顾客参与对新产品开发作用机理研究

传统观念威胁到企业竞争力,新产品开发大多集中在企业角度探讨,却忽略了真正使用产品的顾客。从知识分享的角度,分析顾客参与对产品创新的作用机理,即顾客参与通过关系涉入影响顾客与新产品开发企业的知识分享,进而改善新产品绩效,同时加入了顾客知识异性作为调节变量,更好解释了这一机制。     

Influence of customer engagement with company social networks on stickiness: Mediating effect of customer value creation

Company social networks have become an important means for the socialized marketing of a company, forming a new challenge to companies on how to attract customers. Based on such theories as customer engagement, value co-creation, and relationship marketing, this paper presents a model of the influence of customer engagement on stickiness. Data collected from 260 valid questionnaires from Sina’s enterprise microblog users were analyzed by structural equation modeling. Empirical results show that customer engagement has a direct and positive influence on customer stickiness as well as an indirect influence through customer value creation. This study enriches previous researches on existing theories of customer engagement, value co-creation, and stickiness, and gives practical guidance for companies to encourage customer engagement and enhance the stickiness of company social networks.     

Market orientation and product strategies in small internationalising software companies

This study examines how market orientation is manifested in the context of small, rapidly internationalising software companies. It is concluded from the four company cases that any internationalising software company should focus its market orientation more on the individual-customer level than on the overall-market level. For providers of standardised software products, cooperating with global leading customers plays a key role in terms of obtaining the most accurate market intelligence and staying ahead in the competition. In contrast, providers of tailor-made software projects should emphasise interfunctional coordination: the quality of the response to customer needs should be well managed regardless of the geographical location. Further, the concepts of reactive and proactive market orientation are intertwined: neither contributes to better customer satisfaction in isolation. However, depending on the time needed for responding to customer needs, either may require more attention.     

虚拟顾客参与平台的运行机制:理论与实证研究

通过虚拟顾客参与平台（Virtual Customer Environments, VCE）来吸引顾客参与创新、利用顾客大数据、创建顾客中心型组织成为企业的重要策略，但VCE的运行机制并未得到理论界的足够重视。本文以VCE功能为主线，将VCE分为信息展示、需求收集和深度互动三个子平台。基于刺激-机体-反应模型，分析每个子平台的运行机制。具体而言，IT环境和市场环境的发展构成VCE的运行基础，企业和顾客之间的交互关系构成VCE的运行动因，企业吸引行为和顾客参与行为构成VCE的运行过程。进一步运用内容分析法，本文检验了中国500强企业网站中VCE子平台的运行，实证研究结果支持理论模型所提出的运行机制。但同时也发现，大多数中国500强企业尚未充分构建需求收集和深度互动这两个子平台，表现为在实际运行中未能有效吸引顾客参与。本研究不仅推动了VCE理论的发展，对顾客参与理论进行了拓展，并且有助于指导企业完善VCE平台的构建，从信息技术的角度塑造顾客导向型组织，提升创新绩效。     

授人以鱼不如授人以渔——创新视角下的政府背景客户与企业成长

本文利用2007-2015年间A股上市公司年报中公开披露的客户数据,从研发创新的角度检验了政府大客户的存在对企业成长性的影响。实证结果表明,上市公司的大客户中政府背景订单占比约大,则企业未来的盈利增长能力就越强,与此同时,资本市场对企业的估值水平也越高,进一步地从作用机制来看,政府背景订单之所以能促进企业成长壮大,很大程度上可以归结于它们对企业研发创新的促进作用,尤其是增加公司的创新投入与创新产出,继而助推企业成长。总而言之,本文的研究发现有助于我们客观地了解政府背景客户在企业成长发展中扮演的角色,辩证地看待以政府采购为代表的另类政府干预机制对实体经济产生的影响作用。     

How humans obtain information from AI: Categorizing user messages in human-AI collaborative conversations

Although there is an increasingly number of research about the design and use of conversational agents, it is still difficult for conversational agents to completely replace human service. Therefore, more and more companies have adopted human-AI collaborative systems to deliver customer service. It is important to understand how people obtain information from human-AI collaborative conversations. While the existing work relies on self-reported methods to elicit qualitative feedback from users, we have concluded a categorization system for user messages in human-AI collaborative conversations after a thorough examination of a real-world customer service log, which could objectively reflect the user's information needs. We categorize user messages into five categories and 15 specific types related to three high-level intentions. Two annotators independently classified the same set of 1,478 user messages from 300 conversations and reached a moderate consistency. We summarize and report the characteristics of different message types and compare their usage in sessions with only human, AI, or both representatives. Our results show that different message types vary significantly in usage frequency, length, and text similarities with other messages in a session. Also, the frequency of using different message types in our dataset seems consistent over sessions with different types of representatives. But we also observed some significant differences in a few specific message types across the sessions with different representatives. Our results are used to suggest some areas for improvement and future work in human-AI collaborative conversational systems.     

Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach

Nowadays, Artificial Intelligence (AI) based modeling is the major consideration to build efficient, automated, and smart systems for our today's needs. Many companies are benefited from these modeling methods for their marketing efforts. Each firm has expected to increase its product development in an innovative way to improve its business growth. Successful firm marketing is to offer the right product to the right person at the right time. To market the product to the customer successfully, it is needed to segment the customer by finding their behavioral patterns. The customer behaviors and their purchasing patterns are used to generate profit for the company. Customer segmentation is the process of grouping customers based on commonalities. Developing an efficient AI-based customer segmentation to improve digital marketing growth is a challenging task. In this paper, an unsupervised deep learning model called a Self-organizing map with an Improved social spider optimization approach has been used for efficient customer segmentation. The customer data are analyzed by a feature engineering process using a swarm intelligence model called Modified social spider optimization to select the behavioral features of the customer. Then, the customers are clustered using Self Organizing neural network (SONN). Based on the clusters, the customers are classified using the Deep neural network (DNN) model. The experimental results prove the performance of the proposed model with high clustering and segmentation capability to improve the business profit in marketing.     

How social media brand pages contribute to functional conflict: The central role of commitment

Although brand pages on social media platforms are burgeoning, companies frequently have difficulty in sustaining customer relationships on their brand pages. Consequently, this study focuses on how a social media brand page develops customer commitment and encourages them to perceive that future conflicts with the company can be resolved for their mutual benefit. On the basis of a review of the literature on customer value theory and commitment, this study develops an integrative model that explores the antecedents of functional conflict and the boundary condition under which brand page commitment enhances functional conflict. The model is tested using data collected from 293 followers of brand pages on a social networking site. The results demonstrate the salient roles of customer values and commitment in determining customer perceptions of future conflicts. By shifting scholarly attention from economic outcomes characterized by purchase intention to relationship outcomes characterized by functional conflict, the findings contribute to the research of the business implications of social networking sites.     

Social media competitive analysis and text mining: A case study in the pizza industry

Social media have been adopted by many businesses. More and more companies are using social media tools such as Facebook and Twitter to provide various services and interact with customers. As a result, a large amount of user-generated content is freely available on social media sites. To increase competitive advantage and effectively assess the competitive environment of businesses, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the textual information on their competitors’ social media sites. In an effort to help companies understand how to perform a social media competitive analysis and transform social media data into knowledge for decision makers and e-marketers, this paper describes an in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut, Domino's Pizza and Papa John's Pizza. The results reveal the value of social media competitive analysis and the power of text mining as an effective technique to extract business value from the vast amount of available social media data. Recommendations are also provided to help companies develop their social media competitive analysis strategy.     

电子商务B2C企业发展:顾客服务模式研究

本文首先分析早期大批B2C企业失败的原因，得出在发展B2C应注重顾客需求，进而提出顾客服务模式。重点分析B2C对于顾客的意义。了解顾客在B2C中的需求，分析企业应该提供怎么样的服务去实现顾客的期望。满足顾客现实或潜在的需求。     

Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors

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.     

Eye tracking technology to audit google analytics: Analysing digital consumer shopping journey in fashion m-retail

Rapid changes in digital consumer behaviour, influenced by exponential adoption of smartphones, require timely and sophisticated responses from retailers to these consumer needs. With a growing amount of big data available, fashion retailers need to innovate and develop more sophisticated analytics for new consumer behaviours such as m-commerce. This research has considered how Google Analytics data can be applied to develop customer journey maps to understand digital consumer behaviour. A multi-method research design was used, incorporating an innovative mobile eye tracking technology and mobile fashion consumer data from Google Analytics. Digital customers’ shopping journeys on the fashion retailer’s website were mapped using data sourced from eye tracking and Google Analytics, gathered in parallel. The comparison of these shopping journeys allowed a critical evaluation of the precision and usefulness of the Google Analytics database. Google Analytics tracked only half of the digital users’ activities, whereas eye tracking data documented digital consumers’ interaction with all elements of the fashion retailer’s website. The conclusion reached is that eye tracking can be used to audit the Google Analytics database for potential gaps in data and also to inform and improve marketing decision-making.     

