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With the growing climate problem, it has become a consensus to develop low-carbon technologies to reduce emissions. Electric industry is a major carbon-emitting industry, accounting for 35% of global carbon emissions. Universities, as an important patent application sector in China, promote their patent application and transformation to enhance Chinese technological innovation capability. This study aims to analyze low-carbon electricity technology transformation in Chinese universities.

This paper uses IncoPat to collect patent data. The trend of low-carbon electricity technology patent applications in Chinese universities, the status, patent technology distribution, patent transformation status and patent transformation path of valid patent is analyzed.

Low-carbon electricity technology in Chinese universities has been promoted, and the number of patents has shown rapid growth. Invention patents proportion is increasing, and the transformation has become increasingly active. Low-carbon electricity technology in Chinese universities is mainly concentrated in individual cooperative patent classification (CPC) classification numbers, and innovative technologies will be an important development for electric reduction.

This paper innovatively uses valid patents to study the development of low-carbon electricity technology in Chinese universities, and defines low-carbon technology patents by CPC patent classification system. A new attempt focuses on the development status and direction in low-carbon electricity technology in Chinese universities, and highlights the contribution of valid patents to patent value.

This study aims to validate the content of an instrument which identifies the organizational, sociocultural and technological characteristics that foster digital transformation (DT) in higher education institutions (HEIs) through the Delphi method.

The methodology is quantitative, non-experimental, and descriptive in scope. First, expert judges were selected; Second, Aiken's V coefficients were obtained. Nine experts were considered for the validation.

This study’s findings show that the instrument has content validity and there was strong consensus among the judges. The instrument consists of 29 questions; 13 items adjusted and 2 merged.

A novel instrument for measuring the DT at HEIs was designed and has content validity, evidenced by Aiken's V coefficients of 0.91 with a 0.05 significance, and consensus among judges evidenced by consensus coefficient of 0.81.

Going beyond the traditional approach of formal and informal institutions as antecedents of entrepreneurship (directly) and development (indirectly), this paper seeks to explore knowledge institutions as a necessary input for entrepreneurship and the development of societies.

Institutional economics lenses are utilized to observe other factors (e.g. the number of R&D staff and researchers from the public sector) that involve laws and socialization processes, which at the same time create knowledge useful for entrepreneurs and society. These ideas are tested through a sample of 281 observations from 17 autonomous communities and two autonomous cities in Spain. The information coming from the Global Entrepreneurship Monitor (GEM), Ministry of Economics, Industry, and Competitiveness, and INE (Instituto Nacional de Estadística), was analyzed through 3SLS, which is useful for a simultaneous equation strategy.

Knowledge institutions such as the number of R&D staff and researchers from the public sector are found positively associated with entrepreneurship, which is a factor directly and positively linked to economic development across Spanish regions.

The findings help the operationalization of other institutions considered in institutional economics theory and its application to entrepreneurship research. Moreover, the results bring new insights into the knowledge spillover theory of entrepreneurship in the public sector, in which the institutional analysis is implicit.

This study aims to analyze the impact of Artificial Intelligence (AI) and Machine Learning (ML) on heritage conservation and preservation, and to identify relevant future research trends, by applying scientometrics.

A total of 1,646 articles, published between 1985 and 2021, concerning research on the application of ML and AI in cultural heritage were collected from the Scopus database and analyzed using bibliometric methodologies.

The findings of this study have shown that although there is a very important increase in academic literature in relation to AI and ML, publications that specifically deal with these issues in relation to cultural heritage and its conservation and preservation are significantly limited.

This study enriches the academic outline by highlighting the limited literature in this context and therefore the need to advance the study of AI and ML as key elements that support heritage researchers and practitioners in conservation and preservation work.

Family business sustainability is a critical issue. This study considers if adopting a strategic entrepreneurship orientation can support the sustainability of a family business.

A qualitative approach is used, in which semi-structured interviews were conducted with twelve family business owners. Data collected during the interviews provides insights into understanding, practices, motivations, behaviours and attitudes relating to sustainability.

Although awareness of sustainability processes and procedures is found to be low, sustainability is important to the family business. However, sustainability is not managed or implemented systematically.

The paper presents a new model to describe the sustainability practices of family businesses. Adoption of strategic entrepreneurship is advocated as mechanism for improving sustainability. Practical and policy implications are suggested to enhance the effectiveness of sustainability initiatives in family business settings.

