scholarly journals Predicting Most Productive Requirements Elicitation Teams using MBTI Personality Traits Model

2019 ◽  
Vol 9 (1) ◽  
pp. 3809-3814
Keyword(s):  
Personality Traits ◽  
Personality Assessment ◽  
Soft Skills ◽  
Requirements Elicitation ◽  
Assessment Model ◽  
Personality Characteristics ◽  
Job Descriptions ◽  
The Social ◽  
Elicitation Process ◽  
Personality Prediction

The social and collaborative nature of requirements elicitation process bases its core dependency on aptitude, attitudes, and personality characteristics of its participants. The participant’s personality characteristics are directly related with their personality traits, which can be categorized using different personality assessment models. The MBTI personality assessment model has been used successfully for the assessment of personality of software engineers since last few decades. In this article, the personality traits for requirements elicitation teams have been predicted using MBTI personality assessment model, on the basis of their industry demanded job descriptions/tasks and major soft skills. The article presents a complete personality prediction process using a systematic approach based on major soft skills mapping with job descriptions, personality attributes and personality traits. The obtained results show that extraversion and feeling personality traits are the most suitable personality traits for requirements analysts/engineers who are assigned the task of requirements elicitation. The obtained results are very much aligned with the already published scholar’s work for software engineer’s personality assessment and development team composition.

Personality traits in parents of people with autism

1999 ◽  
Vol 16 (2) ◽  
pp. 59-60 ◽  
Author(s):  
Susan O'Hanrahan ◽  
Michael Fitzgerald ◽  
Myra O'Regan
Keyword(s):  
Personality Traits ◽  
Personality Assessment ◽  
Personality Characteristics ◽  
Assessment Instruments ◽  
Personality Profile ◽  
Mothers And Fathers ◽  
The People

AbstractObjectives: This study set out to explore if there were measurable personality characteristics specific to parents of people with autism.Method: Parents of 12 people with a DSM-III-R diagnosis of autism presented for the study. Each of the people with autism were matched where possible with a counterpart without autism but with a lifelong disability on parameters of age, sex and IQ level. Parents of the ‘autism’ and ‘non-autism’ groups were then interviewed in detail using four personality assessment instruments. Scores were tabulated for both mothers and fathers in each group and intergroup comparisons were made.Results: No significant personality profile difference was identifiable between the two parental groups.Conclusions: Personality traits specific to parents of people with autism are not identifiable in this study thus casting doubt on the validity of personality phenotypes as measurable heritability factors in autism.

scholarly journals SIMPA: Statement-to-Item Matching Personality Assessment from text

2022 ◽  
Author(s):  
Matej Gjurković ◽  
Iva Vukojević ◽  
Jan Šnajder
Keyword(s):  
Language Processing ◽  
Personality Assessment ◽  
Feedback Loop ◽  
State Of The Art ◽  
Proof Of Concept ◽  
Text Data ◽  
Social Media Site ◽  
The Social ◽  
Personality Judgments ◽  
Personality Prediction

Automated text-based personality assessment (ATBPA) methods can analyze large amounts of text data and identify nuanced linguistic personality cues. However, current approaches lack the interpretability, explainability, and validity offered by standard questionnaire instruments. To address these weaknesses, we propose an approach that combines questionnaire-based and text-based approaches to personality assessment. Our Statement-to-Item Matching Personality Assessment (SIMPA) framework uses natural language processing methods to detect self-referencing descriptions of personality in a target’s text and utilizes these descriptions for personality assessment. The core of the framework is the notion of a trait-constrained semantic similarity between the target’s freely expressed statements and questionnaire items. The conceptual basis is provided by the realistic accuracy model (RAM), which describes the process of accurate personality judgments and which we extend with a feedback loop mechanism to improve the accuracy of judgments. We present a simple proof-of-concept implementation of SIMPA for ATBPA on the social media site Reddit. We show how the framework can be used directly for unsupervised estimation of a target’s Big 5 scores and indirectly to produce features for a supervised ATBPA model, demonstrating state-of-the-art results for the personality prediction task on Reddit.

