Advances in Theory and Practice in Store Brand Operations [1st ed. 2021] 9811598762, 9789811598760

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Jiazhen Huo

Advances in Theory and Practice in Store Brand Operations

Advances in Theory and Practice in Store Brand Operations

Jiazhen Huo

Advances in Theory and Practice in Store Brand Operations

Jiazhen Huo School of Economics and Management Tongji University Shanghai, China

ISBN 978-981-15-9876-0 ISBN 978-981-15-9877-7 (eBook) https://doi.org/10.1007/978-981-15-9877-7 Jointly published with Tongji University Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: Tongji University Press. © Tongji University Press 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Private brand (PB), also known as private label (PL) or store brand (SB), refers to a brand created and controlled by a retailer. In the 1960s and 1970s, private labels began to emerge in France and England. Although private labels have grown rapidly worldwide, their market share varies greatly from region to region. According to Nielsen’s 2018 Global Private Label Report, the largest markets for private-label products are found primarily in the more mature European retail markets. With the growth of e-commerce, some online retailers have also launched private-label goods, but the market share for China’s private labels is only 1–3%, which represents a significant gap in comparison with Europe and America. This research monograph project considers: (1) product strategy for private branding; (2) pricing strategy for private brands; (3) channel strategy for private brand introduction; and (4) supply chain coordination for private brand introduction. We focus on the main challenges for Chinese retail companies in developing their private brands and use the practices of the Chinese Lianhua supermarket’s private brand management as a case study. This research includes the results of both empirical and theoretical insights. The empirical study aspect is focused on factors that influence purchasing intentions related to retailer’s private branding. We identify the main factors that influence consumers’ purchasing intentions for private brand products, including the psychological characteristics of consumers; the attributes of private brand products; retailer image; the perceived quality of private brand products; the influence of information value and the influence of manufacturers upon PB decisions. The theoretical study largely relates to introduction strategies for store brands when considering product cost and shelf space opportunity cost and other technical parameters. In relation to the pricing of private brand products we find that the stable price and stable reference price will increase with memory factor, perceived quality of PB and the sharing rate of NB’s revenue, but decrease with the reference effect parameter and the weight coefficient of PB’s history price. Our study of the dynamic assortment planning problem in the presence of heterogeneous brands indicates that ignoring brand heterogeneity will overestimate the retailer’s expected revenues significantly, and that the potential revenue overestimation will depend on initial v

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inventories and prices. We note that the theory behind introducing store brands to combat showrooming is effective. Using game theory, we examine strategic coordination mechanism design and competition and cooperation between retailers and manufacturers by focusing on advertising decisions that consider the competition of retailer and manufacturer, and the Competition of National and Store Brands with advertisement intervention. Our results show that for both manufacturer and retailer, Stackelberg’s leader-follower game strategy is more effective than the Nash non-cooperative proposal. Our findings are able to optimize the supply chain and indicate the best pricing and advertising investment decisions. This manuscript is funded by National Natural Science Foundation of China (71532015). As the leader of this key project, I acknowledge the financial support from the National Science Foundation of China. I extend my thanks to the members of my research team for their excellent contributions and hard work. Acknowledgments This research is supported by the National Natural Science Foundation of China (Grant No. 71532015). I sincerely thank the members of my research team for their contributions. Shanghai, China August 2020

Jiazhen Huo

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 The Development of Store Brands . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 The Market for Store Brands in China . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 The Development of Store Brands in China . . . . . . . . . . . . . . 1.2.2 Reasons for the Low Market Share of China Store Brands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Challenges for China Store Brands . . . . . . . . . . . . . . . . . . . . . 1.2.4 Lianhua’s Practice in Respect of Store Branding . . . . . . . . . . 1.3 The Outline of This Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Product Strategy for Store Branding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Factors That Affect the Success of Store Brands . . . . . . . . . . . . . . . . 2.1.1 Retailers Profit(s) from Store Brands . . . . . . . . . . . . . . . . . . . . 2.1.2 Manufacturers Profit from Store Brands . . . . . . . . . . . . . . . . . 2.1.3 National Brands Profit from Store Brands . . . . . . . . . . . . . . . . 2.1.4 Customers Benefit from Store Brands . . . . . . . . . . . . . . . . . . . 2.1.5 Strategies in Times of Crisis . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Motivation for Introducing Store Brands . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Increasing Market Share Through Additional Product Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Improve Bargaining Power to Compete with National Brand Manufacturers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Strengthen Consumers’ Loyalty and Influence . . . . . . . . . . . . 2.2.4 Control of Product Positioning . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.5 Other Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Factors That Affect Consumers’ Choice of Store Brands . . . . . . . . . 2.3.1 Price and Promotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Customers’ Perception of Risk in Buying Store Brands . . . . 2.3.3 Customers’ Store Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 Familiarity of Store Brands . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 4 4 6 8 9 11 11 12 15 16 16 17 18 18 19 19 20 21 21 23 23 24 24 26 28 30 vii

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2.3.5 Customers’ Perceived Quality of Store Brands . . . . . . . . . . . 2.3.6 Customers’ Perception of Value for Money . . . . . . . . . . . . . . 2.3.7 Customers’ Brand-Loyalty Towards Store Brands . . . . . . . . . 2.3.8 Other Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Strategy for Introducing Store Brands . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 The Strategy of Product Quality . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Strategies for Product Brands . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Strategies Related to Target Market . . . . . . . . . . . . . . . . . . . . . 2.4.4 The Strategy for Product Positioning . . . . . . . . . . . . . . . . . . . . 2.4.5 Strategies for Retailer Advertising of Store Brands . . . . . . . . 2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Research Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.4 Research Conclusions and Management Significance . . . . . . 2.5.5 Deficiencies in the Research . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Introduction of store brands considering product cost and shelf space opportunity cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.2 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.3 Stackelberg Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.4 Numerical Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31 32 32 33 34 34 36 38 39 42

3 Pricing Strategy for Store Brands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Pricing Strategy Based on Marketing Factors . . . . . . . . . . . . . . . . . . . 3.1.1 Multi-brand Pricing Strategy Based on Advertising . . . . . . . 3.1.2 Multi-brand Pricing Strategy Based on Sales Channel . . . . . 3.1.3 Multi-brand Pricing Strategy Based on Brand Positioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.4 Multi-brand Pricing Strategy Based on Layout . . . . . . . . . . . 3.2 Pricing Strategy for Competitive Brands Considering Consumer Consciousness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Pricing Strategy for Competitive Brands Considering Value Consciousness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Pricing Strategy for Competitive Brands Considering Price Consciousness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Pricing Strategy for Store Brands Considering Discount Consciousness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Pricing Strategy Considering Different Sales Structures . . . . . . . . . . 3.4 Pricing Strategy Under Different Classifications of Store Brand Manufacturers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

101 101 102 105

43 44 49 50 57 66 67 68 71 72 81 91 92 93

107 112 115 115 117 119 121 124

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3.5 Dynamic Pricing of Store Brands with Reference Effect . . . . . . . . . . 3.5.1 Problem Description and Model Building . . . . . . . . . . . . . . . . 3.5.2 Fixed Pricing Strategy Without Reference Effect . . . . . . . . . 3.5.3 Dynamic Pricing Strategy Considering Reference Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.4 Numerical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Dynamic Assortment in the Presence of Brand Heterogeneity . . . . . 3.6.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.2 Problem Statement and Model . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.3 Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.4 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

127 128 130

4 Channel Strategy and Conflict Resolution . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Channel Competition Between Traditional Manufacturers and Retailers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 The Competition Between Brick-and-Mortar and Online Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 The Impact of Channel Structure . . . . . . . . . . . . . . . . . . . . . . . 4.1.3 Multi-channel Competition and Strategies . . . . . . . . . . . . . . . 4.2 Channel Competition When Considering the Introduction of a Store Brand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 The Impact of Introducing a Retailer’s Store Brand . . . . . . . 4.2.2 Channel Competition After the Adaptation of a Retailer’s Store Brand . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 A Win-Win Situation for Both Retailers and Manufacturers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Channel Coordination Between Traditional Retailer and Manufacturer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Price Discounts in Traditional Channel Coordination . . . . . . 4.3.2 Revenue Sharing in Traditional Channel Coordination . . . . . 4.3.3 Cooperative Advertising in Traditional Channel Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 Inventory Transfer in Traditional Channel Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.5 Channel Coordination Through Other Methods . . . . . . . . . . . 4.4 Channel Coordination Between Retailers and Manufacturers When Store Brands Are Introduced . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Channel Coordination Through Revenue Sharing . . . . . . . . . 4.4.2 Channel Coordination Through Cooperative Advertising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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131 135 139 140 142 145 148 151 159 160 161

169 170 170 171 172 172 172 173 174 174 175 176 177 178 179 179 181

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4.4.3 Channel Coordination Through Positioning of Store Brand Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.4 Other Methods of Coordination . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Pricing Strategy for Bricks & Mortar (B&M) Stores in a Dual-Channel Supply Chain Based on the Hotelling Model . . . 4.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.3 Model Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.4 Showrooming When the B&M Retailer Implements no Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.5 Showrooming When the B&M Retailer Implements a Store-Brand Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.6 Considering Store-Brand Awareness . . . . . . . . . . . . . . . . . . . . 4.5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Supply Chain Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Supply Chain Coordination Based on Game Theory . . . . . . . . . . . . . 5.1.1 Pricing and Profitability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Strategic Interaction Between Manufacturer and Retailer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 Supply Chain Coordination Based on Positioning of Store Brand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Supply Chain Coordination Mechanism . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Advertising Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Channel Coordination Mechanism . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Promotion and Rebate Strategies Mechanism . . . . . . . . . . . . 5.2.4 Shelf Allocation Strategy Mechanism . . . . . . . . . . . . . . . . . . . 5.2.5 Contracts Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.6 Rest of the Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Supply Chain Competition and Cooperation Between Retailers and Manufacturers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Supply Chain Competition and Cooperation Based on Store Brands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Supply Chain Competition and Cooperation with the Introduction of Store Brands . . . . . . . . . . . . . . . . . . . 5.4 Supply Chain Advertising Decisions with Competition between National and Store Brands . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Model and Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Differential Game Models Based on Stackelberg Game Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Comparison with Nash Non-cooperative Game . . . . . . . . . . . 5.4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

183 184 184 185 187 190 192 192 199 203 204 204 211 213 213 215 217 218 218 220 222 223 224 225 226 226 229 230 231 233 238 240

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5.5 Competition Games for National and Store Brands with Advertisement Intervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Models and Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Stackelberg Games with Shared or Secret Information of Manufacturer’s Advertising Costs . . . . . . . . . . . . . . . . . . . . 5.5.3 Analysis of the Property of the Optimal Solution of the Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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240 241 243 246 248 248 249

About the Author

Jiazhen Huo, PhD, Chair Professor (Supported by BOSCH) of School of Economics and Management, Tongji University, China. His research interests are Logistics and Supply Chain Management, Service Operations Management. In the past decades, he has published more than 30 papers on such as OR, JOM, IJPE, EJOR, IJPR and other high quality journals and presided 7 research projects funded by the National Nature Science Foundation and a number of consulting projects.

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List of Figures

Fig. 1.1 Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 2.5 Fig. 2.6 Fig. 2.7 Fig. 2.8 Fig. 2.9 Fig. 2.10 Fig. 2.11 Fig. 2.12 Fig. 2.13 Fig. 2.14

Fig. 2.15

Fig. 2.16 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 3.5 Fig. 3.6 Fig. 3.7

Store brands of Lianhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research model framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Theoretical model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Standardized model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Standardized cylindrical model . . . . . . . . . . . . . . . . . . . . . . . . . . . Variable results for different values of c . . . . . . . . . . . . . . . . . . . . Retailer’s total profit for different values of c . . . . . . . . . . . . . . . . Manufacturer’s total profit for different values of c . . . . . . . . . . . Variable results for different values of k . . . . . . . . . . . . . . . . . . . . Retailer’s total profit for different values of k . . . . . . . . . . . . . . . . Manufacturer’s total profit for different values of k . . . . . . . . . . . Variable results for different values of αs . . . . . . . . . . . . . . . . . . . Price for different values of αs . . . . . . . . . . . . . . . . . . . . . . . . . . . . Retailer’s total profit for different values of αs . . . . . . . . . . . . . . . Comparison between the periods before and after the introduction of the SB in αs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison between the periods before and after the introduction of the SB in αs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Demand and shelf space proportion for the SB for different values of αs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect of memory coefficient on price and reference price . . . . . . The effect of reference effect coefficient on price and reference price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The effect of weight coefficient on price and reference price . . . The effect of store brand perceived quality on price and reference price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The impact of retailers’ share of NB on price and reference price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Two-stage consumer choice model . . . . . . . . . . . . . . . . . . . . . . . . Effect of inventory levels on revenue overestimation . . . . . . . . . .

10 44 52 53 58 82 82 83 85 86 87 88 88 89

89

90 91 135 135 136 136 137 146 155 xv

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Fig. 3.8 Fig. 4.1 Fig. 4.2 Fig. 4.3

List of Figures

Effect of price on revenue overestimation . . . . . . . . . . . . . . . . . . . The model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Customer’s decision-making process under a store-brand strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Consumers’ decision-making process considering store-brand awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

155 190 194 200

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 2.7 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 3.8 Table 3.9 Table 3.10 Table 4.1 Table 4.2

Judgment principles for internal consistency and reliability coefficient index . . . . . . . . . . . . . . . . . . . . . . . . . . Scale reliability analysis results . . . . . . . . . . . . . . . . . . . . . . . . . . Model fitting index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Standardized parameter inspection . . . . . . . . . . . . . . . . . . . . . . . Correlation Test Between Variables . . . . . . . . . . . . . . . . . . . . . . Correlation Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hypothesis test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of profits with reference effects being positive . . . Comparison of profits with reference effects being negative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data for estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Uniform prior distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimated parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Expected revenues under two consumer choice models . . . . . . Impact of initial inventory on dynamic assortment . . . . . . . . . . Impact of increasing one unit inventory on revenue improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of full price discount on dynamic assortment . . . . . . . . Impact of partial price discount on dynamic assortment . . . . . . Notations used in the paper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The equilibrium solutions of the three models . . . . . . . . . . . . . .

51 52 53 54 54 55 59 138 138 151 152 152 154 156 157 158 159 191 193

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Chapter 1

Introduction

Store brand (SB), also known as private label (PL) or private brand (PB), refers to a brand created and controlled by a retailer. According to the definition from the Private Label Manufacturers’ Association (PLMA), private brand products include all the products sold under a retailer’s brand name, where the retailer controls part of or all the process from design to the sale of products. Although usually produced by third party manufacturers, private brand products are designed by the retailers and sold in their own stores. Private brands stand in contrast to supplier or manufacturer brands, which are also known as national brands (NB). In the main however, for simplicity and consistency we will now prefer the term “store brand” and “store label” over the other “private brand or label” terminology. In the 1960s and 1970s, store labels began to emerge in France and England. Initially, retailers mainly offered low-quality, cheap store label products as an alternative to supplier brands (Steenkamp and Geyskens 2014). Through the 1980s, retailers began to improve the quality of their store brands, with some store label products matching the quality of national brand products, with store label pricing often being lower than the equivalent national brand product (Steenkamp and Geyskens 2014). Retailers are motivated to develop store brands for the following four main reasons: 1. Increasing market share and profits. Retailers expand the variety of product offerings by introducing private brands to meet niche market needs and thereby increase their market share (Amrouche and Zaccour 2008; Kotler 1999; Mills 1999). Retailers can make significant additional profits by selling store brand products, because the profit margin of them is usually much larger than the profits from selling other-brand products (Dimitrieska et al. 2017). 2. Improving the bargaining power of retailers. The introduction of store brands can reduce retailers’ reliance on national brands and increase their bargaining power over manufacturers by lessening their dependency on NBs (Groznik and Heese 2010). At the same time, since retailers control the assortment and positioning of all brands on their shelving, the introduction of store brands can also significantly improve their bargaining power (Morton and Zettelmeyer 2004). © Tongji University Press 2021 J. Huo, Advances in Theory and Practice in Store Brand Operations, https://doi.org/10.1007/978-981-15-9877-7_1

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3. Increasing brand loyalty. Since store labels have the characteristic of being sold only through specific retail channels, the development of store labels by retailers can differentiate themselves from other retailers and increase consumer loyalty (Bontems et al. 1999; Hoch and Banerji 1993). Seenivasan et al. (2016) found that store brands increase brand loyalty by enhancing store differentiation. Lombart and Louis (2016) studied the impact of consumer perceptions of store brands and retailer personality traits on consumer loyalty and found that if consumers believe that the retailer is credible, honest and considerate, then store brands can enhance the retailer’s standing. Consumers’ attitudes toward store brands will affect their loyalty to retailers, so it is of strategic importance and value for retailers to manage store brands effectively. 4. Controlling the positioning of the product. By introducing store label products, retailers can control product positioning, especially when it is difficult for retailers to find products from manufacturer brands that exactly match their requirements (Amrouche and Yan 2012; Morton and Zettelmeyer 2004).

1.1 The Development of Store Brands Early research abroad mainly focused on the significance of store brands for the development of retail enterprises. The store brands that were developed overseas early on had three main characteristics: (1) higher frequency of purchase, often daily consumables, the purpose being to enable them to quickly gain wide attention and interest from customers once launched; (2) obvious differentiation among products with strong selectivity, so that retailers could easily highlight the characteristics of products to meet the needs of subdivided customer groups; (3) moderate price presenting low risk: it was easier to attract consumers to try, and thus increase the possibility of accepting store branded goods. From the initial low-price stage to the modern high-quality stage, the development of store brands can be divided into four types, namely unknown brands, quasibrands, family brands and image brands. Consumers’ attention gradually shifted from price to quality, and retailers store brand product categories also expanded, from low prices and low quality to high quality and low price. The image brand stage was more concerned with attracting consumers through product innovation, enhancing the value of products thus improving their store image (Zhang 2017). In early research, it was clear that consumers’ perception of store brands was of low quality and low prices, but with the development of store brands and their promotion by advertising, more consumers began to perceive that there were no significant differences in the quality of store brand products and national brand products, and that high-quality store label products existed (Xie and Luo 2011). This was due to the retailers increasing investment in product quality, design, style, and packaging, which attracted consumer attention. Over the past ten years, the quality of store brands has

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been greatly improved, although national brands still dominate the market for highend products, including jewelry, household appliances, computers, automobiles and furniture (Dimitrieska et al. 2017). Although store labels have grown rapidly worldwide, market share varies greatly from region to region. According to Nielsen’s 2018 Global Private Label Report,1 the largest markets for store-label products are still found primarily in the more mature European retail markets. Comparatively, store labels still have much room for growth, especially in North America, where penetration is still relatively low. Store labels have the largest market share in Spain at 42% and in Germany the store label share is about 35%. In the Asia Pacific market, store label share is generally low, and only accounts for 4.2% of the market. Europe has the largest store label share because significant numbers of customers hold positive perceptions about store labels. The growth of the store brand market share in various countries is not linear, but when it reaches 5–10%, it appears to have an accelerated effect. The market share of store label products represents diversity among the various product categories, with statistics showing that the market share of store label products in 38 countries averages 32% in refrigerated foods, 5% in personal care products and 2% in infant products (ACNielsen 2005). Overall, the average price of store label products is 31% lower than the NB manufacturer’s price, but there are significant differences depending on the category. For example, the prices of personal care products are 46% lower on average, and the prices of refrigerated foods are 16% lower (Caprice 2017). There are also many examples showing that store label product prices are higher than NB manufacturer brands because these store labels have taken a superior position to some high-end brands. In the past, store brand positioning was mostly as a low-cost alternative to national manufacturer brands, but this situation has changed. Retailers recognized that high-quality store brands were more competitive and profitable, and easily triggered vicious competition with manufacturers. With the development and differentiation of store brands, more and more scholars began to study customers’ purchase preference behavior for store brands. Cho (2019) analyzed the purchasing data of a large Korean supermarket and found that people who are over 50 years old and people with lower incomes preferred store brand products, as opposed to those who are under 30 years old and have higher incomes. In addition, customers who shopped frequently at the store tended to choose store brand products more than customers who purchased larger amounts of goods less frequently. The new e-commerce retail companies have become an indispensable part of the store brand development era, and are showing an enhanced results trend, even as latecomers. This phenomenon results from the intrinsic reliance of e-commerce on data and its better understanding, influence and control of customer purchasing behavior. According to Nielsen’s 2018 Global Private Label Report, e-commerce will be another disrupter to national brand status. Amazon, for example, is not just disrupting the consumer product space; it is fragmenting the path to purchasing goods

1 See the Nielsen’s Global

Private Label Report (2018) available at https://www.nielsen.com/us/en/ insights/report/2018/the-rise-and-rise-again-of-private-label/.

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1 Introduction

and opening new opportunities for store labels. We have entered an age in which store labelers also have strong opportunities to compete by using e-commerce platforms. Store brands usually benefit from online channels, Nenycz-Thiel et al. (2016) surveyed five types of packaged products in the UK and found that in most cases, store label products had a higher market share in online channels than offline channels, and online customers have higher loyalty. Moreover, this difference is more significant in high-end store brand products. Online e-commerce retail companies have a wide range of store brand products, which account for a relatively high proportion, because e-commerce retail enterprises can maximize the variety of their store brands by taking advantage of the visibility and convenience of their online platforms, without considering space factors such as venues. Traditional department store’s own brand development is slow, still in its infancy with slow product replacement (Qin 2017). Research have found that the relative intrinsic loyalty and the conquering power of store brand products differ in different categories or channels, that means some categories of store label products may sell better online. Therefore, different strategies can be formulated according to the category’s competitiveness in different channels (Arce-Urriza and Cebollada 2018). Both traditional retailers and online retailers have gradually realized the importance of understanding their store brand products and formulating reasonable operating strategies. Traditional retailers must especially recognize their shortcomings and take corrective action, or they will struggle in the development of their store brands. In this era of a growing e-commerce economy, retailers have tried online to offline (O2O) operations in pursuit of the combination of online and offline advantages. However, where retailers are not the owners of a brand, it is difficult to achieve uniform prices online and offline. The development of store brands to obtain pricing power for retailers helps the development of O2O operations (Zhang and Zan 2016). This has prompted e-commerce retailers to vigorously develop their store brands.

1.2 The Market for Store Brands in China In this section, we will focus on the market of store brands in China. The remainder of this chapter is organized as follows: we first introduce the development of store brands in China. We then examine the reasons for the low market share of China’s store brands and the challenges for China store brands are analyzed, and the development of the Lianhua store brand model is introduced as a case study.

1.2.1 The Development of Store Brands in China In developed economies, store labelling is a strategic point in the development of retailing, whilst in emerging economies store label market penetration is often not high, and the role of the store label is not so important. It may be that retailers in

1.2 The Market for Store Brands in China

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emerging economies have insufficient knowledge of their own rights and powers, affecting their understanding and management of the potential for store brands; in addition, retailers in emerging or developing countries may lack sufficient skills to fully utilize the real opportunities of store branding (Herstein et al. 2017). In recent years, many large domestic retail enterprises have launched their own brand products, such as Lianhua’s store brands “Better Living”, “Lianhua Jiahui”, “Youpin Life”; Wal-Mart’s store brand “Yekee”, “Great Value”, and so on. China Resources Vanguard also launched “Simple Life”, “Home Run”, “VICTOR” and other store brands. With the growth of e-commerce, some online retailers have also launched store-label goods. JD started to introduce its store brands in 2010, and has developed a number, mainly “dostyle”, “Hommy”, “Truewow”, and “INTERIGHT”, with annual sales of its store brand products reaching several hundred million yuan (Liu 2016). However, at present, the market share for China’s store labels is only 1–3%, which still represents a significant gap compared with Europe and America. At present, there are two main ways to promote store brands: one is to use storebanner branding, and the other is to use stand-alone branding. For the first method, retailers use their own store’s brand name to part-name their store brands, such as “Dangdang Youpin”. The advantages of this method are obvious, but the disadvantage is when the quality of a product is in question, it can easily affect the reputation of the entire store’s name as a brand, which makes for higher management requirements. Geyskens et al. (2018) believe that when brand value is high, store-banner branding is suitable, otherwise stand-alone branding is the best choice. They found that store-banner branding can bring higher sales and profits because customers often cannot distinguish the relationship between store-banner and store brands, so retailers’ clear use of store-banner branding provides support for this. Many large retail companies have begun to adopt their store brand chain operation strategy. Pan (2018) explored how China’s retail companies should develop their store brand chain operations through research on “MINISO”, which is a retail company founded by a Chinese entrepreneur and Japanese designer. As Pan concluded from his research, retail companies should pay attention to the following points during the development of their store brand chains: (1) Establish a market-oriented development concept for store brands, so that products can better meet consumer needs; (2) Optimize products and in-store design in a thematic way, gaining advantages in terms of price, promotional activities, in-store experience and product quality; (3) Accurate positioning of store brand products and clear target markets also help customers to effectively identify the brand. Large supermarkets are the main force for implementing their store brand strategy. The target market for large supermarkets is mainly community residents near certain business districts; the target market for online retailers is young people aged 18–35. Li and Wang (2019) believe that in the process of store brand development, store brand product development and product portfolio management are two issues that require special attention. In the development process, they suggest using the advantages of data to strengthen the analysis of market demand; in the process of portfolio management, they advocate handling the relationship between category (width) and variety (depth), and expanding a portfolio of store brand products gradually.

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By analyzing data from more than 60 retail companies and more than 60 supplier companies distributed throughout the country, the report “2019 China Private Brand Market Research”, found that the proportion of average single product store brand items in the current retail market, the sales of store brands and categories of store brands all show an increasing trend (Li et al. 2019) and the overall strength of China’s retail industry has gradually grown. In 2015, 14 Chinese retailers entered the global retail top 250, such as Suning, Gome, JD, Shanghai Friendship Group and Dashang Group. Compared with foreign-funded retail enterprises however, local retail companies have greater deficiencies in terms of core competitiveness and the capacity for sustainable development (Zhang and Zan 2016). The implementation of local (Chinese) retail companies’ store brand strategy is still in its infancy, and their competitiveness and profitability are insufficient. Compared with foreign-funded enterprises such as Wal-Mart and Auchan, local retailers’ store brand sales and inventory accounts for a significantly smaller proportion. Traditional retailer’s store brand development is struggling, but with the rapid development of online sales, online retailers have become a new force in the development of store brands. Traditional retailer’s store brand product development often adopts the OEM (Original Equipment Manufacturer) model, or imitation development model. In contrast, online retailers mostly adopt the joint development model, which is highly related because of their advantage in valuing the use of data and can quickly react to market demand information. Apart from a few companies such as “China Resources Vanguard” and “Century Lianhua”, traditional retailers have not established an independent operational department for their store brands. Most large online retailers have not only established independent store brand operations departments, but also established independent store brand product development teams (Liu 2016). Li and Wang (2019) summarized the factors that restrict local retail companies from implementing their store brand strategy: these are divided into two aspects, the first being industry factors. When many domestic and foreign retail enterprises enter the market, it leads to fierce competition in the retail market and low profit margins. This limits the enthusiasm of local companies to develop their store brands. The second is the enterprise factor, which includes the lack of comprehensive talents, the difficulty in quality control for commodities, the difficulty of achieving scale effects because of the small scales involved, and weak product development capabilities.

1.2.2 Reasons for the Low Market Share of China Store Brands As an independent director of Lianhua Supermarkets Limited, the largest supermarket chain in China and a listed company on the Hong Kong Stock Exchange, I have been engaged in retail consulting for nearly 20 years and is very familiar with the operation and management practices of the retail industry in China. Based on my understanding of Chinese retail practices and issues over the past 20 years and the team’s research

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and analysis of several large retail enterprises, including Lianhua Supermarket, the reasons for the low market share of China’s retail store label industry are mainly as follows: 1. Store brand recognition is not high. Store brands are still a relatively new thing for Chinese consumers, and their awareness and brand recognition are not high enough. Consumers also lack trust in store label products, preferring to choose products that they are familiar with. In countries with a long time to build their store brands, such as the United States, the United Kingdom, and France, the rate of consumers who trust in their store brands has reached 75, 56, and 38%, respectively (Xu 2016). 2. Unstable quality of store brands. At present, the development of China’s retail enterprise store branding is still in the following stage, where some enterprises are able to reduce store brand OEM costs, and although the advantages, which are the low prices of store brands, are ensured, the quality is difficult to guarantee, affecting customer satisfaction and brand image. Retail companies have inadequate control over the production quality of their store brands and loopholes in the supervision of retail company store brands have also caused store brand products with low quality entering the market (Xu 2016). 3. Inadequate supply chain systems. The development of a retailer’s store brand requires the integration of a complete supply chain system, including category selection, product development, production, quality control, marketing, logistics and sales of the product. 4. Promotion methods are not reached to a certain level. Although some large supermarkets have developed their store brands, enterprises may still use a single promotional method for these types of goods, and discounts, coupons and other promotional methods are less used. 5. Risk aversion affects motivation for new product introduction. For retailers, there is more pressure to sell their store brand than to sell other goods. If there is a problem with the store brand, it may immediately affect the brand image of the entire company. Retailers have therefore reduced their own incentive to introduce their store brands from a risk prevention perspective. In the fiercely competitive market environment, Chinese retail enterprises, in order to be robust in facing the competition, urgently need new theories and methods to guide practice, develop their store brands in a scientific and orderly manner, and improve the differentiated competitiveness of their enterprises. Therefore, it is essential to conduct research on the relevant theories as well as key techniques and methods for developing retail store branding. At the same time, given China’s large population and thus a high demand for goods, store label goods still have huge potential for development. This provides a good practical background for the study of retail development for store label theories and methods.

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1.2.3 Challenges for China Store Brands In China, traditional store brands often mean low prices and serious product homogeneity. Not only is it difficult to persuade people from the premium brands, but it is also extremely difficult to attract new consumers. Although the awareness of Chinese retail companies’ store brands has increased in recent years, the proportion of store brands in most large-scale retail companies are still below 10%, and even in China’s largest supermarket chain, Lianhua Supermarket, it is only 5%. The average level of store brands in the entire Chinese retail industry is only 1–3%. How to develop store label products with high quality and low price like the foreign retail industry and increase the market share of store labels are the practical problems that Chinese retail companies are facing today. For Chinese retail companies to develop their store brands, the following problems need to be solved: 1. Select the correct product. There are between 1 and 2 million kinds of Stock Keeping Units (SKUs) in China’s large physical supermarkets (hypermarkets), and there are more types of products in online retail companies. Which are the most suitable and competitive to choose for the development of store brands in which categories is the first problem to be solved. An important reason for the slow development of store brands in China is that consumers have insufficient confidence in store brands. Therefore, it is necessary to deeply analyze the main factors affecting the introduction of store brands from the perspective of consumers. Although there are some international studies in this area, there are few systematic studies based on the characteristics of the Chinese market. 2. Fixed price. According to LSA/Fournier’s survey of the French market, the importance of retailers’ motivations to introduce their store brands is ranked from high to low: reducing prices (33%), increasing profits (25%), improving their positioning (18%) and increasing consumer loyalty (16%). We can see price is an important factor that affects the introduction of store brands. Before the introduction of store brands, consumers had known expectations about the performance and price of existing products, and price is one of the ways to reflect quality. Lower prices may lead to lower quality expectations, and if the price is too high, it will be difficult to achieve the low-price strategic benefit of store brands. Therefore, how to set prices effectively is also a problem that retail companies must solve when introducing store brands. 3. Restructure channels. After the introduction of store brands, the retailer’s original supply and sales channels will undergo structural changes. To deal with the impact of retailers introducing their store brands, many suppliers have expanded various direct sales channels, so planned channel reconstruction is imperative. At the same time, many retailers have also introduced online sales channels, and changes in the supply network will inevitably bring channel conflicts. How to design a reasonable mechanism to coordinate the “relationship” between channel members and resolve channel conflicts is also a problem that needs to be resolved.

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4. Coordinate the supply chain. After the introduction of store brands, retailers and suppliers have seen increases in the horizontal competitive relationship on the basis of the traditional upstream and downstream flows, and as the supply and sales channels have become more complex, the relationship between retailers and suppliers has become intricate. Under the traditional model, suppliers and retailers usually improve the efficiency of decentralized decision-making and the performance of the supply chain through various established coordination methods. But are these strategies effective after the introduction of store brands and how effective are they? How to optimize the efficiency of the supply chain so that both suppliers and retailers can benefit from it is a key consideration. The “Analysis Report on the Operating Status of China’s Chain Retail Enterprises 2013–2014” shows that although the sales of the top 100 retail companies exceeded 2 trillion in 2013, factors such as slow growth in the macroeconomic environment, weak consumption, e-commerce channel diversion, consumption upgrades, and continued high costs and other factors, together led to a decline in the growth rate of the traditional retail industry. The growth rate has turned into a single digit for the first time at only 9.9%, and the sales of the top 100 retail companies decreased from 10.8% in 2009 to 8.7% of total retail sales of social consumer goods. Regardless of whether it is an online or offline retail enterprise, the high homogeneity of products has shackled market development. Only product differentiation can attract passenger flow, and differentiation that depends on the development of store brands is potentially a good strategy.

1.2.4 Lianhua’s Practice in Respect of Store Branding Lianhua Supermarket introduced its store brand “Lianhua” (LH for short) in the early 90s. With this development at Lianhua Supermarket, they also differentiated the Jiahui (food category) and Youpin Life (nonfood category). Later, Jiahui targeted its product positioning as low-priced consumer goods, and Youpin Life turned to the high-end market. Nowadays (2018) Lianhua has found its store brand department is responsible for the design, development, and management of the store brand products. It has now developed six store brands: Better Living, UPSF (Youpin Life), Youxiang, Youshi (consumer goods), Tasy (high-end market) and Yours. Figure 1.1 shows the store brands of Lianhua. The sales volume of Lianhua store brands accounts for 5.3% of total sales, with ambient temperature products accounting for 3.1% of the total sales volume. Lianhua chooses its manufacturers according to a series of quality process control systems. The store brands of Lianhua are different in various areas of the business. For example, Lianhua has independent store brands in convenience stores, while in supermarkets and online stores, the store brands are uniform. The reasons why Lianhua introduced its store brand products were mainly to increase its market share and profits. The profit margin for store brands is 10% higher compared to similar products. Normally, the price index for general store brand

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1 Introduction

Fig. 1.1 Store brands of Lianhua

products is 90% and for high-end products, it is 110%. Lianhua aims to cover different categories of products using their store brands strategy. Currently, Lianhua’s store brands have covered daily purchasing allocations, for example: fermented rice wine, cereals, oil and groceries, water and beverages, snack foods, home cleaning, personal care, household goods, and home textiles. Home appliances and baby supplies are not covered yet. In Lianhua, grain and oil products have better competitive advantage compared with other categories of store brand products. Lianhua sets the price of its store brands based on costs and profit margin, while the profit margin of store brands is 10% higher than other products within the same category, Lianhua also conducts promotions for a batch of store brand products every two weeks, and it responsible for the cost of this promotion. In terms of national brand products, manufacturers are responsible for the promotions. In the early stage of introducing store brands, the promotion frequency is often higher, to acquire more customers’ cognition of the store brand. The introduction of store branding impacts both the manufacturers and Lianhua. For example, Lianhua displays its goods in a different way after the introduction of store brands. Lianhua has display policies for store brands: store brands have specific exclusive counters, and they are usually located near best-seller goods. From what we see, the conflict between manufacturers and Lianhua is not apparent, as Lianhua cooperates with a lot of manufacturers, the competition between manufacturers exists all the time, and they are usually small manufacturers without the power to affect Lianhua. There exist challenges, though, for Lianhua to develop its store brands. Store brand development differs in different locations, which leads to unbalanced development paths. Compared to Metro, Germany’s largest retail group, the different categories of

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Lianhua store brands are more numerous, which affects brand cognition. The brand image of Lianhua store brands need to be strengthened.

1.3 The Outline of This Work This study is based on the problems that need to be solved to promote the development of store branding as a strategy in China’s retail industry. It is focused on four topics, namely: (1) retail brand product strategy; (2) pricing strategy; (3) channel strategy and (4) supply chain coordination. These key issues are described as follows: 1. The characteristics of consumers who purchase store brand products in the Chinese market and the key factors that affect the success of store brands; the strategic design of the introduction of store brands under different business formats; the distribution and layout of stock shelves after the introduction of store brands 2. Pricing strategies for store brands that consider consumer behavior and competition among the channel members 3. Channel selection and conflict resolution mechanism design in a multi-channel environment after the introduction of store brands 4. Design of supply chain coordination strategy and supply chain optimization after the introduction of store brands. These research results can be applied to the practice of offline and online retail companies to develop their store brands.

1.4 Summary In this chapter, we first discuss the development of store brands. There are four main reasons that motivate retailers to develop store brands: (1) Increasing market share and profits; (2) Improving the bargaining power of retailers; (3) Increasing brand loyalty; (4) Controlling the positioning of the products. The evolution of store brands is often observed in four stages, from unknown brands, to quasi-brands, family brands and finally, image brands. We discuss the characteristics of these store brand evolutionary stages. In the second part, we discussed the market for store brands based on the Chinese experience. We found that the sales of store brands and brand categories of in China are an increasing trend. Many large domestic retail enterprises have launched their own brand products in recent years. The reasons for the low market share of private brands in China are discussed. Using a real-life case study, we illustrated the issues using the practice and challenges experienced by Lianhua supermarket in terms of store brand management. In the final part, we presented our outline for the remaining parts of this work, pointing to the four key topics of retail brand product strategy, pricing strategy, channel strategy and supply chain coordination.

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Qin, L. F. (2017). Comparison of private brand operation modes between e-commerce retail enterprises and traditional department stores. Journal of Commercial Economics, 131–133 (in Chinese with English abstract). Seenivasan, S., Sudhir, K., & Talukdar, D. (2016). Do store brands aid store loyalty? Management Science, 62, 802–816. Steenkamp, J.-B. E. M., & Geyskens, I. (2014). Manufacturer and retailer strategies to impact store brand share: Global integration, local adaptation, and worldwide learning. Marketing Science, 33, 6–26. Xie, Q. H., & Luo, E. F. (2011). A review of research on domestic and foreign retailer’s private brand development. Economic Perspectives, 99–102 (in Chinese with English abstract). Xu, Q. M. (2016). Development status and strategic optimization of retailers’ private brand in China. Journal of Commercial Economics, 49–51 (in Chinese with English abstract). Zhang, H., & Zan, Y. Y. (2016). An economic analysis of the competitiveness of private brands. Journal of Commercial Economics, 47–49 (in Chinese with English abstract). Zhang, Q. W. (2017). Research on the evolution history and development of foreign private brands. Journal of Commercial Economics, 37–39 (in Chinese with English abstract).

Chapter 2

Product Strategy for Store Branding

Manufacturers of store brand products fall into four general classifications: they are (a) large national brand manufacturers that utilize their expertise and excess plant capacity to supply store brands; they are (b) small, quality manufacturers that specialize in particular product lines and concentrate on producing store brands almost exclusively (often such companies are owned by corporations that also produce national brands); they are (c) major retailers and wholesalers that own their own manufacturing facilities and provide store brand products for themselves; and they are also (d) regional brand manufacturers that produce store brand products for specific markets. When it comes to store brands, we need to address the questions that arise, such as, why do retailers develop their own brands; how can they make their store brands successful; what factors affect store brand success; and what factors make a difference to consumer choice of store brands? In this section, we will discuss product strategies for store brands. We have discussed the characteristics of consumers who purchase own brand products in the Chinese market and the key factors that affect the success of retailers’ own brands. We develop all aspects of these issues in the following section. There are mainly four benefits: increasing market share, improving the bargaining power, strengthening consumers’ loyalty and influence, and controlling products positioning. And we analyzed the strategic design of the introduction of store brands in different formats. In addition, it is important to analyze the distribution and layout of shelves after the introduction of store brands. We have listed some of our achievements in the last two parts.

© Tongji University Press 2021 J. Huo, Advances in Theory and Practice in Store Brand Operations, https://doi.org/10.1007/978-981-15-9877-7_2

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2.1 Factors That Affect the Success of Store Brands In recent years, there has been an increasing amount of literature on the factors influencing the success of store brands. In this section, we will introduce some factors that affect the success of store brands and give them a brief analysis by literature reviewing. The main factors that influence the success of store brands are as follows.

2.1.1 Retailers Profit(s) from Store Brands Amrouche and Yan (2012) propose a game-theoretic model through simulation to compare three different contexts, where: (i) only a national brand is offered through a traditional retailer, (ii) a store brand is introduced by a traditional retailer and (iii) a national brand manufacturer opens an online store. The findings of this study suggest that profit does not always increase for the retailer and does not continuously decrease for the manufacturer when the channel starts a national brand. The retailer can profit if they do not compete with their generic brands, or if their brand has a high potential in the market. Morton and Zettelmeyer (2004) used empirical evidence to argue that retailers strongly value control over store-brand positioning because they will be unable to source a national brand with their precisely desired product positioning. That paper is the first to model a retailer’s decision to introduce a store brand in consideration of the fact that space is limited. Morton and Zettelmeyer (2000) use a bargaining framework to model a retailer’s decision as to whether the retailer chooses to introduce store brands through supermarket data from multiple retailers. They also found that when the share of the leading national brands is higher, this will result in the retailers being more likely to introduce their store brands because the retailer can position the store brand to mimic the leading national brand. The retailers can use store brands to price discriminate among consumers and to exploit the marginal-average cost gap of store brands. Narasimhan and Wilcox (1998) showed that store brands, on average, offer retailers substantially higher margins than national brand products. The result of their model shows that the average category retail margin and store-label penetration would be inversely related. Therefore, rational retailers will choose to introduce store brands. Pauwels and Srinivasan (2004) demonstrated that the benefits from store brand entry are different in varied categories. They consistently found two beneficial effects for store brand entry: (1) high unit margins on the store brand itself and (2) higher unit margins on the national brands. In the case of decreasing retail prices, wholesale prices drop even more. In the case of increasing retail prices, wholesale prices rise to a lesser extent; but They note that these unit margin increases are typically not offset by volume loss for the retailer, as premium national brands maintain their sales level, and other price-tier brands lose market share to the store brand. These benefits do not always translate into a higher gross category margin. They

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only found a structural increase in retailer margin for the hot breakfast cereal category, which also experiences higher category demand. Additionally, any beneficial effects of store brand entry appears to be limited to the product category and they do not find any evidence of a structural boost to store traffic or store revenue. Corstjens and Lal (2000) pointed out that the store brand does not have a cost advantage over the national brand. If the retailer is unable to use the store brand to achieve lower procurement costs for the national brands, retailers can increase their profits by marketing. Retailer profits are hypothesized to increase with growing penetration of store brands. They provide empirical support for this hypothesis by presenting retailer-level data from the United Kingdom and France and householdlevel data from the United States and Canada.

2.1.2 Manufacturers Profit from Store Brands For the manufacturers, store brand entry is typically beneficial for premium-price national brands, but not for other price-tier brands. Interestingly, if the premium brands accommodate store brand entry in the price variable, then both retail and wholesale prices increase. Revenues improve because any price increase is not offset by volume loss. A plausible explanation for this phenomenon is that premium brands do not directly compete with the store brand, but instead focus on serving their core quality-conscious consumer segments with the introduction of new product varieties. In contrast, second-tier brands typically retaliate against store brand entry with lower prices and increased promotional activity. As price competition intensifies in the lower end of the market, other national brands differentiate themselves by raising rates (and presumably perceived product quality) (Pauwels and Srinivasan 2004). Previous studies agree that for two-member channels, manufacturers achieve lower profits after a store brand introduction. However, Karray and Zaccour (2006) showed that for specific ranges within cross-price competition levels, the manufacturer could improve profits with the store brand option. Manufacturers will profit from store brand introduction when the retailer chooses a high-quality brand, positioned as if to compete strongly on prices with the national brand. In this case, national brand advantage over store brands relies on higher consumers’ preference. Similarly, Soberman and Parker (2006) also demonstrated that when both the manufacturer and the retailer have market power, the launch of a quality-equivalent store brand by the retailer can lead to higher average category prices. Rather than leading to what some call “store brand competition”, both the manufacturer and the retailer benefit with the launch of quality-equivalent store brands. As a result, even a dominant manufacturer has an incentive to agree to a retailer’s request to supply a quality-equivalent store brand.

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2.1.3 National Brands Profit from Store Brands Wu and Chi-cheng (2012) also used a game-theoretic model to explain why national brands would offer their retailers store brand options in practice. When a lowerquality store brand is introduced, all national brands have less incentive to engage in a promotion. As a result, store brands mitigate competition in development among national brands. Therefore, permitting a store brand can be a credible commitment from a national brand manufacturer that it will not engage in promotions and thus decrease the incentive of a national brand rival to participate in promotion. The response parameter differs significantly across countries in national brand advertising, national brand concentration, the price gap between national brands and store brands, but the variation in the magnitude and direction of effects is predictable, so the experiences from another country could provide a model for managers (Steenkamp and Geyskens 2014). Raju et al. (1995) compared the data on 426 grocery product categories with their key predictions that the introduction of a store brand would lead to an increase in category profits if the cross-price sensitivity among national brands is low. The cross-price sensitivity (between the national brands and the store brand) is high, even when there are many national brands. Sayman et al. (2002) used observational data from two American supermarket chains and found that store brands are more likely to target more influential national brands; using data from 19 product categories they also found the competition between the high-quality store brands and the leading national brands is more intense than the competition between the store brands and secondary national brands.

2.1.4 Customers Benefit from Store Brands Pauwels and Srinivasan (2004) demonstrated that the most consistent consumer benefits are an enlarged product assortment by both store and national brands and intensified price promotional activity. For both hot breakfast cereal and toothbrushes, average price paid is lower and category sales are higher after store brand entry. It appears that some social surplus is created, which benefits the retailer (higher category margin), the premium tier national brand (higher manufacturer revenues), and the consumers (lower average price and enlarged product assortment). In addition to the enlarged product assortment, quality improvement is also an important factor that adds to benefits that accrue to customers. Store brands are usually regarded as low-quality products, but store brands consist of a full quality range of products, including low-price, ‘me-too’, and high-quality. For low-price products, the main target is to attract or keep customers with low a willingness to pay. The me-too product helps in obtaining price concessions from national brands and high-quality products are designed to increase consumer loyalty or to attract new consumers (Fabian et al. 2004). Apelbaum et al. (2003) collected data from various issues of Consumer Reports across 1990–1997, examining 78 product categories and

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found out there were significant differences amongst them in the quality premium for national brands, and the average quality of store brands is higher for one in four product categories.

2.1.5 Strategies in Times of Crisis Kaswengi and Diallo (2015) applied the Binary logit model to assess consumer choice of store brand over national brands in Hypermarkets and supermarkets in times of crisis in France. They found that the two different shop formats (Hypermarket and supermarket) are not affected in the same way by risks, even though marketing variables and consumer characteristics significantly affect store brand choice over national brands in both the Hypermarket and supermarket situations. To elaborate, sale promotion of national brands (brand display and brand feature) could influence store brands in times of crisis. Consumers tended to give their personal adjustment strategies based on their own assessments instead of marketing policy variables when crisis intensity is high. They also provided advice to different elements in the supply chain to help them adjust to the risk period. For manufacturers, strengthening promotion/advertising strategies (brand display, brand feature) to counter store brand market share rise in times of crisis is a good strategy. For retailers, they should change consistently their strategies for running their store brands well during times of crisis. The most recent trends show that store brand sales are growing faster than national brands and have achieved much higher levels of penetration compared with similar figures during the recession. These trends are supported by market share gains of store brands at leading grocery chains in the United States and Canada (Corstjens and Lal 2000).

2.2 Motivation for Introducing Store Brands There are many reasons that explain why retailers capitalize on store brands (Amrouche and Yan 2012). First, it allows retailers to increase their bargaining power with national brand manufacturers. Second, introducing store brands allows retailers to offer more variety for customers (Amrouche and Zaccour 2007). Third, it helps increase the store traffic and store loyalty (Ailawadi et al. 2008). Fabian et al. (2004) proposed two ways to address the question as to why retailers should sell store brands. One is through analysis of retailers without considering upstream firms and their relationships with the retailers. The other does consider the vertical relationships between retailers and producers. In the first case, how firms choose their optimal range of products is influenced by two opposite forces (Champsaur and Rochet 1989). On the one hand, the range of products needs to be as wide as possible to permit more choice among consumers. On the other hand, each firm’s products need to be differentiated to reduce the impact of price competition. As for the way that considers the vertical

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relationships, the focus is generally on coordination within the vertical structure and competition between vertical structures. In general, the literature shows that vertical restraints allow better coordination within the vertical structure. Consumers generally benefit from these vertical restraints depending on the intensity of competition downstream (Fabian et al. 2004). There are some specific reasons why we conclude this, as described in the following sections.

