Relevant bibliographies by topics / Adoption of AI in recruitment

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  3. Books
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Academic literature on the topic 'Adoption of AI in recruitment'

Author: Grafiati
Published: 23 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Adoption of AI in recruitment"

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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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Dissertations / Theses on the topic "Adoption of AI in recruitment"

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Kerey, Ayşegül Begüm, and Enrico D`Alessandro. "AI in recruitment: an exploratory study into the factors that impact its pace of adoption. : A case study to reveal the strategic implications of these factors on AI solution providers from a contingency perspective." Thesis, Uppsala universitet, Industriell teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446406.

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Over the past few years, the adoption of AI in recruitment has accellerated. However, there has been a noticeable resistance from HR managers to invest in AI tools for their deparment. With the aim of understanding the causes prompting this resistance, this thesis investigates the factors that impact the pace of adoption of AI in HR, with a focus on recruitment solutions. While designing an analytical framework inspired by the contingency perspective, the factors have been searched through a literature review and their effects have been tested in terms of magnitude and direction through a qualitative study. To do this, the authors performed a case study involving an external partner, an AI solution provider start-up company. A total of 16 semi-structured interviews have been conducted with different levels of stakeholders, including external partner`s employees, investors, competitors, and end- users. Finally, a strategic analysis of the AI recruitment market has been deployed. Our ambition is that the combination of the information over the factors together with the strategic analysis will empower the companies within the industry in taking better informed strategic decisions.
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Savola, Hannimari, and Bijona Troqe. "Recruiters just wanna have...AI? : Implications of implementing AI in HR recruitment." Thesis, Linköpings universitet, Företagsekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158480.

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The development and implementation of AI is rapidly growing in the Nordic countries, yet the perception and information of AI is still limited. This paper will look deeper into the managerial aspects of implementing AI as part of the recruitment system, specifically the selection process and machine learning in text mining. The data gathering of this research has been conducted via interviews with Linköping's municipality, as well as collecting secondary data from public reports and scientific articles. Afterwards, the data was then scrutinized through theoretical analysis, using frameworks from different academic researches. A set of aspects was found, which affects the implementation of AI in an organisation in Sweden. A managerial view was taken to find a deeper significance on why an understanding of these aspects is necessary when implementing AI as a part of company's recruitment processes. However, while other elements other than the ones identified in this thesis do exist, a coherent picture of the process and the affecting variables can be thoroughly explained through these specifically chosen viewpoints. The paper concludes with drawing a bigger image of the AI in recruitment and selection processes, and the implications of it to an organisation considering to implement AI as part of these processes in near future. The thesis can be seen as a recommendation to any establishment that is making the decision of adopting the usage of AI as part of recruitment.
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Roa, Baez Julian, and Remi Leon Igbekele. "Challenges of AI Adoption in SMEs." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301269.

