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This study aims to develop the first Theory of Technological Response and Progress in Chaos (TRPC) and examine the case of technological development during the COVID-19 pandemic. The research objectives of this study were to: identify the key technologies that act as a response mechanism during the chaos event, specifically in the case of COVID-19; examine how technologies evolve, develop and diffuse in an immediate crisis and a chaotic environment; theorise various types and periods of technological response and progress during the emergence of chaos and the stages that unfold; and develop policy-oriented recommendations and establish technological foundations to address subsequent chaos events.

This study used the grounded theory as a methodology with a mixed-method approach that included quantitative and qualitative methods. The authors used the quantitative method to assist with the qualitative step to build the TRPC theory. Accordingly, this study integrated machine learning and text mining approaches to the qualitative data analysis following the steps of the grounded theory approach.

As a result of the TRPC theory development process, the authors identified three types of technologies (survival, essential and enhancement technologies) and five types of periods (stable, initial, survival-dominant, essential-dominant and enhancement-dominant periods) that are specific to chaos-technology interactions. The policy implications of this study demonstrate that a required technological base and know-how must be established before a chaotic event emerges.

Concerning the limitations of this study, social media data has advantages over other data sources, such as the examination of dynamic areas and analyses of immediate responses to chaos. However, other researchers can examine publications and patent sources to augment the findings concerning scientific approaches and new inventions in relation to COVID-19 and other chaos-specific developments. The authors developed the TRPC theory by studying the COVID-19 pandemic, however, other researchers can utilise it to study other chaos-related conditions, such as chaotic events that are caused by natural disasters. Other scholars can investigate the technological response and progress pattern in other rapidly emerging chaotic events of an uncertain and complex nature to augment these findings.

Following the indications of the OECD (2021a) and considering the study conducted by the European Parliamentary Research Service (Kritikos, 2020), the authors identified the key technologies that are significant for chaos and COVID-19 response using machine learning and text intelligence approach. Accordingly, the authors mapped all technological developments using clustering approaches, and examined the technological progress within the immediate chaos period using social media data.

The key policy implication of this study concerns the need for policymakers to develop policies that will help to establish the required technological base and know-how before chaos emerges. As a result, a rapid response can be implemented to mitigate the chaos and transform it into a competitive advantage. The authors also revealed that this recommendation overlaps with the model of dynamic capabilities in the literature (Teece and Pisano, 2003). Furthermore, this study recommends that nations and organisations establish a technological base that specifically includes technologies that bear 3A characteristics. These are the most crucial technologies for the survival- and essential-dominant stages. Moreover, the results of this study demonstrate that chaos accelerates technological progress through the rapid adoption and diffusion of technologies into different fields. Hence, nations and organisations should regard this rapid progress as an opportunity and establish the prior knowledge base and technologies before chaos emerges.

The authors have contributed to the chaos studies and the relationship between chaos and technological development by establishing the first theoretical foundation using the grounded theory approach, hereafter referred to as the TRPC theory. As part of the TRPC theory, the authors present three periods of technological response in the following sequence: survival technology, essential technology and enhancement technology. Moreover, this study illustrates the evolving technological importance and priorities as the periods of technological progress proceed under rapidly developing chaos.

This research aims to gain a holistic understanding of how video conferencing (VC) apps' media characteristics influence individuals' perceptions of VC apps and, ultimately, their use and continued use in professional settings.

A conceptual research model is developed by integrating constructs from media synchronicity theory (MST), social presence theory and the unified theory of acceptance and use of technology (UTAUT2) model, as well as ubiquity, technicality and perceived fees. Structural equation modeling (SEM) is used to empirically test the conceptual model using data collected from 252 working professionals from the European Union.

The results reveal that while performance expectancy (PE) and facilitating conditions (FC) are fundamental to VC app use, these factors alone do not explain the use and continuing use of VC apps in the professional context. Media characteristics that include synchronicity, social presence, and ubiquity are equally crucial to professionals using VC apps. It also confirms the moderating effect of convergence on the relationship between synchronicity and PE and the moderating effect of technicality and perceived fees on the relationship between ubiquity and FC.

