垃圾分类回收是解决城市垃圾管理问题的重点和难点，也是垃圾回收逆向物流的关键。如何提高居民的垃圾分类回收参与度是有效实施垃圾分类，解决“垃圾围城”困境的重点。随着大数据、物联网、人工智能等信息技术的普及，智能垃圾分类回收系统开始在社区中应用，以新兴技术来推动垃圾分类回收工作的开展，使垃圾分类回收更加精细化。众多学者研究了影响居民垃圾分类回收意图与行为的关键因素，但还未有研究探索信息技术对居民垃圾分类回收行为的影响。

因此，本文基于我国垃圾分类回收的现状及信息系统使用行为研究模型，制定访谈提纲，对受访者进行半结构化访谈。然后基于扎根理论，对原始资料进行深入分析，提炼影响用户使用智能垃圾分类系统的关键因素。通过基于扎根理论的质性分析，得出影响用户接受并使用智能垃圾分类系统的重要因素是感知有用性、感知易用性、奖励积分、感知时间成本、责任意识、环境关心、信任、社会影响。智能垃圾分类系统对居民垃圾分类回收行为有着重要影响作用。质性分析为后续研究模型的建立进行了探索。因此，本文在质性分析基础上，整合动机理论和技术接受模型，建立智能垃圾分类系统使用意愿研究模型，提出假设，针对研究问题设计量表，并通过调查问卷进行数据收集。最后利用结构方程模型对智能垃圾分类系统使用意愿及垃圾分类回收行为进行实证研究。

结果表明，智能垃圾分类系统的奖励积分功能、居民对环境的关心、智能设备素养以及社会影响、信任等个体与社会情感因素对智能垃圾分类系统使用意愿有显著影响作用。由此可以得出，基于新兴技术的智能垃圾分类系统还是比较容易被用户接受并使用的，而且智能垃圾分类系统能够促进居民垃圾分类行为，帮助居民养成垃圾分类习惯，进一步帮助解决垃圾分类的困境。随着科技发展，信息技术的应用有利于促进实施垃圾分类工作。最后，本研究的分析结果对居民垃圾分类行为的学术研究以及垃圾分类回收的实践工作都具有一定的指导意义。

Garbage sorting and recycling are the focus and difficulty in the urban garbage disposal, and it is also the key to the reverse logistics of garbage recycling. How to increase the residents' participation in garbage classification and recycling is the key to effectively implementing garbage sorting and solve the dilemma of "garbage siege". With the popularization of information technologies such as big data, the Internet of Things, and artificial intelligence, intelligent waste sorting and recycling systems have gradually begun to be applied in the community. Emerging technologies is used to promote the development of waste sorting and recycling, making waste sorting and recycling more refined. Many scholars have studied the key factors that affect the residents' waste sorting and recycling intentions and behaviors. However, there is no research to explore the impact of information technology on the residents' waste sorting and recycling behaviors.

Therefore, based on the status quo of waste sorting and recycling in my country and the research model of information system usage behaviors, this paper develops interview outlines, conducts semi-structured in-depth interviews with respondents. Then based on Grounded theory to conduct an in-depth analysis of the original data, this research extracts the key factors that affect the user's use of the intelligent waste classification system. Through a qualitative analysis based on Grounded theory, the important factors that affect the acceptance and use of the intelligent waste classification system are perceived usefulness, perceived ease of use, reward points, perceived time cost, sense of responsibility, environmental care, trust, and social influence. The intelligent waste sorting system has an important influence on residents' waste sorting and recycling behavior. Qualitative analysis explores the establishment of follow-up research models. Therefore, on the basis of qualitative analysis, this article integrates motivation theory and Technology acceptance model, establishes a research model for the willingness to use intelligent waste classification system, proposes hypotheses, designs a scale for research questions, and collects data through questionnaires. Finally, the structural equation model is used to conduct empirical research on the willingness to use the intelligent waste sorting system and the behavior of waste sorting and recycling.

The results show that the influencing factors such as the point incentive function of the intelligent waste classification system, residents' concern for the environment, the literacy of intelligent equipment, social influence and trust, have significant effects on the willingness to use the intelligent waste classification system. It can be concluded that the intelligent waste classification system based on information technology are relatively easy to be accepted and used by users. And the intelligent waste classification systems can also promote residents' waste classification behavior, help residents develop waste classification resources and further help solve the dilemma of waste classification. With the development of science and technology, the application of information technology is conducive to the effective implementation of waste classification. Finally, this research's results have certain guiding significance for the academic research of residents' waste classification behavior and the practical work of garbage recycling.

