Adopt Artificial Intelligence Research Articles

Identifying university librarians’ readiness to adopt artificial intelligence (AI) for innovative learning experiences and smart library services: an empirical investigation

Identifying university librarians’ readiness to adopt artificial intelligence (AI) for innovative learning experiences and smart library services: an empirical investigation

Artificial intelligence in talent acquisition: a multiple case study on multi-national corporations

PurposeThe aim of this paper is to explore how multi-national corporations (MNCs) can effectively adopt artificial intelligence (AI) into their talent acquisition (TA) practices. While the potential of AI to address emerging challenges, such as talent shortages and applicant surges in specific regions, has been anecdotally highlighted, there is limited empirical evidence regarding its effective deployment and adoption in TA. As a result, this paper endeavors to develop a theoretical model that delineates the motives, barriers, procedural steps and critical factors that can aid in the effective adoption of AI in TA within MNCs.Design/methodology/approachGiven the scant empirical literature on our research objective, we utilized a qualitative methodology, encompassing a multiple-case study (consisting of 19 cases across seven industries) and a grounded theory approach.FindingsOur proposed framework, termed the Framework on Effective Adoption of AI in TA, contextualizes the motives, barriers, procedural steps and critical success factors essential for the effective adoption of AI in TA.Research limitations/ implicationsThis paper contributes to literature on effective adoption of AI in TA and adoption theory.Practical implicationsAdditionally, it provides guidance to TA managers seeking effective AI implementation and adoption strategies, especially in the face of emerging challenges.Originality/valueTo the best of the authors' knowledge, this study is unparalleled, being both grounded in theory and based on an expansive dataset that spans firms from various regions and industries. The research delves deeply into corporations' underlying motives and processes concerning the effective adoption of AI in TA.

An extended UTAUT model study on the adoption behavior of artificial intelligence technology in construction industry

BACKGROUND: In recent years, Despite the proven economic growth brought by AI technology globally, the adoption of AI in the construction industry faces obstacles. To better promote the adoption of AI technology in the construction domain, this study, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, delves into the key factors influencing the adoption of AI technology in the construction industry. By introducing personal-level influencing factors such as AI anxiety and personal innovativeness, the UTAUT model is extended to comprehensively understand users’ attitudes and adoption behaviors towards AI technology. METHODOLOGY: The research framework is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) with the added constructs of artificial intelligence anxiety and individual Innovativeness. These data were collected through a combination of online and offline surveys, with a total of 258 valid data collected and analyzed using structural equation modeling. RESULTS: The study found that Usage Behavior (UB) in adopting Artificial Intelligence (AI) is positively influenced by several factors. Specifically, Performance Expectancy (PE) (β= 0.266, 95%), Effort Expectancy (EE) (β= 0.262, 95%), and Social Influence (SI) (β= 0.131, 95%) were identified as significant predictors of UB. Additionally, Facilitating Conditions (FC) (β= 0.168, 95%) also positively influenced UB.Moreover, the study explored the moderating effects of Artificial Intelligence Anxiety and Individual Innovativeness on the relationships between Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) with the Usage Behavior of AI technology. PRACTICAL IMPLICATIONS: This study lie in informing industry stakeholders about the multifaceted dynamics influencing AI adoption. Armed with this knowledge, organizations can make informed decisions, implement effective interventions, and navigate the challenges associated with integrating AI technology into the construction sector.

Determinants of Public Sector Managers' Intentions to Adopt AI in the Workplace

This study investigated the determinants of public sector managers' intentions to adopt artificial intelligence (AI) systems within their organizations. An extended technology acceptance model (TAM) was developed, incorporating additional constructs including fairness, humanity, reliability, safety, transparency, accountability, privacy, security, trust, social norms, tolerance, impact, and isomorphic pressure. A survey was conducted among 330 public sector managers, and the data were analyzed using linear regression tests to evaluate the model. The results showed significant positive influences of both perceived usefulness and perceived impact on managers' attitudes and behavioral intentions toward AI adoption. Isomorphic pressure was also a significant determinant of managers' behavioral intentions toward adopting AI systems. Our findings also indicated that perceptions related to AI ethical principles, such as transparency, privacy, and security, influenced managers' trust in AI systems.

