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Drawing on the literature from cognitive neuroscience and auditing research on professional skepticism (PS), this paper identifies new research questions, determinants, and theories that may resolve current problem areas in PS research. We identify the following PS research areas that neuroscientific perspectives can potentially improve: 1) theory, 2) trust, 3) trait and state skepticism, 4) deception/fraud detection, and 5) skeptical judgment and action. The paper concludes with a discussion of the critical question of whether integrating a neuroscientific perspective in PS research is worthwhile and provides further direction for future research.

Neuromarketing, which is an interdisciplinary area, concentrates on evaluating consumers’ cognitive and emotional reactions to different marketing stimuli. In spite of advantages, neuromarketing still requires development and lacks a strong theoretical framework. Techniques that are used in neuromarketing studies have different superiorities and limitations, and thus, there is a need for the evaluation of the relevance of these techniques. The purpose of this study is to introduce a novel integrated approach for the neuromarketing research area.

The proposed approach combines 2-tuple linguistic representation model and data envelopment analysis to obtain the most efficient neuromarketing technique. It is apt to handle information provided by using both linguistic and numerical scales with multiple information sources. Furthermore, it allows managers to deal with heterogeneous information, without loss of information.

The proposed approach indicates that functional magnetic resonance imaging (fMRI) is the best performing neuromarketing technology. Recently, fMRI has been widely used in neuromarketing research. In spite of its high cost, its main superiorities are improved spatial and temporal resolutions. On the other hand, transcranial magnetic stimulation (TMS) and positron emission tomography (PET) are ranked at the bottom because of their poor resolutions and lower willingness of participants.

This paper proposes a common weight data envelopment analysis (DEA)-based decision model to cope with heterogeneous information collected by the experts to determine the best performing neuromarketing technology. The decision procedure enables the decision-makers to handle the problems of loss of information and multi-granularity by using the fusion of 2-tuple linguistic representation model and fuzzy information. Moreover, a DEA-based common weight model does not require subjective experts’ opinions to weight the evaluation criteria.

Neuroimaging technologies such as electroencephalogram and magnetic resonance imaging allow us to analyze consumers’ brains in real time as they experience emotions. These technologies collect and integrate data on consumers’ brains for big data analytics. The purpose of this paper is to identify new opportunities and challenges for neuromarketing as an applied neuroscience.

The authors discuss conceptual and methodological contributions of neuromarketing based on studies that have employed neural approaches in market-related investigations, explaining the various tools and designs of neuromarketing research. The authors identify marketing-related questions to which neuroscientific approaches can make meaningful contributions, evaluating several challenges that lie ahead for neuromarketing.

The authors summarize the contributions of neuromarketing and discuss synergistic findings that neuromarketing has the potential to yield.

The authors ask: do consumers’ self-reported choices and their neural representations tell different stories?; what are the effects of subtle and peripheral marketing stimuli?; and can neuromarketing help to reveal the underlying causal mechanisms for perceptual and learning processes, such as motivation and emotions?

The authors identify marketing-related questions to which neuroscientific approaches can make meaningful contributions, evaluating several challenges that lie ahead for neuromarketing.

To the best of the authors’ knowledge, no current review has identified avenues for future research in neuromarketing and the emerging challenges that researchers may face. The current paper aims to update readers on what neuroscience and other psychophysiological measures have achieved, as well as what these tools have to offer in the field of marketing. The authors also aim to foster greater application of neuroscientific methods, beyond the more biased/post-test methods such as self-report studies, which currently exist in consumer research.

Neuroarchitecture is a new interdisciplinary research field combining neuroscience and architecture that has developed and expanded since 2000. Neuroarchitecture originated from the divergence of previous multidisciplinary studies on the relationship between humans and the environment. However, scoping reviews of neuroarchitecture in relation to the experience of the built environment are lacking. Thus, this study aimed to provide the background and research trends of neuroarchitecture to contribute to discussions on the built environment.

