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Record W4392829650 · doi:10.62477/jkmp.v23i2.7

Characteristics in Digital Organizational Culture: A Literature Review

2023· review· en· W4392829650 on OpenAlexvenueno aff
Seyma Kocak, Jan Μ. Pawlowski

Bibliographic record

VenueJournal of Knowledge Management and Practice · 2023
Typereview
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetKnowledge managementAgile software developmentDigital transformationOpenness to experienceOrganizational cultureProactivityTeamworkBusinessPsychologyComputer sciencePublic relationsManagementPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Organizational culture is an important aspect that supports a successful digital transformation in companies. It is an essential component of Digital Transformation and requires a crucial development of competencies, characteristics, and attitudes to create acceptance and openness among employees and managers and enable organizations to adapt to the transformation. This paper deals with the main characteristics and implications of digital organizational culture. A systematic literature review was conducted for the methodology. The identified characteristics were integrated into the defined dimensions (digital communication, proactivity, entrepreneurial orientation, personal competencies, and digital skills and attitudes). The results show that, e.g., fault tolerance, innovation, digital skills, and an agile mindset are central to developing a digital organizational culture. Furthermore, some characteristics (participation, teamwork, agile mindset, digital skills, problem-solving, risk-taking) positively affect the digital organizational culture. New research questions are derived from the results, which still show a need for research in IS research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0160.020
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.314
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2023
Admission routes1
Has abstractyes

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