Purpose The purpose of this paper is to compare the effects of organizational and technological barriers to knowledge sharing between large and small firms through the lens of attribution theory. Design/methodology/approach The structural equation modeling approach was applied to estimate the conceptual model by using survey data from a list of Taiwan’s top 1,000 manufacturing and 500 service companies. A total of 229 valid questionnaires were collected. Findings The empirical results show that both organizational and technological barriers have relationships with an individual’s effort and ability with regard to knowledge sharing behavior. When organizational barriers occur, the perceived lack of effort has a direct effect on knowledge sharing behavior for large firms, while negative sharing behavior among employees of small firms is influenced by the perception of low ability through the perceived lack of effort. Originality/value A review of the literature reveals organizational and technological barriers that lead to the negative influences of internal attributions on knowledge sharing. This study, therefore, contributes to a comprehensive perspective on how to encourage knowledge sharing behavior at different sizes of firms.

Merchandise categories inherently form a semantic hierarchy with different levels of concept abstraction, especially for fine-grained categories. This hierarchy encodes rich correlations among various categories across different levels, which can effectively regularize the semantic space and thus make prediction less ambiguous. However, previous studies of fine-grained image retrieval primarily focus on semantic similarities or visual similarities. In real application, merely using visual similarity may not satisfy the need of consumers to search merchandise with real-life images, e.g., given a red coat as query image, we might get red suit in recall results only based on visual similarity, since they are visually similar; But the users actually want coat rather than suit even the coat is with different color or texture attributes. We introduce this new problem based on photo shopping in real practice. That's why semantic information are integrated to regularize the margins to make "semantic" prior to "visual". To solve this new problem, we propose a hierarchical adaptive semantic-visual tree (ASVT) to depict the architecture of merchandise categories, which evaluates semantic similarities between different semantic levels and visual similarities within the same semantic class simultaneously. The semantic information satisfies the demand of consumers for similar merchandise with the query while the visual information optimize the correlations within the semantic class. At each level, we set different margins based on the semantic hierarchy and incorporate them as prior information to learn a fine-grained feature embedding. To evaluate our framework, we propose a new dataset named JDProduct, with hierarchical labels collected from actual image queries and official merchandise images on online shopping application. Extensive experimental results on the public CARS196 and CUB-200-2011 datasets demonstrate the superiority of our ASVT framework against compared state-of-the-art methods.

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Emojis are a form of electronic communication found in text messages that fundamentally alter the exchange of emotion. Images and signs that depict feelings have replaced the nuanced selection of the right words and phrases. However, where feelings subside once expressed, emoticons transcend the spatiotemporality of emotion in ways that can become recurrent acts of aggression and bullying that are impossible not to see and even harder to erase. Across the globe, the expressive range of human emotion through the static ideogram of the emoji, or emoticon, presents an increasing challenge for the visually immediate, nonverbal exchange of capricious emotive communication.