Exploring the Impact of Social Robot Design Characteristics on Users' Privacy Concerns: Evidence from PLS-SEM and FsQCA

Chen, Yongkang1; Wu, Xingting2; Jia, Fusheng1; Yang, Jingyan1; Bai, Xiangtian3; Yu, Ruyang4

2024-09-01

10.1080/10447318.2024.2402126

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION   影响因子和分区

1044-7318

1532-7590

Although an increasing number of studies explore the factors influencing users' privacy concerns regarding social robots, the existing understanding of this issue remains largely fragmented. Previous studies have mainly focused on the "net effect" between variables, leaving the complexity of causal configurations, and the holistic impact of design characteristics of social robots on user privacy concerns remains unclear. Based on the Stimuli-Organism-Response (S-O-R) framework and Communication Privacy Management Theory (CPMT), this study integrates social robot design characteristics such as Anthropomorphism, Warmth, Competence, and Transparency into causal configurations, and uses Perceived Privacy Risk and Perceived Privacy Control as mediating variables to propose a Comprehensive conceptual model. Based on valid data from a sample of 198 Chinese social robot users, this study conducted empirical analyses of the conceptual model using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (FsQCA). PLS-SEM results show that anthropomorphism, warmth, competence, and transparency are key factors influencing privacy concerns, and perceived privacy risk mediates the relationship between warmth, information transparency, and privacy concerns. The FsQCA results further validated the findings of PLS-SEM and identified five configurations of factor combinations that led to higher levels of user privacy concerns. Among them, the combination of high anthropomorphism design, high competence, and low warmth of social robots is the core configuration that leads to users' privacy concerns. Overall, this study broadens our understanding of social robot users' privacy concerns and reveals the causal complexity behind social robot users' privacy concerns. It provides some theoretical and practical insights for subsequent scholars and designers.

Human-computer interaction social robot privacy concern FsQCA PLS-SEM

SCI ; SSCI

英语

其他

Computer Science ; Engineering

Computer Science, Cybernetics ; Ergonomics

WOS:001314341200001

TAYLOR & FRANCIS INC

Web of Science

1.Tongji Univ, Coll Design & Innovat, Shanghai, Peoples R China
2.Swinburne Univ Technol, Swinburne Living Lab, Melbourne, Australia
3.Hunan Univ, Sch Design, Changsha, Peoples R China
4.Southern Univ Sci & Technol, Sch Design, Shenzhen, Peoples R China

Chen, Yongkang,Wu, Xingting,Jia, Fusheng,et al. Exploring the Impact of Social Robot Design Characteristics on Users' Privacy Concerns: Evidence from PLS-SEM and FsQCA[J]. INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION,2024.

Chen, Yongkang,Wu, Xingting,Jia, Fusheng,Yang, Jingyan,Bai, Xiangtian,&Yu, Ruyang.(2024).Exploring the Impact of Social Robot Design Characteristics on Users' Privacy Concerns: Evidence from PLS-SEM and FsQCA.INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION.

Chen, Yongkang,et al."Exploring the Impact of Social Robot Design Characteristics on Users' Privacy Concerns: Evidence from PLS-SEM and FsQCA".INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION (2024).