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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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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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Upadhyay, Ashwani Kumar, and Komal Khandelwal. "Applying artificial intelligence: implications for recruitment." Strategic HR Review 17, no. 5 (October 8, 2018): 255–58. http://dx.doi.org/10.1108/shr-07-2018-0051.
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Purpose This paper aims to review the applications of artificial intelligence (AI) in the hiring process and its practical implications. This paper highlights the strategic shift in recruitment industry caused due to the adoption of AI in the recruitment process. Design/methodology/approach This paper is prepared by independent academicians who have synthesized their views by a review of the latest reports, articles, research papers and other relevant literature. Findings This paper describes the impact of developments in the field of AI on the hiring process and the recruitment industry. The application of AI for managing the recruitment process is leading to efficiency as well as qualitative gains for both clients and candidates. Practical implications This paper offers strategic insights into automation of the recruitment process and presents practical ideas for implementation of AI in the recruitment industry. It also discusses the strategic implications of the usage of AI in the recruitment industry. Originality/value This article describes the role of technological advancements in AI and its application for creating value for the recruitment industry as well as the clients. It saves the valuable reading time of practitioners and researchers by highlighting the AI applications in the recruitment industry in a concise and simple format.
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Wang, Xuhui, Md Jamirul Haque, Wenjing Li, Asad Hassan Butt, Hassan Ahmad, and Hamid Ali Shaikh. "AI-Enabled E-Recruitment Services Make Job Searching, Application Submission, and Employee Selection More Interactive." Information Resources Management Journal 34, no. 4 (October 2021): 48–68. http://dx.doi.org/10.4018/irmj.2021100103.
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Personnel recruitment and selection is changing rapidly with the adoption of artificial intelligence (AI) tools. This chapter looks at how job applicants perceive AI in recruitment. The results show that AI tools encourage a larger number of quality application submissions and for two reasons. First, AI entrains a perception of a novel approach to job searching. Second, AI is perceived to be able to interactively tailor the application experience to what the individual applicant expects and has to offer. These perceptions increase the likelihood the user will submit a job application and so improves the size and quality of the pool from which to recruit personnel.
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Wilfred, Dennis. "AI in Recruitment." NHRD Network Journal 11, no. 2 (April 2018): 15–18. http://dx.doi.org/10.1177/0974173920180204.
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Raveendra, P. V., Y. M. Satish, and Padmalini Singh. "Changing Landscape of Recruitment Industry: A Study on the Impact of Artificial Intelligence on Eliminating Hiring Bias from Recruitment and Selection Process." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4404–7. http://dx.doi.org/10.1166/jctn.2020.9086.
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An emerging trend of implementing Artificial Intelligence (AI) technologies can be seen in such domains that were solely dominated by humans. Today, AI is utilized extensively in HR department to assist and accelerate recruitment and selection process (Martin, F.R., 2019. Employers Are Now Using Artificial Intelligence To Stop Bias In Hiring. Retrieved September 22, 2019, from analyticsindiamag. com: https://analyticsindiamag.com/employersare-using-ai-stop-bias-hiring/.). This paper attempts to present the impact of AI on recruitment and selection process, incorporation of AI in eliminating unconscious biases during hiring. The study addresses the rising questions such as how AI has changed the landscape of recruitment industry, role of AI in recruitment and selection process, whether AI can help in eliminating the unconscious bias during recruitment and selection process. In order to uncover the understanding and figure out the potential solutions that AI brings to the HR process, an extensive review of literature has been carried out. It is concluded by analyzing the past contributions that AI offers potential solution to recruitment managers in optimizing the recruitment and selection process and is able to negate human biases prevalent during hiring. The future waits for augmented intelligence technologies offering better results taking over repetitive administrative jobs completely.
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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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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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Liu, Jia, Shih-Hsuan Chang, Yu-Ci Xu, Guo-An Wu, and Shih-Feng Chang. "Using AI to Enhance Recruitment Effect." Journal of Physics: Conference Series 1827, no. 1 (March 1, 2021): 012150. http://dx.doi.org/10.1088/1742-6596/1827/1/012150.
