Journal articles on the topic 'AI adoption'
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El-Deeb, Ahmed. "AI Adoption." ACM SIGSOFT Software Engineering Notes 47, no. 4 (September 27, 2022): 16–17. http://dx.doi.org/10.1145/3561846.3561851.
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While Artificial Intelligence (AI) has been an industry buzzword the past 15+ years, AI as a subject is not something new. The term AI has been coined by John McCarthy in 1956 and Neural Networks has been a popular subject well in the 1980s. It's just that AI has undergone a long journey of invention and entrepreneurial phase; and seem to still not fully over it. The question now why the industry is not crossing the chasm to the mass production phase? Why most companies are not relying on AI product to reduce their churn and increase their efficiency? In this paper, I will survey the major factors that play critical role in the slow AI adoption across the software industry.
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Pillai, Rajasshrie, and Brijesh Sivathanu. "Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations." Benchmarking: An International Journal 27, no. 9 (August 14, 2020): 2599–629. http://dx.doi.org/10.1108/bij-04-2020-0186.
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PurposeHuman resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.Design/methodology/approachThis study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.FindingsThis research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.Practical implicationsThis paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.Originality/valueThis research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.
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Yuen, Simon, and H. Wu. "Smart Logistics and Artificial Intelligence Practices in Industry 4.0 ERA." International Journal of Managing Value and Supply Chains 13, no. 1 (March 31, 2022): 1–7. http://dx.doi.org/10.5121/ijmvsc.2022.13101.
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The purpose of this paper is to analyze the factors that affect success of logistics companies adopting AI by studying the application of AI in the logistics industry. Although the application of technology and artificial intelligence has been widely studied, the factors affecting the adoption of Artificial Intelligence (AI) are still unknown in the existing literature. Therefore, the main research in this paper is to explore the influence of success factors on AI by integrating technology, organization and environment (TOE) framework. The framework is judged by case analysis of logistics companies. This study provides some suggestions on successful adoption of AI technology fortheir logistics operations.
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Kioko, Peter Mwangangi, Nancy Booker, Njoki Chege, and Paul Kimweli. "The Adoption of Artificial Intelligence in Newsrooms in Kenya: a Multi-case Study." European Scientific Journal, ESJ 18, no. 22 (July 31, 2022): 278. http://dx.doi.org/10.19044/esj.2022.v18n22p278.
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The deployment of Artificial Intelligence (AI) in newsrooms is gaining prominence worldwide, with the technology being used to enhance the processes of news gathering, packaging, and distribution. The study was guided by two research questions: what factors drive/hinder(s) the adoption of AI or lack of it in newsrooms in Kenya? Moreover, what opportunities do journalists feel are offered by adopting AI in newsrooms in Kenya? A qualitative research approach and descriptive research design were employed to investigate the adoption of AI in newsrooms in Kenya. British Broadcasting Corporation (BBC-Africa) and Radio Africa Group (RAG) media organizations were the target population. As a research strategy, a multi-case method was employed. The researchers conducted in-depth interviews with newsroom-based participants. A purposive sampling technique was used to select participants for the research. Collected data were analyzed thematically. The paper identified six factors driving the adoption of AI or lack of it: management buy-in, cost, technical skills, clarity of user case, perception, and company structure. Further, the study identified three challenges presented by adopting AI: lack of quality data, ethical concerns, and unpredictability of the technology’s impact. The study concludes that AI offers excellent opportunities for newsrooms in Kenya to explore. Still, some obstacles need to be addressed before they can benefit fully from the technology. The study projects that human and automated journalism will become closely integrated in the future and recommends that newsrooms in Kenya prepare to embrace AI by laying the foundation for its adoption. Media schools should update curricula to prepare journalists to work with emerging technologies such as AI. Further research is needed to identify the specific skill sets required for Kenyan digital journalists to embrace AI fully. Scholars should investigate how AI can shape new business models given shrinking revenues in the media.
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Freeman, Laura, Abdul Rahman, and Feras A. Batarseh. "Enabling Artificial Intelligence Adoption through Assurance." Social Sciences 10, no. 9 (August 25, 2021): 322. http://dx.doi.org/10.3390/socsci10090322.
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The wide scale adoption of Artificial Intelligence (AI) will require that AI engineers and developers can provide assurances to the user base that an algorithm will perform as intended and without failure. Assurance is the safety valve for reliable, dependable, explainable, and fair intelligent systems. AI assurance provides the necessary tools to enable AI adoption into applications, software, hardware, and complex systems. AI assurance involves quantifying capabilities and associating risks across deployments including: data quality to include inherent biases, algorithm performance, statistical errors, and algorithm trustworthiness and security. Data, algorithmic, and context/domain-specific factors may change over time and impact the ability of AI systems in delivering accurate outcomes. In this paper, we discuss the importance and different angles of AI assurance, and present a general framework that addresses its challenges.
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Somjai, Sudawan, Kittisak Jermsittiparsert, and Thitinan Chankoson. "Determining the initial and subsequent impact of artificial intelligence adoption on economy: a macroeconomic survey from ASEAN." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5459–74. http://dx.doi.org/10.3233/jifs-189029.
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The adoption of AI is an ongoing phenomenon in today’s economy in all the industries. The purpose of this paper is to examine the economic impact of AI adoption in the region of ASEAN. To achieve this objective, structural questionnaire was developed for the various industry experts in targeted region. A sample of 240 experts was finally obtained over a time span of 6 weeks through online structural questionnaire approach. For measuring AI adoption, twelve items, initial economic impact (seven items), and subsequent economic impact (six items) were finally added in the questionnaire. For analyses purpose, descriptive statistics, structural equation modelling, and regression analyseswereapplied, examining the both initial and subsequent economic impact of AI adoption. Findings through structural model indicates that overall both initial and subsequent impact are significantly determined by AI adoption in related industries. Additionally, in depth analyses for the individual AI items as their initial and subsequent economic impact indicate that Usage of the data for AI adoption, clear strategy for AI adoption, successful mapping for AI adoption and overall positive attitude towards AI adoption have their significant and positive influence on initial economic indicators. Whereas, as per subsequent economic impact, factors like effective usage of data for AI adoption, assessing the right skills of individuals for AI adoption and positive attitude towards AI adoption are significantly impacting on material investment, capital investment, increasing unemployment, higher economic output, higher return on capital and higher wages for the existing labor. These findings have provided an outstanding evidence in the field of AI and its economic impact in the region of ASEAN and can be considered as initial contribution in related fields. Both industry exports and macroeconomic decision makers can significantly utilize the findings to develop their conceptual framework and understanding for the integration between AI adoption and economy. Additionally, this study can work as reasonable justification for implementing the more adoption of AI in various industries as it has positive economic outcome (both initial and subsequent). However, one of the key limitations of this study is limited sample size and only 240 industry exports were targeted from selected industries in ASEAN. Future study could be reimplemented on similar topic with expanding the sample size for better findings and more generalization.
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الهادی, محمد. "The Top 7 AI Adoption Challenges." مجلة الجمعیة المصریة لنظم المعلومات وتکنولوجیا الحاسبات 24, no. 24 (April 1, 2021): 18–20. http://dx.doi.org/10.21608/jstc.2021.165200.