Should live broadcasting platforms adopt artificial intelligence? A sales effort perspective

We analytically investigate a supply chain (SC) that is composed of a manufacturer and a live broadcasting platform, and examine whether the latter should adopt artificial intelligence (AI) considering sales effort. We consider several important factors that affect the SC partners’ decision-making including live broadcasting power, consumer expectations of the product, and unfit probability that the consumer is unsatisfied with the bought product. Specifically, the “live broadcasting power” refers to the power of influencers’ personal influence and fans group to enhance product sales. The results are as follows: First, the optimal production quantity of the offline channel (platform) exhibits a positive (negative) correlation with the retail price of the offline channel in the agency mode with AI. Nevertheless, in the resale mode, the retail price of the offline channel has no influence on the two channels’ optimal production quantities. Second, with low marginal cost of adopting AI, the live broadcasting platform should (not) adopt AI under high (low) live broadcasting power. With high marginal cost of adopting AI, the live broadcasting platform should (not) adopt AI under low or high (moderate) live broadcasting power. In addition, the manufacturer without AI should select the agency mode (resale mode) under high (low) live broadcasting power, while the manufacturer with AI should always collaborate with the live broadcasting platform implementing the agency mode. Finally, the agency and resale modes can achieve coordination between the two firms. We also consider the partial unfit probability, hybrid mode, and “webrooming” behavior to extend our study, and numerically demonstrate our analytical findings’ robustness.

AI in Bangladeshi libraries: opportunities and challenges

PurposeThis paper aims to explore the opportunities and challenges associated with adopting artificial intelligence (AI) in libraries in Bangladesh and provide recommendations to guide the responsible integration of AI to enhance library services and accessibility.Design/methodology/approachThe paper reviews relevant literature on the applications of AI in libraries, the current state of technology adoption in Bangladeshi libraries and the ethical considerations surrounding AI implementation. It analyzes the potential benefits of AI tools such as chatbots, intelligent search engines, text-to-speech and language translation for improving user services and inclusion. The challenges of infrastructure constraints, lack of resources and skills, data privacy issues and bias are also examined through the lens of the Bangladeshi context.FindingsAI offers transformative opportunities to automate operations, strengthen user services through 24/7 virtual assistants and personalized recommendations and promote accessibility for diverse users in Bangladeshi libraries. However, significant challenges such as inadequate technology infrastructure, funding limitations, shortage of AI-skilled staff, data privacy risks and potential biases must be addressed. Strategically planning for sustainable implementation, building AI capacity, prioritizing ethical AI development and fostering collaborations are critical factors for successful AI adoption.Originality/valueThis paper provides an in-depth analysis of the prospects and obstacles in leveraging AI specifically for libraries in Bangladesh. It offers original insights and context-specific recommendations tailored to the needs and constraints of a developing nation working to harness AI’s potential to create dynamic, inclusive knowledge centers serving all communities.

Artificial Intelligence and Competitive Advantage of Micro, Small and Medium Enterprises (MSMEs) in Anambra State

Companies are adopting artificial intelligence (AI) to be innovative, improve their strategies and differentiate themselves from competitors. This research on Artificial Intelligence and Competitive Advantage of Micro, Small and Medium Enterprises (MSMEs) in Anambra State. The objective is to examine the extent of the introduction of AI in achieving competitive advantage among MSMEs in Anambra State. This study made use of a Survey Research Design, and the population was 1399 MSMEs from the state. Krejcie and Morgan's 1970 sample size determination formula was used to get a sample size of 301. The analysis for the study was carried out using both descriptive and inferential statistics and the hypotheses were tested at a 5% level of significance. The study revealed that there is a statistically significant positive relationship between data-driven targeted online adverts and increase quality lead generation for (r = .922; p-value < 0.05), The study, therefore, concluded that using data-driven targeted adverts will lead to generating quality that need little effort on converting to paying customers. Sequel to this, the researches recommend among others that the MSMEs in Anambra state need to increasingly rely on data in decision-making, especially in running adverts, as this will give them the opportunity to choose who sees the advert and will lead to generating quality leads which will boost their competitive advantage over their rivals.

