firstName":"xiu","middleName":"","lastName":"qu","suffix":""},{"id":435656185,"identity":"056a8112-79c6-43c4-b8db-5b25726ebbea","order_by":2,"name":"xin xin","email":"","orcid":"","institution":"Xizang University","correspondingAuthor":false,"prefix":"","firstName":"xin","middleName":"","lastName":"xin","suffix":""}],"badges":[],"createdAt":"2025-03-11 08:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6201308/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6201308/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79606686,"identity":"86f74e69-5c52-4d33-bbc4-af80d1d2a5fa","added_by":"auto","created_at":"2025-03-31 16:21:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73095,"visible":true,"origin":"","legend":"\u003cp\u003eConfigurational Analysis Framework of Influencing Factors: \"Behavioral Motivation - Factor Endowment\"\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6201308/v1/12e448f5feacbcba5a6a3d4f.png"},{"id":79606694,"identity":"f75b6b7c-418d-4739-9df4-56190954db1a","added_by":"auto","created_at":"2025-03-31 16:21:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188904,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of Consistency Between Rural E-commerce Sales Behavior and Intention in Seven Cities/Prefectures of Tibet\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6201308/v1/73291056a5de30dada1c437c.png"},{"id":79608343,"identity":"1fc4df64-5f51-4894-b9b1-89b2a17fca6a","added_by":"auto","created_at":"2025-03-31 16:45:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1133069,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6201308/v1/6c9e8e71-9fde-4dc0-b0c8-1b96a52c6b39.pdf"},{"id":79606687,"identity":"83c519fb-fc5c-4b03-9b82-ce0152225241","added_by":"auto","created_at":"2025-03-31 16:21:03","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23036,"visible":true,"origin":"","legend":"","description":"","filename":"data1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6201308/v1/1336737db3dfde2d44b8d9ea.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Promoting Sustainable Farmers’ E-Commerce Sales Participation in Tibet: A fsQCA Analysis of Behavioral Drivers","fulltext":[{"header":"1. Introduction and Literature Review","content":"\u003cp\u003eRural e-commerce has emerged as a new engine for rural economic development in the digital economy era, holding significant potential for advancing agricultural modernization and achieving sustainable development goals. Developed countries like the United States rely on sophisticated cold chain logistics systems for rural e-commerce to achieve a loss rate of less than 2% for crops from field to table (Li X et al,2023) In developing countries, Brazil, Russia, and China, among others, view rural e-commerce as a means of promoting inclusive and sustainable economic growth (Karine H,2021). Academic research indicates that rural e-commerce can narrow the urban-rural income gap, exerting greater economic effects in remote and underdeveloped areas as well as for low-income households, thereby bridging regional development disparities to some extent (Guan X et al,2024). The inclusive growth in income facilitated by rural e-commerce is also reflected in rural women's entrepreneurship, particularly benefiting those with lower education levels (Dong S et al,2024; Li Y and Zhou L,2023). Furthermore, it possesses certain ecological value by increasing the proportion of low-carbon industries, enhancing total factor productivity in agriculture, and improving transport efficiency (Ji K et al,2023). As a development model that combines economic inclusivity and environmental friendliness, rural e-commerce is of great significance for promoting sustainable economic and social development. However, infrastructure gaps and low levels of human capital remain common challenges. India's \"Digital India\" initiative has significantly increased rural internet coverage, but linguistic diversity limits platform usability, and limited physical and human capital restrict digital usage among rural Indian residents (Basu K,2016). Flores (2017) conducted a cross-generational study of 16 families across three generations in U.S.-Mexico border cities and found that low education levels and lack of language skills exacerbate digital inequality (Flores M \u0026Aacute; et al,2017). Research on China revealed that the development capacity of rural e-commerce in China significantly improved from 2011 to 2022, but there is marked polarization among provinces, with eastern and central provinces leading the way, while western and peripheral provinces lag behind (Wang L et al,2024). Regional imbalances in the development of the digital economy may weaken its inclusivity, with developed regions leveraging their economic advantages to accelerate the formation of digital technology monopolies (Yao and Liu,2024), while western regions like Tibet face a \"catch-up dilemma.\" In 2024, Tibet's rural e-commerce achieved an online retail sales volume of RMB 4.24\u0026nbsp;billion, representing a year-on-year growth of 32.73%. However, a horizontal comparison showed that none of Tibet's counties were among the top 50 counties with the fastest growth in agricultural product sales in the \"2024 Taobao Harvest Festival Report.\" This disparity indicates that, although Tibet's rural e-commerce has achieved stage-specific results, it is characterized by \"high growth but a low base,\" and there is an urgent need to explore effective pathways to transition from policy-driven to endogenous growth. The development of rural e-commerce in Tibet faces not only common shortcomings such as low levels of human capital and insufficient proficiency in the national common language but also realistic challenges like high altitude, fragile ecological environments, vast territory, and sparse population. In 2020, data from a nationwide survey of rural households initiated by the Rural Development Institute of the Chinese Academy of Social Sciences showed that the deviation ratio between the willingness to sell via e-commerce and actual behavior was approximately 77%, indicating a significant gap between intention and actual action (Zhang Y and Zhang Y,2024). Similarly, a survey conducted by our research team in agricultural and pastoral areas across seven cities in Tibet (XLDR, 2023) revealed that the participation rate of rural households in e-commerce sales is generally low, with only 33% of those with high willingness converting into actual behavior. The issue of \"high willingness but low action\" is prominent, and Tibet urgently needs to adopt effective measures to increase the participation rate in rural e-commerce sales.\u003c/p\u003e \u003cp\u003eResearch on rural e-commerce has primarily focused on developing countries and regions. In recent years, with the rapid development of rural e-commerce in China, Chinese scholars have discussed it from various perspectives, concentrating mainly on its economic benefits, ecological benefits, and the narrowing of regional disparities. Most studies have explored the mechanisms from a macro perspective. For instance, Li et al. (2024) found that rural e-commerce directly affects farmers' revenue by promoting their market integration and indirectly by expanding affiliated industries that can foster employment growth(Li W and He W,2024). Additionally, rural e-commerce promotes farmers' income growth by enhancing information access, reducing operational costs, and increasing financial support (Qiu H et al,2024). Rural e-commerce also enhances farmers' awareness of green production, reduces the use of fertilizers in agricultural production, and plays a significant role in promoting the green transformation of farmers and agricultural production (Wang C et al,2024). The academic exploration of rural e-commerce mechanisms provides a research direction for this study. However, existing research has not adequately addressed the reality of differentiated development in latecomer regions. The relatively low level of e-commerce development in Tibet indicates that the region is still in the initial stage of e-commerce transformation. In this development context, research on latecomer e-commerce regions such as Tibet should shift its focus to identifying the core factors that influence the participation behavior of stakeholders. This not only aligns with the practical needs of regional economic digital transformation but also fills an academic gap in the study of e-commerce development under special geographical conditions in theory.