Multi-view representation learning aims to extract comprehensive information from multiple sources. It has achieved significant success in applications such as video understanding and 3D rendering. However, how to improve the robustness and generalization of multi-view representations from unsupervised and incomplete scenarios remains an open question in this field. In this study, we discovered a positive correlation between the semantic distance of multi-view representations and the tolerance for data corruption. Moreover, we found that the information ratio of consistency and complementarity significantly impacts the performance of discriminative and generative tasks related to multi-view representations. Based on these observations, we propose an end-to-end CLustering-guided cOntrastiVE fusioN (CLOVEN) method, which enhances the robustness and generalization of multi-view representations simultaneously. To balance consistency and complementarity, we design an asymmetric contrastive fusion module. The module first combines all view-specific representations into a comprehensive representation through a scaling fusion layer. Then, the information of the comprehensive representation and view-specific representations is aligned via contrastive learning loss function, resulting in a view-common representation that includes both consistent and complementary information. We prevent the module from learning suboptimal solutions by not allowing information alignment between view-specific representations. We design a clustering-guided module that encourages the aggregation of semantically similar views. This action reduces the semantic distance of the view-common representation. We quantitatively and qualitatively evaluate CLOVEN on five datasets, demonstrating its superiority over 13 other competitive multi-view learning methods in terms of clustering and classification performance. In the data-corrupted scenario, our proposed method resists noise interference better than competitors. Additionally, the visualization demonstrates that CLOVEN succeeds in preserving the intrinsic structure of view-specific representations and improves the compactness of view-common representations. Our code can be found at https://github.com/guanzhou-ke/cloven.

Purpose: The study aims to understand the role of accountant in corporate social responsibility (CSR) practice.Design/methodology/approach: In this study, the authors examine whether and how chief financial officer (CFO) accounting expertise and previous work experience influence voluntary CSR disclosure, using textual analysis and natural language processing (NLP) techniques. The authors find that firms' CFOs with accounting expertise disclose more CSR issues in their 10-K reports. Overall, this study provides evidence of the impact of CFOs' professional and personal attributes on voluntary CSR disclosure in corporate annual reports. This study has important implications to investors and policy makers in the context of CSR disclosure regulations in annual reports.Findings: Overall, this study provides evidence of the impact of CFOs' professional and personal attributes on voluntary CSR disclosure in corporate annual reports. This study has important implications to practitioners and policy makers in the context of CSR disclosure regulations in annual reports.Research limitations/implications: There is an inherent limitation of textual analysis as the tool tries to read key words from the text.Practical implications: This finding is useful for policy maker and investors as CSR is known to have impact on the share price.Originality/value: This paper is the first attempt to find out accountants' role in CSR activities, which has not been examined in the prior literature.

Multi-view (or -modality) representation learning aims to understand the relationships between different view representations. Existing methods disentangle multi-view representations into consistent and view-specific representations by introducing strong inductive biases, which can limit their generalization ability. In this paper, we propose a novel multi-view representation disentangling method that aims to go beyond inductive biases, ensuring both interpretability and generalizability of the resulting representations. Our method is based on the observation that discovering multi-view consistency in advance can determine the disentangling information boundary, leading to a decoupled learning objective. We also found that the consistency can be easily extracted by maximizing the transformation invariance and clustering consistency between views. These observations drive us to propose a two-stage framework. In the first stage, we obtain multi-view consistency by training a consistent encoder to produce semantically-consistent representations across views as well as their corresponding pseudo-labels. In the second stage, we disentangle specificity from comprehensive representations by minimizing the upper bound of mutual information between consistent and comprehensive representations. Finally, we reconstruct the original data by concatenating pseudo-labels and view-specific representations. Our experiments on four multi-view datasets demonstrate that our proposed method outperforms 12 comparison methods in terms of clustering and classification performance. The visualization results also show that the extracted consistency and specificity are compact and interpretable. Our code can be found at https://github.com/Guanzhou-Ke/DMRIB.

Nearly one-third of the business-to-team (B2T) websites in China closed down in 2011—the casualties of the intensified competition in the group-buying market. To investigate the critical factors to the survival of online B2T businesses in an overcrowded buyer-side market, we propose a model that links convenience, price, product/service, personalization, and website security/safety to customer satisfaction of B2T websites in light of the related literature. A two-stage approach is applied to validate this model. In Stage I, according to the results of an empirical analysis based on questionnaire responses from 157 B2T website users, we found that convenience and product/service are the top two contributors to the success of B2T business, as measured by customers’ repurchase intentions. In Stage II, topic classification and sentiment analysis are applied to 3046 review comments related to 58 B2T websites. The results from the Stage II study confirm the validity of the Stage I results with further evidence to support our arguments.

The purpose of this paper is to study the relationships among the presence of corporate social responsibility (CSR) committee, politicians on CSR committee and CSR disclosure in 10-Ks using data from S&P 500 firms during 2005–2013. The authors manually check the information of CSR committees as well as committees with CSR/sustainability functions from proxy statements (DEF 14a). CSR disclosure from 10-Ks is obtained by using a Python library named Beautiful Soup 4 to clean the rough data from the raw format files from EDGAR. The authors find that superior sustainability governance is associated with more voluntary CSR disclosure in their 10-K reports. More importantly, they find that CSR committee members with working experiences as politicians play an important role to improve CSR disclosure. In the robustness tests, they find that CSR committee and the politicians on CSR committee are also associated with high KLD CSR score ratings. The finding in our paper that politicians on CSR committee can enhance CSR efforts may provide practical implications to some companies. Companies may consider inviting people who have political connections and experience to serve on CSR committees. The authors find the presence of politicians on CSR committee is associated with CSR disclosure and CSR performance. That's new to the CSR governance literature and makes contributions to CSR disclosures and CSR committee expertise and skills.