New Ways of Working: Institutions, Identity, and Power
Bibliographic record
Abstract
The Corona pandemic has accelerated the growing trend for organizations to adopt so-called new ways of working, including practices such as conducting meetings and teachings through video conferencing, working from home, when commuting or in co-working spaces as well as hot desk arrangements, agile methods and holocratic structures. What seems to unite these empirical phenomena is the fact that they grant organizational members intentionally increasing discretion in terms of what to work on (autonomy to set priorities), when to work (temporal autonomy) and where to work (spatial autonomy). However, most of our received wisdom of organizational theories and concepts relies on the implicit premise of a 9-5-fixed-desk-clear-hierarchy work environment. As organizational scholars, we thus seem to be witnessing a substantive shift in our 'object of inquiry' that we have yet to fully understand empirically and to theorize conceptually much better. To increase our knowledge on new ways of working and its implications and effects on theory, we want to elaborate these aspects in three paper presentations on the following three conceptual foci: institutions, organizational identity, as well as power and politics. First of all, we see that new ways of working challenge existing institutions. The flexibilization and digitalization of work led to taken-for-granted assumptions being questioned, e.g. with regard to place, time, interactions and boundaries of work. Secondly, we realize that all of these new ways of working affect the mode and modalities of social interactions. Organizational identity as a social construct, though, rely substantively on social processes of meaning negotiations. Thirdly, we recognize that most of these new ways of working are technology enabled. New practices and technology are never innocent. Rather, they affect how power and politics are distributed and enacted in organizations. At the end of the symposium, similarities and differences of these three perspectives shall be further elaborated together with a discussant and the audience. How Office Space Enables and Restricts New Ways of Working Presenter: Andrea Simone Barth; Alanus U. of Arts and Social Sciences & WU Vienna Presenter: Renate Elisabeth Meyer; WU Vienna & Copenhagen Business School Presenter: Markus A. Höllerer; UNSW Sydney & WU Vienna How ‘New Ways of Working’ Shape Organizational Identity Beliefs Presenter: Emamdeen Fohim; U. of Bern Presenter: Claus Jacobs; U. of Bern From Micro- to Macro-Politics of NWOW Presenter: Michel Ajzen; UCLouvain Presenter: Taskin Laurent; Louvain Research Institute in Management and Organizations -LouRIM
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.065 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".