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To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

The purpose of this study is to analyze the systematic relationships among dynamic capabilities in startups’ survival.

This study is based on a systematic literature review on dynamic capabilities related to startups’ survival, following the content analysis approach.

This study presents four different perspectives of analysis about dynamic capabilities from resources exchange and business factors that meet needs of startups' survival. It also points out new area for future research in this field. In doing so, this study differentiates itself by its approach not limiting dynamic capabilities research and enriching entrepreneurs' capability theory.

By indicating an evolution of dynamic capabilities theory among tangible and intangible resources exchange in a more favorable adaptation to startups growth, this study boosters and contributes to the society, economy in general and to the science of business management in various perspectives such as overcoming cognitive barriers, entrepreneur’s commitment, innovation capabilities and knowledge capacity of startups.

This study amplifies dynamic capabilities vision in startups’ survival as one of the main sources for growth in this type of organizations. It also develops a deeper understanding about new avenues for dynamic capabilities theory among tangible and intangible resources exchange.