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.

Amid intensifying global economic rivalries, China has pinpointed the digital economy and sustainable growth as key accelerators for societal and economic progress. Digital innovation (DI) plays a crucial role in propelling China’s economy towards sustainable growth, by serving as the technological backbone of the digital economy. This study explores how DI influences China’s GTFP through an analysis of panel data covering 30 provinces, municipalities, and autonomous regions from 2005 to 2021. The results indicate that DI greatly contributes to the enhancement of GTFP. DI can also indirectly promote GTFP by increasing the effectiveness of factor allocation efficiency including capital, labor, and technology. Heterogeneity analysis results indicate that the influence of DI on GTFP differs depending on the degree of intellectual property protection (IPP), the development of digital infrastructure construction (DIC), and the geographical location. A higher degree of IPP and developed DIC make areas better suited for the role of DI in advancing GTFP. Furthermore, in the central and eastern areas, the impact of the digital economy on the promotion of GTFP is particularly noticeable. This study offers reliable empirical evidence for the effect of DI on GTFP and contributes to China’s digital economy and sustainable development.

With the advent of social media, the volume of data generated on the internet has exploded due to the growing number of social network users and their interactions on the internet. Given that, in the age of the digital economy, data has become raw material in terms of decision making, it is important and urgent to conduct a study to understand the effect of big data on value creation and value capture. The goal of the current research is to study the share of big data in value creation and capture in the companies Apple and Samsung. The main question addressed by this article is whether the increasing volumes of data in the digital age can improve the creation and capture of value. To achieve this goal, we considered active users of the three main social networks that Samsung and Apple companies use for their advertising to describe “big data”. We measure the “value creation” through the hardware component of the smartphone, such as the battery, camera, CPU speed, RAM (Random-Access Memory), screen size, and storage. Profit, satisfaction, and unit of phone sold are the three manifest variables considered to measure “Value capture”. As a methodology, we used partial least squares and structural equation modeling to obtain the results. The pattern enables us to measure the effect of big data on value creation and value capture. The results indicate that CPU speed, RAMs, and battery capacity are the principal variables that impact the “value creation” in terms of customer need and satisfaction.

In the era of globalization, Chinese firms increasingly leverage sequential cross-border mergers and acquisitions to navigate complex international environments. Understanding the sustained impact of this strategy requires a holistic view beyond isolated transactions. Adopting an open systems perspective, this study examines how sequential cross-border M&As influence the evolution of firms’ innovation boundaries as a dynamic system property. Combining grounded theory with textual analysis of Chinese M&A cases, this study develops an integrated systemic process framework and empirically tests it using data from Chinese listed firms (2002–2021) via fixed-effects models. Results reveal that sequential cross-border M&As act as external inputs that trigger internal system reconfiguration, significantly expanding innovation boundaries. This expansion process is mediated by dynamic capabilities, which constitute the firm’s core adaptive mechanism, and is moderated by resource slack that functions as critical system redundancy. These findings contribute to systems science by elucidating how firms, conceptualized as complex adaptive systems, transition from isolated deal-making to sustained capability building through iterative feedback loops in global competition.