共查询到20条相似文献，搜索用时 62 毫秒
1.
Understanding user experience (UX) becomes more important in a market-driven design paradigm because it helps designers uncover significant factors, such as user’s preference, usage context, product features, as well as their interrelations. Conventional means, such as questionnaire, survey and self-report with predefined questions and prompts, are used to collect information about users’ experience during various UX studies. However, such data is often limited and restricted by initial setups, and they won’t easily allow designers to identify all critical elements such as user profile, context, related product features, etc. Meanwhile, with widely accessible social media, the volume and velocity of customer-generated data are fast-increasing. While it is generally acknowledged that such data contains important elements in understanding and analyzing UX, extracting them to assist product design remains a challenging issue. In this study, how UX data underlying product design can be isolated and restored from customer online reviews is examined. A faceted conceptual model is proposed to elucidate the crucial factors of UX, which serves as an operational mechanism connecting to product design. A methodology of establishing a UX knowledge base from customer online reviews is then proposed to support UX-centered design activities, which consists of three stages, i.e., UX discovery to extract UX data from a single review, UX data integration to group similar data and UX network formalization to build up the causal dependencies among UX groups. Using a case study on smart mobile phone reviews, examples of UX data discovered are demonstrated and both customers and designers concerned key product features and usage situations are exemplified. This study explores the feasibility to discover valuable UX data as well as their relations automatically for product design and business strategic plan by analyzing a large volume of customer online data. 相似文献
2.
[目的/意义]实现海量产品评论数据的快速分析,帮助产品设计人员高效地获取用户需求,在新产品设计的决策中提供参考。[方法/过程]在特征提取和情感分析的基础上,构造了包括"词+词性+词干+位置+依存关系"等节点特征的条件随机场模型,按照"产品特征、谁、在何种情境下、遇到了什么问题"4个要素,以描述手机屏幕和电池的负面评论为例,从产品评论中提取用例。[结果/结论]模型评估和实证研究表明,所构造的模型可以有效地从评论文本中识别产品特征、使用主体、使用情景和遇到的问题,从而快速构造用例,获取用户需求。 相似文献
3.
Ideation is an important phase in the new product development process at which product designers innovate and select novel ideas that can be added as features to an existing product. One way to find novel ideas is to transfer uncommon features of products of other domains and integrate them into the product to be improved. However, before incorporating such targeted features into the product, they need to be evaluated against the customers’ acceptance in social media using sentiment aggregation tools. Despite the many studies in sentiment analysis, mapping the customers’ opinions towards both high-level and technical features of a product extracted from social media to their best corresponding component in that product is still a challenge. Furthermore, none of the existing approaches ascertains the sentiment value of a targeted feature by capturing its dependencies on other features. In this paper, to address these drawbacks, we propose the sentiment aggregation framework for targeted features (SA-TF). SA-TF determines the sentiment of a targeted feature by assisting product designers in the tasks of mapping the features discussed in the reviews to the right product components, sentiment aggregation and considering feature dependencies to determine their polarity. The superiority of the different phases of SA-TF is demonstrated with experiments and comparing it with an existing approach. 相似文献
4.
《International Journal of Information Management》2017,37(6):673-683
With the rapid development of information technology, customers not only shop online—they also post reviews on social media. This user-generated content (UGC) can be useful to understand customers’ shopping experiences and influence future customers’ purchase intentions. Therefore, business intelligence and analytics are increasingly being advocated as a way to analyze customers’ UGC in social media and support firms’ marketing activities. However, because of its open structure, UGC such as customer reviews can be difficult to analyze, and firms find it challenging to harness UGC. To fill this gap, this study aims to examine customer satisfaction and dissatisfaction toward attributes of hotel products and services based on online customer textual reviews. Using a text mining approach, latent semantic analysis (LSA), we identify the key attributes driving customer satisfaction and dissatisfaction toward hotel products and service attributes. Additionally, using a regression approach, we examine the effects of travel purposes, hotel types, star level, and editor recommendations on customers’ perceptions of attributes of hotel products and services. This study bridges customer online textual reviews with customers’ perceptions to help business managers better understand customers’ needs through UGC. 相似文献
5.
6.
