Mobile Application Based on Geolocation for the Recruitment of General Services in Trujillo, La Libertad

Abstract

Currently, there is no technological solution that efficiently facilitates the offering of general services by independent workers in the city of Trujillo. This limitation reduces job opportunities, as workers secure fewer contracts due to reliance on client recommendations, a method that is often inefficient due to long response times and low accessibility. Leveraging the versatility of mobile applications. This study contributes to computer science by demonstrating how cloud-based data management, real-time communication, and location-based service matching using Google APIs optimize service efficiency and user experience. The study follows an applied research approach with a quantitative methodology, employing a pre-experimental explanatory design and a sample of 22 workers selected through non-probabilistic convenience sampling. The development was carried out using the Flutter framework and the Dart programming language, with an SQL database hosted on Microsoft Azure cloud services. The Mobile-D agile methodology guided the development process. After implementing the application, the results showed an 86.79% reduction in the average hiring process time, a 50% increase in the number of contracts completed, and a 51.27% improvement in workers' average satisfaction. These findings highlight the effectiveness of mobile and cloud computing technologies, along with ranking algorithms and geolocation services, in streamlining labor market interactions and improving user experience.