Technological Perceptions and Change: Investigating Imbrication Mechanisms in Flexible IS Assimilation
Bibliographic record
Abstract
As advanced flexible technologies transform business solutions and create new opportunities, organizations must integrate these technologies into their practices to realize their potential benefits. However, many organizations struggle to assimilate technologies into their routines, failing to leverage their capabilities. My thesis explores how actors develop Technological Perception and how these perceptions influence their actions toward potential change within the context of flexible Information System (IS) assimilation, using the Imbrication Framework as a lens. The Imbrication Framework (Leonardi, 2011) explains the gradual, intertwined relationship between IT and organizational processes through incremental improvements. Using this framework allows for consideration of the sociomaterial nature of IS assimilation. Two case studies were conducted at research sites—a municipal government office and a public hospital—where flexible technologies were being implemented and assimilated, using an inductive qualitative design with grounded theory (Urquhart, 2012). The results indicate that participants' Technological Perceptions were shaped by their Realized Technology Experiences, which were a consequence of their direct interactions with the technologies, as well as by their interpretation and understanding of the technologies within their organizational context (Integrated Socio-Technical Frame). The findings suggest that while direct interaction with the technologies shapes participants’ Technological Perceptions, it alone does not fully explain their perception of the technology. The Integrated Socio-Technical Frame echoes and enhances the Technological Frame (Orlikowski & Gash, 1994). Further, Technological Perception influences participants’ actions toward technology (Adaptation Strategies), but perceptions of constraints or affordances do not always lead to changes. Organizational support, such as effective feedback systems, also are needed to facilitate change. The results highlight that successful IS assimilation extends beyond mere technology use, requiring deliberate processes and technological change efforts supported by the organization. The results shed light on the mechanisms through which imbrication occurs, contributing to the literature on the Imbrication Framework (Leonardi, 2011) and IS assimilation while adding new insights to the Technological Frame (Orlikowski & Gash, 1994). Additionally, the findings guide organizations in improving the assimilation process, identifying bottlenecks, and increasing IS implementation success, ultimately realizing the business value of IS investments.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| 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".