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Concrete workers perform physically demanding work in awkward postures, exposing their backs to musculoskeletal disorders. Back-support exoskeletons are promising ergonomic interventions designed to reduce the risks of back disorders. However, the suitability of exoskeletons for enhancing performance of concrete workers has not been largely explored. This study aims to assess a passive back-support exoskeleton for concrete work in terms of the impact on the body, usability and benefits of the exoskeleton, and potential design modifications.

Concrete workers performed work with a passive back-support exoskeleton. Subjective and qualitative measures were employed to capture their perception of the exoskeleton, at the middle and end of the work, in terms of discomfort to their body parts, ease of use, comfort, performance and safety of the exoskeleton, and their experience using the exoskeleton. These were analyzed using descriptive statistics and thematic analysis.

The exoskeleton reduced stress on the lower back but caused discomfort to other body parts. Significant correlations were observed between perceived discomfort and usability measures. Design modifications are needed to improve the compatibility of the exoskeleton with the existing safety gears, reduce discomfort at chest and thigh, and improve ease of use of the exoskeleton.

The study was conducted with eight concrete workers who used the exoskeleton for four hours.

This study contributes to existing knowledge on human-wearable robot interaction and provides suggestions for adapting exoskeleton designs for construction work.

This study aims to analyze corporate mail and wire fraud penalties, using bounded rationality in decision-making and assessing internal and external influences on prosecutorial choices.

The study analyzed 467 cases from 1992 to 2019, using data from the Corporate Prosecution Registry of the University of Virginia School of Law and Duke University School of Law. It examined corporations facing mail and wire fraud charges and other fraud crimes. Multiple regression linked predictor variables to the dependent variable, total payment.

The study found that corporate penalties tend to be lower for financial institutions or corporations in countries with US free trade agreements. Conversely, penalties are higher when the company is a U.S. public company or filed in districts with more pending criminal cases.

This study’s originality lies in applying the bounded rationality model to assess corporate prosecutorial decisions, unveiling external factors’ influence on corporate penalties.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

Low back disorder is one of the most prevalent and costly injuries in the construction industry. Back-support exoskeletons are increasingly perceived as promising solutions. However, the intended benefits of exoskeletons may not be realized if intention-to-use the device is low. Social influence could increase intention-to-use exoskeletons. This study aims to evaluate the impact of social influence on construction workers' intention-to-use back-support exoskeletons.

A field study involving 37 construction workers was conducted, with workers who used exoskeleton for one week, and their peers and supervisors. Data were collected using questionnaires and semi-structured interviews, and analyzed using descriptive statistics and thematic analysis, respectively.

The workers felt that the exoskeleton is easy to use and the functions are well integrated. Workers' intention-to-use exoskeleton was mainly influenced by employers providing and requiring the use of the device. The attitude of the workers and the perception of peers and supervisors did not have a significant impact on workers' intention-to-use exoskeleton, whereas the subjective norm of construction workers had a positive impact on the intention-to-use exoskeletons.

The study involved only 37 workers, including 15 workers who used the exoskeleton, and 14 peers and 8 supervisors of the workers.

This study contributes to existing knowledge on the influence of social influence on intention-to-use exoskeletons. The study also highlights how exoskeleton designs and the construction workplace can influence behavioral intention-to-use exoskeletons.

There is a growing mismatch between the skill demands of the industry and the offerings of academia. One way of reducing this mismatch is by improving collaborations between practitioners and instructors using web-networking platforms. However, it is important to understand practitioners’ considerations while collaborating with instructors. Therefore, this study identified these considerations in order to infer inputs for the design of the graphical user interface (GUI) of a web-based platform for connecting instructors and practitioners.

A mixed method was adopted through a survey and focus group. A survey was used to capture practitioners’ considerations while collaborating with instructors for student development, and a focus group helped uncover an in-depth understanding of the study phenomena. The data were analyzed using descriptive and inferential statistics and thematic analysis.

The results show the willingness of practitioners to collaborate with instructors for student development, the ways by which practitioners are willing to meet instructors' course-support needs and their considerations in deciding to do so. Slight differences were observed between the results of the survey and the focus group regarding the ranking of the practitioners’ considerations. The study highlighted demographic differences in practitioners’ considerations when deciding on meeting instructors' course-support needs. The results provide a basis to deduce the GUI inputs of web-networking platforms for connecting instructors and practitioners.

This study revealed practitioners’ design needs and GUI inputs to facilitate the design of web-networking platforms for connecting instructors and practitioners. This study also contributes to user interface design principles, theories on individual differences and practitioners’ involvement in student professional development.

As video-based interventions are continuously utilized as alternatives to physical site visits, directing students' attention to specific learning contents within videos could increase their comprehension and stimulate their interest. Students' knowledge of construction practice can be reinforced, misconceptions and improper inferences can be reduced by calling out significant learning concepts. However, few studies have formalized practice concepts that could be beneficial in preparing students for the workplace. This paper presents an investigation of construction practice concepts, based on site visits that would be beneficial in complimenting classroom teaching to prepare students for the realities of practice.