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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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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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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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Kazim, Emre, Adriano Soares Koshiyama, Airlie Hilliard, and Roseline Polle. "Systematizing Audit in Algorithmic Recruitment." Journal of Intelligence 9, no. 3 (September 17, 2021): 46. http://dx.doi.org/10.3390/jintelligence9030046.
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Business psychologists study and assess relevant individual differences, such as intelligence and personality, in the context of work. Such studies have informed the development of artificial intelligence systems (AI) designed to measure individual differences. This has been capitalized on by companies who have developed AI-driven recruitment solutions that include aggregation of appropriate candidates (Hiretual), interviewing through a chatbot (Paradox), video interview assessment (MyInterview), and CV-analysis (Textio), as well as estimation of psychometric characteristics through image-(Traitify) and game-based assessments (HireVue) and video interviews (Cammio). However, driven by concern that such high-impact technology must be used responsibly due to the potential for unfair hiring to result from the algorithms used by these tools, there is an active effort towards proving mechanisms of governance for such automation. In this article, we apply a systematic algorithm audit framework in the context of the ethically critical industry of algorithmic recruitment systems, exploring how audit assessments on AI-driven systems can be used to assure that such systems are being responsibly deployed in a fair and well-governed manner. We outline sources of risk for the use of algorithmic hiring tools, suggest the most appropriate opportunities for audits to take place, recommend ways to measure bias in algorithms, and discuss the transparency of algorithms.
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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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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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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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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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Parry, Emma, and Hugh Wilson. "Factors influencing the adoption of online recruitment." Personnel Review 38, no. 6 (September 18, 2009): 655–73. http://dx.doi.org/10.1108/00483480910992265.
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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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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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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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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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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.
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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
Copyright © 2020 Hadley et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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FraiJ, JihaD, and Várallyai László. "literature Review: Artificial Intelligence Impact on the Recruitment ProcessA LITERATURE REVIEW: ARTIFICIAL INTELLIGENCE IMPACT ON THE RECRUITMENT PROCESS." International Journal of Engineering and Management Sciences 6, no. 1 (May 13, 2021): 108–19. http://dx.doi.org/10.21791/ijems.2021.1.10.
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This paper aim is to review the implementation of artificial intelligence (AI) in the Human Resources Management (HRM) recruitment processes. A systematic review was adopted in which academic papers, magazine articles as well as high rated websites with related fields were checked. The findings of this study should contribute to the general understanding of the impact of AI on the HRM recruitment process. It was impossible to track and cover all topics related to the subject. However, the research methodology used seems to be reasonable and acceptable as it covers a good number of articles which are related to the core subject area. The results and findings were almost clear that using AI is advantages in the area of recruitment as technology can serve best in this area. Moreover, time, efforts, and boring daily tasks are transformed to be computerized which makes a good space for humans to focus on more important subjects related to boosting performance and development. Acquiring automation and cognitive insights as well as cognitive engagement in the recruitment process would make it possible for systems to work similarly to the human brain in terms of data analysis and the ability to build an effective systematic engagement to process the data in an unbiased, efficient and fast way.
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Varghese, Julian. "Artificial Intelligence in Medicine: Chances and Challenges for Wide Clinical Adoption." Visceral Medicine 36, no. 6 (2020): 443–49. http://dx.doi.org/10.1159/000511930.
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<b><i>Background:</i></b> Artificial intelligence (AI) applications that utilize machine learning are on the rise in clinical research and provide highly promising applications in specific use cases. However, wide clinical adoption remains far off. This review reflects on common barriers and current solution approaches. <b><i>Summary:</i></b> Key challenges are abbreviated as the RISE criteria: Regulatory aspects, Interpretability, interoperability, and the need for Structured data and Evidence. As reoccurring barriers of AI adoption, these concepts are delineated and complemented by points to consider and possible solutions for effective and safe use of AI applications. <b><i>Key Messages:</i></b> There is a fraction of AI applications with proven clinical benefits and regulatory approval. Many new promising systems are the subject of current research but share common issues for wide clinical adoption. The RISE criteria can support preparation for challenges and pitfalls when designing or introducing AI applications into clinical practice.
