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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.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

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.

Augmented reality (AR) has emerged as a new interactive technology, swiftly transforming the field of marketing. Driven by its rapid uptake in marketing practices, academic research on AR has proliferated. This study aims to offer a holistic view of the past, present and future of augmented reality marketing (ARM) scholarship by analyzing its current and evolving research profile as well as its social and conceptual structures to inspire further research.

This study enriches the ARM scholarship by integrating a quantitative bibliometric analysis with a chronological-thematic review and a qualitative content analysis to develop a more comprehensive understanding of this novel area and suggest future research courses.

The bibliometric analysis reveals the key performance indicators as well as the social and conceptual structure of the ARM research field. The chronological-thematic review exhibits the advancement of ARM research over time and forecasts the emerging trends for the domain. Finally, the content analysis of recent articles reveals the current research hotspots and provides future research directions.

The database limits the selection of literature, as the information in databases such as Scopus is updated regularly, resulting in alterations in the number of articles and citations.

AR developers and brand managers may use this study’s findings to understand the current ARM landscape better and make strategic decisions based on AR adoption and consumption patterns.

This study is singular in using a mixed methods approach by integrating the findings from bibliometric and content analyses for more reliable results and to offer a holistic perspective of the ARM sector, thereby significantly advancing the field of technology-led marketing.

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.

In this study, the authors argue that the host environment of subsidiaries may affect subsidiary initiative via relationships with two stakeholders: public and private stakeholders. On the one hand, the public relationships and private relationships of subsidiaries may affect their tendency to demonstrate initiative. On the other hand, including technological innovation as a moderating effect supports the tendency towards subsidiary initiative.

This study obtained the data through a survey of 216 subsidiaries in China; chief executive officer or senior managers were selected as the data collection sources. AMOS analysis was used to address sophisticated data analysis issues.

Findings based on samples from China support these arguments. The findings contribute to the literature by highlighting that different types of subsidiary initiative coexist within subsidiaries and by accounting for the external environmental relationships and technological innovation.

What determines subsidiary initiatives in the host market? We find that (1) public relationships directly influence subsidiary initiatives, and (2) this effect is moderated by technological innovation. The theoretical framework shows that this interaction arises from the separate impacts of innovation characteristics, especially a foreign subsidiary's interest in entrepreneurial action affecting both growth and maintenance initiatives. In summary, this article concludes that initiatives are not simply the activities of subsidiaries. The authors hope that the strong explanatory and predictive power of these external factors and technological innovation are further enhanced when these concepts are integrated with the charters of internationalizing MNEs.

Unlike traditional industries, the e-cigarette is an epoch-making innovative product originating in China and occupying an absolute competitive advantage in the international market. The traditional A-U model describes the laws and characteristics of technological innovation in developed countries. In contrast, the inverse A-U model depicts the process of “secondary innovation” in late-developing countries through digestion and absorption. This paper aims to find out that if the e-cigarette, as a “first innovation” industry in a late-developing country, conform to the A-U model or conform to the “inverse A-U model”.

This paper takes the patent data of e-cigarettes from 2004 to 2021 as the research object, and uses Python’s Jieba segment words to divide product innovation and process innovation, and then uses statistical analysis methods to conduct empirical analyses on these data.

Thus, an improved A-U model suitable for the e-cigarette industry is proposed. In this model, product innovation in the e-cigarette industry appeared earlier than process innovation, but the synchronous development of product and process innovation is not lagging. The improved A-U model in the e-cigarette industry is not only different from the traditional A-U model but also does not conform to the inverse A-U model.

It is conducive to expanding and clarifying the theoretical contribution and applicable boundaries of the A-U model and has sparked thinking and exploration of the A-U model in e-cigarettes and emerging industries.

On this basis, suggestions on the development path and countermeasures of the e-cigarette industry are put forward.

Based on the e-cigarette industry, this paper takes patents as the research object and provides the method of dividing product innovation and process innovation, and proposes an A-U model suitable for the e-cigarette industry on this basis. By comparing the traditional A-U model with the inverse A-U model in latecomer countries, the background and causes of e-cigarette A-U model heterogeneity are analyzed from different stages and overall morphology. Based on this, the heterogeneity characteristics of e-cigarette innovation are summarized and sorted out.