Relevant bibliographies by topics / Technology Acceptance Model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Technology Acceptance Model'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 October 2024

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Journal articles on the topic "Technology Acceptance Model"

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Khoi, Bui Huy. "Technology Acceptance Model in the Managing Information System." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 869–76. http://dx.doi.org/10.5373/jardcs/v12sp7/20202177.

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Ma, Qingxiong, and Liping Liu. "The Technology Acceptance Model." Journal of Organizational and End User Computing 16, no. 1 (January 2004): 59–72. http://dx.doi.org/10.4018/joeuc.2004010104.

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KOWITLAWAKUL, YANIKA. "The Technology Acceptance Model." CIN: Computers, Informatics, Nursing 29, no. 7 (July 2011): 411–18. http://dx.doi.org/10.1097/ncn.0b013e3181f9dd4a.

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Pierce, Tamra P., Christopher Willy, Robert Roncace, and John Bischoff. "Extending The Technology Acceptance Model: Policy Acceptance Model (PAM)." American Journal of Health Sciences (AJHS) 5, no. 2 (November 24, 2014): 129–44. http://dx.doi.org/10.19030/ajhs.v5i2.8963.

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Many have studied the process of acceptance and adoption of new ideas and technologies as they are introduced into society. While several models have been used to assess various influencing factors, the Technology Acceptance Model (TAM) is one that is most widely accepted. This model examines people’s acceptance of new technologies based on variables that directly correlate to how the end user views the product. This paper introduces the Policy Acceptance Model (PAM), an expansion of TAM, which is designed for the analysis and evaluation of acceptance of new policy implementation. PAM includes the traditional constructs of TAM and adds the variables of age and ethnicity. The model is experimentally assessed using a survey of people’s attitudes toward the upcoming health care reform from 72 survey respondents. The aim is that the theory behind this model can be used as a framework that will be applicable to studies looking at the introduction of any new or modified policies.
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Hatami Kaleshtari, Mohammad, Ileana Ciobanu, Petre Lucian Seiciu, Andreea Georgiana Marin, and Mihai Berteanu. "Towards a Model of Rehabilitation Technology Acceptance and Usability." International Journal of Social Science and Humanity 6, no. 8 (August 2016): 612–16. http://dx.doi.org/10.7763/ijssh.2016.v6.720.

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Fuentes-Martínez, Ana. "From a Technology Acceptance Model to a Practice Acceptance Model." Ars Educandi 17, no. 17 (December 23, 2020): 61–66. http://dx.doi.org/10.26881/ae.2020.17.03.

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The paper delves into the problems of analyzing the introduction of programming in primary and secondary education from the perspective of the traditional Technology Acceptance Model. Instead, a Practice Acceptance model is suggested in which the focus is shifted from digital devices to the everyday practices of curriculum implementation.
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Fuentes-Martínez, Ana. "From a Technology Acceptance Model to a Practice Acceptance Model." Ars Educandi 17, no. 17 (December 23, 2020): 61–66. http://dx.doi.org/10.26881/ae.2020.17.03.

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The paper delves into the problems of analyzing the introduction of programming in primary and secondary education from the perspective of the traditional Technology Acceptance Model. Instead, a Practice Acceptance model is suggested in which the focus is shifted from digital devices to the everyday practices of curriculum implementation.
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Hu, Paul J., Patrick Y. K. Chau, Olivia R. Liu Sheng, and Kar Yan Tam. "Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology." Journal of Management Information Systems 16, no. 2 (September 1999): 91–112. http://dx.doi.org/10.1080/07421222.1999.11518247.

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Teo, Timothy, and Pita Jarupunphol. "Dhammic Technology Acceptance Model (DTAM)." Journal of Educational Computing Research 52, no. 1 (March 2015): 136–51. http://dx.doi.org/10.1177/0735633114568859.

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Mathieson, Kieran, Eileen Peacock, and Wynne W. Chin. "Extending the technology acceptance model." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 32, no. 3 (July 2001): 86–112. http://dx.doi.org/10.1145/506724.506730.

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Dissertations / Theses on the topic "Technology Acceptance Model"

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Kowitlawakul, Yanika. "Technology acceptance model predicting nurses' acceptance of telemedicine technology (eICU®) /." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3058.

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Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 116. Thesis director: Jean Burley Moore. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Nursing. Title from PDF t.p. (viewed July 3, 2008). Includes bibliographical references (p. 108-115). Also issued in print.
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Yousif, Wael K. "Examining engineering & technology students' acceptance of network virtualization technology using the technology acceptance model." Orlando, Fla. : University of Central Florida, 2010. http://purl.fcla.edu/fcla/etd/CFE0003071.

