With the exponential expansion of e-commerce, an immense volume of historical sales data has been generated and amassed. This influx of data has created an opportunity for more accurate sales forecasting. While various sales forecasting methods and models have been applied in practice, existing ones often struggle to fully harness sales data and manage significant fluctuations. As a result, they frequently fail to make accurate predictions, falling short of meeting enterprise needs. Therefore, it is imperative to explore new models to enhance the accuracy and efficiency of sales forecasting. In this paper, we introduce a model tailored for sales forecasting based on a Transformer with encoder–decoder architecture and multi-head attention mechanisms. We have made specific modifications to the standard Transformer model, such as removing the Softmax layer in the last layer and adapting input embedding, position encoding, and feedforward network components to align with the unique characteristics of sales forecast data and the specific requirements of sales forecasting. The multi-head attention mechanism in our proposed model can directly compute the dot product results in a single step, addressing long-term time-dependent computation challenges while maintaining lower time complexity and greater interpretability. This enhancement significantly contributes to improving the model’s accuracy and efficiency. Furthermore, we provide a comprehensive formula representation of the model for the first time, facilitating better understanding and implementation. We conducted experiments using sales datasets that incorporate various factors influencing sales forecasts, such as seasons, holidays, and promotions. The results demonstrate that our proposed model significantly outperforms seven selected benchmark methods, reducing RMSLE, RMSWLE, NWRMSLE, and RMALE by approximately 48.2%, 48.5%, 45.2, and 63.0%, respectively. Additionally, ablation experiments on the multi-head attention and the number of encoder–decoders validate the rationality of our chosen model parameters.

Purpose: This paper aims to use a negative perspective to investigate the effects of perceived deceptiveness and pressure on consumer donation and their underlying mechanisms in the context of asking for donations.Design/methodology/approach: Study 1 used a qualitative approach to clarify the categories and dimensions of the research variables and explore their relationships. Study 2 empirically tested the hypotheses by combining a fictitious context and a real context related to asking for donations.Findings: In the qualitative study, the data provided sufficient evidence to support the relationships in the theoretical model. The results of the empirical study showed that perceived deceptiveness negatively influences consumer donation, while perceived pressure positively affects donation amount. Notably, the discomfort of potential donors plays an important role in mediating these relationships.Practical implications: This paper suggests a way for charities to raise more money, i.e. by cooperating with companies with good reputations, limited scandals and transparent supervisory mechanisms. Meanwhile, solicitors should pay attention to the adverse effects of discomfort to avoid generating resentment among consumers.Originality/value: First, to the best of the authors’ knowledge, this is the first study to use a negative perspective to examine the effects of perceived deceptiveness and pressure on consumer donation. Second, the use of cognitive dissonance theory to highlight the role of discomfort represents a novel contribution to the literature. Third, using a mixed-methods approach to achieve a robust conclusion provides valuable insights and extends the existing literature.

In 2013, China proposed the “Belt & Road Initiative” which aims to invest the “Belt & Road” countries so as to help them develop their infrastructure and economy. China consumes the largest part of fossil energy of the whole world, so it is China’s priority to consider its energy supplying security. Therefore, it becomes urgent for China to invest the “Belt & Road” countries’ energy facilities. There comes a question: how to evaluate the overseas energy investment risk? To answer this question, this paper proposes a deep learning method to assess such risk of the 50 “Belt & Road” countries. Specifically, this paper first proposes an indicator system in which 6 main factors are separated into 36 sub-factors. This paper makes use of hierarchical convolution neural networks (CNN) to encode the historical statistics. The hierarchical structure could help CNN handle the long historical statistics more effectively and efficiently. Afterward, this paper leverages the self-attention layer to calculate the weights of each sub-factor. It could be observed that the resource potential is the most important indicator, while “years of China’s diplomatic relations” is the most important sub-indicator. Finally, we use a conditional random field (CRF) layer and softmax layer to compute the assessment scores of each country. Based on the experimental results, this paper suggests Russia, United Arab Emirates (UAE), Malaysia, Saudi Arabia, Pakistan, Indonesia, and Kazakhstan to be China’s most reliable choices for energy investment.