To promote efficient use of electrical energy, technology-based solutions, along with their corresponding user acceptance assessments, have been seen to facilitate goal fulfillment concerning desired functionality and expected benefits, in an open innovation fashion. This paper simultaneously develops an electrical energy consumption monitoring system (EECMS) device that shall monitor and control the use of energy in real-time and assesses its acceptability to users according to the extended technology acceptance model (TAM) approach. This proposed EECMS device is tested in an academic institution in the Philippines, and it is found that the device can function as desired as well as render a significant favor from its users according to additional key constructs. As such, future developments of the device are encouraged to enhance key constructs identified as suitable for future adoption.

Lockdowns of various forms have prompted higher education institutions (HEIs) to suddenly shift from physical face-to-face classes to e-learning environments on an unprecedented scale in recent history. This sudden shift promotes the continuity of the teaching-learning process in HEIs despite the COVID-19 pandemic, at most on the positive side, while bringing forth challenges related to individual learners and academics. This work is based on a recently reported Values-Enhanced Technology Adoption (VETA) model, which incorporates individual values in technology acceptance modeling. Despite offering crucial insights into academics in evaluating e-learning adoption, the current literature suffers from drawbacks. Motivated by addressing these limitations, this work reevaluates the nine constructs of the VETA model using the decision-making trial and evaluation laboratory (DEMATEL). Results indicate that effort expectancy, hedonic motivation, price value, habit, security, tradition, conformity, achievement, power, and hedonism constructs cause performance expectancy, behavioral intention, and social influence. The DEMATEL captures and models the causal relationships between these constructs within an analytical framework, which induces some variations of the recent empirical findings. Finally, the perception of self-achievement among academics drives the intention to adopt e-learning. The findings offered in this work are crucial to the evolving literature of COVID-19 on education, particularly in informing the design of initiatives and measures to enhance e-learning.

Embedding sustainable practices in higher education institutions remain to be a challenge for universities despite the potential benefits that such integration can provide. For stakeholders in academic institutions to support the successful transition to sustainable practices, there is a need to analyze the educational system according to a more defined framework. As such, the brittle, anxious, nonlinear and incomprehensible (BANI) framework is deemed suitable in framing the perspective of implementing sustainable practices in academic institutions, which the literature failed to provide. In order to proceed with the analysis, an interpretive structural modeling analysis, along with MICMAC approach, is performed to a case study in the Philippines to explore the structural relationships of the sub-components in the brittle systems of academic institutions. Key insights point out rigid policies to be reviewed accordingly to ensure elimination of fragility in the educational system. Furthermore, stakeholders can better prioritize their resources according to the structural relationships found through this analysis.

In the advent of modern technologies used in educational systems, stakeholders in academic institutions are also scrutinizing the use of advanced technologies in special education supervision. Driven by the fact that advanced technologies, such as artificial intelligence (AI), have empirically improved the learning process of students and the monitoring of general systems, it is also highly likely that such technology can also prove worthy of adoption in special education. In this paper, a fuzzy best-worst method (BWM) is utilized to analyze the adoption factors aligned to the supervision process in special education. Key results revealed that AI tools must be selected mainly based on the engagement level it provides to the students as valuable insights can also be gained by supervisors which will be correspondingly used in intervention strategies. The rest of the adoption factors have also been ranked in order of priority for stakeholders to consider in selecting an AI technology to use for special education supervision.