In this study, the researchers illuminated the positive advantages of incorporating augmented reality (AR) technology into the daily practices of service engineers working in an advanced manufacturing environment. AR technology improved the user’s communication with colleagues and content experts through real-time video conferencing and brought valuable information directly to the user on a mobile platform. This effective communication had the potential to reduce the time it takes to complete a work task, even when the user is in a remote location. However, it could not be assumed that people would be willing to use this new technology just because it was available. In order to promote the positive advantages of incorporating AR technology into the daily practices of service engineers, more research was needed to assess the user’s perceived value of AR technology and their willingness to accept AR technology into their daily tasks. The purpose of this research was to demonstrate the advantages of using augmented reality technology to improve communication and access to information as well as to assess the acceptance and use of this technology based on the behavioral intentions of a trained engineer. Using that information and the Unified Theory of Acceptance and Use of Technology including its extensions (UTAUT and UTAUT2) (Venkatesh, Morris, Davis, & Davis, 2003; Venkatesh, 2012) this research determined if AR technology is viable for larger scale adoption.

The professionalization of blogs has elevated blogging to an organizational field whereby bloggers develop a legitimate career path. For many minority women bloggers, the transition from being traditionally employed to managing a one-person digital enterprise is often met with racial and gender imbalances created by nontraditional modes of work. The purpose of this qualitative transcendental phenomenological study was to gain a deeper understanding of how African American women bloggers described their lived experiences with managing a one-person digital enterprise and the implications of their racial and gender identity within this nontraditional mode of work. To address this gap, a transcendental phenomenological method was used to collect data from African American women bloggers. This study was framed by 3 key concepts focused on African American women bloggers: Brydges and Sj00F6holm’s concept of personal style blogger, Martinez Dy et al’s concept of women digital entrepreneurs, and Gabriel’s concept of Black female identity online. Data were gathered using 9 virtual semi-structured interviews and analyzed using the modified Van Kaam method. Eight themes emerged when answering the research question. The findings of the research showed that being an African American woman blogger means conducting entrepreneurial activity, working towards financial solvency, being proud of racial identity, and creating and delivering content as a blogger. Results gleaned from this transcendental phenomenological study may help promote social change by bringing awareness to policymakers on the issues of equity, access, and opportunity for marginalized populations who seek to become digital entrepreneurs.

<div>The manufacturing industry has begun its transition into a digital age, where data-driven decisions aim to improve product quality, output, and efficiency. Decisions made based on manufacturing data can help identify key problem areas in an assembly line and mitigate any defects from progressing through to the next step in the assembly process. But what if the products’ as manufactured data was inaccurate or didn’t exist at all? Decisions based on incorrect data can lead to defective parts being passed as good parts, costing manufacturers millions of dollars in rework or recalls. When specifically referring to mechanically fastened assemblies, products that experience rotation, like an aircraft propeller, or compress to create a seal, like an oil pipe flange, all require specific torque pattern sequences to be followed during assembly. When incorrectly torqued, the parts can have catastrophic failures resulting in consumer injury or ecological contamination. This paper outlines the development and feasibility of a system and its components for tracking and error-proofing the assembly of bolted joints in an industrial environment.</div><div>Using a machine vision system, the system traces the tool location relative to the mechanical fastener and records which order the fasteners were torqued in, if an error is detected, the system does not allow the user to progress through the assembly process, notifying if an error is detected. The system leverages open source machine learning algorithms from TensorFlow2 and OpenCv, that allow efficient object detection model training. The proposed system was tested using a series of tests and evaluated using the STEP method. The data collected aims to understand the system's feasibility and effectiveness in an industrial setting. </div><div>The tests aim to understand the effectiveness of the system under standard and variable industrial work conditions. Using the STEP method and other statistical analysis, an evaluation matrix was completed, ranking the system's ability to successfully meet all predetermined benchmarks and successfully record the torque pattern used to assemble apart</div>