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Mudgal, Keshav Shree, and Neelanjan Das. "The ethical adoption of artificial intelligence in radiology." BJR|Open 2, no. 1 (November 1, 2020): 20190020. http://dx.doi.org/10.1259/bjro.20190020.
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Artificial intelligence (AI) is rapidly transforming healthcare—with radiology at the pioneering forefront. To be trustfully adopted, AI needs to be lawful, ethical and robust. This article covers the different aspects of a safe and sustainable deployment of AI in radiology during: training, integration and regulation. For training, data must be appropriately valued, and deals with AI companies must be centralized. Companies must clearly define anonymization and consent, and patients must be well-informed about their data usage. Data fed into algorithms must be made AI-ready by refining, purification, digitization and centralization. Finally, data must represent various demographics. AI needs to be safely integrated with radiologists-in-the-loop: guiding forming concepts of AI solutions and supervising training and feedback. To be well-regulated, AI systems must be approved by a health authority and agreements must be made upon liability for errors, roles of supervised and unsupervised AI and fair workforce distribution (between AI and radiologists), with a renewal of policy at regular intervals. Any errors made must have a root-cause analysis, with outcomes fedback to companies to close the loop—thus enabling a dynamic best prediction system. In the distant future, AI may act autonomously with little human supervision. Ethical training and integration can ensure a "transparent" technology that will allow insight: helping us reflect on our current understanding of imaging interpretation and fill knowledge gaps, eventually moulding radiological practice. This article proposes recommendations for ethical practise that can guide a nationalized framework to build a sustainable and transparent system.
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Nazri, Shahrizal, Mohamed Azlan Ashaari, and Hazrieffendy Bakri. "EXPLORING THE ADOPTION OF ARTIFICIAL INTELLIGENCE IN INSTITUTIONS OF HIGHER LEARNING." Journal of Information System and Technology Management 7, no. 27 (September 1, 2022): 54–62. http://dx.doi.org/10.35631/jistm.727004.
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In every sector of the economy, artificial intelligence (AI) is becoming more feasible, and higher education service is no exception. At an unparalleled pace, both within and outside the classroom, AI opens the possibility for institutions of higher learning (IHLs) to become scalable. This paper proposed a research framework for AI adoption of in IHLs. The research aims to examine the determinants that significantly affected AI adoption among IHLs. This study presents an interpretation of the Technology-Organisation-Environment (TOE) theory for the adoption of AI. The research framework derived from the TOE theory, where the context of technological, organisational, and environmental are vital for IT adoption. It discussed the development of hypotheses that consisted the determinants for the adoption of AI from the context of technological (relative advantage and compatibility), organisational (resources availability, top management support and organisation size) and environmental (government regulation and competitive pressure).
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Tahiru, Fati. "AI in Education." Journal of Cases on Information Technology 23, no. 1 (January 2021): 1–20. http://dx.doi.org/10.4018/jcit.2021010101.
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Artificial intelligence (AI) is developing and its application is spreading at an alarming rate, and AI has become part of our daily lives. As a matter of fact, AI has changed the way people learn. However, its adoption in the educational sector has been saddled with challenges and ethical issues. The purpose of this study is to analyze the opportunities, benefits, and challenges of AI in education. A review of available and relevant literature was done using the systematic review method to identify the current research focus and provide an in-depth understanding of AI technology in education for educators and future research directions. Findings showed that AI's adoption in education has advanced in the developed countries and most research became popular within the Industry 4.0 era. Other challenges, as well as recommendations, are discussed in the study.
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Wang, Lei, Provash Sarker, Kausar Alam, and Shahneoaj Sumon. "Artificial Intelligence and Economic Growth: A Theoretical Framework." Scientific Annals of Economics and Business 68, no. 4 (November 26, 2021): 421–43. http://dx.doi.org/10.47743/saeb-2021-0027.
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The growing adoption of Artificial Intelligence (AI) has sparked ubiquitous concerns worldwide. Artificial intelligence can affect economic growth and employment. The influence is assumed to be substantial because the adoption of AI technology may lead to increased productivity, lower wages, prices, and labor substitution. Artificial intelligence can affect global economic growth with its widespread adoption and diffusion. We mathematically examined the effects of AI on economic growth, reiterating how AI is unique as a production factor. The models show that AI capital lowers capital prices, increases wages, and augments productivity. Besides, AI capital positively affects the labor share and vice versa, provided that AI and labor are complementary. We improved a task-based model to show AI raises both labor share and wages by generating new tasks. We also present the potential policy implications of AI adoption. We conclude AI can contribute to economic growth. Labor-abundant countries should adopt labor-augmenting technology, while countries with an aging population can adopt capital-augmenting technology. However, caution should be exercised in ensuring that the models are leveraged optimally.
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Tuffaha, Mohand, and M Rosario Perello-Marin. "Adoption Factors of Artificial intelligence in Human Resources Management." Future of Business Administration 1, no. 1 (June 20, 2022): 1–12. http://dx.doi.org/10.33422/fba.v1i1.140.
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The phenomenon of artificial intelligence has been widely studied in several areas. In opposite, in terms of AI in HRM, the literature shows limited research on the adoption factors of artificial intelligence (AI) in HRM. AI has been enrolled in several HRM’s areas starting from staffing till management performance or compensation. A set of suggestions on how to adopt AI in HRM has been raised. This piece of research aims to identify the adoption factors of six scenarios of AI in HRM. These scenarios are turnover prediction with artificial neural networks, candidate search with knowledge-based search engines, staff rostering with genetic algorithms, HR sentiment analysis with text mining, résumé data acquisition with information extraction and employee self-service with interactive voice response. As a result, compatibility, relative advantage, complexity, managerial support, government involvement, and vendor partnership are determinants affected factors of AI adoption in HRM. This paper tries to address new insights for practitioners and academics by minimizing the risks associated with AI adoption in some areas of HRM through exploring determinant factors of adoption.
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Cheng, Mengting, Xianmiao Li, and Jicheng Xu. "Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust." International Journal of Environmental Research and Public Health 19, no. 20 (October 15, 2022): 13311. http://dx.doi.org/10.3390/ijerph192013311.
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Artificial intelligence (AI)-assisted diagnosis and treatment could expand the medical scenarios and augment work efficiency and accuracy. However, factors influencing healthcare workers’ adoption intention of AI-assisted diagnosis and treatment are not well-understood. This study conducted a cross-sectional study of 343 dental healthcare workers from tertiary hospitals and secondary hospitals in Anhui Province. The obtained data were analyzed using structural equation modeling. The results showed that performance expectancy and effort expectancy were both positively related to healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Social influence and human–computer trust, respectively, mediated the relationship between expectancy (performance expectancy and effort expectancy) and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Furthermore, social influence and human–computer trust played a chain mediation role between expectancy and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Our study provided novel insights into the path mechanism of healthcare workers’ adoption intention of AI-assisted diagnosis and treatment.
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Kelley, Stephanie. "Effective Adoption and Implementation of AI Principles." Academy of Management Proceedings 2021, no. 1 (August 2021): 13573. http://dx.doi.org/10.5465/ambpp.2021.13573abstract.