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This thesis paper discusses the adoption of Artificial Intelligence (AI) technologies in the context of small and medium-sized companies. AI is a disruptive innovation currently leading a technological transition in many industries. Academic literature on its adoption in the context of SMEs is limited. This thesis aims to answer the research question "W hat are the main challenges of AI adoption in SMEs?". The authors use a qualitative research approach, interviewing 18 representatives of different Swedish organizations to answer the question. The authors identify six main challenge categories regarding AI adoption in SMEs via a coding process, which are Change Management, Education, Data, Hiring, Project Structuring, and External Help. The authors show similarities to challenges described in the existing literature on AI adoption. However, Hiring and External Help impact AI adoption in SMEs more significantly than in larger corporations, while Change Management can be easier for smaller organizations. Furthermore, a framework that helps to prioritize the above challenges is introduced. In comparison with literature on the adoption of other (non-AI) IT innovations in SMEs, some challenges are more salient to AI: the unclear definition of the term, false expectations as a result of the AI Hype, the uncertainty of project outcome, and the prerequisite of a previous digitalization process. The authors recommend further research in other contexts.
I denna avhandling diskuteras införandet av artificiell intelligens (AI) teknik i små och medelstora företag. AI är en disruptiv innovation som för närvarande leder en teknisk övergång i många branscher. Den akademiska litteraturen kring dess användning i små och medelstora företag är begränsad. Denna avhandling syftar till att besvara forskningsfrågan "Vilka är de största utmaningarna med att införa AI i små och medelstora företag?". Författarna använder en kvalitativ forskningsansats och intervjuar 18 representanter från olika svenska organisationer för att besvara frågan. Författarna identifierar sex huvudsakliga utmaningar områden avseende AI-adoption i små och medelstora företag via en kodningsprocess: förändringshantering, utbildning, data, anställning, projekt strukturering och extern hjälp. Författarna visar på likheter med de utmaningar som beskrivs i den befintliga litteraturen om AI-användning. Anställning och extern hjälp påverkar dock AI-användningen i små och medelstora företag mer påtagligt än i större företag, medan förändringshantering kan vara lättare för mindre organisationer. Vidare introduceras ett ramverk som hjälper till att prioritera ovanstående utmaningar. I jämförelse med litteratur om antagandet av andra (icke-AI) IT-innovationer i små och medelstora företag är vissa utmaningar mer framträdande när det gäller AI: den oklara definitionen av begreppet, falska förväntningar som ett resultat av AI-hypen, osäkerheten från projektresultatet och förutsättningen av tidigare digitaliseringsprocess. Författarna rekommenderar ytterligare forskning i andra sammanhang.
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Savola, Tommi, Tyko Tuohimaa, and Sebastian Berg. "AI-Enhanced Marketing Management – Factors Influencing Adoption in SMEs." Thesis, Högskolan i Jönköping, Internationella Handelshögskolan, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-39908.

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Recent developments and hype around artificial intelligence (AI) have arisen as result of two main factors: increase in computational power and data. Although marketing is considered as one of the main business applications within AI today, there is a lack of literature combining the disciplines. Marketing management tools, which utilise AI in supporting decision making are referred to as knowledge-driven marketing management support systems (MMSS). These systems provide besides quantitative analysis, further qualitative facets into marketing management. Despite the willingness of many SMEs to engage with the technology that may foster competitive advantage, many adoption processes fail. The purpose of this thesis is to explore the factors influencing adoption of knowledge-driven MMSS in SMEs in Finland and Sweden. Qualitative primary data was collected from nine company representatives at top management level in Finnish and Swedish firms. Companies were classified in three categories, providers, adopters and non-adopters of knowledge-driven MMSS.   The findings show that there are several factors influencing adoption of knowledge-driven MMSS. The factors were grouped into technological, organizational and environmental factors, based on the TOE framework. Even though SMEs suffer from a lack of resources compared to large companies, this research suggests that they are at the forefront of adopting AI for marketing purposes. Additionally, it was found that the factors affecting adoption are dependent on whether the knowledge-driven MMSS is built in-house or outsourced.   This study has contributed to the identified gaps in literature by combining the disciplines of AI, marketing and SMEs, and by exploring the factors behind adoption of knowledge-driven MMSS. The authors of this thesis have the aspiration that the developed post-empirical framework will serve as a guiding tool for top management and marketing managers in SMEs looking to adopt knowledge-driven MMSS into their organizations.
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Brishti, Juthika Kabir, and Ayesha Javed. "THE VIABILITY OF AI-BASED RECRUITMENT PROCESS : A systematic literature review." Thesis, Umeå universitet, Institutionen för informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172311.

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Paramita, Dhyana. "Digitalization in Talent Acquisition : A Case Study of AI in Recruitment." Thesis, Uppsala universitet, Institutionen för samhällsbyggnad och industriell teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413081.

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The trend of changing technology has affected different sectors including human resources in the process of recruitment and selection. Different technology solutions offer various benefit for recruitment practices especially in terms of efficiency outcome while it seems to overlook the relationship outcomes. Whether or not to have a balance approach depends on how a firm views their own recruitment process. The purpose of this study is to understand firm’s orientation towards its approach in performing recruiting practices. The analysis and discussion is articulated through the phenomenon of AI in recruiting with the interplay of different views especially from human resources and operations management. This study follows an inductive qualitative single case study that involves 11 HR professionals to participate in semi-structured interviews. The data analysis is performed with thematic analysis to develop grounded theory which is based on approach introduced by Gioia (see Corley and Gioia, 2004; Gioia et al., 2013). The findings proposed by this study is TOP framework which covers competitive advantage through operations, redefining customer orientation, and process enhancement through collaboration.
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Lisa, Aysha Khatun, and Simo Valerie Rostan Talla. "An in-depth study on the stages of AI in recruitment process of HRM and attitudes of recruiters and recruitees towards AI in Sweden." Thesis, Umeå universitet, Företagsekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184521.

