POWER AND PROGRESS: Our Thousand-Year Struggle over Technology and Prosperity by Daron Acemoglu and Simon Johnson. PublicAffairs, 2024. 560 pages. Paperback; $21.99. ISBN: 9781541702547. *In this book, two highly acclaimed MIT economists, and Nobel prize winners, make the bold claim that technological progress does not automatically result in prosperity for all. This is contrary to the claims of what they call the "technology bandwagon," founded on the economic dogma arising from the rise in productivity and wages that occurred over the 20th century. Put simply, this dogma states that "when businesses become more productive they expand their output" which results in "a need for more workers" so they "get busy with hiring" and "collectively bid up wages" (p. 15). *To make its case, the book examines the relationship between technology, wages, and inequality over a thousand years with a view to determining what needs to be done to ensure that all parts of society share in the prosperity arising from innovation. From the opening chapter, it is clear that the authors are concerned about the current direction of digital technology, especially AI and its control by an elite few in Big Tech, what they term "a vision oligarchy" (p. 33) that needs to be "reigned in" (p. 34). Anyone interested in the ethics around technological development and its consequences on society, particularly recent developments in AI, will be interested in these perspectives. *Interpreting the economic and social data over a thousand years through to the present, the authors show how the economic prosperity of the post-World War II years was an outcome of a long struggle over the direction of technological progress and a balancing of power between employer and employee. Various examples are cited by the authors to justify their view that to create an economic elite involves a compelling vision and a social standing that affords opportunity to frame and set the agenda for debates on innovation, prosperity, human flourishing, and how to solve the world's big problems. The influence of the powerful becomes self-perpetuating if they have access to influence policy makers and if their ideas and arguments are persuasive and have broad appeal. *Many illuminating economic facts are employed throughout the book. Typical is that, apart from famine years or other disturbances such as war, food production remained roughly in line with population growth until the early 19th century, and that, despite the innovation of the middle ages, the quality of life of a European peasant changed little over several millennia. Productivity improvements benefited a very small elite of kings and their retinue, nobles, and the clergy. *Turning to the Industrial Revolution, the authors claim the poor did not share the wealth generated through technology innovation because of the bias in automation which favored those wealthy enough to purchase machinery and because of the lack of worker representation in setting wages. They also argue that the "aspirant" class in this period focused on accumulating wealth for themselves and did nothing to alleviate the appalling conditions in the first half of the 19th century. In making this claim, a glaring omission in the authors' analysis of the 18th and 19th century in Britain is the influence of evangelicals in the reform movement, such as the Clapham Sect, and businessmen, such as Cadbury, who conducted his business differently to most, providing homes for his workers and education for their children. This omission is surprising given that these evangelicals shaped institutions and public opinion in ways that the authors view as crucial to bringing about a change of vision in business leaders and institutions, as well as in the public. *The change in direction of technology in the second half of the 19th century plus and institutional changes up to the post-World War II period, ground the authors' conclusion that "the productivity bandwagon depends on new tasks and opportunities for workers and an institutional framework that enables them to share the productivity gains" (p. 218). A key 19th-century transition point was that the direction of technology shifted away from automation and people began to benefit more from the progress of technology. Key examples involve steam and electricity, which created new tasks and job opportunities in transport infrastructure and associated industries, such as steel and coal. Later, as electricity transformed factories by allowing distributed power rather than centralized steam power, there was a significant increase in the demand for engineers and white collar workers, pushing up wages. Contributing to this trend were institutional changes such as trade unions that gave greater bargaining power to workers, creating improved rent sharing between employers and employees. Political representation resulted in regulation with attendant improvements in conditions and public health. After World War II, there was a significant year-on-year increase in the "Total Factor Growth" measure of technological progress, and there was more inclusive economic growth with inequality declining rapidly as wages rose. *The closing chapters of the book focus on digital technology and AI, and detail how the 1,000-year struggle that finally resulted in a more inclusive prosperity began to unravel in the 1980s. Economic growth slowed and labor's share of national income has been on a protracted downward trend in most industrialized economies. The share of wealth in the richest 1% of the population