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Record W2915997164 · doi:10.1108/ict-05-2016-0028

The challenges of organizational agility: part 2

2017· article· en· W2915997164 on OpenAlexaff
Steven H. Appelbaum, Rafael Calla, Dany Desautels, Lisa N. Hasan

Bibliographic record

VenueIndustrial and Commercial Training · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsConcordia University
Fundersnot available
KeywordsAgile software developmentMindsetCompetitive advantageKnowledge managementContext (archaeology)Dynamic capabilitiesBusinessProcess managementEmpirical researchOrganizational performanceOrganizational cultureComputer scienceManagementMarketing

Abstract

fetched live from OpenAlex

Purpose Planned episodic change programs, rigid processes and traditional structures, optimized for efficiency rather than agility, are no longer appropriate in a context where competitive advantage is fueled by high-speed innovation, supported by a more entrepreneurial mindset. The purpose of this paper is to offer a review of relevant research to provide an informed case for continuous strategic transformation facilitated by enhanced organizational agility. The concept of agility is explored, defined and a framework for categorizing agility-enhancing capabilities is presented. Specific aspects of this agility framework are examined to better understand how these interrelated competencies contribute to overall corporate performance in this fast-paced world. Design/methodology/approach A range of published empirical and practitioner research articles were reviewed to study the concepts of organizational agility and transformation as critical factors contributing to sustained competitive advantage, organizational performance and survival in the increasingly competitive global context. This literature review explores how organizations are overcoming the challenges imposed by their traditional structures, cultures and leadership models and identifies dynamic competencies to be developed to achieve a greater level of corporate agility. Findings Increased organizational agility increases the ability to respond proactively to unexpected environmental changes. The commitment to continuous transformation and agile strategies implies changes at all levels of the organization from its structure, through its leadership and decision-making dynamics, down to the skills and interpersonal relationships of the individuals implementing the agile mission. Research limitations/implications There is a gap in the literature with respect to agility, namely that most research focuses on the characteristics of agile organizations, with little attention given to how to develop agile capabilities and embed the commitment to continuous change deep into the corporate DNA, beyond the process level, into the psyche of the people driving the organization. Practical implications Managers should consider agility as an overarching principle guiding strategic and operational activities. Fostering agility-enhancing capabilities will be paramount in ensuring the successful integration of agility as a performance enhancing paradigm. Social implications For small- and medium-sized companies with limited resources, this reality makes staying relevant an uphill battle but also opens windows of opportunity. The challenge of the next century for large organizations will be to rekindle their innovative agile beginnings and for start-ups to continue to foster their dynamic capabilities as they grow. Originality/value The paper provides practical and empirical evidence of the importance of enterprise agility and specific dynamic capabilities on firm performance.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.006
Scholarly communication0.0090.005
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.167
GPT teacher head0.280
Teacher spread0.113 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations65
Published2017
Admission routes1
Has abstractyes

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