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This study addresses the fragmented understanding of the relationship between digital transformation (DT) and innovation by proposing a multi-level framework that integrates diverse disciplinary perspectives. This framework provides deep insight into how DT influences innovation. A systematic literature review was conducted, and based on the findings, a research agenda was outlined as a roadmap to guide future studies. This research contributes to the strategic change and innovation literature by providing a multi-level framework explaining how DT-driven structural change affects innovation, considering various contingencies such as market dynamics, technological advancements, and organizational capacities. Additionally, this study contributes to the innovation and strategic information systems domain by demonstrating that DT's role as a strategic asset for achieving innovation should align with firm capabilities, structural characteristics, and environmental and external dynamics.

The field of MLOps (Machine Learning Operations), which focuses on effectively managing and operationalizing ML workflows, has grown because of the advancements in machine learning (ML). The goal of this study is to examine and contrast the difficulties encountered in the implementation of MLOps in enterprises with those encountered in DevOps. An SLR (Systematic Literature Review) is the first step in the research process to find the issues raised in the literature. The results of this study are based on qualitative content analysis using grounded theory and semi-structured interviews with 12 ML practitioners from different sectors. Organisational, technical, operational, and business problems are the four distinct aspects of challenges for MLOps that our study highlights. These challenges are further defined by eleven different themes. Our research indicates that while some issues, such as data and model complexity, are unique to MLOps, others are shared by DevOps and MLOps as well. The report offers suggestions for further research and summarises the difficulties.

This study examines the influence of digital capabilities in sculpting entrepreneurial orientation (EO) and enhancing metaverse entrepreneurial performance (MEP). Grounded in the theoretical frameworks of the resource-based view (RBV) and dynamic capability theory (DCT), this research explains the mechanisms through which three facets of digital capabilities (infrastructure, business spanning, and proactive stance) cultivate an ecosystem conducive to entrepreneurial expansion and efficacy within the metaverse. Based on a data set of 403 metaverse entrepreneurs, a multifaceted analytical approach is applied. Partial least squares structural equation modeling (PLS-SEM) is used to assess latent variable relationships. Importance-performance map analysis (IPMA) is used to evaluate the performance and importance of various constructs. Fuzzy-set qualitative comparative analysis (fsQCA) is used to identify complex causal relationships. Collectively, these techniques enable the examination of the interplay among digital capabilities, EO, and MEP. The empirical findings show the positive impact of digital capabilities on EO and MEP, with EO mediating this relationship. These results underscore the need for metaverse entrepreneurs to improve their digital capabilities and EO to achieve enhanced performance within this dynamic entrepreneurial ecosystem.

In a world that has rapidly transformed through the advent of artificial intelligence (AI), our systematic review, guided by the PRISMA protocol, investigates a decade of AI research, revealing insights into its evolution and impact. Our study, examining 3,767 articles, has drawn considerable attention, as evidenced by an impressive 63,577 citations, underscoring the scholarly community's profound engagement. Our study reveals a collaborative landscape with 18,189 contributing authors, reflecting a robust network of researchers advancing AI and machine learning applications. Review categories focus on systematic reviews and bibliometric analyses, indicating an increasing emphasis on comprehensive literature synthesis and quantitative analysis. The findings also suggest an opportunity to explore emerging methodologies such as topic modeling and meta-analysis. We dissect the state of the art presented in these reviews, finding themes throughout the broad scholarly discourse through thematic clustering and BERTopic modeling. Categorization of study articles across fields of research indicates dominance in Information and Computing Sciences, followed by Biomedical and Clinical Sciences. Subject categories reveal interconnected clusters across various sectors, notably in healthcare, engineering, business intelligence, and computational technologies. Semantic analysis via BERTopic revealed nineteen clusters mapped to themes such as AI in health innovations, AI for sustainable development, AI and deep learning, AI in education, and ethical considerations. Future research directions are suggested, emphasizing the need for intersectional bias mitigation, holistic health approaches, AI's role in environmental sustainability, and the ethical deployment of generative AI.

Government online services are often considered a core component of e-government, generating significant interest not only among practitioners and scholars but also among policymakers because of their crucial role in enhancing a country's governance capacity. This study, which is based on the United Nations E-Government Survey's Online Service Index, the World Bank's Worldwide Governance Indicators, and the Global Innovation Index, investigates how e-government online services' technology, institutional frameworks, content provision, e-participation, and service provision, as well as innovation, enhance the national governance capacity and offer governance support. The research uses principal component analysis (PCA) for data preprocessing and combines necessary condition analysis (NCA) with fuzzy set qualitative comparative analysis (fsQCA) to provide empirical evidence. The study confirms the critical role of the aforementioned factors in boosting governance capacity and offers three mutually nonexclusive solution types that enhance governance support. These solutions support governance by improving the closeness of interactions between the government and the public, the potential for public participation in governance, and the government's own influence. Additionally, the study reveals that innovation can enhance the impact of e-government online services on national governance capacity, thereby providing a more comprehensive perspective on how e-government online services influence national governance. This study advances our understanding of how e-government enhances a nation's governance capability and offers supplementary insights into the fields of interdisciplinary innovation research and mixed-methods research.

