Valuation judgement bias has been a research topic for several years due to its proclaimed effect on valuation accuracy. However, little is known on the emphasis of literature on judgement bias, with regard to, for instance, research methodologies, research context and robustness of research evidence. A synthesis of available research will establish consistency in the current knowledge base on valuer judgement, identify future research opportunities and support decision-making policy by educational and regulatory stakeholders how to cope with judgement bias. This article therefore, provides a systematic review of empirical research on real estate valuer judgement over the last 30 years. Based on a number of inclusion and exclusion criteria, we have systematically analysed 32 relevant papers on valuation judgement bias. Although we find some consistency in evidence, we also find the underlying research to be biased; the methodology adopted is dominated by a quantitative approach; research context is skewed by timing and origination; and research evidence seems fragmented and needs replication. In order to obtain a deeper understanding of valuation judgement processes and thus extend the current knowledge base, we advocate more use of qualitative research methods and scholars to adopt an interpretative paradigm when studying judgement behaviour.
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Reporting of research findings is often selective. This threatens the validity of the published body of knowledge if the decision to report depends on the nature of the results. The evidence derived from studies on causes and mechanisms underlying selective reporting may help to avoid or reduce reporting bias. Such research should be guided by a theoretical framework of possible causal pathways that lead to reporting bias. We build upon a classification of determinants of selective reporting that we recently developed in a systematic review of the topic. The resulting theoretical framework features four clusters of causes. There are two clusters of necessary causes: (A) motivations (e.g. a preference for particular findings) and (B) means (e.g. a flexible study design). These two combined represent a sufficient cause for reporting bias to occur. The framework also features two clusters of component causes: (C) conflicts and balancing of interests referring to the individual or the team, and (D) pressures from science and society. The component causes may modify the effect of the necessary causes or may lead to reporting bias mediated through the necessary causes. Our theoretical framework is meant to inspire further research and to create awareness among researchers and end-users of research about reporting bias and its causes.
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As artificial intelligence (AI) reshapes hiring, organizations increasingly rely on AI-enhanced selection methods such as chatbot-led interviews and algorithmic resume screening. While AI offers efficiency and scalability, concerns persist regarding fairness, transparency, and trust. This qualitative study applies the Artificially Intelligent Device Use Acceptance (AIDUA) model to examine how job applicants perceive and respond to AI-driven hiring. Drawing on semi-structured interviews with 15 professionals, the study explores how social influence, anthropomorphism, and performance expectancy shape applicant acceptance, while concerns about transparency and fairness emerge as key barriers. Participants expressed a strong preference for hybrid AI-human hiring models, emphasizing the importance of explainability and human oversight. The study refines the AIDUA model in the recruitment context and offers practical recommendations for organizations seeking to implement AI ethically and effectively in selection processes.
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A large body of research has described the influence of context information on forensic decision-making. In this study, we examined the effect of context information on the search for and selection of traces by students (N = 36) and crime scene investigators (N = 58). Participants investigated an ambiguous mock crime scene and received prior information indicating suicide, a violent death or no information. Participants described their impression of the scene and wrote down which traces they wanted to secure. Results showed that con- text information impacted first impression of the scene and crime scene behavior, namely number of traces secured. Participants in the murder condition secured most traces. Furthermore, the students secured more crime-related traces. Students were more confident in their first impres- sion. This study does not indicate that experts outperform novices. We therefore argue for proper training on cognitive processes as an integral part of all forensic education.
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Challenges that surveys are facing are increasing data collection costs and declining budgets. During the past years, many surveys at Statistics Netherlands were redesigned to reduce costs and to increase or maintain response rates. From 2018 onwards, adaptive survey design has been applied in several social surveys to produce more accurate statistics within the same budget. In previous years, research has been done into the effect on quality and costs of reducing the use of interviewers in mixed-mode surveys starting with internet observation, followed by telephone or face-to-face observation of internet nonrespondents. Reducing follow-ups can be done in different ways. By using stratified selection of people eligible for follow-up, nonresponse bias may be reduced. The main decisions to be made are how to divide the population into strata and how to compute the allocation probabilities for face-to-face and telephone observation in the different strata. Currently, adaptive survey design is an option in redesigns of social surveys at Statistics Netherlands. In 2018 it has been implemented in the Health Survey and the Public Opinion Survey, in 2019 in the Life Style Monitor and the Leisure Omnibus, in 2021 in the Labour Force Survey, and in 2022 it is planned for the Social Coherence Survey. This paper elaborates on the development of the adaptive survey design for the Labour Force Survey. Attention is paid to the survey design, in particular the sampling design, the data collection constraints, the choice of the strata for the adaptive design, the calculation of follow-up fractions by mode of observation and stratum, the practical implementation of the adaptive design, and the six-month parallel design with corresponding response results.
