THE IMPACT OF ARTIFICIAL INTELLIGENCE ON CONTEMPORARY BUSINESS: A SCOPING REVIEW

Introduction: Students have widely used Generative Artificial Intelligence (AI) tools to assist them in their daily classroom activities and assignments (with or without the consent of their teachers). These tools are beneficial and, when used critically, can help students complete their tasks and better understand the various associated aspects. Objective: In this paper, we present an experience using AI tools to support the collection, analysis, and organization of user data in projects developed during a User Experience course in undergraduate computing programs. Methodology: The study involved two teachers and 99 students across three classes of the course. AI tools were integrated into project activities, and feedback was gathered from approximately half of the participants to assess their initial impressions. Results: The preliminary findings highlight the potential of generative AI tools to enhance student performance and learning in User Experience classes.

In this paper, we study the vital role of artificial intelligence (AI) in the development of video games, with a focus on various aspects of AI application in this industry. In the introduction, we discuss both the development of video games and the role of AI systems in the user experience, defining the progression of AI’s role in video games. In the following section, we investigate how in-game entities and AI collaborate. Here, we analyze basic concepts such as Non-Playable Characters (NPCs) and how AI enhances their intelligence and reactivity in the game. The mechanisms of AI in video games are a crucial point of consideration in the next part of the paper. We explain how various AI techniques are used for decision-making, player tracking, and adapting the game to their actions. Furthermore, we explore the use of AI in video games beyond NPC control, examining examples such as procedurally generated worlds and player experience modeling. This application of AI contributes to a deeper and more dynamic player experience. In the modern application of AI in video games, we delve into advanced uses of machine learning and deep neural networks in game development. Here, we consider how AI is used for game personalization, user data analysis, and enhancing graphics and sound. Finally, we discuss the future of video games and the role of neural networks in their development. We predict the growth of AI in various aspects of games and how it will shape the future player experience. In conclusion, we assert that artificial intelligence has become an indispensable part of video game development, and its impact will inevitably expand in the future, enabling increasingly rich, dynamic, and personalized games for players worldwide.

PurposeThis paper aims to explore factors impacting citizen intention toward artificial intelligence (AI) adoption, considering government regulation as a moderating variable. It focuses on the Palestinian Cellular Communications Sector in Gaza Strip, providing insights into the citizen-AI relationship dynamics. The research contributes to enhancing comprehension of AI technology from clients’ perspective.Design/methodology/approachTo test the hypotheses, a questionnaire was used in an empirical study to collect primary data. In total, 347 Palestinian citizens responded to the survey.FindingsThe findings of this paper reveal that perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns significantly influence citizen intention toward AI adoption. Furthermore, government regulations as a moderating variable strengthen the impact of perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns on citizen intention toward AI acceptance and adoption. Thus, further research should explore specific domains and cultural contexts to gain a more comprehensive understanding of the factors shaping acceptance and adoption.Research limitations/implicationsThe findings of the study should be understood in the context of their limitations. First, the study ignored cultural or domain-specific subtleties in favor of generic characteristics, which calls for more research in these particular circumstances. Second, relying on self-reported data might result in biases and limitations due to subjectivity in reporting, indicating the necessity for alternate data gathering methods and approaches in future research.Practical implicationsPolicymakers, developers and organizations working to promote the acceptability and implementation of AI applications should consider the practical implications of this study’s results. To secure the long-term use of AI technologies in a responsible and user-centric way, policymakers should give priority to public education and awareness, user-centered design and ethical AI development techniques. They should also stimulate partnerships and create monitoring systems.Originality/valueThis paper investigates the originality of factors that influence citizen intention toward AI acceptance and adoption. It uniquely examines the moderating role of government regulations in shaping this intention. By addressing this novel aspect, the paper contributes to advancing our understanding of the complex dynamics surrounding citizen intentions toward AI applications.

PurposeThe purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.Design/methodology/approachThis paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.FindingsAI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.Research limitations/implicationsThere is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.Social implicationsThis paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.Originality/valueThis paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.