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Yousif, Wael K. Yousif. "EXAMINING ENGINEERING & TECHNOLOGY STUDENTS ACCEPTANCE OF NETWORK VIRTUALIZATION TECHNOLOGY USING THE TECHNOLOGY ACCEPTANCE MODE." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3039.

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This causal and correlational study was designed to extend the Technology Acceptance Model (TAM) and to test its applicability to Valencia Community College (VCC) Engineering and Technology students as the target user group when investigating the factors influencing their decision to adopt and to utilize VMware as the target technology. In addition to the primary three indigenous factors: perceived ease of use, perceived usefulness, and intention toward utilization, the model was also extended with enjoyment, external control, and computer self-efficacy as antecedents to perceived ease of use. In an attempt to further increase the explanatory power of the model, the Task-Technology Fit constructs (TTF) were included as antecedents to perceived usefulness. The model was also expanded with subjective norms and voluntariness to assess the degree to which social influences affect students decision for adoption and utilization. This study was conducted during the fall term of 2009, using 11 instruments: (1) VMware Tools Functions Instrument; (2) Computer Networking Tasks Characteristics Instrument; (3) Perceived Usefulness Instrument; (4) Voluntariness Instrument; (5) Subjective Norms Instrument; (6) Perceived Enjoyment Instrument; (7) Computer Self-Efficacy Instrument; (8) Perception of External Control Instrument; (9) Perceived Ease of Use Instrument; (10) Intention Instrument; and (11) a Utilization Instrument. The 11 instruments collectively contained 58 items. Additionally, a demographics instrument of six items was included to investigate the influence of age, prior experience with the technology, prior experience in computer networking, academic enrollment status, and employment status on student intentions and behavior with regard to VMware as a network virtualization technology. Data were analyzed using path analysis, regressions, and univariate analysis of variance in SPSS and AMOS for Windows. The results suggest that perceived ease of use was found to be the strongest determinant of student intention. The analysis also suggested that external control, measuring the facilitating conditions (knowledge, resources, etc) necessary for adoption was the highest predictor of perceived ease of use. Consistent with previous studies, perceived ease of use was found to be the strongest predictor of perceived usefulness followed by subjective norms as students continued to use the technology. Even though the integration of the task-technology fit construct was not helpful in explaining the variance in student perceived usefulness of the target technology, it was statistically significant in predicting student perception of ease of use. The study concluded with recommendations to investigate other factors (such as service quality and ease of implementation) that might contribute to explaining the variance in perceived ease of use as the primary driving force in influencing student decision for adoption. A recommendation was also made to modify the task-technology fit construct instruments to improve the articulation and the specificity of the task. The need for further examination of the influence of the instructor on student decision for adoption of a target technology was also emphasized.
Ed.D.
Department of Educational Research, Technology and Leadership
Education
Education EdD
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Keeling, Kathleen A. "Customer acceptance of electronic service delivery : : extending the technology acceptance model." Thesis, University of Manchester, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488161.

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Mulero, Senikat Olumayowa. "Acceptance and impact of social networks marketing using extended technology acceptance model." Thesis, Cape Peninsula University of Technology, 2012. http://hdl.handle.net/20.500.11838/1385.

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Thesis (MTech (Information Technology)--)Cape Peninsula University of Technology, 2012
Includes bibliographical references (leaves 127)
In rapid advances in marketing strategies, Social Networks Marketing (SNM) is dramatically becoming an important aspect in promoting businesses. Social networks marketing has been found as one of the most effective tool used in businesses today, by most business owners in South Africa, to expand, and flourish their businesses. The explosion of Internet usage has drawn the attention of researchers towards social networks marketing. Research has shown that potential Internet users are doubtful, and indecisive to use, and accept SNM system in spite of their availability. Therefore, there is a need for researchers to identify some of the factors that determine user’s unwillingness to accept and use the system. According to the Technology Acceptance Model (TAM), Perceived Ease of Use (PEOU), and Perceived Usefulness (PU) are the two major constructs in determining the use and acceptance of various information systems. These beliefs may not fully explain the user’s behaviour towards newly introduced system, such as social networks marketing. Hence, this study had successfully implemented TAM to the newly proposed information system (social networks marketing system). Using TAM as a theoretical framework, the study added two new constructs “perceived credibility” and “awareness” as part of the factors influencing user’s acceptance of social networks marketing. The purpose of the study was to explore the impact of social networks marketing system by SMEs in South Africa, and perception of consumers. In addition, this research investigated factors that influence users’ acceptance of social networks marketing using TAM as the basis for the theoretical framework. Questionnaire-based study done in 2 groups; consumers and small and medium-scale enterprises (SMEs), in this work, represent the major users of social networks marketing in South Africa. The research model was tested using data collected from 143 questionnaires and analysed using linear regression. The results show that user’s intentions to use SNM are strongly and positively correlated with user acceptance. The empirical results confirmed that perceived credibility and perceived usefulness are the strongest determinant in predicting user’s intention to use SNM. In addition, many of the South African SMEs using SNM, have presumed that SNM has positively and negatively impacted their businesses.
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Gabbard, Ralph Barnhart. "Applying the technology acceptance model to online education." [Bloomington, Ind.] : Indiana University, 2004. http://wwwlib.umi.com/dissertations/fullcit/3162232.

