How household food shopping behaviors changed during COVID-19 lockdown period: Evidence from Beijing,China

The COVID-19 pandemic has had a great impact on food shopping behavior. However, changes in food shopping behavior and related decision-making mechanisms remain unexplored. This study conducted a face-to-face questionnaire survey with 900 residents in three communities in Haidian, Beijing, China, to analyze food shopping behavior during the periods of pre-lockdown, lockdown (January–March 2020), and post-lockdown. Our results indicated that the preference for fresh food e-commerce shopping was accelerated by the lockdown with the usage rate increasing by 48%; the wet markets were seriously hit by the lockdown policy and had not fully recovered by the post-lockdown period (with a 75% reduction in the usage rate during lockdown). The psychological mechanisms of changes in food shopping behavior revealed that the context (e.g., community facilities) and the perceived risk of COVID-19 were two significant factors impacting the four shopping choices investigated. Specifically, the context factors affected behavior directly for supermarkets and convenience stores and indirectly for e-commerce and wet markets. The findings are expected to help in guiding emergency responses during a future pandemic as well as the long-term construction of food supply facilities.     

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.     

中国旅游景区功能演变与用地分类构想

研究目的:基于中国旅游景区功能演变、用地特征及问题分析,构建旅游景区用地分类体系,以期为旅游景区用地纳入区域土地利用提供理论基础,为旅游景区规划的深度编制提供实践依据。研究方法:通过调研和问卷厘清现状景区用地情况,对比借鉴相关用地分类体系,基于此构建旅游景区用地分类方案。研究结果:分析并阐明了旅游景区的功能演变、用地特征和现状问题,构建了2大类、9中类、28小类的景区用地分类体系,并与《土地利用现状分类》进行衔接。研究结论:建立可衔接且具可操作性的旅游景区用地分类体系,是实现旅游景区健康可持续发展与用地规范化管控的关键。     

让自然参与设计：一种基于自然演替的 城市自生群落改良设计途径

城市自生群落具有重要的生态服务功能与潜力，但因其外貌不被公众所接受而难以在城市公共空间中应用。将群落生态学和设计学相结合，探索一种介入栽培植物对城市自生群落进行改良，最终形成具有一定美学价值的植物群落的途径。实验以植物群落设计美学原理和种间竞争原理为依据，向已经演替1年的城市自生群落中引入63种栽培植物，按照分层的方式对群落进行改良。3年的观测结果表明，有61.9%的栽培植物可以在城市自生群落中定居并建立优势，能对76.7%的自生植物起到抑制作用，使55.8%的自生植物消失。相比城市自生群落，改良后的群落美观度提高29.4%，公众接受度提高24%，群落的物种丰富度提高至2.29倍。相比一般的人工植物群落，改良后的群落可以节约58.2%的建植成本，降低83.9%的浇水成本和67.8%的除草成本。实验证明改良设计是一种可行的群落设计途径，能有效提高群落的物种丰富度和美学价值，同时让自然参与群落设计过程，实现人与自然的合作。     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

Assessing the influence of goal pursuit and emotional attachment on customer engagement behaviors

This study draws on customer-dominant logic and self-expansion theory to examine the drivers of customer engagement behaviors in the context of emerging online interest communities. The engagement behaviors seen in online communities are operationalized into four types: augmenting, co-developing, influencing, and mobilizing. Goal pursuit (gratifying-the-self, enabling-the-self, and enriching-the-self) and emotional attachment to the community are found to be the key antecedent factors of these behaviors. The attainment of gratifying-the-self has a direct influence on these behaviors, whereas the attainment of enabling-the-self and enriching-the-self influences them via emotional attachment to the community.     

疫情防控常态化下的世界经济战略走向

新冠疫情的出现冲击了世界经济,从经济全球化和全球经济治理体系两个维度分析疫情对世界经济的影响以及世界经济未来走向,研究发现:(1)新冠疫情通过影响全球价值链、贸易投资和就业环境阻碍经济全球化进程,中美矛盾升级、原有治理体系规则未与时俱进和激进政策的不确定性对全球经济治理体系提出新的挑战;(2)新冠疫情强化了世界各国对人类命运共同体的认识,将人类命运共同体融入全球治理体系将是未来治理的重要趋势;(3)新冠疫情可能会使世界经济格局出现新的变化,而中国在此次疫情中高效率的抗疫举措将进一步提升自身在世界经济格局中的地位和参与度。     

Research on a diagnostic system of rural vitalization based on development elements in China

In 2017, the Chinese government implemented a national strategy of "Rural Vitalization" that sought to realize full-scale rural vitalization. However, is it possible to achieve vitalization for all the villages in China? How should their development potential be determined? This paper identified and analyzed the "element-composite" messages of rural development based on 99 exemplary sites of “Beautiful Villages” in China. Combined with the projection pursuit classification method, a diagnostic system of rural vitalization was established; then, Dehua County was taken as a case study for an in-depth analysis. Based on national data analysis, the final results indicated that livelihood resources (LR), agglomeration effects (AE), location and transportation (LT), cultural/natural landscapes (CN), and economic circumstance (EC) are essential elements for successful rural development. Additionally, EC was the only exogenous element, while the remaining elements were endogenous. Furthermore, the villages with better EC presented urbanization rates of 38∼82 % and Engel coefficients of 29∼41 % in their counties; exemplary sites lacking LR, CN, LT, and AE account for 13.13 %, 19.19 %, 26.26 %, and 60.61 % respectively, so the indispensability of these elements decreases progressively in sequence. Only 2 % of villages rely on single element for success, therefore, the composite pattern of development element was also critical; 10 out of 16 types were found to successfully facilitate village development, among which, the type of R-a-L-C (32.32 %) and R-A-L-C (15.15 %) were considered as the greatest potential patterns for vitalization. Finally, by means of the diagnostic system, the ratio of representative villages for high-low potential in Dehua County is evenly split; then, development paths, and land use policies that match with paths were proposed, on the basis of development potential and “element-composite” condition of themselves.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.