EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 86 (2024) BIO Web Conf., 86 (2024) 01060 References
Open Access
Issue
BIO Web Conf.
Volume 86, 2024
International Conference on Recent Trends in Biomedical Sciences (RTBS-2023)
Article Number 01060
Number of page(s) 11
DOI https://doi.org/10.1051/bioconf/20248601060
Published online 12 January 2024
  • S. Vernim, H. Bauer, E. Rauch, M. T. Ziegler, and S. Umbrello, “A value sensitive design approach for designing AI-based worker assistance systems in manufacturing,” Procedia Comput Sci, vol. 200, pp. 505–516, 2022, doi: 10.1016/j.procs.2022.01.248. [CrossRef] [Google Scholar]
  • H. Kong, Z. Yin, Y. Baruch, and Y. Yuan, “The impact of trust in AI on career sustainability: The role of employee–AI collaboration and protean career orientation,” J Vocat Behav, vol. 146, Oct. 2023, doi: 10.1016/j.jvb.2023.103928. [CrossRef] [Google Scholar]
  • Zirar, S. I. Ali, and N. Islam, “Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda,” Technovation, vol. 124, Jun. 2023, doi: 10.1016/j.technovation.2023.102747 [CrossRef] [Google Scholar]
  • H. Jo, “Understanding AI tool engagement: A study of ChatGPT usage and word-of- mouth among university students and office workers,” Telematics and Informatics, vol. 85, p. 102067, Nov. 2023, doi: 10.1016/J.TELE.2023.102067. [CrossRef] [Google Scholar]
  • N. Emaminejad and R. Akhavian, “Trustworthy AI and robotics: Implications for the AEC industry,” Autom Constr, vol. 139, Jul. 2022, doi: 10.1016/j.autcon.2022.104298. [CrossRef] [Google Scholar]
  • F. Nucci, C. Puccioni, and O. Ricchi, “Digital technologies and productivity: A firm-level investigation,” Econ Model, p. 106524, Nov. 2023, doi: 10.1016/j.econmod.2023.106524. [Google Scholar]
  • Parteka and A. Kordalska, “Artificial intelligence and productivity: global evidence from AI patent and bibliometric data,” Technovation, vol. 125, Jul. 2023, doi: 10.1016/j.technovation.2023.102764. [CrossRef] [Google Scholar]
  • S. Chowdhury, P. Budhwar, P. K. Dey, S. Joel-Edgar, and A. Abadie, “AI-employee collaboration and business performance: Integrating knowledge-based view, socio- technical systems and organisational socialisation framework,” J Bus Res, vol. 144, pp. 31–49, May 2022, doi: 10.1016/j.jbusres.2022.01.069. [CrossRef] [Google Scholar]
  • C. H. Yang, “How Artificial Intelligence Technology Affects Productivity and Employment: Firm-level Evidence from Taiwan,” Res Policy, vol. 51, no. 6, Jul. 2022, doi: 10.1016/j.respol.2022.104536. [Google Scholar]
  • N. Omrani, G. Rivieccio, U. Fiore, F. Schiavone, and S. G. Agreda, “To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts,” Technol Forecast Soc Change, vol. 181, Aug. 2022, doi: 10.1016/j.techfore.2022.121763. [CrossRef] [Google Scholar]
  • S. Chowdhury et al., “Unlocking the value of artificial intelligence in human resource management through AI capability framework,” Human Resource Management Review, vol. 33, no. 1, Mar. 2023, doi: 10.1016/j.hrmr.2022.100899. [CrossRef] [Google Scholar]
  • H. Y. Osrof, C. L. Tan, G. Angappa, S. F. Yeo, and K. H. Tan, “Adoption of smart farming technologies in field operations: A systematic review and future research agenda,” Technol Soc, vol. 75, p. 102400, Nov. 2023, doi: 10.1016/J.TECHSOC.2023.102400. [CrossRef] [Google Scholar]
  • O. Rodríguez-Espíndola, S. Chowdhury, P. K. Dey, P. Albores, and A. Emrouznejad, “Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing,” Technol Forecast Soc Change, vol. 178, May 2022, doi: 10.1016/j.techfore.2022.121562. [PubMed] [Google Scholar]
