scholarly journals Consumer Neuroscience Techniques in Advertising Research: A Bibliometric Citation Analysis

2021 ◽  
Vol 13 (3) ◽  
pp. 1589
Author(s):  
Juan Sánchez-Fernández ◽  
Luis-Alberto Casado-Aranda ◽  
Ana-Belén Bastidas-Manzano
Keyword(s):  
Self Report ◽  
Future Research ◽  
Complex Nature ◽  
Clear Understanding ◽  
Continuous Growth ◽  
Main Research ◽  
Consumer Neuroscience ◽  
Starting Point ◽  
Wide Range ◽  
Performance Analysis Tools

The limitations of self-report techniques (i.e., questionnaires or surveys) in measuring consumer response to advertising stimuli have necessitated more objective and accurate tools from the fields of neuroscience and psychology for the study of consumer behavior, resulting in the creation of consumer neuroscience. This recent marketing sub-field stems from a wide range of disciplines and applies multiple types of techniques to diverse advertising subdomains (e.g., advertising constructs, media elements, or prediction strategies). Due to its complex nature and continuous growth, this area of research calls for a clear understanding of its evolution, current scope, and potential domains in the field of advertising. Thus, this current research is among the first to apply a bibliometric approach to clarify the main research streams analyzing advertising persuasion using neuroimaging. Particularly, this paper combines a comprehensive review with performance analysis tools of 203 papers published between 1986 and 2019 in outlets indexed by the ISI Web of Science database. Our findings describe the research tools, journals, and themes that are worth considering in future research. The current study also provides an agenda for future research and therefore constitutes a starting point for advertising academics and professionals intending to use neuroimaging techniques.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

scholarly journals Application of the Socio-Ecological System Framework to Forest Fire Risk Management: A Systematic Literature Review

2021 ◽  
Vol 13 (4) ◽  
pp. 2121 ◽  
Author(s):  
Ingrid Vigna ◽  
Angelo Besana ◽  
Elena Comino ◽  
Alessandro Pezzoli
Keyword(s):  
Risk Management ◽  
Community Involvement ◽  
Forest Ecosystems ◽  
Local Community ◽  
Fire Risk ◽  
Future Research ◽  
Specific Reference ◽  
Clear Understanding ◽  
Wildfire Risk ◽  
Starting Point

Although increasing concern about climate change has raised awareness of the fundamental role of forest ecosystems, forests are threatened by human-induced impacts worldwide. Among them, wildfire risk is clearly the result of the interaction between human activities, ecological domains, and climate. However, a clear understanding of these interactions is still needed both at the global and local levels. Numerous studies have proven the validity of the socioecological system (SES) approach in addressing this kind of interdisciplinary issue. Therefore, a systematic review of the existing literature on the application of SES frameworks to forest ecosystems is carried out, with a specific focus on wildfire risk management. The results demonstrate the existence of different methodological approaches that can be grouped into seven main categories, which range from qualitative analysis to quantitative spatially explicit investigations. The strengths and limitations of the approaches are discussed, with a specific reference to the geographical setting of the works. The research suggests the importance of local community involvement and local knowledge consideration in wildfire risk management. This review provides a starting point for future research on forest SES and a supporting tool for the development of a sustainable wildfire risk adaptation and mitigation strategy.

scholarly journals Education Support Worker's Perspectives on Their Work with Children with Disabilities in New Zealand Kindergarten Settings

2021 ◽  
Author(s):  
◽  
Pamela Jean Backhouse
Keyword(s):  
Early Childhood ◽  
New Zealand ◽  
Students With Disabilities ◽  
Quantitative Research ◽  
Children With Disabilities ◽  
Policy Implications ◽  
Self Report ◽  
Future Research ◽  
Wide Range ◽  
Education Support