2.2.1 Increasing Market Share Through Additional Product Categories Market share relates to company sales expressed as a percentage of total market sales. A useful approach to analyze market share movements is in terms of customer penetration, customer loyalty, customer selectivity, and price selectivity (Kotler et al. 2018). Retailers expand their product categories by introducing their own brands, thereby serving market segments that are not satisfied by national brands, to increase market share. At the same time, introducing store brands can provide more varieties of products to customers (Amrouche and Zaccour 2008). Mills (1999) indicated that although any one retailer will account for a share of the manufacturer’s sales nationwide, the retailer has some degree of market power in their own location. A good example would be a manufacturer in the food processing industry who sells a premium packaged food product to grocery stores whose local market shares are significant (Cotterill 1986). Store brand marketing is a retailer strategy that reduces the dead weight loss (Mills 1999). According to a 2010 Deloitte study, 85% of retail executives were paying more attention to building their store brands, and 70% of them were investing in innovation related to store brand products (Deloitte 2010). For example, Sainsbury in the United Kingdom launched 1300 new store brand products and improved an additional 3500 in 2010 (Sainsbury 2010); the French retailer Carrefour planned to increase its store brand market share from 25 to 40% by adding more than 1500 new products and redesigning its store brand packaging (Store Brands Decisions 2011). Walmart and Kroger (who already have 35% of sales from store brands) revamped their store brand lines to further increase their market share (Peer 2009). Retailers in many emerging markets are also increasingly investing in store brands (Eizenberg and Salvo 2015). Many retail businesses have introduced store brands in significant numbers of product categories. Store brands make their packaging, sizes and labelling like those of the national brand feature or horizontal differentiation product (Mañez et al. 2016). Different varieties of products are sold in different stores. Consumers have, therefore, several alternatives to satisfy their needs. Depending on their tastes, they will be willing to buy either leading branded products (i.e. national brands) or lower quality store brands and they might also have a different willingness to pay for those products when purchased at retailers that offer high-quality service (Mañez et al. 2016).

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In addition to adding product categories, retailers have found that offering variations of those products within the same category may be beneficial. For example, the shampoo brand ‘Head and Shoulders’ supplies dandruff shampoos that come in numerous varieties. It is important to develop strategies to increase variety and depth of products (Aldousari et al. 2017).

2.2.2 Improve Bargaining Power to Compete with National Brand Manufacturers It has been established that introducing a store brand can reduce retailers’ dependence on national brands and improve their bargaining power with manufacturers so that they may secure lower national brand wholesale prices (Groznik and Heese 2010a) and Mills (1995) suggested further that such a wholesale price discount could be elicited by the mere possibility of a store brand introduction. It is also clear that retailers can obtain cost advantages by reducing their production or procurement costs for purchasing their own brand products through bidding and negotiation. In this way, retailers can obtain higher profits through store brand products lines than by selling national brand products. The retailer can strategically promote the store brand to improve their negotiation power with the manufacturer of the national brand. Retailers themselves list bargaining with manufacturers as one of the prime benefits of introducing store brands in a category (from a 1993 PaineWebber retailing conference, see Giblen [1993]). In practice, the negotiation motivation is but one of the reasons why retailers introduce store brands (Morton and Zettelmeyer 2004). Numerical studies show that the introduction of store branding may change a manufacturer’s promotion strategies and can decrease the manufacturer’s profit. However, the manufacturer’s loss due to the introduction of store brand can be mitigated by promoting the national brand’s perceived quality (Zhao et al. 2018).

2.2.3 Strengthen Consumers’ Loyalty and Influence Another rationale for retailers to invest in store brands is that store brands aid the creation of points of retail differentiation and store loyalty (Richardson et al. 1996b; Corstjens and Lal 2000). Strengthening consumer loyalty offers influence on retailers and a positive reputation for the store brand is a competitive advantage which will distinguish it from other retailers. As store brands are specific to each retailer, their introduction enhances differentiation between retailers (Fabian et al. 2004). Since store brands have the feature of selling well only in certain channels, retailers develop store brands to compete with other retailers using a deliberate differentiation strategy to increase consumer loyalty. Hoch and Banerji (1993) note that store brands can

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confer a significant measure of exclusivity to the retailer that runs them. Differentiating store characteristics (e.g. number of checkout counters, size of parking lots, favorable location) draw consumers to the store and create store loyalty. As they are unique to a chain, store brands, like other store characteristics, can serve a differentiating role (Seenivasan et al. 2016). According to a survey among retailers, store brands allow retailers to offer low prices (33%) increase their margin (25%) strengthen their image (18%) and develop customers’ loyalty (16%) (Bontems et al. 1999). Store brands can help retailers establish a trust relationship with customers so that they can build customer loyalty to stores and store brands. This mitigates against potential losses in the case of a store being temporarily out of stock. Since developing store brand products is not just to compete with manufacturers’ brand products, it may be a significant part of a differentiated business operation and obtain meaningful competitive advantage. Branded products reduce consumer risk as they are likely to have lower variance in product quality. As consumers are most interested in quality, retailers can devote their efforts to offering the best quality possible to increase customers’ loyalty (Hoch and Banerji 1993). Using game-theoretic analysis, Corstjens and Lal (2000) posit that store brands can generate store differentiation and loyalty if the quality offered is high enough to satisfy a significant proportion of consumers, inducing them to purchase again. This store differentiation ability is attributed to the store exclusivity of store brands and/or consumers’ inherent brand choice inertia. Seenivasan et al. (2016) have empirically investigated the relationship between store brand loyalty and store loyalty. First, they find a robust, monotonic, positive relationship between store brand loyalty and store loyalty by using multiple loyalty metrics and data from multiple retailers and by controlling for alternative factors that might influence store loyalty. Secondly, they took advantage of a natural experiment involving a store closure and found that the attrition in chain loyalty is lower for households with greater store brand loyalty prior to store closure. Together, their results are consistent with evidence for the store differentiation role of store brands (Seenivasan et al. 2016). Retailers can increase customer loyalty to their chain by focusing their market strategy on ensuring that their label branding is identified with idiosyncratic characteristics that encourage favorable associations and communicates these effectively, such as: quality, originality, respect for the environment, safety, etc. (Rubio et al. 2017). Coelho Do Vale and Verga Matos (2017) assess the impact of store brands on consumers’ store loyalty, across the four store loyalty stages, taking into consideration other identified factors that may influence consumers’ loyalty (i.e. store-related factors: in-store and economic factors). We address this issue by offering an integrative model of store loyalty in which not only is the consumers’ loyalty towards the store brand included as an explanatory variable, but we add several other drivers of store loyalty that have been highlighted in earlier studies that play a relevant role. In addition, this research takes into consideration the multi-dimensional characteristics of loyalty. In their model, Bauner et al. (2019) indicate that the retailer positions their store brand vertically by choosing store brand quality relative to national brand quality and horizontally by choosing the degree of feature differentiation between the store

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brand and the national brand; whilst both Mills (1995) and Bontems et al. (1999) indicate that store brand quality (relative to national brand quality) is the only factor driving the retailer’s choice to offer or not offer the store brand product. Bauner et al. (2019) show that whether the store brand is offered also depends on the degree of feature differentiation between the two products. It is possible for a store brand with features that are like those of the national brand not to be offered, despite high store brand quality.

2.2.4 Control of Product Positioning One of the most important reasons for introducing store brands is for retailers to unilaterally control their own product positioning. Store brands have been developed following different positioning strategies. For instance, some retailers targeted pricesensitive segments by introducing low-price (and low-quality) store brands. Others positioned their store brands as direct competitors to national brands. Retailers can decide the allocation of shelf space to different product categories, thereby benefiting from a larger proportion of the shelf space, which will finally relate to their own profit margins (Amrouche and Zaccour 2008). Meanwhile, Morton and Zettelmeyer (2004) proposed a similar idea, proposing that retailers strongly value control over store brand positioning as they cannot source a national brand with their desired product positioning. This is because it is profitable for retailers to position store brands as close substitutes to leading national brands, which means offering a product space location that national brand manufacturers would not find profitable. The value to retailers of controlling the positioning of their store brands arises when retailers negotiate supply terms for national brands that they stock. As the retailer owns and controls the brand, the choice regarding quality is made by the retailer. This underlines an essential feature of store brand products: the characteristics of store brand products are fixed by the retailers and not by manufacturers. Moreover, these decisions are strategically taken to enable retailers to increase their profits (Caprice 2017). This study shows that store brand positioning leads to less differentiation in product category, which structurally changes a retailer’s product line in return. Consumer’s welfare and total welfare are lower.

2.2.5 Other Motivations External environmental factors, such as weather, may affect the decisions concerning the advantages and disadvantages of introducing store brands. Sun et al. (2018) examine how a change in temperature influences the competition between a retailer and the upstream manufacturers of national brands. Their study focuses on the strategic role of store brands in the vertical interaction between the two players. By estimating a structural model accounting for both the demand and supply-side

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decisions, they find that for some products, with a rise in temperature, the retailer earns both share and margin advantages, compared to the upstream manufacturers. As temperature increases, the increase in consumer demand for store brands is more significant than national brands. This may give the retailer a bargaining advantage for a better contract from the manufacturers of national brands. The results show that store brands and national brands of different qualities are used differently. Manufacturers of premium national brands soften their stance and provide the retailer with price concessions, as temperature rises. As a result, the retailer earns decreasing margins on the premium store brands. In the low-end market, retailer margin rises for the standard store brands, but declines for the standard national brands, with a rise in temperature. By incorporating a weather variable, this study attempts to capture the dynamic nature of retailer-manufacturers vertical interactions where store brands play a key role. Liu and Fu (2019) discussed the introduction of store brands when considering the effect of asymmetric information. The main difference they identify from previous research is the how difference in demand information accepted by retailers and manufacturers is considered. They studied how retailers and manufacturers make decisions for their order strategies when retailers have information advantages and analyzed implications of demand information management for both retailers and manufacturers’ profits.

2.3 Factors That Affect Consumers’ Choice of Store Brands A crucial element in the development of a successful national brand strategy is to understand consumers. Scholars paid attention to positive and negative aspects of retail brand increasing share, while respecting the existing balance between retail and manufacturer brands. A great deal of previous research into store brands has focused on the factors influencing the purchasing intention of customers. Based on our knowledge, we gathered all the factors into those categories as follows.

2.3.1 Price and Promotion Ailawadi and Keller’s (2004) claim that no matter how the characteristics of the consumer, product, store, or purchase situation might differ, price represents the monetary expenditure that a consumer must incur to make a purchase. They point out that a retailer’s price image should be influenced by attributes like average level of prices, how much variation there is in price over time, the frequency and depth of promotions, and whether the retailer positions itself as EDLP (Every Day Low Price) or HILO (High-Low Promotional Pricing). The price format of the retailer in EDLP or HILO orientation could influence purchase intentions of store brands. ‘Large basket shoppers’ prefer EDLP stores while “small basket shoppers” prefer

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HILO stores. These rules are also applicable to store brands when retailers consider which strategies they want to use to attract customers. Mieres et al. (2014) claim that the traditionally lower price of store brand as opposed to national brands, is indeed an appealing factor for consumers who consider price to be the main deciding factor; at the same time it becomes a dissuasive factor for those consumers who establish a direct relationship between price and quality. Yoo et al. (2000) point out that price is an important extrinsic indicator of product quality or benefits. The brands with higher price are often perceived as higher quality and less vulnerable to competitive brands with lower price. This perception, that price is positively related to perceived quality, exists. However, relatively low and affordable prices are the primary benefit that consumers seek in store brands. Beneke et al. (2015) find that relative prices have a strong, positive influence on the willingness of consumers to buy store brands using their conceptual model. Also, in the 2014 report, A.C. Nielsen surveyed consumers in 60 countries through an online questionnaire and pointed out that price is the primary factor that attracts consumers to buy store brands. Excluding low prices, retailers are continually improving the quality of their products: nearly 71% of consumers believe that the variety of store brands is increasing, and 67% of consumers have not only found high-quality products also enjoy the benefits of low prices. Some scholars believe that “one of the most important activities of supermarket retailers is to establish and market their store brands” (Choi and Coughlan 2006). Considering the low-quality perception of store brands, Patricia et al. (2016) propose a boundary condition for this phenomenon by examining the moderating role of brand disclosure in the relationship between brand label and sensory evaluation within different product categories. They find that the brand disclosure can reverse store brands. Bontemps et al. (2008) find out a positive correlation (89%) between brand price and purchase of store brands through homescanned data from a consumer survey for food products. The paper also demonstrates that influence on the cost of second-tier brands is less than the effect on the leading brands. Chintagunta et al. (2002) investigate the motivation to introduce a store brand for retailers from the demand side and supply side. They study change in preference for the national brands, and price elasticities from the demand side, and the effects of the new entrant from the supply side. They are using the data from the oats product category to indicate that the introduction of the store brand could generate changes toward customers. Lower prices are more friendly to lower-income customers: Dubé et al. (2018) point out income and wealth influence the demand for store-brand products. They use empirical analysis based on a comprehensive household-level transactions database matched with price information from store-level scanner data and wealth data based on local house value indices. They find that the effect of wealth and income are negative but not precisely measured by estimating the relationship between income, wealth, and store-label demand. The result of this estimation also indicated a higher share of store-label sales during a recession period. Dick et al. (1995) find out that the relationship between annual family income and store brand proneness is curvilinear. Households can afford to buy national brands across the grocery basket, whereas

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for lower income shoppers, aversion to store brands may reflect limited expertise in brand selection or a lack of need to stretch their shopping credit. Married couples are more likely to be store brand prone than are singles or those divorced or widowed. Grewal et al. (1998) find out that price discounts affected low-knowledge consumers, whilst a high knowledge consumer is more influenced by brand name. Consumers who are more knowledgeable about product and price information may make different decisions to consumers who are less knowledgeable. Specifically, those who are knowledgeable would be less willing to pay prices that do not reflect the quality of the product, compared to those who lack knowledge. Conspicuous instore promotional activity helps in reducing the perception quality gap between NBs and SBs in the mind of consumers. Massara et al. (2018) find that in-store promotional activity positively influences the retailer’s perceived reliability, therefore the promotional activity could affect purchasing intentions indirectly. Calvo Porral and Lévy-Mangin (2004) define price as the overall representation of the relative of food store brand price for a given retailer. They point out that a food store’s brand at an affordable price has a positive influence on purchase intention. Jin and Gu Suh (2005) find that the insignificance of price consciousness and relative importance of perceived quality variation in store brand purchase intention applied in the home appliance category. Therefore, emphasizing the low price of home appliances may not be effective for Korean discount shoppers. Instead what may best be emphasized is comparable quality—a quality that matches or exceeds that of the leading national brands. They also find in the food category that keeping lower prices, rather than offering similar quality to a national brand, is more important. One caution is that just lowering prices is dangerous since it could create fierce price competition between store brand and national brand, thereby resulting in loss of profit in that product category. Therefore, careful leverage of price and quality with national brands may be more desirable. Sellers-Rubio and Nicolau-Gonzalbez (2015) analyze the effect of a promotional run by a manufacturer and studied the effect of a promotion in the presence or absence of a store brand when an asymmetrically dominated alternative (a decoy) is also included in the choice set.

2.3.2 Customers’ Perception of Risk in Buying Store Brands Research has identified as a critical determinant of consumers’ willingness to buy a new product or brand is the perceived risk; that is, a customer-based undertaken variable, associated with the purchase. Perceived risk refers to the subjective anticipation of loss. The greater the perceived risk connected to the retail brand, the lower the likely market share will be, as consumers will be cautious about buying. The research that has evolved around perceived risk has a long history in the marketing literature. Perceived risk is defined as the predicted negative utility connected to buying a specific product or brand. Perceived risk represents a potential for loss caused by bad buying decisions, as perceived by consumers in a purchasing situation. When deciding on a purchase, consumers attempt to reduce uncertainty, which

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requires marketers to decrease perceived risks connected to the retail brand (Abdullah et al. 2017). They grounded the perceived risk into categories including financial risk (consumers’ perceived risk that the store brand product is not worth the cost or is a bad investment), product performance risk (the disappointment buyers may experience if a retail brand does not meet expectations), psychological risk (mental stress and dissatisfaction stress derived from buying an unsatisfactory product) and social risk (the opinion of one’s social circle following the purchase of a store brand). Consumers’ risk perception towards store brands is an important driver in the purchase decision process and may potentially be a vital aspect of the wider formation of quality perceptions towards store brands (Sheau-Fen et al.) González Mieres et al. (2006a, b) divide perceived risk into different parts: functional risk (customers are suspicious of the quality); financial risk (customers think that buying is a waste of money); social risk (customers are worried about opinions of other people); physical risk (customers are afraid that it may not be safe for themselves and their family) and time risk (customers are afraid that it maybe be a waste of time due to a product failure to meet expectations). They used structural equations to investigate the effects that a set of variables related to purchasing behavior had on the difference in perceived risk between store brands and national brands based on the data from two products: kitchen rolls and shampoo. Their results verified that the more equal the perception of quality between store brands and national brands, the less difference there is in perceived risk between the two types of brand. The research also confirmed the functional importance of perceived risk. Similarly, Shimp and Bearden (1982) also regard financial elements as crucial parts of perceived risk. They find that perceived quality of product warranty may perform an instrumental role in allaying consumer’s perceptions of the inherent financial risk in purchasing an innovative product. They also find that in none of the experiments does extrinsic cues significantly reduce a subject’s perception of performance risk. For Narasimhan et al. (1998) perceived risk included three categories, including emotional, social and psychological dimensions, and they recognized that perceived risk is the most significant factor in determining market share of the retail brand and thus that perceived risk leads to greater levels of engagement in information search. Since perceived risk is recognized as the most significant factor in determining store brand purchase intention for customers, it has attracted significant attention in the literature. Grewal et al. (2014) propose that the effect of price on consumer perceptions of risk is moderated by two communication factors: message framing and source credibility. As a result, they find that the influence of price on consumers’ perceptions of performance risk is greater when the message is framed negatively, or the credibility of the source is low. Yasmin et al. (2014) use a survey questionnaire to obtain data from Malaysian hypermarkets to find the factors that influence customers’ attitudes toward store brands. discovered, using structural equation modeling, they find that income level, age, education level, family size, and similar characteristics are significant factors that influence purchase intentions and mediated customers’ perceived risk. Because the relationship between retailers’, manufacturers’ and consumers’ attitudes

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towards store brands is significant, they suggested ways that Malaysian hypermarket industries could reduce perceived risk. Mayer (2016) applies the structural equation modeling to analyze the variables that influence the consumer in their evaluation of store brands. He selected retailer evaluation, category evaluation, category risk, own brand risk, store brand evaluation as research variables and find that the relationship between retailer evaluation and personal evaluation is not essential; the relationship between category evaluation and category risk, store brand risk and store brand evaluation are, however, all significant and positive relationships. Wu et al. (2011) show that when simultaneously examining these two effects, the indirect effect through the mediation of perceived risk outperforms the direct effect. They also verified the negative effect of perceived risk on consumers’ price consciousness and purchase intention for the store brand in other extant literature. Finally, they find that the effect of perceived risk on the purchase intension for store brands outperformed the price consciousness effect, which previous studies had neglected. Diallo and Seck (2018) find that store brand perceived functional risk (negative brand cue) mediates the relationship between store service quality and attitude toward store brands in emerging markets. Richardson et al. (1996b) point out that perceived risk could be minimized by conducting in-store taste tests, publicizing the results of independent testing agencies, offering money back guarantees, and conducting image building campaigns to favorably portray the store brand buyer as “smart” and “quality conscious”. Semeijin et al. (2004) point out that perceived risk consists functional risk, psychosocial risk, and financial risk. Mieres et al. (2014) conclude that it is crucial to determine the proneness to perceived risk that is associated with store branding. The difference in perceived risk dimensions between store and national brands will negatively affect store brand purchase. Aldousari et al. (2017) investigate how various categories of perceived risk connected with attributes, products, and atmosphere of stores impact consumers’ evaluations of store-branded products using a developed and tested structural model. A perceived risk is considered an independent variable and the research identifies the effect of it on purchasing store brands.

2.3.3 Customers’ Store Image Store image is defined in the shopper’s mind, partly by functional qualities and partly by an aura of psychological attributes. Store image develops from customer’s objective and subjective perceptions learned over time (Martineau 1958). A considerable amount of literature focuses on how customers’ store image influences purchase intention. Grewal et al. (1998) pointed out that perceived value and store image positively influenced purchase intentions. Stores continually alter their image to adjust their positioning strategies, because they want to portray an image that is appealing to their current and potential customers. Their results show that store image has a

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direct, positive relationship to purchasing intention. Consumers may derive a sense of ‘added value’ from their image of the store. However, some scholars find that store image affects purchase intention indirectly. For example, using structural equation modelling, Liljander et al. (2009) to find that store image affects purchase intentions indirectly, by reducing perceived risk and increasing the perception of a store brand’s quality. The result showed that overall store product and store image significantly helps to reduce consumers’ perception of financial risk. Store brands are unlikely to be successful at stores with a low image. Stores with a good image can use their reputation to brand their store brands, thus giving the product a quality stamp. However, interestingly, the dimension of store image quality had no effect on store brand quality, whereas store image atmosphere increased store brand quality and reduced social risk. Diallo (2012) proposes and tests a conceptual model of the relationship between image factors and store brand purchasing in the Brazilian market. The result shows that store image could affect purchase intentions strongly. Similarly, they find that store image perceptions and store brand price-image significantly influence store brand purchase intention directly or indirectly via the effect of perceived risk toward store brands. The image of the retailer in the minds of consumers is the basis of the brand equity. A considerable amount of research has been conducted covering variable issues related to store-label brands over recent years. Girard et al. (2017) investigate the influence of awareness/familiarity and perceived quality on reducing perceived risk and increasing perceived value of store-label brands using a PLS-SEM (Partial Least Square-Structural Equation Modeling) model. Also, perceived risk, perceived value, and brand loyalty for Wal-Mart have significant mediating roles in creating store-label brand equity. Perceived risk in their paper is found to have significant, and, as expected, negative relationships with brand association, perceived quality, and perceived value. Kremer and Viot (2012) investigate whether an image transfer takes place between store brands and retailer brands through a qualitative study. The result showed that store brands can influence the brand image of the retailer. Semeijin et al. (2004) point out that perceptions of a store’s image influenced consumers’ judgment of store brand quality in a positive sense, albeit to different degrees for different retailers. At the same time, some risk could be relieved by the perceived store image when they find that all three retailers in their research could neutralize psychosocial risk by their store images. Massara et al. (2018) find that the existence of a positive affect transfer from NB to SB through store image and the existence of a negative direct relationship between NBs and SBs (affect polarization) can be neutralized if consumers have a positive perception of the retailer. Similarly, in researching breakfast cereal products, Beneke et al. (2015) claim, store image influences the willingness of the customer to purchase through perceived product quality and perceived risk. Delgado-Ballester et al. (2014) demonstrate that consumers who are highly value-conscious are sufficiently rational in their decision-making to recognize that those are not necessarily the best choice in terms of their financial characteristics, despite being more prone to the buying of store brands. In contrast, they see store brands as the alternative that can best reduce social risk by bolstering their perception of themselves as ‘smart shoppers’.

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By fitting their personality and inner values, the psychological risk of the purchase is reduced. Wu et al. (2011) use the data from 360 customers of the Watsons and Cosmed chain of drugstores to investigate the direct effects of store image on purchase intention for a store brand product. They treat the store image as a five-dimensional construct consisting of product variety, product quality, price, value for money, and store atmosphere. Store name, as a cue to store image, provides a tremendous amount of information to consumers. As an example, the name “Nordstrom” evokes an image of a luxurious store environment, high levels of customer service and high-quality merchandise (Grewal et al. 1998). Natalia et al. (2016) investigated an interesting topic as to whether a store brand name would affect retailer loyalty for retail consumer products (food and beverages, personal care, and household cleaning) in the Spanish market. Further, the model is analyzed for two strategies for choosing store brands’ name (umbrella store brand connected to label brand and store brand that differs from label brand) which is of great interest to the retail sector. As to strengthening trust in the retailer based on loyalty to store brands, they do not observe a distinct effect of store brands’ name chosen; that is, this variable has no moderating effect. Similarly, Grewal et al. (1998) also investigate whether store name influences the purchase intention, finding that the strong relationship between store name and store image supports the critical cue-providing role of a store’s name. Therefore, when new retailers choose a name, or established retailers change a store’s name, they need to be concerned with consistency between the name and the image they want to project. Yoo et al. (2000) claimed that the store name is a vital extrinsic cue to perceived quality. The quality of a given brand is perceived differently depending on which retailer offers it. Goodimage stores attract more attention, contracts, and visits from potential customers. Therefore, the good store name could be particularly important for store branding.

2.3.4 Familiarity of Store Brands Familiarity is regarded by consumer researchers as an important factor that influences consumers in the buying decision making process. Richardson et al. (1996a) pointed out that familiarity, brand comprehension, product knowledge, or skill in judging criteria are needed to evaluate products and could be used by customers of store brands. Greater familiarity with store brands results in less reliance on extrinsic cues in the quality assessment process. Calvo Porral and Lévy-Mangin (2004) pointed out that familiarity with food store brands has a positive influence on purchase intention. They find out that the main variable influencing food store brand purchase intention and loyalty is familiarity. Sheau-Fen et al. (2012) suggest that marketing programs orientated to improving consumer familiarity associated with store brands could possibly contribute to higher perceived quality. They find that familiarity emerged as the most important factor in predicting store brand purchase intention and thus it has strong implications for store brand retailers.

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2.3.5 Customers’ Perceived Quality of Store Brands Calvo Porral and Lévy-Mangin (2017) investigate whether the consumer perception of product quality influenced store brand purchasing intention. They used Structural Equation Modelling (SEM) to analyze a sample of 439 consumers distinguishing between consumers who expressed high perceived quality (HPQ) and low perceived quality (LPQ). As a result, they find out that store brand purchase intention is strongly influenced by confidence for both HPQ and LPQ customers. In other words, the consumers perception of quality does influence store brands’ purchase intention and perceived value. Steenkamp and Geyskens (2014) combine scanner data for a three to five-year period with consumer survey data (n = 20,987) for scores for food, household care, and personal care categories from 23 countries. They ground the drivers of store brand share into innovation, advertising, price-promotion activity, and national brand concentration for the manufacturer. In addition, they divide the drivers of store brand share into copycatting of national packaging, the quality gap with national brands, price gap with national brands, store brand’s distribution, and retail concentration for retailers. Demand factors also included excluding the factors for manufacturers or retailers. They find that improving the quality of store brands could be helpful for globally integrated strategies. Girard et al. (2016) examine consumer-based brand equity of store-label branding based on brand equity theory and store-label branding research through the data from online Wal-Mart shoppers. They find that awareness/familiarity and perceived quality are keys in reducing the perceived risk and increasing the perceived value of store-label brands in building brand equity. More specifically, as consumers’ perceptions of store-label brand value increases (due to increased familiarity and perceived quality), their perceptions of the retailer would improve as well, resulting in higher loyalty for the retailer. Dick et al. (1995) claim that consumers’ perceptions of store brand quality may also explain brand proneness. They find significant differences in quality perceptions of store brands relative to national brands between the store brands and national brands. Because perception of the store brand is always of lower quality, the value-conscious consumers are hard to draw to store brands. Co-branding could change the influence of the products in some way. The brand equity of national brands and store image both influence purchase intentions through quality perception, but store image also could affect the purchase directly (Chien et al. 2014). Liljander et al. (2014) claim that consumers’ positive perceptions of store image have a positive effect on the perceived quality of store-branded apparel is confirmed by the data for the store image ‘atmosphere’ component, but not for the store image ‘quality’ component. When Beneke et al. (2015) investigate store brand breakfast cereal products, they find that perceived product quality influences the perceived product value. Sometimes perceived quality is treated as the mediating influence about the customers’ purchase intention. For example, Sheau-Fen et al. (2012) explore how the influence of each of the risk dimensions on store brand purchase intention can be intervened through perceived quality. They find that physical risk has a negative

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effect on perceived quality, and its effect on store brand purchase intention is partially mediated by perceived quality.

2.3.6 Customers’ Perception of Value for Money Value for money refers to consideration of quality not in absolute terms but in relation to the price of a specific product. Therefore, the more desirable features (e.g. natural ingredients, real cream, extra virgin olive oil) comprised in one brand, the more value the brand may have. The competition for store brands in the low price bracket should be the more appealing attributes in that price instead of the higher-price product features, as customers are willing to pay for lower-price store brands of relatively good quality (Richardson et al. 1996a). Dick et al. (1995) also point out that value for money implies evaluation of product quality relative to the price required for purchase. Thus, a brand sold at a high price, but with desirable product attributes (e.g. natural flavors, cane sugar instead of artificial sweeteners, vegetable colors instead of chemical dyes) may be viewed as delivering greater value for money than a competing brand sold at the same price but which offers less desirable attributes (e.g. artificial flavors, sugar substitutes). We find that store brand prone shoppers regard store brands as having much greater value for money than do non-store brand prone shoppers. This may partly explain why the latter segment shows such aversion to buying store brands at all. Kremer and Viot (2012) claim that a good price image for the store brand improves the retailer price image and a poor price image for store brands damages the retailer price image. Diallo and Seck (2018) point out that mediation analysis indicates that brand perceived price significantly mediates the relationship between service quality and attitude toward store brands.

2.3.7 Customers’ Brand-Loyalty Towards Store Brands Martos and Lado (2015) use Empirical models to evaluate how different brandloyalty variables proposed by the literature performed in the Spanish market context. They find the market in the Spanish context and the demand in the US and other high-income country markets are similar. They grounded the factors influencing brand-loyalty into six categories included in four different choice models: marketingmix variables; household-specific socio-demographic variables; category-specific shopping variables; loyalty-segmentation variables. As a result, they find factors influencing the brand-loyalty are the same as the American market. Gázquez-Abad et al. (2015) suggests that store loyalty is higher in a mixed brand assortment than ‘store brand-only’ assortment when estimating four product categories, that included yogurt, fresh bread and rolls, toilet tissue, and laundry detergent. Consumers prefer mixed varieties for various purchase categories other than the low-frequency purchase categories.

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Calvo Porral and Lang (2015) use a sample of 362 consumers through a structural equation model to analyze store brand products, retailers, and individual factors that influence consumer loyalty and purchasing intention. They find that loyalty can partially mediate the influence of store brand image and perceived quality on purchase intention. Consumers rely more on the favorable image of store brands than perceived quality. Furthermore, the image of the store and the corporate reputation are both determining factors when consumers are making purchase decisions. The effects of store brand image, store brand quality perception, and loyalty are moderated by the presence of an identified manufacturer. Calvo Porral and Lévy-Mangin (2017) extend previous research on store brands in food products by examining the role of consumer trust in influencing consumers’ purchasing intention and loyalty using a conceptual model that comprised variables such as price, familiarity and store image. The findings highlight the moderating influence of trust on consumers’ loyalty to food store brand brands. In addition, the results obtained reveal the substantially great influence of store brand familiarity on purchase intention and loyalty. Therefore, consumer trust and loyalty are crucial factors influencing food store brands. Rubio et al. (2014) use an empirical study of the Spanish food market through structural equation modeling to estimate the heavy and light buyers of store brands. They find the heavy and light buyers are different when they choose store brands. Brand identification mediates the relationship between consumer value consciousness and loyalty. Perceived risk, perceived value, and consumer satisfaction all affect purchase intentions. Kremer and Viot (2012) claim that the ability of store brands to build the retailer’s brand has direct relevance to loyalty, either with regard to the store or to the retailer as a chain, by which they include all stores with the same brand name.

2.3.8 Other Factors In addition to the factors discussed above that may influence consumer purchasing intentions towards store brands, some additional literature investigate other interesting elements of relevance. Diallo and Seck (2018) find out that cultural context could affect the relationship of service quality, physical aspects, reliability, and personal attention with attitude toward store brands in emerging markets. Budhathoki et al. (2018) use five dimensions of Hofstede’s model through a SEM incremental building model approach using secondary data collected from a sample of 65 countries to investigate whether and how culture impacts store brand performance. Through their research, they find that individualism directly affected store brands positively, and long-term orientation impact on store brand performance negatively. Individualism, masculinity, power distance and uncertainty avoidance all affect the store brand performance significantly as the indirect dimensions: positively for individualism and negatively for the three other dimensions.

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Massara et al. (2018) point out that shelf placement of store brands in the store environment had a greater impact on consumer perceptions of retailer reliability than in-store promotional activity and display appeal, as visual saliency is usually very important in fast moving consumer goods. Dick et al. (1995) point out that family size has a strong influence on store brand proneness. While a majority of those living in a household with five or more members are likely to buy store brands, many smaller households seemed to be more likely to confine their purchases to nationally advertised brands. Thus, differences in financial pressure among the groups may partially determine store brand proneness. Consumer innovativeness, the predisposition to buy new and different products and brands rather than remain with previous choices and consumption patterns, has received little attention. Jin and Gu Suh (2005) find that consumer innovation positively impacts on both attitude and purchase intention in food PB, however, it only influences attitude towards store brands in the home appliance category. It is interesting to note that in both product categories, consumer innovation is the strongest factor in predicting Korean shoppers’ store brand attitude. Moreover, consumer innovation also directly predicts Korean discount shoppers’ store brand purchase intention.

2.4 Strategy for Introducing Store Brands Products are the first things that retailers need to develop their store brands. In the study of marketing, one of the shortest definitions of marketing is ‘meeting needs profitably’. Marketing deals with identifying and satisfying human and social needs and includes ten types of entity: goods, services, experiences, events, persons, places, properties, organizations, information, and ideas (Kotler et al. 2018). The product strategy principally includes four aspects: 1. Product quality, including tangible (such as appearance, strength) and intangible (such as product reputation) characteristics of products. 2. Product brand, that is product name, symbol, design, etc. 3. Target market, that is target customers or target customers’ market which are defined by the product. 4. Product positioning, the consumers’ understanding of product attributes and comparisons between the product and those of their competitors.

2.4.1 The Strategy of Product Quality When developing store brand products, firstly, retailers must consider their strategy for all store brand products. From the perspective of academic research, there are many theoretical and empirical studies on the quality, branding, and positioning of store brand products. With improvements in store brand product quality, retailers

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aim to gain higher market share. As for packaging, retailers often consciously imitate leading national brands (Morton and Zettelmeyer 2004). In this case, it is easily for retailers to find producers for their store brands. Retailer competition has combined at least two strategies: the introduction of one or more store brands and the provision of different services to consumers. Regarding the former, the development of store brands has strategic implications. Store brands are created and controlled by retailers who must consider two layers of competition. On the one hand, they compete with national brands; on the other, store brands help retailers to differentiate themselves from the competition (see Corstjens and Lal [2000], Sayman et al. [2002], Sayman and Raju [2004], Morton and Zettelmeyer [2004], Choi and Coughlan [2006] and Groznik and Heese [2010b] from a theoretical perspective; see also Cotterill and Putsis [2000], Ward et al. [2002] and Bontemps et al. [2008], among others, for empirical evidence that the increase in store brand market share is consistent with an increase in the price of national brands). Service strategies are certainly able to give retailers a competitive advantage. Retailers of the same format, like supermarkets, target a similar consumer segment with similar marketing policies. However, supermarkets and discounters provide different types of consumer benefits. Recent contributions that analyze this fact include GonzalezBenito et al. (2005), Gijsbrechts et al. (2008) and Cleeren et al. (2010), among others. These empirical analyses reveal that store characteristics, related to service level, determine consumer shopping patterns and the competitive structure within the sector, thus emphasizing an important stance in retail price competition. Mañez et al. (2016) developed a theoretical model of retail competition that incorporates two sources of quality, one inherently linked to brand characteristics and the other linked to the retailer level of service. Retailers attempt to go beyond tangible aspects related to concrete features, functionality, and convenience aiming to generate an emotional affect toward store brands (Pandowo 2016). In this context, retailers should ensure high quality, offer to refund money to unsatisfied customers, and offer product replacement (Aldousari et al. 2017). Store label product quality is thus particularly important for defining the retail brand and establishing store attractiveness, and retailers seek sufficiently high quality to avoid negative carryover from their store brand to their other retail brands (Olbrich and Jansen 2014; Vahie and Paswan 2006). Keeping product quality in mind is a competitive advantage that cannot be readily imitated (Olbrich et al. 2017). A study by Mintel (2015) suggests that enhancing the quality, variety and innovation of storebrand products is important in reaching the segment of the population that is inclined to purchase store brands. Since, for example, 42% of millennials consider store brands to be more innovative than national brand products, Górska-Warsewicz et al. (2018) propose that the introduction of organic store brands is a source of competitive advantage via six contributory factors, namely the price, range of assortment, type of store brands, image of the retailer, sustainability and specific process, and product-related attributes of organic food. Extension of offers with organic store brands makes it easier for consumers to buy organic food at more affordable prices and follow the principles of proper nutrition and a sustainable diet with lower environmental impact. At the same time, the international retailers can position themselves

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as chains contributing to more sustainable consumption. Data collected in a recently completed project on the organic food market in Poland shows an increased retail focus on organic products, with store brands gaining share in branded organic sales. The main driver of organic sales growth is expected to be the introduction of new product types and the extension of already-established store brands and branded product lines to also include organic products (Euromonitor International. Organic Packaged Food in Poland; Passport; Euromonitor International: London, UK, 2017, p. 10).

2.4.2 Strategies for Product Brands Nielsen (2014) states that consumers in the Middle East and in Asia exhibit strong brand loyalty to the national brands. It is obvious that brand plays a pivotal role in the decision-making process. Yet the current branding strategy implemented by store brands points to low customization across different markets. This makes it difficult for store brands to penetrate the market. Research regarding store brand branding success places emphasis on many driving factors, including consumer characteristics (Richardson et al. 1996b); category characteristics (Steenkamp and Dekimpe 1997); marketing mix (Ngobo 2011); product innovativeness (Pauwel and Srinivasan 2004); promotional activities (Cotterill et al. 2000) and store image (Collin-Dodd and Lindley 2003; Ailawadi and Keller 2004; Vahie and Paswan 2006). Intuitively, the more similarity there is between the packaging of store brands to major national brands, the more successful store brands should be in the marketplace (Steenkamp and Geyskens 2014). However, Horen and Pieters (2012) argue that the reality is more complex. These authors noted that the effect of ‘accessible information’ (i.e., information that consumers have stored in memory about the leading national brand) on the evaluation of the store brand copycat can be assimilative or contrastive. Assimilation occurs when accessible information guides the interpretation of the store brand, causing a shift to the activated positive national brand evaluation. Contrast occurs when accessible information is used as a contrast comparison standard in evaluation, causing a shift away from the activated positive national brand evaluation. They proposed that the degree of package similarity between copycat and leader brand, and the way consumers make their evaluation, play a critical role in determining whether assimilation (desired by the retailer) or contrast occurs. They provide laboratory-based evidence that if the shopping situation enables consumers to easily compare the copycat store brand with the national brand—which is facilitated by typical shelf layouts (Morton and Zettelmeyer 2004) then “blatant (high-similarity) copycats will lose and subtle copycats (medium similarity) will gain” (Horen and Pieters 2012). This is because, for ‘blatant copycats’, consumers are likely to become more aware of the retailer’s potential ulterior motives in enticing consumers to buy a store brand through (unfair) similarity, which results in negative contrast. In that case, consumers are likely to adjust their evaluation of the store brand by consulting their naïve theories of marketing persuasion. Based on these

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experimental findings, Steenkamp and Geyskens (2014) expect that medium similarity is evaluated more positively than high similarity, which suggests an inverted-U shape response to the copycatting of national brand packaging and store brand share. Steenkamp and Geyskens (2014) argue that even though the copycatting of national brand packaging is a widespread practice, which pays off for store brands, however, it is only up to a certain point. The effect is not as large as perhaps expected (using t-value as an effect size metric; see Nijs et al. 2001), which may allow some retailers to pursue a bolder packaging strategy, where they no longer copy the packaging of a leading national brand in each category. Rather, they set out to bring unity to their store brand assortment by developing a consistent packaging across categories. For example, Walmart recently redesigned its Great Value range to give it the same look and feel across a broad range of foods and beverages, and Target does the same for its Up & Up brand in personal and household care (Steenkamp and Geyskens 2014). Using store brand names different from those of the brands can increase loyalty to the chain from loyalty to its own brands. Retailers should divide marketing resources among the different store brands that they manage, for example, using separate communication campaigns for the different store brands or marketing specific promotions for each store brand (Rubio et al. 2017). Different types of stores sell different types of products, and certain types of stores are associated with specific product categories to some extent in consumers’ minds (Inman et al. 2004). The degree of association between a product category and a certain store in consumers’ minds is known as “product signatureness”. A strong perceptual connection between a store and a product is regarded as a high level of product signatureness (Bao et al. 2011). A store brand in a signature category of a store is likely to receive a perception of high quality, and a higher purchase intention compared to brands introduced in the non-signature categories. Consumers use store image as a cue to infer product quality (Dawar and Parker 1994; Baker et al. 2002). Yoo et al. (2000) identify a positive relationship between store image and product quality. A good store image is a way of reducing any association with poor quality; enhancing the attractiveness of store brands, in addition to their price appeal (Wu et al. 2011). A study by Collins-Dodd and Lindley (2003) also supports a positive relationship between consumer perceptions of an individual store brand and a specific retail store. Store image is clearly classified as a significant predictor of a store brand image. Consumers use store image as an extrinsic cue to speculate on the store brands’ image (Ailawadi and Keller 2004; Vahie and Paswan 2006). Once a positive perception of a store is formed, the positive effects enhance the brands carried by the store and influence the assessment of the store brands’ brand image (Dhar and Hoch 1997). In other word, because the store brand is viewed as an extension of the retail store, consumers use store image as a diagnostic cue to evaluate the store brand (Ailawadi and Keller 2004; Collins-Dodd and Lindley 2003). Research shows that store image and store-category association significantly affect store brand purchasing, with branding strategy only moderately affecting the storecategory association relationship. When own-name branding strategy (same store brand name as the retail store’s name) is implemented, the intention to purchase the store brand goods decreases compared to when the other-name branding strategy

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(different store brand name from the retail store’s name) is employed. It is found that the relationship between store-category association and store brand purchasing is weaker under an own-name branding strategy. These findings provide further theoretical implications for marketing research, as well as practical guidelines for retailers who manage store brand goods. Firstly, there should be more emphasis placed on building up the other-name store brands instead of own-name store brands as suggested by the research results. Secondly, it is recommended that the store should continue to build up its image. A retail store that offers store brands in the market should pay close attention to the management of its service and product assortment; the store should continuously strive to improve its services. Product collection should also contain characteristics of high quality, great variety, and fair value. All these actions would help to create a positive store image. Thirdly, consideration of product category should not be overlooked, since strengthening store image brings with it the requirement of higher investment. A store should be more attentive to the categories which would strengthen their specialization. When consumers believe in the favorable traits of the category, it is likely that the store brand will be presumed to be no different. The intention to purchase store brand products is likely to increase (Thanasuta and Metharom 2016). Store brands have become ever-more important and are slowly turning into brands of their own. Retailers increasingly offer three-level, ‘good, better, best’ store-label programs that include economy, standard, and premium store-label tier goods. For each of these tiers, retailers must decide under what name to market their store brand. They can either assign their store banner name to a store-label tier or go for a unique brand name that is separate from the retailer banner. The authors provide a series of recommendations regarding when to use each brand strategy, based on characteristics of the retailer and the environment in which it operates (Geyskens et al. 2018).

2.4.3 Strategies Related to Target Market Store brands have different features and success rates in different categories. They were introduced earlier for some goods (in particular convenience goods such as pasta, rice, salt, sugar, etc.) and later for others (shopping/specialty goods such as shampoo, home detergents, skin care, etc.) and typically have a greater level of loyalty and confidence by consumers in some departments (such as fresh foods, groceries) and a lower level in others (such as beverages, home and personal care). This implies that, when considering different categories, different stages of store brand development imply, in turn, different levels of penetration. The trade-off dynamics of market shares among brands appear to be increasingly conditioned by an evolution in consumers’ preferences, which tends to favor a long-term psychological and structural consolidation of store brand competitive preferences. This may offer an opportunity to reinforcing trust with consumers by the exploitation of a positive “halo-effect” between store brand loyalty and whole-store loyalty (Fornari et al. 2016). Store brands can be successfully developed and established in every-day use

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categories: food, beverages, plastic and kitchen materials, and toiletries. Retailers should focus development strategies exclusively on other frequently purchased items (Aldousari et al. 2017). To relate the different store brands to each other and connect them to the label, it is advisable that either the communications media, and even the establishment itself, stress that these store brands belong to the label, since consumers will not necessarily relate the store brands’ different brand names, making any transfer of positive associations among store brands less obvious. Consumers’ loyalty to store brands with names that differ from that of the store brands grant the retailer loyalty with less intensity than when the store brand name coincides with that of the store brands. To strengthen customers’ trust, the retailer should opt for marketing strategies with a more relational orientation, such as having the chain’s employees communicate the superiority of the store brand to customers who visit stores, or focusing the retailer’s communication campaign on showing that their store brands are always produced with the goal of respecting their customers’ well-being (Rubio et al. 2017). Recently, retailers have begun offering store brands in categories with consumer preferences that are highly personal, such as organic, health, and wellness products (Scaff et al. 2011). Developing such products requires understanding customers’ preferences and recognizing unmet needs (Hara and Matsubayashi 2017). Coupon strategies, proposed by Bauner et al. (2019), suggest that price-sensitive consumers are not necessarily the target customers of store brand products. In fact, they find that for a fixed store brand quality (fixed degree of feature differentiation), a larger degree of feature differentiation (higher store brand quality) is required for price-sensitive consumers to consume the store brand product than for price-insensitive consumers. Thus, there occurs a scenario in which pricesensitive consumers only purchase the national brand product, while price-insensitive consumers buy both NB and SB products.

2.4.4 The Strategy for Product Positioning The idea of a product or brand’s positioning is widely described, among others, by Ries and Trout (1981), Kapferer (2010) and Keller (2012). The authors agree that the positioning strategy is focused on shaping the preferences of consumers from a brand’s target group, building brand image, and ranking a brand among competitors’ brands. So, the point of reference in the brand positioning is against competitors and their brands. Until a few decades ago, store brands competed against national brands by merely copying or mimicking them. More recently, however, the competition between store brands and national brands has increased in scope and size and matured, with increasingly more retailers currently seeking to reduce their positioning gap with respect to national brands by developing affect toward their own brand, rather than copying the identity of others (Beristain and Zorrilla 2011; Das 2014).

