SSRScientific and Social Research2661-43322981-9946Bio-Byword Scientific Publishing Pty. Ltd.10.26689/ssr.v7i8.12018ArticleArtificial Intelligence and Social Interaction: Evidence from Gift Money Expenditurehttps://artdesignp.com/journal/SSR/7/8/10.26689/ssr.v7i8.12018HanZiyu2025782025-09-17As artificial intelligence proliferates rapidly, understanding its impact on social interaction patterns becomes critical. Using national survey data and gift money expenditure as a proxy for social interaction, the study employs instrumental variable methods to identify the causal effect of AI use on individual social behavior. The study documents three key findings. First, AI use significantly reduces traditional social interaction through substitution effects, with instrumental variable estimates showing that OLS substantially underestimates the true magnitude, confirming the technology substitution hypothesis. Second, diminished social willingness serves as a key mediating mechanism—AI use reduces social behavior by weakening non-family social preferences, demonstrating how technology shapes behavior through preference channels. Third, the substitution effect exhibits significant demographic heterogeneity, with younger, more educated, and higher-income individuals displaying greater sensitivity to technology adoption, consistent with digital divide patterns. These findings provide micro-empirical evidence of social relationship transformation in the digital era. The results suggest policymakers should emphasize social inclusiveness in AI adoption while promoting balanced development between digital innovation and traditional social engagement.

McCarthy J, Minsky ML, Rochester N, et al., 2006, A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence: August 31, 1955. AI Magazine, 27(4): 12–14.

Chui M, Cam A, Hall B, 2019, Global AI Survey: AI Proves Its Worth, but Few Scale Impact. McKinsey & Company, New York.

Rao AS, Verweij G, 2017, Sizing the Prize: What’s the Real Value of AI for Your Business and How Can You Capitalise? PwC Publication, London.

Xie XM, Guo XH, 2024, How Do Artificial Intelligence Deep Learning Platforms Achieve Open Innovation. China Industrial Economics, 2024(8): 174–192.

Trajtenberg M, 2018, AI as the Next GPT: A Political-Economy Perspective (NBER Working Paper No. 24245). National Bureau of Economic Research, Cambridge.

Kromann L, Malchow-Møller N, Skaksen JR, et al., 2020, Automation and Productivity: A Cross-Country, Cross-Industry Comparison. Industrial and Corporate Change, 29(2): 265–287.

Alguacil M, Cuadros A, Orts V, 2022, Inward FDI and Import Demand for Intermediate Inputs: The Role of Backward Linkages and Technology Transfer. Review of World Economics, 158(2): 307–335.

Acemoglu D, Restrepo P, 2018, Artificial Intelligence, Automation and Work (NBER Working Paper No. 24196). National Bureau of Economic Research, Cambridge.

Frey CB, Osborne MA, 2017, The Future of Employment: How Susceptible Are Jobs to Computerisation? Technological Forecasting and Social Change, 2017(114): 254–280.

Cai YZ, Chen N, 2019, Artificial Intelligence and High-Quality Growth and Employment Under the New Technological Revolution. The Journal of Quantitative & Technical Economics, 36(5): 3–22.

Zhao ZJ, Zhuang XY, 2023, High-Quality Development of Artificial Intelligence in China: Current Situation, Problems and Strategies. Reform, 2023(9): 11–20.

Yao JQ, Zhang KP, Guo LP, et al., 2024, How Does Artificial Intelligence Improve Enterprise Production Efficiency? From the Perspective of Labor Skill Structure Adjustment. Management World, 2024(2): 101–116.

Li G, Bai YP, 2024, How Does Artificial Intelligence Application Affect Manufacturing Enterprise Innovation Performance? Finance & Economics, 2024(12): 102–112.

Zhang BW, Ma FH, 2024, How Does Intelligent Manufacturing Enhance Supply Chain Resilience: Theoretical Mechanism and Empirical Test. Economic Perspectives, 2024(11): 20–37.

Chen YB, Lin C, Chen XL, 2019, Artificial Intelligence, Aging and Economic Growth. Economic Research Journal, 54(7): 47–63.