A general form of scoping review was adopted, following the PRISMA-ScR checklist. For this scoping review emphasizing the embodied implication of neuroarchitecture for the built environment, an evaluation framework was developed consisting of four categories: health, performance, aesthetics and emotion.

This study explores objective techniques, including electroencephalography and functional magnetic resonance imaging, electrocardiogram, electrodermal activity and saliva cortisol, to measure neurophysiological impacts, adopting real, virtual and images of environmental settings. An in-depth review of 25 selected papers revealed the existing empirical research on neuroarchitecture using human physiological measurement tools and representational environment settings to examine the impact of human–environment relationships.

A meta-analysis of theoretical and intervention studies on neuroarchitecture that investigates the multisensory characteristics of the environment is lacking. In addition, the development and application of wearable tools to meet the needs of real environment settings can improve the effectiveness of neurophysiological measurement tools.

The purpose of this paper is to uncover the neural basis underlying the negative impact of graffiti on evaluations of neighborhood safety.

While in a functional magnetic resonance imaging scanner, participants rated the perceived safety of neighborhood scenes shown in photographic images. Some scenes were systematically altered to include graffiti, surveillance cameras or framed public art.

Consistent with previous research, the average safety rating for each scene including graffiti was significantly lower than for every other non-graffiti scene presented. The powerful salience of graffiti (as demonstrated by increased activation of the left fusiform gyrus) accompanied a decreased salience of other built environment scene elements (as demonstrated by reduced activation in the right parahippocampal gyrus). It appears as though the presence of graffiti causes the cognitive impact of other scene elements to fade. This redirection of cognitive focus may help to explain the dominant impact of graffiti on neighborhood safety evaluations.

Because of these basic perceptual tendencies, graffiti will necessarily dominate neighborhood safety evaluations. In any attempts to improve neighborhood safety evaluations, combating graffiti should be accorded a position of great importance, in keeping with these cognitive realities.

This is the first neuroimaging study of graffiti and perceived neighborhood safety.

This chapter introduces electroencephalography (EEG), a measure of neurophysiological activity, as a critical method for investigating individual and team decision-making and cognition. EEG is a useful tool for expanding the theoretical and research horizons in organizational cognitive neuroscience, with a lower financial cost and higher portability than other neuroimaging methods (e.g., functional magnetic resonance imaging). This chapter briefly reviews past work that has applied cognitive neuroscience methods to investigate cognitive processes and outcomes. The focus is on describing contemporary EEG measures that reflect individual cognition and compare them to complementary measures in the field of psychology and management. The authors discuss how neurobiological measures of cognition relate to and may predict both individual cognitive performance and team cognitive performance (decision-making). This chapter aims to assist scholars in the field of managerial and organizational cognition in understanding the complementarity between psychological and neurophysiological methods, and how they may be combined to develop new hypotheses in the intersection of these research fields.

The purpose of this paper is to elucidate the factors influencing creativity in information system development (ISD). Specifically, this paper compares two groups, students and practitioners, in order to identify salient factors influencing creativity in this field.

An empirical study was conducted through two online surveys in Taiwan. The subjects were university students (n=220) and practitioners (n=187) who had ISD project experiences. Data were collected via an online survey and analyzed using partial least squares (PLS) structural equation modeling. The authors present state-of-the-art PLS path modeling, including heterotrait-monotrait, standardized root mean square residual, second-order identification, and partial least squares multigroup analysis (PLS-MGA).

Both the student and practitioner results showed that the most salient factor influencing creative behavior was use of creativity technologies. In addition, cognitive style, recognition, and database programming had a significant positive effect on creative behavior for the students but not for the practitioners. The PLS-MGA results indicated that differences in creative behavior between the students and practitioners need to be considered.

