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The purpose of this paper is to investigate the effect of both work-related and emic contextualization of personality measurement in the prediction of work-related outcomes.

In total, 224 employees completed work-contextualized and non-contextualized Big Five model measures, as well as contextualized emic personality measures, together with a number of measures for work-related outcomes.

Results showed that, after controlling for demographic variables and non-contextualized etic factors, etic contextualized factors predicted occupational stress, work engagement, job satisfaction, work frustration, turnover intention, career satisfaction and organizational citizenship behavior. After controlling for demographic variables, non-contextualized etic factors and contextualized etic factors, emic contextualized personality factors predicted work engagement, job satisfaction, absenteeism, counterproductive workplace behaviors and organizational citizenship behaviors.

The study has a number of limitations. First, the sample contained participants recruited from a low number of professional areas. Second, the sample consisted mostly of women, and relying on unbalanced samples may lead to construct irrelevant variance.

By using a combination of etic personality measures and contextualized emic personality measures, organizations can better predict a number of organizational outcomes related to extra-role performance, such as those considered in the present study.

This research showed that, in the case of personality assessment, using a double form of contextualization – frame of reference and culture – an increment in the prediction of organizational behaviors can be obtained.

The purposes of this paper are to (1) explore how personality traits pertaining to the dominance influence steadiness compliance model manifest themselves in terms of user interaction behavior on social media and (2) examine whether social interaction data on social media platforms can predict user personality.

Social interaction data was collected from 198 users of Sina Weibo, a popular social media platform in China. Their personality traits were also measured via questionnaire. Machine learning techniques were applied to predict the personality traits based on the social interaction data.

The results demonstrated that the proposed classifiers had high prediction accuracy, indicating that our approach is reliable and can be used with social interaction data on social media platforms to predict user personality. “Reposting,” “being reposted,” “commenting” and “being commented on” were found to be the key interaction features that reflected Weibo users' personalities, whereas “liking” was not found to be a key feature.

The findings of this study are expected to enrich personality prediction research based on social media data and to provide insights into the potential of employing social media data for the purpose of personality prediction in the context of the Weibo social media platform in China.

Streaming video on demand (SVOD) services are comprised of digital media content creation and content distribution that provide a vast array of genre content playable on an assortment of different technology platforms. Additionally, these digital services are equipped to collect data and information on consumers. However, these services do not capture extensive consumer demographics, lifestyles or personalities information.

To resolve this discrepancy, collecting external information such as complete demographics, personalities and lifestyles of consumers can be useful in advancing SVOD consumer behavior knowledge. This study examined how consumer demographics, lifestyles and personalities may predict SVOD genre consumption and SVOD platform consumption. A survey was executed and disseminated to collect consumer information across these dimensions. Multiple linear regressions and a structural equation model were formed to explicate variance.

Consumer demographics, lifestyles and personalities’ information do predict SVOD genre consumption and SVOD platform consumption.

Media selection and trait theory have not been applied to understanding unexplained variance behind consumer media consumption, and are often used to predict media consumption variance among consumers. These findings illustrate that, while digital consumer touchpoints are necessary to collect and analyze, marketers should not lose sight of easily-obtainable consumer data, much of which dictates consumption choices.

Little is known about how individual differences play out in the process of authentic self-development (ASD) through workplace coaching. This article explores whether the Big Five personality traits and affective, behavioral, cognitive and desire (ABCDs) components of the Big Five personality traits were relevant to ASD, specifically examining the role of affect as a potential mediator.

In total, 176 clients' personality was assessed pre-coaching. Aspects of ASD (perceived competence, goal commitment, self-concordance and goal stability) were assessed post-coaching. Clients' affect balance (AB) scores were obtained post-session.

Multilevel path models showed that higher levels of mean AB (but not the slope) mediated the associations between personality and perceived competence and goal commitment. Personality predicted goal self-concordance, but these effects were not mediated by AB, neither personality nor AB predicted goal stability.

The authors encourage randomized controlled trials to further test findings of this study. Ruling out method variance is not possible completely. However, the authors put forth considerations to support the authors' claim that method variance did not overly influence our results.

These results suggest the necessity of an optimal experience of affect for ASD in workplace coaching and the understanding of how ABCDs, AB and ASD are related beyond coaching psychology.

A deeper understanding of personality processes is important for fostering ASD to meet the challenges of management development in the authors' volatility, uncertainty, complexity and ambiguity (VUCA) world.

This is the first study to test personality as a process in workplace coaching linking personality to one of the most valued leadership skills: authenticity.

