Hasil untuk "Technological innovations. Automation"

A. Tuomi, Iis P. Tussyadiah, Jason L. Stienmetz

Service robots continue to permeate and automate the hospitality sector. In doing so, these technological innovations pose to radically change current service production and delivery practices and, consequently, service management and marketing strategies. This study explores the various impacts of robotization in the sector by offering one of the first empirical accounts on the current state-of-the-art of service robotics as deployed in hospitality service encounters. The results suggest that service robots either support or substitute employees in service encounters. They also offer hospitality businesses a novel point of differentiation, but only if properly integrated as part of wider marketing efforts. Finally, the automation of tasks, processes, and, ultimately, jobs has serious socioeconomic implications both at the microlevel and macrolevel. Consequently, hospitality executives need to consider where and how to apply robotization to strike a balance between operational efficiency and customer expectations. Displaying ethical leadership is key to reaping the benefits of the robot revolution.

David Autor, Caroline Chin, A. Salomons et al.

We answer three core questions about the hypothesized role of newly emerging job categories (‘new work’) in counterbalancing the erosive effect of task-displacing automation on labor demand: what is the substantive content of new work; where does it come from; and what effect does it have on labor demand? We construct a novel database spanning eight decades of new job titles linked both to US Census microdata and to patent-based measures of occupations’ exposure to labor-augmenting and labor-automating innovations. The majority of current employment is in new job specialties introduced since 1940, but the locus of new work creation has shifted from middle-paid production and clerical occupations over 1940–1980 to high-paid professional and, secondarily, low-paid services since 1980. New work emerges in response to technological innovations that complement the outputs of occupations and demand shocks that raise occupational demand. Innovations that automate tasks or reduce occupational demand slow new work emergence. Although the flow of augmentation and automation innovations is positively correlated across occupations, the former boosts occupational labor demand while the latter depresses it. The demand-eroding effects of automation innovations have intensified in the last four decades while the demand-increasing effects of augmentation innovations have not.

Yongchao Martin Ma, Zhongzhun Deng

In the artificial intelligence (AI) age, firms increasingly invest in AI technology innovation to secure competitive advantages. However, the relationship between firms' AI technology innovation and consumer complaints remains insufficiently explored. Drawing on Protection Motivation Theory (PMT), this paper investigates how firms' AI technology innovation influences consumer complaints. Employing a multimethod approach, Study 1 analyzes panel data from S&P 500 firms (N = 2,758 firm-year observations), Study 2 examines user-generated Reddit data (N = 2,033,814 submissions and comments), and Study 3 involves two controlled experiments (N = 410 and N = 500). The results reveal that firms' AI technology innovation significantly increases consumers' threat-related emotions, heightening their complaints. Furthermore, compared to AI process innovation, AI product innovation leads to higher consumer complaints. This paper advances the understanding of consumers' psychological responses to firms' AI innovation and provides practical implications for managing consumer complaints effectively.

Sabin Roman, Francesco Bertolotti

Technological developments and the impact of artificial intelligence (AI) are omnipresent themes and concerns of the present day. Much has been written on these topics but applications of quantitative models to understand the techno-social landscape have been much more limited. We propose a mathematical model that can help understand in a unified manner the patterns underlying technological development and also identify the different regimes in which the technological landscape evolves. First, we develop a model of innovation diffusion between different technologies, the growth of each reinforcing the development of the others. The model has a variable that quantifies the level of development (or innovation, discovery) potential for a given technology. The potential, or market capacity, increases via diffusion from related technologies, reflecting the fact that a technology does not develop in isolation. Hence, the growth of each technology is influenced by how developed its neighboring (related) technologies are. This allows us to reproduce long-term trends seen in computing technology and large language models (LLMs). We then present a three-dimensional system of supply, demand, and investment which shows oscillations (business cycles) emerging if investment is too high into a given technology, product, or market. We finally combine the two models through a common variable and show that if investment or diffusion is too high in the network context, chaotic boom-bust cycles can emerge. These quantitative considerations allow us to reproduce the boom-bust patterns seen in non-fungible token (NFT) transaction data and also have deep implications for the development of AI which we highlight, such as the arrival of a new AI winter.

