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Record W4392701027 · doi:10.7190/shu-thesis-00590

New generation of innovation management: an integrated framework for the digital era

2022· dissertation· en· W4392701027 on OpenAlex
Samah Alnuaimi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSheffield Hallam University · 2022
Typedissertation
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsnot available
Fundersnot available
KeywordsCommercializationAnalytic hierarchy processRevenueProcess (computing)Innovation managementKnowledge managementRevenue modelProcess managementBusinessEngineeringMarketingManagement scienceComputer scienceOperations research

Abstract

fetched live from OpenAlex

The present research highlights the main developments in the generations of innovation management models and systems. Innovation defined as the process of transforming ideas into marketable products or services is vitally important to the industry since it can produce value to the customers and generate revenue for producers. The research aim is to develop a novel generation innovation framework for future digital economy which defines the lifecycle from idea generation to commercialization, illustrating the factors affecting such development and considering the current socio-economic environment, evolution of business processes, technological advancements and market trends. A questionnaire is designed and administered to professionals in industry to elicit their feedback that can be used to validate the framework and to assess its usefulness to organisations. This questionnaire is an essential part of the research methodology. The questions are formulated in a format that allows a pair-wise comparison highlighting the item`s relative importance. Adequate guidance on answering questions is provided. The proposed innovation framework is applied to collect data and to carry out a pair-wise comparison between the components of the main criteria and sub-criteria. It triggers the innovation processes required to handle the demand-pull and to consider the digitalisation push. The model is validated utilizing the practitioner’s contributions from seven countries, namely; the UK, UAE, USA, Germany, Japan, China, and Canada, The Analytical Hierarchy Process (AHP) is utiliesed, combining both quantitative and qualitative methods. The impact of digitalisation-push and of the demand-pull are considered as main criteria, with many sub-criteria associated with each criterion. The findings confirmed that the proposed framework is useful to industry professionals and organisations that focus on creating value for the customer who has become more aware of and demanding regarding lead time delivery services, product availability, and reliability. The model can also be applied to test the ideas of experts to obtain the appropriateness of the innovation framework for manufacturing, firms, and organisations.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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