Impact of Artificial Intelligence Adoption on the Psychological Contract and Job Satisfaction of Chinese Employees - The Moderator Role of Industry Characteristics DOI Creative Commons

J. R. Wang

Interdisciplinary Humanities and Communication Studies, Год журнала: 2024, Номер 1(6)

Опубликована: Апрель 16, 2024

From the perspective of Chinese employees, this study delves into evolving employment relationship amidst digital transformation, specifically examining impact AI on job satisfaction and psychological contracts. Utilizing an online survey, data was collected from 321 subsequent statistical analysis gathered metrics evaluated foundations behavioral outcomes associated with integration in workplace. The findings reveal that, although implementation positively correlates reinforcement contracts, existence transformational leadership tends to attenuate positive correlation.

Язык: Английский

Artificial intelligence and employee outcomes: Investigating the role of job insecurity and technostress in the hospitality industry DOI

Muhammad Hammad Sharif,

Zhang Li,

Muhammad Asif

и другие.

Acta Psychologica, Год журнала: 2025, Номер 253, С. 104733 - 104733

Опубликована: Янв. 17, 2025

Язык: Английский

Процитировано

5

Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors DOI Creative Commons
Julia Stefanie Roppelt, Dominik K. Kanbach, Sascha Kraus

и другие.

Technology in Society, Год журнала: 2023, Номер 76, С. 102443 - 102443

Опубликована: Дек. 9, 2023

The purpose of this review is integrating and contextualizing relevant literature on the factors influencing adoption AI in healthcare industry into a comprehensive framework. Health systems are considered fundamental to creating societal value. However, global health challenged by increasing number patients due population aging growing prevalence chronic diseases cancer. Meanwhile, United Nations calls for equal access healthcare, tackling costs, addressing resource constraints foster sustainable development societies. In context, artificial intelligence (AI) gaining attention as it constitutes promising technology address these burgeoning challenges. Despite opportunities, specifically fragmented across various research fields, lacking overview. It lacks theoretically grounded integrating, example, that influence institutions. Derived from multi-disciplinary systematic review, building 130 studies, we propose Adoption Healthcare Industry Model. This model encompasses five dimensions contextualizes them. We macro-economic, regulatory, technological readiness serve external antecedents whereas organizational individual constitute internal Our has implications acceptance related healthcare. Further, provide hands-on guidance providers, institutions, official bodies such governments leverage

Язык: Английский

Процитировано

43

The knowledge and innovation challenges of ChatGPT: A scoping review DOI
Omar Ali, Peter Murray, Mujtaba M. Momin

и другие.

Technology in Society, Год журнала: 2023, Номер 75, С. 102402 - 102402

Опубликована: Окт. 21, 2023

Язык: Английский

Процитировано

42

AI in knowledge sharing, which ethical challenges are raised in decision-making processes for organisations? DOI
Mojtaba Rezaei,

Marco Pironti,

Roberto Quaglia

и другие.

Management Decision, Год журнала: 2024, Номер unknown

Опубликована: Апрель 24, 2024

Purpose This study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices their implications for decision-making (DM) processes within organisations. Design/methodology/approach The employs a mixed-methods approach, beginning comprehensive literature review extract background information on AI KS potential challenges. Subsequently, confirmatory factor analysis (CFA) is conducted using data collected from individuals employed business settings validate identified impact DM processes. Findings findings reveal that related privacy protection, bias fairness transparency explainability are particularly significant DM. Moreover, accountability responsibility of employment also show relatively high coefficients, highlighting importance process. In contrast, such as intellectual property ownership, algorithmic manipulation global governance regulation found be less central Originality/value research contributes ongoing discourse knowledge management (KM) By providing insights recommendations researchers, managers policymakers, emphasises need holistic collaborative approach harness benefits technologies whilst mitigating risks.

Язык: Английский

Процитировано

12

Applying Artificial Intelligence to Promote Sustainability DOI Open Access

Miriam Du-Phuong Ta,

Stefan Wendt, Þröstur Olaf Sigurjónsson

и другие.

Sustainability, Год журнала: 2024, Номер 16(12), С. 4879 - 4879

Опубликована: Июнь 7, 2024

This study reviews the application of artificial intelligence (AI) throughout food value chain and how it can be leveraged to help companies become more sustainable. A literature review across different parts was conducted provide an overview main themes current future AI applications industry. Moreover, paper focuses on benefits challenges change management when integrating AI. documentary Systematic Review using PRISMA research find analyze aforementioned applications. The key insight is that progress varies significantly. Today’s are primarily found within inspection quality assurance due relatively straightforward in chain. Such technology mainly image-based. Companies use interconnectedness sustainability by becoming efficient through simultaneously saving emissions resources optimizing processes.

