Evaluating museum visitor experiences based on user-generated travel photos

ABSTRACT

This research introduces online travel photos published on social media platforms as a complementary data resource to examine the behavior and experience of museum visitors. The practical value of online travel photos is demonstrated through a case study of popular Hong Kong museums, particularly by using the photo content and metadata available from the Flickr platform. The proposed approach is a generic method for understanding museum visitor behavior and preferences, and supports museum practitioners in developing improved products for visitors. The case study findings are particularly beneficial for tourism managers, especially those in Hong Kong, in promoting and attracting tourists to visit local museums.     

Commemorating the 50‐Year Anniversary of Ball and Brown (1968): The Evolution of Capital Market Research over the Past 50 Years

We commemorate the 50th anniversary of Ball and Brown [1968] by chronicling its impact on capital market research in accounting. We trace the evolution of various research paths that post–Ball and Brown [1968] researchers took as they sought to build on the foundation laid by Ball and Brown [1968] to create a body of research on the usefulness, timeliness, and other properties of accounting numbers. We discuss how those paths often link back to the groundwork laid and questions originally posed in Ball and Brown [1968].     

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.     

The Effectiveness of Brand- and Customer-Centric Content Strategies at Generating Shares, ‘Likes’, and Comments

Brands are constantly competing for user attention on Facebook and are trying to encourage fans to share, click ‘like’, and comment their posts. Yet, it remains unclear what content strategies are successful at encouraging those behaviors. Noting that the effectiveness of brand content strategies still remains an uncharted research area, this study adopts a discovery-oriented approach and employs workshops, quantitative content analysis, and episodic interviews, to link desired user responses (namely clicks share, ‘like’, and comment) to brand content strategies. This study reveals that customer-centric content strategies are the most effective at encouraging user responses. Managers can use provided recommendations to engage their customers by adopting content strategies that result in desired behavioral reactions.     

基于Internet的多通道用户界面

提出了网络多通道界面模型、语音和指点相结合的基于上下文的查询、基于主通道的整合策略,并将受限的自然语言(汉语)理解技术应用于多通道交互中。文章给出了两个实例:汉语语音驱动的多通道文件管理器和网上购物多通道界面的ActiveX控件。结果表明,以上技术有助于提高人机交互的自然性和效率。     

反倾销应诉的集体行动研究——行业协会克服"搭便车"机制的思考

本文针时我国企业在反倾销共同应诉中的不合作或者“搭使车”问题，试图探讨行业协会作为一种治理机制，如何提供或设计一种有效的制度安排（如俱乐部规范），把一群相互依存的企业组织起来，并通过自主性努力克服不合作或“搭便车”，以取得长期的共同利益的实现。     

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。     

中小企业股票市场化发行定价的半参数自组织模型与实证

随着深圳交易所中小企业板块的正式启动，我国中小企业新股发行的市场化定价方式必将启用。本文采用基于自组织数据挖掘GMDH算法的半参数模型的定价方法，将GMDH算法与半参数模型方法结合起来。不仅利用计算机的自动拟合优选最优复杂度模型，从而大大简化了非参数部分估计。创造了半参数模型崭新的实现方式，并达到了理想的定价效果，而且可以发挥半参数模型的特有优势。进行模型结构分析。经过实证和检验证明了这种模型用于我国股票发行定价的有效性和合理性。为完全市场化股票发行方式下确定股票的发行价格提供了新的方法。