Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Mathematics
Special Issues
New Applications of Data Analysis Methodologies and Techniques to the...
share announcement

New Applications of Data Analysis Methodologies and Techniques to the Social Sciences

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 10768

Special Issue Editors

Dr. Santiago Gutiérrez-Broncano

E-Mail Website
Guest Editor
Faculty Social Science, University of Castilla-La Mancha, 45600 Talavera de la Reina, Spain
Interests: high engagement practices; talent management; leadership; organisational behaviour; organisational neuroscience; work climate; well-being; family business; strategy
Dr. Benito Yañez-Araque

E-Mail Website
Guest Editor
Department of Physical Activity and Sports Sciences, University of Castilla-La Mancha, 45071 Toledo, Spain
Interests: healthcare and hospital management; occupational risks prevention; corporate social responsibility; quality management; marketing; strategy; corporate governance; business intelligence; innovation; human resources; sports management
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro Jiménez Estevez

E-Mail Website
Guest Editor
Department of Business Administration, University of Castilla-La Mancha, 45002 Toledo, Spain
Interests: corporate social responsibility; business ethics; organizational behaviour; Human Resource Management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Felipe Hernández-Perlines

E-Mail Website
Guest Editor
Faculty of Legal and Social Sciences, University of Castilla-La Mancha, 45071 Toledo, Spain
Interests: family business; social economy; entrepreneurial orientation; absorptive capacity; innovation; PLS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's world, business and government managers, academics and researchers have at their disposal a wealth of data to analyze for decision making and the discovery of new findings. This requires the establishment of a theoretical framework, the use of sciences such as mathematics and statistics, as well as experience and intuition. In this sense, research in the field of social sciences uses techniques such as multiple regression analysis (MRA), ordinary least squares (OLS) regression, techniques and structural equation models (SEM), partial least squares structural equation modeling (PLS-SEM), fuzzy-set qualitative comparative analysis method (fsQCA), fuzzy logic-based approaches, ordered weighted averaging (OWA), non-linear math, Big Data analytics, neural networks, machine learning, deep learning and data mining. Submissions may present, but are not limited to, applications of the aforementioned methods.

We invite you to contribute to our new Special Issue that seeks high-quality papers with different quantitative and qualitative methodologies, as well as studies of mixed methods. We particularly welcome research studies that adopt a multi-sectoral and transdisciplinary approach. Original manuscripts are welcome on any topic relevant to social sciences, psychology, sociology, business management, economy, healthcare and hospital management, occupational risks prevention, corporate social responsibility, quality management, marketing, strategy, corporate governance, business intelligence, innovation, human resources, tourism management, MICE, sports management, and so on. Reviews following quality criteria will also be considered, including systematic reviews, meta-analysis, MASEM and SEM-based meta-analysis.

Dr. Santiago Gutiérrez-Broncano
Dr. Benito Yáñez-Araque
Dr. Pedro Jiménez Estévez
Prof. Dr. Felipe Hernández-Perlines
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • MRA
  • OLS regression
  • SEM
  • PLS-SEM
  • fsQCA
  • fuzzy logic-based approaches
  • OWA ordered weighted averaging
  • non-linear math
  • big data analytics

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 792 KiB  
Article
The Effects of Dynamic Absorptive Capacity on Innovation Strategy: Evidence from SMEs in a Technological Context
by Mauricio Bedoya-Villa, Elkin Pérez-Sánchez, Hugo Baier-Fuentes, Cesar Zapata-Molina and Edith Román-Castaño
Mathematics 2023, 11(10), 2366; https://doi.org/10.3390/math11102366 - 19 May 2023
Cited by 1 | Viewed by 1670
Abstract
Absorptive capacity and innovation strategies are determining issues for the survival of organizations in current contexts. While organizations are immersed in the knowledge society, managers face great challenges to respond to market needs and performance in innovation ecosystems. This article aims to analyze [...] Read more.
Absorptive capacity and innovation strategies are determining issues for the survival of organizations in current contexts. While organizations are immersed in the knowledge society, managers face great challenges to respond to market needs and performance in innovation ecosystems. This article aims to analyze the effects of absorptive capacity on the implementation of innovation strategy. A quantitative research study was conducted with a sample of 51 SMEs, and the construct model was analyzed using the SEM method. The results indicate that there is a high correlation between the level of absorptive capacity and innovation strategies. Even though firms in this specific sector work with advanced technologies, there is a basic level of development of absorptive capacity, generating some difficulties for the design and implementation of innovation strategies. Furthermore, by using the acquisition, assimilation, transformation, and exploitation of knowledge from the competitive landscape, firms improve their adaptability in the technological environment. The effects of absorptive capacity on innovation strategy contribute to the development of the extant literature on innovation management strategy and provide some managerial implications and future research areas. Full article
(This article belongs to the Special Issue New Applications of Data Analysis Methodologies and Techniques to the Social Sciences)
►▼ Show Figures

