RESUMEN
PurposeThe purpose of the study is to explore how technological capability and exogenous pressure interactively influence business model (BM) dynamics over time in new technology-based ventures.Design/methodology/approachThe study adopts a longitudinal case study of the BM innovations of a Chinese financial technology venture. The structural approach and temporal bracket are used to analyze and theorize the data.FindingsThe findings indicate that distinct contextual changes impel a firm to refine or abandon existing BMs over time. In different stages, the antecedents interactively influence BM dynamics with three successive patterns, namely pressure dominance, parallel influence and hybrid influence. While both antecedents trigger changes during the initiation and implementation of new BMs, they also serve as the filter and the enabler, respectively, during the ideation and integration of BMs.Research limitations/implicationsThe study inductively develops three propositions regarding the relationship between BM dynamics and its antecedents, which is based on the data collected from one single firm. Future research should test the propositions in other domains and take more cross-level antecedents into consideration.Originality/valueThe study contributes to the nascent research stream of BM dynamics by offering in-depth insights into the interaction of internal and external antecedents and by linking the differentiated roles of antecedents to the BM innovation process. The research offers some practical implications for new technology-based ventures seeking to develop BMs in a fast-changing environment.
RESUMEN
The outbreak of COVID-19 has spread worldwide, causing great damage to the global economy. Raman spectroscopy is expected to become a rapid and accurate method for the detection of coronavirus. A classification method of coronavirus spike proteins by Raman spectroscopy based on deep learning was implemented. A Raman spectra dataset of the spike proteins of five coronaviruses (including MERS-CoV, SARS-CoV, SARS-CoV-2, HCoVHKU1, and HCoV-OC43) was generated to establish the neural network model for classification. Even for rapidly acquired spectra with a low signal-to-noise ratio, the average accuracy exceeded 97%. An interpretive analysis of the classification results of the neural network was performed, which indicated that the differences in spectral characteristics captured by the neural network were consistent with the experimental analysis. The interpretative analysis method provided a valuable reference for identifying complex Raman spectra using deep-learning techniques. Our approach exhibited the potential to be applied in clinical practice to identify COVID-19 and other coronaviruses, and it can also be applied to other identification problems such as the identification of viruses or chemical agents, as well as in industrial areas such as oil and gas exploration.
RESUMEN
The outbreak of COVID-19 has spread worldwide, causing great damage to the global economy. Raman spectroscopy is expected to become a rapid and accurate method for the detection of coronavirus. A classification method of coronavirus spike proteins by Raman spectroscopy based on deep learning was implemented. A Raman spectra dataset of the spike proteins of five coronaviruses (including MERS-CoV, SARS-CoV, SARS-CoV-2, HCoVHKU1, and HCoV-OC43) was generated to establish the neural network model for classification. Even for rapidly acquired spectra with a low signal-to-noise ratio, the average accuracy exceeded 97%. An interpretive analysis of the classification results of the neural network was performed, which indicated that the differences in spectral characteristics captured by the neural network were consistent with the experimental analysis. The interpretative analysis method provided a valuable reference for identifying complex Raman spectra using deep-learning techniques. Our approach exhibited the potential to be applied in clinical practice to identify COVID-19 and other coronaviruses, and it can also be applied to other identification problems such as the identification of viruses or chemical agents, as well as in industrial areas such as oil and gas exploration.