Integrating Course-Based Competitions into Strategic Management Instruction: A Reproducible Course Improvement Protocol

Summary

This protocol embeds a diagnosis-decision-defense competition sequence into undergraduate Strategic Management instruction through staged team tasks, structured feedback, rater training, implementation-fidelity monitoring, and separate student- and team-level outcome review.

Abstract

Strategic Management instruction requires students to apply analytical frameworks to uncertain, resource-constrained, and trade-off-based business situations. Course competitions can support this process when they are organized as a structured instructional system rather than as a single presentation activity. This article describes a reproducible protocol for integrating a three-stage course-based competition into an undergraduate Strategic Management course. The protocol follows a 16-week sequence that includes baseline assessment, core instruction, enterprise case introduction, team formation, strategic diagnosis, strategic decision simulation or decision-sheet completion, final strategy defense, post-course assessment, and course review. The instructional mechanism combines staged practice, enterprise case exposure, structured formative feedback, team accountability, oral defense, and rubric-based scoring. Locally operationalized process indicators are used to monitor classroom interaction, case-design completeness, feedback timeliness, task-rubric alignment, student engagement, and implementation fidelity. Student-level outcomes, team-level competition outcomes, rater agreement, missing-data patterns, artificial intelligence use disclosure, and score-inflation warning patterns are analyzed separately to avoid unit-of-analysis problems. Representative results, when reported, should be interpreted as context-specific implementation outputs from a retrospective, non-randomized educational-record comparison, not as evidence of causal effectiveness. The protocol provides an auditable procedure for designing, implementing, documenting, evaluating, and refining competition-integrated Strategic Management instruction within a course-improvement cycle.

Introduction

Strategic Management courses require students to move beyond conceptual recall. Students must apply ideas such as industry structure, competitive advantage, resource allocation, strategic choice, and implementation under conditions of incomplete information and competing priorities. Competitive positioning explains how firms choose positions under external constraints¹, whereas the resource-based view emphasizes firm-specific resources and capabilities that shape strategic options². These perspectives make Strategic Management suitable for applied teaching designs in which students diagnose a business situation, make a strategic choice, defend the choice, and revise their reasoning after feedback. Lectures and isolated case discussions remain useful, but they may not fully expose how students reason through uncertainty, trade-offs, and implementation constraints. A course-based competition can create a more demanding learning environment only when it is connected to course objectives, task sequencing, scoring criteria, and feedback cycles. Without these elements, competition may reward presentation confidence or task division rather than strategic reasoning.

Prior work on business games and simulations has shown that these activities require explicit rules, decision cycles, assessment evidence, and debriefing if they are to function as educational methods rather than classroom entertainment^3,4. Active learning also depends on task design rather than activity alone. Students may appear engaged while still applying concepts superficially. A structured Strategic Management competition should therefore require students to analyze a situation, justify a decision, receive formative feedback, revise their reasoning, and defend an implementation plan. This sequence reflects active learning principles because students produce, test, and refine their own reasoning rather than only listen to instruction⁵. It also reflects experiential learning because the competition cycle links concrete case exposure, reflective analysis, decision making, and revised action⁶.

Published business simulation and competition reports vary widely in their procedural detail. Some describe the use of a simulation or competition but provide limited information about team formation, task staging, feedback timing, rater training, rubric construction, missing-data handling, or separation of individual and team outcomes. Previous reviews and taxonomies have also shown that business games are not a single instructional method; they differ in rules, fidelity, learner roles, decision structure, technology use, assessment design, and debriefing procedures^7,8. A reproducible Strategic Management protocol, therefore, needs to specify not only that a competition occurred, but how the competition was embedded into the course and how its implementation was monitored.

This protocol is best understood as a multicomponent competition-integrated course improvement package. The expected instructional effect cannot be attributed solely to competition, as the protocol also includes enterprise case exposure, repeated practice, structured feedback, team accountability, public defense, rater calibration, and systematic course review. The competition element is educationally useful because it creates deadlines, comparison, accountability, and public justification, but these elements are deliberately combined with staged learning tasks and assessment controls. The procedure differs from conventional experiential, simulation-based, and competition-based Strategic Management teaching in three ways. First, the competition is staged across diagnosis, decision, and final defense rather than concentrated in a single final presentation. Second, implementation fidelity is monitored before learning outcomes are interpreted. Third, student-level learning outcomes are separated from team-level competition outcomes. This distinction is important because a team may perform well in a final defense while some members show limited individual learning. Team learning requires accountability, interaction, and shared reasoning, not only group membership⁹. The protocol therefore uses team charters, process logs, peer contribution forms, rater training, rater agreement checks, and score-inflation warning rules.

The protocol was implemented as a routine teaching activity in an undergraduate Strategic Management course at the Business School, Zhuhai College of Science and Technology, China. The protocol cohort was taught in course section SM-2025-F01 during the fall semester of the 2025-2026 academic year. The comparison cohort was a historical cohort taught in course section SM-2024-F01 during the fall semester of the 2024–2025 academic year. The protocol course section enrolled 96 students; 94 students provided written or electronic consent for research use of de-identified course records after ethics approval and before inclusion in the research dataset, 2 students were excluded because of course withdrawal before Week 8 or absence of any valid post-course outcome, and 92 students formed the final analytic protocol cohort. The historical comparison section enrolled 92 students; 90 students consented to research use of de-identified course records after ethics approval and before retrospective extraction, 2 were excluded after applying the same missing-data and withdrawal rules, and 88 students formed the final analytic comparison cohort.

Both cohorts consisted of third-year undergraduate business students. Majors included Business Administration, Marketing, E-Commerce, and International Business. The protocol cohort included 51 female and 41 male students, with a mean age of 20.4 ± 0.7 years. The comparison cohort included 49 female and 39 male students, with a mean age of 20.5 ± 0.8 years. Prior competition experience was reported by 27 students in the protocol cohort and 24 students in the comparison cohort. Attrition before final analysis was 4.2% in the protocol cohort and 4.3% in the comparison cohort. Missing-data counts in the protocol cohort were 2 baseline knowledge tests, 3 postcourse knowledge tests, 4 post-course learning surveys, and 4 incomplete competition-task records. Missing-data counts in the comparison cohort were 3 baseline knowledge tests, 4 postcourse knowledge tests, 5 postcourse learning surveys, and 70 incomplete competition-task records because the historical course did not contain the full three-stage competition process.

The comparison cohort received conventional case-based Strategic Management instruction without the complete three-stage competition sequence. The same instructor taught both cohorts. Both cohorts used comparable course learning outcomes, core Strategic Management topics, knowledge-assessment coverage, individual case-analysis assessment, and final course grading structure. The protocol cohort additionally received the enterprise case package, staged competition tasks, structured feedback cycles, formal team process records, rater training procedures, and implementation-fidelity monitoring. The present article describes the protocol as a reproducible course-improvement method rather than as a claim of causal effectiveness. The representative results show the types of implementation, instructional quality, student-level, team-level, scoring reliability, missing data, and troubleshooting outputs generated by the protocol. Because the comparison is historical, non-randomized, and context-specific, differences between cohorts should be interpreted as course-improvement evidence within one implementation context, not as proof that course-based competition alone caused the observed outcomes.

The broader purpose of the protocol is to help instructors design, document, evaluate, and refine competition-integrated Strategic Management instruction. It supports assurance of learning by linking teaching activities, student outputs, scoring rubrics, implementation records, and course review decisions¹⁰. It also addresses concerns in gamification and competitive learning research that game-like elements may yield mixed outcomes when motivation is emphasized without sufficient attention to task design, feedback quality, and assessment validity^11,12. The expected outputs include implementation records, locally operationalized instructional quality indicators, student-level learning outcomes, team-level competition outcomes, rater agreement evidence, artificial intelligence use disclosures, missing-data documentation, and troubleshooting records.

