122 research outputs found
Information Outlook, August 2006
Volume 10, Issue 8https://scholarworks.sjsu.edu/sla_io_2006/1007/thumbnail.jp
Information Outlook, December 2006
Volume 10, Issue 12https://scholarworks.sjsu.edu/sla_io_2006/1011/thumbnail.jp
Evaluating online review helpfulness based on Elaboration Likelihood Model: the moderating role of readability
It is important to understand factors affecting the perceived online review helpfulness as it helps solve the problem of information overload in online shopping. Moreover, it is also crucial to explore the factors’ relative importance in predicting review helpfulness in order to effectively detect potential helpful reviews before they exert influences. Applying Elaboration Likelihood Model (ELM), this study first investigates the effects of central cues (review subjectivity and elaborateness) and peripheral cues (reviewer rank) on review helpfulness with readability as a moderator. Second, it also explores their relative predicting power using the machine learning technique. ELM is tested in online context and the results are compared between experience and search goods. Our results provide evidence that for both types of products review subjectivity can play a more significant role when the content readability is high. Furthermore, this study reveals that the dominant predictor is varied for different product types
FAKE REVIEWS AND MANIPULATION: DO CUSTOMER REVIEWS MATTER?
With the prevalence of fake reviews across web and e-commerce platforms it has become difficult for the customers to make an informed purchase decision. Considering this we examine the influence of review manipulation on customer’s purchase decision. A qualitative approach employing interviews with frequent online shoppers was employed to explore the phenomenon. The results of the study suggest that customers accord recommendations from their social network more weightage than the reviews available on an e-commerce platform. Further, we found that customers apply either or both interactive and extractive strategies to deal with review manipulation. Keywords: information processing, review manipulation, fake reviews, grounded theory
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Human-Centered Technologies for Inclusive Collection and Analysis of Public-Generated Data
The meteoric rise in the popularity of public engagement platforms such as social media, customer review websites, and public input solicitation efforts strives for establishing an inclusive environment for the public to share their thoughts, ideas, opinions, and experiences. Many decisions made at a personal, local, or national scale are often fueled by data generated by the public. As such, inclusive collection, analysis, sensemaking, and utilization of pubic-generated data are crucial to support the exercise of successful decision-making processes. However, people often struggle to engage, participate, and share their opinions due to inaccessibility, the rigidity of traditional public engagement methods, and the lack of options to provide opinions while avoiding potential confrontations. Concurrently, data analysts and decision-makers grapple with the challenges of analyzing, sensemaking, and making informed decisions based on public-generated data, which includes high dimensionality, ambiguity present in human language, and a lack of tools and techniques catered to their needs. Novel technological interventions are therefore necessary to enable the public to share their input without barriers and allow decision-makers to capture, forage, peruse, and sublimate public-generated data into concrete and actionable insights.
The goal of this dissertation is to demonstrate how human-centered approaches involve the stakeholders in the design, development, and evaluation of tools and techniques that can lead to inclusive, effective, and efficient approaches to public-generated data collection and analysis to support informed decision-making. To that end, in this dissertation, I first addressed the challenges of empowering the public to share their opinions by exploring two major opinion-sharing avenues --- social media and public consultation. To learn more about people\u27s social media experiences and challenges, I built two technology probes and conducted a qualitative exploratory study with 16 participants. This study is followed up by exploring the challenges of inclusive participation during public consultations such as town halls. Based on a formative study with 66 participants and 20 organizers, I designed and developed CommunityClick to enable reticent share their opinions silently and anonymously during town halls. Equipped with the knowledge and experiences from these works, I designed, developed, and evaluated technologies and methods to facilitate and accelerate informed data-driven decision-making based on increased public-generated data. Based on interviews with 14 analysts and decision-makers in the civic domain, I built a visual analytics system CommunityClick that can facilitate public input analysis by surfacing hidden insights, people\u27s reflections, and priorities. Leveraging the lessons learned during this work, I created a visual text analytics system that supports serendipitous discovery and balanced analysis of textual data to help make informed decisions.