Discovering the influences of the patent innovations on the stock market

With the implementation of the innovation-driven development strategy, increasing technical innovations are patented by the individuals or the companies. As a form of intellectual properties, the patent has attracted attention from individuals and companies. Although there are some researches on the economic function of patent, few quantitative researches discuss on whether patents can work on the company stock market. To discover the relations between the company patents and the stock market, we explore a method to analyze the influence of patent activity on the company stock market. We collect the patent data and the stock data of listed companies, from which patent and market activities are extracted. By the recursive discrete wavelet transform, the patent and market activities are decomposed into multi-scale wavelets. These wavelets are fed into a patent and market activity based stock market trend prediction model, in which the influences of patent activity are analyzed. We compare our model with the state-of-the-art model on 4 measurements for 3 manufacturing datasets. The experimental results show that the patent activities have positive effect on market trend prediction in about 30% manufacturing listed companies and that the measurements of Shanghai/Shenzhen Stock Exchange often outperform that of USA in years 2016–2019 for the manufacturing listed companies.     

The evolution of argumentation mining: From models to social media and emerging tools

Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.     

Going beyond technology: Knowledge sharing as a tool for enhancing customer-oriented attitudes

Numerous articles, books and essays have shown that ongoing efforts to meet customers’ needs characterize market leaders. For a firm to successfully develop customer-oriented attitudes throughout the entire organization, employees at all levels and sections need to be committed to providing a high level of service. It is therefore essential to understand what drives employees – especially those who do not interact daily with customers – to adopt a customer-oriented attitude. The aim of this study is to shed some light on one facet of this relationship, namely the mechanism of knowledge sharing that may impact employee customer orientation. The underlying notion is that technology, in its various advanced forms of CRM software, enhances and enables client “knowing.” In other words, it leads to a greater ability on the part of employees to share knowledge, which brings about real customer orientation.This paper provides empirical evidence, based on a sample of 345 Hi-Tech workers from a telecommunication company, of a link between sharing explicit knowledge and tacit knowledge to enhance customer orientation. The findings indicate that sharing tacit knowledge has a positive effect on increasing employee customer orientation and increases sharing of explicit knowledge, while also strengthening communication about customers in the organization. It is concluded that firms should encourage tacit knowledge sharing and its transformation by technology into explicit knowledge to augment employees’ customer-oriented attitudes.     

消费者序列购买影响因素及路径选择研究

消费者经常会依照一定的顺序从同一企业购买多种产品,这种购买顺序反映了消费者的购买路径.这种现象也为企业提供了销售多种产品的有利的机会.本文通过对影响消费者序列购买路径因素进行识别.建立购买效用模型,从而对消费者序列购买路径进行预测.期望能够帮助企业将有限的营销资源分配给那些最可能为企业带来利润的消费者,同时帮助消费者更加理性地进行决策.     

浅谈跨境电商背景下的高职外贸人才培养途径

随着跨境电商的快速发展,跨境电商企业需要大量既懂外贸业务又懂电子商务的复合型外贸人才。本文从师资建设、课程整合、提高学生综合能力、校企合作四个方面,指出目前外贸人才培养上存在的不足,同时阐明如何培养高职外贸人才以适应跨境电商企业的需要。     

3PL服务质量与C2C顾客满意度的实证研究

物流业务外包后,第三方物流(3PL)公司代替企业与顾客互动,这在电子商务中尤为明显,因此第三方物流公司的服务质量是否影响顾客满意度成为一个重要问题。在C2C网络购物环境中,基于物流服务质量模型,建立了第三方物流服务质量与顾客满意度的测评模型(LSQ-CS)。对LSQ-CS模型的实证研究表明:物流交互和运作质量均影响顾客对第三方物流服务商的满意度;成本质量并不影响顾客对第三方物流服务商的满意度;物流交互质量对运作和成本质量产生影响;此外,第三方物流服务质量也影响着顾客对企业的满意度。这一研究结论表明当前物流市场中第三方物流公司应该保证运作质量,提高交互质量,避免低成本恶性竞争;同时除了物流成本外,企业也应根据物流服务质量来选择物流服务商。     

机构投资者持股能提高上市公司盈余持续性吗?——基于中国A股上市公司的经验证据

随着机构投资者在中国资本市场发挥越来越重要的作用,其行为会影响公司业绩和盈余质量。本文从盈余持续性角度探讨机构投资者作用,研究结果发现,较没有机构投资者持股的上市公司而言,有机构投资者持股的上市公司的盈余持续性显著更低;机构投资者持股比例越高,上市公司的盈余持续性越低;机构投资者年度间的增持行为,会显著降低上市公司的盈余持续性。研究结果表明在我国机构投资者会促使上市公司增加更多的暂时性盈余,从而降低上市公司的盈余持续性。可见,现阶段我国机构投资者并没有发挥促使上市公司改善经营实质的积极作用,反而使上市公司增加了更多的短期行为。