Developing Theory Through Integrating Human and Machine Pattern Recognition

Lindberg, Aron

doi:10.17705/1jais.00593

Cited by 29 publications

(39 citation statements)

References 82 publications

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“…Studies included in this review combine economics, machine learning, statistical methods, linguistics and many other fields using method after method and creating hybrids to address challenges, so it is to be expected that the new methods to be as complex as well. In the future, the division between qualitative and quantitative research might become increasingly tenuous as posited in [6]. A more sensible divisionif one must be mademight be distinguishing between small and big data parts for the sake of iteration and abduction.…”

Section: Resultsmentioning

confidence: 99%

“…When many studies use a few selected methods, BDR might risk pipe vision and research that can only answer certain kinds of questions and deliver certain kinds of insights. A mixed-methods approach could be supplemented with abductive research iterating between discovery and justification and pragmatist philosophy as suggested by Lindberg [6] to help maintain the richness of qualitative data. Abduction would also allow BDRresearcher to address the "why" behind patterns [7].…”

Section: Discussionmentioning

confidence: 99%

“…However, in BDR, the first step might well be looking at the big picture and then focusing on interesting relationships. As portrayed in [6], large-scale patterns and structures are typically analysed with machine pattern recognition whereas human pattern recognition is used when zooming in human dynamics. It is noted in [7] that algorithms can be used to uncover novel patterns in data to offer initial structural frames for deeper theory-building via the study of small samples.…”

Section: Discussionmentioning

confidence: 99%

“…text (emails, web pages, social media, blogs, documents), video, audio and images [2][3]. This has led to a situation where there is more data available than traditional qualitative tools can cope with [4][5][6]. In big data research (BDR), sample sizes might be measured in millions and a system could produce petabytes worth of data in a matter of hours.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Qualitative Big Data’s Challenges and Solutions: An Organizing Review

Suvivuo¹

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Digitalization of everyday lives has tremendously increased the amount of digital (trace) data of people's behaviour available for researchers. However, traditional qualitative research methods struggle with the width and breadth of the data. This paper reviewed 61 recent studies that had utilized qualitative big data for the practical challenges they had encountered and how they were addressed. While quantitative and qualitative big data share many common issues, the review points at that lack of qualitative methods and dataset reduction required by algorithms in big data research decreases the richness of the qualitative data. Locating relevant data and reducing noise are further challenges. Currently, these challenges can be only partially addressed with a combination of human and computer pattern recognition and crowdsourcing. The review describes many "tricks of the trade" but abduction research and pragmatist philosophy seem promising starting places for a more pervasive framework.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Qualitative Big Data’s Challenges and Solutions: An Organizing Review

Suvivuo¹

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…In this regard, our study showed the intention by Volvo Cars to acquire certain competences, but not necessarily the extent to which it was successful. Lastly, we employed a number of computational techniques in combination with qualitative work, which involves the challenges in combining human and machine pattern recognition [58].…”

Section: Conclusion and Limitationsmentioning

confidence: 99%

Resourcing Digital Competence in Product Development: A Computational Study of Recruitment at Volvo Cars

Mankevich

Svahn

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Competence renewal is an intrinsic part of digital transformation. However, digital competence is generic competence-it is function-agnostic and not tied to the specifics of a firm's product portfolio. It typically does not fit established institutional structures. Therefore, it is particularly complicated for product-developing firms to develop digital competence, since functional decomposition and profound specialization prevent necessary knowledge mobility. We adopt a resourcing perspective to analyze how Volvo Cars identified, engaged, and deployed human resources to balance supply and demand for digital competence. Our study relies on over 5000 published job postings, which we compared with the European Skills, Competences, and Occupations (ESCO) framework on the basis of natural language processing. While broadly confirming the idea that digital competence spread from IT departments into mainstream operations, our study also demonstrates asymmetry in the resourcing environment, reflecting tension between emerging and existing structures. Our study also reveals a tendency to close digital competence deficits through external recruitment rather than internal hiring, and by creating new positions rather than replacements.

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Research Methods

Recker

2021

Progress in IS

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Developing Theory Through Integrating Human and Machine Pattern Recognition

Cited by 29 publications

References 82 publications

Qualitative Big Data’s Challenges and Solutions: An Organizing Review

Qualitative Big Data’s Challenges and Solutions: An Organizing Review

Resourcing Digital Competence in Product Development: A Computational Study of Recruitment at Volvo Cars

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