广西高校校园媒体发展状况调查与分析

通过以广西为个案对广西高校校园媒体相关要素进行调查并进行数据统计分析,从而掌握民族地区高校媒体的发展现状,进一步深化民族地区高校校园媒体对于构建和谐校园、促进校园文化生态环境可持续发展的重要性的认识。     

大规模定制的企业核心竞争力

大规模定制的价值取向在于满足客户个性化需求,大规模定制的核心竞争力在于拥有提供给客户的充分商品空间, 大规模定制的生产理念是“专为你生产”。大规模定制供应链信息传输的信源是客户,产品制造的主导信息也来自于客户对 商品的自主选择,这与传统大规模生产迥然不同。     

HPLC-ICP-MS法同时测定贝类中5种砷形态化合物

采用水-甲醇(1:1,V/V)溶液超声波辅助萃取,建立了同时快速测定贝类中砷胆碱、砷甜菜碱、亚砷酸、砷酸和4-氨基苯胂酸等5种砷形态的高效液相色谱-电感耦合等离子体质谱(HPLC-ICP-MS)分析方法。对萃取溶液浓度、超声波辅助萃取温度和时间等条件进行了优化。选用Hamilton PRP-X100阴离子分析柱,以50 mmol/L碳酸铵-甲醇(99:1,V/V)溶液为流动相,进行梯度洗脱对5种砷形态分离,并优化了质谱测试条件。试验结果表明,5种砷形态达到很好的分离效果,在质量浓度为0~100μg/L的范围内与其对应的峰面积均具有良好的线性关系,线性相关系数r²均大于0.999,检出限在0.2~0.60μg/L之间,两水平样品加标回收率在90.5%~103.5%之间,方法的精密度在1.6%~4.2%之间。     

Social Media Index Valuation: Impact of Technological,Social, Economic,and Ethical Dimensions

Marketing practitioners have recognized a growing need to measure consumer-generated social media in a standard way since there are numerous social media indicators in use, making intra- and inter-company comparisons difficult. This paper identifies four social media dimensions for measurement and evaluation: technological, social, economic, and ethical; and, subsequently, measures social media. The study makes a contribution to social media literature by using analytic hierarchy process of developing a mathematical model for social media index valuation. The “social media composite index number” will serve as an industry benchmark signifying the organization's share and commitment to social media.     

大规模定制下基于交互式遗传算法的谈判模型研究

越来越多的企业已经认识到大规模定制是一种竞争策略,可以给企业带来竞争优势。随着大规模定制程度的提高,企业间对信息交互的及时性、有效性的要求也越来越高,企业迫切需要一种快速高效的谈判解决方案来适应大规模定制的需要。运用交互式遗传算法理论来建立谈判模型,以适应大规模定制的需要。     

网络媒体与思想政治工作

网络媒体给思想政治工作带来了方便,也增添了难度,同时对思想政治工作者的素质提出了更 高的要求。在信息网络技术飞速发展的今天,必须重视和发挥网络媒体在思想政治工作中的作用。     

The effect of social media communication on consumer perceptions of brands

Researchers and brand managers have limited understanding of the effects social media communication has on how consumers perceive brands. We investigated 504 Facebook users in order to observe the impact of firm-created and user-generated (UG) social media communication on brand equity (BE), brand attitude (BA) and purchase intention (PI) by using a standardized online survey throughout Poland. To test the conceptual model, we analyzed 60 brands across three different industries: non-alcoholic beverages, clothing and mobile network operators. When analyzing the data, we applied the structural equation modeling technique to both investigate the interplay of firm-created and user-generated social media communication and examine industry-specific differences. The results of the empirical studies showed that user-generated social media communication had a positive influence on both brand equity and brand attitude, whereas firm-created social media communication affected only brand attitude. Both brand equity and brand attitude were shown to have a positive influence on purchase intention. In addition, we assessed measurement invariance using a multi-group structural modeling equation. The findings revealed that the proposed measurement model was invariant across the researched industries. However, structural path differences were detected across the models.     

An analysis of media violence in commercials aired during the telecasts of sports events

The violence contained in television commercials has received less attention than other areas of media violence research. This study analyzed violent content in the Super Bowl commercials that aired between 2001 and 2009. Twenty-two percent of the Super Bowl commercials appearing from 2001 to 2009 contained some type of violence. A number of quantifications related to types of violence, targets and perpetrators of violent acts, and reasons for violence, among others, are reported. Subsequently, the relationships between the contextual factors of the violence and commercial likeability were examined.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

A Historical Review of Postponement Research

This historical review traces the development of postponement research starting in the fifties. The focus is on seven related themes: connecting form and time postponement, conceptual extensions to postponement, decoupling points, design for postponement, customization and mass customization, factors favoring postponement implementation, and postponement in global settings. Within these themes, the review includes concepts such as postponement costs, decoupling points, part commonality, and the impact of demand correlations on the benefits of postponement. The review suggests that substantial progress has been made in postponement research.