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Articles de revues sur le sujet « Technology influence »

Auteur : Grafiati

Publié le 10 mars 2023

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1

ROSTAN, Martin. « 0406 Influence of Ultra-fast Communication Technology on Manufacturing Equipment ». Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015) : _0406–1_—_0406–5_. http://dx.doi.org/10.1299/jsmelem.2015.8._0406-1_.

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Loureiro, Ruben Joaquim Pinto, Jorge Manuel Marques Simões, Sílvio Manuel Valente da Silva et António Gabriel Simão de Castro. « Technology and Influence on Innovation ». Journal of Business and Economics 10, n^o 4 (20 avril 2019) : 335–42. http://dx.doi.org/10.15341/jbe(2155-7950)/04.10.2019/006.

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Innovation can be the key element of a company, yet it is dependent on business resources. The relationship between innovation and performance is addressed by several authors who advocate this theme as a driver to solve problems at the business level and subsequently improve performance. Innovation is essentially under great uncertainty, but little information exists about innovation in developing countries. The present research aims to add knowledge about innovation in developing countries, analyzing whether innovation improves business performance (influenced by technology). Thus, the research question is: What is the relationship between innovation and performance in companies? These hypotheses are related to a specific objective: to identify the factors that improve the company’s performance. Using secondary data obtained by the World Bank, and seeking to make known developments in innovation and the relationship with technology and performance in developing countries, the correlation of several variables was worked out in a first approach to the theme through an analysis factorial. It is concluded that there is no positive relationship between performance and innovation in developing countries.

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Kozinets, Robert V. « Technology/Ideology : How Ideological Fields Influence Consumers' Technology Narratives ». Journal of Consumer Research 34, n^o 6 (avril 2008) : 865–81. http://dx.doi.org/10.1086/523289.

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Havrland, B., M. Kavka et M. Růžička. « Influence of factors on the maize-grain mechanized technology net margin ». Research in Agricultural Engineering 52, No. 2 (7 février 2012) : 69–79. http://dx.doi.org/10.17221/4882-rae.

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a tendency to reduce number of operations by their association carries technical, environmental as well as economic aspects. Technical and environmental features are apparent and can be described with couple of quite logic positive effects and consequences. It is rather difficult to exactly identify economic proceeds that must especially be seen in producer’s final perception (net margin). Methodology of net margin calculation is complicated and sometimes not fully transparent. A new (proper) methodological approach has been conceived in the concept of ATMP (Agricultural Technology Management Program). The Program is meant to provide the art of work to the extension worker in formulating sound and exact technological advice based upon both the availability of technological information (particularly on machinery sets and agronomic requirements) and rapid economic (costs) calculations. The program demonstrates an attempt to put into practice the concept of “precision technology” based on precision machinery inputs, which reduces machinery input costs. Preceding field survey carried out in August–September 2003 supplied basic data for technologies design and economic calculations. Five different mechanized technologies were conceived as provided with various operations and inputs: (1) classic mechanized technology composed of all necessary soil preparation, seeding, cultivation and harvesting operations; (2) direct sowing as a form of the minimum tillage (no soil preparation operations); (3) classic mechanized technology with farmyard fertilizing, (4) classical mechanized technology with combined cultivation operations; (5) classical mechanized technology with green manure. All technologies have been designed using Czech currency. The economic appropriateness of the respective technologies has been judged according to the main parameters of the crop budget that were selected for export to the comparison table. The parameters included in the comparison table were displayed in chart form. This enabled a better comparison of different parameters of the technologies. The main economic indicators that have been considered are the gross and net margins and own market price per ton of the product.

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Issroff, Kim, et Eileen Scanlon. « Educational Technology : The Influence of Theory ». Journal of Interactive Media in Education 2002, n^o 1 (25 juillet 2002) : 6. http://dx.doi.org/10.5334/2002-6.

