scholarly journals Machine Learning Approach to Personality Assessment and Its Application to Personnel Selection

2021 ◽  
Vol 34 (2) ◽  
pp. 213-236
Author(s):  
JiSoo Ock ◽  
HyeRyeon An
Keyword(s):  
Machine Learning ◽  
Personality Assessment ◽  
Personnel Selection ◽  
Scientific Evidence ◽  
New Technology ◽  
Empirical Studies ◽  
Assessment Tools ◽  
Personality Testing ◽  
Future Research ◽  
Legal Standards

As we enter the digital age, new methods of personality testing-namely, machine learning-based personality assessment scales-are quickly gaining attraction. Because machine learning-based personality assessments are made based on algorithms that analyze digital footprints of people’s online behaviors, they are supposedly less prone to human biases or cognitive fallacies that are often cited as limitations of traditional personality tests. As a result, machine learning-based assessment tools are becoming increasingly popular in operational settings across the globe with the anticipation that they can effectively overcome the limitations of traditional personality testing. However, the provision of scientific evidence regarding the psychometric soundness and the fairness of machine learning-based assessment tools have lagged behind their use in practice. The current paper provides a brief review of empirical studies that have examined the validity of machine learning-based personality assessment, focusing primarily on social media text mining method. Based on this review, we offer some suggestions about future research directions, particularly regarding the important and immediate need to examine the machine learning-based personality assessment tools’ compliance with the practical and legal standards for use in practice (such as inter-algorithm reliability, test-retest reliability, and differential prediction across demographic groups). Additionally, we emphasize that the goal of machine learning-based personality assessment tools should not be to simply maximize the prediction of personality ratings. Rather, we should explore ways to use this new technology to further develop our fundamental understanding of human personality and to contribute to the development of personality theory.

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.

Test-Taking Motivation and Personality Test Validity

2010 ◽  
Vol 9 (3) ◽  
pp. 117-125 ◽  
Author(s):  
Thomas A. O’Neill ◽  
Richard D. Goffin ◽  
Ian R. Gellatly
Keyword(s):  
Personality Assessment ◽  
Personnel Selection ◽  
Test Validity ◽  
Personality Testing ◽  
Personality Test ◽  
Field Sample ◽  
Moderation Effect ◽  
Test Taking ◽  
Low Levels ◽  
Valid Criterion

In this study we assessed whether the predictive validity of personality scores is stronger when respondent test-taking motivation (TTM) is higher rather than lower. Results from a field sample comprising 269 employees provided evidence for this moderation effect for one trait, Steadfastness. However, for Conscientiousness, valid criterion prediction was only obtained at low levels of TTM. Thus, it appears that TTM relates to the criterion validity of personality testing differently depending on the personality trait assessed. Overall, these and additional findings regarding the nomological net of TTM suggest that it is a unique construct that may have significant implications when personality assessment is used in personnel selection.

Co-production strategy, retail competition, and market segmentation

2019 ◽  
Vol 32 (2) ◽  
pp. 607-630 ◽  
Author(s):  
Junbin Wang ◽  
Xiaojun Fan
Keyword(s):  
Market Segmentation ◽  
New Technology ◽  
Empirical Studies ◽  
Analytical Framework ◽  
Future Research ◽  
Retail Competition ◽  
Business World ◽  
Content Type ◽  
Production Strategy ◽  
The Impact

Purpose The purpose of this paper is to examine the effect of manufacturers’ co-production strategy on market segmentation and channel performance under retail competition. Design/methodology/approach It differs from previous empirical studies by primarily focusing on the increment in consumer value accompanying co-production. The authors establish a game-theoretical model to analyze the impact of co-production on market segmentation and the profitability of channel members in a competitive retail environment. Findings The results reveal that manufacturers introducing co-production expand market coverage and benefit all channel members, when the intensity of competition is sufficiently high, especially for retailers with low-quality levels, who are out of the market without co-production. Furthermore, with the increase in customer valuation through co-production, employing a co-production strategy is always a dominant strategy for manufacturers. Research limitations/implications First, although the authors assume a monopoly manufacturer and two duopoly retailers, adding competition between manufacturers should enrich the model. Multiple products with vertical or horizontal differentiation could also be introduced into the model. Second, the authors use the multiplicative utility function to model the value co-creation effect on consumers; however, different utility functions may yield significantly different results and implications. Third, the authors model a one-shot game in a single product selling period; future studies may employ multi-period games to obtain further insight into co-production strategy. Finally, the model assumes that all consumers are homogenous in the extent of value creation and hassle cost. Future research may find it interesting to consider heterogeneity in these characteristics. Practical implications The business world today already sees the power of leadership in a supply chain to have shifted from manufacturers to retail giants such as Walmart, Home Depot and Best Buy. The findings also propose a new route to counteract the emergence and rise of dominant retailers. On the other hand, with the application of new technology in the retail industry such as 3D avatar, AR/VR, Internet of Things, consumers are more likely to participate in various forms of co-production activities, how to execute the co-production strategy has become more and more important for managers. Social implications The conclusion of this study points out the way to achieve a win–win outcome under which both channel members including manufacturer and retailers and consumers can be better off, that is, the channel can reach Pareto improvement, so the social welfare is increased accordingly. Originality/value The authors propose an analytical framework to examine the effects of co-production and competition on market segmentation and profitability, and prove that co-production is a powerful marketing tool that can attract consumers and increase profitability, which manufacturers can incorporate into their products even in a competitive environment.

