Critical purchasing incidents in e-business

This research utilized the critical incident technique (CIT) to identify factors influencing customer satisfaction and retention of customers participating in e-commerce transactions. Customers were asked in telephone interviews to discuss both particularly satisfying and dissatisfying (or critical) incidents they had experienced when using web sites to conduct transactions. Each customer also provided demographic information, rated their satisfaction with the experience and ecommerce provider, and was asked how often they purchased products from the provider prior to and after the incident. Analysis revealed 662 citations by customers of items contributing to either positive or negative experiences. Exploratory text-mining analysis revealed that the majority of positive items pertained to the transaction (38%), product (25%), or website (23%), and to customer support (35%) or the transaction for negative items (30%). Approximately 86% of customers citing positive items said they were very likely to use the e-commerce site again, compared to 22% of customers citing negative items. Customer support and user experience both seemed to play an important mediating role on the criticality of negative incidents. Over 70% of first time users and customers who said customer support ignored or refused their requests for assistance said they were unlikely to return to the site, compared to 20% when customer support was said to be responsive. Correlation analysis confirmed that negative incidents tended to be more critical than positive ones, and more so for first time customers.     

Online consumer retention: contingent effects of online shopping habit and online shopping experience

In this study, we further develop the information systems continuance model in the context of online shopping, using a contingency theory that accounts for the roles of online shopping habit and online shopping experience. Specifically, we argue and empirically demonstrate that although conceptually distinct, online shopping habit and online shopping experience have similar effects on repurchase intention. They both have positive mediated effects through satisfaction and moderate the relationship between satisfaction and online repurchase intention. The results of a survey study involving 122 online customers provide strong support for our research model. We also identify after-sale service, transaction efficiency, security, convenience, and cost savings as important online shopping usefulness drivers. Theoretical and practical implications include establishing a contingency theory to more fully explain online customer retention as well as guidelines for development of customer relationship management initiatives.     

Product recommendation with latent review topics

Online customer reviews complement information from product and service providers. While the latter is directly from the source of the product and/or service, the former is generally from users of these products and/or services. Clearly, these two information sets are generated from different perspectives with possibly different sets of intentions. For a prospective customer, both these perspectives together provide a complementary set of information and support their purchase decisions. Given the different perspective and incentive structure, the information from these two source sets tends to be necessarily biased, clearly with the high probability of negative information omission from that provided by the product/service providers. Moreover, customers oftentimes face information overload during their attempts at deciphering existing online customer reviews. We attempt to alleviate this through mining hidden information in online customer reviews. We use a variant of the Latent Dirichlet Allocation (LDA) model and clustering to generate equivalent options that the customer could then use in their purchase decisions. We illustrate this using online hotel review data.     

Design by customer: concept and applications

Customer satisfaction can be increased by reducing the gap between what customer really needs (customer requirements) and what manufacturer can provide (product specifications). The approach of Design for Customer where products are generated by translating customer needs into product specifications (in mass production system) or into product variety (in mass customization system) is not able to give optimum satisfaction to all customers. Some customers are still forced to relax their requirements and to accept predefined product in the assortment. This study proposes a new concept of Design by Customer to increase customer satisfaction by opening maximum possible channel for customers to involve in value creation so that they are no longer only searching for goods but they can also, when necessary, involve in production cycle to specify their own design. In order to ensure the viability of the proposed concept, the integration of multi customer involvement decoupling point, product attribute analysis, crowdscreening and new manufacturing strategy are introduced in this paper. Real products of resin-based table clocks are used as practical example to verify the concept applicability and to demonstrate its merit.     