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Randle, Jacqueline. « Past caring ? The influence of technology ». Nurse Education in Practice 1, n^o 3 (septembre 2001) : 157–65. http://dx.doi.org/10.1054/nepr.2001.0025.

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Destriana, D. B., et Juanda. « Influence of Information Technology on Society ». IOP Conference Series : Materials Science and Engineering 662 (20 novembre 2019) : 022075. http://dx.doi.org/10.1088/1757-899x/662/2/022075.

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Sinfelt, John H. « Influence of technology on catalytic science ». Industrial & ; Engineering Chemistry Fundamentals 25, n^o 1 (février 1986) : 2–9. http://dx.doi.org/10.1021/i100021a001.

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Gahl, K. « How does technology influence medical acting ? » DMW - Deutsche Medizinische Wochenschrift 129, n^o 3 (janvier 2004) : 103–6. http://dx.doi.org/10.1055/s-2004-816286.

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Morey, Marcos, Ana Fernández-Marmiesse, Jose Angel Cocho et María L. Couce. « Influence of technology in genetic epidemiology ». AIMS Genetics 2, n^o 3 (2015) : 219–29. http://dx.doi.org/10.3934/genet.2015.3.219.

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Lee, Younghwa, Jintae Lee et Zoonky Lee. « Social influence on technology acceptance behavior ». ACM SIGMIS Database : the DATABASE for Advances in Information Systems 37, n^o 2-3 (19 septembre 2006) : 60–75. http://dx.doi.org/10.1145/1161345.1161355.

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Zhang, Bochen. « The Influence of Intelligent Technology on Photography Technology and Art ». IOP Conference Series : Materials Science and Engineering 382 (juillet 2018) : 052022. http://dx.doi.org/10.1088/1757-899x/382/5/052022.

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Li, YiZhi. « The Influence of Computer Application Technology on Enterprise Computer Technology ». Journal of Physics : Conference Series 1533 (avril 2020) : 022007. http://dx.doi.org/10.1088/1742-6596/1533/2/022007.

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Hailey, David, Sophie Werkö, Måns Rosén, Karen Macpherson, Susan Myles, Verónica Gallegos Rivero, Cecilia Hipólito-Olivares et al. « INFLUENCE OF HEALTH TECHNOLOGY ASSESSMENT AND ITS MEASUREMENT ». International Journal of Technology Assessment in Health Care 32, n^o 6 (2016) : 376–84. http://dx.doi.org/10.1017/s0266462316000611.

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Objectives: The aim of this study was to obtain information on methods used to measure health technology assessment (HTA) influence, decisions that were influenced, and outcomes linked to HTA.Methods: Electronic databases were used to locate studies in which HTA influence had been demonstrated. Inclusion criteria were studies that reliably reported consideration by decision makers of HTA findings; comparative studies of technology use before and after HTA; and details of changes in policy, health outcomes, or research that could be credibly linked to an HTA.Results: Fifty-one studies were selected for review. Settings were national (24), regional (12), both national and regional (3) hospitals (9), and multinational (3). The most common approach to appraisal of influence was review of policy or administrative decisions following HTA recommendations (51 percent). Eighteen studies (35 percent) reported interview or survey findings, thirteen (26 percent) reviewed administrative data, and six considered the influence of primary studies. Of 142 decisions informed by HTA, the most common types were on routine clinical practice (67 percent of studies), coverage (63 percent), and program operation (37 percent). The most frequent indications of HTA influence were on decisions related to resource allocation (59 percent), change in practice pattern (31 percent), and incorporation of HTA details in reference material (18 percent). Few publications assessed the contribution of HTA to changing patient outcomes.Conclusions: The literature on HTA influence remains limited, with little on longer term effects on practice and outcomes. The reviewed publications indicated how HTA is being used in different settings and approaches to measuring its influence that might be more widely applied, such as surveys and monitoring administrative data.