Wu J, Huang JY, 2023, Culture and Intergenerational Entrepreneurship in Family Businesses: Cultural Embedding in Intergenerational Inheritance. China Industrial Economics, 2023(11): 174–192.

Manski CF, 2000, Economic Analysis of Social Interactions. Journal of Economic Perspectives, 14(3): 115–136.

Brown JR, Ivković Z, Smith PA, et al., 2008, Neighbors Matter: Causal Community Effects and Stock Market Participation. The Journal of Finance, 63(3): 1509–1531.

Li T, 2006, Social Interaction, Information Channels and Household Stock Market Participation. Economic Research Journal, 2006(1): 34–44.

Sun WJ, Lin HM, 2018, Financial Exclusion, Social Interaction and Household Asset Allocation. Journal of Central University of Finance & Economics, 2018(3): 21–38.

Liu H, Ma WH, 2017, Social Interaction and Household Capital Market Participation Behavior in the Internet Era. Studies of International Finance, 2017(3): 55–66.

Yang H, Zhang K, 2020, Cognitive Ability, Social Interaction Modes and Household Asset Choice: Empirical Analysis Based on China Family Panel Studies (CFPS) Data. Investment Research, 39(5): 67–81.

Guo JY, Wang YY, Wang CX, 2023, The Impact and Mechanism of Social Interaction on Rural Residents’ Online Shopping Behavior. Journal of Hunan Agricultural University (Social Sciences), 24(6): 45–54.

Li D, Ding JS, Ma S, 2019, The Impact of Social Interaction on Household Commercial Insurance Participation: Empirical Analysis from China Household Finance Survey (CHFS) Data. Studies of International Finance, 2019(7): 96–114.

Ma XL, 2024, Micro-Test of Internet Development’s Impact on Urban-Rural Consumption Gap: Considering the Mediating Role of Residents’ Income and Social Interaction. Commercial Research, 2024(2): 51–54.

Putnam RD, 2000, Bowling Alone: The Collapse and Revival of American Community. Simon & Schuster, New York.

Turkle S, 2011, Alone Together: Why We Expect More from Technology and Less from Each Other. Basic Books, New York.

Acemoglu D, Restrepo P, 2020, Robots and Jobs: Evidence from US Labor Markets. Journal of Political Economy, 128(6): 2188–2244.

Zhang H, Gao ZH, Xu Y, 2024, The Impact Mechanism of AI Technology Substitution Perception on Human-AI Cooperation Willingness in the Workplace. Soft Science, 38(3): 107–114.

Wei W, 2022, Research on the Differentiation of Artificial Intelligence Employment Substitution Effects and Innovation Effects. Soft Science, 36(3): 55–61.

Wang LH, Hu SM, Dong ZQ, 2022, Artificial Intelligence Technology, Task Attributes and Occupational Substitution Risk: Empirical Evidence from Micro-Level. Management World, 38(7): 60–79.

Bandura A, 1986, Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice-Hall, Englewood Cliffs.

Zhan XJ, Wan Y, Li ZC, et al., 2025, Research on the Impact Mechanism of Gig Workers’ Self-Leadership on Proactive Service Behavior from the Perspective of Social Cognitive Theory. Chinese Journal of Management, 22(1): 65–73.

Wang XP, Zhong RY, 2025, Research on the Impact Mechanism of New Media Contact on Depression in the Elderly: Empirical Research Based on Social Cognitive Theory. Journal of Yunnan Minzu University (Philosophy and Social Sciences Edition), 42(3): 62–71.

Hu XY, 2024, Media Literacy Deficiency and Penetration of Silver-Haired Groups in the New Media Era: Discussion Based on the Perspective of Digital Divide Theory. Journalism & Communication, 2024(1): 49–51.

Xu WB, Li PW, 2022, Research on the Impact of Social Interaction on Chinese Urban Household Out-of-School Education Expenditure: Based on CFPS2018 Cross-Sectional Data. Education & Economy, 38(6): 56–65.