increased from 10% in 1980 to 19% in 2019. Several factors that brought about these changes are reviewed, including the advent of the digital age and the automation of manual labor that it afforded, along with a change in economic doctrine, the erosion of union power, and deregulation that has favored cutting labor costs. All of this, it is argued, has led to a change of vision, often expressed as, "the social responsibility of business is to increase profits" and to generate "high returns for their shareholders" (p. 271), views now taught in most business schools. *The authors also argue that the "move fast and break things" mentality is symptomatic of a shift in the direction of digital technology and that the current AI vision of technology leaders is an illusion. This vision claims that AI will benefit humankind, yet in reality, it sidelines humans while generating huge wealth by reshaping our view of digital and AI technology away from creating new tasks and opportunities toward automating work and cutting labor costs, re-creating the old two-tier society of the previous millennia. Nevertheless, while some data is provided to justify this assertion of the authors in the use of robotics, there is much debate about the real impact of AI among white collar workers, a topic about which the authors offer no projections of their own. *Central to the book's thesis is the claim that a deterministic view of technology is a fallacy. Different choices could have been made in developing AI, away from automation and in directions more beneficial to society. However, what these directions might be are not really examined in any detail. A Christian redemptive approach to culture, while resonating with this nondeterministic view, would want to frame the argument in terms of responsible design choices involving stewardship, love for neighbor, and avoiding technological design that dumbs down humanity or leads to addiction or results in idolatry. *The final chapter outlines how Progressive movement activists, reformers, and journalists changed the views of the public, organized politically, and challenged institutions and government in America in the late 19th and early 20th century, leading to a redistribution of power and a change in direction for technological progress. A three-pronged formula is proposed as a way out of our current predicament: (1) "altering the narrative" and "changing the norms," (2) "cultivating countervailing powers," and (3) providing "policy solutions." How this would work is then sketched out using examples, such as how the environmental movement worked to redirect technologies. The authors' proposals for "Remaking Digital Technologies" were rather weak. Their suggestion that "improving productivity in workers' current jobs" (p. 394) is precisely what companies such as Microsoft would argue they are offering through their "co-pilot." I was also not convinced by the longer section on policy solutions that missed any reflection on proposed standards for responsible AI or policy proposals, such as the EU AI Act, details of which have been under discussion for the last few years. *In the complex world of social history and economics, it is often hard to prove a causal link between one factor and another, let alone when there are several variables in play. No doubt other economists and social historians will have a different take on the role of power and technological progress in shaping our world, and Christians will want to provide an interpretation through the lens of biblical truth. This book does, however, provide a helpful counterpoint to the prevailing AI vision that innovation is essential for growth and prosperity and that regulation stifles progress. *Reviewed by Jeremy Peckham, AI entrepreneur, ethicist, and former CEO, Bewdley, UK.
Breakthrough technologies increasingly shape social institutions, economic systems, and political futures. Yet models of research excellence associated with such technologies often prioritize technical performance, scalability, and short-term innovation metrics while treating ethical, social, and cultural dimensions as secondary considerations. This perspective article argues that such separation is no longer tenable. We propose a broader understanding of excellence that combines technical rigor with ethical robustness, social intelligibility, and long-term relevance. The rapid emergence of generative and agentic artificial intelligence further underscores this argument. As technological systems increasingly operate through language, interpretation, and normative alignment, expertise traditionally cultivated in the humanities and social sciences becomes integral to the design, governance, and responsible deployment of such systems. Drawing on historical examples and contemporary research practices, this article examines five interconnected domains where the humanities and social sciences, treated as integrated dimensions of research practice, can strengthen technological development: (1) ethical, legal, and social integration in agenda-setting and research design; (2) plural and reflexive foresight practices that shape technological futures; (3) graduate education as a leverage point for cross-disciplinary literacy; (4) visualization and communication as epistemic and civic practices; and (5) institutional frameworks that move beyond rigid distinctions between basic and applied research. Across these dimensions, we propose practical strategies for embedding interdisciplinary collaboration structurally rather than symbolically.
Miglena Angelova, Tsvetana Stoyanova, Philip Stoyanov
The present research is dedicated to the good practices of using artificial intelligence (AI) in the overall process of digitalization of higher education. The major objective is to identify good practices used so far by the universities in implementation of AI tech-nologies in their everyday life for educational and administrative needs. At the same time, our additional goal is to understand the expectation of students for further implementation of AI in universities. We developed our research in 2 main stages: first one – collection and identification of good practices of universities with AI and digitalization and second one – dedicated to students’ expectations. We conducted 25 interviews with representatives of 18 Bulgarian universities, responsible for digitalization. Second one was to provide survey among students (n=254). Our findings reveal that digitalization is defined as one of the leading priori-ties for universities; the major part of universities have specific strategic document devoted to digitalization. Several challenges are outlined for the smooth digitalization: lack of sufficient resources (including financial, administrative capacity etc.); unwell prepared infrastructure (including lack of common vision for the different systems used so far within the university); emergence of various types of resistance (both in academic and administrative staff) etc. Big advantage for universities using AI is seen in improved quality of the educational process (including introduction of new educational perspectives trough new technologies), improved administrative service, improved public image which is considered to reflect to increased number of potential candi-dates.
The paper analyzes which manufacturing firms in Russia face shortage of skilled workforce more often — effective growing firms or non-competitive enterprises. The regression analysis of the survey data of 1860 Russian manufacturing firms conducted in 2022 showed that growing and effective firms, as well as firms integrated into the global economy, face skilled labour shortage more often. At the same time, firms with higher technological level of production are more effective in solving skilled workforce shortage problems. The shortage of qualified personnel seems to be not a subjective but a real problem of Russian manufacturing firms and an obstacle to the growth and development of competitive enterprises. Additionally, the results are illustrated with materials from 12 expert interviews with enterprise managers. A potential key to addressing the workforce shortage could be an intensive growth model for enterprises based on innovations and a high level of technological organization in production and labour, with the development of production automation that reduces the need for human resources.
Pristiwanto Bani, Nurhayati Siregar, Bambang Subiyanto
et al.
The rapid advancement of digital technologies has driven a fundamental transformation in auditing practices, shifting from manual, sampling-based methods toward automation and data analytics. Although innovations such as Artificial Intelligence, Big Data Analytics, Blockchain, Cloud Computing, and Robotic Process Automation have been introduced, a comprehensive understanding of their applications, challenges, and impacts on audit quality remains limited. This study aims to (1) identify digital innovations applied in the audit process, (2) analyze key challenges in their implementation, and (3) evaluate the impact of digital transformation on audit quality in terms of effectiveness, efficiency, transparency, accuracy, and timeliness. The research employs a systematic literature review (SLR) using the PRISMA protocol and the PICOC framework, covering 84 academic articles published between 2015 and 2025. Data were analyzed through thematic synthesis and narrative synthesis. Findings reveal that AI and data analytics dominate digital audit innovations, while Blockchain enhances transparency and RPA accelerates routine procedures. Key challenges include limited IT infrastructure, organizational resistance, auditors’ skill gaps, and a lack of specific regulations for digital auditing. Nevertheless, digital transformation significantly improves accuracy, efficiency, and real-time anomaly detection, although issues of data integrity and professional ethics persist. Digital transformation enhances audit quality and repositions auditors as strategic partners within organizations. However, its success largely depends on technological readiness, human capital competence, and adaptive regulatory frameworks.
Technology infrastructure development has become a critical determinant of competitiveness, efficiency, and safety within modern confectionery business environments. As production processes evolve toward higher levels of automation, digitization, and interconnectedness, confectionery firms increasingly rely on robust digital architectures to support real-time monitoring, quality assurance, supply-chain coordination, and data-driven decision-making. This review examines the current state, challenges, and strategic direction of technology infrastructure advancement in the confectionery sector, emphasizing its role in operational performance, regulatory compliance, and long-term innovation capacity. The review highlights the transition from traditional, stand-alone production systems to integrated cyber-physical environments characterized by advanced sensors, industrial IoT platforms, cloud-based manufacturing execution systems, and AI-enabled analytics. These technologies enable more accurate control of ingredient management, batch consistency, temperature and humidity regulation, energy optimization, and predictive asset maintenance factors that are essential for ensuring product quality and minimizing waste. Strengthened digital connectivity also enhances traceability from raw material procurement to final packaging, supporting transparency and adherence to food safety regulations. However, the development of such infrastructure introduces challenges related to cybersecurity vulnerabilities, interoperability gaps across legacy equipment, high capital expenditure, and the need for workforce upskilling. The review underscores that sustained progress requires a balanced approach that aligns technological upgrades with governance frameworks, risk management programs, and human capability development. Moreover, emerging innovations such as edge computing, blockchain-enabled traceability, digital twins for production optimization, and AI-driven demand forecasting are expanding the strategic possibilities for confectionery manufacturers seeking greater agility and resilience. Overall, technology infrastructure development represents both an operational necessity and a strategic enabler for the confectionery industry. By investing in secure, scalable, and intelligent digital systems, organizations can achieve improved efficiency, enhanced product safety, stronger regulatory compliance, and greater trust among consumers and supply-chain partners.
Oladotun Solomon, Fagbenle Emmanuel, J. Simon
et al.
This study investigated the growing influence of data driven technologies specifically Data Analytics (DA), Business Intelligence (BI), Artificial Intelligence (AI), and Predictive Modeling (PM) in fostering innovation, reducing operational risk, and accelerating economic growth within the United States. As economies undergo rapid digital transformation, these technologies have become critical enablers of startup scalability and strategic agility. The research aimed to evaluate the extent to which these tools contribute to entrepreneurial success and broader macroeconomic outcomes. A mixed methods approach was adopted, combining quantitative analysis of national startup and economic datasets using regression and machine learning models, alongside qualitative case studies from the fintech, healthtech, and edtech sectors. The findings revealed that predictive modeling significantly improved early stage forecasting and investment decisions, while AI and BI were widely adopted for automation, personalization, and strategic monitoring across sectors. However, barriers such as regulatory uncertainty, disparities in data infrastructure, and limited digital maturity constrained broader implementation. The study’s integration of empirical and thematic insights offers a robust framework for understanding how technological capabilities can be strategically harnessed to advance national competitiveness. These results hold critical implications for policymakers shaping digital innovation ecosystems, startup leaders pursuing sustainable growth, and researchers developing interdisciplinary models of economic development in the era of intelligent systems.
In recent decades, society has witnessed significant advancements in emerging mobility systems. These systems refer to transportation solutions that incorporate digital technologies, automation, connectivity, and sustainability to create safer, more efficient, and user-centered mobility. Examples include connected and automated vehicles (CAVs), shared mobility services (car-pooling), electric vehicles, and mobility-as-a-service platforms. These innovations have the potential to greatly impact areas such as safety, pollution, comfort, travel time, and fairness. In this article, we explore the current landscape of CAVs. We discuss their role in daily life and their future potential, while also addressing the challenges they may introduce. Following, we also examine the practical difficulties in research associated with CAVs especially simulating and testing CAV-related algorithms in real-world settings. We present existing solutions that aim to overcome these limitations. Finally, we provide an accessible introduction to modeling CAVs using basic kinematic principles and offer an open-source tutorial to help interested students begin exploring the field.
Web tracking (WT) systems are advanced technologies used to monitor and analyze online user behavior. Initially focused on HTML and static webpages, these systems have evolved with the proliferation of IoT, edge computing, and Big Data, encompassing a broad array of interconnected devices with APIs, interfaces and computing nodes for interaction. WT systems are pivotal in technological innovation and business development, although trends like GDPR complicate data extraction and mandate transparency. Specifically, this study examines WT systems purely from a technological perspective, excluding organizational and privacy implications. A novel classification scheme based on technological architecture and principles is proposed, compared to two preexisting frameworks. The scheme categorizes WT systems into six classes, emphasizing technological mechanisms such as HTTP proto-cols, APIs, and user identification techniques. Additionally, a survey of over 1,000 internet users, conducted via Google Forms, explores user awareness of WT systems. Findings indicate that knowledge of WT technologies is largely unrelated to demographic factors such as age or gender but is strongly influenced by a user's background in computer science. Most users demonstrate only a basic understanding of WT tools, and this awareness does not correlate with heightened concerns about data misuse. As such, the research highlights gaps in user education about WT technologies and underscores the need for a deeper examination of their technical underpinnings. This study provides a foundation for further exploration of WT systems from multiple perspectives, contributing to advance-ments in classification, implementation, and user awareness.
Ege Erdil, Andrei Potlogea, Tamay Besiroglu
et al.
