An Intelligent Online Shopping Guide Based On Product Review Mining

This position paper describes an on-going work on a novel recommendation framework for assisting online shoppers in choosing the most desired products, in accordance with requirements input in natural language. Existing feature-based Shopping Guidance Systems fail when the customer lacks domain expertise. This framework enables the customer to use natural language in the query text to retrieve preferred products interactively. In addition, it is intelligent enough to allow a customer to use objective and subjective terms when querying, or even the purpose of purchase, to screen out the expected products

Shopbot: An Image Based Search Application for E-Commerce Domain

For the past few years, e-commerce has changed the way people buy and sell products. People use this business model to do business over the Internet. In this domain, Human-Computer Interaction has been gaining momentum. Lately, there has been an upsurge in agent based applications in the form of intelligent personal assistants (also known as Chatbots) which make it easier for users to interact with digital services via a conversation, in the same way we talk to humans. In e- commerce, these assistants offer mainly text-based or speech based search capabilities. They can handle search for most products, but cannot handle search that is based on product features, for instance color or pattern of a T-shirt. Most of the times, it is difficult for users to define these characteristics while searching for a product. Furthermore, a growing number of consumers rely on social media to make a purchasing decision. They try to find out what is trending right now and look for similar items. This brings us the need of a virtual shopping assistant or a shopbot which recommends products based on an image of the product provided by a user. It will be designed to provide relevant responses to the user queries by performing image recognition. This report explains the proposed approach along with the implementation for the virtual shopping assistant

Shopbot: An Image Based Search Application for E-Commerce Domain

For the past few years, e-commerce has changed the way people buy and sell products. People use this business model to do business over the Internet. In this domain, Human-Computer Interaction has been gaining momentum. Lately, there has been an upsurge in agent based applications in the form of intelligent personal assistants (also known as Chatbots) which make it easier for users to interact with digital services via a conversation, in the same way we talk to humans. In e- commerce, these assistants offer mainly text-based or speech based search capabilities. They can handle search for most products, but cannot handle search that is based on product features, for instance color or pattern of a T-shirt. Most of the times, it is difficult for users to define these characteristics while searching for a product. Furthermore, a growing number of consumers rely on social media to make a purchasing decision. They try to find out what is trending right now and look for similar items. This brings us the need of a virtual shopping assistant or a shopbot which recommends products based on an image of the product provided by a user. It will be designed to provide relevant responses to the user queries by performing image recognition. This report explains the proposed approach along with the implementation for the virtual shopping assistant

Semantic Shopping: A Literature Study

The digitalization of the economy and society overall has a significant impact on customers’ shopping behavior. After being conditioned by experiences in entertainment or simple Internet search, customers increasingly expect that a smart shopping assistant understands his/her shopping intentions and transfers these to shopping recommendations. Thus, the emerging opportunity in this context is to facilitate an intention-based shopping experience similar to the way semantic search engines provide responses to enquiries. In order to progress this new area, we differentiate alternative types of shopping intentions to provide the first set of conversation patterns. Grounded in the Speech Act Theory and a structured literature review, semantic shopping is defined and different types of shopping intentions are deduced

The Impact of Sentiment Analysis Output on Decision Outcomes: An Empirical Evaluation

User-generated online content serves as a source of product- and service-related information that reduces the uncertainty in consumer decision making, yet the abundance of such content makes it prohibitively costly to use all relevant information. Dealing with this (big data) problem requires a consumer to decide what subset of information to focus on. Peer-generated star ratings are excellent tools for one to decide what subset of information to focus on as they indicate a review’s “tone”. However, star ratings are not available for all user-generated content and not detailed enough in other cases. Sentiment analysis, a text-analytic technique that automatically detects the polarity of text, provides sentiment scores that are comparable to, and potentially more refined than, star ratings. Despite its popularity as an active topic in analytics research, sentiment analysis outcomes have not been evaluated through rigorous user studies. We fill that gap by investigating the impact of sentiment scores on purchase decisions through a controlled experiment using 100 participants. The results suggest that, consistent with the effort-accuracy trade off and effort-minimization concepts, sentiment scores on review documents improve the efficiency (speed) of purchase decisions without significantly affecting decision effectiveness (confidence)

