Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology–Organisation–Environment (TOE) Framework

Factors Influencing Willingness to Continue Using Online Sports Videos: Expansion Based on ECT and TPB Theoretical Models

Digital sports, also known as online sports, are a new form of sport that users have tried in recent years. Despite the rapid growth of online sports, the factors influencing users' willingness to sustain their use are currently unknown. Based on the theory of planned behavior (TPB) and expectation-confirmation theory (ECT), this study empirically investigates the factors influencing the persistent use of online sports videos. Questionnaires were administered to participants. A total of 305 participants completed the questionnaire. Structural equation modeling showed that all hypotheses' paths were significant, except for H11 and H12. The results indicated that perceived usefulness, expectation confirmation, and coach social presence had a significant positive effect on users' satisfaction in using online sports videos. Moreover, satisfaction, behavioral attitudes, subjective norms, and perceived behavioral control had a significant positive effect on users' willingness to consistently use online sports videos, with gender moderating the impact of satisfaction and behavioral attitudes on the willingness to consistently use. We discuss the practical implications and recommendations for applying this study's findings.

Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective

AI-powered chatbots hold great promise for enhancing learning experiences and outcomes in today's rapidly evolving education system. However, despite the increasing demand for such technologies, there remains a significant research gap regarding the factors influencing users' acceptance and adoption of AI-powered chatbots in educational contexts. This study aims to address this gap by investigating the factors that shape users' attitudes, intentions, and behaviors towards adopting ChatGPT for smart education systems. This research employed a quantitative research approach, data were collected from 458 of participants through a structured questionnaire designed to measure various constructs related to technology acceptance, including perceived ease of use, perceived usefulness, feedback quality, assessment quality, subject norms, attitude towards use, and behavioral intention to use ChatGPT. Structural model analysis (SEM) Statistical techniques were then utilized to examine the relationships between these constructs. The findings of the study revealed that Perceived ease of use and perceived usefulness emerged as significant predictors of users' attitudes towards ChatGPT for smart education. Additionally, feedback quality, assessment quality, and subject norms were found to positively influence users' behavioral intentions to use ChatGPT for smart educational purposes. Moreover, users' attitudes towards use and behavioral intentions were significantly proved for the actual adoption of ChatGPT. However, a few hypotheses, such as the relationship between trust in ChatGPT and perceived usefulness, were not supported by the data. This study contributes to the existing body information systems applications for the determining factor of technology acceptance in smart education context.

A critical review of the feasibility of emerging technologies for improving safety behavior on construction sites

INTRODUCTION

Advancements in the modern construction industry have contributed to the development of a range of technology-based interventions to improve the safety behavior of front-line construction workers. Notwithstanding the extensive research on safety behavior, there is still a paucity of research on assessing technology interventions of safety behavior to provide an overview of their strengths and limitations. The present study aims to bridge this gap in the literature and identify the main trends of research.

METHOD

A systematic review and critical content analysis are adopted to capture an overview of the state of knowledge on safety behavior technologies. As a result of searching Scopus, Web of Science, and Google Scholar databases in the period from Jan 2010 to Feb 2023, a total of 359 potential studies went through the systematic screening process and finally, 48 representative studies were selected followed by an assessment of the feasibility and applicability of the safety behavior technologies.

RESULTS

It was found that safety behavior technology is characterized by seven technologies including virtual-reality simulation (T1), eye-tracking technology (T2), prediction modeling of safety behavior (T3), computer-based training (T4), drone/sensor-based hazard monitoring (T5), vision-based behavior monitoring (T6), and real-time positioning (T7).

CONCLUSIONS

This research improves understanding of the status of safety behavior technologies and provides a critical review of their feasibility from the perspective of four assessment criteria including application, limitation, benefit, and feasibility.

PRACTICAL APPLICATIONS

The categorizations of technologies add value to the body of knowledge in terms of generic requirements for their implementation and adaptation on construction sites.

