Development,analysis and applications of a quantitative methodology for assessing customer satisfaction using evolutionary optimization

Consumer-oriented companies are getting increasingly more sensitive about customer's perception of their products, not only to get a feedback on their popularity, but also to improve the quality and service through a better understanding of design issues for further development. However, a consumer's perception is often qualitative and is achieved through third party surveys or the company's recording of after-sale feedback through explicit surveys or warranty based commitments. In this paper, we consider an automobile company's warranty records for different vehicle models and suggest a data mining procedure to assign a customer satisfaction index (CSI) to each vehicle model based on the perceived notion of the level of satisfaction of customers. Based on the developed CSI function, customers are then divided into satisfied and dissatisfied customer groups. The warranty data are then clustered separately for each group and analyzed to find possible causes (field failures) and their relative effects on customer's satisfaction (or dissatisfaction) for a vehicle model. Finally, speculative introspection has been made to identify the amount of improvement in CSI that can be achieved by the reduction of some critical field failures through better design practices. Thus, this paper shows how warranty data from customers can be utilized to have a better perception of ranking of a product compared to its competitors in the market and also to identify possible causes for making some customers dissatisfied and eventually to help percolate these issues at the design level. This closes the design cycle loop in which after a design is converted into a product, its perceived level of satisfaction by customers can also provide valuable information to help make the design better in an iterative manner. The proposed methodology is generic and novel, and can be applied to other consumer products as well.     

The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators

The purposes of this study are to examine whether perceived playfulness and perceived flow would mediate the relationships among website quality, customer satisfaction, and purchase intention, as well as to assess the degree of reciprocity between perceived playfulness and perceived flow in an online travel agency context. This study suggested a research framework for testing the relationships among the constructs based on the stimulus-organism-response framework. In addition, this study developed a non-recursive model. After validating the measurement scales, empirical analyses were conducted using structural equation modelling. The findings confirm that website quality affects customers’ perceived playfulness and perceived flow, and in turn, would influence their satisfaction and purchase intention. Notably, this study finds that the service quality is more important than information and system quality in influencing customer satisfaction and purchase intention. Furthermore, the study reveals that the relationship between perceived playfulness and perceived flow is reciprocal. Based on the findings, the implications are discussed in the paper and directions for future research are also highlighted.     

Customer knowledge discovery from online reviews

The explosive growth of Chinese electronic market has made it possible for companies to better understand consumers?? opinion towards their products in a timely fashion through their online reviews. This study proposes a framework for extracting knowledge from online reviews through text mining and econometric analysis. Specifically, we extract product features, detect topics, and identify determinants of customer satisfaction. An experiment on the online reviews from a Chinese leading B2C (Business-to-Customer) website demonstrated the feasibility of the proposed method. We also present some findings about the characteristics of Chinese reviewers.     

The determinants of online customer ratings: a combined domain ontology and topic text analytics approach

Merchants, as well as customers, have noticed the importance of online product reviews and numeric ratings in electronic commerce websites. It is valuable if merchants can discover some potential customer value from the sheer volume of data. This paper contributes a semantic text analytics approach that can dig out the customers’ most basic concerns about their online purchase choices. More specifically, based on the hypothesis that the product reviews and overall ratings estimated by same person in a tiny time interval have a great relevance, we dexterously utilize this relevance to realize the embedded customer value. In the proposed method, take the single lens reflex camera for example, an innovative aspect extraction method that comprehensively considers the product ontology and results of the topic modeling method latent Dirichlet allocation is applied. As a result, 8 specific aspects are identified from the experimental results. For each aspect, a self-contained review feature corpus is created as an extension of some seed terms. After aspect-based sentence segmentation and context-sensitive sentiments preprocessing, aspect-oriented sentiment analysis is applied. Multiple regression analysis is then used as a statistical measure to discover determinant aspects of overall ratings. The results reveal that cost performance, image quality and product integrity are the three most influential aspects. The practical implication of our research is that merchants can efficiently modify their products, to satisfy more customers and also boost sales performance.     

Developing the index for product design communication and evaluation from emotional perspectives

Often customers make their purchase decision based on price, quality and functionality of the product. Sometimes the decision is influenced by the perceived value, which is always subjective and emotion-driven. In order to ensure successful launch of a product, it is extremely important to predict the perceived value of design alternatives systematically based on the common language understood by both target users and designers. However, the index for communicating and evaluating such value from emotional perspective is not available in the literature. Therefore, the objective of this research is to extract key indexes of perceived value from emotional perspectives and develop an effective algorithm to evaluate products. First, through literature review and the interview of participants, many scenarios of purchase decision were collected. A focus group was invited to identify the essential elements that influence the perceived value of products. Followed by a large scale questionnaire survey and factor analysis, four indexes were extracted. These indexes, named as FASE Index in brief, included features, association, social-esteem, and engagement. Second, by combining the fuzzy mathematics and the pairwise comparison method, an evaluation model was developed. Third, the perception differences of sample products were conducted to verify the validity of FASE index. The findings of this study demonstrated that FASE index was effective for decision making in product design.     