scholarly journals 2.5D Facial Personality Prediction Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jia Xu ◽  
Weijian Tian ◽  
Guoyun Lv ◽  
Shiya Liu ◽  
Yangyu Fan
Keyword(s):  
Personality Traits ◽  
Real Life ◽  
Previous Method ◽  
Personality Characteristics ◽  
Facial Contour ◽  
Related Information ◽  
New Findings ◽  
Positive Images ◽  
Personality Prediction ◽  
Personality Computing

The assessment of personality traits is now a key part of many important social activities, such as job hunting, accident prevention in transportation, disease treatment, policing, and interpersonal interactions. In a previous study, we predicted personality based on positive images of college students. Although this method achieved a high accuracy, the reliance on positive images alone results in the loss of much personality-related information. Our new findings show that using real-life 2.5D static facial contour images, it is possible to make statistically significant predictions about a wider range of personality traits for both men and women. We address the objective of comprehensive understanding of a person’s personality traits by developing a multiperspective 2.5D hybrid personality-computing model to evaluate the potential correlation between static facial contour images and personality characteristics. Our experimental results show that the deep neural network trained by large labeled datasets can reliably predict people’s multidimensional personality characteristics through 2.5D static facial contour images, and the prediction accuracy is better than the previous method using 2D images.

Handwriting Analysis and Personality Assessment

2000 ◽  
Vol 5 (1) ◽  
pp. 44-51 ◽  
Author(s):  
Peter Greasley
Keyword(s):  
Personality Traits ◽  
Personality Assessment ◽  
Personnel Selection ◽  
Empirical Studies ◽  
Handwriting Analysis ◽  
Recruitment Process ◽  
Academic Literature ◽  
Personnel Recruitment ◽  
The Subject ◽  
Better Than

It has been estimated that graphology is used by over 80% of European companies as part of their personnel recruitment process. And yet, after over three decades of research into the validity of graphology as a means of assessing personality, we are left with a legacy of equivocal results. For every experiment that has provided evidence to show that graphologists are able to identify personality traits from features of handwriting, there are just as many to show that, under rigorously controlled conditions, graphologists perform no better than chance expectations. In light of this confusion, this paper takes a different approach to the subject by focusing on the rationale and modus operandi of graphology. When we take a closer look at the academic literature, we note that there is no discussion of the actual rules by which graphologists make their assessments of personality from handwriting samples. Examination of these rules reveals a practice founded upon analogy, symbolism, and metaphor in the absence of empirical studies that have established the associations between particular features of handwriting and personality traits proposed by graphologists. These rules guide both popular graphology and that practiced by professional graphologists in personnel selection.

Systems of Systems Paradigm in a Hospital Environment: Benefits for Requirements Elicitation Process

2014 ◽  
Vol 9 (10) ◽  
pp. 1798 ◽  
Author(s):  
Farid Lahboube ◽  
Saida Haidrar ◽  
Ounsa Roudiès ◽  
Nissrine Souissi ◽  
Anwar Adil
Keyword(s):  
Requirements Elicitation ◽  
Hospital Environment ◽  
Systems Of Systems ◽  
Elicitation Process

The Influence of Personality Traits on Consumer Impulsive Buying Behaviour

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Khagendra Nath Gangai ◽  
Rachna Agrawal
Keyword(s):  
Personality Traits ◽  
Cultural Influences ◽  
Buying Behavior ◽  
Doing Business ◽  
The Social ◽  
Impulsive Buying ◽  
Social And Physical Environment ◽  
The Common ◽  
Buying Behaviour