[1]吴宏基,岳江静. 基于大数据的垃圾分类智能化应用研究[J].营销界,2020(35): p. 109-110.
[2]刘慧敏.上海垃圾分类实施现状及对策研究[J].农村经济与科技,2020(22): p. 23-24.
[3]鲁先锋.垃圾分类管理中的外压机制与诱导机制[J].城市问题,2013(1): p. 86－91.
[4]Calabrò, P.S. Greenhouse gases emission from municipal waste management: the role of separate collection[J]. Waste Management, 2009. 29(7): p. 2178-2187.
[5]Wadehra, S. and A. Mishra. Encouraging urban households to segregate the waste they generate: insights from a field experiment in Delhi,. India[J]. Resourc. Conserv. Recycl., 2018. 134: p. 239-247.
[6]Pakpour, A.H., et al. Household waste behaviours among a community sample in Iran: An application of the theory of planned behaviour[J]. Waste management, 2014. 34(6): p. 980-986.
[7]Wang, Z., et al. How does information publicity influence residents’ behaviour intentions around e-waste recycling[J]? Resources, Conservation and Recycling, 2018. 133: p. 1-9.
[8]Ajzen, I. From intentions to actions: a theory of planned behavior[J]. Action-Control: Cogn. Behav., 1985: p. 1-63.
[9]Guagnano, G.A., P.C. Stern, and T. Dietz. Inflfluences on attitude-behavior relationships: a natural experiment with curbside recycling[J]. Environ. Behav, 1995. 27(5): p. 699–718.
[10]Meng, X., et al. Investigation on decision-making mechanism of residents’ household solid waste classification and recycling behaviors[J]. Resources, Conservation and Recycling, 2019. 140: p. 224-234.
[11]Liu, X., et al. Mechanisms of public education influencing waste classification willingness of urban residents[J]. Resources, Conservation and Recycling, 2019. 149: p. 381-390.
[12]Boonrod, K., et al. Enhancing organic waste separation at the source behavior: A case study of the application of motivation mechanisms in communities in Thailand[J]. Resources, Conservation and Recycling, 2015. 95: p. 77-90.
[13]Fan, B., W. Yang, and X. Shen. A comparison study of ‘motivation–intention–behavior’model on household solid waste sorting in China and Singapore[J]. Journal of cleaner production, 2019. 211: p. 442-454.
[14]Chen, F., et al. Impact of regulatory focus on express packaging waste recycling behavior: Moderating role of psychological empowerment perception[J]. Environmental Science and Pollution Research, 2019. 26(9): p. 8862-8874.
[15]Botetzagias, I., A.F. Dima, and C. Malesios. Extending the theory of planned behavior in the context of recycling: The role of moral norms and of demographic predictors[J]. Resources, conservation and recycling, 2015. 95(58-67).
[16]Davis, G., A.D. Read, and Y. Iida. Demonstrating the need for the development of internal research capacity: Understanding recycling participation using the theory of planned behavior in West Oxford shire,UK[J]. Resour. Conserv. Recy, 2006. 46: p. 115–127.
[17]Ramayah, T., J.W.C. Lee, and S. Lim. Sustaining the environment through recycling: An empirical study[J]. Environ. Manage, 2012. 102: p. 141–147.
[18]Bernstad, A. Household food waste separation behavior and the importance of convenience[J]. Waste Management, 2014. 34: p. 1317-1323.
[19]Nguyen, T.T.P., D. Zhu, and N.P. Le. Factors influencing waste separation intention of residential households in a developing country: Evidence from Hanoi, Vietnam[J]. Habitat International, 2015. 48: p. 169-176.
[20]Sidique, S.F., F. Lupi, and S.V. Joshi. The effects of behavior and attitude on drop-off recycling activities[J]. Resources, Conservation and Recycling, 2010. 54: p. 163-170.
[21]徐林, 凌卯亮, 卢昱杰. 城市居民垃圾分类的影响因素研究[J]. 公共管理学报, 2017. 14(1): p. 142-153.
[22]刘俊娥, 刘彭. 区域性餐厨垃圾逆向物流系统设计与管理研究[J]. 物流技术, 2013(15): p. 19-21.
[23]叶健飞, 马慧民, 李祥祥. 城市垃圾分类回收逆向物流监管机制的演化博弈分析[J]. 物流科技, 2015. 38(7): p. 78-82.