Protocol

The research use of de-identified educational records from both cohorts was approved by the Teaching Research Ethics Committee of Zhuhai College of Science and Technology, Zhuhai, China (Approval No.20260507001; approval date: 7 May 2026). The approval covered the protocol cohort and the historical comparison cohort, including routine course records, surveys, classroom observations, team process records, competition scores, rater scores, learning platform records, and de-identified gradebook variables. The 16-week course was delivered as routine Strategic Management teaching prior to data extraction for research, and no identifiable research dataset was extracted, coded, analyzed, or reported before ethics approval. Written or electronic consent for research use of de-identified course records was obtained after ethics approval and before dataset inclusion. Course participation was separated from research data use; non-consenting students completed the course normally but were excluded from research analysis. Consent records were stored separately from the gradebook, the code-to-identity file was restricted to the data manager, and the analysis-ready dataset was created only after grade submission, consent verification, code assignment, and removal of direct identifiers and identifiable free-text comments.

1. Establish the competition-integrated course improvement protocol.

1. Define the protocol as a 16-week competition-integrated course improvement process for an undergraduate Strategic Management course. The instructional mechanism is a staged diagnosis-decision-defense cycle linking strategic analysis, evidence-based decision making, feedback, team accountability, oral defense, and course review.
2. Define instructional quality through classroom interaction, case-design completeness, feedback timeliness, task-rubric alignment, and student engagement. Define implementation fidelity through planned activity completion, enterprise or case-session completion, task completion, feedback turnaround, rubric completion, observation completion, and rater-training completion. Aggregate indicators only when reliability and dimensional evidence support aggregation.
3. Align learning outcomes, teaching activities, competition tasks, rubrics, feedback points, and outcome measures before course delivery to maintain constructive alignment¹³.
4. Assign the roles of course instructor, competition coordinator, enterprise liaison, classroom observer, rater coordinator, and data manager. Teaching roles may be combined in small programs, but the data manager should remain separate from final grading and research dataset preparation whenever possible.
5. Prepare a version-controlled course implementation folder containing the syllabus, case files, handbook, task templates, rubrics, observation forms, surveys, team records, scoring forms, feedback logs, AI-use disclosure forms, and version-control records. After ethics approval, create a research reproducibility folder containing the approval record, consent-verification record, de-identification log, de-identified datasets, data dictionary, cleaning log, analysis scripts, statistical outputs, figure source files, tables, and supplementary files (Figure 1 and the Table of Materials; operational templates are provided in Supplementary File 1).

Figure 1: Workflow of the competition-integrated Strategic Management course improvement protocol. The workflow shows the sequence from course preparation to 16 week routine course implementation, routine educational-record generation, grade submission, ethics-approved research-use preparation, consent verification, de-identification, dataset locking, analysis, reporting, troubleshooting, and course refinement. The figure separates routine teaching activities from post-grade research data extraction and analysis. Please click here to view a larger version of this figure.

2. Recruit the student cohort and prepare the research dataset.

1. Include students formally enrolled in the selected undergraduate Strategic Management section. Record institution, academic year, semester, section, cohort type, major, year level, teaching weeks, contact hours, enrollment, consented sample, exclusions, final analytic sample, demographics, prior competition experience, attrition, and missing-data counts.
2. Define the protocol cohort as the class receiving the complete competition-integrated protocol and the comparison cohort as a historical class receiving conventional case-based Strategic Management instruction without the full three-stage competition sequence. Record whether the same instructor taught both cohorts and whether learning outcomes, assessment coverage, individual case-analysis requirements, and grading structure were comparable.
3. After ethics approval, verify consent, assign non-identifiable student and team codes, remove direct identifiers, and apply the inclusion, exclusion, and missing-data rules in Table 1.
4. Include consenting students with at least one valid post-course outcome. Exclude students from paired knowledge analysis when either knowledge test is missing and from competition-related analysis when all three task records are missing. Retain available records for other eligible analyses.
5. Code missing research data as NA. Do not assign zero unless zero reflects an actual course score under the approved course policy. Create a participant-flow record for each cohort.

Item	Operational definition	Application rule	Dataset variable	Analysis implication
Target course	Undergraduate Strategic Management course delivered in the selected semester	Include students formally enrolled in the selected course section	course_section	Defines the eligible teaching population
Protocol cohort	Class receiving the complete competition-integrated course improvement protocol	Code as 1	cohort_type	Protocol cohort
Historical comparison cohort	Historical class receiving conventional case-based Strategic Management instruction without the complete three-stage competition process	Code as 0	cohort_type	Non-randomized historical comparison cohort
Official enrollment	Student appears on the official course roster	Screen as potentially eligible	eligible_status	Required before research-use screening
Research consent	Student provides written or electronic consent for de-identified course-record use after ethics approval	Include only consenting students in the research dataset	consent_status	Non-consenting students complete the course normally but are excluded from research analysis
Course participation	Routine course learning activities completed regardless of research consent	Allow all enrolled students to attend class, join teams, complete course tasks, and receive grades	course_participation_status	Separates teaching participation from research participation
Valid post-course outcome	At least one valid post-course outcome is available	Include in analyses for which the relevant outcome is available	outcome_available	Allows available-case analysis
Course withdrawal before midpoint	Student withdraws before Week 8	Exclude from the final research dataset	withdrawal_status	Reduces incomplete-exposure bias
Missing baseline knowledge test	Week 1 knowledge test is missing	Code as NA	pre_knowledge_missing	Exclude from paired knowledge analysis only
Missing post-course knowledge test	Week 16 knowledge test is missing	Code as NA	post_knowledge_missing	Exclude from paired knowledge analysis only
Missing both knowledge tests	Both baseline and post-course knowledge scores are missing	Exclude from knowledge-score analysis	knowledge_pair_status	Retain for other eligible analyses if data are available
Missing post-course survey	Anonymous post-course survey is missing	Code as NA	survey_missing	Retain in objective performance analyses
Missing one or two competition-task records	One or two task or mapped project records are missing	Code missing records as NA unless course policy assigns zero	task_score_missing	Retain available task or mapped project records
Missing all three competition-task records	No valid record for Task 1, Task 2, or Task 3 is available	Exclude from competition-related analyses	competition_completion	Retain for non-competition analyses if other outcomes are available
Duplicate record	More than one row appears for the same student code	Keep the verified final record after source-file checking	duplicate_check	Resolve before analysis
Identifier leakage	Name, student number, phone number, email, platform account, device identifier, or identifiable free-text comment appears	Remove identifier before analysis	identifier_check	Dataset cannot be analyzed until de-identified
Final analytic sample	Consenting students with usable data after exclusion and missing-data rules	Report final number and exclusion reasons	analysis_sample	Required for participant-flow reporting

Table 1: Cohort structure, inclusion criteria, exclusion criteria, and missing-data rules. This table defines the protocol cohort, historical comparison cohort, eligibility criteria, research-use permission, exclusion criteria, missing-data handling, and analytic sample construction.

3. Implement the 16-week course sequence.

1. Deliver the course over 16 teaching weeks, with 32 classroom contact hours and approximately 24-30 independent teamwork hours. Record instructional hours, teamwork time, case interaction, feedback, rehearsal, and assessment time separately.
2. In Week 1, conduct the course briefing, baseline knowledge test, and baseline engagement survey as routine course activities. Explain the course process, task sequence, assessment rules, data-protection principles, and later research use of de-identified records. Deliver core Strategic Management instruction in Weeks 2–3 and short concept reinforcement during Weeks 4–15.
3. In Week 4, introduce the enterprise case package and competition handbook, including task sequence, submission rules, rubrics, feedback timeline, team responsibilities, academic integrity rules, AI disclosure, oral-check procedure, and appeal process.
4. Implement Task 1 in Weeks 5–6 and return feedback within 7 calendar days in Week 7. Require revision of the strategic issue statement before Task 2¹⁴. Implement Task 2 in Weeks 8–10, enterprise mentor or structured case coaching in Week 11, and Task 3 in Weeks 12–15.
5. In Week 16, conduct the post-course knowledge test, postcourse survey, data audit, and course review as routine course activities. Export, code, de-identify, and analyze records for research use only after ethics approval, consent verification, and grade submission (see Table 2).
   PAUSE POINT: Pause after Week 4 if team allocation, case files, handbook, rubrics, AI disclosure form, task templates, or data-recording forms are incomplete. For research reporting, pause analysis until ethics approval, consent verification, grade submission, de-identification, and dataset locking are complete.