<p>International literature has focused on paraprofessionals working with students with disabilities in schools and similarly there is some investigative research on teacher aides working with children with disabilities in New Zealand schools. However there is little enquiry into Education Support Workers (ESWs) perspectives of working with children with disabilities in New Zealand Early Childhood Education settings. This study is intended to contribute to addressing this important gap in the literature. ESWs are allocated as primary supports for children with disabilities who need extra learning support and require intervention. This qualitative and quantitative research study is positioned within a sociocultural framework of the Te Whāriki (1996) Early Childhood curriculum which promotes inclusive practices for all children. One-hundred and three ESW respondents from the kindergarten sector completed and returned a questionnaire. Data collection included the role and proximity of an ESW, the child’s interactions with others, and the ESW’s relationship with the child with disabilities. The results revealed ESWs have a wide range of roles and responsibilities in their work with children with disabilities. They work in collaboration with teachers in determining their work with a child and integrate a child into the environment. The development of social skills and involving everyone in the child’s learning was a top priority. Also included was the building of relationships between the child, peers, teachers, and parents. In this study ESWs used a combination of positions such as working alongside, hovering, opposite, and behind and at the same time the child primarily interacted with the ESW, teachers, and peers. Even though there were some ESWs who worked exclusively with a child, the child still interacted in combination with the ESW, teachers, and peers. This result showed inclusion of others irrespective of the ESW’s close proximity. The ESW’s relationship with a child was reported as warm, caring, and positive and also described as very close, perhaps due to the nature of support for some children. This study explored ESWs’ perspectives on their work with children with disabilities and used self report. Theoretical and policy implications are discussed in the context of the ECE curriculum. Although some insight has been generated by ESWs’ participation in this study, there is still an urgent need for future research to ensure Ministry of Education policy and practice line up for children with disabilities and their families, in order for them to receive an equitable fair education as valued members of our community.</p>

Meaningful Ways of Understanding and Measuring Change for People with Borderline Personality Disorder: A Thematic Analysis

2018 ◽  
Vol 46 (5) ◽  
pp. 528-540 ◽  
Author(s):  
Louise McCusker ◽  
Marie-Louise Turner ◽  
Georgina Pike ◽  
Helen Startup
Keyword(s):  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Focus Groups ◽  
Thematic Analysis ◽  
Borderline Personality ◽  
Complex Nature ◽  
Service Users ◽  
Starting Point ◽  
Wide Range ◽  
Measuring Change

Background:The effective treatment of Borderline Personality Disorder (BPD) presents healthcare providers with a significant challenge. The evidence base remains limited partially due to a lack of professional consensus and service user involvement regarding ways of measuring change. As a result, the limited evidence that is available draws on such a wide range of outcome measures, that comparison across treatment types is hindered, maintaining a lack of clarity regarding the clinical needs of this group.Aims:This investigation aimed to follow the National Institute of Clinical Excellence (NICE, 2009) research recommendations by asking service users about meaningful change within their recovery. This forms a starting point for the future development of a tailored outcome measure.Method:Fifteen service users with a diagnosis of BPD participated in three focus groups across two specialist Personality Disorder services. The focus groups were analysed using Thematic Analysis.Results:Two superordinate themes were synthesized from the data: (1) recovery to what?: ‘How do you rewrite who you are?’; and (2) conditions for change. Each superordinate theme further consisted of three subordinate themes which elucidated the over-arching themes.Conclusion:This investigation highlights the complex nature of measuring change in people who have received a BPD diagnosis. Further research is needed to develop meaningful ways of measuring change according to the needs and priorities of people with BPD.

scholarly journals El pensamiento computacional en Educación. Análisis bibliométrico y temático.

2020 ◽  
Vol 20 (63) ◽  
Author(s):  
Rosabel Roig-Vila ◽  
Víctor Moreno-Isac
Keyword(s):  
Research Methods ◽  
Web Of Science ◽  
Computational Thinking ◽  
Future Research ◽  
Main Research ◽  
Documentary Sources ◽  
Starting Point ◽  
The Status ◽  
Number Of Publications ◽  
Educación Primaria

El pensamiento computacional se está considerando actualmente como una de las competencias más demandadas y, de ahí, su planteamiento en el contexto educativo. Este trabajo trata de analizar la literatura científica sobre la aplicación del pensamiento computacional en el ámbito educativo publicada en las colecciones principales de la base de datos Web of Science. Se han tenido en cuenta las variables de año de publicación, los países con más producciones, los autores más productivos en este campo y fuentes documentales con mayor número de publicaciones. Asimismo, se ha realizado una clasificación según los tipos de documentos y los métodos de investigación utilizados, así como las etapas educativas objeto de estudio y los lenguajes de programación utilizados. Se ha hallado una tendencia creciente de publicaciones en esta temática, donde España es uno de los países donde más se publica. Además, se ha observado cómo este campo de estudio se ha abordado desde los dos principales métodos de investigación –cuantitativo y cualitativo— y la etapa educativa más investigada es la educación primaria. Por último, se lleva a cabo una discusión de los resultados y conclusiones, sirviendo este documento como punto de partida para futuras investigaciones sobre el pensamiento computacional en educación. The status of computational thinking as one of the most demanded skills explains why suggestions are currently being made to apply it within the educational context. This paper constitutes an attempt to analyze the scientific literature on the implementation of computational thinking in the field of education published in the most important Web of Science database collections. Attention was paid to four variables, namely: publication year; countries with more productions; the most productive authors in this field; and documentary sources with a higher number of publications. Added to this, we carried out a classification according to the types of documents and the research methods used, along with the educational stages under study and the programming language adopted. Publications are undoubtedly on an upward trend, Spain standing out as one of the most productive countries in this area. Likewise, evidence demonstrates not only that this field of study has been addressed using the two main research methods —quantitative and qualitative— but also that the emphasis has traditionally been placed on the primary education stage. A discussion of the results obtained as well as the conclusions drawn will put an end to this paper, which can hopefully serve as a starting point for future research works on the utilization of computational thinking in education.

