Abstract: Electronic and electrical waste (e-waste) is growing fast. The purpose of this study is to examine young consumers’ purchase intention of refurbished electronic devices (REDs) such as laptop, tablet, mobile phone and game console. From literature review the factors that influence young consumers’ purchase intention were identified as ‘environmental awareness’, ‘social acceptance’, ‘seller/brand reputation and availability’, and ‘affordability and value’. For each factor a few statements were developed and used as independent variables in a questionnaire. One statement was added about purchase intention as dependent variable. A Pearson correlation coefficient test us showed a clear positive correlation of ‘environmental awareness’ and ‘affordability and value’ with the intention to purchase REDs, but not for the other two factors. This analysis contributes to knowledge on young consumers’ perceptions of refurbished electronic devices and can inform the design of innovative value propositions and new business models for REDs that contribute to a circular economy
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Purpose: To provide an overview of factors influencing the acceptance of electronic tech-nologies that support aging in place by community-dwelling older adults. Since technologyacceptance factors fluctuate over time, a distinction was made between factors in the pre-implementation stage and factors in the post-implementation stage. Methods: A systematic review of mixed studies. Seven major scientific databases (includingMEDLINE, Scopus and CINAHL) were searched. Inclusion criteria were as follows: (1) originaland peer-reviewed research, (2) qualitative, quantitative or mixed methods research, (3)research in which participants are community-dwelling older adults aged 60 years or older,and (4) research aimed at investigating factors that influence the intention to use or theactual use of electronic technology for aging in place. Three researchers each read the articlesand extracted factors. Results: Sixteen out of 2841 articles were included. Most articles investigated acceptance oftechnology that enhances safety or provides social interaction. The majority of data wasbased on qualitative research investigating factors in the pre-implementation stage. Accep-tance in this stage is influenced by 27 factors, divided into six themes: concerns regardingtechnology (e.g., high cost, privacy implications and usability factors); expected benefits oftechnology (e.g., increased safety and perceived usefulness); need for technology (e.g., per-ceived need and subjective health status); alternatives to technology (e.g., help by family orspouse), social influence (e.g., influence of family, friends and professional caregivers); andcharacteristics of older adults (e.g., desire to age in place). When comparing these results to qualitative results on post-implementation acceptance, our analysis showed that some factors are persistent while new factors also emerge. Quantitative results showed that a small number of variables have a significant influence in the pre-implementation stage. Fourteen out of the sixteen included articles did not use an existing technology acceptance framework or model. Conclusions: Acceptance of technology in the pre-implementation stage is influenced by multiple factors. However, post-implementation research on technology acceptance by community-dwelling older adults is scarce and most of the factors in this review have not been tested by using quantitative methods. Further research is needed to determine if and how the factors in this review are interrelated, and how they relate to existing models of technology acceptance.
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Background: This paper presents the findings of a pilot research survey which assessed the degree of balance between safety and productivity, and its relationship with awareness and communication of human factors and safety rules in the aircraft manufacturing environment.Methods: The study was carried out at two Australian aircraft manufacturing facilities where a Likertscale questionnaire was administered to a representative sample. The research instrument included topics relevant to the safety and human factors training provided to the target workforce. The answers were processed in overall, and against demographic characteristics of the sample population.Results: The workers were sufficiently aware of how human factors and safety rules influence their performance and acknowledged that supervisors had adequately communicated such topics. Safety and productivity seemed equally balanced across the sample. A preference for the former over the latter wasassociated with a higher awareness about human factors and safety rules, but not linked with safety communication. The size of the facility and the length and type of employment were occasionally correlated with responses to some communication and human factors topics and the equilibrium between productivity and safety.Conclusion: Although human factors training had been provided and sufficient bidirectional communication was present across the sample, it seems that quality and complexity factors might have influencedthe effects of those safety related practices on the safety-productivity balance for specific parts of the population studied. Customization of safety training and communication to specific characteristics of employees may be necessary to achieve the desired outcomes.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
