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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 study aims to investigate the effect of technological progress on employment in Egypt in the period 1990–2019.

The study applies a vector autoregressive (VAR) model and uses patents as a measure of innovation outputs.

The study concludes, as shown by impulse response functions, that a shock to patents affects employment negatively in Egypt throughout the period, as expected.

Since there is still no decisive answer about the impact of technological progress and innovation on employment, this study attempts to contribute to this debate. Most existing studies focus on how technological change affects workers with different job types and skill levels, covering manufacturing and service sectors, mainly in developed countries, but there is still little research on its effect on employment at the macrolevel and in developing countries.

A new model called the G‐C‐D model, which is used to measure the technological advance, is built in this paper. The progress in non‐technical elements in Solow's “remaining value” is removed by using the idea, method and modeling technique of grey system theory. So, the difficult technical problem in the measurement of technological advance has been solved to a certain extent. The periodic G‐C‐D model of Henan Province is built in four different periods and the contribution rate of periodic technological advance of Henan Province is measured.

In this paper, first a new model, the G‐C‐D model, which is used to measure the technological advance, is built. The progress with non‐technical elements in Solow's “remaining value” is removed by using the idea, method and modeling technique of grey system theory. So the difficult technical problem in measurement of technological advance has been solved to a certain extent. Secondly, another new model, the G‐E model, which combines the Grey model with the econometrics model, is built. Using the principle of grey incidence to analyse and cluster system factors, adopting the GM(1,1) simulated values of system's variables to build the econometrics model and confirming the predicted values with grey models, some difficult techniques in econometrics model building have been solved. Thirdly, the periodic G‐C‐D model of Henan Province is built in four different periods and the contribution rate of the periodic technological advance of Henan Province is measured. Lastly, the technical change and the relation between the technical change and the funds for science and technology of Henan Province are analysed with the grey production function (the G‐C‐D) and the grey‐econometrics combined model (the G‐E), and some useful outcome for policy‐making body are obtained.

The purpose of this paper is to investigate the effects of technology progress on carbon intensity in China. Abatement of carbon emission has become one of the most important targets for the Chinese government. Numerous studies confirm that technology progress is the main factor responsible for reduction in CI. However, very few studies analyze the impacts of technology progress on CI for various regions. There is also inadequate knowledge on the transmission mechanisms of the impacts. These are the motivations for this research.

Given energy consumption and CO₂ emissions, an improved MLPI, which stands for the generalized technology progress related to energy and environment, is introduced and decomposed into technical and efficiency changes. Using a panel data of 30 provinces from 1997 to 2012, the authors construct different panel data models to investigate the effects of technology progress (and its decomposition elements) on CI.

Results show that technology progress is conducive for reducing CI, with the main factor being technical change. It also finds that the two components of technology progress have completely different effects in the three regions of China. Dynamic panel data models with threshold effects indicate that capital deepening enforces and weakens the negative effects of technical change and efficiency change on CI.

The above conclusions provide new evidences for policy-makers with respect to capital deepening, technical innovation and allocative efficiency enhancement with a view to achieving CI reduction.

The New Economic Society was formed in 1973 to promote the aims of the School of Economic Synthesis. Economic synthesis, since its early formation in the 1930s, has sought to integrate historical economics with social and neo‐classical economics. As the academic movement toward economic synthesis broadened, a more formal organisation became necessary. The New Economic Society (International School of Economic Synthesis) is an interdisciplinary association open to economists and others who are interested in developing a more social and humanistic economics, and a more realistic and scientific understanding of modern developed and less developed societies. The membership includes persons from numerous academic disciplines in many countries; formal chapters of the Society exist in the United Kingdom, Germany, India and Israel. At present, the membership is developing on an informal basis and no dues are requested. Membership information may be obtained from the following persons.

The purpose of this paper is to analyse aspects of technological change in rice agriculture, related to adoption of the Green Revolution (GR) in Indonesia. Rice production is selected in this study because it plays an important role in the development of Indonesian economy. Particular attention is paid to the use of agrochemicals that has potentials of contaminating the environment.

The paper uses an econometric model to investigate the impact of different technologies and policies related to rice production. Production function technology that enables non-neutrality of input use is the underlying concept of this paper. Types of land and transformation in policies related to rice production were accounted for to determine biased technological change. National-wide data were compiled from the Indonesian Bureau of Statistics.

The results show that rice agriculture underwent technological progress with biased technological change. The technological change was capital- and labour-saving, and agrochemical-augmenting. Production system in wetland led to technological change less capital- and labour-intensive, whilst the GR led to technological change more labour-saving and more agrochemical-augmenting.