  • C. Peng, J. van Doorn, F. Eggers, and J. E. Wieringa, “The effect of required warmth on consumer acceptance of artificial intelligence in service: The moderating role of AI- human collaboration,” Int J Inf Manage, vol. 66, Oct. 2022, doi: 10.1016/j.ijinfomgt.2022.102533. [CrossRef] [Google Scholar]
  • M. Cholo, S. Marisennayya, E. Bojago, D. Leja, and R. K. Divya, “Determinants of adoption and intensity of improved haricot bean (Phaseolus vulgaris L.) varieties: A Socio-agronomic study from southern Ethiopia,” J Agric Food Res, vol. 13, Sep. 2023, doi: 10.1016/j.jafr.2023.100656. [PubMed] [Google Scholar]
  • L. Nazareno and D. S. Schiff, “The impact of automation and artificial intelligence on worker well-being,” Technol Soc, vol. 67, Nov. 2021, doi: 10.1016/j.techsoc.2021.101679. [CrossRef] [Google Scholar]
  • Y. K. Dwivedi et al., “‘So what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy,” Int J Inf Manage, vol. 71, Aug. 2023, doi: 10.1016/j.ijinfomgt.2023.102642. [CrossRef] [Google Scholar]
  • E. Saha, P. Rathore, R. Parida, and N. P. Rana, “The interplay of emerging technologies in pharmaceutical supply chain performance: An empirical investigation for the rise of Pharma 4.0,” Technol Forecast Soc Change, vol. 181, Aug. 2022, doi: 10.1016/j.techfore.2022.121768. [CrossRef] [Google Scholar]
  • X. Liu, X. He, M. Wang, and H. Shen, “What influences patients’ continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics,” Technol Soc, vol. 70, Aug. 2022, doi: 10.1016/j.techsoc.2022.101996. [Google Scholar]
  • S. M. M. Hasan and A. Trianni, “Boosting the adoption of industrial energy efficiency measures through industry 4.0 technologies to improve operational performance,” J Clean Prod, p. 138597, Nov. 2023, doi: 10.1016/j.jclepro.2023.138597. [Google Scholar]
  • Malik, P. Budhwar, and B. A. Kazmi, “Artificial intelligence (AI)-assisted HRM: Towards an extended strategic framework,” Human Resource Management Review, vol. 33, no. 1, Mar. 2023, doi: 10.1016/j.hrmr.2022.100940. [CrossRef] [Google Scholar]
  • K. Sowa, A. Przegalinska, and L. Ciechanowski, “Cobots in knowledge work: Human – AI collaboration in managerial professions,” J Bus Res, vol. 125, pp. 135–142, Mar. 2021, doi: 10.1016/j.jbusres.2020.11.038. [CrossRef] [Google Scholar]
  • Abadie, M. Roux, S. Chowdhury, and P. Dey, “Interlinking organisational resources, AI adoption and omnichannel integration quality in Ghana’s healthcare supply chain,” J Bus Res, vol. 162, Jul. 2023, doi: 10.1016/j.jbusres.2023.113866. [CrossRef] [Google Scholar]
  • S. Siriwardhana and R. C. Moehler, “Enabling productivity goals through construction 4.0 skills: Theories, debates, definitions,” J Clean Prod, vol. 425, Nov. 2023, doi: 10.1016/j.jclepro.2023.139011. [CrossRef] [Google Scholar]
  • M. Zhu, C. Liang, A. C. L. Yeung, and H. Zhou, “The impact of intelligent manufacturing on labor productivity: An empirical analysis of Chinese listed manufacturing companies,” Int J Prod Econ, vol. 267, Jan. 2024, doi: 10.1016/j.ijpe.2023.109070. [Google Scholar]
  • Md. Z. ul Haq, H. Sood, and R. Kumar, “Effect of using plastic waste on mechanical properties of fly ash based geopolymer concrete,” Mater Today Proc, 2022. [Google Scholar]
  • H. Sood, R. Kumar, P. C. Jena, and S. K. Joshi, “Optimizing the strength of geopolymer concrete incorporating waste plastic,” Mater Today Proc, 2023. [Google Scholar]
  • H. Sood, R. Kumar, P. C. Jena, and S. K. Joshi, “Eco-friendly approach to construction: Incorporating waste plastic in geopolymer concrete,” Mater Today Proc, 2023. [Google Scholar]