E. Pilalis, Dimitrios Zisis, Christina Andrinopoulou et al.

The recent advancement of sequencing technologies marks a significant shift in the character and complexity of the digital genomic data universe, encompassing diverse types of molecular data, screened through manifold technological platforms. As a result, a plethora of fully assembled genomes are generated that span vertically the evolutionary scale. Notwithstanding the tsunami of thriving innovations that accomplish unprecedented, nucleotide-level, structural and functional annotation, an exhaustive, systemic, massive genome-wide functional annotation remains elusive, particularly when the criterion is automation and efficiency in data-agnostic interpretation. The latter is of paramount importance for the elaboration of strategies for sophisticated, data-driven genome-wide annotation, which aim to impart a sustainable and comprehensive systemic approach to addressing whole genome variation. Therefore, it is essential to develop methods and tools that promote systematic functional genomic annotation, with emphasis on mechanistic information exceeding the limits of coding regions, and exploiting the chunks of pertinent information residing in non-coding regions, including promoter and enhancer sequences, non-coding RNAs, DNA methylation sites, transcription factor binding sites, transposable elements and more. This review provides an overview of the current state-of-the-art in genome-wide functional annotation of genetic variation, including existing bioinformatic tools, resources, databases and platforms currently available or reported in the literature. Particular emphasis is placed on the functional annotation of variants that lie outside protein-coding genomic regions (intronic or intergenic), their potential co-localization with regulatory element areas, such as putative non-coding RNA regions, and the assessment of their functional impact on the investigated phenotype. In addition, state-of-the-art tools that leverage data obtained from WGS and GWAS-based analyses are discussed, along with future bioinformatics directions and developments. These future directions emphasize efficient, comprehensive, and largely automated functional annotation of both coding and non-coding genomic variants, as well as their optimal evaluation.

Maximilian Eder, Helle Sjøvaag

ABSTRACT This theoretical framework-driven article addresses the challenges faced by local journalism in adapting to technological innovations, particularly in the context of declining traditional funding models and an increasing reliance on digital platforms. As journalism shifts towards automation and data-driven processes, local media organisations’ slow adoption of artificial intelligence (AI) has raised concerns about their sustainability. The study explores how the unique characteristics of local journalism contribute to path dependencies that hinder innovation adoption by drawing on institutional theory. By reviewing policy and funding initiatives, as well as extant literature, the article proposes a model for local journalism’s adoption curve and examines its application in Germany and Norway. The findings highlight the need for long-term incentives and support schemes to address local journalism’s current economic challenges.

Aso Hajirasouli, Ayrin Assadimoghadam, Muhammad Atif Bashir et al.

The rise of Construction 4.0—driven by digitalisation, automation, and data-intensive technologies—is radically reshaping the construction industry. While its technological innovations are widely acknowledged, their implications for industrial relations remain underexplored. In this study, we conduct a systematic literature review (SLR) of 91 peer-reviewed articles published between 2010 and 2024, aiming to synthesise emerging knowledge on how Construction 4.0 is transforming workforce dynamics, employment models, and labour relations. Using NVivo software and an inductive thematic approach, we identify seven key themes: workforce transformation, the attraction of new generations and women, skill requirements and workforce development, supply chain and logistics optimisation, digital twin technology in project management, the emergence of new business models, and safety and risk assessment. Our findings highlight both opportunities—such as improved collaboration, skill diversification, and enhanced productivity—and challenges, including job displacement, digital ethics, and widening disparities between developed and developing countries. Recent studies from 2023 and 2024 underscore routine-biased changes in workforce structure, evolving project management practices through digital twins, and critical skill shortages within the sector. Furthermore, contemporary policy shifts and increasing labour tensions in some regions reveal deeper socio-economic implications of digital construction. This review contributes to a more holistic understanding of how technological innovation intersects with social systems in the built environment. The insights presented offer valuable guidance for policymakers, educators, and industry leaders seeking to navigate the evolving landscape of Construction 4.0.

Pragnaleena Debroy, Priyanka Majumder, Parijata Majumdar et al.

Andrés Fernández-Miguel, F. García‐Muiña, Susana Ortíz-Marcos et al.

