Relationships between the “Big Five” personality types and consumer attitudes in Indian students toward augmented reality advertising

The Personality and Resilience of Competitive Athletes as BMW Drivers-Data from India, Latvia, Lithuania, Poland, Romania, Slovakia, and Spain

BACKGROUND

Individual differences in personality and resilience are related to a variety of social behaviors. The current study sought to answer the question of whether BMW drivers exhibit different personality profiles and resilience levels compared with drivers of other car brands.

PARTICIPANTS AND PROCEDURE

An international study was carried out in India, Latvia, Lithuania, Poland, Romania, Slovakia, and Spain on a sample of 448 athletes using the 20-item Mini-IPIP and the Resilience Scale. The results of BMW drivers (n = 91) were compared with the results of drivers of other German car brands (n = 357).

RESULTS

BMW drivers were characterized by higher neuroticism compared with drivers of other German car brands. They also showed higher resiliency, both in terms of total score and scores on the subscales of: personal coping competences and tolerance of negative emotions, tolerance of failures and perceiving life as a challenge, and optimistic attitude towards life and capacity for self-mobilization in difficult situations. The greatest difference was observed for the factor of tolerance of failures and perceiving life as a challenge. Using the Dwass-Steel-Critchlow-Fligner (DSCF) pairwise comparison test, gender differences between athletes (as BMW drivers and drivers of other German car brands, respectively) were discussed. Additionally, the results of the main logistic regression analyses emphasized that neuroticism represents a better predictor of BMW preference in the case of athletes (as drivers) than the scores obtained for resilience.

CONCLUSIONS

BMW drivers differed from drivers of other German car brands only with regard to neuroticism. A higher level of neuroticism can affect mental health and the overall quality of life in athletes; aggression and distress management are essential. Athletes (as BMW drivers) also showed differences in resiliency levels. Understanding the mechanisms of behavior among BMW drivers is possible through considering their personality and individual differences.

The relation between players' motivation, virtual communities and stickiness in augmented reality games

Purpose

The purpose of this research was to explore the stickiness of players' motivation in a virtual community and to explore the important factors for gamers.

Design/methodology/approach

In this research, motivation was the independent variable; the virtual community was the mediator; and stickiness was the dependent variable. An online questionnaire survey was conducted, with users of augmented reality (AR) as the research objects. Statistical analysis was carried out using SPSS and AMOS software to verify the research model and research hypotheses, to understand the relation between player motivation and stickiness and to determine whether there were any changes in the virtual community.

Findings

The authors found that the relation between players' motivation in AR-based games and the virtual community had a significant positive impact. Ingress had a significant positive impact on the virtual community and stickiness, and Pokémon had a significant positive impact too. The virtual community of the Ingress game played a completely mediating role in motivation and stickiness, but the virtual community in Pokémon did not have a mediating effect.

Originality/value

The novel approach adopted in this study enabled us to determine the causal relation between player motivation, the virtual community and stickiness, on the basis of the theoretical framework formulated, and the latter was used to construct a path analysis model diagram. The correlation between motivation and the virtual community, between the virtual community and stickiness, and the causal relation between all three was verified. The study results and conclusions may help companies understand how to use virtual communities in AR games to improve stickiness and motivate gamers to continue playing.

The role of five big personality traits and entrepreneurial mindset on entrepreneurial intentions among university students in Saudi Arabia

The big five personality traits and entrepreneurial mindset (EM) are crucial individual-level elements that determine entrepreneurial intention (EI). This study examines the impact of big five personality traits and EM, on EI using the theory of planned behavior. Besides, this study examined the role of entrepreneurial self-efficacy (ESE) and attitude toward entrepreneurship (ATE) influences EI. To achieve the research objectives, a quantitative approach was used. Structural equation modeling (SEM) and path analysis were conducted using SmartPLS software. Data were collected from 270 respondents through online questionnaires. Findings of the study revealed that big five personality traits influence ESE and ATE which led to EI. Finally, the moderating role of entrepreneurial passion was also found to have strong effect on influence ESE and ATE. This study offers evidence and insights that academics, educators, and others involved in the creation or expansion of entrepreneurial knowledge can use as a reference point.

Augmented Reality Marketing: A Systematic Literature Review and an Agenda for Future Inquiry

Augmented reality (AR) is a potentially disruptive technology that enriches the consumer experience and transforms marketing. With the surging popularity of AR in marketing practice, academic efforts to investigate its effects on consumer experience, response, and behavior have increased significantly. To obtain an integrated and comprehensive view of the front-line in AR marketing research and identify the gaps for future research, we analyze the existing AR marketing literature through a systematic literature review. Using 99 journal articles selected from the Web of Science core collections, this research sheds light on the general characteristics such as publication year, publication outlet, research design, and research method. Moreover, this research also gains insight into the AR marketing relevant factors such as application area, application context, AR type, and theoretical lenses. The findings of the analyses reveal the state-of-the-art of scholarly publications on AR marketing research. First, the number of journal articles on AR marketing increased rapidly in the past few years, and the journals that published articles on AR marketing cover a wide range of disciplines. Second, the empirical studies in most literature adopted the quantitative research design and used survey or experiment methods. Third, the studies in more than half of the journal articles used mobile AR applications in various online contexts. Fourth, the Technology Acceptance Model (TAM) and the Stimulus-Organism-Response (S-O-R) framework are the two most widely used theoretical lenses used in the literature. After that, the major application areas of AR in marketing are retail, tourism, and advertising. To identify the focal themes discussed in the three application areas, this research summarizes the studies by the outcome variables. Specifically, the outcome variables have five categories: technology-related, product-related, brand-related, tourist destination-related, and advertisement-related. Finally, this research proposes the agenda for future academic efforts in AR marketing.

Predicting ratings of social media feeds: combining latent-factors and emotional aspects for improving performance of different classifiers

Purpose

The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.

Design/methodology/approach

In this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.

Findings

Overall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.

Originality/value

This study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

Recognizing Personality Traits Using Consumer Behavior Patterns in a Virtual Retail Store

Virtual reality (VR) is a useful tool to study consumer behavior while they are immersed in a realistic scenario. Among several other factors, personality traits have been shown to have a substantial influence on purchasing behavior. The primary objective of this study was to classify consumers based on the Big Five personality domains using their behavior while performing different tasks in a virtual shop. The personality recognition was ascertained using behavioral measures received from VR hardware, including eye-tracking, navigation, posture and interaction. Responses from 60 participants were collected while performing free and directed search tasks in a virtual hypermarket. A set of behavioral features was processed, and the personality domains were recognized using a statistical supervised machine learning classifier algorithm via a support vector machine. The results suggest that the open-mindedness personality type can be classified using eye gaze patterns, while extraversion is related to posture and interactions. However, a combination of signals must be exhibited to detect conscientiousness and negative emotionality. The combination of all measures and tasks provides better classification accuracy for all personality domains. The study indicates that a consumer's personality can be recognized using the behavioral sensors included in commercial VR devices during a purchase in a virtual retail store.