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With the constant technological changes globally, organizations are now at the forefront of changing their business processes to be more competitive. These technological developments have brought in several shifts within business processes. Human resource management (HRM) has been dramatically affected by such changes more than ever, especially the recruitment process. As such, there is rising concern to shift from a traditional to an AI recruitment process. The adoption of AI in the recruitment process has remained relatively unexplored, especially within Swedish organizations. Despite its great discussion within academia and organizations, the limited amount of literature on the subject makes it interesting and current. Therefore, the main aims of this research are: (1) to analyze in which stages till date organizations are using AI in recruitment practices in Sweden and (2) to ascertain the attitudes of recruiters and recruitees towards the use of AI in the recruitment process in Sweden organizational context. This research adopted a qualitative approach with semi-structured approach interviews conducted with three recruiters and five recruitees in Sweden. The empirical findings of the study reveal that organizations have not fully implemented AI in the recruitment process. Factors such as timeframe, recruitment cost, work efficiency, and human biases were considered the top challenges of the traditional recruitment process. AI in the recruitment process can help reduce the lengthy time while increasing work efficiency with faster-recruiting methods. Organizations can share recruitment costs. Human biases can significantly be reduced with the use of AI at the pre-screening and selection stages. It was also discovered that the attitudes of recruiters and recruiters were seemingly positive towards the acceptance of AI in the recruitment process. Furthermore, AI was not seen as a threat to human jobs instead as a complementary role. This leads to the conclusion that AI can complement the recruitment process and AI cannot take human jobs since humans will still be needed for software development. This research provides contributions towards theoretical, practical, and social. This research offers an extent of the existing knowledge on the subject matter. It will help recruiters understand the importance of AI in the recruitment process. Furthermore, recruitees will be more accustomed to the idea of AI. In addition, the findings of this research can assist in the curriculum adjustment of educational institutions to best serve the needs of the changing business climate. At the government level, the findings can be used to encourage continuous innovation and learning. Furthermore, this research can be a starting point for other future research.
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Tersander, Jacob. "AI – Can You Afford To Wait?" Thesis, KTH, Skolan för industriell teknik och management (ITM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241051.