Towards Easy Comparison of Local Businesses Using Online Reviews

With the rapid development of e‐commerce, there is an increasing number of online review websites, such as Yelp, to help customers make better purchase decisions. Viewing online reviews, including the rating score and text comments by other customers, and conducting a comparison between different businesses are the key to making an optimal decision. However, due to the massive amount of online reviews, the potential difference of user rating standards, and the significant variance of review time, length, details and quality, it is difficult for customers to achieve a quick and comprehensive comparison. In this paper, we present E‐Comp, a carefully‐designed visual analytics system based on online reviews, to help customers compare local businesses at different levels of details. More specifically, intuitive glyphs overlaid on maps are designed for quick candidate selection. Grouped Sankey diagram visualizing the rating difference by common customers is chosen for more reliable comparison of two businesses. Augmented word cloud showing adjective‐noun word pairs, combined with a temporal view, is proposed to facilitate in‐depth comparison of businesses in terms of different time periods, rating scores and features. The effectiveness and usability of E‐Comp are demonstrated through a case study and in‐depth user interviews.     

基于情感词抽取与LDA特征表示的情感分析方法

情感分析作为文本挖掘的一个新型领域,可用于分类、归纳用户发布的产品评论,从而有助于商家改善服务,提高产品质量;同时为其他消费者提供购买决策。本文提出一种基于情感词抽取与LDA特征表示的情感分析方法,对产品评论进行褒贬二元分类。在情感词抽取中,采用人工构造的情感词典对预处理之后的文本抽取情感词;用LDA模型建立文档的主题分布,以评论-主题分布作为特征,用SVM分类器进行分类。实验结果表明,本文方法在评论褒贬分类方面有着良好的效果。     

Agent and data mining based decision support system and its adaptation to a new customer-centric electronic commerce

Recently, as the Internet has become more widely used, Electronic Commerce (EC) has emerged and has developed a high-level business environment. The customer-centric EC model is important for the success of EC and this study presents a new customer-centric EC model in make-to-order (MTO) semiconductor manufacturing environment. In this study we proposed the EC model providing the process transparency of process sampling method that can provide online semiconductor customers with the performance information of available process sampling methods which can be used at all manufacturing process steps for their own products in MTO manufacturing environment, and then the capability to select a desirable one among them based on their purchase situations on EC web site. In the proposed EC model the customer can select a process sampling method that is most suitable to him/her according to the customer's purchase situation. In this model the use of intelligent decision support system called customized sampling decision support system (CSDSS) that can autonomously generate available customized sampling methods and provide the performance information of those methods to EC system is requisite. We implemented an Internet-based prototype of CSDSS which had an architecture based on intelligent agent technology and also the successful integration of data mining process for the generation of optimal sampling method into DSS framework by means of applying that technology.     

Does reliable information matter? Towards a trustworthy co-created recommendation model by mining unboxing reviews

Online review forums provide customers with powerful platforms to express opinions and influence business trends, while allowing firms to collaborate and co-create value with customers. However, information overload due to the huge amount of reviews posted daily complicates the efforts of consumers to locate reliable information when making a purchase decision. Therefore, this study develops a trustworthy co-created recommendation model. The proposed model mines unboxing reviews, calculates the trust scores of the reviewers, and then generates the recommended products by combing this information with customer preferences using a multi-criteria decision-making method. An illustrative example of mobile phones demonstrates the recommendation procedure of the proposed model. The proposed model is evaluated via an empirical experiment to examine the satisfaction of study participants by using a seven-point Likert scale. An analysis of the structural equation modelling results indicates that three factors (i.e. confidence in decision quality, enhanced problem-solving ability, and satisfaction with resource expenditure) significantly and positively affect the purchase decision-making process. Moreover, the proposed model outperforms a baseline model in all four factors, ultimately increasing user satisfaction. In addition to its theoretical framework for co-creating value with customers to develop a trustworthy co-created recommendation model, as supported by various theories of trust, the proposed model provides further insights into the role of customer reviews in designing recommendation models, as well as the extent to which such models impact user decisions.     

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.     

Which is more important in Internet shopping,perceived price or trust?

Price and trust are considered to be two important factors that influence customer purchasing decisions in Internet shopping. This paper examines the relative influence they have on online purchasing decisions for both potential and repeat customers. The knowledge of their relative impacts and changes in their relative roles over customer transaction experience is useful in developing customized sales strategies to target different groups of customers. The results of this study revealed that perceived trust exerted a stronger effect than perceived price on purchase intentions for both potential and repeat customers of an online store. The results also revealed that perceived price exerted a stronger influence on purchase decisions of repeat customers as compared to that of potential customers. Perceived trust exerted a stronger influence on purchase decisions of potential customers as compared to that of repeat customers.     

