{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:dissertations-1867"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:dissertations-1867","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"Sifting customers from the clickstream : behavior pattern discovery in a virtual shopping environment","abstract":"While shopping online, customers' needs and goals may change dynamically, based on a variety of factors such as product information and characteristics, time pressure and perceived risk. While these changes create emergent information needs, decisions about what information to present to customers are typically made before customers have visited a web site, using data such as purchase histories and logs of web pages visited. Better understanding of customer cognition and behavior as a function of various factors is needed in order to enable the right information to be presented at the right time. One approach to achieving this understanding is to develop predictions about what information to present based on inferences made from cognitively-grounded models of the customer, calibrated according to an analysis of what behaviors can be observed during the online shopping experience (e.g., clickstream produced by mouse clicks and typing). As a step in achieving this objective, this research tests hypotheses about how differences in product involvement, time pressure, and uncertainty and riskiness of choice may impact a customer's search and decision strategies, time on task, and perceived risk while shopping online. It draws upon the results of prior research, as well as two pilot studies, to motivate the design of a study involving human participants making purchasing decisions in an online shopping environment. The main data sources are the think-aloud protocols and clickstreams of the participants, as well as pre- and post-experiment questionnaires. This work is expected to improve understanding of how contextual, personal and product-related factors help shape online shopping behavior, and to generate insights into the cognitive processes that inform this behavior. Future work beyond the thesis is likely to involve more formal modeling of human cognition in online shopping environments.","abstract_html":"While shopping online, customers' needs and goals may change dynamically, based on a variety of factors such as product information and characteristics, time pressure and perceived risk. While these changes create emergent information needs, decisions about what information to present to customers are typically made before customers have visited a web site, using data such as purchase histories and logs of web pages visited. Better understanding of customer cognition and behavior as a function of various factors is needed in order to enable the right information to be presented at the right time. One approach to achieving this understanding is to develop predictions about what information to present based on inferences made from cognitively-grounded models of the customer, calibrated according to an analysis of what behaviors can be observed during the online shopping experience (e.g., clickstream produced by mouse clicks and typing). As a step in achieving this objective, this research tests hypotheses about how differences in product involvement, time pressure, and uncertainty and riskiness of choice may impact a customer's search and decision strategies, time on task, and perceived risk while shopping online. It draws upon the results of prior research, as well as two pilot studies, to motivate the design of a study involving human participants making purchasing decisions in an online shopping environment. The main data sources are the think-aloud protocols and clickstreams of the participants, as well as pre- and post-experiment questionnaires. This work is expected to improve understanding of how contextual, personal and product-related factors help shape online shopping behavior, and to generate insights into the cognitive processes that inform this behavior. Future work beyond the thesis is likely to involve more formal modeling of human cognition in online shopping environments.","abstract_has_math":false,"creators":["Chang, Peishih"],"institution":null,"degree_name":"Doctor of Philosophy in Information Systems - (Ph.D.)","degree_level":null,"degree_discipline":"Information Systems","degree_department":null,"school":null,"contributors":["David Mendonca","Starr Roxanne Hiltz","Il Im"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007-05-31T07:00:00Z","date_published":"2007-05-31T07:00:00Z","updated_at":"2026-07-24T03:23:04Z","subjects":["Cognitive modeling","Customer behavior","Online shopping","Information seeking","Clickstream data","Information display board","Databases and Information Systems","Management Information Systems"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/dissertations/812","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["David Mendonca","Starr Roxanne Hiltz","Il Im"]},{"key":"dc:creator","label":"Author","values":["Chang, Peishih"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Information Systems"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Information Systems - (Ph.D.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Cognitive modeling","Customer behavior","Online shopping","Information seeking","Clickstream data","Information display board","Databases and Information Systems","Management Information Systems"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.njit.edu/dissertations/812"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["While shopping online, customers' needs and goals may change dynamically, based on a variety of factors such as product information and characteristics, time pressure and perceived risk. While these changes create emergent information needs, decisions about what information to present to customers are typically made before customers have visited a web site, using data such as purchase histories and logs of web pages visited. Better understanding of customer cognition and behavior as a function of various factors is needed in order to enable the right information to be presented at the right time. One approach to achieving this understanding is to develop predictions about what information to present based on inferences made from cognitively-grounded models of the customer, calibrated according to an analysis of what behaviors can be observed during the online shopping experience (e.g., clickstream produced by mouse clicks and typing). As a step in achieving this objective, this research tests hypotheses about how differences in product involvement, time pressure, and uncertainty and riskiness of choice may impact a customer's search and decision strategies, time on task, and perceived risk while shopping online. It draws upon the results of prior research, as well as two pilot studies, to motivate the design of a study involving human participants making purchasing decisions in an online shopping environment. The main data sources are the think-aloud protocols and clickstreams of the participants, as well as pre- and post-experiment questionnaires. This work is expected to improve understanding of how contextual, personal and product-related factors help shape online shopping behavior, and to generate insights into the cognitive processes that inform this behavior. Future work beyond the thesis is likely to involve more formal modeling of human cognition in online shopping environments."]},{"key":"dc:title","label":"Title","values":["Sifting customers from the clickstream : behavior pattern discovery in a virtual shopping environment"]}]}],"canonical_facts":{"dc:contributor":["David Mendonca","Starr Roxanne Hiltz","Il Im"],"dc:creator":["Chang, Peishih"],"dc:description.abstract":["While shopping online, customers' needs and goals may change dynamically, based on a variety of factors such as product information and characteristics, time pressure and perceived risk. While these changes create emergent information needs, decisions about what information to present to customers are typically made before customers have visited a web site, using data such as purchase histories and logs of web pages visited. Better understanding of customer cognition and behavior as a function of various factors is needed in order to enable the right information to be presented at the right time. One approach to achieving this understanding is to develop predictions about what information to present based on inferences made from cognitively-grounded models of the customer, calibrated according to an analysis of what behaviors can be observed during the online shopping experience (e.g., clickstream produced by mouse clicks and typing). As a step in achieving this objective, this research tests hypotheses about how differences in product involvement, time pressure, and uncertainty and riskiness of choice may impact a customer's search and decision strategies, time on task, and perceived risk while shopping online. It draws upon the results of prior research, as well as two pilot studies, to motivate the design of a study involving human participants making purchasing decisions in an online shopping environment. The main data sources are the think-aloud protocols and clickstreams of the participants, as well as pre- and post-experiment questionnaires. This work is expected to improve understanding of how contextual, personal and product-related factors help shape online shopping behavior, and to generate insights into the cognitive processes that inform this behavior. Future work beyond the thesis is likely to involve more formal modeling of human cognition in online shopping environments."],"dc:identifier":["https://digitalcommons.njit.edu/dissertations/812"],"dc:subject":["Cognitive modeling","Customer behavior","Online shopping","Information seeking","Clickstream data","Information display board","Databases and Information Systems","Management Information Systems"],"dc:title":["Sifting customers from the clickstream : behavior pattern discovery in a virtual shopping environment"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Information Systems"],"thesis:degree_name":["Doctor of Philosophy in Information Systems - (Ph.D.)"]},"updated_at":"2026-07-24T03:23:04Z"}