Toward an Understanding of Consumer Attitudes on Online Review Usage

ABSTRACT

With the rapid e-commerce growth and changes in consumers’ behaviors, many businesses are forced to adapt their business model to match their target customers’ needs. To provide consumers with more product details and increase their confidence in making online purchases, online businesses offer an online review as an alternative to physically interacting with a product. Although consumers have become familiar with the use of online product reviews, many aspects of user behavior toward the usage of online reviews are still not well understood. This study explores the factors underlying the acceptance of consumers’ online review usage when considering purchasing an item. The study results provide insight into the factors that affect customers’ use of online reviews prior to a purchase. This study furthers the body of knowledge that deals with online reviews and system usage, providing results that allow e-commerce businesses to adapt their business model to better fit consumers’ expectations.     

Multimedia mobile application e-commerce service satisfaction

Previous research on e-commerce through multimedia mobile applications primarily focused on the relationships between e-service factors and positive customer behavior. However, these studies did not consider the consumer purchasing decision process sufficiently to develop firms’ strategic priorities in the context of e-commerce services through multimedia mobile applications. To fill this gap, we conducted a choice-based conjoint analysis (CBCA) to identify the preferences for each attribute and the interaction effects between each pair of attributes related to the purchasing decision, and used an analytic hierarchy process (AHP) analysis to calculate the overall priorities for every sub-criterion and partial priorities for each main criterion. The main findings from the integrated CBCA and AHP are that consumers must be able to fulfill their most basic needs through multimedia mobile applications’ e-commerce service, e-commerce service providers using multimedia mobile applications should enhance their existing advantages to complement their shortcomings, and e-commerce service providers besides social commerce operators should change their business structures to adapt to the new e-commerce environment. We also discuss the main contributions and implications to improve e-commerce service satisfaction and strategic decision-making.

     

Password Security: An Empirical Investigation into E-Commerce Passwords and Their Crack Times

Abstract

Strong passwords are essential to the security of any e-commerce site as well as to individual users. Without them, hackers can penetrate a network and stop critical processes that assist consumers and keep companies operating. For most e-commerce sites, consumers have the responsibility of creating their own passwords and often do so without guidance from the web site or system administrator. One fact is well known about password creation—consumers do not create long or complicated passwords because they cannot remember them. Through an empirical analysis, this paper examines whether the passwords created by individuals on an e-commerce site use either positive or negative password practices. This paper also addresses the issue of crack times in relationship to password choices. The results of this study will show the actual password practices of current consumers, which could enforce the need for systems administrators to recommend secure password practices on e-commerce sites and in general.     

中国移动电子商务技术应用分析报告

移动互联网作为目前最广泛最流行的平台,也是现代许多行业大众服务的必备渠道,其强大的用户群是许多行业瞄准的必争之地。目前国内外移动电子商务发展迅速,通过分门别类的移动终端来实现最方便的用户体验被视为移动因特网业务的最重要方面。通过因特网与移动技术的结合也为服务提供商创造了更多新的机会,在用户最方便的时间为他提供所需要的服务,从而和客户建立更信赖的关系。文章主要通过对移动电子商务的概念、发展现状以及技术基础、存在的问题等进行分析,并且结合当今移动设备中的新思想,提出了相应的应用方案和策略。     

Is paid search overrated? When bricks-and-mortar-only retailers should not use paid search

While bricks-and-mortar-only retailers do not offer online purchasing, they often take advantage of multi-channel management strategies to reach consumers in a pre-purchase phase. We investigate whether paid search can increase the sales of brick-and-mortar retailers who promote their offers via an informational website. Although a sizeable one third of all retailers still trade without an online-shop, previous work has been silent about the effects of paid search for them. We make use of a randomized field experiment and an end-to-end tracking mechanism to investigate the cross-channel behavior of individual consumers. Our empirical results suggest that, whilst paid search increases the number of potential customers through enhancing the reach of marketing initiatives, store sales are not increased. We conclude that customers who search online to buy offline primarily use paid search as a navigational shortcut to the retailer’s website. Consequently, bricks-and-mortar-only retailers seeking to increase store purchases should approach paid search with caution.     

Perceived information transparency in B2C e-commerce: An empirical investigation

Consumers abandon their online purchases at an e-commerce website partly due to the lack of information transparency of the website. We identify the antecedents of consumers’ perceived information transparency of an e-commerce website and its effects on consumers’ online purchase intention. We collected data through a scenario-based survey conducted in a laboratory setting. We found that (1) product transparency, vendor transparency, and transaction transparency significantly influence perceived information transparency; (2) perceived information transparency significantly increases consumers’ online purchase intention; and (3) perceived risk partially mediates the effects of perceived information transparency on purchase intention.     

The impact of e-retail characteristics on initiating mobile retail services: A modular innovation perspective

The rise and challenges of m-commerce have led to an urgent need to examine how firms adopt the emerging sales channel. Early studies mainly discussed the differences between e-commerce and m-commerce. Our study shows the modular innovation from e-retailing to m-retailing, which changes the core component of service delivery but keeps the operations intact, provides more opportunities for well-entrenched firms. Using a dataset of e-retailers, we find e-retail characteristics have an impact on firm's migration to the mobile domain. Firms with online service competencies, economies of scale, and physical outlets are more inclined to exploit opportunities provided by mobile technologies.     