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The paradigm of diffusion research can be traced back all the way to the 1940s when Ryan and Gross investigated the diffusion of hybrid seed among farmers in Iowa. Since the 1960s diffusion research has been applied in a wide variety of disciplines, for instance, to study the diffusion of the Internet and the non-diffusion of the Dvorak keyboard. Currently, the technologies that are on top of the Gartner Hype Cycle are all associated with Artificial Intelligence (AI), which shortly can be defined as learning devices that perceive their environment and take actions to maximize their success at some goal. Consequently, some people suggest that the current hype surrounding AI can be the end of the human kind, while others believe it will give way for millions of fresh jobs and cleverer decision-making. In recent years both media and political organizations have shown great interest in AI. In addition, the industry is captivated by the potential uses of AI. In the last years, AI-related companies in the US have raised billions of dollars in the stock market together with a large number of acquisitions. The large flow of capital into AI technology underpins the fast development of AI solutions. The purpose of this study is to investigate how groups approach AI. What can be concluded after reviewing different sectors is that organizations seem to share a common interest of AI. Furthermore, organizations share the opinion that eventually AI will be a more natural part of their processes. Organizations investing a larger share of their budget in R&D have a longer experience of using AI and are currently doing projects utilizing more advanced technologies within AI. In organizations from other sectors, the investments in AI depend on the people with the authority to invest money in projects and their view on AI. Organizations generally seem to approach AI in a similar way. Firstly, they evaluate what AI is. Secondly, they find areas to make small iterative PoC-projects utilizing AI, usually with machine learning. Finally, more money is invested if the PoC-projects were successful and the organization starts looking at how to acquire more competence within the area to fully exploit the value of AI.
Paradigmet för innovationsspridning kan spåras ända tillbaka till 1940-talet när Ryan och Gross undersökte spridningen av hybridfrön bland bönder i Iowa. Sedan 1960-talet har forskningen tillämpats inom en mängd olika discipliner, till exempel för att studera spridningen av Internet och icke-spridningen av Dvorak-tangentbordet. För närvarande är teknologierna som ligger på toppen av Gartner Hype-cykeln alla förknippade med artificiell intelligens (AI), som kan definieras som lärande enheter som uppfattar sin miljö och vidtar åtgärder för att maximera sin framgång gällande något mål. Hypen som nu finns kring AI har lett till att vissa människor tror att det kan innebära slutet för mänskligheten medan andra tror att det kommer att ge plats för miljoner nya jobb och smartare beslutsfattande. Under de senaste åren har både medier och politiska organisationer visat stort intresse för AI samt visat intresse för potentiella användningsområden av AI. AI-relaterade företag i USA har under de senaste åren har tagit in miljarder dollar i riskkapital. Ett stort antal förvärv och kapitalflödet till AI-teknik ökar den snabba utvecklingen av AI-lösningar. Syftet med denna studie är att beskriva spridningen av AI i organisationer från ett antal olika sektorer. Vad som kan sägas efter att ha studerat olika sektorer är att organisationer delar en gemensam nyfikenhet för AI och att de tror att AI kommer bli en allt mer naturlig del av sina processer. De företag som spenderar mycket pengar på FoU har längre erfarenhet av att använda AI och gör för närvarande projekt som använder mer avancerade tekniker. I andra organisationer är investeringarna inom AI beroende av de anställda som har rätt att investera pengar i projekt och deras syn på AI. Organisationer verkar allmänt närma sig AI på ett liknande sätt där de först utvärderar vad AI är. Därefter väljer de ett antal områden där de gör små iterativa projekt där de utnyttjar AI, vanligtvis via ML. Därefter investerades mer pengar om de små projekten lyckas och företaget börjar titta på hur man kan förvärva mer kompetens inom området.
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Johansson, Jennifer, and Senja Herranen. "The application of Artificial Intelligence (AI) in Human Resource Management: Current state of AI and its impact on the traditional recruitment process." Thesis, Högskolan i Jönköping, Internationella Handelshögskolan, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-44323.

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Background: The world is constantly becoming more prone to technology due to globalization which implies organizations have to stay up to date in order to be competitive. Human Resource Management (HRM) is more important than ever, especially with a focus on the recruitment of new employees which will bring skills and knowledge to an organization. With technological advances also comes the opportunity to streamline activities that previously have had to be carried out by humans. Therefore, it is of the highest importance to consider and evaluate the impact technology might have on the area of HRM and specifically the recruitment process. Purpose: The purpose of this thesis is to research the implications that technological advancements, in particular Artificial Intelligence (AI), have for the recruitment process. It aims to investigate where AI can be implemented in the traditional recruitment process and possibly make the process more effective, as well as what the implications would be of having AI within recruitment. Method: This thesis uses a qualitative study with semi-structured interviews conducted with eight international companies from all over the world. It is viewed through an interpretivism research philosophy with an inductive research approach. Conclusion: The results show that the area of AI in recruitment is relatively new and there are not many companies that utilize AI in all parts of their recruitment process. The most suitable parts to implement AI in traditional recruitment include recruitment activities such as pre-selection and communication with candidates and sending out recruitment results for applicants. The main benefits of AI were seen as the speeded quality and elimination of routine tasks, while the major challenge was seen as the companies’ overall readiness towards new technologies.
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Stenberg, Louise, and Svante Nilsson. "Factors influencing readiness of adopting AI : A qualitative study of how the TOE framework applies to AI adoption in governmental authorities." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279583.