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Thesis (Ph.D.)--Indiana University, 2004.
Source: Dissertation Abstracts International, Volume: 66-01, Section: A, page: 0011. Chair: Javed Mostafa. Title from dissertation home page (viewed Oct. 12, 2006).
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Blue, Jon T. "Rebuilding Theories of Technology Acceptance: A Qualitative Case Study of Physicians' Acceptance of Technology." VCU Scholars Compass, 2006. http://hdl.handle.net/10156/1543.

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Vrablova, Adriana, and Stjepan Kalinic. "Technology acceptance of IKEA mobile application." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Företagsekonomi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-26676.

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In the past few years, rapid development of mobile technologies has been changing the way people approach purchasing. Using Technology Acceptance Model (Davis, 1986), the authors believe that IKEA’s furniture mobile application creates a certain value to its users. The study aims at examining the importance of perceived usefulness, perceived ease of use and compatibility dimensions of IKEA’s app and their impact on consumers’ behavioral intentions to see whether or not they lead to actual purchase.   The thesis findings reveal that IKEA mobile application is not widely used. The results should have been applicable for similar companies as IKEA especially those which promote in-store app usage. However, it is not possible since the thesis contradicts the assumption of broad usage of such mobile application.   The analysis of the surveys releaved gender having a role in IKEA mobile app perception as well as occupation. The analysis is also contributing by a realization that mobile technologies lead to faster decision-making, more information availability, and therefore, can create better marketing communication strategies.
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Ducey, Adam J. "Predicting Tablet Computer Use: An Extended Technology Acceptance Model." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4471.

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While information technology has rapidly changed work in the United States in the past 50 years, some businesses and industries have been slow to adopt new technologies. Healthcare is one industry that has lagged behind in information technology investment for a variety of reasons. Recent federal initiatives to encourage IT adoption in the healthcare industry provide an ideal context to study factors that influence technology acceptance. Data from 261 practicing pediatricians were collected to evaluate an extended Technology Acceptance Model. Results indicated that individual (i.e., perceived usefulness, perceived ease of use), organizational (i.e., subjective norm), and device (i.e., compatibility, reliability) characteristics collectively influence pediatricians' intention to adopt tablet computers in their medical practice. Theoretical and practical implications are discussed. Future research should examine additional variables that influence information technology adoption in organizations.
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Hur, Youngjin. "Determinants of sport website acceptance : an application and extension of the technology acceptance model." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Dissertations/Summer2007/y_hur_072707.pdf.

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Books on the topic "Technology Acceptance Model"

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Davis, Fred D., and Andrina Granić. The Technology Acceptance Model. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-030-45274-2.

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Kamel, Sherif. Assessing the introduction of electronic banking in Egypt using the technology acceptance model. Hershey, PA: Idea Group Pub., 2003.

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Al-Emran, Mostafa, and Khaled Shaalan, eds. Recent Advances in Technology Acceptance Models and Theories. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64987-6.

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Kock, Johannes. Technology Acceptance Model (Tam). an Overview. GRIN Verlag GmbH, 2017.

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The Technology Acceptance Model: 30 Years of TAM. Springer, 2020.

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Kock, Johannes. Understanding the Acceptance of 3D Printing Toolkits. an Extension of the Technology Acceptance Model. GRIN Verlag GmbH, 2017.

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Ortiz, Michael. Loss of Control and Technology Acceptance in Transformation: Acceptance and Design Factors of a Heuristic Model. Springer Fachmedien Wiesbaden GmbH, 2023.

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Tan, Tio Boon. Technology Acceptance Model(TAM): A study of word processing usage in Singapore. 1996.

