Determinants of users’ continuance of social networking sites: A self-regulation perspective

Social networking sites (SNS) have transformed how individuals interact, build and maintain social relationships. We proposed a research model on the determinants of user continuance using Bagozzi's framework of self-regulation as the theoretical foundation. Following the process of appraisal → emotional reactions → coping responses, we developed the model by leveraging findings from social presence and IS continuance research. Based on survey data from Facebook users, we found that appraisal factors (pleasure, awareness, connectedness, and system quality) were strong determinants of emotional reaction (user satisfaction and sense of belonging). User satisfaction and sense of belonging together positively influenced continuance intention.     

An exploratory study of buyers’ participation intentions in reputation systems: The relationship quality perspective

Drawing upon a relationship quality framework, this study identifies how satisfaction, trust and different styles of handling conflict influence online buyers’ participation intentions in reputation systems associated with a C2C online shopping platform. Furthermore, we investigate how these effects are moderated by social conformity and the perceived value of knowledge. The results of a survey of 269 online buyers indicate that satisfaction, trust and an accommodating conflict handling style positively impact the intention to submit positive ratings, whereas an avoiding conflict handling style negatively affects the intention to submit positive ratings. Implications and suggestions for future research are provided.     

A UNIFIED FRAMEWORK OF TARGETED MARKETING USING CUSTOMER PREFERENCES

One of the fundamental tasks of targeted marketing is to elicit associations between customers and products. Based on the results from information retrieval and utility theory, this article proposes a unified framework of targeted marketing. The customer judgments of products are formally described by preference relations and the connections of customers and products are quantitatively measured by market value functions. Two marketing strategies, known as the customer‐oriented and product‐oriented marketing strategies, are investigated. Four marketing models are introduced and examined. They represent, respectively, the relationships between a group of customers and a group of products, between a group of customers and a single product, between a single customer and a group of products, and between a single customer and a single product. Linear and bilinear market value functions are suggested and studied. The required parameters of a market value function can be estimated by exploring three types of information, namely, customer profiles, product profiles, and transaction data. Experiments on a real‐world data set are performed to demonstrate the effectiveness of the proposed framework.     

Inferring social nature of conversations from words: Experiments on a corpus of everyday telephone conversations

Language is being increasingly harnessed to not only create natural human–machine interfaces but also to infer social behaviors and interactions. In the same vein, we investigate a novel spoken language task, of inferring social relationships in two-party conversations: whether the two parties are related as family, strangers or are involved in business transactions. For our study, we created a corpus of all incoming and outgoing calls from a few homes over the span of a year. On this unique naturalistic corpus of everyday telephone conversations, which is unlike Switchboard or any other public domain corpora, we demonstrate that standard natural language processing techniques can achieve accuracies of about 88%, 82%, 74% and 80% in differentiating business from personal calls, family from non-family calls, familiar from unfamiliar calls and family from other personal calls respectively. Through a series of experiments with our classifiers, we characterize the properties of telephone conversations and find: (a) that 30 words of openings (beginnings) are sufficient to predict business from personal calls, which could potentially be exploited in designing context sensitive interfaces in smart phones; (b) our corpus-based analysis does not support Schegloff and Sack's manual analysis of exemplars in which they conclude that pre-closings differ significantly between business and personal calls – closing fared no better than a random segment; and (c) the distribution of different types of calls are stable over durations as short as 1–2 months. In summary, our results show that social relationships can be inferred automatically in two-party conversations with sufficient accuracy to support practical applications.     

年最大洪峰流量群居蜘蛛优化投影寻踪预测模型

年最大洪峰流量预测,受较多的复杂因素的影响,不确定性较强,用常规统计方法做出准确预报具有较大困难。从水文序列本身出发,提出将投影回归模型应用于年最大洪峰流量预测,为了更好获得投影寻踪模型参数和预测精度,提出了运用延迟相关系数法确定回归预测因子、群居蜘蛛算法优化投影寻踪模型最佳投影方向参数a、利用最小二乘法确定多项式的权系数c、岭函数个数M的群居蜘蛛优化投影寻踪年最大洪峰流量预测模型,结合长江宜昌站(1882年-2004年)的年最大洪峰流量资料进行实例预测,训练阶段平均绝对相对误差为8.61%,预测阶段平均绝对相对误差为10.51%,该模型预测效果较好,模型结果稳定,可有效应用于年最大洪峰流量预测。     

民族文化差异与日用陶瓷的文化营销

在经济全球化的背景下,由于民族文化、区域文化差异性和多样性的存在,日用陶瓷企业在开展国际营销活动时需要一个行之有效的营销策略。本文通过分析民族文化差异,为日用陶瓷企业实施国际文化营销提供一些建议。     

A framework for tag-aware recommender systems

In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.     

Modeling and verifying probabilistic Multi-Agent Systems using knowledge and social commitments

Multi-Agent Systems (MASs) have long been modeled through knowledge and social commitments independently. In this paper, we present a new method that merges the two concepts to model and verify MASs in the presence of uncertainty. To express knowledge and social commitments simultaneously in uncertain settings, we define a new multi-modal logic called Probabilistic Computation Tree Logic of Knowledge and Commitments (PCTL^kc in short) which combines two existing probabilistic logics namely, probabilistic logic of knowledge PCTLK and probabilistic logic of commitments PCTLC. To model stochastic MASs, we present a new version of interpreted systems that captures the probabilistic behavior and accounts for the communication between interacting components. Then, we introduce a new probabilistic model checking procedure to check the compliance of target systems against some desirable properties written in PCTL^kc and report the obtained verification results. Our proposed model checking technique is reduction-based and consists in transforming the problem of model checking PCTL^kc into the problem of model checking a well established logic, namely PCTL. So doing provides us with the privilege of re-using the PRISM model checker to implement the proposed model checking approach. Finally, we demonstrate the effectiveness of our approach by presenting a real case study. This framework can be considered as a step forward towards closing the gap of capturing interactions between knowledge and social commitments in stochastic agent-based systems.     

奥妙洗衣粉广告之魅力

阐述了要把去除污渍的能力转化成自由生活方式的源动力和妈妈对孩子的培育与关爱融人到每件干净的衣物中，通过一系列广告战役和概念营销捕获目标消费群体的心。