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Artificial intelligence is increasing in interest and it is creating value to many organizations world-wide. Due to the potential, governmental authorities in Sweden who work with large volumes of text documents are interested in natural language processing models, which is a sub field of AI and have started to incorporate it to their organizations. This study explores and discusses factors that are influential for governmental authorities when adopting AI and highlights ethical aspects which are of importance for the adoption process. This is explored through a literature review which lead to a frame of reference built on the Technology Organization Environment framework (TOE), which then was tested through interviews with project leaders and AI architects at governmental authorities who are working with language models. The results show that the TOE framework is suitable for analysing AI adoption for governmental authorities. The factors that are found influential are Relative Advantage, Compatibility and Complexity, Management support, Staff capacity, Regulatory environment and Cooperation. Furthermore, the findings suggest that AI Ethics and Data access are influential in all three contexts of technology, organization and environment. The findings of this study confirm results from previous research regarding adoption of new technology, and also provides the literature with exploring the adoption process of AI in governmental authorities, which was not widely explored in literature on beforehand.
Allt fler intresserar sig för artificiell intelligens då det skapar värde för många organisationer. Svenska myndigheter som arbetar med stora mängder textdokument ser potentialen i AI och har börjat implementera språkmodeller, ett sorts AI, i sina organisationer. Den här studien utforskar och diskuterar faktorer som är inflytelserika inför implementering av AI och belyser etiska aspekter som är viktiga för implementationsprocessen. Detta har utforskats först genom en litteraturstudie, ur vilken ett ramverk som bygger på Teknologi Organisation Miljö-ramverket (TOE) har tagits fram. Detta har sedan testats genom intervjuer med projektledare och AI arkitekter på svenska myndigheter som arbetar med språkmodeller. Resultaten visar att TOE-ramverket lämpar sig väl för att analysera adoptering av AI i myndigheter. Faktorerna som har identifierats som inflytelserika är relativ fördel, kompatibilitet, komplexitet, ledningsstöd, anställdas kapacitet, regleringskontext och samarbete. Dessutom föreslås det att etik för AI och datatillgång ska spänna över alla tre kontexter inom TOE. Resultaten av studien bekräftar tidigare forskning gällande adoptering av nya teknologier, och den bidrar även till litteraturen genom att utforska adopteringsprocessen av AI i myndigheter, vilket inte har utforskats i större utsträckning tidigare.
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Books on the topic "Adoption of AI in recruitment"

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Songqiao, Li, ed. Sheng ming zhi ai: A love for life. Beijing Shi: Wai yu jiao xue yu yan jiu chu ban she, 2002.

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Pellegrino, Piero. Gli impedimenti relativi ai vincoli etico-giuridici tra le persone nel matrimonio canonico. Torino: G. Giappichelli, 2002.

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ter Haar, Barend J. Guan Yu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803645.001.0001.

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Guan Yu was a minor general in his own day, who supported one of numerous claimants to the throne in the early third century CE. He was captured and executed by enemy forces in 219. He eventually became one the most popular and influential deities of imperial China under the name Lord Guan or Emperor Guan, of the same importance as the Buddhist bodhisattva Guanyin. This is a study of his cult, but also of the tremendous power of oral culture in a world where writing became increasingly important. The book follows the rise of the deity through his earliest stage as a hungry ghost, his subsequent adoption by a prominent Buddhist monastery during the Tang (617–907) as its miraculous supporter, and his recruitment by Daoist ritual specialists during the Song dynasty (960–1276) as an exorcist general. It continues on with his subsequent roles as a rain god, protector against demons and barbarians, and, eventually, moral paragon and almost messianic saviour. Throughout his divine life, the physical prowess of the deity, more specifically Lord Guan’s ability to use violent action for doing good, remained an essential dimension of his image. Most research ascribes a decisive role in the rise of his cult to the literary traditions of the Three Kingdoms, best known from the famous novel by this name. This book argues that the cult arose from oral culture and spread first and foremost as an oral practice.
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Book chapters on the topic "Adoption of AI in recruitment"

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Amigoni, Francesco, and Mauro Villa. "An Algorithm for Recruitment of Agents in Agency Design." In AI*IA 99: Advances in Artificial Intelligence, 321–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-46238-4_28.