For new product development, previous segmentation methods based on demographic, psychographic, and purchase behavior information cannot identify a group of customers with unsatisfied needs. Moreover, segmentation is limited to sales promotions in marketing. Although needs-based segmentation considering customer sentiments on product features can be conducted to develop a new product concept, it cannot identify commonalities among customers owing to their diverse preferences. Therefore, this paper proposes an interpretable machine learning-based approach for customer segmentation for new product development based on the importance of product features from online product reviews. The technical challenges of determining the importance of product features in each review are identifying and interpreting the nonlinear relations between satisfaction with product features and overall customer satisfaction. In this study, interpretable machine learning is used to identify these nonlinear relations with high performance and transparency. A case study on a wearable device is conducted to validate the proposed approach. Customer segmentation using the proposed approach based on importance is compared with that employing a previous approach based on sentiments. The results show that the proposed approach presents a higher clustering performance than the previous approach and offers opportunities to identify new product concepts. 相似文献
7.
以期望与感知服务质量、顾客满意间的关系为研究对象,旨在揭示期望对感知服务质量、顾客满意影响的效应机理,从而帮助企业通过科学管理顾客期望,实现对感知服务质量和顾客满意的有效提升.本文基于卡诺模型划分期望类别,构建必备属性期望、一维属性期望、魅力属性期望和感知服务质量、顾客满意关系的结构模型,并提出假设.以餐饮行业为实证研究背景,进行实证检验.结果表明不同属性期望对感知服务质量和顾客满意产生不同的影响.其中,必备属性期望对感知服务质量和顾客满意有显著负向影响,一维属性期望与魅力属性期望对感知服务质量和顾客满意有显著正向影响. 相似文献
8.
《Information processing & management》2022,59(1):102800
At present, the focus of marketing research is mostly on the influencing factors, composition, and measurement of brand equity. The meta-combined brand equity analysis is based on two main research perspectives: financial perspective and customer perspective. While the financial perspective is based on the incremental discounted future cash flows that would result from a branded product's revenue over the revenue of an unbranded product, the brand equity from the customer's perspective is the consumer's reaction to brand marketing behavior, the impact on brand knowledge. The decision-making of marketing behaviors often faces choices related to ethics. Therefore, once the moral value of a company through marketing behavior is recognized by consumers, the ethical behavior presented in this article through marketing behavior will make consumers feel more about the brand. How does the brand equity of your customer's products affect you? In this experiment, shopping groups with the same shopping experience were selected. During the survey process, all customers in different periods and the same time were selected as far as possible based on the practicability of the survey. The study survey covered 4 main aspects; customer satisfaction, overall overview of customer satisfaction; the advantages and disadvantages of marketing strategies through quantitative analysis and to put forward reasonable marketing strategy improvement opinions and suggestions to improve customer satisfaction. Using the technique of parameter prediction of the financial industry, the experiment proved that the non-standard promotion behavior, the integrity of the enterprise and the social responsibility are three aspects (P<0.05) that have an impact on the customer's brand equity among the corporate marketing components. It was a detailed study of the current state of brand marketing strategies and customer satisfaction, found key indicators of brands that could improve customer satisfaction, and presented corresponding suggestions for optimizing marketing strategies. It shows that. It has the importance of good guidance and references to improve customer satisfaction in the industry. 相似文献
9.
由于科学技术发展越来越快,产品生命周期缩短,生产成本降低空间缩小,技术创新空间降低,各企业对顾客满意度的关注度越来越高,但是随着产品生命周期的缩短,产品更新换代速度的加快,企业对于产品改计的切入点寻找难度增加,改进成本也随之提高,本文从顾客满意度的角度,对卡诺(Kano)模型进行改进,引入优先指数,将待改进指标进行排序,使企业的改进方向更为明确,为改进决策起导向作用。 相似文献
10.
To develop highly competitive products, companies need to understand customer needs (CNs) by effectively gathering and analysing customer data. With the advances in Information Technology, customer data comes not only from surveys and focus groups but also from social media and networking sites. Few studies have focused on developing algorithms that are devised exclusively to help to understand customer needs from big opinion data. Topic mining, aspect-based sentiment analysis and word embedding are some of the techniques adopted to identify CNs from text data. However, most of them do not consider the possibility that part of the customer data analysed is already known by companies. With the aim to continuously enhance company understanding of CNs, this paper presents an autonomous methodology for automatically classifying a set of text data (customer sentences) as referring to known or unknown CN statements by the company. For verification purposes, an example regarding a set of customer answers from an open survey questionnaire regarding the climate system of a car is illustrated. Results indicate that the proposed methodology helps companies to validate and update the customer need database with an average of 90 % precision and 60 % recall. 相似文献
11.