\u003c/p\u003e \u003cp\u003eResearch on the factors influencing participation in rural e-commerce predominantly employs traditional linear regression analysis, supplemented by configurational analysis. Zhang et al. (2016) used field survey data from fruit farmers in major cherry-producing areas and applied iterative GMM and ordered Probit methods to analyze the data. The findings revealed that the planting scale of farmers and the convenience of logistics positively influence farmers' participation in rural e-commerce sales (Zhang Y et al,2016). Ma et al. (2017) based on the perspectives of farmers' endowments and regional environment, utilized a binary Logistic regression model to find that different resource endowments have heterogeneous effects on farmers' participation in rural e-commerce for poverty alleviation. Education level is positively correlated with participation, while income level is negatively correlated (Ma Z et al,2017). Xiang et al. (2019), from the perspective of livelihood risk perception, constructed a multi-group structural equation model to investigate the intergenerational differences and influencing factors of farmers' participation in e-commerce for poverty alleviation. The study found that human capital has a significant impact on farmers' participation in rural e-commerce. Additionally, factors such as livelihood risk perception and rural e-commerce policies also have a positive influence (Xiang L et al,2019). In recent years, Huang et al. (2021) based on the entrepreneurial event model, conducted an online survey of rural e-commerce practitioners from Jieyang and Chaozhou in Guangdong Province, China, and used a structural equation model for their research. The results showed that professional knowledge and resource endowment have a significant positive impact on Base of the Pyramid (BoP) entrepreneurship in rural e-commerce (Huang L et al,2021). Liu et al. (2021) performed regression analysis based on rural household data collected in 2019 from Shandong, Henan, and Shaanxi provinces in China and found that education level and social capital have significant influences on farmers' adoption of e-commerce (Liu M et al,2021). Luo et al. (2022), based on the Theory of Reasoned Action and the extended Technology Acceptance Model, used a structural equation model to analyze and found that perceived usefulness and government support promote the use of e-commerce platforms (Luo S et al,2022). Ye et al. (2023) based on the Theory of Planned Behavior and the entrepreneurial event-related theory, used a structural equation model to investigate and found that entrepreneurial attitude, subjective norm, and perceived behavioral control are positively correlated with farmers' use of digital technology for inclusive entrepreneurship (Ye R and Zhou D,2023). Luo et al. (2024) using models such as Oprobit, found that digital literacy positively influences farmers' willingness to participate in e-commerce, and e-commerce cognitive literacy has a significant positive impact on the willingness of two generations of farmers to participate in e-commerce (Luo L et al,2024).\u003c/p\u003e \u003cp\u003eIn general, the aforementioned studies lack empirical analysis addressing the phenomenon of the \"intention-behavior\" gap. Recently, although literature has acknowledged the distinction between \"intention\" and \"behavior,\" recognizing that there is a gap between the two and they cannot be treated equivalently, most research methods rely primarily on linear regression, neglecting the fundamental characteristic of \"multiple concurrent causes\" underlying behavior. For example, Wang et al.(2021) and Xue et al. (2023) systematically examined the factors influencing the \"intention-behavior\" gap in farmers' participation in agricultural product e-commerce operations by constructing Logistic regression models (Wang X et al,2021;Xue F and Xia C,2023). Zhang et al. (2024), based on data from the 2020 China Rural Revitalization Survey, used a probit model to find that digital literacy can mitigate the intention-behavior gap in farmers' participation in rural e-commerce sales (Zhang Y and Zhang Y,2024).\u003c/p\u003e \u003cp\u003eIn terms of configurational analysis, few scholars have approached the topic from a micro perspective. Some scholars, taking a macro perspective and using 30 Chinese provinces as samples, analyzed the driving paths of the rural e-commerce entrepreneurial ecosystem based on the CAS (Complex Adaptive System) theory and the TOE (Technological, Organizational, and Environmental) framework (Huang L et al,2024). Configurational analysis has advantages over traditional linear regression in handling multiple concurrent causal relationships and conditional combination effects, particularly excelling in exploring pathways in complex environments. It has been extensively applied in studies on the factors influencing entrepreneurship in rural areas, which are closely related to the present study. Kimmitt J et al. (2020), based on an fsQCA (fuzzy-set qualitative comparative analysis) of changes in life circumstances of 166 farm households in rural Kenya, explored how different combinations of conversion factors enable distinct forms of entrepreneurship in the pursuit of prosperity (Kimmitt J et al,2020). Zhang et al. (2024), from a configurational perspective, used the fsQCA method with 85 typical rural innovation and entrepreneurship demonstration counties in China as research samples to explore the impact path of the rural entrepreneurship ecosystem composed of multiple factors on entrepreneurial performance and the complex causal mechanisms behind it (Zhang X et al,2024). Huang et al. (2023), using the entrepreneurial ecosystem theory as the study framework and the fsQCA method, analyzed the shaping of entrepreneurial opportunity (EO) (Huang L et al,2023). The above studies provide relevant support for the methodological choice of this research.\u003c/p\u003e \u003cp\u003eThe objective of this study is to identify the combinations of driving factors that facilitate the transition from \"intention\" to \"behavior\" in rural e-commerce sales in Tibet, addressing the challenge of \"intention without action\" in rural e-commerce, and providing relevant insights for countries and regions facing similar issues, thereby promoting rural economic growth and sustainable development. In summary, the innovations of this study are as follows:\u003c/p\u003e \u003cp\u003eFirstly, it focuses on the mechanism of sustainable behavior transformation in plateau border regions, filling the research gap in the \"intention-behavior\" domain. Existing research predominantly concentrates on central and eastern China, neglecting the unique decision-making processes of farmers in ethnic border areas. This study targets Tibetan farmers and herdsmen, utilizing survey data on the livelihood development in Tibetan agricultural and pastoral areas (XLDR, 2023), to address the paradox of \"high intention-low behavior\" in rural e-commerce sales, thereby bridging the geographical gap in sustainable e-commerce behavior research.\u003c/p\u003e \u003cp\u003eSecondly, it reveals sustainable driving pathways through the synergy of multiple factors, innovating the application scenarios of methodology. Current literature on the micro-level factors influencing smallholders' participation in rural e-commerce typically employs linear regression to quantify the \"net effect\" of individual factors, making it difficult to capture the complex causal mechanisms in unevenly developed regions. This study introduces fuzzy-set qualitative comparative analysis (fsQCA) to analyze the influencing factors of smallholders' participation in rural e-commerce sales from a configurational perspective, identifying equivalent sustainable transformation pathways and promoting a paradigm shift in rural e-commerce research from \"whether it works\" to \"how it works.\"\u003c/p\u003e \u003cp\u003eThirdly, it constructs a dual-drive framework of \"behavioral motivation-resource endowment\" by integrating the Theory of Planned Behavior and the Factor Endowment Theory, transcending the traditional one-dimensional analysis paradigm. This framework not only focuses on the internal drivers of behavior generation but also considers external constraints, enriching the theoretical application of factors influencing rural e-commerce participation behavior.\u003c/p\u003e \u003cp\u003eThe rest of this paper is organized as follows. The second section introduces the research analytical framework. The three section discusses the fsQCA research method. The four section presents the fsQCA analysis process and empirical results. Finally, the five section provides conclusions, limitations, and directions for future research.