Qualidade de revistas científicas: uma revisão sistemática da literatura

The evaluation of scientific activity is a fact that has grown and been well evidenced in the academic community in recent decades. Scientific journals, the main sources of information at the present time and responsible for the diffusion of knowledge through scientific publications, are the means of evaluating the scientific activity most viewed. During the digital age and the need for publication for the validation of studies, the increase in scientific production is imminent, which consequently entails the appearance of new journal titles. The emergence of these new titles spreads in the need of the evaluation of scientific journals. It is believed that it is necessary to propose parameters and quality indicators to assist in this evaluation. As is known, quality indicators already exist that aid in the evaluation of scientific journals, of which the impact factor and the Qualis Capes are the most used and emphasized in the literature. But to what extent can we consider the impact factor and the Qualis Capes as the main quality indicators of scientific journals and use them in large majority to classify the quality of a scientific journal. Based on the points, the research in question intends to carry out an analysis of the literature, more specifically a systematic review of the literature in order to answer the following question: what characterizes a scientific journal of quality according to the scientific literature? In advance, it can be concluded that the literature is still very restricted to the quality of scientific journals, using as main indicator the impact factor, but other indicators not less important are also gaining space. It is also perceived as an incipient subject, despite the great concern of authors of articles in publishing their manuscripts in renowned journals.La evaluación de la actividad científica es un hecho que ha crecido y ha sido bien evidenciado en la comunidad académica en las últimas décadas. Las revistas científicas, las principales fuentes de información en la actualidad y responsables de la difusión del conocimiento a través de publicaciones científicas, son los medios para evaluar la actividad científicas más vistas. En medio de la era digital y la necesidad de publicación para la validación de estudios, el aumento de la producción científica es inminente, lo que conlleva la aparición de nuevos títulos de revistas. La aparición de estos nuevos títulos se propaga en la necesidad de la evaluación de revistas científicas. Se cree que es necesario proponer parámetros e indicadores de calidad para ayudar en esta evaluación. Como se sabe, ya existen indicadores de calidad que ayudan en la evaluación de revistas científicas, de las cuales el factor de impacto y los Qualis Capes son los más utilizadas y destacados en la literatura. Pero ¿hasta qué punto podemos considerar el factor de impacto y los Qualis Capes como los principales indicadores de calidad de las revistas científicas y utilizarlos en gran mayoría para clasificar la calidad de una revista científica? Sobre la base de los puntos antes mencionados, la investigación en cuestión pretende realizar un análisis de la literatura, más específicamente una revisión sistemática de la literatura para responder la siguiente pregunta: ¿qué caracteriza a una revista científica de calidad según la literatura científica? De antemano, se puede concluir que la literatura todavía está muy restringida a la calidad de las revistas científicas, utilizando como indicador principal el factor de impacto, pero otros indicadores no menos importantes también están ganando espacio. También se percibe como un tema incipiente, a pesar de la gran preocupación de los autores por los artículos que publican sus manuscritos en revistas de renombre.A avaliação da atividade científica é um fato que tem crescido e sido bastante evidenciado na comunidade acadêmica nas últimas décadas. As revistas científicas, principais fontes de informação na atualidade e responsáveis pela difusão do conhecimento por meio de publicações científicas, são os meios de avaliação da atividade científica mais visionados. Em meio a era digital e a necessidade da publicação para a validação dos estudos, é eminente o aumento da produção científica, que consequentemente acarreta o surgimento de novos títulos de revistas. O surgimento destes novos títulos esparra na necessidade da avaliação das revistas científicas. Acredita-se que seja necessário propor parâmetros e indicadores de qualidade que auxiliem nesta avaliação. Como sabe-se, já existem indicadores de qualidade que auxiliam na avaliação das revistas científicas, dos quais o fator de impacto e o Qualis Capes são os mais utilizados e enfatizados na literatura. Mas até que ponto podemos considerar o fator de impacto e o Qualis Capes como os principais indicadores de qualidade das revistas científicas e utilizá-los em grande maioria para classificar a qualidade de uma revista científica? Partindo dos pontos supracitados, a pesquisa em questão pretende realizar uma análise da literatura, mais especificamente uma revisão sistemática da literatura a fim de responder a seguinte questão: o que caracteriza uma revista científica de qualidade de acordo com a literatura científica? De antemão, pode-se concluir que a literatura ainda está muito restrita à qualidade de revistas científicas, utilizando como principal indicador o fator de impacto, mas outros indicadores não menos importantes também estão ganhando espaço. Percebe-se ainda que é um tema incipiente, apesar da grande preocupação dos autores de artigos em publicarem seus manuscritos em revistas de renome

Toward the consolidation of a multi-metric-based journal ranking and categorization system for computer science subject areas