For researchers, the study offers insights into the extent to which technological and socially derived characteristics of VC apps influence the routine tasks undertaken by professionals in virtual work settings. For practitioners, recommendations pivotal to the use of VC apps are presented to promote higher acceptance and improved well-being of the professional workforce.

This study aims to examine how the knowledge breadth and depth of a patent affect its likelihood of being pledged. It also seeks to explore whether these relationships change diversely in different technological environments.

A complementary log-log model with random effects was conducted to test the hypotheses using a unique data set consisting of 348,927 invention patents granted by the China National Intellectual Property Administration from 1985 to 2015 belonging to 74,996 firms.

The findings reveal that both knowledge breadth and depth of a patent positively affect its likelihood of being pledged. Furthermore, the knowledge breadth and depth entail different degrees of superiority in different technological environments.

This study focuses on the effect of an individual patent’s knowledge base on its likelihood of being selected as collateral. It does not consider the influence of the overall knowledge characteristics of the selected patent portfolio.

Managers need to pay attention to patents’ knowledge characteristics and the changes in technological environments to select the most suitable patents as collateral and thus improve the success rate of pledge financing.

This study explores the impact of multidimensional characteristics of knowledge base on patent pledge financing within a systematic theoretical framework and incorporates technological environments into this framework.

This article empirically tests the impact of risk appetite of the executive team on the re-innovation strategy after technological innovation failure using a panel regression model from the perspective of regional financial development level of enterprises.

By means of time series global principal component analysis and panel regression model method, the study validated and analyzed the impact of risk appetite of the executive team on the re-innovation strategy after enterprise technological innovation failure.

The research found that the higher the risk appetite of executive team, the more inclined the enterprise is to choose the “focusing on quantity, ignoring quality” re-innovation strategy after technological innovation failure. The better the financial development level of the region where the enterprise is located, the better it can effectively reduce the re-innovation strategy of “focusing on quantity, ignoring quality” of the enterprise due to the high risk appetite of the executive team.

The findings of this study are helpful in improving the financial development level of the region where the enterprise is located. It can help the executive team of the enterprise to more objectively choose the innovation strategy after technological innovation failure, and reduce the phenomenon that the executive team of the enterprise only pays attention to the quantity of re-innovation and underestimates the quality of re-innovation after technological innovation failure due to its high risk appetite.

This study aims to investigate the structural characteristics, spatial evolution paths and internal driving mechanisms of the knowledge transfer (KT) network in China’s patent-intensive industries (PIIs). The authors' goal is to provide valuable insights to inform policy-making that fosters the development of relevant industries. The authors also aim to offer a fresh perspective for future spatiotemporal studies on industrial KT and innovation networks.

In this study, the authors analyze the patent transfer (PT) data of listed companies in China’s information and communication technology (ICT) industry, spanning from 2010 to 2021. The authors use social network analysis and the quadratic assignment procedure (QAP) method to explore the problem of China’s PIIs KT from the perspectives of technical characteristics evolution, network and spatial evolution and internal driving mechanisms.

The results indicate that the knowledge fields involved in the PT of China’s ICT industry primarily focus on digital information transmission technology. From 2010 to 2021, the scale of the ICT industry’s KT network expanded rapidly. However, the polarization of industrial knowledge distribution is becoming more serious. QAP regression analysis shows that economic proximity and geographical proximity do not affect KT activities. The similarity of knowledge application capacity, innovation capacity and technology demand categories in various regions has a certain degree of impact on KT in the ICT industry.

The current research on PIIs mainly focuses on measuring economic contributions and innovation efficiency, but less on KT in PIIs. This study explores KT in PIIs from the perspectives of technological characteristics, network and spatial evolution. The authors propose a theoretical framework to understand the internal driving mechanisms of industrial KT networks.

Complex technology not only provides potential economic benefits but also increases the difficulty of application. Whether and how upstream technological complexity affects downstream manufacturers' innovation through vertical separation structure is worth discussing, but it has not been effectively discussed.

