Technology in Society最新文献

To sustain economic growth and generate new ideas for innovation, academia, industries, and governments should cooperate to develop successful research and development ecosystems. Indeed, the collaboration between these parties is required to extend beyond the basic funding provided by industries to research projects conducted by universities. Hence, collaboration must occur in an integrated environment so that each party plays its role including problem identification, setting the regulations, funding, conducting the research, etc. On the other hand, due to globalization, collaboration in research and development has become international where teams from different countries or continents collaborate in various ways. Indeed, the integrated collaboration between industry, governments, and academia improves the impact of the research outcome on society by addressing problems of interest to society while involving all the concerned parties. One of the known frameworks in this context, the Triple Helix Model (THM), has been used in this study to foster international collaboration in aviation. The international collaboration involved two universities representing academia, civil aviation authorities, public funding bodies, and air navigation services representing the government bodies, in addition to aviation industries from Sweden and the United Arab Emirates. The focus of the collaboration was the development of knowledge-based open innovation in the form of a traffic management tool that optimizes, simulates, and visualizes delivery scenarios of unmanned aerial vehicles (UAVs).

This paper aims to formalize the collaborative framework that has been used in the international collaboration mentioned above, and that combines the THM and PMBOK models so that it can be used for managing innovation projects of similar nature throughout their lifecycle. Moreover, the challenges that have been encountered by the teams involved in the collaboration are discussed in this paper in addition to the mitigation strategies that have been used to overcome them which can be beneficial for other similar collaborations. Finally, the study results encourage industries, governments, and universities to establish further projects based on the same framework to spur and maximize knowledge-based innovation for social and financial growth.

The preservation of urban heritage is one of the main challenges for contemporary society and an important task for governments. The administration of urban heritage preservation (UHP) requires effective methods, as well as sufficient material and human resources. The administrative practice of UHP is embedded in the relevant regulatory framework, which comprises legislative and normative acts at municipal, national, and international levels. The scale and range of heritage objects to be monitored, the complex statutory basis of the regulatory framework, and the traditionally low human and material resources of city administrations all contribute to the ineffectiveness of UHP practice in the UNESCO World Heritage Site of Vilnius.

Advances in technologies such as 3D spatial scanning and AI-based image processing offer the promise of a technological fix to the ineffective administrative practice. In this paper, we present a grounded design study aimed at developing a technology prototype for an automated urban heritage risk monitoring tool. Originally conceived as a techno-fix to the inefficiency of administrative practice, the prototype development reveals tensions and stumbling blocks within digitalisation efforts. Using the analytical lens of the Trifecta model of IT-based regulation, the case study examines the interaction between technological and administrative domains in shaping technology design choices.

Based on this case study, we critique the notion that IT is perceived as capable of providing “techno-fix” solutions to tasks requiring reflective human decision-making, especially when such solutions are expected to be effective without first redesigning the entire organisational system. The paper concludes with a discussion on the limits of automating administrative practices and identifies specific avenues for further research into the digitalisation of urban heritage preservation.

Global health system is rapidly transitioning from conventional models to inclusive digital healthcare due to the pandemic and technological advancements. This transition has led to widespread telemedicine adoption in response to healthcare needs. In Indonesia, the implementation of telemedicine has increased significantly after enacting relevant policies due to limited healthcare accessibility. The younger demographic, namely Millennials and Gen Z plays an important role in adopting this technology, offering significant potential for future development and research. Despite the significance of this technology, existing literature lacks a comprehensive psychological analysis of digital transformation in telemedicine adoption, particularly concerning Millennials-Gen Z. Therefore, this research aimed to construct telemedicine adoption model by integrating psychological aspects of digital transformation and multidimensional planned behavior. A total of 205 Millennials-Gen Z telemedicine users were included in the survey. The results showed that commitment to digital health transformation among these age groups significantly influenced telemedicine adoption. Readiness to engage in transformation influenced behavioral planning, while beliefs, attitudes, social norms, and self-control intricately impacted an inclination and willingness. Furthermore, it was discovered that behavioral intention contributed to enhancing the user experience of telemedicine services. These results offered substantial theoretical and practical implications, guiding providers and policymakers in constructing telemedicine services that catered to public health equity.

With the rapid rise of artificial intelligence (AI), seemingly unrelated technological domains have become closely interconnected, creating favorable conditions for achieving deep convergence between technologies. This study, drawing on the resource-based view theory, investigates the relationship between AI applications and technology convergence, while analyzing the moderating role of digital orientation. We chose Chinese A-share listed manufacturing companies from 2015 to 2022 as our research sample to validate our hypotheses. Empirical results indicate that the application of AI has a positive impact on both novel and reinforced technology convergence. Furthermore, firms with a high level of digital orientation enhance the positive effects of AI applications on novel and reinforced technology convergence. The research findings contribute empirically to the field of AI and technology convergence, offering valuable insights for firms on how to leverage AI to drive technology convergence.