Week	Activity	Instructional focus	Student or team output	Required record	Quality check
Week 1	Course briefing and baseline assessment	Course process, assessment rules, baseline knowledge test, baseline engagement survey	Baseline knowledge test; baseline survey	Attendance; baseline test file; baseline survey file; course-briefing record	Confirm baseline assessment completion and routine course-record separation from later research use
Week 2	Core Strategic Management instruction I	Environmental analysis, industry analysis, competitor analysis	In-class worksheet	Attendance; worksheet record	Check worksheet completion
Week 3	Core Strategic Management instruction II	Internal resource analysis, strategic choice, implementation planning, performance evaluation	Short case-discussion response	Attendance; case-discussion record	Confirm core concepts before competition launch
Week 4	Enterprise case introduction and competition briefing	Case package, handbook, task sequence, rubrics, AI disclosure, team rules, appeal procedure	Team list; team charter; AI-use acknowledgement	Team list; team charter; handbook access record; case access record	Pause if case files, handbook, rubrics, team records, task templates, or AI-use forms are incomplete
Week 5	Task 1 begins	Strategic diagnosis report structure and evidence requirements	Draft strategic diagnosis report	Team process log; consultation record	Confirm team role allocation
Week 6	Task 1 submission	Strategic diagnosis, industry and internal analysis, evidence use	Final Task 1 report	Submission record; report file	Identify missing or incorrectly formatted submissions
Week 7	Task 1 feedback	Rubric-based feedback and revision direction	Revised strategic issue statement	Feedback log; feedback turnaround record	Pause if >15% of reports fail to meet the required structure
Week 8	Task 2 begins	Decision sheet, simulation assumptions, decision variables	Initial strategic decision sheet	Team process log; observation record	Confirm understanding of decision variables
Week 9	Strategic decision refinement	Market positioning, resource allocation, implementation sequence, risk assumptions	Revised decision sheet	Coaching record; observation checklist	Check evidence-based justification
Week 10	Task 2 submission	Final strategic decision logic	Final Task 2 decision sheet	Task 2 score; submission record; decision-sheet file	Check submission completeness
Week 11	Enterprise or structured case coaching	Mentor or case-based feedback on strategic dilemmas	Mentor reflection sheet; team revision plan	Enterprise engagement log; student questions; reflection record	Confirm attendance and reflection completion
Week 12	Task 3 begins	Final defense format, executive summary, slide structure, Q&A rules	Draft presentation outline	Team process log; AI-disclosure update	Check workload balance using peer contribution form
Week 13	Final defense preparation	Strategic coherence, feasibility, risk control, evidence use	Draft slide deck; executive summary	Coaching log; observation checklist	Confirm diagnosis-choice-implementation linkage
Week 14	Rehearsal and rater calibration	Rehearsal defense and rater training	Rehearsal presentation	Rater training record; preliminary rubric check	Confirm scoring calibration
Week 15	Final strategy defense	8-min presentation and 5-min Q&A	Final slide deck; executive summary; oral defense	Task 3 scores; rater scores; Q&A notes	Calculate ICC for overlapping subjective scoring
Week 16	Post-course assessment and course review	Post-course knowledge test, post-course survey, course review, routine data audit	Post-course knowledge test; post-course survey	Post-course test file; survey file; course-review record	Pause research analysis until grade submission, ethics approval, consent verification, de-identification, and dataset lock are complete

Table 2: Sixteen-week implementation schedule for the competition-integrated Strategic Management course protocol. This table summarizes weekly protocol activities, instructional focus, student or team outputs, required records, and quality-control checkpoints across the 16-week course.

4. Prepare enterprise cases and competition materials.

1. Select one main enterprise case and two optional supporting cases from public, anonymized, or enterprise-approved teaching materials. Screen each case for industry context, managerial decision problem, resource constraint, strategic trade-off, implementation challenge, and measurable consequence. Use “case-design completeness” rather than “case authenticity” unless independent validation is available.
2. Prepare an anonymized model case when enterprise-approved materials are unavailable. Specify the industry, firm size, customer segment, competitive pressure, resource constraint, strategic decision, implementation risk, and expected consequence. Remove confidential enterprise information, including company names, exact financial figures, customer information, employee information, proprietary data, and strategic documents without written permission. The case template is provided in Supplementary File 1.
3. Prepare the handbook, task templates, peer contribution form, team process log, feedback log, AI-use disclosure form, oral-check record form, rubrics, and version-control table. Permit disclosed AI use for language polishing, formatting, grammar checking, general brainstorming, and slide-layout improvement. Prohibit AI use that replaces student-generated diagnosis, evidence selection, decision justification, risk analysis, implementation planning, or oral defense.
4. Design rubrics to prioritize strategic reasoning over polished text. Score evidence use, trade-off reasoning, implementation feasibility, internal consistency, and oral defense separately from presentation style. Upload the handbook, templates, rubrics, case materials, AI-use rules, and appeal procedure before Week 4.

5. Form student teams and assign team roles.

1. Form teams of 4–6 students. Use stratified grouping when baseline course performance, major, gender, or prior competition experience is available and permitted by institutional course policy.
2. Assign one primary role to each student before Task 1. Require each team to submit a team charter and maintain a process log that documents role allocation, communication rules, contribution expectations, deadlines, AI use responsibilities, attendance, decisions, evidence reviewed, unresolved problems, and next actions.
3. Collect peer contribution scores at the midpoint and final stage to identify contribution imbalance. Conduct individual oral checks during coaching, rehearsal, or final defense by asking selected students to explain the team’s diagnosis, evidence base, decision logic, risk assumptions, or implementation plan in their own words.

6. Conduct baseline assessment.

1. Administer the baseline Strategic Management knowledge test in Week 1 before enterprise case introduction and competition tasks as a routine course assessment.
2. Use a 24-item test scored from 0 to 100, covering environmental analysis, industry and competitor analysis, internal resource analysis, strategic choice, implementation planning, and strategic evaluation. The blueprint, item-development rules, review form, and scoring rules are provided in Supplementary File 1.
3. Develop Form A and Form B as equivalent but non-identical versions matched by topic coverage, item format, cognitive level, score range, expected difficulty, and scoring rules. Have two Strategic Management instructors and one business education assessment reviewer evaluate item relevance, clarity, cognitive level, and topic coverage. Pilot both forms with 24 students outside the analytic sample.
4. Retain items with difficulty values of 0.30–0.80 and item discrimination values above 0.20 when feasible. Treat internal consistency as acceptable when KR-20 or Cronbach’s alpha is ≥0.70. Treat the forms as acceptable for paired use when the pilot mean difference is ≤3 points and the standardized difference is <0.20.
5. Counterbalance forms when feasible. Assign half of the students to Form A at baseline and Form B post-course, and the other half to Form B at baseline and Form A post-course. If one fixed order is used, record it as a limitation.
6. Administer the baseline engagement survey after the knowledge test. Use 5-point items to record interest in Strategic Management, perceived practical relevance, case-analysis confidence, willingness to participate in team tasks, teamwork readiness, and prior competition experience. Analyze conceptually different items separately unless reliability and dimensional evidence support a composite score.
7. Record baseline attendance, knowledge-test completion, survey completion, and form version during routine course delivery. Export baseline data for research use only after ethics approval, consent verification, code assignment, and removal of direct identifiers.

7. Deliver enterprise case-based Strategic Management instruction.

1. Deliver case-based instruction through short lectures, guided discussion, team worksheets, and brief decision exercises. Use active learning tasks to move students from concept reception to analysis, decision making, team discussion, and reflection¹⁵.
2. Invite enterprise managers, entrepreneurs, or industry mentors when available. Provide course objectives, student level, case topic, confidentiality requirements, discussion focus, and time commitment at least 7 days before the session. Use an anonymized or public case briefing when a live mentor is unavailable.
3. Present one non-confidential strategic problem involving market uncertainty, competitive pressure, business model adjustment, resource constraints, implementation barriers, or risk control. Require each team to identify the problem, evidence base, alternatives, risks, and recommended decision using a structured worksheet.
4. Require each team to submit a 300–500 word reflection linking the case problem with one Strategic Management concept and one active competition decision. Record mentor background, case source, attendance, student questions, worksheet completion, and reflection submission in the enterprise engagement log. Link case exposure, reflection, decision making, and revision across the competition cycle through experiential learning activities¹⁶.