An expert system for extracting knowledge from customers’ reviews: The case of Amazon.com,Inc.

E-commerce has proliferated in the daily activities of end-consumers and firms alike. For firms, consumer satisfaction is an important indicator of e-commerce success. Today, consumers’ reviews and feedback are increasingly shaping consumer intentions regarding new purchases and repeated purchases, while helping to attract new customers. In our work, we use an expert system to predict the sentiment of a product considering a subset of available customers’ reviews.     

The design and evaluation of an intelligent sales agent for online persuasion and negotiation

Online purchases from e-stores are getting popular among Internet users. Although many e-commerce activities, such as auctions, bargaining, and recommendation services are available, most e-stores lack clerk-like mechanisms to persuade potential buyers into buying products and to bargain with them for making a good deal. The objectives of this research are to design a lab prototype of a sales agent with persuasion and negotiation capabilities and to evaluate its effectiveness as a virtual clerk in an e-store. The prototypical intelligent sales agent (ISA) is equipped with reinforcement learning capabilities and an abstract argument framework. We conduct both laboratory and Internet experiments to assess ISA’s performance. The experimental results reveal that an e-store embedded within such a sales agent can improve a seller’s surplus and increase a buyer’s product valuation, willingness-to-pay, and satisfaction with the e-store.     

Is Online Consumers’ Impulsive Buying Beneficial for E-Commerce Companies? An Empirical Investigation of Online Consumers’ Past Impulsive Buying Behaviors

Previous studies emphasized that e-businesses could increase sales with marketing strategies on shopping web sites that induce impulsive buying behavior, and lacked an empirical approach to problems caused by impulsive buying. This study proposes a research model that deals with online consumers’ impulsive buying behavior and empirically validates it. The results show that impulsive buying could have a strong impact on post e-commerce purchase intention and behavior such as actual behavioral return tendency of goods.     

A study on factors that affect users’ behavioral intention to transfer usage from the offline to the online channel

Increasingly more service providers offer both offline and online services simultaneously, but consumers still seem to prefer using offline services rather than online ones. Our research focuses on factors that influence users’ intention to transfer their usage from the offline to the online channel that offer similar services. Drawing on the valence framework and prior research related to habit, innovativeness, and Internet experience, we propose a research model that incorporates motivators and inhibitors of usage transfer from both the offline and online channels. Using banking services in China as the research context, our study reveals that innovativeness in new technology and relative benefit have positive effects on users’ intention to transfer usage. Conversely, habits that consumers form in the offline channel have a negative effect on the intention to transfer usage. Moreover, our findings indicate that Internet experience moderates the relationship between relative benefit and consumers’ intention to transfer usage from offline to online services. These results provide a better understanding of consumers’ usage-transfer behavior and offer suggestions to providers in boosting their consumers’ use of online services.     

Effects of the pricing and cooperative advertising policies in a two-echelon dual-channel supply chain

The rapid development of e-commerce has reduced the intermediary channels between manufacturers and consumers and changed consumption patterns. However, channel conflicts occur when manufacturers engage in direct sales. Using traditional and direct online channels as its setting, this paper evaluates the impact of price schemes and cooperative advertising mechanisms on dual-channel supply chain competition. Our analysis offers structural and quantitative insights into the interplay between upstream and downstream entities in the supply chain, helping managers to understand the interplay between the upstream and downstream entities of a dual channel structure.     

Reconciling predictive and interpretable performance in repeat buyer prediction via model distillation and heterogeneous classifiers fusion

Repeat buyer prediction is crucial for e-commerce companies to enhance their customer services and product sales. In particular, being aware of which factors or rules drive repeat purchases is as significant as knowing the outcomes of predictions in the business field. Therefore, an interpretable model with excellent prediction performance is required. Many classifiers, such as the multilayer perceptron, have exceptional predictive abilities but lack model interpretability. Tree-based models possess interpretability; however, their predictive performances usually cannot achieve high levels. Based on these observations, we design an approach to balance the predictive and interpretable performance of a decision tree with model distillation and heterogeneous classifier fusion. Specifically, we first train multiple heterogeneous classifiers and integrate them through diverse combination operators. Then, classifier combination plays the role of teacher model. Subsequently, soft targets are obtained from the teacher and guide training of the decision tree. A real-world repeat buyer prediction dataset is utilized in this paper, and we adopt features with respect to three aspects: users, merchants, and user–merchant pairs. Our experimental results show that the accuracy and AUC of the decision tree are both improved, and we provide model interpretations of three aspects.

