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Digital technologies over time are becoming increasingly pervasive and relatively affordable, finding a large diffusion in Small and Medium Enterprises (SMEs) also for internationalization purposes. However, less is known about the specific mechanisms by which this can be achieved. Specifically, we focus on how SMEs can face the international environment, leveraging digital technologies and thanks to their intellectual capital (IC).

We analyze the relationship between digital technologies and the internationalization of SMEs, exploring the mediating role of IC in its three dimensions: human, relational and innovation capital, and assessing the possible moderating effects posed by international institutional conditions, specifically the Sino-US trade frictions. The relationships are tested using a sample of companies listed on China’s A-share Growth Enterprise Market (GEM) from 2010 to 2021.

Digital technologies help to internationalize SMEs. However, this positive relationship is affected (mediated) by the presence of an already consolidated IC. In addition, the institutional conditions of the international market, such as the Sino-US trade friction, moderate the components of IC differently. Specifically, the overall mediating effect of human and relational capital is boosted, while this does not happen for innovation capital.

First, this study contributes to the literature on organizational resilience, especially digital resilience, confirming its validity in the context of internationalization and, in particular, those processes adopted by SMEs. Second, we clarify the mechanisms through which digital technologies exert their impact on the process of internationalization and in particular the prominent necessity of having IC. Third, our conclusions enrich the understanding of how IC components react to turbulence in international markets.

The purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and improve quality control in textile manufacturing.

This paper proposed a dual-branch balance saliency model based on discriminative feature for fabric defect detection. A saliency branch is firstly designed to address the problems of scale variation and contextual information integration, which is realized through the cooperation of a multi-scale discriminative feature extraction module (MDFEM) and a bidirectional stage-wise integration module (BSIM). These modules are respectively adopted to extract multi-scale discriminative context information and enrich the contextual information of features at each stage. In addition, another branch is proposed to balance the network, in which a bootstrap refinement module (BRM) is trained to guide the restoration of feature details.

To evaluate the performance of the proposed network, we conduct extensive experiments, and the experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) approaches on seven evaluation metrics. We also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed method.

The dual-branch balance saliency model was proposed and applied into the fabric defect detection. The qualitative and quantitative experimental results show the effectiveness of the detection method. Therefore, the proposed method can be used for accurate fabric defect detection and even surface defect detection of other industrial products.