Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Web personalization research: an information systems perspective

Web personalization research: an information systems perspective Purpose – Web personalization has been studied in different streams of research such as Marketing, Human Computer Interaction and Computer Science. However, an information systems perspective of web personalization research is very scarcely visible in this body of knowledge. This research review seeks to address two important questions: how has web personalization evolved as an integrative discipline? How has web personalization been treated in IS literature and where should researchers focus next? Design/methodology/approach – The paper intently follows an information systems perspective in its thematic classification of web personalization research which is consistent with the early conceptualization of information systems by logically mapping IS categories into web personalization research streams. Articles from 100+ journals were analyzed and important concepts related to web personalization were classified from an information systems perspective. Findings – Surrounding the theme of web personalization two parallel streams of research evolved. First stream consisted of internet business models, computer science algorithms and web mining. Second stream focussed on human computer Interaction studies, user modelling and targeted marketing. Future information systems researchers in web personalization must focus on four important areas of social media, web development methodologies, emerging Internet accessing gadgets and domains other than e‐Commerce. Originality/value – Web personalization has been studied previously in separate research streams. But no integrated view from different research streams exists. Although research interest in web mining has been growing as evidenced by growing number of publications an information systems perspective of web personalization research is very scarcely visible in the body of knowledge. The authors intently follow an information systems perspective in their thematic classification of web personalization research which is consistent with the early conceptualization of information systems by logically mapping IS categories into web personalization research streams. This thematic segregation of different research streams into IS framework makes our study distinct from other early reviews. They also identify four important areas where future IS researchers should focus on. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Systems and Information Technology Emerald Publishing

Web personalization research: an information systems perspective

Download PDF
15 pages

Loading next page...
 
/lp/emerald-publishing/web-personalization-research-an-information-systems-perspective-XMgep0aqU6
Publisher
Emerald Publishing
Copyright
Copyright © 2013 Emerald Group Publishing Limited. All rights reserved.
ISSN
1328-7265
DOI
10.1108/JSIT-11-2012-0065
Publisher site
See Article on Publisher Site

Abstract

Purpose – Web personalization has been studied in different streams of research such as Marketing, Human Computer Interaction and Computer Science. However, an information systems perspective of web personalization research is very scarcely visible in this body of knowledge. This research review seeks to address two important questions: how has web personalization evolved as an integrative discipline? How has web personalization been treated in IS literature and where should researchers focus next? Design/methodology/approach – The paper intently follows an information systems perspective in its thematic classification of web personalization research which is consistent with the early conceptualization of information systems by logically mapping IS categories into web personalization research streams. Articles from 100+ journals were analyzed and important concepts related to web personalization were classified from an information systems perspective. Findings – Surrounding the theme of web personalization two parallel streams of research evolved. First stream consisted of internet business models, computer science algorithms and web mining. Second stream focussed on human computer Interaction studies, user modelling and targeted marketing. Future information systems researchers in web personalization must focus on four important areas of social media, web development methodologies, emerging Internet accessing gadgets and domains other than e‐Commerce. Originality/value – Web personalization has been studied previously in separate research streams. But no integrated view from different research streams exists. Although research interest in web mining has been growing as evidenced by growing number of publications an information systems perspective of web personalization research is very scarcely visible in the body of knowledge. The authors intently follow an information systems perspective in their thematic classification of web personalization research which is consistent with the early conceptualization of information systems by logically mapping IS categories into web personalization research streams. This thematic segregation of different research streams into IS framework makes our study distinct from other early reviews. They also identify four important areas where future IS researchers should focus on.

