Development of an application for the comprehensive evaluation of structural equation modeling findings

Throughout history, increasingly crowded and complex structures have been managed by dividing them into subgroups to ensure sustainability. Technological advancements have drastically shortened the production time of scientific publications, causing academic data to grow and become increasingly intricate. This study develops a desktop application—specialized in scientific method—that aims to enhance efficiency and speed for researchers during the literature review process in the face of vast and complex academic data. The developed application processes findings from the "Structural Equation Modeling" (SEM) method as input. In studies utilizing SEM, statistical path coefficients, sample sizes, sample countries, gender distributions, publication years, statistical relationship directions, and similar data are typically reported. The application is designed to take these inputs and produce the required information in a flexible and user-defined perspective through combinations of highly diverse search, filtering, sorting, and grouping options. The application was developed using the "Waterfall Model" software development methodology. During the requirements phase, studies employing SEM were meticulously reviewed to identify the attributes consistently reported (the application's inputs). Additionally, consultations were conducted with academicians from two public universities who specialize in this method. In the design phase, processes such as creating entity-relationship diagrams, normalization, and developing data dictionaries were undertaken to establish an effective database. Furthermore, flow diagrams were created to guide the coding of the user interface. During the coding phase, "Microsoft Visual Studio" was employed as the development environment, and the "C#" programming language was used. Upon completion of coding, testing was performed, and necessary improvements were implemented. The final application offers researchers employing SEM the ability to perform various searches within the context of statistical relationships, filter results with extensive options, and group filtered results using different criteria. Additionally, as part of this study, 721 statistical relationship data points from 91 studies conducted to explain the phenomenon of "Social Media Continuance Intention" were entered into the application's database. By leveraging the application's functionality, the most frequently observed relationships explaining this phenomenon were identified and discussed in detail, along with their theoretical foundations, in the fourth chapter of the study. This study underscores the necessity of developing method-specific tools to manage and access large and complex academic datasets, providing an example of such a specialized application to address the "overcrowding" problem in the realm of academic data. Beyond its contribution to academic data management, the comprehensive evaluation of the literature in a specific field constitutes a secondary significant contribution of this study.