Rosen LD, Whaling K, Carrier LM, et al., 2013, The Media and Technology Usage and Attitudes Scale: An Empirical Investigation. Computers in Human Behavior, 29(6): 2501–2511.

Araujo T, Wonneberger A, Neijens P, et al., 2017, How Much Time Do You Spend Online? Understanding and Improving the Accuracy of Self-Reported Measures of Internet Use. Communication Methods and Measures, 11(3): 173–190.

Junco R, 2013, Comparing Actual and Self-Reported Measures of Facebook Use. Computers in Human Behavior, 29(3): 626–631.

Araujo T, Boeschoten L, Oberski DL, et al., 2023, Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking. Communication Methods and Measures, 17(2): 89–110.

Marres N, 2017, Digital Sociology: The Reinvention of Social Research. Polity Press, Cambridge.

Tian ZF, Li T, Fu L, 2022, Family Relationships and Household Consumption. Economic Research Journal, 57(6): 173–190.

Jiang T, 2022, Mediation Effects and Moderation Effects in Causal Inference Empirical Research. China Industrial Economics, 2022(5): 100–120.

Skågeby J, 2010, Gift-Giving as a Conceptual Framework: Framing Social Behavior in Online Networks. Journal of Information Technology, 25(2): 170–177.

Li QQ, Fan YW, Song WJ, 2021, Research on the Impact of Social Context on Interest in Experiential Consumption Participation. Nankai Business Review, 24(3): 4–17.

Feng DW, Gao MT, Zhou L, 2022, Internet and Urban-Rural Consumption Gap: Evidence from Household Micro-Survey. China Economic Studies, 2022(3): 98–114.

Ramulu B, Justin P, 2023, Social Influence Research in Consumer Behavior: What We Learned and What We Need to Learn?—A Hybrid Systematic Literature Review. Journal of Business Research, 2023(162): 113916.

Liu SH, Zhang X, Zhu XH, et al., 2020, Empirical Research on Rural Education Oriented to Scientific Decision-Making. Journal of East China Normal University (Educational Sciences), 38(9): 103–118.

Lin X, Xie JY, Lin SX, 2022, Research on the Impact of Mobile Internet Use on Different Consumption Categories of Rural Households: Empirical Analysis Based on 2018 CFPS Data. Lanzhou Academic Journal, 2022(2): 113–123.

Sun DY, Wang SS, Liang F, 2025, How Do Cultural Consumption Policies Affect Rural Household Consumption? A Quasi-Natural Experiment Based on Cultural Consumption Pilot Policies. Journal of Huazhong Agricultural University (Social Sciences Edition), 2025(3): 128–138.

Chen L, Gao YP, Yu LH, 2024, How Does Artificial Intelligence Change Enterprise Demand for Labor? Analysis from Big Data of Recruitment Platforms. Management World, 40(6): 74–93.

Xu CY, Qian C, Kong FB, 2024, Can Digital Rural Construction Narrow the Urban-Rural Income Gap? Empirical Test Based on Agricultural Technological Progress Mediation and Threshold Effects. Journal of Agrotechnical Economics, 2024(12): 4–24.

Zhou YX, 2012, Is Social Capital the Capital of the Poor? Empirical Evidence Based on Chinese Farmers’ Income. Management World, 2012(7): 83–95.

Pan LQ, Zhang SH, 2022, Can Social Networks Improve Migrant Workers’ Wages? Analysis Based on Path and Heterogeneity. Journal of Guangdong University of Finance & Economics, 37(2): 15–28.

Chen YZ, Zhou Q, 2024, Research on the Willingness and Influencing Factors of Mutual Support for Rural Elderly in Northeast China: Based on the Fifth Sample Survey Data of Living Conditions of Urban and Rural Elderly in China. Study & Exploration, 2024(12): 103–110.

Dong MJ, Liang YM, Zhang QZ, 2015, China’s Industrial Capacity Utilization: Industry Comparison, Regional Gap and Influencing Factors. Economic Research Journal, 50(1): 84–98.