The aim of this study was to determine data literacy (DL) training needs of researchers at South African public universities. The outcome of this study would assist librarians and researchers in developing a DL training programme which addressed identified needs.

A survey research method was used to gather data from researchers at these universities by convenience. Online questionnaires were distributed to public universities through library directors for further distribution to researchers.

The results indicate low levels of DL training at the respondent South African public universities with most researchers indicating that they had not received any formal training on DL. A few researchers indicated that they would welcome DL training.

This study was exploratory in nature and data was received from eight universities, which is not representative of all the 26 public universities in South Africa. Nonetheless, the low DL confirmed by the majority in the realised sample is indicative of the need to further investigate the subject.

Librarians and research support personnel should collaborate on the development of DL training courses, workshops and materials used by researchers at institutions of higher learning to enhance DLs on campus.

This study may be novel in South Africa in investigating the DL training needs of researchers at several universities and contributes to the growing body of literature on research data management

This paper is a bibliometric analysis of articles published on the influence of Covid-19 on consumer behaviour.

Biblioshiny and VOSviewer applications are employed for the bibliometric analysis and visualisation, respectively.

The most influential documents, authors, affiliations, countries and journals are presented. Citation, Co-citation and keyword co-occurrence analysis is conducted and presented in the form of a clustered network diagram.

This paper found three main themes of the research in consumer behaviour amid Covid-19 a) Food purchasing decisions and food wastage, b) Adoption of technology and c) Intrinsic and extrinsic influence on consumer behaviour.

The Covid-19 pandemic has shaken the world’s economy and left behind its adverse effect on almost every walk of life. Consumer behaviour is no exception, studies have reported paradigm shifts in the way consumers are reacting to marketing stimuli, making purchase and consumption decisions. For the marketers to sustain profitability, they need to understand the changing behaviour and tailor their offerings accordingly.

The article offers the emerging theme and sub-themes in the consumer behaviour research that leads to future expansion of this research domain.

This paper investigates the moderating-mediation roles of synchronous and asynchronous learning, as well as virtual self-efficacy between digital learning space experience and continuous use among learners in Ghanaian institutions of higher learning.

A convenience sampling technique was used in the selection of 604 students who answered questions on digital learning space experience, synchronous and asynchronous learning, virtual self-efficacy and learner continuous use within the Greater Accra Region of Ghana. The study employed regression analysis to measure the hypothesized paths.

The findings show that asynchronous learning partially mediates between digital learning space experience and learner continuous use, but the mediating effect of synchronous learning between digital learning space experience and learner continuous use was not significant. Further, virtual self-efficacy significantly moderates the mediated relationship between asynchronous learning and learner continuous use, but the moderated mediated role of synchronous learning was not established in the study.

Generalization of the study findings is limited due to the sampling scope, which was restricted to students of IHL in the Greater Accra Region of Ghana.

In this research, the academic scope of digital transformation was expanded from both digital structure elements and psychological perspectives within the domain of higher education literature.

All entrepreneurs interact simultaneously with multiple entrepreneurial contexts throughout their entrepreneurial journey. This conceptual paper has two central aims: (1) it synthesises the current literature on gender and entrepreneurship, and (2) it increases our understanding of how gender norms, contextual embeddedness and (in)equality mechanisms interact within contexts. Illustrative contexts that are discussed include entrepreneurship education, business networks and finance.

This conceptual paper draws upon extant literature to develop its proposed conceptual framework. It provides suggestions for systemic policy interventions as well as pointing to promising paths for future research.

A literature-generated conceptual framework is developed to explain and address the systemic barriers faced by opportunity-driven women as they engage in entrepreneurial contexts. This conceptual framework visualises the interplay between gender norms, contextual embeddedness and inequality mechanisms to explain systemic disparities. An extra dimension is integrated in the framework to account for the power of agency within women and with others, whereby agency, either individually or collectively, may disrupt and subvert the current interplay with inequality mechanisms.

This work advances understanding of the underrepresentation of women entrepreneurs. The paper offers a conceptual framework that provides policymakers with a useful tool to understand how to intervene and increase contextual embeddedness for all entrepreneurs. Additionally, this paper suggests moving beyond “fixing” women entrepreneurs and points towards disrupting systemic disparities to accomplish this contextual embeddedness for all entrepreneurs. By doing so, this research adds to academic knowledge on the construction and reconstruction of gender in the field of entrepreneurship.

Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.

To selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.

The ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.

Since ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.

This research has not been published anywhere else.