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An Evaluation of GeoBEST Contingency Beddown Planning Software Using the Technology Acceptance Model. Storming Media, 2002.

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Weibe, Aaron. Qualitative Investigation of the Technology Acceptance Model in the U.S. Cooperative Extension Service on the Adoption of Customer Relationship Management Systems. Extension Foundation, 2024.

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Book chapters on the topic "Technology Acceptance Model"

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Iyamu, Tiko. "Technology acceptance model." In Applying Theories for Information Systems Research, 143–54. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003184119-9.

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Davis, Fred D., and Andrina Granić. "Epilogue: What Will the Future of TAM Be Like?" In The Technology Acceptance Model, 103–8. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-030-45274-2_4.

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Davis, Fred D., and Andrina Granić. "Evolution of TAM." In The Technology Acceptance Model, 19–57. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-030-45274-2_2.

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Davis, Fred D., and Andrina Granić. "Introduction: “Once Upon a TAM”." In The Technology Acceptance Model, 1–18. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-030-45274-2_1.

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Davis, Fred D., and Andrina Granić. "Revolution of TAM." In The Technology Acceptance Model, 59–101. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-030-45274-2_3.

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Davis, Fred D., and Andrina Granić. "Actionable Principles: The Seven Pillars Framework." In The Technology Acceptance Model, 109–17. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-030-45274-2_5.

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Jumaah, Fawaz, Sani Salisu, and Shahad Alfahad. "Technology Acceptance Model in Social Commerce." In Artificial Neural Networks and Structural Equation Modeling, 37–49. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6509-8_3.

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Granić, Andrina. "Technology Acceptance and Adoption in Education." In Handbook of Open, Distance and Digital Education, 1–15. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0351-9_11-1.

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AbstractThe chapter provides a comprehensive and up-to-date insight into main research findings in the area of educational technology acceptance, adoption, and usage. Over the past decades, a variety of theoretical perspectives have been advanced to provide an understanding of the determinants of adoption of various technologies used to support the process of knowledge transfer and acquisition. Although some prominent theoretical approaches in educational contexts include Innovation Diffusion Theory (IDT), Unified Theory of Acceptance and Use of Technology (UTAUT), as well as Motivational Model (MM), research reveals the Technology Acceptance Model (TAM) as the most influential model and leading scientific paradigm in investigating acceptance of educational technology by students, teachers, and other stakeholders. Aiming to increase their predictive validity, in numerous empirical studies, models have been extended with different predictive factors, like the most often validated self-efficacy, subjective norm, perceived enjoyment, perceived playfulness, anxiety, social influence, system quality, and facilitating conditions. Research revealed electronic learning (e-learning) as the most common validated mode of delivery, followed by mobile learning (m-learning), learning management system (LMS), personal learning environment (PLE), and massive open online course (MOOC), along with different supportive facilitating technologies used in education such as social media platforms, teaching assistant robots, simulators, as well as virtual reality (VR) and augmented reality (AR) technologies. To enhance explanatory power, new developments in educational technology acceptance and adoption have suggested the need of integration of TAM and UTAUT with other contributing adoption and post-adoption theories and models, together with several established approaches from other fields.
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Granić, Andrina. "Technology Acceptance and Adoption in Education." In Handbook of Open, Distance and Digital Education, 183–97. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-2080-6_11.

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AbstractThe chapter provides a comprehensive and up-to-date insight into main research findings in the area of educational technology acceptance, adoption, and usage. Over the past decades, a variety of theoretical perspectives have been advanced to provide an understanding of the determinants of adoption of various technologies used to support the process of knowledge transfer and acquisition. Although some prominent theoretical approaches in educational contexts include Innovation Diffusion Theory (IDT), Unified Theory of Acceptance and Use of Technology (UTAUT), as well as Motivational Model (MM), research reveals the Technology Acceptance Model (TAM) as the most influential model and leading scientific paradigm in investigating acceptance of educational technology by students, teachers, and other stakeholders. Aiming to increase their predictive validity, in numerous empirical studies, models have been extended with different predictive factors, like the most often validated self-efficacy, subjective norm, perceived enjoyment, perceived playfulness, anxiety, social influence, system quality, and facilitating conditions. Research revealed electronic learning (e-learning) as the most common validated mode of delivery, followed by mobile learning (m-learning), learning management system (LMS), personal learning environment (PLE), and massive open online course (MOOC), along with different supportive facilitating technologies used in education such as social media platforms, teaching assistant robots, simulators, as well as virtual reality (VR) and augmented reality (AR) technologies. To enhance explanatory power, new developments in educational technology acceptance and adoption have suggested the need of integration of TAM and UTAUT with other contributing adoption and post-adoption theories and models, together with several established approaches from other fields.
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Ursavaş, Ömer Faruk. "Technology Acceptance Model: History, Theory, and Application." In Springer Texts in Education, 57–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10846-4_4.