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Niederhauser, Dale S., et Serkan Perkmen. « Validation of the Intrapersonal Technology Integration Scale : Assessing the Influence of Intrapersonal Factors that Influence Technology Integration ». Computers in the Schools 25, n^o 1-2 (4 juillet 2008) : 98–111. http://dx.doi.org/10.1080/07380560802157956.

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Dampil, F. « The Influence of Educational Technology in Improving the Performance of Students in Chemistry ». International Journal of Information and Education Technology 5, n^o 11 (2015) : 822–25. http://dx.doi.org/10.7763/ijiet.2015.v5.619.

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Bhaumik, Amiya, Kian Aun Law et Yanping Xu. « INFLUENCE OF PSYCHOLOGICAL CAPITAL OF EMPLOYEES IN TECHNOLOGY ENTERPRISES AT GUANGDONG IN CHINA ». International Journal of Psychosocial Rehabilitation 24, n^o 04 (29 février 2020) : 1463–73. http://dx.doi.org/10.37200/ijpr/v24i4/pr201115.

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Makarenko, Liliia Petrivna. « Political performance as a technology of influence ». Politicus 1 (2018) : 66–70. http://dx.doi.org/10.24195/2414-9616-2018-1-66-70.

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19

Keefe, Jeffrey H. « Do Unions Influence the Diffusion of Technology ? » Industrial and Labor Relations Review 44, n^o 2 (janvier 1991) : 261. http://dx.doi.org/10.2307/2524807.

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20

Adeyeye, Olajumoke, Jemimah Njuki et Maureen Miruka. « Does women’s agency influence agriculture technology adoption ? » Food Chain 7, n^o 1 (janvier 2018) : 38–54. http://dx.doi.org/10.3362/2046-1887.17-00005.

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21

Fogg-Rogers, Laura. « Does being human influence science and technology ? » Journal of Science Communication 16, n^o 04 (20 septembre 2017) : C04. http://dx.doi.org/10.22323/2.16040304.

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This article addresses two major questions about women and science. Firstly, the commentary looks at the ways science and technology are discussed and represented all around us in society. Secondly, I ask whether this matters. The defining issue is therefore whether or not being human affects the type of science and technology that is conducted and valued within our society. By addressing these questions in science communication, we can add much to the debate about gender diversity and affirmative action being portrayed in our media and culture.

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Adel Albla, Alaulddin. « Influence Technology of Wireless Sensors Network communication ». International Journal of Scientific and Research Publications (IJSRP) 11, n^o 8 (24 août 2021) : 478–81. http://dx.doi.org/10.29322/ijsrp.11.08.2021.p11659.

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Segrest, Sharon L., Darla J. Domke‐Damonte, Angela K. Miles et William P. Anthony. « Following the crowd:social influence and technology usage ». Journal of Organizational Change Management 11, n^o 5 (octobre 1998) : 425–45. http://dx.doi.org/10.1108/09534819810234841.

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Sibanda, Mabutho, et Durrel Ramrathan. « Influence of Information Technology on Organization Strategy ». Foundations of Management 9, n^o 1 (23 février 2017) : 191–202. http://dx.doi.org/10.1515/fman-2017-0015.

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AbstractThe exponential development of information technology has presented many opportunities to organizations; however, it has also presented several challenges. A key challenge is how do organizations effectively use information technology and incorporate it into their strategies to make full use of its capabilities as an enabler. The fast-changing nature of information technology has resulted in little empirical evidence on how it influences organization strategy. The Strategic Alignment Model was a popular model created to assist organizations to align their information technology and their business strategy; however, the growth of technology may have made this model irrelevant in this age. Therefore, organizations need to determine what factors drive this alignment. Using hermeneutic phenomenology, 12 in-depth interviews were conducted within IBM South Africa to determine real-life drivers that help create this alignment. The themes derived from the interview texts reveal that consumers are becoming more empowered; therefore, organizations need to be more flexible in their business models and strategies. Furthermore, the integration of cross-functional roles in the organization at the management level allow for improved alignment between information technology and strategy as better integrated roles bring a combination of these two elements.

