Abstract:
The goals of this study are to evaluate an effective web application platform for energy data communication in the office building by comparing between visual data and statistical data in order to design and evaluate the results of the proposed persuasive effects model. The participants are forty office workers who work in Chamchuri 5 building, Chulalongkorn University. The study has been applied mixed research methods which include qualitative and quantitative research. The information was collected by a focus group and rating scale surveys to examine the persuasive levels of energy reduction in the office building. The study is to be conducted in three weeks and participants will receive three different web application platforms for each week which included 1) Statistical data, as the same platform of CU BEMS 2) Visual pet data and 3) Visual farm data. The result was adopted to second web application design which is the fourth type (mixed web application) to compare persuasion levels with the first design. Evaluation through remote usability testing and analyzed with post-study system usability questionnaire regarding to ISO 9241-11. The major findings have revealed that the effective energy communication web application must include six following elements such as 1) Suggestion of how to decrease energy consumption, 2) Energy self-monitoring, 3) Energy consumption comparison, 4) Virtual Rewards, 5) Application instructions and 6) Notification message. There are different results in each application which indicates the persuasion levels with the statistical significance level at .05. And the mixed web application is the most persuasive. The result from usability testing shown that the mixed web application is 100% effective as all tasks were completed by all participants and overall satisfaction was in highest level. This research is in the middle of the coronavirus (COVID-19) situation, so the energy consumption of the building could not be measured. In addition, the suggestions for future research are to analyze an actual energy data before and after web application testing for more information.