Decision-making strategy for detecting authenticated recommendations and identification of valuable customers in online shopping sites

2020 ◽  
Vol 1 (4) ◽  
pp. 351
Author(s):  
Goldina Ghosh ◽  
Bidushi Chakraborty
Keyword(s):  
Decision Making ◽  
Online Shopping

Design, Development and Comparison of Heuristic Driven Algorithms Based on the Crossed Domain Products’ Reviews for User’s Summarization

2020 ◽  
Vol 13 (5) ◽  
pp. 884-892
Author(s):  
Sartaj Ahmad ◽  
Ashutosh Gupta ◽  
Neeraj Kumar Gupta
Keyword(s):  
Decision Making ◽  
Online Shopping ◽  
Recent Time ◽  
Design Development ◽  
Different Types ◽  
Feature Based ◽  
Use Of Internet ◽  
Day By Day ◽  
Better Than

Background: In recent time, people love online shopping but before any shopping feedbacks or reviews always required. These feedbacks help customers in decision making for buying any product or availing any service. In the country like India this trend of online shopping is increasing very rapidly because awareness and the use of internet which is increasing day by day. As result numbers of customers and their feedbacks are also increasing. It is creating a problem that how to read all reviews manually. So there should be some computerized mechanism that provides customers a summary without spending time in reading feedbacks. Besides big number of reviews another problem is that reviews are not structured. Objective: In this paper, we try to design, implement and compare two algorithms with manual approach for the crossed domain Product’s reviews. Methods: Lexicon based model is used and different types of reviews are tested and analyzed to check the performance of these algorithms. Results: Algorithm based on opinions and feature based opinions are designed, implemented, applied and compared with the manual results and it is found that algorithm # 2 is performing better than algorithm # 1 and near to manual results. Conclusion: Algorithm # 2 is found better on the different product’s reviews and still to be applied on other product’s reviews to enhance its scope. Finally, it will be helpful to automate existing manual process.

The Next Generation of Shopbots

2011 ◽  
pp. 19-36 ◽  
Author(s):  
Maria Fasli
Keyword(s):  
Decision Making ◽  
Web Services ◽  
Semantic Web ◽  
Online Shopping ◽  
Vast Number ◽  
High Expectations ◽  
Profound Impact ◽  
Comparison Shopping ◽  
Shopping Experience ◽  
Immense Potential

The huge growth of e-commerce has had a profound impact on users who can now choose from a vast number of options online. Inevitably, as the number of choices has increased, so has the need for tools to help users organize, manage and utilize information on these for better decision-making. Comparison shopping agents or shopbots can help users decide what to buy and enhance their online shopping experience. However, despite the high expectations, the immense potential of shopbots has not been fully realized. In this chapter, the author identifies the limitations and drawbacks of current shopbots, in particular, with regard to the underlying technology for building such systems. She then discusses how these technical limitations can be overcome by making use of the Semantic Web and Web Services. She also considers how shopbots can truly serve the user by providing personalized, impartial and flexible services.

scholarly journals The impact of branding on purchasing decision-making in mall shopping and online shopping

2020 ◽  
Vol 801 ◽  
pp. 012146
Author(s):  
Aulia Agung Dermawan ◽  
Harmein Nasution ◽  
Muhammad Haikal Sitepu
Keyword(s):  
Decision Making ◽  
Online Shopping ◽  
Purchasing Decision ◽  
The Impact

Intelligent decision-making of online shopping behavior based on internet of things

2020 ◽  
Vol 50 ◽  
pp. 515-525 ◽  
Author(s):  
Hanliang Fu ◽  
Gunasekaran Manogaran ◽  
Kuang Wu ◽  
Ming Cao ◽  
Song Jiang ◽  
...  
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Online Shopping ◽  
Shopping Behavior ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Behavior Based ◽  
Online Shopping Behavior

scholarly journals The Impact of the purchase channel on unplanned purchases

2019 ◽  
Vol 19 (34) ◽  
pp. 249-268
Author(s):  
Inês Henriques ◽  
Ana Margarida Barreto
Keyword(s):  
Decision Making ◽  
Online Shopping ◽  
Online Advertising ◽  
Future Research ◽  
Decision Making Process ◽  
Exploratory Research ◽  
Shopping List ◽  
Purchase Situation ◽  
The Impact ◽  
Web Designers