[24]徐颖, 葛新权, 王伟. 城市垃圾分类回收逆向物流三方主体进化博弈行为分析——基于垃圾分类回收市场化运作模式的探索[J]. 经济研究参考, 2017(40): p. 10.
[25]孙其昂, 孙旭友, 张虎彪. 为何不能与何以可能：城市生活垃圾分类难以实施的“结”与“解”[J]. 中国地质大学学报, 2014(11): p. 63-67.
[26]韩明敏, 袁凤林. 居民垃圾分类的意愿及影响因素调查研究——以扬州市为例[J]. 现代商贸工业, 2018. 39(12): p. 126-127.
[27]杨洋. 城市生活垃圾分类的认知影响因素研究——以天长市为例[J]. 智库时代, 2018(42): p. 188.
[28]金沙田科技. OFweek维科号[EB].
https://mp.ofweek.com/ecep/a645693228976.2020
[29]吴晓菁. 厦门新闻[EB]. https://fj.qq.com/a/20170505/008535.htm.2017
[30]曾莹莹. 北极星固废网[EB]. http://huanbao.bjx.com.cn/news/20201110/1114899.shtml.2020
[31]Glaser, B.G., A.L. Strauss, and E. Strutzel., The Discovery of Grounded Theory; Strategies for Qualitative Research[J]. Nursing Research, 1968(4).
[32]武慧娟. 微阅读用户持续使用行为影响因素及作用路径研究[J]. 情报科学, 2020. 38(06): p. 76-82+102.
[33]孙晓娥. 扎根理论在深度访谈研究中的实例探析[J]. 西安交通大学学报(社会科学版), 2011. 31(06): p. 87-92.
[34]张冰倩. 智慧城市App用户适应性信息行为过程研究[J]. 情报理论与实践, 2021(1-12).
[35]Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS quarterly, 1989: p. 319-340.
[36]Holden, H. and R. Rada. Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance[J]. Journal of Research on Technology in Education, 2011. 43(4): p. 343–367.
[37]Venkatesh, V. and F.D. Davis. A theoretical extension of the technology acceptance model: Four longitudinal field studies[J]. Management science, 2000. 46(2): p. 186-204.
[38]Venkatesh, V., et al. User acceptance of information technology: Toward a unified view[J]. MIS quarterly, 2003: p. 425-478.
[39]Al-Alak, B.A. and I.A.M. Alnawas. Measuring the acceptance and adoption of e-learning by academic staff[J]. Knowledge Management and E-Learning, 2011. 3(2): p. 201–221.
[40]Alharbi, S. and S. Drew. Using the Technology Acceptance Model in Understanding Academics’ Behavioural Intention to Use Learning Management Systems[J]. International Journal of Advanced Computer Science and Applications, 2014. 5(1): p. 142–155.
[41]Jie, L. and Z. Jing. An Empirical Study into Consumer Acceptance of Dockless Bikes Sharing System Based on TAM[J]. Sustainability, 2021. 13(4).
[42]Ngabiyanto, et al. Teacher’s intention to use online learning; an extended technology acceptance model (TAM) investigation[J]. Journal of Physics: Conference Series, 2021. 1783(1).
[43]ShinYi, L., J. PinJu, and L. ShengWei. A TAM Framework to Evaluate the Effect of Smartphone Application on Tourism Information Search Behavior of Foreign Independent Travelers[J]. Sustainability, 2020. 12(22).
[44]Deci, E.L. and R.M. Ryan. A motivational approach to self: Integration in personality[J]. Nebraska Symposium on Motivation Nebraska Symposium on Motivation, 1961. 38(2): p. 237.
[45]Deci, E.L. Intrinsic motivation,extrinsic reinforcement,and inequity[J]. Pers.Soc.Psychol., 1972. 22(1): p. 113.
[46]Davis, F.D., R.P. Bagozzi, and P.R. Warshaw. Extrinsic and intrinsic motivation to use computers in the workplace[J]. Appl.Soc.Psychol., 1992. 22(14): p. 1111-1132.
[47]Lin, C.S., S. Wu, and R.J. Tsai. Integrating perceived playfulness into expectation-confirmation model for web portal context[J]. Tnform.Manag., 2005. 42(5): p. 683-693.
[48]Shang, R.A., Y.C. Chen, and L. Shen. Extrinsic versus intrinsic motivations for consumers to shop on-line[J]. Inf.Manage., 2005. 42(3): p. 401-413.
