M2CIM-DSS: A Model for Measuring Continuance Intention in Decision Support Systems

Abstract

Currently, the core trend of Higher Education Institutes (HEI) to invest in decision support systems (DSS) to improve their decision-making process. Due to technology emergence, HEI has been experiencing noteworthy changes. Many techniques such as DSS been adopted developed and implemented to support the educational process. Even though DSS has adopted and invested mainly in most sectors, a lack of research in investigating confirmed, the influencing factors on the intention of stakeholders to continue to use them. Consequently, the purpose of the study is to examine postadoption users' satisfaction and users' intention to continue using DSS. This study combining two theoretical models, the Technology Acceptance Model, and The Technology Organization Environment Framework, to examine users' intentions to continue using DSS. The data collection process has conducted using 240 respondents, who belong to HEI institutions (Academia and management staff), who work on DSS. Structural Equation Modeling was utilized to analyze structural relationships among the proposed model's factors. The authors used several methods such as hierarchical regression, oneway ANOVA, descriptive statistics, as well as t-test have applied to evaluate the model's components' relevancy, understanding, and pertinence to each other. The result shows the proposed model fits the data and had a good explanation than the existing models. On the other hand, the results show the importance of equipping DSS with real-time support because they have positive repercussions in the decision-making process The implications as well as the limitations of this study have been extensively discussed.

The Success Factors in Measuring the Millennial Generation’s Energy-Saving Behavior Toward the Smart Campus

“…The application of the DSS Fuzzy-AHP concept is adopted effectively in this questionnaire to accommodate the diversity of perspectives in translating information, both data and linguistic issues [24] [25]. As a result, the priority value analysis of each criterion and sub-criteria is ranked and enriched in the measurement model [53] [54].…”