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Weber, Felix Dominik, and Reinhard Schütte. "State-of-the-art and adoption of artificial intelligence in retailing." Digital Policy, Regulation and Governance 21, no. 3 (May 13, 2019): 264–79. http://dx.doi.org/10.1108/dprg-09-2018-0050.
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PurposeIn the most abstract way, artificial intelligence (AI) allows human work to be shifted toward technological systems that are currently not fully capable. Following this, the domain of retail can be sketched as a natural fit for the application of AI tools, which are known for their high proportion of human work and concurrent low profit margins. This paper aims to explore the current dissemination of the application of AI within the industry. The value-added core tasks of retail companies are examined to determine the possible utilization and the market adoption within the globally largest retail companies is given.Design/methodology/approachThe paper uses two different approaches to identify the scientific state-of-the-art: a search on the major scientific databases and an empirical study of the ten largest international retail companies and their adoption of AI technologies in the domains of wholesale and retail.FindingsThe application within the different value-added core tasks varies greatly depending on the area. In summary, there are numerous possible applications in all areas. Especially, in areas where future forecasts are needed within the task areas (such as marketing or replenishment), the use of AI, today, is both scientifically and practically highly developed. In contrast, the market adoption of AI is highly variable. The pioneers have integrated extensive applications into everyday business, while the challengers are investing heavily in new initiatives. Some others, however, show neither active use nor any effort to adopt such technology.Originality/valueTo the best of the author’s knowledge, this is one of the first research contributions to analyze the areas of application and the impact of AI structured along the value-added core processes of retail companies.
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Wan Hooi, Lai. "The adoption of Japanese recruitment practices in Malaysia." International Journal of Manpower 29, no. 4 (July 11, 2008): 362–78. http://dx.doi.org/10.1108/01437720810884764.
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Gyulai, Attila, and Anna Ujlaki. "The political AI." Információs Társadalom 21, no. 2 (June 1, 2021): 29. http://dx.doi.org/10.22503/inftars.xxi.2021.2.3.
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This article adopts a political theoretical perspective to address the problem of AI regulation. By disregarding the political problem of enforceability, it is argued that the applied ethics approach dominant in the discussions on AI regulation is incomplete. Applying realist political theory, the article demonstrates how prescriptive accounts of the development, use, and functioning of AI are necessarily political. First, the political nature of the problem is investigated by focusing on the use of AI in politics on the one hand and the political nature of the AI regulation problem on the other. Second, the article claims that by revisiting some of the oldest political and theoretical questions, the discourse on guidelines and regulation can be enriched through the adoption of AGI and superintelligence as tools for political theoretical inquiry.
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Limenih, Beliyu. "Women farmers’ adoption challenges on artificial inseminations service in outskirt of Addis Ababa." International Journal of Agricultural Extension 6, no. 2 (September 9, 2018): 81–88. http://dx.doi.org/10.33687/ijae.006.02.2417.
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The study was conducted in purposively selected Oromia National Regional State aiming at to suggest strategies to better involve women farmers in Artificial Insemination service. Experts from office of livestock and fishery represented the study population. Group discussion were undertaken separately for Women and men farmers. Collected information was analyzed qualitatively and interpreted accordingly. The study found that institutional and cultural barriers hindered many number of women AI (Artificial Insemination) technicians less involved in AI service delivery. Specifically, institutions don’t encourage women applicant to apply on AI, they thought the position is full of hardship and risky for women to give a service basing in rural areas. In addition to that, the service needs some physical fitness and it would be more difficult for women to move long distance caring containers. Moreover, there is animal disease (brucella) that can be easily transmit to human and can cause gynaecological problem for women. Culturally, even if it is not boldly pronounced by the community, there is a feeling of indignity when women provide AI services. On the other hand, the community also ashamed women farmers, if they ask AI service provision. Moreover, culturally women farmers are not allowed to watch inseminations service. So, in order to increase number of women AI technicians: nominating women technicians from local communities (at least grade 8 completed) and animal science graduate, and train more in practical way would made women AI technicians to be more capable. Support women AI technicians to work privately through post payment service for a while, would raise women acceptance by community. In order to increase number of women farmers attendees on training: conduct on farm training, invite women farmers directly for training, organize training between march to May seasons, prepare pictured based training (including production manuals) would change the number of women dairy producer on AI service provision.