The objective of this scoping review will be to chart the available evidence on user experience and adoption of automation and artificial intelligence (AI) technologies for evidence synthesis. Evidence syntheses are crucial for informing health care practice and policy; however, they are constrained by the ever-increasing volume of research and labor-intensive methods. With reviews often taking over a year to complete, automation and AI offer promising solutions by streamlining evidence synthesis workflows. However, while these technologies may offer significant time savings, their adoption depends on usability, trustworthiness, and workflow integration-elements that are currently poorly understood. This review will include primary research articles, all types of reviews, expert opinions, and gray literature that discuss user experience/adoption of automation and AI technologies for evidence synthesis across all disciplines. Following JBI scoping review methodology, the search strategy will identify published and unpublished evidence sources using a 3-step process. An initial exploratory search of PubMed was conducted to identify relevant keywords and terms. This will be followed by searches of PubMed, Web of Science Core Collection, Scopus, ProQuest Central, and ACM Digital Library databases, as well as online gray literature sources to identify eligible studies. A date limit of October 2015 will be applied to the searches, with no language limitations. Three reviewers will independently screen, select, and extract data from relevant evidence sources. Data extraction and analysis will be charted and mapped through the lenses of 4 distinct frameworks: Unified Theory of Acceptance and Use of Technology (UTAUT), Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM), Human-AI Interaction (HAI), and user experience (UX) principles. OSF https://osf.io/ayqjc/overview.

The evolving structure of the modern workplace—driven by hybrid work models and remote collaboration—has necessitated a redefinition of User Experience (UX) frameworks in digital enterprise ecosystems. In this context, Artificial Intelligence (AI) has emerged as a pivotal enabler for enhancing UX by facilitating intelligent, adaptive, and personalized interactions across distributed digital environments. This study presents a comprehensive systematic literature review examining how AI-driven tools such as chatbots, recommendation engines, emotion-aware systems, and context-aware automation contribute to UX optimization in digital workplaces. Drawing on 87 peer-reviewed articles published between 2010 and 2024, and employing the PRISMA 2020 methodology, the review synthesizes empirical and theoretical insights across key themes, including AI-powered remote support, personalized interfaces, intelligent user guidance, and emotional intelligence integration in hybrid systems. The findings reveal that AI enhances UX at multiple levels: (1) by automating routine support functions to reduce user friction and improve response accuracy; (2) through adaptive personalization based on user behavior, roles, and preferences; (3) by enabling emotional intelligence features that detect and respond to user moods, stress, and disengagement; and (4) through real-time contextual adaptations that adjust interfaces based on environmental cues. AI systems integrated into platforms such as Microsoft Teams, Zoom, Slack, Salesforce, and Google Workspace were found to improve usability, satisfaction, and task efficiency while supporting digital wellbeing. Additionally, trust and transparency emerged as critical UX factors in AI adoption, emphasizing the importance of explainable AI and user autonomy in interface design. This review contributes to the evolving discourse on human-centered AI by framing UX not just as a functional outcome but as a multi-dimensional construct shaped by affective, cognitive, and behavioral interactions across AI-augmented platforms. By analyzing the convergence of AI technologies and UX principles in enterprise settings, the study provides a structured framework for designing adaptive, inclusive, and ethically aligned digital work environments. The synthesis also identifies gaps in longitudinal evaluations, emotional diversity modeling, and cross-cultural personalization strategies, offering directions for future empirical and design-focused research in AI-powered UX. Ultimately, this review underscores the transformative impact of AI in redefining the contours of user interaction, engagement, and satisfaction within the digital workplace paradigm.

Backgrounds. Artificial Intelligence (AI) has emerged as a transformative force in modern business, influencing competitive dynamics and reshaping consumer behavior. As AI applications expand across industries, understanding their strategic impact on market competition and consumer engagement becomes increasingly vital for business sustainability and innovation. Methods. This study employs a mixed-method approach combining bibliometric analysis and systematic literature review to examine the interrelationship between AI, market competition, and consumer behaviour. Bibliometric analysis was conducted using the Scopus database for the period 2015–2025, with VOSviewer utilized to map keyword co-occurrences and thematic clusters. Subsequently, a qualitative literature review was performed on thematically relevant and highly cited articles to extract insights on AI’s practical implementations, competitive implications, consumer analytics, and ethical concerns. Results. The findings reveal a marked increase in scholarly attention to AI-driven business strategies, particularly between 2023 and 2024. AI is shown to influence market competition by enhancing operational efficiency, fostering innovation, supporting data-driven decision-making, and improving strategic adaptability. In terms of consumer behaviour, AI enables pattern recognition, real-time responsiveness, personalized marketing, and demand forecasting, contributing to customer satisfaction and loyalty. Additionally, AI-powered business operations—such as dynamic pricing and product recommendation systems—further optimize performance. However, ethical challenges, including data privacy, algorithmic bias, and regulatory gaps, underscore the need for responsible AI adoption. Conclusions. AI serves as both a technological enabler and strategic asset in contemporary business ecosystems. Its influence extends beyond automation, offering firms competitive advantage through improved agility and consumer-centered strategies. To fully leverage AI’s potential, businesses must balance innovation with ethical considerations, ensuring transparent governance and human oversight in AI integration.

