{"title":"电脑并不总是做得更好!计算机自动奖励计算对消费者低碳行为数字激励满意度的影响","authors":"Xin Jiang , Zhihua Ding , Yupeng Mou , Yue Liu , Manqiong Shen","doi":"10.1016/j.resconrec.2024.107991","DOIUrl":null,"url":null,"abstract":"<div><div>Digital incentive tools encourage participants by recording and rewarding their daily low-carbon behavior on digital platforms, ultimately fostering a low-carbon lifestyle. This research explores the contextual factor affecting the effectiveness of rewards in digital incentive tools, specifically the impact of computer-automated calculation design (vs. self-calculation design) on satisfaction towards rewards. Through four controlled experiments focused on green commuting with American samples and one field experiment on clothing recycling with a Chinese sample, this research finds when participants notified of rewards, the computer-automated calculation design (vs. self-calculation design) reduces their satisfaction towards rewards. That is, when participants notified of potential rewards, presented computer-calculated outcomes automatically (rather than allowed to self-calculate their own rewards) would diminish their satisfaction towards rewards. This effect is mediated by the reduced salience of reward elements rather than decreased self-involvement. Furthermore, listing reward components can alleviate this negative impact. This research enhances the literature on extrinsic rewards and low-carbon behavior by identifying the design of automated reward calculations as a novel factor undermining reward effectiveness, and recommending practitioners to enhance participants' perception of elements constituting the rewards.</div></div>","PeriodicalId":21153,"journal":{"name":"Resources Conservation and Recycling","volume":"212 ","pages":"Article 107991"},"PeriodicalIF":11.2000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The computer does not always do it better! The effect of computer-automated reward calculations on consumer satisfaction with digital incentives for low-carbon behavior\",\"authors\":\"Xin Jiang , Zhihua Ding , Yupeng Mou , Yue Liu , Manqiong Shen\",\"doi\":\"10.1016/j.resconrec.2024.107991\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Digital incentive tools encourage participants by recording and rewarding their daily low-carbon behavior on digital platforms, ultimately fostering a low-carbon lifestyle. This research explores the contextual factor affecting the effectiveness of rewards in digital incentive tools, specifically the impact of computer-automated calculation design (vs. self-calculation design) on satisfaction towards rewards. Through four controlled experiments focused on green commuting with American samples and one field experiment on clothing recycling with a Chinese sample, this research finds when participants notified of rewards, the computer-automated calculation design (vs. self-calculation design) reduces their satisfaction towards rewards. That is, when participants notified of potential rewards, presented computer-calculated outcomes automatically (rather than allowed to self-calculate their own rewards) would diminish their satisfaction towards rewards. This effect is mediated by the reduced salience of reward elements rather than decreased self-involvement. Furthermore, listing reward components can alleviate this negative impact. This research enhances the literature on extrinsic rewards and low-carbon behavior by identifying the design of automated reward calculations as a novel factor undermining reward effectiveness, and recommending practitioners to enhance participants' perception of elements constituting the rewards.</div></div>\",\"PeriodicalId\":21153,\"journal\":{\"name\":\"Resources Conservation and Recycling\",\"volume\":\"212 \",\"pages\":\"Article 107991\"},\"PeriodicalIF\":11.2000,\"publicationDate\":\"2024-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Resources Conservation and Recycling\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0921344924005822\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ENVIRONMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Resources Conservation and Recycling","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0921344924005822","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
The computer does not always do it better! The effect of computer-automated reward calculations on consumer satisfaction with digital incentives for low-carbon behavior
Digital incentive tools encourage participants by recording and rewarding their daily low-carbon behavior on digital platforms, ultimately fostering a low-carbon lifestyle. This research explores the contextual factor affecting the effectiveness of rewards in digital incentive tools, specifically the impact of computer-automated calculation design (vs. self-calculation design) on satisfaction towards rewards. Through four controlled experiments focused on green commuting with American samples and one field experiment on clothing recycling with a Chinese sample, this research finds when participants notified of rewards, the computer-automated calculation design (vs. self-calculation design) reduces their satisfaction towards rewards. That is, when participants notified of potential rewards, presented computer-calculated outcomes automatically (rather than allowed to self-calculate their own rewards) would diminish their satisfaction towards rewards. This effect is mediated by the reduced salience of reward elements rather than decreased self-involvement. Furthermore, listing reward components can alleviate this negative impact. This research enhances the literature on extrinsic rewards and low-carbon behavior by identifying the design of automated reward calculations as a novel factor undermining reward effectiveness, and recommending practitioners to enhance participants' perception of elements constituting the rewards.
期刊介绍:
The journal Resources, Conservation & Recycling welcomes contributions from research, which consider sustainable management and conservation of resources. The journal prioritizes understanding the transformation processes crucial for transitioning toward more sustainable production and consumption systems. It highlights technological, economic, institutional, and policy aspects related to specific resource management practices such as conservation, recycling, and resource substitution, as well as broader strategies like improving resource productivity and restructuring production and consumption patterns.
Contributions may address regional, national, or international scales and can range from individual resources or technologies to entire sectors or systems. Authors are encouraged to explore scientific and methodological issues alongside practical, environmental, and economic implications. However, manuscripts focusing solely on laboratory experiments without discussing their broader implications will not be considered for publication in the journal.