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Miasato, Alessandra, and Fabiana Reis Silva. "Artificial Intelligence as an Instrument of Discrimination in Workforce Recruitment." Acta Universitatis Sapientiae Legal Studies 8, no. 2 (January 19, 2020): 191–212. http://dx.doi.org/10.47745/ausleg.2019.8.2.04.
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The purpose of this article is to reflect on the use of artificial intelligence in the process of hiring and on how biased algorithms can pose a great risk of discrimination to particular groups if artificial intelligence is not used properly with an emphasis on labour relations. Based on current research, we present the wide range of uses how AI technology can be deployed in the search for employees who satisfy the needs of employers on the labour market. The various manifestations of bias in AI implementations utilized in the field of human resources as well as their causes are presented. We conclude that in order to avoid discrimination due to either wilful programmer behaviour or implicit in the data used to train AI agents, the observance of legal and ethical norms, as outlined in tentative projects underway worldwide, is necessary.
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Pillai, Rajasshrie, and Brijesh Sivathanu. "Adoption of AI-based chatbots for hospitality and tourism." International Journal of Contemporary Hospitality Management 32, no. 10 (September 11, 2020): 3199–226. http://dx.doi.org/10.1108/ijchm-04-2020-0259.
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Purpose This study aims to investigate the customers’ behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending the technology adoption model (TAM) with context-specific variables. Design/methodology/approach To understand the customers’ behavioral intention and AUE of AI-powered chatbots for tourism, the mixed-method design was used whereby qualitative and quantitative techniques were combined. A total of 36 senior managers and executives from the travel agencies were interviewed and the analysis of interview data was done using NVivo 8.0 software. A total of 1,480 customers were surveyed and the partial least squares structural equation modeling technique was used for data analysis. Findings As per the results, the predictors of chatbot adoption intention (AIN) are perceived ease of use, perceived usefulness, perceived trust (PTR), perceived intelligence (PNT) and anthropomorphism (ANM). Technological anxiety (TXN) does not influence the chatbot AIN. Stickiness to traditional human travel agents negatively moderates the relation of AIN and AUE of chatbots in tourism and provides deeper insights into manager’s commitment to providing travel planning services using AI-based chatbots. Practical implications This research presents unique practical insights to the practitioners, managers and executives in the tourism industry, system designers and developers of AI-based chatbot technologies to understand the antecedents of chatbot adoption by travelers. TXN is a vital concern for the customers; so, designers and developers should ensure that chatbots are easily accessible, have a user-friendly interface, be more human-like and communicate in various native languages with the customers. Originality/value This study contributes theoretically by extending the TAM to provide better explanatory power with human–robot interaction context-specific constructs – PTR, PNT, ANM and TXN – to examine the customers’ chatbot AIN. This is the first step in the direction to empirically test and validate a theoretical model for chatbots’ adoption and usage, which is a disruptive technology in the hospitality and tourism sector in an emerging economy such as India.
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Masod, Muhammad Yusuf, Siti Farhana Zakaria, and Ruslan Abdul Rahim. "AI Adoption in the Printing Industry: A FVM perspective." Environment-Behaviour Proceedings Journal 6, SI5 (September 1, 2021): 167–71. http://dx.doi.org/10.21834/ebpj.v6isi5.2945.