8. Implement the three-stage teaching competition.

1. Conduct the formal competition briefing in Week 4. Explain the task sequence, timeline, rubrics, feedback rules, academic integrity requirements, AI disclosure, oral-check procedure, and appeal procedure.
2. Implement Task 1 in Weeks 5–6 as a 1,500–2,000-word strategic diagnosis report covering industry analysis, competitor analysis, internal resource analysis, strategic issue diagnosis, evidence use, and initial recommendation. Return feedback within 7 calendar days and use the revised strategic issue statement as the starting point for Task 2.
3. Implement Task 2 in Weeks 8–10 as a strategic decision simulation or structured decision sheet covering market positioning, resource allocation, pricing or cost logic, implementation sequence, risk assumptions, and expected outcome.
4. Implement Task 3 in Weeks 12–15 as a final strategy defense. Require an 8–12-slide deck, a 1,000–1,500-word executive summary, an 8 min presentation, and 5 min of questions. Confirm that the defense connects Task 1 diagnosis, Task 2 decision logic, implementation plan, risk control, and expected outcome.
5. Score all three tasks using 100 point rubrics and apply the structure and scoring dimensions in Table 3.
   PAUSE POINT: Pause after Task 1 scoring when more than 15% of submissions fail to follow the required structure or when raters identify unclear descriptors. Revise task instructions or rater guidance before Task 2.

Task	Weeks	Required Output	Main Content	Score	Scoring Dimensions
Task 1: Strategic diagnosis report	Weeks 5–6	1,500–2,000-word team report	Industry analysis, competitor analysis, internal resource analysis, strategic issue diagnosis, evidence use, initial recommendation	100	Industry and market analysis, 20
					internal resource analysis, 20
					strategic issue diagnosis, 20
					evidence use, 20
					written logic and clarity, 20
Task 2: Strategic decision simulation or decision sheet	Weeks 8–10	Structured team decision sheet	Market positioning, resource allocation, pricing or cost logic, implementation sequence, risk assumptions, expected outcome	100	Strategic coherence, 25
					market logic, 20
					operational feasibility, 20
					risk analysis, 20
					decision justification, 15
Task 3: Final strategy defense	Weeks 12–15	8–12-slide deck, 1,000–1,500-word executive summary, 8-min presentation, 5-min Q&A	Strategic diagnosis, strategic choice, implementation plan, risk control, expected performance	100	Strategic diagnosis, 20
					strategic choice, 25
					implementation feasibility, 20
					innovation, 15
					evidence use, 10
					oral defense, 10

Table 3: Three-stage teaching competition structure and scoring dimensions. This table defines the three competition tasks, implementation weeks, required outputs, main content requirements, total score, and scoring dimensions.

9. Standardize scoring and train raters.

1. Recruit at least two raters for subjective scoring when staffing allows; document single-rater scoring when a second rater is unavailable.
2. Train raters with rubrics, descriptors, calibration submissions, common scoring errors, AI disclosure rules, and oral-check criteria. Raters independently score two calibration submissions and discuss score differences greater than 10 points.
3. Freeze the rubric after training and use the same version throughout the implementation cycle. Calculate ICC when two or more raters score the same submission. Treat ICC ≥ 0.75 as acceptable and reconcile scores when ICC is <0.75 or score gaps exceed 10 points. Preserve raw scores, reconciled scores, and reconciliation notes.
4. Use rubric-based scoring to clarify expectations and support formative assessment¹⁷. Follow the rater training, scoring agreement, and reconciliation procedure in Supplementary Table S1.

10. Collect instructional quality and implementation fidelity data.

1. Measure instructional quality through classroom interaction, case-design completeness, feedback timeliness, task-rubric alignment, and student engagement. Analyze indicators separately unless reliability and dimensional evidence support aggregation.
2. Use “case-design completeness” rather than “case authenticity” unless independent authenticity validation is available.
3. Measure implementation fidelity through planned activity completion, enterprise or case-session completion, task submission, feedback turnaround, rubric completion, observation completion, and rater-training completion.
4. Treat Table 4 thresholds as predefined operational quality-control rules, not externally validated cutoffs. The thresholds were set before research analysis based on pilot feasibility, teaching team consensus, and workload constraints.
5. Use the indicators, data sources, thresholds, warning rules, and corrective actions in Table 4. Additional troubleshooting procedures are provided in Supplementary Table S2.
6. Treat implementation as adequate when ≥85% of planned weekly activities are completed, ≥85% of protocol-cohort students complete all three tasks, ≥80% of feedback is returned within 7 calendar days, all major rubrics are completed, and required observations are available. Treat implementation as suboptimal when task completion is <75%, feedback exceeds 14 calendar days for >20% of submissions, ICC is <0.75, or >25% of planned observations are missing.

Category	Indicator	Data source	Time point	Scale	Positive signal or threshold	Warning rule	Corrective action
Implementation fidelity	Planned activity completion	Course implementation log	Weeks 1–16	% completed	≥85% planned activities completed	<85% completed	Review missed sessions and revise schedule control
Implementation fidelity	Enterprise or structured case session	Enterprise engagement log	Week 11	Implemented / not implemented	Planned session implemented or replaced by structured case coaching	Planned session missing without replacement	Use anonymized case session or structured case coaching
Implementation fidelity	Three-task sequence completion	Submission records	Weeks 6, 10, 15	% students	≥85% protocol-cohort students complete all three tasks	<75% complete all tasks	Simplify instructions and add progress checks
Implementation fidelity	Feedback timeliness	Feedback log	Weeks 7, 10, 15	Calendar days	≥80% feedback returned within 7 days	>20% feedback returned after 14 days	Shorten feedback form and distribute scoring workload
Implementation fidelity	Observation completion	Observation forms	Weeks 1–16	% planned observations	≥75% planned observations completed	>25% planned observations missing	Add observer backup and fixed observation schedule
Scoring quality	Rater agreement	Rater scores	Task 1 subset and Task 3 defense	ICC	ICC ≥0.75	ICC <0.75	Retrain raters, clarify descriptors, reconcile affected scores
Instructional quality	Classroom interaction	Observation checklist	Selected sessions	1–5 score	Mean ≥3.50 or increase ≥0.30	Mean <3.50	Strengthen discussion prompts and facilitation
Instructional quality	Case-design completeness	Case checklist	Before Week 4	0–5 or 1–5 score	Mean ≥4.00	Mean <3.50	Revise case context, decision problem, constraints, and consequence
Instructional quality	Task-rubric alignment	Alignment checklist	Before Week 4 and post-course review	0–5 or 1–5 score	Mean ≥4.00	Mean <3.50	Revise task-output-rubric-feedback alignment
Instructional quality	Student engagement	Attendance, participation, logs, survey items	Weeks 1–16	Item-level or 1–5 score	Increase ≥0.30 points	Decrease >0.30 points	Reduce overload, clarify expectations, increase formative support
Learning outcome	Strategic Management knowledge	Baseline and post-course tests	Weeks 1 and 16	0–100	Mean increase ≥5 points	Increase <3 points	Review teaching coverage, item alignment, and test equivalence
Learning outcome	Individual case analysis	Case-analysis rubric	Late semester	0–100	Protocol-cohort advantage ≥3 points	No advantage or decrease	Add case-analysis practice and annotated examples
Team outcome	Team competition performance	Competition rubrics	Weeks 6, 10, 15	0–100	Progressive improvement across tasks	High final score with weak earlier evidence	Check score inflation and rebalance rubric toward reasoning
Student perception	Satisfaction	Post-course survey	Week 16	1–5 score	Mean ≥4.00	Mean <3.50	Review workload, task clarity, feedback quality, and team coordination
Student perception	Practical relevance	Post-course survey	Week 16	1–5 score	Mean ≥4.00	Mean <3.50	Increase case specificity and links to real strategic decisions
Workload safety	Competition pressure	Post-course survey	Week 16	1–5 score or %	High pressure ≤20% students	High pressure >20% students	Reduce task overlap and adjust deadline spacing
Interpretation warning	Ceiling effect	Baseline test and survey	Week 1	0–100 or 1–5	Baseline knowledge ≤85 and satisfaction ≤4.50	Knowledge >85 or satisfaction >4.50	Interpret small improvement cautiously
Interpretation warning	Score inflation	Team and individual outcomes	End of semester	Pattern judgment	Team gains accompanied by individual learning evidence	Team scores improve but individual learning does not	Increase individual accountability and reasoning-based scoring
Team process	Peer contribution balance	Peer contribution form	Midpoint and final stage	1–5 score	No repeated major imbalance	One or more members repeatedly rated very low	Require role reallocation and instructor check-in
Academic integrity	AI-use transparency	AI disclosure and oral check	Each major task	Disclosed / not disclosed	AI use disclosed according to institutional rules	Undisclosed or inappropriate AI use	Apply institutional academic integrity procedure and clarify AI rules

Table 4: Implementation fidelity, instructional quality, outcome-interpretation, and troubleshooting indicators. This table specifies implementation fidelity indicators, instructional quality indicators, student-level and team-level outcome indicators, scoring-quality indicators, interpretation thresholds, warning rules, and corrective actions.