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Conference papers on the topic "Technology Acceptance Model"

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Michel, Christine, Marc-Eric Bobillier-Chaumon, and Philippe Sarnin. "Technology acceptance model." In the 2014 Ergonomie et Informatique Avancée Conference - Design, Ergonomie et IHM: quelle articulation pour la co-conception de l'interaction. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2671470.2671489.

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Thaneshan, L., Fadhilah Mat Yamin, and Siti Norezam Othman. "Mobile technology acceptance framework using extended technology acceptance model." In THE 11TH INTERNATIONAL CONFERENCE ON THEORETICAL AND APPLIED PHYSICS: The Spirit of Research and Collaboration Facing the COVID-19 Pandemic. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0124183.

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Wang, Xinli, and Mostafa El-Said. "Technology Acceptance Model Revisited." In SIGITE '19: The 20th Annual Conference on Information Technology Education. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3349266.3351379.

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Mustafa, A'dillah Bt, Norasiah Hj Harun, and Mohd Zailan Endin. "Understanding e-book acceptance through Technology Acceptance Model (TAM Model)." In 2014 2nd International Conference on Information and Communication Technology (ICoICT). IEEE, 2014. http://dx.doi.org/10.1109/icoict.2014.6914052.

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Handoko, Bambang Leo, Ameliya Rosita, and Nada Ayuanda. "Auditor Acceptance of Metaverse: Approach from Technology Acceptance Model." In ICEME 2023: 2023 the 14th International Conference on E-business, Management and Economics. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3616712.3616733.

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Santoso, Cahyono Budy, Wayan Suparta, Edi Abdurachman, and Agung Trisetyarso. "Jakarta Government Official Portal Acceptance Based On Technology Acceptance Model." In 2020 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, 2020. http://dx.doi.org/10.1109/icitsi50517.2020.9264916.

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Busolo, Gilbert, Lawrence Nderu, and Kennedy Ogada. "A Multilevel Technology Acceptance Management Model." In 6th International Conference on Computer Science, Engineering And Applications (CSEA 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101802.

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Knowledge is a strategic resource for successful data driven decision making in any organization. To harness this knowledge, successful adoption of a technological intervention is key. Institutions leverage on technology to drive knowledge management (KM) initiatives for quality service delivery and prudent data management. These initiatives provide the overall strategy for managing data resources. They make available knowledge organization tools and techniques while enabling regular updates. Derived benefits of positive deployment of a technological intervention are competency enhancement through gained knowledge, raised quality of service and promotion of healthy development of e-commerce. Successful and timely adoption of technological interventions through which knowledge management initiatives are deployed remains a key challenge to many organizations. This paper proposes a wholesome multilevel technology acceptance management model. The proposed model takes into account human, technological and organizational variables, which exist in a deployment environment. This model will be vital in driving early technology acceptance prediction and timely deployment of mitigation measures to deploy technological interventions successfully.
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Akbari, Elham. "Technology Acceptance Model and Mobile learning." In 2021 14th National and 8th International Conference on e-Learning and e-Teaching (ICELET). IEEE, 2021. http://dx.doi.org/10.1109/icelet202153025.2021.9712606.

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Gao, Shang, John Krogstie, and Per Anton Gransæther. "Mobile Services Acceptance Model." In 2008 International Conference on Convergence and Hybrid Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/ichit.2008.252.

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Mohamad, Mohamad Amiruddin, Mohd Talmizie Amron, and Nur Hidayah Md Noh. "Assessing the Acceptance of E-Learning via Technology Acceptance Model (TAM)." In 2021 6th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE). IEEE, 2021. http://dx.doi.org/10.1109/icraie52900.2021.9704019.

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Reports on the topic "Technology Acceptance Model"

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Hwang, Chanmi. Consumers' acceptance of wearable technology: Antecedents in a technology acceptance model (MS - 2nd place). Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-1125.

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Ma, Yoon Jin, Hae Jin Gam, and Jennifer Banning. Acceptance of a Socially Responsible Label Model for Apparel Products: Application of the Technology Acceptance Model. Ames: Iowa State University, Digital Repository, 2013. http://dx.doi.org/10.31274/itaa_proceedings-180814-842.