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Gates, J. W. C. « The influence of holography on measurement technology ». Journal of Physics E : Scientific Instruments 19, n^o 12 (décembre 1986) : 998–1007. http://dx.doi.org/10.1088/0022-3735/19/12/001.

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Lahm, Elizabeth A., et Leslie Sizemore. « Factors that Influence Assistive Technology Decision Making ». Journal of Special Education Technology 17, n^o 1 (décembre 2001) : 15–26. http://dx.doi.org/10.1177/016264340201700102.

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Ejye Omar, Ogenyi. « Retail influence on food technology and innovation ». International Journal of Retail & ; Distribution Management 23, n^o 3 (mars 1995) : 11–16. http://dx.doi.org/10.1108/09590559510083957.

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Wahab, D. A., et T. F. Putra. « Influence of Information Technology on Company Development ». IOP Conference Series : Materials Science and Engineering 662 (20 novembre 2019) : 022073. http://dx.doi.org/10.1088/1757-899x/662/2/022073.

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Klein, John J. « The Influence of Technology on Space Strategy ». Astropolitics 10, n^o 1 (janvier 2012) : 8–26. http://dx.doi.org/10.1080/14777622.2012.651700.

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Loescher, Lois J. « The influence of technology on cancer nursing ». Seminars in Oncology Nursing 16, n^o 1 (février 2000) : 3–11. http://dx.doi.org/10.1016/s0749-2081(00)80003-0.

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Sharma, Somesh Kumar, Swatantra Vishwakarma et Nishant Jha. « Prognosis agent technology : influence on manufacturing organizations ». International Journal of Advanced Manufacturing Technology 92, n^o 1-4 (25 février 2017) : 435–46. http://dx.doi.org/10.1007/s00170-017-0025-7.

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Phan, Dinh Hoang Bach, Paresh Kumar Narayan, R. Eki Rahman et Akhis R. Hutabarat. « Do financial technology firms influence bank performance ? » Pacific-Basin Finance Journal 62 (septembre 2020) : 101210. http://dx.doi.org/10.1016/j.pacfin.2019.101210.

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Keefe, Jeffrey H. « Do Unions Influence the Diffusion of Technology ? » ILR Review 44, n^o 2 (janvier 1991) : 261–74. http://dx.doi.org/10.1177/001979399104400205.

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Linton, Jonathan D. « Technology innovation management's growing influence and impact ». Technovation 29, n^o 10 (octobre 2009) : 643–44. http://dx.doi.org/10.1016/j.technovation.2009.07.001.

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Lowther, Deborah, Steven M. Ross et J. Daniel Strahl. « The Influence of Technology on Instructional Practices ». International Journal of Knowledge, Culture, and Change Management : Annual Review 6, n^o 5 (2006) : 131–42. http://dx.doi.org/10.18848/1447-9524/cgp/v06i05/49383.

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Vannoy, Sandra A., et Prashant Palvia. « The social influence model of technology adoption ». Communications of the ACM 53, n^o 6 (juin 2010) : 149–53. http://dx.doi.org/10.1145/1743546.1743585.

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Deutsch, H. « Public opinion's influence on voting system technology ». Computer 38, n^o 3 (mars 2005) : 93–95. http://dx.doi.org/10.1109/mc.2005.102.

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Krau, Stephen D. « The Influence of Technology in Nursing Education ». Nursing Clinics of North America 50, n^o 2 (juin 2015) : 379–87. http://dx.doi.org/10.1016/j.cnur.2015.02.002.

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Achar, Sandesh. « Influence of IoT Technology on Environmental Monitoring ». Asia Pacific Journal of Energy and Environment 7, n^o 2 (31 décembre 2020) : 87–92. http://dx.doi.org/10.18034/apjee.v7i2.649.

