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An Integrational Strategy as a Response to the Corporate-Community Engagement Challenges

2015· article· en· W2602868549 on OpenAlexaffabout
Nolywé Delannon, Emmanuel Raufflet, Sofiane Baba

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsSalientStakeholder engagementStakeholderPublic relationsContext (archaeology)BusinessFlexibility (engineering)Local communityCommunity engagementPolitical scienceManagementEconomics

Abstract

fetched live from OpenAlex

In recent years, issues related to companies' relations with their local stakeholders, namely the local community, have become increasingly salient. There is a growing consensus among practitioners, business associations, international financial institutions and consultants that these relations matter to a company's long-term success. However, little is known about how companies actually organize their internal resources to implement their community engagement strategy. Understanding these internal arrangements is timely, especially in the context of environmentally sensitive industries (e.g., mining, forestry, energy, etc.) where corporate-community relations are of tantamount importance. Based on an empirical study with 17 Canadian companies, this article opens a black box by linking the strategies of community engagement, well documented in the existing literature, to the organizational arrangements that support them. In doing so, this article contributes to the stakeholder literature by providing an in-depth examination of the experience of the very actors that are in the front line of corporate-community engagement. More specifically, it sheds light on a strategy of engagement that has been overlooked in previous works, i.e., the integrational strategy. This strategy is distinctive by its transverse and dynamic dimensions and yields tangible results for those companies that embrace flexibility.

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)0.007
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.344
GPT teacher head0.330
Teacher spread0.014 · 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 designTheoretical or conceptual
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

Citations0
Published2015
Admission routes2
Has abstractyes

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