\u003c/p\u003e"},{"header":"2. Framework for analysis","content":"\u003cp\u003eIn the existing analytical framework for examining the factors influencing the \"intention-behavior gap,\" most scholars have centered their research around the Theory of Planned Behavior (Wang X et al,2021;Cao H et al,2024;Chen H and Mu Y,2024). However, this study posits that there is room for expansion. The Theory of Planned Behavior, which evolved from the Rational Behavior Theory proposed by Fishbein (1963), emphasizes the central role of behavioral intention in behavioral decision-making (Duan W et al,2008). According to this theory, behavioral attitude, subjective norm, and perceived behavioral control constitute a three-dimensional driving mechanism for behavioral intention. These components are both independent and interrelated, sharing a common belief basis, and form a classic framework for explaining individual behavior. It is noteworthy that Sheppard's (1988) meta-analysis indicated that the Theory of Planned Behavior has limited explanatory power for actual behavior, suggesting the need to introduce external contextual variables to enhance the theory's explanatory power (Sheppard B H et al,1988). Loch (1996) also argued that relying solely on the Theory of Planned Behavior to explain individual behavior is inadequate (Loch K D and Conger S,1996). This theoretical gap provides a logical foundation for the introduction of the Factor Endowment Theory. Originally developed by Heckscher and Ohlin in the field of international trade to explain the reasons for trade between two countries, the Factor Endowment Theory posits that differences in factor endowments lead to variations in productivity among countries (Tsai W and Ghoshal S,1998). It represents an extension and development of comparative advantage and is predominantly studied at the macro level. Some scholars, based on this theory, have categorized factor endowments into natural capital, physical capital, human capital, economic capital, and social capital for micro-level research (Liu Ke et al, 2019 ; Zhang Y et al,2015;Zhang T et al,2017; Zhang F et al,2020).The relationship between factor endowments and behavior can be summarized as follows: differences in factor endowments directly influence farmers' production behavior (Zhang T et al,2017; Jamison D T and Gaag J V D,1987; Qiu H et al,2012) and factor endowments can serve as external incentives, driving individuals to act or make specific choices in particular directions (Liu J et al,2024). Farmers may choose to abandon behaviors if their own endowments do not meet the requirements for engaging in those behaviors (Liu K et al,2019). Variations in factor endowments also affect farmers' production attitudes and, consequently, their agricultural production behavior (He Yue,2019). The availability of resources such as individual economic and human capital reflects behavioral control (Ajzen I,1991). Therefore, it can be seen that there is an interactive relationship between the Theory of Planned Behavior and the Factor Endowment Theory, satisfying the presupposition that the influencing factors in QCA should be interrelated. By integrating the intrinsic behavioral motivation driving conditions of the Theory of Planned Behavior with the extrinsic resource endowment constraints of the Factor Endowment Theory, this study constructs a configurational analysis framework of \"behavioral motivation-factor endowment\" influencing factors to explain smallholders' participation in e-commerce sales. The specific framework is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Theory of Planned Behavior\u003c/h2\u003e \u003cp\u003eSpecifically, the three core dimensions of the Theory of Planned Behavior exhibit unique connotations in the context of e-commerce participation. Behavioral attitudes refer to an individual's tendency to perform a specific behavior, representing the actor's mental disposition towards engaging in a particular action. It is generally regarded as a positive factor and the most effective variable for predicting individual behavior. The more positive an actor's attitude towards a certain action, the stronger their intention to take action. Generally speaking, if farmers have a positive evaluation of e-commerce, their willingness to participate in it will be stronger; conversely, if farmers perceive risks and uncertainties associated with e-commerce, their willingness to participate will diminish.\u003c/p\u003e \u003cp\u003eSubjective norms refers to the extent to which others, outside of the actor, influence the actor's behavior, specifically referring to the impact of others' expectations on the actor (Patrick S,1991). Due to the strong connectivity of rural social networks, the policy mobilization by village officials, the demonstration effect of neighbors, and the opinion tendencies of relatives form a composite pressure field. This \"ripple effect\" of group cognition significantly influences individuals' conformity tendencies in decision-making, and subjective norm has an amplifying effect in rural social contexts.\u003c/p\u003e \u003cp\u003ePerceived behavioural control is equivalent to self-efficacy. When an individual is performing a certain behavior, their perception of the ease or difficulty of the behavior directly affects whether the behavior occurs. It reflects the individual's judgment, based on past experiences, of the resources they can control to engage in the behavior. In the context of e-commerce participation, perceived behavioral control is a self-assessment of the relevant knowledge and skills required for e-commerce behavior. The stronger farmers' perceived usefulness and ease of use of e-commerce, the stronger their willingness to participate in e-commerce sales, and they are more willing to take action, leading to a greater consistency between intention and behavior (Wang X et al,2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Factor Endowment Theory\u003c/h2\u003e \u003cp\u003eResource endowment, comprising resources and capabilities innate or acquired by family members, is the most direct factor constraining individuals' behavioral decisions (Zhang C et al,2016). Agriculture is a sector highly dependent on natural conditions, and superior natural capital provides favorable conditions for large-scale operations. Factors such as land size and geographical location determine the e-commerce suitability of product supply. For example, the spatial agglomeration advantage of specialty agricultural product production areas can foster e-commerce economies of scale, enhance farmers' long-term expectations, and make them more likely to choose rural e-commerce for online sales (Zhang N et al,2023).\u003c/p\u003e \u003cp\u003ePhysical capital primarily refers to the infrastructure on which farmers rely to participate in e-commerce. Road facilities and network coverage are the foundations for farmers' engagement in rural e-commerce, and mobile phone ownership is a necessary element for conducting e-commerce (Lin H et al,2019). The continuous improvement of rural transportation infrastructure enables rural logistics to provide a range of services including packaging, warehousing, and delivery (Zhou D et al,2019). Internet infrastructure development is a crucial driver of rural e-commerce growth, offering farmers more possibilities for internet use. Rao (2008) argues that government investment in network infrastructure significantly supports e-commerce development (Rao S S,2008).\u003c/p\u003e \u003cp\u003eHuman capital mainly encompasses the quantity and quality of human capital (Schultz T W,1961). The number of family labor represents the quantity of human capital (Zhang T et al,2017), while the quality of human capital refers to farmers' knowledge reserves and abilities. Educational experience within human capital has a direct impact on entrepreneurial success (Wang S et al,2010). Farmers with higher education levels have broader perspectives and are more receptive to new technologies. Proficiency in Chinese is also crucial for farmers' participation in rural e-commerce, as language skills are an important form of human capital. For ethnic minority practitioners, a lack of language skills can hinder entry into the mainstream market (Pisani M J et al,2017). Generally, health status is an important aspect of measuring human capital quality; however, e-commerce employment opportunities break the physical limitations for people with disabilities and represent a new direction for their future employment (Liao J,2015). Therefore, in this study, human capital quality does not include health status but is measured primarily by the number of family labor, education level, and proficiency in listening, speaking, reading, and writing the national language.