Online shopping adoption during COVID-19 and social isolation: Extending the UTAUT model with herd behavior

Determinants of users' unverified information sharing on social media platforms: A herding behavior perspective

The proliferation of unverified or false information by irresponsible users can significantly amplify the spread of misinformation or fake news. Despite growing research on unverified information sharing, a comprehensive understanding of the varying influences of different factors and strategies to mitigate this issue remains under investigation. To address this research gap, this study, rooted in the theory of herd behavior, develops, and tests a model theorizing the reasons behind social media users' unverified information sharing. Data was collected from 510 respondents across six regions of China using a convenience sampling method. The collected data was analyzed using Mplus. The results from this study indicated that perceived severity, state uncertainty, and herding have a significant positive influence on unverified information sharing. These results enrich the understanding of unverified information-sharing behavior among Chinese social media users. Drawing from these results, we suggest platform administrators and policymakers mitigate herd behavior tendencies and stem the spread of misinformation by disseminating timely, accurate, and authoritative information. Since this action will reduce users' perceptions of severity and uncertainty. Social media users are also advised to stay vigilant over the implications of herd behavior and foster a more critical attitude towards information sharing.

The influence mechanism analysis on the farmers' intention to adopt Internet of Things based on UTAUT-TOE model

Internet of Things (IoT) technologies are widely recognized as one of the most important infrastructures for economic development and technological innovation. By analyzing the influencing factors of vegetable farmers' intention to adopt agricultural IoT, it helps to formulate effective IoT promotion policies and accelerate the realization of agricultural modernization. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology-Organization-Environment (TOE) theory, this study constructed for the first time a mechanism model of the influence of vegetable farmers' intention to adopt IoT, expanding the scope of current research on agricultural IoT and revealing the intrinsic influence mechanism of farmers' adoption of IoT technologies. In this study, 357 quantitative data were obtained by a questionnaire survey, and structural equation modeling was used to test the direct and indirect effects of each factor on vegetable farmers' intention to adopt IoT. The results show that almost all variables in TOE have significant direct impacts on the intention, while no variables in UTAUT have significant direct impacts. Among variables in TOE, government support and complexity are the two most important elements influencing the intention. Although the interactions among variables in TOE and UTAUT are also found, the indirect effects of variables are non-significant. Therefore, it is proposed to reduce the complexity of operation and use of IoT technologies; improve rural information infrastructure and compatibility of IoT platforms and devices; and governments should increase subsidies, and incentives to promote the use of IoT in agriculture and agricultural practices.

Unlocking future learning: Exploring higher education students' intention to adopt meta-education

Despite the potential of meta-education to transform higher education, there remains a scarcity of research investigating students' adoption intentions. This study aimed to identify factors influencing students' intentions to adopt meta-education using an extended Decomposed Theory of Planned Behavior model (DTPB). Data was collected via an online survey of 596 higher education students from Jordan who were purposefully selected. Structural equation modeling using partial least squares analysis revealed attitude, social influence, and perceived behavioral control as key antecedents of adoption intention. Furthermore, newly added variables including perceived enjoyment, herd behavior, student autonomy, and student innovativeness showed efficiency in explaining variance in attitude, social influence, and perceived behavioral control. Overall, the extended model provided meaningful insights on factors driving students' willingness to adopt meta-education. The study contributes to theory by extending the decomposed TPB model in the context of emerging educational technologies. It also provides practical implications for policymakers and educators aiming to encourage meta-education adoption.

Green Consumer Profiling and Online Shopping of Imperfect Foods: Extending UTAUT with Web-Based Label Quality for Misshapen Organic Produce

