An Evaluation of Key Adoption Factors towards Using the Fog Technology

Fog technology is one of the recent improvements in cloud technology that is designed to reduce some of its drawbacks. Fog technology architecture is often widely distributed to minimize the time required for data processing and enable Internet of Things (IoT) innovations. The purpose of this paper is to evaluate the main factors that might influence the adoption of fog technology. This paper offers a combined framework that addresses fog technology adoption based on the technology adoption perspective, which has been comprehensively researched in the information systems discipline. The proposed integrated framework combines the technology acceptance model (TAM) and diffusion of innovation (DOI) theory to develop a holistic perspective on the adoption of fog technology. The factors that might affect the adoption of fog technology are analyzed from the results of an online survey in 43 different organizations across a wide range of industries. These factors are observed based on data collected from 216 participants, including professional IT staff and senior business executives. This analysis was conducted by using structural equation modeling (SEM). The research results identified nine factors with a statistically significant impact on the adoption of fog technology, and these factors included relative advantage, compatibility, awareness, cost-effectiveness, security, infrastructure, ease of use, usefulness, and location. The findings from this research offer insight to organizations looking to implement fog technology to enable IoT and tap into the digital transformation opportunities presented by this new digital economy.

Interdisciplinary Confusion and Resolution in the Context of Moral Machines

Recent advancements in artificial intelligence (AI) have fueled widespread academic discourse on the ethics of AI within and across a diverse set of disciplines. One notable subfield of AI ethics is machine ethics, which seeks to implement ethical considerations into AI systems. However, since different research efforts within machine ethics have discipline-specific concepts, practices, and goals, the resulting body of work is pestered with conflict and confusion as opposed to fruitful synergies. The aim of this paper is to explore ways to alleviate these issues, both on a practical and theoretical level of analysis. First, we describe two approaches to machine ethics: the philosophical approach and the engineering approach and show how tensions between the two arise due to discipline specific practices and aims. Using the concept of disciplinary capture, we then discuss potential promises and pitfalls to cross-disciplinary collaboration. Drawing on recent work in philosophy of science, we finally describe how metacognitive scaffolds can be used to avoid epistemological obstacles and foster innovative collaboration in AI ethics in general and machine ethics in particular.