\u003c/p\u003e \u003cp\u003eEconomic capital primarily refers to the economic capacity required for farmers to participate in rural e-commerce, reflecting farmers' financial strength, economic status, and ability to invest in new technologies and cope with new risks (Chen Y and Zhao M ,2024). The higher the income proportion, the greater the likelihood of farmers participating in industrial projects (Tian Y and Zhang W,2018). Farmers with higher economic capital have lower trial-and-error costs and stronger financial support to promote e-commerce operations, making them more inclined to participate in rural e-commerce.\u003c/p\u003e \u003cp\u003eSocial capital refers to farmers' relationship networks and mobilizable resources (Zhu Q et al,2020). As the first recipients of policies, village officials have a high degree of identification with and expectation for policies and closely follow national policy guidance to internalize rural e-commerce into actual production and sales. The social networks and reciprocal norms formed between small farmers and other social entities (Yang Y and Shi Z,2012), facilitate agricultural production cooperation through organized diffusion, reducing farmers' transaction costs for technology adoption and enabling resource integration to empower small farmers to connect with the e-commerce market. Cooperatives have strong negotiation capabilities, coordinate market service dilemmas in the process of e-commerce development, and help small farmers address the blindness and disorder in connecting with the e-commerce market on a small scale (Zhang J and Xi Y,2019;Gan Y,2022). Therefore, the higher the social capital endowment, the easier it is to participate in rural e-commerce.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Methodology: fsQCA\u003c/h2\u003e \u003cp\u003eResearch on influencing factors often employs a single-factor analysis approach. However, in reality, the transition from intention to behavior in rural e-commerce sales is typically the result of collaborative evolution, interaction, and interlinked development among multiple factors influencing each other. Fuzzy Set Qualitative Comparative Analysis (fsQCA) combines quantitative and qualitative research methods, offering advantages in analyzing pathways to enhance rural e-commerce participation behavior. It comprehensively considers the relationships between multiple antecedent conditions and outcome variables, making it suitable for exploring how various factors jointly influence the occurrence of behavior. As the range of sample data gradually increases, fsQCA is widely applied in small and medium-sized sample studies. Moreover, it can convert any data into a 0\u0026ndash;1 membership score, thereby imposing lower requirements on data (Chi M et al,2021). Based on this, the present study adopts the fsQCA research method and utilizes survey data on the livelihood development in Tibetan agricultural and pastoral areas (XLDR, 2023) to identify the configurational conditions that influence the transformation of rural e-commerce sales intentions into actual behaviors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Variable measurement and data sources\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Outcome variable\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study aims to explore the pathways for enhancing rural e-commerce sales participation behavior, specifically by improving the consistency between farmers' \"intention to participate\" and their \"actual behavior\" in e-commerce. Drawing on relevant research (Chang Q et al,2021;Yan B et al,2024), define inconsistency in rural e-commerce participation intention and behavior as the scenario where \"farmers have the intention to participate in rural e-commerce but do not engage in actual behavior.\" Based on this, samples from farmers without strong participation intentions were excluded, retaining only those who responded with \"strongly agree\" to the question, \"If circumstances permit, would you engage in online sales?\" The rationale behind this is that the focus of this study is on enhancing the e-commerce participation behavior of farmers with strong intentions to engage in rural e-commerce sales. The issue with farmers lacking strong participation intentions lies in cultivating their willingness, which is not the core scope of this study. According to the sample data, 68 samples demonstrated consistency between rural e-commerce sales behavior and intention, accounting for only 33% of the total research sample. Among these, Chamda had the highest proportion at 16.43%, while Lhoka had the lowest. The specific details are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below. Therefore, it is necessary to explore pathways for enhancing the consistency between intention and behavior.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Conditional Variables\u003c/h2\u003e \u003cp\u003e(1) Behavioural attitudes: individuals showing positive or negative evaluations of a certain behaviour affects the positive and negative feelings of individuals when they participate in the action (Song Y et al., 2023). When farmers hold a positive evaluation of rural e-commerce, the stronger the active participation behaviour. \u0026lsquo;Safety and Reliability Approval\u0026rsquo; and \u0026lsquo;Improvement in Quality of Life Approval\u0026rsquo; were chosen to characterize behavioural attitudes.\u003c/p\u003e \u003cp\u003e(2) Subjective norms: Individuals tend to adopt the norms and behavioural patterns of the group in order to maintain consistency with the group, and subjective norms are measured by the degree of social support (Chen M et al., 2024). The questions \u0026lsquo;Is there an e-commerce service station in this village to help support villagers to sell online\u0026rsquo; and \u0026lsquo;Do you agree with the statement \u0026ldquo;Many people around you are selling online\u0026rdquo;\u0026rsquo; were used to characterise the degree of subjective norms.\u003c/p\u003e \u003cp\u003e(3) Perceived behavioural control: High self-efficacy improves individuals' persistence in the face of challenges and makes them more likely to overcome obstacles and complete tasks successfully (Deng Z et al., 2022). Perceived behavioural control is characterized by \u0026lsquo;Do you think online selling is easy\u0026rsquo; and \u0026lsquo;Recognition of training related to participation in online selling\u0026rsquo;.\u003c/p\u003e \u003cp\u003e(4) Natural endowment: As an indispensable resource element for farmers\u0026lsquo; production, natural capital is the basis for farmers\u0026rsquo; survival, and \u0026lsquo;the area of agricultural land (including pasture, arable land, and forest land)\u0026rsquo; was chosen to measure natural capital.\u003c/p\u003e \u003cp\u003e(5) Physical capital: Internet infrastructure and mobile terminal equipment are the most basic hardware facilities for e-commerce development, providing a platform for farmers to participate in rural e-commerce, providing comprehensive and high-quality information, narrowing the information gap, and cracking the \u0026lsquo;access gap\u0026rsquo; (Tian L et al., 2024). Road infrastructure is the foundation of efficient and fast logistics system in rural areas, based on this, this study originally set \u0026lsquo;network infrastructure\u0026rsquo;, \u0026lsquo;road infrastructure\u0026rsquo;, \u0026lsquo;with or without communication equipment\u0026rsquo; to characterise the physical capital. Physical capital, but in reality, the sample farmers have network infrastructure and communication devices (mobile phones), so only \u0026lsquo;road infrastructure\u0026rsquo; is left to characterise physical capital.\u003c/p\u003e \u003cp\u003e(6) Human capital: The level of human capital mainly consists of quantity and quality, with the quantity of human capital mainly referring to the number of labourers in the household, and the quality of human capital is generally characterised by the level of education (Chen M et al., 2024), and the national common language, as a widely-used linguistic tool, plays an important role in the rural areas and ethnic minority-populated regions. The human capital is characterised by the education level of farmers, the number of labourers in the household, and the level of proficiency in listening, speaking, reading and writing in the national language.\u003c/p\u003e \u003cp\u003e(7) Economic capital: economic capital mainly refers to the economic capacity needed in the production and management of farming households (Zhang T et al., 2017), and is characterised by \u0026lsquo;the level of household income (including agricultural and animal husbandry income, non-agricultural and animal husbandry, and subsidy income)\u0026rsquo;.