In this work, I contribute an understanding of how people collect and analyze public-generated data to fuel their decisions when they have increased exposure to alternative avenues for opinion-sharing. Through a series of human-centered studies, I highlight the challenges that inhibit inclusivity in opinion sharing and shortcomings of existing methods that prevent decision-makers to account for comprehensive public input that includes marginalized or unpopular opinions. To address these challenges, I designed, developed, and evaluated a collection of interactive systems including CommunityClick, CommunityPulse, and Serendyze. Through a rigorous set of evaluation strategies which include creativity sessions, controlled lab studies, in-the-wild deployment, and field experiments, I involved stakeholders to assess the effectiveness and utility of the built systems. Through the empirical evidence from these studies, I demonstrate how alternative designs for social media could enhance people\u27s social media experiences and enable them to make new connections with others to share opinions. In addition, I show how CommunityClick can be utilized to enable reticent attendees during public consultation to share their opinions while avoiding unwanted confrontation and allowing organizers to capture and account for silent feedback. I highlight how CommunityPulse allowed analysts and decision-makers to examine public input from multiple angles for an accelerated analysis and more informed decision-making. Furthermore, I demonstrate how supporting serendipitous discovery and balanced analysis using Serendyze can lead to more informed data-driven decision-making. I conclude the dissertation with a discussion on future avenues to expand this research including the facilitation of multi-user collaborative analysis, integration of multi-modal signals in the analysis of public-generated data, and potential adoption strategies for decision-support systems designed for inclusive collection and analysis of public-generated data
Information Outlook, August 2007
Volume 11, Issue 8https://scholarworks.sjsu.edu/sla_io_2007/1007/thumbnail.jp
Internet shopping - A taxonomy of consumer online actions.
This thesis applied the theory of activity and goal-directed action to the study of online shopping actions. It first studied qualitatively the structures of online shopping actions using the self-confrontation interview method. The qualitative findings established the structural, cognitive and dispositional dimensions of online shopping actions including knowledge and value structures, attention processes and flow. The typical behavioural traits of online shoppers were also identified. Findings also emerged about the tensions between consumers' online and offline actions and the consequences of the technological mediation of shopping. From these qualitative findings, a survey instrument was developed to query online shoppers on various dimensions of their online shopping actions. Cluster analysis of the survey results produced a taxonomy of consumer online actions from which a typology of online shoppers was generated. The qualitative findings on the typical behavioural traits of online shoppers were then used as criteria for the qualitative usability analysis of retail websites. Retail websites of four product and service categories were analysed for their usability, i.e. ability to accommodate the typical behavioural traits of online shoppers such as propensity to experience information overload and to multi-task, potential for experiencing affect and flow etc. This thesis made several theoretical, methodological and practical contributions. It extended goal-directed action theory beyond its traditional scope of work actions and group activity to the realm of consumer behaviour. It also introduced a different theoretical framework to consumer psychology by applying the theory of activity and goal directed action to consumer behaviour. It made a methodological contribution by applying the self-confrontation interview method to the study of online behaviour. This thesis' findings also have practical implications for the understanding of online behaviour, the diffusion of e-commerce and the design of Internet interfaces
Personalizing online reviews for better customer decision making
Online consumer reviews have become an important source of information for understanding
markets and customer preferences. When making purchase decisions, customers
increasingly rely on user-generated online reviews; some even consider the information
in online reviews more credible and trustworthy than information provided
by vendors. Many studies have revealed that online reviews influence demand and
sales. Others have shown the possibility of identifying customer interest in product
attributes. However, little work has been done to address customer and review diversity
in the process of examining reviews. This research intends to answer the research
question: how can we solve the problem of customer and review diversity in the context
of online reviews to recommend useful reviews based on customer preferences and
improve product recommendation? Our approach to the question is through personalization.
Similar to other personalization research, we use an attribute-based model
to represent products and customer preferences. Unlike existing personalization research
that uses a set of pre-defined product attributes, we explore the possibility of a
data-driven approach for identifying more comprehensive product attributes from online
reviews to model products and customer preferences. Specifically, we introduce
a new topic model for product attribute identification and sentiment analysis. By
differentiating word co-occurrences at the sentence level from at the document level,
the model better identifies interpretable topics. The use of an inference network with
shared structure enables the model to predict product attribute ratings accurately.
Based on this topic model, we develop attribute-based representations of products,
reviews and customer preferences and use them to construct the personalization of online reviews. We examine personalization from the lens of consumer search theory
and human information processing theory and test the hypotheses with an experiment.
The personalization of online reviews can 1) recommend products matching
customer's preferences; 2) improve custom's intention towards recommended products;
3) best distinguish recommended products from products that do not match
customer's preferences; and 4) reduce decision effort
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