Through theoretical analysis and empirical testing, this article discusses the cost effect and market competition effect caused by upstream technological complexity on downstream manufacturers and further elucidates the impact of upstream technological complexity on downstream manufacturers' innovation.

Research has found that the impact of upstream technological complexity on the downstream manufacturers' innovation depends on the cost effect and market competition effect. The cost effect caused by the complexity of upstream technology inhibits the innovation of downstream manufacturers. In contrast, the market competition effect promotes the innovation of downstream manufacturers. There are differences in the cost effect and market competition effect of upstream technological complexity on different types of downstream manufacturers, so there is also significant heterogeneity in the impact of upstream technological complexity on innovation of different types of downstream manufacturers.

The conclusions of this article improve the understanding of the relationship between upstream technological complexity and downstream innovation and provide helpful implications for industrial chain innovation.

The purpose of this paper is examining how financial inclusion can be enhanced in rural and remote areas in emerging economies through the adoption of Financial technologies. The study examines the direct influence of technological characteristics, facilitating conditions, trust and perceived risk on adoption of FinTech. Furthermore, the study examines the mediating effects of facilitating conditions and moderating effects of education level in adoption of FinTech.

The study employed quantitative research design. Stratified and simple random sampling were employed to identify areas in which respondents will be collected. Drop and Pick method was further employed to collect 275 valid and reliable responses. Structural equation modeling was used to analyze the data and assess the reliability and validity of the measurement model. Structural model analysis was employed to examine the hypothesized relationship.

The results show that compatibility, complexity agent trust, perceived risk and facilitating have significant influence on adoption of FinTech. Furthermore, the findings show that facilitating conditions mediate the relationship between all technological characteristics and FinTech adoption except the relationship between compatibility and FinTech. The study also has found that education level moderates the relationship between observability and FinTech adoption as well as trialability and FinTech adoption.

This study employed cross-sectional research-based techniques in collecting data for analyzing the hypothesized relationship. However, the drawback of cross-sectional may impact the findings of this study over a long time. Therefore, future studies must consider conducting longitudinal studies which may collect data over some time for analysis of the FinTech adoption. Furthermore, the generalization of these findings should be considered carefully by other countries which are not economically similar to Tanzania and developed countries. This also provides room for future studies to examine the same area in countries with different economic development.

This study contributes to knowledge into two folds: First there is limited knowledge on adoption of FinTech in emerging economies; the study addresses the knowledge gap by providing empirical findings which will help scholar. Furthermore, less studies have examined the moderating effects of facilitating condition in adoption of technology. This study addresses the knowledge gap by examining the moderating influence of moderating influence of facilitating conditions. Also, this study provides empirical findings which will help policymakers to implement strategy that will speed-up financial inclusion in rural and isolated areas in emerging economies.

The construction industry has shown an inherent inability to adopt advanced technological innovations. The construction innovation literature is highly fragmented and outlines an overwhelming number of factors that arguably influence the transfer of technological innovations in construction. Factor-based research in the construction innovation literature tends to set out technological innovations and their adoption as socially neutral events, overlooking the role of context in the adoption process. Hence, the authors’ understanding of how technological innovations are transferred and implemented in the construction industry remains limited by the constraints of reductionist approaches adopted by factor-based research. This paper aims to advance the authors’ understanding of the actual experiences of technological innovations transfer in construction. Instead of introducing another set of factors, the research, through empirical investigation, develops a comprehensive and holistic framework to re-interpret the existing factor-based literature taking into consideration the role of the construction context and the socially constructed nature of technological innovations.

This research uses a participatory exploratory multiple case study design strategy, which has much in common with action research. The factors that could influence the transfer of technological innovations were investigated in three selected case studies over an extended period of time. During the research, a prototype system application of construction integration was used as a basis to discuss the adoption of technological innovations.

