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Record W4313435918 · doi:10.1080/21650349.2022.2157888

Facing extreme uncertainty – how the onset of the COVID-19 pandemic influenced product development

2022· article· en· W4313435918 on OpenAlexaff
Katja Hölttä‐Otto, Tua Björklund, Monika Klippert, Kevin Otto, Dieter Krause, Claudia Eckert, Oscar Nespoli, Albert Albers

Bibliographic record

VenueInternational Journal of Design Creativity and Innovation · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNew product developmentProduct (mathematics)Flexibility (engineering)Process managementCoronavirus disease 2019 (COVID-19)BusinessResilience (materials science)Quality (philosophy)Psychological resilienceProcess (computing)PandemicRisk analysis (engineering)Computer scienceMarketingPsychologyEconomicsManagementSocial psychology

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has been a global disruption, but little is known about how it impacted product development. Based on interviews of 24 practicing product development leaders, we find that COVID-19 generated a unique combination of external and internal uncertainties and thus had several direct impacts on product development. Initial adaptations were reported in the level of innovation pursued, the development processes and the resourcing. In terms of level of innovation and resourcing, no uniformity in adaptations were observed: opposite changes were made across radical/incremental, expanding/reducing, local/international and internal/external collaboration balances in the different companies’ product development activities. Process adaptations were more uniform in direction, focusing on increasing flexibility and agility. In terms of product development methods for different phases, we find companies quickly seeking creative approaches to replace their traditional methods in idea generation, prototyping, customer interaction, validation etc. with virtual means. Furthermore, changes in human interaction quality, particularly informal interaction, were seen to have far-reaching, unintended negative consequences on their creative efforts, whether in product development, development process or resourcing. Overall, the results highlight the diversity of adaptive choices available to respond to external uncertainties, though more research is still needed on how these influence longer term resilience.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.101
GPT teacher head0.286
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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