This study only pays attention to environmentally detrimental inputs as a cause of externalities. This is a not full representation of real environmental consequences. In some studies on environmental degradation associated with intensive agricultural practices, however, there are other factors that can degrade the environment, such as soil erosion and soil compaction resulting from certain agricultural practices, and deforestation resulting from agricultural expansion. These are also important environmental impacts. The author expects that these factors are interesting and challenging subjects to be modelled in future research on sustainability of agricultural productivity growth, both theoretically and empirically.

Increase in use of agrochemicals was strongly GR linked. Moving from the GR towards more environmentally friendly policy was a wise step to reach sustainable rice production. After the GR, an act that removed pesticide subsidies and disseminated environmentally friendly technology, called integrated pest management was able to reduce the intensity of agrochemical use in rice agriculture. Further actions to support environmentally friendly policy could be the use of bio-agents such as bio-fertilisers and bio-pesticides. Enhancing farmers’ knowledge on the environmental issues and engaging farmers as a part of agro-ecosystem would synergise the actions.

This study uses the concept of biased technological change, estimated econometrically using national-level data. The production function used in this analysis enables non-neutrality of agrochemical use. When the result significantly shows the agrochemical-augmenting technological change, it is a convincing evidence, not just by accident, that the GR really led to environmental problem.

Technical progress is an important technique within improving China’s comparative advantages, as new and renewable technologies will be beneficial for energy security. Productive technical progress and green technical innovation are necessary to improve working conditions and productivity of industries. Therefore, the purpose of this paper is to study technical progress in China under such harsh competitive circumstances, as well as types of technical progress that can be promoted, productive technical progress or green technology progress, and how technical progress will affect China’s competitive advantages.

The authors perform a multi-index multi-factor constitutive model based on a sample of 468 Chinese industries, and divide the industries into four categories.

The results indicate that there is a “U”-shape relationship between green technology progress and comparative advantages and an inverted “U”-shape relationship between the intensity of market competition and comparative advantages.

China has crossed the inflection point of the “U”-shaped curve. This, coupled with the slowing of economic growth, demonstrates the need for advocating green technology in China to decrease the pollutant discharge. Establishing Chinese national brands within overseas markets and earning a profit through the downstream of production chain enhance China’s international competitiveness.

One of the most original findings of this paper points out that China is faced with a situation in which exports are severely decreased and domestic environment pollution is increased. Vigorous promotion of green technology progress, improvement of the quality and the technical content of exported products, the establishment of national brand within the overseas market, as well as enhancement of China’s international competitiveness, is needed.

In times of organizational change leaders often tell stories that justify publicly the directions in which organizations move. Such stories are always political in nature and often reflect the motives of the storyteller. We observe how leaders in high‐tech organizations use the story of technological determinism in organizational settings as a discursive practice through which they invoke the “inevitability” of technology to justify managerial decisions to the public. Rather than taking ownership of certain actions, managers are able to use this story to claim that certain organizational changes are inevitable, and to eliminate alternative stories. We examine this strategy as it appears in the public discourse produced during two mergers in the high‐tech and telecommunications industries occurring from 1998 to 2002: US West and Qwest, and AOL and TimeWarner. Finally, we demonstrate that the story of technological determinism performs discursive closure around each merger.

The Indian garment industry has witnessed a significant change since the inception of the New Textile Policy 2000 that suggests removing the industry from the list of small‐scale industries with a view to improving its competitiveness in the global market. As productivity is the driving factor in enhancing the competitiveness of any decision‐making entity (firm), a study of total factor productivity (TFP) and its sources can provide vital inputs to a firm for improving its competitiveness. Keeping this as a backdrop, the paper attempts to measure the TFP in the Indian garment‐manufacturing firms; identify sources of the TFP; and suggest measures for the firms to enhance their productivity.

The study is based on the firm‐level panel data collected from the Centre for Monitoring Indian Economy for the years 2002‐2007. One output variable, namely, gross sale and four input variables, namely, net fixed assets, wages & salaries, raw material, and energy & fuel, have been selected. The DEA‐based Malmquist Productivity Index (MPI) approach has been applied to measure the TFP.

The Indian garment industry has achieved a moderate average TFP growth rate of 1.7 per cent per annum during the study period. The small‐scale firms are found to be more productive than the medium‐ and large‐scale firms. The decomposition of TFP growth into technical efficiency change (catch‐up effect) and technological change (frontier shift) reveals that the productivity growth is contributed largely by technical efficiency change rather than by technological change.

Earlier studies on the Indian garment industry have applied the partial factor productivity approach, which has several limitations. This paper measures the TFP and identifies its sources through applying a non‐parametric DEA‐based MPI approach. Through this approach, the productivity growth is decomposed into technical efficiency change and technological change. Further, an attempt has also been made to study the variation in the productivity growth rates across location, scale‐size and type of garments.