Why Do Retail Customers Adopt Artificial Intelligence (AI) Based Autonomous Decision-Making Systems?

Advancements in Artificial Intelligence (AI) have led to the development of autonomous decision-making processes, allowing customers to delegate decisions and tasks. Such technologies have the potential to alter the retailing landscape. Grounded in the unified theory of acceptance and use of technology and Hofstede's cultural theory, this article investigates customers’ adoption of AI-based autonomous decision-making processes by analyzing 454 customer responses using covariance-based structural equation modeling. The results reveal that effort expectancy, performance expectancy, facilitating conditions, and social influence are positively associated with customers’ adoption of autonomous decision-making processes. Collectivism strengthened the positive association of social influence with customer attitude, whereas uncertainty avoidance dampened the associations of performance expectancy, effort expectancy, and social influence with attitude. The findings provide useful implications for system developers and managers while providing future researchers with directions to further explore autonomous decision-making processes.

Exploring Challenges and Opportunities: Evaluating the Awareness and Readiness of Selected Government Agencies in Adopting Artificial Intelligence (AI)

This study undertakes a comprehensive examination of the awareness, skills, attitude, and readiness of respondents regarding the adoption of Artificial Intelligence (AI) applications in their professional settings. While the research evaluates respondents' familiarity with AI tools, proficiency levels, and overall attitude towards AI integration, it also strives to present a nuanced perspective by exploring potential challenges and reservations. The data, collected through a structured survey employing a Likert scale, captures diverse viewpoints on awareness, skills, attitude, and readiness towards AI applications. The findings reveal a generally positive outlook among respondents, emphasizing their commendable awareness of AI technologies and a strong inclination towards potential benefits. Despite varying levels of proficiency with specific AI tools, respondents express a collective willingness to embrace new technologies. The study identifies a positive attitude towards AI integration in work processes, accompanied by a proactive approach towards skill development and troubleshooting. However, it is crucial to note the potential challenges and reservations reported by some respondents, offering a balanced view of their preparedness for AI adoption. While the overall disposition towards AI technologies is favorable, the study underscores the importance of tailored training and development programs. The varying levels of proficiency reported highlight the need for targeted initiatives to address specific skill gaps. Organizations aiming to leverage AI technologies can benefit from the insights provided, emphasizing the significance of accessible training and creating a supportive environment for employees. By acknowledging challenges and reservations, this study contributes to a more comprehensive understanding of the landscape, facilitating informed strategies for successful AI integration in the workplace.

Federated Learning and Blockchain-Enabled Privacy-Preserving Healthcare 5.0 System: A Comprehensive Approach to Fraud Prevention and Security in IoMT

The proliferation of Internet and Communication Technologies (ICTs) has ushered in a period often referred to as Industry 5.0. The subsequent development is accompanied by the healthcare industry coining Healthcare 5.0. Healthcare 5.0 incorporates the Internet of Things (IoT), enabling medical imaging technologies to facilitate early diagnosis of diseases and enhance the quality of healthcare facilities' service. Nevertheless, the healthcare sector is currently experiencing a delay in adopting Artificial Intelligence (AI) and big data technologies compared to other sectors under the umbrella of Industry 5.0. This delay may be attributed to the prevailing concerns about data privacy within the healthcare domain. In recent times, there has been a noticeable increase in the use of Machine Learning (ML) enabled adaptive Internet of Medical Things (IoMT) systems with different technologies for medical applications. ML is an essential component of the IoMT system, as it optimizes the trade-off between delay and energy consumption. The issue of data fraud in classical learning models inside the distributed IoMT system for medical applications remains a significant research challenge in practical settings. This paper proposes Federated Learning and Blockchain-Enabled Privacy-Preserving (FL-BEPP) for Fraud Prevention and Security (FPS) in the IoMT framework. The system incorporates numerous dynamic strategies. This research examines the medical applications that exhibit hard constraints, such as deadlines, and soft constraints, such as resource consumption, when executed on distributed fog and cloud nodes. The primary objective of FL-BEPP is to effectively detect and safeguard the confidentiality and integrity of data across many tiers, including local fog nodes and faraway clouds. This is achieved by minimizing power use and delay while simultaneously meeting the time constraints associated with healthcare workloads.