Consumer behavior is a complex phenomenon which is evolving according to the time, situations, demographic characteristics of individuals, personality traits, cultural influences etc. The personality of individuals is a unique dynamic organization of the characteristics of a particular person, physical and psychological, which influence behavior and responses to the social and physical environment. It gives the impression that consumer buying is always influenced by their personality. Therefore, many marketers make use of personality traits in the advertisement of products and at the same time they enhance their marketing strategy. The marketers always designed different products and target specific market segments which commonly addressed on individuals personality traits. The individuals few personality traits influence consumer for impulsive buying behavior. The aim of present research is to study the personality traits influence on consumer impulsive buying behavior as it will help to create opportunities of doing business and dealing with customers. The objectives of this research are: (1) to investigate the influence of personality traits on consumer impulsive buying behavior, and (2) to identify the role of gender and their personality traits influence on consumer impulsive buying behavior. To fulfill the purpose of the study, the researchers randomly collected sample and divided them on the basis of gender, 60 males and 60 females. Data were collected from Delhi and NCR region. The data were analyzed using statistical applications such as correlation and t Test. The result was revealed that the common personality traits have a significant relationship with impulsive buying behavior that is psychoticism in the case of male and female. The role of gender has significant differences in impulsive buying behavior. The man showed more impulsive buying behavior compare to women.

Brokers in the tea trade between China and West Africa

2021 ◽  
pp. 092137402110218
Author(s):  
Ute Röschenthaler
Keyword(s):  
Social Movements ◽  
West Africa ◽  
19Th Century ◽  
Green Tea ◽  
Soft Skills ◽  
Field Research ◽  
Extensive Field ◽  
The Social ◽  
Entrepreneurial Activities ◽  
The 19Th Century

Brokers have played important roles in the trade of green tea between China and Mali, from the 19th century when tea first came to Mali up to the present. They mediate between tea buyers and sellers, work on their own account, use soft skills, knowledge and networks and make a living from the commission they gain. This article examines the work of brokers in the tea trade, the social constellations in which they are active and the scope of their activity. Based on extensive field research in Mali and China, this article shows how brokers create their own jobs in a dynamic business landscape, which is often delimited by governmental policies, competing entrepreneurial activities and social movements.

scholarly journals The Association Between Personality and Homebound Status in Older Adults: Results From the NHATS

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 233-233
Author(s):  
Xiaocao Sun ◽  
Minhui Liu ◽  
Christina E Miyawaki ◽  
Yuxiao Li ◽  
Tianxue Hou ◽  
...  
Keyword(s):  
Older Adults ◽  
Personality Traits ◽  
Personality Assessment ◽  
Rating Scale ◽  
Community Dwelling ◽  
Cross Sectional ◽  
Health Related ◽  
Community Dwelling Older Adults ◽  
Ordinal Logistic Regression Model ◽  
Hispanic White

Abstract Personality is associated with predictors of homebound status like frailty, incident falls, and depression. It has been rarely investigated whether personality predicts homebound status among older adults. Using the combining cross-sectional data of the Year 2013 and Year 2014 data from the National Health and Aging Trends Study (NHATS), this study examined the association between personality traits and homebound status in a sample of community-dwelling older adults aged 65 years and older (N=2,788). Homebound status (non-homebound, semi-homebound, and homebound) was determined by the frequency, difficulty, and help of outdoor mobility. Personality traits, including conscientiousness, agreeableness, openness, extraversion, and neuroticism were assessed using the 10-item Midlife Development Inventory on a rating scale from 1 (not at all) to 4 (a lot). Each personality trait was included as a predictor in an ordinal logistic regression model to examine its association with homebound status after adjusting demographic and health-related covariates. The sample was on average 79±7.53 years old, non-Hispanic White (72.0%), female (58.6%), living alone (35.4%) or with spouse/partner only (37.4%). Seventy-four percent, 18%, and 8% of participants were non-homebound, semi-homebound, and homebound, respectively. Homebound participants tended to be less-educated older females. The average scores of conscientiousness, agreeableness, openness, extraversion, and neuroticism were 3.19±0.75, 3.57±0.56, 2.81±0.83, 3.13±0.75, and 2.22±0.86, respectively. Among these five personality traits, high conscientiousness (OR=1.34, p<0.001) and extraversion (OR=1.16, p=.03) were associated with a reduced likelihood of being homebound. These findings provided a basis for potential personality assessment to identify and protect individuals with high homebound risk.

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

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