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Online employee reviews have emerged as a crucial information source for business managers to evaluate employee behavior and firm performance. The purpose of this paper is to test the relationship between employee personality traits, derived from online employee reviews and job satisfaction and turnover behavior at the individual level.

The authors apply text-mining techniques to extract personality traits from online employee reviews on Indeed.com based on the Big Five theory. They also apply a machine learning classification algorithm to demonstrate that incorporating personality traits can significantly enhance employee turnover prediction accuracy.

Personality traits such as agreeableness, conscientiousness and openness are positively associated with job satisfaction, while extraversion and neuroticism are negatively related to job satisfaction. Moreover, the impact of personality traits on overall job satisfaction is stronger for former employees than for current employees. Personality traits are significantly linked to employee turnover behavior, with a one-unit increase in the neuroticism score raising the probability of an employee becoming a former employee by 0.6%.

These findings have implications for firm managers looking to gain insights into employee online review behavior and improve firm performance. Online employee review websites are recommended to include the identified personality traits.

This study identifies employee personality traits from automated analysis of employee-generated data and verifies their relationship with employee satisfaction and employee turnover, providing new insights into the development of human resources in the era of big data.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

Given the cruciality of construction workers' safe behaviors, the possible influential factors on workers' behaviors should be studied, and one of these factors is characteristics. The authors identified emotional intelligence (EI), motivation and job burnout as characteristics that might affect a worker's safety behavior, and the aim of this study is to investigate these possible relationships.

Workers' EI, motivation and job burnout status were assessed by a structured interview. Furthermore, workers' safety behaviors were assessed by a checklist derived from national codes, regulations and other research studies. Then, the researcher's observations took place, and the data were acquired.

EI and motivation of workers were able to predict safety behaviors, and the effect of job burnout on safety behaviors was not significant. In addition, motivation's influence on job burnout was not significant. Therefore, in order to promote safety behaviors, the EI and motivation of workers need to be taken into consideration.

The results indicate why construction managers should consider the workers' EI and motivation competencies and how this consideration could lead to safer and better performance in construction projects.

The possible effects of EI, motivation and job burnout on the safety behaviors of construction workers haven't been paid enough attention. Moreover, the authors couldn't find a study similar to the present one that was conducted in Iran. Also, an original model was presented, and safety behaviors were studied through fieldwork rather than using questionnaires.

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.

This study aims to understand how brand personality affects purchase intention through brand love and perceived quality in e-commerce.

Three hundred forty-eight users of e-commerce sites in New Delhi, India, were surveyed for the study. The data set was examined using confirmatory factor analysis, and the research hypotheses were assessed using structural equation modeling.

Two important conclusions emerged from the study. First, brand love and perceived quality have been considerably and favorably influenced by all six dimensions of brand personality of e-commerce brands. Second, the purchase intention toward the e-commerce sites is significantly and positively impacted by brand love and perceived quality.

This study by exploring various dimensions of brand personality, will assist e-commerce executives in increasing purchase intention toward the e-retailing sites.

This research is supposed to be the foremost to look at how brand personality, through brand love and perceived quality affects purchase intention toward e-commerce websites. The attachment theory is used in this study as a theoretical foundation for linking e-commerce brand personality to customers’ purchase intentions via brand love and perceived quality.

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.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

This article presents two Brazilian Portuguese corpora collected from different media concerning public security issues in a specific location. The primary motivation is supporting analyses, so security authorities can make appropriate decisions about their actions.

The corpora were obtained through web scraping from a newspaper's website and tweets from a Brazilian metropolitan region. Natural language processing was applied considering: text cleaning, lemmatization, summarization, part-of-speech and dependencies parsing, named entities recognition, and topic modeling.

Several results were obtained based on the methodology used, highlighting some: an example of a summarization using an automated process; dependency parsing; the most common topics in each corpus; the forty named entities and the most common slogans were extracted, highlighting those linked to public security.

Some critical tasks were identified for the research perspective, related to the applied methodology: the treatment of noise from obtaining news on their source websites, passing through textual elements quite present in social network posts such as abbreviations, emojis/emoticons, and even writing errors; the treatment of subjectivity, to eliminate noise from irony and sarcasm; the search for authentic news of issues within the target domain. All these tasks aim to improve the process to enable interested authorities to perform accurate analyses.

The corpora dedicated to the public security domain enable several analyses, such as mining public opinion on security actions in a given location; understanding criminals' behaviors reported in the news or even on social networks and drawing their attitudes timeline; detecting movements that may cause damage to public property and people welfare through texts from social networks; extracting the history and repercussions of police actions, crossing news with records on social networks; among many other possibilities.

The work on behalf of the corpora reported in this text represents one of the first initiatives to create textual bases in Portuguese, dedicated to Brazil's specific public security domain.

Microblogs are communication platforms for companies and consumers that challenge companies' brand marketing strategies. This paper provides a theoretical basis for expanding microblog applications and a practical basis for improving the effectiveness of brand marketing.

The authors use factor analysis to extract the factors of microblog user influence and construct a structural equation model to reveal the interaction mechanism of the influencing factors. Additionally, the authors clarify the promotion and enhancement effects of these factors.

Microblog user influence can be converted into richness, interaction and value factors. The richness factor significantly affects the latter two, whereas the interaction factor does not affect the value factor.

First, the sample used is limited to media industry practitioners. To increase generalizability, diverse groups should be included in future studies. Second, this model's theoretical explanatory ability can be further developed by adding other meaningful factors beyond the existing ones.

This study analyzes the factors of microblog user influence in China and validates the relevant elements. As a result, it improves the influence research on social media users and benefits the practice of information recommendation and microblog marketing.