A mixed methods research approach was employed combining qualitative and quantitative data collection and analysis. An online questionnaire, semi-structured interviews and a focus group were conducted with industry practitioners and instructors to identify the topics and practice concepts significant for supporting classroom teaching with site visits.

The findings suggest that the most relevant topics typically supported with site visits are preconstruction management, excavation and foundation work, construction equipment, construction means and methods, project management, road construction, sustainability, building systems, structures, construction technology, building construction, capstone, site logistics and safety. Practice concepts were identified for each of these topics.

The study will guide researchers in the design of video-based pedagogical tools to be used as an effective complement of or alternative to site visit experiences. The findings will support instructors on how to structure their teaching practices to prepare students for some of the complexities of the workplace.

This study adds value to the existing literature by providing insights into industry perception of practice concepts for complementing classroom teaching.

Recognizing construction workers' activities is critical for on-site performance and safety management. Thus, this study presents the potential of automatically recognizing construction workers' actions from activations of the erector spinae muscles.

A lab study was conducted wherein the participants (n = 10) performed rebar task, which involved placing and tying subtasks, with and without a wearable robot (exoskeleton). Trunk muscle activations for both conditions were trained with nine well-established supervised machine learning algorithms. Hold-out validation was carried out, and the performance of the models was evaluated using accuracy, precision, recall and F1 score.

Results indicate that classification models performed well for both experimental conditions with support vector machine, achieving the highest accuracy of 83.8% for the “exoskeleton” condition and 74.1% for the “without exoskeleton” condition.

The study paves the way for the development of smart wearable robotic technology which can augment itself based on the tasks performed by the construction workers.

This study contributes to the research on construction workers' action recognition using trunk muscle activity. Most of the human actions are largely performed with hands, and the advancements in ergonomic research have provided evidence for relationship between trunk muscles and the movements of hands. This relationship has not been explored for action recognition of construction workers, which is a gap in literature that this study attempts to address.

Both the SEC (Securities and Exchange Commission) and the popular press have routinely criticized firms for the complexity of their financial disclosures. This study aims to investigate how financial analysts respond to the tone complexity of firm disclosures.

Using approximately 20,000 earnings conference call transcripts of S&P 1,500 firms between 2005 and 2015, the authors first calculate the abnormal negative tone, the measure of tone complexity; then use such tone measure in econometric models to examine analyst forecast behavior. The authors also test the robustness of the results under different model specifications, tone word lists and alternative tone measure calculations.

Consistent with the notion that analysts respond to the information demand from investors and incur more costs and effort to analyze firm disclosure when the tone is more complex, the authors find that higher tone complexity is positively and significantly associated with more analyst following, longer report duration, more forecast revisions, larger forecast error and larger forecast dispersion. In addition, the authors find that tone complexity has a long-term impact on analyst following but has a limited long-term impact on analyst report duration, analyst revision, forecast error and dispersion.

This study complements existing literature by highlighting the information role of financial analysts and by providing evidence that analysts incorporate the management tone disclosed during conference calls to adjust their forecasting behaviors. The results can be used by policymakers as evidence and support for further improving firm communication from a new dimension of disclosure tone.

There are a variety of benefits associated with the use of YouTube for learning purposes, such as YouTube is a free open-access tool students can use to facilitate their learning. This study investigates whether an attitudinal factor (i.e. perceived usefulness) and the factor's antecedents, resource quality factors (i.e. credibility, currency, coverage and relevance), normative factor (i.e. subjective norm) and control factor (i.e. perceived ease of use) all affect college freshmen's behavioral intentions to use YouTube for academic learning purposes.

This research employs the theory of planned behavior (TPB) to explore the attitudinal, normative and control factors associated with college freshmen's behavioral intentions to use YouTube for academic learning. After developing a quantitative survey given to 182 college freshmen in a Southeastern institution in the United States of America, structural equation modeling (SEM) was used to examine the seven hypotheses and the research constructs.

The results indicate that attitudinal factor (i.e. perceived usefulness) and its antecedents, resource quality factors (i.e. currency, coverage and relevance) and normative factor (i.e. subjective norm) have a statistically significant effect on college freshmen's intentions to use YouTube for academic learning purposes.

This study suggests that individual motivations (i.e. perceived usefulness and subjective norm) and resource quality factors (i.e. currency, coverage and relevance) play into college freshmen's decisions to use YouTube for learning purposes, while other research indicates that the system or application itself factors into students' decisions to use technology for learning.

This study suggests that college freshmen are more likely to use YouTube for academic learning purposes when the freshmen hold favorable attitudes about the platform and when the freshmen believe the freshmen's peers are also using YouTube to supplement in-class learning.

This is an initial study that focuses on college freshmen's behavioral intentions to use YouTube for academic learning purposes. This research demonstrates the roles that peers as well as resource quality factors play in students' decisions to use specific technology to enhance the students' learning.

Construction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.

The study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.

The classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.

The findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.

The classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.