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Ghani, Erlane K., Nurshahiirah Ariffin, and Citra Sukmadilaga. "Factors Influencing Artificial Intelligence Adoption in Publicly Listed Manufacturing Companies: A Technology, Organisation, and Environment Approach." International Journal of Applied Economics, Finance and Accounting 14, no. 2 (September 23, 2022): 108–17. http://dx.doi.org/10.33094/ijaefa.v14i2.667.
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This study examines the factors influencing artificial intelligence (AI) adoption in publicly listed manufacturing companies in Malaysia. Specifically, three factors are investigated based on the technology, organisation, and environment (TOE) framework: information technology (IT) capability, top management support, and government support. Using a questionnaire survey of 127 respondents from publicly listed manufacturing companies in Malaysia, this study shows that top management support and government support significantly affect AI adoption in publicly listed manufacturing companies. However, the results show that IT capability does not significantly influence the AI adoption of publicly listed manufacturing companies. Thus, the findings provide evidence of the influence of IT capability, top management support, and government support on AI adoption in publicly listed manufacturing companies. In addition, the findings of this study contribute to the existing literature on AI adoption in publicly listed manufacturing companies in Malaysia.
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Regona, Massimo, Tan Yigitcanlar, Bo Xia, and Rita Yi Man Li. "Artificial Intelligent Technologies for the Construction Industry: How Are They Perceived and Utilized in Australia?" Journal of Open Innovation: Technology, Market, and Complexity 8, no. 1 (January 10, 2022): 16. http://dx.doi.org/10.3390/joitmc8010016.
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Artificial intelligence (AI) is a powerful technology that can be utilized throughout a construction project lifecycle. Transition to incorporate AI technologies in the construction industry has been delayed due to the lack of know-how and research. There is also a knowledge gap regarding how the public perceives AI technologies, their areas of application, prospects, and constraints in the construction industry. This study aims to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. This study adopted social media analytics, along with sentiment and content analyses of Twitter messages (n = 7906), as the methodological approach. The results revealed that: (a) robotics, internet-of-things, and machine learning are the most popular AI technologies in Australia; (b) Australian public sentiments toward AI are mostly positive, whilst some negative perceptions exist; (c) there are distinctive views on the opportunities and constraints of AI among the Australian states/territories; (d) timesaving, innovation, and digitalization are the most common AI prospects; and (e) project risk, security of data, and lack of capabilities are the most common AI constraints. This study is the first to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. The findings inform the construction industry on public perceptions and prospects and constraints of AI adoption. In addition, it advocates the search for finding the most efficient means to utilize AI technologies. The study helps public perceptions and prospects and constraints of AI adoption to be factored in construction industry technology adoption.
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Zeng, Xiaochun, Suicheng Li, and Zahid Yousaf. "Artificial Intelligence Adoption and Digital Innovation: How Does Digital Resilience Act as a Mediator and Training Protocols as a Moderator?" Sustainability 14, no. 14 (July 6, 2022): 8286. http://dx.doi.org/10.3390/su14148286.
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This study aims to discover how technology firms accomplish digital innovation through AI adoption. The current research also investigated digital resilience’s role as a mediator and training protocol’s role as a moderator between AI adoption and digital innovation links. The data collection and analysis were conducted using a quantitative method. To examine the research hypotheses, we chose technology firms that face problems regarding the enhancement of digital innovation. The findings confirmed that the digital innovation of technology firms is forecasted through AI adoption. The results proved that digital resilience plays a mediating role between AI adoption and digital innovation links. Technology firms play a key role in the advancement of digital technology. This research study adds to the existing knowledge by offering a digital innovation model with the combined influence of AI adoption, digital resilience, and training protocol. This study will be helpful for top management by showing when, why, and how AI adoption helps firms in their achievement of digital innovation. Moreover, digital resilience’s role is also important in the current digitalized world; thus, we used digital resilience as mediator in this research.
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Shin, Yooncheol, and Jaewoo Joo. "Home Alone: Loneliness Increases Adoption of AI Speakers." Journal of the Ergonomics Society of Korea 38, no. 6 (December 31, 2019): 499–515. http://dx.doi.org/10.5143/jesk.2019.38.6.499.
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Phuoc, Nguyen Van. "The Critical Factors Impacting Artificial Intelligence Applications Adoption in Vietnam: A Structural Equation Modeling Analysis." Economies 10, no. 6 (June 1, 2022): 129. http://dx.doi.org/10.3390/economies10060129.
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The concept of artificial intelligence (AI) is the transformation of inanimate objects into intelligent beings that can reason similarly to humans. Computer systems are capable of imitating a number of human intelligence functions, such as learning, reasoning, problem solving, speech recognition, and planning. In this regard, artificial intelligence applications have been developed to assist corporations and entrepreneurs in making business decisions. Hence, the aim of the study is to investigate the adoption of AI applications at the Vietnamese organizational level. Using the core structures of the technology–organization–environment (TOE), the theoretical model was constructed based on how technical and environmental elements influence companies’ technological innovation adoption decisions. Ten critical factors related to AI adoption are identified. To test the model, data were obtained from 193 senior managers who are directly in charge of information systems in both private and public companies in Vietnam. Subsequently, the Structural Equation Modeling (SEM) approach was used to analyze the data. The findings indicate that technical compatibility, relative advantage, technical complexity, technical capability, managerial capability, organizational readiness, government involvement, market uncertainty, and vendor partnership are significantly related to AI application adoption. Interestingly, the study results indicated that the relationship between organization size and AI adoption was not statistically significant. Therefore, the suggested adoption of the AI application could contribute to the existing research on the adoption of AI through the TOE. Finally, the significant government law implications and future research directions are further addressed.
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Neller, Todd W. "AI education matters." AI Matters 8, no. 1 (March 2022): 9–11. http://dx.doi.org/10.1145/3544897.3544900.
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In this column, we describe the Model AI Assignment "FairKalah: Fair Mancala Competition". After introducing the rules of Mancala (a.k.a. Kalah), we discuss the primary difficulty that its unfairness causes for AI competition assessment, and present a solution along with a description of a set of resources to aid in assignment adoption.
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Yigitcanlar, Tan, Rita Yi Man Li, Tommi Inkinen, and Alexander Paz. "Public Perceptions on Application Areas and Adoption Challenges of AI in Urban Services." Emerging Science Journal 6, no. 6 (September 13, 2022): 1199–236. http://dx.doi.org/10.28991/esj-2022-06-06-01.
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Artificial intelligence (AI) deployment is exceedingly relevant to local governments, for example, in planning and delivering urban services. AI adoption in urban services, however, is an understudied area, particularly because there is limited knowledge and hence a research gap on the public's perceptions-users/receivers of these services. This study aims to examine people’s behaviors and preferences regarding the most suited urban services for application of AI technology and the challenges for governments to adopt AI for urban service delivery. The methodological approach includes data collection through an online survey from Australia and Hong Kong and statistical analysis of the data through binary logistic regression modeling. The study finds that: (a) Attitudes toward AI applications and ease of use have significant effects on forming an opinion on AI; (b) initial thoughts regarding the meaning of AI have a significant impact on AI application areas and adoption challenges; (c) perception differences between the two countries in AI application areas are significant; and (d) perception differences between the two countries in government AI adoption challenges are minimal. The study consolidates our understanding of how the public perceives the application areas and adoption challenges of AI, particularly in urban services, which informs local authorities that deploy or plan to adopt AI in their urban services. Doi: 10.28991ESJ-2022-06-06-01 Full Text: PDF
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Paul, Souma Kanti, Sadia Riaz, and Suchismita Das. "Adoption of Artificial Intelligence in Supply Chain Risk Management." Journal of Global Information Management 30, no. 8 (September 1, 2021): 1–18. http://dx.doi.org/10.4018/jgim.307569.
