Special Issue "Big Data Research for Open Innovation"

Special Issue Editors

Prof. Dr. Josep Domenech
Website
Guest Editor
Faculty of Business Administration and Management, Universitat Politècnica de Valéncia, Valencia, Spain
Interests: digital footprint, big data, innovation systems, internet economics
Prof. Dr. María Rosalía Vicente
Website
Guest Editor
University of Oviedo, Calle San Francisco, 3, 33003 Oviedo, Asturias, Spain
Interests: big data; ICT; digital economy; innovation

Special Issue Information

Dear Colleagues,

Big data approaches in an open innovation environment may enable the flows of knowledge and external cooperation at many different levels. The increasing availability of open and big data sources of information has drawn attention to the different research methods that can be used to extract useful information and support the creation of innovative solutions. This Special Issue will present some of the most recent advances in the application of big data-related research methods to different fields of social sciences. Suitable topics include, but are not limited to, the following:

Open and big data sources in economics and social sciences:

  • Google trends and search engine data;
  • Web scraping;
  • Social media and public opinion mining;
  • Geospatial and mobile phone data;

Big data solutions in innovation systems:

  • Sentiment analysis;
  • Internet econometrics;
  • Machine learning econometrics;
  • Information quality and assessment;
  • Crowdsourcing;

Internet and big data applications to innovation systems:

  • Official statistics;
  • Tourism forecasting;
  • Business analytics with social media;
  • Social behavior and mobility patterns;
  • Consumer behavior, eWOM, and social media marketing;
  • Politics and social media;
  • Bibliometrics and sciencetometrics.

Prof. Dr. Josep Domenech
Prof. Dr. María Rosalía Vicente
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 papers will be 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. Journal of Open Innovation: Technology, Market, and Complexity is an international peer-reviewed open access quarterly 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 800 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

  • big data
  • open innovation
  • open data
  • social media
  • webscraping
  • machine learning

Published Papers (5 papers)

