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Interdependencies between People and Information Systems in Organizations

2011· book-chapter· en· W150965403 on OpenAlexaff
Andrew Burton‐Jones, Alan Burton‐Jones

Bibliographic record

VenueOxford University Press eBooks · 2011
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInterdependenceKnowledge managementInformation systemInteroperationComplementarity (molecular biology)BusinessComputer scienceEngineeringSociologySocial scienceInteroperability

Abstract

fetched live from OpenAlex

Abstract This article argues that while people and information systems (ISs) represent the two single largest areas of investment for many organizations and are increasingly interconnected resources, there has been very little research on the nature of their interdependencies and how these interdependencies affect their functioning and complementarity. It discusses how a better understanding of the dynamics of interdependencies between people and ISs can help researchers study organizations and help organizations improve the interoperation of their human and technological assets, and thus returns on investments in them. The article begins by reviewing the concept of capital and its application to people – human capital – and information systems: ISs capital. Next, it surveys past literature on interdependencies and recent literature relating to interdependencies between people and information systems. Based on the analysis, the article proposes an agenda for future research aiming to conceptualize interdependencies between people and ISs in a richer fashion.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.996
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.162
Teacher spread0.149 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations4
Published2011
Admission routes1
Has abstractyes

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