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Li, Ailin, and Yingjiao Xu. Chinese Consumers� Adoption Behaviors Toward Virtual Fitting Rooms: From the Perspective of Technology Acceptance Model. Ames (Iowa): Iowa State University. Library, January 2019. http://dx.doi.org/10.31274/itaa.8331.

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Contreras Salamanca, Luz Briyid, and Yon Garzón Ávila. Generational Lagging of Dignitaries, Main Cause of Technological Gaps in Community Leaders. Analysis of Generation X and Boomers from the Technology Acceptance Model. Universidad Nacional Abierta y a Distancia, May 2021. http://dx.doi.org/10.22490/ecacen.4709.

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Community and neighborhood organizations are in the process of renewing the organizational culture, considering technological environments in the way of training, and advancing communally, being competitive in adaptation and learning, creating new solutions, promoting change, and altering the status quo, based on the advancement of technology over the last few years, currently applied in most organizations. The decisive factor is the ability of true leaders to appropriate the Technological Acceptance Model –TAM– principles, participating in programs and projects, adopting new technologies from the different actors involved, contributing to the welfare of each community. There is, however, a relative resistance to the use of technology as support in community management, due to the generational differences in leaders and dignitaries, according to collected reports in this study, in relation to the age range of dignitaries –Generation X and Baby Boomers predominate–. They present a challenge to digital inclusion with difficulties related to age, cognitive, sensory, difficulty in developing skills, and abilities required in Digital Technologies, necessary to face new scenarios post-pandemic and, in general, the need to use technological facilities.
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Naim, Michael, Andrew Spielman, Shlomo Nir, and Ann Noble. Bitter Taste Transduction: Cellular Pathways, Inhibition and Implications for Human Acceptance of Agricultural Food Products. United States Department of Agriculture, February 2000. http://dx.doi.org/10.32747/2000.7695839.bard.

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Historically, the aversive response of humans and other mammals to bitter-taste substances has been useful for survival, since many toxic constituents taste bitter. Today, the range of foods available is more diverse. Many bitter foods are not only safe for consumption but contain bitter constituents that provide nutritional benefits. Despite this, these foods are often eliminated from our current diets because of their unacceptable bitterness. Extensive technology has been developed to remove or mask bitterness in foods, but a lack of understanding of the mechanisms of bitterness perception at the taste receptor level has prevented the development of inhibitors or efficient methods for reducing bitterness. In our original application we proposed to: (a) investigate the time course and effect of selected bitter tastants relevant to agricultural products on the formation of intracellular signal molecules (cAMP, IP3, Ca2+) in intact taste cells, in model cells and in membranes derived therefrom; (b) study the effect of specific bitter taste inhibitors on messenger formation and identify G-proteins that may be involved in tastant-induced bitter sensation; (c) investigate interactions and self-aggregation of bitter tastants within membranes; (d) study human sensory responses over time to these bitter-taste stimuli and inhibitors in order to validate the biochemical data. Quench-flow module (QFM) and fast pipetting system (FPS) allowed us to monitor fast release of the aforementioned signal molecules (cGMP, as a putative initial signal was substituted for Ca2+ ions) - using taste membranes and intact taste cells in a time range below 500 ms (real time of taste sensation) - in response to bitter-taste stimulation. Limonin (citrus) and catechin (wine) were found to reduce cellular cAMP and increase IP3 contents. Naringin (citrus) stimulated an IP3 increase whereas the cheese-derived bitter peptide cyclo(leu-Trp) reduced IP3 but significantly increased cAMP levels. Thus, specific transduction pathways were identified, the results support the notion of multiple transduction pathways for bitter taste and cross-talk between a few of those transduction pathways. Furthermore, amphipathic tastants permeate rapidly (within seconds) into liposomes and taste cells suggesting their availability for direct activation of signal transduction components by means of receptor-independent mechanisms within the time course of taste sensation. The activation of pigment movement and transduction pathways in frog melanophores by these tastants supports such mechanisms. Some bitter tastants, due to their amphipathic properties, permeated (or interacted with) into a bitter tastant inhibitor (specific phospholipid mixture) which apparently forms micelles. Thus, a mechanism via which this bitter taste inhibitor acts is proposed. Human sensory evaluation experiments humans performed according to their 6-n-propyl thiouracil (PROP) status (non-tasters, tasters, super-tasters), indicated differential perception of bitterness threshold and intensity of these bitter compounds by different individuals independent of PROP status. This suggests that natural products containing bitter compounds (e.g., naringin and limonin in citrus), are perceived very differently, and are in line with multiple transduction pathways suggested in the biochemical experiments. This project provides the first comprehensive effort to explore the molecular basis of bitter taste at the taste-cell level induced by economically important and agriculturally relevant food products. The findings, proposing a mechanism for bitter-taste inhibition by a bitter taste inhibitor (made up of food components) pave the way for the development of new, and perhaps more potent bitter-taste inhibitors which may eventually become economically relevant.
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Committee on Toxicology. COT FSA PBPK for Regulators Workshop Report 2021. Food Standards Agency, April 2024. http://dx.doi.org/10.46756/sci.fsa.tyy821.