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The study aims to examine factors that influence the adoption-diffusion process of Artificial Intelligence (AI) in Supply Chain Risk Management (SCRM) across manufacturing, wholesale trade, retail trade, and transportation industries in India. As part of this study, eleven constructs that influence the adoption-diffusion stages of AI in SCRM were identified and examined. A survey was conducted to collect data from supply chain executives, risk professionals, and AI consultants across the manufacturing, wholesale trade, retail trade, and transportation industries in India. Partial least squares structural equation modeling (PLS-SEM) was used to study the data. Results show that these factors have varying degrees of influence and direction on the three stages of adoption of AI in SCRM. The study will enable the leadership team in the organizations to build a roadmap for the adoption, implementation, and routinization of AI in SCRM.
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Regona, Massimo, Tan Yigitcanlar, Bo Xia, and Rita Yi Man Li. "Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review." Journal of Open Innovation: Technology, Market, and Complexity 8, no. 1 (February 28, 2022): 45. http://dx.doi.org/10.3390/joitmc8010045.
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Artificial intelligence (AI) is a powerful technology with a range of capabilities, which are beginning to become apparent in all industries nowadays. The increased popularity of AI in the construction industry, however, is rather limited in comparison to other industry sectors. Moreover, despite AI being a hot topic in built environment research, there are limited review studies that investigate the reasons for the low-level AI adoption in the construction industry. This study aims to reduce this gap by identifying the adoption challenges of AI, along with the opportunities offered, for the construction industry. To achieve the aim, the study adopts a systematic literature review approach using the PRISMA protocol. In addition, the systematic review of the literature focuses on the planning, design, and construction stages of the construction project lifecycle. The results of the review reveal that (a) AI is particularly beneficial in the planning stage as the success of construction projects depends on accurate events, risks, and cost forecasting; (b) the major opportunity in adopting AI is to reduce the time spent on repetitive tasks by using big data analytics and improving the work processes; and (c) the biggest challenge to incorporate AI on a construction site is the fragmented nature of the industry, which has resulted in issues of data acquisition and retention. The findings of the study inform a range of parties that operate in the construction industry concerning the opportunities and challenges of AI adaptability and help increase the market acceptance of AI practices.
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Frank, Darius-Aurel, Christian T. Elbæk, Caroline Kjær Børsting, Panagiotis Mitkidis, Tobias Otterbring, and Sylvie Borau. "Drivers and social implications of Artificial Intelligence adoption in healthcare during the COVID-19 pandemic." PLOS ONE 16, no. 11 (November 22, 2021): e0259928. http://dx.doi.org/10.1371/journal.pone.0259928.
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The COVID-19 pandemic continues to impact people worldwide–steadily depleting scarce resources in healthcare. Medical Artificial Intelligence (AI) promises a much-needed relief but only if the technology gets adopted at scale. The present research investigates people’s intention to adopt medical AI as well as the drivers of this adoption in a representative study of two European countries (Denmark and France, N = 1068) during the initial phase of the COVID-19 pandemic. Results reveal AI aversion; only 1 of 10 individuals choose medical AI over human physicians in a hypothetical triage-phase of COVID-19 pre-hospital entrance. Key predictors of medical AI adoption are people’s trust in medical AI and, to a lesser extent, the trait of open-mindedness. More importantly, our results reveal that mistrust and perceived uniqueness neglect from human physicians, as well as a lack of social belonging significantly increase people’s medical AI adoption. These results suggest that for medical AI to be widely adopted, people may need to express less confidence in human physicians and to even feel disconnected from humanity. We discuss the social implications of these findings and propose that successful medical AI adoption policy should focus on trust building measures–without eroding trust in human physicians.
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Paton, Chris, and Shinji Kobayashi. "An Open Science Approach to Artificial Intelligence in Healthcare." Yearbook of Medical Informatics 28, no. 01 (April 25, 2019): 047–51. http://dx.doi.org/10.1055/s-0039-1677898.
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Objectives: Artificial Intelligence (AI) offers significant potential for improving healthcare. This paper discusses how an “open science” approach to AI tool development, data sharing, education, and research can support the clinical adoption of AI systems. Method: In response to the call for participation for the 2019 International Medical Informatics Association (IMIA) Yearbook theme issue on AI in healthcare, the IMIA Open Source Working Group conducted a rapid review of recent literature relating to open science and AI in healthcare and discussed how an open science approach could help overcome concerns about the adoption of new AI technology in healthcare settings. Results: The recent literature reveals that open science approaches to AI system development are well established. The ecosystem of software development, data sharing, education, and research in the AI community has, in general, adopted an open science ethos that has driven much of the recent innovation and adoption of new AI techniques. However, within the healthcare domain, adoption may be inhibited by the use of “black-box” AI systems, where only the inputs and outputs of those systems are understood, and clinical effectiveness and implementation studies are missing. Conclusions: As AI-based data analysis and clinical decision support systems begin to be implemented in healthcare systems around the world, further openness of clinical effectiveness and mechanisms of action may be required by safety-conscious healthcare policy-makers to ensure they are clinically effective in real world use.
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Shivaprakash, Kadukothanahally Nagaraju, Niraj Swami, Sagar Mysorekar, Roshni Arora, Aditya Gangadharan, Karishma Vohra, Madegowda Jadeyegowda, and Joseph M. Kiesecker. "Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India." Sustainability 14, no. 12 (June 10, 2022): 7154. http://dx.doi.org/10.3390/su14127154.
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The recent advancement in data science coupled with the revolution in digital and satellite technology has improved the potential for artificial intelligence (AI) applications in the forestry and wildlife sectors. India shares 7% of global forest cover and is the 8th most biodiverse region in the world. However, rapid expansion of developmental projects, agriculture, and urban areas threaten the country’s rich biodiversity. Therefore, the adoption of new technologies like AI in Indian forests and biodiversity sectors can help in effective monitoring, management, and conservation of biodiversity and forest resources. We conducted a systematic search of literature related to the application of artificial intelligence (AI) and machine learning algorithms (ML) in the forestry sector and biodiversity conservation across globe and in India (using ISI Web of Science and Google Scholar). Additionally, we also collected data on AI-based startups and non-profits in forest and wildlife sectors to understand the growth and adoption of AI technology in biodiversity conservation, forest management, and related services. Here, we first provide a global overview of AI research and application in forestry and biodiversity conservation. Next, we discuss adoption challenges of AI technologies in the Indian forestry and biodiversity sectors. Overall, we find that adoption of AI technology in Indian forestry and biodiversity sectors has been slow compared to developed, and to other developing countries. However, improving access to big data related to forest and biodiversity, cloud computing, and digital and satellite technology can help improve adoption of AI technology in India. We hope that this synthesis will motivate forest officials, scientists, and conservationists in India to explore AI technology for biodiversity conservation and forest management.
