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Record W1497717160

IMPACTS OF SOCIAL EMBEDDEDNESS ON IOS PLANNING EFFECTIVENESS: A MULTILEVEL PERSPECTIVE

2008· article· en· W1497717160 on OpenAlexaff
Saeed Akhlaghpour, Liette Lapointe

Bibliographic record

VenueQueensland's institutional digital repository (The University of Queensland) · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsMcGill University
Fundersnot available
KeywordsEmbeddednessDyadPerspective (graphical)Salience (neuroscience)ExploitBusinessProcess managementKnowledge managementInterpersonal tiesOperations managementComputer scienceSociologyEconomicsPsychologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

This conceptual paper introduces the logic of embeddedness as a new theoretical angle to the IS planning stream of research. Taking two levels of analysis into account, we examine the impact of socially embedded ties on strategic interorganizational system (IOS) planning. We propose that from a dyad perspective, embedded ties facilitate the planning process, and can significantly increase the number of identified systems and their potential strategic value. Yet, paradoxically, having solely embedded ties in a network can adversely impact planning effectiveness by increasing (a) the risks of being insulated from novel ideas, (b) the likelihood of a lock-in effect, and (c) the salience of isomorphic pressures. We argue that, from a network perspective, those firms that manage to exploit an integrated mix of both embedded and arm’s length ties are more likely to succeed in their IOS planning. This paper contributes to SISP theory by identifying new interorganizational-level antecedents of planning effectiveness. It provides guidelines that IS executives can use to improve the effectiveness of their planning activities and ultimately achieve superior IT-business alignment.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.216
Teacher spread0.200 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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