Journal

Journal of Systems and Information Technology – Emerald Publishing

Published: Aug 9, 2013

Keywords: Information systems; Web personalization; Information systems framework; Recommendation systems; User modelling; Electronic commerce; Web mining

References

  • Data data everywhere – and not a byte of use?
    Abbott, J.
  • Using data mining methods to build customer profiles
    Adomavicius, G.; Tuzhilin, A.
  • Toward the next generation of recommender systems: a survey of the state‐of‐the‐art and possible extensions
    Adomavicius, G.; Tuzhilin, A.
  • Context‐aware recommender systems
    Adomavicius, G.; Tuzhilin, A.
  • Internet Business Models and Strategies: Text and Cases
    Afuah, A.; Tucci, C.
  • Fast algorithms for projected clustering
    Aggarwal, C.; Wolf, J.; Yu, P.; Procopiuc, C.; Park, J.
  • Developing and validating an instrument for measuring user‐perceived web quality
    Aladwani, A.; Palvia, P.
  • Internet recommendation systems
    Ansari, A.; Essegaier, S.; Kohli, R.
  • Establishing trust in electronic commerce through online word of mouth: an examination across genders
    Awad, N.; Ragowsky, A.
  • Fab: content‐based, collaborative recommendation
    Balabanovic, M.; Shoham, Y.
  • Electronic markets and virtual value chains on the information superhighway
    Benjamin, R.; Wigand, R.
  • Conceptual modeling and code generation for rich internet applications
    Bozzon, A.; Comai, S.; Fraternali, P.; Carughi, G.
  • Empirical analysis of predictive algorithms for collaborative filtering
    Breese, J.; Heckerman, D.; Kadie, C.
  • Methods and techniques of adaptive hypermedia
    Brusilovsky, P.
  • Adaptive hypermedia
    Brusilovsky, P.
  • Mining the web's link structure
    Chakrabarti, S.; Dom, B.; Kumar, S.; Raghavan, P.; Rajagopalan, S.; Tomkins, A.; Gibson, D.; Kleinberg, J.
  • When online reviews meet hyperdifferentiation: a study of the craft beer industry
    Clemons, E.; Gao, G.; Hitt, L.
  • Web mining: information and pattern discovery on the world wide web
    Cooley, R.; Mobasher, B.; Srivastava, J.
  • I didn't buy it for myself
    Cranor, L.
  • Google news personalization: scalable online collaborative filtering
    Das, A.; Datar, M.; Garg, A.; Rajaram, S.
  • Perceived usefulness, perceived ease of use, and user acceptance of information technology
    Davis, F.
  • User acceptance of computer technology: a comparison of two theoretical models
    Davis, F.; Bagozzi, R.; Warshaw, P.
  • Web mining for web personalization
    Eirinaki, M.; Vazirgiannis, M.
  • Strategy and the new economics of information
    Evans, P.; Wurster, T.
  • On power‐law relationships of the internet topology
    Faloutsos, M.; Faloutsos, P.; Faloutsos, C.
  • Adaptable and adaptive information provision for all users, including disabled and elderly people
    Fink, J.; Kobsa, A.; Nill, A.
  • The Oxford Handbook of Management Information Systems: Critical Perspectives and New Directions
    Galliers, R.; Currie, W.
  • An algorithmic framework for performing collaborative filtering
    Herlocker, J.; Konstan, J.; Borchers, A.; Riedl, J.
  • Commercial scenarios for the web: opportunities and challenges
    Hoffman, D.; Novak, T.; Chatterjee, P.
  • Does animation attract online users' attention? The effects of flash on information search performance and perceptions
    Hong, W.; Thong, J.; Tam, K.
  • Searching for experience on the web: an empirical examination of consumer behavior for search and experience goods
    Huang, P.; Lurie, N.; Mitra, S.
  • Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering
    Huang, Z.; Chen, H.; Zeng, D.
  • Using the lens of max Weber's theory of bureaucracy to examine e‐government research
    Jain, A.
  • The effects of presentation formats and task complexity on online consumers' product understanding
    Jiang, Z.
  • Referral web: combining social networks and collaborative filtering
    Kautz, H.; Selman, B.; Shah, M.
  • Adrosa – adaptive personalization of web advertising
    Kazienko, P.; Adamski, M.
