How do IT misalignments and IT ambidexterity imbalances lead to organizational agility? Substitution, complementarity, and contingency interdependencies with a configurational approach
Bibliographic record
Abstract
Two main and separate Information Systems (IS) research streams have investigated the link between information technology (IT) and organizational agility via the concepts of IT ambidexterity and IT alignment. This study examines IT ambidexterity and IT alignment for agility by breaking down each concept and investigating questions regarding the balance between exploitation and exploration alongside those of fit between the IT and business domains. To do so, we apply configurational theory inductively to empirically identify and theorize how interdependencies between capability types (i.e., exploitation and exploration) and domains (i.e., IT and business) lead to agility. A fuzzy set qualitative comparative analysis (fsQCA) of data gathered through a survey of manufacturing SMEs unveils three specific forms of interdependencies: (1) complementarity of exploration between the IT and business domains; (2) substitution of exploitation between the two domains; and (3) contingent effects (positive vs. negative) of each, IT exploitation and IT exploration, depending upon the intensity of the remaining elements. These interdependencies enable the derivation of four theoretically meaningful propositions for future research that reconcile inconsistent findings in both research streams. Overall, this study contributes beyond past research focused on either IT ambidexterity or IT alignment by providing a compelling parsimonious theoretical explanation of how – and which – IT misalignments and imbalances between exploitation and exploration lead to high agility and which do not. These insights are also of high practical value, as they provide manufacturing SMEs with more options to reach agility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".