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This study aims to examine brand personality and its application to political branding. This study focuses on the brand personality of a political leader from the BJP Party brand (Bharatiya Janta Party). The development of a strong political brand personality is crucial for success at the polls. Little research has been dedicated to this phenomenon particularly beyond Western political and post-election contexts.

The scope and development of the study required a qualitative approach. The theoretical frameworks of the study acted as the deductive base of the study. The insights of the respondents were the inductive base of the study. Semi-structured interviews were conducted with external stakeholders [voters]. In addition, semi-structured interviews were also adopted to capture the branding activities used by internal stakeholders [BJP].

The brand personality dimensions such as sincerity; agreeableness, competence, energy, openness, conscientiousness and emotional stability were clearly associated with a political leader. Negative qualities such as dictatorial attitudes and arrogance affected the political leader’s brand personality. Religious partisanship was another strong negative trait affecting the brand personality of the political leader.

The study has an actionable framework for political brand personality in the post-election context. It offers negative qualities to be avoided in the development of the political brand personality of the leader. It offers insights about the political brand personality of the leader in terms of young digitally savvy voters.

Este trabajo examina la aplicación de la personalidad de marca al ámbito del marketing político y de la marca personal política. Concretamente se centra en la personalidad de marca de un líder político del partido Bharantiya Janta Party (BJP). El desarrollo de una fuerte marca personal política es crucial para el éxito en las elecciones. Pocos trabajos se han centrado hasta el momento en este fenómeno más allá del contexto político occidental.

El alcance y desarrollo del estudio requirió la adopción de un enfoque cualitativo. El marco teórico sirvió de base deductiva al tiempo que las entrevistas realizadas sirvieron de base inductiva. Estas entrevistas fueron semi-estructuradas y dirigidas a grupos de interés externos del BJP (los votantes). Además, se realizaron entrevistas también semi-estructuradas para capturar las actividades de marca desarrolladas por los grupos de interés internos (candidatos, políticos, trabajadores y gerentes del partido).

Las dimensiones de personalidad de marca sinceridad, competencia, energía, estabilidad emocional, franqueza y escrupulosidad están claramente asociadas con un líder político. Por el contrario, rasgos negativos como las actitudes arrogantes y dictatoriales dañan la personalidad de marca de dicho líder, pero sobretodo el partidismo religioso.

El trabajo proporciona un marco de acción para la marca personal política en un contexto post-electoral. Proporciona indicaciones de los rasgos y cualidades negativas que deben de evitarse en el desarrollo de una marca personal para un líder político. Ofrece también evidencias sobre la personalidad de marca que tiene que desarrollar un líder de cara a los votantes más dinámicos y digitales.

The purpose of this paper is the better understanding of the increasing relation between big data 2.0 and neuromarketing, particularly to influence election outcomes, along with a special aim to discuss some raised doubts about Trump’s presidential campaign 2016 and its ability to hijack American political consumers’ minds, and to direct their votes.

This paper combines deductive/inductive methodology to define the term of political neuromarketing 2.0 through a brief literature review of related concepts of big data 2.0, virtual identity and neuromarketing. It then applies a single qualitative case study by presenting the history and causes of online voter microtargeting in the USA, and analyzing the political neuromarketing 2.0 mechanisms adopted by Trump’s political campaign team in the 2016 presidential election.

Based on Trump’s political marketing mechanisms analysis, the paper believes that big data 2.0 and neuromarketing techniques played an unusual role in reading political consumers’ minds and helping the controversial candidate to meet one of the most unexpected victories in the presidential elections. Nevertheless, this paper argues that the ethics of using political neuromarketing 2.0 to sell candidates and its negative impacts on the quality of democracy are and will continue to be a subject of ongoing debates.

The marriage of big data 2.0 and political neuromarketing is a new interdisciplinary field of inquiry. This paper provides a useful introduction and further explanations for why and how Trump’s campaign defied initial loss predictions and attained victory during this election.

The study utilises unique survey data gathered from 12,427 party members. The dependent variable measures party members’ in-party commitment and is based on willingness to donate money, to contribute effort, the feeling of belonging in the party network and social trust in the party network.

In this article, we study how different extra-parliamentary online and offline activities are associated with in-party commitment amongst political party members from the six largest Finnish parties. We especially delve into the differences between members of the Finnish parties.

We found that extra-parliamentary political activity, including connective action through social media networks and collective action through civic organisations, is highly associated with members’ in-party commitment. Additionally, members of the newer identity parties more effectively utilised social media networks, whilst the traditional interest parties were still more linked to traditional forms of extra-parliamentary political action.

By employing the sociological network theory perspective, the study contributes to ongoing discussions surrounding the impact of social media on political participation amongst party members, both within and beyond the confines of political parties.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

The pandemic has enhanced the global phenomenon of disinformation. This paper aims to study the false news concerning COVID-19, spread through social media in Spain, by using the LatamChequea database for a duration from 01/22/2020, when the first false information has been detected, up to 03/09/2021.

A quantitative analysis has been conducted with regard to the correlation between fake news stories and the pandemic state, the motive to share them, their dissemination in other countries and the effectiveness of fact checking. This study is complemented by a qualitative method: a focus group conducted with representatives of different groups within the society.

Fake news has been primarily disseminated through several social networks at the same time, with two peaks taking place in over a half of the said false stories. The first took place from March to April of 2020 during complete lockdown, and we were informed of prevention measures, the country’s situation and the origin of the virus, whereas the second was related to news revolving around the coming vaccines, which occurred between October and November. The audience tends to neither cross-check the information received nor report fake news to competent authorities, and fact-checking methods fail to stop their spread. Further awareness and digital literacy campaigns are thus required in addition to more involvement from governments and technological platforms.

The main limitation of the research is the fact that it was only possible to conduct a focus group of five individuals who do not belong to generation Z due to the restrictions imposed by the pandemic, although a clear contribution to the analysis of the impact of fake news on social networks during the COVID-19 pandemic in Spain can be seen from the privileged experiences in each of the fields of work that were identified. In this sense, the results of the study are not generalizable to a larger population. On the other hand, and with a view to future research, it would be advisable to carry out a more specific study of how fake news affects generation Z.

This research is original in nature, and the findings of this study are valuable for business practitioners and scholars, brand marketers, social media platform owners, opinion leaders and policymakers.

The purpose of this paper is to inform the reader of some emerging trends in placemaking and digital destination management, while providing a conceptual background on shifts in architectural design.

The trend paper is based on a fundamental bibliographic view on evolutions in placemaking, from architectural design to spatial agency, integrated by and contextualized in tourism trends, however possibly anecdotal.

The trend paper identifies a fundamental shift from architectural processes to spatial agency as organizing principle for placemaking, discussing how digital tourism trends are formed or forming change in this.

The trend paper newly relates otherwise distant and unrelated fields, namely architectural design theory and tourism trends, by connecting at the level of IoT and IT digital technologies, exploring the impact and the mutual role played by its two constituencies.

Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media.

For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance.

The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts.

Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.