Misshapen organic vegetables in the food supply chain can easily be discarded in the market if they do not meet aesthetic standards. E-commerce technology enables the distribution of imperfect organic produce from farmers to potential customers, mitigating agri-food waste issues. Green consumers are prospective customers of imperfect produce because their purchasing decisions are made based on sustainability and environmental considerations. However, each individual's degree of green consumption differs, impacting their preferences and behaviors toward green buying activity. Considering the gap between the varying levels of green consumers and their intention to purchase imperfect organic produce through e-commerce, this study aimed to profile three distinct green consumers and explore factors affecting their Online Green Purchase Intention (OGPI) for imperfect organic vegetables. The Unified Theory of Acceptance and Use of Technology (UTAUT) was applied in this study, and Web-Based Label Quality (WLQ) was introduced as an extended construct to describe green consumers' perceptions of the credibility and reliability of labels or product-related information displayed on e-commerce platforms. This study involved 668 internet users from environmental platforms and online communities of organic food enthusiasts. First, the consumers were classified into dark-green, semi/light green, and non-green using a cluster analysis approach. Then, Structural Equation Modeling (SEM) and Multi-Group Analysis (MGA) were employed to determine the factors affecting OGPI among green consumer groups. This research found that Performance Expectancy (PE), Social Influence (SI), and WLQ positively influenced dark-green consumers' online green purchase intention. Only Performance Expectancy (PE) positively affected semi/light-green consumers' OGPI. Meanwhile, the Facilitating Condition (FC) positively affected non-green consumers' online green purchase intentions. This research revealed dark-green consumers as the target segment, broadening customers' perspectives on accepting imperfect organic products, including e-commerce technology. Moreover, the research results can be utilized for marketing and business purposes and contribute to food policy.

Social media heterogeneity and preventive behaviours during the COVID-19 outbreak: a survey on online shopping

BACKGROUND

Residents' adoption of preventive behaviours proved beneficial in preventing the large-scale transmission of the virus during the early stages of the COVID-19 outbreak. It is critical to investigate how social media triggers residents' preventive behaviour decisions during the COVID-19 outbreak.

METHODS

This paper selected online shopping as a specific preventive behaviour for empirical investigation. An online cross-sectional survey was conducted through the Sojump website from 1 to 15 March 2020, and a total of 1,289 valid questionnaires were collected from China. This paper uses multiple regression analysis to investigate the heterogeneous impacts of different information sources on residents' online shopping willingness and online shopping behaviour and the heterogeneous impacts of different information content in social media on the transformation of residents' online shopping willingness and online shopping behaviour.

RESULTS

The findings indicate that both official-media and self-media positively promote residents' online shopping willingness and behaviour, with official-media having a stronger promotional effect than self-media. Furthermore, official-media and self-media can collaboratively promote residents' online shopping willingness and online shopping behaviour. The ease-of-use and usefulness of information significantly promoted the transformation of residents' online shopping willingness.

CONCLUSIONS

This study analyses the heterogeneous impacts of social media on residents' preventive behaviours from the perspectives of information source differentiation and information content differentiation, which enriches related studies and provides feasible paths for promoting residents' preventive behaviours.

Understanding the Purchase Decisions of Silver Consumers in Short-Form Video Platforms from the Perspective of Existence, Relatedness, and Growth Needs

The differentiated characteristics of the silver (elderly) group from other groups means that the previous interaction mechanism in short-form video (SFV) e-commerce is no longer applicable. Drawing on sociotechnical systems theory and the ERG theory, this study is motivated to explore the purchase intention of silver consumers in SFV platforms. We categorize the characteristics of SFV platforms into social and technical aspects, and analyze silver consumers' purchasing decisions in terms of existence, relatedness, and growth needs. The empirical results of 284 samples show that social belonging, perceived trust, and product relevance are positive factors that promote purchase. Information diversity and social interaction have significant positive effects on social belonging and perceived trust. Recommendation affordance is significantly positively associated with perceived trust and product relevance, while platform ease of use did not have a significant effect on perceived trust. The findings provide management insights into SFV platforms to better understand the digital divide faced by silver consumers and to facilitate increased consumption.

Post-COVID-19's impact on green supply chain management and sustainable E-commerce performance: the moderating role of big data analytics

This study investigates the relationship between COVID-19 and the adoption of green supply chain management practices (GSCM) in the Pakistani e-commerce industry. It also assesses the impact of these practices on ecological sustainability across three dimensions and explores the role of big data analytics (BDA) in enhancing them after the pandemic. The research utilized partial least squares structural equation modeling to evaluate data and test hypotheses. The research sample was composed of 390 managers operating within Pakistan's e-commerce industry. The study's preliminary findings reveal that COVID-19 has positively influenced the adoption of GSCM practices, which are moderated by BDA. Implementing GSCM has a positive impact on the perceived environmental and social resilience of e-commerce enterprises, but no significant effect on their perceived economic resilience. Additionally, GSCM acts as a mediator in the relationship between the impact of COVID-19 and the perceived environmental and social resilience of e-commerce firms, but not for economic resilience. This research focuses on Pakistan's e-commerce industry and investigates how COVID-19 affects the adoption of eco-friendly supply chain practices. It measures the impact of these practices on ecological sustainability using three dimensions. The study also examines how BDA can improve the adoption of GSCM in e-commerce, offering new insights into sustainability during and post-pandemic. The results recommend that the e-commerce industry can use BDA and GSCM practices to improve e-commerce sustainable performance. This is initial research that integrates COVID-19 impact, BDA, GSCM practices, and e-commerce sustainability in a single framework that was overlooked previously.

