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Record W4414106747 · doi:10.1016/j.joitmc.2025.100627

Managing operational alignment complexity: A recommender system approach

2025· article· en· W4414106747 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Open Innovation Technology Market and Complexity · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsUniversity of British ColumbiaUniversity Canada West
Fundersnot available
KeywordsPairwise comparisonRecommender systemAbstractionDelphi methodComplexity managementStructural complexityBenchmark (surveying)Information system

Abstract

fetched live from OpenAlex

Operational alignment, defined as the alignment between business processes (BPs) and information systems (ISs), is essential for ensuring that IS capabilities effectively support organizational operations. Despite extensive efforts, existing approaches to operational alignment remain constrained by a trade-off between simplicity and comprehensiveness. Coarse-grained methods overlook critical details, while fine-grained methods, though more precise, generate overwhelming complexity that impedes practical application. Drawing on complexity theory and systems thinking, this study conceptualizes operational alignment as a complex, nonlinear phenomenon characterized by emergent behaviors and intricate coevolutionary interactions among numerous detailed BP activities and IS tasks. While acknowledging dynamic/process complexity conceptually, this study targets the structural complexity at the BP-IS interface (i.e., the many-to-many mapping between BP activities and IS tasks) and operationalizes it through activity-task matching. To address structural complexity, this research proposes a novel operational alignment technique that balances abstraction and idealization through the logic of recommender systems (RSs). Using the Delphi method, the relationships between BPs and ISs, including the importance and performance of specific BP activities and IS tasks, were identified and used to parameterize an RS-based operational alignment technique. This technique manages structural complexity by defining alignment indicators derived from a fit-as-matching perspective, yielding pairwise BP-IS correspondences. The technique employs collaborative filtering to estimate missing values and prioritize high-impact alignment areas. It was empirically validated at Top Public Universities (TPUs) in the Middle East, where it generated actionable recommendations for aligning ISs with BPs and vice versa. Results from expert evaluations and a practical workshop confirmed the technique’s usefulness, usability, and applicability, emphasizing its effectiveness in reducing structural complexity. By translating structural alignment complexity into actionable empirical solutions, this study contributes to design science research by providing a practical, theoretically grounded artifact that addresses operational alignment challenges, preserves alignment accuracy, and supports informed decision-making in dynamic organizational environments.

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)0.001
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: none
Teacher disagreement score0.877
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.053
GPT teacher head0.281
Teacher spread0.228 · 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