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Choi and Coughlan (2006) find that a store brand’s best positioning strategy depends on the nature of the national brands’ competition and its own quality. In many cases, product feature and quality are two key factors influencing the product positioning of store brand. Choi and Coughlan (2006) establish a model of two competing national brands and one store brand. They find that when the two national brands are significantly differentiated in their features, the store brands’ optimal strategy is to minimize feature differentiation from one of the national brands, and the target brand to imitate depends on the store brand’s own quality level. To be specific, when store brand quality is high, the store brand should imitate the stronger brand; otherwise, when its quality is low, the store brand should position closer to a weaker national brand. Hoch and Lodish (1998) argue that unlike the goal of national brand manufacturers to maximize profits on their own products, a retailer’s goal is to maximize the benefits of all the product categories it sells, including store brands and national brands. Retailers could increase category profitability by raising store brand prices while holding the line on national brand prices and experience little if any negative spillover onto their long-term price image. Sayman et al. (2002) have studied how retailers maximize profits in their product categories. Firstly, they model how the retailer should position the store brand to maximize category profits within the context of a category with two national brands, one of which is stronger. They find that store brand targeting of the leading national brand can lead to: 1. Lower wholesale prices from both national brand 1 and to a lesser extent national brand 2. 2. Higher margins for the retailer on national brands. 3. Higher profits from the store brands. 4. Increased category demand. All these may result in increased category profit relative to other positioning strategies. Further, they find that this targeting strategy is relatively more profitable in categories where the leading national brand is stronger. This is because targeting the leading national brand can minimize the double marginalization problem. Then they collect 75 categories of product data from two leading U.S. grocery chains and these 75 categories were selected randomly from the Marketing Fact Book (IRI 1998). With such data, researchers perform regression analysis and empirical studies to verify the product strategy for the store brand, targeting the leading national brand. In addition, Morton and Zettelmeyer (2004) also verify the conclusion (from Sayman et al. 2002) through establishing a theoretical model and empirical study using real data for regression analysis. Du et al. (2005) further compete these conclusions and pointed out that it is not always optimal for a retailer to position its store brand against the leading national brand. Instead, there are many situations where it is best to position the store brand close to the weaker national brand or to position it in the ‘middle’ so it not only appeals to the strong national brand’s target segments, but also attracts the market segments of weaker national brands. Sayman and Raju (2004) study how certain product category characteristics affect the number of store brands and suggested that retailers are more likely to carry two store brands in categories where the national brands are similar in strength (low market concentration) and the

2.4 Strategy for Introducing Store Brands

41

cross price sensitivity between the national brands is low. Introducing multiple store brands that are positioned to compete with different national brands is a strategic alternative for retailers. Firstly, this can help the retailer to extend the base demand for store brands, which is necessary for profitable multiple store brands when store brands target different national brands. Secondly, targeting may exert pressure on the national brand manufacturers to offer better trade terms (Sayman et al. 2002). Fang et al. (2013) analyze whether retailers should introduce their own store brands, how to price the store brand, and what quantities of products to order from suppliers while they carry some national brands for sale. Intuitively, if the national brand wholesale price is low, the retailer will only carry the national brand. However, if the wholesale price is set to a medium level, the retailer will carry both national brands and store brands. If the wholesale price is particularly high, then the retailer will carry the store brand only. In their studies, they establish a game theoretic model to solve the problem and identify national brand cost per unit quality (CPUQ) as a key indicator for retailers to make decisions. CPUQ is the cost of production divided by quality rating, which is a driver of supply chain behavior. As their conclusions illustrate, if the national brand’s CPUQ is larger than the store brand’s CPUQ, the retailer may be inclined to introduce the store brand and the supplier does not have the ability to prevent its introduction. When the national brand has the cost advantage, meaning that its CPUQ is lower than the store brand’s CPUQ, the national brand supplier could charge a wholesale price low enough to deter the retailer from introducing its store brand (Fang et al. 2013). Chung and Lee (2017) explicitly compare a manufacturer’s national brand positioning problem and a retailer’s store brand quality positioning problem to demonstrate their differences, and show how store brand quality positioning is shaped by three underlying strategic forces: the market expansion force, the consumer profitability force, and the retail margin force, leading to different optimal product line designs for store brands across different category environments. Interestingly, against multiple incumbent national brands, the retailer’s optimal product line design includes a store brand positioned at the highest quality level in the category only if most consumers are moderately quality conscious. Their findings extend the product quality and positioning literature by providing new insights into the unique nature of the retailer’s quality positioning decisions for its own brands. To retail strategists, their study highlights the multifaceted strategic benefits of introducing store brands and how to maximize these benefits via strategic positioning analysis. Specifically, their findings indicate that it is critical for retailers to pay close attention to category (or store) specific market conditions and to understand their implications for the impact of store brand quality positions on the category demand, customer profitability, and retailer’s channel power (Chung and Lee 2017). Store brand positioning plays an important role in determining couponing strategies in national brand versus store brand competition. Bauner et al., (2019) find that the two dimensions of store brand positioning have different impacts on couponing strategies. For a fixed store brand quality, the manufacturer tends to increase its coupon value as the degree of feature differentiation between the two products increases, while the retailer responds by decreasing the value of its store coupon on

42

2 Product Strategy for Store Branding

the national brand. In contrast, for a fixed degree of feature differentiation, couponing strategies depend on the actions of price-sensitive consumers. Interestingly, if store brand quality is low, price-sensitive consumers are not interested in the store brand product and only price-insensitive consumers consider purchasing it. In this case, the manufacturer’s coupon value decreases and the retailer’s national brand coupon increases with increasing store brand quality. As store brand quality increases to a certain level, it becomes attractive to price-sensitive consumers, at which point the manufacturer increases its coupon value to mitigate the loss associated with pricesensitive consumers’ uptake of the store brand. The retailer responds by decreasing the national brand store coupon. For even higher store brand quality, the manufacturer reduces its wholesale price rather than changing the coupon value as competition with the store brand product gets more intense for all consumers (both price-sensitive and price-insensitive); manufacturer and store coupons are unaffected by further changes in store brand quality. The store brand coupon, meanwhile, is not at all affected by store brands positioning once it is offered (Bauner et al. 2019). These results are consistent with Chung and Lee (2017), who find that the best position for store brands may not be as close as possible to the national brand. Witek-Hajduk and Grudecka (2018) argue that retailers in Poland can be divided into six clusters in terms of their own-brand positioning. Members of each cluster rely on different predominant factors when positioning their own brands, mostly using “Tradition and Country/region of origin”, “CSR and Health”, “Premium and Innovation” and “Quality and Value for money” differentiators. In the case of one cluster (with only six cases), its members does not refer to any positioning strategy provided for the study. Clusters were characterized by exogenous variables (e.g. scope of action, the number of own brands registered in Poland, capital structure, etc.) which helped to profile each cluster. The results of this study have practical implications and may be helpful in the decision-making process concerning retailer brand strategies, especially those referring to the positioning strategies in Poland and other emerging markets. This study shows the differentiation of the positioning strategies of the retailer brands and that retailers move away from positioning their own brands exclusively or primarily as lower-price brands. The results of the study may serve as a recommendation for retail chain managers, pointing to directions for their own brands’ development in transition economies.

2.4.5 Strategies for Retailer Advertising of Store Brands Griffith et al. (2018) document patterns in store brand penetration across product categories and retailers using detailed data on sales and advertising of products in the British grocery market. They show that there is considerable variation in the degree of store brand penetration across product categories, which appears to be correlated with advertising activity in the category. Different types of retailers appear to follow different strategies with respect to their store brands, with high-value and discount retailers selling a greater share of store brands compared to the mid-range

2.4 Strategy for Introducing Store Brands

43

supermarkets. However, while the store brands sold by the high-value supermarkets are potentially of a higher quality than the national brands in the same market, discount supermarkets opt to sell cheaper ‘budget equivalents.’ They show that these patterns of store brand penetration are stable over time. They develop a model to understand these empirical patterns. In the model, manufacturers and retailers choose advertising levels, wholesale, and retail prices. The sub-game perfect equilibrium of the model gives the values of advertising and prices as functions of characteristics of the market and retailer. The model incorporates the effect of differential brand attractiveness on both retail and wholesale price setting, capturing the competing incentives faced by retailers. The model explains the cross-category variation in store brand penetration via variation in primitive demand conditions, i.e. how consumer demand responds to advertising. This contributes to the existing literature, which has hitherto focused on variation in advertising without explicitly modelling why retailer and manufacturers’ advertising strategies vary across product categories. Griffith et al. (2018) use their model to gain insight into the possible impact of store brands on welfare. When advertising is rivalrous, advertising is lower in the presence of a store brand, because retailers internalize some of the negative externalities associated with rivalrous advertising. They show that under the assumptions of the model, consumer surplus is higher in the presence of store brands, due to the price discrimination that the store brand facilitates. If the market is sufficiently concentrated, then producer surplus is also higher in the presence of store brands. They argue that these conclusions would survive relaxing some assumptions, but that, given the growing importance of store brands in the retail market, an important avenue for future research is to further investigate the robustness of these results.

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands In this section we report our analysis of the influence of five latent variables on purchase intention related to store brand products: (1) the psychological characteristics of consumers; (2) the attributes of store brand products; (3) retailer image; (4) the influence of information value, and (5) the influence of manufacturers. We tested the adjustment function of the store brand products’ perceived quality as an intermediary variable. Through empirical study we know that consumers’ perceived quality for store brand products has a decisive influence on purchase intention for store brand products. The variable of store brand product attributes, the intervening variable, produces a positive impact indirectly on purchase intention through perceived quality; the influence of manufacturers has a negative impact on purchase intention for store brand products; consumers’ brand awareness has no direct effect on the purchase intention for store brand products. However, when combined with the high correlation of manufacturers’ influence, consumer brand awareness has an indirect impact on purchase intention.

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2 Product Strategy for Store Branding

2.5.1 Research Hypothesis The theoretical model proposed by this paper is shown as Fig. 2.1. The psychological characteristics of consumer variables relate mainly to consumers’ brand awareness. For the attributes of store labels, the ways in which price and packaging of products are perceived are two key considerations. Retailer image considers three aspects: brand image, store image and product image positioning. The influence of information value mainly considers two aspects: the degree of the retailer’s dissemination of store brand product information and the willingness and intensity of consumers’ information search. The influence of manufacturers considers the following elements: the value of manufacturer’s brands, the capabilities of their product design, sales channels, advertising ability and discount promotions. Finally, this study considers how the moderating effect that consumer attributes like gender, age, education level, income and so on operates on the various latent variables. From the research model, it can be seen that the five factors, including the psychological characteristics of consumers, the attributes of store brand products, retailer image, the influence of information value and the influence of manufacturers, could be used as exogenous variables, and the purchase intention of store brand products can be used as endogenous variables. The perceived quality of store brand products can be used as mediation variable. Brand awareness relates to the importance of how a brand is valued by consumers during the purchase decision process. Western retailers have a wide range of store label products in low-end, mid-end and high-end selections. However, in China, many retailers only position their store labels to low-end products with low prices. Based on this positioning, in the minds of most consumers, the perception of ‘low price and poor quality’ is formed, resulting in a subjectively negative impression of the retailer’s store labels. Consumer brand awareness decides purchasing attitudes to retailers’ store labels and manufacturer national brands. Customers with stronger brand awareness are more inclined to choose products with high brand reputation. Hence, if the store

Fig. 2.1 Research model framework

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 45

image established by the retailer is positive, their store brand products will be more easily accepted by consumers. Conversely, customers with weaker brand awareness will be relatively less affected in their purchase decision by the brand. Even if the retailer’s store label is positive, it will not have a significant influence on the purchase decision. Accordingly, this paper makes the following hypotheses: H1 Consumers’ brand awareness is significantly related to the perceived quality of store brand products. The attributes of store brands mainly consider two aspects: the perceived price of products and packaging of products. William B. Dodds, Kent B. Monroe and Dhruv Grewal find that there is a positive linear correlation between price and consumer perception of quality, but the result does not support this linear trend. Instead, they find that the perceived value of consumers will rise firstly and then go down when the price increase to the relatively high level from the lower level. The same relationship exists between price and the purchase intention, and the negative correlation between them is significant. Richardson et al. study how packaging influences customer perception of store brand product quality. They find that when store brand products are packaged like national brand products with the same price, consumers are likely to give a higher score to the perceived quality of the store brand products. In addition, when the national brand is displayed with store brand, the evaluation of consumers for the perceived quality will decrease significantly. Above all, to large extent, consumer impression of store labels depends on the packaging instead of the actual quality of the store brand product. Packaging plays an important role in consumer perception of store brand product quality. Accordingly, this paper makes hypotheses as the following. H2.1 The higher the price of store brand products is, the higher the consumer’s perceived quality will be. H2.2 Store brand products with good packaging can improve the consumer’s perceived quality. Retailer image considers three aspects: brand image, store image and product image positioning. Brand value has an invisible impact on consumers, but it has a great impact on consumers’ perceived quality and purchase intention. If consumers are familiar with this brand and have good evaluation of it, they will have a higher perceived quality and are more willing to buy it. Dodds et al. (1991) also find that brand name has a positive correlation with perceived quality. Ruthe and Elaine find that store image can affect a buyer’s emotions, and this emotion will directly affect the consumer’s rate of consumption in the store, the number of patronage visits and willingness to purchase in the store continually. A positive emotional assessment of the store may also stimulate consumers into

46

2 Product Strategy for Store Branding

making additional purchases. William and Jeen. Su’s research find that store image will affect how often consumers patronize stores. As for retailers, the store image not only affects consumers’ perception of the quality of store brand products, but has a significant influence on consumers’ attitudes and purchase intentions in relation to the store products. In respect of the research concerning store labels imitating a national manufacturer’s brand, some scholars have suggested that retailers might be making the difference between store labels and national labels minimal, such that the differential perception of consumers is eliminated or blurred through similarities with national manufacturers in appearance, such as deliberately similar packaging, labelling, colors and so on (Herstein and Gamliel 2006). However, Yang and Wang (2008) find that retailers could improve consumer perception of store brand product quality by imitating the packaging of the national brand. Loken et al. (1986) propose that imitating national brand, to some extent, could improve consumers’ perceptions of the store brand product quality and affect their purchase intentions. This is because the similarity in the external attributes of store labels and national brands may induce cognitive dissonance thereby blurring consumers’ feelings about the difference between different brand products. However, Feng (1999) proposes that using the strategy of product differentiation, especially in providing special products to the market, could not only reduce the sensitivity to the target market and effectively offset the cost leadership of competitors, but also set a higher barrier to market entry for competitors if a higher brand trust can be established in the minds of customers. Strategy related to a target market refers to the way in which a company puts their effort into making running in a small market worthwhile. Accordingly, this paper makes hypotheses as the following. H3.1 The more positive the consumer’s evaluation of store labels, the more positive the perceived quality will be. H3.2 The better the store image is, the more positive the consumer’s perceived quality will be. H3.3 The more positive the consumer’s evaluation of store labels is, the more extensive the purchase intention will be. H3.4 The more positive the store image is, the more positive the consumer’s purchase intention will be. The influence of information value mainly considers two aspects: what is the intensity of consumer’s willingness to search for information in relation to store brands and to what degree do retailers disseminate store brand product information? Peng and Zhang (2013) study how the willingness and intensity of consumer information searching has a moderating effect on the consumer’s purchase intentions for store brand products. They argued that when the willingness to search for information is at a higher level, the value of a customer’s perception of quality has a positive correlation with the purchase intention towards the store brand product and

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 47

that this correlation is higher than when the willingness to search for information is low. The willingness to search for information is a prerequisite for consumers to conduct information search at all. If consumers do not have the desire to search for information, they are unlikely to take any note of the issue. If consumers realize that there is a demand for a certain commodity, they will form a willingness to search for information about that commodity and this may even afford the consumer some emotional satisfaction. Therefore, when the willingness to search for information is high, the customer’s perceived quality value is positively correlated with the purchase intention of store brand products, and this relationship is higher than when the willingness to search for information is low. There is little research on the degree of retailer’s dissemination of store brand information. Some scholars have studied the setting of shelves and spatial positioning to increase the exposure of retail store brand products, to increase consumer’s purchase intentions. Some scholars have conducted research from the perspective of promotion. Hongbo Qi studies the relationship between promotion and perceived quality and believed that promotion could improve consumer perception of quality re store labels. Accordingly, this paper proposes the following hypotheses: H4.1 The more a retailer disseminates information about a store brand product, the higher the consumer’s perception of the quality of the store brand product will be. H4.2 The intensity and willingness of a consumer to search for information is significantly related to the consumer’s perceived quality of a store brand product. H4.3 The more a retailer disseminations store brand information, the more positive the consumer’s purchase intention re the store brand products will be. H4.4 The intensity and willingness of a consumer to search for information is significantly related to the consumer’s purchase intention of store brand products. Yi (2005) introduces the influenced factor of manufacturers’ brands as the intermediary variable, selecting two dimensions: the number of manufacturer brands and the advertising and promotion of manufacturer brands. Yi finds that the moderating effect of manufacturer brand factors, overall, is not obvious. Only the number of brands has a significant weakening effect on the relationship between perceived social risk and the intention to purchase retailer’s store brands. We believe that when the influence of manufacturers in the same market segment is strong, the consumer’s perception of the quality of retailers’ store labels will be improved. On the contrary, if the influence of manufacturers with similar positioning as retailers is not strong, it is easy to reduce the consumer’s perception of quality relating to retailers’ store labels. This is because consumers will consciously or unconsciously make comparisons when purchasing commodities. When consumers lack information about store brand products, it is easy to judge by other information about other products with similar positioning. Besides, we believe that when consumers buy goods, most of

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them will choose products with perceived higher cost-effectiveness. When the costeffectiveness of a national brand products is improved, it is easy to lead consumers to reduce their purchase intentions towards the store brand products. Accordingly, this paper makes hypotheses as the following. H5.1 The higher the influence of a manufacturer’s brand is, the higher the consumer’s perceived quality of the store brand product will be. H5.2 The higher the influence of a manufacturer’s brand is, the lower the consumer’s purchase intention for store brand products will be. When consumers buy products, the perceived quality of the products will have an impact on their purchase intentions. If consumers feel that the quality of products is unacceptable, they will lower their purchase intentions. Monroe and Krishnan (1985) proposed that the higher the consumer’s perception of quality is, the higher the consumer’s perceived value of the product will be, and the higher perceived value will bring about an increase in purchase intention. Therefore, we believe that there is a certain correlation between the perceived quality of store labels and consumer purchase intentions. In addition, although there is relevant evidence to support the hypothesis that consumers’ perception of risk, product attributes and retailer’s image have a direct impact on consumer’s purchase intentions for store labels, we believe that whether these factors are significantly able to independently drive consumer’s purchase intentions for store labels is worth discussing. Accordingly, this paper proposes the following hypotheses: H6 There is a relationship between consumer’s perception of the quality and their purchase intention. The higher the consumer’s perception of the quality of the store brand products, the higher the consumer’s purchase intention will be. H7.1 The relationship the perceived quality of intermediary brand awareness and the purchase intention of store brands. H7.2 The relationship the perceived quality of intermediary attributes of products and the purchase intention of store brands. H7.3 The relationship the perceived quality of intermediary retailer’s image and the purchase intention of store brands. H7.4 The relationship the perceived quality of intermediary the influence of information value and the purchase intention of store brands. H7.5 The relationship the perceived quality of intermediary the influence of manufacturers and the purchase intention of store brands.

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 49

2.5.2 Research Design (1) The scale design. The rating approach used for this research adopts the Likert 5-point scale. The author designs seven scales: (1) consumers’ psychological characteristic scale; (2) retailers’ store brand products attributes scale; (3) retailer image scale; (4) the influence of information value on purchasing behavior scale; (5) the influence of manufacturers scale; (6) consumers’ perceived quality of store brand products scale and (7) consumers’ purchase intention to store brands scale. (2) Research scheme design. (a) Selection of questionnaire respondents. Part of the questionnaire is distributed to students and staff at school, and the other part is distributed to consumers in large supermarkets. Since domestic store labels are generally positioned in low-end products ranges, most of the surveyed respondents’ income are thus at the middle and lower consumption levels. (b) Research program design. (i) Exploratory research. Part of the scale of this study is based on the scale used by previous scholars, and part of the scale is designed by the research team. To ensure the reliability and validity of the questionnaire design and explore the relevant hypotheses that need to be tested in this study, the questionnaire is first issued for exploratory research and analysis. In this pilot study, 150 questionnaires are distributed and 137 are collected. Then authors use SPSS 16.0 software to analyze the reliability of the questionnaire. Authors adjust the questionnaire through analyzing the results and then used SPSS 16.0 software to conduct exploratory factor analysis to clarify the general relationship between each factor, finally adjusting the theoretical hypothesis model and hypothesis relationship by analyzing the results. (ii) Confirmatory research. This research uses structural equation modelling (SEM) to analyze factors that consumers may influence in respect of their purchase intentions related to store labels. Moustaki et al. (2004) suggest that structural equation modelling may also be called latent variable model. In the field of social science, SEM is mainly used to analyze complex relationships between observation variables. A ‘latent variable’ is an intangible factor that cannot be directly measured, such as motivation, belief, satisfaction, and so forth. These unobservable concepts can, however, be measured using a group of observational variables as indicators. Because variables such as consumers’ purchase intention and perceived quality cannot be measured directly, they need to be measured indirectly by subdividing the measurement indicators, so

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the structural equation model has more data for approximating the direct relationship of each latent variable. Based on the initial exploratory research, a large-scale formal survey is carried out. 320 questionnaires are sent out and 309 questionnaires are collected. AMOS 17.0 software is used to construct the structural equation model and modify the model through the collected data. According to the fitting result, we modify the model to make the model fitting index meet the relevant requirements. Finally, by constructing the structural equation model, this paper tests the proposed research hypothesis and analyses the moderating effects of background variables (gender, age, education level, income) to the research variables.

2.5.3 Empirical Analysis (1) Scale reliability analysis. Reliability is a measure of the ability of a scale device to produce a stable, replicable, transparent and meaningful outcome related to the research focus. The commonly used method of testing reliability for the attitude scale is the  method  2 Si K Cronbach α coefficient, as represented by the formula: α = K −1 1 − S 2 .  2 Where K is the total number of questions included in the scale; Si is the total variance of the scale items, and S 2 is the total back variance of the scale items. This section uses the judgment method of scholar Wu Minglong (2010) to test the reliability of the scale, which is shown in Table 2.1. This questionnaire includes seven subscales, using SPSS for scale reliability analysis, the results are shown in Table 2.2. These results demonstrate that the internal consistency reliability coefficient indexes of the seven scales all exceed 0.7, indicating that the reliability of each scale is good, and subsequent research is justified. (2) Descriptive analysis of survey results. Based on the results of the previous reliability analysis and the needs of the actual survey, the questionnaire is revised, and the questionnaire is reissued. This time 320 questionnaires were distributed, and 309 questionnaires were recovered. The survey results show the distribution ratio of background variables (gender, age, education level, income) among the survey respondents. Among them, there are slightly more women than men; the age group of the respondents is mainly distributed between 18 and 24 years old; and the academic qualifications of the respondents are mainly distributed in colleges and universities. The monthly income of the respondents is mainly less than 3000 yuan. The survey respondents’ focus on retailer’s own brand selection is mainly concentrated on cooked food, daily necessities, apparel and footwear, beauty and skin care products, and some consumers have a strong willingness to buy hardware store brand products.

Ideal (good, reliable)

0.80α coefficient < 0.90 Very ideal (good reliability)

Good (high reliability)

0.70α coefficient < 0.80

α coefficient 0.90

Not bad

0.60α coefficient < 0.70

Not ideal, abandoned

Acceptable, add items or modify sentences

Level or construct

0.50α coefficient < 0.60

α coefficient < 0.50

Internal consistency α coefficient value

Table 2.1 Judgment principles for internal consistency and reliability coefficient index Very unsatisfactory Not ideal, reorganized or revised

Very ideal (good reliability)

Good (high reliability)

Acceptable

Reluctantly accept, it is better to add items or modify sentences

Whole scale

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 51

52 Table 2.2 Scale reliability analysis results

2 Product Strategy for Store Branding Scale

Cronbach’s alpha

Consumer psychological characteristics scale

0.806

Attribute scale of store brand products

0.773

Retailer image scale

0.813

Information value influence table

0.848

Manufacturer influence table

0.812

Perceptible quality scale

0.705

Purchase willingness scale

0.881

(3) Structural model result analysis. Using Amos software for the modeling and result analysis, Fig. 2.2 represents the bit structure ‘theoretical’ model, and Fig. 2.3 Represents the bit standardized model path coefficients. (a) Model fitting index. To evaluate the fitness degree of the structural equation model, we refer to the evaluation index and scale given by Wu Minglong (2010). The following indicators are mainly used: Absolute fitting index: x 2 /D F (chi-square degree of freedom ratio), GFI (fitness index), RMSEA (asymptotic residual mean square root) and relative fitting index: NFI (regular adaptation index), IFI (value added adaptation index), CFI (comparative

Fig. 2.2 Theoretical model

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 53

Fig. 2.3 Standardized model

Table 2.3 Model fitting index Absolute fit index

Relative fit index

Simple fitting

Index

x 2 /D F

GFI

RMSEA NFI

IFI

CFI

PNFI

PCFI

Reference

0.9

< 0.06

> 0.9

> 0.9

> 0.5

> 0.5

This model

1.696

0.910

0.048

> 0.9 0.900

0.956

0.943

0.701

0.744

adaptation index) Simple fit index: PNFI (standard adaptation index after simple adjustment), PGFI (simple fit index). It can be seen from Table 2.3 that all indexes reach the reference value required by model simulation, so it can be considered that the effect of structural equation simulation in this paper is very good. (b) Model normalization coefficient. The standardized path coefficients of all latent variable measurement indexes are greater than 0.55, and most are greater than 0.6, which means that each measurement index could be better reflect the latent variable. Table 2.4 shows that product attributes have a significant positive predictive effect on perceived quality. Retailer image, information value influence, and perception of quality have a significant positive predictive effect on purchase intention; and manufacturer influence has a significant negative prediction effect on purchase intention. Table 2.5 shows that there is no significant correlation between the manufacturer’s influence and product attributes. As to the rest, the manufacturer’s influence has a

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2 Product Strategy for Store Branding

Table 2.4 Standardized parameter inspection Estimate

S.E

C.R

P

−0.285

0.228

−1.25

0.211

product properties

0.799

0.193

4.14

***

Manufacturer influence

0.366

0.235

1.561

0.119

Information value influence

0.129

0.109

1.18

0.238

Retailer image

−0.111

0.157

−0.709

0.478

Manufacturer influence

−0.484

0.201

−2.405

0.016

0.208

0.088

2.365

0.018

Perceptible quality

0.797

0.119

6.171

product properties

−0.095

0.177

−0.535

0.593

Brand awareness

0.283

0.189

1.494

0.135

Retailer image

0.246

0.121

2.04

0.041

Estimate

S.E

C.R

P

Perceptible quality

< –-

Brand awareness

Perceptible quality

< –-

Perceptible quality

< –-

Perceptible quality

< –-

Perceptible quality

< –-

Purchase Intention

< –-

Purchase Intention

< –-

Information value influence

Purchase Intention

< –-

Purchase Intention

< –-

Purchase Intention

< –-

Purchase Intention

< –-

***

Table 2.5 Correlation Test Between Variables Relationship between variables product properties

Brand awareness

0.061

0.023

2.685

0.007

Retailer image

Brand awareness

0.121

0.021

5.681

***

Information value influence

Brand awareness

0.076

0.029

2.615

0.009

Brand awareness

Manufacturer influence

0.221

0.032

6.827

***

product properties

Retailer image

0.124

0.024

5.125

***

product properties

Information value influence

0.259

0.038

6.843

***

Retailer image

Manufacturer influence

0.142

0.024

5.966

***

Retailer image

Information value influence

0.157

0.029

5.469

***

Information value influence

Manufacturer influence

0.067

0.029

2.282

0.022

product properties

Manufacturer influence

0.019

0.022

0.868

0.386

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 55 Table 2.6 Correlation Coefficient

Relationship between variables

Estimate

Product properties

Brand awareness

0.229

Retailer image

Brand awareness

0.513

Information value influence

Brand awareness

0.215

Brand awareness

Manufacturer influence

0.811

Product properties

Retailer image

0.500

Product properties

Information value influence

0.699

Retailer image

Manufacturer influence

0.563

Retailer image

Information value influence

0.481

Information value influence

Manufacturer influence

0.177

Product properties

Manufacturer influence

0.066

significantly positive correlation with the retailer’s image, brand awareness, and information value influence. With the retailer’s image and brand awareness, there is a significant positive correlation between product attributes, brand awareness and product attributes. There is a significant positive correlation between information value influence and brand awareness, product attributes, and retailer image. Table 2.6 shows the correlation between different variables. The correlation coefficient between brand awareness and manufacturer influence is 0.811, indicating a high degree of correlation. The correlation coefficient between product attributes and the influence of information value is 0.699, indicating a high correlation between the two. Besides, variables with a lower degree of correlation are as the following: the correlation coefficient between retailer image and manufacturer influence is 0.563; the correlation coefficient between retailer image and brand awareness is 0.513; the correlation coefficient between retailer image and product attributes is 0.500; the correlation coefficient between the image of the retailer and the value of information impact is 0.481. (4) Regulation test. Most analyzes the adjustment effect of original background variables (gender, age, education, level, income) on latent variables. The author intended to use independent sample T test for dichotomous variables such as gender, and analysis of variance for categorical variables such as age, education level, and income. However, when using SPSS software for analysis of variance, if the HSD method or N-K test is used, it is usually applicable to multiple comparisons with equal numbers of groups or comparisons when the number of people in each group is not too large. The data collected in this study does not meet this requirement, so this analysis method will

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appear: Although the F value of the overall test reaches a significant value, there is no significant difference between the averages of any two groups in the multiple comparison summary table. Accordingly, this study used SPSS16.0 for multiple regression analysis. In multiple regression analysis, the independent variable should be a measurement variable. If the independent variable is a discontinuous variable, it should first be converted into a dummy variable when investing in a regression model. In summary, the following virtual variables are established for the background variables gender, age, education level, and income: male–female, under 18-(45–54) years old, (18– 24)-(45–54) years old, (25–34)-(45–54) years old, (35–44)-(45–54) years old, junior high school and below-graduate student and above; high school/technical secondary school/vocational high-tech school-graduate student and above, college/universitygraduate students and above, below 1000 yuan-6000 yuan and above, (1000–3000) yuan-6000 yuan and above, (3000–6000) yuan-6000 yuan and above. The adjustment effect of background variables on consumers’ psychological characteristics (brand awareness): the F value of the significance test of the regression model’s variation is 1.821, and the p value of the significance test is 0.05, indicating that the overall interpretation of the regression model does not reach the level of significance The influence of variables on brand awareness is not significant. The adjustment effect of background variables on the perceptible quality: the F value of the significance test of the regression model variance is 2.129, and the p value of the significance test is 0.018, which is a significance level of less than 0.05, indicating that the overall interpretation of the regression model reaches a significant level. As a result, the adjustment factors that reach the significance level are mainly concentrated on the monthly income of consumers. The standardized regression coefficient of the ‘less than 1,000 yuan -000 yuan and above’ dummy variable is 0.306 > 0, indicating that consumers with a monthly income of 6000 yuan and above have a lower perceived quality of store brands compared to consumers with a monthly income of 6000 yuan and above. Cross high. The standardized regression coefficient of the ‘(3000–6000) yuan-6000 yuan and above’ dummy variable is 0.313 > 0, indicating that comparing with consumers with a monthly income of 6000 yuan and above, consumers earning 3000–6000-yuan have a higher perception of quality. The moderating effect of background variables on purchase intention: the F value of significance test of regression model variance is 2.405, and the o value of significance test is 0.007, which is less than the significance level of 0.05, indicating that the overall explanatory variance of the regression model reached a level of significance. As can be seen, the moderating factors to reach the significance level are mainly concentrated on the monthly income of consumers. The standardized regression coefficient of the “(1000–3000) yuan -6000 yuan and above” virtual variable is 0.391 > 0, indicating comparing with consumers with income of 6000 yuan or above, consumers earning 1000 yuan to 3000 yuan are more willing to buy store brands. The standardized regression coefficient of the “3,000–6,000 yuan” virtual variable is 0.350 > 0, indicating that consumers with a monthly income of 3000–6000 yuan or more have a higher willingness to buy their own brands than consumers with a monthly income of 6000 yuan or more.

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 57

The regulating effect of background variables on product attributes: the F value of significance test of variance of regression model is 2.150, and the p value of significance test is 0.017, which is less than the significance level of 0.05, indicating that the overall explanatory variance of regression model reaches the level of significance. It can be seen that the moderating factors to reach the level of significance are mainly concentrated on consumers’ education level and monthly income. The standardized regression coefficient of the “high school/technical secondary school/vocational high-tech school- graduate student and above” virtual variable is 0.205 > 0, indicating that comparing with graduate students, consumers with high school/secondary/vocational technical school education level have a higher score on product attributes. The standardized regression coefficient of the “(3000–6000) yuan-6000 yuan or more” virtual variable is 0.259 > 0, showing that comparing with consumers earning 6000 yuan and above, consumers earning 3000–6000 yuan have a higher score on product attributes. The moderating effect of background variables on the influence of manufacturers: the F value of the significance test of variance in the regression model is 1.283, and the p value of the significance column test is 0.233, which is greater than the significance level of 0.05, indicating that the overall explanatory variance of the regression model does not reach the significance level. It shows that background variables have no significant influence on the perception of manufacturers influence. The moderating effect of background variables on the influence of information value: the F value of the regression model variance significance test is 2.430, and the p value of the significance test is 0.007, which is less than the significance level of 0.05, indicating that the overall explanatory variance of the regression model reaches the significance level. The moderating factors to reach the significance level mainly focus on the gender of the consumer. The standardized plot coefficient of the ‘male– female’ dummy variable is −0.119–0, indicating that compared with female, the male have a lower score on the influence of information value. The regulating effect of background variables on the image of retail image: the F value of the significance test of regression building type variation is 0.743, and the p value of the significance test is 0.697, which is greater than the significance level of 0.05, indicating that the variance of the overall explanation of the regression model does not reach the significant level. The effect of background variables on retailers’ image perception is not significant.

2.5.4 Research Conclusions and Management Significance (1) Result analysis. (a) Hypothesis test results. According to the adjusted model, the results of the AMOS analysis were resolved as shown in Fig. 2.4. To simplify the model and highlight the key information diagram,

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Fig. 2.4 Standardized cylindrical model

only the path number of variables related to the hypothesis test are identified, and the results of hypothesis test are summarized in Table 2.7. (b) Structural equation model results. In the previous chapter, the hypotheses proposed have been analysed by the model. First, the structural equation related to purchase intention is given according to the calculation results of the model. Perceived quality = 0.679 × product attributes.

(2.1)

purchase intention − −0.172 × retailer image + 0.504 × product attributes + 0.218 × information value influence− − 0.390 × manufacturer influence.

(2.2)

Retailer image − 0.843 × brand image + 0.771 × store image.

(2.3)

own − brand product attributes = 0.809 × price level + 0.664 × product packaging.

(2.4)

information value influence − 0.790 × information dissemination degree + 0.731 × information search behavior and meaning. Channel + 0.556 × promotion ability + 0.663 × publicity ability

(2.5)

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 59 Table 2.7 Hypothesis test results Serial number

Research hypothesis

Standardized parameter estimates

The verification results

H1

Consumer brand awareness is significantly correlated with consumer perception of quality of store brand goods

H2.1

The higher the price of store brand goods, the more consumers can perceive the quality

= 0.809*0.679 = 0.549

valid

H2.2

Better packaged store brand goods will improve the perceived quality of consumers

= 0.664*0.679 = 0.451

valid

H3.1

The higher the consumer’s brand evaluation of store brand products, the higher the consumer perception of quality

Not valid

H3.2

The better the retailer’s store image, the higher the perceived quality of consumers’ store brand products

Not valid

H3.3

The higher the brand = 0.843*0.172 = 0.145 evaluation of consumers to the store brand products, the higher the purchase intention

Valid

H3.4

The better the retailer’s store image, the more willing consumers are to buy store brand products

= 0.771*0.172 = 0.133

Valid

H4.1

The higher the degree of retailers’ dissemination of store brand information, the higher the perceived quality of consumers’ store brand products

Not valid

H4.2

There is a significant correlation between the intensity and willingness of consumers to search for information and the perceived quality of store brand products

Not valid

Not valid

(continued)

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Table 2.7 (continued) Serial number

Research hypothesis

Standardized parameter estimates

The verification results

H4.3

The higher the degree of retailers’ dissemination of store brand information, the higher consumers’ willingness to buy store brand products

= 0.790*0.218 = 0.172

Valid

H4.4

There is a significant correlation between the intensity and willingness of consumers to search for information and their willingness to purchase store brand products

= 0.731*0.218 = 0.159

Valid

H5.1

The higher the brand influence of the manufacturer, the higher the perceived quality of the consumers to the store brand products

H5.2

The higher the brand influence of the manufacturer, the lower the purchase intention of consumers to the store brand products

−0.390

Valid

H6.1

The higher the perceived quality of store brand is, the more consumers will buy it

0.743

Valid

H7.1

The relationship between perceived quality intermediary product awareness and purchase intention of store brand

Not valid

H7.2

The relationship between perceived quality intermediary product attributes and purchase intention of store brand

Valid

H7.3

The relationship between perceived quality intermediary retailers’ image and their intention to buy store brand goods

Not valid

H7.4

The relationship between perceived quality intermediary retailers’ influence on the information value of store brand goods and their intention to buy store brand goods

Not valid

Not valid

(continued)

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 61 Table 2.7 (continued) Serial number

Research hypothesis

H7.5

The relationship between perceived quality intermediary manufacturer’s influence and purchase intention of store brand

Standardized parameter estimates

The verification results Not valid

+ 0.603 × promotion ability.

(2.6)

From formula (2.2), the factors influencing the purchase intention for retailers’ store brands are: product attributes (0.504), manufacturer influence (−0.390), and information value influence (0.218), retailer image (0.172). It can be seen from formula (2.3)–(2.6) that the image of the retailer is composed of brand image (0.843) and store image (0.771). Product attributes are composed of price level (0.809) and product packaging (0.664). The influence force of information value is composed of the degree of information transmission (0.79) and the intensity of information search and intention (0.731). The manufacturer’s influence is composed of brand value (0.717), sales channel (0.775), new promotion ability (0.556), publicity ability (0.663) and promotion ability (0.603). (c) Analysis results of interaction between latent variables. Based on the data analysis results, qualitative analysis is conducted to analyse the correlation among various latent variables, and extrapolating from these correlation relationships, to further find the entry point to improve consumers’ willingness to buy retailer’s own brand goods. (i) Consumer psychological characteristics (brand awareness) perspective. The role of background variables (gender, age, income, education level) in regulating consumer brand awareness is not significant, indicating that most store brands involve many homogenized brands in market segments, and consumers have no particular preference for retailers’ store brands and do not form brand loyalty. From the analysis results, although the consumer’s brand awareness has no direct effect on the consumer’s willingness to purchase store branded goods, there is a high correlation between brand awareness and the manufacturer’s influence. The influence of the variable transmission of influence on consumers’ purchase intentions shows that if the store brand competes with similar manufacturers’ brands, the manufacturer’s brand value will be significantly reduced when the store brand value is strong. This explains why, when the prices of chicken legs in supermarkets and KFC are similar, we are more likely to buy from KFC, indicating that brand value is an intangible competitiveness factor that determines consumers’ buying behavior. (ii) Product attribute perspective.

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Part of background variables (gender, age, income, education level) in regulating product attributes is significant. Compared with graduate students, consumers with high education levels of secondary schools, technical secondary schools, and vocational high schools have higher scores on product attributes; compared with consumers with monthly income of 6000 yuan and above, consumers with monthly income of 3000–6000 Product attributes have higher ratings. This shows that some consumers with high income or high education level are less price sensitive, while some consumers with lower income or low education level are more price sensitive, so these price sensitive. One of the reasons why consumers rate their own-brand products higher is that their prices are generally lower, so they will attract such consumers to purchase. From the analysis results, the attribute variable of store brand products indirectly has a positive effect on consumers’ willingness to purchase store brands through the intermediary variable of perceived quality. It shows that the low price of the goods or good packaging does not substantially stimulate consumers’ purchase intention. The purchase behavior will only be triggered when consumers really feel that the product is useful. In addition, through analysis, we can see that product attributes have a strong correlation with the influence of information value, indicating that in the information era, consumers exposure to product information will directly affect consumers direct evaluation of products. Through analysis, we see that there is also a strong correlation between product attributes and the retailer’s image, indicating that consumer evaluation of retailer’s store brand products will also depend on the retailer’s image. Consumers will evaluate products based on their shopping environment and the quality of services they receive. (iii) Retailer image perspective. The role of background variables (gender, age, income, education level) in regulating the image of retailers has not reached a significant level, indicating that most existing retailers are seriously homogenized, it is difficult to form their own brand characteristics, and consumers have not formed loyalty to specific retailers. According to the results of the analysis, the image of retailers has a positive impact on the purchase intention of store brands. It shows that the image of the retailer will directly stimulate consumers’ purchase intention. In addition, by analyzing the relationship between the image of the retailer and the influence of the information value, it shows that the degree of consumer contact with product information will directly affect the direct evaluation of the image of the retailer. The analysis shows that there is also a strong correlation between the manufacturer’s influence and the retailer’s image, indicating that the manufacturer’s series of marketing activities in the retail store can improve the consumer’s evaluation of the retail image to a certain extent, indicating that the relationship between

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 63

manufacturers and retailers is not simply a competitive relationship, there will be an indirect cooperative relationship. (iv) Perceivable quality perspective. Part of background variables (gender, age, income, education level) in regulating the perception of quality is significant. Compared with consumers with a monthly income of 6000 yuan and above, consumers below 1000 yuan have a higher perception of the quality of own brands; compared with consumers with a monthly income of 6000 yuan and above, for consumers between 3000 and 6000 yuan the perceived quality of store brands is high. It shows that the existing market retailers’ own brand products are mainly low-priced and low-end products, which have lower technical content and lower prices, so the quality of store brand products perceived by consumers with lower income is higher. From the results of the analysis, the perceivable quality variable is the most important factor that directly affects the purchase intention, indicating that most consumers’ purchase behavior depends upon consumers’ perception of product quality. As mentioned earlier, product attributes are an important factor that affects consumers’ perception of quality. Therefore, to improve consumers’ perceived quality for own brand products, it is also necessary to start with the improvement of store brand products themselves. (v) Manufacturer influence angle. Part of background variables (gender, age, income, education level) in regulating the influence of manufacturers has not reached a significant level, indicating that consumers have a higher evaluation of brand products, of the same category, of manufacturers than retailers’ own brands. From the results of the analysis, the influence of the manufacturer has a negative impact on the purchase intention, which reflects the competitive relationship between the manufacturer’s brand and the retailer’s brand. (vi) Value influence perspective. Part of background variables (gender, age, income, education level) in regulating perception of quality is significant. Compared with women, men have lower scores on the influence of information value. This shows that the influence of store brand information dissemination on women is relatively high. From the analysis results, the influence of information value has a positive influence on purchase intention. Therefore, it shows that consumers’ exposure to own brand product information and their willingness to search will have a significant effect on consumers’ willingness to buy. (2) Management enlightenment. Although the development of store brand strategy brings new opportunities to retail enterprises, there are also many problems. In view of the current development situation, the author believes that retailers must realize that the ultimate goal of

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developing own-brand products is to meet the needs of consumers, and in the process of providing products that satisfy consumers, it requires the joint efforts of retailers, manufacturers and enterprises related to the supply chain. What retailers want to improve is not limited to their own brand products, but also to focus on the whole process of production, processing, distribution, marketing, and sales. Based on this, the author puts forward the following suggestions for the development of store brand products for China’s retailers: a) Strengthen brand management. There are two main types of retailer’s store brand strategy: hard brand and soft brand. A hard brand is a retailer’s own brand, such as Carrefour’s “Harmonie” line, that replaces the original manufacturers. Soft brand refers to keeping the original manufacturer’s brand, but supplemented by the retailer’s own brand, such as Carrefour’s “excellent” series, with the product trademark “supervised by Carrefour”. In today’s Chinese market where retailers’ store brands are less well-known and occupy the market with low prices, considering the weakness of retailers’ own brand influence, we can best consider adopting the soft brand strategy to enhance the influence of store brand products through the influence of well-known manufacturers’ brands. (b) Strengthen research, design, and development of own-brand products to form strategically differentiated business characteristics. Retailers should develop their own unique branded products, rather than blindly following suit, and produce many products like those of existing manufacturers. From the data analysis results, consumers’ loyalty to store brand products is low. The most important reason is that most store brand products are “reproductions”, which rely on low-cost advantages to obtain consumers’ purchase intention in the short term, but if cheaper replicas appear in the market, this fragile consumer relationship will collapse. Above all, the author believes that an important direction for the development of store brand products is to form consumption loyalty. An important reason for the success of Watson’s own brand is their differentiation strategy. Product design starts from the needs of consumers’ lives and develops distinctive products to meet those consumers’ needs in daily life, thereby enhancing consumer loyalty. For example, in the early days, Watsons developed a pad to prevent the chafing that women experienced from wearing high heels that damaged their feet. Once they were launched, they were well received, and the Watsons brand became the preferred brand for many women. The results of Zhao Linying’s (2012) research show that retailers attracting premium store brands will improve the perceived quality of conventional store brands, while the introduction of cheap store brands will reduce the perceived quality of conventional store brands. One of the advantages of retailers over manufacturers is that they can directly contact consumers and directly obtain consumer demand information, so they can quickly develop their own brand products that cater to market needs, based on consumers’ actual requirements. The demand is customized and developed, so consumers are willing to pay higher prices for such products. The author believes that the development of premium store brands can enhance

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 65

the retailer’s brand image, thereby attracting consumers’ attention and improving consumers’ perception of store quality. Therefore, retailers should use their own channel advantages to develop premium store brands that cater to consumer demand. (c) Improve the information dissemination of store brand products. Alongside the development of store brands, retailers need to improve the level of marketing, by strengthening product publicity. Because, relative to the manufacturers’ national brands, retailers brand sales channels are limited, usually being restricted to their own retail stores. Since advertising for the brand is limited, consumers’ brand knowledge will be insufficient, affecting consumer purchase intentions. However, retailers are not restricted in their dissemination of store brand information to passive local advertising or promotion. The author believes that improving the degree of information dissemination of own-brand products to attract more attention from consumers, retailers may actively engage in wider initiatives, such as advertising by micro-blog marketing, box marketing, gold display positioning of own-brand products in supermarkets, and repeated placement of own-brand products in different locations. For example, Watsons’ method of selling its new own products is originally a questionnaire about shopping habits. However, nearly 10 questions in the 70 questions set related to its own new products, and the questions were straightforward and clear, such as “have you ever heard of the Watsons series?” If you choose no, it will prompt you to click the link for more information. (d) Handle the relationship with the manufacturer from the perspective of game playing. Retailers should not restrict the sales of products of well-known manufacturers to develop their own brand products, which overall will have a negative impact on retailers. When the retailer’s own influence is insufficient, it can use the influence of manufacturers’ products to enhance its own image. There is a competitive relationship between manufacturers and retailers. Retailers rely on the manufacturer’s brand value to enhance their brand image, but in turn, when the manufacturer’s brand influence is high, it will inhibit consumers from purchasing store brands products. The willingness to buy a given brand is a game relationship. Many scholars have analyzed the competitive game relationship between manufacturers and retailers from a quantitative perspective. The author believes that reasonable pricing strategies, brand strategies, and channel competition structures can alleviate this, and these methods can be considered when conflicts occur. (e) Improve shopping environment and shopping experience. First, we should improve store decoration, goods placement, shelf layout, etc. to create a warm atmosphere for consumers to shop. Second, improve the overall service level which is key to the process of raising consumption levels. This must include the whole process of service quality, as the perception of high-quality service enhances the quality perception of all store brand commodities. This forms positive consumer evaluations across the board. Retailers should invest in their staff and strengthen the training of service personnel, to develop and implement strategies that reassure

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consumers through helpful and friendly shopping guarantee refund systems and so forth. (f) Improve product quality and enhance consumers’ perceived quality of ownbrand products. The analysis results show that consumers cannot be motivated to buy goods only by high or low prices, or good or bad packaging: it is only when consumers really feel that products are useful will they buy them. Therefore, the property variable of store brand products indirectly exerts a positive influence on consumers’ willingness to buy store brand products through the intermediate variable of perceived quality. To substantially improve consumers’ purchase intentions and comprehensively improve consumers’ perception of quality, the first step is to really improve product quality, which is the key to brand success. Then, through brand management, product design, information dissemination related to own-brand products, and a positive shopping environment, consumers’ perception of the quality of own-brand products will be realized.

2.5.5 Deficiencies in the Research In the theoretical analysis and empirical research, this paper tries to be scientific and strict, but due to unavoidable limiting factors, the research of this paper is, in parts, inadequate. (1) Limitation of sample size. There is no unified, clear, academic conclusion as to how many samples should be included in the construction of a structural equation model. In this paper, based on the recommendations of Rigdon (2005), although the sample size meets the basic requirements, the explanatory power of the sample size of this study given in the model needs to be improved in view of the requirement that SEM is applicable to the statistical analysis of larger samples. (2) Ignore the influencing factors of consumer perceived risks. In the reliability verification part of the main questionnaire, after analysis, it is found that most consumers purchased store brand products concentrated in lowpriced products such as stationery, cooked food, daily necessities, beauty skin care products, and snacks. The store brand’s ability to interpret perceived quality is weak. To improve the reliability of the questionnaire, we deleted the part of the question about consumers’ perception of risks. When the category of the researched store brand product changes, the research conclusion may change. (3) Limited factors to be considered.

