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Record W2010116412 · doi:10.1145/1104004.1104008

Complexities in IS sourcing

2005· article· en· W2010116412 on OpenAlexaff
Barbara L. Marcolin, Alain Ross

Bibliographic record

VenueACM SIGMIS Database the DATABASE for Advances in Information Systems · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEquifinalityConfusionOutsourcingFunction (biology)Strategic sourcingLEAPSScope (computer science)BusinessComputer scienceRisk analysis (engineering)Management scienceMarketingEconomicsArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Growth in IS outsourcing spurred research in the area that spans many perspectives but contains contradictory findings. Inconsistent findings raise confusion and doubt, leaving managers uncertain about IS sourcing directions and researchers unclear about theoretical perspectives relevant to IS sourcing.We argue that the concept of equifinality accounts for much of this confusion. Equifinality suggests that, in the struggle to match conflicting functional demands with structural options, many equally viable alternatives may exist. For IS sourcing, this means that different sourcing choices can be leveraged producing similar IS capabilities. Thus, embracing equifinality requires that we understand more fully the complexities in the IS function and in the range of sourcing options available. We argue, as well, that the existence of multiple paths raises the importance of implementation and execution issues.Illustrating the complexities of the IS function, we outline several IS function elements, including IT resources, IS activities and IS strategy. An IS-Business Partnering framework is then presented demonstrating the multiplicity of partnering options, situating sourcing as one aspect. We conclude with several company illustrations showing these multiplicities for successful company outcomes.For IS sourcing researchers, equifinality holds important implications. No longer can we afford to view the IS function as a homogeneous entity nor can we limit our scope to a narrow range of sourcing and partnering options. More than one path exists to achieve a particular outcome. Hence, the pursuit in IS sourcing research can no longer be for one good answer but for a few good answers.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.016
Open science0.0010.001
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 head0.026
GPT teacher head0.266
Teacher spread0.241 · 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.

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

Citations20
Published2005
Admission routes1
Has abstractyes

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Same venueACM SIGMIS Database the DATABASE for Advances in Information SystemsSame topicOutsourcing and Supply Chain ManagementFrench-language works237,207