A Framework for Collaborative Practices Platforms for Humans and Machines in Industry 4.0–Oriented Smart and Sustainable Manufacturing Environments

Ferreira, Luı́s Pinto; Putnik, Goran D.; Varela, Leonilde; Manupati, Vijaya K; Lopes, Nuno; Cunha, Manuela Ivone P. da; Alves, Cátia; Castro, Hélio

doi:10.1201/9781003123866-1

Cited by 7 publications

(8 citation statements)

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“…However, access to traditional online apps such as Mural and Trello and access to web apps may require less exertion. Past studies, however, have claimed that effort expectancy is less important than performance expectancy in determining collaboration since it has a more significant impact on comment utilization (Ferreira et al, 2022;Yueh et al, 2015). (Ferreira et al, 2022;Yueh et al, 2015).…”

Section: Effort Expectancy and Actual Use Of Collaborative System Pla...mentioning

confidence: 97%

“…Collaborative learning emphasizes the benefits of using digital tools to help individuals learn together (Ferreira et al, 2022). Longitudinal research has examined the interplay between education and technology in the context of collaborative learning, focusing on how computers can facilitate the formation of learning communities in which students work together in a planned manner toward the common goal of knowledge acquisition (Han & Trimi, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Murals are vital because they encourage individual expression among the populace. Because of the time, money, and effort involved in creating a Mural, it is usually commissioned by a sponsor (Ferreira et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the intention to use mobile apps like Mural and Trello, the relative advantage is the strongest predictor of behavior, as shown by Shaikh et al (2021). Before downloading a networking app, users will consider the store's performance in light of their needs for information dissemination and evocative discourse (Ferreira et al, 2022;Shchetynina et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Employees’ Continued Use Intention of Collaborative System Platforms: Examining Mural and Trello

Alobidyeen¹

2022

J. Digi.Inf.Sys

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This survey investigated the intentions of current users of the project management tools Mural and Trello in the United States. This study focused on small and medium enterprises (SMEs) in the United States. The survey results of 291 employees whose companies had them use either Mural or Trello to coordinate and organize their work were analyzed. Through structural equation modelling (SEM), this study confirmed that the components of effort expectation have direct and meaningful effects in the conceptual model, which in turn affects the staff's actual usage of the Mural and Trello systems. As a bonus, the aspiration to use Mural and Trello in the future affects their actual usage. Moreover, it suggests future research and implementation directions, including the theoretical and practical consideration of technologies in collaboration platform systems.

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Section: Effort Expectancy and Actual Use Of Collaborative System Pla...mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Employees’ Continued Use Intention of Collaborative System Platforms: Examining Mural and Trello

Alobidyeen¹

2022

J. Digi.Inf.Sys

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“…Valero et al [7] have designed a coherent framework for smart manufacturing. A concept has been proposed for supporting the collaborative practice platforms in smart manufacturing by Luís Ferreira et al [8] Using various sensors, an Edge-to-Cloud Industry Internet of Things framework has been developed for condition monitoring in manufacturing systems by Zhi Li et al [9]. In addition, object detection and text recognition are two promising branches of computer vision that can potentially push automated supervision to a new level.…”

Section: Related Workmentioning

confidence: 99%

Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers

Bian

et al. 2022

Sensors

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With the rapid concurrent advance of artificial intelligence (AI) and Internet of Things (IoT) technology, manufacturing environments are being upgraded or equipped with a smart and connected infrastructure that empowers workers and supervisors to optimize manufacturing workflow and processes for improved energy efficiency, equipment reliability, quality, safety, and productivity. This challenges capital cost and complexity for many small and medium-sized manufacturers (SMMs) who heavily rely on people to supervise manufacturing processes and facilities. This research aims to create an affordable, scalable, accessible, and portable (ASAP) solution to automate the supervision of manufacturing processes. The proposed approach seeks to reduce the cost and complexity of smart manufacturing deployment for SMMs through the deployment of consumer-grade electronics and a novel AI development methodology. The proposed system, AI-assisted Machine Supervision (AIMS), provides SMMs with two major subsystems: direct machine monitoring (DMM) and human-machine interaction monitoring (HIM). The AIMS system was evaluated and validated with a case study in 3D printing through the affordable AI accelerator solution of the vision processing unit (VPU).

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Evaluating the Statistical Process Control Data Acquisition System in a Heat Exchanger Factory

Costa

Shah

Varela

et al. 2022

Lecture Notes in Mechanical Engineering

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A Framework for Collaborative Practices Platforms for Humans and Machines in Industry 4.0–Oriented Smart and Sustainable Manufacturing Environments

Cited by 7 publications

References 0 publications

Employees’ Continued Use Intention of Collaborative System Platforms: Examining Mural and Trello

Employees’ Continued Use Intention of Collaborative System Platforms: Examining Mural and Trello

Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers

Evaluating the Statistical Process Control Data Acquisition System in a Heat Exchanger Factory

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