Figure 1

19 pages, 391 KiB  
Article
A QCA Analysis of Knowledge Co-Creation Based on University–Industry Relationships
by Cristina Bianca Pocol, Liana Stanca, Dan-Cristian Dabija, Veronica Câmpian, Sergiu Mișcoiu and Ioana Delia Pop
Mathematics 2023, 11(2), 388; https://doi.org/10.3390/math11020388 - 11 Jan 2023
Cited by 5 | Viewed by 1650
Abstract
This research aims to identify typologies of companies willing to cooperate with universities to foster knowledge co-creation and ease knowledge transfer to students within courses, training, communities of practises, etc., regardless of the business sector they are active in. To implement the research [...] Read more.
This research aims to identify typologies of companies willing to cooperate with universities to foster knowledge co-creation and ease knowledge transfer to students within courses, training, communities of practises, etc., regardless of the business sector they are active in. To implement the research scope, we rely on the qualitative comparative analysis method (QCA). Interactions between causal factors within the university–industry relations, and knowledge co-creation have been examined. The results obtained indicate two typologies. Type 1 includes companies oriented towards supporting interactions with universities based on education, research, student placements, training, and community services such as consultancy, and product development. These acknowledge both the necessity of creating platforms to establish more ties with universities and the importance of alumni connections to develop effective campus management. Type 2 includes companies that are not interested in understanding or supporting the mission of universities in society, not developing ties with universities, and generating only a superficial interaction, which hinders their involvement in the creation of knowledge with universities. From a managerial perspective, this paper highlights the relationship between universities and industry and how this could contribute to increased resilience for a society facing unexpected challenges, such as the global crisis related to COVID-19 and the present state of international political instability. Full article
(This article belongs to the Special Issue New Applications of Data Analysis Methodologies and Techniques to the Social Sciences)
17 pages, 4945 KiB  
Article
A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences
by Tuba Bircan and Almila Alkim Akdag Salah
Mathematics 2022, 10(23), 4398; https://doi.org/10.3390/math10234398 - 22 Nov 2022
Cited by 6 | Viewed by 4664
Abstract
The use of Artificial Intelligence (AI) and Big Data analysis algorithms is complementary to theory-driven analysis approaches and becoming more popular also in social sciences. This paper describes the use of Big Data and computational approaches in social sciences by bibliometric analyses of [...] Read more.
The use of Artificial Intelligence (AI) and Big Data analysis algorithms is complementary to theory-driven analysis approaches and becoming more popular also in social sciences. This paper describes the use of Big Data and computational approaches in social sciences by bibliometric analyses of articles indexed between 2015 and 2020 in Social Sciences Citation Index (SSCI) of the Web of Science repository. We have analysed especially the recent research direction called Computational Social Sciences (CSS) that bridges computer analytical approaches with social science challenges, generating new methodologies of Big Data and AI analytics for social sciences. The results indicate that AI and Big Data practices are not confined to CSS only and are diffused in a wide variety of disciplines under Social Sciences and are made use of in many main research lines as well. Thus, the anticipated overlap between the Social Sciences & AI specialization and CSS has yet to be crystallised. Moreover, the impact of computational social science studies is not permeated to social science citation networks yet. Lastly, we demonstrate that the AI and Big Data publications that appear under the SSCI index are more oriented towards computational studies than addressing social science concepts, concerns, and challenges. Full article
(This article belongs to the Special Issue New Applications of Data Analysis Methodologies and Techniques to the Social Sciences)
►▼ Show Figures

Figure 1

17 pages, 334 KiB  
Article
An Aggregation Metric Based on Partitioning and Consensus for Asymmetric Distributions in Likert Scale Responses
by Juan Moreno-Garcia, Benito Yáñez-Araque, Felipe Hernández-Perlines  and Luis Rodriguez-Benitez
Mathematics 2022, 10(21), 4115; https://doi.org/10.3390/math10214115 - 04 Nov 2022
Cited by 2 | Viewed by 1609
Abstract
A questionnaire is a basic tool for collecting information in survey research. Often, these questions are measured using a Likert scale. With multiple items on the same broad object, these codes could be summed or averaged to give an indication of each respondent’s [...] Read more.
A questionnaire is a basic tool for collecting information in survey research. Often, these questions are measured using a Likert scale. With multiple items on the same broad object, these codes could be summed or averaged to give an indication of each respondent’s overall positive or negative orientation towards that object. This is the basis for Likert scales. Aggregation methods have been widely used in different research areas. Most of them are mathematical methods, such as the arithmetic mean, the weighted arithmetic mean, or the OWA (Ordered Weighted Averaging) operator. The usual presentation of Likert scale derived data are Mean. This paper presents a new approach to compute an aggregate value that represents Likert scale responses as a histogram adequate to treat better than Mean with asymmetric distributions. This method generates a set of partitions using an approach based on successive division. After every division, each partition is evaluated using a consensus measure and the one with the best value is then selected. Once the process of division has finished, the aggregate value is computed using the resulting partitions. Promising results have been obtained. Experiments show that our method is appropriate for distributions with large asymmetry and is not far from the behavior of the arithmetic mean for symmetric distributions. Overall, the article sheds light on the need to consider other presentations of Likert scale derived data beyond Mean more suitable for asymmetric distributions. Full article
(This article belongs to the Special Issue New Applications of Data Analysis Methodologies and Techniques to the Social Sciences)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop