article

Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems

Authors:

Anthony M. Townsend,

Brian E. MenneckeAuthors Info & Claims

Information Systems and e-Business Management, Volume 13, Issue 4

Pages 769 - 799

https://doi.org/10.1007/s10257-014-0269-9

Published: 01 November 2015 Publication History

Abstract

This study investigates how consumers assess the quality of two types of recommender systems-collaborative filtering and content-based--in the context of e-commerce by using a modified version of the unified theory of acceptance and use of technology (UTAUT) model. Specifically, the concept of trust in the technological artifact is adapted to examine the intention to use recommender systems. Additionally, this study also considers hedonic and utilitarian product characteristics with the goal of presenting a comprehensive picture on recommender systems literature. This study utilized a 2 2 crossover within-subjects experimental design involving a total of 80 participants, who all evaluated each recommender system. The results suggest that the type of recommender system significantly moderates many relationships of the determinants of customer behavioral intent on behavioral intention to use recommender systems. Surprisingly, the type of product does not moderate any relationship on behavioral intention. This study holds importance in explaining the factors contributing to the use of recommender systems and understanding the relative influence of the two types of recommender systems on customer behavioral intention to use recommender systems. The finding also sheds light for designers on how to provide more effective recommender systems.

References

[1]

Adomavicius G, Tuzhilin A (2005) Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734-749.

Digital Library

[2]

Aggarwal P, Vaidyanathan R (2003) The perceived effectiveness of virtual shopping agents for search vs. experience goods. Adv Consum Res 30:347-348.

[3]

Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179-211.

[4]

Anderson NH, Barrios AA (1961) Primacy-recency in impression formation. J Abnorm Soc Psychol 63(2):346-350.

[5]

Ansari A, Essegaier S, Kohli R (2000) Internet recommendation systems. Mark Res 37(3):363-375.

[6]

Archak N, Ghose A, Ipeirotis PG (2011) Deriving the pricing power of product features by mining consumer reviews. Manage Sci 57(8):1485-1509.

Digital Library

[7]

Bandura A (1977) Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 84:191-215.

[8]

Bearden WO, Etzel MJ (1982) Reference group influence on product and brand purchase decisions. Consum Res 9(2):183-194.

[9]

Bettman JR (1979) An information processing theory of consumer choice. Addison-Wesley, Reading.

[10]

Brown S, Dennis A, Venkatesh V (2010) Predicting collaborative technology use: integrating technology adoption and collaboration research. J Manag Inf Syst 27(2):9-53.

Digital Library

[11]

Cheung W, Chang MK, Lai VS (2000) Prediction of internet and world wide web usage at work: a test of an extended triandis model. Decis Support Syst 30(1):83-100.

Digital Library

[12]

Childersa TL, Carrb CL, Peckc J, Carson S (2001) Hedonic and utilitarian motivations for online retail shopping behavior. J Retail 77(4):511-535.

[13]

Choi J, Lee HJ, Kim YC (2011) The influence of social presence on customer intention to reuse online recommender systems: the roles of personalization and product type. Int J Electron Commer 16(1):129-154.

Digital Library

[14]

Cooley R, Tan PN, Srivastava J (1999) WebSift: the web site information filter system. In: Paper presented at the the 1999 KDD workshop on web mining, San Diego, CA.

[15]

Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319-340.

Digital Library

[16]

Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manage Sci 37(1):36-48.

[17]

Davis FD, Bagozzi RP, Warshaw PR (1992) Extrinsic and intrinsic motivation to use computers in the workplace1. J Appl Soc Psychol 22(14):1111-1132.

[18]

Degemmis M, Lops P, Semeraro G, Costabile M, Guida S, Licchelli O (2004) Improving collaborative recommender systems by means of user profiles. In: Karat C-M, Blom JO, Karat J (eds) Designing personalized user experiences in eCommerce. Kluwer Academic Publishers, The Netherlands.

[19]

Dhar R, Wertenbroch K (2000) Consumer choice between hedonic and utilitarian goods. J Mark Res 37(1):60-71.

[20]

Dickinger A, Arami M, Meyer D (2008) The role of perceived enjoyment and social norm in the adoption of technology with network externalities. Eur J Inf Syst 17(1):4-11.

[21]

Feick L, Higie RA (1992) The effects of preference heterogeneity and source characteristics on ad processing and judgments about endorsers. J Advert 21:9-24.