  • Automatic recommendations for e‐learning personalization based on web usage mining techniques and information retrieval
    Khribi, M.; Jemni, M.; Nasraoui, O.
  • Generic user modeling systems
    Kobsa, A.
  • Personalised hypermedia presentation techniques for improving online customer relationships
    Kobsa, A.; Koenemann, J.; Pohl, W.
  • Grouplens: applying collaborative filtering to usenet news
    Konstan, J.; Miller, B.; Maltz, D.; Herlocker, J.; Gordon, L.; Riedl, J.
  • Web mining research: a survey
    Kosala, R.; Blockeel, H.
  • Personalising web page presentation for older people
    Kurniawan, S.; King, A.; Evans, D.; Blenkhorn, P.
  • Internet Companion: A Beginner's Guide to Global Networking
    LaQuey, T.
  • Amazon.com recommendations: item‐to‐item collaborative filtering
    Linden, G.; Smith, B.; York, J.
  • Enterprise 2.0: the dawn of emergent collaboration
    McAfee, A.
  • Business models for internet‐based e‐commerce
    Mahadevan, B.
  • There's gold in them thar networks! or searching for treasure in all wrong places
    Martin, J.
  • A program for research on management information systems
    Mason, R.; Mitroff, I.
  • Data mining for web personalization
    Mobasher, B.
  • Creating adaptive web sites through usage‐based clustering of URLs
    Mobasher, B.; Cooley, R.; Srivastava, J.
  • Automatic personalization based on web usage mining
    Mobasher, B.; Cooley, R.; Srivastava, J.
  • Effective personalization based on association rule discovery from web usage data
    Mobasher, B.; Dai, H.; Luo, T.; Nakagawa, M.
  • A taxonomy of recommender agents on the internet
    Montaner, M.; López, B.; De La Rosa, J.
  • What makes a helpful online review? A study of customer reviews on Amazon.com
    Mudambi, S.; Schuff, D.
  • Personalization on the net using web mining: introduction
    Mulvenna, M.; Anand, S.; Büchner, A.
  • Web engineering: a new discipline for development of web‐based systems
    Murugesan, S.; Deshpande, Y.; Hansen, S.; Ginige, A.
  • The effects of consumer knowledge on message processing of electronic word‐of‐mouth via online consumer reviews
    Park, D.; Kim, S.
  • Exploring the implications of the internet for consumer marketing
    Peterson, R.; Balasubramanian, S.; Bronnenberg, B.
  • Managing in the marketspace
    Rayport, J.; Sviokla, J.
  • Recommender systems in e‐commerce
    Schafer, J.; Konstan, J.; Riedl, J.
  • E‐commerce recommendation applications
    Schafer, J.; Konstan, J.; Riedl, J.
  • Mining query logs: turning search usage data into knowledge
    Silvestri, F.
  • Web usage mining: discovery and applications of usage patterns from web data
    Srivastava, J.; Cooley, R.; Deshpande, M.; Tan, P.
  • Mygrid: personalised bioinformatics on the information grid
    Stevens, R.; Robinson, A.; Goble, C.
  • Web personalization as a persuasion strategy: an elaboration likelihood model perspective
    Tam, K.; Ho, S.
  • Understanding the impact of web personalization on user information processing and decision outcomes
    Tam, K.; Ho, S.
  • Reinventing local governments and the e‐government initiative
    Tat‐Kei Ho, A.
  • Understanding information technology usage: a test of competing models
    Taylor, S.; Todd, P.
  • Business models for electronic markets
    Timmers, P.
  • A survey on context‐aware web service systems
    Truong, H.; Dustdar, S.
  • Designing the interface of rich internet applications
    Urbieta, M.; Rossi, G.; Ginzburg, J.; Schwabe, D.
  • Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology
    Venkatesh, V.; Thong, J.; Xu, X.
  • Quantitative metrics for measuring web accessibility
    Vigo, M.; Arrue, M.; Brajnik, G.; Lomuscio, R.; Abascal, J.
  • E‐government and the transformation of service delivery and citizen attitudes
    West, D.
  • Implementing core IS capabilities: Feeny‐Willcocks IT governance and management framework revisited
    Willcocks, L.; Feeny, D.; Olson, N.
  • E‐government research: reviewing the literature, limitations, and ways forward
    Yildiz, M.