Purpose This study aims to examine that personality traits are associated with the investor’s ability to exhibit disposition effect, herding behavior and overconfidence. It also explores how risk-attitude can modify investor behavior by moderating the association between personality traits, disposition effect, herding and overconfidence. Design/methodology/approach Data were collected from 396 respondents by using personally administrated survey. Confirmatory factor analysis (CFA) was used to confirm the validity and reliability of data. Regression analysis was used to test the proposed hypotheses. Findings The results supported the proposed hypotheses and showed that extravert investors were more likely to exhibit disposition effect, herding and overconfidence. The conscientiousness trait was associated with disposition effect and overconfidence, while neuroticism was associated with herding behavior. The results confirmed the moderating effect of risk aversion on the association between personality traits, disposition effect, herding and overconfidence. Originality/value This study demonstrates how risk aversion modes the strength of association between psychological characteristics (represented by personality traits) and cognitive biases (disposition effect, herding and overconfidence). The results support the “auction” interpretation of investors' behavior by suggesting that personality traits are associated with investment decision-making and that investors are marginal price setters.

This systematic literature review provides the association between memory processes, auditors judgement and decision-making process under the influence of cognitive errors. Due to limited cognitive resources, auditors are unable to analyze the population of accounting transactions, therefore, they use sampling and heuristics for information processing. In the context of Big Data (BD), auditors may face a similar problem of information overload and exhibit cognitive errors, resulting in the selection and analysis of irrelevant information cues. But Big Data analytics (BDA) can facilitate information processing and analysis of complex diverse Big Data by reducing the influence of auditor’s cognitive errors. The current study adapts Ding et al., (2017) framework in the auditing context that identify causes of cognitive errors influencing auditor’s information processing. This review identified 75 auditing related studies to elaborate the role of BD and BDA in improving audit judgement. In addition, role of memory, cognitive errors, and judgement and decision-making are highlighted by using 61 studies. The analysis provides useful insight in different open areas by proposing research propositions and research questions that can be explored by future research to gain extensive understanding on the association between memory and audit judgement in the context of BD and BDA. La revisión sistemática de la literatura proporciona la asociación entre los procesos de la memoria, el juicio de los auditores y el proceso de toma de decisiones bajo la influencia de errores cognitivos. Debido a los limitados recursos cognitivos, los auditores no pueden analizar la población de transacciones contables; por lo tanto, utilizan el muestreo y la heurística para el procesamiento de la información. En el contexto de Big Data (BD), los auditores pueden enfrentarse a un problema similar de sobrecarga de información y exhibir errores cognitivos, lo que resulta en la selección y análisis de indicios de información irrelevantes. No obstante, la analítica de Big Data (BDA) puede facilitar el procesamiento de información y el análisis de datos complejos y diversos al reducir la influencia de los errores cognitivos del auditor. El presente estudio adapta el marco de trabajo de Ding et al (2017) en el contexto de la auditoría que identifica las causas de los errores cognitivos que influyen en el procesamiento de la información del auditor. Esta revisión identificó 75 estudios relacionados con la auditoría para elaborar el papel de BD y BDA en la mejora del juicio de auditoría. Además, el papel de la memoria, los errores cognitivos y el juicio y la toma de decisiones se destacan mediante el uso de 61 estudios. El análisis proporciona una visión útil de los diferentes aspectos abiertos de la cuestión proponiendo propuestas y preguntas de estudio que puedan ser exploradas por la investigación futura para obtener una comprensión amplia de la asociación entre la memoria y el juicio de auditoría en el contexto de BD y BDA.

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PurposeValue-added intellectual coefficient (VAIC) is extensively used as a measure of intellectual capital (IC), but it is criticized for not capturing the totality of IC. Therefore, this study aims to analyse critiques of the original VAIC and proposes a modified VAIC by adding missing IC components and adjusting for exogenous factors. The study uses a modified VAIC model to investigate the relationship between IC, firm performance (FP) and market value (MV) for US non-financial firms.Design/methodology/approachThis study employed fundamental data of US non-financial firms listed on the NYSE and NASDAQ from 1980 to 2019. A final sample consisted of 6,019 firms and 62,686 firm-year observations.FindingsThe results provide a significant positive effect of aggregate and components of modified VAIC on FP and MV. Moreover, results validate the modified VAIC model and find that the modified VAIC explains changes in shareholders' MV. In addition, findings indicate that modified VAIC serves as an additional intangible factor to explain firms' capital structure decisions.Practical implicationsThe findings have important implications for management, owners, researchers and investors.Originality/valueThe modified VAIC model differs from the original VAIC model in four ways: first, it corrects the measurement of structural capital efficiency (SCE) following the accounting principle. Second, it replaces SCE with innovation capital efficiency (InVCE) and relational capital efficiency (RCE) to account for missing components of information of structural capital (SC). Third, the modified VAIC model adjusts for exogenous factors like business cycles and cross-industry variations. Finally, with the addition of InVCE and RCE as components of SCE, innovation capital (InVC) and relational capital (RC) are added to the calculation of value-added (VA) as components of IC.

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