[22]

Fishbein M, Ajzen I (1975) Belief, attitude, intention, and behavior: an introduction to theory and research. Addison-Wesley, Reading.

[23]

Fornell C, Larcker DF (1981) Structural equation models with unobservable variables and measurement error: algebra and statistics. J Mark Res 18(3):382-388.

[24]

Gallagher K, Parsons J, Foster KD (2001) A tale of two studies: replicating advertising effectiveness and content evaluation in print and on the web. J Advert Res 41(4):71-82.

[25]

Gefen D (2000) E-commerce: the role of familiarity and trust. Omega 28(6):725-737.

[26]

Gefen D, Straub DW (2003) Managing user trust in B2C e-services. E-service J 2(2):7-24.

[27]

Gefen D, Straub DW (2004) Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services. Omega 32(6):407-424.

[28]

Gefen D, Karahanna E, Straub DW (2003) Trust and TAM in online shopping: an integrated model. MIS Q 27(1):51-90.

[29]

Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans Knowl Data Eng 23(10):1498-1512.

Digital Library

[30]

Goodhue D, Thompson R (1995) Task-technology fit and individual performance. MIS Q 19(2):213-236.

Digital Library

[31]

Gutzman A (2000) Real time chat: what are you waiting for? http://www.ecommerce-guide.com/solutions/technology/article.php/322551.

[32]

Hassanein K, Head M (2005) The impact of infusing social presence in the web interface: an investigation across product types. Int J Electron Commer 10(2):31-55.

Digital Library

[33]

Helson H (1964) Adaptation-level theory: an experimental and systematic approach to behavior. Harper & Row, New York.

[34]

Heppner PP, Kivlighan DM, Wampold BE (2008) Research design in counseling Belmont. Thomson Brooks/Cole, CA.

[35]

Herlocker J, Konstan J, Riedl J (2000) Explaining collaborative filtering recommendations. In: Paper presented at the 2000 ACM conference on computer supported cooperative work, New York.

[36]

Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5-53.

Digital Library

[37]

Hirschman EC, Holbrook MB (1982) Hedonic consumption: emerging concepts, methods and propositions. J Mark 46(3):92-101.

[38]

Holbrook MB, Hirschman EC (1982) The experiential aspects of consumption: consumer fantasies, feelings, and fun. J Consum Res 9(2):132-140.

[39]

Hong W, Chan FKY, Thong JYL, Chasalow LC, Dhillon G (2014) A framework and guidelines for context-specific theorizing in information systems research. Inf Syst Res 25(1):111-136.

Digital Library

[40]

Hostler RE, Yoon YV, Guimaraes T (2005) Assessing the impact of internet agent on end users' performance. Decis Support Syst 41(1):313-323.

Digital Library

[41]

Igbaria M, Iivari J, Maragahh H (1995) Why do individuals use computer technology? A Finnish case study. Inf Manag 29(5):227-238.

Digital Library

[42]

Jones N, Pu P (2007) User technology adoption issues in recommender systems. In: Proceedings of NAEC, ATSMA, pp 379-339.

[43]

Jupiter Research (2003) Jupiter research reports that web site 'personalization' does not always provide positive results. Jupitermedia Corporation, Darien, Connecticut.

[44]

King MF, Balasubramanian SK (1994) The effects of expertise, end goal, and product type on adoption of preference formation strategy. Acad Mark Sci 22(2):146-159.

[45]

Kobsa A, Koenenmann J, Pohl W (2001) Personalized hypermedia presentation techniques for improving online customer relationships. Knowl Eng Rev 16(2):111-155.

Digital Library

[46]

Konstan JA, Riedl J (2012) Recommender systems: from algorithms to user experience. User Model User-Adap Inter 22:101-123.

Digital Library

[47]

Kramer T (2007) The effect of measurement task transparency on preference construction and evaluations of personalized recommendations. J Mark Res 44(2):224-233.

[48]

Krulwich B (1997) Lifestyle finder: intelligent user profiling using large-scale demographic data. AI Mag 18(2):37-54.

Digital Library

[49]

Kumar N, Benbasat I (2006) The influence of recommendations and consumer reviews on evaluations of websites. Inf Syst Res 17(4):425-429.

Digital Library

[50]

Liang T-P, Lai H-J, Ku Y-C (2007) Personalized content recommendation and user satisfaction: theoretical synthesis and empirical findings. J Manag Inf Syst 23(3):45-70.

