Cross-country analysis of alternative five factor personality trait profiles

Kövi, Zsuzsanna; Aluja, Antón; Glicksohn, Joseph; Blanch, Ángel; Morizot, Julien; Wang, Wei; Barry, Oumar; Hansenne, Michel; Carvalho, André F.; Valdivia, Mauricio; Desrichard, Olivier; Hyphantis, Thomas; Rossier, Jérôme; Pascalis, Vilfredo De; León-Mayer, Elizabeth; Пискунов, А. К.; Stivers, Adam W.; Ostendorf, Fritz; Čekrlija, Đorđe; Markiewicz, Dorota; Motevalian, Seyed Abbas; Karagonlar, Gökhan

doi:10.1016/j.paid.2019.01.042

Cited by 8 publications

(9 citation statements)

References 40 publications

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“…There is evidence that there may be differences in participants from different countries in relation to key elements of the SDCM. These include appraisal (e.g., Imada and Ellsworth, 2011 ), morality (e.g., An and Trafimow, 2014 ), social norms (e.g., Shen et al, 2011 ), self-esteem ( Brown and Cai, 2010 ), and personality ( Kövi et al, 2019 ). For these reasons, it could be argued that doping models could be tested among athletes residing in different countries.…”

Section: The Sport Drug Control Model For Adolescent Athletesmentioning

confidence: 99%

The Susceptibles, Chancers, Pragmatists, and Fair Players: An Examination of the Sport Drug Control Model for Adolescent Athletes, Cluster Effects, and Norm Values Among Adolescent Athletes

et al. 2020

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Although there are few high-profile cases of adolescent athletes being caught doping, up to a third of young athletes may dope. In order to generate a more accurate understanding of why adolescent athletes dope, it is important to validate models that help to explain this behavior. The aims of this study were 3-fold: firstly, to test the Sport Drug Control Model for Adolescent Athletes (SDCM-AA); secondly, to generate athlete profiles that would help quantify the proportion of athletes who are at risk of doping; and thirdly, to create norm values for the Adolescent Sport Doping Inventory (ASDI), which would allow national doping organizations, sporting organizations, and clubs to benchmark the scores of their athletes for key psycho-social variables linked to doping. A total of 2208 adolescent athletes from the United Kingdom, Australia, Hong Kong, and the United States completed the ASDI. The data presented an appropriate fit to the SDCM-AA model, in which 54% of the variance in susceptibility to doping was explained in the model, and 44.8% of attitudes toward doping was accounted for. Four distinct clusters of athletes emerged: the Susceptibles (i.e., identified with the benefits of doping, were willing to cheat, and viewed little threat), the Chancers (i.e., identified with the benefits of doping, scored high on willingness to cheat, and were highly influenced by their reference group, but had an average score for threat, self-esteem, and legitimacy), the Pragmatists (i.e., did not engage with any aspects of doping, but were more susceptible than the fair players), and Fair Players (i.e., high levels of sportspersonship, unwilling to cheat, and viewed doping as a threat). The revised SDCM-AA appears a valid model that helps explain the factors associated with doping attitudes and doping susceptibility. Adolescent athletes can be classified into one of four clusters, in relation to doping. Their cluster group could influence the content of the anti-doping education they receive.

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Section: The Sport Drug Control Model For Adolescent Athletesmentioning

confidence: 99%

The Susceptibles, Chancers, Pragmatists, and Fair Players: An Examination of the Sport Drug Control Model for Adolescent Athletes, Cluster Effects, and Norm Values Among Adolescent Athletes

et al. 2020

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“…The second centers on the person, and is based on the configuration of a set of attributes that define each individual, grouping individuals with a similar personality pattern and exploring the relationships of these groups with variables of interest. Although the first approach has dominated psychological research, in recent decades, the importance of the person-centered approach has also been emphasized [42]. The motivation of this last approach arises from the seminal study of Block (1971) [43], and although the typology proposed by Block could not be replicated [44], its formulation served as the basis for the work of Robins et al (1996) [45].…”

Section: Personality Typesmentioning

confidence: 99%

“…The motivation of this last approach arises from the seminal study of Block (1971) [43], and although the typology proposed by Block could not be replicated [44], its formulation served as the basis for the work of Robins et al (1996) [45]. Robins and colleagues identified, for the first time, three personality types that have been subsequently confirmed through diverse replicates [42]. These types were called resilient, over-controlled, and under-controlled.…”