再多一次接触:顾客反馈机制对服务失败后顾客评价的影响

It is difficult to control service quality for any service firm and service failure is inevitable due to human or non-human factors. As a result, service recovery stirred the interest of researchers and practitioners. However, service failure may not be identified by firms because a majority of dissatisfied customers will not complain to the service provider. Thus, this research proposes a proactive customer feedback mechanism comprised of proactive solicitation of customer feedback, customers providing feedback and follow-up communication and examine their impact of customer evaluations in the context of service failure. This research tries to establish that through adding a continuous series of satisfied service encounters or "moment of truth" to service failure encounters, the customer evaluations (e.g. satisfaction, trust, purchase intent and positive WOM) toward the service will be significantly improved. Our research employed a 2×2×2 between subjects experimental design. Three independent variables were manipulated as presence and absence level, that is, firms either proactively solicitted customer feedback or employed no solicitation of feedback, Customers either provided feedback to the service firm or refused to provide feedback, and service firm either conducted follow-up communication or no follow-up communication. Our research adoptted a scenario in the context of patronizing a three-star hotel on a business trip. In total, 221 business administration graduates from a famous university in Shanghai participated in the experiment. MANOVA was used to test the hypotheses. The results indicated that proactive solicitation significantly influenced trust (p<0.01). Besides satisfaction, the other dependent variables such as trust, purchase intent and positive WOM significantly differred between with and without follow-up communication groups (p<0.05). Third, a significant difference was detected regarding satisfaction, trust, purchase intent and positive WOM between customer providing feedback groups and no customer feedback groups (p<0.05). Moreover, significant interactive effects were found of customers provided feedback and follow-up communication on trust and purchase intent. Trust and purchase intent reached highest when a follow-up communication occurred after customers provided feedback. However, no interactive effects were found of proactive solicitation of customer feedback and customer providing feedback. This research suggests that service providers should create new encounters with customers when they are confronted with service failure, and in this study, through proactive customer feedback mechanism. That is, first, the service provider should encourage customers to provide feedback, which can improve customer evaluations after service failure and after customers providing feedback. At the same time, the firm should employ follow-up communication with the customer and inform them about how the firm responded to their complaints, suggestions and comments. It is worth to note that companies just need to have follow-up communication with those customers who have provided feedback. More important, as information technology and internet prevails, it is very likely sending email and short messages after a service failure to maintain customer relationships could significantly enhance customer evaluations of the service provider. Of course, how consumers respond to such measures online is an promising research area and needs further investigation.     

Exploring the influence of personal schema on trust transfer and switching costs in brick-and-click bookstores

Due to competition, most firms try to retain and lock in customers; this is likely to ensure a greater market profit than that gained by acquiring new customers. While many studies have investigated customer satisfaction and retention in online and offline firms, scant research exists on customer satisfaction and customer retention in the context of brick-and-click firms: i.e., those that conduct business both online and in stores. Such firms encourage trust transfer among their customers. However, most studies have failed to consider the role of personal schema in trust transfer. Based on social exchange, investment, and information integration theories, we analyzed the impact of trust transfer, switching costs, and personal schema on customer satisfaction and repurchase intentions in the context of an offline bookstore that has now launched an online business. Our results indicated that trust transfer and switching costs had a small effect on customer satisfaction and repurchase intentions. Personal schema, however, affected both customer satisfaction and repurchase intention.     

Augmenting feature model through customer preference mining by hybrid sentiment analysis

A feature model is an essential tool to identify variability and commonality within a product line of an enterprise, assisting stakeholders to configure product lines and to discover opportunities for reuse. However, the number of product variants needed to satisfy individual customer needs is still an open question, as feature models do not incorporate any direct customer preference information. In this paper, we propose to incorporate customer preference information into feature models using sentiment analysis of user-generated online product reviews. The proposed sentiment analysis method is a hybrid combination of affective lexicons and a rough-set technique. It is able to predict sentence sentiments for individual product features with acceptable accuracy, and thus augment a feature model by integrating positive and negative opinions of the customers. Such opinionated customer preference information is regarded as one attribute of the features, which helps to decide the number of variants needed within a product line. Finally, we demonstrate the feasibility and potential of the proposed method via an application case of Kindle Fire HD tablets.     

基于在线评论获取产品优化辅助决策信息的算法研究

在大数据时代,如何通过数据分析抓住顾客需求,增加产品优化的科学性,对企业有着至关重要的战略意义.本文将在线评论数据应用于企业产品的辅助优化中,提出了产品优化信息的获取技术与方法,从评论中获取产品优化所需要的优化信息.首先计算在线评论中的顾客关注度和满意度等指标,构建客户意见的权重算法模型;然后,提取出产品特征和顾客意见的词对,并根据权重算法模型计算出顾客意见的权重;接着,通过关联矩阵找到对应的产品优化信息;最后并通过实例分析验证的方法的可行性.     

Sentiment analysis from Customer-generated online videos on product review using topic modeling and Multi-attention BLSTM

With the popularity of social websites and mobile applications including Instagram, YouTube, TikTok, etc., online videos shared by customers presenting their thoughts and reviews on products are posted daily in increasing numbers. Such online videos containing Voice of Customer (VOC) are precious for product designers or managers to capture customer sentiment and understand customer preference. For this purpose, we propose a novel method for analyzing customer sentiment from online videos on product review. Firstly, latent Dirichlet allocation (LDA) modeling is applied to identify the topics from the online videos after data preprocessing. Then sentiment polarity corresponding to each topic of each speaker in videos can be identified using our newly designed multi-attention bi-directional LSTM (BLSTM(MA)), which can better mine complex relationships among a speaker’s sentiments on different topics. This paper is of great practical value for company managers and researchers to better understand a large number of customer opinions on specific products. To explain the application of this method and prove its effectiveness, two cases respectively on smartphones and several published datasets are developed finally.     