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The future of food safety assessment in the UK depends on the Food Standards Agency’s (FSA) adaptability and flexibility in responding to and adopting the accelerating developments in science and technology. The Tox21 approach is an example of one recent advancement in the development of alternative toxicity testing approaches and computer modelling strategies for the evaluation of hazard and exposure (New Approach Methodologies (NAMs). A key aspect is the ability to link active concentrations in vitro to likely concentrations in vivo, for which physiologically based pharmacokinetic (PBPK) modelling is ideally suited. The UK FSA and the Committee on Toxicity of Chemicals in Food, Consumer Products, and the Environment (COT) held an “PBPK for Regulators” workshop with multidisciplinary participation, involving delegates from regulatory agencies, government bodies, academics, and industry. The workshop provided a platform to enable expert discussions on the application of PBPK to health risk assessment in a regulatory context. Presentations covered current application of PBPK modelling in the agrochemical industry for in vitro to in vivo extrapolation (IVIVE), pharmaceutical industry for drug absorption related issues (e.g., the effect of food on drug absorption) and drug-drug interaction studies, as well as dose extrapolations to special populations (e.g., those with a specific disease state, paediatric/geriatric age groups, and different ethnicities), environmental chemical risk assessment, an overview of the current regulatory guidance and a PBPK model run-through. This enabled attendees to consider the wide potential and fitness for purpose of the application of PBPK modelling in these fields. Attendees considered applicability in the context of future food safety assessment for refining exposure assessments of chemicals with narrow margins of exposure and/or to fill data gaps from more traditional approaches (i.e., data from animal testing). The overall conclusions from the workshop were as follows: PBPK modelling tools were applicable in the areas of use covered, and that expertise was available (though it is in small numbers). PBPK modelling offers opportunities to address questions for compounds that are otherwise not possible (e.g., considerations of human variability in kinetics) and allows identification of “at risk” subpopulations. The use of PBPK modelling tends to be applied on a case-by-case basis and there appears to be a barrier to widespread acceptance amongst regulatory bodies due to the lack of available in-house expertise (apart from some medical and environmental agencies such as the European Medicines Agency, United States Food and Drug Administration, and the US Environmental Protection Agency, respectively). Familiarisation and further training opportunities on the application of PBPK modelling using real world case studies would help in generating interest and developing more experts in the field, as well as furthering acceptance. In a regulatory context, establishing fitness for purpose for the use of PBPK models requires transparent discussion between regulatory agencies, government bodies, academics, and industry and the development of a harmonised guidance such as by the Organisation for Economic Co-operation and Development (OECD) would provide a starting point. Finally, PBPK modelling is part of the wider “new approach methodologies” for risk assessment, and there should be particular emphasis in modelling both toxicodynamics and toxicokinetics.
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Lehmann, Johannes, and Nina Dwerlkotte. Carbon Farming and Its Impact on Agricultural Technology. SAE International, November 2023. http://dx.doi.org/10.4271/epr2023026.

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<div class="section abstract"><div class="htmlview paragraph">The agricultural sector is responsible for a large share of anthropogenic greenhouse gases. At the same time, methods such as targeted land use change can reduce emissions from landscape elements and sequester carbon from the atmosphere in the soil. This process, also known as carbon farming, has not been uniformly defined, and faces challenges regarding establishing new requirments for agricultural vehicles and technology, creating profitable business models (that preclude “greenwashing”), and developing governmental frameworks and industry acceptance.</div><div class="htmlview paragraph"><b>Carbon Farming and Its Impact on Agricultural Technology</b> discusses the large development gap for carbon farming methods, especially with regard to agricultural technology. In addition to the new hardware requirements arising from land use change, there is also a need for the further development of software. The establishment of suitable interfaces and solutions that are interoperable with existing technologies is also crucial at this point. This report clearly shows that more funding for research and development is needed today so that appropriate standards can be set and carbon farming can contribute to climate protection in the future.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>
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Cavicchia, Rebecca, Jonas Kačkus Tybjerg, Hilma Salonen, Maja Brynteson, Nicola Wendt-Lucas, Sæunn Gísladóttir, and Hjalti Jóhannesson. Ten-year Regional Outlook: Future Perspectives for Electric Aviation in the Nordic Region. Nordregio, March 2024. http://dx.doi.org/10.6027/r2024:81403-2503.

