Revealing the Senses in Organizations - An Exploration Into How the Five Senses Inform the Experience of Doing and Being at Work
Bibliographic record
Abstract
Despite the criticality of sensory experience in the workplace, limited empirical studies of this issue have been undertaken.The domain of sensory exploration in work settings is an area of scholarship in the management field of organizational aesthetics (OA).This research was designed to inquire into the aesthetic dimensions of organizational life by investigating how the five senses inform the experiences of doing work as well as those of being at work.To accomplish this, twenty phenomenological interviews were conducted with men and women in both office jobs and "artistic" professions.This approach provides in-depth first-person accounts of the phenomenon under study (Allen-Collinson, 2009).Ontologically constructivist and epistemologically subjectivist, Constructivist Grounded Theory (CGT; Charmaz, 1995a) is particularly fitting for analyzing the sensorial at work, as it takes into account the researcher's view and felt knowledge.Furthermore, CGT supports the notion that an inter-relationship exists between the researcher and the participant (Mills et al., 2006).Interview transcripts were first manually coded during the interview process, then uploaded into NVivo 12 software for further analysis.Applying CGT principles and approach to the analysis of selective coding allowed four theoretical insights to emerge: (A) a clear hierarchy of the senses in doing and being at work and how this hierarchy at work is culturally rooted; (B) an inverted hierarchy of the senses in doing and being at work reflects a sensory disconnect, (C) the senses help build ii human connections at work through organizational aesthetic moments and (D) sensory connection to nature is paramount in a (work)day.This research into how the five senses inform the experiences of doing and being at work is timely, given the post-pandemic (Covid-19) context and the onset of new ways of working.As organizations and their employees maneuver through such challenging contexts, this research encourages management scholars and organizational actors to consider thoughtful approaches to the following issues:fostering human connections at work, maintaining the sensory divide between work and home, and encouraging and facilitating access to nature.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".