Digital content recommender on the Internet

On the Web, where information is vast and users are numerous, personalization that aims to offer suitable information to suitable users is essential. To sustain their competitive advantage, portal sites attract many users' attention by supplying personalized content. Most Web content providers offer all users the same content, failing to satisfy individual users' needs. Providers should be able to offer suitable users suitable content with suitable speed. To do so, they must be able to identify customers, predict their interests, determine appropriate content, and deliver it in a personalized format during customers' online sessions. In this paper, the author presents a digital-content recommender system that suggests Web content, in this case news articles, based on a user's preference when he or she visits an Internet news site and reads the published articles. This recommender system creates a one-to-one relationship between the content provider and the user, raises the user's satisfaction, and increases loyalty toward the content provider.     

The Mechanisms through Which Certain Variables Influence Customer Loyalty: The Mediating Roles of Perceived Value and Satisfaction

This study surveyed 123 bank users who experienced problems with their banks’ online banking services, with the aim of understanding the mechanisms through which certain variables influence customer loyalty. Principally, the possible mediating roles of value and satisfaction on the relationships between recovery and loyalty and between ISO 9001 and loyalty were scrutinized. The dimensionalities of the scales were assessed using exploratory and confirmatory factor analysis. Thereafter, structural equation modeling and multiregression analyses were used to test the proposed model. The overall results showed that service recovery can be used to enhance customers’ satisfaction and perception of value and, therefore, customer loyalty. This study also confirmed the partial mediating roles of customers’ satisfaction and perception of value in the relationship between service recovery and loyalty. In contrast, ISO 9001 has no influence on perceived value, satisfaction, and loyalty; the mediating roles of both perceived value and satisfaction were not supported in the relationship between ISO 9001 and loyalty. In practice, even though ISO 9001 appeared to offer few extras to satisfy and retain customers, its use is still advisable because of the other potential benefits that it provides.     

The effect of online social value on satisfaction and continued use of social media

Social media offers customers a unique service value proposition. Recognizing service value as a pivotal concept, this study develops an aggregate construct of online social value, whereby customers evaluate utilitarian and hedonic benefits relative to what they must sacrifice in effort and risk in deriving a value calculation of online social networking services which predicts satisfaction and continued use of online social media, such as Facebook. By empirically testing a model that explains online social value, this research contributes to information systems (IS) theory by introducing a customer value perspective in the social media context and helps service providers by identifying factors predicting satisfaction and continued use that might be employed to improve offerings to keep customers coming back.     

A multi-objective genetic algorithm approach for solving feature addition problem in feature fatigue analysis

Feature fatigue (FF) is used to represent the phenomenon of customer’s inconsistent satisfaction with products: customers prefer to choose products with more features and capabilities initially, but after having worked with a product, they become frustrated or dissatisfied with the usability problems caused by too many features. To “defeat” FF, it is essential for designers to decide what features should be added when developing a product to make the product attractive enough and not too hard to use at the same time. In this paper, a feature fatigue multi-objective genetic algorithm (FFMOGA) method is reported for solving the feature addition problem. In the proposed method, fitness functions are established based on Bayesian networks, which can represent the uncertain customer preferences and reflect the relationships among features. The computational experiments on a smart phone case show that the FFMOGA approach can find multiple solutions along the Pareto-optimal frontier for designers to select from, and these obtained solutions have good performance in convergence.     

An approach for capturing the Voice of the Customer based on Virtual Prototyping

The need for companies to improve their competitiveness may lead to innovation and the reconceptualization of traditional products and processes, with companies making an effort to enhance product elements related to functionality, attractiveness, technology and sustainability, and implementing mass-customisation concepts. Mass-customised products are developed to satisfy specific customer needs, in line with increasing demand for product variety and customisation. The analysis of what customers really want, capturing the Voice of the Customer (VOC), is one of the strategies used to establish effective product development processes. Using a VOC survey, it is possible to transform customer needs into the functional and psychological requirements of the product. This paper presents a methodology based on Virtual Reality (VR) technologies to support the capturing of the VOC in regard to the visual, haptic and auditory characteristics of products. This method can be applied to the beginning of the product development process, to allow companies to deduce from the data the requirements of new industrial customised products. A flexible and interactive Virtual Prototype (VP) of a product category is then developed as a product platform in a draft version by designers and configured according to customer needs, using an immersive VR environment. This method, based on the use of VP, reduces the number of physical prototypes that need to be manufactured during the product development process, thus reducing overall costs. In addition, the VP based method supports the mass-customisation process of products through the real-time integration and collection of data for product configuration preferences, involving as many users as possible representative of the target users of the new products. To demonstrate this process a case study concerning the development of the VP for a washing machine, a summary of test sessions with users and results are presented. Specifically, the results presented in this paper are related to improvements in capturing the VOC and reductions in Virtual Prototyping cost and time.     