11. Collect postcourse outcome data.

1. Administer the post-course Strategic Management knowledge test in Week 16 using the equivalent post-course form rather than the exact baseline form.
2. Collect the late-semester individual case-analysis score using a rubric covering problem identification, evidence use, strategic logic, feasibility, and written clarity. Report it as a post-course performance indicator unless a true baseline case-analysis assessment was administered.
3. Collect team scores from Tasks 1–3 and keep them separate from individual-level learning outcomes.
4. Collect satisfaction, perceived practical relevance, feedback usefulness, team collaboration, competition pressure, workload, and perceived improvement through an anonymous post-course survey. Do not combine items into one score unless reliability and dimensional evidence support a composite.
5. Collect final course scores only after grade submission. Export post-course records for research use only after ethics approval, consent verification, code assignment, and removal of direct identifiers and identifiable comments.
   PAUSE POINT: Pause research analysis until final grades are submitted, ethics approval is obtained, consent verification is completed, the consent list is separated from the research dataset, and de-identification is confirmed.

12. Clean and merge the de-identified dataset.

1. After ethics approval and consent verification, create de-identified student-level, team-level, and class-session datasets. Check duplicate records, impossible values, missing codes, inconsistent team codes, and direct identifiers.
2. Merge student- and team-level data by team code only. Clean and code all datasets using the template in Supplementary File 1 and the variable dictionary in Supplementary Table S3.
3. Code missing values as NA. Do not replace missing outcomes unless the course policy defines a make-up assessment or zero score for a specific requirement. Exclude students missing either the knowledge test from paired knowledge analysis only, and exclude students missing all three competition tasks from competition-related analyses. Retain available records for other eligible analyses.
4. Save the cleaned dataset, data dictionary, cleaning log, analysis-ready file, and locked output file. Modify locked data only with a dated correction log.

13. Analyze implementation fidelity, instructional quality, and learning outcomes.

1. Analyze implementation fidelity before interpreting learning outcomes. Interpret outcome differences cautiously when fidelity thresholds are not met.
2. Report descriptive statistics for continuous variables and frequencies for categorical variables, including cohort type, major group, prior competition experience, task completion, missing-data category, and protocol-cohort award level. Report the award level only for the protocol cohort unless the comparison cohort used the same award structure.
3. Compare baseline characteristics using independent-samples t-tests, chi-square tests, or Fisher’s exact tests, as appropriate, to describe baseline comparability rather than causal equivalence.
4. Compare within-cohort knowledge change using paired-samples t-tests or Wilcoxon signed-rank tests. Compare post-course outcomes between cohorts using independent-samples t-tests, Mann-Whitney U tests, ANCOVA, or regression models adjusted for baseline differences. Interpret all cohort comparisons as context-specific educational-record analyses, not causal estimates.
5. Use repeated-measures or mixed-effects models when multiple time points are analyzed. Include student-level random effects for repeated measurements and team-level random effects or cluster-robust standard errors when students are nested within teams.
6. Analyze team-level outcomes separately from student-level outcomes. Do not treat team scores as independent individual observations unless clustering is addressed.
7. Report Cohen’s d for major mean differences, with 0.20, 0.50, and 0.80 interpreted as small, moderate, and large. Report 95% confidence intervals when possible and compare complete-case with available-case results in sensitivity analyses.
8. Save the statistical script, software output, figure source files, and final tables. Submit all tables as separate editable files and archive the figure source data.

14. Interpret successful and suboptimal implementation.

1. Define implementation success before interpreting final outcomes. Treat the protocol as successfully implemented when the core fidelity thresholds in Table 4 are met.
2. Interpret learning outcomes only after checking fidelity, scoring reliability, missing-data patterns, baseline comparability, and unit of analysis.
3. Treat a post-course knowledge increase of ≥5 points, a late-semester individual case-analysis advantage of ≥3 points, or an instructional process-indicator increase of ≥0.30 on a 5 point scale as a positive implementation signal when fidelity is adequate.
4. Define warning patterns before analysis: ceiling effect when baseline knowledge exceeds 85/100 or baseline satisfaction exceeds 4.50/5; score inflation when team scores improve without individual learning evidence; excessive competition pressure when satisfaction decreases by >0.30 points or >20% of students report high workload pressure.
5. Interpret evaluation as a layered process from implementation fidelity to student reaction, learning evidence, and course refinement, not as a single score¹⁸. Avoid causal wording because the design uses routine educational cohorts, a historical comparison group, and retrospective de-identified course records rather than randomized assignment.

15. Conduct troubleshooting, adaptation, and course iteration.

1. Review fidelity after each major task and identify delayed feedback, low team participation, unclear instructions, incomplete scoring forms, missing observations, weak case-design completeness, rater disagreement, or inappropriate AI use.
2. Apply the corrective actions in Supplementary Table S2, including instruction revision, additional coaching, rater retraining, role reallocation, workload adjustment, or institutional academic integrity review.
3. For shorter terms, combine Tasks 1 and 2 into a diagnosis-decision brief while preserving the final defense, feedback record, and fidelity checks. For online, hybrid, or large multi-section delivery, use digital briefings, submission logs, online oral checks, standardized feedback templates, common rubrics, shared rater training, section-level observers, team dashboards, and central data management.
4. After Week 16, review fidelity, process indicators, student outcomes, rater notes, missing-data patterns, AI-use records, student comments, and instructor reflections. Revise the handbook, case package, rubrics, templates, feedback schedule, and data-recording forms before the next cycle.
5. After ethics approval and research data preparation, archive the protocol map, schedule, handbook, case files, rubrics, observation forms, survey instruments, de-identified dataset, data dictionary, analysis script, statistical output, figure source files, tables, and supplementary materials. Record all revisions in the version-control table.

Results

The representative results show the outputs generated by the 16 week competition-integrated Strategic Management course improvement protocol. Results are organized by implementation fidelity, instructional quality, student-level outcomes, team-level outcomes, and predefined warning patterns. Because the data came from routine educational cohorts with a historical comparison group rather than randomized assignment, all results should be interpreted as context-specific implementation and evaluation outputs, not causal evidence of effectiveness.

Implementation fidelity and instructional quality

The available educational-record dataset included 88 students in the historical comparison cohort and 92 students in the protocol cohort. The team-level dataset included 22 comparison-cohort teams and 23 protocol-cohort teams. After applying the paired pre-post knowledge-test rule, 79 comparison-cohort students and 84 protocol-cohort students were retained for knowledge-gain analysis.

Implementation fidelity was checked before learning outcomes were interpreted. The protocol cohort completed 15 of 16 planned weekly activities (93.8%), whereas the comparison cohort completed 13 of 16 (81.3%). The enterprise or structured case session was completed in the protocol cohort but was not part of the full historical comparison structure. In the protocol cohort, 88 of 92 students completed all three competition tasks, corresponding to 95.7%. In the comparison cohort, 18 of 88 students had complete records for three comparable project-based assessments, corresponding to 20.5%; these records were retained for descriptive comparison only because the historical course did not implement the full competition sequence. Feedback was returned within 7 calendar days for 243 of 268 protocol-cohort submissions, corresponding to 90.7%, compared with 49 of 66 comparison-cohort submissions, corresponding to 74.2%. Mean feedback turnaround was 5.8 ± 1.9 days and 8.4 ± 2.7 days, respectively. Observation completion reached 81.3% and 62.5%. The protocol-cohort two-rater ICC for overlapping subjective scoring was 0.82, exceeding the predefined 0.75 threshold. Overall, the protocol cohort achieved 5 of 5 core implementation-fidelity indicators, whereas the comparison cohort achieved 2 of 5.