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Akiyama, Nobumasa. "AI Nuclear Winter or AI That Saves Humanity? AI and Nuclear Deterrence." In Robotics, AI, and Humanity, 161–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54173-6_13.

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AbstractNuclear deterrence is an integral aspect of the current security architecture and the question has arisen whether adoption of AI will enhance the stability of this architecture or weaken it. The stakes are very high. Stable deterrence depends on a complex web of risk perceptions. All sorts of distortions and errors are possible, especially in moments of crisis. AI might contribute toward reinforcing the rationality of decision-making under these conditions (easily affected by the emotional disturbances and fallacious inferences to which human beings are prone), thereby preventing an accidental launch or unintended escalation. Conversely, judgments about what does or does not suit the “national interest” are not well suited to AI (at least in its current state of development). A purely logical reasoning process based on the wrong values could have disastrous consequences, which would clearly be the case if an AI-based machine were allowed to make the launch decision (this virtually all experts would emphatically exclude), but grave problems could similarly arise if a human actor relied too heavily on AI input.
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Zhu, Yiwei, and Shiwei Sun. "Exploring Patients’ AI Adoption Intention in the Context of Healthcare." In Communications in Computer and Information Science, 27–39. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3631-8_4.

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Falcone, Rino, and Cristiano Castelfranchi. "Levels of Delegation and Levels of Adoption as the basis for Adjustable Autonomy." In AI*IA 99: Advances in Artificial Intelligence, 273–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-46238-4_24.

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Misra, Santosh K., Satyasiba Das, Sumeet Gupta, and Sujeet K. Sharma. "Public Policy and Regulatory Challenges of Artificial Intelligence (AI)." In Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation, 100–111. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64849-7_10.

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Goel, Apoorva, and Richa Awasthy. "Are Video Resumes Preferred by Job Applicants? Information Technology in Recruitment." In Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation, 138–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64861-9_13.

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Jain, Vranda, Nidhi Singh, Sajeet Pradhan, and Prashant Gupta. "Factors Influencing AI Implementation Decision in Indian Healthcare Industry: A Qualitative Inquiry." In Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation, 635–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64849-7_56.

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Chatterjee, Sheshadri, Kuttimani Tamilmani, Nripendra P. Rana, and Yogesh K. Dwivedi. "Employees’ Acceptance of AI Integrated CRM System: Development of a Conceptual Model." In Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation, 679–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64861-9_59.

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Peisl, Thomas, and Raphael Edlmann. "Exploring Technology Acceptance and Planned Behaviour by the Adoption of Predictive HR Analytics During Recruitment." In Communications in Computer and Information Science, 177–90. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56441-4_13.

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Jahić, Jasmin, and Robin Roitsch. "State of the Practice Survey: Predicting the Influence of AI Adoption on System Software Architecture in Traditional Embedded Systems." In Communications in Computer and Information Science, 155–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59155-7_12.

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Conference papers on the topic "Adoption of AI in recruitment"

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Mujtaba, Dena F., and Nihar R. Mahapatra. "Ethical Considerations in AI-Based Recruitment." In 2019 IEEE International Symposium on Technology and Society (ISTAS). IEEE, 2019. http://dx.doi.org/10.1109/istas48451.2019.8937920.

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Pena, Alejandro, Ignacio Serna, Aythami Morales, and Julian Fierrez. "Bias in Multimodal AI: Testbed for Fair Automatic Recruitment." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00022.

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Yazdani, Ghulam, Farheen Qazi, and Sadiq Ali Khan. "Adoption of VR influencing AI on 3D objects." In 2019 International Conference on Information Science and Communication Technology (ICISCT). IEEE, 2019. http://dx.doi.org/10.1109/cisct.2019.8777408.

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Ben David, Daniel, Yehezkel S. Resheff, and Talia Tron. "Explainable AI and Adoption of Financial Algorithmic Advisors." In AIES '21: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3461702.3462565.

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Lee, Jung Hee, Ju Hyung Kim, Yong Hwan Kim, and Yong Min Song. "A Study on Priorities for Utilization of AI Recruitment System." In 2021 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter). IEEE, 2021. http://dx.doi.org/10.1109/snpdwinter52325.2021.00072.