面向产品设计改进的在线评论挖掘

以竞争市场环境中的产品在线评论数据为研究对象,基于支持产品设计改进的视角,采用数据挖掘的方法与工具,开展面向产品设计改进的在线评论大数据分析研究。重点开展在线评论数据挖掘过程模型中的有用性建模和特征评价值情感分析。以某智能手机产品的在线评论数据为对象进行了实验,得到该产品各个属性的评价值,与更新换代后的产品属性进行比较,验证了此方法的有效性。     

Mining demand chain knowledge of life insurance market for new product development

Demand chain management (DCM) can be defined as “extending the view of operations from a single business unit or a company to the whole chain. Essentially, demand chain management focuses not only on generating drawing power from customers to purchase merchandises on the supply chain; but also on exploring satisfaction, participation, and involvement from customers in order for enterprises to understand customer needs and wants. Thus, customers have changed their position in the demand chain to assume a leading role in bringing more benefit for enterprises. This article investigates what functionalities best fit the consumers’ needs and wants for life insurance products by extracting specific knowledge patterns and rules from consumers and their demand chain. By doing so, this paper uses the a priori algorithm and clustering analysis as methodologies for data mining. Knowledge extraction from data mining results is illustrated as market segments and demand chain analysis on life insurance market in Taiwan in order to propose suggestions and solutions to the insurance firms for new product development and marketing.     

Does online interactivity matter? Exploring the role of interactivity strategies in consumer decision making

Given that the Internet does not afford an opportunity to inspect products before purchase, some customers hesitate to shop online. Online interactivity can supplement online decision-making with added product information. Based on the theories of impression management and deception, this study focuses on sellers’ online interactivity strategies (SOIS) and aims to explore the role of SOIS played in online purchase decision-making process. According to the stimulus–organism–response (S–O–R) paradigm, this study aims to understand how each component of SOIS affects transaction intention through consumer perceptions (perceived deception and perceived diagnosticity) and how this affect is moderated by product types (search goods and experience goods) in online marketplaces. Data collected from 475 respondents support most of our hypotheses. Product type positively moderates only the link between image creation and perceived deception. Implications for theory and practice are also discussed.     

Understanding the role of an IT artifact in online service continuance: An extended perspective of user satisfaction

Given increasing investment in an IT (information technology) artifact (i.e., online service website), it is becoming important to retain existing customers. In order to help link website design and investment decisions to the strategy for retaining customers, we propose a model by extending the user satisfaction perspective into research on online service continuance. We empirically tested the model within the context of a social network service. The analysis results found that website information satisfaction and system satisfaction play key roles in forming continuance intention through perceived usefulness and perceived enjoyment. It is also noted that computer anxiety serves as an important moderator toward continuance intention. Theoretical and practical implications are offered for better understanding of the role of the IT artifact in online service post-adoption phenomena.     

An integrated decision-making method for product design scheme evaluation based on cloud model and EEG data

Selecting the optimal design scheme is a vital task in the product design area. It not only improves the performance of the product, but also leads to the greatest satisfaction of customers. However, existing methods express qualitative evaluation information roughly, and none of them has taken the implicit psychological states of customers into consideration. Therefore, an integrated decision-making method for product design scheme evaluation is proposed. This method applies the cloud model to facilitate the evaluation process of experts and uses the EEG data to reveal the psychological states of customers. Benefit from the probability theory and fuzzy set theory, the cloud model deals with the fuzziness and randomness simultaneously. It can decrease the cognitive discrepancy of experts and allow the information distortion to be neutralized to a great extent. Since the experts are not the final users of products, the evaluation results from experts cannot truly reflect the psychological states of customers when they use the product. An experiment is designed to collect the EEG data which can reveal the implicit psychological states of customers. The recorded data are segmented based on the operation process and tagged with the self-reported psychological states. Subsequently, the wavelet packet decomposition is applied and the sample entropy of each EEG frequency band is extracted as the feature. Taking advantage of the random forest classifier, the psychological states of customers can be classified with the average accuracy of 90.76%. This study can lead to a practical system for automatic assessment of psychological states in future applications. The evaluation process of elevator design schemes is conducted as a case study to illustrate the feasibility of the proposed method.     

基于观点挖掘的笔记本电脑评论分析系统

主要以商业领域的需求和应用为背景,构建一个智能化的笔记本电脑评论分析系统.该系统对国内大型购物网站上非结构化、自由式的笔记本电脑评论文本进行情感倾向识别和产品特征归纳,实现了利用数据挖掘和商务智能的手段分析网络消费者对特定产品的反馈,帮助企业管理人员了解特定产品的市场需求、制定商业决策.实验结果证明该系统能够较准确的得出分类结果并归纳出产品特征.     

How does justice matter in online retailers’ reputation and purchase intentions: an empirical study of China

In recent years, online shopping has been proliferated around the world. Online retailers’ reputation and purchase intentions are critical for survival and profitability of any online store. Thus, this study proposes a research framework to examine the perceived justice effects on customers purchase intention and online retailers’ reputation. A confirmatory factor analysis was conducted to demonstrate the reliability and validity of the measurement model, and the structural equation modelling technique was used to test the research model. The hypothesised model was validated empirically using data collected from 383 online shopping customers in China. The results indicated that perceived procedural, distributive and interactional justice components were strong predictors of customers purchase intention and online retailers’ reputation while online retailers’ reputation had significant effects on purchase intentions. Finally, theoretical and managerial implications are also presented in the paper.