Instructional quality indicators were higher in the protocol cohort. Classroom interaction was 3.70 ± 0.44 in the protocol cohort and 3.15 ± 0.48 in the comparison cohort. Case-design completeness was 4.08 ± 0.36 and 3.23 ± 0.42. Task-rubric alignment was 4.01 ± 0.39 and 2.30 ± 0.51. Student engagement increased by 0.42 ± 0.41 points in the protocol cohort, exceeding the predefined 0.30-point signal threshold, whereas the comparison cohort increased by 0.07 ± 0.34 points. Attendance was similar between cohorts: 89.6% ± 5.8% and 89.1% ± 6.2%. Table 5 summarizes these indicators.

Indicator	Data source	Predefined threshold	Historical comparison cohort	Protocol cohort	Interpretation
Planned weekly activity completion	Course implementation log	≥85%	13/16 weeks, 81.3%	15/16 weeks, 93.8%	Achieved in protocol cohort
Enterprise or structured case-session implementation	Enterprise engagement log	≥1 implemented session	0/1 session, 0.0%	1/1 session, 100.0%	Achieved in protocol cohort
Complete task or mapped project-record completion	Submission records	≥85%	18/88 students, 20.5%	88/92 students, 95.7%	Achieved in protocol cohort; historical records descriptive only
Feedback returned within 7 days	Feedback log	≥80%	49/66 submissions, 74.2%	243/268 submissions, 90.7%	Achieved in protocol cohort
Observation checklist completion	Observation forms	≥75%	10/16 sessions, 62.5%	13/16 sessions, 81.3%	Achieved in protocol cohort
Two-rater agreement	Rater scores	ICC ≥0.75	Not required	ICC = 0.82	Achieved in protocol cohort
Feedback turnaround time	Feedback log	Mean ≤7 days preferred	8.4 ± 2.7 days	5.8 ± 1.9 days	Within preferred range in protocol cohort
Classroom interaction	Observation checklist	Mean ≥3.50 or increase ≥0.30	3.15 ± 0.48	3.70 ± 0.44	Higher in protocol cohort
Case-design completeness	Case-design checklist	Mean ≥4.00 preferred	3.23 ± 0.42	4.08 ± 0.36	Higher in protocol cohort
Task-rubric alignment	Alignment checklist	Mean ≥4.00	2.30 ± 0.51	4.01 ± 0.39	Achieved in protocol cohort
Student engagement gain	Attendance, logs, survey items	Increase ≥0.30 points	+0.07 ± 0.34	+0.42 ± 0.41	Achieved in protocol cohort
Attendance rate	Attendance record	Descriptive	89.1% ± 6.2%	89.6% ± 5.8%	Similar across cohorts
Core implementation-fidelity summary	Implementation indicators	Most core thresholds achieved	2/5 thresholds achieved	5/5 thresholds achieved	Protocol implemented as intended

Table 5: Representative implementation fidelity and instructional quality indicators. This table reports representative implementation fidelity and instructional quality outputs for the historical comparison cohort and protocol cohort and indicates whether the predefined operational thresholds were achieved.

Figure 2 presents the implementation fidelity and instructional quality profile. Figure 2A shows planned weekly activity completion. Figure 2B shows three-task sequence completion. Figure 2C shows feedback returned within 7 calendar days. Figure 2D shows classroom interaction, case design completeness, task rubric alignment, and student engagement gains. Figure 2E shows observation completion and enterprise or structured case-session completion. Percentage-based fidelity measures and scale-based instructional indicators should be interpreted within their own measurement scales.

Figure 2: Implementation fidelity and instructional quality profile. This figure summarizes implementation fidelity and instructional quality indicators for the historical comparison cohort and the protocol cohort. (A) Planned weekly activity completion. (B) Complete task or mapped project-record completion, distinguishing the protocol cohort’s full three-task sequence from mapped project records in the historical comparison cohort. (C) Feedback returned within 7 calendar days, with feedback-turnaround time shown as supporting information. (D) Instructional quality indicators, including classroom interaction, case-design completeness, task-rubric alignment, and student engagement gain. (E) Observation checklist completion and enterprise or structured case-session implementation. Please click here to view a larger version of this figure.

Student-level learning outcomes

Student-level outcomes were examined separately from team-level competition outcomes. Baseline knowledge scores were 64.18 ± 8.74 in the protocol cohort and 62.37 ± 8.91 in the comparison cohort, with a small baseline difference of 1.81 points (d = 0.21). Post-course knowledge scores were 71.08 ± 8.58 and 66.41 ± 9.06, with a difference of 4.67 points (d = 0.53). Knowledge gain was 6.90 ± 5.41 points and 4.04 ± 5.26 points, with a difference of 2.86 points (d = 0.54). The protocol cohort exceeded the predefined 5-point knowledge-gain signal.

Late-semester individual case-analysis performance was 82.31 ± 7.06 in the protocol cohort and 76.42 ± 7.38 in the comparison cohort, with a difference of 5.89 points (d = 0.82). This outcome was interpreted as a postcourse performance indicator rather than a paired gain because no true baseline case-analysis assessment was administered. Satisfaction was 3.66 ± 0.58 and 3.34 ± 0.62 (d = 0.53). Perceived practical relevance was 3.91 ± 0.55 and 3.41 ± 0.59 (d = 0.88). Competition pressure was higher in the protocol cohort, 3.12 ± 0.76, compared with 2.48 ± 0.71 (d = 0.87), but high workload pressure was reported by 13 of 92 protocol-cohort students, corresponding to 14.1%, below the predefined 20% warning threshold. Final course score was treated as a secondary educational outcome and was 87.20 ± 6.10 in the protocol cohort and 84.80 ± 6.70 in the comparison cohort (d = 0.37). Table 6 summarizes student-level and team-level outcomes.

Outcome	Level	Historical comparison cohort	Protocol cohort	Difference	Effect size	Interpretation
Available educational-record sample	Student	n = 88	n = 92	—	—	Usable de-identified course records
Paired knowledge-analysis sample	Student	n = 79	n = 84	—	—	Students with baseline and post-course knowledge scores
Baseline knowledge score	Student	62.37 ± 8.91	64.18 ± 8.74	1.81	0.21	Small baseline difference
Post-course knowledge score	Student	66.41 ± 9.06	71.08 ± 8.58	4.67	0.53	Higher in protocol cohort
Knowledge gain	Student	4.04 ± 5.26	6.90 ± 5.41	2.86	0.54	Protocol cohort exceeded the ≥5-point signal
Individual case-analysis score	Student	76.42 ± 7.38	82.31 ± 7.06	5.89	0.82	Exceeded the ≥3-point case-analysis signal
Student engagement gain	Student	+0.07 ± 0.34	+0.42 ± 0.41	+0.35	0.93	Exceeded the ≥0.30-point signal
Satisfaction score	Student	3.34 ± 0.62	3.66 ± 0.58	0.32	0.53	Higher in protocol cohort
Perceived practical relevance	Student	3.41 ± 0.59	3.91 ± 0.55	0.5	0.88	Higher in protocol cohort
Competition pressure score	Student	2.48 ± 0.71	3.12 ± 0.76	0.64	0.87	Higher pressure in protocol cohort
High workload pressure	Student	Not assessed as competition pressure	13/92, 14.1%	—	—	Below the 20% warning threshold
Final course score	Student	84.80 ± 6.70	87.20 ± 6.10	2.4	0.37	Secondary educational outcome
Number of teams	Team	n = 22	n = 23	—	—	Team-level analyses reported separately
Decision-round exposure	Team	0.77 ± 0.81 rounds	5.09 ± 0.79 rounds	4.32	Not estimated	Implementation exposure indicator
Task 1 or mapped diagnosis score	Team	69.84 ± 6.45	72.18 ± 6.28	2.34	0.37	Modest team-level difference
Task 2 or mapped decision score	Team	71.09 ± 6.12	73.52 ± 5.91	2.43	0.4	Modest team-level difference
Task 3 or mapped final-defense score	Team	72.71 ± 6.36	77.20 ± 6.04	4.49	0.72	Largest team-level difference
Final team score	Team	71.21 ± 5.81	74.30 ± 5.52	3.09	0.55	Moderate team-level difference
Strategic diagnosis dimension	Team	14.60 ± 1.80	15.60 ± 1.70	1	—	Final-defense rubric dimension
Strategic choice dimension	Team	17.90 ± 2.20	19.10 ± 2.10	1.2	—	Final-defense rubric dimension
Implementation feasibility dimension	Team	14.40 ± 1.90	15.50 ± 1.80	1.1	—	Final-defense rubric dimension
Innovation dimension	Team	10.80 ± 1.60	11.50 ± 1.50	0.7	—	Final-defense rubric dimension
Evidence use dimension	Team	7.10 ± 1.00	7.50 ± 0.90	0.4	—	Final-defense rubric dimension
Oral defense dimension	Team	7.91 ± 1.10	8.00 ± 1.00	0.09	—	Final-defense rubric dimension
Peer contribution mean	Team	3.72 ± 0.41	3.91 ± 0.36	0.19	0.49	No major team-process failure
Teams with contribution imbalance	Team	4/22, 18.2%	3/23, 13.0%	−5.2 percentage points	—	No systematic imbalance pattern
Award distribution	Team	Not applicable	10 none; 8 third; 2 second; 3 first	—	—	Protocol cohort only
Score-inflation warning pattern	Team	Not applicable	2/23 teams, 8.7%	—	—	Flagged for troubleshooting