\u003c/p\u003e \u003cp\u003e(8) Social capital: social capital is an individual's possession of social resources with livelihood value (Cao T and Zou W, 2022), using \u0026lsquo;whether there is a co-operative in the village\u0026rsquo; and \u0026lsquo;whether he/she is a village cadre or holds a position in a township or a higher-level administrative unit\u0026rsquo;. Finally, the scores of each component were measured using the entropy method, and the definitions of the variables and descriptive statistics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable definitions and descriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable Assignment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lsquo;Willingness-behaviour\u0026rsquo; agreement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;no bias (willingness and actual participation in rural e-commerce)\u003c/p\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;Bias exists (willing but not involved in rural e-commerce)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDo you have any experience in online selling? Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003cp\u003eIf conditions permit, would you sell online?\u003c/p\u003e \u003cp\u003eStrongly disagree\u0026thinsp;=\u0026thinsp;1; Quite disagree\u0026thinsp;=\u0026thinsp;2.\u003c/p\u003e \u003cp\u003eAverage\u0026thinsp;=\u0026thinsp;3; More agree\u0026thinsp;=\u0026thinsp;4; Strongly agree\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eConditional variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBehavioural attitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSafety and Reliability Approval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDo you agree with the statement \"Rural e-commerce is safe and reliable\"?\u003c/p\u003e \u003cp\u003eStrongly disagree\u0026thinsp;=\u0026thinsp;1; Quite disagree\u0026thinsp;=\u0026thinsp;2.\u003c/p\u003e \u003cp\u003eAverage\u0026thinsp;=\u0026thinsp;3; More agree\u0026thinsp;=\u0026thinsp;4; Strongly agree\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImprovement in Quality of Life Approval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDo you agree with the statement \"Participating in rural e-commerce can improve the quality of life\"?\u003c/p\u003e \u003cp\u003eStrongly disagree\u0026thinsp;=\u0026thinsp;1; Quite disagree\u0026thinsp;=\u0026thinsp;2.\u003c/p\u003e \u003cp\u003eAverage\u0026thinsp;=\u0026thinsp;3; More agree\u0026thinsp;=\u0026thinsp;4; Strongly agree\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.7005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.1563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubjective norms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree of social support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIs there an e-commerce service station in this village to help support villagers to sell online? Yes\u0026thinsp;=\u0026thinsp;2; No\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree of online sales by people around you\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDo you agree with the statement \u0026lsquo;Many people around you are selling online\u0026rsquo;?\u003c/p\u003e \u003cp\u003eStrongly disagree\u0026thinsp;=\u0026thinsp;1; Quite disagree\u0026thinsp;=\u0026thinsp;2.\u003c/p\u003e \u003cp\u003eAverage\u0026thinsp;=\u0026thinsp;3; More agree\u0026thinsp;=\u0026thinsp;4; Strongly agree\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.9420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.1768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerceived behavioural control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternet selling is easy Approval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDo you think online selling is simple?\u003c/p\u003e \u003cp\u003eStrongly disagree\u0026thinsp;=\u0026thinsp;1; Quite disagree\u0026thinsp;=\u0026thinsp;2.\u003c/p\u003e \u003cp\u003eAverage\u0026thinsp;=\u0026thinsp;3; More agree\u0026thinsp;=\u0026thinsp;4; Strongly agree\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree of participation in training related to online sales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWould you like to participate in training related to online sales?\u003c/p\u003e \u003cp\u003eStrongly disagree\u0026thinsp;=\u0026thinsp;1; Quite disagree\u0026thinsp;=\u0026thinsp;2.\u003c/p\u003e \u003cp\u003eAverage\u0026thinsp;=\u0026thinsp;3; More agree\u0026thinsp;=\u0026thinsp;4; Strongly agree\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural endowment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgricultural land area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea of pasture/cropland/woodland ________ acres.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1344.4720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2820.5097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoad infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWhat is the type of road connecting this village to the township?\u003c/p\u003e \u003cp\u003eNo road connectivity\u0026thinsp;=\u0026thinsp;1; levelled unpaved gravel/dirt road\u0026thinsp;=\u0026thinsp;2; concrete road\u0026thinsp;=\u0026thinsp;3; tarmac road\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.4396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHuman capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of labour force\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObtained by summing the number of household workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.0290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighest level of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther\u0026thinsp;=\u0026thinsp;0; No schooling or temple study\u0026thinsp;=\u0026thinsp;1; No schooling but temple study\u0026thinsp;=\u0026thinsp;2; Primary school\u0026thinsp;=\u0026thinsp;3; Junior high school\u0026thinsp;=\u0026thinsp;4; Ordinary high school\u0026thinsp;=\u0026thinsp;5; Secondary school, vocational high school, technical school\u0026thinsp;=\u0026thinsp;6; University college\u0026thinsp;=\u0026thinsp;7; Undergraduate college\u0026thinsp;=\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.3623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eListening and speaking skills in the national common language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree of mastery of listening and speaking skills in national common language\u003c/p\u003e \u003cp\u003eVery unskilled\u0026thinsp;=\u0026thinsp;1; Quite unskilled\u0026thinsp;=\u0026thinsp;2; Fair\u0026thinsp;=\u0026thinsp;3; Quite skilled\u0026thinsp;=\u0026thinsp;4; Very skilled\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiteracy in national languages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree of proficiency in reading and writing the national common language\u003c/p\u003e \u003cp\u003eVery unskilled\u0026thinsp;=\u0026thinsp;1; Quite unskilled\u0026thinsp;=\u0026thinsp;2; Fair\u0026thinsp;=\u0026thinsp;3; Quite skilled\u0026thinsp;=\u0026thinsp;4; Very skilled\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.2947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncome level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSum of farming, non-farming and subsidised income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54856.7600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45404.1121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocial capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhether a village cadre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHave you served as a village cadre or in a township or higher level administrative unit?\u003c/p\u003e \u003cp\u003eNever served\u0026thinsp;=\u0026thinsp;1; Served in the past, not now =\u0026thinsp;2; Current village cadre\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExistence of co-operative societies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIs there a co-operative in this village? Yes\u0026thinsp;=\u0026thinsp;2; No\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.7101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Data source\u003c/h2\u003e \u003cp\u003eThe data of this study comes from the survey data of livelihood development in Xizangan agricultural and pastoral areas (XLDR, 2023), the group conducted a face-to-face household questionnaire survey on Xizangan farmers and herdsmen from December 2023 to January 2024, and the data samples cover 20 villages in 7 prefectures and municipalities in the whole region, including 8 agricultural areas, 7 pastoral areas, and 5 semi-agricultural and semi-pastoral areas. The questionnaire content mainly includes supervisory questionnaire and household questionnaire, in order to ensure the questionnaire's scientific and rationality, firstly, pre-survey was conducted in the villages around Lhasa city, and then the questionnaire was modified and perfected in response to the pre-survey situation, and due to the difference in minority languages, Tibetan students from the same region were recruited in the whole school for training, and after eliminating the samples that do not meet the focus of the present study, 207 valid questionnaires were obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. Data calibration\u003c/h2\u003e \u003cp\u003eCombined with previous studies, this study calibrated the data, and the condition variables were calibrated using the direct calibration method, which set the upper quartile, mean, and lower quartile of the sample data to three anchors, namely, not at all affiliated, intersection, and fully affiliated, respectively, and the outcome variable was a 0\u0026ndash;1 variable, which did not need to be calibrated.