“…Sofiyabadi et al ( 2020 ) designed strategies for measuring innovation practice, development, and exploration to investigate the impact of open technological innovation used before and now on enterprise knowledge management strategies and innovation output types; results found that IPR (Intellectual Property Rights) management and internal innovation network management of enterprises were essential for open technological innovation of enterprises. Qureshi et al ( 2021 ) used a structural equation model to analyze the correlation between intellectual property, open technological innovation, and organizational performance; analysis results found that intellectual property and open technological innovation had a significant positive correlation; the impact of intellectual property had a positive impact on organizational performance through open technological innovation. Hameed et al ( 2021 ) used quantitative research and cross-sectional research methods to solve the problem of declining open technological innovation capabilities that hindered the overall performance of small- and medium-sized enterprises; consequently, intellectual property, network strategy, and research and development design were the decisive factors of enterprises’ open technological innovation performance.…”
Section: Development Status Of Open Technology Innovation In Enterprisesmentioning
The problems faced by the open technological innovation of China’s new ICT (information and communications technology) industry under IoT (Internet of Things) technology are expected to be analyzed to improve the overall innovation ability and ensure the sustainable development of related industries. An evaluation model is constructed for open technological innovation in the IoT industry by analyzing the development of IPR (Intellectual Property Rights) management, network strategy, and AI (artificial intelligence) technology under the development of IoT technology. Meanwhile, IBM SPSS Statistics 20.0 and IBM SPSS Amos 19.0 are used to analyze the data information of 306 enterprises in the information technology industry. Besides, the proposed hypotheses are verified by factor analysis, multiple regression, and Back Propagation Neural Network. Finally, a new evaluation index system is constructed for open technological innovation in the new ICT industry. The development of IoT technology provides a primary guarantee for the open technological innovation of the new ICT industry, and the network strategy has the greatest influence on the internal knowledge output mode. Besides, the experimental results indicate that the IoT and artificial intelligence have a critical display value for the open technological innovation of the emerging ICT industry, with the highest weight ratio of 48.25%. This result demonstrates that artificial intelligence is positively correlated with the external input information. Intellectual property management is a crucial guarantee of open technology innovation in the ICT industry. The evaluation model of open technological innovation in the ICT industry has good performance through case analysis, with the highest accuracy of 91.25%. Therefore, the evaluation index system reported here can reflect the important factors affecting the development of innovative technology, which can provide a theoretical basis and practical value for improving the existing open technology innovation system.
“…Sofiyabadi et al ( 2020 ) designed strategies for measuring innovation practice, development, and exploration to investigate the impact of open technological innovation used before and now on enterprise knowledge management strategies and innovation output types; results found that IPR (Intellectual Property Rights) management and internal innovation network management of enterprises were essential for open technological innovation of enterprises. Qureshi et al ( 2021 ) used a structural equation model to analyze the correlation between intellectual property, open technological innovation, and organizational performance; analysis results found that intellectual property and open technological innovation had a significant positive correlation; the impact of intellectual property had a positive impact on organizational performance through open technological innovation. Hameed et al ( 2021 ) used quantitative research and cross-sectional research methods to solve the problem of declining open technological innovation capabilities that hindered the overall performance of small- and medium-sized enterprises; consequently, intellectual property, network strategy, and research and development design were the decisive factors of enterprises’ open technological innovation performance.…”
Section: Development Status Of Open Technology Innovation In Enterprisesmentioning
The problems faced by the open technological innovation of China’s new ICT (information and communications technology) industry under IoT (Internet of Things) technology are expected to be analyzed to improve the overall innovation ability and ensure the sustainable development of related industries. An evaluation model is constructed for open technological innovation in the IoT industry by analyzing the development of IPR (Intellectual Property Rights) management, network strategy, and AI (artificial intelligence) technology under the development of IoT technology. Meanwhile, IBM SPSS Statistics 20.0 and IBM SPSS Amos 19.0 are used to analyze the data information of 306 enterprises in the information technology industry. Besides, the proposed hypotheses are verified by factor analysis, multiple regression, and Back Propagation Neural Network. Finally, a new evaluation index system is constructed for open technological innovation in the new ICT industry. The development of IoT technology provides a primary guarantee for the open technological innovation of the new ICT industry, and the network strategy has the greatest influence on the internal knowledge output mode. Besides, the experimental results indicate that the IoT and artificial intelligence have a critical display value for the open technological innovation of the emerging ICT industry, with the highest weight ratio of 48.25%. This result demonstrates that artificial intelligence is positively correlated with the external input information. Intellectual property management is a crucial guarantee of open technology innovation in the ICT industry. The evaluation model of open technological innovation in the ICT industry has good performance through case analysis, with the highest accuracy of 91.25%. Therefore, the evaluation index system reported here can reflect the important factors affecting the development of innovative technology, which can provide a theoretical basis and practical value for improving the existing open technology innovation system.