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Rawashdeh, Awni, Mashael Bakhit, and Layla Abaalkhail. "Determinants of artificial intelligence adoption in SMEs: The mediating role of accounting automation." International Journal of Data and Network Science 7, no. 1 (2023): 25–34. http://dx.doi.org/10.5267/j.ijdns.2022.12.010.
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The study examines the technological factors influencing the adoption of artificial intelligence (AI) technology. In addition, this study examines the mediating role of accounting automation on AI adoption in a small and medium-sized enterprise (SMEs) context. The owners and managers of SMEs were surveyed online using a convenience sampling technique. The proposed model was tested using SEM. The findings confirmed the relationships between the predictive variables and AI adoption. The results showed that accounting automation partially mediated the relationship between predictive variables and the adoption of AI. The results contribute to the TOE model by incorporating accounting automation into the TOE framework as a mediating variable. The study also contributed to the literature by including new variables in the model, such as saving time and efficiency-improving.
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Singh, Gagandeep. "A Move Towards Intelligent Economy: Indian Evidence." Management and Labour Studies 46, no. 2 (February 25, 2021): 192–203. http://dx.doi.org/10.1177/0258042x21989941.
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The current study accounts for a transitional move initiated by NITI Aayog and Securities and Exchange Board of India (SEBI) recently in the form of adoption of artificial intelligence (AI) in Indian economy. Empirical analysis of data of top 500 Indian companies reveals that adoption of this transformation is likely to enhance firm performance. The annual reports of Indian companies mention the use of AI in the business, which clearly indicates that Indian economy has initiated a move towards intelligent economy. It is observed that the banking companies are using AI and chatbots at a wider scale. Firms belonging to other sectors are gradually following the adoption of AI in the business process which results in improved financial performance. The findings of the study suggest that Indian legislators should gradually move towards mandatory adoption of AI in business economy at large in line with the global trend, which began its footprints through Digital India.
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Chatterjee, Sheshadri. "AI strategy of India: policy framework, adoption challenges and actions for government." Transforming Government: People, Process and Policy 14, no. 5 (June 3, 2020): 757–75. http://dx.doi.org/10.1108/tg-05-2019-0031.
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Purpose The purpose of this study is to provide recommendations for policy framework on artificial intelligence (AI) in India. Design/methodology/approach Studies have been conducted through focus group discussion and the other sources such as different company websites using AI, Indian Government strategy reports on AI, literature studies, different policies implemented on AI in different locations and other relevant documents. After those studies, a charter of recommendation has been provided. This will help the authority to frame the AI policy for India. Findings This study highlights that “National Strategy for AI” for India needs improvement to provide comprehensive inputs for framing policy on AI. This study also implies that focus is to be given on security, privacy issues including issues of governance. Research limitations/implications AI-related technology has immense potential toward the development of organizations. This study implies the necessity of framing a comprehensive policy on AI for India. If there is a comprehensive policy on AI for India, the Indian industries will derive many benefits. Practical implications This study provides inputs on how the industries of India can be benefitted with the help of AI and how R&D can develop the AI activities to harness maximum benefits from this innovative technology. Social implications AI-related policy will have appreciable influence on the society in terms of human–device interactions and communications. The policy framework on AI for India is expected to project far-reaching effects toward deriving benefits to the society. Originality/value This paper has taken a holistic and unique attempt to provide inputs to the policymakers for framing a comprehensive and meaningful policy on AI for India.
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Nugraha, Angga, Agustina Abdullah, and Nurani Sirajuddin. "Tingkat Adopsi Inovasi Ib (Inseminasi Buatan) Pada Peternak Sapi Potong Di Kecamatan Lalabata Kabupaten Soppeng Adoption Of Innovation Ai ( Artificial Insemination ) Breeder In." AVES: Jurnal Ilmu Peternakan 10, no. 2 (December 8, 2016): 3. http://dx.doi.org/10.35457/aves.v10i2.187.
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The purpose of this study was to determine the extent to which farmers in adopting the technology of artificial insemination by measuring the time it takes breeders from receipt of the information to be applied , the quality of AI ( Application and recommendation ) , and Area Applied IB in cattle . The research was conducted in August - September 2014 against 30 respondents from 30 populations breeders who use technology AI ( Artificial Insemination ) . Data were analyzed using descriptive statistics by using tables Distribution Frekuwensi results showed adoption rate of technological innovation Artificial Insemination ( AI ) in beef cattle farms in the district Lalabata Soppeng ie Stage time is needed breeders from the receipt of the information to be obtained on application High category in the sense of the ability of farmers to adopt an innovation has been rapid , stage area located on the application of Low Category this suggests that the broad application of the implementation of the AI in the district as a whole Lalabata the breeder has done these activities , but not all animals in peliharanya apply technology Artificial Insemination ( AI ) , and Phase AI quality is in the category Medium this suggests that the quality of AI in District Lalabata Soppeng ie almost entirely of farmers already know the signs of estrus in livestock before conducting AI. Keywords : Adoption Levels , Artificial Insemination , Beef Cattle Breeders
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Nugraha, Angga, Agustina Abdullah, and Nurani Sirajuddin. "Tingkat Adopsi Inovasi Ib (Inseminasi Buatan) Pada Peternak Sapi Potong Di Kecamatan Lalabata Kabupaten Soppeng Adoption Of Innovation Ai ( Artificial Insemination ) Breeder In." AVES: Jurnal Ilmu Peternakan 10, no. 2 (December 8, 2016): 3. http://dx.doi.org/10.30957/aves.v10i2.187.
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The purpose of this study was to determine the extent to which farmers in adopting the technology of artificial insemination by measuring the time it takes breeders from receipt of the information to be applied , the quality of AI ( Application and recommendation ) , and Area Applied IB in cattle . The research was conducted in August - September 2014 against 30 respondents from 30 populations breeders who use technology AI ( Artificial Insemination ) . Data were analyzed using descriptive statistics by using tables Distribution Frekuwensi results showed adoption rate of technological innovation Artificial Insemination ( AI ) in beef cattle farms in the district Lalabata Soppeng ie Stage time is needed breeders from the receipt of the information to be obtained on application High category in the sense of the ability of farmers to adopt an innovation has been rapid , stage area located on the application of Low Category this suggests that the broad application of the implementation of the AI in the district as a whole Lalabata the breeder has done these activities , but not all animals in peliharanya apply technology Artificial Insemination ( AI ) , and Phase AI quality is in the category Medium this suggests that the quality of AI in District Lalabata Soppeng ie almost entirely of farmers already know the signs of estrus in livestock before conducting AI. Keywords : Adoption Levels , Artificial Insemination , Beef Cattle Breeders
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Goldfarb, Avi, Bledi Taska, and Florenta Teodoridis. "Artificial Intelligence in Health Care? Evidence from Online Job Postings." AEA Papers and Proceedings 110 (May 1, 2020): 400–404. http://dx.doi.org/10.1257/pandp.20201006.
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This paper documents a puzzle. Despite the numerous popular press discussions of artificial intelligence (AI) in health care, there has been relatively little adoption. Using data from Burning Glass Technologies on millions of online job postings, we find that AI adoption in health care remains substantially lower than in most other industries and that under 3 percent of the hospitals in our data posted any jobs requiring AI skills from 2015-2018. The low adoption rates mean any statistical analysis is limited. Nevertheless, the adoption we do observe shows that larger hospitals, larger counties, and integrated salary model hospitals are more likely to adopt.