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. fsQCA analysis process","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1. Necessary conditions analysis\u003c/h2\u003e\n \u003cp\u003eBefore conducting the fsQCA (fuzzy set qualitative comparative analysis) configuration analysis, it is necessary to perform a necessity analysis on individual conditions to identify whether a single condition is a necessary condition for the occurrence of the outcome. If the consistency level is above 0.9, then a particular condition variable is considered a necessary condition for the occurrence of the outcome variable (Schneider C Q and Wagemann C,2012). The necessity analysis reveals that the consistency levels of all condition variables are below 0.9, as shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. This indicates that the condition variables do not constitute necessary conditions for the outcome when considered individually. Therefore, a configuration analysis is required.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNecessity analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCoherence of will and behaviour\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBehavioural attitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Behavioural attitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.449944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubjective norms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.259391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.450056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Subjective norms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.740641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.450056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived Behavioural Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.211292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Perceived Behavioural Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.602321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.518822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural endowment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.695209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.333713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ \u003cstrong\u003eNatural\u003c/strong\u003e endowment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.304995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.317622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysical capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.376296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Physical capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.835327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.320584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.448597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.364831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Human capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.551562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.304076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomic capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.278109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Economic capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.721968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.318836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.365377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.436283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~ Social capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.287749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Sufficiency analysis of conditional configurations\u003c/h2\u003e\n \u003cp\u003eConditional grouping sufficiency analysis is a core part of QCA analysis by analysing different combinations of conditional variables affecting the outcome generation. The judgement criterion is that the consistency level of sufficiency is greater than or equal to 0.75. Based on previous studies and the specifics of this study, in constructing the truth table, the consistency threshold was chosen to be 0.8, the PRI threshold was 0.7, and given that the sample was a larger one, the frequency of cases was set to 2 (Zeng F and Chen Y,2024). The study covered 207 case samples, and due to inter-individual differences, it was not clear how the antecedent condition acted on the outcome variable, so the direction was not preset in the counterfactual operation step, and the conditional variable was set to \u0026lsquo;presence or absence\u0026rsquo;(Zhang F,2023). The complex solution does not simplify the result, the parsimonious solution contains only the core conditions, and the intermediate solution contains both the core conditions and presents the edge conditions. Therefore, the study searched for appropriate grouping paths with the intermediate solution as the main one and the parsimonious solution as the secondary one, and the results are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.The overall consistency of the three groupings is 0.81, which is higher than 0.75, indicating that the overall consistency has a high explanatory strength, and that the three groupings are a sufficient condition for the generation of congruence between willingness and behaviours. Further to verify the robustness of the results, the consistency threshold was increased to 0.85 and the PRI threshold was increased to 0.75, which produced the three groupings of groupings of states that were basically consistent, and therefore this study has good robustness, and the results are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cimg 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\" style=\"width: 1044px; height: 808.034px;\" width=\"1044\" height=\"808.034\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that natural endowment exists in each of the three enhancement paths of rural e-commerce willingness and behavioural consistency, indicating that natural capital endowment is the basic condition for the development of rural e-commerce, an important factor in the formation of brand advantages, and an important support for promoting the sustainable development of rural e-commerce. Rural e-commerce is dominated by agricultural e-commerce, which is highly dependent on the natural environment. According to the basic law of agricultural development, the scale of agricultural operation is positively correlated with agricultural economic efficiency, that is, through the scale effect, reduce production costs and improve production efficiency (Zhu T and Xia Y,2022), good natural capital endowment is conducive to enhancing the long-term expectations of farmers, so that farmers use more powerful agricultural production and management methods.\u003c/p\u003e\n \u003cp\u003e(1) The dual-drive model combining subjective norms and natural endowments presents two configurations where subjective norms serve as the core condition, while natural capital functions as a peripheral condition. These configurations enhance the consistency between willingness and actual participation in e-commerce sales. This model suggests that when subjective norms reach a high level, further enhancing natural capital endowments can boost participation in e-commerce sales behaviors.Rural areas are static societies characterized by collectivism, where individual behaviors are easily influenced by others\u0026apos; thoughts and attitudes. Farmers\u0026apos; participation in e-commerce is more susceptible to the influence of other farmers in the village community, as well as incentives or constraints from government and social forces (Wang M and Cao X,2008). When the external environment supports e-commerce participation, it has a positive impact on farmers\u0026apos; involvement in e-commerce. The No. 1 Central Document of 2020 explicitly emphasizes strengthening the construction of village-level e-commerce service stations to facilitate the two-way flow of products between urban and rural areas. The Tibet Autonomous Region Department of Commerce issued the \u0026quot;Work Guidelines for Comprehensive Demonstration of E-commerce in Rural Tibet,\u0026quot; stating that \u0026quot;the construction of rural e-commerce service stations must cover more than 30% of the total administrative villages in demonstration counties.