Dynamic Pricing Framework for Live-Streaming Commerce Considering Time and Priority-Based Promotion

국문 초록 (Abstract)

라이브 스트리밍 커머스는 인도네시아 디지털 유통 시장에서 빠르게 성장하고 있으며, 특히 한국 뷰티(K-Beauty) 시장에서 중요한 판매 채널로 활용되고 있다. Shopee Live와 TikTok Shop은 실시간 제품 소개, 인플루언서 마케팅, 플래시 할인, 한정 시간 쿠폰 등을 통해 소비자의 구매 행동에 영향을 미친다. 소비자는 라이브 방송 중 즉시 구매하거나 향후 할인 혜택을 기다리는 전략적 의사결정을 하며, 이러한 행동은 기업의 가격 전략과 수익성에 영향을 준다. 그러나 인도네시아 K-Beauty 라이브 커머스 환경에서 소비자 행동을 고려한 가격 결정 연구는 아직 부족하다.

본 연구는 Chen, Xie, Dai 및 Song(2025)의 2기간 전략적 소비자 모델을 기반으로 라이브 스트리밍 커머스를 위한 동적 가격 결정 프레임워크를 개발한다. 본 연구에서는 라이브 방송 중 제한된 시간 동안 제공되는 시간 제한 프로모션 가격(Time-Limited Promotion Pricing, TLPP, YT) 전략을 제안하고, 이를 동적 가격 결정(YD), 가격 고정 전략(YC), 가격 매칭 전략(YM)과 비교 분석한다. 모델에는 소비자의 기다림 성향, 스트리머 역량, 프로모션 긴급성, 라이브 방송 운영 비용을 주요 변수로 반영하였다. 또한 2021년부터 2025년까지 Shopee Live와 TikTok Shop에서 진행된 TIRTIR, Laneige, Innisfree, Etude House, 3CE, CLIO 등 K-Beauty 브랜드 사례를 활용하여 모델을 검증하였다.

분석 결과, 인도네시아 K-Beauty 소비자는 할인 종료 시간, 한정 수량, 플래시 쿠폰과 같은 긴급성 기반 프로모션에 높은 반응을 보였다. 또한 스트리머의 제품 설명과 신뢰성은 소비자의 구매 결정에 중요한 영향을 미쳤다. 수치 분석 결과, TLPP 전략은 비교한 가격 전략 중 가장 높은 기업 이익을 제공하며, 특히 젊은 소비자층과 대중 시장 K-Beauty 브랜드에서 효과적인 것으로 나타났다. 그러나 TLPP는 가격 고정 전략 대비 소비자 잉여를 감소시킬 가능성이 있다.

본 연구는 인도네시아 K-Beauty 라이브 커머스 시장을 대상으로 전략적 소비자 행동을 반영한 가격 결정 프레임워크를 제시하며, 라이브 커머스 운영 및 동적 가격 전략 수립에 학문적·실무적 기여를 제공한다.

다국어 초록 (Multilingual Abstract)

Live-streaming commerce has become one of the fastest-growing digital retail channels in Indonesia, especially in the Korean beauty (K-Beauty) makeup market. Platforms such as Shopee Live and TikTok Shop allow K-Beauty brands to promote products through real-time influencer demonstrations, skincare consultations, flash sales, and limited-time vouchers. In this environment, consumers must decide whether to purchase immediately during live-stream sessions or wait for future discounts, creating strategic consumer behavior that influences pricing effectiveness and sales performance. However, limited research has examined pricing strategies in Indonesian K-Beauty live-stream commerce.
This study develops a mixed-method framework based on the two-period strategic consumer model proposed by Chen Xie Dai and Song (2025). The research introduces a new pricing strategy called Time-Limited Promotion Pricing (TLPP) denoted as YT, which refers to temporary live-stream discounts that expire before second-period marketplace sales begin. The study compares TLPP with Dynamic Pricing (YD), Price Commitment (YC), and Price Matching (YM). The analytical model incorporates important parameters such as customer patience, streamer ability, urgency intensity, and live-stream operational costs. The framework is calibrated using publicly documented K-Beauty live-stream cases in Indonesia from brands including TIRTIR, Laneige, Innisfree, Etude House, 3CE, and CLIO on Shopee Live and TikTok Shop between 2021 and 2025.
The findings show that Indonesian K-Beauty consumers have low strategic patience and are highly responsive to urgency-based promotions such as flash vouchers, countdown discounts, and limited-stock campaigns. Streamer ability also strongly affects purchasing decisions through product explanations, skincare education, and influencer credibility. The numerical analysis demonstrates that TLPP consistently generates the highest firm profit among all pricing strategies, particularly for younger consumers and mass-market K-Beauty brands. Although TLPP improves profitability, it may reduce consumer surplus compared to Price Commitment strategies. Overall, this study contributes to the literature on live-stream commerce, strategic consumer behavior, and two-period pricing by providing analytical and empirical evidence from Indonesia’s rapidly growing K-Beauty live-stream market. 

