Investigating consumers’ reluctance to give up local hard drives after adopting the Cloud
Bibliographic record
Abstract
Purpose The next phase of hardware technology development is focused on alternative ways to manage and store consumers’ personal content. However, even consumers who have adopted Cloud-based services have demonstrated a reluctance to move all of their personal content into the Cloud and continue to resist giving up local hard drives. This paper aims to investigate the characteristics of local hard drives and the Cloud that lead to simultaneous use. Design/methodology/approach This paper uses content analysis of online comments and ten depth interviews with simultaneous users of local hard drives and the Cloud. Findings Three factors influence the resistance to giving up local hard drives. Simultaneous users utilize local hard drives as a redundancy system and as a way to ensure the permanence of their digital content. They are unsure of the Cloud’s ability to support their content creation, management and storage activities (task-technology fit). Research limitations/implications Study findings are based on qualitative methods and thus the results cannot be considered conclusive. Practical implications The authors speculate that it is unlikely that Cloud-only will fully replace hard drives until these factors are understood and addressed by information technology developers. Cloud service providers may not be aware of how little that users understand the Cloud. In contrast to their certainty and confidence in local hard drives, simultaneous users are confused as to what the Cloud is and how it functions. This uncertainty exacerbates their risk perception and need for control. Originality/value This is the first study exploring simultaneous use of local hard drives and the Cloud with a view to understanding this behaviour in terms of the relative advantage of the incumbent technology over the new technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".