Institutional factors driving citizen perceptions of AI in government: Evidence from a survey experiment on policing

AbstractLaw enforcement agencies are increasingly adopting artificial intelligence (AI)‐powered tools. While prior work emphasizes the technological features driving public opinion, we investigate how public trust and support for AI in government vary with the institutional context. We administer a pre‐registered survey experiment to 4200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national Federal Bureau of Investigation), algorithmic targets (i.e., public targets via predictive policing versus detecting officer misconduct through automated case review), and agency capacity (i.e., necessary resources and expertise). We find that the public clearly prefers local over national law enforcement use of AI, while reactions to different algorithmic targets are more limited and politicized. However, we find no responsiveness to agency capacity or lack thereof. The findings suggest the need for greater scholarly, practitioner, and public attention to organizational, not only technical, prerequisites for successful government implementation of AI.

Student nurses' attitudes, perceived utilization, and intention to adopt artificial intelligence (AI) technology in nursing practice: A cross-sectional study

AimThe aim of this study was to investigate the attitudes and intentions of student nurses towards Artificial Intelligence (AI) in the context of nursing practice and to explore the relationship between their attitudes towards AI, their perceptions of AI utilization in nursing practice, and their intentions to adopt AI technology. The study hypothesized that perceived utilization of AI in nursing practice would positively influence the intention to use AI and that attitudes towards AI would mediate this relationship. BackgroundAI has the potential to revolutionize various aspects of healthcare, including nursing practice. As AI technology continues to advance, it becomes increasingly important for nurse education to prepare student nurses to leverage AI technology and be willing to adopt it in their nursing practice. DesignCross-sectional design. MethodsA total of 200 student nurses from two government-owned nursing schools participated in the study. Mediation testing was performed using Hayes' PROCESS macro in SPSS (Model 4). ResultsPerceived AI utilization in nursing practice had a significant positive effect on student nurses' attitudes towards AI (β = 0.450, p < 0.001) and their intention to adopt AI technology (β = 0.458, p < 0.001). Attitudes towards AI partially mediated the relationship between perceived AI utilization in nursing practice and the intention to adopt AI technology (β = 0.255). ConclusionThe findings suggest that student nurses had favorable perceptions of AI utilization in nursing practice, expressed high intentions to adopt AI technology, and held positive attitudes towards AI. Furthermore, student nurses' perceptions of AI utilization in nursing practice influenced their attitudes towards AI, which, in turn, affected their intentions to adopt AI technology. Nursing education programs should incorporate AI-focused coursework, training, and experiential learning to further enhance students' readiness and proficiency in utilizing AI technology. Additionally, healthcare institutions should consider creating a supportive environment for nursing students to explore and embrace AI, ultimately preparing them for the evolving landscape of AI-enhanced healthcare practice. Tweetable abstractStudent nurses' attitudes towards AI technology were influenced by their perceptions of AI utilization in nursing practice, which subsequently influenced their intentions to adopt AI technology.

Adopting artificial intelligence (AI) for employee recruitment: the influence of contextual factors

Adopting artificial intelligence (AI) for employee recruitment: the influence of contextual factors

The application of artificial intelligence in implant dentistry

Implant dentistry is rapidly adopting artificial intelligence (AI), revolutionizing various aspects of the field. AI facilitates efficient systematic reviews, predicts implant failure rates, and aids in detecting complications using radiographs and CT scans. Additionally, AI enhances patient education and engagement by providing personalized reports. Advancements include automated implant planning, image analysis, predictive modeling, virtual surgical simulation, and AI-assisted robotic surgery. These applications optimize treatment processes, improve accuracy, and ensure better outcomes. Continued research will further enhance AI integration in implant dentistry, benefitting both patients and clinicians.

