Earticle

초록

영어

This study explored the factors contributing to the activation of residential space user management platforms through a multiple case study approach. Twenty platforms that existed from 2012 to April 2024 were analyzed, focusing on 11 key features, with 8 selected as variables. Service activation was measured based on Play Store downloads and ratings, classifying 10 platforms as successful and 10 as needing development. Using an inductive reasoning approach with the ID3 algorithm, supported by GPT-4's Advanced Data Analytics, the analysis identified the "Neighbor Communication Function" as the most influential variable, followed by the "Newsletter Function" and "Facility Status Check Function." The study found that platforms including a neighbor communication function are more likely to be activated. Platforms without both the neighbor communication and newsletter functions are less likely to succeed. Even without the neighbor communication function, platforms with both the newsletter and facility status check functions tend to be activated. Platforms lacking the neighbor communication function will not be activated unless both the newsletter and facility status check functions are present. These findings provide strategic insights for the commercial success of residential space user management services. The results suggest that interaction services, such as the neighbor communication function, are crucial for success. However, success can still be achieved with robust monitoring and information services, even in the absence of user interaction features.

목차

Abstract
1. Introduction
2. Theoretical Background
2.1 The Scientific Management of Residential Space User Services
2.2 The Importance of Information Delivery and Interactive Communication
2.3 Case Studies Based on Inductive Reasoning
2.4 ID3 Algorithm
3. Methodology
3.1 Variable Identification
3.2Analysis of Cases with Activation Level &quat;D (Deactivated)&quat;
3.3Analysis of Cases with Activation Level &quat;S (Activated)&quat;
3.4Case Table and Decision Tree Extraction
4. Results and Discussion
4.1 Hypothesis Development
4.2 Implications for Activating Residential Space User Management Services
5. Conclusion
References

키워드

저자

• InSoo Kim [ Graduate student, Dept. of Management, Kyung Hee University, Seoul, Korea ]
• Baek Jeong [ Lecturer, Dept. of Big Data Analytics, Kyung Hee University, Seoul, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380