Contribution of AI’s neural networks on the business performance of a startup company with the moderating variable of innovation: The case of MedHyve
The researchers conducted a single case study to examine the effect of neural networks on the business performance of a start-up company, Medhyve, with the moderating variable of innovation. A qualitative study was conducted to ascertain whether neural networks are truly advantageous for start-ups....
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2022
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etdb_dsi/126 https://animorepository.dlsu.edu.ph/context/etdb_dsi/article/1045/viewcontent/Contribution_of_AI_s2_Neural_Networks_on_the_Business_Performance_Redacted.pdf |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | The researchers conducted a single case study to examine the effect of neural networks on the business performance of a start-up company, Medhyve, with the moderating variable of innovation. A qualitative study was conducted to ascertain whether neural networks are truly advantageous for start-ups. The founders of MedHyve, as well as industry experts, were interviewed to strengthen the data received from respondents. It was discovered that neural networks significantly enhanced MedHyve's business operations and performance. Additionally, it was recognized that innovation is a critical component of success, particularly for start-up businesses. Clustering and recurrent neural networks assisted MedHyve in developing a more efficient procurement procedure that saves both the company and its clients significant time. The use of neural networks enhances the service given by the company and adds value to MedHyve's customers, hence increasing client retention. Due to the automation of numerous processes, neural networks help greatly to the reduction of menial duties while enhancing productivity. Constant vigilance in handling accurate data that is inputted in the network is needed to avoid losing money for the firm due to unreliable results. Additionally, recommendations were given to MedHyve and selected individuals in light of the research's findings and critical assessments. |
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