Synchronizing innovation: unveiling the synergy of need-based and curiosity-based experimentation in AI technology adoption for libraries

PurposeIn the context of libraries adopting artificial intelligence (AI) technology, this paper aims to demonstrate the synergy between two different yet complimentary frameworks, need-based experimentation (NBE) and curiosity-based experimentation (CBE). It looks at how these frameworks interact and operate together to promote technological innovation and innovation in libraries.Design/methodology/approachThe authors’ extensive professional experience in the AI adoption and innovation of libraries is drew upon in this paper. The methodology encompasses empirical observations of various libraries engaging in digital innovations through experimentations with AI technology adoption practices. Using the frameworks of NBE and CB), these observations are examined to find patterns, relationships and mutual reinforcement between the two methods. The analysis of this study is built on the authors’ observations and real-world case studies.FindingsThe research reveals that NBE and CBE work together to provide libraries with all-encompassing adoption methods for AI technology. This study indicates a dynamic interaction between NBE and CBE that boosts libraries’ methods for adopting AI technology. NBE acts as a catalyst for CBE by raising awareness of specific library needs, prompting librarians to explore AI technologies aligned with those needs. This synergy empowers librarians to creatively experiment with technology solutions that directly address pressing library challenges. Conversely, CBE fuels NBE by promoting group learning among diverse team members and fostering individual motivation to tackle library needs collaboratively. As they explore AI technology out of personal curiosity, librarians make important contributions that enhance NBE.Originality/valueThe novel aspect of this study is the recognition of the complementarity between NBE and CBE frameworks, which suggests that libraries should view them as intertwined rather than two separate approaches. Focusing on both methodologies increases the culture of experimentation and improves the problem-solving abilities of librarians. Innovation is fueled by controlled experimentation and innate curiosity in an atmosphere that is fostered by the mutual influence of NBE and CBE. This synthesis offers libraries a comprehensive strategy for adopting AI technology, empowering them to manage the shifting environment and realize the revolutionary promise of AI technologies.

Factors influencing student nurses' readiness to adopt artificial intelligence (AI) in their studies and their perceived barriers to accessing AI technology: A cross-sectional study

BackgroundWith the projected significant increase in the use of AI in nursing education, it becomes vital for nurse faculty to adequately equip student nurses with the necessary competences to effectively utilize AI in their studies. Ensuring that student nurses are prepared and ready to embrace AI technology is imperative for their successful integration into the healthcare workforce. ObjectiveThis study aimed to examine student nurses' readiness to embrace AI technology, explore associated factors, and identify perceived barriers to accessing AI technology. DesignCross sectional study. SettingsOne public-owned nursing school in the Philippines. ParticipantsThree hundred twenty-three student nurses. MethodsData were collected using structured questionnaires. Descriptive statistics and multivariable analysis were performed to analyze the data. ResultsThe results revealed that student nurses demonstrated moderate readiness to embrace AI in their studies (M = 2.906, SD = 0.692) and perceived moderate barriers to accessing AI technology (M = 2.336, SD = 0.719). Factors associated with students' readiness to embrace AI included self-rated technological proficiency (β = 0.170, p = 0.014), understanding of AI-powered technologies (β = 0.260, p < 0.001), and perceived AI use in nursing practice (β = 0.153, p = 0.022). The study also identified potential barriers to accessing AI technology, such as lack of computer skills to navigate AI, lack of AI knowledge and awareness, and time constraints. ConclusionThe findings of this study provided valuable insights into the factors influencing student nurses' attitudes towards AI and shed light on their perceived barriers to accessing AI technology. By enhancing technological proficiency, increasing AI understanding, and providing practical experiences, nurse faculty can better prepare future nurses to effectively navigate the AI-driven healthcare environment and contribute to improved patient care outcomes.