Assessing the economic impacts of artificial intelligence requires integrating insights from both computer science and economics. We present the Growth and AI Transition Endogenous model (GATE), a dynamic integrated assessment model that simulates the economic effects of AI automation. GATE combines three key ingredients that have not been brought together in previous work: (1) a compute-based model of AI development, (2) an AI automation framework, and (3) a semi-endogenous growth model featuring endogenous investment and adjustment costs. The model allows users to simulate the economic effects of the transition to advanced AI across a range of potential scenarios. GATE captures the interactions between economic variables, including investment, automation, innovation, and growth, as well as AI-related inputs such as compute and algorithms. This paper explains the model's structure and functionality, emphasizing AI development for economists and economic modeling for the AI community. The model is implemented in an interactive sandbox, enabling users to explore the impact of AI under different parameter choices and policy interventions. The modeling sandbox is available at: www.epoch.ai/GATE.
This paper explores how the U.S. Department of Treasury, particularly the Internal Revenue Service (IRS), is adopting artificial intelligence (AI) to modernize tax administration. Using publicly available information, the survey highlights the applications of AI for taxpayer support, operational efficiency, fraud detection, and audit optimization. Key initiatives include AI-powered chatbots, robotic process automation, machine learning for case selection, and advanced analytics for fraud prevention. These technologies aim to reduce errors, improve efficiency, and improve taxpayer experiences. At the same time, the IRS is implementing governance measures to ensure responsible use of AI, including privacy safeguards, transparency initiatives, and oversight mechanisms. The analysis shows that the Treasury AI strategy balances technological innovation with legal compliance, confidentiality, and public trust, reflecting a wider effort to modernize aging systems while maintaining accountability in tax collection and enforcement.
Over the past decade, object detection has advanced significantly, with the YOLO (You Only Look Once) family of models transforming the landscape of real-time vision applications through unified, end-to-end detection frameworks. From YOLOv1's pioneering regression-based detection to the latest YOLOv9, each version has systematically enhanced the balance between speed, accuracy, and deployment efficiency through continuous architectural and algorithmic advancements.. Beyond core object detection, modern YOLO architectures have expanded to support tasks such as instance segmentation, pose estimation, object tracking, and domain-specific applications including medical imaging and industrial automation. This paper offers a comprehensive review of the YOLO family, highlighting architectural innovations, performance benchmarks, extended capabilities, and real-world use cases. We critically analyze the evolution of YOLO models and discuss emerging research directions that extend their impact across diverse computer vision domains.
The rapid rise of compound AI systems (a.k.a., AI agents) is reshaping the labor market, raising concerns about job displacement, diminished human agency, and overreliance on automation. Yet, we lack a systematic understanding of the evolving landscape. In this paper, we address this gap by introducing a novel auditing framework to assess which occupational tasks workers want AI agents to automate or augment, and how those desires align with the current technological capabilities. Our framework features an audio-enhanced mini-interview to capture nuanced worker desires and introduces the Human Agency Scale (HAS) as a shared language to quantify the preferred level of human involvement. Using this framework, we construct the WORKBank database, building on the U.S. Department of Labor's O*NET database, to capture preferences from 1,500 domain workers and capability assessments from AI experts across over 844 tasks spanning 104 occupations. Jointly considering the desire and technological capability divides tasks in WORKBank into four zones: Automation "Green Light" Zone, Automation "Red Light" Zone, R&D Opportunity Zone, Low Priority Zone. This highlights critical mismatches and opportunities for AI agent development. Moving beyond a simple automate-or-not dichotomy, our results reveal diverse HAS profiles across occupations, reflecting heterogeneous expectations for human involvement. Moreover, our study offers early signals of how AI agent integration may reshape the core human competencies, shifting from information-focused skills to interpersonal ones. These findings underscore the importance of aligning AI agent development with human desires and preparing workers for evolving workplace dynamics.
Catherine Diaz-Asper, Mathias K Hauglid, Chelsea Chandler
et al.
