A model of tourism advertising effects

This study develops the AIEDA tourism advertising effects model and examines this model by tourism destination types and advertising formats in a field experiment. The AIEDA model extends the traditional AIDA model in the advertising field and additionally considers the unique features of tourism products. It includes five hierarchical stages: Attention→ Interest→Evaluation (Perceived Usefulness→ Perceived Credibility) → Desire →Action. Findings of experimental research indicate that destination type and advertising format have main effects and interaction effects on tourism advertising effects. In addition, this study discovered that, for natural and cultural destinations, video ads yielded similar or more positive advertising effects than virtual reality ads, whereas print was the least effective advertising format.     

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.     

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。     

Selecting Sites for New Facilities Using Data Envelopment Analysis

This paper develops a mathematical programming model for obtaining a best set of sites for planned facilities. The model is concerned with those situations where resource constraints are present. The specific setting for the paper involves the selection of sites for a set of retail outlets, wherein the ratio of aggregate outputs to inputs for the selected set is maximal among all possible sets that could be chosen. At the same time, the model guarantees that the only sets of stores allowable are those for which the available resources are used to the maximum extent possible.     

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。     

脱贫与经济增长:"贵州新现象"探析——贵州云南两省的比较研究

经济增长通常意味着贫困率的下降,然而,对于增长很慢的经济体或经济增长没有带动贫困率降低的案例,这种假设似乎并不成立.中国两个较为贫困省份的数据展示了这样一个谜题:贵州省的经济增长缓慢但农村贫困率的下降较为显著,对这一现象不妨称之为"贵州新现象";与此同时,云南省经济高速增长但贫困率下降较为迟缓.根据相关考察和统计数据的分析,交通、流动人口、旅游业和矿产业四个因素是解释这个谜题的关键.这四个因素在两个省的不同分布和构造解释了它们经济增长与贫困率下降关系中的不平衡性.