Based on the matching data in the China Customs, Chinese industrial enterprises, and patent databases, this study uses the propensity score matching method (PSM) and the double difference model (DID) to study the impact of Internet use on enterprise innovation. The results show that Internet use can significantly promote the innovation level of China's manufacturing export enterprises, with obvious heterogeneity among regions. Compared with the central and western regions, the role of the Internet in promoting manufacturing export enterprises’ innovation in the eastern region is more significant. Compared with the non-coastal regions, manufacturing export enterprises in the coastal regions benefit more. The promotion effect of the Internet on manufacturing export enterprises’ innovation has a scale threshold and varies by ownership type: it is more significant on large and medium-sized enterprises, while state-owned enterprises have a higher innovation level than private enterprises. Mechanism analysis shows that Internet applications can, indeed, promote manufacturing export enterprises’ innovation by reducing export costs and management innovation costs.The digital economy is becoming the driving force of development leading the era, and the use of the Internet has an important impact on enterprise digitalization and enterprise innovation. Based on the data of Chinese manufacturing enterprises, this study uses the propensity score matching method (PSM) and the double difference model (DID) to study the impact of Internet use on enterprise innovation. The results show that Internet use can significantly promote the innovation level of China's manufacturing export enterprises, with obvious heterogeneity among regions. Compared with the central and western regions, the role of the Internet in promoting manufacturing export enterprises’ innovation in the eastern region is more significant. Compared with the non-coastal regions, manufacturing export enterprises in the coastal regions benefit more. The promotion effect of the Internet on manufacturing export enterprises’ innovation has a scale threshold and varies by ownership type: it is more significant on large and medium-sized enterprises, while state-owned enterprises have a higher innovation level than private enterprises. Mechanism analysis shows that Internet applications can, indeed, promote manufacturing export enterprises’ innovation by reducing export costs and management innovation costs. This study contributes to the micro analysis from the perspective of dual heterogeneity of enterprises and regions, which provides new evidence for explaining the development of internet economy and enterprise innovation in developing countries.

Globally, the sharing economy is considered a significant factor in achieving energy efficiency and sustainable economic development (SED) which needs regulatory and academic focus. This article investigates the impact of the sharing economy, including sharing economy users and sharing economy values, on efficient energy use and SED in China. The research uses exports, population growth, and foreign direct investment (FDI) as control variables. The article uses secondary data extracted from Statista, the sustainable development solution network, and World Development Indicators (WDI), from 1986 to 2020. The research applies the nonlinear autoregressive distributed lagged (NARDL) model to examine the associations among the variables. The results reveal that sharing economy users, sharing economy values, exports, population growth, and FDI have a positive association with efficient energy use and SED in China. This article guides policymakers establishing policies related to the achievement of SED and energy efficiency using the sharing economy. Governments, in particular, are recommended to actively encourage the growth of the sharing economy and create governance structures that support this growth.

Recently, energy efficiency and the sharing economy have become significant factors for sustainable economic development (SED), which require the attention of researchers and policymakers. Thus, the present research investigates the impact of energy efficiency and the sharing economy on SED in China. Secondary data is used covering the period from 1991 to 2020. The study runs the augmented Dickey-Fuller (ADF) test to check the unit root and the quantile autoregressive distributed lag (QARDL) model to investigate the linkages amongst the variables. The results indicate that efficient energy use, sharing economy users, sharing economy values, population growth, urbanization and industrialization have positive linkages with SED in China. This study guides policymakers developing policies related to the achievement of SED using efficient energy and a valuable sharing economy.

This study uses text mining technology to construct an index of digital transformation and discusses the impact of digital transformation on enterprise innovation and its mechanisms from theoretical and empirical perspectives. It also analyzes whether digital transformation can significantly enhance enterprises’ value through innovation. The findings are presented as follows: first, digital transformation has a positive and significant impact on enterprise innovation, and this finding holds true when we conduct robustness testing and endogeneity processing. Second, the influence of digital transformation on enterprise innovation varies significantly according to enterprise size, ownership, and industry. Third, risk-taking plays an intermediary role between digital transformation and innovation. Fourth, the innovation incentive effect of digital transformation has a value enhancement function with a two-year lag, while it lacks a value enhancement function in the current year, following year, or next three years. In the modern era of innovation-driven and cross-border integration, this study deepens the theoretical understanding of innovation-driven and digital transformation. It also promotes the deeper integration of real and digital economies in practice.