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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.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

While there exist many surveys on the use stochastic frontier analysis (SFA), many important issues and techniques in SFA were not well elaborated in the previous surveys, namely, regular models, copula modeling, nonparametric estimation by Grenander’s method of sieves, empirical likelihood and causality issues in SFA using regression discontinuity design (RDD) (sharp and fuzzy RDD). The purpose of this paper is to encourage more research in these directions.

A literature survey.

While there are many useful applications of SFA to econometrics, there are also many important open problems.

This is the first survey of SFA in econometrics that emphasizes important issues and techniques such as copulas.

Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.

The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty.

It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice.

This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.