This study investigates how AI-driven innovations are reshaping manufacturing value chains through the transition from Industry 4.0 to Industry 6.0, particularly in resource-intensive sectors such as ceramics. Addressing a gap in the literature, the research situates the evolution of manufacturing within the broader context of digital transformation, sustainability, and regulatory demands. A mixed-methods approach was employed, combining semi-structured interviews with key industry stakeholders and an extensive review of secondary data, to develop an Industry 6.0 model tailored to the ceramics industry. The findings demonstrate that artificial intelligence, digital twins, and cognitive automation significantly enhance predictive maintenance, real-time supply chain optimization, and regulatory compliance, notably with the Corporate Sustainability Reporting Directive (CSRD). These technological advancements also facilitate circular economy practices and cognitive logistics, thereby fostering greater transparency and sustainability in B2B manufacturing networks. The study concludes that integrating AI-driven automation and cognitive logistics into digital ecosystems and supply chain management serves as a strategic enabler of operational resilience, regulatory alignment, and long-term competitiveness. While the industry-specific focus may limit generalizability, the study underscores the need for further research in diverse manufacturing sectors and longitudinal analyses to fully assess the long-term impact of AI-enabled Industry 6.0 frameworks.

A. Rahim, R. Khatoon, T. Khan et al.

Introduction Healthcare amelioration is exponential to technological advancement. In the recent era of automation, the consolidation of artificial intelligence (AI) in dentistry has rendered transformation in oral healthcare from a hardware-centric approach to a software-centric approach, leading to enhanced efficiency and improved educational and clinical outcomes. Objectives The aim of this narrative overview is to extend the succinct of the major events and innovations that led to the creation of modern-day AI and dentistry and the applicability of the former in dentistry. This article also prompts oral healthcare workers to endeavor a liable and optimal approach for effective incorporation of AI technology into their practice to promote oral health by exploring the potentials, constraints, and ethical considerations of AI in dentistry. Methods A comprehensive approach for searching the white and grey literature was carried out to collect and assess the data on AI, its use in dentistry, and the associated challenges and ethical concerns. Results AI in dentistry is still in its evolving phase with paramount applicabilities relevant to risk prediction, diagnosis, decision-making, prognosis, tailored treatment plans, patient management, and academia as well as the associated challenges and ethical concerns in its implementation. Conclusion The upsurging advancements in AI have resulted in transformations and promising outcomes across all domains of dentistry. In futurity, AI may be capable of executing a multitude of tasks in the domain of oral healthcare, at the level of or surpassing the ability of mankind. However, AI could be of significant benefit to oral health only if it is utilized under responsibility, ethicality and universality.

Khaled M. Salem, M. S. Mohamed, Mohamed H. ElMessmary et al.

Industry Revolution Five (Industry 5.0) will shift the focus away from technology and rely more on to the collaboration between humans and AI-powered robots. This approach emphasizes a more human-centric perspective, enhanced resilience, optimized workplace processes, and a stronger commitment to sustainability. The humanoid robot market has experienced substantial growth, fueled by technological advancements and the increasing need for automation in industries such as service, customer support, and education. However, challenges like high costs, complex maintenance, and societal concerns about job displacement remain. Despite these issues, the market is expected to continue expanding, supported by innovations that enhance both accessibility and performance. Therefore, this article proposes the design and implementation of low-cost, remotely controlled humanoid robots via a mobile application for home-assistant applications. The humanoid robot boasts an advanced mechanical structure, high-performance actuators, and an array of sensors that empower it to execute a wide range of tasks with human-like dexterity and mobility. Incorporating sophisticated control algorithms and a user-friendly Graphical User Interface (GUI) provides precise and stable robot operation and control. Through an in-house developed code, our research contributes to the growing field of humanoid robotics and underscores the significance of advanced control systems in fully harnessing the capabilities of these human-like machines. The implications of our findings extend to the future development and deployment of humanoid robots across various industries and societal contexts, making this an ideal area for students and researchers to explore innovative solutions.

Andre K. Y. Low, J. J. W. Cheng, K. Hippalgaonkar et al.

