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Record W4406451527 · doi:10.1007/s12599-025-00924-8

Affordance-Based Information Technology Sensemaking [ABITS]

2025· article· en· W4406451527 on OpenAlex

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

VenueBusiness & Information Systems Engineering · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsHEC Montréal
FundersLoyola Marymount University
KeywordsAffordanceSensemakingKnowledge managementHuman–computer interactionComputer scienceData scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Information Technology (IT) use gives rise to a wide variety of outcomes. This stems in part from the divergent ways in which individuals understand technology. While the sensemaking literature unveils how meaning is attached to organizational phenomena via cognitive and social processes, it overlooks the discovery dimension of making sense, that is detecting the role of the IT artifact in bringing about outcomes. In other words, there is a need to explain how the IT artifact contributes to technology sensemaking and its outcomes. This paper presents a framework that enables scholars to analyze the IT artifact’s role in technology sensemaking and its outcomes. The paper proposes an Affordance-Based IT Sensemaking (ABITS) framework that explicates IT sense-made as a distinctive ontological arrangement among the users’ perceptions of technology affordances, the affordances that users actualize, and the user characteristics that underpin optimal adaptation. The study shows how these sense-made configurations lead to outcomes for individuals and organizations. This conceptual combination allows for the examination of user appropriations of new technology, as well as the integration of the IT artifact into accounts of IT sensemaking and its outcomes.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.006
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 head0.006
GPT teacher head0.253
Teacher spread0.247 · 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