ISJ inaugural editorial

Davison, Robert M.; Powell, Philip; Trauth, Eileen M.

doi:10.1111/j.1365-2575.2012.00417.x

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…7 This result indicates a growing interest in ML within the IS research domain. On closer inspection, journals with a more technical focus like DSS, or top-journals like ISJ, that state "publishing high quality, yet risky, papers that help to move the IS field forward" (Davison et al 2012;Davison 2017) as part of their mission and tradition are highly benefiting from this growing interest.…”

Section: Bibliographic Reviewmentioning

confidence: 99%

Machine learning in information systems - a bibliographic review and open research issues

2021

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Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in industry and business practice, while management-oriented research disciplines seem reluctant to adopt these sophisticated data analytics methods as research instruments. Even the Information Systems (IS) discipline with its close connections to Computer Science seems to be conservative when conducting empirical research endeavors. To assess the magnitude of the problem and to understand its causes, we conducted a bibliographic review on publications in high-level IS journals. We reviewed 1,838 articles that matched corresponding keyword-queries in journals from the AIS senior scholar basket, Electronic Markets and Decision Support Systems (Ranked B). In addition, we conducted a survey among IS researchers (N = 110). Based on the findings from our sample we evaluate different potential causes that could explain why ML methods are rather underrepresented in top-tier journals and discuss how the IS discipline could successfully incorporate ML methods in research undertakings.

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Section: Bibliographic Reviewmentioning

confidence: 99%

Machine learning in information systems - a bibliographic review and open research issues

2021

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“…It will also continue to develop its international footprint with global recruitment of authors, reviewers and AEs. Finally, it will continue the tradition of publishing high quality, yet ‘risky’, papers that help to move the IS field forward (Davison et al ., ). These papers will not be restricted to traditional research articles, but also include research commentaries, opinion papers and shorter communications.…”

mentioning

confidence: 97%

Transition arrangements to a new editorial structure

Davison¹

2016

Information Systems Journal

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“…One of them is the interpretive tradition. So while we welcome positivist research that uses quantitative data, we also expect to see data that can ‘motivate rich interpretations of phenomena’ (Davison et al ., , p. 258). But the provision of a rich description should apply not only to the empirical data but also to the literature review.…”

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confidence: 97%

Editorial

Trauth

2015

Information Systems Journal

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EditorialIn several of the editorials since taking on the role of editors-in-chief in 2012 we have written about our intention to remain true to the founding principles of Information Systems Journal. One of them is the interpretive tradition. So while we welcome positivist research that uses quantitative data, we also expect to see data that can 'motivate rich interpretations of phenomena' (Davison et al., 2012, p. 258). But the provision of a rich description should apply not only to the empirical data but also to the literature review. As I tell my Ph.D. students and as I have commented in reviews of papers submitted to ISJ, a literature review is not a bibliographic checklist; it should tell a story. All too often I have read literature reviews that reveal an author's lack of familiarity with the sources being discussed. On some occasions, theoretical viewpoints or conclusions attributed to a source being cited are in conflict with what the author actually wrote. I attribute this to a literature review based on a reading of journal article titles and abstracts rather than a deep reading of the papers (or perhaps a literature review based on the literature reviews of others). The purpose of a literature review is to tell a story about what is known about a phenomenon so that the gaps motivating new research can be identified. In addition to superficial treatment of the literature another issue I have observed is that some authors will base their literature reviews on a small subset of journals. These authors will then declare that 'the literature is silent' on a particular topic if nothing on it has been published in these journals. Part of telling the story in a literature review is listening to the voices no matter where they are, even if this involves going beyond a set of 'top journals' or outside the domain of the information systems field. A strong literature review that tells a compelling story about what is known and what remains to be known about a phenomenon provides an equally compelling motivation for the research that is subsequently discussed in the paper.The story told by Newman and Lyytinen (2015) is about shifting coalitions among different stakeholder groups in an enterprise resource planning (ERP) implementation. The point of their story is to illustrate how such dynamics helped to move the ERP system towards a successful implementation. In the process, they show how some groups were excluded as a condition of that success. They illustrate these themes by presenting an analysis of the successful implementation outcome of an ERP system that emerged from the merger of two previously independent universities. This system, which replaced several legacy student administration systems, was deemed successful by both project consultants and the new university's management even as the users were marginalised in the process. Using a process approach and an actor-network theory reading of related socio-technical events, they demonstrate how three networks of actors-management, the project team and the...

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ISJ inaugural editorial

Cited by 6 publications

References 3 publications

Machine learning in information systems - a bibliographic review and open research issues

Machine learning in information systems - a bibliographic review and open research issues

Transition arrangements to a new editorial structure

Editorial

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