목차 (Table of Contents)

• Chapter I Introduction 1
• 1.1 Background and Motivation 1
• 1.2 Research Problem 3
• 1.3 Research Questions and Objectives 4
• 1.4 Scope and Contributions 5
• 1.5 Thesis Organization 6
• Chapter II Background and Literature Review 7
• 2.1 Live-streaming Commerce and Strategic Consumer Theory 7
• 2.2 Two-period Pricing in Strategic Consumer Markets 9
• 2.3 Time-Limited Promotion Pricing and Urgency Mechanisms 10
• 2.4 K-Beauty Industry in Indonesia and Live-stream Commerce 11
• 2.4.1 Korean Cosmetics Industry Structure 13
• 2.4.2 Indonesian Cosmetics Regulatory Environment 14
• 2.4.3 Indonesian K-Beauty Consumer Demographics 15
• 2.4.4 Indonesian Influencer Ecosystem 16
• 2.4.4 TikTok Shop Indonesia Returning and Implications 16
• 2.5 Indonesian E-commerce Platforms: Shopee and TikTok Shop 18
• 2.6 Comparative Literature Mapping 20
• 2.7 Research Gap 23
• Chapter III Analytical Model and Extension 24
• 3.1 Baseline Two-period Strategic-Consumer Framework 24
• 3.2 Benchmark Schemes Without Live-streaming (ND, NC, NM) 28
• 3.2.1 Scheme ND (No Live-streaming, Dynamic Price) 28
• 3.2.2 Scheme NC (No Live-streaming, Commitment Price) 28
• 3.2.3 Scheme NM (No Live-streaming, Matching Price) 29
• 3.2.4 Comparative Result for Benchmark Schemes 29
• 3.3 Three Schemes with Live-streaming (YD, YC, YM) 29
• 3.3.1 Scheme YD (Yes Live-streaming, Dynamic Price) 29
• 3.3.2 Scheme YC (Yes Live-streaming, Commitment Price) 30
• 3.3.3 Scheme YM (Yes Live-streaming, Matching Price) 31
• 3.4 The Time-Limited Promotion Scheme (YT): Formulation 32
• 3.4.1 Modeling Choice 33
• 3.4.2 Sanity Checks for the YT Formulation 33
• 3.4.3 Alternative Formulations Considered and Rejected 33
• 3.5 Lemma 5: YT Optimal Solution 34
• 3.5.1 Concavity Verification 35
• 3.6 Proposition 4: Properties of YT 36
• 3.6.1 Proof of Proposition 4 (1): Monotonicity 37
• 3.6.2 Proof of Proposition 4 (2): YD Boundary 38
• 3.6.3 Proof of Proposition 4 (3): Commitment-Substitute Frontier 38
• 3.6.4 Proof of Proposition 4 (4): Welfare Structure 39
• 3.6.5 Numerical Verification of Proposition 4 (1) Monotonicity 40
• 3.6.6 Numerical Computation of the Commitment-Substitute Frontier \alpha* 40
• 3.6.7 Comparison with the Static Monopoly Limit 41
• 3.6.8 Comparison of Schemes at Selected Parameter Configurations 42
• 3.7 Consumer Surplus and Social Welfare under YT 43
• 3.7.1 Consumer Surplus under YT 43
• 3.7.2 Social Welfare under YT 43
• 3.8 Empirical Calibration Approach for Indonesian K-Beauty 44
• Chapter IV Empirical Case Analyses and Numerical Results 46
• 4.1 Baseline Numerical Reproduction (Chen et al., 2025 with YT) 46
• 4.1.1 Preferred Pricing Scheme Map 47
• 4.1.2 Prices Across Schemes as Functions of \theta 47
• 4.1.3 Profit Gap from Live-stream Adoption 48
• 4.1.4 Sensitivity of Preferred Scheme to Urgency \alpha 50
• 4.1.5 \alpha* Iso-Contour Frontier 51
• 4.1.6 Consumer Surplus and Social Welfare under YT 51
• 4.1.7 Calibration Robustness Across Mass-Market Scenarios 53
• 4.1.8 Tabulated Baseline Results 53
• 4.2 Baseline Calibration: Shopee 11.11 (11th Nov) Big Sale (Generic) 54
• 4.2.1 Calibration for Generic Shopee 11.11 (11th Nov) Big Sale 54
• 4.2.2 Results 55
• 4.2.3 Baseline Comparison: X The League Round 2 57
• 4.2.3 X The League (XTL) Round 2 Calibration 57
• 4.3 Case 1: TIRTIR x StyleKorean Indonesia TikTok Live 24-hr 60
• 4.3 Brand and Distribution Anchors 60
• 4.3 Documented TLPP Event 61
• 4.3 Calibration 61
• 4.3 Results 62
• 4.3 Robustness: \lambda Sensitivity 62
• 4.3 Welfare Results 63
• 4.4 Case 2: Laneige Shopee 11.11 Maison Kitsun Limited Edition 63
• 4.4.1 Brand and Distribution Anchors 64
• 4.4.2 Documented Recurring TLPP Event 64
• 4.4.3 Calibration 65
• 4.4.4 Results 65
• 4.4.5 Fragility Analysis 67
• 4.4.6 Welfare Result 67
• 4.5 Case 3: Innisfree Shopee Indonesia Re-launch November 2025 68
• 4.5.1 Brand and Distribution Anchors 68
• 4.5.2 Documented Repositioning Event 68
• 4.5.3 Calibration 69
• 4.5.4 Results 69
• 4.5.5 Parameter-Shift Decomposition 70
• 4.5.6 Robustness 71
• 4.5.7 Welfare Result 71
• 4.6 Case 4: Etude House TikTok Shop Indonesia 71
• 4.6.1 Brand and Distribution Anchors 71
• 4.6.2 Documented TLPP Mechanisms 72
• 4.6.3 Calibration 72
• 4.6.4 Results 72
• 4.6.5 Robustness 73
• 4.7 Case 5: 3CE TikTok Branded Mission with Pop-up 73
• 4.7.1 Brand and Distribution Anchors 73
• 4.7.2 Documented Branded Mission Campaign 74
• 4.7.3 Calibration 74
• 4.7.4 Results 75
• 4.8 Case 6: CLIO Kill Cover Launch on Shopee Indonesia 75
• 4.8.1 Brand and Distribution Anchors 75
• 4.8.2 Documented TLPP Mechanisms 76
• 4.8.3 Calibration 76
• 4.8.4 Results 77
• 4.9 Cross-case YT-Adoption Frontier and Robustness Analysis 79
• 4.9.1 Cross-case Summary Table 79
• 4.9.2 Empirically Calibrated YT-Adoption Frontier 81
• 4.9.3 Boundary Cases and Fragility 81
• 4.9.4 Comparison with the Analytical Frontier 81
• 4.9.5 Robustness Checks Across All Six Cases 81
• 4.9.5 \alpha-Sensitivity Robustness 83
• 4.10 Welfare Implications and Regulatory Discussion 83
• 4.10.1 Welfare Decomposition Across All Six K-Beauty Cases 84
• 4.10.2 Welfare Trade-off Mechanism 85
• 4.10.3 Regulatory Implications for Indonesian Consumer Protection 85
• 4.10.4 Calibration of Regulatory Thresholds 87
• 4.10.5 Limitations of the Welfare Analysis 87
• 4.10.6 Cross-case Welfare Frontier Visualization 88
• 4.10.7 International Comparison Notes 88
• 4.10.8 Summary of Cross-case Findings 89
• 4.11 Grid Analysis: Joint Parameter Sweeps 90
• 4.11.1 Grid 1: (\alpha,\ \lambda) plane at brand-average calibration 90
• 4.11.2 Grid 2: (θ, δ) plane at brand-average calibration 91
• 4.11.3 Per-brand grid analysis 92
• 4.11.4 Empirical frontier accuracy 93
• 4.12 Sensitivity Analysis: One-at-a-time Parameter Sweeps 94
• 4.12.1 Sensitivity to each parameter 94
• 4.12.2 Tornado diagrams: parameter ranking by impact magnitude 96
• 4.12.3 Cross-case sensitivity findings 97
• Chapter V Conclusion 93
• 5.1 Summary of Findings 93
• 5.2 Theoretical Contributions 95
• 5.3 Managerial Implications for K-Beauty Brands 96
• 5.4 Policy Implications for Indonesian Consumer Protection 97
• 5.5 Limitations and Future Research 97
• [References] 104
• [Acknowledgement] 108