《Information processing & management》2019,56(3):823-843
A vast number of user opinions are available from reviews posted on e-commerce websites. Although these opinions are a valuable source of knowledge for both manufacturers and customers, they provide volumes of information that exceeds the human cognitive processing capacity, which can be a major bottleneck for their effective use. To address this problem, a number of opinion-summarization methods have been proposed to organize these opinions by grouping them around aspects. However, these methods tend to generate an excessive number of aspect groups that are frequently overly generic and difficult to interpret. We argue that a superior alternative would be to organize opinions around product attributes as defined in a product catalog. Typically, product attributes correspond to the most important characteristics of the products. Furthermore, they are common to all products in a given category and thus, form a more stable set than aspects. In this paper, we propose a novel approach called OpinionLink to products in a catalog at the attribute granularity level with opinions extracted from product reviews. The proposed approach is divided into two phases. In the first phase, OpinionLink uses a classifier to identify opinionated sentences in the reviews on a particular product. In the second phase, another classifier is used to map the opinions that were previously extracted from the user reviews to the attributes of the products in the product catalog. We performed a series of experiments on these phases. For the first phase, our experiments indicated that using classifiers with the proposed features achieved an average of 0.87 in terms of F1 measure for the task of identifying opinionated sentences. In the second phase, the method we proposed for the opinion-mapping task achieved an average of 0.85 in terms of F1. Further, we verified the effectiveness of the proposed approach as a realistic end-to-end application, indicating that we can use OpinionLink in a real setting. Finally, we empirically demonstrate the feasibility of using the proposed approach with an extremely large volume of opinions available in a collection of more than 600,000 real reviews. We also set forth a number of directions for future research. 相似文献
12.
住宅产品顾客满意度评价模型研究 总被引：1，自引：0，他引：1
在深入研究顾客满意的影响因素、住宅产品的特点和住宅产品消费者行为分析的基础上,提出了住宅产品顾客满意度评价模型,为住宅产品企业分析顾客满意状态,实施顾客满意战略提供了方法和手段。 相似文献
13.
运用顾客忠诚理论和满意理论,揭示顾客满意、转换成本和品牌认同等构面对忠诚意愿的影响关系和特性,设计基于顾客忠诚意愿的影响因素差异分析框架及方法,提出影响因素综合分析流程及显著影响因子获取方法;并以移动通讯服务消费顾客为实例背景,针对三类忠诚意向顾客群,运用Logistic回归模型、因子分析、单因素方差分析等技术,分析得到忠诚意向的影响主因子构成及显著影响强度差异特性。 相似文献
14.
《Information processing & management》2020,57(6):102304
The way that users provide feedback on items regarding their satisfaction varies among systems: in some systems, only explicit ratings can be entered; in other systems textual reviews are accepted; and in some systems, both feedback types are accommodated. Recommender systems can readily exploit explicit ratings in the rating prediction and recommendation formulation process, however textual reviews -which in the context of many social networks are in abundance and significantly outnumber numeric ratings- need to be converted to numeric ratings. While numerous approaches exist that calculate a user's rating based on the respective textual review, all such approaches may introduce errors, in the sense that the process of rating calculation based on textual reviews involves an uncertainty level, due to the characteristics of the human language, and therefore the calculated ratings may not accurately reflect the actual ratings that the corresponding user would enter. In this work (1) we examine the features of textual reviews, which affect the reliability of the review-to-rating conversion procedure, (2) we compute a confidence level for each rating, which reflects the uncertainty level for each conversion process, (3) we exploit this metric both in the users’ similarity computation and in the prediction formulation phases in recommender systems, by presenting a novel rating prediction algorithm and (4) we validate the accuracy of the presented algorithm in terms of (i) rating prediction accuracy, using widely-used recommender systems datasets and (ii) recommendations generated for social network user satisfaction and precision, where textual reviews are abundant. 相似文献
15.
