Factors driving memory fallibility: A conceptual framework for accounting and finance studies

Ding, Yike; Hellmann, Andreas; Mello, Lurion De

doi:10.1016/j.jbef.2017.03.003

Cited by 14 publications

(17 citation statements)

References 82 publications

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“…Recently, Ding et al, (2017) extended the framework by suggesting that cognitive errors significantly distort JDM under the influence of cognitive limitations. Ding et al, (2017) framework consisted of two main levels: an association between memory processes and judgement, and cognitive errors exhibited during the information processing. They divided the judgement process into two groups: judgement without memory retrieval, and judgement with memory retrieval.…”

Section: Data Extraction For Systematic Literature Reviewmentioning

confidence: 99%

“…The current study adapts Ding et al, (2017) framework for a systematic literature review because it associates memory processes with JDM and also considers the influence of cognitive errors on memory processes. Ding et al, (2017) developed their framework in the context of accounting and finance but the focus of the current study is on the auditing process with reference to the application of BDA. Therefore, judgement with memory retrieval pathway is adapted because auditors make audit judgement based on both available information and past information.…”

Section: Data Extraction For Systematic Literature Reviewmentioning

confidence: 99%

“…Proposition 1: Big Data Analytics facilitate auditor's conscious selection of relevant information cues (quality audit evidence). 2017Note: Author adapted framework from Ding et al, (2017) 2. Cue competition Decision makers exhibit tendency to prefer some cues relatively more than others (Ding et al, 2017), which results in higher attention to irrelevant information cues and negligence towards relevant cues (Blay, 2005;Kruschke and Johansen, 1999).…”

Section: Selective Attentionmentioning

confidence: 99%

“…The influence of memory processes on the accounting JDM process was established by . Recently, Ding, Hellmann and Mello (2017) provided the general framework in the context of both accounting and finance that integrates memory processes with JDM and identifies the cognitive errors that hinder the memory processes. Similarly, Alles andGray (2016), Brown-Liburd et al, (2015) highlighted the main cognitive weakness that auditors can exhibit during the BD analysis.…”

Section: Introductionmentioning

confidence: 99%

“…There is limited evidence of behavioral implications on the relationship between memory process and JDM in the context of BD and BDA in auditing. To fill this gap, the current study adapts the framework of Ding et al, (2017) in the context of auditing with the application of BD and BDA. The current study uses the PRISMA methodology to conduct a systematic literature review.…”

Section: Introductionmentioning

confidence: 99%

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A systematic review of the role of Big Data Analytics in reducing the influence of cognitive errors on the audit judgement

Ahmad

2019

RC-SAR

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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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Section: Data Extraction For Systematic Literature Reviewmentioning

confidence: 99%

Section: Data Extraction For Systematic Literature Reviewmentioning

confidence: 99%

Section: Selective Attentionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A systematic review of the role of Big Data Analytics in reducing the influence of cognitive errors on the audit judgement

Ahmad

2019

RC-SAR

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Robotic spray coating of self-sensing metakaolin geopolymer for concrete monitoring

McAlorum

Perry

Vlachakis

et al. 2021

Automation in Construction

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Sensors and new materials can support optimised concrete maintenance, and produce the data needed to justify new, low carbon structural designs. While these technologies are affordable, the process of manual installation in a construction context comes with acute and unfamiliar risks to productivity, personnel safety, and confidence in the quality of workmanship. The installation of smart materials using robotics could address some of these issues, but there are few proofs-of-concept at the time of writing. Here, we present a robotically controlled process for spray coating geopolymers -a class of self-sensing concrete repair materials. By tuning mix design, robotic toolpaths and spray dispenser parameters, we show reliable and automated spray coating of 250 mm 2 patches in a laboratory setting. The cured geopolymer has a compressive strength of 20 MPa, and a bond strength to the concrete substrate of 0.5 MPa. Electrical interrogation of patches, via a set of four electrodes, produces strain and temperature measurements of the underlying concrete substrate with resolutions of 1 µε and 0.2 • C, respectively. This demonstration multifunctional material deposition using robotics is a step towards remote, traceable, and low-risk technology deployment across civil engineering sectors. This could support more widespread adoption of novel concrete health monitoring and repair systems in future.

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Towards a conceptual framework for analysing impression management during face-to-face communication

Hellmann

Ang

Sood³

2020

Journal of Behavioral and Experimental Finance

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Factors driving memory fallibility: A conceptual framework for accounting and finance studies

Cited by 14 publications

References 82 publications

A systematic review of the role of Big Data Analytics in reducing the influence of cognitive errors on the audit judgement

A systematic review of the role of Big Data Analytics in reducing the influence of cognitive errors on the audit judgement

Robotic spray coating of self-sensing metakaolin geopolymer for concrete monitoring

Towards a conceptual framework for analysing impression management during face-to-face communication

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