School Level Information System (IS) Discontinuance Intention: A Case Study on Information System (IS) Discontinuance of Surigao State College of Technology SSCT

Educational institutions implement school information system (IS) solutions for efficient academic processes and improve the student experience, and thus tend to upgrade to new school IS, ceasing the use of old school IS. Extant studies of information system discontinuance are in organizational in general. This study is solely a school organization setting, specifically a thorough understanding of contributing factors that facilitate the intention of school information system (IS) discontinuance. Organizational level IS discontinuance intention OLIDI model was used as a lens and anchored by grounded theory. Semi-Structured interviews were used to collect data from school administrators and users of information system (IS) of Surigao State College of Technology. A qualitative statistics approach and coding of words are used to analyze the intention of IS discontinuance and provide model for school setting IS discontinuance. The findings show that system shortcomings, organizational initiative, environmental change, system investment, and institutional pressures were the critical determinants of increased intentions to replace the existing school information system (IS). The study findings are useful to school administrators to identify long term flexibility required for policies to overcome the capability of shortcomings that emerge over the system life span and underline risk associated with continued use of unsupported systems.

Evaluating different levels of information on the calibration of building energy simulation models

A poorly calibrated model undermines confidence in the effectiveness of building energy simulation, impeding the widespread application of advanced energy conservation measures (ECMs). Striking a balance between information-gathering efforts and achieving sufficient model credibility is crucial but often obscured by ambiguities. To address this gap, we model and calibrate a test bed with different levels of information (LOI). Beginning with an initial model based on building geometry (LOI 1), we progressively introduce additional information, including nameplate information (LOI 2), envelope conductivity (LOI 3), zone infiltration rate (LOI 4), AHU fan power (LOI 5), and HVAC data (LOI 6). The models are evaluated for accuracy, consistency, and the robustness of their predictions. Our results indicate that adding more information for calibration leads to improved data fit. However, this improvement is not uniform across all observed outputs due to identifiability issues. Furthermore, for energy-saving analysis, adding more information can significantly affect the projected energy savings by up to two times. Nevertheless, for ECM ranking, models that did not meet ASHRAE 14 accuracy thresholds can yield correct retrofit decisions. These findings underscore equifinality in modeling complex building systems. Clearly, predictive accuracy is not synonymous with model credibility. Therefore, to balance efforts in information-gathering and model reliability, it is crucial to (1) determine the minimum level of information required for calibration compatible with its intended purpose and (2) calibrate models with information closely linked to all outputs of interest, particularly when simultaneous accuracy for multiple outputs is necessary.