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Despite its application in high-profile areas, AI's utilisation in the print manufacturing sector is still scarce. Competitiveness and advances in this sector urgently require higher value-added processes, including digitisation, incorporation of advanced manufacturing technologies, and efficient resource utilisation. This paper outlined challenges in organisational adoption of AI demands systematic assessment of the fit and viability of software implementations in heterogeneous, concurrent, and integrated systems while accounting for the performance, efficiency, stability, and sustainability of the sector. We develop a working framework for further assessment based on multiple theories and case studies, with particular attention to the Fit-Viability Model (FVM). Keywords: AI technologies adoption and implementation; Fit-Viability Model; printing industry; Industry 4.0 eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6iSI5.2945
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Chatterjee, Sheshadri, Soumya Kanti Ghosh, and Ranjan Chaudhuri. "Knowledge management in improving business process: an interpretative framework for successful implementation of AI–CRM–KM system in organizations." Business Process Management Journal 26, no. 6 (March 22, 2020): 1261–81. http://dx.doi.org/10.1108/bpmj-05-2019-0183.
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PurposeThe purpose of this paper is to identify the critical success factors (CSFs) for AI-integrated CRM system for better knowledge management (KM) in organizations to improve business process.Design/methodology/approachThe factors critical for adoption of AI-integrated CRM system for efficient knowledge management are innumerable. The salient factors may be identified by several means. Methods like brainstorming and Delphi have been applied here. Sixteen CSFs have been identified. Then the interrelationship among these 16 factors, levels of their importance and the principal driving factors have been established by interpretative structural modelling (ISM) methodology.FindingsThe results show that out of 16 CSFs, leadership support, adequate fund and support of functional area leads are the most important CSFs for AI–CRM–KM integration.Practical implicationsThe results show that support of top management is essential for successful adoption of AI-integrated CRM system for better knowledge management to improve the business process.Originality/valueThis paper has taken a novel attempt to identify CSFs for AI-integrated CRM adoption for efficient knowledge management system in organizations for improvement of business process and to establish interrelationship among those CSFs with the help of ISM methodology.
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Karippur, Nanda Kumar, Shaohong Liang, and Pushpa Rani Balaramachandran. "Factors Influencing the Adoption Intention of Artificial Intelligence for Public Engagement in Singapore." International Journal of Electronic Government Research 16, no. 4 (October 2020): 73–93. http://dx.doi.org/10.4018/ijegr.2020100105.
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This study aims at examining the key factors influencing the adoption intention of artificial intelligence (AI)-enabled mobile application for public engagement. Digital technologies such as AI provide the opportunity for public agencies to be inclusive and invite citizens to participate in shaping and reshaping the future of public policies and methods of governance. The authors test the proposed research model and results highlight the significant roles of collaboration, hedonic motivation, reliability, and degree of app savviness in the adoption intention of AI application for public engagement. The article reports valuable insights and relevant implications for public agencies, service providers and researchers.
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Mugisha, Anthony, Vincent Kayiizi, David Owiny, and John Mburu. "Breeding Services and the Factors Influencing Their Use on Smallholder Dairy Farms in Central Uganda." Veterinary Medicine International 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/169380.
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Dairy cattle breeding is an important technology in the enhancement and promotion of dairy production in Uganda. The introduction of germplasm through AI is crucial to enhance the production potential of the local breeds. A study was conducted in six districts of Uganda in the central region using a questionnaire survey involving 450 randomly selected households to profile the dairy breeding services in use and investigate the factors that affect the success of dairy breeding focusing on AI. Adoption of the AI service was highly (P<0.05) dependent on ava ilability of extension services, record keeping practice (P<0.05), and availability of milk markets (P<0.05). On the other hand AI adoption was independent of formal education, age of farmer, labor availability, and feed/water availability (P>0.05). Use or nonuse of AI did not significantly (P>0.05) influence the sex of the calf born. While preference for AI was marked, very few farmers actually used it. This implies that focus should be put on improved AI service delivery alongside improved extension services.
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Hickman, Sarah E., Gabrielle C. Baxter, and Fiona J. Gilbert. "Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations." British Journal of Cancer 125, no. 1 (March 26, 2021): 15–22. http://dx.doi.org/10.1038/s41416-021-01333-w.
