Social media firestorms pose a significant challenge for firms in the digital age. Tackling firestorms is difficult because the judgments and responses from social media users are influenced by not only the nature of the transgressions but also by the reactions and opinions of other social media users. Drawing on the heuristic-systematic information processing model, we propose a research model to explain the effects of social impact (the heuristic mode) and argument quality and moral intensity (the systematic mode) on perceptions of firm wrongness (the judgment outcome) as well as the effects of perceptions of firm wrongness on vindictive complaining and patronage reduction. We adopted a mixed methods approach in our investigation, including a survey, an experiment, and a focus group study. Our findings show that the heuristic and systematic modes of information processing exert both direct and interaction effects on individuals’ judgment. Specifically, the heuristic mode of information processing dominates overall and also biases the systematic mode. Our study advances the literature by offering an alternative explanation for the emergence of social media firestorms and identifying a novel context in which the heuristic mode dominates in dual information processing. It also sheds light on the formulation of response strategies to mitigate the adverse impacts resulting from social media firestorms. We conclude our paper with limitations and future research directions.
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Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.
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Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.
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People tend to disclose personal identifiable information (PII) that could be used by cybercriminals against them. Often, persuasion techniques are used by cybercriminals to trick people to disclose PII. This research investigates whether people can be made less susceptible to persuasion by reciprocation (i.e., making people feel obligated to return a favour) and authority, particularly in regard to whether information security knowledge and positive affect moderate the relation between susceptibility to persuasion and disclosing PII. Data are used from a population-based survey experiment that measured the actual disclosure of PII in an experimental setting (N = 2426). The results demonstrate a persuasion–disclosure link, indicating that people disclose more PII when persuaded by reciprocation, but not by authority. Knowledge of information security was also found to relate to disclosure. People disclosed less PII when they possessed more knowledge of information security. Positive affect was not related to the disclosure of PII. And contrary to expectations, no moderating effects were found of information security knowledge nor positive affect on the persuasion–disclosure link. Possible explanations are discussed, as well as limitations and future research directions. Uitgegeven door Sage, APA beschrijving: van der Kleij, R., van ‘t Hoff—De Goede, S., van de Weijer, S., & Leukfeldt, R. (2023). Social engineering and the disclosure of personal identifiable information: Examining the relationship and moderating factors using a population-based survey experiment. Journal of Criminology, 56(2-3), 278-293. https://doi.org/10.1177/26338076231162660
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Pressure on natural resources, unsustainable production and consumption, inequality and a growing global population lie at the base of the big challenges that people face. This chapter investigates how businesses can take responsibility in dealing with these challenges by means of frugal business model innovation. The notion of ‘frugal innovation’ was first introduced in the context of emerging markets, giving non-affluent customers opportunities to consume affordable products and services suited to their needs. Business modelling with a frugal mindset opens up a path that provides significant value while minimizing the use of resources such as energy, capital and time. Business models require intentional design if they are to deliver aspired sustainability impacts. Diminish or simplify resources can be described as the means to remove or reduce features, resources, required activities and/or waste streams. Decompose can be described as the removal of resources from the commercial value proposition and replacing them with resources the user/consumer already can access or uses. This is an Accepted Manuscript of a book chapter published by Routledge/CRC Press in Circular Economy : Challenges and Opportunities for Ethical and Sustainable Business on 2021, available online: https://doi.org/10.4324/9780367816650
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The studies in this thesis aim to increase understanding of the effects of various characteristics of scientific news about a common chronic disease, i.e., diabetes, on the cognitive responses (e.g., emotions, attitudes, intentions) of diabetes patients. The research questions presented in this thesis are guided by the Health Belief Model, a theoretical framework developed to explain and predict healthrelated behaviours based on an individual’s beliefs and attitudes. The model asserts that perceived barriers to a recommended health behavior, advantages of the behavior, self-efficacy in executing the behavior, and disease severity and personal susceptibility to the disease are important predictors of a health behavior. Communication is one of the cues to action (i.e., stimuli) that may trigger the decision-making process relating to accepting a medical or lifestyle recommendation.
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Recently, there has been an increase in interest for the integration of insights from the behavioural sciences into the design process. The Persuasive by Design model aims to provide an evidence-based framework by which designers gain access to relevant theoretical insights from the behavioural sciences. This paper examines the use of the model in two case studies that dealt with complex behavioural change situations. In both studies, the model proved to be a valuable aid in determining target behaviours and operationalizing intervention concepts, especially in the early stages of the design process. Some shortcomings of the model also transpired. The model was seen as too complex, and its psychological frame does not prevent designers to overlook possible systemic moderators of behaviour. Implications for further development of tools that give access to model insights are discussed.
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In this thesis several studies are presented that have targeted decision making about case management plans in probation. In a case management plan probation officers describe the goals and interventions that should help offenders stop reoffending, and the specific measures necessary to reduce acute risks of recidivism and harm. Such a plan is embedded in a judicial framework, a sanction or advice about the sanction in which these interventions and measures should be executed. The topic of this thesis is the use of structured decision support, and the question is if this can improve decision making about case management plans in probation and subsequently improve the effectiveness of offender supervision. In this chapter we first sketch why structured decision making was introduced in the Dutch probation services. Next we describe the instrument for risk and needs assessment as well as the procedure to develop case management plans that are used by the Dutch probation services and that are investigated in this thesis. Then we describe the setting of the studies and the research questions, and we conclude with an overview of this thesis.
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Purpose – Information verification is an important factor in commercial valuation practice.Valuers use their professional autonomy to decide on the level of verification required, thereby creating an opportunity for client-related judgement bias in valuation. The purpose of this paper is to assess the manifestation of client attachment risks in information verification. Design/methodology/approach – A case-based questionnaire was used to retrieve data from 290 commercial valuation professionals in the Netherlands, providing a 15 per cent response rate of the Dutch commercial valuation population. Descriptive and inferential statistics have been used to test research hypotheses involving relations between information verification and professional features that may indicate client attachment such as an executive job level and brokerage experience. Findings – The results reveal that valuers acting at partner level within their organisation obtain lower scores on information verification compared to lower-ranked valuers. Also, brokerage experience correlates negatively to information verification of valuation professionals. Both findings have statistical significance. Research limitations/implications – The results reflect valuers’ reasoning behaviour rather than actual behaviour. Replication of findings through experimental design will contribute to research validity. Practical implications – Maintaining close client contact in a competitive environment is important for business continuity yet may foster client attachment.The associated downside risks in valuation practice call for higher awareness of (subconscious) client influence and the development of attitudinal scepticism in valuer training programmes. Originality/value – This paper is one of the few that explore possible sources of valuer judgement bias by relating client-friendly valuer features to a key area of valuation i.e. information verification.
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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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