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Food SMEs is the backbone of local and world economy. Even while food SMEs are aware of the potential advantages of implementing supply chain analytics (SCA), only a small number of companies use data-based decision-making. This is because of technophobia. In light of this, the purpose of this study is to investigate the factors that have an impact on SCA adoption which in turn influence the sustainable performance of firms.

The data were collected from 221 managers working in food-related SMEs in China by using a questionnaire-based survey. The framework of this study was validated using a rigorous statistical procedure using the technique, namely, partial least squares structural equation modelling.

The findings of this study suggest that all modified UTAUT components (i.e. performance expectancy, effort expectancy, social influence, facilitating conditions and technophobia) significantly influence SCA adoption. Moreover, the existing study highlights and confirms the significance of adopting SCA to improve sustainable performance.

This research is novel, as it extends and investigates the theoretical framework based on UTAUT theory in SCA context and its impact on sustainable organizational performance. In addition, the factor of technophobia is tested in SCA context. This study has several contributory managerial implications for food SMEs.

This study aims to provide a critical reflection of the application of image recognition techniques in visual information mining in hospitality and tourism.

This study begins by reviewing the progress of image recognition and advantages of convolutional neural network-based image recognition models. Next, this study explains and exemplifies the mechanisms and functions of two relevant image recognition applications: object recognition and facial recognition. This study concludes by providing theoretical and practical implications and potential directions for future research.

After this study presents different potential applications and compares the use of image recognition with traditional manual methods, the main findings of this critical reflection revolve around the feasibility of the described techniques.

Knowledge on how to extract valuable visual information from large-scale user-generated photos to infer the online behavior of consumers and service providers and its influence on purchase decisions and firm performance is crucial to business practices in hospitality and tourism.

Visual information plays a crucial role in online travel agencies and peer-to-peer accommodation platforms from the side of sellers and buyers. However, extant studies relied heavily on traditional manual identification with small samples and subjective judgment. With the development of deep learning and computer vision techniques, current studies were able to extract various types of visual information from large-scale datasets with high accuracy and efficiency. To the best of the authors’ knowledge, this study is the first to offer an outlook of image recognition techniques for mining visual information in hospitality and tourism.