[49]Wamba, S.F., et al. Role of intrinsic and extrinsic factors in user social media acceptance within workspace:assessing unobserved heterogeneity[J]. Inf.Manage., 2017. 37(2): p. 1-13.
[50]Fassinger, R.E. Paradigms, praxis, problems, and promise: Grounded theory in counseling psychology research[J]. Journal of Counseling Psychology, 2005. 52(2): p. 156-166.
[51]P, O.A., L.-S.C. F, and e.a. Wong S F. An expectation-confirmation model of continuance intention to use mobile instant messaging[J]. Telematics and Informatics, 2016. 33(1): p. 34-47.
[52] Sun, Y., Liu, L., et al. Understanding Chinese users’ continuance intention toward online social networks: an integrative theoretical model[J]. Electronic Markets, 2014. 24(1): p. 57-66.
[53]Hsieh, J.P.A., A. Rai, and M. Keil. Understanding digital inequality: Comparing continued use behavioral models of the socio-economically advantaged and disadvantaged[J]. MIS quarterly, 2008: p. 97-126.
[54]Iyer, E.S. and R.K. Kashyap. Consumer recycling: Role of incentives, information, and social class. [J] . Journal of Consumer Behaviour: An International Research Review, 2007. 6(1): p. 32-47.
[55]Matsumoto, S. The opportunity cost of pro-environmental activities:separating time to promote the environment or earning more money[J]? Journal of Family and Economic Issues, 2014. 35: p. 119-130.
[56]Stern, P.C. A Value-Belief-Norm theory of support for social movements: the case of environmentalism[J]. Research in Human Ecology, 1999. 6(2): p. 81-97.
[57]Vicente, P. and E. Reis. Factors influencing households’ participation in recycling[J]. Waste Management & Research, 2008. 26(2): p. 140-146.
[58]Simmons, D. and R. Widmar. Motivations and barriers to recycling: toward a strategy for Public Education[J]. The Journal of Environmental Education, 1990. 22(1): p. 13-18.
[59]Merritt, R. Nine predictions for CES 2011[EB].http://www.eetimes.com/electronics-news/4211787/Nine-predictions-for-CES-2011.2011
[60]Phillips, S.R. Student Discussions in Cooperative Learning Groups in a High School Mathematics Classroom: A Descriptive Multiple Case Study[J]. Doctoral Thesis, The University of Akron, United States, 2010.
[61]C-L., H. and L.J. C-C. What drives purchase intention for paid mobile apps?–An expectation confirmation model with perceived value[J]. Electronic Commerce Research and Applications, 2015. 14(1): p. 46-57.
[62]Crano, W.D. and R.Prislin. Attitudes and persuasion[J]. Annual Review of Psychology, 1995. 57(1): p. 345.
[63]P., P., et al., Implementation intention and reminder effects on behavior change in a mobile health system: a predictive cognitive model[J]. Journal of medical Internet research, 2017. 19(11).
[64]M, K., S. S, and H. A. Charity donation: intentions and behaviour[J]. Marketing Intelligence & Planning, 2015. 33(1): p. 90-102.
[65]C, L., C. J-L, and Y.D. C. Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model[J]. Computers in Human Behavior, 2007. 23(6): p. 2804-2822.
[66]Bhattacherjee, A., Understanding information systems continuance: an expectation-confirmation model[J]. MIS quarterly, 2001: p. 351-370.
[67]Shen, L., et al. Factors Influencing Young People’s Intention toward Municipal Solid Waste Sorting[J]. International journal of environmental research and public health, 2019. 16(10): p. 1708.
[68]Zheng, Y., et al. The impact of organisational support, technical support, and self-efficacy on faculty perceived benefits of using learning management system[J]. Behaviour & Information Technology, 2018. 37(4): p. 311-319.
[69]S, M. Factors influencing continuance intention to use social network sites: The Facebook case[J]. Computers in Human Behavior, 2015. 53: p. 102-110.
[70]Kaiser, F.G. A General Measure of Ecological Behavior[J]. Journal of Applied Social Psychology, 1998. 28(5).
[71]Podsakoff, P.M., et al. Common method biases in behavioral research: a critical review of the literature and recommended remedies[J]. Appl Psychol, 2003. 88(5): p. 879-903.
[72]C, F. and L.D. F. Evaluating structural equation models with unobservable variables and measurement error[J]. Journal of marketing research, 1981. 18(1): p. 39-50.
[73]Leguina, A. A primer on partial least squares structural equation modeling (PLS-SEM) [J]. International Journal of Research & Method in Education, 2015. 38(2).