Digital Library

[51]

Liberman H (1995) Letizia: an agent that assists web browsing. In: Paper presented at the international joint conference on artificial intelligence, Montreal, Canada.

[52]

Luhmann N (1979) Trust and power. Wiley, London.

[53]

McKnight DH, Choudhury V, Kacmar C (2002) Developing and validating trust measures for e-commerce: an integrative typology. Inf Syst Res 13(3):334-359.

Digital Library

[54]

McNee S, Riedl J, Konstan J (2006) Making recommendations better: an analytic model for human-recommender interaction. In: Paper presented at the 24th international conference human factors in computing systems, Montreál, Canada.

[55]

Mobasher B, Cooley R, Srivastava J (2000) Automatic personalization based on web usage mining. Commun ACM 43(8):142-151.

Digital Library

[56]

Montaner M, López B, de la Rosa JL (2003) A taxonomy of recommender agents on the internet. Artif Intell Rev 19(4):285-330.

Digital Library

[57]

Moore GC, Benbasat I (1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf Syst Res 2(3):192-222.

Digital Library

[58]

Muller KE, Stewart PW (2006) Linear model theory: univariate, multivariate, and mixed models. Wiley, New York.

[59]

Olshavsky RW (1985) Perceived quality in consumer decision-making: an integrated theoretical perspective. In: Olson JC (ed) Perceived uality: how consumers view stores and merchandise. D. C. Heath and Company, Lexington, pp 3-29.

[60]

Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web: methods and strategies of web personalization. Lecture notes in computer science. Springer, Berlin, pp 325-341.

[61]

Pu P, Chen L (2006) Trust building with explanation interfaces. In: Paper presented at the proceedings of the 11th international conference on intelligent user interfaces.

[62]

Pu P, Chen L, Hu R (2012) Evaluating recommender systems from the user's perspective: survey of the state of the art. User Model User-Adap Inter 22:317-355.

Digital Library

[63]

Rayport J, Jaworski B (2001) Introduction to e-Commerce. McGraw-Hill/Irwin, New York.

[64]

Resnick P, Varian HR (1997) Recommender Systems. Commun ACM 40(3):56-58.

Digital Library

[65]

Ryu G, Park J, Feick L (2006) The role of product type and country-of-origin in decisions about choice of endorser ethnicity in advertising. Psychol Mark 23(6):487-513.

[66]

Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web: methods and strategies of web personalization. Lecture notes in computer science. Springer, Berlin, pp 291-324.

[67]

Senecal S, Nantel J (2004) The influence of online product recommendations on consumers' online choices. J Retail 80(2):159-169.

[68]

Sinha R, Swearingen K (2001) Comparing recommendations made by online systems and friends proceedings. In: Paper presented at the 2nd DELOS network of excellence workshop on personalisation and recommender systems in Digital Libraries, Dublin, Ireland.

[69]

Sinha R, Swearingen K (2002) The role of transparency in recommender systems. In: Paper presented at the CHI 2002 extended abstracts on human factors in computing systems, Minneapolis, MN.

[70]

Stafford MR, Stafford TF, Day E (2002) A contingency approach: the effects of spokesperson type and service type on service advertising perceptions. J Advert 31:17-35.

[71]

Swearingen K, Sinha R (2002) Interaction design for recommender systems. In: Paper presented at the Designing Interactive System, London.

[72]

Taylor S, Todd PA (1995) Assessing IT usage: the role of prior experience. MIS Q 19(4):561-570.

Digital Library

[73]

Terveen L, Hill W (2001) Beyond recommender systems: helping people help each other. In: Carroll JM (ed) HCI in the New Millennium, vol 46. Addison-Wesley, New York, pp 487-509.

[74]

Thibaut JW, Kelley HH (1978) Interpersonal relations: a theory of interdependence. Wiley, New York.

[75]

van der Heijden H (2004) User acceptance of hedonic information systems. MIS Q 28(4):695-704.

[76]

Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci 46(2):186-204.

[77]

Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425-478.

[78]

Vijayasarathy LR, Jones JM (2001) Do internet shopping aids make a difference? An empirical investigation. Electron Mark 11(1):75-83.

[79]

Wang W, Benbasat I (2005) Trust in and adoption of online recommendation agents. J Assoc Inf Syst 6(3):72-101.

[80]

Wang W, Benbasat I (2007) Recommendation agents for electronic commerce: effects of explanation facilities on trusting beliefs. J Manag Inf Syst 23(4):217-246.