\u0026quot; The government encourages farmers to participate in rural e-commerce and provides them with a high-quality service environment.A typical example of this configuration is found in Markam County, Changdu City. Markam County is abundant in natural capital, with diverse products such as Sodoxi chili sauce, Yanjing wine, and wild mushrooms. The richness of natural capital contributes to the diversity of rural e-commerce products. In 2020, Tibet Post established a rural e-commerce service center in Markam County, allowing farmers and herdsmen to enjoy high-quality e-commerce services within the village. The incentive support from the external environment enhances rural farmers\u0026apos; participation in e-commerce sales.In summary, this dual-drive model highlights the importance of both subjective norms and natural endowments in promoting farmers\u0026apos; participation in e-commerce. When these two conditions are met, especially with strong external support, farmers are more likely to engage in e-commerce activities, thereby contributing to the development of rural e-commerce.\u003c/p\u003e\n \u003cp\u003e(2) Capital endowment diversification driven.The multi-drive model based on diverse capital endowments is represented by Configuration 3, which indicates that a combination of high human capital and economic capital, complemented by natural and social capital as peripheral conditions, can enhance the consistency between willingness and actual participation in rural e-commerce. This configuration suggests that individuals with high levels of human and economic capital, when supported by natural and social capital, are more likely to engage in rural e-commerce activities.High-capital-endowed groups are more capable of converting digital technology into their own benefits (Chen H and Xie K,2024). Households with abundant human capital are more likely to improve the efficiency of e-commerce collaboration and possess stronger risk resistance. The quality of human capital directly affects farmers\u0026apos; adoption of digital technology, with farmers leveraging the internet and mobile payments to create a driving effect for rural e-commerce (Su Q and Xing H,2025). Human capital also influences farmers\u0026apos; subjective initiative; high human capital enhances farmers\u0026apos; vigilance towards e-commerce entrepreneurship, increases the probability of opportunity recognition, and reduces the risk of blindly participating in e-commerce startups (Zeng Y et al,2019). In ethnic minority areas, human capital also includes proficiency in the national common language. Groups with high proficiency in foreign languages are more likely to obtain information from official institutions or other formal channels (Underhill E et al,2019), while farmers with poor national common language skills struggle to learn relevant skills from online social media, reducing the effectiveness of information (Zhang W et al,2022).Economic capital, such as startup funds, is a critical factor affecting farmers\u0026apos; entrepreneurial activities, including rural e-commerce. It provides financial security for farmers in the early stages of rural e-commerce. The more abundant the economic capital, the wider the budget line, the greater the decision-making space for investment and consumption, and the more generous the startup funds willing to be invested in rural e-commerce. Social capital serves as a primary channel for accessing external information and resources, significantly improving the availability of entrepreneurial resources and facilitating successful entrepreneurial activities (Zhang Q et al,2022).Taking Nyemo County in Lhasa as an example, Nyemo County is a county dominated by agriculture with a combination of agriculture and animal husbandry. It is rich in agricultural and animal husbandry resources and is one of the important agricultural production areas in Lhasa. In recent years, Nyemo County has held multiple special conferences on increasing farmers\u0026apos; and herdsmen\u0026apos;s income to effectively improve their economic capital. According to statistical bulletins, the per capita disposable income of farmers and herdsmen in 2023 was 22,403 yuan, far exceeding the regional average. Nyemo County has leveraged cooperatives to actively organize rural e-commerce training, enhancing farmers\u0026apos; and herdsmen\u0026apos;s human capital. Training has been conducted for different groups, including rural e-commerce practitioners, entrepreneurs, staff of agriculture-related enterprises, and farmers and herdsmen, covering e-commerce policies, theories, operations, and practical skills, with a cumulative impact on employment and entrepreneurship for about 30 people. It is evident that Nyemo County has adopted a multi-pronged approach to enhance participation in rural e-commerce sales through multiple dimensions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusions and policy recommendations","content":"\u003cp\u003eThe digital economy, as a catalyst for sustainable development, offers innovative pathways for economic growth in underdeveloped regions. In Tibet, the digital divide intertwines with the constraints of high-altitude environments. E-commerce platforms not only bridge the market access for small-scale farmers but also further narrow the digital divide. This study further responds to the call for research on how digital tools advance Sustainable Development Goals (SDGs) in underdeveloped regions. The primary research content involves utilizing survey data on people\u0026apos;s livelihood development in Tibet to empirically analyze, from the micro-perspective of small-scale farmers, the pathways for enhancing farmers\u0026apos; e-commerce sales behavior and facilitating the transition from \u0026quot;behavioral intention\u0026quot; to \u0026quot;behavioral realization.\u0026quot; By integrating the internal behavioral motivation drivers from the Theory of Planned Behavior (TPB) with the external resource endowment constraints from the Factor Endowment Theory, this study constructs an analytical framework for the configuration of \u0026quot;behavioral motivation-resource endowment\u0026quot; influencing factors. The research indicator system is built based on the three elements of the TPB and the five elements of resource endowment. Empirical research is conducted using the fsQCA (fuzzy-set Qualitative Comparative Analysis) method, and the robustness of the research results is demonstrated through threshold-raising tests. The findings are as follows: Firstly, the necessity analysis of fsQCA shows that among the eight antecedent conditions encompassed by the TPB and the Factor Endowment Theory, no single variable constitutes a necessary condition for the consistency between intention and behavior. This conclusion illustrates the complex causal mechanism of participation in rural e-commerce sales behavior in the context of the digital economy, where a single policy stimulus is unlikely to produce systematic effects. Secondly, through configuration analysis, three equivalent pathways are identified, which can be aggregated into two typical driving modes. One is the \u0026quot;norm-endowment dual-drive mode,\u0026quot; with subjective norms as the core condition and natural capital as the auxiliary condition, indicating that when the demonstration effect and regional resource advantages are coupled, the behavioral transformation threshold can be effectively crossed. The other is the \u0026quot;capital endowment multi-drive mode,\u0026quot; with human capital (including proficiency in listening, speaking, reading, and writing the national common language) and economic capital as the core, and natural capital and social capital as marginal conditions, which can enhance the transformation of rural e-commerce sales behavior.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the above conclusions, this paper proposes the following policy recommendations:\u003c/p\u003e\n\u003cp\u003e1. Enhance the drive for natural endowment development. Given the ubiquity of natural endowments across all configurations, it is recommended to implement the \u0026quot;Digitalization Project for Distinctive Resources of the Qinghai-Tibet Plateau\u0026quot;. Firstly, strengthen investments to improve agricultural infrastructure such as irrigation systems, roads, and drainage facilities to enhance agricultural production conditions; promote land circulation and strengthen the provision of agricultural socialized services to achieve large-scale agricultural operations. Secondly, establish a special fund for geographical indication certification of distinctive agricultural products in Tibetan areas and build a blockchain traceability system for products such as cordyceps sinensis and Tibetan medicine. Finally, introduce the \u0026quot;enclave economy\u0026quot; model, leveraging the East-West collaboration mechanism to deeply integrate data resources from coastal e-commerce platforms with physical resources in Tibetan areas.\u003c/p\u003e\n\u003cp\u003e2. Strengthen the hierarchical cultivation mechanism for human capital. Firstly, implement a \u0026quot;three-tier\u0026quot; e-commerce training program, with the basic tier focusing on national common language and mobile payment skills, potentially through the development of an AI assistant for Tibetan-Chinese bilingual e-commerce platforms to reduce language barriers. The advanced tier should train participants in short video marketing and live streaming sales techniques, while the elite tier should cultivate data analysis and supply chain management capabilities. Secondly, establish a \u0026quot;digital entrepreneur\u0026quot; certification system and incorporate e-commerce skills training into the certification standards for new-type professional farmers. Finally, promote a \u0026quot;village official + local influencer\u0026quot; pairing mechanism, with publicly selected e-commerce mentors stationed in villages for guidance each year.\u003c/p\u003e\n\u003cp\u003e3. Innovate social capital activation models. To overcome the marginalization of social capital, a trinity development model of \u0026quot;cooperative + platform + finance\u0026quot; can be established, along with the implementation of the \u0026quot;Thousand Villages, Thousand Influencers\u0026quot; plan, to incubate Tibetan e-commerce IPs with cultural identity and leverage platforms such as Douyin and Kuaishou to create \u0026quot;Roof of the World Live Streaming Rooms\u0026quot;. Finally, establish an e-commerce credit community in Tibetan areas to amplify the financial empowerment effect of social networks.\u003c/p\u003e\n\u003cp\u003e4. Improve the economic capital supply system. Addressing the capital constraints faced by capital endowment-driven pathways, a \u0026quot;Snowy Land E-commerce Growth Enterprise Market\u0026quot; can be created to provide interest-free loans to startup projects with appropriately extended repayment periods. Additionally, an East-West e-commerce revenue-sharing mechanism can be established to guide eastern enterprises to enjoy tax incentives for the Western Development Strategy when setting up \u0026quot;cloud warehousing\u0026quot; centers in Tibetan areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, this study also has two limitations that urgently need to be addressed: Firstly, the cross-sectional data characteristics limit the dynamic testing of causality. With the long-term tracking survey of Tibetan agricultural and pastoral areas conducted by the research team, dynamic QCA methods will be adopted in the future to capture the evolution of configurations. Secondly, digital technology is reconstructing social network forms, and future research will continuously incorporate virtual community influence indices to construct a subjective norm measurement system that integrates online and offline dimensions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFoundation Support:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Investigation and Research On the Current Situation of Consolidating the Results of Poverty Alleviation and Rural Revitalisation in Agricultural and Pastoral Areas of Tibet under the Project of the National Social Science Foundation[22BMZ126]; Cultivation Plan for Postgraduate Students\u0026apos; Scientific Research Ability in Chinese Minority Economics of School of Economics and Management, Tibet University; Postgraduate High-level Talent Cultivation Plan of Tibet University [2022-GSP-B001].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material , further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Tibet University human research ethics committee approved this study(XLDR,2023), under the condition that it be conducted with integrity, respect for life, and adherence to human rights. This study has been performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the survey period from December 2023 to January 2024, written informed consent forms were obtained from all participants using on-site questionnaires. Participation is entirely voluntary and without any form of compensation. Participants agree to publish or display research articles and/or share anonymous data with other researchers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData curation, A.X. (Aiyan Xu); investigation, A.X. (Aiyan Xu) X.Q. (Xiu Qu) and X.X.(Xin Xin); writing\u0026mdash;original draft preparation, X.Q. (Xiu Qu); writing\u0026mdash;review and editing, A.X. (Aiyan Xu) and X.X.(Xin Xin). 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Agricultural Economics and Management, 2022, (03): 28-41.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"rural e-commerce, fsQCA, Theory of Planned Behavior, Factor Endowment Theory","lastPublishedDoi":"10.21203/rs.3.rs-6201308/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6201308/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRural e-commerce, as a new engine for rural development in the digital economy era, is highly expected to promote economic growth and sustainable development. Based on the reality of \"high willingness but low action\" in rural e-commerce sales participation in Tibet, this study integrates the intrinsic motivational drivers of behavior from the Theory of Planned Behavior with the external resource endowment constraints from the Factor Endowment Theory, constructing an analytical framework of \"behavioral motivation-resource endowment\" influencing factors. Utilizing survey data from the Livelihood Development in Tibetan Agricultural and Pastoral Areas (XLDR, 2023) and employing the fuzzy-set Qualitative Comparative Analysis (fsQCA) method, this study identifies the conditional configurations that influence the translation of rural e-commerce sales intentions into actual behaviors. The research findings indicate that a single condition does not constitute a necessary condition for the consistency between intentions and behaviors. In the sufficiency analysis of conditional configurations, three primary paths are identified, which can be further refined into two models: the \"subjective norm-natural endowment dual-drive model\" and the \"capital endowment multi-drive model.\" This study overcomes the limitations of previous literature that only considered the impact of single variables, systematically proposes multiple causal paths of antecedent configurations for enhancing rural e-commerce sales participation, and thus provides new insights for improving rural e-commerce sales participation rates.\u003c/p\u003e","manuscriptTitle":"Promoting Sustainable Farmers’ E-Commerce Sales Participation in Tibet: A fsQCA Analysis of Behavioral Drivers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 16:20:58","doi":"10.21203/rs.3.rs-6201308/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-28T16:01:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-08T14:15:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197923443516880827692401272676690857416","date":"2025-04-03T05:18:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-29T08:37:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62005926706373436264523408393284723150","date":"2025-03-23T05:59:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-23T05:53:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-22T12:29:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-22T12:28:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-21T06:40:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-03-11T08:24:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0e57b421-5bea-4151-932a-149679f54cae","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":46391079,"name":"Social science/Development studies"},{"id":46391080,"name":"Social science/Economics"},{"id":46391081,"name":"Social science/Social policy"}],"tags":[],"updatedAt":"2025-10-16T13:23:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-31 16:20:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6201308","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6201308","identity":"rs-6201308","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}