Integrating the technology acceptance model for social media-based learning with authentic leadership development: symmetric and asymmetric modeling

Introduction

The growing trend of social media use has influenced all segments of society, including education, during the COVID-19 pandemic. At the same time, authentic leadership is an emerging concept in positive psychology for dealing with emergencies in the digital era. The possibility of a relationship between the two was checked in preservice teachers studying in a teacher education department of a university in Pakistan. The conceptual framework of the study was built around teaching acceptance model (TAM) and Authentic leadership theory.

Methods

The survey method suited the aim of the research, and questionnaires aided us in gathering participant self-reporting responses. We conducted two surveys, and in between them, a course was taught online using social media as a teaching and learning platform. The survey results checked preservice teachers' positive intentions toward social media-based learning, and the post-course survey studied the development of Authentic leadership attributes among the preservice teachers. Technology acceptance was measured across three constructs: ease of use, usefulness, and positive intentions. The results also reported the development of three authentic leadership characteristics: ethics, self-regulation, and self-awareness.

Results and discussion

This study is among the pioneering studies integrating TAM (i.e., the acceptance of social media-based learning) with leadership theory (i.e., authentic leadership). It also adds a methodological contribution by combining symmetrical (i.e., partial least squares structural equation modeling) and asymmetrical (i.e., fuzzy set qualitative comparative technique) for data analysis. The study's findings are valuable for teacher education institutions, as they help prepare future teachers to become authentic leaders capable of addressing future crises by leveraging education through social media-based teaching and learning platforms.

Investigating the impact of artificial intelligence on human resource functions in the health sector of China: A mediated moderation model

Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in the health sector of China. This study aims to scrutinize the impact of artificial intelligence on the human resource functions operating in the healthcare sector through technological awareness, social media influence, and personal innovativeness. Additionally, this study examines the moderating role of perceived risk between technological awareness and human resources functions. An online questionnaire was administered to human resources professionals in the health sector of China to gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, is implemented to investigate the hypothesis of the projected model of artificial intelligence and human resource functions. The research findings reveal that artificial intelligence significantly influences human resource functions through technological awareness, social media influence, and personal innovativeness. Furthermore, perceived risk significantly moderates the relationship between technological awareness and human resource functions. The findings of this study have important implications for HR practitioners and policymakers in the health sectors of China, who can leverage artificial intelligence technologies to optimize and improve organizational performance. However, its adoption needs to be carefully planned and managed to reap the full benefits of this transformative technology.

Clearing the way for participatory data stewardship in artificial intelligence development: a mixed methods approach

Participatory data stewardship (PDS) empowers individuals to shape and govern their data via responsible collection and use. As artificial intelligence (AI) requires massive amounts of data, research must assess what factors predict consumers' willingness to provide their data to AI. This mixed-methods study applied the extended Technology Acceptance Model (TAM) with additional predictors of trust and subjective norms. Participants' data donation profile was also measured to assess the influence of individuals' social duty, understanding of the purpose and guilt. Participants (N = 322) completed an experimental survey. Individuals were willing to provide data to AI via PDS when they believed it was their social duty, understood the purpose and trusted AI. However, the TAM may not be a complete model for assessing user willingness. This study establishes that individuals value the importance of trusting and comprehending the broader societal impact of AI when providing their data to AI.Practitioner summary: To build responsible and representative AI, individuals are needed to participate in data stewardship. The factors driving willingness to participate in such methods were studied via an online survey. Trust, social duty and understanding the purpose significantly predicted willingness to provide data to AI via participatory data stewardship.