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Öztürk, Rezan, and Veysel Kula. "A General Profile of Artificial Intelligence Adoption in Banking Sector." Journal of corporate governance, insurance and risk management 8, no. 2 (May 15, 2021): 146–57. http://dx.doi.org/10.51410/jcgirm.8.2.10.
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Artificial Intelligence (AI) is rapidly transforming the global financial services industry. The new digitalization model is powered by artificial intelligence technology, and AI has the potential to disrupt and refine the existing financial services industry. The increasing amount of data in banking has revealed the need for fast and reliable service. Banks are financial service organizations that have used AI effectively in recent years. This paper reveals the general profile of artificial intelligence adoption by banks. Based on the evidence from all 17 banks operating in the Afyonkarahisar province of Turkey, it is concluded that AI technologies are applied in almost every area of the banking sector to improve the overall service offered. Moreover, the use of AI is evaluated as a potential that provides ease of use and reduces costs. As for the operations in future, the participants think AI will provide high levels of benefit to banks in their financial services in the incoming years. Given no similar study, this study appears to provide an original contribution to the literature regarding the use of AI in banking services within the Turkish context.
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Alekseeva, Liudmila, Mireia Gine, Sampsa Samila, and Bledi Taska. "AI Adoption and Firm Performance: Management versus IT." Academy of Management Proceedings 2021, no. 1 (August 2021): 15935. http://dx.doi.org/10.5465/ambpp.2021.15935abstract.
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Schmitt, Bernd. "Speciesism: an obstacle to AI and robot adoption." Marketing Letters 31, no. 1 (November 30, 2019): 3–6. http://dx.doi.org/10.1007/s11002-019-09499-3.
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Ukoh, Edidiong Enyeneokpon, and Jude Nicholas. "AI Adoption for Teaching and Learning of Physics." International Journal for Infonomics 15, no. 1 (June 30, 2022): 2121–31. http://dx.doi.org/10.20533/iji.1742.4712.2022.0222.
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Huisman, Merel, Erik Ranschaert, William Parker, Domenico Mastrodicasa, Martin Koci, Daniel Pinto de Santos, Francesca Coppola, et al. "An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude." European Radiology 31, no. 9 (March 20, 2021): 7058–66. http://dx.doi.org/10.1007/s00330-021-07781-5.
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Abstract Objectives Radiologists’ perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond. Methods Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression. Results The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24–74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10–2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20–0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21–0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25–31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16–50.54, p < 0.001). Conclusions Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption. Key Points • Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption.
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Alhamad, Ibrahim Abdullah, and Harman Preet Singh. "Digital technologies and information translucence in healthcare management: An institutional theory perspective for adopting electronic incidence reporting systems." Revista Amazonia Investiga 11, no. 57 (November 8, 2022): 30–38. http://dx.doi.org/10.34069/ai/2022.57.09.3.
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The purpose of this study was to provide an institutional theory perspective on the adoption of electronic IRS technology for healthcare management. This research employs institutional theory to investigate the adoption of electronic IRS for healthcare management. The study’s conceptual analysis demonstrates that coercive, normative, and imitative forces influence the adoption of electronic IRS for healthcare management. International healthcare regulations and standards reflect the presence of coercive forces. International healthcare societies and professional networks mirror normative forces. Imitative forces exert pressure on smaller enterprises and developing nations to adopt electronic IRS. This research contributes to the literature and theory by extending the application of institutional theory to the adoption of digital technologies such as the electronic IRS. In addition, the study has practical implications because it demonstrates the importance of digital technologies such as electronic IRS for information translucence and healthcare management. Small businesses in developing nations can learn from large businesses in developed nations to adopt electronic IRS for efficient and effective healthcare management.
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Chaibi, Asma, and Imed Zaiem. "Doctor Resistance of Artificial Intelligence in Healthcare." International Journal of Healthcare Information Systems and Informatics 17, no. 1 (January 1, 2022): 1–13. http://dx.doi.org/10.4018/ijhisi.315618.
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Artificial intelligence (AI) has revolutionized healthcare by enhancing the quality of patient care. Despite its advantages, doctors are still reluctant to use AI in healthcare. Thus, the authors' main objective is to obtain an in-depth understanding of the barriers to doctors' adoption of AI in healthcare. The authors conducted semi-structured interviews with 11 doctors. Thematic analysis as chosen to identify patterns using QSR NVivo (version 12). The results showed that the barriers to AI adoption are lack of financial resources, need for special training, performance risk, perceived cost, technology dependency, need for human interaction, and fear of AI replacing human work.
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Lee, One-Ki Daniel, Ramakrishna Ayyagari, Farzaneh Nasirian, and Mohsen Ahmadian. "Role of interaction quality and trust in use of AI-based voice-assistant systems." Journal of Systems and Information Technology 23, no. 2 (August 17, 2021): 154–70. http://dx.doi.org/10.1108/jsit-07-2020-0132.
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PurposeThe rapid growth of artificial intelligence (AI)-based voice-assistant systems (VASs) has created many opportunities for individuals to use VASs for various purposes in their daily lives. However, traditional quality success factors, such as information quality and system quality, may not be sufficient in explaining the adoption and use of AI-based VASs. This study aims to propose interaction quality as an additional, yet more important quality measure that leads to trust in an AI-based VAS and its adoption. Design/methodology/approachThe authors propose a research model that highlights the importance of interaction quality and trust as underlying mechanisms in the adoption of AI-based VASs. Based on survey methodology and data from 221 respondents, the proposed research model is tested with a partial least squares approach. FindingsThe results suggest that interaction quality and trust are critical factors influencing the adoption of AI-based VASs. The findings also indicate that the impacts of traditional quality factors (i.e. information quality and system quality) occur through interaction quality in the context of AI-based VASs. Originality/valueThis research adds interaction quality as a new quality factor to the traditional quality factors in the information systems success model. Further, given the interactive nature of VASs, the authors use social response theory to explain the importance of the trust mechanism when individuals interact with AI-based VASs. Contribution to Impact
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Priya Gupta, Kriti, and Preeti Bhaskar. "Inhibiting and Motivating Factors Influencing Teachers’ Adoption of AI-Based Teaching and Learning Solutions: Prioritization Using Analytic Hierarchy Process." Journal of Information Technology Education: Research 19 (2020): 693–723. http://dx.doi.org/10.28945/4640.