Язык: Английский

Процитировано

11

A consumer acceptance model in the artificial intelligence era DOI
Paritosh Pramanik, Rabin K. Jana

Management Decision, Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

Purpose This paper identifies consumer acceptance criteria of artificial intelligence (AI)-enabled products and services in the business. We first investigate existing three models. They are technology model (TAM), unified theory use (UTAUT) (CAT). then discuss applicability these models for AI-enabled services. Finally, we outline shortcomings propose an product service (AIEPSAM). also validate proposed AIEPSAM with empirical results using primary survey data. Design/methodology/approach To understand customer’s point view on AI applications services, identify some critical factors present a conceptual framework consumers' based literature, prior research prominent management theories. Then, study broadens horizon beyond established principles associated to accommodate AI-specific factors/variables like data privacy, explainability apparent opacity algorithms. In this paper, that Findings argue although TAM, UTAUT CAT generally applicable explain attitudes towards technology, alone insufficient encompass entire spectrum AI-related issues must not be ignored. The model, namely AIEPSAM, accommodates limitations modifies make it suitable technology. Originality/value attempt articulate discover useful insights, leading examination formulating validation through is criticize TAM other but incorporate into those Through study, required modifications considering additional factors. will assist companies building better understanding emergence (TE) opportunities (TO).

Язык: Английский

Процитировано

2

Understanding GAI risk awareness among higher vocational education students: An AI literacy perspective DOI

Helen Wu,

Dantong Li,

Xiaolan Mo

и другие.

Education and Information Technologies, Год журнала: 2025, Номер unknown

Опубликована: Янв. 27, 2025

Язык: Английский

Процитировано

1

Artificial intelligence in live streaming: How can virtual streamers bring more sales? DOI
Yaping Chang, Han Wang, Zitao Guo

и другие.

Journal of Retailing and Consumer Services, Год журнала: 2025, Номер 84, С. 104247 - 104247

Опубликована: Фев. 3, 2025

Язык: Английский

Процитировано

1

Automation and Its Influence on Sustainable Development: Economic, Social, and Environmental Dimensions DOI Open Access
Ahlam Almusharraf

Sustainability, Год журнала: 2025, Номер 17(4), С. 1754 - 1754

Опубликована: Фев. 19, 2025

This study investigates the complex duality of automation and its impact on sustainable development, encompassing factors economic growth, social equity, environmental sustainability. Innovations in artificial intelligence, robotics, machine learning are driving transforming industries through improved production, operational efficiency, resource optimization. However, rapid integration has created a paradox. While it offers opportunities for optimization technological advancement, exacerbates challenges such as income inequality, degradation, displacement. These issues underline need balanced inclusive approaches to automation’s implementation. Automation contributes substantively GDP growth because raises labor productivity, yet arguably enhanced inequality by eliminating low-skilled jobs. improves energy efficiency aids renewable but increases overall effectiveness, leading concerns regarding ecological applied quantitative methodology using longitudinal data from 2000 2023 regression models examine sustainability metrics influenced automation. The findings highlight potential reform effective forms manufacturing, encourage innovation, identify systemic governmental policies. Specifically, results indicate that contributed 25% increase productivity across sectors, 15% reduction intensity per unit GDP, 12% rise Gini index, signaling growing inequality. outcomes emphasize both posed By integrating advancements with goals, can act transformative instrument promote conservation, equitable justice. paper concludes recommendations governments industry leaders incorporate into development objectives, ensuring distribution advantages, while alleviating socio-environmental hazards.

Язык: Английский

Процитировано

1

Application of CHATGPT in civil engineering DOI Creative Commons
Martin Aluga

East African Journal of Engineering, Год журнала: 2023, Номер 6(1), С. 104 - 112

Опубликована: Июнь 28, 2023

Artificial Intelligence, machine learning, and the Internet of Things (IoT) are changing way tasks accomplished. CHATGPT is a well-known conversational artificial intelligence (AI) system based on generative pre-trained transformer (GPT) architecture, launched by OpenAI. trained through reinforcement learning human feedback. There advantages to use in Civil engineering, including but not limited design planning: structural analysis simulation, code compliance regulations construction management, knowledge repository information retrieval, education, research. The limitation bias datasets used training, requirement sufficient input information, as well risk transparency issues, negative consequences if generating inaccurate content. other language models civil engineering requires careful consideration ensure bypassing expert consultation particular cases. Deep Learning would have positive impact rather than replacing expertise improving infrastructure development world solving challenges facing mankind

Язык: Английский

Процитировано

23