Assessing Digital Transformation of Cost Accounting Tools in Healthcare

The expansion of digital technologies has significantly changed most economic activities and professions. Digital technologies penetrated managerial accounting and have a vast potential to transform this profession. Implementing emerging digital technologies, such as artificial intelligence, blockchain, the Internet of Things, big data, and cloud computing, can trigger a crucial leap forward, leading to a paradigm-shifting in healthcare organizations' accounting management. The paper's main objective is to investigate the perception of Romanian accountants on implementing digital technologies in healthcare organizations' accounting management. The paper implies a study based on a questionnaire among Romanian accountants who use various digital technologies implemented in traditional and innovative cost accounting tools. Based on structural equation modeling, the results emphasize the prevalence of innovative tools over traditional cost accounting tools improved through digital transformation, digital technologies assuming the most complex and time-consuming tasks. Moreover, the influence of cost accounting tools improved through digital transformation on healthcare organizations' performance is much more robust in the case of innovative tools than in the case of traditional cost accounting tools. The proposed model provides managers in healthcare organizations with information on the most effective methods in the context of digital transformation.

Explaining the effect of artificial intelligence on the technology acceptance model in media: a cloud computing approach

Purpose

The purpose of this paper is to explain the effect of the technology acceptance model in the media environment by using the mediating role of artificial intelligence and the cloud computing approach.

Design/methodology/approach

After reviewing the theoretical foundations, a conceptual model framework and research hypotheses were formed. The statistical population of the study included managers, deputies and experts from the National Iranian Oil Company, and a statistical sample of 368 people was selected by simple random sampling.

Findings

The results of structural equation modelling with PLS 2.0 software show a positive and significant effect on the artificial intelligence variable in the technology acceptance model with the cloud approach. Artificial intelligence has opened a new space in the digital world, especially in the media, so that its profound impact is quite evident and has affected people’s lives.

Originality/value

The acceleration of various technologies has severely challenged the approach of organizations, especially the media. The media environment with word of the technologies of the Industry 4.0, especially cloud computing technology, has changed the ways of accessing and using products and services.

Analysis of the Conditions Influencing the Assimilation of the Robotic Process Automation by Enterprises

More and more companies are implementing the RPA (Robotic Process Automation) tools that belong to the newly emerging category of IT solutions used to automate business processes and enable the development of the so-called software robots. The term robot has a metaphorical meaning here – it is a special kind of software, not a device. Due to the fact that this is a new product category and many companies do not have extensive experience in this area yet, the use of the RPA tools is associated with many risks. At the same time, due to the increasingly competitive environment, it seems that there is no turning back from the implementation thereof. For this reason, two goals have been set in the article. The first is to build and verify a research model based on the TOE model (Technology-Organization-Environment), allowing for the identification of the determinants (drivers) influencing the assimilation of the robotic process automation by enterprises. The second goal is to develop recommendations for the managers responsible for implementing the RPA tools that will allow for increasing the assimilation of the robotic process automation. The following methods were used to accomplish these goals: literature research, a survey (conducted on 267 Polish enterprises) and statistical analysis (with the use of the structural equation models).

Technological Acceptance of Industry 4.0 by Students from Rural Areas

In this study, our objective was to identify the factors that explain the acceptance of Industry 4.0 technologies by technical students. Industry 4.0 is made up of a series of technologies, such as the Internet of Things; cyber-physical systems; big data, data analytics, or data mining; cloud computing or the cloud; augmented reality or mixed reality; additive manufacturing or 3D printing; cybersecurity; collaborative robots; artificial intelligence; 3D simulation; digital twin or digital twin; drones. We designed a theoretical model based on the technology acceptance model to explain the acceptance of these technologies. The study was carried out on a sample of 326 technical professional students. Students are considered ideal samples to test theoretical predictions regarding the relationships between variables in emerging technologies. The results show the positive effect of technological optimism on perceived usefulness and ease of use. However, there was not a direct effect on the attitude towards the use. A mediating effect was established. In addition, the facilitating conditions influence optimism and the ease of using the technology. These elements influence the attitude and intention to use, which is consistent with previous studies on technology acceptance. The results will guide the design of public policies to incorporate technologies into education.