Impact of review valence and perceived uncertainty on purchase of
time-constrained and discounted search goods
- URL: http://arxiv.org/abs/2110.09313v1
- Date: Sat, 9 Oct 2021 19:50:08 GMT
- Title: Impact of review valence and perceived uncertainty on purchase of
time-constrained and discounted search goods
- Authors: Prathamesh Muzumdar
- Abstract summary: This study investigates how purchase decisions for new products are affected by past customer aggregate ratings when a soon-to-expire discount is being offered.
Considering review credibility, diagnosticity, and effectiveness as determinants of consumer attitude in a time-constrained search and purchase environment, we follow the approach-avoidance conflict theory.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Increasing online shoppers have generated enormous amount of data in form of
reviews (text) and sales data. Aggregate reviews in form of rating (stars) have
become noticeable indicators of product quality and vendor performance to
prospective consumers at first sight. Consumers subjected to product discount
deadlines search for ways in which they could evaluate product and vendor
service using a comprehensible benchmark. Considering the effect of time
pressure on consumers, aggregate reviews, known as review valence, become a
viable indicator of product quality. This study investigates how purchase
decisions for new products are affected by past customer aggregate ratings when
a soon-to-expire discount is being offered. We examine the role that a
consumer's attitude towards review valence (RV) plays as an antecedent to that
consumer's reliance on RV in a purchase decision for time-discounted search
goods. Considering review credibility, diagnosticity, and effectiveness as
determinants of consumer attitude in a time-constrained search and purchase
environment, we follow the approach-avoidance conflict theory to examine the
role of review valence and perceived uncertainty in a time-constrained
environment. The data was collected through an online survey and analyzed using
structural equation modelling. This study provides significant implications for
practitioners as they can better understand how review valence can influence a
purchase decision. Empirical analysis includes two contributions: 1. It helps
to understand how consumer attitude toward review valence, when positively
influenced by the determinants, can lead to reliance on review valence, further
influencing purchase decision; 2. Time constrained purchase-related perceived
uncertainty negatively moderates the relationship between consumer attitude and
reliance on review valence.
Related papers
- Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs [57.16442740983528]
In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback.
The role of user feedback in annotators' assessment of turns in a conversational perception has been little studied.
We focus on how the evaluation of task-oriented dialogue systems ( TDSs) is affected by considering user feedback, explicit or implicit, as provided through the follow-up utterance of a turn being evaluated.
arXiv Detail & Related papers (2024-04-19T16:45:50Z) - Language Models Can Reduce Asymmetry in Information Markets [100.38786498942702]
We introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants.
The central mechanism enabling this marketplace is the agents' dual capabilities: they have the capacity to assess the quality of privileged information but also come equipped with the ability to forget.
To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information.
arXiv Detail & Related papers (2024-03-21T14:48:37Z) - Painsight: An Extendable Opinion Mining Framework for Detecting Pain
Points Based on Online Customer Reviews [7.897859138153238]
We propose Painsight, an unsupervised framework for extracting dissatisfaction factors from customer reviews.
Painsight employs pre-trained language models to construct sentiment analysis and topic models.
It successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type.
arXiv Detail & Related papers (2023-06-03T07:51:57Z) - Generating Persuasive Responses to Customer Reviews with Multi-Source
Prior Knowledge in E-commerce [11.586256303135329]
Customer reviews usually contain much information about one's online shopping experience.
It is of vital importance to carefully and persuasively reply to each negative review and minimize its disadvantageous effect.
We propose a Multi-Source Multi-Aspect Attentive Generation model for persuasive response generation.
arXiv Detail & Related papers (2022-09-20T06:20:45Z) - Exploring the Distribution Regularities of User Attention and Sentiment
toward Product Aspects in Online Reviews [7.23135508361981]
This paper explores the distribution regularities of user attention and sentiment toward product aspects from the temporal perspective of online reviews.
The empirical results show that a power-law distribution can fit user attention to product aspects, and the reviews posted in short time intervals contain more product aspects.
arXiv Detail & Related papers (2022-09-08T10:23:16Z) - Estimating Causal Effects of Multi-Aspect Online Reviews with
Multi-Modal Proxies [24.246450472404614]
This work empirically examines the causal effects of user-generated online reviews on a granular level.
We consider multiple aspects, e.g., the Food and Service of a restaurant.
arXiv Detail & Related papers (2021-12-19T22:29:02Z) - Stateful Offline Contextual Policy Evaluation and Learning [88.9134799076718]
We study off-policy evaluation and learning from sequential data.
We formalize the relevant causal structure of problems such as dynamic personalized pricing.
We show improved out-of-sample policy performance in this class of relevant problems.
arXiv Detail & Related papers (2021-10-19T16:15:56Z) - Fairness, Welfare, and Equity in Personalized Pricing [88.9134799076718]
We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features.
We show the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.
arXiv Detail & Related papers (2020-12-21T01:01:56Z) - Face to Purchase: Predicting Consumer Choices with Structured Facial and
Behavioral Traits Embedding [53.02059906193556]
We propose to predict consumers' purchases based on their facial features and purchasing histories.
We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers.
Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
arXiv Detail & Related papers (2020-07-14T06:06:41Z) - Inverse Active Sensing: Modeling and Understanding Timely
Decision-Making [111.07204912245841]
We develop a framework for the general setting of evidence-based decision-making under endogenous, context-dependent time pressure.
We demonstrate how it enables modeling intuitive notions of surprise, suspense, and optimality in decision strategies.
arXiv Detail & Related papers (2020-06-25T02:30:45Z) - How do online consumers review negatively? [1.52292571922932]
Using 1, 450, 000 negative reviews from JD.com, the largest B2C platform in China, the behavioral patterns from temporal, perceptional and emotional perspectives are explored.
Consumers from lower levels express more intensive negative feelings, especially on product pricing and seller attitudes.
The value of negative reviews from higher-level consumers is unexpectedly highlighted because of less emotionalization and less biased narration.
arXiv Detail & Related papers (2020-04-28T12:54:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.