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This study aims to conduct a bibliometric analysis to provide a comprehensive picture and identify future research directions to enrich the existing literature on behavioral biases.

The data set comprises 518 articles from the Web of Science database. Performance analysis is used to highlight the significant contributors (authors, institutions, countries and journals) and contributions (highly influential articles) in the field of behavioral biases. In addition, network analysis is used to delve into the conceptual and social structure of the research domain.

The current review has identified four major themes: “Influence of behavioral biases on investment decisions,” “Determinants of home bias,” “Impact of biases on stock market variables” and “Investors’ decision-making under uncertainty.” These themes reveal that a majority of studies have focused on equity markets, and research on other asset classes remains underexplored.

This study extracted data from a single database (Web of Science) to ensure standardization of results. Consequently, future research could broaden the scope of the bibliometric review by incorporating multiple databases.

The novelty of this research is to provide valuable guidance by evaluating the existing literature and advancing the knowledge base on the conceptual and social structure of behavioral biases.

Supporting community college transfer students represents a critical strategy for broadening participation in STEM. In addition to being a racially diverse group, students who pursue STEM degrees by way of community college report frequent interests in graduate study and academic careers. Thus, supporting and expanding transfer students’ PhD interests can help to diversify the STEM professoriate. This study aims to identify the experiences that predict PhD interests among students who transferred into the computer science major from a community college.

Relying on longitudinal survey data from over 150 community college transfer students throughout their first year at their receiving four-year university, we used regression analysis to identify the post-transfer college experiences that predict early interest in PhDs.

We found that receiving information about PhDs from a professor strongly predicted PhD interest among transfer students. Relationships with other variables indicate that the provision of information about graduate school was more likely to occur for students who participated in undergraduate research experiences than for those participating in internships. Descriptive data document inequities in who has access to these types of experiences.

This paper provides new insight into how STEM departments can develop targeted efforts to ensure that information about PhD training is equitably available to all transfer students. Working to ensure that faculty equitably communicate with students about PhD opportunities may go a long way in countering potential deterrents among transfer students who may be interested in such pathways.

The purpose of the paper is to present an integrated methodology for identifying trends in a particular subject area based on a combination of advanced text mining and expert methods. The authors aim to test it in an area of clinical psychology and psychotherapy in 2010–2019.

The authors demonstrate the way of applying text-mining and the Word2Vec model to identify hot topics (HT) and emerging trends (ET) in clinical psychology and psychotherapy. The analysis of 11.3 million scientific publications in the Microsoft Academic Graph database revealed the most rapidly growing clinical psychology and psychotherapy terms – those with the largest increase in the number of publications reflecting real or potential trends.

The proposed approach allows one to identify HT and ET for the six thematic clusters related to mental disorders, symptoms, pharmacology, psychotherapy, treatment techniques and important psychological skills.

The developed methodology allows one to see the broad picture of the most dynamic research areas in the field of clinical psychology and psychotherapy in 2010–2019. For clinicians, who are often overwhelmed by practical work, this map of the current research can help identify the areas worthy of further attention to improve the effectiveness of their clinical work. This methodology might be applied for the identification of trends in any other subject area by taking into account its specificity.

The paper demonstrates the value of the advanced text-mining approach for understanding trends in a subject area. To the best of the authors’ knowledge, for the first time, text-mining and the Word2Vec model have been applied to identifying trends in the field of clinical psychology and psychotherapy.

This study aims to examine the role of hope and hate in political leaders’ messages in influencing liberals versus conservatives’ social-distancing behavior during the COVID-19 pandemic. Given the increasing political partisanship across the world today, using the appropriate message framing has important implications for social and public policy.

The authors use two Natural Language Processing (NLP) methods – a pretrained package (HateSonar) and a classifier built to implement our supervised neural network-based model architecture using RoBERTa – to analyze 61,466 tweets by each US state’s governor and two senators with the goal of examining the association between message factors invoking hate and hope and increased or decreased social distancing from March to May 2020. The authors examine individuals’ social-distancing behaviors (the amount of nonessential driving undertaken) using data from 3,047 US counties between March 13 and May 31, 2020, as reported by Google COVID-19 Community Mobility Reports and the New York Times repository of COVID-19 data.

The results show that for conservative state leaders, the use of hate increases nonessential driving of state residents. However, when these leaders use hope in their speech, nonessential driving of state residents decreases. For liberal state leaders, the use of hate displays a directionally different result as compared to their conservative counterparts.

Amid the emergence of new analytic techniques and novel data sources, the findings demonstrate that the use of global positioning systems data and social media analysis can provide valuable and precise insights into individual behavior. They also contribute to the literature on political ideology and emotion by demonstrating the use of specific emotion appeals in targeting specific consumer segments based on their political ideology.

The findings have significant implications for policymakers and public health officials regarding the importance of considering partisanship when developing and implementing public health policies. As partisanship continues to increase, applying the appropriate emotion appeal in messages will become increasingly crucial. The findings can help marketers and policymakers develop more effective social marketing campaigns by tailoring specific appeals given the political identity of the consumer.

Using Neural NLP methods, this study identifies the specific factors linking social media messaging from political leaders and increased compliance with health directives in a partisan population.

The purpose of this paper is to introduce the relational view of unethical pro-organisational behaviour (UPB) to explain interpersonal paths of influence on employees’ engagement in UPB. The proposed relational view of UPB is grounded in Darwall’s second-person philosophy.

This research design involves two quantitative studies – a pilot study with 340 subjects and the main study with 310 employees. The structural equation modelling data analysis was conducted using the R language software.

The findings provided initial support for the relational view of UPB. Study 1 revealed that employees’ accountability (perceived as personal obligation) influenced their engagement in UPB. Furthermore, Study 2 strengthens the theory and findings from Study 1 that employees’ moral organisational identification influences their engagement in UPB over the influence of employees’ identification with the organisation.

The findings extend the nomological network of UPB and extant theoretical knowledge on the moral self by uncovering how moral accountability and personal obligation have a “dark side”.

The findings indicate that practitioners should address the impact of employee interpersonal relationships on their perceived obligation to engage in UPB.

The authors provided an original use of Darwall’s second-person standpoint as the philosophical foundation to integrate accountability and identity theories, to explain interpersonal influences on employees’ engagement in UPB.

In a competitive environment, eHealth small and medium-sized enterprises’ (SMEs’) barriers to survival differ from those of large enterprises. Empirical research on barriers to eHealth SMEs in less prosperous areas has been largely neglected. This study fills this gap by employing an integrated approach to analyze barriers to the development of eHealth SMEs. The purpose of this paper is to address this issue.

The authors collected data through semi-structured interviews and conducted thematic analysis to identify 16 barriers, which were used as inputs into total interpretive structural modeling (TISM) to build interrelationships among them and identify key barriers. Cross-impact matrix multiplication applied to classification (MICMAC) was then applied validate the TISM model and classify the 16 barriers into four categories.

This study makes significant contributions to theory by identifying new barriers and their interrelationships, distinguishing key barriers and classifying the barriers into four categories. The authors identify that transcultural problems are the key barrier and deserve particular attention. eHealth SMEs originating from regions with cultural value orientations, such as hierarchy and embeddedness, that differ from the UK’s affective autonomy orientation should strengthen their transcultural awareness when seeking to expand into UK markets.

By employing an integrated approach to analyze barriers that impede the development of eHealth SMEs in a less prosperous area of the UK, this study raises entrepreneurs’ awareness of running businesses in places with different cultural value orientations.