Table 6:Representative student-level and team-level outcomes. This table reports representative student-level and team-level outcomes, including available educational-record sample, paired knowledge-analysis sample, knowledge scores, knowledge gain, individual case-analysis score, engagement, satisfaction, perceived practical relevance, workload pressure, final course score, team task scores, final-defense rubric-dimension scores, peer contribution, award distribution, and warning indicators.

Figure 3 presents student-level outcomes with uncertainty estimates where applicable. Figure 3A shows baseline and post-course knowledge scores. Figure 3B shows knowledge gain. Figure 3C shows individual case-analysis performance. Figure 3D shows engagement gain, satisfaction, and perceived practical relevance. Figure 3E shows the final course score as a secondary outcome.

Figure 3: Student-level representative learning outcomes. This figure presents individual-level outcomes separately from team-level competition outcomes. (A) Baseline and post-course Strategic Management knowledge scores. (B) Knowledge gain from baseline to post-course assessment. (C) Late-semester individual case-analysis score. (D) Engagement gain and survey perception indicators, including satisfaction and perceived practical relevance, are shown with separate scales where required. (E) Final course score as a secondary educational outcome and protocol-cohort high workload pressure relative to the predefined warning threshold. Values are presented as mean ± SD where ± values are shown, and error bars represent SD. In (E), the dashed reference line indicates the predefined high-workload-pressure warning threshold of 20%. Please click here to view a larger version of this figure.

Team-level competition outcomes

Team-level outcomes were analyzed separately from student-level outcomes. For descriptive reporting, comparison-cohort case-project records were mapped to comparable assessment points when available, but these records did not represent completion of the full competition-integrated sequence. Decision-round exposure was reported as an implementation exposure indicator rather than a learning-effect estimate. The protocol cohort completed 5.09 ± 0.79 decision rounds, whereas the comparison cohort completed 0.77 ± 0.81 mapped rounds.

In the protocol cohort, the mean Task 1 score was 72.18 ± 6.28, the mean Task 2 score was 73.52 ± 5.91, and the mean Task 3 score was 77.20 ± 6.04. The corresponding mapped comparison-cohort scores were 69.84 ± 6.45, 71.09 ± 6.12, and 72.71 ± 6.36. The final team score was 74.30 ± 5.52 in the protocol cohort and 71.21 ± 5.81 in the comparison cohort. Final-defense or mapped final-project rubric dimensions were as follows: strategic diagnosis, 15.60 ± 1.70 and 14.60 ± 1.80; strategic choice, 19.10 ± 2.10 and 17.90 ± 2.20; implementation feasibility, 15.50 ± 1.80 and 14.40 ± 1.90; innovation, 11.50 ± 1.50 and 10.80 ± 1.60; evidence use, 7.50 ± 0.90 and 7.10 ± 1.00; and oral defense, 8.00 ± 1.00 and 7.91 ± 1.10, respectively.

Award distribution was reported for the protocol cohort only because the comparison cohort did not use the full award-based competition structure. In the protocol cohort, 10 teams received no award, 8 received third prize, 2 received second prize, and 3 received first prize. Mean peer contribution was 3.91 ± 0.36 in the protocol cohort and 3.72 ± 0.41 in the comparison cohort. Contribution imbalance was identified in 3 of 23 protocol-cohort teams and 4 of 22 comparison-cohort teams. A score-inflation warning pattern was identified in 2 of 23 protocol-cohort teams, corresponding to 8.7%.

Figure 4 presents team-level outcomes and warning patterns. Figure 4A shows Task 1, Task 2, and Task 3 team scores. Figure 4B shows final-defense rubric-dimension scores. Figure 4C shows protocol-cohort award distribution only. Figure 4D shows score-inflation warning cases. Figure 4E shows peer contribution balance and contribution-imbalance counts.

Figure 4: Team-level competition outcomes and warning patterns. This figure presents team-level outcomes separately from student-level learning outcomes. (A) Task-stage team scores for Task 1 strategic diagnosis, Task 2 strategic decision, and Task 3 final defense, with mapped project scores shown for the historical comparison cohort where available. (B) Final-defense rubric-dimension scores, including strategic diagnosis, strategic choice, implementation feasibility, innovation, evidence use, and oral defense. (C) Protocol-cohort award distribution only, because the historical comparison cohort did not use the full award-based competition structure. (D) Score-inflation warning pattern, defined as high final-defense performance without supporting diagnostic strength or individual learning evidence. (E) Peer contribution score and contribution-imbalance counts are displayed as separate team-level indicators. Please click here to view a larger version of this figure.

Suboptimal patterns and troubleshooting indicators

Two of 23 protocol-cohort teams showed a predefined score-inflation warning pattern. These teams had relatively high final defense scores but weaker Task 1 diagnostic scores and below-average individual knowledge gains. This pattern was flagged for follow-up in the next course iteration rather than interpreted as evidence of learning improvement. No major ceiling-effect warning was observed because baseline knowledge scores were below 85 out of 100, and baseline satisfaction was below 4.50 out of 5. Attendance was similar between cohorts, and no systematic team contribution imbalance was observed, although three protocol-cohort teams required monitoring.

Overall, the representative results showed that the protocol generated interpretable outputs at three levels: implementation fidelity, student-level learning evidence, and team-level competition performance. The warning cases supported the use of the protocol for course monitoring, troubleshooting, and subsequent course refinement.

The de-identified dataset supporting this study has been deposited in figshare.com and is available at https://doi.org/10.6084/m9.figshare.33094931. The dataset includes student-level records, team-level competition records, implementation indicators, participant-flow records, missing-data audit records, and rater-score records. Student identifiers, consent records, linkage files, and identifiable free-text comments are not included.

Supplementary Table S1: Rater training, scoring agreement, and score reconciliation procedure. This table provides the rater assignment, independence check, rubric orientation, calibration scoring, scoring agreement check, score-gap rule, reconciliation procedure, data-entry audit, score lock, and archive procedure.Please click here to download this file.

Supplementary Table S2: Troubleshooting matrix for protocol deviations and suboptimal implementation. This table lists protocol deviations and suboptimal implementation patterns, including trigger rules, corrective actions, required records, and interpretation notes.Please click here to download this file.

Supplementary Table S3: Dataset structure and variable dictionary for the de-identified educational dataset. This table defines student-level, team-level, and class-session-level variables, including variable codes, descriptions, data types, coding ranges, data sources, and missing-data rules.Please click here to download this file.

Supplementary File 1: Operational templates, assessment materials, and de-identified dataset workbook. This supplementary file provides the operational templates, assessment materials, scoring forms, survey items, case-design check, missing-data rules, de-identified dataset workbook structure, cleaning log, analysis summary template, and version-control record used to support reproducibility.Please click here to download this file.

Discussion

This protocol provides a reproducible approach for embedding a teaching-competition sequence into an undergraduate Strategic Management course. Its purpose is not to add a competition event to an existing course, but to convert competition into a structured course-improvement process that links strategic diagnosis, decision making, formative feedback, final defense, and post-course evaluation. The instructional value, therefore, depends on the design of the sequence rather than on the presence of competition alone. Competition-based or game-based activities may increase participation, but their educational value becomes difficult to interpret when learning goals, task outputs, scoring criteria, and data records are not clearly specified¹⁹. This protocol addresses that problem by defining the teaching sequence, scoring rubrics, feedback window, implementation records, and de-identified data structure needed for replication.

