Do consumers always believe humans create better boxes than AI? The context-dependent role of recommender creativity

创造力 背景(考古学) 产品(数学) 独创性 推荐系统 感知 心理学 消费(社会学) 消费者行为 众包 广告 营销 计算机科学 应用心理学 社会心理学 业务 万维网 社会学 生物 古生物学 神经科学 社会科学 数学 几何学
作者
Hyunjoo Im,Garim Lee
出处
期刊:International Journal of Retail & Distribution Management [Emerald (MCB UP)]
卷期号:51 (8): 1045-1060 被引量:6
标识
DOI:10.1108/ijrdm-09-2021-0449
摘要

Purpose The recent surge of subscription box services calls for research to understand how consumers respond to curation services. This study aims to develop and test a theoretical model to predict consumer response to AI (vs human). Particularly, the authors tested the role of stereotyping in shaping consumer perception of creativity in this context while considering the contextual moderators, shopping goals (hedonic vs utilitarian) and product category (fashion vs meal). Design/methodology/approach Two preliminary studies and the main study (total n = 761) tested the assumptions and hypotheses of the study. Preliminary study 1 ( n = 511 Amazon mTurk, online survey) confirmed consumer stereotypes of humans and machines. Preliminary study 2, a single-factor between-subjects online experiment (recommender: human vs AI), was conducted at a large Midwestern university in the US ( n = 56). The main study was conducted as a 2(recommender: human vs AI) × 2(product: fashion vs meal) × 2(goal: utilitarian vs hedonic) between-subjects online experiment ( n = 194, Amazon mTurk). Findings The results confirmed that consumers are more likely to follow recommendations made by a human more than recommendations made by AI and the perceived creativity of the recommender explained the effect. Significant differences across product categories and shopping goals of the consumers were observed, calling for attention to the context of consumption. Originality/value This study extends the understanding of consumers' responses to recommendations in curation subscription services by highlighting the role of perceived creativity of humans versus AI.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
ll完成签到 ,获得积分10
刚刚
刚刚
加菲丰丰给元迎夏的求助进行了留言
1秒前
852应助张鱼小丸子采纳,获得10
2秒前
翠翠发布了新的文献求助80
4秒前
6秒前
6秒前
毛豆应助古今奇观采纳,获得10
11秒前
123456qi发布了新的文献求助10
11秒前
Garlician发布了新的文献求助10
12秒前
12秒前
不配.应助科研菜鸟采纳,获得10
13秒前
13秒前
科研通AI2S应助altman88采纳,获得10
14秒前
twob发布了新的文献求助10
15秒前
16秒前
16秒前
张鱼小丸子完成签到,获得积分10
16秒前
灵巧慕凝完成签到,获得积分10
19秒前
充电宝应助深海之镜采纳,获得10
19秒前
19秒前
20秒前
斯文败类应助123456qi采纳,获得10
21秒前
21秒前
醉生梦死发布了新的文献求助10
23秒前
霁星河发布了新的文献求助10
23秒前
gaowei完成签到,获得积分10
25秒前
27秒前
28秒前
糊涂发布了新的文献求助10
29秒前
一只特立独行的猫完成签到,获得积分10
32秒前
哈哈哈发布了新的文献求助10
33秒前
con完成签到 ,获得积分10
34秒前
YYDS发布了新的文献求助10
34秒前
growing发布了新的文献求助10
35秒前
rosalieshi应助bamboo采纳,获得30
36秒前
kydd完成签到,获得积分10
38秒前
Promise发布了新的文献求助20
39秒前
Garlician关注了科研通微信公众号
40秒前
不配.应助魁梧的雨双采纳,获得80
40秒前
高分求助中
Histotechnology: A Self-Instructional Text 5th Edition 2000
Rock-Forming Minerals, Volume 3C, Sheet Silicates: Clay Minerals 2000
The late Devonian Standard Conodont Zonation 2000
Nickel superalloy market size, share, growth, trends, and forecast 2023-2030 2000
The Lali Section: An Excellent Reference Section for Upper - Devonian in South China 1500
Encyclopedia of Computational Mechanics,2 edition 800
The Healthy Socialist Life in Maoist China 600
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3270960
求助须知:如何正确求助?哪些是违规求助? 2910251
关于积分的说明 8353362
捐赠科研通 2580767
什么是DOI,文献DOI怎么找? 1403723
科研通“疑难数据库(出版商)”最低求助积分说明 655921
邀请新用户注册赠送积分活动 635309