Understanding online customers’ ties to merchants: the moderating influence of trust on the relationship between switching costs and e-loyalty

Fostering customer loyalty is a key objective for online businesses. Initial transactions with new customers are less profitable than transactions with existing customers, making loyalty an important strategy. Drawing on research examining online customer loyalty (termed e-loyalty), switching costs, and trust, this study provides an empirical test of the relative influence of trust vs switching costs on e-loyalty for e-service providers. We further examine whether trust moderates the relationship between switching costs and e-loyalty. We propose that in the presence of high customer trust, e-service providers should have less need to rely on switching costs as a driver of e-loyalty. We test the hypothesized relationships using data collected from 299 repeat users of online travel services. Our results confirm that trust is a more important predictor of e-loyalty than switching costs. In addition, we find that the impact of switching costs on e-loyalty depends on the level of trust felt by customers. This study extends our understanding of customer loyalty, switching costs, and trust in e-commerce environments and provides practical, theory-driven guidelines to e-businesses seeking to develop customer loyalty programmes.     

Application of neural network in QFD matrix

The major challenge for contemporary enterprises is to make products fulfill customers’ expectations. Conceptual design is the foundation of the development process, as it starts from the customer needs identification and decides about the whole product life cycle. Quality function deployment (QFD) helps to extract product characteristics from customer demands. Optimization of the product development process requires different product variant information at the early stage of product development. At the early designing stage, designers lack sufficient product information and have difficulty in determining it. The idea of the paper is to provide measurable engineering information for the quality function deployment method. For this purpose, a chosen artificial intelligence method was used. In the experiment, artificial neural network (NN) was applied. The results of analyses show that the intelligent estimation methods are useful and effective. The methods of estimation consist of four stages: goal setting, data acquisition, configuration of NN architecture, fulfilling of the QFD matrix. Finally, to illustrate the procedure of the chosen engineering characteristic estimation, a toothed gear box example was used.     

Understanding customers' loyalty intentions towards online shopping: an integration of technology acceptance model and fairness theory

As with any other information system (IS), the success of online shopping depends largely on customer satisfaction and other factors that will eventually increase customers' loyalty intentions. This article integrates two major variables of technology acceptance model (TAM), trust, and fairness to construct a model for investigating the motivations behind customers' loyalty intentions towards online shopping. The hypothesised model is validated empirically using data collected from 311 customers of an online shopping store. The results indicated that distributive, procedural and interactional fairness were strong predictors of trust, which in turn influenced satisfaction. Distributive fairness and interactional fairness exhibited significant positive effects on satisfaction. Perceived usefulness and satisfaction influenced loyalty intention towards online shopping. Perceived ease of use acts indirectly on loyalty intention through the mediating effect of perceived usefulness. Implications for theory and practice and future research directions are discussed.     

浅介几种常用搜索引擎的选择比较

搜索引擎自动从英特网搜集信息,经过一定整理以后,提供给用户进行查询。英特网上的信息浩瀚万千,而且毫无秩序,工作需要搜索技术文档、客户信息;购物需要搜索商品信息和指南;娱乐需要搜索相关背景资料和图片。搜索已经变得无处不在,而搜索引擎则相应的成了工作和生活中的一个必备工具。     

A multigranular linguistic content‐based recommendation model

Recommendation systems are a clear example of an e‐service that helps the users to find the most suitable products they are looking for, according to their preferences, among a vast quantity of information. These preferences are usually related to human perceptions because the customers express their needs, taste, and so forth to find a suitable product. The perceptions are better modeled by means of linguistic information due to the uncertainty involved in this type of information. In this article, we propose a content‐based recommendation model that will offer a more flexible context to improve the final recommendations where the preferences provided by the sources will be modeled by means of linguistic variables assessed in different linguistic term sets. The proposal consists of offering a multigranular linguistic context for expressing the preferences instead of forcing users to use a unique scale. Then the content‐based recommendation model will look for the most suitable product(s), comparing them with the customer(s) information according to its resemblance. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 419–434, 2007.     

基于云计算神经网络物流车辆调度算法研究

研究了物流车辆调度优化问题。针对云计算下任务调度算法没有考虑调度的服务质量和用户满意度的问题,特别是在物流任务调度问题中存在复杂的计算网络,造成计算率降低,为了解决上述问题,提出了一种新的有关云计算和神经网络相结合的物流作业调度算法。算法充分考虑了调度的服务质量以及用户满意度,建立一个参数化的处理模型,计算用户在各个资源上的综合满意度,再将任务分配到满足用户需求和使系统资源达到均衡的资源上执行,最后采用改进的神经网络进行优化车辆调度。实验结果表明,改进算法不仅能满足用户的多种需求,提高了用户的满意度,同时也提高了资源调度率和系统资源的利用率。