Data science

developing theoretical contributions in information systems via text analytics

Document identifier: oai:DiVA.org:ltu-77324
Access full text here:10.1186/s40537-019-0280-6
Keyword: Natural Sciences, Medie- och kommunikationsvetenskap, Methodology, Text analytics, Information systems, Contribution, Theory, Data science, Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning, Samhällsvetenskap, Computer and Information Sciences, Information Systems, Social aspects, Media and Communications, Social Sciences, Systemvetenskap, informationssystem och informatik, Data- och informationsvetenskap, Naturvetenskap, Information Systems, Informationssystem
Publication year: 2020
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Abstract:

Scholars have been increasingly calling for innovative research in the organizational sciences in general, and the information systems (IS) field in specific, one that breaks from the dominance of gap-spotting and specific methodical confinements. Hence, pushing the boundaries of information systems is needed, and one way to do so is by relying more on data and less on a priori theory. Data, being considered one of the most important resources in research, and society at large, requires the application of scientific methods to extract valuable knowledge towards theoretical development. However, the nature of knowledge varies from a scientific discipline to another, and the views on data science (DS) studies are substantially diverse. These views vary from being seen as a new scientific (fourth) paradigm, to an extension of existing paradigms with new tools and methods, to a phenomenon or object of study. In this paper, we review these perspectives and expand on the view of data science as a methodology for scientific inquiry. Motivated by the IS discipline’s history and accumulated knowledge in using DS methods for understanding organizational and societal phenomena, IS theory and theoretical contributions are given particular attention as the key outcome of adopting such methodology. Exemplar studies are analyzed to show how rigor can be achieved, and an illustrative example using text analytics to study digital innovation is provided to guide researchers.

Authors

Aya Rizk

Luleå tekniska universitet; Digitala tjänster och system
Other publications >>

Ahmed Elragal

Luleå tekniska universitet; Digitala tjänster och system
Other publications >>

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        identifier: oai:DiVA.org:ltu-77324
        datestamp: 2021-04-19T12:57:24Z
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                version: 3.7
            recordInfo: 
                recordContentSource: ltu
                recordCreationDate: 2020-01-09
            identifier: 
                http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77324
                10.1186/s40537-019-0280-6
                2-s2.0-85077584057
            titleInfo: 
                @attributes: 
                    lang: eng
                title: Data science
                subTitle: developing theoretical contributions in information systems via text analytics
            abstract: 
Scholars have been increasingly calling for innovative research in the organizational sciences in general and the information systems (IS) field in specific one that breaks from the dominance of gap-spotting and specific methodical confinements. Hence pushing the boundaries of information systems is needed and one way to do so is by relying more on data and less on a priori theory. Data being considered one of the most important resources in research and society at large requires the application of scientific methods to extract valuable knowledge towards theoretical development. However the nature of knowledge varies from a scientific discipline to another and the views on data science (DS) studies are substantially diverse. These views vary from being seen as a new scientific (fourth) paradigm to an extension of existing paradigms with new tools and methods to a phenomenon or object of study. In this paper we review these perspectives and expand on the view of data science as a methodology for scientific inquiry. Motivated by the IS discipline’s history and accumulated knowledge in using DS methods for understanding organizational and societal phenomena IS theory and theoretical contributions are given particular attention as the key outcome of adopting such methodology. Exemplar studies are analyzed to show how rigor can be achieved and an illustrative example using text analytics to study digital innovation is provided to guide researchers.

            subject: 
                    @attributes: 
                        lang: eng
                        authority: uka.se
                    topic: 
                        Natural Sciences
                        Computer and Information Sciences
                        Information Systems
                    @attributes: 
                        lang: swe
                        authority: uka.se
                    topic: 
                        Naturvetenskap
                        Data- och informationsvetenskap
                        Systemvetenskap informationssystem och informatik
                    @attributes: 
                        lang: eng
                        authority: uka.se
                    topic: 
                        Social Sciences
                        Media and Communications
                        Information Systems Social aspects
                    @attributes: 
                        lang: swe
                        authority: uka.se
                    topic: 
                        Samhällsvetenskap
                        Medie- och kommunikationsvetenskap
                        Systemvetenskap informationssystem och informatik med samhällsvetenskaplig inriktning
                    @attributes: 
                        lang: eng
                    topic: Data science
                    @attributes: 
                        lang: eng
                    topic: Theory
                    @attributes: 
                        lang: eng
                    topic: Contribution
                    @attributes: 
                        lang: eng
                    topic: Information systems
                    @attributes: 
                        lang: eng
                    topic: Text analytics
                    @attributes: 
                        lang: eng
                    topic: Methodology
                    @attributes: 
                        lang: eng
                        authority: ltu
                    topic: Information systems
                    genre: Research subject
                    @attributes: 
                        lang: swe
                        authority: ltu
                    topic: Informationssystem
                    genre: Research subject
            language: 
                languageTerm: eng
            genre: 
                publication/journal-article
                ref
            note: 
                Published
                2
                
Validerad;2020;Nivå 1;2020-01-24 (johcin)

            name: 
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                        type: personal
                        authority: ltu
                    namePart: 
                        Rizk
                        Aya
                        1988-
                    role: 
                        roleTerm: aut
                    affiliation: 
                        Luleå tekniska universitet
                        Digitala tjänster och system
                    nameIdentifier: 
                        ayariz
                        0000-0001-8693-2295
                    @attributes: 
                        type: personal
                        authority: ltu
                    namePart: 
                        Elragal
                        Ahmed
                    role: 
                        roleTerm: aut
                    affiliation: 
                        Luleå tekniska universitet
                        Digitala tjänster och system
                    nameIdentifier: 
                        ahmelr
                        0000-0003-4250-4752
            originInfo: 
                dateIssued: 2020
                publisher: Springer
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                    type: host
                titleInfo: 
                    title: Journal of Big Data
                identifier: 2196-1115
                part: 
                    detail: 
                            @attributes: 
                                type: volume
                            number: 7
                            @attributes: 
                                type: artNo
                            number: 7
            location: 
                    url: https://doi.org/10.1186/s40537-019-0280-6
                    url: http://ltu.diva-portal.org/smash/get/diva2:1384175/FULLTEXT01.pdf
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                gratis
                gratis
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                form: electronic
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