2.5 Empirical Study of the Factors Influencing Purchasing Intention for Retailer Store Brands 67

There are many factors that affect consumer’s purchase intention for store brands of retailers, besides psychological characteristics, products attributes of store brands products, retailer’s image, information value influence, manufacturer influence, the perception of quality of store brands products. The author has not analyzed all factors. In addition, this study take the domestic retail stores as a whole to analyze without doing a comparison analysis between foreign retail stores and local retail stores. From these two perspectives to study the factors that affect consumer’s purchase intention, it is more beneficial for local retailers to develop their store brands. (4) No research on sub-category of store brand products is conducted. This section does not delve into specific products or categories. This study does not analyze the classification based on store brands categories. However, it is to study the influence of consumer psychological characteristics, private brand product attributes, retailer image, information value influence, manufacturer influence, and store brand product perception of quality on store brand purchase intention from the overall perspective. The research conclusions inevitably have some shortcomings. (5) The influence of price on purchase intention is not discussed deeply enough. According to the general situation, high prices should improve consumers’ perception of quality and reduce consumers’ willingness to buy, but the results of this study show that although price has a positive impact on perceptible quality, the effect of price on willingness to purchase is not significant The author believes that part of the reason is that most of the store brand products in the Chinese market are functioning within low-price strategies, resulting in consumers being insensitive to the prices of such low-priced store brand products. As a result, the relationship between specific prices and purchase intentions needs further analysis.

2.6 Introduction of store brands considering product cost and shelf space opportunity cost The section studies the introduction of store brand (SB) when the producing cost, shelf space allocation and base-line sales are taken into consideration. We construct a Stackelberg model, in which one retailer, acting as the leader, sells a national brand (NB) and its store brand, and maximizes category profit by allocating shelf space and determining the prices for the store brand and national brand products. Meanwhile, an NB manufacturer, acting as the follower, maximizes its profit based on the decisions of the retailer. Our results demonstrate that the product cost of the SB (NB) and the shelf-space opportunity cost are the dominating factors to decide the optimal pricing strategy. If the two costs are low, then the optimal pricing strategy is the me-too strategy (competitive strategy); otherwise, the optimal pricing strategy is the differentiation strategy. There exists threshold of the product cost, shelf-space opportunity cost and baseline sales to decide the pricing strategy and introduction of SB.

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2.6.1 Introduction Store brands account for 14% of total retail sales in US supermarkets, and their share ranges from 20 to 45% of total retail sales in the UK, Belgium, Germany, Spain, and France (Morton and Zettelmeyer 2004). Why are retailers eager to introduce store brands? Store brands are the exclusive brands for which the retailer is responsible for shelf placement, pricing, quality, packaging, and promotion. In contrast to a national brand (NB), which is provided by the manufacturer, a retailer’s SB product is entirely and independently controlled by the retailer, including research and development, design, sales, and market management. Retailers expand their market share by introducing SB products to extend their product line and to meet demands in different market segments (Mills 1999; Kumar 2007). Moreover, retailers develop their SB products to compete with other retailers and enhance consumer loyalty (Bontems et al. 1999). Retailers also introduce SBs into the original sales category to obtain profits from their sales and, more importantly, to leverage negotiation power with the manufacturer (Narasimhan and Wilcox 1998). Furthermore, Groznik and Heese (2010a) demonstrate that retailers are in a position to gain competitive advantages in the supply chain and increase profits by introducing SB products. Lamey et al. (2007) also reveal that retailers are more likely to increase their SBs market share when the economy is suffering and shrinks. However, according to Nielsen’s SBs report (the data come from Nielsen global store brand report November 2014), the share of SBs in the Asia–Pacific market is generally low, and China’s market share of SBs is only 1–3%. Hence, the SB market potential is extremely large and therefore attractive to growing numbers of retailers in China. Increasing numbers of retailers, including international brand retailers such as Wal-mart, ALDI, and Lidl and domestic brand retailers in China such as Lianhua, Vanguard, and Wumart, have begun to introduce SB products. ALDI opened an online store on TMall on Mar. 20, 2017. In ALDI supermarkets, most products are SBs, and generally, for each given category, there is a maximum of two other brands. ALDI’s main competitor, Lidl supermarket, which is famous as an SB retailer in Germany, opened an online store in China on Sept. 28, 2017. Interestingly, the two retailers entered China’s market via online stores. Usually, the retailer develops its own SB products (see Fig. 2.1). Manufacturer A sells its NB to the distributor at product price, and the distributor delivers the NB to retailers at wholesale price, and then the retailer sells the NB products at retail prices to the consumers through displays offline (shelf) or online (web store). Now, ALDI, as a powerful retailer, obtains the same quality of goods directly from manufacturer B, and it wants the product itself without any brand premium. Ten, it sells these goods as SB products at retail prices P to consumers through the same displays. This is why ALDI’s product quality is as good as that of any other retailer, while its prices are not high as others’. Therefore, it is natural to consider the retailer’s purchasing cost and the display method as the dominant factors that affect retailers’ profits and price positioning strategies. Motivated by these issues, we propose the following research questions:

2.6 Introduction of store brands considering product cost and shelf space opportunity cost

69

1. Which factors will affect the price positioning strategy of a powerful retailer that is a leader and has the power to introduce SBs? 2. What is a suitable price positioning strategy for a dominant retailer following the entry of an SB? 3. Who will bene t from the different price positioning strategies? To answer these questions, in this paper, we propose a Stackelberg model involving an NB manufacturer (this is the distributor in Fig. 2.1) and a retailer selling its store brand and the NB. The decision variables include the following: the wholesale price for the manufacturer, the retail prices of both brands, and the shelf space that the retailer allocates to each brand. We assume that the retailer is the leader in the Stackelberg game, and we characterize the resulting equilibrium in terms of price, shelf space, and profit for both players. Generally, there are three-tiered store brands: economy store brands, standard store brands, and premium store brands (PSBs). Consumers generally perceive store brands to be lower quality and higher risk products. Geyskens and Steenkamp (2014) note that initially retailers provide low-quality and low-cost SB products mainly as substitutes for NB products. Furthermore, standard SBs imitate the quality of leading NB products for slightly lower prices. With the improvement in the quality of SBs, PSBs have become increasingly common in retail stores. Seenivasan et al. (2016) report that “store brands have also gained in consumer esteem, with almost 77% of American consumers considering them to be as good as or better than national brands”. The quality of some SB products has caught up to that of NB products, but SB pricing is usually lower than NB pricing. Nenycz-Tiel and Romaniuk (2016) and Hara and Matsubayashi (2017) show that the quality of some PSB products has caught up with that of NB products. In response to the introduction of SBs, manufacturers of NBs are searching for ways to expand their business and help retailers introduce their SB products. Hara and Matsubayashi (2017) find that, in essence, some NB manufacturers have gradually become original equipment manufacturers (OEMs) of premium SBs. In this section, we study the introduction of standard SBs. To the best of our knowledge, no previous papers have studied the impact of product costs and shelf space opportunity costs on the entry of SBs. We fill the gap by proposing pricing strategy games between a retailer and a manufacturer when the retailer introduces SBs. There are two streams of literature, those on product cost and shelf space opportunity cost, that relate to the present research. The frameworks employed in previous papers typically assume that the manufacturer who provides the SB product to the retailer does not play any strategic role, and thus they set the retailer’s purchasing cost of the store brand at zero (see Raju et al. 1995; Amrouche and Zaccour 2007). However, in a recent contribution, Fang et al. (2013) study a wholesale price contract between an NB supplier and retailer, and they consider the cost per unit quality (CPUQ), which can determine whether the retailer can introduce the SB and whether the supplier can affect and deter its introduction. In another recent work, Mai et al. (2017) study an extended warranty as a means of coordinating the quality decisions for SB products. They consider the unit repair cost and unit production cost, which ensure that the product has a zero

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probability of failure during the extended warranty period. In contrast, our research shows that the product cost is the dominant factor that affects the price positioning strategy in the introduction of SBs by a powerful retailer. The Stackelberg equilibrium solution will be adopted in this work. Amrouche and Zaccour (2007) and Li et al. (2013) study the shelf space allocation and pricing decisions in the marketing channel by applying static and dynamic games. Kurtulu¸s and Toktay (2011) construct a supply chain with two manufacturers and one retailer and study a three-stage sequential dynamic game. They demonstrate that a retailer, acting as the leader in the supply chain, can use category management and categorize shelf space to control the intensity of competition between manufacturers. However, there they do not consider the impact of SBs or shelf space effects in the demand function. Kuo and Yang (2013) develop a competitive shelf space model for NBs versus SBs based on Kurtulu¸s and Toktay’s settings and find that if the cross-price effect is not too large, the retailer should position its SB’s quality closer to that of the NB. Kuo and Yang consider the shelf space opportunity cost in operation and channel conflict, but they do not consider product cost. In retailing, the shelf space allocation problem is crucial and has been studied by both operations research and marketing scholars for years. Corstjens and Doyle (1981) develop a model to address the shelf space allocation problem. Bultez and Naert (1988) and Dreze et al. (1994) con rm that shelf space has a positive effect on a retailer’s sales and profitability. Irion et al. (2012) develop a shelf space allocation optimization model that combines essential in-store costs and considers space-and cross-elasticities to study shelf space management. Valenzuela et al. (2013) propose that consumers hold vertical schemas that higher is better on shelves and that more expensive pro-ducts should be placed higher on a display than cheaper pro-ducts. They test whether retailer shelf space layouts reflect consumer beliefs and illustrate that consumers’ beliefs about shelf space layouts are not always reflected in the real marketplace. These studies all focus on the shelf space allocation of general products; however, they do not consider SBs or analyze the pricing issue. However, the competition for shelf space is prevalent in supermarkets, especially for new product introductions. Dreze et al. (1994) demonstrate that retailers want to maximize category sales and profits and must allocate a certain amount of shelf space to do so. Moreover, manufacturers want to maximize the sales and profits of their NBs and therefore always want more and better space to be allocated to their NBs. Thus, retailers often earn a positive profit margin on each product they sell in addition to collecting the slotting fees, given that their role goes beyond shelf space leasing (Martínez-de-Albéniz and Roels 2011). Because the slotting fee includes not only shelf space leasing but also logistics, merchandising, and promotion, among other services, shelf space is so scarce that manufacturers have to provide retailers with slotting fees to secure shelf space for their SBs. Our research assumes that the shelf space opportunity cost represents a slotting fee that is a dominant factor affecting the introduction and price positioning strategy of SBs. In contrast to all of the above research streams, our paper discusses the store brand entry problem under varying product cost and SB opportunity scenarios. Our results provide guidance for retailers regarding marketing strategies under different

2.6 Introduction of store brands considering product cost and shelf space opportunity cost

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product cost, shelf space opportunity, and baseline sales settings. This is one of our contributions to the existing literature. The remainder of the paper is organized as follows: In Sect. 2.6.2, we construct an economic pro t model for the supply chain under study. In Sect. 2.6.3, we derive the Stackelberg equilibrium. In section, we seek the optimal pricing strategy by conducting a numerical study with different scenarios. In section, we conclude the paper.

2.6.2 The Model We consider a two-stage supply chain that consists of one retailer and one manufacturer. The manufacturer provides one product in a given category and sells it to consumers through the retailer. The retailer maximizes profit by allocating shelf space to each brand. We normalize the total shelf space available for each category to one. S denotes the share of this space that is dedicated to the SB, and S > 0. We assume that the total shelf space is allocated; thus, the share of the NB is 1 − S (see [13]). We assume that the demand for each brand depends on the exposure each receives, as measured by shelf space and the price of each brand. Here, we assume the NB baseline sales are normalized to one and the baseline sales of the SB are captured by the parameter αs ∈ (0,1) (see Raju et al. 1995). The demands of the two products are as follows: Dn =

1 ((1 + αs )(1 − S) + ψ(Ps − Pn ) − Pn ) 1 + αs

(2.7)

1 ((1 + αs )S + ψ(Pn − Ps ) − Ps ) 1 + αs

(2.8)

Ds =

where Dn and Ds represent the demand for the NB and for the SB, respectively. 1 + α s represents the total baseline sales (potential market) of the NB and SB (Groznik and Heese 2010b). ψ ∈ (0,1) denotes the cross-price competition between the NB and SB. Pn and Ps are the reference prices for each, respectively, which is a common assumption in the literature (see Raju et al. 1995; Kurtulu¸s and Toktay 2011; Rajendran and Tellis 1994). In addition, the demand for each brand increases in its proportion of shelf space. The rationale is that if a product has more shelf space, the probability of being noticed, perceived, and selected by the consumer will increase (see Yang and Chen 1999; Martín-Herrán and Taboubi 2005; Eisend 2014; Hübner and Schaal 2017). Furthermore, many studies demonstrate that each brand’s demand increases in the competing brand’s price and decreases in its own price (see Raju et al. 1995; Kuo and Yang 2013). According to (2.7) and (2.8), the marginal price effect on demand depends on the shelf space allocated to the brand. This specification has been used extensively in the literature (see Nogales and Suarez 2005; Zhao et al. 2016).

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In addition, c is the unit cost. As in Nenycz-Thiel and Romaniuk (2016) and Hara and Matsubayashi (2017), we assume that the costs of the SB and the NB are equal. To simplify the computation, we assume that there is no significant quality differentiation between the SB and NB, and the prices of the two products satisfy Ps = γ Pn , 0 < γ < 1

(2.9)

where Ps and Pn represent the retail prices of the SB and NB, respectively. γ is the price difference coefficient. Parameter c represents the cost of the SB and NB. Assuming that the manufacturer and the retailer are profit maximizers, their objectives are as follows: max  M = (w − c)Dn max  R = Ds (Ps − c) + m Dn −

(2.10) k S2 2

(2.11)

where M and R represent the profit of the manufacturer and of the retailer, respectively, and Dn and Ds are given by (2.7) and (2.8), respectively. w represents the wholesale price, and m represents the unit markup from selling unit NB; therefore, Pn = w + m. Because shelf space is a scarce resource, space allocated to one product means relinquishing profits from another product. If the shelf spaces of NBs are occupied by the SB, there exists the loss of the opportunity cost for the retailer. That means the retailer will forgo the slotting fee for the new NB from manufacturer. Assume that the retailer incurs a shelf space cost, i.e., the opportunity cost k of the shelf space, where k > 0. The shelf space proportion, S, is a continuous endogenous variable. The assumption is standard in economics (see Kurtulu¸s and Toktay 2011; Martín-Herrán et al. 2005). In this part, we assume that the retailer is the leader and the manufacturer is the follower. The sequence of events is as follows: The retailer (leader) first announces its marketing strategy, including the unit markup value m and shelf space proportion S. The manufacturer reacts to this information by deciding the wholesale price w.

2.6.3 Stackelberg Equilibrium To determine the reaction function of the manufacturer to the retailer’s unit markup value m and shelf space proportion S, we must solve the following optimization problem. First, we consider the manufacturer’s problem. Substituting (2.7) and (2.9) into (2.10) yields M =

(w − c)(ψ(γ (m + w) − m − w) − m + (1 − S)(αs + 1) − w) αs + 1

(2.12)

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First-order conditions are d M =0⇔ dw c((γ − 1)ψ − 1) − γ mψ + mψ + m + (S − 1)αs + S − 1 w(m, S) = 2(γ − 1)ψ − 2

(2.13)

and d 2M 2((γ − 1)ψ − 1) (K(K + ψ − 1) − 4ψ + 1)/Kψ, then the retailer’s profit function R is jointly concave in m and S, where 1/3   K = −4ψ 2 + 2 4ψ 4 + 4ψ 3 − ψ 2 + 6ψ − 1

(2.17)

Proof The first and second order derivatives of R with respect to m and S are as follows: ∂ 2  R (m, S) γ 2 + (1 − γ )(2 − γ )ψ + 2 αs + 1, then the retailer’s profit function R is jointly concave in m and S.

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Proof . The first- and second-order derivatives of R with respect to m and S are as follows:   2 γ 2 + (γ − 1)2 ψ + 1 ∂ 2  R (m, S) = − αs + 1, then |H | =

  2k(γ 2 + γ − 1)2 ψ + 1 − (γ − 1)2 > 0 αs + 1

(2.54)

H is a negative definite integral and the profit function is concave. By the concavity of R, the first-order condition yields ∂ R = 0 ⇔ m(w, S) ∂m cγ ψ + cγ − cψ + αs ((γ − 1)S + 1) + (γ − 1)S − 2γ 2 wψ − 2γ 2 w + 3γ wψ − wψ − w + 1   = 2 γ 2 (ψ + 1) − 2γ ψ + ψ + 1

(2.55) −c + (γ − 1)m + γ w ∂ R = 0 ⇔ S(w, m) = ∂S k

(2.56)

Thus, m(w) = − +

−cγ + cγ k + αs (−γ c + c + k + (γ − 1)γ w)   (γ − 1)2 − 2k γ 2 + (γ − 1)2 ψ + 1 + (γ − 1)2 αs (γ − 1)kψ(c − 2γ w + w) + c − 2γ 2 kw − kw + k + γ 2 w − γ w   (γ − 1)2 − 2k γ 2 + (γ − 1)2 ψ + 1 + (γ − 1)2 αs (2.57)

2.6 Introduction of store brands considering product cost and shelf space opportunity cost

  −c γ 2 + (γ − 1)2 ψ + γ + 2 + γ + (γ − 1)αs   S(w) = − (γ − 1)2 − 2k γ 2 + (γ − 1)2 ψ + 1 + (γ − 1)2 αs   w 2γ 2 + (γ − 1)2 ψ + γ + 1 − 1   + (γ − 1)2 − 2k γ 2 + (γ − 1)2 ψ + 1 + (γ − 1)2 αs

79

(2.58)

Then, M =

(w−c)(ψ(γ (m+w)−m−w)−m+(1−S)(αs +1)−w) αs +1

(2.59)

Substituting (2.57) and (2.58) into (2.59) yields M =

(M1 + M2 + M3 )(c − w) M4

(2.60)

where    M1 = c (γ − 1)2 kψ 2 + (γ − 1)2 kψ + γ (γ − k + 2) + 1  M2 = αs c(γ + 1)2 + k 2γ 2 (ψ + 1) − 3γ ψ + ψ +1) − (γ  − 1)γ αs − 2(γ (γ + γ w + w − 1))) M3 = γ + k 2γ 2 (ψ + 1) − 3γ ψ − w(−γ ψ + ψ + 1)2   +ψ + 1) − γ (γ + 2(γ + 1)w)  M4 = (αs + 1) (γ − 1)2 − 2 k γ 2 + (γ − 1)2 ψ + 1 + (γ − 1)2 αs

(2.61)

The first-order optimality conditions are w∗ =

w1 + w2 + w3 w4

(2.62)

where w1 = c((γ +1)(3γ + 1) + (γ − 1)((γ − 1)ψ − 1)(k(2ψ + 1))) w2 = αs (3γ + 1)(c(γ + 1)) + 2γ + 2γ 2 (kψ + k − 1) −3γ kψ + kψ + k − (γ − 1)γ αs ) w3 = γ + γ 2 (2(k(ψ + 1)) − 1) − 3γ kψ + kψ + k  w4 = 2 2γ (γ + 1) + k(−γ ψ + ψ + 1)2 + 2(γ (γ + 1))αs

(2.63)

Substituting (2.62) into (2.57) and (2.58) yields m∗ = −

   c m 1 + k 2 (m 2 − m 3 ) + αs (c∗m 7 + (γ (m 11 − m 10 ))αs − 3∗m 6 + m 8 + m 9 )

m4 + m5 − m6 + m 12 ∗m 13

where

m 12 ∗m 13

(2.64)

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m 1 = 3(γ (γ + 1))(γ − 1)2     + k γ −5γ 3 + 18γ − 16 ψ + γ −6γ 3 − 5γ 2  +γ − 7) + 2(γ − 1)4 ψ 2 + 3ψ + 1 m 2 = −2(γ (γ ((γ − 5)γ + 8) − 7))ψ + γ (2(γ − 1)γ + 3) − 6ψ − 1 m 3 = 3(γ − 1)2 (γ (2γ − 3) + 3)ψ 2 − 4(γ − 1)4 ψ 3  m 4 = k 2 −4γ 4 (ψ + 1)2 + 12γ 3 (ψ(ψ + 1)) −γ 2 (ψ(11ψ + 8) + 4) + 2γ (ψ(ψ + 1)) + (ψ + 1)2



m 5 = 2k(γ (γ (γ (2γ (ψ + 1) − 5ψ − 2) + 4ψ + 3) − ψ + 1)) m 6 = (γ − 1)2 γ 2

   m 7 = 6(γ (γ + 1))(γ − 1)2 + k γ −5γ 3 + 18γ − 16 ψ     +γ −6γ 3 − 5γ 2 + γ − 7 + 2(γ − 1)4 ψ 2 + 3ψ + 1  m 8 = k 2 −4γ 4 (ψ + 1)2 + 12γ 3 (ψ(ψ + 1))  −γ 2 (ψ(11ψ + 8) + 4) + 2γ (ψ(ψ + 1)) + (ψ + 1)2 m 9 = 4k(γ (γ (γ (2γ (ψ + 1) − 5ψ − 2) + 4ψ + 3) − ψ + 1)) m 10 = 3c(γ + 1)(γ − 1)2 + 3γ (γ − 1)2 − γ (γ − 1)2 · αs m 11 = 2(k(γ (γ (2γ (ψ + 1) − 5ψ − 2) + 4ψ + 3) − ψ + 1))      m 12 = 2 (γ − 1)2 − 2 k γ 2 + (γ − 1)2 ψ + 1 + (γ − 1)2 αs m 13 = 2γ (γ + 1) + k(−γ ψ + ψ + 1)2 + 2(γ (γ + 1))αs S∗ =

S1 S3 (S4 + S5 ) − S2 S6 S7

(2.65) (2.66)

where   S1 = c γ 2 + (γ − 1)2 ψ + γ + 2 − γ − (γ − 1)αs + 1    S2 = (γ − 1)2 − 2 k γ 2 + (γ − 1)2 ψ + 1 + (γ − 1)2 αs S3 = γ 2 (ψ + 2) − 2γ ψ + γ + ψ + 1 S4 = c((γ + 1)(3γ + 1) + (γ − 1)((γ − 1)ψ − 1)(k(2ψ + 1))) + γ + γ 2 (2(k(ψ + 1)) − 1) − 3γ kψ + kψ + k  S5 = αs (3γ + 1)(c(γ + 1)) + 2γ + 2γ 2 (kψ + k − 1) −3γ kψ + kψ + k − (γ − 1)γ αs )      S6 = 2 (γ − 1)2 − 2 k γ 2 + (γ − 1)2 ψ + 1 + (γ − 1)2 αs S7 = 2γ (γ + 1) + k(−γ ψ + ψ + 1)2 + 2(γ (γ + 1)) · αs Thus,

(2.67)

2.6 Introduction of store brands considering product cost and shelf space opportunity cost

Dn∗ =

Pn∗ = w ∗ + m ∗

(2.68)

Ps∗ = γ Pn∗

(2.69)

    1  (1 + αs ) 1 − S ∗ − Pn∗ + ψ Ps∗ − Pn∗ 1 + αs

Ds∗ =

81

  1  (1 + αs )S ∗ − Ps∗ + ψ Pn∗ − Ps∗ 1 + αs   ∗M = w ∗ − c Dn∗

  1  2 ∗R = m ∗ Dn∗ + Ps∗ − c Ds∗ − k S ∗ 2

(2.70) (2.71) (2.72) (2.73)

The data used to support the findings of this study are included within this part. All authors made substantial contributions to this paper. Yongrui Duan developed the original idea and provided guidance. Zhixin Mao designed the game and calculated the process. Jiazhen Huo provided additional guidance and advice. All authors have read and approved the final manuscript.

2.6.4 Numerical Studies The purpose of this section is to reveal the effects of the product cost and the baseline sales of SBs on profitability and shelf space allocation. (1) Scenario 1: Varying product cost of SB. In this subsection, we will study the relationships between the product cost c and the parameters, such as the decision variables, demand, price and the profitability of SBs and NBs. Let αs = 0.8, ψ = 0.8, k = 0.1, and γ = 0.7; 0.8; 0.9. Figure 2.5 (a) shows that SB shelf space S and markup m decrease as product cost c increases and conversely, the wholesale price of NB w increase. In Fig. 2.5 (b), the demand of SB, Ds, and total demand, D, decrease as product cost c increase and meanwhile, the demand of NB, Dn increase. Figure 2.5 (c) shows that product prices of both NBs and SBs increase as product cost c increase. That is to say, most of the decision variables and other parameters (except the demand of NB) are sensitive to product cost c, and any slight change in the product cost results in a great change in the parameters (such as S, m, w, D, Ds). In Figs. 2.6 and 2.7, we aim to study (2.7) the relationship between the product cost c and the profit of retailer and manufacturer when the SB is introduced, as well as (2.8) the profit of the retailer and manufacturer before and after the introduction of SB.

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Fig. 2.5 Variable results for different values of c

Fig. 2.6 Retailer’s total profit for different values of c

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2.6 Introduction of store brands considering product cost and shelf space opportunity cost

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Fig. 2.7 Manufacturer’s total profit for different values of c

Figure 2.6 (a) shows that the retailer’s total profit decreases as product cost c increases. There exists a cost threshold c = 0.425 (whereαs = 0.8, ψ = 0.8, k = 0.1, and γ = 0.7 or 0.9). The retailer should use a different pricing strategy for different product costs. If the cost is less than the threshold, the me-too strategy is preferred; if the cost is larger than the threshold, the differentiation strategy can increase profit. In other words, when the product cost c is high, the retailer uses a differentiation strategy; however, the me-too strategy is better when the product cost c is low. Figure 2.6 (b) demonstrates the profit before and after the introduction of SB when c changes. It shows that the total profits of the retailer decrease as product cost c increases. There exists a cost threshold˜ c = 0.389 (where αs = 0.8, ψ = 0.8, k =

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0.1, and γ = 0.8) such that if the cost is less than the threshold, the introduction of the SB is profitable; if the cost is larger than the threshold, the introduction of the SB will not increase profit, and the retailer will not have enough incentive to introduce the SB. However, the retailer is less affected when differentiation strategies are used. Within the range [0,˜ c], the retailer uses differentiation strategies, and its total profit will reach a minimum value and then rise again. That is, differentiation strategies are used for the, and the introduction of SB will bring more profits for the retailers, due to the existence of big price differential. This conclusion is different from the previous research; Sayman et al. (2002) do not find the significant effect of price differential; in present paper, we demonstrate that the significant effect exists when considering the product cost. Figure 2.7 demonstrates that the manufacturer’s profits will be very low when the retailer introduces the SB. However, the manufacturer’s total profits increase as product cost c increases. Furthermore, when the retailer introduces the SB and the product cost increases, the manufacturer’s total profit is higher than before. However, this situation will not occur because it is not profitable for the retailer when parameter c is too high; the retailer, as a leader, will not introduce the SB in this interval. To put it differently, the retailer will prudently consider whether to introduce the SB. (2) Scenario 2: Varying Shelf Space Opportunity Cost of SBs. In this subsection, we will study the relationships between the shelf space opportunity cost of SB k and the parameters, such as decision variables, demand, price, and the profitability of SBs and NBs. Let αs = 0.8, ψ = 0.8, c = 0.1, and γ = 0.7;0.8;0.9. The results in Fig. 2.8 (a) show that when in the high competition situation (ψ = 0.8) with high baseline sales of SBs (αs = 0.8), increasing the opportunity cost parameter of shelf space k leads to a sharp decrease in the retailer’s shelf space proportion S, as low decrease in the unit markup value m, and a sharp increase in the wholesale price w. Figure 2.8 (b) demonstrates that increasing the opportunity cost of shelf space k causes a sharp decrease in the retailer’s demand for SBs but an increase in the manufacturer’s demand for NBs and, subsequently, a sharp increase in the total demand for the product category. Figure 2.8 (c) indicates as low increase in the sales price for both SBs and NBs when k increases. In Figs. 2.9 and 2.10, we aim to study (2.7) the relationship between the opportunity cost of the shelf space k and the profit of retailer and manufacturer when the SB is introduced, as well as (2.8) the profit difference of the retailer and the manufacturer before and after the introduction of SB. Figure 2.9 (a) shows that the retailer’s total profits decrease as the opportunity cost of the shelf space k increases. Meanwhile, the retailer should use a different pricing strategy when the opportunity cost takes a different value. Particularly, there exists a threshold k= 0.252 (where αs = 0.8, ψ = 0.8, c = 0.1, and γ = 0.7 or 0.9), such that if the opportunity cost of the shelf space is less than the threshold, the retailer uses the me-too strategy; if the opportunity cost of the shelf space is larger than the threshold, a differentiation strategy is optimal. Figure 2.9 (b) visualizes the difference in profit before and after the introduction of the SB; the retailer’s total profits decrease as opportunity cost parameter of shelf space k increases. There exists a threshold˜ k =

2.6 Introduction of store brands considering product cost and shelf space opportunity cost

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Fig. 2.8 Variable results for different values of k

0.374 (where αs = 0.8, ψ = 0.8, c = 0.1, and γ = 0.8). When the opportunity cost parameter k is high, the retailer uses a differentiation strategy; on the other hand, the competitive strategy (me-too strategy) is better when parameter k is low. Figure 2.10 demonstrates that the manufacturer’s total profit increases as the opportunity cost of shelf space k increases. That is to say, when the retailer introduces SB, the manufacturer’s total profit is higher than before. However, this situation will not occur because the retailer is not profitable when parameter k is high; in this interval, the retailer, as a leader, will not introduce the SB. (3) Scenario 3: Varying Baseline Sales of SBs. Let ψ = 0.8, k = 0.8, c = 0.1, and γ = 0.8; we draw the plots for the relationships between the baseline sales of SBs, αs, and parameters such as the decision variables, demand, price, and profitability of SBs and NBs. Figure 2.8 (a) indicates that in a high-competition situation where ψ = 0.8 and the low-cost parameters k = 0.1, c = 0.1, and γ = 0.8, an increase in the baseline sales of SB αs leads to a slow increase in the SB’s proportion of retail shelf space, S, and a sharp increase in the unit markup value, m. Meanwhile, the manufacturer’s wholesale price, w, increases slowly as baseline sales of the SB, αs, increases. Figure 2.8 (b) demonstrates that increasing the baseline sales of SBs αs causes a sharp increase in the retailer’s demand for SBs, a decrease in the manufacturer’s demand for NBs, and, subsequently, an increase in the total

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Fig. 2.9 Retailer’s total profit for different values of k

demand for the product category. When the retailer, as a leader, introduces the SB, actual sales increase as the baseline sales increase. That is to say, as the baseline sales of the SB increase, the retailer can increase the proportion of shelf space allocated to the SB; meanwhile, the retailer is better o because it can obtain more pro t from the manufacturer’s product. In this situation, the increase of the wholesale price can be perceived as compensation for the shelf space occupied by the SB, and the manufacturer deliberately increases the wholesale price to o set the manufacturer’s loss from NB sales. Meanwhile, Fig. 2.11 also

2.6 Introduction of store brands considering product cost and shelf space opportunity cost

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Fig. 2.10 Manufacturer’s total profit for different values of k

demonstrates that as increases, the retailer can increase the SB’s proportion of shelf space. The actual demand of the SB increases more quickly than that of the shelf space, which aligns with the conclusion in Eisend (2014), that a small increase in shelf space elasticity can also promote a rapid growth in product sales. In Fig. 2.12, we study the relationship between the retail price of SB (NB) and the baseline sales of the SB. Figure 2.12 (a) shows that in a high-competition situation where ψ = 0.8 and the low-cost parameters k = 0.1, c = 0.1, and γ = 0.8, the prices of both the SB and NB increase as the baseline sales of the SB increase. Figure 2.12(b) demonstrates that when the retailer introduces SB and implements the me-too strategy (competitive strategy), the price of the NB is lower than the

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Fig. 2.11 Variable results for different values of αs

Fig. 2.12 Price for different values of αs

differentiation strategy, which aligns with the conclusion in Gabrielsen and Sørgard (2007), that the introduction of SB leads to price concessions from the NB. Eventually, it will be beneficial for consumers to purchase the NB. In Fig. 2.13, we study the profitability of retailer in different marketing environments. Let ψ = 0.8, k = 0.1; 0.35, c = 0.5; 0.1, and γ = 0.7; 0.8; 0.9. As αs increases, the retailer’s total profit increases; however, there is a significant difference when the retailer uses different pricing strategies. (1) When c or k is small, the retailer uses the me-too strategy, and the profit of the retailer will increase. (2) When c or k is large, then the differentiation strategy (i.e., = 0.7) results in more profit than the me-too strategy (i.e., γ = 0.9). In Fig. 2.14, we study the difference in profit before and after the introduction of SB. The result shows that if retailer introduces the SB, there exists the threshold αs = 0.455 (where ψ = 0.8, k = 0.1, c = 0.1, and γ = 0.8), such that (1) under the differentiation strategy, if αs is greater than 0.492 (where ψ = 0.8, k = 0.1, c = 0.1, and γ = 0.7), it will be profitable to introduce the SB; (2)under the me-too strategy, if αs is greater than 0.414 (where ψ = 0.8, k = 0.1, c = 0.1, and γ = 0.9), it will be

2.6 Introduction of store brands considering product cost and shelf space opportunity cost

Fig. 2.13 Retailer’s total profit for different values of αs

Fig. 2.14 Comparison between the periods before and after the introduction of the SB in αs

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profitable to introduce the SB. In Fig. 2.15, we study the differences in profit before and after the introduction of SB. The result demonstrates that when the retailer, as the leader, introduces the SB, the manufacturer will gain little profit; however, a comparison of the profit before and after SB is introduced shows that the profit of the manufacturer reduces considerably. In other words, when the retailer is the leader, the introduction of the SB is detrimental to the manufacturer, and this result aligns with Kuo and Yang (2013). In addition, Fig. 2.15 (a) demonstrates that when the retailer uses the me-too strategy (γ = 0.9), as αs increases, the manufacturer’s profits gradually decrease. When the retailer uses the differentiation strategy (γ = 0.7), as αs increases, the manufacturer’s profits gradually increase. Thus, if the retailer, as the leader, introduces the SB, the manufacturer is eager to increase its profit when the retailer adopts a differentiation strategy. Therefore, when the SB and the NB have roughly the same product quality, a differentiation strategy helps to cultivate consumers’ preferences and to improve consumer loyalty to the SB. Furthermore, Fig. 2.15 Comparison between the periods before and after the introduction of the SB in αs

2.6 Introduction of store brands considering product cost and shelf space opportunity cost

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Fig. 2.16 Demand and shelf space proportion for the SB for different values of αs

the differentiation strategy is more conducive to the introduction of other categories of the SB. (4) Scenario 4: Manufacturer as the Leader in the Supply Chain. Figure 2.16 demonstrates the results when the manufacturer is the leader (please see the proof in Appendix B).It indicates that in this case, although the shelf space proportion S is greater than 0 (where ψ = 0.8, k = 0.1, c = 0.1, and γ = 0.9), the actual demand for the SB is less than 0. That is, the SB should be introduced only when the retailer is the leader and has sufficient power. When the manufacturer is the leader in the supply chain, the retailer does not have an incentive to introduce the SB.

2.6.5 Conclusion In this part, we investigate the introduction of an SB product when the retailer is the supply chain leader. In particular, our aim is to answer the following questions: (1) What is the price positioning strategy of the SB—the differentiation or the me-too strategy—when the product cost and the shelf space opportunity cost are considered? (2) What are the factors that influence the pricing position of the retailer? (3) Who will benefit from the different price strategies? To answer these questions, this section examines a two-echelon supply chain that consists of a manufacturer and a retailer. The retailer sells an NB product produced by the manufacturer and an SB product. The retailer needs to determine the price markup of the NB, the price of the SB, and the shelf space allocated to the SB. The manufacturer needs to determine the wholesale price of the product. To this end, we formulate a Stackelberg game model in which the retailer is the leader and the manufacturer is the follower. Our contribution is twofold. On the one hand, we prove the condition that an optimal solution exists. On the other hand, to distinguish the factors that influence the introduction and pricing position strategy of the SB, we conduct an experimental analysis of the parameters. Our results indicate that if both the product cost of the

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SB and the shelf space opportunity cost are low, then the optimal pricing strategy is the me-too strategy (competitive strategy). Otherwise, the optimal pricing strategy is the differentiation strategy. To the best of our knowledge, previous papers have not studied the impact of product cost and shelf space opportunity cost on the entry of SBs. With regard to retailers, our findings have a number of managerial implications: (1) according to the numerical analysis, there is a significant effect of the price differential between the SB and NB; that is, an SB with a price positioned as close as possible to the NB price will not generate more profit for the retailer when the SB is a standard SB. This conclusion is different from those of previous research (Sayman et al. 2002). This is because our research considers the role of product cost, and we observe a significant effect. (2) There exist threshold˜s c and˜ k of costs such that if the cost is less than the threshold, the introduction of the SB is profitable; if the cost is larger than the threshold, then the introduction of the SB will not increase profits, and the retailer will not have sufficient incentive to introduce the SB. According to our numerical analysis, the introduction of an SB and the optimal pricing strategy cannot be fully captured by only one parameter. That is, the product cost and shelf space opportunity cost are the dominant factors affecting the introduction of an SB. The conclusion contrasts with the findings of previous research (see Raju et al. 1995; Kurtulu¸s and Toktay 2011). This is because the effect of shelf space is reflected not only in the demand function but also in the profit function. (3)There also exists a threshold of baseline sales such that if the baseline sales of the SB are less than the threshold, the introduction of the SB will not increase profits, and the retailer will not have sufficient incentive to introduce the SB; if the baseline sales are larger than the threshold, the introduction of the SB is profitable. (4) The numerical analyses also show that the manufacturer is better off when the retailer adopts a differentiation strategy and enlarges the price differential. However, the retailer’s pricing strategies are dependent on the product costs and shelf opportunity cost. In addition, the retailer use same-too strategy; in this case, the prices of both the NB and the SB are lower, and consumers will therefore be better off when they purchase either the NB or the SB. This study has several shortcomings that are worthy of further investigation in the future. First, we assume that the product cost for each brand is the same. In reality, most products do not have the same cost. Therefore, it would be interesting to extend our model to include different costs, Second, our model does not consider competition between retailers or between manufacturers. In fact, with the improvement of SB quality, retailers have their own SBs, and SB competition needs to be considered even though the resulting model would certainly be difficult to analyze.

2.7 Summary This section has discussed product strategies for store brands. Domestic and foreign research on store brand product strategies mainly focuses on the four aspects: factors that affect store brand success; the motivation for introducing store brands; factors

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that affect consumers’ choice of store brands, and strategies for introducing the store brands. We have offered a review of the literature relating to such issues covering the period up to 2019. The last two parts focus on store brands in the Chinese market. We have related our empirical study on the factors influencing purchasing intention for retailer store brands. In this study, we defined several factors connected with consumers’ purchase intentions for store brand products: the psychological characteristics of consumers; the attributes of store brand products; retailer image; the perceived quality of store brand products; the influence of information value, and the influence of manufacturers. We have introduced the concept of store brands considering product cost and shelf space opportunity cost. We have proven that an optimal solution exists. At the same time, to distinguish the factors that influence the introduction and pricing position strategy of store brands, we have conducted an experimental analysis of the relevant parameters. Our results indicate that if both the product cost of the store brand and the shelf space opportunity cost are low, then the optimal pricing strategy is the me-too (competitive) strategy. Otherwise, the optimal pricing strategy is the differentiation strategy.

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Tonghui, Z. (2009). Research on the development strategy of private brands of large retail enterprises in China. Master, Shandong University (in Chinese with English abstract). Toshio, N. (1996). The strategy of private brands in the era of price destruction. Translated by Liu Meifen, International Trade Bureau, 80 (in Chinese with English abstract). Vahie, A., & Paswan, A. (2006). Private label brand image: Its relationship with store image and national brand. International Journal of Retail & Distribution Management. Valenzuela, A., Raghubir, P., & Mitakakis, C. (2013). Shelf space schemas: Myth or reality? Journal of Business Research, 66, 881–888. van Horen, F., & Pieters, R. (2012). When high-similarity copycats lose and moderate-similarity copycats gain: The impact of comparative evaluation. Journal of Marketing Research, 49, 83–91. Verneau, F., Griffith, C. J., Porral, C. C., & Lévy-Mangin, J.-P. (2016). Food private label brands: The role of consumer trust on loyalty and purchase intention. British Food Journal. Weihong, W. (2004). How retailers create their own brands. Business News Business Economics, 53–56 (in Chinese with English abstract). Witek-Hajduk, M. K., & Grudecka, A. (2018). Positioning strategies of retailers’ brands in the emerging market—A cluster analysis. International Journal of Emerging Markets, 13, 925–942. Wu, C.-C., & Wang, C.-J. (2005). A positive theory of private label: A strategic role of private label in a duopoly national-brand market. Marketing Letters, 16, 143–161. Wu, P. C. S., Yeh, G.Y.-Y., & Hsiao, C.-R. (2011). The effect of store image and service quality on brand image and purchase intention for private label brands. Australasian Marketing Journal (AMJ), 19, 30–39. Wu, W.-M., & Lin, J.-R. (2015). Productivity growth, scale economies, ship size economies and technical progress for the container shipping industry in Taiwan. Transportation Research Part E: Logistics and Transportation Review, 73, 1–16. Xia, W., Ping, Z., Gao, W., & Jia, L. (2004). Characteristics of Chinese consumers’ price tolerance. Psychological Journal, 593–600 (in Chinese with English abstract). Xiaomeng, L. Y. C. (2012). The sales of China private labels are below the Asian moving average. Beijing Commercial Daily (in Chinese with English abstract). Xinxin, W. (2003). Chinese retailers should actively develop their own brands. Enterprise Research, 47–49 (in Chinese with English abstract). Xu, J., Huang, R., & Ji, Y. (2018). Private labels in China—Case studies of RT-Mart and ICA. Xuebing, P., & Fanshou, Z. (2013). Research on the relationship between customer perceived value and purchase intention of supermarket private brands. Economic Forum, 144–146. Yang, X., Xu, M., & Zhang, W. (2020). Can design for the environment be worthwhile? Green design for manufacturers brands when confronted with competition from store brands. Sustainability, 12(3), 1078. Yang, M.-H., & Chen, W.-C. (1999). A study on shelf space allocation and management. International Journal of Production Economics, 60, 309–317. Yasmin, F., Ab Yajid, S., & Khatibi, A. (2014). Customer perceived risk on store brand products: A study on Malaysian hypermarket consumer perspective. International Journal of Academic Research in Business and Social Sciences, 4(9), 572. Yoo, B., Donthu, N., & Lee, S. (2000). An examination of selected marketing mix elements and brand equity. Journal of the Academy of Marketing Science, 28, 195–211. Yuexiang, L. (2006). Research on brand awareness. Journal of China Youth College for Political Sciences, 98–101 (in Chinese with English abstract). Zhao, J., Zhou, Y.-W., & Wahab, M. (2016). Joint optimization models for shelf display and inventory control considering the impact of spatial relationship on demand. European Journal of Operational Research, 255, 797–808. (in Chinese with English abstract). Zonglin, W. (2003). Research on the relationship between the impression of coffee chain stores in Kaohsiung City and consumer’s buying behavior. Master, Sun Yat-Sen University (in Chinese with English abstract).

Chapter 3

Pricing Strategy for Store Brands

In recent years, store branding has become an important strategic tool for retail stores to enhance their status and brand influence. In particular, the production and purchase cost of store brands is often lower, and the circulation process of commodities has become less. The gross profit rate is often higher than that of the national brands. If a retail pricing strategy can be reasonably formulated, the profit space will be rich Anselmsson et al. (2008). However, when retailers sell both national brands and store brands at the same time, it is only when they have a good understanding of consumers’ purchase intention and cognitive response to brands, that they will know how to manage the sales channels and balance or rethink the relationship with channel members. Similarly, it is only when retailers use suitable marketing means to attract more demand or avoid the negative impact of competition, that they can win more consumer favor and improve their profit levels. The pricing problems that we perceive for retailers’ store brands are summarized and addressed in the following section.

3.1 Pricing Strategy Based on Marketing Factors Store branding affects the interaction between the retailer and national brand manufacturer, and it therefore impacts both of their strategies. With the entry of store brands, on one hand, the retailer continues to cooperate with the manufacturer through selling the national brand products; on the other hand, the retailer seizes some demand from the national brand by establishing the store brand and becomes a competitor to the manufacturer. This new environment may induce reconsideration of marketing strategies for retailers and/or manufacturers. Many marketing activities have been shown to have an impact on brand management, especially pricing strategies. Such marketing-related activities include advertising, sale channels management, brand positioning and layout.

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3.1.1 Multi-brand Pricing Strategy Based on Advertising The impact of advertising on the sales of different brands within a retail store may be multifaceted, either share-stealing or category-building. Increasing the advertising of the national brand can promote its sales within the retail store, but it will impact sales of related store brand; on the contrary, if the retailer promotes its own store brand products, it may attract more traffic, which then promotes sales of national brands. Therefore, the attributes and spillover effects of advertisement will affect sales of SBs and NBs. Hence, the advertising options for SBs are crucial business strategies to consider. In markets in which advertising is rivalrous (it benefits a specific product rather than the entire category), advertising is typically over-provided by the market, because firms do not account for the negative externalities of their advertising on other firms. However, with a store brand the retailer internalizes some of the negative externalities from rivalrous advertising, and so spends less on advertising. This effect can explain the cross-category variation in store brand advertising to a certain extent. Griffith et al. (2018) provide new evidence on retailers’ pricing and advertising of store brands in the U.K. grocery market and account for the impact these decisions have on retailer–manufacturer bargaining (via wholesale price setting) and downstream competition (via retail price setting). They analyze a simple Hotelling model in which retailers and manufacturers endogenously advertise their respective brands to demonstrate the potential welfare enhancing effects that can arise from the presence of store brand products. The sub game perfect equilibrium of the model gives the values of advertising and prices as functions of characteristics of the market and retailer, and explicitly model why retailer and manufacturers’ advertising strategies vary across product categories. Some of the literature analyzes the carryover effects of brand advertising over time for both the manufacturer and the retailer and account for the complementary and competitive roles of advertising, such as Amrouche et al. (2008), Karray and Martín-Herrán (2009). For a simple supply chain of one manufacturer and one retailer, considering a scenario in which both sides invest in advertising in order to increase own brand equity and reduce the competitor’s equity (that is to acknowledge advertising may have negative effects on the competitor’s brand sales). Amrouche et al. (2008) show that investing in building up some equity for each brand is more effective for both, as it reduces the price competition between them and enhances their differentiation enhancing market power for both. On this basis, Karray and Martín-Herrán (2009) study the interaction between the pricing and advertising decisions in a channel under a national brand and a store brand. They consider more general conditions where advertising may have positive and negative effects on the brand sales of both. The manufacturer reacts to higher competitive retailer’s advertising levels by offering price concessions and limiting their own advertising expenditure. This study finds that the manufacturer advertising will increase the wholesale price, but because of the effects of advertising on brand sales diversity, the types of retailer advertisements (competitive or complementary)

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will have different impacts on wholesale prices. Variation in retail prices for different brands is related to the degree of price competition for products of different brands and the influence of advertisements of both sides on brand demand. Some literature studies the impact of “brand imitation” as an “alternative advertisement” on the reputation for incumbent retailers. Now, brand equity (reputation or goodwill) and product status are a firm’s assets that are acquired by investing in advertising and by implementing an appropriate pricing policy. Clearly, such assets cannot be built overnight but only over time. These features lead to the retention of a dynamic game model and focus on advertising and pricing strategies to assess the impact of brand imitation on the incumbent (Crettez et al. 2018). The evolution of the brand reputation will not only depend on the incumbent’s investment in advertising but may also be influenced by an imitator’s sales. More specifically, there are two cases: (i) a negative effect (brand dilution) and (ii) a positive effect (brand or reputation enhancement). Brand dilution refers to the loss of reputation by a prominent brand and the devaluation of its exclusive features due to their use by a third party (here, the entrant). In the other case, the producer of the original brand gains in popularity thanks to the unintended free advertising done by the imitator. To illustrate, if one sees a “BlueBerry” or a “BleckBarry” mobile phone, one obviously and immediately thinks of “BlackBerry”. A T-shirt displaying the slogan “Naik: Just do it” is providing free advertising for Nike. A T-shirt displaying the trademark “Adidass” is clearly providing free advertising for “Adidas”. Another example is “Hamossy” liquor offered in some outlets in China whose packaging clones Hennessy, the well-known brand of French cognac. Here, it is Hennessy’s brand goodwill that is used to sell the alternative brand. The incumbent invests in advertising to raise its brand’s reputation, a variable that influences the current and future market size, and consequently, its demand and profit over time. The impact of advertising on product prices also depends on the type of advertising. Many scholars consider the impact of informational and persuasive advertising on the pricing of competitive products. Observers argue that evidence for the persuasive role of advertising comes from competitive categories where increases in advertising leads consumers to buy certain brands. Conversely, others claim that advertising serves a purely informational role. Here, higher levels of advertising lead to better-informed consumers which may influence demand, thereby intensifying competition and affecting pricing. Soberman (2004) shows that increases in informative advertising alone can lead to both higher or lower prices and the direction of this relationship depends on the level of differentiation between competing firms. Both relationships are found because of informative advertising affects the firms’ demand differently. Philp and Kim (1997) considers competition between nationally advertised brands and quality-equivalent store brands, and they reveal that heavy advertising among national brands can increase prices, revenues, and profits for both national brands and store brands. They report nine tests to support the conclusion that a battle with store brands may result in an alliance with store brands. From a theoretical perspective, the study rejects the advertising as information hypothesis and the authors find that advertising sustains (or significantly increases) market power and/or facilitates collusive strategies.