This chapter proposes the development of a compound personality trait termed “goal propensity”. Motivation is a key determinant of performance in virtually all contexts, and personality has long been viewed as an important influence on motivation. Despite the long history of exploring how personality influences motivation, we do not have a clear understanding of the linkage between individual differences in personality and work motivation or the tools to reliably and accurately predict individual differences in motivation. Advances in our understanding of personality and the convergence of motivation theories around models of self-regulation present the opportunity to achieve that understanding and predictive efficacy. Goal propensity would be a theoretically derived trait that would explain the role of personality in self-regulation models of motivation as well as allow the prediction of tendencies to engage in self-regulation. This chapter provides the rationale for the development of this construct, articulates the nature of the proposed goal propensity construct, and explores the value of such a construct for theory, future research, and human resource practice.

For an enterprise, it is essential to win as many customers as possible. The key to successfully winning customers is often determined by understanding the personality characteristics of the object of communication in order to employ an effective communication strategy. An enterprise needs to obtain the personality information of target or potential customers. However, the traditional method for personality evaluation is extremely costly in terms of time and labor, and it cannot acquire customer personality information without their awareness. Therefore, the manner in which to effectively conduct automated personality predictions for a large number of objects is an important issue. The paper aims to discuss these issues.

The diverse social media that have emerged in recent years represent a digital platform on which users can publicly deliver speeches and interact with others. Thus, social media may be able to serve the needs of automated personality predictions. Based on user data of Facebook, the main social media platform around the world, this research developed a method for predicting personality types based on interaction logs.

Experimental results show that the Naïve Bayes classification algorithm combined with a feature selection algorithm produces the best performance for predicting personality types, with 70-80 percent accuracy.

In this research, the dominance, inducement, submission, and compliance (DISC) theory was used to determine personality types. Some specific limitations were encountered. As Facebook was used as the main data source, it was necessary to obtain related data via Facebook’s API (FB API). However, the data types accessible via FB API are very limited.

This research serves to build a universal model for social media interaction, and can be used to propose an efficient method for designing interaction features.

This research has developed an approach for automatically predicting the personality types of network users based on their Facebook interactions.

The purpose of this paper is to contribute to existing models of human motivation by measuring transcendental needs along with the more commonly studied basic and social needs in order to take into account the multitude of needs experienced in the workplace.

A survey combining the Big Five Inventory (John and Srivastava, 1999) with the authors’ 30-item need-level questionnaire was administered to 366 participants (162 males, 204 females). The authors examined the relation between need levels, particularly transcendental needs, and personality to detect the predictors of the different personality traits.

The results showed that extraversion (r=0.24; p<0.001) and agreeableness (r=0.19; p<0.001) are predicted by high social needs, whereas openness to experience is predicted by high transcendental needs (r=0.35; p<0.001). While the authors made no hypotheses about conscientiousness and neuroticism, they found that neuroticism was significantly correlated with transcendental needs (r=−0.15; p<0.01).

The relations between needs and other individual dimensions, such as values or interests, could be further examined. The nature of the sample could be extended in additional studies.

Since need levels were able to predict personality traits, measuring need levels instead of personality could be a better predictor of both adaptability and performance in specific contexts.

In a position that requires a specific vocation and sense of purpose (priest, counselor, etc.), transcendental needs could be a better predictor of job achievement than personality traits. Monitoring all levels of needs could also be valuable in helping managers develop a deeper understanding of their team members’ expectations for meaning and purpose.

This research contributes to the model on human motivation by adding one more level (transcendental) and by testing the hypothesis of a link between a need scale and personality traits.

The purpose of this paper is to clarify whether the dark side of personality adds information beyond the bright side when predicting career success.

In total, 287 participants (150♀, M_age=37.74 and SD_age=10.38) completed questionnaires on the Dark Triad (narcissism, Machiavellianism and psychopathy) and the Big Five (emotional stability, extraversion, openness, agreeableness and conscientiousness). They also provided information on their objective (salary and leadership position) and subjective (job satisfaction and satisfaction with income) career success. Regression analyses were used to estimate the Dark Triad’s incremental predictive value.

The results show that the Dark Triad only provides incremental information beyond the Big Five when predicting salary (ΔR²=0.02*) and leadership position (ΔR²=0.04*). In contrast, the Dark Triad does not explain unique variance when predicting job satisfaction or satisfaction with income.

The exclusive use of self-rated success criteria may increase the risk of same-source biases. Thus, future studies should include ratings derived from multiple perspectives.

Considering the Dark Triad in employee selection and development seems particularly promising in the context of competitive behaviour.

The results are discussed in light of the socioanalytic theory. This may help to better understand behaviour in organisational contexts.

This study is the first that simultaneously investigates all three traits of the Dark Triad and the Big Five in combination with objective and subjective career success. In addition, it extends previous findings by answering the question of whether the Dark Triad offers incremental or redundant information to the Big Five when predicting success.