The field of materials science stands at a critical inflection point. While laboratory innovations continue to emerge at an unprecedented pace, the traditional timeline from discovery to market in 10–20 years has become an unacceptable bottleneck in addressing urgent technological challenges. We argue that self-driving laboratories (SDLs) represent not merely another step in automation, but a fundamental reimagining of the materials development pipeline. By integrating manufacturing constraints and scalability considerations from the earliest stages of discovery, SDLs can collapse the laboratory-to-factory timeline while improving reproducibility and success rates. This requires abandoning the traditional sequential approach of materials screening, device optimization and manufacturing scale-up; in favor of concurrent cross-scale development. Here, we critically examine current SDL implementations, challenge prevailing assumptions about automation in materials science, and propose a roadmap for truly integrated materials development platforms that could revolutionize how we translate laboratory discoveries into commercial products.

Bahir Abdul Ghani

Artificial Intelligence (AI) has emerged as a transformative force in the construction industry, revolutionizing traditional processes and introducing unprecedented efficiency and precision. The construction sector's integration of AI technologies has catalyzed significant advancements in automation, project management, and sustainable practices. Innovations in predictive analytics and machine learning have enhanced cost estimation accuracy, while real-time monitoring systems have revolutionized risk management and safety protocols. The synergy between AI and Building Information Modeling (BIM) has created powerful tools for project visualization and coordination, while digital twin technology provides comprehensive virtual replicas for optimized decision-making. Advanced Building Energy Management Systems powered by AI algorithms have markedly improved energy efficiency and sustainable construction practices. These technological innovations collectively represent a paradigm shift in construction management, fostering improved productivity, reduced risks, and enhanced environmental sustainability in the built environment.

Kun Aulia Widyadhana, Rina Tjandra Kirana DP

Cloud-Based Accounting Systems (CBAS) are technological innovations that support digital transformation in financial management by increasing efficiency, transparency, and real-time data access. This system helps companies reduce manual recording errors, improve reporting accuracy, and accelerate data-based decision-making processes. In addition, CBAS also has advantages in terms of flexibility of use and compliance with accounting standards, making it a relevant solution for modern companies. Although CBAS offers many benefits, its implementation can also create a perception of technological superiority that does not fully reflect reality if it is not balanced with organizational and human resource readiness. Challenges often faced in implementing CBAS include dependence on cloud services, data security threats, and errors in system input or configuration that can lead to inaccurate financial reports. In addition, overly high expectations for automation can lead to neglect of manual supervision that is still needed to maintain the reliability of financial information. The success of CBAS implementation is highly dependent on a well-designed implementation strategy, adequate user training, and appropriate risk mitigation measures. With careful planning and a strategic approach, CBAS can serve as a tool that increases financial transparency and efficiency, not just a technology trend that provides no real benefits.

Ram Naresh, Sagar K Jadav, Monika Singh et al.

Hydroponic agriculture offers a soilless cultivation method that can enhance crop yields and sustainability. With decreasing arable land and water availability, hydroponics is positioned to complement conventional farming approaches to support global food security. This paper reviews the current status and future innovations in precision hydroponic technologies. Leading application crops, geographic adoption patterns, growth potential in developing countries, and technological advances are analyzed. Key challenges limiting widespread implementation are discussed, including infrastructural costs, lack of expertise, and inadequate research investments. Proposed legislation and standardization efforts in major markets are outlined. Ongoing improvements in automation, renewable energy integration, biocontrols and tailored crop varieties can further overcome limitations. The paper offers recommendations to promote hydroponics through targeted research initiatives, public incentives and localized equipment development. With appropriate regulatory support and sustained funding commitment, hydroponic systems can bolster food ecosystem resilience. The COVID-19 crisis has highlighted the interlinked risks in concentrated, centralized agriculture. More decentralized precision approaches can enhance stability. Hydroponics and vertical farming innovations can enable sustainable intensification to meet future nutritional demands. Adoption efforts to date have focused on profitable vegetable and herb markets in advanced economies, but expanding technical skills training and appropriate technologies globally would support wider implementation. With further commercial maturation and policy regulations keeping pace with innovations, hydroponics can be an integral strategy for sustainable crop production worldwide.

A. Dimitrov, M. Tytgat, K. Mota Amarilo et al.

This paper presents a streamlined framework for real-time processing and analysis of condition data from the CMS experiment Resistive Plate Chambers (RPC). Leveraging data streaming, it uncovers correlations between RPC performance metrics, like currents and rates, and LHC luminosity or environmental conditions. The Java-based framework automates data handling and predictive modeling, integrating extensive datasets into synchronized, query-optimized tables. By segmenting LHC operations and analyzing larger virtual detector objects, the automation enhances monitoring precision, accelerates visualization, and provides predictive insights, revolutionizing RPC performance evaluation and future behavior modeling.