The integration of Artificial Intelligence (AI) into the operations of modern businesses represents a pivotal shift in the way organizations approach innovation, efficiency, and maintaining a competitive edge in today’s rapidly evolving market landscape. This qualitative research article delves deeply into the multifaceted realm of AI adoption within businesses, aiming to uncover the extensive array of possibilities it presents as well as the formidable challenges it poses. At its core, this study seeks to comprehensively explore not only the promising horizons that AI adoption opens up but also the formidable hurdles that businesses must overcome to effectively leverage these transformative technologies. In examining the potential benefits of AI integration, this research embarks on an exploration of the vast opportunities it brings forth. Among these potential advantages are heightened levels of productivity, marked advancements in innovation capabilities, and the ability to deliver unparalleled customer experiences. Through the automation of repetitive tasks, the enhancement of decision-making processes with data-driven insights, and the facilitation of personalized interactions, AI holds the promise of revolutionizing the way businesses operate and interact with their customers, clients, and stakeholders. However, amidst these promising prospects, a complex tapestry of challenges emerges, casting a shadow over the path to AI adoption. These obstacles encompass a broad spectrum of concerns, from the intricate nuances of data privacy and security to the profound ethical considerations inherent in the deployment of AI systems. Additionally, businesses must grapple with the intricacies of regulatory compliance, navigate the daunting skills gap in AI expertise, and confront the substantial costs associated with implementation and maintenance. Through a meticulous examination of these opportunities and challenges, this study sheds light on the intricate dynamics that underpin the adoption of AI technologies by modern businesses. It underscores the critical importance of developing proactive strategies, establishing robust ethical frameworks, and embracing human-centric approaches to ensure the responsible deployment of AI solutions. By illuminating the complexities inherent in the adoption of AI, this research aims to provide valuable insights that can inform decision-making processes for businesses, policymakers, and stakeholders alike. Ultimately, it seeks to contribute to the advancement of a sustainable and inclusive future, where the transformative potential of AI is harnessed to benefit society as a whole.

For several years empirical studies have spanned the spectrum of research from software productivity, quality, reliability, performance to human computer interaction. Analyses have involved software systems ranging from desktop software to telecommunication switching systems. But surprising there has been little work done on the emerging digital game industry, one of the fastest growing domains today. To the best of our knowledge, our work is one of the first empirical analysis of a large commercially successful game system. In this paper, we introduce an analysis of the significant user data generated in the gaming industry by using a successful game: Project Gotham Racing 4.

Rapid developments in artificial intelligence (AI) have transformed e-commerce by enabling personalized recommendations and enhancing user engagement. However, consumer acceptance of AI-driven systems remains influenced by various psychological factors. This study aims to examine the effects of subjective norms, attitudes, and perceived behavioral control (PBC) on purchase intention through AI in e-commerce platforms. The research integrates the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to explain how these psychological variables shape users’ acceptance of AI technology. Data were collected through an online survey of 250 respondents and analyzed using the Structural Equation Modeling–Partial Least Squares (SEM-PLS) approach. The results reveal that subjective norms (β = 0.274, p = 0.002) and PBC (β = 0.455, p = 0.000) significantly and positively influence AI adoption, which in turn has a strong positive effect on purchase intention (β = 0.664, p = 0.000). Additionally, subjective norms (β = 0.182, p = 0.008) and PBC (β = 0.302, p = 0.003) indirectly affect purchase intention through AI. In contrast, attitude does not significantly affect AI adoption (β = 0.095, p = 0.311) or purchase intention (β = 0.063, p = 0.310). These findings extend TPB and TAM applications to AI-based contexts and provide practical insights for e-commerce firms to optimize AI-driven personalization and user experience

Application of artificial intelligence: benefits and limitations for human potential and labor-intensive economy – an empirical investigation into pandemic ridden Indian industry

