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The purpose of this study is to identify papers that have produced the most significant impact on research on strategic alliances in the biotechnology industry. The authors attempt to illustrate the thematic evolution of its intellectual structure through 616 papers published between 1992 and 2021.

The present research methodology relies on three distinct techniques, implemented using SciMat software: (1) bibliometric techniques, (2) scientific map analysis and (3) content analysis of research documents from the Web of Science (WoS). In this manner, the authors analyse the intellectual structure of the field of strategic alliances in the biotechnology industry, tracking its evolution over a period of three decades.

The study emphasises the relevance of “innovation” as a key theme and identifies several potential areas for future research, which could serve as a foundation for further investigations.

This study represents a novel contribution to the literature as it is the first to use the SciMat tool to analyse strategic alliances in the biotechnology industry. This research reveals that while strategic alliances have been assessed extensively across various industries, some topics, such as the types and formation of alliances, have not been specifically studied in the biotechnology industry. These areas as well as the barriers and variables influencing the formation of alliances offer promising avenues for future research in this field.

本研究旨在確定對關於生物科技產業內的策略聯盟的探究產生極其顯著影響的學術論文。我們擬透過探討於1992年至2021年期間發表的616篇學術論文，去闡明策略聯盟的知識結構的主題演變。

研究依賴三個不同的技術來進行，並以SciMat 可視化軟件來做具體實施的工作。這三個技術為、(一) 文獻計量技術;(二) 科學製圖分析;和 (三) 就取自 Web of Science 的學術文章而進行的內容分析。我們採用這研究法，對在生物科技產業內的策略聯盟的知識結構進行分析，俾能對有關的知識結構的主題演變進行一個涵蓋三十載的跟蹤調查。

研究強調了創新，並視之為主要的主題的重要相關概念;研究亦確定了一些今後可供研究的潛在領域，這或許會成為進一步研究的基礎。

由於本研究是首個研究、使用SciMat這工具去分析在生物科技產業內的策略聯盟，故就有關的文獻而言，它給予新穎的貢獻。研究結果顯示，雖然策略聯盟已在各個不同的產業裡被廣泛評價，但在生物科技產業裡，一些如聯盟的種類和形成方式等的課題仍未得到適切的研究。這些課題，以及影響著聯盟的形成的障礙和變數，會為這領域內今後的學術研究、提供光明的途徑。

As the comparability concept has recently garnered increased attention of policymakers and standard setters in the sustainability reporting (SR) arena, this paper aims to provide a reflexive viewpoint of this concept in this context.

To inform the authors’ viewpoint and disentangle the concept of comparability into different facets, the authors review policymakers’ and standard setters’ (including the Global reporting initiative) comparability principles, as well as relevant studies in the field. To provide insights into the different ways in which the comparability facets can be approached, the authors use multi-perspective reflexive practices and focus on the multiple purposes that reporting can serve. To empirically animate the authors’ reflection on the facets, the authors analyse the sustainability disclosures of two Italian banks over three years.

This study reveals that three facets form valuable starting points for extending the understanding of the meanings the comparability concept can carry in the SR arena. These facets are materiality and comparability, benchmarking/monitoring and comparability and operationalisation and comparability.

This study is intended to elicit policymakers’ and standard setters’ thoughts on the role of comparability and its complexities in SR.

By taking a critical and reflexive approach, the authors encourage policymakers and standard setters to reconsider the comparability principle, so it effectively embeds the accountability purpose of SR.

In this paper, the authors propose three facets for disentangling the concept of comparability.

Higher education performance is boosted through cross-border cooperation and increased transnational mobility of students. In addition, exchange students have better employability skills after staying abroad compared to the students' peers. A number of studies have investigated factors that determine whether a student studies abroad. In this study, the authors focused on the role of personality trait openness to experience and cultural intelligence (CI) in explaining Croatian students' experience with and/or intention to travel abroad for studying purposes.

The authors analyzed results from 482 students (M = 22.61, standard deviation (SD) = 2.24, 66% female), of whom 35% reported that they studied abroad or intended to study abroad. They filled in The Cultural Intelligence Scale and openness facets items from the International Personality Item Pool (IPIP-300) questionnaire.

The authors conducted a hierarchical binary logistic regression analysis and found that students who were younger, had higher adventurousness and higher motivational CI were more likely to study abroad. Results of the mediation analysis showed that the association between openness to experience facet adventurousness and intention to study abroad was partially mediated by the motivational aspect of CI.

This study contributes to the better understanding of complex interrelations between personality traits and CI in the context of higher education internationalization processes. This study offers unique insight into the mediating role CI has in the association between personality and mobility behavior.

This paper purposed a multi-facet sentiment analysis system.

Hence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.

The proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.

The construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.