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AbstractRetrospective studies have shown artificial intelligence (AI) algorithms can match as well as enhance radiologist’s performance in breast screening. These tools can facilitate tasks not feasible by humans such as the automatic triage of patients and prediction of treatment outcomes. Breast imaging faces growing pressure with the exponential growth in imaging requests and a predicted reduced workforce to provide reports. Solutions to alleviate these pressures are being sought with an increasing interest in the adoption of AI to improve workflow efficiency as well as patient outcomes. Vast quantities of data are needed to test and monitor AI algorithms before and after their incorporation into healthcare systems. Availability of data is currently limited, although strategies are being devised to harness the data that already exists within healthcare institutions. Challenges that underpin the realisation of AI into everyday breast imaging cannot be underestimated and the provision of guidance from national agencies to tackle these challenges, taking into account views from a societal, industrial and healthcare prospective is essential. This review provides background on the evaluation and use of AI in breast imaging in addition to exploring key ethical, technical, legal and regulatory challenges that have been identified so far.
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Richins, Daniel, Dharmisha Doshi, Matthew Blackmore, Aswathy Thulaseedharan Nair, Neha Pathapati, Ankit Patel, Brainard Daguman, et al. "AI Tax." ACM Transactions on Computer Systems 37, no. 1-4 (June 2021): 1–32. http://dx.doi.org/10.1145/3440689.
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Artificial intelligence and machine learning are experiencing widespread adoption in industry and academia. This has been driven by rapid advances in the applications and accuracy of AI through increasingly complex algorithms and models; this, in turn, has spurred research into specialized hardware AI accelerators. Given the rapid pace of advances, it is easy to forget that they are often developed and evaluated in a vacuum without considering the full application environment. This article emphasizes the need for a holistic, end-to-end analysis of artificial intelligence (AI) workloads and reveals the “AI tax.” We deploy and characterize Face Recognition in an edge data center. The application is an AI-centric edge video analytics application built using popular open source infrastructure and machine learning (ML) tools. Despite using state-of-the-art AI and ML algorithms, the application relies heavily on pre- and post-processing code. As AI-centric applications benefit from the acceleration promised by accelerators, we find they impose stresses on the hardware and software infrastructure: storage and network bandwidth become major bottlenecks with increasing AI acceleration. By specializing for AI applications, we show that a purpose-built edge data center can be designed for the stresses of accelerated AI at 15% lower TCO than one derived from homogeneous servers and infrastructure.
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Chatterjee, Sheshadri, Bang Nguyen, Soumya Kanti Ghosh, Kalyan Kumar Bhattacharjee, and Sumana Chaudhuri. "Adoption of artificial intelligence integrated CRM system: an empirical study of Indian organizations." Bottom Line 33, no. 4 (October 23, 2020): 359–75. http://dx.doi.org/10.1108/bl-08-2020-0057.
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Purpose The purpose of this study is to explore the behavioral intention of the employees to adopt artificial intelligence (AI) integrated customer relationship management (CRM) system in Indian organizations. Design/methodology/approach To identify the factors impacting the behavioral intention of the employees to adopt AI integrated CRM system in Indian organizations helps of literature review and theories have been taken. Thereafter, some hypotheses have been formulated followed by the development of a theoretical model conceptually. The model has been tested statistically for validation using a survey by considering 308 usable respondents. Findings The results of this study show that perceived usefulness and perceived ease of use directly impact the behavioral intention of the employees to adopt an AI integrated CRM system in organizations. Also, these two exogenous factors impact the behavioral intention of the employees to adopt an AI integrated CRM system mediating through two intermediate variables such as utilitarian attitude (UTA) and hedonic attitude (HEA). The proposed model has achieved predictive power of 67%. Research limitations/implications By the help of the technology acceptance model and motivational theory, the predictors of behavioral intention to adopt AI integrated CRM systems in organizations were identified. The effectiveness of the model was strengthened by the consideration of two employee-centric attitudinal attributes such as UTA and HEA, which is claimed to have provided contributions to the extant literature. The proposed theoretical model claims a special theoretical contribution as no extant literature considered the effects of leadership support as a moderator for the adoption of an AI integrated CRM system in Indian organizations. Practical implications The model implies that the employees using AI integrated CRM system in organizations must be made aware of the usefulness of the system and the employees must not face any complexity to use the system. For this, the managers of the concerned organizations must create a conducive atmosphere congenial for the employees to use the AI integrated CRM system in the organizations. Originality/value Studies covering exploration of the adoption of AI integrated CRM systems in Indian organizations are found to be in a rudimentary stage and in that respect, this study claims to have possessed its uniqueness.
