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While the branding of individuals has attracted increasing attention from practitioners in recent decades, understanding of personal branding still remains limited, especially with regard to the branding of celebrity CEOs. To contribute to this debate, this paper aims to explore the co-branding of celebrity CEOs and corporate brands, integrating endorsement theory and the concept of meaning transfer at a level of brand attributes.

A between-subjects true experimental design was chosen for each of the two empirical studies with a total of 268 participants, using mock newspaper articles about a succession scenario at the CEO level of different companies. The study is designed to analyse the meaning transfer from celebrity CEO to corporate brand and vice versa using 16 personality attributes.

This study gives empirical support for meaning transfer effects at the brand attribute level in both the celebrity-CEO-to-corporate-brand and corporate-brand-to-celebrity-CEO direction, which confirms the applicability of the concept of brand endorsement to celebrity CEOs and the mutuality in co-branding models. Furthermore, a more detailed and expansive perspective on the definition of endorsement is provided as well as managerial guidance for building celebrity CEOs and corporate brands in consideration of meaning transfer effects.

This study is one of only few analysing the phenomenon of meaning transfer between brands that focus on non-evaluative associations (i.e. personality attributes). It is unique in its scope, insofar as the partnering relationship between celebrity CEOs and corporate brands have not been analysed empirically from this perspective yet. It bridges the gap between application in practice and the academic foundations, and it contributes to a broader understanding and definition of celebrity endorsement.

The aim of this paper, which analyses the use of sports celebrities in advertising discourse, is to understand the strategic use to which brands put them in their commercial and corporate communication on Instagram.

To this end, a content analysis was performed on the Instagram posts of the brands Santander, Movistar, Red Bull and Iberdrola during the period 2021-2022.

The results indicate that, strategically speaking, these brands use the celebrity endorsement strategy to pursue emotional objectives and to adopt a position depending on the type of user. Likewise, these findings show that they single out uniqueness as the principal celebrity characteristic, while also mainly leveraging sports values, especially competence. These values represented by sports celebrities are markedly social in nature, which implies that they enjoy a degree of public recognition that is transferred to the brand to which they lend their image.

The conclusions connect celebrity endorsers with strategic branding issues and aspects of sports.

An empirical approach is followed here to study the representation of sports celebrities in the advertising of well-known brands linked to the sports world.

Background: Educational institutions are strategic tools in disseminating knowledge on Sustainable Development Goals (SDGs) since education is an effective developmental tool. All the 17 SDGs are tied in one way or the other to education, that is, the ability of people to learn and apply. This study applies unorthodox theories which include convergence models, neo-functionalism, intergovernmentalism, neorealism and the Hofstede model to explain how educational institutions are an essential enabling environment which accelerates the attainment of SDGs.

Methods: These factors are analysed in the context of the new coronavirus (COVID-19) pandemic. Empirically, some university case studies were highlighted in addition to unclear modus operandi, small, fragmented and heterogeneous markets and economies, political stability, deficient political will, and lack of standardisation of products and procedures among other factors. These dynamics affect both the quality of educational institutions and the quality of education thereby directly or indirectly affecting the attainment of the 17 SDGs and are compounded with the emergence of the coronavirus pandemic.

Results: The study reveals that acceleration of the 17 SDGs will require a holistic approach as opposed to silos (scientific, economic, political, academic) which usually emerge when pursuing overarching goals of this magnitude.

Conclusions: It concludes that accelerating progress towards the attainment of SDGs will not only require dynamic and visionary leadership but also well-functioning institutions which are based on economic feasibility as opposed to political alliances. Priorities should be placed on addressing poverty, inequality and quality education. Moreover, partnerships will be key in achieving sustainability especially given that the COVID-19 pandemic has compounded existing challenges.

This paper examines the relationship between transport connectivity and regional economic development in China. It develops measurements appropriate for transport connectivity based on a set of evaluation models. This model is used to analyze the logistic connectivity of China’s 31 provinces by focusing on 11 variables, including some new factors (Density of road network, Density of railway network, Number of Internet Users) not used in previous studies, over the 13-year period from 2002 to 2014. Using panel data regression analysis, the empirical results show a statistically significant and positive impact of transport connectivity (factors like Density of road network, Density of railway network and Number of Internet Users) on economic development in China. In particular, the Number of internet users is a key factor reflecting information connectivity in all the variables. Comparative analysis regarding economic development is conducted to benchmark between coastal provinces and interior provinces. Like most previous research, this study yields the same finding of higher impact of transport connectivity on economic development in eastern provinces than in western provinces. This study suggests that decentralized decision-making will be significantly more efficient for analyzing regional infrastructure development. It also shows that the influence of transport connectivity on economic development is dependent on a certain developmental stage. This suggests that an economic region should adopt different development strategies for transport connectivity during different stages of development.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

The purpose of this paper is to use tenets of the complexity theory in order to study the effect of various determinants of firm’s performance, such as CEO’s compensation and age, for the case of 72 insurance companies.

The authors identify the asymmetries in the data set by creating quantiles and using contrarian analysis. Instead of ignoring this information and use a main effects approach, all the available information in the data set is taken into account. For this purpose, the authors use qualitative comparative analysis to find alternative equifinal routes toward high firm performance.

Five configurations are found which lead to high performance. Every one of the five configurations is found to be sufficient but not necessary for high firm performance.

The research findings contribute to a better understanding of the determinants of firm’s performance taking into account the asymmetries in the data set. The authors identify alternative paths toward high firm performance, which could be vital information for the decision maker inside a firm.