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The recent COVID-19 crisis has been followed by an epidemic of fraud. This study aims to evaluate cases of COVID-19-related fraud to identify cognitive heuristics that influence decision-making under the pressure of crisis conditions.

An analysis of fraud advisories and cases relating to COVID-19 is conducted and matched against various types of cognitive heuristics to explain their influence on victims of crisis fraud.

The affect, availability, cue-familiarity, representativeness and scarcity heuristics are identified and explained to have a substantial influence on risk evaluations of crisis fraud.

The findings from this study can help individuals avoid fraud victimisation by helping them understand psychological vulnerabilities that they may be unaware of under the pressure of crisis conditions.

The COVID-19 pandemic has spurred a concurrent outbreak of false information online. Debunking false information about a health crisis is critical as misinformation can trigger protests or panic, which necessitates a better understanding of it. This exploratory study examined the effects of debunking messages on a COVID-19-related public chat on WhatsApp in Singapore.

To understand the effects of debunking messages about COVID-19 on WhatsApp conversations, the following was studied. The relationship between source credibility (i.e. characteristics of a communicator that affect the receiver's acceptance of the message) of different debunking message types and their effects on the length of the conversation, sentiments towards various aspects of a crisis, and the information distortions in a message thread were studied. Deep learning techniques, knowledge graphs (KG), and content analyses were used to perform aspect-based sentiment analysis (ABSA) of the messages and measure information distortion.

Debunking messages with higher source credibility (e.g. providing evidence from authoritative sources like health authorities) help close a discussion thread earlier. Shifts in sentiments towards some aspects of the crisis highlight the value of ABSA in monitoring the effectiveness of debunking messages. Finally, debunking messages with lower source credibility (e.g. stating that the information is false without any substantiation) are likely to increase information distortion in conversation threads.

The study supports the importance of source credibility in debunking and an ABSA approach in analysing the effect of debunking messages during a health crisis, which have practical value for public agencies during a health crisis. Studying differences in the source credibility of debunking messages on WhatsApp is a novel shift from the existing approaches. Additionally, a novel approach to measuring information distortion using KGs was used to shed insights on how debunking can reduce information distortions.

The purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.

Using a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.

The findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.

Even with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.

First, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.

This study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

This paper aims to share how an Asian university enhanced students’ global competence through international business study missions (BSMs). More specifically, it focuses on how the design of these BSMs enabled “deep” learning beyond industry tourism and how 21st-century competencies such as “global competence” can be acquired through participation in short-term, faculty-led study missions.

Using the case study approach, it critically analyzes the learning goals and objectives, design decisions, implementation details and learning outcomes underlying three BSMs led by three instructors from the same university to the USA (New York), Germany (Berlin and Stuttgart) and South Korea (Seoul).

The study shows that students gained global competencies related to specific fields of study such as the creative industries, urban sustainability and entrepreneurship. It shows how design choices such as destination, range of organizations, length of individual visits, range of pedagogical techniques, intensity of preparation and quality of management contribute to students’ acquisition of global competencies.

This research presents a subset of case studies that may limit the generalization of the findings; the bias that results from an unrepresentative, opportunistic sample (selection bias); and lack of quantitative causality in a qualitative evaluation.

The course design described here provides practical information for designing study abroad “deep” learning goals, objectives and outcomes focusing on global competence.

The detailed case studies of three instructors from different disciplines to achieve the country’s education vision of globally competent students.

Internet fraud is an epidemic that costs US$7.1 billion as of 2007. The advent of the internet and proliferation of its use makes it an attractive medium for communicating the fraud, particularly through the use of e‐mail. This paper aims to explain how victims of online fraud can be influenced by judgmental heuristics and cognition when they make nonnormative or sub‐optimal decisions.

The paper will analyse the content of 14 recent fraud e‐mails to explain how victims of online fraud can be influenced from a psychological perspective, using theories of bounded rationality, judgmental heuristics and cognition.

The paper suggests that e‐mail fraudsters, whether intentionally or not, employ specific methods that correspond closely to how the human mind works within a context of bounded rationality. These methods have a propensity to exploit psychological blind spots in victims caused by selective perception and post‐decisional dissonance, as well as sub‐optimal or nonnormative responses in automatic behaviour due to the common use of heuristics (for example, representativeness, availability and affect) when making decisions in complex task environments.

Considering the current and widespread problem of online fraud, this paper is expected to inform and prepare internet users against such deception by offering a better understanding of how fraudsters can psychologically influence the way victims and potential victims make their decisions.

This chapter describes a neurodynamic modeling approach which may be useful for dynamically assessing teamwork in healthcare and military situations. It begins with a description of electroencephalographic (EEG) signal acquisition and the transformation of the physical units of EEG signals into quantities of information. This transformation provides quantitative, dynamic, and generalizable neurodynamic models that are directly comparable across teams, tasks, training protocols, and team experience levels using the same measurement scale, bits of information. These bits of information can be further used to dynamically guide team performance or to provide after-action feedback that is linked to task events and team actions.

These ideas are instantiated and expanded in the second section of the chapter by showing how these data abstractions, compressions, and transformations take advantage of the natural information redundancy in biologic signals to substantially reduce the number of data dimensions, making the incorporation of neurodynamic feedback into Intelligent Tutoring Systems (ITSs) achievable.