The central design principle is alignment. Each task is linked to a course outcome, a classroom topic, a student output, and a scoring criterion. Task 1 requires students to diagnose a strategic problem, Task 2 requires them to select and justify a strategic decision, and Task 3 requires them to defend the final strategy through written and oral evidence. This diagnosis-decision-defense sequence is intended to prevent students from treating the competition as a presentation exercise only. It also gives students repeated opportunities to revise their reasoning before the final defense. This structure is consistent with simulation-supported and experiential learning research, which suggests that applied learning is more useful when students engage in active decision cycles, receive feedback, and explain their choices rather than only observing or playing through a scenario^20,21.

Several quality-control steps are necessary for the protocol to work as intended. First, feedback must be timely enough to influence the next task. The 7 day feedback window is therefore used as an instructional design requirement, not only as an administrative target. Second, scoring quality must be monitored. Rubrics can improve transparency, but only when raters share a common understanding of the descriptors and evidence requirements²². For this reason, the protocol includes rater training, calibration scoring, score-gap checks, ICC monitoring, reconciliation notes, and score locking. Third, team performance must be separated from individual learning evidence. A high final defense score may reflect presentation strength or uneven team contribution rather than broad individual learning. The protocol, therefore, analyzes knowledge scores, individual case-analysis scores, student perceptions, peer contribution, and team scores separately.

The protocol also includes troubleshooting rules because competition-integrated teaching can fail in predictable ways. Feedback may be delayed, team contributions may become uneven, students may rely too heavily on presentation polish, and group scores may improve without corresponding evidence of individual learning. These risks are monitored through feedback logs, peer contribution records, AI-use disclosure forms, oral checks, rater agreement outputs, and score-inflation warning rules. The AI-use disclosure and oral-check components are especially important because students may use digital tools for language polishing, formatting, or brainstorming, yet are still required to demonstrate their own strategic reasoning. The protocol does not prohibit all AI support, but it requires disclosure and protects the central learning task: students must be able to explain the diagnosis, evidence, decision logic, implementation risk, and final recommendation.

The protocol is adaptable across institutions, but the core sequence should be preserved. If a business simulation platform is unavailable, the structured decision sheet can serve as a substitute for the simulation round. If enterprise mentors cannot attend, an anonymized case, public case briefing, or recorded mentor comment can be used. Large classes may require parallel judging groups and standardized feedback templates, whereas smaller classes may allow longer defense sessions. These adaptations should be documented because course-improvement protocols depend not only on the instructional idea but also on implementation fidelity, instructor support, assessment conditions, and data completeness²³. The essential requirement is that the course retains the linked sequence of diagnosis, decision, defense, feedback, and evaluation.

Several limitations should be acknowledged. This is a course-improvement protocol, not a randomized controlled trial. Differences between the protocol cohort and the comparison cohort should therefore be interpreted as representative implementation and evaluation outputs rather than causal proof of effectiveness. Historical or parallel comparison cohorts may differ in student composition, classroom dynamics, institutional timing, or assessment conditions. Final course scores are also secondary outcomes because they may include routine course components beyond the competition tasks. In addition, team-level outcomes cannot be used as substitutes for student-level learning outcomes. These limitations are addressed by reporting implementation fidelity, instructional quality indicators, student-level outcomes, team-level outcomes, workload pressure, and warning patterns separately²⁴.

The protocol is most suitable for courses in which students must connect conceptual knowledge with applied managerial judgment. Strategic Management is a natural setting because students are expected to analyze environments, evaluate resources, compare strategic options, and defend implementation choices. The same structure could be adapted to entrepreneurship, marketing strategy, innovation management, business model design, and other management courses that require evidence-based decision making. Its broader contribution is practical: it provides instructors with an auditable way to document how a course-improvement activity was implemented, monitored, scored, de-identified, analyzed, and revised. In management education, experiential learning is often valued for realism, but its procedures are not always reported in enough detail for replication. This protocol addresses that gap by combining implementation fidelity, scoring consistency, data governance, and troubleshooting into a single reproducible process²⁵.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
AI-use disclosure form	Author-developed	AI-Disclosure-v1.0; Supplementary File 1	Records disclosed AI use for each major submission
Baseline Strategic Management knowledge test	Author-developed	SMKT-Pre-v1.0; Supplementary File 1	Measures Week 1 baseline knowledge
Classroom observation checklist	Author-developed	Observation-v1.0; Supplementary File 1	Records classroom interaction and instructional process quality
Cleaning log	Author-developed	Cleaning-Log-v1.0; Supplementary File 1	Records data checks, corrections, exclusions, and dataset lock
Competition handbook	Author-developed	Handbook-v1.0; Supplementary File 1	Defines task sequence, submission rules, rubrics, AI disclosure, oral checks, appeals, and deadlines
Data dictionary	Author-developed	Data-Dictionary-v1.0; Supplementary Table 2	Defines variables, coding ranges, data sources, and missing-data rules
De-identified dataset workbook	Author-developed	Dataset-Workbook-v1.0; Supplementary File 1	Stores student-level, team-level, and class-session-level variables
Enterprise or anonymized case package	Author-developed or enterprise-approved	CasePack-v1.0; Supplementary File 1	Provides the strategic decision context
Final defense slide template	Author-developed	FSD-Template-v1.0; Supplementary File 1	Guides Task 3 slide deck structure
Final strategy defense scoring form	Author-developed	FSDSF-v1.0; Supplementary File 1	Records Task 3 rater scores and Q&A assessment
IBM SPSS Statistics	IBM	Version 27.0; institutional or personal license	Descriptive statistics, group comparisons, effect sizes, and ICC analysis
Individual case-analysis task sheet	Author-developed	ICATS-v1.0; Supplementary File 1	Defines individual case-analysis requirements and scoring domains
Knowledge-test answer key	Author-developed	SMKT-Key-v1.0; Supplementary File 1	Supports knowledge-test scoring
Microsoft Excel	Microsoft	Excel 2021; institutional or personal license	Data entry, data dictionary, cleaning log, table preparation, and source-data checks
Oral-check record form	Author-developed	Oral-Check-v1.0; Supplementary File 1	Records individual oral-check questions and responses
Peer contribution form	Author-developed	PCF-v1.0; Supplementary File 1	Records peer contribution and contribution imbalance
Post-course learning survey	Author-developed	PCLS-v1.0; Supplementary File 1	Measures satisfaction, relevance, feedback usefulness, collaboration, workload, and perceived improvement
Post-course Strategic Management knowledge test	Author-developed	SMKT-Post-v1.0; Supplementary File 1	Measures Week 16 post-course knowledge using an equivalent form
Rater training file	Author-developed	Rater-Training-v1.0; Supplementary File 1	Provides calibration files, descriptors, examples, and common scoring errors
Statistical output file	Software-generated	Statistical-Output-v1.0; archived with locked analysis folder	Stores statistical outputs with the analysis file and dataset version
Strategic decision sheet	Author-developed	SDS-v1.0; Supplementary File 1	Guides Task 2 decision output
Strategic diagnosis report template	Author-developed	SDR-Template-v1.0; Supplementary File 1	Guides Task 1 report structure
Student code linkage file	Author-developed restricted file	Student-Code-Linkage-v1.0; stored separately by data manager	Links student identity and research code before de-identification
Task-rubric alignment checklist	Author-developed	Alignment-v1.0; Supplementary File 1	Checks alignment among learning outcomes, task outputs, scoring criteria, and feedback points
Team charter template	Author-developed	Team-Charter-v1.0; Supplementary File 1	Records role allocation, communication rules, deadlines, and contribution expectations
Team process log	Author-developed	Team-Process-Log-v1.0; Supplementary File 1	Records meeting attendance, decisions, progress, problems, and next actions
Version-control table	Author-developed	Version-Control-v1.0; Supplementary File 1	Tracks version number, date, editor, reason, and approval status
Wenjuanxing online survey platform	Wenjuanxing	Web-based platform; institutional or user account access	Baseline survey, post-course survey, peer contribution form, and AI-disclosure exports