scholarly journals Multi-Approach Formulation in Dynamic Personality-Situation Interaction for Personnel Selection

2019 ◽  
Author(s):  
Mohd Hanafiah Ahmad ◽  
Gusman Nawanir ◽  
Mohd Rashid Ab Hamid
Keyword(s):  
Job Performance ◽  
Personality Assessment ◽  
Personnel Selection ◽  
Empirical Studies ◽  
Assessment Center ◽  
Work Outcomes ◽  
Mixed Methods Approach ◽  
Selection Decisions ◽  
Fundamental Research ◽  
Conceptual Paper

The purpose of personnel selection is to measure knowledge, skills, and abilities that are necessary to perform a job effectively. The process involves various assessments, including personality assessment. This conceptual paper discussed the potential of using a learning factory to develop multiple simulations for assessment center activities in assessing personality in different situations. Although traditional personality assessment contributes to the effectiveness of selection decisions and prediction, it tended to ignore that trait-related behaviors may differ across situations. Study on dynamic personality is essential as empirical studies showed that within-person fluctuations in personality states relate to a variety of work outcomes, including job performance. To further understand this fundamental issue, this paper discussed further how personality–situation interplay influences performance by using a learning factory assessment center method. This study also discussed how the adaptation of exploratory mixed methods approach could be used. The mixed exploratory methods are suitable as this topic is related to fundamental research and empirical study, besides the investigation on this area is still limited. This paper could benefit other researchers, industry players, and policymakers in understanding better how dynamic personality may influence performance, especially in the activities related to Industry 4.0.

scholarly journals Validity Concerns in Research Using Organic Data

2019 ◽  
Vol 46 (7) ◽  
pp. 1257-1274
Author(s):  
Heng Xu ◽  
Nan Zhang ◽  
Le Zhou
Keyword(s):  
Social Networking Sites ◽  
Scientific Evidence ◽  
Empirical Studies ◽  
Future Research ◽  
Design Elements ◽  
Digital Devices ◽  
Ubiquitous Sensing ◽  
Research Findings ◽  
Complex Methods ◽  
Analyze Data

With the advent of computing technologies, researchers across social science fields are using increasingly complex methods to collect, process, and analyze data in pursuit of scientific evidence. Given the complexity of research methods used, it is important to ensure that the research findings produced by a study are robust instead of being affected significantly by uncertainties associated with the design or implementation of the study. The field of metascience—the use of scientific methodology to study science itself—has examined various aspects of this robustness requirement for research that uses conventional designed studies (e.g., surveys, laboratory experiments) to collect data. Largely missing, however, are efforts to examine the robustness of empirical research using “organic data,” namely, data that are generated without any explicit research design elements and are continuously documented by digital devices (e.g., video captured by ubiquitous sensing devices; content and social interactions extracted from social networking sites, Twitter feeds, and click streams). Given the growing popularity of using organic data in management research, it is essential to understand issues concerning the usage and processing of organic data that may affect the robustness of research findings. This commentary first provides an overview of commonly present issues that threaten the validity of inferences drawn from empirical studies using organic data. This is followed by a discussion on some key considerations and suggestions for making organic data a robust and integral part of future research endeavors in management.

scholarly journals A validation of machine learning-based risk scores in the prehospital setting

2019 ◽  
Author(s):  
Douglas Spangler ◽  
Thomas Hermansson ◽  
David Smekal ◽  
Hans Blomberg
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Assessment Tools ◽  
Risk Scores ◽  
Gradient Boosting ◽  
Future Research ◽  
Operating Characteristics ◽  
Hospital Data ◽  
Hospital Outcomes ◽  
The Impact

AbstractBackgroundThe triage of patients in pre-hospital care is a difficult task, and improved risk assessment tools are needed both at the dispatch center and on the ambulance to differentiate between low- and high-risk patients. This study develops and validates a machine learning-based approach to predicting hospital outcomes based on routinely collected prehospital data.MethodsDispatch, ambulance, and hospital data were collected in one Swedish region from 2016 - 2017. Dispatch center and ambulance records were used to develop gradient boosting models predicting hospital admission, critical care (defined as admission to an intensive care unit or in-hospital mortality), and two-day mortality. Model predictions were used to generate composite risk scores which were compared to National Early Warning System (NEWS) scores and actual dispatched priorities in a similar but prospectively gathered dataset from 2018.ResultsA total of 38203 patients were included from 2016-2018. Concordance indexes (or area under the receiver operating characteristics curve) for dispatched priorities ranged from 0.51 – 0.66, while those for NEWS scores ranged from 0.66 - 0.85. Concordance ranged from 0.71 – 0.80 for risk scores based only on dispatch data, and 0.79 – 0.89 for risk scores including ambulance data. Dispatch data-based risk scores consistently outperformed dispatched priorities in predicting hospital outcomes, while models including ambulance data also consistently outperformed NEWS scores. Model performance in the prospective test dataset was similar to that found using cross-validation, and calibration was comparable to that of NEWS scores.ConclusionsMachine learning-based risk scores outperformed a widely-used rule-based triage algorithm and human prioritization decisions in predicting hospital outcomes. Performance was robust in a prospectively gathered dataset, and scores demonstrated adequate calibration. Future research should investigate the generality of these results to prehospital triage in other settings, and establish the impact of triage tools based on these methods by means of randomized trial.