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Some scholars focus on a traditional type of cooperative advertising when the retailer makes a decision regarding how much to spend on a local advertising campaign while the manufacturer pays a percentage of the costs; other scholars focus on the either manufacturer or retailer offering full support for the NB. In the scenario where retailers provide support for the NB, the manufacturer could propose an incentive to lower the wholesale price for the retailer as a gesture of reciprocity, and which in return, encourages the latter indirectly to further advertise the NB and thus create a win-win result. Thus, manufacturer incentive policies may have effect on advertising investment and product pricing. Knowing that fact, the manufacturer may be interested to offer incentives to push the retailer to invest further in its brand. By using such a strategy, the manufacturer is interested in generating further profit surplus for the whole supply chain that could be shared between both supply chain players. This means that the manufacturer is indirectly pushing the retailer to advertise further the NB to improve the demand, and ultimately, to improve their wholesale price discounts from the manufacturer. However, the actual situation may not play out as expected under the incentive policy, hence it becomes crucial to share information to maximize the benefit of decisions. While retailers are putting pressure on manufacturers to decrease their wholesale prices, manufacturers do not have any control over how their brands are promoted and displayed, specifically in the presence of the store brand competition. In other words, even if manufacturers are willing to offer wholesale price reduction (such as an incentive), they need a guarantee that retailers will boost their marketing efforts toward the NB as well. Considering wholesale price incentives and information sharing under the context of one store brand competing against one national brand through a unique retailer, Amrouche and Yan (2017) investigated how supply chain players could use the advertising collaboration and the wholesale price incentive in order to rebuild and enhance their relationship and the effect of the NB local advertising strategy on supply chain players’ profits when the advertising is supported by either one of the players. Cooperative advertising to deter or benefit from the introduction of SBs is widely studied in the literature. For example, Karray and Zaccour (2006) investigate whether the manufacturer can counter any harmful effects related to the introduction of store brand, by implementing a cooperative advertising program. Their model accounts for prices and for local advertising undertaken by the retailer for the national brand. Aust and Buscher (2012) revealed that vertical cooperative advertising in a cooperation relationship can help the manufacturer-retailer supply chain achieve the highest total profits and lead to the lowest retail price for consumers. Some papers investigate whether manufacturers can use the timing (sequence) of their pricing and advertising decisions to benefit from or to deter store brand introductions. Karray and MartínHerrán (2019) develop and solve six sequential game-theoretic models for a bilateral channel where different timing of these decisions is considered before and after the retailer introduces a store brand. Comparisons of equilibrium solutions show that the sequencing of pricing and advertising decisions in the channel significantly impact the profitability of a store brand entry by the retailer. In particular, the SB entry leads to losses for the manufacturer when the sequence of advertising and pricing

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decisions is kept unchanged after the SB entry even when it is much differentiated from the NB. For low levels of competition intensity between the NB and the SB, the manufacturer can either prevent or benefit from the retailer’s brand given an adjustment in the sequence of the manufacturer’s decisions. Many studies in the literature show the impact of quality on price and on advertising, respectively. Considering jointly the price–quality and advertising–quality relationships, studies show how advertisements affect the price–quality relationship and how price changes the advertising–quality relationship (Chenavaz and Jasimuddin 2017) or explore conditions to determine when a product of better quality is more or less expensive (Chenavaz 2017). Chenavaz and Jasimuddin (2017) investigate when a product with better quality increases or decreases in advertising, price is assumed to be given by an inverse demand function, that is, price is not directly controlled by the firm. In practice though, the firm, which differentiates its product by leveraging the quality and advertising levels, has also some freedom in price setting. There it must consider that setting the price in turn affects the advertising– quality relationship. It shows that quality impacts both pricing and advertising in a separable manner and it measures the linkage between the impacts on price and advertising. They find that when quality increases, price and advertising may both increase or decrease together, or the one may increase while the other decreases. If advertising is constant, price may increase or decrease after a quality increase. Similarly, if price is constant, advertising may rise or fall following the introduction of better quality. Related to Chenavaz (2017) and Chenavaz and Jasimuddin (2017), Chenavaz et al. (2020) foster the understanding of more complex marketing-mix opportunities, they investigate the interplay between price, advertising, and quality in an optimal control model. Their results generalize the condition of Dorfman– Steiner in a dynamic context, and they point to the impact of greater product quality on dynamic policies for pricing and advertising.

3.1.2 Multi-brand Pricing Strategy Based on Sales Channel As more and more firms begin to integrate their online and traditional operations and share more information over the Internet, real-time supply chain management models on product life-cycle management, dynamic pricing and production coordination, integrated models for supply consortia, and the coordination of Internet and traditional channels, are going to become more significant (Swaminathan and Tayur 2003). Kurata et al. (2007) formulate a Nash equilibrium pricing game to explore crossbrand and cross-channel pricing policies and find that marketing decisions are more restrictive for a NB channel than they are for a SB channel. What’s more, they find that an appropriate combination of markup and markdown prices can achieve a win–win outcome for each channel. Considering the two channels of direct sales and retailer sales available to national brands, while store brands have only the one retailer sales channel, Heese (2010) develops a Nash game model with channel sales

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price as the decision variable, and compares this outcome with the optimal price of centralized supply chain. As a result, the author proposes a price adjustment strategy (price increase/ decrease). In recent years, the Internet channel has provided a convenient and secure environment for the manufacturers/retailers and consumers to make transactions, thereby leading to what has been called the “clicks-and-mortar” phenomenon. Some scholars have found that manufacturers, retailers, and consumers can benefit from the introduction of a direct channel by the manufacturer (Arya et al. 2007). However, the result may be different when considering the investment spillover effect (Yoon 2016). As a result, it seems necessary to investigate the strategic choice of channel structure or channel management holistically. The introduction of a store brand will inevitably have a huge impact on the original supply chain. This effect is often reflected in the price strategy, especially as the low-price strategy of the store brand has been popular in developed countries under the current economic environment (Nielsen 2011). Some scholars, such as Amrouche and Yan (2012), Jin et al. (2017) and Li et al. (2018) explore the interaction between manufacturer channel strategies and retailer brand strategies and discuss their impact on pricing strategies. Amrouche and Yan (2012) built a game model to theoretically compare the competition between store brands and national brands in channel sales under three contexts. Firstly, the model of traditional retailers selling only supplier brands is analyzed, then on this basis, the issues for retailers’ store brands is studied, and finally on the basis of the second model they analyze the pricing issues for SBs and NBs and different channels when manufacturers are opening online channels. They find that the quality differential between the NB and SB, the SB’s potential and the cross-price competitions are all important factors in determining the result of a SB introduction. The manufacturer opens an online store either to counter the threat of such strategy or to expand their market. While the retailer benefits from the expansion if they do not compete in terms of price for their generic brands or their brand is not very well established in the market for premium brands. However, a small difference in the cross-price competitions could be detrimental for the retailer. Li et al. (2018) investigate the strategic interplay between a national brand manufacturer and a retailer by modeling four different scenarios depending on the retailer’s SB introduction strategy (i.e. whether the SB is introduced) and the NB manufacturer’s channel strategy (i.e. whether the online direct channel is introduced) based on utility-demand functions. They compare and analyze the scenarios where manufacturer and retailer engage in quantity competition and price competition. Here, the online direct channel is treated as a complementary marketing channel. They show that at equilibrium, the store brand is introduced, but the online direct channel may or may not be introduced and introduce online sales channel does not necessarily lead to a lower retail price. The manufacturer can charge different prices to retailers under a flexible wholesale price (FWP) scheme while the manufacturer offers consistent prices under a uniform wholesale price (UWP) scheme. Considering both FWP and UWP, and taking into account channel management, Jin et al. (2017) study retailers’ store brand introduction decisions, and find that under the FWP scheme, the retailers’ store brand introduction decisions are mostly symmetrical under dual channel conditions, due to the

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less dependent wholesale prices charged by the manufacturer and their symmetrical roles. But under the UWP scheme, a retailer may gain more profit by not introducing a store brand if its competitor has already introduced one, which gives rise to a much wider range of asymmetric dual-channel setting. Consider the situation in which a manufacturer selling its national brand product to customers through a retail channel, under the assumption that the manufacturer has the option of exploiting the direct channel to compete with the retail channel. The retailer now has the option of introducing a store brand product to compete with the NB product, Cao et al. (2015) study the impact of competition on channel structure, pricing policy and the related profits engendered in a Nash pricing game framework. They show that the competition between the manufacturer and the retailer may decrease retail prices, weaken the negative effects of double marginalization, and eventually achieve a win-win outcome. Taking into account the heterogeneity of consumers, Hsiao and Chen (2014) attempt to provide an answer to when and why the manufacturer and the retailer should introduce Internet channels, given that they are both capable of doing so and that they actively respond to their channel and pricing decisions. They classify consumers into two segments: grocery shoppers who will attach a higher utility from purchasing through the physical channel, whereas a priori Internet shoppers who prefer purchasing online. Their findings suggest that the probability of shoppers attaching utility to the online channel is the determinant for the introduction of an internet channel and a pricing policy. Some literature investigates the role of information sharing in the sale channels as a tool to impact the behaviors of both supply chain players, and therefore impact the wholesale price and retail price. Generally, the manufacturer and the retailer make decisions based on their own demand forecasts. There is informational asymmetry between the manufacturer and the retailer channels. With the entry of a store brand, the retailer and the manufacturer will become more discreet in their information strategy, e.g. whether to share their information. Zhang et al. (2017) comprehensively discuss how information collection, information sharing and forecast accuracy between these channels affect firms’ prices and profits when considering store brand entry. They consider pricing decisions based on accurate demand forecasting and show how different information sharing scenarios might affect price decision for store brand products and national brand products.

3.1.3 Multi-brand Pricing Strategy Based on Brand Positioning The growing importance and complexity of store brand management begs for deeper understanding of optimal decision-making on brand positioning, e.g. store brand

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quality and product line design. Next, we will discuss multi-brand pricing strategies for retailers based on brand positioning by presenting six aspects derived from previous research. First, the early literature on product positioning is usually discussed from a manufacturer’s point of view assuming no active product positioning by retailers. There are two positioning options, one is that the store brand is positioned asymmetrically with the national brand, and the other one is that store brand is positioned so as to mimic a national brand, and is therefore targeted at a specific national brand. Vandenbosch and Weinberg (1995) develop a parsimonious analytical model to captures and compare these two positioning options. The authors find that the equilibrium price of the store brand is lower than the equilibrium price of either of the two national brands. Further, the results show that the introduction of a store brand could make retailer’s profits in a product category increase when the cross-price sensitivity among national brands is low and the sensitivity between the national brands and the store brand is high. However, it is unclear how the literature, with its manufacturer focus, offers applicable strategic guidelines for retailers. With the rapid growth of store brands, the issue of positioning is no longer the exclusive concern of manufacturers that assumes no active product positioning by retailers. Retailers have right to make positioning decision on store brands. Retailers control the positioning of store brands and this is one of the key factors that make store brands so valuable to them. This is not because retailers are necessarily any better than national brand manufacturers at positioning brands—they may in fact be less skilled at identifying consumers’ preferences. Instead, retailers attach importance to the control of store brand positioning because they could never source a national brand with their desired product positioning. Second, abundant empirical evidence indicates that there are significant variations about store brand positioning practices in the real world, and there is no “one-sizefits-all” store brand positioning strategy that is optimal in all situations. For instance, Sayman et al. (2002) examine whether the implications of their model are consistent with market data by conducting three empirical studies. Their analysis of 75 product categories reveals that their recommended strategy for emulating the leading national brand was followed in less than 1/3 of the categories. This finding indicates that retailers prefer to have a store brand that competes heavily with the national brands. Further, this study provides evidence that store brands indeed target the leading national brand in the category and stronger evidence that optimal positioning leads to greater competition between the store brand and the national brand from categories with high quality store brand alternatives. Similarly, Morton and Zettelmeyer (2004) survey two stores and found that only 15–20% of store brands matched a major national brand in size, shape, color, and so forth. They find that control over the positioning of store brands allows retailers to mimic leading national brands—a positioning which the manufacturer of another national brand would not want to choose for its own brand, which clearly explains why retailers value store brands instead of additional national or regional brands in negotiating with national brand manufacturers. In addition, the observations of

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Chung and Lee (2017) from multiple retail stores exhibit different store brand positioning strategies across retailers and across product categories. For instance, a wellknown mass-merchandiser, targeting more quality conscious consumers, has been observed in the Northeastern United States to position its high quality store brand mayonnaise at a higher price position than leading national brands such as Kraft and Hellmann’s, whereas another competing mass merchandiser catering to more price sensitive consumers has its store brand mayonnaise positioned as the lowest-priced item in the category. Similar differences were found across competing retailers in the same region in other product categories. Different store brand positioning strategies are often observed across product categories in the same store. For example, one U.S. supermarket chain offers a store brand applesauce as the most expensive item in the category but positions its store brand sliced bacon as a cheaper brand than leading national brands. Other categories (e.g. dish-washing liquid and apple juice) have store brands priced higher than some national brands but lower than top-tier national brands, as observed in multiple retailers. These empirical results show that there is no unified paradigm for positioning options, the results may develop in opposing directions under different conditions. Third, previous studies of store brand positioning primarily focus on the horizontal positioning of store brands. Hoch and Banerji (1993) conduct empirical investigations to examine horizontal factors through various metrics including price data from a large supermarket chain in Chicago. They use the average price of the store brand (some categories have multiple store brand items) and the average price of the leading national brands over a one-year period for each category. They find that depth of price discount is not a significant factor in explaining market share, but that quality is more important than price. Further, they show that retailers would do well to concentrate on categories where they can offer quality levels comparable to those of national brands and should be less concerned with delivering deeply discounted prices. Du et al. (2005) assume a market consisting of two consumer segments and identify four distinct types of category management strategy. These authors investigate how a monopolist retailer positions a store brand horizontally compared with the existing national brands and how a monopolist retailer prices each of the brands. They find that it is best for the retailer to price store brand near to the national brand’s monopoly price except when the national brands are highly differentiated. Choi and Coughlan (2006) investigate how the retailer chooses the store brand price based only on its own quality and variable cost: and how the retailer chooses to position their store brand against two national brands in terms of both product quality and product features. Nalca et al. (2018) also assume a market composed of two consumer segments and further consider more factors. They establish a spatial model to investigate vertical differentiation, horizontal differentiation, uncertainty and heterogeneity in consumer taste, and heterogeneity in consumer valuations. They find that the retailer will make pricing decisions after the information acquisition, and the sharing and positioning stages. The authors find that the retailer benefits from acquiring consumer taste information, and that the manufacturer is harmed when the retailer acquires information—unless the fixed cost of store brand introduction is high. These findings also

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show that neither the degree of horizontal differentiation between the two consumer segments, nor the restriction of possible product positioning decisions poses any limits on the above findings. Fourth, consumers have well-established positioning for most of the leading national brands in frequently purchased consumer product categories, and multiple retailers distribute national brands with varying target market characteristics. National brands have established positions that enable them to enjoy benefits, such as economies of scale in production and marketing. Therefore, the national brand manufacturers are more likely to keep their current positions and are unlikely to reposition existing national brands in response to each new store brand’s entry. Chintagunta et al. (2002) study the effects of introducing a store-brand into a given product category, especially its effect on the demand as well as on the supply side. On the demand side, the authors investigated the changes in preferences for the national brands and price elasticities in the category. On the supply side, they study the effects of the new entrant on the interactions between the national brand manufacturers and the retailer introducing the store brand, including how these interactions influence the retailer’s pricing behavior. Halstead and Ward (1995) find the primary competitive advantage of store brands—good quality at low prices—may be lost if store brand retailers continue to modify and expand how their brands are marketed. If strategy changes by store brand retailers can be implemented at low/no cost or absorbed (i.e. not passed on to retailers or consumers in the form of higher prices) then the wheel hypothesis for store brands (and resulting vulnerability) would not hold. That is, store brand strategy changes/improvements would not be a good predictor of the likelihood of future margin/price/status changes, and store brands would not be vulnerable to potentially new forms of brand competition. Pauwels and Srinivasan (2004) offer a potential ‘win-win’ scenario for the retailer and premium brand manufacturers on category demand and invite national brands to rethink their perception of store brands as detrimental. While store brands and national brands compete for market share, they may mutually benefit by the stimulation of primary demand in certain categories. Consider national brand manufacturers’ strategic options in the presence of store brands. Bontems et al. (1999) focus on the national brand manufacturer’s wholesale pricing response to store brand quality positioning. Nasser et al. (2013) focus on two important drivers: the national brand manufacturer’s ability to differentiate on the quality dimensions and their cost advantage over the outside supplier of the store brand. To completely characterize the national brand manufacturer’s response, they develop a descriptive theory that clarifies the incentives of the national brand manufacturer to accommodate, displace, or buffer. In doing this, they determine how the national brand manufacturer’s whole product portfolio should be designed, such as deciding the positioning (quality levels) and prices of all its offerings. Fifth, previous theoretical studies model the difference between national brands and store brands mainly in terms of the manufacturer’s pricing power, by assuming that national brand manufacturers set profit maximizing wholesale prices, while store brand manufacturers lack such pricing power and supply their products at cost or with

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minimal reservation margins (Raju et al. 1995). Schmalensee (1978) noted that store brands often imitate the category leader, presumably to signal comparable quality at a lower price. Although this represents an important difference between national brands and store brands indeed, these studies ignore the fact that national brands often enjoy superior brand equity in comparison to the inferior or no brand equity of store brands. Choi and Coughlan (2006) investigate how the ability to change objective product quality, as well as how consumers perceive it, is of central importance to a retailer concerned with the best positioning of its store brand. Research examining the retailer’s problem of positioning a store brand against national competitors suggests that the best positioning depends on the nature of competition, as well as on the store brand’s objective quality. While this research can inform retailers about the best positioning, it does not address the issue of how to successfully position a store brand as a quality brand. Chung and Lee (2017) capture different market environments by incorporating four different distributions of consumers’ willingness to pay for quality and demonstrate that the relative proportions of quality conscious consumers and price sensitive consumers have substantial influence on optimal store brand positioning strategy. The analysis reveals that the nature of a retailer’s store brand quality positioning is quite different from the manufacturer’s national brand positioning decision, and that the best position for a store brand is not ‘as close to a national brand as possible’ as previous studies suggest. Instead, the optimal quality position of each store brand is remarkably sensitive to the distribution of consumers’ willingness to pay. Liao et al. (2020) address the individual and joint effects of sourcing and pricing power considering a retailer’s store brand/ quality positioning problem. They consider two forms of price leadership, Manufacturer-Stackelberg and Retailer-Stackelberg under in-house, a leading national-brand manufacturer with a competing product, and a strategic third-party manufacturer. They fully characterize the retailer’s optimal quality levels and their relative values across the six scenarios, as well as equilibrium prices, retail profits, consumer welfare, and supply chain profits. They show that the power to decide sourcing is more important to the retailer than having pricing power. Finally, we will discuss premium store brand. Understandably, issues of quality and pricing of store brands relative to national brands have been a dominant focus of research in the literature. They are typically sold at a price 20–30% below that of the national brands that they are competing with (Steenkamp et al. 2010). In contrast, premium store brands are positioned at the top end of the market and deliver quality that is alike or even better than premium-quality national brands at prices comparable with, or higher than, the premium-quality national brands. Geyskens et al. (2010) postulate how the introduction of economy and premium store brands may affect the choice of mainstream-quality and premium-quality national brands and the choice of the retailer’s existing SB offering. The authors use a natural experiment offered by Asda and Sainsburys introduction of economy and premium SB tiers in the corn flakes and canned soup categories in the United Kingdom to test their framework. Using brand choice models that accommodate context (compromise,

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similarity, and attraction) effects, the authors find that both economy and premium store brands cannibalize incumbent store brands. Economy store brand introductions benefit mainstream-quality national brands because these national brands become a compromise or middle option in terms of quality in the retailer’s assortment. The effects of premium store brand introductions on premium-quality national brands are mixed: their share improves in two of four cases but decreases in the other two. The consumer makes their quality decisions based on price and believes the expensive products have higher quality. Thus, Lichtenstein and Burton (1989) also investigate how the consumer infers the higher quality corresponds with the experience products when the products are the same category. This initial positioning, reinforced over the years by low prices, leads consumers to associate store brands with a quality level lower than leading national brands (Soberman and Parker 2006; Sprott and Shimp 2004). Palmeira and Thomas (2011) address the quality positioning problem by examining consumers’ reactions to multi-tier store brands. The authors find that consumers infer lower quality when a retailer provides a single store brand, even when it is defined as a premium brand, thus a premium store brand has the beneficial impact of a value store brand on quality perceptions. Akcura et al. (2019) conduct an empirical study to investigate how shares of standard and premium store brand products affect retailers’ marketing mix decisions toward national brands. They used a comprehensive store-level data set covering 52 categories and 130 stores of two retailer chains during 2003–2009. The paper examines how shares of standard and premium store brands affect retailer marketing strategies for national brand retail prices, promotions and product assortments and the results indicate that retailers make strategic national brand decisions through multitier store brands. Specifically, the evidence suggests that retailers use standard and premium store brands differently in promotions and assortment decisions toward national brands. National brand manufacturers need to be cognizant of the increasing marketing power of retailers through their multitier store brands.

3.1.4 Multi-brand Pricing Strategy Based on Layout Shelf space is a resource that can arrange different brand products and the layout has an important effect on consumer purchase behavior. Therefore, shelf space is one of the retailer’s most important and very scarcest assets and it is a limited resource that must be optimally divided among the different categories and their various brands. Both marketing and operations research scholars pay attention to the issue of shelf-space allocation and its impact on retailers’ performance. First, empirical in nature verifies that shelf space has indeed a positive impact despite a decreasing marginal effect. Curhan (1973) reviews conceptual models and empirical studies of the relationship of shelf space allocation to unit sales that is organized to support specific recommendations for the practical management of shelf space for profit maximization. Drèze et al. (1994) measure the effectiveness of two

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shelf management techniques through a series of field experiments. One is spaceto-movement where they customized shelf sets based on store-specific movement patterns. The other is product reorganization where they manipulated product placement to facilitate cross-category merchandising for ease of shopping. The authors model the impact of shelf positioning and facing allocations on sales of individual items using the field experiment data. They find that location had a large impact on sales, whereas changes in the number of facings allocated to a brand had much less impact if a minimum threshold was maintained. A retail chain manager must draw on experience based on data available from the points of sale records to diagnose space misallocations in stores and to make recommendations. Desmet and Renaudin (1998) focus on the control exerted by a store chain on a major merchandising decision: the allocation of space among product categories within the set of stores. Their results show that space elasticities increase with the impulse buying rate of the product category and do not depend on the type of store. More, some of the literature tackles the joint problem of item selection and pricing using either experimentation or conjoint analysis. Mcintyre and Miller (1999) develop and test an empirical approach, applied to the problem of selecting an optimal assortment of backpacks from a field of eight available items, that simultaneously addresses the selection and pricing problems. The authors show that their new approach does yield significantly more profitable retail assortments in all cases (e.g. for all assortment sizes) and predicts sales and profitability more accurately compared with more traditional approaches. The second stream developed mathematical programming models to provide optimal shelf-space allocation policies for retailers. Anderson and Amato (1974) discussed the specific brands that should be displayed and the amount of retail product-display area that should be assigned to these brands to maximize the retailer profit. Their paper decomposes total market demand according to the various levels of brand preference that could conceivably exist in final markets. The authors use an algorithm, like the one used to solve the fixed-charge problem and find the optimal brand mix and display-area allocation. Corstjens and Doyle (1981) develop a model which uniquely incorporates the demand function and the cost function. They use a case study to estimate the parameters and solve the problem within a geometrical programming framework. The paper suggests this general model leads to significantly different allocation rules and superior profit performance compared with alternative procedures. Zufryden (1986) proposes a dynamic programming approach to allocate shelf-space units to the selected products in supermarkets considering general objective function specifications that account for space elasticity, costs of sales, and potential demand-related marketing variables. One may conclude that space management tools are essential. Addressing this need, Bultez and Naert (1988) elaborate a general, theoretical shelf space allocation model, which focuses on the demand interdependencies prevailing across and within product-groups. Urban (1998) proposes a greedy heuristic and a genetic algorithm to generalize and integrate existing inventorycontrol models, product assortment models, and shelf-space allocation models. Yang (2001) formulates a model and proposes an approach which is akin to the algorithm used for solving a knapsack problem. The approach allocates shelf space item by

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item according to a descending order of sales profit for each item per display area or length. The author proposes three options for improving the heuristics that is a very efficient algorithm to allocates shelf space at near optimal levels. However, all these studies had a vested interest in the retailer’s perspective and disregarded the element of interdependence with manufacturers. A third stream of studies of recent vintage considered the allocation problem within the framework of the strategic interaction between the partners in a marketing channel, typically formed of two competing manufacturers and a retailer. Cox (1970) measures sales of two brands of salt and powdered coffee cream and tests the influence of shelf space upon sales of branded products in a randomized block field experiment. Their paper indicates that retailers might limit shelf allocations for several brands to some minimal level. Morton and Zettelmeyer (2004) show store brands are the only brands for which the retailer is responsible not only for promotion, shelf placement, and pricing, but also for positioning the brand in product space. MartínHerrán and Taboubi (2005) consider a network composed of a unique retailer offering the products of two competing manufacturers. The retailer controls the amount of shelf-space to allocate to both brands, while the manufacturers make advertising decisions to build their brand image. The paper indicates that the shelf-space allocated to each brand, manufacturers’ advertising strategies at the equilibrium and channel members’ value functions are affected by the goodwill levels of both products. Martín-Herrán et al. (2005) characterize an open-loop Stackelberg equilibrium for shelf-space allocation and propose a differential game to study retailer’s allocation strategy of shelf-space shares between the manufacturers of two competing brands. An assumption in this last group of studies is that the manufacturer can influence, by wholesale price or advertising policy, the retailer’s shelf-space allocation. The result is no longer an optimal solution to a mathematical programming problem but rather, an equilibrium in a noncooperative game. Amrouche and Zaccour (2007) propose a game-theoretic model with one national-brand manufacturer as a leader and one retailer as a follower. This paper is an attempt to simultaneously tackle pricing strategies and shelf-space allocation in the context of store brands and solves the resulting Stackelberg equilibrium through the amount of shelf space assigned to these brands and their prices. The authors find that the quality of the store brand can affect the assignment of the shelf space, and that quality is measured by the baseline sales (or brand equity), the degree of brand substitution and the price positioning. Amrouche and Zaccour (2009) investigate the idea of a shelf-space-dependent incentive in the context of national brand and store brand competition. The authors think the manufacturer, who acts as a leader, proposes an incentive to the retailer (who acts as a follower) in order to incite the retailer to allocate a larger part of the shelf space to the NB, to ultimately increase NB profit. The profitability for the manufacturer of such a mechanism was characterized in terms of the store brand types offered by the retailer, as well as in terms of the shelf-space allocation and wholesale price in the benchmark case. In the case of more general two brands and two to two supply chain structure, Li et al. (2013) examine the influence of competition among supply chain partners on product demand. A power law demand function that depends on product pricing and shelf-space allocation (SSA) is used.

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The exponents in the power law are given by the elasticities of demand. To achieve the optimal pricing and SSA strategies in the presence of competition, two gametheory-based methodologies—Cournot and Stackelberg games—are employed. For each type of game, a Nash equilibrium is achieved by optimizing the profit as a function of demand and price. A case study is presented to demonstrate the potential of this methodology. The results of this study indicate that both prices and profits decrease using the Stackelberg game as compared with the Cournot game, and that coordination beyond simple knowledge of price would be beneficial for improving overall profits. Shelf management is a difficult task in which rules of thumb rather than good theory and hard evidence tend to guide practice. The allocation of scarce shelf space among competing products is a central problem in retailing.

3.2 Pricing Strategy for Competitive Brands Considering Consumer Consciousness Shankar and Bolton (2004) empirically investigate the determinants of retailers’ pricing decisions and they classify retailers’ pricing strategies based on four underlying dimensions: price consistency, price-promotion intensity, price-promotion coordination, and relative brand price. These four pricing dimensions, which are statistically related to competitor factors, category factors, chain factors, store factors, brand factors, customer factors and competitor factors, best explain variance in retailer pricing strategy. Here, this book integrates these factors into consumer behavior. Consumer consciousness correlates with product knowledge, purchasing behavior and their decision-making. Thus price, quality, and promotional offers are assessed by consumers, and all such factors will influence a store brand purchase.

3.2.1 Pricing Strategy for Competitive Brands Considering Value Consciousness Value consciousness can be described as the level of quality for the price paid to the product, and willingness to receive good quality for the amount paid (Burton et al. 1998). Typical store brand buyers are value conscious and attracted by good quality and product image. These buyers prefer search goods to experience goods and buy product categories like milk, complete meals, paper products, paper bags, and wraps, where involvement and cost-switching were found to be low (Kwon et al. 2008). This trait displays satisfaction with the price asked for and paid. Value-conscious consumers evaluate products for quality (Kara et al. 2009), tend to pay less, look for price savings, are less loyal, and exhibit brand switching behavior (Garretsona et al.

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2002). This trait varies with product category (Jin and Suh 2005) and is typical of store brand product purchasers. It has been suggested that people are more likely to use price as an indicator of quality for experience products. As the price level increases, the risk of an incorrect assessment increases and buyers often are less familiar with the product because of the infrequency of purchases. In such situations, simple learned heuristics based on folk wisdom such as “you get what you pay for” are likely to be used. Rao and Monroe (1989) show that the greater the relative difference between the prices used as levels of an independent variable in a given study, the greater will be the observed effect on the consumers’ perception of quality and, all things being equal, the better the chance of detecting a statistically significant effect. In addition, value-conscious consumers are sensitive to prices and constantly tend to maximize value for money in their purchases. These are people who show a psychological need to perceive themselves as smart shoppers, that is, they see themselves able to obtain an advantage in their own evolved shopping method by consistently achieving price savings (Garretsona et al. 2002). Therefore, these consumers tend to prefer SB products whenever the price differential between the latter and NBs is significant and can compensate for any perceived quality gap between the two brand types. Wang and Li (2011) point out that when consumers face different product choices, they will choose products with high perceived quality at the same price, so it is perceived quality rather than actual quality that affects consumer behavior. Considering the different conditions of the two, this paper constructs a utility function for consumers, analyzes the pricing principle of retailers according to the perceived quality and the actual quality, and finds that when the actual quality and perceived quality of the store brand products are high, the retailers position the store brand in the high-end high-priced products, and compete with the supplier brand in the highend market; when the store brand products’ actual quality and perceived quality are low, the pricing of store brand products is also relatively low. Currently, store brand products tend to adopt the low-cost strategy to compete with manufacturers’ middle and low-end products at a competitive low price. Manikandan (2020) proposes a structural equation model, based upon a standardized questionnaire, to study the influence of retailer equity (retailer awareness, retailer association, retailer perceived quality and retailer loyalty) and perceived risk factors (functional risk, financial risk and social risk) on attitudes towards store brand products. Their study reveals that the Retailer Equity variables referred to as retailer perceived quality and retailer loyalty have a positive influence on the store brand attitude, while the other two variables (retailer awareness and association) do not show any influence. Retailer perceived quality shows negative influence over the functional risk while retailer loyalty negatively influences social risk. Money-back guarantee (MBG) is a well-known service offered by many retailers, which is regarded as an effective way to reduce perceived risk and achieve high value consciousness. Under the MBG policy, an item purchased by a consumer can be returned for a full refund (Davis et al. 1995; Akçay et al. 2013; Heydari et al. 2017), thus MBG plays a vital role in reducing the perceived risk of inappropriate or

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impulse purchases, and ultimately enhances the willingness to pay (Desmet 2014). However, the increase in willingness to pay is related to some other factors. Desmet (2014) shows that a double money-back guarantee does not make consumers prefer to choose a national brand. The effect of a money-back guarantee differs, depending on the customer–retailer relationship: and a retailer with high credibility will influence regular customers less by the use of guarantees. Huang and Feng (2020) investigate the MBG choice problem in the process of store brand introduction in a two-echelon supply chain consisted of one manufacturer and one retailer. Their research implies that the manufacturer sells the NB products at a higher wholesale price when MBG is only offered for the NB product. The return policy of offering MBG for the SB products can generate more competitive advantages for the store brand and attract more consumers to purchase the SB product, which prompts the manufacturer to decrease the wholesale price. Another implication is that the manufacturer sells the NB products at a higher wholesale price when the retailer provides MBG for the NB products regardless of whether MBG is offered for the SB products.

3.2.2 Pricing Strategy for Competitive Brands Considering Price Consciousness Price consciousness is an important criterion that defines the purchasing behavior of store brand consumers. It is defined as the “a consumer’s reluctance to pay for the distinguishing features of a product if the price difference for these features is too large” (Monroe and Petroshius 1981, p. 44) and it is affected by store brand favoring variables like lower incomes, high deal proneness and a decreased belief in price-quality association (Hsu and Lai 2008). Price is an extrinsic cue and offers important information to customers for making purchasing decision (Beneke et al. 2013). Price is amongst the most important choice criteria for customers, although their price knowledge is often surprisingly inaccurate. One frequent assumption when studying consumer choice has been that consumers have sufficient information on product prices. For decades, economists have examined and tested the knowledge or recollection of prices by consumers for various products in many countries (Jensen and Grunert 2014). Earlier reports attribute this trait to low income and a desire to pay less for purchased goods (Burton et al. 1998). Groceries belong to the price-conscious food category (Kwon et al. 2008) therefore, retailers focus more on this section to entice consumers. While most consumers react quite sensitively to price changes in food retailing (Estelami and Lehmann 2001), most studies suggest serious inaccuracies in consumers’ price knowledge, thus contradicting classical theory. Significant differences in the recollection of prices between consumers (Burton et al. 1998) and/or for certain products (Estelami and Maeyer 2004) are observed. Prices are amongst the most important factors for product and store choice; especially when the difference

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in locality is small, price as search attribute is of high consequence for both sellers and customers. A considerable mismatch between actual prices and recalled prices might change or decrease the effects of marketing and pricing on consumer choices. Sinha and Batra (1999) show several reasons have been advanced to explain the remarkable success and growth of store brands in Western Europe and North America. One important factor that has not been adequately highlighted is the role of consumer price consciousness and consequent consumer resistance to the prices of national brands. The authors have developed a framework for understanding consumer price consciousness, why it varies across product categories, and how it may result in store brands purchase, and calibrated the model on category-level field data. Their findings establish that perceived category risk and perceived price unfairness of national brands in that category are significant antecedents of consumer price consciousness, and that variations in such price consciousness across categories is a significant reason why consumers buy store brands more in some categories than in others. Additionally, they show that perceived price–quality association has a significant effect on store brand purchase in risky categories. Pre-shopping price recall, which is relevant for store choice, is considered to rely on reference prices (Jensen and Grunert 2014). For consumers, inaccurate price knowledge might motivate them to patronize stores and chose products that do not maximize their utility. The evolution of reference prices and the comparison with observed prices towards formation of long-term price knowledge is a complex subconscious process. Focus on a convenience store chain that is using a price-matching guarantee (refund the difference between the retail price and the lowest selling price) for its store brands, Loy et al. (2020) provide some evidence to evaluate the role of the price-matching guarantee in this process by comparing consumers’ price recalls of convenience store brands with discounter store brands and consider the nature of the recall bias by calculating both over and under estimation of prices. Despite a pricematching guarantee for the store brands in the convenience store, the price image is still in favor of the discounter. This result raises doubts on the effectiveness of the price-matching guarantee. Retailers may adopt pricing policies, for example, a retailer that adopts a high–low (HiLo) promotion strategy tries to stimulate customer demand through time-limited price promotions. Price-based special offers aim to attract consumers to the store whilst also signaling price competency for the assortment. In contrast, a retailer with an everyday low price (EDLP) strategy largely eliminates price promotions and offers products for a consistently low, non-varying price. The everyday low-price strategy of the discounter seems to pay off in terms of the price image. Though both stores may charge the exact same prices for their store brands, prices at the discounter are on average perceived to be significantly lower. Sethuraman and Cole (1999) identify some managerially relevant factors that influence the size of the price premium that consumers will pay for national brands over store brands in grocery products. The authors define price premium as the maximum price consumers will pay for a national brand over a store brand, expressed as the proportionate price differential between a national brand and a store brand.

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Olbrich et al. (2017) use product test ratings and panel data from more than 35,000 participating households in Germany, and this study address the impacts of price, quality, and promotion shares on the market shares of different products, including national brands and store brands, as well as food and non-food product categories. The results of a path analysis reveal important differences across the four segments, as well as insights regarding the use of everyday low price and high–low retail pricing strategies.

3.2.3 Pricing Strategy for Store Brands Considering Discount Consciousness Factors influencing store brand price promotions include the degree of store brand loyalty (Anselmsson et al. 2008), frequency of price promotions of store brands and depth of price promotions of store brands (Sethuraman 2009). Store brand consumers look for discounts and promotional offers. “Discount consciousness” is defined as purchasing of store brand products in a discount sale or using sales promotions to buy products. Store brand buyers are vulnerable to in-store promotions, special displays, and coupons for favorite brands (especially groceries). Price differential coupons are an important and dependable way of attracting deal-prone and dealoriented consumers, who favor low-priced products (Burton et al. 1998), and display psychographic features like shopping enjoyment, financial constraint, and impulsive buying. From an individual consumer’s perspective, coupons represent a simple price discount to induce purchases. However, from a manufacturer’s perspective, coupons can be used to discriminate between more and less price-sensitive consumers. Retailers no longer only act as distributors for manufacturers’ products but carry NBs on shelves while simultaneously offering consumers SBs, functioning as manufacturers. Whereas once only NB manufacturers issued coupons, retailers now issue coupons for both NBs and SBs. Due to their increasing market share, SBs are increasingly taken seriously as competitors to NBs in many product categories. Retailers thus face dual roles as downstream parts of the NB supply chain and as competitors to the NB. This duality provides a fascinating backdrop for studying couponing strategies. Bauner et al. (2019) explore the couponing strategies when a national brand competes with a store brand by discussing three different kinds of coupons: manufacturers’ coupons, retailers’ national brand coupons, and retailers’ store brand coupons. Concentrating on money-off (as opposed to percent-off or buy-n-get-onefree) coupons, they model couponing strategies by a brand name manufacturer and a retailer that may choose to offer a SB product. They explore the interaction between coupon value and the degree of feature differentiation. Greater feature differentiation will drive the manufacturer to increase its coupon value, and the retailer will respond by decreasing the value of its own coupon for the brand name product.

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Based on the analysis of factors determining cannibalization between NBs and SBs, Fornari et al. (2016) develop a theoretical framework. The framework structure assumes that cannibalization of sales between these two brand types depends on competitive factors both of price and non-price type. For price type, baseline price can result in long-term competition, while price promotion or price discount can cause short-term price competition. In the case of non-price dimensions, NB–SB competition is influenced both by supply-side and demand-side variables; while store loyalty and SB image are two main demand-side variables that condition SB non-price competitiveness. Retailers may adopt promotional pricing policies to show discount. Olbrich et al. (2017) study the effect of pricing strategy on store brand and national brand performance. They show that for the national brands they carry, retailers should rely on price promotions and adopt a high–low promotion strategy. National brands tend to be better known than store brands, so price promotions on these familiar products may increase store attractiveness. To avoid negative carryover effects to the retail brand, retailers should avoid including poor quality products, with low test ratings, in their price promotions. However, other than these price promotions, national brands do not need to be priced low. Evidence of a harmful impact from higher prices on market shares is rather weak, and in some cases, a higher price even can increase market shares. Furthermore, retailers might use high-priced national brands to signal the attractiveness of their low-priced, store brand alternatives (i.e. umbrella pricing). The primary purpose of a store brand discount is to protect its own base from encroachment by the national brands. If store brand loyalty is higher, then sales are not threatened by the national brand manufacturer and therefore the retailer does not discount often. When a store brand has high loyalty, the national brand needs to discount deeply to attract store brand consumers. For the same reason, the store brand does not have to offer deep discounts to protect its market share because consumers are already loyal to the store brand. As loyalty to a store brand increases, that is, it takes a larger price differential to switch store brand consumers, the store brand should be promoted less often. Perloff et al. (2012) divide consumers into two categories: those who are highly loyal to suppliers’ brands, and those who will switch to store brands under certain price conditions. This condition is transformed into two constraints by introducing two parameters: the reference value of brand price difference of consumers and the threshold value of store brand price. On this basis, considering the deterministic demand form, which is linearly related to the price, the Lagrangian relaxation function for retailer profit is constructed, and the optimal prices of two brands in different regions are discussed in detail. Sun Xixiu (2013) put forward six pricing models for setting retail prices according to several possibilities of store brand and supplier brand entering the market successively. Under the linear form, like the demand for replaceable products, it can be concluded that if the operating cost of a store brand is low, then the sales with low price strategy can help to improve the profit. Arce-Urriza et al. (2012) investigate competition between store brands and national brands across online and offline retail channels using data supplied by a multichannel supermarket chain describing a full year’s purchase records for 2,742

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households in 36 product categories. They analyze competition between these two types of brands by estimating the following competition indicators: market share, loyalty, and conquering power (a measure of the ability of a brand to attract new customers). Using empirical analysis for the actual data, they compare the online sales channel under market share, and the number of consumer brand loyalty and brand switching potential consumers. They find that the online channel can increase the number of loyal customers; however, based on the analysis of product category, online channels have no obvious effect on competition between brands. Corstjens and Lal (2000) study the role of a store brand in building store loyalty through a game theoretic analysis. In a market in which a segment of consumers is sensitive to product quality and consumers’ brand choice in low-involvement packaged goods categories is characterized by inertia, the authors show that quality store brands can be an instrument for retailers to generate store differentiation, store loyalty, and store profitability, even when the store brand does not have a margin advantage over the national brand. This loyalty argument does not apply for the “cheap and nasty” store brand strategy and such a store brand policy, on the contrary, would reinforce rather than reduce price competition among stores. The quality store brands create store differentiation and loyalty, whereas the national brands enable the retailer to raise prices and increase store profitability. Some authors explore the loyalty of different brands from “double jeopardy” (DJ) theory. In any given period, a small brand typically has far fewer buyers than a larger brand. In addition, those buyers tend to buy less often. This pattern is an instance of a widespread phenomenon called DJ. Researchers describe the wide range of empirical evidence for DJ, the theories that account for its occurrence, known exceptions and deviations, and practical implications. McPhee’s (1963) theoretical explanation of DJ arose from his noting an asymmetry in people’s familiarity with, or exposure to different brands. Stochastic models of buying behavior have been developed that predict the size as well as the presence and direction of DJ trends for competitive brands. They outline three models for DJ in buying behavior, in increasing order of sophistication. Two other models are much more realistic in that they allow for consumer heterogeneity. One is usually referred to in the literature as w (l - b) and the other as the Dirichlet, which is more flexible and much wider ranging. Ehrenberg et al. (1990) review a little-known but widely occurring and theoretically supported regularity in competitive markets, namely that small brands generally attract less “loyalty” among their buyers than large brands do among theirs. In many markets larger brands have more advertising support and possibly also wider distribution, either of which might lead both to more buyers and to greater loyalty.

3.3 Pricing Strategy Considering Different Sales Structures The retailer usually purchases its store brand products at a wholesale price from a product manufacturer and sells them to consumers as their store brand. The existence of store brands will lead to increasing sales competition among different brands,

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which must inevitably require retailers and manufacturers to adjust their product prices to maximize benefits. Therefore, the pricing of store and national brand is an important issue to discuss. Next, we will discuss the competition structures among brands, including one store and national brand, multiple national brands, and multiple store brands. First, much of the literature concerns the situation related to one national brand and one store brand, where a retailer can gain extra revenues and expand sales from selling the store brand, thus attracting new consumers and how the retailer that introduces the store brand can obtain better price terms from national brand manufacturers. Narasimhan and Wilcox (1998) assume that consumers prefer the national brand to the store brand. The national brand product price is higher than the store brand and the manufacturer will use lower wholesale prices or other ways to maintain a certain store brand price. Bonfrer and Chintaunta (2004) investigate a store brand with a national brand in one category and examine a panel of data in five stores located in a competing market area. What happens to the retail prices of the incumbent national brand with the introduction of a store brand in the category? Using category level market structure measures to help distinguish the conditions for category prices to rise or fall, they find mixed evidence as to how the retailer may affect prices of the incumbent national brand after a store brand is introduced to the category. Fang et al. (2013) investigate strategies for retailers and suppliers regarding pricing and contracting with trade intermediaries. The authors analyze a supply chain, which includes one national brand with the retailer deciding whether to allow a store brand to enter, how to set the store brand price, and what quantities of the product to order. They find that this sale structure required revisiting the national brand manufacturer’s pricing contract. Abundant research focuses on competition between store brands and national brands and counterstrategies that national-brand manufacturers can take to counter store-brand introduction. Of the few research studies concerning store-brand production issues all are related to single-retailer scenarios. However, this common wisdom is obtained under single-retailer scenarios. Mills (1995) presents a model of retailermanufacturer interaction that focuses on retail competition between national brands and store brands positioned by retailers to compete with them. When a manufacturer distributes its national brand through multiple local monopolist retailers it should comply with the Robinson–Patman Act, in which the national brand manufacturer may not behave the same as in the single-retailer setting. According to the Robinson– Patman Act, the national brand manufacturer should charge a uniform wholesale price to all its retailers (unless there is a good and fair business reason for differentiation), and thus the national brand manufacturer might have to adjust its wholesale price globally in reaction to a retailers’ store brand entry (Groznik and Heese 2010). Therefore, Mills finds the national brand manufacturer may not reduce its national brand wholesale price as much as it might under single-retailer scenarios. Consequently, store brand entry may not be so beneficial to the retailer and an incentive to the withdraw national brand from its shelves may arise. To avoid this situation, the national brand manufacturer should find ways, other than lowering wholesale price, to compensate the retailer.

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Second, we discuss some of the literature concerning multiple national brands. Not only does the retailer gain profits directly from introducing their own store brand, but also uses store branding as a strategic weapon to elicit concessions from the national brand manufacturer. Chintagunta et al. (2002) study a sales structure consisting of multiple national brands and one store brand. They detect whether their conclusions are consistent with some of the commonly held assumptions regarding retailer pricing behavior by examining the relevant data. The authors study the nature of the retailer-manufacturer interaction in connection with how this would affect the retailer’s pricing behavior. By observing the retailer’s actual markup, the authors predict markup to be conditional on the demand specification, the deviation between the two can be ascribed to the nature of interactions quite unambiguously. Pauwels and Srinivasan (2004) analyze data for premium national brands, second-tier national brands and store brands. The authors also describe the permanent performance effects of store brand entry that are beneficial to the retailer, the consumers, and premium-brand manufacturers and those that are harmful for second-tier brand manufacturers. Chung and Lee (2017) establish a game theoretic model composed of one national brand manufacturer and a retailer. Further the authors investigate the structure consisting of one national brand manufacturer and a retailer. They find the retailer has an opportunity to transform quality conscious consumers from the national brand to a new store brand by targeting the store brand at higher quality and setting price levels to exact higher profits. Store brands may expand the product categories at their retail stores by either attracting new customers or by better serving national brand consumers. Despite these benefits, store brand introductions are not always successful. Given the harmful effects of store brand introductions for national manufacturers, a few studies have examined strategies that manufacturers can implement to either preempt or deter store brand entry. Notably, Nasser et al. (2013) consider one store brand and multiple national brands that were offered by a national brand manufacture. Their findings propose that store brand entry can be deterred by national brand manufacturers by using their pricing and product line strategies in a targeted way. They identify the conditions under which the national brand manufacturer should either reposition their product, choose not to supply the store brand, or extend their product line by introducing a new national brand product to flank or otherwise undermine the store brand product. According to study by Chung and Lee (2017), when a substantially high degree of horizontal differentiation exists between two national brands, the retailer should position its store brand about half way between the national brand positions, so that competitive pressure is applied to both the manufacturers, resulting in lower wholesale prices and higher retail margins. Third, multiple store brands develop correspondingly with the real world. Store brand products become more diverse and multiple lines form, expanding with the retailer’s strategy. The literature concerning store brand introductions shows that retailers can benefit from introducing store brands by expanding the product categories, ensuring stronger margins, and differentiating their retail offerings. Du et al. (2005) find that the two national brands’ that are in a moderately competitive situation offers a store brand the most beneficial position for introduction. More retailers

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now carry a line of multiple store brands with different price and quality levels in a product category. When there are several retailers selling their own brands, the competition not only exists between their own brands and suppliers’ brands, but also between the retailers implementing their own brands at the same time. Choi and Coughlan (2006) emphasize that quality is the key determinant for success, derived from using a manufacturer Stackelberg pricing game to investigate the case where a retailer carrying two national brands introduces one store brand as the lowest quality product in the product category. The authors generalize the benefit of positioning store brands against the leading national brands by concluding that the introduction of multiple store brands in the same category is optimal when there are multiple differentiated national brands. For a more comprehensive set of results about the impact on retailers, see excellent reviews by (Sethuraman 2009). Meza and Sudhir (2010) consider multiple store brand entry by empirical analysis. Retailers can ensure lower wholesale prices as they are gaining bargaining power against manufacturers after the entry of their store brand. The authors found that retailers choose to strategically set prices to help store brands gain more market share on initial introduction of the new item. But the retailers will reset retail prices to maximize profit when the store brand gains a stable market share. Choi and Fredj (2013) use a game-theoretic model to examine a market channel composed of one national brand manufacturer and two retailers who, each, carry their own store brand and a national brand products. The model accounts for product competition between store brands and national brand products, as well as for inter-store competition between retailers. From a consumer’s perspective, a manufacturer’s leadership in this situation represents the worst-case scenario as it leads to the highest retail prices for all brands. The lowest prices for store brands are reached when the retailers have vertical price leadership and for the national brand when there is no leadership. Choi et al. (2018) consider the market of one manufacturer and two retailers where two retailers produce their own store brands and show how the retailers strategically optimize the price and quality of store brands based on research into customer tastes and preferences, and by lowering the production costs of the items in order to make their store brands more sustainable and profitable.

3.4 Pricing Strategy Under Different Classifications of Store Brand Manufacturers According to the classifications of the Store brand Manufacturers Association, the manufacturers that produce store brand products fall into the following three categories: national brand manufacturers that produce both national and store brands; specialist manufacturers who concentrate on producing store brands exclusively; retailers who operate their own manufacturing plants and provide store brands for their own stores. More commonly, in the literature, there are generally two possibilities for a retailer when they decide on a store brand production strategy. We

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125

discuss the classifications of store brand manufacturers from two perspectives: the national brand manufacturers that also produce the store brands, and the specialist manufacturers who produce goods only for store branding. National brand manufacturers producing their counterpart retailers’ store brands is the prevalent model. In America, more than 50% of national brand manufacturers of packaged goods also produce store brands that total more than 60% of store brands. Here, we first discuss the literature concerning pricing strategy for the situation in which a national brand manufacturer produces store brands. In Rao’s theoretical work (Rao 1991) for pricing and promotions in asymmetric duopolies, he suggested that the most likely behavior that the NB manufacturer engages in would be to induce SB retailers to raise their price to lessen competition with those who will pay some premium for NB: in equilibrium, SB retailers would choose a sufficiently high regular price for their SB and would be unlikely to engage in price promotions. Rao conjectures that SBs would not promote an equilibrium context for an extension to two or more NBs in the market. This conclusion can be supported by an empirical investigation by Cotterill et al. (2000), which suggested that cross-price elasticities are decidedly asymmetric with NB price having a major impact on SB sales, whereas SB price has a considerably smaller impact on NB sales. It appears that price is not an important strategic weapon when SB share is low (Cotterill et al. 2000). Store brands may give up fighting for price sensitive consumers and instead concentrate on consumers with low demand elasticity if SB can maintain a sufficiently high regular price and does not need promotions to retain price conscious consumers (Perloff et al. 1996). Wu and Wang (2005) discuss one common retailer and two national brand manufacturers, where only one manufacturer can also provide store brand products. The authors find that there exists no equilibrium where both national brands introduce their respective store brands. Producer choice for store brand introduction has been discussed less widely in the literature. Bergès-Sennou (2006) reports that the retailer can either entrust the production of their store brand to the national brand manufacturer at a low unit cost, with the disadvantage that both national brand and store brand is produced by the same agent, or the retailer can choose a firm from the competitive fringe with a higher unit production cost. The author finds that the distributor will entrust store brand production to the national brand manufacturer when the retailer’s bargaining power or the consumers’ store loyalty is high enough. In addition to the bargaining power versus efficiency trade-off demonstrated in Bergès-Sennou (2006), Bergès and BouamraMechemache (2012) consider not only a supplementary strategy for the retailer concerning the choice of store brand quality, but also a brand manufacturer’s potential counter-strategy by adapting national brand product characteristics. Depending on the structure of their capacity constraints, they find that a retailer may prefer to choose a specialist manufacturer to produce their store brand whereas they are likely to choose the national brand manufacturer in the case of having excess capacity. Hara and Matsubayashi (2017) examine single- retailer scenarios. They find that a premium store brand produced by one of the national brand manufacturers would make all channel members better off when a store brand of the retailer faces intense competition from the national brands of two competing national brand manufacturers.

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Cheng et al. (2020) examine this issue under a multiple-retailer scenario which has more correspondences with reality in business practice. The authors find that when the store brand perceived quality and the store brand supply price from a competitive fringe manufacturer are both relatively high, it is a better choice for the retailer to choose the national brand manufacturer to supply its store brand with a contract. Some literature investigates the specialist manufacturers, or the retailers produced produce store brand product. We now discuss the literature that concerns pricing strategy under the specialist manufacturers or the retailers own-produced store brands. Mills (1995) discusses the market composed of national brands that are offered by manufacturers and store brands that are provided by competitive manufacturers and investigates how the wholesale price of national brands affect store brand introductions and market share. Narasimhan and Wilcox (1998) develop a model using one national brand and one store brand product offered by the retailer. Using their empirical research to analyze the results, they find that market share alone may very well not be good a measurement of for explaining store brand success. Morton and Zettelmeyer (2004) discuss two national brand products provided by two different manufacturers and one store brand product provided by a special manufacturer. The authors look at how price and packaging information are used together in an analysis. Gabrielsen and Sørgard (2007) compare their research results before and after lowquality store brands were introduced into a retail situation. The authors find that the potential for store brand entry may lead to price concessions from the manufacturer of the national brand. If an exclusivity contract is offered by the national brand manufacturer, the retailer will introduce the store brand, and this will raise retail prices on the national brand. Tarziján (2007) establishes a model for one national brand and one store brand, finding that the national brand manufacturer would be well-advised to produce store brands if an independent manufacturer may otherwise produce a store brand product that is closer in quality to the national brand. National brand manufacturer gains from producing store brands are increasing with the concentration of the retail market. Amrouche and Zaccour (2009) establish an economic model of vertical differentiation to analyze the effect of store brand entry. The authors show that national brand manufacturers choose situations in which to produce store brands or situations where they allow or even prefer another manufacturer to produce the store brand, depending on the quality positioning chosen by the retailer, once the decision to introduce a store brand has been made. Nasser et al. (2013) consider a supply chain with a monopolist retailer, a national brand manufacturer, and a competitive market in which a manufacturer is supplying a store brand product at marginal cost. In such a case, the market will expand, and the retail price average will drop, due to the incumbent national brand competing with the store brand of lower quality. The retailer will always benefit from the potential to offer a store brand entry.

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3.5 Dynamic Pricing of Store Brands with Reference Effect Store brand refers to a brand independently created by a retailer. Unlike manufacturers’ national brands, store brands are only sold in retailers’ own stores. It is estimated that store brands account for a large proportion of sales in many regions, such as 30% in Europe, 22% in Australia and 17% in the United States (Palmeira and Thomas 2011). In China, many large retailers have introduced their own brands, such as “Lianhua Jiahui” of Lianhua and “Great Value” of Wal Mart. Many manufacturers adopt a national unified pricing model for national brand products, for example, some national brand products will directly mark the words “uniform retail price” or “suggested retail price” on the packaging bags. When a retailer dynamically adjusts the price of its store brand products, the manufacturer often finds this difficult to detect, and is not willing to change the unified pricing strategy for the sake of competition with a certain own brand. Compared with national brands with a wider sales scope, store brands are more flexible in pricing strategy because they are limited to retailers’ own stores. The historical price of store brand products and the price of national brand products form a reference price structure, which has a certain impact on consumers’ purchase decisions. Due to low awareness of real quality issues, the perceived quality of SB products and the difference between SBs and NBs will also affect consumer demand. Many scholars have conducted theoretical research on the pricing of store brands. For example, Choi and Fredj (2013) consider a market channel composed of one manufacturer and two competitive retailers, and examine competitive strategies between store brands and national brands as well as retailers. Kurata et al. (2007) analyze the pricing problem of store brand and national brand within multi-channel retailing. In this research, the national brand is sold in both online direct sales and retail stores, whilst the store brand is only sold in retail stores. The research find that brand loyalty can bring profits to both brands. Based on the limited shelf space of retailers factor, some of the literature studies the pricing of store brands (Kuo and Yang 2013; Wang et al. 2015). Using the conditions in which the advertising cost information of manufacturers is shared with retailers or secret, Wang et al. (2015) analyze the product pricing and advertising game between retailers and manufacturers with their own brands, and discuss optimal decision-making approaches. For additional relevant research, please refer to the literature review by Hyman et al. (2010). Most of these studies concern static models, while only a small part of the literature discusses the pricing of store brands from a dynamic perspective. For example, Karray and Martín-Herrán (2009) use a differential game system to analyze pricing and advertising strategies for NBs and SBs. The results show that the relationship between advertising and pricing decisions largely depends on the competition and complementary effects of advertising among supply chain players. There is some literature that considers the quality differences between brands. Under the premise of retailers as leaders, Ru et al. (2015) analyze the impact of introducing SB on manufacturer, retailer and the whole supply chain, and found

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that the introduction of a SB may enable manufacturers to create more profit, and may reduce the double marginal problem of the supply chain. Assuming that the perceived quality of SBs is lower than that of NBs, and on the basis of considering the introduction cost of store brands, Chen et al. (2011) discuss the optimal introduction conditions for SBs. However, none of the above studies considered the influence of a reference effect on consumption demand. In recent years, the dynamic pricing problem under the reference effect has become a contentious issue. Popescu and Wu (2007) study dynamic pricing strategy under the reference effect, and find that if the consumer is loss averse, the optimal price will tend to the steady-state value, otherwise, the optimal strategy is circular pricing. Nasiry and Popescu (2011) further investigate the dynamic pricing problem of enterprises when demand is affected by both price and reference price and consumers are loss averse based on the peak-end law. The results show that peak-end law and loss aversion limit the benefits of dynamic pricing. These reports in the literature are based on the discrete dynamic programming model, and some of them build a continuous form of reference price function to explore the influence of reference effect on the dynamic pricing of products. For example, Dye and Yang (2016) study the dynamic pricing and fresh-keeping technology investment of perishable products under the reference effect. In a supply chain composed of a manufacturer and a retailer, Zhang et al. (2014) study the dynamic pricing strategy under centralized and decentralized decision-making conditions, and the effect of reference price on pricing and profit. They find that the higher the initial reference price, the more sensitive the reference price effect would be, and that higher product loyalty would bring more benefits to the supply chain.

3.5.1 Problem Description and Model Building Consider a monopoly retailer that sells both manufacturer’s national brand products and its own brand products in its stores. Suppose the manufacturer adopts a fixed pricing strategy for the national brand products, and the price is Pn . Retailers can make a certain profit share from the sales of national brand products. The share proportion is k. Retailers can dynamically adjust the price of their own brand products, and their goal is to maximize the long-term benefits. In the existing theoretical models, the continuous reference price is often only related to the historical price of the product, that is, it has the following forms:    t eθt p(u)du , t ≥ 0 r (t) = e−θt r0 + θ 0

where, t is the time and θ is the memory coefficient of the consumer. The larger θ is, the shorter the memory time is, which infers a lower likelihood of loyalty by consumers to the products (Zhang et al. 2014).

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However, relevant research shows that the main factors that constitute consumer reference price include two aspects: internal reference price and external reference price (Kumar et al. 1998). The historical price of a product can be regarded as an internal reference price. When a retailer introduces its own brand, the manufacturer’s national brand product price constitutes an external reference price to the store brand. Therefore, this study constructs the following reference price function: r (t) = e

−θt



t

[r0 + θ

eθt (τ p(u) + (1 − τ ) pn )du], t ≥ 0

(3.1)

0

where, τ is the weight coefficient of the historical price of store brand products in the reference price. The larger τ is, the greater the impact of the reference price on the historical price of the store brand; the smaller τ is, the greater the impact of the reference price on the national brand product price. For the derivation of formula (3.1), the equation of motion regarding the reference price is obtained as follows: r˙ (t) = θ (τ p(t) + (1 − τ ) pn − r (t))

(3.2)

Assuming the product value v is uniformly distributed from 0 to v, the perceived quality of national brands is 1. When consumers purchase national brand products, the utility is as follows: u n (t) = v − pn . Suppose the perceived quality of store brand is q, in which 0 < q < 1. When consumers purchase store brand products, the utility is as follows: u s (t) = qv − p(t) + φ(r (t) − p(t)), Where φ is the influence coefficient of reference price on consumer utility. When the reference price is higher than the price of the store brand, consumers will gain a certain utility increase; otherwise, they will suffer a certain utility loss. (t)− p(t)) }, consumers will choose store When P{u s ≥ u n } = P{v ≤ pn − p(t)+φ(r 1−q brand products; on the contrary, they will choose national brand products. We define (t)− p(t)) . v˜ represents the critical utility of two brand products. Further, v˜ as pn − p(t)+φ(r 1−q we can obtain the demands of store brands and national brands as follows: Ds (t) = N Dn (t) = N

N v˜ = [ pn − (1 + φ) p(t) + φr (t)] v¯ v(1 ¯ − q)

N v¯ − v˜ = [v(1 ¯ − q) − pn + (1 + φ) p(t) − φr (t)] v¯ v(1 ¯ − q)

(3.3) (3.4)

Where N is the total market size. Property 3.1 When the reference price of store brand is larger, the demand for store brand products is larger, and the demand for national brand products is smaller.

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Property 3.2 When the perceived quality of the store brand is greater, the demand for the store brand product is greater, and the demand for the national brand product is smaller. When the perceived quality of a store brand product is higher, the production cost is generally higher. To simplify the analysis, it is assumed that the production cost of store brands is a linear function of perceived quality, i.e. c(q) = ωq, where ω is the production cost coefficient. The retailer’s profit function at time t is: R(t) = kpn Dn (t) + ( p(t) − c(q))Ds (t) The dynamic optimization problem for retailers aiming at maximizing long-term revenue is as follows:  ∞ e−ρt [kpn Dn (t) + ( p(t) − c(q))Ds (t)]dt max Π = p(t) 0  ∞ N {kpn [v(1 e−ρt ¯ − q) − pn + (1 + φ) p(t) − φr (t)] = v(1 ¯ − q) 0 +( p(t) − ωq)[ pn − (1 + φ) p(t) + φr (t)]}dt s.t. r˙ (t) = θ (τ p(t) + (1 − τ ) pn − r (t)), r (0) = r0 (3.5)

3.5.2 Fixed Pricing Strategy Without Reference Effect When the reference effect is not considered, there is no equation of motion of state variables. Thus, we have P(t) = P, that is, retailers adopt fixed pricing strategy. At time t, the demands for national brands and store brands are as follows: Ds = Dn =

N ( pn − p) v(1 ¯ − q)

N (v(1 ¯ − q) − pn + p) v(1 ¯ − q)

The decision problem for retailers becomes: 

∞

max Π = p

0

N {kpn [v(1 ¯ − q) − pn + p] + ( p − ωq)( pn − p)}dt v(1 ¯ − q) N = {kpn [v(1 ¯ − q) − pn + p] + ( p − ωq)( pn − p)} ρ v(1 ¯ − q) (3.6)

e−ρt

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Theorem 3.1 When the reference effect is not considered, we can obtain the retailer’s optimal price and the maximum profit:

p∗ =

(k + 1) pn + ωq , 2

N (k − 1) pn + ωq ¯ − q) + {kpn [v(1 ] ρ v(1 ¯ − q) 2 [(k + 1) pn − ωq][(1 − k) pn − ωq] } + 4

Π∗ =

. Proof. The second-order derivative of (3.5) is

∂ 2Π −2(1 + φ)N =

W2 , the reference price decreases with time. The reference price will eventually tend to the stable value W2 . ∗

Proof.. Because of v1 < 0, when r0 < W2 , we have drdt(t) = v1 (r0 − W2 )ev1 t > 0. Thus, r ∗ (t) increases with time; Similarly, when r0 > W2 , r ∗ (t) decreases with time. Because ev1 t decreases with t and lim ev1 t = 0, so we have lim r ∗ (t) = W2 . t→∞

t→∞

∗

Corollary 3.3 The optimal price p (t) eventually tends to a stable value: ¯ − q)θ τ W1 N [k(1 + φ) + 1] pn + N ω(1 + φ)q + N φW2 + v(1 2N (1 + φ) From corollaries 3.2 and 3.3, it can be seen that the reference effect, the perceived quality of store brand and the retailer’s share of the national brand’s revenue will have an impact on the stable value of the price and the reference price, which will be further discussed in the numerical analysis section.

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3.5.4 Numerical Analysis This section further analyzes the influence of reference effect and other main parameters on retailer pricing and profit through numerical simulation. The basic parameters are set as follows: N = 50 , v = 40, ρ = 0.1, θ = 0.5, τ = 0.7, q = 0.8, φ = 0.4, k = 0.08, ω = 4, pn = 20, r0 = 18. 1. The influence of the main parameters on price and reference price (1) Let θ be 0.3, 0.5, and 0.8, respectively, and keep the other parameters unchanged, as shown in Fig. 3.1. It can be seen from this figure that the larger the memory coefficient, the larger the stable price and the stable reference price. (2) Let φ be 0.2, 0.4, and 0.6, respectively, and keep the other parameters unchanged, as shown in Fig. 3.2. It can be seen from this that the larger the reference effect coefficient, the smaller the stable price and the reference price.

Fig. 3.1 Effect of memory coefficient on price and reference price

Fig. 3.2 The effect of reference effect coefficient on price and reference price

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(3) Let τ be 0.5, 0.7, and 0.9, respectively, and other parameters remain unchanged, as shown in Fig. 3.3. When the historical price-weight coefficient of the store brand is larger, the stable price and the steady-state reference price are smaller. (4) Let q takes the values 0.4, 0.6, and 0.8, respectively, and keep the other parameters unchanged, as shown in Fig. 3.4. The greater the perceived quality of store brands, the greater the stable price and the steady-state reference price. (5) Let k take the values 0.02, 0.08 and 0.12 respectively, and keep the other parameters unchanged, as shown in Fig. 3.5. It can be seen from this that the greater the proportion of retailers’ share of national brand revenue, the greater the stable price and steady-state reference price. Based on the above analysis, it can be found that the correlation coefficients (θ, φ, τ et al.) of the reference effect will have an impact on the optimal price and the optimal reference price, which means that if the manager of the enterprise ignores the role of the reference price and the reference effect, then it will result in a decrease in revenue due to the inappropriate prices. When the perceived quality of store brands

Fig. 3.3 The effect of weight coefficient on price and reference price

Fig. 3.4 The effect of store brand perceived quality on price and reference price

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Fig. 3.5 The impact of retailers’ share of NB on price and reference price

increases, retailers can increase the prices of store brands. When the proportion of retailers’ share of national brand revenue increases, retailers can increase the price of their own brands Since the demand for national brands will increase at this time, and the share of revenue for retailers will increase, this will increase the total revenue. 2. Reference effect on retailer’s income This section compares the benefits of a fixed pricing strategy without reference effects and a dynamic pricing strategy with reference effects. The reference effect may bring either an increase in revenue to retailers or a decrease in revenue. In this section,two examples are used.  the Let ∗1 and ∗2 respectively denote the maximum return withoutconsidering  ∗ − ∗ reference effect and considering the reference effect, and let  = 2∗ 1 × 100% 1 denote the rate of change of the return caused by the reference effect. (1) Select the basic parameters and calculate, we get Table 3.1. It can be seen from Table 3.1 that when the reference effect is positive, the return under the dynamic pricing strategy when considering the reference effect is always higher than the return under the fixed pricing strategy without considering the reference effect. (2) Let r0 = 5, τ = 0.9, and keeping the other parameters unchanged, the calculation results are shown in Table 3.2. Table 3.2 shows that the reference effect may reduce the retailer’s revenue, that is, the reference effect is negative. It may be seen that the return under the dynamic pricing strategy when considering the reference effect is always lower than the return under the fixed pricing strategy without considering the reference effect. Combining the above two examples, then we can draw the following conclusions: (1) When the price of the national brand is higher, the retailer’s income is greater no matter whether reference effect is considered or not; when the reference price brings a positive effect, the smaller the rate of change in income will be (as shown in the Table 3.1); while when the reference price brings a negative effect, the rate of change in revenue caused by the reference effect is negative and the absolute value is greater (as shown in Table 3.2).

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Table 3.1 Comparison of profits with reference effects being positive Π1∗

Π2∗

 (%)

16

2713.6

3111.91

14.68

18

3508.9

3976.44

13.32

20

4410.0

4949.98

12.24

22

5416.9

6032.52

11.36

Parameter pn

k

q

24

6529.6

7224.08

10.64

0.04

4400.0

5001.89

13.68

0.06

4402.5

4973.01

12.96

0.08

4410.0

4949.98

12.24

0.10

4422.5

4932.78

11.54

0.12

4440.0

4921.42

10.84

0.5

2481.0

2734.64

10.22

0.6

2800.0

3100.94

10.75

0.7

3335.0

3715.34

11.4

0.8

4410.0

4949.98

12.24

0.9

7645.0

8665.56

13.35

Table 3.2 Comparison of profits with reference effects being negative Π1∗

Π2∗



16

2713.6

2668.51

−1.662%

18

3508.9

3442.28

−1.899%

20

4410.0

4317.92

−2.088%

22

5416.9

5295.43

−2.242%

24

6529.6

6374.82

−2.37%

0.04

4400.0

4321.22

−1.79%

0.06

4402.5

4316.82

−1.946%

0.08

4410.0

4317.92

−2.088%

0.10

4422.5

4324.53

−2.215%

Parameters pn

k

q

0.12

4440.0

4336.65

−2.328%

0.5

2481.0

2452.45

−1.151%

0.6

2800.0

2760.61

−1.407%

0.7

3335.0

3277.88

−1.713%

0.8

4410.0

4317.92

−0.02088

0.9

7645.0

7449.06

−0.02563

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139

(2) The greater the retailer’s share of the national brand’s revenue, the greater the retailer’s revenue when the reference effect is not considered; when the reference price brings a positive effect, the smaller the retailer’s revenue when the reference effect is considered, and the smaller the rate of change in revenue due to the reference effect (as shown in Table 3.1); when the reference price brings a negative effect, the rate of change in revenue due to the reference effect is negative and the greater the absolute value (as shown in Table 3.2). (3) When the perceived quality of a store brand is greater, regardless of whether the reference effect is considered, the retailer’s revenue will be greater; when the reference price brings a positive effect, the greater the rate of change in revenue caused by the reference effect (as shown in Table 3.1); when the reference price brings a negative effect, the rate of change in revenue caused by the reference effect is negative and the smaller the absolute value (as shown in Table 3.2). The above conclusions indicate that when retailers do not consider the impact of the reference effect on consumers, they will underestimate or overestimate the long-term returns of their enterprises under different conditions.

3.5.5 Conclusion This study constructs a dynamic optimal control model to explore the pricing of store brand products for retailers when reference price and perceived quality affect the utility of consumers at the same time. This research shows that when the reference effect is not considered, the optimal price for retailers increases with NB price, the perceived quality of SB, the production cost coefficient of SB and the profit-sharing ratio of retailers. When the reference price is considered, if the initial reference price is less than the critical value, then the reference price will gradually increase over time. If the initial reference price is greater than the critical value, the reference price will gradually decrease with time: the reference price will eventually tend to a stable value, and thus the optimal price will eventually tend to be stable with time. Through numerical analysis, it is further clear that the correlation coefficient of reference effect, the perceived quality of SB and the change of retailer’s share proportion will affect the optimal price. When the reference effect is positive, the income under the dynamic pricing strategy is higher than that under the fixed pricing strategy; when the reference effect is negative, the income under the dynamic pricing strategy is lower than that under the fixed pricing strategy. In reality, not all retailers will introduce store brands. Scholars can further consider the situation with multiple retailers’ in competition and analyze the conditions for introducing their own brands at any relevant time. Since retail shelf space is often limited, it would be rational to add shelf space constraints based on this study to further improve predictive accuracy.

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3.6 Dynamic Assortment in the Presence of Brand Heterogeneity Retailers attach great importance to assortment planning, because the set of products that a retailer carries is one of the main determinants of consumers’ store choice and purchasing decisions. To attract more consumers and increase profits, retailers expanded their assortments dramatically in the past decades and have reached unprecedented levels of variety. However, such a high level of variety doesn’t testify to making consumers more satisfied and straightly brings about more operational costs. Thus retailers need to decide a reasonable set of products offering to consumers within a category. In addition, during the selling season, inventories of each product vary because of consumers’ choice. Some products are more popular among consumers, which leads these products to run out more quickly before the ending of the selling season. Then the fixed assortment will definitely restrict consumers’ purchasing decision, which causes retailers’ revenues to decrease. In order to make full use of these limited inventories and increase retailers’ revenues, dynamic assortment is introduced. In the setting of dynamic assortment, retailers are able to revise or change assortment selection in each selling period, rationing inventories of some products. On the other hand, hoping to expand market share, and obtain brand loyalty and bar- gaining ability, more and more retailers introduce a store brand (or a private label) to all kinds of categories. As Nielsen reported in 2017, the largest markets for store brand products are primarily found in the more mature European retail markets. For example, the store brand’s market share in Spain is up to 42% in 2016. In Asia-Pacific area, the store brand’s market share is relatively low, but store brand will continue growing up in the future at a higher speed. Many retailers in China, like Vanguard, LianHua and YongHui, are making great efforts for constructing their own store brands. Taking YongHui as an example, in 2018, it formally launched its own store brand whose quantity is about three hundred SKUs, covering kinds of categories, like household items, snack foods, etc. Therefore, there is sufficient evidence to believe that store brands will occupy a significant part of the retail market in the future. As a result, it is so common that retail categories consist of heterogeneous brands. For example, toothpastes including store brands and national brands are presented in the same shelf space. It is highly possible that consumers treat store brands and national brands unequally because of perceived quality difference. In China, consumers always associate relatively low quality and price with store brands. Conversely, they believe that products of national brands are of high quality. Under this circumstance, the assumption in some models that products in a category are homogeneous is becoming not reasonable. Obviously, the products from different brands are heterogeneous. In the view of consumers, products are divided into different subgroups according to products’ brands. Then we could only assume a homogeneous group of products under a brand of a category. We infer that products within one brand are closer substitution to each other than are products from another brand. Thus, brand heterogeneity makes the consumer choice process more

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complex. In this paper, we use a two-stage consumer choice model to depict the consumer choice process. Without a sharp understanding of consumer choice process, retailers tend to treat store brands and national brands equally in the dynamic assortment planning. However, the reality is different as we have mentioned above. If retailers could not realize brand heterogeneity in theoretical analysis stage and put related results into practice, the actual outcome will certainly deviate from the expectations. This has a negative effect on retailers’ operational decisions, which should be examined and avoided in retailers’ operation. When reviewing the literature about dynamic assortment, we find that there are still no papers focusing on heterogeneous brands in the context of dynamic assortment. Therefore, we want to fill the gap between literature and reality. The goal of this paper is to explore the revenue impact of dynamic assortment with heterogeneous brands, which contain a store brand and a national brand. In particular, we want to examine how brand heterogeneity affects the performance of the dynamic assortment. Using sales transaction data from a supermarket chain’s data set, we want to estimate consumer choice behavior and demonstrate the potential revenue improvements from dynamic assortment optimization. We consider a product category with multiple product types from one store brand and one national brand. We characterize consumer choice process as a two-stage nested multinomial logit (NMNL) model Ben-Akiva and Lerman (1985). From national brand and store brand, consumers first choose which brand to buy and then select a product type within the chosen brand. Over a finite selling season, the retailer sells limited inventories of products from two brands. To maximize her profit, the retailer needs to make assortment decisions for the whole category. In each selling period, the retailer determines an assortment offered to consumers, which may include products from two brands. We formulate this decision problem as a dynamic assortment planning problem with brand heterogeneity. Using available sales transaction data from a supermarket chain in China, we first estimate consumers’ two-stage choice behavior and preferences. Our estimation results show that consumers have different perceived utilities for products of the store brand and the national brand. In particular, consumers present higher price sensitivity and lower utilities for products of the store brand, in comparison with those of the national brand. Furthermore, our results show that the products within one brand are closer substitution to each other than are products from another brand. That is brand heterogeneity, which means that the retailer should not treat store brand and national brand equally, while characterizing consumer choice process. Otherwise as we have demonstrated, the retailer’s expected revenues will be significantly overestimated. We found that the degree of this revenue overestimation is affected by products’ initial inventory levels and prices. Such an effect of prices differs between two brands. Our empirical study suggests that the bene- fits that could be obtained from dynamically optimizing assortment controls to account for choice behavior are significant: the dynamic assortment leads to 11.40% revenue gain in comparison with the offer-all policy, under which all available products are offered to consumers in each period. We further show that the performance of dynamic assortment depends

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on initial inventory levels and prices of products. Although more inventories could bring out more revenues, we found that there were no significant revenue improvements when the inventory levels exceed some threshold. We finally find that lowering products’ prices not only reduces the retailer’s expected revenues but also weakens the performance of dynamic assortment. The same price discount on products whose prices are relatively lower in one brand would reinforce the performance of dynamic assortment. In summary, the main contributions of our paper can be described as follows. • We first study the dynamic assortment optimization problem with multiple products from one national brand and one store brand. • A two-stage NMNL model is used to characterize the consumer choice process. We estimate the consumer choice behavior from readily available retailing sales transaction data. • We empirically demonstrate existence of brand heterogeneity in real retail market. • Our results suggest that ignoring brand heterogeneity will make the retailer’s expected revenues significantly overestimated. The potential revenue overestimation depends on initial inventories and prices of products of national brand and store brand. • We demonstrate and assess the potential revenue improvements of implementing the dynamic assortment with the estimated consumer choice model.

3.6.1 Literature Review Our work is related to three streams of research. The first one includes work on the characteristics of consumers’ buying preferences when retailers offer store brand products. The second one is the literature on consumer choice model. The third stream is related to retail assortment planning. 1. Consumer Purchase Preference for Store Brand Facing products of store brands and national brands, consumers always have a trade-off between some factors, like quality (Bontems et al. 1999), price (Sethuraman and Cole 1999), brand loyalty (Anselmsson et al. 2008). There are two main research approaches: (1) Using a random model to describe consumers’ purchase behavior of store brand. Based on a random coefficient Logit model, Chintagunta et al. (2002) explore effects of introduction of a store brand into a particular product category on both demand side and supply side. Hansen et al. (2006) investigate the behavior of store brand buyers and develop a multi-category brand-choice model which is applied to a set of food and nonfood product categories. They show that there are strong correlations in household preferences for brands across categories. (2) Using empirical analysis. Baltas (2003) study store brand demand and its determinants using the method of empirical research in a two-stage model to describe the demand for store brand products. Diallo (2012) investigate jointly the effect

3.6 Dynamic Assortment in the Presence of Brand Heterogeneity

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of store image perceptions, store brand price-image and perceived risk toward store brand on consumers’ purchase intention in the context of an emerging market Brazil. Using data from a consumer survey, they find that store image perceptions and store brand price-image influence significantly the purchase intention directly or indirectly via the effect of perceived risk toward store brand. Our study focuses on the operational level and intends to examine the impact of heterogeneous brands on retailers’ dynamic assortment. 2. Consumer Choice Process The multinomial logit (MNL) model has been widely used in demand estimation (e.g., Vulcano et al. 2012; Newman et al. 2014) and assortment related issues (Van Ryzin and Mahajan 1999; Rusmevichientong et al. 2010). These MNL-based models assume a homogeneous group of products in one category possibly variants of the same product, such as the same garment with different styles, colors, or sizes. The applicability of these models may be limited because most retail categories consist of heterogeneous product subgroups such as coexistence of store brand products and national brand products. And we believe that the products within a subgroup are closer substitution to each other than are products from another subgroup when consumers have strong brand concept. Because of the property of the so-called independence of irrelevant alternatives (IIA) (Anderson and De Palma 1992), the MNL model fall short of capturing these interactions in a category with heterogeneous product subgroups. In a category containing heterogeneous product subgroups, a nested multinomial logit (NMNL) model can be a better alternative to depict consumer choice process (Ben-Akiva and Lerman 1985). Under the NMNL model, customers follow a hierarchical choice process. Consumers first choose among subgroups and then choose a product in the chosen subgroup. The NMNL model provides closed-form choice probabilities much like the MNL model and has been widely used in modeling consumer choice process. Kök and Xu (2011) model brand choice and product type choice using NMNL model with two different hierarchical structures, and study assortment planning and pricing. Wan et al. (2018) build NMNL model to capture consumer choice process under which consumers choose the store at the first level and select the product at the second level. We are interested in a nested structure that reflects brand heterogeneity within a product category. 3. Retail Assortment Planning Assortment planning is defined by the set of products carried in each store at each point of time. Kök et al. (2008) provide an excellent review of this literature. The assortment planning problem can be divided into static assortment problem and dynamic assortment problem where retailers can revise or change assortment selection as time elapses. Regarding static assortment planning, many decision models have been developed with different demand models. Van Ryzin and Mahajan (1999) derive the optimal assortment policy for a category using the MNL model. Based on this study, Cachon et al. (2005) incorporate consumer search costs into model, and point out that failing to incorporate consumer search into an assortment planning process may cause a retailer to have an assortment with less variety and significantly

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lower expected profits compared to the optimal solution. Smith and Agrawal (2000) develop a general demand model characterized by the first-choice probabilities and a substitution matrix in an assortment and a methodology for selecting item inventory levels. Chong et al. (2001) present an empirically based modeling framework using an NMNL model to assess the revenue and lost sales implication of alternative category assortments. Mahajan and Van Ryzin (2001) consider assortment planning and inventory management problem under dynamic substitution using a simple utility maximization mechanism. They develop a sample path gradient algorithm to determine the optimal assortment and inventory levels. Gaur and Honhon (2006) use a Hotelling-type location choice model to study assortment planning and inventory management problem in a single category. Kök and Fisher (2007) describe a methodology for demand estimation and substitution rates which are applied to assortment optimization using data from a supermarket chain. All of these papers consider homogeneous product subgroups within categories. Kök and Xu (2011) study assortment planning and pricing decisions in one category with multiple subgroups of products, which is closer to our formulation. They show whether consumers first choose the product type or first choose the brand under a centralized regime and a decentralized regime, has a critical effect on the optimal management policy. Davis et al. (2014) study a class of assortment optimization problems where customers choose among the offered products according to the nested logit model. Dynamic assortment planning problem appeared in fashion and apparel retailers at first. Innovative firms such as Zara, Mango, and World Co. created highly responsive and flexible supply chains and cut the design-to-shelf lead time down to 2–5 weeks, which enabled them to make design and assortment selection decisions during the selling season. Caro and Gallien (2007) first study the dynamic assortment optimization. Using the multiarmed bandit for the assortment design problem faced by fast-fashion retailers, they derive bounds on the value function and propose an indexbased policy that is shown to be near optimal when there is some prior information on demand. Rusmevichientong et al. (2010) consider an assortment optimization problem subject to a capacity constraint. They propose an adaptive algorithm that learns the unknown parameters from past data and at the same time optimizes the profit. Sauré and Zeevi (2013) study a family of stylized assortment planning problems in which a general random utility model characterizes consumer choice. They develop dynamic policies that balance trade-off between exploration and exploitation and prove that these policies satisfy some performance bounds. Ulu et al. (2012) use a Hotelling locational model with unknown demand distributions that can be discovered by varying the assortment over time. Talebian et al. (2014) propose a stochastic dynamic programming model for simultaneously making assortment and pricing decisions which incorporates demand learning using Bayesian updates. They analytically show that it is profitable for the retailer to use price reductions early in the sales season to accelerate demand learning. All of the dynamic assortment planning papers reviewed above consider learning consumer demand during the selling season.

3.6 Dynamic Assortment in the Presence of Brand Heterogeneity

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There are still some papers studying dynamic assortment planning problem without demand learning. Rusmevichientong and Topaloglu (2012) study robust formulations of assortment optimization problems using the MNL choice model under both static and dynamic settings. Caro et al. (2014) propose an attraction model in which product preference weights decay over time. They formulate the assortment packing problem, in which given a collection, a firm must decide in advance the release date of each product to maximize the total profit over the entire selling season. Cınar and Martınez-de Albéniz (2013) model dynamic assortment planning problem by assuming that products lose their attractiveness over time and they have a cost for enhancing the assortment. They characterize the optimal closedloop policy to maximize firm’s profits. Bernstein et al. (2015) propose the idea of assortment customization based on limited inventory conditions and in the presence of heterogeneous customer segments. Note that all these papers do not examine the effect of brand heterogeneity on the dynamic assortment, which is the focus of this paper.

3.6.2 Problem Statement and Model We study the dynamic assortment optimization problem for one product category (e.g., toothpastes, shampoos and purified water category). Over a finite selling season, the retailer sells a set of heterogeneous products from one store brand and one national brand. Time is discrete and indexed by t = 1, 2, . . . , T . T can be viewed as the number of revision points where the assortment can be refreshed. Let T = {1,2, · · ·, T}. In this paper, we use SB and NB to denote store brand and national brand, respectively. Let Nb be the set of products

of brand b ∈ B = {SB, NB}, which are sold by the retailer. Define N = N S B N N B . For a given product j, let b( j) be the brand of this product. In each period, the retailer determines an assortment of products from two brands to offer to consumers, given available inventory. There is no replenishment during the selling season. For each period t, let Sbt denote the Sbt , ∀t ∈ T. assortment of products of brand b ∈ B. Hereafter, we define S = b∈B

The retailer needs to determine an optimal assortment in each period, so that the expected revenues are maximized over the selling season. 1. Consumer Choice Model Given assortment St , consumers make purchase decisions following a two-stage hierarchical choice process. In this paper, we use the nested multinomial logit (NMNL) model to depict consumer choice behavior (see Fig. 3.6 for illustration). In this model, a consumer first decides which brand or the no-purchase option to choose. If the consumer chooses a brand instead of no-purchase option, then he decides which product offered by this brand to purchase. We assume consumers are utility maximizers and individual consumer’s utilities for alternatives are random variables. Let Uit be the utility of a consumer purchasing product i in period t, which can be decomposed into two parts: a representative vit

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Fig. 3.6 Two-stage consumer choice model

and a random component ∈it , That is, Uit = vit + ∈it The representative component vit is deterministic, which is modeled as a linear-inparameter combination of observable attributes: vit = β0i + β1i p1i + β2i f i where β0i is a specific constant associated with product i; β1i and β2i are unknown parameters. These parameters will be estimated from retailers’ sales transaction data. Let β be parameter vector in the utility function. p1i is the price of product i in period t; f i is one specific feature of product i. In each period, the utility of no-purchase is defined as U0t = v0t + ∈0t In the NMNL model, it is assumed that the random components ∈it S and ∈0t S are independent and identically distributed random variables with a Gumbel (or double-exponential) distribution (Gumbel 1958). The cumulative density function of the Gumbel distribution is F(x) = exp[−exp( μx1 + γ )], where γ is the Eulers constant (= 0.5722…) and μ1 is the scale parameter. Here μ1 is a positive constant, with a higher value of μ1 corresponding to a higher degree of heterogeneity among the population. We let μ1 = 1 to simplify exposition. As we know, the Gmubel distribution has some useful analytical properties, the most important of which is that the distribution of the maximum of n independent Gumbel random variables with the same scale parameter μ1 = 1 is also a Gumbel random variable. The assumption of the Gumbel distribution in the NMNL model, while restrictive, leads to a simple form of the choice probabilities. Without loss of generality, we assume that no-purchase utility is zero; i.e., v0t = 0.

3.6 Dynamic Assortment in the Presence of Brand Heterogeneity

147

Under the NMNL, the probability that a consumer chooses product i offered by brand b in period t, is given by Pibt (β, μ, St ) = Pbt (β, μ, St ) ∗ Pt (i|b, β, Sbt ), ∀i ∈ Sbt , ∀b ∈ B, ∀t ∈ T where Pbt (β, μ, St ) denotes the probability that a consumer chooses brand b in period t, which depends on parameters β, μ, and assortment St . It is given by  Pbt (β, μ, St ) =





j∈Sbt exp(vjt )



b ∈B

1/μ

j∈Sbt exp(vjt )

1/μ

+1

,

where μ is the scale parameter that controls the brand heterogeneity. Pt (i|b, β, Sbt ) is the probability of a consumer choosing product i in an assortment Sbt of brand b. It can be defined using the MNL model, Pt ( i|b,β, Sbt ) = 

exp(vit ) . k∈Sbt exp(vkt )

We let P0t (β, μ, St ) be the probability that a consumer does not purchase from the offered assortment; i.e., P0t (β, μ, St ) =





b ∈B

1

 j∈Sbt

exp(vjt )

1/μ

+1

Note that if μ = 1, the NMNL model reduces to the standard MNL model, where all products of different brands form a homogeneous set. The value of μ will be also estimated from retailer’s sales transaction data. 2. The Dynamic Assortment Optimization Model In this section, we give the dynamic programming formulation for the dynamic assortment optimization problem. Given a limited initial inventories of products of two brands, the retailer needs to determine the assortments for both SB and NB in each selling period, to maximize the expected revenues over the whole selling season. Without loss of generality, we assume that the salvage value for unsold units at the end of the selling season is zero (it is a common assumption in the literature). We assume that the number of consumer arrivals in each period t follows a Poisson distribution with mean λt (arrival rate). In this paper, we consider an identical arrival rate λ over all the T periods. We assume a sufficiently small time interval such that in each period there is at most one consumer arrival (this assumption is the same as in Talluri and van Ryzin (2004). Thus, the probability of no consumer arrival is 1 − λ. We further assume that a product’s prices remains unchanged over time; that is, we do not consider pricing decision in the dynamic assortment.

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For each product i, let yit and yit respectively be the initial inventory levels at the beginning of the selling season and in period t. Let yt be the corresponding inventory vector in period t, yt = {yit : i ∈ N }. Given inventory yt , let S b (yt ) be theset of possible assortments of brand b in period t. Define the value function (yt ) as the maximum revenue obtainable from periods t

t, t + 1, · · · , T , given that for each product  i, there are yit inventory units remaining at time t. Then, the Bellman equation for (yt ) is and the boundary condition is  t      t (yt ) = max { λPib (β, μ, St ) pi + (yt − ei ) +(λP0t (β, μ, St )+1− Sbt ⊂S b (yt ) b∈B i∈Sbt t t+1  λ) (yt )}, and the boundary condition is t+1

  yT+1 = 0 T+1

The first term is the expected value from an arriving consumer (an arrival occurs with probability λ). For a given assortment St, this term accounts for the probability of selling one unit of product i, earning a revenue of pi from the sale, plus the revenue-to-go function in period t + 1 evaluated at the current inventory level minus the unit sold in period t. ei is a vector with the ith component being 1 and others being 0 s. The second term accounts for the possibility that no consumer arrives (with probability 1 − λ), or the arriving consumer does not make a purchase. In this case, the revenue is the revenue-to-go function in period t + 1 evaluated at the current vector of inventory levels.

3.6.3 Parameter Estimation In this section, we discuss how to estimate parameters in the consumer choice model, using available sales transaction data. Let z it be the number of purchases of product t observed in period  t. We denote the total number of observed purchases in period z it . We denote vector of parameters β, μ and λ by φ; i.e., t by mt; i.e., m t = i∈N

φ = (β, μ, λ). A major incompleteness in the sales transaction data is that there is no information about the number of consumers who arrive in a period but do not make a purchase; that is, the total number of transactions in a given period is a censored approximation to the true demand of the period. We treat the no-purchase option as a separate product that is always available. Following Abdallah and Vulcano (2016) and Newman et al. (2014), we can form the incomplete data likelihood function, L(φ) as: L(φ) =

T  t=1

⎛

 t P jb(  j)(β,μ,St ) ⎝ P(m t customers buy in period t|φ)  m t !  t i∈N z it ! i∈St P j∈St

ib(i)(β,μ,St )

z jt ⎞ ⎠

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149

where   m t    t t λ i∈St Pib(i)(β,μ,S ex p −λ i∈St Pib(i)(β,μ,S ) ) t t  . P(m t customers buy in period t|φ) = i∈N z it !

Taking logarithm, we can write the log-likelihood function L L(φ) as L L(φ) =

T  t=1

⎛  m t     t P jb( ex p −λ i∈St P t λ i∈St P t  j)(β,μ,St ) ib(i)(β,μ,St ) ib(i)(β,μ,St ) ⎜   log⎝ t z ! it i∈N i∈St P

z jt

⎞ ⎟ ⎠

ib(i)(β,μ,St )

j∈St

⎡ ⎛    m t ⎞ t t T Pt ex p −λ i∈St Pib(i)(β,μ,S λ i∈St Pib(i)(β,μ,S   jb( j)(β,μ,St ) ⎢ ⎜ t) t) ⎟  = log  ⎣log⎝ ⎠+ t z ! it i∈N i∈St P t=1

=

z jt

⎤ ⎥ ⎦

ib(i)(β,μ,St )

j∈St

⎡ ⎞ ⎛ ⎤ T ! "      t t t ⎣m t log λ + m t log⎝ ⎠−λ ⎦ Pib(i)(β,μ,S P − log(z !) + z log P it jt ib(i)(β,μ,S ) ib(i)(β,μ,S ) ) t

t=1

t

i∈St

i∈St

t

i∈N

⎡ ⎤ T ! "     t t ⎣m t log λ − λ ⎦ Pib(i)(β,μ,S − log(z !) + z log P = it jt jb( j)(β,μ,S ) ) t

t=1

i∈St

t

i∈N

The last equality holds since m t = # $  t  z jt log Pib(i)(β,μ,S ) .

j∈N

j∈St

j∈St



# z jt , and thus m t log

j∈N



i∈St

$ t Pib(i)(β,μ,S t)

=

t

i∈St

To estimate parameter β, μ and λ, we use the Markov Chain Monte Carlo (MCMC) algorithm, where a Bayesian method is utilized for sampling (Musalem et al. 2009, 2010). The MCMC estimation approach is described in Algorithm 3.1. Let π(φ) be the prior distribution of φ. A uniform prior distribution is assumed for each parameter. In addition, we use the prior distribution as the proposal distribution. Algorithm 3.1. The MCMC estimation method Input sales transaction data D. Set the prior distribution π(φ) of φ. Sample φ 0 according to π(φ). Set r = 1. while r ≤ 100, 000 Set φ (r ) = φ (r −1) . for each parameter φ j , do Sample φ ∗j from the proposal distribution. ∗(r )

Generate φ j

.

