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The purpose of this paper is to use Twitter data to mine personality traits of basketball players to predict their performance in the National Basketball Association (NBA).

Automated personality mining and robotic process automation were used to gather data (player statistics and big five personality traits) of n = 185 professional basketball players. Correlation analysis and multiple linear regressions were computed to predict the performance of their NBA careers based on previous college performance and personality traits.

Automated personality mining of Tweets can be used to gather additional information about basketball players. Extraversion, agreeableness and conscientiousness correlate with basketball performance and can be used, in combination with previous game statistics, to predict future performance.

The study presents a novel approach to use automated personality mining of Twitter data as a predictor for future basketball performance. The contribution advances the understanding of the importance of personality for sports performance and the use of cognitive systems (automated personality mining) and the social media data for predictions. Scouts can use our findings to enhance their recruiting criteria in a multi-million dollar business, such as the NBA.

This paper aims to explore a hotel brand personality performance through electronic word-of-mouth. A complementary attribute is designed and tested in addition to the already existing five dimensions from the brand personality scale, denominated sustainability.

A sample of 16,175 reviews from the rating session of three hotel properties behind a brand was retrieved from TripAdvisor for a data mining procedure. A complementary list of associated words was considered in addition to the 42 personality traits of Aaker’s model, and a brief inventory was developed based on the 17 sustainable development goals (SDGs) to compose the sustainability dimension.

This study registered sincerity as the most representative dimension in its results, and ruggedness as the lowest. This is evidence that the latter is not suitable for representing a brand personality scale for hotels and could be replaced by sustainability.

Despite the relevant findings, new surveys and tests are recommended to provide better support to the new proposed dimension.

This investigation enables hotel managers to work more effectively on their brand strategies based on sustainability-oriented brand personality, which could deliver economic, social and environmental benefits to the world by influencing consumption behavior in association with the SDGs.

This study differs from existing literature by attempting to fill a gap on the limitations of studies focused on linking brand personality to sustainability, and using data mining to reach this goal.

本论文探索通过电子口碑形式的酒店品牌个性效用。本论文设计和检测了一个附加要素 （计价可持续性）, 对现有的五项维度品牌个性量表进行补充

本文样本为TripAdvisor同一品牌的三家酒店的16,175评论, 对其进行数据挖掘。本文扩充了Aaker模型的42项个性特点外的相关词汇, 并且建立了基于17项可持续发展战略目标（SDGs）的词汇库, 以确定可持续性维度

本论文确立了真诚度为结果中最具代表性的维度, 坚固性为最低代表度。显而易见, 坚固性不适合代表酒店品牌个性, 需要被可持续性取代

尽管相关结果, 本文建议采用新问卷和测试来为新提出的维度做更好的理论支持

hx672C;论文使得酒店经理能够更高效地运作, 基于可持续品牌个性的品牌战略, 这将带来结合SDGs的消费行为, 从而对世界带来经济、社会、和环境效益

本论文区别于以往的文献, 连接品牌个性与可持续性, 使用数据挖掘的方法, 来实现研究目的, 对有限的相关文献做出贡献

While extant research has increasingly examined minority entrepreneurs, less attention has been paid to Eastern European immigrant entrepreneurs and the role that marketing agility and risk propensity play in their resilience and survival in Nordic countries. This paper aims to highlight the importance of these factors for Eastern European immigrant entrepreneurs in the developed Nordic economy of Denmark.

This paper adopts the dynamic capabilities view as a theoretical framework and uses a qualitative research approach with interviews as the main data collection method. The empirical sample comprises 12 entrepreneurs originating from Hungary, Slovakia, Latvia, Lithuania and Romania, who operate in Denmark.

The findings show that contrary to prior studies that have highlighted a reliance among the migrant entrepreneurial community on ethnic networks as their dominant target market, Eastern European immigrant entrepreneurs located in Denmark, in contrast, focused on attracting Danish consumers as their target market audience. Leveraging multiple networks was therefore found to be critical to the survival of these immigrant ventures. Additionally, the entrepreneurs' marketing agility, underpinned by their optimistic approach, growth ambitions and passion for entrepreneurship, was found to play a pivotal role in their survival. Finally, despite the stable institutional environment in Denmark and the ease of doing business (both of which are influential factors in shaping the risk propensity and risk perception of entrepreneurs), the authors found immigrant entrepreneurs' risk propensity to be rather low, which was contrary to the expectations.

The current paper is one of the first studies that explicitly analyzes the roles of marketing agility and risk propensity in the resilience and survival of the ventures of relatively skilled immigrant entrepreneurs from Eastern Europe in a developed Nordic economy (Denmark). The paper's findings also challenge the notion associated with immigrant entrepreneurial ventures being primarily focused on ethnic customers or enclaves. The paper also specifies the peculiarities of marketing agility in immigrant entrepreneurial contexts and solidifies the importance of diverse networks in immigrant business survival and development.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

The purpose of this paper is to propose a research approach to investigate brand alignment by exploiting textual data from online brand communities in the coffee industry. Specifically, consumer brand associations from user-generated content (UGC) and company brand associations from firm-generated content (FGC) are explored to measure the alignment between brand identity and brand image. The selected context of research is the beverage industry wherein companies are called on to develop appropriate digital websites and brand communication strategies to enhance the consumers' brand experience.

The authors introduce a research approach that integrates netnography with text mining analysis. Since brand associations were the basis of the study’s analysis, the authors focused on text mining procedures, providing data (co-occurrences) corresponding to brand associations that consumers perceive and that the company communicates. Data were used to develop the measurements of brand alignment.

The main findings of this research highlight the importance for both scholars and practitioners of determining brand alignment of beverage products in online communities. Knowing the alignment between the way a company communicates its brand identity and how this is perceived by consumers allows for effectively reviewing brand communication.

Although the combined analysis of the alignment between brand image and brand identification has received attention in marketing literature, most scholars have neglected how to measure brand alignment. This is a need for many marketing managers in the coffee industry who are now moving in digital environments where the role of consumers is not that of receivers of brand communication but rather that of cocreators of brand value.

Gathering knowledge regarding personality traits has long been the interest of academics and researchers in the fields of psychology and in computer science. Analyzing profile data from personal social media accounts reduces data collection time, as this method does not require users to fill any questionnaires. A pure natural language processing (NLP) approach can give decent results, and its reliability can be improved by combining it with machine learning (as shown by previous studies).

In this, cleaning the dataset and extracting relevant potential features “as assessed by psychological experts” are essential, as Indonesians tend to mix formal words, non-formal words, slang and abbreviations when writing social media posts. For this article, raw data were derived from a predefined dominance, influence, stability and conscientious (DISC) quiz website, returning 316,967 tweets from 1,244 Twitter accounts “filtered to include only personal and Indonesian-language accounts”. Using a combination of NLP techniques and machine learning, the authors aim to develop a better approach and more robust model, especially for the Indonesian language.

The authors find that employing a SMOTETomek re-sampling technique and hyperparameter tuning boosts the model’s performance on formalized datasets by 57% (as measured through the F1-score).

The process of cleaning dataset and extracting relevant potential features assessed by psychological experts from it are essential because Indonesian people tend to mix formal words, non-formal words, slang words and abbreviations when writing tweets. Organic data derived from a predefined DISC quiz website resulting 1244 records of Twitter accounts and 316.967 tweets.