Search results

The aim of this paper is to examine the impact of personality traits of the Big Five model on training outcomes to help explain variation in training effectiveness.

Associations of the Big Five with self-reported learning following training were tested in a pre- and post-design in a field sample of junior medical practitioners (N = 99), who attended a training workshop on self-awareness. Associations are reported of personality traits with post-training learning measured immediately following the workshop and one-month later controlling for pre-training learning.

Conscientiousness was related to post-training learning at both times. None of the remaining Big Five factors were associated with post-training learning.

The study contributes to the literature on personality and training outcomes, clarifying the associations of traits with outcomes in a pre-and-post design. Although the study sample has limitations, the findings have implications for numerous lines of future research, in particular in understanding the role of training in relations of personality and job performance.

Practitioners should consider ways to encourage training participants to approach training conscientiously. Personality assessment might help people reflect on their approach to learning to adapt it during training.

No study has previously examined the role of personality traits in training outcomes using a pre- and post-design. The role of conscientiousness in workplace learning is underlined by the findings. While dimensions such as openness and extraversion may encourage people to participate in training, conscientiousness may make the difference in promoting internalized individual development and change following training.

The purpose of this paper is to suggest a comprehensive framework to elucidate the relationship between personality and networking. Using the Five Factor Model as a framework, the paper aims to argue that traits tapping into social (i.e. extraversion, agreeableness) and informational (i.e. openness to experience) features are relevant in explaining how individual dispositions facilitate networking behaviors. Moreover, it aims to delineate structural and functional differences in networking (i.e. building, maintaining, and using contacts within and outside the organization) and to theorize how these differences yield differential relationships of personality traits with networking dimensions.

Online surveys were administered to two samples, from Germany and the UK, respectively (n=351). Structural equation modeling is used to test the hypotheses.

Personality traits reflecting social (extraversion) and informational aspects (openness to experience) are broadly related to networking in general. The paper also finds support for differential relationships, for example, agreeableness is related to internal, but not external networking. Both conscientiousness and emotional stability are not related to networking behaviors.

The findings help explain why some individuals experience more barriers to networking than others and can be used in networking trainings. Practitioners should also note that there is more than extraversion to accurately predict networking skills in selection assessments.

The paper provides further insights into determinants of networking, which is an important career self‐management strategy. It also offers an integrative framework on the personality‐networking relationship as prior research has been fragmentary. Establishing differential relations also furthers understanding on core differences between networking dimensions.

The purpose of this study is to explore the potential and growth of big data across several industries between 2016 and 2020. This study aims to analyze the behavior of interest in big data within the community and to identify areas with the greatest potential for future big data adoption.

This research uses Google Trends to characterize the community’s interest in big data. Community interest is measured on a scale of 0–100 from weekly observations over the past five years. A total of 16 industries were considered to explore the relative interest in big data for each industry.

The findings revealed that big data has been of high interest to the community over the past five years, particularly in the manufacturing, computers and electronics industries. However, over the 2020s the interest in the theme decreased by more than 15%, especially in the areas where big data typically had the greatest potential interest. In contrast, areas with less potential interest in big data such as real estate, sport and travel have registered an average growth of less than 10%.

To the best of the author’s knowledge, this study is original in complementing the traditional survey approaches launched among the business communities to discover the potential of big data in specific industries. The knowledge of big data growth potential is relevant for players in the field to identify saturation and emerging opportunities for big data adoption.

Financial professionals are increasingly important in the Indian financial system. Our study examines the association between the Big Five personality traits and Indian financial professionals' behavioral biases when making investment decisions.

After testing our questionnaire's reliability and validity, we used it to obtain the sample responses. We used multiple regression analysis and other statistical tools to identify the relationships between the Big Five personality traits and behavioral biases.

Our findings reveal a high level of extraversion and conscientiousness, a moderate level of agreeableness and openness and a low neuroticism level among financial professionals. The results show a significant association between neuroticism, extraversion, openness and all behavioral biases except anchoring bias. The neuroticism trait has a statistically significant relationship with all behavioral biases examined, whereas agreeableness and conscientiousness traits lack a significant association with behavioral biases. The openness trait is associated with many emotional biases and cognitive heuristics, while the extraversion trait has a significantly positive relationship with availability bias.

Future researchers could analyze primary (survey) and secondary investor data from brokerage houses. Using a larger sample could provide more generalizable findings. Researchers could also consider other aspects of investment decision-making using various asset classes. Understanding financial professionals' personality traits and behavioral biases could help them develop strategies to suit client needs.

This study provides the first comprehensive examination of the association between personality traits and behavioral biases of Indian financial professionals.

The purpose of the present study is to identify key personality traits which distinguish customer service (CS) employees from other occupations and are related to their career satisfaction. As hypothesized, 2,610 CS employees were differentiated from other occupational groups by higher levels of conscientiousness, customer service orientation, and lower tough‐mindedness. Conscientiousness, customer service orientation, emotional stability, extraversion, and tough‐mindedness were significantly, positively related to customer service representatives’ (CSRs’) career satisfaction. Results are discussed in terms of the adaptive value of these traits for the recruitment, selection, and management of customer service employees.

Data for this study were extracted from an archival database containing information on individuals’ many different occupations and industries, including 2,641 CSRs and 76,788 individuals in other occupations. Measures included demographic items and the Big Five personality traits as well six other narrow personality traits.