How does herd behaviour impact the purchase intention? Explore the moderating effect of risk aversion in the context of Vietnamese consumers

Managers have been applying the influence of herd behaviour to stimulate purchase intention but have not achieved precise results because they do not clearly understand the mechanism of this relationship. This article aims to determine the effect of herd behaviour on the relationship between information adoption and purchase intention and the moderating of risk aversion. With 17 variables for four concepts, data was collected online with 340 respondents and analysed with a partial least squares structural equation modelling. When considering herd behaviour in terms of subjective norms with corroborated information, this study upgrades the classic role of the theory of planned behaviour in purchase intention. The findings indicate a positive relationship between herd behaviour, information adoption and purchase intention. In particular, risk aversion is a positive moderator of the relationship between herd behaviour and purchase intention. This result is a significant addition to the theoretical body and shows that risk aversion can drive purchase intention through herd behaviour. In other words, this study upgrades the perception of information source authentication through herd behaviour to explain the purchase intentions of Vietnamese consumers. These results show that managers can create risky situations to promote herd behaviour. They can also apply information adoption stimuli to stimulate herd behaviour. At the same time, through the information adoption mechanism, consumers can be more alert to the effect of herd behaviour and have more opinions when making purchases.

A machine learning ensemble approach to predicting factors affecting the intention and usage behavior towards online groceries applications in the Philippines

The emergence of e-commerce platforms, especially online grocery shopping, is heightened by the COVID-19 pandemic. Filipino consumers started to adapt online due to the strict quarantine implementations in the country. This study intended to predict and evaluate factors influencing the intention and usage behavior towards online groceries incorporating the integrated Protection Motivation Theory and an extended Unified Theory of Acceptance and Use of Technology applying machine learning ensemble. A total of 373 Filipino consumers of online groceries responded to the survey and evaluated factors under the integrated framework. Artificial Neural Network that is 96.63 % accurate with aligned with the result of the Random Forest Classifier (96 % accuracy with 0.00 standard deviation) having Perceived Benefits as the most significant factor followed by Perceived Vulnerability, Behavioral Intention, Performance Expectancy, and Perceived. These factors will lead to very high usage of online grocery applications. It was established that machine learning algorithms can be used in predicting consumer behavior. These findings may be applied and extended to serve as a framework for government agencies and grocers to market convenient and safe grocery shopping globally.

Online grocery shopping intention: Elderly's perspective in Malaysia

Purpose

This study investigates the factors associated with the elderly's intention to use OGS. Building on the Technology Acceptance Model, we propose several elderly-specific and functional-ageing constructs related to the perceived ease of use and usefulness of OGS.

Methods

The questionnaire was distributed physically and digitally to non-OGS users aged above 60, living in the Klang Valley, Malaysia, using a non-probability sampling approach. A total of 302 data were analysed using PLS structural equation modelling.

Results

The findings indicate that perceived ease of use, functional ability, life-course events, and digital literacy are associated with the elderly's perception of OGS's usefulness, while life-course events, digital literacy, technology anxiety, and facilitating conditions are associated with the perceived ease of use.

Conclusion

This study contributes to the OGS literature by addressing ageing complexities with OGS adoption among the elderly population, thereby offering insights for marketers and the government to tailor services and provide relevant support.

