Purpose: The aim of this study is to estimate the relationship between technological innovation and corporate environmental performance among energy companies working in Egypt.Design/methodology/approach: The study extended the aim with the intention to assess the role of green financing in enhancing corporate environmental performance. Partial least squares (PLS)-based structural equation modeling (SEM) is applied to estimate the nexus among study variables.Findings: The results indicated that technological innovation influenced environmental performance and has a positive impact on company performance. The role of green financing for environmental performance is also significant and positive. Moreover, corporate social responsibility (CSR) has insignificant role in environmental performance of the energy companies in the study context.Research limitations/implications: The study offers a valuable model for general managers of manufacturing organizations and policymakers to manage CSR, environmental strategy and green innovation in examining environmental performance. It can help to assist general managers of large manufacturing organizations to strengthen their internal resources like CSR, environmental strategy and green innovation to enhance environmental performance.Practical implications: The findings of this article will help the practitioners to design policies regarding sustainable energy systems and green finance in the presence of any natural calamity.Originality/value: This study primarily complements the existing literature by establishing how green financing and CSR can augment and/or interact between technological innovation and corporate environmental performance under COVID-19 crises, in a developing country.

The main purpose of this empirical study is to explore the entrepreneurial self-efficacy, and entrepreneurial competency as antecedents to the entrepreneurial intentions in smart city of Neom Saudi Arabia. Additionally, the study has also examined the mediating role of entrepreneurial education on artificial intelligence in the relationship between the entrepreneurial self-efficacy, entrepreneurial competency, and the entrepreneurial intentions in smart city of Neom Saudi Arabia. The study has employed the SEM-PLS for the data analysis. A sample based on 550 students from private (n = 291, 52.9 %) and public universities (n = 259, 47.1 %) of KSA were randomly selected. The entrepreneurial self-efficacy, and entrepreneurial competency are reported to have a positive and significant influence on entrepreneurial intentions. The entrepreneurial education on artificial intelligence, medicate the relationship between the entrepreneurial competency, entrepreneurial self-efficacy, and entrepreneurial intentions The model is contextually suitable for motivating individuals to become self-sufficient and for enabling them to harness their creativity through entrepreneurial and profit-driven endeavors in smart cities. This study has significant implications for entrepreneurs, practitioners, and governments alike. The findings provide a guideline for governments in establishing an environment that supports the individual drive to embark on entrepreneurial endeavors. Among the stimulus for encouraging entrepreneurship include the provision of loans with reasonable interest rates and knowledge sharing sessions by prominent entrepreneurs. Drawing inferences from the empirical findings and discussions, this present study has made appreciable theoretical contributions to the existing body of literature, specifically, in the area of entrepreneurial intentions in smart city.

Purpose: This study investigates the impact of traditional marketing analytics and big data analytics on the success of a new product. Moreover, it assesses the mediating effects of the quality of big data system. Design/methodology/approach: This study is based on primary data that were collected through an online questionnaire survey from large manufacturing firms operating in UAE. Out of total distributed 421 samples, 327 samples were used for final data analysis. The survey was conducted from March–April 2020, and data analysis was done via Structural Equation Modelling (SEM-PLS). Findings: It emerges that big data analysis (BDA), traditional marketing analysis (TMA) and big data system quality (BDSQ) are significant determinants of new product development (NPD) success. Meanwhile, the BDA and TMA significantly affect the BDSQ. Results of the mediating role of BDSQ in the relationship between the BDA and NPD, as well as TMA and NPD, are significant. Practical implications: There are significant policy implications for practitioners and researchers concerning the role of analytics, particularly big data analytics and big data system quality, when attempting to achieve success in developing new products. Originality/value: This is an original study based on primary data from UAE.

Purpose: The aim of the study is to examine the impact of the big data analytics capabilities (BDAC) on the organizational performance. The study also examines the mediating role of ambidexterity and the moderating role of business value of big data (BVBD) analytics in the relationship between the big data analytics capabilities and the organizational performance. Design/methodology/approach: This study collected primary data based on a questionnaire survey among the large manufacturing firms operating in UAE. A total of 650 questionnaires were distributed among the manufacturing firms and 295 samples were used for final data analysis. The survey was conducted from September to November in 2019, and data were analyzed based on partial least squares structural equation modeling (PLS-SEM). Findings: The big data analysis (BDA) scalability is supported by the findings on the performance of firm and its determinants such as system, value of business and quality of information. The roles of business value as a moderator and ambidexterity as mediator are found significant. The results reveal that there is a need for managers to consider the business value and quality dynamics as crucial strategic objectives to achieve high performance of the firm. Research limitations/implications: The study has significant policy implication for practitioners and researchers for understanding the issues related to big data analytics. Originality/value: This is an original study based on primary data from UAE manufacturing firms.