Yuqing Wang, Xiao Yang

Cloud computing adoption across industries has revolutionized enterprise operations while introducing significant challenges in compliance management. Organizations must continuously meet evolving regulatory requirements such as GDPR and ISO 27001, yet traditional manual review processes have become increasingly inadequate for modern business scales. This paper presents a novel machine learning-based framework for automating cloud computing compliance processes, addressing critical challenges including resource-intensive manual reviews, extended compliance cycles, and delayed risk identification. Our proposed framework integrates multiple machine learning technologies, including BERT-based document processing (94.5% accuracy), One-Class SVM for anomaly detection (88.7% accuracy), and an improved CNN-LSTM architecture for sequential compliance data analysis (90.2% accuracy). Implementation results demonstrate significant improvements: reducing compliance process duration from 7 days to 1.5 days, improving accuracy from 78% to 93%, and decreasing manual effort by 73.3%. A real-world deployment at a major securities firm validated these results, processing 800,000 daily transactions with 94.2% accuracy in risk identification.

Christian Friedrich, Ralf Gulde, Armin Lechler et al.

Automating complex tasks using robotic systems requires skills for planning, control and execution. This paper proposes a complete robotic system for maintenance automation, which can automate disassembly and assembly operations under environmental uncertainties (e.g. deviations between prior plan information). The cognition of the robotic system is based on a planning approach (using CAD and RGBD data) and includes a method to interpret a symbolic plan and transform it to a set of executable robot instructions. The complete system is experimentally evaluated using real-world applications. This work shows the first step to transfer these theoretical results into a practical robotic solution.

Andreas Holzinger, J. Schweier, Christoph Gollob et al.

Recent technological innovations in Artificial Intelligence (AI) have successfully revolutionized many industrial processes, enhancing productivity and sustainability, under the paradigm of Industry 5.0. It offers opportunities for the forestry sector such as predictive analytics, automation, and precision management, which could transform traditional forest operations into smart, effective, and sustainable practices. The paper sets forth to outline the evolution from Industry 5.0 and its promising transition into Forestry 5.0. The purpose is to elucidate the status of these developments, identify enabling technologies, particularly AI, and uncover the challenges hindering the efficient adoption of these techniques in forestry by presenting a framework. However, the gap between potential and practical implementation is primarily due to logistical, infrastructural, and environmental challenges unique to the forestry sector. The solution lies in Human-Centered AI, which, unlike the Industry 4.0 paradigm, aims to integrate humans into the loop rather than replace them, thereby fostering safe, secure, and trustworthy Human-AI interactions. The paper concludes by highlighting the need for Human-Centered AI development for the successful transition to Forestry 5.0 – where the goal is to support the human workers rather than substituting them. A multidisciplinary approach involving technologists, ecologists, policymakers, and forestry practitioners is essential to navigate these challenges, leading to a sustainable and technologically advanced future for the forestry sector. In this transformation, our focus remains on ensuring a balance between increased productivity, nature conservation and social licence, worker safety and satisfaction.

U. Sumalatha, K. Prakasha, Srikanth Prabhu et al.

Effective cardiovascular health monitoring relies on precise electrocardiogram (ECG) analysis for early diagnosis and treatment of heart conditions. Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNNs), have significantly enhanced the automation, accuracy, and personalization of ECG analysis. This review targets both medical professionals and a broader audience interested in deep learning applications. Our work explores the evolution of deep learning techniques in ECG analysis, from early CNN applications to current innovations in real-time processing and privacy-preserving methods. The paper discusses various deep learning models, including hybrid models, Recurrent Neural Networks (RNNs), and attention mechanisms, and their impact on diagnostic accuracy for diseases like myocardial infarction. Additionally, our paper examines ECG-based authentication systems, addressing challenges related to security and privacy, and highlighting recent technological advancements. By providing a detailed overview of these developments, the review offers valuable insights into future directions for deep learning in cardiovascular health monitoring and ECG-based authentication.