PI (Productivity Index) degradation is a common issue in many oil fields. To obtain a highly reliable production forecast, it is critical to include well and completion performance in the analysis. A new workflow is developed to assess and incorporate the damage mechanisms at the wellbore, fracture and reservoir into production forecasting. Currently, most reservoir models use a skin factor to represent the combined well damages mechanisms. The skin factor is adjusted based on the user's experience or data analysis instead of physical modeling. In this workflow, a detailed model is built to explicitly simulate the damage mechanisms, assess the dynamic performance of the well and completion with depletion, and generate a physics-based proxy function for reservoir modeling. The new workflow closes the modeling gap in production forecasting and provides insights into which damage mechanisms impact PI degradation. In the workflow, a detailed model is built, which includes an explicit wellbore, an explicit fracture and the reservoir. Subsurface rock and flow damage mechanisms are represented explicitly in the model. Running the model with an optimization tool, the damage mechanisms’ impact on productivity can be assessed separately or in a combination. A physics-based proxy is generated linking the change in productivity to typical well parameters such as cumulative production, drainage region depletion and drawdown. This proxy is then incorporated into a standard reservoir simulator through the utilization of scripts linking the PI evolution of the well to the typical well parameters stated above. The workflow increases the reliability of generated production forecasts by incorporating the best representation of the near wellbore flow patterns. By varying the damage mechanism inputs the workflow is capable of history matching and forecasting the observed field behavior. The workflow has been validated for a high permeability, over pressured deep-water reservoir. The history match, PI prediction and damage mechanism analysis are presented in this paper. The new workflow can help assets to: (1) history match and forecast well performance under varying operating conditions; (2) identify the key damage mechanisms which allows for potential mitigation and remediation solutions and; (3) set operational limits that reduce the likelihood of future PI degradation and maintain current performance.

Aim/Purpose: This study investigates the key factors influencing the adoption and use of artificial intelligence (AI) applications among researchers, focusing on effort expectancy, satisfaction, perceived ease of use, and perceived usefulness, which shaped attitudes and drove AI adoption as a research assistant. Background: AI tools have rapidly become game-changers in academic research, transforming tasks such as literature retrieval, writing, editing, and data analysis. Despite their potential, barriers like high effort expectancy, inconsistent user satisfaction, and ethical concerns regarding over-reliance and plagiarism continue to hinder widespread adoption. A pressing gap exists in understanding how AI impacts the efficiency and integrity of academic research workflows. Methodology: A quantitative approach using structural equation modeling (SEM) was employed. Data was collected from 120 active researchers who use AI tools for academic tasks, including literature reviews, writing support, and data visualization. Contribution: This study contributes to the understanding of how key factors, such as effort expectancy and satisfaction, affect AI adoption in academic research. It emphasizes the importance of reducing cognitive load and improving user satisfaction to promote widespread AI adoption. It also underscores the importance of intuitive AI design and institutional support in shaping researchers’ engagement with AI tools, which could enhance productivity and research outcomes. Findings: The findings reveal that effort expectancy, satisfaction, perceived ease of use, and perceived usefulness significantly influence attitude and actual use of AI tools, with attitude serving as a key mediator. The model demonstrated moderate to high explanatory power (R² = 0.409 to 0.459) and predictive relevance (Q² = 0.171 to 0.409), highlighting the substantial role of effort expectancy and satisfaction in shaping perceived ease of use and usefulness. These findings emphasize the importance of reducing cognitive load and improving user satisfaction to encourage the adoption of AI tools in research. Recommendations for Practitioners: Institutions and AI developers should focus on reducing the learning curve of AI tools by enhancing their intuitiveness and providing targeted training and technical support. Ethical AI use should also be promoted to address concerns about over-reliance and plagiarism. Institutions should foster a culture that normalizes AI integration in research practices. Recommendation for Researchers: Researchers should be informed of the long-term effects of AI adoption on research quality and integrity and how institutional support can foster positive attitudes toward AI tools in academic research. Impact on Society: The broader adoption of AI tools in academic research could enhance productivity and efficiency, leading to more breakthroughs in various fields and benefiting society by accelerating research and innovation. Additionally, AI can democratize access to research resources, particularly for underfunded institutions and early-career researchers, by enabling broader participation in cutting-edge research and fostering equity and diversity in academic contributions. Future Research: Future studies should focus on the role of user experience in AI adoption, particularly how different user groups interact with AI tools. Longitudinal studies could provide insights into how attitudes toward AI change as users become more familiar with the tools.