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Jahic, Jasmin, Robin Roitsch, and Lukasz Grzymkowski. "Knowledge-based Adequacy assessment Approach to support AI adoption." In 2021 IEEE 18th International Conference on Software Architecture Companion (ICSA-C). IEEE, 2021. http://dx.doi.org/10.1109/icsa-c52384.2021.00008.

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Kwon, Ohbyung, Sujin Bae, and Bongsik Shin. "Understanding the Adoption Intention of AI through the Ethics Lens." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2020. http://dx.doi.org/10.24251/hicss.2020.611.

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Fernandes, Pedro M., Francisco C. Santos, and Manuel Lopes. "Adoption Dynamics and Societal Impact of AI Systems in Complex Networks." In AIES '20: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3375627.3375847.

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"ADOPTION OF AI IN AGRICULTURE: THE GAME-CHANGER FOR INDIAN FARMERS." In International Conferences on ICT, Society and Human Beings (ICT 2020), Connected Smart Cities (CSC 2020) and Web Based Communities and Social Media (WBC 2020). IADIS Press, 2020. http://dx.doi.org/10.33965/ict_csc_wbc_2020_202008c025.

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Moradi, Morteza, Mohammad Moradi, and Farhad Bayat. "On robot acceptance and adoption a case study." In 2018 8th Conference of AI & Robotics and 10th RoboCup Iranopen International Symposium (IRANOPEN). IEEE, 2018. http://dx.doi.org/10.1109/rios.2018.8406626.

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Reports on the topic "Adoption of AI in recruitment"

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Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. AI Adoption and System-Wide Change. Cambridge, MA: National Bureau of Economic Research, May 2021. http://dx.doi.org/10.3386/w28811.

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Cary, Dakota. Academics, AI, and APTs. Center for Security and Emerging Technology, March 2021. http://dx.doi.org/10.51593/2020ca010.

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Six Chinese universities have relationships with Advanced Persistent Threat (APT) hacking teams. Their activities range from recruitment to running cyber operations. These partnerships, themselves a case study in military-civil fusion, allow state-sponsored hackers to quickly move research from the lab to the field. This report examines these universities’ relationships with known APTs and analyzes the schools’ AI/ML research that may translate to future operational capabilities.
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Borrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot, et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, December 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.

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The integration of novel technologies for monitoring and investigating compliance can enhance the effectiveness of regimes related to weapons of mass destruction (WMD). This report looks at the potential role of four novel approaches based on recent technological advances – remote sensing tools; open-source satellite data; open-source trade data; and artificial intelligence (AI) – in monitoring and investigating compliance with WMD treaties. The report consists of short essays from leading experts that introduce particular technologies, discuss their applications in WMD regimes, and consider some of the wider economic and political requirements for their adoption. The growing number of space-based sensors is raising confidence in what open-source satellite systems can observe and record. These systems are being combined with local knowledge and technical expertise through social media platforms, resulting in dramatically improved coverage of the Earth’s surface. These open-source tools can complement and augment existing treaty verification and monitoring capabilities in the nuclear regime. Remote sensing tools, such as uncrewed vehicles, can assist investigators by enabling the remote collection of data and chemical samples. In turn, this data can provide valuable indicators, which, in combination with other data, can inform assessments of compliance with the chemical weapons regime. In addition, remote sensing tools can provide inspectors with real time two- or three-dimensional images of a site prior to entry or at the point of inspection. This can facilitate on-site investigations. In the past, trade data has proven valuable in informing assessments of non-compliance with the biological weapons regime. Today, it is possible to analyse trade data through online, public databases. In combination with other methods, open-source trade data could be used to detect anomalies in the biological weapons regime. AI and the digitization of data create new ways to enhance confidence in compliance with WMD regimes. In the context of the chemical weapons regime, the digitization of the chemical industry as part of a wider shift to Industry 4.0 presents possibilities for streamlining declarations under the Chemical Weapons Convention (CWC) and for facilitating CWC regulatory requirements.
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