The multi‐tasking paradox: perceptions, problems and strategies
Bibliographic record
Abstract
Purpose The purpose of this article is to describe multi‐tasking behaviour in the workplace; to link its cause to the increasing prevalence of low‐cost information and communications technologies and to the changing organizational structures that have evolved to meet the demands and opportunities of these technologies. Design/methodology/approach This article is a presentation of the current literature on multi‐tasking behaviour among knowledge workers with a selective bibliography addressing empirical research into the behavioural, managerial and technological aspects of this phenomenon. It then expands to comprehensive coverage of the literature on past and current thinking about task structuring, strategies for coping in a multi‐tasking environment and the changing nature of work and organizations, which fuels the need to multi‐task in response to these changes. Findings Among knowledge workers, multi‐tasking behaviour appears to be an inevitable consequence of the presence of increasingly easy access to information. Despite the detrimental effect that multi‐tasking has on specific task completion, the paradox is that this does not seem to have an effect on overall organizational productivity. For the USA at least, an average 4 per cent growth rate over the past several years of the late twentieth and early twenty‐first centuries shows that productivity has increased in tandem with an increase in multi‐tasking behaviour and information technologies. Practical implications Multi‐tasking behaviour needs to be understood in the context of its manifestation as a variable that is at least partially dependent on the existence of relatively “cheap” information. In essence, in an information economy, task completion by knowledge workers to a set deadline may be counterproductive to the interests of the organization as a whole. This article describes certain strategies that can be used to minimize the harmful aspects of continuous task switching and to maximize the returns to experience that multi‐tasking can bring to an organization. Originality/value Multi‐tasking behaviour and its link to complexity theory may lead to a new understanding of organizations as highly fluid and variable entities that are impossible to design or maintain centrally and yet whose goals lead to the moment by moment creation of micro‐organizational structures that accomplish tasks in a manner that engages the full resources of knowledge workers.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".