No abstract

As Industry 4.0 offers significant productivity improvements, its relevance has grown across various organisations. While it captures the attention of both the industry and the academia, very few efforts have been made to streamline useful indicators across stages of its implementation. Such work facilitates the development of strategies that are appropriate for a specific stage of implementation; therefore, it would be significant to a variety of stakeholders. As a result, this paper aims to establish an indicator system for adopting Industry 4.0 within the context of the three stages of the innovation adoption: (i) pre-adoption, (ii) adoption, and (iii) post-adoption. First, a comprehensive review was performed with a search expanding into the literature on innovation and technology adoption. Second, the resulting indicators were filtered for relevance, redundancy, description, and thorough focus discussions. Finally, they were categorised by their stage of adoption. From 469 innovation adoption indicators found in the literature, this work identified a total of 62 indicators relevant for the Industry 4.0 adoption, in which 11, 14, and 37 of them comprised the three stages, respectively. Case studies from two manufacturing firms in the Philippines were reported to demonstrate the applicability of the proposed indicator system. This work pioneers the establishment of an indicator system for the Industry 4.0 adoption and the classification of such indicators into three stages — pre-adoption, adoption, and post-adoption — which would serve as a framework for decision-makers, practitioners, and stakeholders in planning, strategy development, resource allocation, and performance evaluation of the Industry 4.0 adoption.

No abstract

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.

This paper attempts to extract Industry 4.0 indicators from relevant literature and integrate these indicators in strategy formulation by providing a categorization system according to technological, organizational, inter-organizational, and social and regulatory clusters. The identified 62 indicators are found to cover a wide range of responsibilities accounted for by specific functional teams and cross-functional teams, which collectively aim to support strategic decision-making among stakeholders. The categorization of indicators is necessary to efficiently facilitate corporate level, business level, and functional level strategies.

No abstract

No abstract