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The newly connected globe has the potential to become a more environmentally friendly and sustainable place if more efficiencies can be brought to various industries. We will look at examples of how the Internet of Things technology is helping to improve the world. Relatively recently, the technology of the fourth industrial revolution has given rise to the characteristics of things constantly expanding, and everything, including people, things, people, and the environment, is connected based on the Internet. In particular, the network structure is connected to various Internet of Things devices and transitioning from wired to wireless. Gateways both within and outside the smart home can now be used to control other devices, in contrast to users who previously owned each device individually. However, due to these changes, the environment is now susceptible to attacks from the outside. For example, when an attacker gains access to a gateway, he can attempt various attacks, such as port scans, OS and service detection, and denial of service assaults on Internet of Things devices. As a result, the findings of the study reveal the influence of IoT technology, which has the potential to improve environmental monitoring to enhance environmental safety.

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Sox, Carole B., Jeffrey M. Campbell, Sheryl F. Kline, Sandra K. Strick et Tena B. Crews. « Technology use within meetings : a generational perspective ». Journal of Hospitality and Tourism Technology 7, n^o 2 (3 mai 2016) : 158–81. http://dx.doi.org/10.1108/jhtt-09-2015-0035.

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Purpose This paper aims to examine generational formative referents as factors that influence meeting attendees’ adoption and technology use within virtual and hybrid meetings, and test the applicability of the technology acceptance model (TAM) as presented by Davis (1986). This study investigates how attendees’ experiences from their respective formative years (i.e. generational formative referents), the basis of the Generational Cohort Theory (GCT), influence the TAM model constructs. Design/methodology/approach A partial least squares analysis test is utilized to determine technology acceptance within meetings across three generations: Baby Boomers (1946-1964), Generation X (1965-1978) and Generation Y (1979-2000). Findings The multi-group comparison determined all three generations responded similarly with regard to the paths being tested, indicating each of the three generational cohorts within this study are influenced by the experiences of their formative years, which are different for each generation. Research limitations/implications The findings add to the limited foundation for scholars wanting to further analyze technology use within meetings, and for those interested in generational influences. Practical implications This study provides useful information for marketers and planners to increase meeting attendance, enhance attendee satisfaction, and further explore meeting engagement opportunities. Originality/value Underpinning the GCT, this study is the first within hospitality and tourism studies to investigate a theoretical model on generational technology use within meetings.

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R., Dr Vijay Raja, et Dr Soundarapandian K. « Impact of Factors on Work Life Balance Due to Technology Transformation with Respect to Information Technology Employees ». Webology 19, n^o 1 (20 janvier 2022) : 5487–96. http://dx.doi.org/10.14704/web/v19i1/web19369.

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A balanced work and family life leads to a strong work life balance. Due to the increase of employment, longer working hours due to inadequate family income, target driven work culture makes it problematic for a good work life balance. It is also seen that the advancement in the digital technology have made everyone reachable beyond traditional working hours. This study focuses and investigates on the factors which influence the ‘Work-Life’ balance of employees in the selective IT companies. After various references from articles, a research model was derived. Remote Work (RW), Increased Communication (SE), Work Extension (WE), Permeability (PER) and Flexibility (FLE) are the independent Variables and Work Life balance (WLB) is the dependent variable taken into consideration for the study. Convenience sampling is adopted for the study. Out of the 266 questionnaires distributed, 213 responded. After neglecting the 9 incomplete or invalid responses, the sample size was determined to be 204. The study shows that remote work has a positive influence on work life balance whereas the permeability and flexibility has a significant influence on the other way.

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Rosmayanti, Vivit, Nurdin Noni et Andi Anto Patak. « Students’ Acceptance of Technology Use in Learning English Pharmacy ». International Journal of Language Education 6, n^o 3 (1 novembre 2022) : 314. http://dx.doi.org/10.26858/ijole.v6i3.24144.