Generate a random number ω from interval [0,1]. (continued)

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3 Pricing Strategy for Store Brands

(continued) Algorithm 3.1. The MCMC estimation method & ! ! " %  " ∗(r ) ∗(r ) = min{exp L L φ j − L L φ (r ) , 1} if ω < P accept φ j ∗(r )

Update φ (r ) using φ j

.

end if end for Set r = r + 1. end while '

Set φ =

1 10,000

100,000 

φ (r ) .

t=90,001

In our implementation, we choose the starting point for each parameter by simply taking a random point from a specific uniform distribution. To sample parameter φ, we use a hybrid Gibbs sampling method, which embeds the Metropolis-Hastings algorithm within Gibbs sampling. Gibbs sampling breaks down the problem by drawing samples for each parameter directly from that parameters conditional posterior distribution, or the prob- ability distribution of a parameter given a specific value of other parameters. However, sampling φ from their conditional posterior distributions is challenging. We use the independent Metropolis-Hastings algorithm to sample φ. Particularly, at iteration r , Gibbs sampling consists of a series of 26 (the number of parameters) steps, with step j of iteration r corresponding to an update of the subvector φ j conditional on given all the other elements of φ. Let φ (r ) be the parameter value sampled at iteration r , which is initialized equal to φ (r −1) . Suppose we are sampling the jth parameter (say φ j ) in φ. As a result, let φ ∗j denote the sampled value ) denote the updated parameter vector of this parameter. At iteration r , we let φ ∗(r j after the jth parameter is sampled; i.e., the jth component is replaced by φ ∗j . In the ) Metropolis-Hastings algorithm, φ ∗(r is accepted with probability j % & ! ! "  " ) ) P accept φ ∗(r = min{exp L L φ ∗(r − L L φ (r ) , 1} j j ) ) (r ) We set φ (r ) equal to φ ∗(r if we accept φ ∗(r unchanged. j j . Otherwise, we keep φ The MCMC estimation procedure terminates with 100,000 iterations. We discard the first 90,000 estimation results. The average of the last 10,000 sampled φ (r ) is used 100,000  1 φ (r ) . as the estimand φ ; i.e., φ = 10,000 '

'

t=90,001

3.6 Dynamic Assortment in the Presence of Brand Heterogeneity

151

3.6.4 Empirical Analysis In this section, we first report the estimation results on a real-world sales transaction data set from a large supermarket chain in Shanghai, China. In particular, we want to estimate consumer choice behavior and examine how the brand heterogeneity affects the performance of dynamic assortment. We then assess the potential revenue improvements from dynamic assortment. To measure the performance of dynamic assortment, we calculate the percentage of revenue improvements relative to a benchmark called offer-all policy, under which all available products are offered to consumers in each period. 1. Estimation Results In order to estimate parameters in the consumer choice model, we obtained sales trans- action data from one of the largest supermarket chains for consumer products in Shanghai, China. We were provided data with one SB (called “JiaHui”) and one NB (called “YiBao”) from the purified water category. These two brands are both top brands in this super- market. Under these brands, there are diversified products with different characteristics. For each brand, we selected four products that differ in volumes. These eight products are labeled as Pi , ∀i = 1, 2, · · · , 8. The data was collected from a store in downtown Shanghai, and covered 122-day sales transaction, ranging from June 1 to September 30, 2015. All these eight products of two brands were sold during this period. The data description is shown in Table 3.3. In this empirical study, we include prices and volume in definition of consumer utility. The last row of Table 3.3 reports the number of transactions of each product during the considered selling season. For each transaction, the sales data includes information of sales time, selling price, and the number of units sold. The sales transaction data can give us the assortments St every day. The assortments St can vary in the considered selling season of 122 days. But we assume that the assortments St do not vary during different periods of one day, because the retailer did not record this information. That is, the assortments in one day remain unchanged. Note that our proposed model and methodology do not require that the assortments cannot change in one day. As previously stated, we assume that each consumer purchases at most one unit product in each period. Time slice is determined so as to guarantee that at most one consumer arrives in each period. For eight products of two brands, we recorded time span between two adjacent transactions and conducted the statistical analysis. We found that it is small Table 3.3 Data for estimation Item

Store brand: JiaHui P1

P2

P3

P4

P5

P6

P7

P8

Volume

350 mL

550 mL

4L

5L

350 mL

555 mL

1.555 L

4.5 L

Price (RMB) # of transactions

1.6 390

2 583

National brand: YiBao

5.9 590

6.5 1112

1.3 356

1.5 3328

3.1 1381

7.8 1129

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3 Pricing Strategy for Store Brands

enough to set time slice equal to 30 s, so that at most one purchase occurs in this time slice. The supermarket is operated from 7:30 a.m. to 10:00 p.m. every day. Thus, the selling season is divided into T = 122 ∗ 14.5 ∗ 3, 600/30 = 212, 280 time periods. After preprocessing data, we finally applied the MCMC algorithm to estimate the parameters. We assume a uniform prior distribution for each parameter, which is detailed in Table 3.4. The estimation results are shown in Table 3.5. From these results, we find that there are significant distinctions between SB products and NB products. The price sensitivity of the SB products is higher than that of the NB products. This makes sense. In Chinese retail market, the popularity of SB is far behind that in mature European retail markets. However, nowadays more and more retailers launch their own store brands, such as “Better Living” of LianHua, “Family of Run” of Vanguard, etc. A majority of consumers attach low price and low quality to SB. Therefore, it is reasonable that consumers are more sensitive to prices of SB products. It is interesting to interpret the relative values of the coefficients as indicators of the sensitivity of the choices. Let us look at P1 and P5. These two products have the same volume, 350 mL. The mean utilities of these two products are, respectively, v1t = 3.2062 − 5.1192 p1t + 0.7727 × 0.35 = 3.4766 − 5.1192 p1t and v5t = −0.2783 − 1.1757 p5t + 0.8334 × 0.35 = −0.0116 − 1.1757 p5t . Table 3.4 Uniform prior distributions Parameter

Store brand: JiaHui

National brand: YiBao

P1

P2

P3

P4

P5

P6

P7

P8

β0

(3, 4)

(−2, − 1)

(−1, 0)

(2, 3)

(−1, 0)

(0, 1)

(−2, −1)

(1, 2)

β1

(−6, − 5)

(−2, − 1)

(−2, − 1)

(−2, − 1)

(−2, − 1)

(−0.1, 0)

(−0.1, 0)

(−1, 0)

β2

(0.5, 1.5)

(2, 3)

(2, 3)

(0.5, 1.5)

(0.5, 1.5)

(0, 1)

(1, 2)

(0, 1)

μ

(4, 5)

λ

(0, 0.1)

Table 3.5 Estimated parameters Item

Store brand: JiaHui P1

P3

3.2062

−1.8639

−0.9194

2.8945

−0.2783

0.8822

−1.3001

1.3577

−5.1192

−1.6170

−1.9444

−1.8845

−1.1757

−0.0143

−0.0170

−0.5888

0.7727

2.6304

2.5332

1.4797

0.8334

0.6168

1.0774

0.7229

−3.6512

−2.2585

−1.9557

−1.5150

1.2031

0.3226

0.0180

'

β0

'

β1

National brand: YiBao

P2

P4

P5

P6

P7

P8

'

β2 '

μ

4.6069

'

λ '

v

0.0348 −4.7140

3.6 Dynamic Assortment in the Presence of Brand Heterogeneity

153

We then have v1t > v5t , when p1t < 0.6814 + 0.2297 p5t . The current price of P5 is 1.3RMB. Therefore, setting p1t less than 0.98RMB is able to make P1 a more attractive alternative (on average). However, the current selling price of P1 is 1.6RMB. This type of analysis could be useful for the retailer to determine price strategies of SBs. Further we can see the volume coefficients have the positive sign. That is, the retailer is able to increase her revenues through product volume increment. More importantly, the factor of “volume” has a larger effect on the products of SB. If such a volume-based strategy is implemented, the retailer may focus on those products with larger β 2 e.g., products P2 and P3. Substituting parameters’ values into equation vit = β0i + β1i p1i + β2i f i , we can get the mean utility of each product. For instance, the mean utility of P1 is v1 = 3.2062 − 5.1192 × 1.6 + 0.7727 × 0.35 = −4.7140. The mean utility of each product is shown in the last row of Table 3.5. The results show that products of SB have lower utilities than those of NB. Therefore, the SB products have smaller probabilities of being chosen by consumers. Finally we can see that the estimated value of μ is greater than 1. It is concluded that products across two brands are less replaceable than products within a brand. Note that under the MNL model, the products of SB and NB are put in one product pool and treated equally. Naturally, we conjecture that brand heterogeneity may affect the performance of the dynamic assortment. Such an effect will be examined explicitly in the next section. 2. Assessing Effect of brand Heterogeneity on Revenue Improvements The above estimation results have demonstrated that consumers have different utilities for SB and NB products. If such a hierarchical choice behavior is ignored, the expected revenues from the assortment planning may deviate from the reality. We next used the estimation results fit from the real-word sales transaction data to assess the potential revenue overestimation from brand heterogeneity. Toward this end, we respectively solved the dynamic assortment optimization problem with the MNL and NMNL models. To solve the dynamic assortment optimization problem, we need to set the initial inventory level of each product at the beginning of the selling season. To this end, we assume all products are offered to consumers in each period and set its initial  inventoryequal to the rounded expected sales quantity of each product, i.e., yi0 = λ ∗ Pib ∗ T . where Pib is the probability of choosing product i of brand b, given that all the products are displayed to the customers. λ is the one determined in Section 3.6.4. Now the setting of the initial inventory depends on the length of the selling season T . A larger T will give a larger initial inventory. Thus, a lager T will produce a largescale dynamic programming, which is required for solving the dynamic assortment problem. To obtain a tradeoff between the required computing effort and reporting meaningful managerial insights from the dynamic assortment, we set T = 600 in our experiment. As a result, the initial inventory is y0 = (1, 1, 2, 3, 1, 6, 3, 2). The price of each product is set to the real one derived from the sales transaction data; i.e., the price vector p0 = (1.6, 2, 5.9, 6.5, 1.3, 1.5, 3.1, 7.8). As a comparison benchmark, '

'

'

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3 Pricing Strategy for Store Brands

we also solved an assortment optimization problem under the offer-all policy, where all the available products are offered to consumers. Table 3.6 reports the expected revenues from the assortment optimization under two consumer choice models. In the last row of Table 3.6, we show the percentage of revenue overestimation when the retailer treats SB and NB equally. The results in Table 3.6 clearly show that the dynamic assortment with the MNL model yields larger expected revenues than that with the NMNL model. In other words, the expected revenues will be significantly overestimated if consumers’ hierarchical choice behavior is ignored. Under offer-all and dynamic assortment strategies, the expected revenues are overestimated 8.79 and 9.58%, respectively. The results demonstrate that it is necessary for the retailer to take into consideration the impact of brand heterogeneity, while capturing consumer choice process. More importantly, we can see that the retailer does benefit from dynamically adjusting products offered to consumers. In comparison with the offer-all policy, the dynamic assortment leads to 11.40% increase of the expected revenues, when the estimated NMNL model is used. The above results have demonstrated that customers’ hierarchical choice behavior incurs revenue overestimation. We further examined whether the degree of revenue deviation is related to the initial inventory level. To this end, we solved the assortment optimization problem with different levels of initial inventory under dynamic assortment. In particular, we want to show how the revenue overestimation evolves with change of inventories of SB and NB, respectively. We denote y0 = (1, 1, 2, 3, 1, 6, 3, 2) in the last comparison study as “basic inventory”. Figure 3.7 depicts the results. In Fig. 3.7a, the inventory levels of SB products are simultaneously increased, or decreased based on the basic inventory level y0. Figure 3.7b applies to NB products. Results in Fig. 3.7a show that the percentage of revenue overestimation rises up, when we simultaneously increase the inventory levels for SB products. The same observation can be derived from the case of adjusting inventory levels of NB products (see Fig. 3.7b). We further examine whether simultaneously adjusting prices of SB, or NB products will affect the revenue overestimation under dynamic assortment. To this end, we adjusted basic price vector p0 by a discount factor δ to create six new price scenarios. The results are reported in Fig. 3.8. “SB-20%” means that the  discount factor δ = 20% applies to all the SB products. That is p = {1.6 × (1 − 0.2), 2 × (1 − 0.2), 5.9 × (1 − 0.2), 6.5 × (1 − 0.2), 1.3, 1.5, 3.1, 7.8} = Table 3.6 Expected revenues under two consumer choice models

Choice model

Expected revenues Offer-all Dynamic assortment

Revenue improvements

MNL

56.67

63.58

12.21%

NMNL

52.09

58.03

11.40%

Revenue overestimation

8.79%

9.58%

3.6 Dynamic Assortment in the Presence of Brand Heterogeneity

155

Fig. 3.7 Effect of inventory levels on revenue overestimation

Fig. 3.8 Effect of price on revenue overestimation

(1.28, 1.6, 4.72, 5.2, 1.3, 1.5, 3.1, 7.8). Results in Fig. 3.8 show that under these price scenarios, the expected revenues are all overestimated, when the retailer ignore customers’ perceived utility difference between SB and NB products. We can see that this revenue overestimation evolves differently for SB and NB. The percentage of revenue overestimation will decline with increasing price discount factor on SB products. Conversely, increasing price discount factor of NB products would enlarge the percentage of revenue overestimation. Furthermore, we find that the same price discount factor on NB products will produce greater revenue overestimation than on SB products. 3. Impact of Initial Inventory on the Dynamic Assortment Performance In this section, we assess the impact of initial inventory on revenue improvements from the dynamic assortment. In particular, we aim to examine the following two issues:

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• How do revenue improvements from the dynamic assortment change, when we simultaneously adjust initial inventory levels of SB and NB products? • Suppose the storage space is fixed. If we increase inventory for products of one brand and decrease inventory for another brand, how will the revenue improvements evolve? To examine these above issues, we solved the dynamic assortment problem under seven scenarios of initial inventory: • Scenario 2. It corresponds to the basic inventory y0 . • Scenarios 1 and 3. They are created by increasing or decreasing one unit inventory for all the products, respectively. • Scenarios 4 and 5. Based on the basic inventory, we add one unit inventory to SB products and decrease by one unit inventory for NB products in scenario 4. Conversely, in scenario 5, inventories of SB and NB products are respectively decreased and increased one unit. • Scenarios 6 and 7. They are created by adding three and seven units inventory to product 1. As a comparison benchmark, we also solved the assortment optimization problem under the offer-all policy. Table 3.7 summarizes the results. We first answer question 1 (refer to results of scenarios 1–3). In comparison with the offer- all policy, the dynamic assortment yields more revenue improvements, when we increase initial inventory levels of all the products of two brands. This can be explained as follows. In scenario 1, the initial inventory is larger than the expected demand in the selling season. The retailer has more flexibility in implementation of dynamic assortment. Therefore, there is larger revenue increase after rationing inventory. However, when the inventory level of any product is greater than some threshold, there is no significant improvement in expected revenues because of the limited demand. This circumstance is shown in scenarios 6 and 7. When the initial inventory of product 1 increases from 4 units to 8 units, the expected revenues under two policies increase a little. Therefore, Table 3.7 Impact of initial inventory on dynamic assortment Scenario

Initial inventory

Expected revenues Offer-all

Revenue improvements (%)

Dynamic assortment

1

(2, 2, 3, 4, 2, 7, 4, 3)

55.33

69.16

24.99

2

(1, 1, 2, 3, 1, 6, 3, 2)

52.09

58.03

11.40

3

(0, 0, 1, 2, 0, 5, 2, 1)

38.52

39.00

1.25

4

(2, 2, 3, 4, 0, 5, 2, 1)

51.02

55.49

8.77

5

(0, 0, 1, 2, 2, 7, 4, 3)

49.11

57.16

16.39

6

(4, 1, 2, 3, 1, 6, 3, 2)

52.13

58.14

11.54

7

(8, 1, 2, 3, 1, 6, 3, 2)

52.13

58.15

11.54

3.6 Dynamic Assortment in the Presence of Brand Heterogeneity

157

increasing initial inventory could bring out more revenues, but its function is limited. In scenario 3, the initial inventory is very small and thus the retailer has enough time to sell out these products. All these products are likely to be included in the optimal assortment. As a result, the dynamic assortment is similar to the offer-all policy, and the percentage of revenue improvements will become very small. We next answer question 2 (refer to results of scenarios 4 and 5). As we can see, the dynamic assortment yields a larger percentage of revenue improvements in scenario 5 than in scenario 4. Because higher utilities are associated with NB products, they are more possible to be chosen by consumers. That is, given a fixed storage space, the retailer prefers to add inventory for NB products in order to maximize the expected revenues. Suppose the retailer has a chance of storing extra one unit inventory in the warehouse. Then which product should the retailer choose? For this purpose, we further examine the effect of extra one unit inventory on expected revenues. Table 3.8 reports the percentage of revenue improvements, which are calculated based on the expected revenues with basic inventory y0 = (1, 1, 2, 3, 1, 6, 3, 2). In Table 3.8, we consider eight scenarios, where each one corresponds to adding one unit inventory to only one product. Scenarios 1–4 apply to SB products, whereas scenarios 5–8 are for NB products. As one may observe, increasing one extra unit inventory to these eight products does yield revenue improvements, under the dynamic assortment. More inventory makes the retailer have more flexibility to implement the dynamic assortment. Then the expected revenues increase after rationing inventory. However, under the offer-all Table 3.8 Impact of increasing one unit inventory on revenue improvements

Scenario

Initial inventory

Percentage of revenue improvements Offer-all (%)

Dynamic assortment (%)

1

(2, 1, 2, 3, 1, 6, 3, 2)

0.03

0.13

2

(1, 2, 2, 3, 1, 6, 3, 2)

−0.12

0.30

3

(1, 1, 3, 3, 1, 6, 3, 2)

2.51

4.12

4

(1, 1, 2, 4, 1, 6, 3, 2)

2.97

4.89

5

(1, 1, 2, 3, 2, 6, 3, 2)

−0.14

0.10

6

(1, 1, 2, 3, 1, 7, 3, 2)

−0.81

0.17

7

(1, 1, 2, 3, 1, 6, 4, 2)

0.65

2.26

8

(1, 1, 2, 3, 1, 6, 3, 3)

4.31

10.11

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policy, extra inventory may not necessarily result in revenue increase. In scenarios 2, 5 and 6, the extra inventory conversely decreases the expected revenues. Intuitively, we believe that more inventory will bring more revenues. But this belief is not true in our numerical experiments. It can be explained as follows. Products 2, 5 and 6 all have lower prices. But their utilities are not so low. If consumers choose these products in the offered assortment, the retailer’s expected revenues will be hurt. We further can see that under both offer-all policy and dynamic assortment, extra inventory could produce much more expected revenue improvements for scenarios 3, 4, 7 and 8 than for other scenarios. These products have higher prices. So in order to increase revenues, the retailer can put extra inventory on products with higher prices. Finally it can be observed that extra inventory can produce more revenues under the dynamic assortment than under the offer-all policy. 4. Impact of Initial Price on the Dynamic Assortment Performance The prices of products significantly affect the retailer’s revenues. Although we do not consider pricing decision in the context of dynamic assortment, we want to give some insights for the impact of initial prices on the performance of dynamic assortment. In supermarkets, it is so common that there are various price discounts on products. Table 3.9 reports the revenue improvements from dynamic assortment, under different price discounts. Here the price discounts apply to eight products of two brands simultaneously. Results in Table 3.9 show that offering price discount will decline the expected revenues under both offer-all policy and dynamic assortment. However, despite that, the dynamic assortment yields more revenues, in comparison with the offer-all policy. More importantly, increasing price discount will hurt the percentage of revenue improvements from the dynamic assortment. That is, price discount has a negative impact on the dynamic assortment. It is because price discount makes the difference among products’ prices smaller and thus different products present similar utilities to consumers. Then the function of the dynamic assortment is weakened. We finally examine the performance of dynamic assortment, when we only lower price of one product. Table 3.10 summarizes the results, where each scenario corresponds to reducing price of one product by 20%. Here the initial inventory is set equal to y0 = {1, 1, 2, 3, 1, 6, 3, 2}. “P1-20%” means that the price discount factor δ = 20% only applies to P1. From Table 3.10, we can observe some meaningful insights. Under dynamic assortment, having price discount on more expensive products within one brand leads to more revenue loss. Under offer-all policy, the same price discount Table 3.9 Impact of full price discount on dynamic assortment Scenario

Price discount (%)

Expected revenues Offer-all

Dynamic assortment

Revenue improvements (%)

1

0

52.09

58.03

11.40

2

10

50.21

54.02

7.58

3

20

46.81

49.10

4.91

4

30

42.17

43.57

3.34

3.6 Dynamic Assortment in the Presence of Brand Heterogeneity

159

Table 3.10 Impact of partial price discount on dynamic assortment Scenario

Price discount (%)

Expected revenues Offer-all

Dynamic assortment

Revenue improvements (%)

1

P1-20

51.08

57.98

13.53

2

P2-20

51.48

57.90

12.47

3

P3-20

51.00

56.61

11.01

4

P4-20

50.87

55.93

9.95

5

P5-20

51.85

58.00

11.87

6

P6-20

50.58

57.32

13.34

7

P7-20

50.71

56.25

10.92

8

P8-20

51.39

55.06

7.13

on more expensive products within NB do not mean losing more revenues, e.g. cases 7 and 8. Although P7 and P8 are more expensive than P6, applying the same price discount to them brings more expected revenues than to P6. For products with similar prices, like P1 and P6, the same price discount yields different impacts on expected revenues under both policies. Expected revenues of P6-20% decrease more than these of P1-20%. Besides, for scenarios 1, 2, 5 and 6, we find that price discount on products whose prices are relatively lower within one brand would reinforce the performance of dynamic assortment.

3.6.5 Conclusions We consider the dynamic assortment optimization problem, where a retailer has limited inventories of products of two brands from a category. The heterogeneous brands include one store brand and one national brand. We characterize consumer choice process using a two-stage NMNL model, in which a consumer first decides which brand or the no-purchase option to choose and then decides which product offered by that brand to purchase. In each period of the selling season, the retailer determines an assortment from a set of products offered to consumers given available inventory and the remaining time in the season so that the expected revenues are maximized. Using the MCMC procedure based on the sales transaction data collected from a super- market chain, we estimate consumer choice behavior. Our estimation results show that products of store brand and national brand are different in price sensitivity and consumer perceived utilities. And the products within one brand are closer substitution to each other than are products from another brand. Hence, it is necessary for the retailer to take brand heterogeneity into account when making operation decisions. We examine how the brand heterogeneity affects the performance of dynamic assortment. If the retailer is not able to distinguish between store brand and national

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brand while characterizing consumer choice behavior, the expected revenues will be significantly overestimated. And the degree of this revenue deviation will be affected by products’ initial inventory level and prices. We further examine the potential revenue improvements from implementing dynamic assortment by benchmarking a myopic policy where all available products are shown to any arriving consumers. We empirically show that factors like initial inventory levels and initial prices will affect the performance of dynamic assortment. More inventories could bring out more revenues and intensify the performance of dynamic assortment. But there are no significant revenue improvements when the inventory levels exceed some threshold. We also find that lowering products’ prices not only reduces the expected revenues, but also weakens the performance of dynamic assortment. The same price discount on low-price products in one brand would reinforce the performance of dynamic assortment. Our analytical results and case study suggest that it is necessary and valuable to distinguish store brand from national brand, and dynamic assortment with heterogeneous brands is an effective lever for increasing retailers’ revenues. Our empirical study can offer some advice on how to characterize consumer choice behavior and implement the dynamic assortment in the retail market.

3.7 Summary In this chapter, we have reviewed previous research on the pricing behavior of retailers in the marketing of store brands from different directions. We mainly discuss and summarize pricing strategy for store brands from four perspectives: marketing factors; consumer consciousness; sales structures and store brand manufacturer classifications. We found that competitive factors between SBs and NBs depend on both price and non-price types. For price type, baseline price can result in long-term competition, while price promotion or price discount can cause short-term price competition. In the case of non-price dimensions, NBs–SBs competition is influenced both by supply-side and demand-side variables. Sales structures and power structures are main supply-side variables: store loyalty, SB image and consumer consciousness are the main demand-side variables that condition SB non-price competitiveness. We conduct a study which contributes to understanding consumer consciousness in relation to price type competition. We analyze two scenarios including constant pricing without reference effect and dynamic pricing considering both reference price and perceived quality, finding that the stable price and stable reference price will increase with memory factor, perceived quality of SB and the sharing rate of NB’s revenue, but decrease with the reference effect parameter and the weight coefficient of SB’s history price. Finally, the result shows that the profit under dynamic pricing is higher than that of constant pricing when the reference effect is positive. We also contribute to understanding multi-brand positioning by studying the dynamic assortment planning problem in the presence of heterogeneous brands. We use the nested multinomial logit framework to model consumer choice process,

3.7 Summary

161

in which consumers choose which brand to buy first and then a product within that brand. The result finds that ignoring brand heterogeneity will make the retailer’s expected revenues significantly overestimated, with the potential revenue overestimation depending on initial inventories and the prices of products from two brands. We also investigate how the retailer will benefit from dynamic assortment optimization using the estimated consumer choice model.

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Chapter 4

Channel Strategy and Conflict Resolution

The introduction of a retailers’ store brand is sure to bring channel competition to the relationship between manufacturers and retailers because of channel encroachment. The reality is that it is easy for store brand products to compete with the national brand products of manufacturers, which may be problematic for the manufacturers. Most of the profit from store brand products accrue to the retailer, manufacturers cannot afford for their products to lose significant market share to the competition. Thus, even if national brand manufacturers become co-producers of store brand products, they still need to find a reasonable channel coordination method to mitigate channel conflicts caused by the introduction of store brands and achieve win-win outcomes. In this chapter, we discuss aspects of channel competition between traditional manufacturers and retailers, followed by aspects of channel competition that further consider store brand issues. We consider the relevant in the appropriate sections following.

4.1 Channel Competition Between Traditional Manufacturers and Retailers Traditional manufacturers sell their products through brick-and-mortar retailers. With the development of e-business, manufacturers, even retailers, have the option to introduce direct online sales channels. The introduction of online channels brings different channel structures, and channel competition. In this section, we first review the literature that depicts the competition between a brick-and-mortar channel and an online channel; then the impact of channel structures on the equilibrium result is reviewed: finally, we review aspects of competition and strategies for manufacturers and retailers within multi-channel environments.

© Tongji University Press 2021 J. Huo, Advances in Theory and Practice in Store Brand Operations, https://doi.org/10.1007/978-981-15-9877-7_4

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4.1.1 The Competition Between Brick-and-Mortar and Online Channels Wolfinbarger and Gilly (2001) found that online shopping gives customers more choices through comparing it with the offline shopping environment; that online shoppers could engage in both goal-directed shopping and a form of experiential shopping in the online environment. Cattani et al. (2006) construct a customer model to address the pricing problems for manufacturing firms in the competition between traditional and direct Internet channels. Yan and Ghose (2010) found that the profits of online as well as traditional retailers always increase with forecast accuracy, and that forecast accuracy has a greater effect on the performance of the traditional retailer than on that of the online retailer. They also argue that the profit differentials between traditional and online retailers can increase the accuracy in predicting customer spending levels. Arya and Mittendorf (2018) studied a model of the bricks-and-mortar entry choice of online retailers considering consumer sale taxes. Another stream in the literatures focusses on the impact of the introduction of direct channels on the traditional brick-and-mortar channel. Chiang et al. (2003) consider the price competition between traditional and electronic channels and find that the manufacturer’s opening up of electronic channels lowers the wholesale price and will not threaten the retailer’s interests, ultimately achieving coordination between traditional and electronic channels by reducing the degree of inefficient price double marginalization. Tsay and Agrawal (2009) developed a model that captures key attributes of channel conflict, including various sources of inefficiency. They found that the addition of a direct channel alongside a reseller channel is not necessarily detrimental to the reseller, given associated adjustments in the manufacturer’s pricing, and both parties can benefit. Cai (2010) investigated four different supply chain structures on the supplier, the retailer and the entire supply chain, and find the existence of the channel-adding Pareto zone, in which both the supplier and the retailer benefit from adding a new channel to the traditional single-channel supply chain.

4.1.2 The Impact of Channel Structure There is some research based on the competition between traditional manufacturers and retailers in multichannel environments. Some scholars have studied the impact of channel structure on the equilibrium result in the competition between manufacturers and retailers. McGuire and Staelin (1983) investigate the effect of product substitutability on Nash equilibrium distribution structures in a duopoly where each manufacturer distributes its goods through a single exclusive retailer. They find that for more highly competitive goods, manufacturers will be more likely to use a decentralized distribution system. Moorthy (1988) continues to study this problem with focus on the effects of strategy interaction between manufacturers, and he finds that

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strategic interaction makes it possible for a manufacturer’s retail demand curve to rise when he decentralizes. Choi (1991) compares the equilibrium results with two competing manufacturers and one intermediary (a common retailer) that sells both manufacturers’ products under three different game settings: Stackelberg, Vertical Nash and Retailer Stackelberg. Chen et al. (2008) studied a problem for manufacturers in managing their direct online sales channel together with an independently owned bricks-and-mortar retail channel, when both channels compete in service. Hsiao and Chen (2014) investigated pricing strategies under three channel structures with different capabilities for introducing the Internet channels. They found that when Internet shoppers are either highly profitable or fairly unimportant, the manufacturer prefers to facilitate channel separation, otherwise, it prefers to steals demand from the retailers’ physical channel. Yang et al. (2015) studied how the asymmetric characteristics of substitutability and brand equity affect the equilibrium channel structures for manufacturers that sell competing products. They examined three possible types of competition between the two supply chains: Cournot (quantity) competition; Bertrand (price) competition and Bertrand-Cournot competition. They found that, in equilibrium, a manufacturer always sells directly when its rival competes on quantity; and when there is sufficient asymmetry in either brand equity or substitutability.

4.1.3 Multi-channel Competition and Strategies Balasubramanian (1998) studied the modelling competition in a multichannel environment from a strategic perspective, and the role of information as a strategic lever in the multiple-channel market. Levary and Mathieu (2000) studied the profits of physical retail, e-tail and hybrid retail and concluded that, despite a few disadvantages, hybrid retail stores combine the best of e-retail with the best of physical retail and thus will generate the optimal profits to the maximum extent. King et al. (2004) use a game-theoretic approach to study the impact of Web-based e-commerce on retailers’ choices of distribution channel strategies, and state that a retailer’s multichannel strategy is in an equilibrium state after gaming with other retailers. Rosenbloom (2007) believes that managers should be responsible for channel management in order to deal with the variety of challenging issues arising from coordinating and integrating multiple channels These issues include the role of e-commerce in the multi-channel structure; finding an optimal channel mix; creating synergies across channels; building strategic alliances; creating sustainable competitive advantages; managing more complex supply chains; dealing with conflict and providing the leadership necessary to attain well integrated multiple channels. Yan (2010) analysis yields strategic information related to the implementation of brand differentiation strategies that can help multichannel retailers effectively maximize revenue in competition, particularly when consumers are not price sensitive and the market base is large. Yan and Pei (2011) examined the effect of an information sharing strategy

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on performance of all channel members and concluded that the multi-channel manufacturer (with online and traditional retail channels) will always benefit from an information sharing strategy.

4.2 Channel Competition When Considering the Introduction of a Store Brand In this section, we study channel competition issues when consideration is being given to the introduction of a store brand. We first review the stream of literature that studies the impact of introducing a retailer’s store brand on channel members, especially manufacturers; then we summarize the channel competition after the manufacturer’s adaptation of the retailer’s store brand; finally a win-win situation for both manufacturers and retailers is studied.

4.2.1 The Impact of Introducing a Retailer’s Store Brand Channel competition between manufacturers and retailers initially focused on the impact of the introduction of the retailer’s store brand on the manufacturer’s brand. Mills (1999) examined several counterstrategies adopted by manufacturers to mitigate the impact of retailers’ store brand programs so that manufacturers can remedy their potential profit losses. Morton and Zettelmeyer (2004) argued that the introduction of a store brand increases the retailer’s profits through a pure redistribution of channel profits, not through a price discrimination effect. Karray and Zaccour (2006) consider that manufacturers can counter the harmful effects of the introduction of a store brand by implementing cooperative advertising program, but only if the national brand competes strongly with the store brand. Fan and Chen (2011) address the channel structure of two symmetric manufacturers and a single retailer, using game theory to explore the impact of retailer store brand introduction and positioning on channel price decisions under different channel power structures. They show that the introduction of store brands will lower the profit of manufacturer under either channel structure.

4.2.2 Channel Competition After the Adaptation of a Retailer’s Store Brand The main manifestation of channel competition after the adaptation of a manufacturer’s brand for a retailer store brand is as follows. Wedel and Zhang (2004) show asymmetrical price competition not only within but also across subcategories: the

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cross-subcategory impact of national brands on store brands appears to be substantially greater than that of store brands on national brands. De Wulf et al. (2005) studied customer perception between the retailer’s store brand and the manufacturer’s brand and those results show that the retailer’s store brand can offer better quality and lower prices than the manufacturer’s brand. Sethuraman (2009) reviews the literature on mathematical models of national brand and store brand competition between retailers and manufacturers and assess the external validity of the results using three criteria: robustness, empirical support, and credibility. The competition between retailers and manufacturers at this time focused on price competition between brands (Choi 1991; Sethuraman et al. 1999). Retail store brands force manufacturers to lower retail prices and compete for demand for their manufacturer brands (Raju et al. 1995; Mills 1995, 1999; Narasimhan and Wilcox 1998; Horowitz 2000; Sayman and Raju 2004; Choi and Coughlan 2006; Morton and Zettelmeyer 2000, 2004; Bontems et al. 1999; Sayman et al. 2002). Cotterill et al. (2000) analyzed the strategic price interaction between national brands and store brands, and found that (a) the counterintuitive pricing findings of previous cross-section analyses disappeared when we accounted for the simultaneity of demand and supply, and (b) there are important differences in the pricing power of store brands across categories implied by residual demand elasticities versus the more commonly used partial own-demand elasticity. Brynjolfsson and Smith (2000) show through empirical analysis that prices in the online channel are 9–16% lower than prices in conventional outlets, and price dispersion is lower in Internet channels than in conventional channels, reflecting the dominance of certain heavily branded retailers. Chiang et al. (2003) show that vertically integrated direct sales channels can help manufacturers to limit the pricing behavior of retailers with whom they partner, but that reductions in wholesale prices are beneficial to retailers.

4.2.3 A Win-Win Situation for Both Retailers and Manufacturers Some studies point out that the introduction of store branding is beneficial to both retailers and manufacturers. Wu and Wang (2005) consider a market consisting of two manufacturers, each offering one product, and sales through a common retailer. They find that manufacturers and retailers could reach a Pareto zone in which all three channel members benefit because the store brand strategically mitigates the promotion competition between the two national brands. Li et al. (2016) study the profits of manufacturer and retailer under different game theory equilibriums. They found that the one necessary condition for a win-win situation for both retailers and manufacturers is that the quality of the store brand should not exceed three quarters of the quality of the national brand. Based on their earlier paper, Liu et al. (2020) consider the situation in which retailers give up selling the national brand and yield the Nash equilibriums under different conditions. Other studies include Mills (1995), Narasimhan and Wilcox (1998) and Morton and Zettelmeyer (2004).

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4.3 Channel Coordination Between Traditional Retailer and Manufacturer As more manufacturers begin to adopt multi-channel supply chains, that is, introducing electronic direct sales channels in combination with traditional retail channels, the interests of retailers will be damaged without cooperation, and it causes channel conflicts among manufacturer and retailer. Many manufacturers cooperate with regional retailers to reduce channel conflicts. For example, some electric appliance manufacturers hand over orders from electronic direct sales channels to local retailers, who provide distribution, installation, and after-sales service. Despite this, there are still few supply chains implementing channel cooperation, mainly due to retailers’ lack of trust in manufacturers, concern about their cooperation motives, and difficulty in dividing responsibilities in cooperation. In this situation, it is necessary to seek a reasonable channel coordination method to reduce the negative impact of channel competition. Zhang et al. (2013) studied the driving factors for cooperation in multi-channel supply chains and found that the factors that significantly affect channel cooperation intentions are competitive pressure, consumer demand, organizational resources, channel complementarity, perceived relative advantage and perceived complexity. Wang and Zhang (2019) found that consumer cross-channel behavior has a positive impact on channel cooperation performance and that retailers would be well-advised to follow consumer behavior and complement each other through channel coordination rather than resist. Through Wang and Zhang’s investigation, it was found that three dimensions of cooperation behavior, namely special investment (capital, site, equipment, etc.); information sharing (sales, goods, logistics, inventory, etc.) and joint action (product pricing, supply, promotion, cost sharing and profit distribution, etc.) had a positive effect on the performance of channel cooperation. In this section, we discuss channel coordination methods in traditional supply chains when the direct sales (online) channel is adopted by national brand manufacturers.

4.3.1 Price Discounts in Traditional Channel Coordination Yan and Pei (2009) believe that manufacturers’ development of electronic direct sales channels can stimulate retailers to improve service levels to reduce the impact of direct sales channels on traditional distribution channels. Based on the reality that retailers may obtain lower wholesale prices and improved service levels from manufacturers, Yao and Liu (2005) and Cattani et al. (2006) demonstrated that retail prices can effectively balance the competition between manufacturers’ dual-source channels, and that optimized wholesale prices can reduce conflicts with retailers due to the introduction of direct sales channels. According to the research of Park and Keh (2003), the total profit within the supply chain can increase under the mixed channel model based on cooperation between manufacturer and retailer, but the

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retailer’s profit share will decrease accordingly. Because higher price charged by the retailer leads to lower sales volume. One feasible method for manufacturers is to use quantity discounts to encourage retailers to cooperate with them, which props up retailers’ profit shares. Xie and Huang (2007) established a quantity discount game model, and found that under the mixed channel model, if a manufacturer implements a certain quantity discount, its profits with retailers are greater than when they are not cooperating. Thus, by adjusting the quantity discount rate, the dualchannel supply chain can achieve a win-win situation. Xu and Dan (2012) researched the dual-channel model of traditional retail and electronic direct sales coexisting in the context of e-commerce. Using a manufacturer based Stackelberg game, they demonstrated the effectiveness of a price discount mechanism for promoting the supply chain coordination. Meanwhile, a transfer payment mechanism is suggested with the combination of discount model to achieve a win-win situation. Transfer payment mechanism is to provide financial support to others, which is one way of profit sharing discussed in the next section.

4.3.2 Revenue Sharing in Traditional Channel Coordination Yan (2011) studied the effectiveness of using differentiated brands and profit sharing to coordinate channels under conditions of multi-level channel manufacturers and retailers. The results show that although the strategy of differentiating brands can alleviate competition it does not function as a fully coordinated method. Since retailers’ profits are still negatively affected by manufacturers opening direct sales channels, manufacturers need to use revenue-sharing mechanisms to persuade retailers to participate in channel cooperation to fully mitigate channel conflicts. Sales through different channels will cause competition, so an appropriate sales strategy is necessary. Boyaci (2005) stated that the application of penalty contracts to rectify inequities in the supply chain execution process are too complicated, and when retailers only earn from additional sales, retailers and national brand manufacturers can coordinate the dual-channel supply chain through creative revenue sharing mechanisms. Contract strategy is one of the core methods for channel coordination. The combined mechanism designed by Chiang (2010) can share inventory holding costs and share direct sales channel revenue, and ultimately achieve dual-channel coordination. Guo and Zhao (2008) demonstrated that manufacturers using a mixed channel model can increase market demand and force retailers to lower prices but can increase overall supply chain profits. Through the analysis of equilibrium in dualchannel supply chain based on game theory, Guo and Zhao (2008) found that the transfer payment mechanism can coordinate the conflict between electronic direct sales channels and traditional distribution channels. Yan et al. (2016) have proposed a coordination method called the ‘triple cooperation strategy’, which is designed to ease channel conflicts by expanding market share and revenue sharing. The implementation of this method requires three layers of effort. First, manufacturers need to provide support to retailers through financial

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or other means to help retailers improve their sales efforts. This initiative helps to stimulate consumers to purchase goods from traditional channels. Second, members of the supply chain need to improve the performance of the entire channel through a coordinated pricing strategy, such as reducing the wholesale price, or the prescribed retail price given to retailers, depending on the mode of cooperation between them. This method can increase the sales volume of traditional channels and cover the impact caused by the decrease in the profit of a single product. Thirdly, a cooperative mechanism of revenue sharing is still needed to reasonably distribute the profits of the entire channel. Such a triple cooperation method can reduce the channel’s negative competition and conflict, and maximize the profit of channel members (Yan et al. 2016).

4.3.3 Cooperative Advertising in Traditional Channel Coordination Zhang and Chen (2016) studied the problem of cooperative advertising under a brand differentiation strategy implemented by manufacturers in a dual-channel supply chain and determined the optimal advertising input within the range of cost sharing rates that both parties are willing to accept under centralized decision-making and Stackelberg game theory. The marginal profit level of manufacturers and retailers in different channels and the degree of brand differentiation are two factors that affect the cooperative advertising strategy. Channel coordination can be achieved by designing a traditional channel product advertising cost-sharing contract. Cooperative advertising requires bilateral participation by definition; that is, manufacturers share a certain cost of local advertising for retailers, and retailers share a certain cost of national advertising for manufacturers. The research by Chen et al. (2017) shows that the national advertising investment of manufacturers and local advertising investment of retailers under centralized decision-making are higher than that of decentralized decision-making, and that the overall profit of the supply chain under centralized decision-making is higher. Therefore, a bilateral cooperative advertising contract is necessary, so that supply chain members under decentralized decision-making may adopt an optimal advertising investment strategy under centralized decision-making to achieve a win-win situation. Under the influence of bilateral cooperative advertising contracts, total profit of the supply chain has increased, but the profits of retailers have decreased. The manufacturer may need to transfer a certain profit value to the retailer to make the retailer willing to accept a cooperative advertising contract. Since methods for sharing profits is often used in combination with other methods to coordinate channel conflicts, considering how to combine these multiple methods to play an effective role in conflict resolution is essential. Pei and Yan (2013) found that NB manufacturers could increase the performance of the entire channel by increasing their investment of national advertising, to alleviate channel conflicts. The size of manufacturers’ investment in national advertising

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depends on the type of product and the degree of channel substitution. If the product’s compatibility with online sales or channel substitutability is higher, the investment in national advertising can greatly increase profits, and manufacturers should increase investment in national advertising. Increased brand influence can increase the profits of traditional channels and ease the channel conflicts caused by the introduction of direct sales channels. The research of Buratto et al. (2019) is based on a manufacturer leading the supply chain and determining the retail price of products. Here, a retailer obtains a certain profit share by selling the product and they compare the coordination of the cooperative advertising and price discount mechanisms. The study found that the manufacturer as leader always recommends the use of coordination mechanisms. To mitigate channel conflicts through a simple commission contract, cooperative advertising will be a better choice. Compared with the price discount mechanism, retailers are more willing to choose cooperative advertising because it can bring better economic performance. However, when the production cost of a product is high, price discounts can greatly reduce the retail price, potentially increasing demand significantly. In such cases, retailers may be more willing to accept the price discount mechanism in the case of high production costs.

4.3.4 Inventory Transfer in Traditional Channel Coordination Xu et al. (2015) studied an inventory transshipment cooperation strategy under the dual-channel supply chain. This method of cooperation is to transship the remaining inventory in the retail channel to meets the needs of online channel. Through simulation, they found that cooperative dual-channel supply chains can effectively reduce channel inventory and reduce the risk of items being out of stock. By reducing outof-stock events, replenishment, and inventory costs, and improving service levels, the profits of the dual-channel supply chain increase. Manufacturers need to give retailers certain quarter-end compensation and inventory transfer compensation to encourage retailers to actively participate in inventory transshipment cooperation. In order to encourage retailers to order more, manufacturers give certain compensation for unsold products at the end of quarter, which is called quarter-end compensation. As the O2O model became more and more popular in business, relevant research has aroused the interest of scholars of channel coordination. Zhu et al. (2018) compared the optimal order decision of a dual-channel supply chain based on wholesale price and transshipment contracts, under an O2O model. The simulation data came from an apparel company and the influence of order quantity, inventory quantity, and out-of-stock rate on profit were explored. The result showed that transshipment contracts are more conducive to the coordination of dual channels compared with price discount contracts. The reason is that they can reduce order risks

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and out-of-stock costs caused by uncertain market demand through transfer cargos between franchisees, direct stores, and online platforms. Due to the coordination role of the transshipment contract, the profits from dual channel working can be increased. Yu et al. (2019) obtained the same conclusion through numerical example analysis. When only manufacturers pay attention to fairness, this strategy is easily accepted by members in the supply chain. In other cases, retailers will have concerns about fairness, and they may need to be compensated to promote cooperation.

4.3.5 Channel Coordination Through Other Methods The results of Cao et al. (2015) showed that when the operating costs of manufacturers introducing direct sales channels and the unit costs for retailers introducing store brands are low, the competition between the two parties is beneficial and can reduce the impact of “double marginalization” and achieves a win-win situation for the entire supply chain. Manufacturers and retailers should focus on improving their products and services to increase customer loyalty to direct sales channels or store brands. Such benign competition can increase the profits of their channels and effectively avoid conflicts. Shi et al. (2017) believe that domestic strategies for solving online and offline channel conflicts are mainly divided into two aspects: differentiation strategy and the same products with same price strategy. Their study established a competition and cooperation model and substituted sales data from “Uniqlo” and “CHIUSHUI” into the model, and found that although the two companies adopted different dual-channel integration strategies (Uniqlo tried the same products with same price strategy, and “CHIUSHUI” implemented product differentiation strategy) both achieved win-win results. In the process of selecting strategies, it depends on each situation. For example, Uniqlo drove the growth of online store sales with its strong store strength, and “CHIUSHUI” seized upon the needs of different customers. Scholars have demonstrated that an effective promotion strategy can expand consumers’ spending in various categories and increase market capacity to avoid increased competition. Putsis and Dhar (2001) analyzed the conversion strategies of manufacturer brands and retailer brands and discussed the effectiveness of expanding category promotion. The placement of goods has a certain impact on customers’ purchasing behavior, so the necessary shelf strategy is indispensable. Morton and Zettelmeyer (2004) found that retailers’ control of brand categories and shelf placement can help pressure manufacturers with national brands to obtain more favorable trade agreements. Li (2016) discussed the value of information sharing in mitigating the channel conflict between retailers and manufacturers, and believed that information sharing can coordinate the relationship between the two, reasonably arrange the sales through both traditional and electronic direct sales channels, and effectively promote organizational cooperation to improve overall channel performance. To alleviate channel conflicts, a combination of vertical and horizontal information sharing methods is required. Zhu et al. (2017) research aimed at economic sustainability rather than profit maximization, and found that under traditional contracts

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(revenue sharing and repurchase contracts), although channel conflicts were alleviated to a certain degree, the risk degree of retailers is still restricted. Therefore, a risk-sharing contract was proposed, which can be executed to stabilize the changing market environment and coordinate channel conflicts.

4.4 Channel Coordination Between Retailers and Manufacturers When Store Brands Are Introduced More scholars have begun to pay attention to channel and brand competition in the dual-channel supply chain, that is, where manufacturers adopt a dual-channel model of combining online direct sales channels with traditional retail channels, and retailers sell their store brand products and national brand products. According to Chen et al. (2011) the introduction of retailers’ store brands can play a role in channel coordination when manufacturers are also developing direct sales channels, especially in decentralized supply chains. Although the development of store brands will reduce the profits of national brand manufacturers, it does not necessarily reduce the profits of the entire supply chain; so, the possibility of solving the problem of channel conflicts remains. If store brand products are not developed by the vertically integrated supply chain, the condition for overall profit improvement is that if the development cost is low enough, it can make a retailer’s profit higher than the manufacturer’s loss. For manufacturers, if retailers introduce store brands, their profits will be affected. Although some manufacturers prevent the introduction of store brands through compensatory payments or the introduction of additional national brands, retailers still provide multiple levels of store brands for balance. It has been found that the expansion of store brands will reduce the differences between products and improve the profit of all channels and the welfare of consumers. This may be contrary to popular belief, but the reality is that if retailers can sell products at prices which are lower than average prices, they can expand the market. Overall, a reduction in product price is more significant than a reduction in quality. Therefore, the gradual introduction of store brands has become an irreversible trend, especially for the rise of store brands in the primary form of the Chinese market. Under such circumstances, manufacturers and retailers, have a common interest in resolving channel conflicts to achieve win-win cooperation. This section introduces methods for channel coordination between retailers and manufacturers when retailers have their store brands.