“…After checking the reliability, the specified data were further analyzed to ensure convergent validity. Convergent validity explains, how accurate the indicators of a construct converge on their corresponding construct (Qureshi et al , 2021). Researchers have emphasized that the value of average variance extracted (AVE) should be above 0.50 in the case of reflective constructs.…”
Purpose
This study aims to assess the acceptability of online classes among university students of Pakistan through the extension of the unified theory of acceptance and use of technology (UTAUT) model.
Design/methodology/approach
This study follows a quantitative research approach and data were collected from 662 university students of 10 different universities of Pakistan through a self-administrative Web survey. Structural equation modeling through SmartPLS was used to analyze the data.
Findings
Findings of the research show that performance expectancy, effort expectancy, social influence and facilitating conditions play a significant role in developing the intention to adopt online classes. Furthermore, facilitating conditions and intention to adopt online classes have further resulted in frequent use behavior. The authors also investigated the moderating role effect of active learning in relationship behavioral intention and use behavior. Findings show that active learning is an important component of online classes that interacts with the behavioral intention to develop the behavior of attending the online mode of learning by students. However, no significant moderation of uncertainty was found in the relationship between four components of acceptance of technology and behavioral intention to adopt the technology.
Originality/value
The authors have extended the UTUAT model by establishing the relationship between facilitating conditions and behavioral intention that supports e-learning. Furthermore, this study tests the moderating role of uncertainty and active learning on the UTUAT model.
“…A logical explanation is firms’ failure to fully prepare and acquire knowledge and resources, a mismatch between environmental actions and organizational strategy (Ervin et al 2013 ). Studies have categorized several dynamic capabilities, namely the existence of relative environmental strategies and responsibilities to actively mobilize resources (Ali et al 2021a ; Arda et al 2019 ; Yang et al 2015 ; Lee et al 2016 ), moving toward flexibility and reorganization (Russo and Harrison 2005 ; Girod and Whittington 2017 ; Abeelen et al 2013 ), experienced R&D (Mithani 2017; Lee and Min 2015 ), technical and innovative knowledge and skill (Ali et al 2021b ; Cainelli et al 2015 ; Bhupendra and Sangle 2015 ; Arvanitis and Ley 2013 ), and reshaping human capital management to achieve economic, social, and environmental sustainability goals (Aravind 2012 ; Qureshi et al 2021 ; Singh et al 2020 ; Yunus, 2021 ).…”
The integration of Industry 4.0 (I4.0) has emerged as an innovative paradigm for industrial firms contemplating environmental and economic issues. This study explicates the role of I4.0 technologies (I4.0TEC) in reinforcing the management of environmental assets (ENVASS) as well as optimizing financial performance (FP). The data in this research was collected from 738 industrial firms in Malaysia between 2009 and 2018. The analyses of ordinary least square statistics (OLS) and structural equation modeling (SEM) delineated three major findings. The individual effect of ENVASS, robotization, and flexibility in production technologies has a marginal impact on sales, exports, and labor productivity indicators. The complementarities of these variables represent a similar effect on the performance indicators. The findings related to gross operating margin elucidate that ENVASS and I4.0TEC have neither individual nor complementarity effects. This was explained by developing a robust model by integrating ENVASS, I4.0TEC, spending and investing in R&D, flexibility in production, and human capital management. Our findings have confirmed that the proposed model offers a functional toolkit for the firms considering optimizing their profitability by leveraging ENVASS and I4.0TEC. This research also contributes to developing an ethical business model for the circular economy.
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