There are two main limitations. The first one is its design. This study was cross-sectional and there is a likelihood of common source bias due to self-reported data. In the future, researchers can use brain-imaging tools (e.g. functional magnetic resonance imaging (fMRI)) or eye tracking measurement to reduce self-reporting bias in creative behavior research. The second limitation is related to the sample. The subjects were university students and practitioners who had ISD project experiences; however, the heterogeneity of the investigated individuals (e.g. background, ability or experience) in university or industry may limit the power and generalizability of this study. Future studies may take same samples into consideration.

No previous study has investigated factors influencing the creativity of information system (IS) students and practitioners by using a decomposed and second-order research model. Thus, this paper, based on Amabile’s creativity theory, attempts to elucidate the factors influencing IS students’ and practitioners’ creativity.

The emotion regulation research has drawn considerable attention from academicians and scholars in the contemporary world. As a result, the publications that are specifically dedicated to emotion regulation research are rapidly escalating. Therefore, this study aims to conduct a bibliometric analysis of research articles that have been published in the field of “emotion regulation.” The study primarily examines the growth and development of scholarly publications, seminal studies, influential authors, productive journals, research production and collaboration among countries, emerging research themes, research hotspots and thematic evolution of emotion regulation research.

The Web of Science Core Collection database was used to gather the study’s data, which was then analysed using VOSviewer and Bibliometrix, Biblioshiney open-source package of the R language environment.

The study’s results reveal that the research on emotion regulation has grown significantly over the last three decades. Notably, Emotion and Frontiers in Psychology are the most dominant and productive journals in the field of emotion regulation research. The most prominent author in the area of emotion regulation is identified as James Gross, followed by Gratz, Wang and Tull. The USA is at the forefront of research on emotion regulation and has collaborated with most of the developed countries like Germany, England and Canada. The keyword analysis revealed that the most potential research areas in the field of emotion regulation are functional magnetic resonance imaging, amygdala, post-traumatic stress disorder, borderline personality disorder, alexithymia, emotion dysregulation, depression, anxiety, functional connectivity, neuroimaging, mindfulness, self-regulation, resilience and coping. The thematic evolution reflects that the research on emotion regulation has recently focused on issues including Covid-19, non-suicidal self-injury, psychological distress, intimate partner violence and mental health.

The results of this study highlighted the current knowledge gaps in emotion regulation research and suggested areas for further investigation. The present study could be useful for researchers, academicians, planners, publishers and universities engaged in emotion regulation research.

The purpose of this paper is to propose a novel method for evaluation of human-robot affection. The model is inspired by the scientific methods of human-human love evaluation. This paper would benefit the researchers in the field of developing new technologies where emotional interaction is involved.

Among the two available options of Functional Magnetic Resonance Imaging (fMRI) and user study, the latter is adopted and the conventional method of Love Attitude Scale is transformed for human-robot interaction as Lovotics (love + robotics) Love Attitude Scale. A user study is conducted to evaluate the emotional effect of interaction with the robot.

The proposed method is employed in order to evaluate the performance of Lovotics robot. In total, 20 users experienced interaction with Lovotics robot and answered questionnaires which were designed based on the psychology of love, especially to measure love scales between the participants and the robot. Data from the user study are analyzed statistically to evaluate the overall performance of the designed robot.

Various aspects including human to robot love styles, robot to human love styles, overall love values and gender study are investigated during the data analysis. The concept of human-robot affection is still in initial stage of development. Personal and social robots are increasing and much limitation from artificial intelligence, mechanical development and integration still exist.

This is a multidisciplinary research field utilizing fundamentals concepts from robotics, artificial intelligence, philosophy, psychology, biology, anthropology, neuroscience, social science, computer science and engineering.

Considering the recent technical advancement in robotics which is brining robots closer to home, this paper aims to bridge the gap between human and robot affection measurement. The final goal is to introduce robots to the society which are useful and can be especially used to take care of those in need such as elderly.

This paper is one of the first kind to get inspired from scientific human love evaluation methods and apply that to human-robot application.