Date of Publication


Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Business Management

Subject Categories

Entrepreneurial and Small Business Operations


Ramon V. Del Rosario College of Business


Decision Sciences and Innovation Dept

Thesis Advisor

Harvey Ong

Defense Panel Chair

Theresa Gerial

Defense Panel Member

Raymund Dimaranan


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.

Abstract Format







Technological innovations; Neural networks (Computer science)

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