Characterizing Competencies for Human-Centered Design

2016 ◽  
Author(s):  
Julia Kramer ◽  
Alice M. Agogino ◽  
Celeste Roschuni
Keyword(s):  
Core Competencies ◽  
Future Research ◽  
Support Design ◽  
Human Centered Design ◽  
Future Design ◽  
Core Set ◽  
Starting Point ◽  
Wide Range ◽  
Successful Design ◽  
Research Interviews

Employees and employers alike increasingly value human-centered design, as it can drive innovation across a wide range of industries. With the growing interest in understanding human-centered design processes as they apply in different professions, there is a rising need to recognize the specific competencies necessary to perform these jobs well. Though there is a body of research on how people discover, create, and use design methods, there is a lack of understanding of what core competencies are necessary for people to apply these methods. Previous interactions with target users of theDesignExchange, an interactive community-driven portal to support design researchers and practitioners, have demonstrated a desire for increased awareness of the competencies required for employability and for successful design practice. This paper reports on a portion of an expansive competency-finding project aimed at identifying the core set of competencies that human-centered design practitioners need and employers seek. In this paper, we present our lists of cultivated mindsets, specialized disciplinary skills, contextualized tasks, and basic skills in human-centered design. These lists represent a first pass at identifying the essential and underlying competencies a practicing or aspiring human-centered designer must have in order to perform their current or future design tasks. The work we present in this paper serves as a preliminary starting point for future research interviews with design practitioners and employers, as we seek to understand human-centered design competencies.

scholarly journals COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

scholarly journals Education Support Worker's Perspectives on Their Work with Children with Disabilities in New Zealand Kindergarten Settings

2021 ◽  
Author(s):  
◽  
Pamela Jean Backhouse
Keyword(s):  
Early Childhood ◽  
New Zealand ◽  
Students With Disabilities ◽  
Quantitative Research ◽  
Children With Disabilities ◽  
Policy Implications ◽  
Self Report ◽  
Future Research ◽  
Wide Range ◽  
Education Support

<p>International literature has focused on paraprofessionals working with students with disabilities in schools and similarly there is some investigative research on teacher aides working with children with disabilities in New Zealand schools. However there is little enquiry into Education Support Workers (ESWs) perspectives of working with children with disabilities in New Zealand Early Childhood Education settings. This study is intended to contribute to addressing this important gap in the literature. ESWs are allocated as primary supports for children with disabilities who need extra learning support and require intervention. This qualitative and quantitative research study is positioned within a sociocultural framework of the Te Whāriki (1996) Early Childhood curriculum which promotes inclusive practices for all children. One-hundred and three ESW respondents from the kindergarten sector completed and returned a questionnaire. Data collection included the role and proximity of an ESW, the child’s interactions with others, and the ESW’s relationship with the child with disabilities. The results revealed ESWs have a wide range of roles and responsibilities in their work with children with disabilities. They work in collaboration with teachers in determining their work with a child and integrate a child into the environment. The development of social skills and involving everyone in the child’s learning was a top priority. Also included was the building of relationships between the child, peers, teachers, and parents. In this study ESWs used a combination of positions such as working alongside, hovering, opposite, and behind and at the same time the child primarily interacted with the ESW, teachers, and peers. Even though there were some ESWs who worked exclusively with a child, the child still interacted in combination with the ESW, teachers, and peers. This result showed inclusion of others irrespective of the ESW’s close proximity. The ESW’s relationship with a child was reported as warm, caring, and positive and also described as very close, perhaps due to the nature of support for some children. This study explored ESWs’ perspectives on their work with children with disabilities and used self report. Theoretical and policy implications are discussed in the context of the ECE curriculum. Although some insight has been generated by ESWs’ participation in this study, there is still an urgent need for future research to ensure Ministry of Education policy and practice line up for children with disabilities and their families, in order for them to receive an equitable fair education as valued members of our community.</p>

scholarly journals COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina F. Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Abstract Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible-infected-recovered (SIR) and susceptible-exposed-infectious-recovered (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

scholarly journals COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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