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Record W2032723187 · doi:10.5539/ies.v6n6p95

Developing Creative Teaching Module: Business Simulation in Teaching Strategic Management

2013· article· en· W2032723187 on OpenAlexvenueno aff
Nor Liza Abdullah, Mohd Hizam Hanafiah, Noor Azuan Hashim

Bibliographic record

VenueInternational Education Studies · 2013
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness simulationMultidisciplinary approachContext (archaeology)Knowledge managementStrategic managementBusiness analysisBusiness educationGlobalizationBusinessBusiness modelHigher educationComputer scienceMarketingSociologyEconomics

Abstract

fetched live from OpenAlex

Globalization and liberalization in the business environment have changed the requirements of types and qualities of human capital needed by the corporate sector. In relation to this, business graduates not only need to have theoretical understanding, but they also need to have creative thinking, communication skills and decision making skills based on multidisciplinary knowledge. Simulation game in business education is suggested to fill the gap by exposing students to real business situations. This study evaluates the effectiveness of business simulation in teaching Strategic Management in Faculty of Economics and Management, Universiti Kebangsaan Malaysia (UKM). A total of 48 students participated in the business simulation game and answered a survey at the end of the Strategic Management course. The objective of this paper is to present the findings in terms of contextual and processual context of using business simulation as an approach in teaching strategic management. The important findings of this research are the ability of simulation in transferring theory to practice, applying multidisciplinary knowledge, managing team dynamics, making decisions in uncertainties and managing in realistic situation. This study highlights the potential of business simulations in developing competent business graduates that fulfill the requirements of the industry.

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.

How this classification was reachedexpand

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.120
GPT teacher head0.460
Teacher spread0.340 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2013
Admission routes1
Has abstractyes

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