Engagement in proactive recommendations The role of recommendation accuracy, information privacy concerns and personality traits

被引:12
作者
Rook, Laurens [1 ]
Sabic, Adem [2 ]
Zanker, Markus [3 ]
机构
[1] Delft Univ Technol, Delft, Netherlands
[2] Alpen Adria Univ Klagenfurt, Klagenfurt, Austria
[3] Free Univ Bozen Bolzano, Bolzano, Italy
关键词
Information privacy; Concerns for information privacy; Proactive recommendation delivery; Personality-aware recommendations; Human-computer interaction; USER ACCEPTANCE; 5-FACTOR MODEL; E-COMMERCE; PARADOX; ONLINE; ANTECEDENTS; NEUROTICISM; DIMENSIONS; BEHAVIOR; SYSTEMS;
D O I
10.1007/s10844-018-0529-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The present research explored to what extent user engagement in proactive recommendation scenarios is influenced by the accuracy of recommendations, concerns with information privacy, and trait personality. We hypothesized that people's self-reported information privacy concerns would matter more when they received accurate (vs. inaccurate) proactive recommendations, because these pieces of advice would seem fair to them. We further hypothesized that this would particularly be the case for people high on the social personality trait Extraversion, who are by inclination prone to behaving in a more socially engaging manner. We put this to the test in a controlled experiment, in which users received manipulated proactive recommendations of high or low accuracy on their smartphone. Results indicated that information privacy concerns positively influenced a user's engagement with proactive recommendations. Recommendation accuracy influenced user engagement in interaction with information privacy concerns and personality traits. Implications for the design of human-computer interaction for recommender systems are addressed.
引用
收藏
页码:79 / 100
页数:22
相关论文
共 73 条
[1]   Personalization technologies: A process-oriented perspective [J].
Adomavicius, G ;
Tuzhilin, A .
COMMUNICATIONS OF THE ACM, 2005, 48 (10) :83-90
[2]  
Adomavicius G., 2015, RECOMMENDER SYSTEMS, P191
[3]   Analyzing count variables in individuals and groups: Single level and multilevel models [J].
Aiken, Leona S. ;
Mistler, Stephen A. ;
Coxe, Stefany ;
West, Stephen G. .
GROUP PROCESSES & INTERGROUP RELATIONS, 2015, 18 (03) :290-314
[4]  
Aiken LS., 1991, MULTIPLE REGRESSION
[5]   The impact of personality traits on users' information-seeking behavior [J].
Al-Samarraie, Hosam ;
Eldenfria, Atef ;
Dawoud, Husameddin .
INFORMATION PROCESSING & MANAGEMENT, 2017, 53 (01) :237-247
[6]   On the Internet no one knows I'm an introvert: Extroversion, neuroticism, and Internet interaction [J].
Amichai-Hamburger, Y ;
Wainapel, G ;
Fox, S .
CYBERPSYCHOLOGY & BEHAVIOR, 2002, 5 (02) :125-128
[7]  
[Anonymous], 1992, Revised NEO personality inventory (NEO-PI-R)
[8]  
[Anonymous], 2013, REGRESSION ANAL COUN
[9]  
Awad NF, 2006, MIS QUART, V30, P13
[10]  
Bachrach Y, 2012, PROCEEDINGS OF THE 3RD ANNUAL ACM WEB SCIENCE CONFERENCE, 2012, P24