Technological advances in the assessment and understanding of speech and language within the domains of automatic speech recognition, natural language processing, and machine learning present a remarkable opportunity for psychologists to learn more about human thought and communication, evaluate a variety of clinical conditions, and predict cognitive and psychological states. These innovations can be leveraged to automate traditionally time-intensive assessment tasks (e.g., educational assessment), provide psychological information and care (e.g., chatbots), and when delivered remotely (e.g., by mobile phone or wearable sensors) promise underserved communities greater access to health care. Indeed, the automatic analysis of speech provides a wealth of information that can be used for patient care in a wide range of settings (e.g., mHealth applications) and for diverse purposes (e.g., behavioral and clinical research, medical tools that are implemented into practice) and patient types (e.g., numerous psychological disorders and in psychiatry and neurology). However, automation of speech analysis is a complex task that requires the integration of several different technologies within a large distributed process with numerous stakeholders. Many organizations have raised awareness about the need for robust systems for ensuring transparency, oversight, and regulation of technologies utilizing artificial intelligence. Since there is limited knowledge about the ethical and legal implications of these applications in psychological science, we provide a balanced view of both the optimism that is widely published on and also the challenges and risks of use, including discrimination and exacerbation of structural inequalities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
The laying of power cables is a crucial aspect of developing and maintaining modern electrical infrastructure, which is vital for transmitting electricity reliably and efficiently. This review discusses the challenges and advancements in cable laying technologies, emphasizing the critical role of these techniques in meeting the increasing demands for power transmission in the backdrop of the global shift to renewable energy. Three main traditional cable laying methods are explored, including underground, overhead, and submarine, each suited to specific environmental and operational conditions. Then, the cable faults due to the impropriate laying process are discussed. Subsequently, the challenges and advancements encountered in cable laying processes are investigated, especially the difficulties of the cable laying of underground cable, submarine cable, and high-temperature superconductivity cable. This review also considers the impact of technological innovations on improving efficiency in cable laying processes, highlighting the advances driven by digitalization and automation.
With the significant role that Unmanned Surface Vessels (USVs) could play in industry, the military and the transformation of ocean engineering, a growing research interest in USVs is attracted to their innovation, new technology and automation. Yet, there has been no comprehensive review grounded in bibliometric analysis, which concentrates on the most recent technological advancements and developments in USVs. To provide deeper insight into the relevant research trends, this study employs a bibliometric analysis to examine the basic features of the literature from 2000 to 2023, and identifies the key research hotspots and modeling techniques by reviewing their current statuses and the recent efforts made in these areas. Based on the analysis of the temporal and spatial trends, disciplines and journals’ distribution, institutions, authors and citations, the publications relating to the new technology of USVs are assessed based on their keywords and the term analysis in the literature; six future research directions are proposed, including enhanced intelligence and autonomy, highly integrated sensor systems and multi-modal task execution, extended endurance and resilience, satellite communication and interconnectivity, eco-friendly and sustainable practices and safety and defense. The scientific literature is reviewed in a systematic way using a comparative analysis of existing tools, and the results greatly contribute to understanding the overall situation of new technology in USVs. This paper is enlightening to students, international scholars and institutions, as it can facilitate partnerships between industry and academia to allow for concerted efforts to be made in the domain of USVs.
The advent of generative artificial intelligence (AI) technologies heralds a new era in industrial innovation, offering unprecedented capabilities for content creation, predictive analytics, and automation. This paper delves into the transformative potential of generative AI across key industrial sectors, emphasizing its role in catalyzing technological advancements, enhancing operational efficiencies, and fostering sustainable practices. By exploring the technical characteristics, developmental trajectory, and application scenarios of generative AI, alongside a critical examination of its limitations and ethical considerations, this study aims to provide a comprehensive understanding of how generative AI is reshaping the landscape of automotive, manufacturing, and energy industries.
The integration of technology in accounting practices has spurred transformative implications, reshaping traditional processes and redefining the roles of accountants in the digital era. This study aims to explore these implications, employing a systematic literature review methodology to analyze existing scholarly works. Technological advancements, including cloud computing, Artificial Intelligence (AI), and Big Data analytics, have emerged as key enablers, offering automation, efficiency, and real-time data access. Findings indicate that these technologies have significantly enhanced efficiency and accuracy in financial reporting, allowing accountants to focus on higher-value tasks. However, challenges such as cybersecurity threats and ethical dilemmas accompany the adoption of technology in accounting. The evolving role of accountants, from data processors to strategic advisors, underscores the need for continuous upskilling and adaptation to remain relevant. This evolution also highlights the importance of fostering a culture of innovation and collaboration within organizations. The implications of these findings extend to educational institutions, which must integrate technological competencies into accounting curricula, and to policymakers, who play a crucial role in ensuring responsible and inclusive technology deployment. Overall, this study contributes to the understanding of technology's transformative impact on accounting practices and underscores the importance of adapting to the changing digital landscape.