     

Optimal pricing of online products based on customer anchoring-adjustment psychology

Online product pricing is an important factor that affects the purchase of customers and the earnings of e-commerce platform enterprises. It has an increasingly prominent influence on consumers’ purchasing decisions. To study the influence of customers’ anchoring psychology and product pricing on customers’ purchasing behaviors, this paper proposes a pricing model that considers online consumers to be anchored by the product price and sales. The cognitive bias of online consumers is described by a utility function based on an anchoring-adjustment heuristic, and consumer choice behavior and an online product pricing strategy are studied with a multinomial logit model. Then, it studies the influence of the anchoring point and anchoring degree on optimal pricing, sales and profit. In particular, when consumers are anchored only by sales of goods, the optimal solution is monotonic with respect to the anchoring degree. However, when consumers are anchored by both price and sales, both factors affect consumer behaviors and produce more complex results. This paper provides a flexible pricing mechanism for platform enterprises, and it can provide a theoretical basis and decision support for dynamic pricing of platform enterprises based on historical consumption data.     

Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques

As the competition between mobile telecom operators becomes severe, it becomes critical for operators to diversify their business areas. Especially, the mobile operators are turning from traditional voice communication to mobile value-added services (VAS), which are new services to generate more average revenue per user (ARPU). That is, cross-selling is critical for mobile telecom operators to expand their revenues and profits. In this study, we propose a customer classification model, which may be used for facilitating cross-selling in a mobile telecom market. Our model uses the cumulated data on the existing customers including their demographic data and the patterns for using old products or services to find new products and services with high sales potential. The various data mining techniques are applied to our proposed model in two steps. In the first step, several classification techniques such as logistic regression, artificial neural networks, and decision trees are applied independently to predict the purchase of new products, and each model produces the results of their prediction as a form of probabilities. In the second step, our model compromises all these probabilities by using genetic algorithm (GA), and makes the final decision for a target customer whether he or she would purchase a new product. To validate the usefulness of our model, we applied it to a real-world mobile telecom company’s case in Korea. As a result, we found that our model produced high-quality information for cross-selling, and that GA in the second step contributed to significantly improve the performance.     

Do web sites change customers’ beliefs? A study of prior–posterior beliefs in e-commerce

Web sites are the main interface between online merchants and their customers. Despite the consequent importance of web-site design, there is little theoretical foundation that can be used to assess how web-design factors lead e-customers to revise their earlier beliefs. Our study examined web customers’ salient beliefs and the role of web-design elements in altering customers’ existing beliefs after their exposure to an e-commerce web site. Using salient beliefs and categories of web-design elements identified previously, we developed a conceptual model of belief revisions due to exposure to web-design elements and internal cues. The empirical analysis of the model indicated that web customers were influenced by the external cues from the design factors as well as the internal ones from their prior beliefs. Our findings have implications in designing web sites for e-commerce.     

How impulsivity affects consumer decision-making in e-commerce

This research investigates whether a person’s mood can influence impulsivity in online shopping decisions, and how involvement can regulate it. We adopt a process view of impulsivity, and recorded the detailed information search patterns of consumers using an eye-tracker methodology. The results show that incidental moods tend to increase process impulsivity, and this effect may not be restrained by involvement. We also demonstrate that the decision-making process can be separated into two stages – orientation and evaluation. We further find that differences in impulsivity are most evident in the evaluation stage. These results suggest the importance of mood-elicited impulsivity of purchases in e-commerce.     

The effect of mobile retailing on consumers' purchasing experiences: A dynamic perspective

The emerging retail culture is characterized by the extensive use of mobile technologies, high connectivity, ubiquitous computing and contactless technologies, which enable consumers to experience shopping differently. In fact, innovative mobile technologies provide new tools (apps) which are able to separate the moment of purchase from the moment of effective consumption, by allowing consumers to make purchases by mobile phone and collect them at home or at a store (a pick-up boutique or collection point), in addition to the traditional in-store service (purchase in the store and collect/consume in the store). The aim of this paper is to understand the extent to which mobile technologies have an impact on consumer behaviour, with emphasis on the drivers motivating consumers to adopt the consumer experience of mobile shopping. To achieve this goal we used a qualitative approach involving 29 consumers in the Italian market, where mobile shopping is still at an early stage. The findings shed a light on the extent to which consumers are moving from e-channels to mobile channels and take into account the effect of these technological innovations in retail settings from a cognitive standpoint, where studies are limited. The implications for researchers and practitioners are then discussed, with emphasis on retailers need to develop new mobile service competences, and integrate and synthetize physical retail settings with mobile opportunities and functionalities.     

A time-based approach to effective recommender systems using implicit feedback

Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations using explicit ratings on items from users. In some e-commerce environments, however, it is difficult to collect explicit feedback data; only implicit feedback is available.

In this paper, we present a method of building an effective collaborative filtering-based recommender system for an e-commerce environment without explicit feedback data. Our method constructs pseudo rating data from the implicit feedback data. When building the pseudo rating matrix, we incorporate temporal information such as the user’s purchase time and the item’s launch time in order to increase recommendation accuracy.

Based on this method, we built both user-based and item-based collaborative filtering-based recommender systems for character images (wallpaper) in a mobile e-commerce environment and conducted a variety of experiments. Empirical results show our time-incorporated recommender system is significantly more accurate than a pure collaborative filtering system.