The current natural language processing algorithms are still lacking in judgment criteria, and these approaches often require deep knowledge of political or social contexts. Seeing the damage done by the spreading of fake news in various sectors have attracted the attention of several low-level regional communities. However, such methods are widely developed for English language and low-resource languages remain unfocused. This study aims to provide analysis of Hindi fake news and develop a referral system with advanced techniques to identify fake news in Hindi.

The technique deployed in this model uses bidirectional long short-term memory (B-LSTM) as compared with other models like naïve bayes, logistic regression, random forest, support vector machine, decision tree classifier, kth nearest neighbor, gated recurrent unit and long short-term models.

The deep learning model such as B-LSTM yields an accuracy of 95.01%.

This study anticipates that this model will be a beneficial resource for building technologies to prevent the spreading of fake news and contribute to research with low resource languages.

Focusing on managerial problems related to the measurement of intangibles, this paper develops and validates a hedonic-pricing methodology for the evaluation of the intangible resources of companies obtaining their shadow prices.

The paper adapts a hedonic-pricing methodology developed primarily for markets in real estate and secondhand cars to define how much intangibles may contribute to companies' market value. A certain calibration of the original tool has been developed to make this methodology appropriate for interpretation and practical use. The main advantage of this approach is that it allows for an evaluation of the shadow prices of intangible resources. These prices can be interpreted as the market value of the intangible resources which are not reflected on the balance sheet.

The results of this study demonstrate that hedonic pricing with a self-selection correction generates robust estimates. As one can see, the positive contribution of a high endowment of intangibles for all shadow prices is confirmed through estimations using two different techniques. Meanwhile, the negative effect of a low endowment is even more evident for the baseline model. This model shows consistent negative shadow prices for the majority of underinvested intangibles. Brands have the highest shadow prices in the introduced models; human capital, as measured by the qualification of top management and investments in employees, has likewise demonstrated high prices. However, most structural resources seem to be not reflected to a large degree in companies' market value.

This paper brings new opportunities to obtain the monetary value of intangible resources based on estimated market prices of a corporation's resource portfolio. These prices may be used for several purposes – for example, benchmarking for performance management, capital budgeting or knowledge-management practices. Moreover, by having methodological value, this study opens ways to evaluate any other intangibles which are not explicitly discussed in the empirical test of this particular study.

This study primarily contributes to the methodological advancement of evaluation of corporate intangible resources. It departs from the conventional hedonic-pricing mechanism to identify cogent estimates to intangibles in monetary terms. Importantly, this mechanism implies individual shadow prices for specific intangible resources which makes the contribution of this study unique for the existing literature, both within resource-based and value-based views.

The OSCE is regarded as the gold standard of competence assessment in many healthcare programs, however, there are numerous internal and external sources of variation contributing to checklist marks. There is concern amongst organisers that candidates may be unfairly disadvantaged if they follow an “excellent” preceding candidate. This study assessed if average checklist scores differed depending on who a candidate follows accounted for different sources of variation.

This study examined assessment data from final year MBChB OSCEs at the University of Aberdeen and categorised candidates into three levels dependent on examiner awarded global scores of preceding candidates for each station. Data were modelled using a linear mixed model incorporating fixed and random effects.

A total of 349 candidates sat the OSCEs. The predicted mean (95% CI) score for students following an “excellent” candidate was 21.6 (20.6, 22.6), followed “others” was 21.5 (20.5, 22.4) and followed an “unsatisfactory” student was 22.2 (21.1, 23.3). When accounted for individual, examiner and station levels variabilities, students following an “excellent” candidate did not have different mean scores compared to those who followed “other” (p = 0.829) or “unsatisfactory” candidates (p = 0.162), however, students who followed an “unsatisfactory” student scored slightly higher on average compared to those who followed “other” (p = 0.038).

There was weak evidence that candidate's checklist variations could be attributed to who they followed, particularly those following unsatisfactory students; the difference in predicted mean scores may be of little practical relevance. Further studies with multiple centres may be warranted assuring perceived fairness of the OSCE to candidates and educators.

The purpose of this paper is to assess the perceived importance and actual level of implementation of five preliminary success factors, four knowledge management (KM) strategies and three KM processes towards the performance of the Malaysian telecommunication industry.

A questionnaire survey was conducted on telecommunication companies based on a convenience sampling technique. Data were analyzed using paired t‐tests and multiple regression analyses.

The results show that Malaysian telecommunication organizations view all the KM preliminary success factors, strategies and process as important and critical but fall short of implementation. K Audit, K Map, leadership, measurement, construction and embodiment are the variables that have the largest gap between perceived importance and actual implementation. For perceived importance, culture is the only important variable associated with organizational performance whereas for actual implementation, both business strategy and construction process are found to be significantly associated with organizational performance.

This paper was conducted in only one industry in Malaysia. Furthermore, it focuses on KM implementation rather than on learning and knowledge utilization. This paper provides a framework for developing an instrument for assessing the use of preliminary elements in the Malaysian telecommunication industry. Telecommunication organizations have to overcome problems identified and enhance their implementation level in order to achieve better organizational performance.

This paper has extended knowledge in KM, especially concerning implementation issues at the beginning stage of KM. Moreover, it is among the first empirical work to specifically examine preliminary success factors, processes and strategies that affect the preliminary implementation of KM.