With the rapid advancement of technology and societies, the global energy sector now acknowledges that by integrating contemporary digital technologies into their operations and capabilities, can improve their competitive advantage and innovation performance and processes. Moreover, energy operators are also facing a significant undertaking: how to best use and secure large amounts of data that promote sustainable productivity performance and minimise potential threats in the oil and gas value chain and project operations. In view of the foregoing, various facets like Generative Artificial Intelligence (GAI) and Machine Learning Algorithms (MLA) are increasingly gaining popularity within oil and gas sector operations. Thus, we explored how GAI and ML algorithms can enhance oil and gas value chain productivity performance. The Principal Component Analysis (PCA) was employed to identify significant GAI and MLA variables influencing performance in the oil and gas value chain, while Structural Equation Modelling (SEM) was used to test regression equations related to their application. The study found that risk portfolios and profiles can be appraised throughout the value chain by effectively utilising GAI and ML algorithms in upstream, midstream and downstream undertakings. While these findings are noteworthy and have significant implications for current practice, the paper advocates that an array of digital technologies beyond GAI and ML can still be examined during future studies to demonstrate a holistic perspective on how digital transformation can be achieved across the energy sector value and project operations.

Global warming is intensifying, with Belt and Road Initiative (BRI) countries accounting for over two-thirds of global carbon dioxide emissions. As a pivotal technology in advanced manufacturing, industrial robots significantly impact the overall carbon footprint through their role in production processes. This study examines how the use of industrial robots influences the Low-Carbon Green Performance (LCGP) across BRI nations, utilizing a dataset spanning from 2004 to 2020. The findings reveal that the integration of industrial robots notably boosts the LCGP within these countries, a conclusion supported by extensive robustness evaluations. The novelty of this study lies in uncovering the underlying mechanism by which industrial robots improve LCGP through the promotion of technological innovation. Specifically, we find that industrial robots have a pronounced effect on technological progress (TC), an impact that is further amplified by increases in labor productivity and human capital levels. This discovery provides policy implications for BRI governments, suggesting that actively promoting the development of industrial robots can accelerate energy transformation and upgrading, thereby reducing carbon emissions. Our research fills a significant void concerning the environmental impact of industrial robots and offers new perspectives and strategies for BRI countries to achieve low-carbon development goals. These findings contribute significant theoretical and practical value to global environmental protection and sustainable development.

This study offers a novel empirical examination of the interplay between industrial digitalization and urban spatial dynamics, addressing a significant gap in the existing literature. Using data from 1609 firms in South Korea and employing a generalized structural equation model, this study examines how digitalization influences industries' preferences for urban locations. It diverges from previous theoretical and intuitive approaches, by providing concrete data-driven insights. The role of digitalization in enhancing productivity and spatial efficiency is a critical outcome here, thereby encouraging industries to favor urban areas. It also reveals an unexpected nuance: digitalization does not directly correlate with clean production, challenging prevalent assumptions. By empirically validating the indirect pathways linking digitalization with urban industrial setting, this study contributes uniquely toward understanding technological advancements’ reshaping of urban industrial landscapes. This nuanced insight extends beyond the current discourse, emphasizing the indirect yet substantial influence of digitalization on industrial location choices. This marks a critical advancement in the academic examination of urban industrial dynamics.

As the world increasingly faces intersecting and complex environmental, social, and economic challenges, there are rising demands on emerging science and technology to produce innovative and potentially disruptive solutions. However, these solutions may also introduce different problems, uncertainties, and risks. Responsible innovation (RI) offers a way to identify and mitigate the social and ethical risks associated with new science and technology developments while delivering socially desirable and responsible outcomes. But how does the public view RI? In this paper, we argue that those undertaking disruptive science and technology research need a better understanding of the drivers of public trust in the research and innovation sector, and broader societal expectations of what constitutes socially responsible outcomes arising from their work. Few studies have explored if and how RI, trust, and social expectations are interrelated in the eyes of the public. This research investigates public perceptions of RI relevant to the development of novel and potentially disruptive science and technology, and their relationships with two key social outcomes: (1) public trust in the research and innovation sector; and (2) public expectations that innovative and potentially disruptive research can deliver socially responsible outcomes. Through surveying 4080 Australians, this research identifies how these four elements of RI – (i) practices of scientists, (ii) institutional compliance with research ethics, (iii) risk management effectiveness, and (iv) governance arrangements – are associated with public trust and expectations of socially responsible outcomes. Our best fitting path model showed that these elements of RI explain a large proportion of variability in trust in scientists and research institutions undertaking disruptive science, and most of the variability in public expectations that such research can deliver socially responsible outcomes. Of the four elements of RI, practices of scientists are most important for explaining trust in the research and innovation sector, and risk management effectiveness is most important for expectations of socially responsible outcomes from disruptive science and technology.