Adopt Artificial Intelligence Research Articles

Determinants Of Artificial Intelligence Adoption In Accounting Among Malaysia Small And Medium-Sized Enterprises

Malaysia Small and Medium-Sized Enterprises (MSMEs) are still in the early phases of adopting artificial intelligence (AI) to transform manual or conventional processes into ones that are supported by AI technology. Most MSMEs have yet to fully grasp the essence of digitalization as a general movement in corporate strategy and work processes to improve decision-making, boost efficiency, and discover untapped possibilities. The purpose of this research is to examine the determinants of AI adoption in accounting among MSMEs. The independent variables are perceived ease of use, management support, organizational readiness, government support, and external pressure. The dependent variable is AI adoption in accounting among MSMEs. This research applied a quantitative method by using a questionnaire consisting of 24 questions. The study used a convenience sampling method, with a sample size of 200 respondents from MSMEs. A total of 150 samples are valid. The collected data was analyzed using SPSS. The collected information was assessed using demographic descriptive analysis, data descriptive analysis, normality test, validity analysis, reliability test, correlation analysis, and regression analysis. The findings indicated that the adoption of AI in accounting among MSMEs is positively influenced by perceived ease of use, management support, organizational readiness, government support, and external pressure. The results and suggestions will be advantageous for SMEs looking to adopt AI in their accounting functions. Additionally, this research proposes a methodology and framework to assist SMEs owners and management in comprehending the determinants that drive significant AI adoption in accounting.

Ai-Driven Big Data Transformation And Personally Identifiable Information Security In Financial Data: A Systematic Review

This systematic review explores the impact of adopting artificial intelligence (AI) to analyze and transform big data in financial and economic contexts, with a specific focus on the privacy and security of personally identifiable information (PII). By examining 37 articles spanning the latest advancements in AI-driven big data technologies, the review identifies both opportunities and challenges in safeguarding PII during financial data transformation. Key findings reveal that while AI enhances data processing capabilities—enabling faster insights and predictive accuracy in economic trends—PII faces increased risks due to sophisticated data aggregation and correlation techniques. The review categorizes major AI methodologies used, including machine learning algorithms, natural language processing, and predictive analytics, highlighting how each can affect data privacy. The findings suggest that, to maintain trust, organizations must adopt AI responsibly, integrating privacy-by-design principles and adhering to data governance standards. This review contributes to a clearer understanding of the interplay between AI and PII protection, offering practical insights for stakeholders in the financial sector aiming to harness AI while prioritizing ethical data handling.

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

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

Adoption of artificial intelligence for manufacturing SMEs’ growth and survival in South Africa

This study advances research and practice related to adopting artificial intelligence (AI) in the context of South Africa (SA). The study evaluated AI adoption by South African manufacturing Small and Medium Enterprises (SMEs); established the challenges faced by manufacturing SMEs in adopting AI; and developed a framework for adopting AI for manufacturing SMEs’ growth and survival. The study adopted a systematic literature review approach. Articles from Scopus and Google scholar databases, ranging from the years 2018 to 2024, were used. Of the 206 articles found, 54 were shortlisted. The systematic review analysis was performed using the PRISMA framework. The results identified AI adoption by South African manufacturing SMEs is low, limiting their innovation and productivity. The results also show, despite the numerous benefits AI adoption can offer manufacturing SMEs in the country, a major constraint is the lack of a framework to enhance adoption and implementation. Hence, this study was conducted to develop a framework to improve AI adoption by South African manufacturing SMEs. The findings contribute to the body of knowledge and provide new insights to manufacturing SME owners/managers, policymakers and practitioners into AI adoption to enhance manufacturing SMEs’ ability to compete on the global stage.

CANCEL: A feature engineering method for churn prediction in a privacy-preserving context

This paper proposes a solution for predicting churn with privacy preservation by using edge computing. With the increasing popularity of smartphones, users are becoming more demanding regarding mobile app usage. Installing and removing an app are frequent routines and the ease of uninstallation can facilitate churn, which is customer abandonment. Companies seek to minimize churn since the cost of acquiring new customers is much higher than retaining current ones. To predict possible abandonment, organizations are increasingly adopting artificial intelligence (AI) techniques. Nevertheless, customers are becoming more concerned about their data privacy. In this context, we propose a technique called CANCEL, which creates attributes based on users' temporal behavior, with edge computing to predict churn locally, without transmitting users' data. The paper presents the evaluation of CANCEL in comparison to baseline solutions, the development of a mobile app integrated with the proposed method and deployed as an edge computing solution.

