Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions

Technological Forecasting and Social Change, Journal Year: 2023, Volume and Issue: 192, P. 122579 - 122579

Published: April 21, 2023

Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With proliferation digital computing devices emergence big data, AI increasingly offering significant opportunities for society business organizations. The growing interest scholars practitioners in has resulted diversity research topics explored bulks scholarly literature published leading outlets. This study aims map intellectual structure evolution conceptual overall Technological Forecasting Social Change (TF&SC). uses machine learning-based structural topic modeling (STM) extract, report, visualize latent from literature. Further, disciplinary patterns examined with additional objective assessing impact AI. results reveal eight key topics, out which concerning healthcare, circular economy sustainable supply chain, adoption by consumers, decision-making showing rising trend over years. influence on disciplines such as management, accounting, social science, engineering, computer mathematics. provides an insightful agenda future based evidence-based directions would benefit identify contemporary issues develop impactful solve complex societal problems.

Language: Английский

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Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research

Technological Forecasting and Social Change, Journal Year: 2022, Volume and Issue: 186, P. 122120 - 122120

Published: Nov. 6, 2022

The final search query for the Systematic Literature Review (SLR) was conducted on 15th July 2022. Initially, we extracted 1707 journal and conference articles from Scopus Web of Science databases. Inclusion exclusion criteria were then applied, 58 selected SLR. findings show four dimensions that shape AI explanation, which are format (explanation representation format), completeness should contain all required information, including supplementary information), accuracy (information regarding explanation), currency recent information). Moreover, along with automatic users can request additional information if needed. We have also found five XAI effects: trust, transparency, understandability, usability, fairness. In addition, investigated current knowledge to problematize future research agendas as questions possible paths. Consequently, a comprehensive framework its effects user behavior has been developed.

Language: Английский

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Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk

Regulation & Governance, Journal Year: 2023, Volume and Issue: 18(1), P. 3 - 32

Published: Feb. 6, 2023

Abstract In its AI Act, the European Union chose to understand trustworthiness of in terms acceptability risks. Based on a narrative systematic literature review institutional trust and public sector, this article argues that EU adopted simplistic conceptualization is overselling regulatory ambition. The paper begins by reconstructing conflation “trustworthiness” with “acceptability” Act. It continues developing prescriptive set variables for reviewing research context AI. then uses those prior sector. Finally, it relates findings EU's policy. Its prospects successfully engineer citizen's are uncertain. There remains threat misalignment between levels actual applied

Language: Английский

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Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability

International Journal of Information Management, Journal Year: 2022, Volume and Issue: 69, P. 102538 - 102538

Published: June 17, 2022

Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance and explainability. models with higher are often based on more complex therefore lack explainability vice versa. However, little to no empirical evidence of this from an end user perspective. We aim provide by conducting two experiments. Using distinct datasets, we first measure the for five common classes machine algorithms. Second, address problem perceptions explainable artificial intelligence augmentations aimed at increasing understanding logic high-performing models. Our results diverge widespread assumption curve indicate much less gradual user's perception. This stark contrast assumed inherent interpretability. Further, found be situational example due data complexity. Results our second experiment show while can used increase explainability, type explanation plays essential role

Language: Английский

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From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change

Technological Forecasting and Social Change, Journal Year: 2023, Volume and Issue: 189, P. 122381 - 122381

Published: Feb. 8, 2023

Technological Forecasting and Social Change (TFSC) is one of the most prominent journals to focus on methodologies practices technological forecasting futures studies. This study aims analyse topical structure TFSC track cited articles published in journal using a combination structural topic model (STM) bibliometric analysis. The STM reveals 18 topics TFSC, quality results verified based semantic coherence exclusivity scores as well an assessment correlations among topics. also tracks temporal variations prevalence that occurred from 1969 2022, shedding light changing popularity each topic. analysis presents decade-by-decade perspective geographical dispersion authors affiliated with thereby providing truly global journal's publishing activity.

Language: Английский

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Virtual agents and flow experience: An empirical examination of AI-powered chatbots

Technological Forecasting and Social Change, Journal Year: 2022, Volume and Issue: 181, P. 121772 - 121772

Published: May 31, 2022

Language: Английский

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Application of supportive and substitutive technologies in manual warehouse order picking: a content analysis

International Journal of Production Research, Journal Year: 2023, Volume and Issue: 62(3), P. 685 - 704

Published: Jan. 24, 2023

Order picking in warehouses is a labour- and time-intensive logistical process that significantly impacts the efficiency of supply chains. Although technical progress facilitates automation specific order tasks, human workers remain primary actors picking. Owing to high operating costs associated with manual picking, its design management have been increasingly researched for decades. Because systems are socio-technical systems, factors workers’ interaction technology essential operational success. As innovative technologies become utilised, such as augmented reality or exoskeletons, warehouse managers need consider effects supportive substitutive on outcomes. However, potentials obstacles using require further investigations. Therefore, this study analyses literature content investigates existing state research field. Text mining employed enhance insights regarding analysis. Additionally, future opportunities integration proposed improvement development sustainable human-centered logistics according Industry 5.0 vision.

Language: Английский

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Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity

Journal of Retailing and Consumer Services, Journal Year: 2024, Volume and Issue: 79, P. 103790 - 103790

Published: March 12, 2024

Recent studies have demonstrated the potential of generative artificial intelligence (GenAI) in enhancing marketing content. However, its impact on consumer behavior has remained empirically untested. In response to social media platforms mandating disclosure GenAI content, we investigate how followers perceive brands that use for content creation. Drawing from literature algorithm aversion and brand authenticity, results three experimental indicate brands' adoption induces negative attitudinal behavioral follower reactions. These effects are mediated by followers' perceptions authenticity can be triggered disclosure. Negative reactions attenuated if is used assist humans creation rather than replace them through automation. Our findings underscore need nuance unlock economic benefits without compromising relationships with consumers.

Language: Английский

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Artificial intelligence and consumer behavior: From predictive to generative AI

Journal of Business Research, Journal Year: 2024, Volume and Issue: 180, P. 114720 - 114720

Published: May 23, 2024

Language: Английский

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Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA

Insights into Imaging, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 22, 2024

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI radiology holds to revolutionize healthcare practices by advancing diagnosis, quantification, management multiple medical conditions. Nevertheless, ever-growing availability tools highlights an increasing need critically evaluate claims its utility differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.This multi-society paper, presenting views Radiology Societies USA, Canada, Europe, Australia, New Zealand, defines practical problems ethical issues surrounding incorporation into radiological practice. In addition delineating main points concern that developers, regulators, purchasers should consider prior their introduction clinical practice, this statement also suggests methods monitor stability safety use, suitability autonomous function. This is intended serve as a useful summary which be considered all parties involved development resources, implementation tools.Key • artificial intelligence practice demands increased monitoring safety.• Cooperation between clinicians, regulators will allow address performance.• can fulfil promise advance patient well-being if steps are rigorously evaluated.

Language: Английский

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