Generating Big Spatial Data on Firm Innovation Activity from Text- Mined Firm Websites

Kinne, Jan; Resch, Bernd

doi:10.1553/giscience2018_01_s82

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…For example, the information in company websites is self-reported. Moreover, it is not standardized, and thus needs accurate processing for appropriate analysis (Arora et al , 2016; Kinne and Resch, 2018). Nevertheless, there is an increasing number of applications to facilitate the use of data collected from the Web for economics and tourism analysis.…”

Section: Methodsmentioning

confidence: 99%

A network perspective of cognitive and geographical proximity of sustainable tourism organizations: evidence from Italy

Blasi

Fano²,

Sedita

et al. 2022

IJCHM

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Purpose This research aims to contribute to the literature on sustainable hospitality and tourism by applying social network analysis to identify sustainable tourism business networks and untangle the role of cognitive and geographical proximity in their formation. Design/methodology/approach Data mining and machine learning techniques were applied to data collected from the websites of tourism companies located in northeastern Italy, namely, the Veneto region. Specifically, the authors used Web scraping to extract relevant information from the internet. Findings The results support the existence of geographical clusters of tourist accommodation providers that are linked by strong cognitive proximity based on sustainability principles that are well communicated via their websites. This does not appear to be greenwashing because companies that have agreed on sustainability principles have also implemented concrete actions and tend to signal these actions through a variety of sustainability certifications. Practical implications The results may guide tourism managers and policymakers in developing tourism initiatives directed at the creation of fruitful collaborations between similarly oriented organizations and methods to support clusters of sustainable tourism accommodation. Identifying sustainable tourism networks may assist in the identification of potential actors of change, fueling a widespread transition toward sustainability. Originality/value In this study, the authors adopted an innovative methodology to detect sustainability-oriented tourism business networks. Additionally, to the best of the authors’ knowledge, this study is one of the first to simultaneously explore the cognitive and geographical connections between tourism businesses.

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Section: Methodsmentioning

confidence: 99%

A network perspective of cognitive and geographical proximity of sustainable tourism organizations: evidence from Italy

Blasi

Fano²,

Sedita

et al. 2022

IJCHM

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“…For the extraction of the diabetes apps' metadata, we first devised the architecture [28] and subsequently developed the corresponding software module for the automatic extraction of mobile app metadata using the web-based API of 42Matters. The output of this module is a data set stored locally in a comma-separated values (CSV) file.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Diabetes Apps to Assess Privacy-Related Permissions: Systematic Search of Apps (Preprint)

Flors-Sidro¹,

Househ²,

Abd‐Alrazaq³

et al. 2019

Preprint

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BACKGROUND Mobile health has become a major channel for the support of people living with diabetes. Accordingly, the availability of diabetes mobile apps has been steadily increasing. Most of the previous reviews of diabetes apps have focused on the apps’ features and their alignment with clinical guidelines. However, there is a lack of knowledge on the actual compliance of diabetes apps with privacy and data security aspects. OBJECTIVE The aim of this study was to assess the level of privacy of diabetes mobile applications to contribute to raising the awareness of final users, developers and data-protection governmental regulators towards privacy issues. METHODS A web scraper capable of retrieving Android apps’ privacy-related information, particularly the dangerous permissions required by the apps, was developed with the aim of analyzing privacy aspects related to diabetes apps. Following the research selection criteria, the original 882 apps were narrowed down to 497 apps, which were finally included in the analysis. RESULTS 60% of diabetes apps may request dangerous permissions, which poses a significant risk for the users’ data privacy. In addition, 30% of the apps do not return their privacy policy website. Moreover, it was found that 40% of apps contain advertising, and that some apps that declared not to contain it actually had ads. 95.4% of the apps were free of cost, and those belonging to the Medical and Health and Fitness categories were the most popular. However, final users do not always realize that the free-apps’ business model is largely based on advertising, and consequently, on sharing or selling their private data, either directly or indirectly, to unknown third-parties. CONCLUSIONS The aforementioned findings unquestionably confirm the necessity to educate users and raise their awareness regarding diabetes apps privacy aspects. For this purpose, this research recommends properly and comprehensively training users, ensuring that governments and regulatory bodies enforce strict data protection laws, devising much tougher security policies and protocols in Android and in the Google Play Store, and the implication and supervision of all stakeholders in the apps’ development process.

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“…The main difference between the two studies is the way the text on websites has been prepared, the type of model used, and the innovation indicator considered. Lenz (2019, 2021) use a web scraping approach described in Axenbeck (2018, 2020) and Kinne and Resch (2018) and developed a deep neural network for analysing website content. The text analysis rests on a dictionary of all words that occurred on websites as long as the document frequency is between 1.5% and 65% (i.e.…”

Section: Large-scale Web Scrapingmentioning

confidence: 99%

Using big data for generating firm-level innovation indicators - a literature review

Rammer,

Es-Sadki

2023

Technological Forecasting and Social Change

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Generating Big Spatial Data on Firm Innovation Activity from Text- Mined Firm Websites

Cited by 8 publications

References 19 publications

A network perspective of cognitive and geographical proximity of sustainable tourism organizations: evidence from Italy

A network perspective of cognitive and geographical proximity of sustainable tourism organizations: evidence from Italy

Analysis of Diabetes Apps to Assess Privacy-Related Permissions: Systematic Search of Apps (Preprint)

Using big data for generating firm-level innovation indicators - a literature review

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