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Willis, Kate, and Chris Woods. "Managing invasive Styela clava populations: inhibiting larval recruitment with medetomidine." Aquatic Invasions 6, no. 4 (December 2011): 511–14. http://dx.doi.org/10.3391/ai.2011.6.4.16.
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Alghamdi, Mohammed I. "Assessing Factors Affecting Intention to Adopt AI and ML: The Case of the Jordanian Retail Industry." MENDEL 26, no. 2 (December 21, 2020): 39–44. http://dx.doi.org/10.13164/mendel.2020.2.039.
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Aim: The aim of this research is to evaluate the factors that affect the adoption intention of AI and ML in the context of Jordan’s retail industry
Method: For this research paper, primary data was collected with the help of surveying different retail companies that are operational in Jordan with a sample of 400 participants. The survey questionnaire was based on a Likert scale where five points ranging from strongly agree to strongly disagree were provided to the participants. Structural Equation Modelling (SEM) used to analyse the impact and significance of the different factors on the adoption of AI and ML in Jordanian retail sector.
Results: It has been concluded from this research paper that communication, government regulations, market structure, and technological infrastructure are important factors that influence the adoption of AI and ML in the retail industry of Jordan. However, the results of this research have pointed out that managerial support and vendor relationship do not have a significant influence on the adoption of AI and ML.
Limitations: The scope of the research is restricted to the context of the retail industry only. This research has been carried out in the context of Jordan thus it cannot be applied on to other geographical backgrounds. Due to the time and scope limitations, there are restricted factors considered in the framework.
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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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Ahmed, Shuaib, Hammad Tahir, and Syed Waleed Warsi. "E -Recruitment Transforming the Dimensions of Online Job Seeking: A case of Pakistan." International Journal of Human Resource Studies 5, no. 1 (February 10, 2015): 96. http://dx.doi.org/10.5296/ijhrs.v5i1.6161.
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This research study aims to investigate the influence of adoption of e-recruitment method by job seekers in karachi, Pakistan. It also analyzes the relationship between e-recruitment and their adoption behavior. A convenient sample of 250 job seekers from different universities students of Karachi was taken. After validating the instrument, one sample t-test was performed to test the relationship of selected variables i.e. cost saving, time saving, extensive search and unlimited excess with the behavior of job seekers in adopting a e-recruitment method. Using SPSS 17.0 Statistical evidence at 0.05 level of significance proved that cost saving, time saving, extensive search and unlimited excess are significantly correlated with a e-recruitment adoption. The findings are helpful for academicians, organizations and other institutions to design and market new web pages to attract more and more job seekers.
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Hmoud, Bilal Ibrahim, and László Várallyai. "Artificial Intelligence in Human Resources Information Systems: Investigating its Trust and Adoption Determinants." International Journal of Engineering and Management Sciences 5, no. 1 (April 14, 2020): 749–65. http://dx.doi.org/10.21791/ijems.2020.1.65.
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With the rapidly emerging trend of employing Artificial Intelligence technologies within modern economics. This study is an attempt to fill the research gap associated with the factors that have influence with the adoption of artificial intelligence in human resources information systems on HR-leaders intention to use it. It empirically investigates the influences that trust, technological readiness, facilitating condition and performance expectancy on HR-professional’s behavioral intention to use AI in HRM. Besides, examine the moderating effect of age and experience on the proposed associations. Data were collected from by online questionnaire from 185 HR managers. A structural framework was introduced to test the relationship between study latent variables. Result exhibited that trust and performance expectancy has a significant influence on HR-professionals behavioral intention to use AI-HRIS. Trust and technological readiness showed a significant influence on HR-professionals performance expectancy of using AI-HRIS. While facilitating condition, organizational size and technological readiness did not show a significant influence on HR-professionals behavioral intention toward using AI-HRIS. Lastly, Age and Experience did not have a moderating effect on trust and performance expectancy association with the behavioral intention toward using AI-HRIS. The findings of this study contribute to the theory development of information technology diffusion in HRM.
