Use for end-to-end writing of Information Systems research papers targeting UTD24 IS journals (MISQ, ISR, Management Science IS section). Invoke whenever the user is drafting, revising, or positioning an IS paper — covers contribution framing, theory selection, methodology design, and submission strategy. Use this skill proactively when the user mentions IS research, IT artifacts, digital systems, or any paper targeting MISQ, ISR, or Management Science.
jluo410 スター2026/04/16
職業
カテゴリ
学術
スキル内容
End-to-end workflow for Information Systems papers targeting MISQ, ISR, and Management Science (IS section).
Stage 0 — Contribution Audit
Before writing a word, answer these four questions. If any is unclear, resolve it first.
What is the central IT artifact or IS phenomenon? IS papers must be grounded in a specific IT artifact (system, platform, algorithm, digital service) or IS phenomenon (adoption, use, IT-enabled change). Abstract "technology" stories fail.
What is the theoretical contribution? Choose one: (a) new theory, (b) extension of existing theory, (c) theory application to new context, (d) theory testing/replication. Know which — reviewers will ask.
What is the empirical or design contribution? Know the method and what it uniquely enables: survey, experiment, archival, design science, econometrics, simulation, qualitative.
What does this paper change for IS research or practice? If the honest answer is "not much," reframe or reconsider.
Stage 1 — Venue Selection
Choose venue before deep drafting. Each journal has distinct tolerance for theory vs. empirics vs. design.
関連 Skill
Signal
Lean MISQ
Lean ISR
Lean MS-IS
Theory-forward, pluralistic method
✓
Tight hypothetico-deductive, survey/experiment
✓
Causal identification, economics framing
✓
Design science, IT artifact evaluation
✓
Computational methods, large-scale data
✓
✓
Behavioral theory, organizational IS
✓
✓
Markets, platforms, economic mechanisms
✓
See misq-playbook, isr-playbook, ms-is-playbook for venue-specific depth.
Stage 2 — Theory Scaffolding
IS papers live or die on theory. Do this before writing the intro.
Pick one primary theory. Resist stacking 3–4 theories. Reviewers read stacked theories as conceptual confusion. One theory, used rigorously, outperforms three theories used loosely.
Common IS theory families:
Behavioral/cognitive: TAM, TPB, social cognitive theory, cognitive fit
Economic: information asymmetry, transaction cost economics, signaling, two-sided markets
Sociotechnical: structuration, sociomateriality, affordance theory
Design-oriented: design science research methodology (DSRM), action design research
Theory-to-contribution mapping:
If testing theory in new IS context → contribution is boundary condition or moderator evidence
If extending theory with IS construct → contribution is theoretical refinement
If theory predicts artifact design → contribution is design principle + evaluation
If theory is wrong in IS context → contribution is theoretical challenge (high bar — must be airtight)
Stage 3 — Paper Structure
Introduction (1.5–2 pages)
Open with the IS phenomenon or practical problem. Establish that existing work does not fully address it. State your approach and contributions explicitly. End with a roadmap paragraph.
Do NOT open with generic "technology is everywhere" statements. Reviewers skip these. Open with the specific gap.
Contributions statement format:
This paper makes three contributions. First, ... Second, ... Third, ...
Be concrete. Avoid "we provide insight into" — state what you find and why it matters.
Literature Review / Theoretical Background
Purpose: establish what is known, expose the gap, and introduce your theoretical lens.
Review the IS literature, not the general management literature
Cite the papers reviewers expect to see — missing a canonical IS paper is a rejection signal
End with an explicit gap statement and research questions
Hypotheses / Propositions / Design Principles
Match your theory to your method:
Hypothetico-deductive → formal H1, H2, ... with theoretical justification per hypothesis
Design science → design principles derived from kernel theory
Qualitative/interpretive → research questions, not hypotheses
Each hypothesis needs: theoretical mechanism (why), directional prediction (what), and boundary conditions (when/who).
Methodology
IS reviewers scrutinize methods. Address these proactively:
For surveys/experiments:
Sample size, source, response rate
Common method bias mitigation (Harman's test alone is no longer sufficient — use procedural remedies)
Endogeneity threats and identification strategy (IV, DiD, RD, matching)
Robustness checks required
For design science:
Problem instance and meta-requirements
Artifact description (not just overview — enough to replicate)
Evaluation against naturalistic or controlled setting
Design principles formalized, not just implied
For qualitative/interpretive:
Research site access and informant count
Data triangulation
Coding scheme and inter-rater reliability
Theory-to-data linkage explicit
Results
Report what the data show, not what you wanted to find. For quantitative work: effect sizes, confidence intervals, model fit statistics. For qualitative: representative quotes tied to codes and themes.