  • M. Z. ul Haq et al., “Sustainable Infrastructure Solutions: Advancing Geopolymer Bricks via Eco-Polymerization of Plastic Waste,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01203. [Google Scholar]
  • M. Z. ul Haq et al., “Geopolymerization of Plastic Waste for Sustainable Construction: Unveiling Novel Opportunities in Building Materials,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01204. [Google Scholar]
  • M. Z. ul Haq et al., “Eco-Friendly Building Material Innovation: Geopolymer Bricks from Repurposed Plastic Waste,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01201. [Google Scholar]
  • M. Z. ul Haq et al., “Circular Economy Enabler: Enhancing High-Performance Bricks through Geopolymerization of Plastic Waste,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01202. [Google Scholar]
  • S. K. Samal et al., “3D-Printed Satellite Brackets: Materials, Manufacturing and Applications,” Crystals (Basel), vol. 12, no. 8, Aug. 2022, doi: 10.3390/CRYST12081148. [Google Scholar]
  • K. Zheng Yang et al., “Application of coolants during tool-based machining – A review,” Ain Shams Engineering Journal, 2022, doi: 10.1016/J.ASEJ.2022.101830. [Google Scholar]
  • S. Subramaniam et al., “Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review,” Sustainability (Switzerland), vol. 14, no. 16, Aug. 2022, doi: 10.3390/SU14169951. [Google Scholar]
  • V. S. Rana et al., “Assortment of latent heat storage materials using multi criterion decision making techniques in Scheffler solar reflector,” International Journal on Interactive Design and Manufacturing, 2023, doi: 10.1007/S12008-023-01456-9. [Google Scholar]
  • S. Bali et al., “A framework to assess the smartphone buying behaviour using DEMATEL method in the Indian context,” Ain Shams Engineering Journal, 2023, doi: 10.1016/J.ASEJ.2023.102129. [Google Scholar]
  • P. Singh et al., “Comparative Study of Concrete Cylinders Confined Using Natural and Artificial Fibre Reinforced Polymers,” Lecture Notes in Mechanical Engineering, pp. 79–91, 2023, doi: 10.1007/978-981-19-4147-4_8. [Google Scholar]
  • Garg, V., Singh, H., Bimbrawh, S., Kumar Singh, S., Gulati, M., Vaidya, Y. and Kaur, P., 2017. Ethosomes and transfersomes: Principles, perspectives and practices. Current drug delivery, 14(5), pp.613-633. [CrossRef] [PubMed] [Google Scholar]
  • 70. Bhatia, A., Singh, B., Raza, K., Wadhwa, S. and Katare, O.P., 2013. Tamoxifen- loaded lecithin organogel (LO) for topical application: development, optimization and characterization. International Journal of Pharmaceutics, 444(1-2), pp.47-59. [CrossRef] [PubMed] [Google Scholar]
  • 71. Elsheikh, A.H., Saba, A.I., Abd Elaziz, M., Lu, S., Shanmugan, S., Muthuramalingam, T., Kumar, R., Mosleh, A.O., Essa, F.A. and Shehabeldeen, T.A., 2021. Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia. Process Safety and Environmental Protection, 149, pp.223-233. [CrossRef] [PubMed] [Google Scholar]
  • 72. Singh, M., Rathi, R. and Garza-Reyes, J.A., 2021. Analysis and prioritization of Lean Six Sigma enablers with environmental facets using best worst method: A case of Indian MSMEs. Journal of cleaner production, 279, p.123592. [CrossRef] [Google Scholar]
  • 73. Sharma, A., Sarishma, Tomar, R., Chilamkurti, N. and Kim, B.G., 2020. Blockchain based smart contracts for internet of medical things in e-healthcare. Electronics, 9(10), p.1609. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.