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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.

Blockchain is expected to have a significant impact on Systems of Innovation as the new General Purpose Technology. The purpose of this study is to investigate how Blockchain can revolutionise the Systems of Innovation by investigating its overall structure, actors and relationships.

This study used the systematic mapping method to explore and integrate the Blockchain and Systems of Innovation literature for the creation of a new conceptual model of Blockchain-enabled Systems of Innovation. In that scope, 37 Blockchain-related and 32 Systems of Innovation-related papers, besides two major books in the field of Blockchain, have been reviewed and then integrated based on the Systems Thinking approach.

The key findings for Blockchain-enabled Systems of Innovation are that there is (1) an increased distribution of networks and collaborations, (2) increased trust through the use of reputation systems, (3) an emerging new nature of platform characteristics, (4) a democratisation of entrepreneurship by the new funding landscape and (5) an increased significance of technological drivers, such as energy.

The study shows new Systems of Innovation-related research implications. Accordingly, a new type of actor, relationship and attribute has been introduced where the boundaries of the role definitions are blurred and more distributed. This is where larger organisations can expect to lose their central position. The different types of actors are replaced by a network of actors as a result of the distributed new Blockchain-based system. The threshold for the Bottom of the Pyramid is expected to be reduced, leading to a more democratised innovation system.

Blockchain appears to reduce the effects of distrust in collaborative innovation practices with its consensus mechanisms and the new Blockchain-enabled Systems of Innovation is expected to revolutionise the interactions in the future.

There are very few studies that have been found to integrate innovation management practices with Blockchain. This is the first Blockchain-based Systems of Innovation study enabling the fundamental revision of its structure, types of relationships and actors.