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Electric aviation has gained momentum in the Nordic region in recent years. Given its unique geographical features and strong commitment to climate-neutral transport, the Nordic region represents an ideal testing ground for electric aviation. In five to ten years from now, electric aviation has the potential to become reality, so it is crucial to explore which factors may affect its implementation and how regional development may be impacted. The “Electric Aviation and the Effects on the Nordic Region” project presents a ten-year future scenario for electric aviation in the Nordic region. In this report, we explore future scenarios for five selected Nordic routes and identify the key driving forces behind that transformative shift, as well as the positive and negative impacts of electric aviation. The scenarios were developed through focus group discussions conducted with key stakeholders from each of the five Nordic countries (Denmark, Finland, Iceland, Norway and Sweden). The main findings of the study show that electric aviation is generally perceived positively in the various Nordic countries. However, there are also certain contextual differences relating to whether electric aviation is viewed as a pathway to achieving climate targets and removing emissions, stimulating regional development and accessibility of remote areas, or as a new travel option compared to conventional flights. Even though the general attitude towards electric aviation seems to be positive, scepticism and questions regarding the social acceptance of the new technology emerged, as well as the uncertainty around who will be the investors to kick-off the new technology. Furthermore, the future scenarios suggest that a high level of support from local and national governing bodies is required in order to make electric aviation a viable air transport mode in the Nordic region. The insights of this report are intended both to inform decision-makers and to provide nuanced observations from each Nordic country and explore future pathways towards a sustainable and more integrated Nordic region in line with the 2030 vision of the Nordic Council of Ministers.
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Muelaner, Jody, ed. Unsettled Issues in Commercial Vehicle Platooning. SAE International, November 2021. http://dx.doi.org/10.4271/epr2021027.

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Platooning has the potential to reduce the energy consumption of commercial vehicles while improving safety; however, both advantages are currently difficult to quantify due to insufficient data and the wide range of variables affecting models. Platooning will significantly reduce the use of energy when compared to trucks driven alone, or at a safe distance for a driver without any automated assistance. Platooning will also reduce stopping distances—multiple states in the US have passed laws authorizing truck platoons to operate at shorter gaps than are authorized for normal, human-driven trucks. However, drivers typically do not currently leave the recommended gaps and, therefore, already gain much of the potential energy savings by drafting lead vehicles, albeit illegally. The automated systems associated with platooning cannot be programmed to flout safety recommendations in the way that human drivers routinely do. Therefore, actual energy savings may be minimal while safety may be greatly improved. More data will be needed to conclusively demonstrate a safety gain. Recommended safe gaps are currently highly generalized and must necessarily assume worst-case braking performance. Using a combination of condition monitoring and vehicle-to-vehicle communications, platooning systems will be able to account for the braking performance of other vehicles within the platoon. If all the vehicles in a platoon have a high level of braking performance, the platoon will be able to operate in a more efficient, tighter formation. Driver acceptance of platooning technology will increase as the systems become more effective and do not displace jobs. The increased loading of infrastructure must also be considered, and there may be requirements for upgrades on bridges or restrictions on platooning operation.
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Burton, Simon. The Path to Safe Machine Learning for Automotive Applications. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, October 2023. http://dx.doi.org/10.4271/epr2023023.

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<div class="section abstract"><div class="htmlview paragraph">Recent rapid advancement in machine learning (ML) technologies have unlocked the potential for realizing advanced vehicle functions that were previously not feasible using traditional approaches to software development. One prominent example is the area of automated driving. However, there is much discussion regarding whether ML-based vehicle functions can be engineered to be acceptably safe, with concerns related to the inherent difficulty and ambiguity of the tasks to which the technology is applied. This leads to challenges in defining adequately safe responses for all possible situations and an acceptable level of residual risk, which is then compounded by the reliance on training data.</div><div class="htmlview paragraph"><b>The Path to Safe Machine Learning for Automotive Applications</b> discusses the challenges involved in the application of ML to safety-critical vehicle functions and provides a set of recommendations within the context of current and upcoming safety standards. In summary, the potential of ML will only be unlocked for safety-related functions if the inevitable uncertainties associated with both the specification and performance of the trained models can be sufficiently well understood and controlled within the application-specific context.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>
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