Technoidentology: Towards An Explication Of Individual Relationships With IS/IT
Bibliographic record
Abstract
This paper argues that IS theories should not ignore or background the relationship between individuals and technologies in explaining concepts such as adoption, innovation, diffusion, and practice. The relationships that individuals have with technologies should, arguably, be a core interest of IS because of the centrality of people and IT to the discipline. The neglect of individual relationships with IS/IT is surprising given the growth of customisation and personalisation of systems, as well as the increasing prevalence of devices such a smartphones and tablets that blur the boundary between corporate and personal. Other disciplines recognise the importance of relationships in explaining core concepts, for example, the relationship between individuals and brands in marketing, individuals and others in sociology, and individuals and their thoughts in psychology. This paper draws on such work to consider the relationships between individuals and IS/IT, which we refer to as ‘technoidentology’, in examining the immediacy of individuals’ reactions to technology. Having done so, we conclude that theoretical work in the area of personal construct theory and terror management theory is likely to prove fruitful in helping IS researchers address key aspects of technoidentology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".