This exploratory research aimed to observe if the purchase channel used (online versus physical store) could influence the number and the type of unplanned purchases in a supermarket purchase situation. 64 participants were asked to simulate a supermarket purchase using a shopping list and a predefined budget. Participants were divided into two conditions: online shopping and physical store shopping simulation.Findings show that consumers purchase more unplanned items (and spent more money on unplanned purchases) when they buy in physical stores, as well as items on promotion. They also tend to spend more time in the decision-making process when compared to participants shopping online. In addition, online consumers spend more money on items that were on their shopping list.Our findings are important to the literature, demonstrating that consumer reactions towards shopping differ according to the channel. Advertisers and web designers can also benefit from these findings by making better decisions regarding online advertising, specifically in the retail domain. Suggestions for future research are provided in the end.

scholarly journals The Influence of Commodity Presentation Mode on Online Shopping Decision Preference Induced by the Serial Position Effect

2021 ◽  
Vol 11 (20) ◽  
pp. 9671
Author(s):  
Zhiman Zhu ◽  
Ningyue Peng ◽  
Yafeng Niu ◽  
Haiyan Wang ◽  
Chengqi Xue
Keyword(s):  
Decision Making ◽  
Serial Position ◽  
Human Computer Interaction ◽  
Online Shopping ◽  
Serial Position Effect ◽  
Position Effect ◽  
Presentation Mode ◽  
Decision Bias ◽  
Evaluation Mode ◽  
Computer Interaction

The information cluster that supports the final decision in a decision task is usually presented as a series of information. According to the serial position effect, the decision result is easily affected by the presentation order of the information. In this study, we seek to investigate how the presentation mode of commodities and the informativeness on a shopping website will influence online shopping decisions. To this end, we constructed two experiments via a virtual online shopping environment. The first experiment suggests that the serial position effect can induce human computer interaction decision-making bias, and user decision-making results in separate evaluation mode are more prone to the recency effect, whereas user decision-making results in joint evaluation mode are more prone to the primacy effect. The second experiment confirms the influence of explicit and implicit details of information on the decision bias of the human computer interaction caused by the serial position effect. The results of the research will be better applied to the design and development of shopping websites or further applied to the interactive design of complex information systems to alleviate user decision-making biases and induce users to make more rational decisions.

scholarly journals Critical Success Factors influencing Web-Based Decision Support Systems in Online Shopping Context

2014 ◽  
Vol 8 (3) ◽  
pp. 1364-1371
Author(s):  
Mohammed A. I. Ayoub
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Critical Success Factors ◽  
Success Factors ◽  
Online Shopping ◽  
Support Systems ◽  
Quality Data ◽  
Web Based ◽  
Critical Success

Web-based decision support systems are increasingly used over the past years. However, few studies have been conducted on evaluation of web-based decision support systems especially in the field of online shopping. This paper attempts to explore the critical success factors that influence decision making satisfaction in online shopping context by providing a conceptual model for this purpose. Although there are various factors which contribute in making online shopping decisions but this study focuses on five factors i.e. web site quality, data quality, knowledge management, decision making satisfaction, and perceived net benefit. Also, this research will use existing models that explain and predict information systems success. However, these success models need to be updated to recurrent industry developments since the updating existing IS success models, a better understanding of web-based DSS practitioner success can be achieved.

A Novel Fuzzy Integrated Customer Needs Prioritization Software Tool for Effective Design of Online Shopping Websites

2017 ◽  
Vol 8 (4) ◽  
pp. 23-42
Author(s):  
Ashish K. Sharma ◽  
Sunanda Khandait
Keyword(s):  
Decision Making ◽  
Quality Function Deployment ◽  
Online Shopping ◽  
Real Life ◽  
Software Tool ◽  
Visual Basic ◽  
Website Design ◽  
Customer Needs ◽  
Technical Requirements ◽  
Effective Design

Albeit, online shopping has grown much recently, users' rate of satisfaction has declined due to the ineffective design of online shopping websites. Thus, the companies involved are craving for well-designed websites. Effective website design involves decision making and thus this paper considers Quality Function Deployment (QFD) as it is a strong decision-making tool. However, QFD uses crisp scoring approach that generates uncertainty and vagueness resulting in impreciseness and inconsistency in results. The issue can be addressed using fuzzy integration. QFD involves prioritization of Customer Needs (CNs) and Technical Requirements (TRs). However, the paper focuses on only CNs prioritization. Also, the existing software's lack the indispensable fuzzy support feature to handle the uncertainty and vagueness. Thus, the paper presents a novel fuzzy integrated customer needs prioritization software tool. The tool is built using Visual Basic Dot Net (VB.Net) and MS-Access. A real-life example is presented to demonstrate the viability of the software tool.

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