How are US hospitals adopting artificial intelligence? Early evidence from 2022.

US hospitals are rapidly adopting artificial intelligence (AI), but there is a lack of knowledge about AI-adopting hospitals' characteristics, trends, and spread. This study aims to fill this gap by analyzing the 2022 American Hospital Association (AHA) data. The novel Hospital AI Adoption Model (HAIAM) is developed to categorize hospitals based on their AI adoption characteristics in the fields of (1) predicting patient demand, (2) optimizing workflow, (3) automating routine tasks, (4) staff scheduling, and (5) predicting staffing needs. Nearly one-fifth of US hospitals (1107 or 18.70%) have adopted some form of AI by 2022. The HAIAM shows that only 3.82% of hospitals are high adopters, followed by 6.22% moderate and 8.67% low adopters. Artificial intelligence adoption rates are highest in optimizing workflow (12.91%), while staff scheduling (9.53%) has the lowest growth rate. Hospitals with large bed sizes and outpatient surgical departments, private not-for-profit ownership, teaching status, and part of health systems are more likely to adopt different forms of AI. New Jersey (48.94%) is the leading hospital AI-adopting state, whereas New Mexico (0%) is the most lagging. These data can help policymakers better understand variations in AI adoption by hospitals and inform potential policy responses.

Adopting artificial intelligence algorithms for remote fetal heart rate monitoring and classification using wearable fetal phonocardiography

Fetal phonocardiography (FPCG) is a non-invasive Fetal Heart Rate (FHR) monitoring technique that can detect vibrations and murmurs in heart sounds. However, acquiring fetal heart sounds from a wearable FPCG device is challenging due to noise and artefacts. This research contributes a resilient solution to overcome the conventional issues by adopting Artificial Intelligence (AI) with FPCG for automated FHR monitoring in an end-to-end manner, named (AI-FHR). Four sequential methodologies were used to ensure reliable and accurate FHR monitoring. The proposed method removes low-frequency noises and high-frequency noises by using Chebyshev II high-pass filters and Enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ECEEMDAN) in combination with Phase Shifted Maximal Overlap Discrete Wavelet Transform (PS-MODWT) filters, respectively. The denoised signals are segmented to reduce complexity, and the segmentation is performed using multi-agent deep Q-learning (MA-DQL). The segmented signal is provided to reduce the redundancies in cardiac cycles using the Artificial Hummingbird Optimization (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm called variational auto-encoder-general adversarial networks (VAE-GAN). The feature extraction and classification are carried out by adopting a hybrid of the bidirectional gated recurrent unit (BiGRU) and the multi-boosted capsule network (MBCapsNet). The proposed method was implemented and simulated using MATLAB R2020a and validated by adopting effective validation metrics. The results demonstrate that the proposed method performed better than the current method with accuracy (81.34%), sensitivity (72%), F1-score (83%), Energy (0.808 J), and complexity index (13.34). Like other optimization methods, AHO needs precise parameter adjustment in order to function well. Its performance may be greatly impacted by the selection of parameters, including population size, exploration rate, and learning rate.

An Intelligent Vehicle Price Estimation Approach Using a Deep Neural Network Model

In recent years, the market for used-vehicle trade in the Kingdom of Saudi Arabia has grown significantly. This is due to the high cost of new vehicles that are not affordable by most buyers and lifting the ban on women drivers. Recently, several online websites for selling vehicles are available with different functions. However, estimating the vehicle price is based on traditional calculation methods, and this is inaccurate in several selling situations, as there are many factors that may affect the vehicle price, and these factors must be taken into consideration when estimating the vehicle’s price. Therefore, there is high demand to develop an automated vehicle price estimation system through adopting artificial intelligence (AI) technologies. Hence, this paper proposes an efficient vehicle price estimation system through developing an efficient deep neural network (DNN) model. The developed DNN model has been trained using a recent collected dataset for used-vehicle prices in the Kingdom of Saudi Arabia. The developed system has been validated using a recent vehicle price dataset, and the obtained results are compared with seven different machine learning models and showed a promising regression accuracy. In addition, we developed a reliable graphical user interface (GUI) for the purpose of allowing the user to estimate the price of any vehicle using the pre-trained DNN model.

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

Assessment of University Lecturers' Readiness to Adopt Artificial Intelligence (AI) Technology in North-East of Nigeria

Assessment of University Lecturers' Readiness to Adopt Artificial Intelligence (AI) Technology in North-East of Nigeria

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.

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.

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.