Product search in e‐shopping: a review and research propositions

By Jennifer E. Rowley. 2000

304 citations recorded by the source corpus.

Subfields: Information Systems and Management, Marketing, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/200b6c0862263c8fcd184d600dba8053bdedf089

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In Information Systems and Management

In Marketing

In Organizational Behavior and Human Resource Management

Citations in the corpus

The Effects of Information Format and Shopping Task on Consumers' Online Shopping Behavior: A Cognitive Fit Perspective (2004, background): “First, although it is generally acknowledged that consumers go online for different purposes [9, 50, 64], there is little research into the design of the Web interface to support the different shopping tasks.”
Modeling the impact of internet atmospherics on surfer behavior (2005, background): “…may be reached; and is general or purposeful. b Purposive Q browsing occurs when surfers have specific requirements, whereas b general Q browsing allows surfers to fine-tune knowledge of their requirements or to keep themselves up-to-date on the latest changes in a specific field (Rowley, 2000).”
How credibility affects eWOM reading: The influences of expertise, trustworthiness, and similarity on utilitarian and social functions (2014, background): “Furthermore, an ongoing interest in a product or a brand often leads to a constant search for related information (Rowley 2000), similar to browsing behavior (Bloch and Richins 1986).”
Relationships in online communities: the potential for marketers (2010, background): “An increasing acceptance of technology and the growth in confidence which comes with familiarity of use of online searches and purchasing, online consumers have become more sophisticated in their use of the internet ( Rowley, 2000”
Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval (2023, background): “Image retrieval brings great convenience to our daily life in various areas such as e-commerce search [8, 16], face recognition [17, 32], and landmark localization [13, 24].”
Explainable Product Search with a Dynamic Relation Embedding Model (2019, background): “Because users often search for “a producer’s name, a brand or a set of terms which describe the category of the product" in e-shopping [48], queries extracted with this paradigm are usually sufficient to simulate real-world product search queries [6, 57].”