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This study aims at determining how Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions influenced Behavioral intentions to use technology in learning English Pharmacy. This study employed a sample of 100 pharmacy students who were enrolled in an English pharmacy course. The samples were chosen using the quota sampling technique. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was performed to measure students' acceptance of the use of technology in this study. The findings of this study indicated that the four aspects mentioned above, such as Performance Expectations, Effort Expectations, Social Influences, and Facilitation Conditions, have a favorable impact on students' Behavioral intentions when using technology to learn English Pharmacy. However, only 40% of the four factors could explain the influence on Behavioral intention. The impact of this study is that it can lead the way and serve as a reference for future research on the UTAUT approach in a larger scope. In addition, the university should be able to increase the benefits and usability of technology as a learning resource by developing its own Learning Management System (LMS), which is easy to use.

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Yuyin, Wu, et Chen Yuhang. « Influence of virtual imaging technology based on html5 technology on digital painting ». Microprocessors and Microsystems 82 (avril 2021) : 103855. http://dx.doi.org/10.1016/j.micpro.2021.103855.

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Koch, Christian. « Collective Influence on Information Technology in Virtual Organizations-Emancipatory Management of Technology ? » Technology Analysis & ; Strategic Management 12, n^o 3 (septembre 2000) : 357–68. http://dx.doi.org/10.1080/713698475.

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Baikenov. « Investigation of the influence of electrohydroimpulse technology on physico-chemical characteristics of oil sludges ». Bulletin of the Karaganda University. "Chemistry" series 87, n^o 3 (29 septembre 2017) : 131–36. http://dx.doi.org/10.31489/2017ch3/131-136.

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Olschewski, Mark, Uta Renken et Benjamin Mueller. « Collaboration Technology Adoption ». International Journal of Technology Diffusion 9, n^o 3 (juillet 2018) : 13–28. http://dx.doi.org/10.4018/ijtd.2018070102.

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This article investigates technology readiness and social influence in the context of collaboration technology acceptance. The authors also explore these factors in terms of their impact on technology appropriation and repurposing to go beyond classical adoption research focused on initial acceptance of technology. The authors find that social influence is a dominant factor in the context of individuals' acceptance of collaboration technologies, outperforming technology readiness and classical adoption measures. Based on an exploratory PLS study conducted among student users of a collaboration technology, this study is among the first to explore and conceptualize the role of social influence and technology readiness in technology acceptance. This is a particularly valuable advance for studying collaboration technologies because individuals' use of these technologies is shaped in social interaction and through personal psychological dispositions. This helps managers to successfully introduce collaboration technologies, a key tool for enabling workplace and customer engagement innovation.

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Hughes, Thomas. « Shaped Technology : An Afterword ». Science in Context 8, n^o 2 (1995) : 451–55. http://dx.doi.org/10.1017/s026988970000209x.

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The informative and engaging essays in the foregoing collection suggest several interesting concepts that deserve further research and reflection. Over the past decade, the “social construction of technology” has become a concept often explored by historians (Bijker, Hughes, and Pinch 1987). Even though it has performed the useful function of discrediting technological determinism, the concept suggests too narrow a set of influences that shape technology. Two other concept, “nature-shaped technology” and “culture-shaped technology,” convey the character of technology more effectively. To designate “nature” as a shaper of technology reminds us that in a relatively prisine world the designer of technology negotiates with natural forces more than with human-built ones. To see culture as a shaper of technology suggests a broader range of influences affecting technology, not simply the social. “Shaping” conveys the notion of influence and avoids that of determinism better than “construction.”

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Kaufer, David S., et Kathleen M. Carley. « The Influence of Print on Social and Cultural Change ». Annual Review of Applied Linguistics 16 (mars 1996) : 14–25. http://dx.doi.org/10.1017/s0267190500001410.