The findings suggest moving from the commonly held beliefs in the rational school of technological innovation and the functionalist paradigm towards constructivist approaches to capture the role of context and the stoically constructed nature of technological innovations. It is suggested that construction companies need to develop management capabilities to manage the changing political environments of the adoption process. The research supports the emerging view of technological innovations in construction as a collaborative-based innovation, which is a function of relationships and context.

The research is limited by the settings of the action research design and the use of an interpretive approach. Because the focus was on investigating the “why” question, little attention was given to the “how” question. Accordingly, the findings provide little insights into the way organizations can manage the changing political and social environment of the adoption process. The authors also acknowledge that in spite of their best efforts to build a theoretically grounded framework of factors, they may have overlooked some relevant studies. However, the objective of this research was not to conduct a systematic literature review or to come up with a framework that quantitatively captures all previous research studies; instead, the objective was to build a holistic framework that can be used as a base to analyze the role of construction context and the socially constructed nature of technological innovations in the adoption process.

The research offers several practical insights. First, it suggests that construction companies need to develop management capabilities to be able to lead the changing political environment of the adoption process successfully. Second, the research supports the emerging view of technological innovations in construction as a collaborative-based innovation that is a function of relationship. From a policy perspective, the research provides the basis to reconsider the effectiveness of federal and provincial policies that promote advanced technological Initiatives. The majority of these policies focus on the technical aspects in assessing the adoption of technological innovation, providing little incentives to companies to manage the changing political and social environment of the adoption process.

This research offers several theoretical and methodological contributions. First, it advances the authors’ understanding of the actual experiences of the transfer of technological innovations in construction organizations. Second, instead of introducing another set of factors, the research develops a framework to re-interpret the existing factor-based literature, considering the role of construction context and the socially constructed nature of technological innovations. The research uses the theoretical lens of the social construction of technology framework to analyze the adoption process. The existing literature suggests that such research is scarce and highly needed. Third, unlike the mainstream reductionist approaches, this research adopts an exploratory action research inquiry design that uses both inductive and deductive inquiry approaches to understand the socially constructed nature of innovation transfer in construction. The adopted research design offers a valuable methodological contribution to the existing body of knowledge.

Energy conservation and environmental protection industry (ECEPI) is a strategic choice to promote energy conservation and emission reduction, develop green economy and circular economy. However, China’s ECEPI is still in the stage of rapid development and the overall scale is relatively small, what development periods have the ECEPI experienced? This study aims to contribute to a better understanding of collaborative innovation evolution based on social network analysis from the perspective of multi-dimensional proximity.

Methodologically, this study uses social network analysis method to explore the co-evolution of multidimensional collaboration networks. It divides China’s ECEPI into four periods based on national policies from 2001 to 2020. This contribution constructs collaborative innovation networks from geographical, technological and organizational proximity.

The results show that the collaborative innovation network was initially formed in the central region of China, gradually expanded to neighboring cities and the core positions of Beijing, Jiangsu and Guangdong have been continuously consolidated. C02F has been the core of the collaboration networks, and the research focus has gradually shifted from the treatment of wastewater, sewage or sludge to the separation field. Enterprises always occupy a dominant position in the collaboration networks.

This research investigates the dynamic evolution process of collaborative innovation network in China’s ECEPI from the perspective of multidimensional proximity, explores the community structure, important nodes and multidimensional proximity features in the network, expands the research perspective on evolution characteristics of innovative network and the research field of social network analysis. Theoretically, this study enriches collaborative innovation theory, social network theory and multi-dimensional proximity theory.

This study aims to analyze the determinants influencing Chinese manufacturing companies in implementing lean manufacturing (LM).

The determinants to be explored in this study consist of technological, organizational and environmental (TOE) dimensions. A questionnaire survey was conducted on Chinese manufacturing companies, and 208 samples were analyzed.

The findings show that the relative advantage of LM and organizational support have significantly positive effects on Chinese manufacturing firms’ adoption of LM. The complexity of LM, quality of human resources, organizational readiness, customer pressure, international situation, governmental support and environmental uncertainty do not have significant effects.

This paper contributes to the literature by using the TOE model to explore the factors influencing LM adoption in the Chinese manufacturing industry.