System Dynamics Modeling for Information Systems Research: Theory Development and Practical Application1
Bibliographic record
Abstract
Most information systems (IS) research develops theory for explanation and prediction based on a variance logical structure that assumes one-way, time invariant causal relationships. This approach largely misses the opportunity to extend theory from alternative logical structures that build upon reciprocal and temporal causal mechanisms; for example, the system perspective. This paper introduces system dynamics (SD), a modeling tool capable of capturing the reciprocal and temporal causal mechanisms that underlie many complex and dynamic systems, and demonstrates its ability to extend existing variance theory from a system perspective. To do so, we first describe the basic tenets of SD and discuss the status quo of existing SD applications in the field. Then, we demonstrate how to model SD’s unique theoretical logic of reciprocal and temporal causal structure to extend existing variance theory. To demonstrate the use of SD in theory development, we develop and validate an SD model of the e-commerce resource endowment of a click-and-mortar firm and simulate dynamic causal relationships between the e-commerce resource endowment and firm performance over time, under various scenarios. This case demonstrates how we can extend an existing variance theory by reconciling the inconsistent findings of prior research from a system perspective using the SD approach. The paper concludes by discussing how SD can help IS researchers develop dynamic theories.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".