Inequalities in the social use of the Internet of things: A capital and skills perspective

Zeeuw, Alex van der; Deursen, Alexander Johannes Aloysius Maria van; Jansen, Giedo

doi:10.1177/1461444818821067

Cited by 35 publications

(8 citation statements)

References 50 publications

(80 reference statements)

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“…Moreover, it is a new design and engineering philosophy for adaptive production systems that mainly focus on treating automation as a further enhancement of the human's physical, sensorial, and cognitive capabilities using human cyber-physical system integration [221]. e operator 4.0 typology includes analytical, virtual, augmented, collaborative, heathy, smarter, social, and super- [13,19,26,37,116,200,[202][203][204][205][206].…”

Section: Building Skills Into the Workforce Of Industry 40mentioning

confidence: 99%

Exponential Disruptive Technologies and the Required Skills of Industry 4.0

Bongomin

Ocen

Nganyi

et al. 2020

Journal of Engineering

135

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The 21st century has witnessed precipitous changes spanning from the way of life to the technologies that emerged. We have entered a nascent paradigm shift (industry 4.0) where science fictions have become science facts, and technology fusion is the main driver. Thus, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this study, disruptive technologies of industry 4.0 were explored and quantified in terms of the number of their appearances in published literature. The study aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enumerate the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management was done in multidisciplinary databases: Google Scholar, Science Direct, Scopus, Sage, Taylor & Francis, and Emerald Insight. From the electronic survey, 35 disruptive technologies were quantified and 13 key technologies: Internet of Things, Big Data, 3D printing, Cloud computing, Autonomous robots, Virtual and Augmented reality, Cyber-physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones, and Biotechnology were identified. Both technical and personal skills to be imparted into the human workforce for industry 4.0 were reported. The review identified the need to investigate the capability and the readiness of developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. This study proposes the need to address the integration of industry 4.0 concepts into the current education system.

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Section: Building Skills Into the Workforce Of Industry 40mentioning

confidence: 99%

Exponential Disruptive Technologies and the Required Skills of Industry 4.0

Bongomin

Ocen

Nganyi

et al. 2020

Journal of Engineering

135

View full text Add to dashboard Cite

show abstract

“…Investigating Internet access is particularly important since the Internet is not a monolithic technology and new systems and applications are constantly being developed. For example, while general Internet access might be mostly saturated in rich countries, the same cannot be said about social media access (Blank & Lutz, ; Hargittai, ; Pew, , Pew, ), the mobile Internet (Marler, ; Pearce & Rice, ), and AI‐powered technologies such as smart speakers, smart homes, social robots, and Internet‐of‐things (IoT) applications (Van der Zeeuw, Van Deursen, & Jansen, ). Thus, digital inequalities research in the tradition of the first‐level digital divide is well advised to keep studying inequalities in technology adoption along socioeconomic lines.…”

Section: First‐level Digital Dividementioning

confidence: 99%

Digital inequalities in the age of artificial intelligence and big data

Lutz

2019

Human Behav and Emerg Tech

176

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In this literature review, I summarize key concepts and findings from the rich academic literature on digital inequalities. I propose that digital inequalities research should look more into labor‐ and big data‐related questions such as inequalities in online labor markets and the negative effects of algorithmic decision‐making for vulnerable population groups. The article engages with the sociological literature on digital inequalities and explains the general approach to digital inequalities, based on the distinction of first‐, second‐, and third‐level digital divides. First, inequalities in access to digital technologies are discussed. This discussion is extended to emerging technologies, including the Internet‐of‐things and artificial intelligence‐powered systems such as smart speakers. Second, inequalities in digital skills and technology use are reviewed and connected to the discourse on new forms of work such as the sharing economy or gig economy. Third and finally, the discourse on the outcomes, in the form of benefits or harms, from digital technology use is taken up. Here, I propose to integrate the digital inequalities literature more strongly with critical algorithm studies and recent discussions about datafication, digital footprints, and information privacy.

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“…Regarding educational attainment, those with higher levels possess more advanced internet skills [8,30] and are better able to keep up with technological advancements, resulting in greater inequality between themselves and lower-educated individuals who are unable to keep up [40]. We expect these differences in an IoT environment to become even larger, as a complex system requires even more cognitive capabilities [30,41].…”

Section: Iot Skill Determinantsmentioning

confidence: 99%

Internet-of-Things Skills Among the General Population: Task-Based Performance Test Using Activity Trackers

2020

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Background The health internet-of-things (IoT) can potentially provide insights into the present health condition, potential pitfalls, and support of a healthier lifestyle. However, to enjoy these benefits, people need skills to use the IoT. These IoT skills are expected to differ across the general population, thereby causing a new digital divide. Objective This study aims to assess whether a sample of the general Dutch population can use health IoT by focusing on data and strategic IoT skills. Furthermore, we determine the role of gender, age, and education, and traditional internet skills. Methods From April 1, 2019, to December 12, 2019, 100 individuals participated in this study. Participants were recruited via digital flyers and door-to-door canvassing. A selective quota sample was divided into equal subsamples of gender, age, and education. Additional inclusion criteria were smartphone possession and no previous experience of using activity trackers. This study was conducted in 3 waves over a period of 2 weeks. In wave 1, a questionnaire was administered to measure the operational, mobile, and information internet skills of the participants, and the participants were introduced to the activity tracker. After 1 week of getting acquainted with the activity tracker, a task-based performance test was conducted in wave 2 to measure the levels of data IoT skills and the strategic IoT skill component—action plan construction. A week after the participants were asked to use the activity tracker more deliberately, a performance test was then conducted in wave 3 to measure the level of the strategic IoT skill component—action plan execution. Results The participants successfully completed 54% (13.5/25) of the data IoT skill tasks. Regarding strategic IoT tasks, the completion rates were 56% (10.1/18) for action plan construction and 43% (3.9/9) for action plan execution. None of the participants were able to complete all the data IoT skill tasks, and none of the participants were able to complete all the strategic IoT skill tasks regarding action plan construction or its execution. Age and education were important determinants of the IoT skill levels of the participants, except for the ability to execute an action plan strategically. Furthermore, the level of information internet skills of the participants contributed to their level of data IoT skills. Conclusions This study found that data and strategic IoT skills of Dutch citizens are underdeveloped with regard to health purposes. In particular, those who could benefit the most from health IoT were those who had the most trouble using it, that is, the older and lower-educated individuals.

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Inequalities in the social use of the Internet of things: A capital and skills perspective

Cited by 35 publications

References 50 publications

Exponential Disruptive Technologies and the Required Skills of Industry 4.0

Exponential Disruptive Technologies and the Required Skills of Industry 4.0

Digital inequalities in the age of artificial intelligence and big data

Internet-of-Things Skills Among the General Population: Task-Based Performance Test Using Activity Trackers

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