A NOVEL FRAMEWORK BASED ON WORD-OF-MOUTH MINING FOR NON-PROSUMER DECISION SUPPORT

The deeper penetration of business-to-consumer e-commerce requires that customer decision support systems (CDSS) serve a wider range of users. However, a significant weakness of existing e-shopping assistance programs is their inability to aid non-professional consumers (non-prosumers) in buying highly differentiated products. This paper proposes a novel framework that infers product recommendations with minimal information input. At the heart of the proposed framework is the feature-usage map (FUM), a Bayesian network-based model that encodes the correlations among a product’s technical specifications and its suitability in terms of its using scenario (usage). It also incorporates a query-based lazy learning mechanism that elicits a product’s rating score from product reviews and constructs its corresponding FUM in an on-demand manner. This mechanism allows the knowledge base to be enriched incrementally, with no need for an exhaustive repository of FUMs pertaining to all possible usage queries a user may invoke. The effectiveness of the proposed framework is evaluated through an empirical user study. The results show that the framework is able to effectively derive product ratings based on specified usage. Moreover, this rating information can also be incorporated into a conventional buying guide system to deliver purchase decision support for non-prosumer

Service Quality, Customer Satisfaction and Customer Loyalty in Consumer Electronics E-Tailers: A Structural Equation Modeling Approach

The E-S-QUAL and E-RecS-QUAL scales have been successfully tested in a study by Parasuraman, Zeithaml, and Malhotra (2005). However, E-S-QUAL and E-RecS­ QUAL are newly developed and lack specific application to different types of e-business. This non-experimental, correlational study is the first to examine and explore the relationships among electronic service quality, customer satisfaction, and customer loyalty for consumer electronics e-tailers. Using quota and snowball sampling, participants from the continental United States received e-mail invitations and voluntarily forwarded the e-mail invitations to their friends and family. A total of 276 participants completed the online survey. This study\u27s demographic characteristics included most between the ages of 26 and 35 years (47%), mean age of 35.2, most with graduate degrees (40.6%), and with 40% earning a family income of $75,000 or more. Out of twenty hypotheses (including four sub-hypotheses for H1 and three for H2) in this study, 13 were supported, two were marginally supported, and five were not supported. Findings indicated that electronic service quality was measured by online shoppers\u27 perceptions of service quality of consumer electronic e-tailers through four dimensions of the 17-indicator modified E-S-QUAL (efficiency, system availability, fulfillment, and privacy) . Electronic recovery service quality was measured by online shoppers\u27 perceptions of recovery service quality of consumer electronic e-tailers through two dimensions of modified E-RecS-QUAL (responsiveness and contact, and compensation). Findings also indicated that perceived value and customer satisfaction were two significant variables that mediated the relationships among customer expectations, electronic service quality, customer loyalty, and customer complaints. However, this study also found that electronic service quality and customer expectations had no direct effect on customer satisfaction, but had indirect positive effects on customer satisfaction for consumer electronics e-tailers. Consumer electronics e-tailers\u27 managers could formulate plans to improve service quality and recovery service quality through dimensions of E-S-QUAL and E­ RecS-QUAL. They also could formulate a competitive strategy based on the modified Electronic Customer Satisfaction (e-CS) model to keep current customers and to enhance customer relation management. The limitations and recommendations for future research are also included in this study

An Experimental Investigation of Regulatory Orientation and Post-Choice Regret in Online Product Selection

Delivering product information effectively is fundamental to customer satisfaction and e-retailer success. In this study we examine the way in which the presentation of online customer reviews in peer endorsement systems (PES) impact perceptions of post-choice regret. The theory of Regulatory Orientation is used to account for individual differences in the way that online review content is processed. Results of a laboratory experiment comparing two peer endorsement system formats show that PES content presentation significantly impacts perceptions of post-choice regret. These perceptions are found to be strong influencers of user intention to use the PES. The study’s findings provide theoretical insights into how individual orientation and PES technology influence online decision-making with regards to product selection. As a result, the study has important implications for managers looking to get the most from investment in PES systems deployment and online web retail space design