An FDA Guide on Indications for Use and Device Reporting of Artificial Intelligence-Enabled Devices: Significance for Pediatric Use

Radiology has been a pioneer in adopting artificial intelligence (AI)-enabled devices into the clinic. However, initial clinical experience has identified concerns of inconsistent device performance across different patient populations. Medical devices, including those using AI, are cleared by the FDA for their specific indications for use (IFUs). IFU describes the disease or condition the device will diagnose or treat, including a description of the intended patient population. Performance data evaluated during the premarket submission support the IFU and include the intended patient population. Understanding the IFUs of a given device is thus critical to ensuring that the device is used properly and performs as expected. When devices do not perform as expected or malfunction, medical device reporting is an important way to provide feedback about the device to the manufacturer, the FDA, and other users. This article describes the ways to retrieve the IFU and performance data information as well as the FDA medical device reporting systems for unexpected performance discrepancy. It is crucial that imaging professionals, including radiologists, know how to access and use these tools to improve the informed use of medical devices for patients of all ages.

Food fraud detection using explainable artificial intelligence

AbstractRecently, the global food supply chain has become increasingly complex, and its scalability has grown. From farm to fork, the performance of food‐producing systems is influenced by significant changes in the environment, population and economy. These changes may cause an increase in food fraud and safety hazards and hence, harm human health. Adopting artificial intelligence (AI) technology in the food supply chain is one strategy to reduce these hazards. Although the use of AI has been rising in numerous industries, such as precision nutrition, self‐driving cars, precision agriculture, precision medicine and food safety, much of what AI systems do is a black box due to its poor explainability. This study covers numerous use cases of food fraud risk prediction using explainable artificial intelligence (XAI) techniques, such as LIME, SHAP and WIT. We aimed to interpret the predictions of a machine learning model with the aid of these technologies. The case study was performed on a food fraud dataset using adulteration/fraud notifications retrieved from the Rapid Alert System for Food and Feed system and economically motivated adulteration database. A deep learning model was built based on this dataset and XAI tools have been investigated on the proposed deep learning model. Both features and shortcomings of the current XAI tools in the food fraud area have been presented.

Challenge or hindrance? How and when organizational artificial intelligence adoption influences employee job crafting

While organizations are increasingly adopting artificial intelligence (AI), little is known about employees’ reactions to this new work environment. This study utilizes the transactional theory of stress and coping to establish a framework that examines the impact of organizational AI adoption on employees’ promotion- and prevention-focused job crafting based on the data from a three-wave time-lagged survey of 332 employees of eight companies in Chengdu, China. The findings indicated that the effects of organizational AI adoption on challenge/hindrance appraisals and subsequent job crafting depend on employees’ locus of control. Organizational AI adoption induces challenge appraisals for employees with an internal locus of control and leads to promotion-focused job crafting behaviors. When employees possess an external locus of control, organizational AI adoption induces their hindrance appraisal and leads to prevention-focused job crafting behaviors. These findings have theoretical and practical implications.

The current state of artificial intelligence-augmented digitized neurocognitive screening test.

The cognitive screening test is a brief cognitive examination that could be easily performed in a clinical setting. However, one of the main drawbacks of this test was that only a paper-based version was available, which restricts the test to be manually administered and graded by medical personnel at the health centers. The main solution to these problems was to develop a potential remote assessment for screening individuals with cognitive impairment. Currently, multiple studies have been adopting artificial intelligence (AI) technology into these tests, evolving the conventional paper-based neurocognitive test into a digitized AI-assisted neurocognitive test. These studies provided credible evidence of the potential of AI-augmented cognitive screening tests to be better and provided the framework for future studies to further improve the implementation of AI technology in the cognitive screening test. The objective of this review article is to discuss different types of AI used in digitized cognitive screening tests and their advantages and disadvantages.