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Research

Open AccessArticle
Comparing Methods to Collect and Geolocate Tweets in Great Britain
J. Open Innov. Technol. Mark. Complex. 2021, 7(1), 44; https://doi.org/10.3390/joitmc7010044 (registering DOI) - 25 Jan 2021
Abstract
In the era of Big Data, the Internet has become one of the main data sources: Data can be collected for relatively low costs and can be used for a wide range of purposes. To be able to timely support solid decisions in [...] Read more.
In the era of Big Data, the Internet has become one of the main data sources: Data can be collected for relatively low costs and can be used for a wide range of purposes. To be able to timely support solid decisions in any field, it is essential to increase data production efficiency, data accuracy, and reliability. In this framework, our paper aims at identifying an optimized and flexible method to collect and, at the same time, geolocate social media information over a whole country. In particular, the target of this paper is to compare three alternative methods to collect data from the social media Twitter. This is achieved considering four main comparison criteria: Collection time, dataset size, pre-processing phase load, and geographic distribution. Our findings regarding Great Britain identify one of these methods as the best option, since it is able to collect both the highest number of tweets per hour and the highest percentage of unique tweets per hour. Furthermore, this method reduces the computational effort needed to pre-process the collected tweets (e.g., showing the lowest collection times and the lowest number of duplicates within the geographical areas) and enhances the territorial coverage (if compared to the population distribution). At the same time, the effort required to set up this method is feasible and less prone to the arbitrary decisions of the researcher. Full article
(This article belongs to the Special Issue Big Data Research for Open Innovation)
Open AccessArticle
Online Buyers and Open Innovation: Security, Experience, and Satisfaction
J. Open Innov. Technol. Mark. Complex. 2021, 7(1), 37; https://doi.org/10.3390/joitmc7010037 - 18 Jan 2021
Abstract
The topic of consumer behavior in a social context is important due to its influence on the behaviors and attitudes of individuals. New online business models are adopting open innovation practices focused on improving their sales channels through their technological capacity. In this [...] Read more.
The topic of consumer behavior in a social context is important due to its influence on the behaviors and attitudes of individuals. New online business models are adopting open innovation practices focused on improving their sales channels through their technological capacity. In this paper, we analyze the purchase intentions in a business context to identify consumer needs through the proper purchase decision process. We must also observe the internal and external factors that influence consumer behavior. More exactly, electronic commerce is facing challenges and opportunities manifested by online consumers, such as design, security, trust, risk, uncertainty, and satisfaction with online purchases. Many external factors (economic, political, social, environmental, and health) influence buyers’ intentions and behaviors. The objectives of this study are to (1) determine the influence of the security level of websites on purchasing behaviors (socially responsible and panic buyers), (2) determine the effect of website security on consumer satisfaction, (3) determine the effect of buyers (socially responsible and panic buyers) on the level of satisfaction, and (4) examine if the buyer experience has a moderating effect between the variables (socially responsible and panic buyers) and the dependent variable (customer satisfaction). We focus on a sample of 663 socially responsible online buyers and panic buyers from the Sonora, Baja California, and Sinaloa regions in Mexico. Data were collected from the months of April to August 2020, and an online questionnaire was used address to each of the residents of these regions aged between 20 and 55 and who were economically active. The data were analyzed using the structural equation model–partial least squares (SEM-PLS) model based on variance. The findings show that website security has significant positive effects on socially responsible buyers, panic buyers, and the level of customer satisfaction. Socially responsible buyers also have positive effects on customer satisfaction. However, the relationship between panic buyers and customer satisfaction is not supported. Related to a moderation analysis, that the buyer experience has a significant effect on the relationship between socially responsible online buyers and the level of satisfaction. Conversely, we find empirical evidence of the buyer experience having no significant effect between panic buyers and customer satisfaction. Our findings contribute to the development of various theories: the theory of behavioral reasoning (BRT), social identity theory (SIT), and the technological adaptation model (TAM). From an academic point of view, the findings are positive and encouraging, contributing to the literature on the e-commerce, behaviors, and attitudes of purchase intentions of individuals. Our work is incorporated into the existing literature on purchase intention and virtual business models, whose characteristics need to continue to be outlined, constituting a popular business model in recent years. Full article
(This article belongs to the Special Issue Big Data Research for Open Innovation)
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Open AccessArticle
How Big Data Analytics Boosts Organizational Performance: The Mediating Role of the Sustainable Product Development
J. Open Innov. Technol. Mark. Complex. 2020, 6(4), 190; https://doi.org/10.3390/joitmc6040190 - 13 Dec 2020
Abstract
Increasing haze pollution and its adverse effects on human health is pressuring academics and practitioners to search for different solutions for environmental sustainability around the world. Similar to other countries, Pakistan is also affected by air pollution, and smog has become a fifth [...] Read more.
Increasing haze pollution and its adverse effects on human health is pressuring academics and practitioners to search for different solutions for environmental sustainability around the world. Similar to other countries, Pakistan is also affected by air pollution, and smog has become a fifth season. In Pakistan, one of the main reasons of smog and air pollution is hazardous emissions from vehicles. As a result, the booming automobile industry of Pakistan is now affected by two major challenges: sustainable product development and organizational performance. To meet these challenges, the study has developed a conceptual model to find the effect of big data analytics on organizational performance by adopting a sustainable development program. For the elimination of standard method biases, the study has used a time lag approach to collect the data in three waves and receive 372 usable responses. The empirical results of PLS-SEM suggest that big data analytics have a positive effect on a sustainable product development and sustainable product development has a positive and significant impact on organizational performance. Moreover, mediation of a sustainable program development is also confirmed between big data analytics and organizational performance. The managerial and theoretical implications of these results are discussed. Full article
(This article belongs to the Special Issue Big Data Research for Open Innovation)
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Open AccessArticle
A Field Study on the Impacts of Implementing Concepts and Elements of Industry 4.0 in the Biopharmaceutical Sector
J. Open Innov. Technol. Mark. Complex. 2020, 6(4), 175; https://doi.org/10.3390/joitmc6040175 - 03 Dec 2020
Cited by 1
Abstract
This study proposes a field study, based on a literature review, about the applications and impacts of Industry 4.0 (I4.0) in the biopharmaceutical sector. The world is facing a new industrial revolution and the central idea is the integration between the virtual and [...] Read more.
This study proposes a field study, based on a literature review, about the applications and impacts of Industry 4.0 (I4.0) in the biopharmaceutical sector. The world is facing a new industrial revolution and the central idea is the integration between the virtual and the real world through elements that will allow for a greater degree of automation and digitization of processes. The production of medicines via biological processes is a booming domain in the pharmaceutical sector, that involves extraordinary technological challenges. The fieldwork, carried out between August 2019 and February 2020, involved semi-structured interviews with managers of pharmaceutical companies and specialists in the I4.0 theme. The interviews allowed for the identification of trends and key benefits and barriers for implementing I4.0 in the biopharmaceutical sector. While the perceptions were considerably diversified, benefits in productivity, competitiveness and quality ranked among the most scored items. The main barriers, highlighted by the interviewees, refer to the need to break organizational cultural standards, the regulatory requirements, the lack of organizational strategies for implementation, and the lack of qualified professionals. This work offers a contribution to the biopharmaceutical sector and reinforces the imminent need for companies to adapt to this new reality. Full article
(This article belongs to the Special Issue Big Data Research for Open Innovation)
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Open AccessArticle
Social-Media Analysis for Disaster Prevention: Forest Fire in Artenara and Valleseco, Canary Islands
J. Open Innov. Technol. Mark. Complex. 2020, 6(4), 169; https://doi.org/10.3390/joitmc6040169 - 29 Nov 2020
Cited by 1
Abstract
This manuscript investigates the use of social media, specifically Twitter, during the forest fires in Artenara and Valleseco, Canary Islands, Spain, during summer 2019. The used methodology was big-data analysis through the Union Metrics and Twlets tools, as well as content analysis of [...] Read more.
This manuscript investigates the use of social media, specifically Twitter, during the forest fires in Artenara and Valleseco, Canary Islands, Spain, during summer 2019. The used methodology was big-data analysis through the Union Metrics and Twlets tools, as well as content analysis of posts related to the fires written by seven relevant accounts on the days when the fires were active, which was between 17 August and 26 September, when 9636.40 hectares were burned. The accounts selected for analysis were the following: Ángel Víctor Torres, autonomous president; Canary Islands Government; Civil Protection of Las Palmas; Military Emergency Unit of the Spanish Army; Delegation of the Spanish Government in the Canary Islands; Citizen’s Service of the Canary Islands Government; and the information account of the Security and Emergency area of the Canary Islands Government. The study concludes that the Canary Islands authorities did not use social media as a preventive element, but almost exclusively as a live-information channel. Future recommendations are presented for the management of social media during natural disasters. Full article
(This article belongs to the Special Issue Big Data Research for Open Innovation)
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