Analysis of Driving Factors in the Intention to Use the Virtual Nursing Home for the Elderly: A Modified UTAUT Model in the Chinese Context

As a unique form of mobile health service (m-Health) for elderly care in China, the virtual nursing home is considered a reliable alternative to the traditional model of home-based care, but services from virtual nursing homes are infrequently used by the elderly in practice. Thus, this study aims to measure the driving factors affecting the behavioral intention to use the virtual nursing home by designing a research framework that combined the unified theory of acceptance and use of technology (UTAUT) with the technology acceptance model (TAM). Furthermore, a new variable, conformist mentality, is proposed as a moderator. In order to validate the model, a well-structured questionnaire using mature scales was used, and the linear regression analysis method was conducted on 200 valid data samples collected during a field study in Harbin, China. The results show that performance expectancy, effort expectancy, and social influence significantly affect behavioral intention. However, the effect of facilitating conditions is not significant. Moreover, performance expectancy and effort expectancy have a positive effect on attitude toward use, and attitude toward use not only affects behavioral intention but also plays a mediating role in the effect of performance expectancy and effort expectancy on behavioral intention. This study also innovatively proposes and confirms conformist mentality as a moderator to strengthen the driving effect of social influence on behavioral intention. This is the first time that conformist mentality is introduced as a moderator in a study on the behavioral perception and acceptance of virtual nursing homes among Chinese older adults. Based on these findings, this study offers theoretical contributions and management implications that are conducive to the sustainable development of virtual nursing homes, thereby making extensive contributions to this field. Additionally, it also aids in the contextual expansion of the UTAUT model.

What motivates customers to repurchase online under social distancing?

Global e-commerce is growing rapidly during the COVID pandemic. Previous research on customers' online shopping decisions rarely considered social distancing. To investigate customers' continued intention toward online purchases while socially isolated, we propose a framework based on the UTAUT model. A survey of 330 valid samples was collected through an online survey among internet users during a period of social distancing in Indonesia. Hypotheses were validated using a structural equation modeling approach. The results showed that social contingency is the most influential factor on customers' intention to repurchase online under social restriction conditions, followed by customer perceived value and other significant factors. The findings contribute to providing a new understanding of customers' online repurchase intentions when they are in a contingency situation.

A Markovian-based fuzzy decision-making approach for the customer-based sustainable-resilient supplier selection problem

The supplier selection problem is one of the most important issues in supply chain management. So, many papers have investigated the mentioned problem. However, the related literature shows that researchers had less attention to the sustainability and resilience aspects based on the customer preferences in supplier selection problem. To cover this gap, this research tries to investigate the customer-based sustainable-resilient supplier selection problem. In this way, a Markovian-based fuzzy decision-making method is proposed. At the outset, the customer preferences are evaluated using a combination of the quality function deployment and the Markov transition matrix. Then, by combining the transition matrix and the fuzzy best-worst method, the weights of the indicators are calculated. Finally, the decision matrix is formed and the performance of suppliers is measured based on the multiplication of the decision matrix and vector of sub-criteria weights. Regarding the recent pandemic disruption (COVID-19), the importance of online marketplaces is highlighted more than the past. Hence, this study considers an online marketplace as a case study. Results show that in a pandemic situation, the preferences of customers when they cannot go shopping normally will change after a while. Based on the Markov steady state, these changes are from the priority of price, availability, and performance in initial time to serviceability, reliability, and availability in the future. Finally, based on the FBWM results, from the customer point of view, the top five sub-criteria for sustainable-resilient supplier selection include cost, quality, delivery, responsiveness, and service. So, based on these priorities, the case study potential suppliers are prioritized, respectively.

Consumer Intention to Utilize an E-Commerce Platform for Imperfect Vegetables Based on Health-Consciousness

Thailand has a problem with fruit and vegetable waste because consumers have a negative attitude towards fruit and vegetables with imperfect shapes; however, those imperfections have no impact on nutritional quality. Young adults are most aware of the problem of food waste. Here, we study factors influencing consumer’ intention to adopt an e-commerce platform that commercializes imperfect vegetables (ugly veggies). In all, 390 respondents from four provinces of Thailand were enrolled in the study. Cluster analysis, structural equation modeling, and multigroup moderation analysis approaches were used. The main conceptual framework was adapted from the unified theory of acceptance and the use of technology. Respondents were classified into low, moderate, and high health-conscious segments. We found that performance expectancy positively influences the behavioral intention of highly health-conscious consumers. For consumers in the moderate health-consciousness segment, effort expectancy and social influence positively affect behavioral intention. None of the hypothesized factors influence the low health-consciousness segment’s behavioral intention. This paper expands the understanding of consumer’ attitudes toward accepting an imperfect vegetable e-commerce platform. Additionally, the research can guide platform development and marketing efforts.

The applicability of scarcity, originality, and transaction tracking characteristics of the dermatological industry and non-fungible token

BACKGROUND

The K-beauty industry has entered the global market and encompasses the global economy. In the future, it is necessary to recognize the need for rapid recognition and application of digital technologies such as non-fungible token (NFT) in a continuously changing and shared society.

OBJECTIVES

This literature review presents various examples of ways to increase the value of beauty brands according to the latest digital trends. The potential value of blockchain technology was reviewed while understanding the current usage status of NFT.

METHODS

As of July 2022, NFT's brand application cases can be confirmed, and comparisons and cases can be applied to how much it can contribute to the rise in the value of beauty brands in the future. The 41 references were finally selected using PRISMA flow diagram.

RESULTS

In a rapidly changing time, the beauty industry pays attention to the NFT market that can develop economically. Therefore, the parts that various industries are preemptively trying were prepared by confirming the cases.

CONCLUSION

The beauty industry should pay attention to the NFT market, which is an issue, and by creating a brand with scarcity value, it can apply economic activities in the beauty industry to the trend by releasing products that both consumers and sellers can satisfy.

Drivers of e-government use during the COVID-19 pandemic: the case of Vietnam

Purpose

The COVID-19 pandemic has disrupted various systems that drove people to adapt to certain technologies, such as electronic government services, for daily survival and to meet social distancing requirements. Therefore, this study aims to determine the antecedents of e-government use based on prospect theory and modified unified theory of acceptance use of technology (UTAUT) during a pandemic.

Design/methodology/approach

Convenience sampling of 368 respondents from Vietnam was conducted, and questionnaires were distributed personally or by email. The data were analyzed following a two-stage structural equation modeling (SEM) using SPSS v23 and AMOS v23. The validity and reliability of the instrument were tested and ensured.

Findings

Results show that perceived severity drives government support and social influence while perceived security drives government support, social influence and trust. Social influence enhances government support and trust, which both drives e-government use. Mediation analysis shows that government support mediates perceived the influence of perceived severity on e-government use.

Practical implications

The integration of prospect theory and UTAUT brings into light what will drive the adoption of e-government in the context of Vietnam. Supporting mechanisms, such as security measures, trust-building, government support and social influence, will drive citizens to adapt to technologies provided by the government but would also rely on the perceived risks and benefits.

Originality/value

This study integrates prospect theory and a modified version of UTAUT to explain the drivers of e-government use. The results reveal that under uncertainties, government support is critical in driving the use of e-government for people to manage the daily lives for survival.

Analysis and modeling of changes in online shopping behavior due to Covid-19 pandemic: A Florida case study

The emergence of eCommerce and online shopping commenced a new episode in human life and changed trading patterns. Online shopping provided access to a broader range of products and facilitated their delivery, which increased demand. To respond to the increased demand, more heavy commercial vehicles need to be on the roads to deliver orders. This is while the road infrastructure is not ready for such a swift shift, and most roads and bridges were planned and constructed during the 19s when online shopping was not coined yet. The continued increase of heavy vehicles on roads can intensify the deterioration of roads and structures such as bridges. Therefore, there is a significant need for an update on new shopping trends, especially changes in people's behavior due to the ongoing Covid-19 pandemic, and to assess if the pandemic permanently changed the trends of in-store and online shopping. This study first examines the NHTS 2017 data to find the attributes that are significant to online shoppers' behavior. Then a survey is developed to scrutinize Covid-19 effects on the online shopping behavior of users before, during, and after the Covid-19 pandemic. 206 records of data are interpreted through descriptive analysis and discrete choice modeling of users' responses to find the most significant attributes affecting their online shopping behavior. The findings of discrete choice modeling and descriptive analysis support that people tend to go back to stores after the pandemic. The findings of this study show that online and in-store shopping would be balanced after the pandemic and would pursue their normal trends as they were before the pandemic. Based on the findings of this study, it is hard to state that online shopping can vanish in-store shopping due to Covid-19. People still need to go to stores to fulfill their needs for the joy of shopping, interactions with other people, and touching the products they would like to buy. Therefore, transportation stakeholders need to pay special attention to both in-store and online shopping for their planning and operation management of ground transportation infrastructure.

A New Destination on the Palm? The Moderating Effect of Travel Anxiety on Digital Tourism Behavior in Extended UTAUT2 and TTF Models

Digital tourism has developed rapidly, especially in museums. However, as people become increasingly familiar with digital museums, their use intentions and behavior have changed. Taking the Digital Palace Museum in China as an example, applying the PLS-SEM method, this study uncovers visitors' use intentions and actual use behavior for digital museums by integrating the new UTAUT model (UTAUT2) and TTF model (TTF under social distancing) and introduces the PATS (Pandemic Anxiety Travel Scale) model to reveal how pandemic anxiety promotes the transformation of use intentions into use behavior more easily. The results show that performance expectations, hedonic motivations, habits, and task-technology-fit positively affect use intentions for digital museums. However, the price-saving orientation negatively affects use intentions. Pandemic anxiety moderates the effect of use intentions on actual behavior. When travel anxiety is relatively high, use intentions have a greater effect on use behavior for digital museums. The results reveal the influencing factors on use intentions of digital museums and the moderating effect of pandemic anxiety on the relation between use intentions and actual behavior.

Using Online Grocery Applications during the COVID-19 Pandemic: Their Relationship with Open Innovation

This present research examines the behavioral intentions of Filipinos to use online grocery applications during the novel COVID-19 pandemic. The study proposes an integration of the health belief model (HBM) and the Unified Theory of Acceptance and Use of Technology (UTAUT2) to identify the factors affecting the acceptance and usage of Filipinos of online grocery applications in terms of the impact of health risk for COVID-19. To accurately measure the factors and their relationship to behavioral intentions and usage behavior, a questionnaire was developed and distributed to 373 residents in the Philippines. Partial least squares structural equation modeling (PLS-SEM) was applied as an analytical method for this study. The results revealed that performance expectancy, perceived benefits, perceived severity, and cues to action significantly influenced the behavioral intentions and usage of online grocery apps during the COVID-19 pandemic. The study’s findings can be utilized as a theoretical framework for future researchers of consumer behavior; e-commerce developers; and grocery industry retailers, to enhance the innovation and services of online grocery applications. The results of this study may also be used and capitalized on by investors and managers to apply in strategizing when developing and marketing online grocery applications among consumers. Moreover, the framework of this study may be adopted and utilized by other online markets, even in different counties. Further theoretical and practical aspects are discussed in this paper.

Exploration on the Influencing Factors of Overseas Students' Online Chinese Learning Intention During the Epidemic Period

In the information age, online teaching has become an essential field of educational development. The work aims to analyze the factors influencing learning intention of Overseas Students (OSs) during the Coronavirus Disease 2019 (COVID-19). The work adopts the method of Unified Theory of Acceptance and Use of Technology (UTAUT), and implements the influencing factor model of open source software online Chinese learning (OCL). Specifically, the OSs of five colleges and universities in Fuzhou are taken as the research objects. The data is collected through questionnaire survey (QS) and Spss25.0 to analyze the reliability and validity of the data, and Amos23.0 is used to validate the model. The results present that autonomy and self-discipline have become the necessary factors affecting open source software OCL, and personal interests and hobbies are the main factors affecting open source software OCL. Meantime, perceived interest, self-learning management, effort expectation, community influence, performance expectation, and promotion conditions have a significant positive impact on the willingness to promote Chinese learning, and their influence degree increases in turn. The conclusion can provide a novel online teaching and learning strategy for open source software under the COVID-19 situation.

Factors Influencing Residents' Behavior in Internet Recycling: From the Perspective of the Adoption of New Technology

Achieving carbon neutrality has become a major national strategy for sustainability, and the recycling of recyclable resources is an important direction toward doing so. Due to the huge amounts of recyclable resources generated every year and the low recycling rate, a new Internet recycling model with great potential to increase the recycling rate has developed rapidly in China. However, low participation from residents hinders the sustainable development of Internet recycling. Through this study, we aim to uncover potential avenues for improving Internet recycling behavior. The factors influencing Internet recycling from the perspective of new technologies have scarcely been investigated. Therefore, this study used the Unified Theory of Acceptance and Use of Technology theoretical framework to explore the factors influencing residents’ intentions and behavior toward Internet recycling. A questionnaire survey was conducted with 500 residents of Beijing, China, and empirical analysis was conducted using the structural equation model. The results indicated that social influence and performance expectancy significantly influence residents’ intentions to participate in Internet recycling, whereas effort expectancy and perceived risk do not. Facilitating conditions and behavioral intentions were identified as influential factors for use behavior. Relevant recommendations for promoting residents’ Internet recycling behavior were proposed.