The evaluation of scientific journals poses challenges owing to the existence of various impact measures. This is because journal ranking is a multidimensional construct that may not be assessed effectively using a single metric such as an impact factor. A few studies have proposed an ensemble of metrics to prevent the bias induced by an individual metric. In this study, a multi-metric journal ranking method based on the standardized average index (SA index) was adopted to develop an extended standardized average index (ESA index). The ESA index utilizes six metrics: the CiteScore, Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), Hirsh index (H-index), Eigenfactor Score, and Journal Impact Factor from three well-known databases (Scopus, SCImago Journal & Country Rank, and Web of Science). Experiments were conducted in two computer science subject areas: (1) artificial intelligence and (2) computer vision and pattern recognition. Comparing the results of the multi-metric-based journal ranking system with the SA index, it was demonstrated that the multi-metric ESA index exhibited high correlation with all other indicators and significantly outperformed the SA index. To further evaluate the performance of the model and determine the aggregate impact of bibliometric indices with the ESA index, we employed unsupervised machine learning techniques such as clustering coupled with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). These techniques were utilized to measure the clustering impact of various bibliometric indicators on both the complete set of bibliometric features and the reduced set of features. Furthermore, the results of the ESA index were compared with those of other ranking systems, including the internationally recognized Scopus, SJR, and HEC Journal Recognition System (HJRS) used in Pakistan. These comparisons demonstrated that the multi-metric-based ESA index can serve as a valuable reference for publishers, journal editors, researchers, policymakers, librarians, and practitioners in journal selection, decision making, and professional assessment

The Effects of Knowledge Spillovers, Incubators and Accelerator Programmes on the Product Innovation of High-Tech Start-Ups: A Mixed Methods Approach

The Knowledge Spillover Theory of Entrepreneurship (KTSE) focuses on exploring how entrepreneurs use uncommercialised knowledge spillovers into funding a new venture. This phenomenon explores the role of geographical proximity on the exploration of entrepreneurial opportunities that result in the creation of start-ups that promote the evaluation of the economic growth in regions. However, the definition of knowledge spillovers and the mechanisms measurements to evaluate high-tech entrepreneurs during the first years of operation continues to be an elusive research area in the field of entrepreneurship and innovation. This doctoral thesis seeks to shed light on the effects of knowledge spillovers, incubators, and accelerators on high-tech start-ups performance and survival. Knowledge Spillovers research focuses on the effects of economics and the characteristics of countries on start-ups. However, there is a clear gap in stating a definition of knowledge spillovers and taxonomy with other disciplines. Research so far assumes that entrepreneurs automatically absorb knowledge spillovers. This work takes a different approach by identifying the processes, mechanisms and companies that facilitate using knowledge spillovers towards innovation. The doctoral research focused on obtaining primary data from entrepreneurs at the individual level. The study conducted a sequential mixed method exploratory design to empirically develop a model that identifies the types of knowledge spillovers used by companies at the seed and growth stages. A qualitative phase conducted a multiple-case study approach involving 32 semi-structured interviews with chief executive officers and co-founders of high-tech start-ups that attended incubator and accelerator programmes in Greater London, United Kingdom. The resultant conceptual model identified the start-up's strategic decisions to form alliances and partnerships through accelerator programmes, incubators and networking events. The results also suggest that entrepreneurs are likely to allocate Research and Development (R&D) budgets to hire human capital and invest in training to implement information technologies that allow them to overcome geographical proximity and engage in product innovation. The qualitative phase's objective was to identify the mechanisms, processes, definitions of knowledge spillovers, and to guide factor analysis to generalise the findings. The qualitative findings guided the development of incoming and network knowledge spillovers formative constructs that evaluate alliances with organisations and information sources. The results led to quantitative models' development to evaluate the start-up's absorptive capacity and product innovation. The quantitative phase conducted a validation and generalisation of the qualitative model using factor analysis from a sample of 556 founders of high and medium-tech start-ups operating in the United Kingdom. The findings highlighted that tacit and explicit knowledge spillovers positively affect the company's creation during the process of potential absorptive capacity. The results suggested that the entrepreneur valuation of the business idea based on their experience, or by conducting market research through interviews, surveys, and asking experts in the field. The entrepreneurial journey is supported by incubators or accelerator programmes through networking events and the provision of headquarters. The activities undertaken in these programmes provide access to investment from venture capitalists, and headquarters for start-ups to run their operations. This process leads to the development of alliances and partnerships that enable access to knowledge spillovers. Entrepreneurs wound to take the managerial decisions to hire highly skilled human capital and incorporate technological tools and conduct R&D. Furthermore, the model three variant of KST-QNCM proves that the founder's start-ups type of industry's background and academic qualifications influence start-ups operations and objectives. The research's main contribution to knowledge is the developed Knowledge Spillovers model of High-Tech Start-ups (KMS-HTS). The model states propositions and the statistical effects from constructs and variables during the phases of identifying the business idea and creation of the company, establishment and development, and scaling up and the company's future. The model provides a clear description of entrepreneurs' processes and mechanisms to implement knowledge spillovers towards innovation. The model also provides a taxonomy and sources of knowledge under the classification of network and incoming knowledge spillovers that can be implemented in disciplines not linked to economic and econometric models. The thesis provides strong empirical evidence on different approaches taken by entrepreneurs based on the type of industry. The model revealed that high-technology start-ups follow a unidirectional process of absorption and implementation of knowledge spillovers to develop new products through exploratory innovation. Thus, high-tech start-ups become potential sources of knowledge spillovers for entrepreneurs and companies through R&D that generate research outputs, patents, and academic publications. On the other hand, Medium-high technology and knowledge-intensive companies aim to engage in a product development cycle focused on developing a product prototype from existing technology to participate in local and international markets. Under this category, companies can engage in exploratory or exploitative innovation by using information technologies to acquire additional knowledge spillovers