集群企业协同竞争研究——以“中国锰都”矿业集群为例

从博弈论角度出发,以"囚徒困境"模型为基础构建了"中国锰都"集群矿业企业协同竞争模型,分析了集群内企业协同的影响因素。发现协同意愿的提高有赖于协同额外收益、协同因子、行业集中度、协同平等关系的提高以及协同成本的降低。     

银行客户风险统计的国际比较与建议

银行客户风险统计旨在实现客户风险信息在各家商业银行间的共享,为银行风险管理和监管决策服务.但其数据信息来源单一、采集成本高昂的问题不可忽视.通过国际经验的分析比较,本文建议建立专业的第三方征信机构,以共享交流的机制,采集客户风险数据信息,既提高数据的质量,又降低采集成本;利用数据挖掘技术,开发利用海量的原始数据,将庞杂无序的"数据坟墓"转换成有价值的"知识金块",建立全方位的风险预警管理体系.     

依靠技术创新 科学发展煤化工产业

兖矿集团20世纪90年代就已进入煤化工领域,在煤化工产业发展领域积累了很多有益的经验。文章介绍了兖矿集团煤化工产业的发展现状、煤化工技术创新的主要成果及其具体实践,提出兖矿集团煤化工产业"十二五"期间的重点技术研发目标。     

兖矿集团煤电铝产业链模式浅论

回顾了兖矿集团煤电铝产业链的发展现状,分析了兖矿集团煤电铝产业链形成和发展的原因,论述了兖矿集团实施煤电铝产业链发展模式的具体实践。     

Exploring the multi-dimensionality of authenticity in dining experiences using online reviews

The quest for authenticity in dining experiences has become increasingly important. This paper explores authenticity dimensions that are of value to customers in dining experiences, and by that gains a multi-dimensional understanding of authenticity in this context. Following an integrated learning approach using text mining and classification techniques, this paper explores and confirms different dimensions of authenticity by identifying and classifying authenticity judgements in online restaurant reviews. The results suggest that authenticity is a multi-dimensional concept encompassing Authenticity of the Other, Authenticity of the Producer, and Authenticity of the Self as first-level dimensions. Additionally, besides historical and categorical authenticity which have been previously explored in the literature, a new type of authenticity - Deviated Authenticity - emerged as a second-level dimension falling under Authenticity of the Other. This paper enhances existing conceptualisations of authenticity and establishes avenues for exploring the multi-dimensionality of other consumer research concepts using user-generated content.     

Discovering bank risk factors from financial statements based on a new semi‐supervised text mining algorithm

This paper aims to comprehensively uncover bank risk factors from qualitative textual risk disclosures reported in financial statements, which contain a huge amount of information on bank risks. We propose a new semi‐supervised text mining approach named naive collision algorithm to analyse the textual risk disclosures, which can more accurately identify bank risk factors compared with the typical unsupervised text mining approach. We identified 21 bank risk factors in total, which is far more than identified in previous studies. We further analyse the importance of each bank risk factor and how the importance of each risk factor changes over time.     

Evaluation of forestland use in mining operation activities in Turkey in terms of sustainable natural resources

Mining has been necessary for human activities and is conducted in line with this need. The location of mines must sometimes be where land use overlaps with other activities because the location of the mined substance cannot be changed. In Turkey, forestland are the most common of these overlapping areas. Therefore, mining has frequently occurred on forestland in Turkey—and worldwide. After the mining operation activities are conducted, the forestland are rehabilitated and returned to the forest administration. The examination of used and returned areas provides an opportunity to create an optimal situation between “mining for sustainable development” and “protection of forestland.”Accordingly, several questions, such as mining production amounts, degrees of social and economic development of the cities in which enterprises are conducting mining, the quantity of the areas they used for mining activities in forestland, the areas which were returned to the forest administration, operating license areas and operation permit areas, and the life of mining operation, were asked to the mining enterprises in Turkey through the “Survey Monkey” program in 2018. Thus, according to mineral groups, different land use rates were compared with the operating license areas, and the land uses for each mineral group were analyzed by considering the operation activity periods. The results indicate that the sustainability of the use of forestry in mining activities in Turkey has changed in a positive direction, particularly because of changes in mining and environmental legislation in Turkey over the last decade.     

环境规制科技政策对科技进步与经济增长的影响

基于事件系统理论构建系统性、多层次性和综合性的政策量化分析框架，从政策属性、政策目标和政策工具3个维度对我国1985—2015年制定的198条环境规制科技政策进行量化研究。基于政策评估中的工具理性和价值理性两个层面，从环保科技进步效果和经济增长效应两个维度对环境规制科技政策有效性进行评估。研究发现，我国环境规制科技政策工具及政策工具协同对环保科技进步和经济增长的影响存在显著方向性差异。为此，进一步讨论了我国环境规制科技政策工具及工具协同的政策有效性评估结果，可为我国环境规制科技政策的完善和有效实施提供决策依据。     

Motivation and satisfaction of Chinese and U.S. tourists in restaurants: A cross-cultural text mining of online reviews

Tourists with dissimilar cultural backgrounds think and behave differently. Precisely capturing and correctly understanding the cultural difference will help tourism managers generate greater customer satisfaction and increased business revenue. To this end, this paper uncovers and compares the motivation and satisfaction of restaurant tourist customers coming from China and U.S. by investigating their online ratings and reviews. From two major online review communities, customer ratings and reviews have been retrieved, quantified, text-mined, compared, and interpreted using statistics, latent Dirichlet allocation, and frequency analysis. Results suggest that Chinese tourists are less inclined to assign lower ratings to restaurants, and are more strongly fascinated by the food offered, whereas U.S. tourists are more apt to be fun-seeking, and are less uncomfortable with crowdedness.     

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.