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Zerfass, Ansgar, Jens Hagelstein, and Ralph Tench. "Artificial intelligence in communication management: a cross-national study on adoption and knowledge, impact, challenges and risks." Journal of Communication Management 24, no. 4 (May 7, 2020): 377–89. http://dx.doi.org/10.1108/jcom-10-2019-0137.
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PurposeArtificial intelligence (AI) might change the communication profession immensely, but the academic discourse is lacking an investigation of the perspective of practitioners on this. This article addresses this research gap. It offers a literature overview and reports about an empirical study on AI in communications, presenting first insights on how professionals in the field assess the technology.Design/methodology/approachA quantitative cross-national study among 2,689 European communication practitioners investigated four research questions: RQ1 – How much do professionals know about AI and to what extent are they already using AI technologies in their everyday lives? RQ2 – How do professionals rate the impact of AI on communication management? RQ3 – Which challenges do professionals identify for implementing AI in communication management? RQ4 – Which risks do they perceive?FindingsCommunication professionals revealed a limited understanding of AI and expected the technology to impact the profession as a whole more than the way their organisations or themselves work. Lack of individual competencies and organisations struggling with different levels of competency and unclear responsibilities were identified as key challenges and risks.Research limitations/implicationsThe results highlight the need for communication managers to educate themselves and their teams about the technology and to identify the implementation of AI as a leadership issue.Originality/valueThe article offers the first cross-national quantitative study on AI in communication management. It presents valuable empirical insights on a trending topic in the discipline, highly relevant for both academics and practitioners.
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Sanusi, Ahmad, and Ahmad Martadha Mohamed. "Relationship between E-recruitment Adoption and Good Governance Practices in Nigerian Public Sector: An Empirical Study." Journal of Public Administration and Governance 2, no. 4 (November 11, 2012): 57. http://dx.doi.org/10.5296/jpag.v2i4.1965.
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The universal quest for good governance forces different countries to adopt e-recruitment as recruiting strategy in hiring their teeming workforce in line with global best practices. As part of public service reform Nigerian government encourage public sector organisations to jettison conventional recruitment method in favour of electronic recruitment for transparency, accessibility and efficiency in recruitment exercises. This study empirically investigates the relationship between e-recruitment adoption and good governance practices in Nigerian public service. The study Modified Technology Acceptance Model (TAM) to analyse the response generated from 326 survey respondents. The findings indicated that e-recruitment adoption is yet to provide good governance. In the overall, this study offer important insight and recommended that government agencies involved should put effective machinery in motion in order to enrich and improve good governance practices in e-recruitment in Nigerian public sector.
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Cowgill, Bo, Fabrizio Dell’Acqua, and Sandra Matz. "The Managerial Effects of Algorithmic Fairness Activism." AEA Papers and Proceedings 110 (May 1, 2020): 85–90. http://dx.doi.org/10.1257/pandp.20201035.
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How do ethical arguments affect AI adoption in business? We randomly expose business decision-makers to arguments used in AI fairness activism. Arguments emphasizing the inescapability of algorithmic bias lead managers to abandon AI for manual review by humans and report greater expectations about lawsuits and negative PR. These effects persist even when AI lowers gender and racial disparities and when engineering investments to address AI fairness are feasible. Emphasis on status quo comparisons yields opposite effects. We also measure the effects of “scientific veneer” in AI ethics arguments. Scientific veneer changes managerial behavior but does not asymmetrically benefit favorable (versus critical) AI activism.
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Kebede, Addisu Kasa. "Adoption of E-recruitment in the Ethiopian Banking Industry." IBMRD's Journal of Management & Research 6, no. 2 (September 1, 2017): 29. http://dx.doi.org/10.17697/ibmrd/2017/v6i2/120446.
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