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Aim/Purpose: The purpose of the present study is to prioritize the inhibiting and motivating factors underlying the adoption of AI based teaching and learning solutions by teachers in the higher education sector of India. Background: AI based teaching and learning solutions are amongst the most important educational innovations. The intervention of AI in instructional methods can result in personalized teaching and learning experiences. AI enabled teaching and learning systems can give teachers a better understanding regarding their students’ learning abilities, learning styles and progress. Methodology: The Analytic Hierarchy Process (AHP) is employed to find the relative importance of inhibiting and motivating factors. The primary data for making the pair-wise comparisons between the factors were obtained from a convenient sample of 32 teachers, teaching in various higher educational institutions (HEIs) in the National Capital Region (NCR) of Delhi, India. Contribution: Though, the acceptance of AI based solutions has been studied in other contexts such as retail, banking, ecommerce, and so on; nonetheless, the acceptance of AI in the education sector has not grabbed much attention of researchers. Hence the study has made worthwhile contributions to the literature as it has specifically focused on the adoption of AI based teaching methods by teachers in higher education Findings: The findings suggest that institutional barriers are the major inhibitors and recognition is the main motivator that affect teachers’ behaviour towards adopting AI based teaching solutions. Overall, the findings of the study highlight the importance of institutional support in terms of resources, time, and recognition that may be provided to the teachers so that they can willingly integrate AI based methodologies into their teaching. Recommendations for Practitioners: The study provides several implications for HEIs and developers of AI based educational solutions. The HEIs should provide adequate support to their teachers in terms of financial support, infrastructure and technical support. The developers should focus on developing such solutions that are compatible with the teachers’ existing work style. Recommendation for Researchers: Future studies can employ statistical techniques such as multiple regression analysis or structural equation modelling to examine the impact of these factors on the actual use behaviour of teachers regarding AI based teaching methods. More diversified samples that are statistically significant in size, can be considered to examine the teachers’ behaviour regarding AI based instructional methods. Impact on Society: AI technology can play a pivotal role in reshaping and remodeling higher education. AI is the technology of todays’ times that has the capability of transforming the instructional methods. The educators need to understand that nowadays, teaching and learning are heading towards creative styles that embrace the use of innovative technologies such as AI. Future Research: The adoption of AI in the field of education is at a very nascent stage in India, constant changes are likely to happen in the factors influencing the adoption of AI enabled teaching solutions. Future studies may come up with a more holistic model of factors to address this research problem.
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Kot, Sebastian, Hafezali Iqbal Hussain, Svitlana Bilan, Muhammad Haseeb, and Leonardus W. W. Mihardjo. "THE ROLE OF ARTIFICIAL INTELLIGENCE RECRUITMENT AND QUALITY TO EXPLAIN THE PHENOMENON OF EMPLOYER REPUTATION." Journal of Business Economics and Management 22, no. 4 (May 14, 2021): 867–83. http://dx.doi.org/10.3846/jbem.2021.14606.
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The prime contribution of current research entails the explanation of role of artificial intelligence based human resource management function to determine the employer reputation among pharmaceutical industry of Indonesia. The study intends to examine the empirically investigation the role and impact of artificial intelligence-based recruitment and artificial intelligence-based quality to determine the employer reputation with mediating role of artificial intelligence adoption. The study contributes to the body of knowledge and claims to be novel in explaining the AI based HR function to explain the phenomenon of employer reputation. The study examined the empirical investigation between AI based recruitment and AI based quality to influence the AI adoption that further predicts the phenomenon of employer reputation. The study was conducted on pharmaceutical industry of Indonesia and convenience sampling was used for data collected and Smart-PLS was utilized for data analysis. The study found that AI based recruitment and quality significantly influences the AI adoption and further it influences the employer reputation. The mediation role of artificial intelligence adoption is significant where it is found that artificial intelligence mediates the relationship between artificial intelligence recruitment and employer reputation, with similar significant mediation role between artificial intelligence quality and employer reputation.
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Ansari, Tarique. "Conversational AI Assistant." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 1169–72. http://dx.doi.org/10.22214/ijraset.2022.47554.
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Abstract: A conversational assistant is an intelligent conversational computing system designed to mimic human speech to provide automated online guidance and support. The growing benefits of conversational support have led to widespread adoption across many industries to provide virtual support to their customers. Conversation assistance uses methods and algorithms from his two fields of artificial intelligence: natural language processing and machine learning. However, the application has many challenges and limitations. This research reviews recent advances in conversation support using artificial intelligence and natural language processing. We highlight the main challenges and limitations of the current work and provide recommendations for future research investigations.
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Barry, Barbara, Xuan Zhu, Emma Behnken, Jonathan Inselman, Karen Schaepe, Rozalina McCoy, David Rushlow, et al. "Provider Perspectives on Artificial Intelligence–Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study." JMIR AI 1, no. 1 (October 14, 2022): e41940. http://dx.doi.org/10.2196/41940.
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Background The promise of artificial intelligence (AI) to transform health care is threatened by a tangle of challenges that emerge as new AI tools are introduced into clinical practice. AI tools with high accuracy, especially those that detect asymptomatic cases, may be hindered by barriers to adoption. Understanding provider needs and concerns is critical to inform implementation strategies that improve provider buy-in and adoption of AI tools in medicine. Objective This study aimed to describe provider perspectives on the adoption of an AI-enabled screening tool in primary care to inform effective integration and sustained use. Methods A qualitative study was conducted between December 2019 and February 2020 as part of a pragmatic randomized controlled trial at a large academic medical center in the United States. In all, 29 primary care providers were purposively sampled using a positive deviance approach for participation in semistructured focus groups after their use of the AI tool in the randomized controlled trial was complete. Focus group data were analyzed using a grounded theory approach; iterative analysis was conducted to identify codes and themes, which were synthesized into findings. Results Our findings revealed that providers understood the purpose and functionality of the AI tool and saw potential value for more accurate and faster diagnoses. However, successful adoption into routine patient care requires the smooth integration of the tool with clinical decision-making and existing workflow to address provider needs and preferences during implementation. To fulfill the AI tool’s promise of clinical value, providers identified areas for improvement including integration with clinical decision-making, cost-effectiveness and resource allocation, provider training, workflow integration, care pathway coordination, and provider-patient communication. Conclusions The implementation of AI-enabled tools in medicine can benefit from sensitivity to the nuanced context of care and provider needs to enable the useful adoption of AI tools at the point of care. Trial Registration ClinicalTrials.gov NCT04000087; https://clinicaltrials.gov/ct2/show/NCT04000087
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Bhavani, Josephine Rajendra, and Ambikai S. Thuraisingam. "ARTIFICIAL INTELLIGENCE AND ITS IMPACT ON THE LEGAL FRATERNITY." UUM Journal of Legal Studies 13, No.2 (July 21, 2022): 129–61. http://dx.doi.org/10.32890/uumjls2022.13.2.6.
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The article endeavors to analyze the implications of artificial intelligence (AI) in the legal fraternity. There have been various reports on the impact and challenges of AI in the legal fraternity in recent years. AI is used to perform legal work previously completed solely by human lawyers. The rise of AI technology has caused a great deal of apprehension among members of the legal fraternity both in Malaysia and globally. AI promises to disrupt the substratum of how legal work is practiced and delivered. Nevertheless, there are implications encountered by the legal fraternity in adopting AI in legal practice such as ethical responsibility, algorithm bias, data privacy and the lack of regulations for AI. The doctrinal method was employed in conducting this study. The primary objective of this article is to evaluate the implications of AI adoption in the legal fraternity and to propose recommendations for better integration of AI in the legal industry.
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Asher Austen Fainman. "The problem with Opaque AI." Thinker 82, no. 4 (October 1, 2019): 44–55. http://dx.doi.org/10.36615/thethinker.v82i4.373.