Digital Library

[81]

Xiao B, Benbasat I (2007) E-commerce product recommendation agents: use, characteristics, and impact. MIS Q 31(1):137-209.

[82]

Zhang TC, Agarwal R, Lucas HC Jr (2011) The value of IT-enabled retailer learning: personalized product recommendations and customer store loyalty in electronic markets. MIS Q 35(4):859-881.

Cited By

Jin YChen LCai WZhao X(2023)CRS-Que: A User-centric Evaluation Framework for Conversational Recommender SystemsACM Transactions on Recommender Systems10.1145/36315342:1(1-34)Online publication date: 2-Nov-2023
https://dl.acm.org/doi/10.1145/3631534
Gupta AShrinath P(2023)Link Prediction based on bipartite graph for recommendation system using optimized SVD++Procedia Computer Science10.1016/j.procs.2023.01.114218:C(1353-1365)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.procs.2023.01.114
Raffaghelli JRodríguez MGuerrero-Roldán ABañeres D(2022)Applying the UTAUT model to explain the students' acceptance of an early warning system in Higher EducationComputers & Education10.1016/j.compedu.2022.104468182:COnline publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1016/j.compedu.2022.104468
Show More Cited By

Index Terms

Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems

Index terms have been assigned to the content through auto-classification.

Recommendations

Determinants of behavioral intention to mobile banking

With the improvement of mobile technologies and devices, banking users are able to conduct banking services at anyplace and at anytime. Recently, many banks in the world have provided mobile access to financial information. The reason to understand what ...
Multi-faceted trust and distrust prediction for recommender systems

Many trust-aware recommender systems have explored the value of explicit trust, which is specified by users with binary values and simply treated as a concept with a single aspect. However, in social science, trust is known as a complex term with ...
The Influence of Social Presence on Customer Intention to Reuse Online Recommender Systems: The Roles of Personalization and Product Type

Providing recommendations is acknowledged to be an important feature of a business-to-consumer online storefront. Although many studies have examined the algorithms and operational procedures relevant to personalized recommender systems, empirical ...

Comments

Information & Contributors

Information

Published In

cover image Information Systems and e-Business Management

Information Systems and e-Business Management Volume 13, Issue 4

Nov 2015

200 pages

ISSN:1617-9846

Issue’s Table of Contents

Copyright © Copyright © 2015 Springer-Verlag Berlin Heidelberg.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2015

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Jin YChen LCai WZhao X(2023)CRS-Que: A User-centric Evaluation Framework for Conversational Recommender SystemsACM Transactions on Recommender Systems10.1145/36315342:1(1-34)Online publication date: 2-Nov-2023
https://dl.acm.org/doi/10.1145/3631534
Gupta AShrinath P(2023)Link Prediction based on bipartite graph for recommendation system using optimized SVD++Procedia Computer Science10.1016/j.procs.2023.01.114218:C(1353-1365)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.procs.2023.01.114
Raffaghelli JRodríguez MGuerrero-Roldán ABañeres D(2022)Applying the UTAUT model to explain the students' acceptance of an early warning system in Higher EducationComputers & Education10.1016/j.compedu.2022.104468182:COnline publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1016/j.compedu.2022.104468
Sun YWang P(2021)The E-Commerce Investment and Enterprise Performance Based on Customer Relationship ManagementJournal of Global Information Management10.4018/JGIM.20220701.oa930:3(1-15)Online publication date: 29-Sep-2021
https://dl.acm.org/doi/10.4018/JGIM.20220701.oa9
Jin YChen LCai WPu P(2021)Key Qualities of Conversational Recommender Systems: From Users’ PerspectiveProceedings of the 9th International Conference on Human-Agent Interaction10.1145/3472307.3484164(93-102)Online publication date: 9-Nov-2021
https://dl.acm.org/doi/10.1145/3472307.3484164
Seebacher SSchüritz RSatzger G(2021)Towards an understanding of technology fit and appropriation in business networks: evidence from blockchain implementationsInformation Systems and e-Business Management10.1007/s10257-020-00485-119:1(183-204)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s10257-020-00485-1
Sohn KKwon O(2020)Technology acceptance theories and factors influencing artificial Intelligence-based intelligent productsTelematics and Informatics10.1016/j.tele.2019.10132447:COnline publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.tele.2019.101324

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents