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The role that corporations play in environmental and social sustainability has become increasingly important due to their size and embeddedness in our everyday lives. This study aims to examine the relationship between corporate social responsibility (CSR) and corporate interlocks for the Fortune 500 companies.

To collect data, various sources were used including data web scraped from US Securities and Exchange Commission, Bloomberg ESG, ASSET4, Carbon Disclosure Project and data from each companies’ websites. To measure CSR, this paper uses an original United Nations Sustainable Development Goals (SDG) index, and to measure corporate interlocks, it uses the Bonacich centrality score and has a sample of 401 companies. To account for missing data, Bayesian multiple imputation was used. For the final analysis, linear regression analysis was conducted, for which all the assumptions are met.

The findings show that for most SDGs, corporate interlocks is an important predictor, but not for all SDGs. In other words, they indicate that corporate centrality remains to be an important variable in most aspects of CSR, but a more nuanced approach is required.

This paper uses the SDGs to provide a granular perspective of CSR, which is stronger and more methodologically rigorous compared to the existing metrics of CSR. Consequently, it provides an original insight into the corporate interlocks literature, which has not been empirically researched using granular CSR data.

The purpose of this study is to explore, develop, and evaluate a new sustainable development goals (SDG) index that quantifies corporate social responsibility (CSR). By providing a granular perspective with clear justification for methods, this index is more applicable to academic research in comparison with the CSR indices published by private companies.

Focusing on the Fortune 500 companies in 2017, this study uses data from Bloomberg, ASSET4, and the Carbon Disclosure Project. A z-score was calculated for each variable, which was then aggregated according to the SDG indicator list to calculate each SDG score. Various robust analyses were conducted.

The SDG index shows that companies tend to score worse on environment-related goals compared with social goals. Furthermore, for each SDG, there are differences across industrial sectors, a finding that is enabled by the more granular approach of this index. Additionally, the leaders and laggards are identified for each of the SDGs.

This study identifies the methodological weaknesses of the existing CSR indices and introduces and evaluates an alternative index based on the SDGs. This alterative index provides methodological clarity and granularity of data, which were lacking in previously established indices.

Most previous studies predicting review helpfulness ignored the significance of deep features embedded in review text and instead relied on hand-crafted features. Hand-crafted and deep features have the advantages of high interpretability and predictive accuracy. This study aims to propose a novel review helpfulness prediction model that uses deep learning (DL) techniques to consider the complementarity between hand-crafted and deep features.

First, an advanced convolutional neural network was applied to extract deep features from unstructured review text. Second, this study used previous studies to extract hand-crafted features that impact the helpfulness of reviews and enhance their interpretability. Third, this study incorporated deep and hand-crafted features into a review helpfulness prediction model and evaluated its performance using the Yelp.com data set. To measure the performance of the proposed model, this study used 2,417,796 restaurant reviews.

Extensive experiments confirmed that the proposed methodology performs better than traditional machine learning methods. Moreover, this study confirms through an empirical analysis that combining hand-crafted and deep features demonstrates better prediction performance.

To the best of the authors’ knowledge, this is one of the first studies to apply DL techniques and use structured and unstructured data to predict review helpfulness in the restaurant context. In addition, an advanced feature-fusion method was adopted to better use the extracted feature information and identify the complementarity between features.

大多数先前预测评论有用性的研究忽视了嵌入在评论文本中的深层特征的重要性, 而主要依赖手工制作的特征。手工制作和深层特征具有高解释性和预测准确性的优势。本研究提出了一种新颖的评论有用性预测模型, 利用深度学习技术来考虑手工制作特征和深层特征之间的互补性。

首先, 采用先进的卷积神经网络从非结构化的评论文本中提取深层特征。其次, 本研究利用先前研究中提取的手工制作特征, 这些特征影响了评论的有用性并增强了其解释性。第三, 本研究将深层特征和手工制作特征结合到一个评论有用性预测模型中, 并使用Yelp.com数据集对其性能进行评估。为了衡量所提出模型的性能, 本研究使用了2,417,796条餐厅评论。

广泛的实验验证了所提出的方法优于传统的机器学习方法。此外, 通过实证分析, 本研究证实了结合手工制作和深层特征可以展现出更好的预测性能。

据我们所知, 这是首个在餐厅评论预测中应用深度学习技术, 并结合了结构化和非结构化数据来预测评论有用性的研究之一。此外, 本研究采用了先进的特征融合方法, 更好地利用了提取的特征信息, 并识别了特征之间的互补性。

The purpose of this paper is to examine the thermal insulation and water vapor transmission rate (WVTR) according to the type of the filling material, and compared the thermal insulation in the dynamic state considering actual wearing conditions.

The thermal insulation and WVTR were evaluated in a standard state depending on the type of filling material (goose down (GD), duck down (DD), Thinsulate700 (T700), Thinsulate600 (T600) and Polyester (PET)), and the changes in thermal insulation were examined by measuring the microclimate in the case of an environmental change from a high temperature to a low temperature. In addition, the clumping of filling material and the changes in the thickness/weight depending on the laundry process were observed, and the relationships with the thermal insulation were analyzed.

The results showed that for natural filling materials (GD and DD), the thermal insulation deteriorated significantly due to changes in the thickness/weight after laundering ten times, and water washing was more appropriate than the dry cleaning. On the other hand, the artificial filling materials (T700, T600 and PET) showed a relatively smaller difference, except for clumping, when they went through more dry cleaning or water washing cycles compared to the natural filling materials.

The results showed that the laundry methods have different effects on the damage to the filling material, the change in thermal insulation, and the change in the comfort-related physical property. Therefore, it is important to select the optimal laundry method depending on the filling material.