International Journal of Human-Computer Interaction, Год журнала: 2024, Номер unknown, С. 1 - 16
Опубликована: Дек. 12, 2024
Язык: Английский
Heliyon, Год журнала: 2024, Номер unknown, С. e37569 - e37569
Опубликована: Сен. 1, 2024
Язык: Английский
Процитировано
9
PLoS ONE, Год журнала: 2025, Номер 20(1), С. e0313837 - e0313837
Опубликована: Янв. 9, 2025
This research explores the determinants affecting academic researchers’ acceptance of AI writing tools using Theory Reasoned Action (TRA). The impact attitudes, subjective norms, and perceived barriers on intentions to adopt these technologies is examined through a cross-sectional survey 150 researchers. Structural Equation Modeling (SEM) employed evaluate measurement structural models. Findings confirm positive influence favorable attitudes norms use tools. Interestingly, did not significantly or intentions, suggesting that in context, potential benefits may outweigh obstacles tool adoption. Contrarily, do affect directly. TRA model demonstrates considerable explanatory predictive capabilities, indicating its effectiveness understanding adoption among study’s diverse sample across various disciplines career stages provides insights be generalizable similar contexts, though further with larger samples needed broader applicability. Results offer practical guidance for developers, institutions, publishers aiming foster responsible efficient academia. suggest strategies such as demonstrating clear productivity gains, establishing Writing Tool programs, developing comprehensive training initiatives could promote Strategies focusing cultivating leveraging social influence, addressing particularly effective promoting pioneering study investigates technology model, contributing professional contexts highlighting importance field-specific factors examining behaviors.
Язык: Английский
Процитировано
1
Systems Research and Behavioral Science, Год журнала: 2025, Номер unknown
Опубликована: Фев. 18, 2025
ABSTRACT This study looks at perceptions of artificial intelligence (AI) systems in human resources (HR) management within Swiss organizations. Based on a survey experiment provided to 324 private and public HR professionals, it explores how UTAUT's predictors—performance expectancy, effort social influence facilitating conditions—as well as top support, the Private/Public dimension control variables—age, gender, time with organization hierarchical position—influence their acceptability four different type AI tools. To do this, this article is based multiple regression method. Its main findings are that, irrespective tool, performance expectancy positively tools studied, whereas working has systematically negative influence. makes significant contribution literature by offering valuable insights into these factors collectively shape willingness professionals embrace technologies practices. It also offers an overview levers that organizations aiming adopt could act upon.
Язык: Английский
Процитировано
1
Sustainability, Год журнала: 2024, Номер 16(9), С. 3554 - 3554
Опубликована: Апрель 24, 2024
The analysis of students’ attitudes and perceptions represents a basis for enhancing different types activities, including teaching, learning, assessment, etc. Emphasis might be placed on the implementation modern procedures technologies, which play an important role in process digital transformation. Among them is artificial intelligence—a technology that has already been found to applicable various sectors. When it comes education, several AI-based tools platforms can used by students teachers. Besides offering customized learning experiences, AI may significant part establishing concept sustainability, especially when concerning achievement sustainable development goal 4. This paper investigates intention use intelligence taking three predictors from UTAUT model awareness as moderator. included Autonomous Province Vojvodina, Republic Serbia. For purpose research, partial least squares structural equation modeling (PLS-SEM) method was applied. Hereby, two models (without with moderator) were tested examine main moderating effects, respectively. Regarding results, while interaction terms non-significant, impacts performance expectancy, effort social influence behavioral positive.