Fast development of IT and ICT facilitate customers to post a large volume of their concerns and expectation online, which are widely accepted to be a valuable resource for product designers. However, it is found that only a small number of small and medium-sized enterprises (SMEs) have capabilities to leverage customer online insights for design innovation, which often demonstrate a significant share in national economies growth. To discover the beneath reasons regarding the barrier that prevent them to make effective utilization, in this study, as a concrete example, manufacturing SMEs in the South Wales and Greater Manchester industrial areas of the UK are focused and their potential motivations for using and knowledge of big data-based customer analytics are investigated. An exploratory survey was conducted in terms of the type of customer data they have, the storage approaches, the volume of customer data, etc. Next, a carefully devised exploratory study was undertaken to understand how SMEs perceive the relations between customer data and product design, how about their expectations from big customer data analytics and what really challenges SMEs to exploit the value of big customer data. Besides, a demonstration platform is developed to present SMEs an automatic process of analysing customer online reviews and the capacity on customer insights acquisition and strategic decision making. Finally, findings from two focus groups indicate the different managerial and technical considerations required for SMEs considering implementing big data and customer analytics. This study encourages SMEs to welcome big customer data and suggests that a cloud-based approach may be the most appropriate way of giving access to big data analytics techniques. 相似文献
16.
服务创新过程中如何充分有效地利用企业内外部创新主体是服务企业面临的重要课题。本文在对相关文献进行回顾与分析的基础上提出了服务创新过程中员工、顾客、供应商等多主体参与和顾客满意度之间关系的理论假设模型。采用实证研究的方法,进行问卷调查并进行结构方程分析,得出结果包括:顾客参与对员工参与、供应商参与有直接正向影响;供应商参与对员工参与有直接正向影响;顾客参与、供应商参与对顾客满意度有直接正向影响;员工参与对顾客满意度并没有直接影响。发现了员工参与、顾客参与、供应商参与及顾客满意度的综合影响机制,并得到一些启示。 相似文献
17.
Social media data have recently attracted considerable attention as an emerging voice of the customer as it has rapidly become a channel for exchanging and storing customer-generated, large-scale, and unregulated voices about products. Although product planning studies using social media data have used systematic methods for product planning, their methods have limitations, such as the difficulty of identifying latent product features due to the use of only term-level analysis and insufficient consideration of opportunity potential analysis of the identified features. Therefore, an opportunity mining approach is proposed in this study to identify product opportunities based on topic modeling and sentiment analysis of social media data. For a multifunctional product, this approach can identify latent product topics discussed by product customers in social media using topic modeling, thereby quantifying the importance of each product topic. Next, the satisfaction level of each product topic is evaluated using sentiment analysis. Finally, the opportunity value and improvement direction of each product topic from a customer-centered view are identified by an opportunity algorithm based on product topics’ importance and satisfaction. We expect that our approach for product planning will contribute to the systematic identification of product opportunities from large-scale customer-generated social media data and will be used as a real-time monitoring tool for changing customer needs analysis in rapidly evolving product environments. 相似文献
18.
在线商品评论对产品销量影响研究 总被引：3，自引：0，他引：3
作为一种新型的口碑传播方式,在线产品评论成为了消费者和商家了解产品质量和服务的最为重要的信息来源。在线产品评论哪些因素影响到消费者的购买决策,对产品的销量产生多大的影响已经成为人们关注的重要问题。通过对在线手机评论研究发现,"在线评论数量"、"商品的关注度"对在线手机销量有显著性影响,更为重要的是我们发现"评论的时效性"和"顾客认为评论的有用率"对手机的销量也有非常重要的显著性影响,而"评论的正负情感倾向性"等对产品的销量无明显影响。 相似文献
19.
【目的/意义】目前,越来越多的消费者参与在线评论进行信息交互和需求表达。从丰富的在线产品评论中
识别并分析用户需求有助于企业有针对性地提升产品及服务质量,从而推动企业可持续发展。【方法/过程】本文利
用LDA模型对在线手机评论进行评论主题及产品特征挖掘,有效识别用户需求要素。基于Kano模型设置用户需
求调查问卷,结合用户满意指数分析各项需求对用户满意度的影响,确定各类用户需求重要度和供给优先级顺
序。【结果/结论】本文将24项用户需求要素划分为6项高魅力型需求、8项低魅力型需求、3项高期望型需求、3项高
必备型需求、2项低必备型需求、2项无差异型需求,进一步提出企业产品管理的优化策略。【创新/局限】本文利用文
本挖掘方法对真实的在线评论进行用户需求分析,有效克服传统用户需求调查方法中存在的需求来源滞后及可靠
性不足等问题。此外,本文所选产品的品牌相同,后续研究可向多平台及多品牌的产品需求分析进行改进和深化。 相似文献
20.
根据用户满意度理论,构建了图像搜索引擎用户满意度测评理论模型和图像搜索引擎用户满意度评价指标体系。选择知名的图像搜索引擎Google、百度和Ditto,以问卷调查的方式,调查用户对此三个图像搜索引擎的满意度。 相似文献