scholarly journals Machine learning in Governments: Benefits, Challenges and Future Directions

2021 ◽  
Vol 13 (1) ◽  
pp. 203-219
Author(s):  
Yulu Pi
Keyword(s):  
Machine Learning ◽  
Public Sector ◽  
Empirical Studies ◽  
Data Availability ◽  
Future Research ◽  
Future Directions ◽  
Computing Power ◽  
Technical Aspects ◽  
The Public ◽  
Machine Learning Applications

The unprecedented increase in computing power and data availability has signifi-cantly altered the way and the scope that organizations make decisions relying on technologies. There is a conspicuous trend that organizations are seeking the use of frontier technologies with the purpose of helping the delivery of services and making day-to-day operational deci-sions. Machine learning (ML) is the fastest growing and at the same time, the most debated and controversial of these technologies. Although there is a great deal of research in the literature related to machine learning applications, most of them focus on the technical aspects or pri-vate sector use. The governmental machine learning applications suffer the lack of theoretical and empirical studies and unclear governance framework. This paper reviews the literature on the use of machine learning by government, aiming to identify the benefits and challenges of wider adoption of machine learning applications in the public sector and to propose the direc-tions for future research.

scholarly journals Using Machine Learning to Advance Personality Assessment and Theory

2018 ◽  
Vol 23 (2) ◽  
pp. 190-203 ◽  
Author(s):  
Wiebke Bleidorn ◽  
Christopher James Hopwood
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Personality Assessment ◽  
Construct Validation ◽  
Assessment Tools ◽  
Learning Approaches ◽  
Psychological Science ◽  
Validation Framework ◽  
Learning Research ◽  
Applications Of Machine Learning

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.

scholarly journals Health care for transgender people in Brazil: a systematic review protocol

2020 ◽  
Author(s):  
Rafael Rodolfo Tomaz de Lima ◽  
Taiana Brito Menezes Flor ◽  
Alexandre Bezerra Silva ◽  
Luiz Roberto Augusto Noro
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Health System ◽  
Scientific Evidence ◽  
Empirical Studies ◽  
Assessment Tools ◽  
Systematic Review Protocol ◽  
Transgender People ◽  
Unified Health System ◽  
Review Protocol

Abstract Background At the global level, transgender people (transvestites and transsexuals) live in worse health and living conditions due to the stigma and violence they face, including within health services. In Brazil, the Unified Health System must offer comprehensive care to the population, regardless of social class and gender. The aim of this article is to establish a systematic review protocol to analyze how health care occurs for the transgender population in the Unified Health System. Methods This protocol was guided by PRISMA-P and the bases SCIELO, LILACS, BVS and PUBMED will be consulted. Empirical studies, with a qualitative or quantitative approach, that deal with health care for the transgender population in the Unified Health System, where the respondents are transgender people, health professionals or health managers, will be included in the systematic review. For the definition of the final sample, the articles will undergo an assessment of methodological quality and risk of bias, with the help of the critical assessment tools of the Joanna Briggs Institute, using a specific checklist for each type of study. The synthesis of the findings will be carried out through the formal narrative and the presentation of tables with summarized data, with the final writing of the review guided by PRISMA. Discussion Based on scientific evidence, it is intended to present an overview of health care for the transgender population in the Unified Health System. In addition, to point out possible strategies to qualify the political, organizational and attitudinal aspects related to health care for that population. Systematic review registration: PROSPERO CRD42020188719.

Personality testing on the internet

2012 ◽  
Author(s):  
Tom Buchanan
Keyword(s):  
Personality Assessment ◽  
Online Survey ◽  
Survey Methodology ◽  
Self Report ◽  
Personality Testing ◽  
Future Research ◽  
Personality Tests ◽  
Self Disclosure ◽  
Personality Inventories ◽  
Key Issues

This article provides an overview of some of the key issues in online personality assessment, offers practical advice for people planning to use such tests in research or applied settings, and highlights some priorities for future research. As well as personality inventories, it considers other forms of self-report-questionnaire-based psychological assessment that may reflect relatively stable individual differences but not strictly fall into traditional models of personality. For example, these are considered in the discussion of equivalence between online and offline tests, because it is likely that any psychological processes affecting the completion of online personality tests (e.g., increased self-disclosure) will be shared with these instruments as well. In terms of methodology, if not the constructs being measured, there are strong similarities that will inform discussion of issues such as equivalence. The same is true of research on online survey methodology – again, there are valuable lessons to be learned from that body of literature.

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