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The ability to uncover, evaluate and predict causality is fundamental in disciplines of inquiry, such as law. Effective adoption of Artificial Intelligence (AI) applications in domains in which legally significant consequences result will depend heavily on the user’s ability to provide explanons and contest decisions.
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Nugroho, Aras Prasetiyo, Lucie Setiana, Dadang Mulyadi Saleh, and Dayu Lingga Lana. "Factors in the Adoption of Beef Cattle Artificial Insemination (AI) Technology in Brebes Regency." Jurnal Penyuluhan 16, no. 1 (March 18, 2020): 16–23. http://dx.doi.org/10.25015/16202027574.
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Efforts to increase beef cattle population and genetic in Brebes Regency can be done by using artificial insemination (AI) technology approach.Therefore this study aims to determine the factors that influence the ability of beef cattle farmers in adopting artificial insemination technology (AI) in Brebes Regency. Survey method was applied to observe beef cattle and farmers. Sampling technique used Slovin formula with 90% significant rate to observe variables, namely the ability of farmers to adopt AI technology (Y); social factors (X1) consisting of age (X1.1), educational background (X1.2), farming experience (X1.3), herd size (X1.4); technical factors consisting of S/C (X2.1) and oestrous detection (X2.2); and economic factor is AI costs (X3). The research data obtained were analysed using descriptive analysis and correlation. The results showed that adoption of artificial insemination in beef cattle in Brebes Regency had a negative correlation with age (X1.1) (rs = -0.498), did not correlate with educational background (X1.2) (rs = 0.221), farming experience ( rs = X1.3) (rs = -0.056), and the herd size (X1.4) (rs = 0.094) as social factors; does not correlate with the value of S/C (X2.1) (rs = 0.203) and estrous detection (X2.2) (rs = 0.259) as technical factors; and negatively correlate.ed with AI cost (X3) (rs = -0,661) as an economic factor. From the results of the study, it can be concluded that the adoption of artificial insemination in beef cattle in Brebes Regency is influenced by social and economic factors, especially from the age factor and AI cost factor that is less supportive.
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Albert, Edward Tristram. "AI in talent acquisition: a review of AI-applications used in recruitment and selection." Strategic HR Review 18, no. 5 (October 14, 2019): 215–21. http://dx.doi.org/10.1108/shr-04-2019-0024.
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Purpose The purpose of this study is to explore the current use of artificial intelligence (AI) in the recruitment and selection of candidates. More specifically, this research investigates the level, rate and potential adoption areas for AI-tools across the hiring process. Design/methodology/approach To fulfill that purpose, a two-step approach was adopted. First, the literature was extensively reviewed to identify potential AI-application areas supporting the recruitment and selection (R&S) process. Second, primary research was carried out in the form of semi-structured thematic interviews with different types of R&S specialists including HR managers, consultants and academics to evaluate how much of the AI-applications areas identified in the literature review are being used in practice. Findings This study presents a multitude of findings. First, it identifies 11 areas across the R&S Process where AI-applications can be applied. However, practitioners currently seem to rely mostly on three: chatbots, screening software and task automation tools. Second, most companies adopting these AI-tools tend to be larger, tech-focussed and/or innovative firms. Finally, despite the exponential rate of AI-adoption, companies have yet to reach an inflection point as they currently show reluctance to invest in that technology for R&S. Research limitations/implications Due to the qualitative and exploratory nature behind the research, this study displays a significant amount of subjectivity, and therefore, lacks generalisability. Despite this limitation, this study opens the door to many opportunities for academic research, both qualitative and quantitative. Originality/value This paper addresses the huge research gap surrounding AI in R&S, pertaining specifically to the scarcity and poor quality of the current academic literature. Furthermore, this research provides a comprehensive overview of the state of AI in R&S, which will be helpful for academics and practitioners looking to rapidly gain a holistic understanding of AI in R&S.
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Almarashda, Hassan Ali Hassan Altaffaq, Ishak Bin Baba, Azizul Azhar Ramli, Aftab Hameed Memon, and Ismail Abdul Rahman. "Human Resource Management and Technology Development in Artificial Intelligence Adoption in the UAE Energy Sector." Journal of Applied Engineering Sciences 11, no. 2 (December 1, 2021): 69–76. http://dx.doi.org/10.2478/jaes-2021-0010.
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Abstract Artificial Intelligence (AI) is proved a very effective technique in reducing complexity and making suitable quick decisions for achieving success. Artificial Intelligence is an emerging area and growing fast. It is used successfully in various fields. In UAE also, AI is being used in several sectors. Now the government of UAE has promoted AI in the energy sector to get the maximum of its benefits. Hence, this study evaluated various indicators that affect the promotion of AI in the energy section of the UAE. With the help of a questionnaire survey, 350 questionnaire forms were analyzed to prioritize the parameters affecting AII adoption. From the analysis results it was found that “Organization use AI to provide effective business innovation”, “Organizations use AI to align with its business strategy”, and “Organization use AI to improve the levels of production” are the key motivating factor to adopt AI. Significant parameters of AI technology include are; AI Technology is user friendly, AI Technology is able to improve the quality of the work and AI Technology fits well with the tasks involved; are reported as significant technological parameters to adopt AI. On the other hand, Referral person is required if facing difficulty with AI technology, and teammate support in using AI technology are essential parameters of the human resource management which affect AI adoption in the energy sector of the UAE. In addition, reliability and normality tests validated the data. Hence, these findings can be used to promote AI and understand the situation for making proper decisions.
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Hadley, Trevor D., Rowland W. Pettit, Tahir Malik, Amelia A. Khoei, and Hamisu M. Salihu. "Artificial Intelligence in Global Health —A Framework and Strategy for Adoption and Sustainability." International Journal of Maternal and Child Health and AIDS (IJMA) 9, no. 1 (February 10, 2020): 121–27. http://dx.doi.org/10.21106/ijma.296.
Full textAbstract:
Artificial Intelligence (AI) applications in medicine have grown considerably in recent years. AI in the forms of Machine Learning, Natural Language Processing, Expert Systems, Planning and Logistics methods, and Image Processing networks provide great analytical aptitude. While AI methods were first conceptualized for radiology, investigations today are established across all medical specialties. The necessity for proper infrastructure, skilled labor, and access to large, well-organized data sets has kept the majority of medical AI applications in higher-income countries. However, critical technological improvements, such as cloud computing and the near-ubiquity of smartphones, have paved the way for use of medical AI applications in resource-poor areas. Global health initiatives (GHI) have already begun to explore ways to leverage medical AI technologies to detect and mitigate public health inequities. For example, AI tools can help optimize vaccine delivery and community healthcare worker routes, thus enabling limited resources to have a maximal impact. Other promising AI tools have demonstrated an ability to: predict burn healing time from smartphone photos; track regions of socioeconomic disparity combined with environmental trends to predict communicable disease outbreaks; and accurately predict pregnancy complications such as birth asphyxia in low resource settings with limited patient clinical data. In this commentary, we discuss the current state of AI-driven GHI and explore relevant lessons from past technology-centered GHI. Additionally, we propose a conceptual framework to guide the development of sustainable strategies for AI-driven GHI, and we outline areas for future research.
Keywords: • Artificial Intelligence • AI Framework • Global Health • Implementation • Sustainability • AI Strategy
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