As hypothesized, CS employees differed from other occupational groups by having higher levels of conscientiousness, customer service orientation, and lower tough‐mindedness. Also, conscientiousness, customer service orientation, emotional stability, extraversion, and tough‐mindedness were significantly, positively related to career satisfaction. Using hierarchical multiple regression, the Big Five traits (Openness, Agreeableness, Conscientiousness, Extraversion, and Emotional Stability) accounted for 22 percent of the variance in CSR career satisfaction, while the narrow traits added an additional 6 percent.

The findings of the present study are original in that the authors used a relatively large sample to identify key personality traits which distinguish CS employees from other occupations and are related to their career satisfaction. An empirically validated personality profile of CS workers was presented. The typical CS representative is more: conscientious, optimistic, intrinsically motivated, tender‐minded, deferential, conventional, willing to serve other people, and reluctant to work long hours or become workaholics.

This chapter provides a contemporary view of state-of-the science research and thinking done in the areas of selection and classification. It takes as a starting point the observation that the world of work is undergoing important changes that are likely to result in different occupational and organizational structures. In this context, we review recent research on criteria, especially models of job performance, followed by sections on predictors, including ability, personality, vocational interests, biodata, and situational judgment tests. The paper also discusses person-organization fit models, as alternatives or complements to the traditional person-job fit paradigm.

The purpose of this paper is to examine the efficacy of the Japanese ten-item personality inventory (TIPI-J), a short version of the big five (BF) questionnaire, on crowdsourcing. The BF traits are indicators of personality and are said to be an effective predictor of study performance in various occupations. BF can be used in crowdsourcing to predict crowd workers’ performance; however, it will be difficult to use in practice for two reasons like the time-and-effort issue and the bias issue. In this study, an empirical analysis is conducted on crowdsourcing to examine if TIPI-J can solve those issues.

To investigate the issues, two tasks are posted on a crowdsourcing provider. Both TIPI-J and full version BF are conducted before and after selecting crowd workers. Structural validity and convergence validity are tested with correlation analysis between before (TIPI-J) and after (full version BF) data to examine the bias issue. Additionally, those correlations are compared with previous study and significances are examined.

The correlations in “conscientiousness” is 0.45-0.50, respectively, compared with a previous study, those two correlations did not show significance. This indicates that no clear bias exists.

This is the first research to investigate the efficacy of TIPI-J on crowdsourcing and showed that TIPI-J can be a useful tool for predicting crowd workers’ performance and thus it can help to select appropriate crowd workers.

Not all pressured, greedy, and opportunistic individuals actually commit white-collar crime. So what exactly is the common denominator for individuals to commit white-collar crime? This study investigates the propensity of an individual to commit white-collar crime and advances personality as an explanatory factor. Questionnaire survey data is collected from 357 undergraduate accounting students in a later year accounting course at a large university in Australia. Personality is measured using the Big Five Inventory. Support is provided for the view that individuals scoring lower in agreeableness and lower in conscientiousness have a higher propensity to commit white-collar crime. While no significant main effect associations emerged for extraversion, neuroticism, or openness to experience, inspection of individual parameter estimates revealed a significant negative association between neuroticism and propensity to commit white-collar crime but only in certain circumstances.

This paper aims to extend our understanding of how mid-tier firm auditors legitimise and institutionalise the logic of commercialism within their profession. This paper is responsive to research that shows how Big Four auditors have restructured the market and re-cast the relationality between the two logics to forge an identity that suits them commercially. Such research provides insight into auditor agency and intentionality, illustrating how auditors maintain and indeed grow their status and role within society.

Semi-structured interviews with audit executives situated in a strategically challenging regulatory context are interpreted through a theoretical framework developed from institutional complexity theory, coupled with the understanding that institutional logics are a socially constructed phenomenon.

Mid-tier auditors appear to be as commercially orientated as their Big Four counterparts, expressing the logics of professionalism and commercialism as highly complementary. In response to competitive pressures and the difficulty of replicating the multi-disciplinary practice business model of the Big Four, mid-tier auditors present a competitive and contrasting identity as “more devoted experts”, using various legitimation techniques and “heroic” representations. This identity representation is strategic, allowing them to forge a consistent and coherent “collective identity defining story” designed to counter the “versatile expert” identity of the Big Four and establish social legitimacy with their potential client base.

These findings contribute to our understanding of how mid-tier auditors are “catching up” to the Big Four in the construction of their commercial business model. By shedding light on the rhetoric and “identity experimentation” of auditors, the findings can aid legislators and regulators to exercise democratic control over the profession and promulgate regulations that better align auditors’ interests with the public interest. As regulators encourage mid-tier firms to compete with the Big Four and lower supply concentration in the market, this study believes the tensions inherent in the logics, as well as the strategic necessity for firms to represent themselves in a favourable manner, will become more prominent.

The objective of this paper is to test the extent to which combinations of financial information with non‐financial variables, such as audit fees and type of audit firm, can be used in predicting qualified and unqualified audit reports.

The data were taken from a sample of 185 Greek companies listed at the Athens stock exchange and were analysed using logistic and OLS regression models.

It is found that audit fees and the type of audit firm (Big five vs non‐Big five) do not affect auditors' propensity to qualify their opinions. Instead, the occurrence of audit qualifications is associated with financial metrics such as operating margin to total assets and the current ratio. The model developed was successful in classifying 90 per cent of the total sample.

This study has implications for external auditors, regulators and investors. Also contributes to auditing and accounting research by examining the suggested variables to identify those that can best discriminate cases of audit opinion.