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In this paper, we report on the initial results of an explorative study that aims to investigate the occurrence of cognitive biases when designers use generative AI in the ideation phase of a creative design process. When observing current AI models utilised as creative design tools, potential negative impacts on creativity can be identified, namely deepening already existing cognitive biases but also introducing new ones that might not have been present before. Within our study, we analysed the emergence of several cognitive biases and the possible appearance of a negative synergy when designers use generative AI tools in a creative ideation process. Additionally, we identified a new potential bias that emerges from interacting with AI tools, namely prompt bias.
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Limited evidence is available about (non)-representativeness of participants in health-promoting interventions. The Dutch Healthy Primary School of the Future (HPSF)-study is a school-based study aiming to improve health through altering physical activity and dietary behaviour, that started in 2015 (registered in ClinicalTrials.gov on14-06-2016, NCT02800616). The study has a response rate of 60%. A comprehensive non-responder analysis was carried out, and responders were compared with schoolchildren from the region and the Netherlands using a cross-sectional design. External sources were consulted to collect non-responder, regional, and national data regarding relevant characteristics including sex, demographics, health, and lifestyle. The Chi-square test, Mann-Whitney U test, or Student's t-test were used to analyse differences.
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Over the past 10 years, different types of financing have become available in the Netherlands. It is now possible to combine bank loans, crowdfunding loans and risk capital. Moreover, fintech applications lower the threshold for applications and reduce response times from weeks to just days or even hours. Fraser, Bhaumik and Wright (2015) point out there is a lack of knowledge of the cognitive process involved in selecting SME financing. This paper looks into the selection process financial advisers use, against the backdrop of the growing range of funding possibilities. To assess this process, we try to understand dominant habits and related heuristics. Within our explorative study, 19 experienced and independent SME financial advisers were interviewed. The questions address their knowledge, skills, experiences and choices in the selection process on the financing or refinancing of working capital and growth. Taking a grounded theoretical approach, we use Atlas TI to label all answers and statements step by step. The findings suggest a strong bias of decision-making towards the more traditional banking products. Yet advisers state they are aware of, and familiar with, other solutions. We have also found that fintech solutions are hardly used to prepare financing solutions up front. Financial advisers estimate the likelihood of acceptance by a few financial providers they know well within their personal network. We suggest that there is a behavioural approach to financing in the day-to-day decisions made by financial advisers. As long as automated selections are not fully transparent and are unable to combine all types of financing up front, financial advisers will be guided by habit or by availability, confirmation and affect heuristics, rather than looking for new financing solutions and combinations.
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Over the past 10 years, different types of financing have become available in the Netherlands. It is now possible to combine bank loans, crowdfunding loans and risk capital. Moreover, fintech applications lower the threshold for applications and reduce response times from weeks to just days or even hours. Fraser, Bhaumik and Wright (2015) point out there is a lack of knowledge of the cognitive process involved in selecting SME financing. This paper looks into the selection process financial advisers use, against the backdrop of the growing range of funding possibilities. To assess this process, we try to understand dominant habits and related heuristics. Within our explorative study, 19 experienced and independent SME financial advisers were interviewed. The questions address their knowledge, skills, experiences and choices in the selection process on the financing or refinancing of working capital and growth. Taking a grounded theoretical approach, we use Atlas TI to label all answers and statements step by step. The findings suggest a strong bias of decision-making towards the more traditional banking products. Yet advisers state they are aware of, and familiar with, other solutions. We have also found that fintech solutions are hardly used to prepare financing solutions up front. Financial advisers estimate the likelihood of acceptance by a few financial providers they know well within their personal network. We suggest that there is a behavioural approach to financing in the day-to-day decisions made by financial advisers. As long as automated selections are not fully transparent and are unable to combine all types of financing up front, financial advisers will be guided by habit or by availability, confirmation and affect heuristics, rather than looking for new financing solutions and combinations.
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Like many countries, the COVID-19 pandemic has forced Statistics Netherlands to make changes in its fieldwork strategy. Since mid-March 2020, there have been limited opportunities to conduct face-to-face interviews. Therefore, from September 2020, CAPI sampled people are offered the opportunity to respond by telephone. For this purpose, face-to-face interviewers are instructed to persuade the potential respondent at the doorway. When people refuse a face-to-face interview, interviewers ask for a telephone number and try to make an appointment to conduct the interview by telephone. The aim of our study was to investigate the effects of conducting the interview by telephone instead of face-to-face on important survey outcome variables. We were particularly interested in whether differences are due to selection effects or caused by mode-specific measurement errors. Because we did not have the time or capacity to set up a controlled experiment, we performed regression analyses to decompensate the differences between selection effects and mode-specific measurement errors. We used data of the Labour Force Survey (LFS) and the Housing Survey (WoON). Our analysis showed that there were differences in important target variables, for both LFS and WoON. These differences were, however, mainly caused by selection effects – which can be taken into account for during weighting – and were less likely to be caused by mode specific measurement errors. Although there are important limitations and caveats, these findings are supportive to further implement this field strategy.
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