Local television,citizen knowledge and U.S. senators' roll-call voting

I study the effect of access to local television on citizens' political knowledge. I do so by utilizing the mismatch between U.S. television markets and state borders, causing some citizens to receive local television which primarily covers neighboring state politics. I find that access to relevant local television causes citizens to be more informed about their senators' roll-call votes, and more likely to hold opinions about these senators. I also find that citizens with access to relevant local television are more likely to assess their senators based on how well the senators' roll-call votes align with the citizens' policy preferences. These results suggest that passively acquired information through television can help individuals evaluate their elected representatives.     

Comparing consumer-produced product reviews across multiple websites with sentiment classification

ABSTRACT

Online consumer reviews have been extensively studied. However, existing literature analyzing online consumer review data mostly relies on a single data source, resulting in potentially biased analytics conclusions. Many websites encourage consumers to post reviews of their purchased products, so that new consumers can evaluate these reviews for the same product across different websites to help them make purchasing decisions. Confusions often arise in this process, because there often exist substantial discrepancies in customer reviews across different retailers on the same product. Clarifying such confusions can help consumers reduce concerns to make up their mind for their purchases, therefore benefiting both consumers and retailers. Through text analytics and sentiment analysis, we comparatively examine the underlying patterns of online consumer reviews of three large retailers including Sears, Home Depot, and Best Buy for a same product. Afterward, we combine online consumer reviews from these large retailers and conduct an overall text analytics and sentiment analysis. The overall results are further compared with the results from individual retailers. The findings show that the sentiment of the online consumer reviews could vary substantially so relying on a single data source to make purchase decision is not a wise idea. Based on the results, we further devise a framework to comparatively examine and integrate multiple data sources for social media analytics of online consumer reviews. This study offers important managerial implications and identifies several new research directions for social media analytics.     

Natural resource rents and internal conflicts: Can decentralization lift the curse?

We study how natural resource rents affect the risk of internal conflict within countries and how the federal structure of countries influences this relationship. Natural resource abundance may induce excessive rent-seeking and thus increase the risk of internal conflict. Fiscal and political decentralization as an institutional arrangement for rent-sharing and political codetermination of regions within a country may limit the destructive effect of natural resource rents on internal stability. Using cross-country and panel data from more than 90 countries covering the period 1984–2004, we find evidence that natural resource rents indeed increase the risk of internal conflict, but this relationship is significantly mitigated by political decentralization.     

Does tourism really cause stress? A natural experiment utilizing ArcGIS Survey123

ABSTRACT

Tourism related stress has recently been revealed as an issue for tourism host community residents. Prior studies have relied on directly asking residents if tourism caused them stress, resulting in possible participant priming. This study utilized the ArcGIS Survey123 app to collect data on resident stress from a community with high levels of tourism visitation and a community with low levels of tourism visitation. During the study, researchers never mentioned tourism. Half of study participants in the community with high tourism visitation discussed tourism directly as a stressor, accounting for almost 5% of all stressful experiences, while no participants in the community with low tourism visitation mentioned tourism as a stressor. Geospatial data revealed that tourism related stressors occurred outside of the ‘tourism zone’ in the community with high tourism visitation, and tourism related stressors mirrored perceived tourism impacts measured in a post experiment survey of study participants.     

Understanding the drivers of organic foods purchasing of millennials: Evidence from Brazil and Spain

This paper examines the antecedents of millennials' organic food purchasing. A conceptual framework, based on product characteristics, and consumers' concerns and consciousness, is proposed. Data collection was conducted through a survey in two culturally and socioeconomically distinct countries (Brazil vs. Spain) to increase the robustness and generalizability of the results. The results show that product characteristics and consumer concerns improve millennials’ health consciousness and increase their social consciousness, which, in turn, increase their willingness to pay a price premium and their purchase frequency of organic foods. The findings show that the proposed model has high validity, with only one significant difference between the two countries. Theoretical contributions and managerial implications are discussed.     

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.     

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.     

化妆品灌装间紫外线消毒后的效果观察

文章采用自然沉降法,对化妆品灌装间紫外线消毒后的效果进行监测。监测3次得出结论：在消毒后一个工作日内至22个小时内,灌装车间空气质量符合化妆品卫生规范要求。     

Natural resources and international labour mobility

We study the relationship between natural resource wealth and (skilled and unskilled) emigration. The source data for international migration rates come from Brücker, Capuano, and Marfouk (2013) and measures of resource wealth from the World Bank database. We find that natural resource abundance reduces skilled emigration regardless of countries’ natural wealth, but that natural resources reduce unskilled emigration only in resource-rich countries.