Economic growth is a tool for measuring the development and progress of countries, and technological innovation is one of the factors affecting economic growth and contributes to the development and modernization of production methods. Therefore, technological innovation is the main driver for economic growth and human progress. Spending on innovation, research and development as well as investment in innovation supports competition and progress. Accordingly, sustainable economic growth is achieved. This ensures the preservation of resources for future generations and the achievement of economic and social growth. Moreover, a sustainable educational level of the workforce, investment in research, creation of new products, and investor access to stock markets will be ensured through the development of the public and private sectors and the improvement of people’s living conditions. Our study aimed to measure the impact of technological innovation on economic growth in developing countries during the period 1990–2018. To this end, the error correction model (ECM) method has been applied. The results showed that the variables are unstable in the level and stable after taking the first difference. Co-integration was also tested using the ECM, and Granger’s causality test for the direction of causation. The test results showed that an increase in technological innovation indicators (such as spending on education, number of patents for residents and non-residents, R&D expenditures, number of researchers in R&D, high-tech exports, and scientific and technical research papers.) leads to an increase in economic growth in the short term and the long-run with a long-run and two-way causal relationship between technological innovation and GDP, and short-run causation spanning from technological innovation to GDP. The study also concluded that technological innovation has a direct impact on the sustainability of a country’s economic growth, which is why it is crucial to adopt strong policies that encourage international investors to allocate capital for development in developing countries and thus encourage more research and development.

The competitiveness of national economies is increasingly dependent on their ability to produce and use knowledge, as knowledge, education, and innovation are the main indicators of economic growth in a globalizing world. Many countries have adopted policies related to the production of knowledge and its transformation into wealth that stimulates the growth and competitiveness of their economies. Through our study, we measured some knowledge economy (KE) variables for a sample of 20 developing countries. During the period (1996–2020), using panel data, the estimate was made using three models: the cumulative regression model, the fixed-effects model, and the random-effects model. The results of the statistical tests indicated that the fixed-effects model is the appropriate model, and that the estimates of the proposed model parameters do not contradict the assumptions of economic theory, nor do they contradict the practical reality. In addition, the results showed that 93% one of the changes that occur in economic growth in the developing countries under study is due to the dependence on the knowledge economy. It became clear through the study that the use of cross-sectional time-series models increases the accuracy of statistical forecasting, because it considers the information with a time dimension in the time series, as well as the cross-sectional dimension in different units. Our study found a positive impact on economic growth of the internet and mobile phone users, control of corruption, political stability, foreign direct investment, and the total value of international trade. Additionally, spending on education, patents for residents, and trade openness had a negative impact on economic growth. Accordingly, the knowledge economy in developing countries contributed greatly to their economic growth and had a prominent role in maintaining high rates of growth for decades. Our study contributes by expanding the scope of developing countries in deciding to focus on the important indicators in the knowledge economy (KE), so that they can increase the added value of their economic growth.

Both technological innovation and foreign direct investment have received widespread attention in the literature on their role in promoting economic growth. Therefore, this study aims to test the relationship between foreign direct investment, technological innovation, and economic growth of the Egyptian economy during the period between 1990–2019 using the autoregressive distributed lag model simultaneous integration test. Our findings show of the ARDL (Autoregressive Distributed Lag) model estimation a joint complementary relationship between the rate of growth of per capita gross domestic product (GDP) in US dollars and the independent variables in the model in the long and short term, which are statistically significant results. We found a positive significant relationship between the variables of incoming foreign direct investment and share of total capital formation in economic growth. Therefore, in the long term, the rate of inflation and the innovation index had a negative impact in the long term and the speed of adjustment towards equilibrium was very large, as it was estimated at 1.5 years (1/0.651). Furthermore, the study also provides valuable lessons and a strategic vision for the Egyptian government, which aspires to advance technology and attract more foreign direct investment.

This paper studies the EKC hypothesis and STIRPAT model. Based on the panel data of carbon emission intensity and other influencing factors of 30 provinces in China from 2000 to 2018, the spatial effect of per capita food nitrogen footprint (FNF) and the effect of different socio-economic factors in China were studied by using exploratory spatial data analysis and fixed effect spatial Durbin model for the first time. The results show that: (1) there is a spatial agglomeration effect and a positive spatial dependence relationship in China’s provincial per capita FNF (FNFP), which verifies that the relationship between China’s FNF and economy is in the early stage of EKC hypothesis curve. (2) The driving forces of China’s FNF were explored, including Engel’s coefficient of urban households (ECU), population density (PDEN), urbanization, nitrogen use efficiency (NUE) and technology. (3) The results show that there is a significant spatial spillover effect of FNFP. The ECU and NUE can reduce the regional FNFP, and can slow down the FNFP of surrounding provinces. (4) Policy makers need to formulate food nitrogen emission reduction policies from the food demand side, food consumption side and regional level.

Public opinion inversion and other nonlinear phenomena often occur during online social network information dissemination (OSNID) in modern society. To explore the influence of overconfidence on OSNID, we develop a multi-agent simulation model integrating overconfidence and evolutionary games, as previous research has scarcely paid any attention to irrational behavior in information dissemination. This integrated paradigm provides an effective tool for managers to control the spread of OSNID and reduce the harm caused by rumors. The theoretical model is constructed from the perspective of an evolutionary game and, combined with overconfidence theory, three overconfidence scenarios are designed: benefit overconfidence, cost overconfidence, and both benefit and cost overconfidence. Then, a multi-agent simulation model of OSNID under different overconfidence scenarios is realized. The proposed simulation model exhibited better performance (e.g., faster diffusion speed) than the traditional Bass model; its performance was also validated by comparison with real-world cases. The results demonstrate that (1) with increasing benefit overconfidence, the convergence speed of OSNID will be accelerated, and the user group will reach stability at a faster speed. (2) With increasing cost overconfidence, the user's decision will change from adopting dominant to tit-for-tat, and the adoption ratio will gradually decrease. (3) Compared with overconfidence in earnings, overconfidence in costs is more conducive to improving stability. This study attempts to provide new ideas for OSNID and attempts to propose an integration framework for behavioral theory and simulation methods.

The rapid development of online social media has significantly promoted product diffusion in online social networks (PDOSN). However, prior studies focusing on irrational behavior, such as overconfidence, in PDOSN are scarce. To investigate the effect of overconfidence on PDOSN, this study combined overconfidence and an evolutionary game to conduct a multiagent simulation on PDOSN. This combined method provided an effective reference to examine product diffusion in the context of irrational behavior. After careful consideration, this study identified three overconfidence scenarios, benefit, cost, and benefit and cost overconfidence, developed a multiagent simulation model for PDOSN using various overconfidence scenarios, and conducted a comparison with real‐world cases to validate the model’s feasibility. The findings indicated that adoption benefits and betrayal penalties had a positive effect on the results in all models, while adoption costs had the opposite effect. When benefit and cost overconfidence occurred simultaneously, benefit overconfidence offset the negative effect of cost overconfidence. Moderate connectivity, a large number of core nodes, and high reconnection probability fully promoted product diffusion. Benefit overconfidence and cost overconfidence had a significant impact on the results in different networks. As such, this study combined psychological theory with simulation methods, providing insights for future research on product diffusion.