A B S T R A C TWe investigate the impact of family ownership on core business transformation and the moderating role of political connections in this relation through a Probit model, conditional Logit model, and Heckman selection model with instrumental variable using data from Chinese listed companies covering 2001-2010. The results demonstrate that, compared with non-family firms, family firms are more likely to transform their core business, enter strongly correlative industries and non-regulated industries, and adopt a mergers and acquisitions (M&A) mode. Furthermore, compared with politically non-connected family firms, family firms with political connections are more likely to conduct business transformation and adopt M&A rather than an internal cultivation mode to realize transformation. In addition, political connections make family firms more likely to enter weakly correlative industries and increase their chances of entering government-regulated industries.

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CCA is a powerful tool for analyzing paired multi-view data. However, when facing semi-paired multi-view data which widely exist in real-world problems, CCA usually performs poorly due to its requirement of data pairing between different views in nature. To cope with this problem, we propose a semi-paired variant of CCA named SemiPCCA based on the probabilistic model for CCA. Experiments with artificially generated samples demonstrate the effectiveness of the proposed method.

In the context of the depth adjustment of the global economy and wild fluctuations in energy prices, the vulnerability issue of the coal mining industrial ecosystem (CMIES) has seriously affected the sustainable development of the regional economy. Comparisons of CMIES health status at a regional level are worthy of being conducted. This not only contributes to understanding a particular coal mining area's situation in regards to CMIES vulnerability, but also helps to discover a meaningful benchmark to learn the experiences in terms of action programmes formulation. In this study, based on the analysis of the vulnerability response mechanism of CMIES to economic fluctuations, an initial indicator system for vulnerability assessment of CMIES was constructed. Ultimately, 14 vulnerability-evaluating indicators and their weights were obtained using rough set attribute reduction. Based on a composite CMIES Vulnerability Index (CVI), the Rough Set-Technique for Order Preference by Similarity to Ideal Solution-Rank-sum Ratio (RS-TOPSIS-RSR) methodology is proposed to conduct the CMIES vulnerability assessment process from an overall perspective. Using this methodology, 33 coal mining areas in China are ranked as well as grouped into three specific groups based on the CVI score. The results demonstrate the feasibility of the proposed method as a valuable tool for decision making and performance evaluation with multiple alternatives and criteria.

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