Section: Personality Typesmentioning

confidence: 99%

“…At present, and although there is evidence about the existence of personality types, there is also controversy regarding the number of types and the configuration of the traits of each type [41,46]. For example, the study of Kövi et al (2019) [42], which used a sample of 15,529 participants including 23 subsamples from 22 countries and with 16 different languages, proposes the existence of five types of personalities: over-controlled, resilient, under-controlled, reserved, and ordinary. On the other hand, the study of Gerlach et al (2018) [12], based on the analysis of four large datasets comprising a total of more than 1.5 million participants, identified four types: reserved, self-centered, role model, and average.…”

Section: Personality Typesmentioning

confidence: 99%

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Personality Types as Moderators of the Acceptance of Information Technologies in Organizations: A Multi-Group Analysis in PLS-SEM

et al. 2019

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This study aims to examine the influence of personality types on the acceptance of information technologies at work. Based on the model of the five dominant personality traits and the unified theory of acceptance and use of technology, 155 users of Enterprise Resource Planning systems were examined in two Chilean organizations. A cluster analysis applied to personality traits identified three different types of personalities. Subsequently, a multi-group analysis in Partial Least Squares of the technology acceptance model detected statistically significant differences among these types of personalities. Specifically, although for all personality types, the intention to use technology is explained in more than 60 percent, the strength of the antecedent variables changes radically depending on the type of personality. These findings indicate that personality type plays an essential role as a moderator of technology acceptance at work. This study is one of the first attempts where personality types, instead of specific personality traits, have been associated with technology acceptance models. In it, we performed an analysis of statistically significant differences among the types. Its practical implications are to identify the personality type of employees and adapt the implementation of innovations accordingly. This can help organizations to implement technology successfully, which, in turn, contributes to their sustainability.

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“…A likely possibility is that some personality measures cluster more often together than others, giving rise to personality “profiles” or “types” 59 – 61 . For example, high extraversion and high conscientiousness tend to cluster together but are both separate from high neuroticism 60 , 62 . Therefore, it might be wiser to investigate the neural correlates of personality profiles instead of individual factors.…”

Section: Introductionmentioning

confidence: 99%

Whole-brain white matter correlates of personality profiles predictive of subjective well-being

Kotikalapudi

Dricu

Moser

et al. 2022

Sci Rep

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We investigated the white matter correlates of personality profiles predictive of subjective well-being. Using principal component analysis to first determine the possible personality profiles onto which core personality measures would load, we subsequently searched for whole-brain white matter correlations with these profiles. We found three personality profiles that correlated with the integrity of white matter tracts. The correlates of an “optimistic” personality profile suggest (a) an intricate network for self-referential processing that helps regulate negative affect and maintain a positive outlook on life, (b) a sustained capacity for visually tracking rewards in the environment and (c) a motor readiness to act upon the conviction that desired rewards are imminent. The correlates of a “short-term approach behavior” profile was indicative of minimal loss of integrity in white matter tracts supportive of lifting certain behavioral barriers, possibly allowing individuals to act more outgoing and carefree in approaching people and rewards. Lastly, a “long-term approach behavior” profile’s association with white matter tracts suggests lowered sensitivity to transient updates of stimulus-based associations of rewards and setbacks, thus facilitating the successful long-term pursuit of goals. Together, our findings yield convincing evidence that subjective well-being has its manifestations in the brain.

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Cross-country analysis of alternative five factor personality trait profiles

Cited by 8 publications

References 40 publications

The Susceptibles, Chancers, Pragmatists, and Fair Players: An Examination of the Sport Drug Control Model for Adolescent Athletes, Cluster Effects, and Norm Values Among Adolescent Athletes

The Susceptibles, Chancers, Pragmatists, and Fair Players: An Examination of the Sport Drug Control Model for Adolescent Athletes, Cluster Effects, and Norm Values Among Adolescent Athletes

Personality Types as Moderators of the Acceptance of Information Technologies in Organizations: A Multi-Group Analysis in PLS-SEM

Whole-brain white matter correlates of personality profiles predictive of subjective well-being

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