The impact of influencers' multi-SNS use on followers’ behavioral intentions: An integration of cue consistency theory and social identity theory

The considerable marketing potential brought by the rapid growth of social media has been extensively examined in the literature, particularly in terms of the impact of influencers' recommendations on their followers' decisions. However, little attention has been given to how the individual influencers' use of multiple social networking sites affects their followers' behavioral intentions. Hence, this academic gap warrants further exploration. Drawing on cue consistency theory, social identity theory, and stimulus-organism-response framework, this research proposes three research questions and constructs an integrated research model to explore how the influencers' cue consistency affects the social identification of their followers, subsequently influencing their behavioral intentions. A structural equation modeling technique is performed to analyze the data of 510 valid respondents. The findings show that influencers' consistent cues (i.e., information and image consistency) across social media have positive impacts on followers' cognitive and affective identification toward the influencers. Furthermore, the determinants for followers' purchase intention and electronic word-of-mouth intention are affirmed to serve as both cognitive and affective identification. Especially, the results indicate that the relationships between followers’ social identifications and behavioral intentions are moderated by the type of influencer (i.e., lifestyle vs. review). Accordingly, the results indicate how the use of multiple social networking sites by influencers impacts the behavioral intentions of their followers. The findings offer new insights into influencer marketing and provide important lessons for marketers.     

Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.     

Crowdsourcing-based semantic relation recognition for natural language questions over RDF data

ABSTRACT

Natural language query systems over RDF data need to rely on the semantic relations in query. First, we propose the new crowdsourcing model that used to produce semantic relations dataset. The model not only inherits completeness of the iterative model and accuracy of the parallel model, but also saves human resources. Second, we mine the rules of semantic relation recognition from the correlations between dependency structures and semantic relations. Third, we propose an algorithm of semantic relation recognition for natural language query over RDF data, and experiments demonstrate that it can recognize more semantic relations than existing methods.     

Comparing the impacts of Internet technology and national culture on online usage and purchase from a four-country perspective

The Internet has successfully generated an ever-expanding cohort of users for all its major concomitant activities, including information gathering, communications and transactions. So far no attempt has been made to validate whether such a success is so deep as to transcend national cultures. Nor any work has been conducted to compare the internationalisation¹ performances between online usage activities. The current study addresses these two research gaps from the perspective of four countries, i.e. Britain, Germany, Japan and Taiwan. Results show that although the technological forces have been quite successful in internationalising overall online usage activities, they succumb to the cultural forces as far as only the transactions activity, or more colloquially online purchase, is concerned. This indicates the relative difficulty in internationalising online purchase vis-à-vis other online usage activities. Further research on locating a series of step functions or kick-off time points regarding the development of online purchase is suggested.     

Web数据挖掘技术探索与应用

Web数据挖掘是当今世界上的热门研究领域,本文从Web数据挖掘的定义开始分析研究Web数据挖掘的难点、过程等相关技术,并计论其在电子商务领域中的相关应用。     

基于Web和数据仓库的房地产市场预警预报系统研究

介绍了基于Web和数据仓库的房地产市场预警预报系统的体系结构，探索设计了房地产市场预警预报系统的技术与方法，并提出了基于Web和数据仓库的房地产市场预警预报系统的完整解决方案。     

数据挖掘在信用风险管理中的应用

本文详细分析了在现代信用风险管理中不同方法对数据质量的要求,并在此基础上提出采用数据挖掘方法改进数据质量、提高计算精度的建议,供我国金融机构信用风险管理之借鉴。     

数据挖掘在高校体育数据分析中的应用

此文通过对数据挖掘技术的基本原理研究，分析和探讨了它在高校体育数据分析中的应用。     

给排水工程专业课程体系构造与网页制作

根据给排水工程专业特点,构造给排水专业的课程体系及其相互关系,制作课程体系信息库并应用Frontpage2000软件将其链接起来,在互联网上供本专业学生查阅,达到了指导、帮助学生综合利用自己所学知识进行课程预习、复习及毕业设计的目的.