Our current era is witnessing continuous change and rapid development in various aspects of life, including technical development and scientific progress. Modern technologies like simulation and artificial intelligence (AI) significantly enhance media content creation and distribution in various ways: Content Creation: AI tools can generate scripts, edit videos, create animations, and produce music, allowing creators to streamline the production process and reduce costs. Personalization: AI algorithms analyze user preferences and behavior to deliver personalized content recommendations, improving user engagement and satisfaction. Simulations: Simulation technologies can create realistic virtual environments for training, gaming, and storytelling, providing immersive experiences for users. Quality Enhancement: AI can automatically enhance audio and video quality, detect errors or inconsistencies, and improve overall production value. Data Analysis: AI helps analyze audience data to inform content strategies, predict trends, and optimize marketing efforts. Simulation and AI are transforming media content by making it more efficient, engaging, and tailored to audiences. The study aims to identify the impact of using these modern technologies in the field of media, which includes the latest technological developments, by utilizing virtual viewer simulations through artificial intelligence techniques, and achieving the highest levels of awareness for producing various media materials. The research also addresses the key stages that a designer goes through and how we can benefit from the study to develop these stages in order to enhance the role of visual communication and interaction between the recipient and the sender through the use of digital media designed within the media content via artificial intelligence to convey the advertising message, and its role in influencing consumer motivations to attract and guide them in making decisions through artificial intelligence techniques that provide a clear vision of the design environment. The research highlights the importance of graphic design as an essential element in improving viewers’ experience of modern digital media and achieving a more realistic simulation, pointing to the development of the concept of simulation used in modern digital media and Computer-Generated Image (CGI) techniques. This research also aims to explore the vital role that graphic design plays at every stage of the production of digital technologies to achieve a more realistic simulation of virtual scenes in digital media. Hence, the research problem the extent to which media content, in terms of form and substance, benefits from these modern technologies (simulation - Artificial intelligence) and the impact of these technologies in making media content more credible through their use. The research hypotheses: Has the media content become more attractive with the use of these technologies (simulation Artificial intelligence)? Has the media content become more shareable across different platforms in form and substance with the use of these technologies (simulation - Artificial intelligence)? Has the media content become more appealing with the use of these technologies (simulation - Artificial intelligence)? Has the media content become more effective in the visual communication process with the use of these technologies (simulation - Artificial intelligence)? Has the use of these technologies (simulation - Artificial intelligence) provided competitive advantages for various media outlets? Has the use of these technologies (simulation - Artificial intelligence) offered marketing and promotional advantages for various media outlets? What are the positives and negatives of these technologies (simulation - Artificial intelligence) in the media? The main aim of the research the extent to which media content, in terms of form and substance, benefits from modern technologies (simulation – Artificial intelligence) and the impact of these technologies on enriching, supporting, and developing media content to make it more competitive and credible through their use. The importance of research in studying the role and impact of modern technologies, such as virtual scene simulation, using artificial intelligence techniques to produce various types of media content. It emphasizes the role of these technologies in influencing different media outlets regarding competitive advantages, marketing, and promotion, as well as highlighting the positives and negatives of these technologies. The research methodology is based on the analytical description approach.

Artificial intelligence (AI) is quickly transforming business operations and society, as AI capabilities are incorporated into applications ranging from mobile personal assistants to self-driving cars. The potentially disruptive nature of AI calls for an extensive investigation into all aspects of AI-human interactions at individual, group, organizational and market levels. However, there is paucity of academic information systems (IS) research in this area that goes beyond the development and testing of specific narrow AI capabilities. AI represents an important opportunity for organizational and behavioral IS researchers, but also presents challenges associated with the underlying complexity of AI technologies and the diversity of AI applications. Understanding how existing AI research and business practice relate to traditional areas of IS research is an important step towards creating a comprehensive behavioral and organizational AI research agenda. This dissertation seeks to achieve a dual purpose in a series of three essays. Essay 1 seeks to understand the current state of business AI research and practice in business through a quantitative literature review, relate the findings to traditional IS research areas, and identify potentially fruitful research areas for AI-focused IS research. Essays 2 and 3 seek to address specific research questions related to one of such research areas, namely, human interactions with AI enabled applications. Essay 2 focus on user experience with a chatbot, a popular AI application, and Essay 3 explores how user experiences with AI assistant apps differ from their interactions with more traditional IT artifacts.