Язык: Английский
Процитировано
5
SSRN Electronic Journal, Год журнала: 2024, Номер unknown
Опубликована: Янв. 1, 2024
The integration of generative artificial intelligence (AI) models, particularly ChatGPT, into resilience represents a significant advancement in how societies can adapt to and thrive amid various challenges. This study explores the diverse applications ChatGPT enhancing across multiple sectors, including climate resilience, urban business continuity, cybersecurity, community social supply chain management, hospitality tourism, psychological infrastructure, disaster response, environmental ecological healthcare. improves communication, supports policy development, provides real-time data analysis aiding effective decision-making preparedness. In AI optimizes resource predicts hazards, engages communities, city resilience. For continuity crisis offers tools for risk assessment, ongoing learning, ensuring organizational robustness during disruptions. also plays crucial role by facilitating supporting mental health, improving response. ChatGPT's capabilities threat detection incident management significantly strengthen defenses. healthcare sector benefits from through remote patient monitoring, predictive analytics disease outbreaks, telehealth support, resilient systems. paper highlights transformative potential technologies like building more adaptive, knowledgeable, interconnected global community. These AI-driven solutions not only address immediate challenges but provide long-term strategies sustainable development improved quality life, underscoring essential fostering overall societal
Язык: Английский
Процитировано
5
Education and Information Technologies, Год журнала: 2024, Номер unknown
Опубликована: Ноя. 22, 2024
Язык: Английский
Процитировано
5
Applied Sciences, Год журнала: 2025, Номер 15(2), С. 746 - 746
Опубликована: Янв. 14, 2025
The integration of new technologies in professional contexts has emerged as a critical determinant organizational efficiency and competitiveness. In this regard, the application Artificial Intelligence (AI) recruitment processes facilitates faster more accurate decision-making by processing large volumes data, minimizing human bias, offering personalized recommendations to enhance talent development candidate selection. Technology Acceptance Model (TAM) provides valuable framework for understanding recruiters’ perceptions innovative technologies, such AI tools GenAI. Drawing on TAM, model was developed explain intention use tools, proposing that perceived ease usefulness influence attitudes toward AI, which subsequently affect selection processes. Two studies were conducted Portugal address research objective. first qualitative exploratory study involving 100 interviews with recruiters who regularly utilize their activities. second employed quantitative confirmatory approach, utilizing an online questionnaire completed 355 recruiters. findings underscored transformative role recruitment, emphasizing its potential optimize resource management. However, also highlighted concerns regarding loss personal interaction need adapt roles within domain. results supported indirect effect via positive these tools. This suggests is best positioned complementary tool rather than replacement decision-making. insights gathered from perspectives provide actionable organizations seeking leverage Specifically, show importance ethical considerations maintaining involvement ensure balanced effective
Язык: Английский
Процитировано
0
International Journal of Educational Technology in Higher Education, Год журнала: 2025, Номер 22(1)
Опубликована: Март 13, 2025
Abstract Artificial intelligence (AI) is ushering in an era of potential transformation various fields, especially educational communication technologies, with tools like ChatGPT and other generative AI (GenAI) applications. This rapid proliferation adoption GenAI have sparked significant interest concern among college professors, who are dealing evolving dynamics digital within the classroom. Yet, effect implications education remain understudied. Therefore, this study employs Technology Acceptance Model (TAM) Social Cognitive Theory (SCT) as theoretical frameworks to explore higher faculty’s perceptions, attitudes, usage, motivations, underlying factors that influence their or rejection tools. A survey was conducted full-time faculty members ( N = 294) recruited from two mid-size public universities US. Results found professors’ perceived usefulness predicted attitudes intention use adopt technology, more than ease use. Trust social reinforcement strongly influenced decisions acted mediators better understand relationship between TAM SCT. Findings emphasized power shaping self-efficacy, GenAI. enhances peer affects how shapes users’ willingness whereas self-efficacy has a minimal impact. research provides valuable insights inform policies aimed at improving experience for students AI-driven workforce.
Язык: Английский
Процитировано
0
Tourism Management, Год журнала: 2025, Номер 110, С. 105179 - 105179
Опубликована: Март 31, 2025
Язык: Английский
Процитировано
0
Journal of Family Business Management, Год журнала: 2024, Номер unknown
Опубликована: Окт. 10, 2024
Purpose To describe how decision-making in the selection processes of managerial successors business families is influenced by use cutting-edge technologies such as AI. Design/methodology/approach Systematic literature review 65 articles indexed Scopus and main specialized journals on family businesses. Findings The integration AI algorithms, specifically procedures, raises major questions faces legal ethical issues that affect employee performance, moral commitment fairness processes. These aspects are important to ensure transparency, accountability they provide insight into practices succession challenges possibility using signaling games addressing gender biases information asymmetries have been reported past research could be complemented these actions. Research limitations/implications limitations this mainly attributed exclusive a single database (Scopus), which limit access relevant literature; Furthermore, exclusion certain articles, despite focusing prestigious families, may overlooked contributions; 20-year scope ended February August 2024 omits subsequent publications enriched findings study. Originality/value best author’s knowledge, study first its kind conduct bibliometric analysis covering line successor process leveraged new AI, an aspect has little addressed literature. In addition, work traces selection. great value since it allows illustrate consistent way relationship between executive affected different identify gaps make strategic decisions regarding management successions BFs. provides framework for future area.
Язык: Английский
Процитировано
3