Unraveling the What and How of Organizational Communication to Employees During COVID-19 Pandemic: Adopting an Attributional Lens
Bibliographic record
Abstract
The coronavirus (COVID-19) pandemic has resulted in a massive, unexpected, and sudden disruption to billions of employees in many sectors, including the higher education sector.Employees turn to organizational leaders for information, which heightens demands for effective communication of critical decisions.While human resources (HR), communication and organizational change research has developed some knowledge about when communications are successful in 'getting the message across', insights about the antecedents of the 'what' and 'how' elements of these communications have not been a focus of previous research efforts.There may be a significant variability in 'what' and 'how' institutions communicate with staff.In this context, a team of international scholars have started an international project with the goal of improving our understanding on the antecedents of messages sent by universities to their staff using the attribution perspective as an overarching analytical framework.Although, 30+ participants from Australasia, North America, Europe and Asia are already involved, participants from across the globe are still joining the project to allow for a cross-cultural analysis of the data.In 2020, the coronavirus (COVID-19) pandemic has resulted in a massive, unexpected, and sudden disruption to billions of employees around the world.Organizations and employees have been forced to transform their operational routines virtually overnight.This has resulted in unprecedented demands on managers to make decisions in very uncertain conditions.In times of crises such as those, employees turn to organizational leaders for information, which heightens demands for effective communication of critical decisions (Van der Meer, Verhoeven, Beentjes, & Vliegenhart, 2017;Van Zoonen & Van der Meer, 2015).In general, in the 'new normal' resulted from the COVID-19 pandemic many white-collar and professional employees are working from home.This presents a whole range of communication challenges.In response, organizations have adopted technologydriven solutions, where managers communicate time-critical information via multiple channels including but not limited to email, intranet, video conferencing, and other tools.Globally, the impact of COVID-19 has been devastating in many sectors including the higher education sector (Wang, Cheng, Yue, & McAleer, 2020).An important question within this sector is
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".