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Theoretical Constructs and Relationships in Information Systems Research

2009· book-chapter· en· W2483352255 on OpenAlex
Brent Furneaux, Michael Wade

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIGI Global eBooks · 2009
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsYork University
Fundersnot available
KeywordsPremiseSalientNomological networkPerspective (graphical)Set (abstract data type)Sample (material)Management scienceKnowledge managementEmpirical researchData scienceComputer sciencePsychologyEpistemologyEngineeringArtificial intelligenceStructural equation modeling

Abstract

fetched live from OpenAlex

Constructs and the relationships between them are widely considered to be central to theory development and testing. Over time, information systems (IS) researchers have identified and explored an extensive set of relationships amongst a broad range of constructs. The result of these initiatives is a body of literature that can be considered to represent the cumulative learning of the discipline. Based on the premise that this cumulative learning is capable of providing valuable guidance to future theory development, the authors present a review and analysis of a large sample of empirical research published in two leading IS journals. The objective of this endeavor is to offer a broad perspective on the nature of the constructs and relationships explored in IS research and to develop a nomological network of the most salient relationships that can then serve to guide future research and to lend support to new and existing theory.

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.

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)0.003
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: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.061
GPT teacher head0.356
Teacher spread0.295 · 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