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Tracing the sociocultural influence of any technology is fraught with problems. First, many of the influences cited are likely to be too large and diffuse to be tested under experimental conditions in the laboratory. Second, the technology is likely to be, at most, an accessory to many other influencing factors rather than a singular cause. Third, insofar as the technology can be isolated as a factor of influence, the direction of the influence is often two way. The technology may cause changes in sociocultural states, but existing sociocultural states are also likely to result in the technology being used and evolved in unanticipated ways.

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Indriati, Fibria, et Priyanka Agustina. « THE INFLUENCE OF UTAUT FACTORS ON E-RETENTION WITH E-SATISFACTION AS MEDIATING VARIABLE IN E-LEARNING ». Hasanuddin Economics and Business Review 2, n^o 1 (30 juin 2018) : 19. http://dx.doi.org/10.26487/hebr.v2i1.1465.

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Factors on Unified Theory of Acceptance and Use of Technology (UTAUT) are factors derived from UTAUT technology acceptance model, which is used to describe the factors that influence the acceptance of individuals on the implementation of a new technology in the enterprise. This study aimed to analyze the influence of UTAUT factors on E-retention with E-satisfaction as mediating variable. This study uses a quantitative approach with the total samples. The results showed that: (1) Performance Expectancy influences E-retention significantly, (2) Effort Expectancy influences E-retention significantly, (3) Social Influence influences E-retention not significantly, (4) Facilitating Conditions influences E-retention significantly, (5) E-satisfaction influences E-retention significantly, and (6) UTAUT Factors influence E-retention with E-satisfaction as mediating variable. However, the bigger impact is shown through the influence of UTAUT Factors on E-retention directly without going through the E-satisfaction.

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Zhang, Tingting (Christina), Behzad Abound Omran et Cihan Cobanoglu. « Generation Y’s positive and negative eWOM : use of social media and mobile technology ». International Journal of Contemporary Hospitality Management 29, n^o 2 (13 février 2017) : 732–61. http://dx.doi.org/10.1108/ijchm-10-2015-0611.

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Purpose This paper aims to explore the factors that influence Generation Y’s positive or negative electronic word-of-mouth (eWOM) behavior via social media and mobile technology in the foodservice sector. Three types of dining experiences were examined: positive and negative customer experiences and negative customer service followed by a satisfactory recovery package. Design/methodology/approach A survey was adopted to test the factors posited to influence Generation Y consumers in these service contexts. Participants were recruited through Amazon Mechanical Turk , and multi-group structural equation modeling was performed to analyze the data. Findings Active use of social media and peer influence had a sweeping influence on Generation Y’s intentions to engage in eWOM about their service experiences. Technological sophistication with mobile technology influenced Generation Y to spread positive or negative service experiences, rather than satisfactory recovery experiences. Family influence had a mixed influence on Generation Y subgroups (21-24 years old vs 25-35 years old) to engage in eWOM about their satisfactory or poor service experiences. In satisfactory recovery experiences, family influence showed no significant influence on Generation Y’s eWOM behaviors. Research limitations/implications This study enriches online reviews and eWOM marketing theories, adds to service failure and recovery literature and enhances understanding of consumer behavior expressed by Generation Y through the empirical investigation of Generation Y consumers’ behavioral motivations to engage in eWOM through social media and mobile technology. Practical implications Engaging Generation Y consumers with social media campaigns and mobile technology development is not merely sufficient in eWOM marketing strategies. Instead, it is essential to create integrative peer communities to motivate Generation Y consumers to engage in eWOM marketing. Marketers need to pay attention to the mixed effects of family influences on the eWOM behaviors of subgroups of Generation Y in positive or negative service experiences. Originality/value Given the scarcity of consumer behavior research into Generation Y as an emerging market segment, this paper makes an incremental contribution by developing and validating a model of factors that influence Generation Y consumers’ eWOM intentions through social networking and mobile technologies in three major service contexts: positive, negative and recovery following a service failure.

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