4.4.1 Channel Coordination Through Revenue Sharing It has been shown that the introduction of store brands by retailers will reduce the profits of national brand manufacturers, but the cooperation with manufacturers

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can improve the profits of the whole supply chain. Channel conflicts can be mitigated through the reasonable distribution of supply chain profits, which leads to the coordination method of revenue sharing. Store brand products usually have lower retail prices, which will cause the transfer of market demand, so they may make considerable profits. The way that retailers compensate manufacturers with a certain percentage of their profits is called revenue sharing, which has many different manifestations. Retailers, for example, may give manufacturers a profit percentage for products they fail to sell to make up for the loss of national brand product sales caused by the market share taken by store brands. It can also be compensated to the manufacturer by a percentage of the profits from the sale of retailers’ store brand products. Retailers can strengthen channel coordination by purchasing store brands from national brand manufacturers (Amaldoss and Shin 2015), but this approach will still need to meet certain conditions to increase the overall supply chain profits. Therefore, research on the coordination of mixed channels composed of a retailer’s OEM (Original Equipment Manufacturer) channel and manufacturer’s direct sales channel can provide us with insights. The research of Jiang et al. (2016) was established in the context of mixed channels lead by retailers. They compared the optimal prices, sales volume, and profits of supply chain members under centralized and decentralized decisions, respectively. Under centralized decision-making, the drop in retail price of the OEM channel triggered a shift in market demand, and profit from the OEM channel was been greatly improved. Retailers should compensate a certain percentage of their profits to the manufacturer, so that the overall increased profits under mixed channel with centralized decision-making are shared and coordinated. Research shows that retailers need to compensate 25–50% of their profits to manufacturers to achieve a win-win situation. The wholesale price discount rate can be adjusted so that the retail prices of direct sales channels and OEM channels are equal to the optimal decision under centralized decision-making conditions. The wholesale price discount rate depends on the elasticity of market demand for prices and channel transfer coefficients. When the channel transfer coefficient is smaller, and the brand premium is closer to the maximum, the overall profit increase in the mixed channel supply chain after coordination is the largest. Scholars have found that a money-back guarantee policy is beneficial to retailers and can be used as a strategic tool for retailers to develop their store brands. The money-back guarantee policy will transfer some customers from the national brand channel to the store brand channel, so the profit of the manufacturer will be affected. Li et al. (2018) based their research on the condition that both retailers and manufacturers face the problem of customer returns, and that the products of their respective brands have quality differences. The study found that retailers can alleviate price competition between the two brands when they provide a money-back guarantee policy for both store and national brands. A centralized supply chain will intensify the brand competition between retailers and manufacturers. A simple and easy-toexecute coordination contract can be used for channel coordination. The coordination mechanism contains three parameters, namely the retail price of the store brands,

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the sales profit rate of the national brand, and profit share of the national brand manufacturers. Huang and Feng (2020) also studied the impact of the money-back guarantee policy on retailers and national brand manufacturers when retailers sell their store brands and national brands and discussed how manufacturers provide coordinated contracts to reduce channel competition. The money-back guarantee policy benefits retailers because store-branded products are often considered low-end alternatives to national brand products. Even if the retailer provides a fair money-back guarantee policy for the two brand products, the manufacturer will still lose part of its profits. Therefore, designing a two-part tariff and a benefit-sharing contract for the manufacturer to coordinate the channels can improve the manufacturer’s profit, while the retailer can obtain a profit close to the best case under the decentralized supply chain. The results of the study provide evidential support to prevent retailers from making wrong decisions due to estimation or intuitive thinking. Retailers should insist on the money-back guarantee policy when introducing store brands. Even if the customer satisfaction and return rate of store brand products are higher compared to national brand products, the existence of this policy is beneficial to the development of store brands and the increased profit is greater than the loss caused by the return. The coordination method of revenue sharing has the following advantages: the method is easy to implement; it is easily recognized by manufacturers and retailers and manufacturers’ losses caused by the reduction of market share will be compensated. Store brands and manufacturers’ national brands will focus on improving product quality and service rather than fighting with each other. The improvement in product competitiveness will continuously increase the overall market share. It helps retailers and manufacturers to realize the advantages brought by channel coordination and improve their cooperation level.

4.4.2 Channel Coordination Through Cooperative Advertising To improve brand awareness, manufacturers often use forms of advertising promotion. The advertisements which appear on television, internet or in newspapers, are called national advertisements if they are spread throughout the country; if they appear only within a certain area, such as sky drawing, posters and brochures, they are called local advertising. The strategy of cooperative advertising refers to the ways in which national brand manufacturers raise the awareness of national brands through national advertising. However, since retailers also benefit from this, they need to bear part of the cost of national advertising. Thus, manufacturers and retailers cooperate to share the cost of national advertising. National advertising can increase consumer’s demand for national brand products and increase sales through traditional channels. Retailers benefit from increased sales and manufacturers benefit from increased sales as well as increasing number of cooperative retailers.

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Scholars have studied the problem of cooperative advertising strategies when national brand manufacturers adopt the dual-channel model and retailers introduce store brands. When the marginal profit obtained through the cooperative advertising model is low, the Stackelberg leaders tend to reduce the investment in cooperative advertising. The cooperative advertising strategy can mitigate the channel conflicts brought by the introduction of online channels by national brand manufacturers and the introduction of store brands by retailers. In a situation where no member can dominate the supply chain, the party with the lower marginal profit under the cooperative advertising model should have greater power to determine the costsharing rate (Yang et al. 2018). Research by Chen and Zhang (2019) is based on the assumption that both manufacturers and retailers invest in advertising to enhance their brand reputations. Through a stochastic differential game model, the impact on cooperative advertising strategies from the channel structure changes, supply chain members’ marginal profit levels, and brand reputation are explored. Their study found that designing a two-way cooperative advertising coordination contract can achieve dual-channel coordination. Making both parties willing to participate in the cooperative advertising contract, can be achieved by sharing a part of the profit transfer payment between the manufacturer and the retailer. Qian (2008) gave guidance about bilateral cooperative advertising subsidy contract measures based on the finding that a given proportion of exogenous bilateral advertising subsidies can coordinate the supply chain. Similarly, Abe (1995) demonstrated that combining pricing and advertising strategies is one of the coordinating mechanisms, and the degree of advertising depends on the quality differences between manufacturers and retailers’ products. Through cooperative advertising, a benign competition can be formed between retailers and manufacturers, which is reflected in the following ways: (1) Manufacturers take full advantage of their brand strengths and increase the income brought by their brand premium; (2) Retailers benefit from the price advantage brought by reduced circulation to meet the needs of different types of consumers and expand their market share; (3) The existence of national brands ensures the diversity of retailers’ products, and the increase of sales volume can increase the profits of retailers. However, the application of cooperative advertising strategy also has its limitations and challenges. First, not all national brand manufacturers adopt national advertising as their marketing strategy. Some manufacturers attach more importance to product development and quality improvement than advertising investment. For medium-sized manufacturers, spending on national advertising can cost a large part of their profits. Second, it is difficult for retailers and national brand manufacturers to agree on cost-sharing ratios. Without scientific research into the impact of the introduction of store brands on the profits of retailers and manufacturers, the costsharing ratio may depend simply on the bargaining power of the parties. Finally, if the impact of national advertising is incalculable, it may lead to a large loss of market share of store brand products. Therefore, it is necessary to adjust factors such as the cost-sharing ratio scientifically according to the influence of advertising, so that the interests of all sides are always guaranteed.

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4.4.3 Channel Coordination Through Positioning of Store Brand Quality It is inevitable for retailers to compete with manufacturers after the introduction of store brand. Differentiated brand strategies are considered as one of the methods to reduce competition, that is, adjusting the quality positioning of store brand products. In the perception of consumers, national brands are usually high-quality products, while store brands are regarded as substitutes for national brands, characterized by low quality and low price. There are, of course, some retailers who have succeeded in developing high quality store brand products, which are often considered to have the same level of quality as national brands, but these account for only a small percentage of store brand products. Adjusting the quality positioning of store brands can be used as one of the methods of channel coordination, although national brand owners will wish to be satisfied that the quality of store brands remains lower than that of the competing national brand. This approach can reduce the competition between brands, meet different market demands as much as possible, and expand the overall market share. As national brand products can meet the demand of consumers who want and are prepared to pay for their high quality, store brand products can meet the needs of price-sensitive consumers. Li et al. (2019a) studied whether retailers with store brands should stop sharing offline channels with manufacturers when national brand manufacturers open online direct sales channels. The study found that when online channels are more economical, manufacturers may increase wholesale prices and reduce the profits of retailers. The way that retailers stop channel sharing does not pose a threat to national brand manufacturers. The effectiveness of the threat to manufacturers depends on the quality of the store brand. When the quality of the store brand is higher than a critical value, the retailer’s profit will be affected because it makes the manufacturer more inclined to enter the online channel, resulting in a change in the equilibrium between the two sales games. Retailers should keep the quality of store brands at a relatively reasonable and low level, as this is more conducive to coordination and reconciliation with manufacturers on channel conflict issues and improve the profits of the whole supply chain. The research of Li et al. (2016) found that when retailers introduce store brands and manufacturers introduce direct sales channels, in order to achieve a win-win situation for both parties, the quality of store brand products must not exceed three quarters of the national brand. The quality level of store brands has negative and positive effects on the profits of manufacturers and retailers, respectively. It should be noted that if the quality level of store brand products is low and the sales cost of direct sales channels is moderate, the two parties are prone to “prisoner’s dilemma”. This is a situation that is best avoided by advance negotiation. The introduction of high-quality store brands is becoming more common among retailers, and national brand manufacturers are the best choice to produce those highquality store brands. Therefore, strategic cooperation between retailers and manufacturers, to introduce high-quality store brands is vital. Hara and Matsubayashi (2017) studied the conditions for cooperation between retailers and manufacturers from

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the perspective of introducing high-quality store brands. They found that achieving cooperation between the two sides is difficult. When the value of the store brands is at a low or medium level, retailers are reluctant to develop high-quality store brands because the competitiveness is weak and will deepen the “bilateral effect”. However, when the value of store brands is relatively high, this presents a better basis on which to cooperate with manufacturers to develop high-quality store brands, because it can reduce the negative effects of competition and reduce the “bilateral effect”. In this way the manufacturers would also benefit from using high-quality store brands to complement their national brands.

4.4.4 Other Methods of Coordination Fang et al. (2013) proposed that when the retailer’s unit quality cost of producing its store brand products has an advantage over manufacturers, the introduction of store brands can play a decentralized role in the supply chain. They found that a simple minimum order quantity contract can coordinate the retailer’s store brand and the manufacturer’s brand. Kurata et al. (2007) found that the establishment of brand loyalty is beneficial to both manufacturers and retailers. Compared with retailers, manufacturers’ marketing strategies are more restricted. They believe that when brand competition and channel contention coexist, wholesale prices do not coordinate the dual-channel supply chain, and an appropriate dynamic pricing strategy and a combination of high and low prices is necessary to coordinate the dual-channel supply chain and benefit all parties in the game. The research of Luo et al. (2019) found that in a supply chain composed of national brand manufacturers and retailers with their store brands, if both parties jointly implement consumer rebate promotions, the profits of both parties can be improved under the condition that the manufacturer dominates the supply chain.

4.5 Pricing Strategy for Bricks & Mortar (B&M) Stores in a Dual-Channel Supply Chain Based on the Hotelling Model This section uses the “hotelling law” model to analyze store brands as a strategy for B&M (brick-and-mortar) retailers to combat showrooming. It is based on investigations into how national-brand product mismatch and store-brand awareness affect a supply chain’s performance. Four major conclusions were reached. First, store-brand strategy may be an effective means for B&M stores to mitigate showrooming. This is most effective when introducing premium store brands. Second, the B&M store’s profit grows—and the online store’s profit declines—as national-brand

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product mismatch increases in breadth. When many consumers feel the nationalbrand product does not match their needs, a product positioning strategy for the store brand can help B&M retailers improve profit margins. Third, as national-brand product mismatch increases, the B&M store’s profit rises and online store profit falls. If national-brand products lack features that consumers need, a product differentiation strategy can be implemented to use store brands to fill in the gaps left by national brands. Finally, the growth of store-brand awareness will not necessarily benefit the B&M store. The impact of store-brand awareness on the B&M store’s profit depends on the hassle cost factor t, and a brand promotion strategy will reduce the loss of B&M retailer’s profit.

4.5.1 Introduction With the rise of smartphones and digital information, more consumers are searching for products online. Showrooming is a form of free riding by consumers. They visit a B&M store to examine the products, but then they complete their purchases through an online store in order to benefit from a lower retail price, time-saving logistics, convenient return services, and so on (Bell et al. 2015; Li et al. 2019b). According to the China Consumer Market Analysis Report (2017), the annual growth rate of online retail sales in 2015 was 53, and 47% of this growth came from the conversion of offline channel sales.1 A survey from Accenture shows that nearly two-thirds (63%) of Canadian consumers engage in “showrooming”.2 Thus, whether in China or elsewhere in the world, consumers may visit a B&M store with no intention of making a purchase. When showrooming occurs, the switch from the offline to the online purchase channel causes physical stores to lose potential customers. Hubert Joly, Executive Chairman, and former CEO of Best Buy (one of the largest chains of B&M stores in North America), said that many consumers expected Best Buy to shut down due to the growth of showrooming. It is a huge challenge for B&M stores to combat the ongoing negative impact of showrooming. Previous studies (e.g., Bell et al. 2015; Zhou et al. 2018; Chen and Chen 2019) have examined some effective strategies, such as price-matching and omnichannel marketing, to help physical stores recapture market share. However, researchers found that store-brand strategy may be another powerful weapon in the hands of B&M retailers resisting the adverse impact of e-commerce, especially in the field of food products. Store brand (SB) products, usually contrasted with national brand

1 See the China Consumer Market Analysis Report (2017) available at https://www.sohu.com/a/209

418195_99923947. 2 See Accenture holiday shopping survey reveals Canadians are “webrooming” and “showrooming”

to save money available at https://newsroom.accenture.com/industries/retail/accenture-holiday-sho pping-survey-reveals-canadians-are-webrooming-and-showrooming-to-save-money.htm.

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(NB) products, are defined as a goods or services sold by a B&M retailer with a store label. For example, Hema, a well-known fresh food store owned by Alibaba, recently developed a SB product called “Hema Fresh Rice”, which is made from an excellent rice variety and adopts cold storage technology to ensure its freshness. Despite a high up-front cost, its sales in the three-month period have increased by more than 57%. For another example, Uchida Shinji, the chairman of China’s 7-Eleven convenience store, said: 7-Eleven is not worried about the shock of e-commerce, because 50% of its sales come from its store brand, and food product account for most of the store-brand sales. It is said that it takes about 4–6 months for 7-Eleven to turn an idea to a SB product on their shelves. Although it costs much upfront time and effort, store branding brings huge profits for 7-Eleven and becomes one of its most important elements for competitiveness. Showrooming is made possible by the homogeneity of products sold both online and offline. It is not possible if the product that interests the consumer is sold only at physical stores. Hence, a major research issue in this paper is whether the negative effects of showrooming can be mitigated by the implementation of a store-brand strategy. Several factors may have decisive influences on the successful introduction of store brands. For example, understanding the product mismatch of existing national brands is of great help when implementing store-brand strategy. Product mismatch, the failure of brand to deliver a product that matches consumer expectations, can be relatively easily observed as B&M stores usually have first-hand information of consumer’s preferences. Brand awareness, familiarity, and willingness to buy the product, are other factors that have strong positive associations with the purchase decision and thus corporate profits. In 2005, Procter & Gamble was willing to acquire Gillette for $57 billion because of Gillette’s high brand awareness among consumers, although Gillette had an accounting book value of only $2 billion in earnings and $11 billion in revenue at that time. Brand awareness is a crucial consideration for a newly introduced store brand, especially when the presence of competing national brand is considered. Hence, we propose the following research questions: 1. Can the implementation of a store-brand strategy enable B&M stores to increase profit, mitigating the effect of showrooming? 2. What pricing strategy should B&M stores adopt when introducing store brands to combat showrooming? 3. How do national-brand product mismatch and store-brand awareness affect the B&M and online stores’ pricing, profit, and demand? This study contributes to existing literature on store brands and showrooming. It presents a systematic examination of how a store-brand strategy can effectively combat showrooming. A premium store-brand strategy for B&M stores is proposed to mitigate the adverse effects of showrooming. This study then develops pricing strategies that online and offline stores can use when introducing store brands. Against this background, this study defines two discrete dimensions of product mismatch—breadth and depth—and investigates their effects on the performance of

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the supply chain. Then this study broadens the model to include store-brand awareness and analyzes its effect on store-brand strategy. Finally, to assist B&M retailers in effectively implementing a store-brand strategy, specific measures regarding product positioning are proposed, relating to product differentiation and brand promotion. The remainder of the paper is structured as follows. Section 4.5.2 reviews relevant antecedent work. In Sects. 4.5.3 and 4.5.4, this study develops the model framework and study the null case in which showrooming is practiced but the B&M retailer does not introduce a store brand. Section 4.5.5 examines the effectiveness of the store-brand strategy and develops pricing strategies for the two members of the supply chain. In this section, it also investigates the effect of national-brand product mismatch. In Sect. 4.5.6, this study extends the model to include store-brand awareness and analyze how this affects the performance of the supply chain. Section 4.5.7 concludes the paper and suggests avenues for future research.

4.5.2 Literature Review Our work draws on three related research streams, namely (1) store brand, (2) brand awareness, and (3) showrooming in a dual-channel supply chain.

4.5.2.1

Store Brand

The literature on store brands related to our study includes research on the introduction of store brands and of premium store brands. Verhoef et al. (2002) conducted a questionnaire survey to investigate the strategies used by NB manufacturers facing competition from store brands in the Netherlands. Their results suggest that NB manufacturers should improve their advertising and product innovation in order to resist the store-brand strategies of B&M retailers. Kurata et al. (2007) studied pricing strategies in dual distribution channels where national brands and store brands compete. They also examined the effects of key marketing activities on the equilibrium price. Groznik and Heese (2010) find that the introduction of a store brand increases the retailer’s bargaining power, leading the NB manufacturer to offer a discount on the wholesale price. Premium store brands, which are usually of similar, or even higher quality than the national brands, have proliferated in recent years. Some of them are more in line with consumer demand, thus enabling B&M retailers to compete directly with the national brand (Geyskens et al. 2010; Hara and Matsubayashi 2017). Braak et al. (2014) find that retailers are more likely to introduce premium store brands in categories with more frequent promotions, a longer interpurchase time, a higher need for variety, and higher functional but lower social, risk. Schnittka (2015) finds that premium store brands are more beneficial for high-priced grocery stores than for low-priced ones, and more promising in product categories of high brand relevance. Hara and Matsubayashi (2017) studied the introduction of

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a premium store brand through collaboration between a retailer and a NB manufacturer. The results indicate that both partners benefit from developing a premium store brand when store brands have relatively high value.

4.5.2.2

Brand Awareness

Brand awareness relates to the degree of probability that a consumer is familiar with the nature, availability and accessibility of a company’s product and service. When selecting a product or service, brand awareness plays a decisive role in a series of brands that consumers are interested in (Barreda et al. 2015). Naik et al. (2008) investigated how to build brand awareness in a dynamic oligopoly model. They developed an estimation approach and offered managers a systematic way to assess advertising effectiveness and predict awareness levels for their own brands as well as those of the competition. Malik et al. (2013) conducted an empirical analysis to identify the effect of brand awareness on purchase intention. They found that brand awareness has strong positive association with purchase intention. Barreda et al. (2015) use SEM to examine the differential effects of Online Social Network (OSN) elements (system quality, virtual interactivity, information quality, and rewards for activities) on brand awareness, which, in turn, influence WoMM (Word of Mouth Marketing). The results show that building brand awareness in OSNs promotes WoMM traffic. Langaro et al. (2018) demonstrated the positive and significant effect of user participation on brand awareness. They also find that user participation has a positive effect on brand attitude, but this relationship is mediated by brand awareness. Some scholars also pay particular attention to the issue of store-brand awareness. Vahie and Paswan (2006) found that for the B&M store it was quality, store convenience, store price/value, and the congruence between national brand and store brand that had a positive effect on the affective dimension of store-brand awareness. Hsu and Hsu (2015) examined whether brand awareness and experiential perceived quality generated consumers’ differing brand attitudes. They found that national brands clearly achieve better brand awareness than store brands do. Store brands need to overcome their disadvantages by improving product quality.

4.5.2.3

Showrooming in a Dual-Channel Supply Chain

Research literature on the impact of showrooming and on strategies to combat it is relevant to our study. Many studies analyze the effects of showrooming. Balakrishnan et al. (2014) examined the ways in which showrooming influences competitive behavior between online and offline stores. Their results demonstrate that with the intensification of competition, showrooming can lead to declining profits not only for B&M retailers, but also for e-retailers. He et al. (2016) evaluated the impact of showrooming on carbon emissions in a closed-loop dual-channel supply chain. They found that although a manufacturers’ online store may benefit from showrooming, total carbon

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emissions in the supply chain as a whole increase. Pu et al. (2017) studied showrooming in a supply chain consisting of a manufacturer’s online direct channel and a traditional offline channel. Their results indicate that as the showrooming phenomenon grows, both the sales effort level of the B&M retailer and the total profit in the supply chain decrease. Setak et al. (2017) examined how showrooming affects supply chain coordination and information-sharing between the manufacturer and the traditional retailer. Zhou et al. (2018) investigated the different effects of consumers’ free-riding behavior on the pricing/service strategies and profits of supply chain members when the manufacturer’s online channel and the traditional channel adopt differential or non-differential pricing scenarios. Reflecting the generally negative attitude in both academic and management circles towards the showrooming phenomenon, an increasing body of research considers ways to reduce or eliminate its adverse impact. Basak et al. (2017) explored the viability of a B&M retailer opening its own online channel to stem its losses. Gu and Tayi (2017) study the optimal product placement strategy allowing an onlineto-offline (O2O) retailer to coordinate the two channels so as to mitigate consumer showrooming. Mehra et al. (2017) proposed price-matching as a short-term strategy and product exclusivity as a long-term strategy for B&M retailers to counter showrooming. They suggest that it is better to implement exclusivity through a store brand rather than a national brand when the product category has few digital attributes. Chen and Chen (2019) studied the circumstances under which a B&M retailer should implement price-matching to combat showrooming. They found that it was better for such a retailer not to adopt a price-matching strategy when the cost of online shopping is either low or high. However, when the cost is moderate, the physical store should try to match the online price. Liu et al. (2019) suggested that a multichannel retailer running both online and offline channels can intentionally establish a channel price gap to facilitate the switch from the offline to the online channel, thus realizing significant cost savings. To the best of our knowledge, studies that consider the use of store brands as a strategy to combat consumer showrooming are scarce. Mehra et al. (2017) considered three competitive anti-showrooming strategies for B&M retailers: price-matching, product exclusivity through known brands, and product exclusivity through store brands. Our paper differs from this study in that we focus on exploring the effects of national-brand product mismatch and of store-brand awareness on the performance of the supply chain. On this basis, we design a series of specific management measures to help B&M stores mitigate the negative impact of showrooming. Thus, our study addresses a limitation in current literature and presents a systematic examination of how a store-brand strategy can effectively combat showrooming.

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Fig. 4.1 The model

4.5.3 Model Framework We model a dual-channel supply chain consisting of a B&M store and an online store, in which both retailers purchase a national-brand product from the manufacturer and sell it through their own channels. Each consumer intends to purchase at most one item. We assume that the managers of the two retail outlets are both rational pursuers of profit maximization. The retail price of the NB product in the offline channel is pr and its price in the online channel is po . In addition, the B&M retailer has introduced a store-brand strategy and also sells SB products at the retail price pa in the offline channel, as shown in Fig. 4.1. We assume that the SB product differs horizontally from the NB product, so they have the same function value, denoted by v(>0). As consumers have developed an increasing focus on store brand programs, store brands have changed consumers’ shopping behavior over recent years (Braak et al. 2014; Hara and Matsubayashi 2017). According to data provided by Acosta (a US sales and marketing company) 53% of shoppers used store brands to determine where they shop in 2017, versus 34% in 2011.3 This proportion is increasing year by year. When a store brand is launched at the physical store, which may be more in line with consumer expectations, consumers will tend to go to the B&M store to experience both products and make their final rational purchasing choice. In the traditional business setting, consumers evaluate the quality of products by looking, touching, and feeling the products. However, for goods that involve an “experience”, whose quality can only be ascertained after purchase (e.g., perfume, clothing, and glass), these traditional ways of searching for more information are not available online, which exposes consumers to a significantly higher risk. Therefore, there is a high probability that consumers choose to try out the experience products offline first to avoid the risk of direct online shopping (Luo et al. 2012). Additionally, in some retail industries, such as electrical appliances and luxury goods retail, the absence of in-person guidance and lack of “touch & feel” in online shopping results in a very low proportion of pure online purchase (Burns et al. 2018). According to the China Luxury Report 2019, 90% of survey respondents claimed that in-person experiences

3 See

the Acosta data available at https://www.grocerydive.com/news/grocery--report-majority-ofconsumers-visit-multiple-stores-for-their-groceries/534424/.

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Table 4.1 Notations used in the paper Notations

The meanings

pr

Price of the NB product at the B&M store

po

Price of the NB product at the online store

pa

Price of the SB product at the B&M store

v

Customer perceived value of the product

t

Hassle cost factor

x

Customer’s location, distance from the B&M store

α

Proportion of customers whose best-matching product is the store brand

β

Store-brand awareness



Mismatch factor of a non-best-matching product

di

Demand for NB or SB product at a B&M or online store

Ui

Customer’s utility

πi

Profit of the B&M or online store

at brand stores is the most impactful source of information that influence purchase.4 The pure online purchase of Luxury goods is still at its infancy. Therefore, we may assume that consumers will continue to go to the B&M store to experience both SB and NB products, and then decide which brand to purchase, and in which channel, so as to maximize their expected surplus. In keeping with previous research (e.g., Tao et al. 2020; Chen and Chen 2019), we use the Hotelling model to describe the competitive relationship between the online and offline channels. We consider the B&M store and online shop lie on a Hotelling line by index 0 and 1, respectively. Consumers have corresponding hassle costs when shopping in B&M store or online shop (Gao and Su 2017; Li and Wang 2019). We assume that index x shows the proportion of hassle cost of visiting B&M store and that a customer whose position is x will pay an offline hassle cost t x (e.g., travelling to the B&M store or searching for the product on shop shelves). On the other hand, if a consumer purchases online, he or she will pay an online hassle cost t(1 − x) (e.g., paying delivery charge or waiting for the package to arrive). We use the hassle cost factor t as the proportionality constant to scale costs. In a sense, the two kinds of hassle costs in the hotelling model show consumers’ different preferences for online and offline channels. As the development cost of the store brand is a sunk cost, we assume that any additional production costs with regard to SB products are normalized to zero for simplicity, as is done in many related studies (e.g., Sayman et al. 2002; Mehra et al. 2017). Table 4.1 summarizes key notations in the paper.

4 See

the China Luxury Report 2019 available at https://www.mckinsey.com.cn.

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4.5.4 Showrooming When the B&M Retailer Implements no Strategy For comparison, we first analyze the optimal pricing strategies when B&M retailers do not take any measures to deal with showrooming. In keeping with previous studies, because the B&M store and the online store are independent of each other, we apply the Nash game model. This means that the B&M retailer and the online store retailer make simultaneous decisions on retail prices to maximize their respective profits. In the first case, the B&M retailer does not implement strategies to combat consumer showrooming. Therefore, both the B&M store and the online store sell only NB products. The utility derived by a customer who purchases at a B&M store is Ur = v − t x − pr while for a customer who practices showrooming it is Uo = v − t x − t(1 − x) − po . Setting v − t x − pr = v − t x − t(1 − x) − po and solving for x, we determine the indifference location as x = 1 − ( pr − po ) t. A customer located at [0, x] will purchase the product at the B&M store; a customer located at [x, 1] will purchase it at the online store. Therefore, the demand for the NB product at the two stores is as follows:  B&M store dr = x = 1 − ( pr − po ) t

(4.1)

 Online store do = 1 − x = ( pr − po ) t

(4.2)

The profit of the two stores can be expressed as follows:    B&M store πr = pr dr = pr 1 − ( pr − po ) t

(4.3)

 Online store πo = po do = po ( pr − po ) t

(4.4)

Taking the first-order derivative of πr with respect to pr and the first-order derivative of πo with respect  to po , respectively, we obtain the optimal retail prices prN = 2t 3 and poN = t 3 by letting thederivatives be equal to zero. Substituting prN = 2t 3 and poN = t 3 into Eqs. (4.1)–(4.4), we determine the two stores’ respective demands and profits, as shown in Table 4.2.

4.5.5 Showrooming When the B&M Retailer Implements a Store-Brand Strategy In the second case, the B&M retailer introduces a store brand and offers a SB product which is so like the NB product that the two can be substituted for each other. While the B&M store now sells both products, the online store still sells only the NB product. Consumers choose only one product, either the national brand or the store brand. Let us assume that

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Table 4.2 The equilibrium solutions of the three models No strategy

Store-brand strategy

Considering store-brand awareness

po

2t 3 t 3

Pa

−−

do dr

1 3 2 3

da

−−

dr +a

−−

4t−α 6 t−α 3 4t+3−α 6 t−α 3t (1−α)(4t−α) 6t (4t+3−α)α 6t 2 α 3 + 3t

4t−αβ 6 t−αβ 3 4t+3−αβ 6 t−αβ 3t (1−αβ)(4t−αβ) 6t (4t+3−αβ)αβ 6t αβ 2 3 + 3t

πr

4t 9

16t 2 +16tα+α2 (9−5α) 36t

16t 2 +16tαβ+αβ2 (9−5αβ) 36t

πo

t 9

(t−α)2 9t

(t−αβ)2 9t

pr

1. for a proportion α(0 < α < 1) of customers, the best-matching product is the store brand offering. Hence, for a proportion 1−α of customers, the best-matching product is the one from the national brand. 2. the utility of any product that is not the best match is v − , where 0 <  < v. A customer whose best-matching product is the store brand and who buys the SB product at the B&M store derives the utility Ua = v − t x − pa . A customer with the same store-brand preference who buys the NB product at the same store derives the utility Ur = v − t x − pr − . Assuming that pa < pr + , the proportion α of customers for whom the SB product is the best match will always, if they buy at the B&M store, choose to purchase the store brand, since Ua > Ur always holds. On the other hand, a customer who switches to the online channel to purchase the NB product will incur two hassle costs and derive the utility Uo = v − t x − t (1 − x) − po − . Setting v − t x − pa =v − t x − t (1 − x) − po − and solving for x, we determine the indifference point x1 = (t − pa + po + ) t. From this discussion, we may conclude that αx1 customers will prefer to purchase the SB product at the B&M store, while α(1 − x1 ) customers will prefer to switch channels and purchase the NB product at the online store. A customer whose best-matching product is the national brand and who purchases it at the B&M store derives the utility Ur = v − t x − pr . A customer with the same NB preference who buys the store-brand product at the same store derives the utility Ua = v − t x − pa − . Assuming that pa > pr − , the proportion 1 − α of customers for whom the NB product is the best match will always, if they buy at the B&M store, choose to purchase the national brand, since Ur > Ua always holds. However, a customer who purchases the NB product online derives the utility Uo = v − t x − t (1 − x) − po . Setting v − t x − pr =v − t x − t (1− x) − po and solving for x, we determine the indifference point x2 = (t − pr + po ) t. From this analysis, we conclude that (1 − α)x2 customers will buy the NB product at the B&M store,

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Fig. 4.2 Customer’s decision-making process under a store-brand strategy

while (1 − α)(1 − x2 ) customers will switch channels and purchase it at the online store. Figure 4.2 illustrates the customer’s decision-making process in this case.

4.5.5.1

Pricing Strategies When a Store-Brand Strategy Is Used to Combat Showrooming

As the above figure shows, the demand for the SB and NB products at B&M and online stores is as follows:  SB product demand at B&M store da = αx1 = α(t − pa + po + ) t

(4.5)

 NB product demand at B&M store dr = (1 − α)x2 = (1 − α)(t − pr + po ) t (4.6) NB product demand at online store do =α(1 − x1 ) + (1 − α)(1 − x2 )  = (αpa − α + pr − αpr − po ) t (4.7) Accordingly, the profits of the B&M and online retailers can be expressed as follows:  B&M retailer’s profit πr = pa da + pr dr = pa α(t − pa + po + ) t  + pr (1 − α)(t − pr + po ) t (4.8)  Online retailer’s profit πo = po do = po (αpa − α + pr − αpr − po ) t

(4.9)

To find the equilibrium solution maximizing πr and πo , we take the first-order derivative of πr with respect to pr , pa and the first-order derivative of πo with respect to po , respectively. Letting the derivatives be equal to zero, we obtain:

4.5 Pricing Strategy for Bricks & Mortar (B&M) Stores …

 ⎧ ⎨ ∂πr  ∂ pr =0 ∂π ∂ p =0 ⎩ r α ∂πo ∂ po =0

195

(4.10)

From the above equations, we determine the optimal retail prices prS , paS and poS . In addition, we verify that the optimal prices satisfy the constraint pr −  < pa < pr + . By substituting prS , paS and poS into Eqs. (4.5)–(4.9), we derive the B&M and online retailers’ demand and profit for this case, as shown in Table 4.2. After obtaining the optimal prices, we discuss the rationality of the assumption on prices.

4.5.5.2

Discussion of the Assumption on Prices

We address the equilibrium solutions under the assumption pr −  < pa < pr + . Now, let us consider the possibility of other optimal solutions, that is, the price order is inconsistent with the assumption. Therefore, we make the following assumptions in the opposite direction. (i) if pa < pr −  By assuming this, we can obtain that each consumer will either purchase their SB product at the B&M store or buy their NB product at the online store. No customers will go to the B&M store to buy NB products even if one consumer’s best-matching product is a NB product, which definitely causes the B&M retailers to no longer have the incentive to sell NB products. Consequently, the showrooming phenomenon will cease to exist. However, the situation that B&M stores only sell SB products is an extreme case, which is out of line with reality. (ii) if pa > pr +  In this case, no customers will purchase their SB products from the B&M store even if one customer’s best-matching product is a SB product. It implies that SB products are completely ruled out and the B&M store would not introduce store branding at all. The situation that only considers the showrooming between NB products has been discussed in many previous papers, but this is inconsistent with the focus of this paper, as we assert that research on implementing store-brand strategy to combat showrooming is valid. In conclusion, these two assumptions that may exist other equilibrium solutions are unsuitable for the research background of this paper. Hence, the rationality of the assumption on prices is proven. Proposition 4.1 Implementing a store-brand strategy increases profit margins and demand for B&M retailers. The store-brand strategy may be an effective tool for such retailers to combat showrooming. Proof of Proposition 4.1

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 Due to πrS − πrN = α(16t + (9 − 5α)) 36t > 0, drS − drN = −α(4t + (1 − α)) 6t < 0 and drS+α − drN = α 3t > 0, Proposition 4.1 is proven. In Proposition 4.1, we know that as the B&M retailer adopts a store-brand strategy, demand for the NB product decreases in both online and offline stores, reducing the profit of the online retailer. However, for the B&M retailer, the increased demand for the SB product offsets the decreased demand for the NB product, leading to growth in the retailer’s overall profit. An immediate implication of this proposition is that the B&M retailer has an incentive to adopt a store-brand strategy to mitigate the negative effect of consumer showrooming. Proposition 4.2 When B&M retailers consider implementing a store-brand strategy to mitigate showrooming, it may be better for them to introduce premium (as opposed to economy or mid-range) store brands. Proof of Proposition 4.2. Due to paS > prS > poS , Proposition 4.2 is proven. From Proposition 4.2, we know that in the B&M store, the price of the SB product is higher than that of the NB product, and that the NB product in the online store has the lowest price of the three. The reason may be that, on the one hand, overall demand is always higher for a B&M retailer than for an online retailer (see Table 4.2), so the online retailer tends to set a lower price for the NB product in order to attract consumers. On the other hand, when α is at a moderate or high level—meaning that SB products are more in line with consumer expectations—demand for the SB product at the B&M store is greater than demand for the NB product. Thus, the B&M retailer may maximize profit by setting a higher price for the SB product than for the NB one. When α is at a low level, meaning that NB products are more popular than SB products, the B&M store relies mainly on the sale of NB products to make a profit. In this case, the retailer may set a higher price for the SB product to avoid price competition between its two products. The management insight we obtain from Proposition 4.2 is that, instead of introducing a mid- to low-end store brand, the B&M retailer should consider introducing a premium store brand that better meets consumer expectations. This conclusion is consistent with some real-world observations. For example, Hema Fresh recently launched a popular store-brand product called “Daily Fresh Milk” at a price of 19.9 yuan per carton. Contrary to the “low price and low quality” image of store brands in the traditional retail era, this price is higher than that of most milk on the market. However, Hema promises not to sell overnight milk, guaranteeing that Daily Fresh Milk is fresher than most milk. Because “fresher” matches consumer expectations of milk, Daily Fresh Milk has become a new benchmark for dairy products. In fact, it is common that many of Hema’s SB products are more expensive than NB products. In short, a premium store-brand strategy can entice some consumers to buy storebrand products, ending their free-riding behavior. In this way, the adverse impact of consumer showrooming on the profit of the B&M retailer is mitigated.

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4.5.5.3

197

Effects of National-Brand Product Mismatch

With global technology integration, product homogeneity is becoming an increasingly serious issue for marketers. It is especially important for enterprises to discover, perhaps even to guide, the new needs of consumers in today’s fiercely competitive markets. When a B&M retailer considers introducing a store brand, discovering the shortcomings of existing products is a top priority. To provide analytical insight, we investigated how national-brand product mismatch (that is, failure to match consumer expectations and needs) affects pricing strategies, demand, and profits in the supply chain. Like previous research (Su 2009), our study considers two discrete dimensions: the breadth of the national-brand product mismatch (i.e., α, the proportion of consumers whose mismatching product is the national brand) and the depth, or degree, of the mismatch (i.e., ). Proposition 4.3 Table 4.2 indicates the effect of α on p, d, π .    (i) ∂ paS  ∂α < 0; ∂ prS  ∂α < 0; ∂ poS ∂α  < 0;  (ii) ∂daS ∂α > 0; ∂drS ∂α < 0; ∂drS+a ∂α > 0; ∂doS ∂α < 0; (iii) ∂πrS ∂α > 0; ∂π0S ∂α < 0. Proof of Proposition 4.3. Proof of Proposition 4.3(i).   Taking the first derivative of  retail prices,we can get ∂ paS ∂α = − 6 < 0,   ∂ prS ∂α = − 6 < 0 and ∂ poS ∂α = − 3 < 0. Proposition 4.3(i) is proven. Proof of Proposition 4.3(ii).  Taking the first of demands, we can obtain ∂daS ∂α =     derivative (4t + (3 − 2α)) 6t> 0, ∂drS+a ∂α= 3t > 0, ∂doS ∂α = − 3t 0. Therefore, ∂drS ∂α = (−2(t − α) − 2t − ) 6t < 0. Proposition 4.3(ii) is proven. Proof of Proposition 4.3(iii). Taking the first derivative of the   physical  and online store profits, we get 2 (16t + 9 − 10α) 36t = 10(t − α) + 6t + 9 36t > ∂πrS ∂α =   0 and ∂πoS ∂α = −2(t − α) 9t < 0. Proposition 4.3(iii) is proven. Proposition 4.3(i) shows that the prices of the SB and NB products at the B&M store and the price of the NB product at the online store are all negatively associated with the breadth of the national-brand product mismatch α. This is plausible because the greater the number of consumers for whom the national-brand product fails to meet expectations, the more likely it is that the online retailer will set a lower price for the NB product in order to retain customers. The B&M store may also reduce its price for the NB product to match the online price. Because of price competition between the national brand and the store brand, the B&M store may also lower the price of the SB product. Proposition 4.3(ii) shows that demand for the SB product increases with α, while demand for the NB product at both B&M and online stores decreases when α becomes greater. As the breadth of national-brand product mismatch increases, more customers switch to SB products at the B&M store, leading to increased demand for

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the SB product and reduced demand for the NB offering at the online store. This trend also hurts the demand for the NB product at the B&M store. However, Proposition 4.3(ii) also shows that the total demand for SB and NB products at the B&M store increases with α. This result implies that although demand for the NB product at the B&M store falls, growing demand for the SB product offsets the loss and may even result in a higher total demand. Proposition 4.3(iii) indicates that the profit of the B&M store increases in a linear fashion with α, while the profit of the online store always declines with the growth of α. Clearly, although demand for the NB product and the prices of both SB and NB products at the B&M store all decrease as α becomes greater, the rising price of the SB product may result in increased total profit for the B&M retailer. These observations highlight the fact that the breadth of the national-brand product mismatch α is a strategic factor affecting the performance of the supply chain. If more customers prefer SB products to NB products, the result is not good for the online store that sells only NB products. However, from the perspective of the B&M retailer, this is an opportunity to enhance its profits and combat consumer showrooming. Hence, the management insight for B&M retailers is to implement a product positioning strategy. These retailers have a richer set of consumer data and insights into customer preferences than do the manufacturers of NB products. Before introducing a store brand, they can scrutinize consumer market surveys, determine the degree of consumer satisfaction with NB products, and explore the market potential. Such analysis will allow the B&M retailer to identify product categories in which customer satisfaction is low, with a view to introducing SB products in these categories. In this way, the retailer can formulate a product positioning strategy that uses store brands to increase customer satisfaction and improve competitiveness. Proposition 4.4 Table 4.2 illustrates the impact of  on p, d, π .    (i) ∂ paS  ∂ > 0; ∂ prS ∂ < 0; ∂ poS ∂ <  0;  S S > 0; ∂d ∂ > 0; ∂d ∂ < 0; ∂drS ∂ < 0, i f  ∈ (ii) ∂daS ∂ r +a o   S 0, 1 2 , other wise,  ∂dr ∂ > 0; S S (iii) ∂πr ∂ > 0; ∂πo ∂ < 0. Proof of Proposition 4.4. Proof of Proposition 4.4(i).  Taking of retail prices with respect to , we can get ∂ paS ∂ =      the first derivative 3 − α 6 > 0, ∂ prS ∂ = −α 6 < 0 and ∂ poS ∂ = −α 3 < 0. Proposition 4.4(i) is proven. Proof of Proposition 4.4(ii). Taking the first derivative of the  demandswith respect to , we can obtain   ∂daS ∂ = α(3 − α) 6t > 0, ∂drS+a ∂=α 3t > 0 and ∂doS ∂ = −α 3t < 0.   − 1) 6t, the value derivative depends on . When As ∂drS ∂ = α(2   of the  0 <  < 1 2,∂drS ∂ < 0; when  > 1 2, ∂drS ∂ ≥ 0. Proposition 4.4(ii) is proven. Proof of Proposition 4.4(iii).

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Similarly, taking stores’ profits with respect to  the first derivative of both   S S ∂ = + 2α(9 − 5α)) 36t > 0 and ∂π ∂ = , we get ∂π (16tα o r −2α(t − α) 9t < 0. Proposition 4.4(iii) is proven. Proposition 4.4(i) indicates that the price of the SB product at the B&M store increases with , while the prices of the NB product at both the B&M and online stores respond negatively to rising . As the depth of national-brand product mismatch becomes greater, consumers are more motivated to buy the SB product, leading to an increase in its retail price. Thus, both the B&M and online stores must reduce the NB product price to attract consumers. Proposition 4.4(ii) indicates that demand for the SB product and total demand at the B&M store increase with , while demand for the NB product at the online store is negatively associated with . However, the relationship between demand for NB product at B&M stores and the depth of the national-brand product mismatch varies at different intervals of. If consumer dissatisfaction with NB product is relatively small (0 <  < 1 2), demand for the NB product at the B&M store shows a negative association with . However, if consumer dissatisfaction with the NB product is relatively large ( ≥ 1 2), demand for the product at the B&M store increases with . This finding is counterintuitive. The explanation may be that as the depth of the national-brand product mismatch becomes greater, the price of the store brand continuously rises. Thus, the NB product gains a price advantage that may stimulate an increase in demand. Proposition 4.4(iii) states that a greater depth of national-brand product mismatch results in higher revenue for the B&M store and declining profit for the online store. The direct implication for management is that a B&M retailer can implement a product differentiation strategy, identifying desired features that are missing from NB products to improve the SB product. In other words, such retailers can move into the market gap resulting from the depth of the national-brand product mismatch by increasing the difference between SB and NB products. Their efforts in this direction will bring greater profits.

4.5.6 Considering Store-Brand Awareness Store brands are generally not as famous as national ones. Many consumers do not understand their benefits, and some do not even know of their existence. Therefore, we introduce β(0 < β < 1) to describe the proportion of store customers who are familiar with the store brand. As in the second case, these customers may choose to purchase either the SB or the NB product. However, a proportion 1 − β of customers will not take the store-brand products into consideration. Therefore, there are only two choices for these customers. If they purchase the NB product directly at the B&M store (without showrooming), their utility is Ur = v − t x − pr . If they switch channels to purchase the NB product at an online store, their utility is Uo = v − t x − t (1 − x) − po . Setting v − t x − pr =  v − t x − t (1 − x) − po , we determine the indifference point x3 = (t + po − pr ) t. A fraction β of customers behave the same

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Fig. 4.3 Consumers’ decision-making process considering store-brand awareness

way as in the second case. As for the rest of the consumers (the 1 − β fraction), we conclude that (1 − β)x3 consumers will buy the NB product directly from the B&M store, while (1 − β)(1 − x3 ) consumers will switch channels to purchase the NB product at an online store. Figure 4.3 depicts consumers’ decision-making process in this case.

4.5.6.1

Pricing Strategies When Considering Store-Brand Awareness

In this case, demand for the SB product and for the NB product marketed in dual channels is as follows:  SB product demand at B&M store da = βαx1 = βα(t − pa + po + ) t (4.11) NB product demand at B&M store dr = β(1 − α)x2 +(1 − β)x3  = (1 − αβ)(t − pr + po ) t

(4.12)

NB product demand at online store do = βα(1 − x1 ) + β(1 − α)(1 − x2 )+(1 − β)(1 − x3 )  (4.13) = (αβpa − αβpr − αβ − po + pr ) t

The respective profits of the two retailers can be expressed as follows:  B&M store’s profit πr = pa da + pr dr = pa βα(t − pa + po + ) t  + pr (1 − αβ)(t − pr + po ) t (4.14)  Online store’s profit πo = po do = po (αβpa − αβpr − αβ − po + pr ) t (4.15)

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By taking the first-order derivatives of πr with respect to pr , pa and the firstorder derivative of πo with respect to po , respectively, we set the derivatives at zero and solve the equations to obtain the optimal retail prices prA , paA and poA , as in the previous case. The optimal retail prices again satisfy the constraint. Substituting prA , paA and poA into Eqs. (4.11)–(4.15), we derive the B&M and online stores’ demand and profit, as shown in Table 4.2. Proposition 4.5 The premium store-brand strategy may also be an effective means for B&M stores to combat showrooming when store-brand awareness is considered. Proof of Proposition 4.5.  Due to πrA − πrN = αβ(16t + (9 − 5αβ)) 36t > 0, drA − drN =  A N −αβ(4t + (1 − αβ)) 6t < 0, dr +α − dr = αβ 3t > 0 and paA > prA > poA , Proposition 4.5 is proven. From the proof, we can see that the demand for NB products at the online store decreases due to the influence of the store-brand strategy. Although the demand for the B&M store’s NB products also declines, the overall demand for SB and NB products at the B&M store increases. Moreover, the SB product commands the highest optimal price, followed by the NB product at the B&M store and the NB product at the online store, a finding that replicates case 2. The management implication is that when store-brand awareness is accounted for, a premium storebrand strategy is still an effective means to combat consumer showrooming. Even if the level of store-brand awareness is not high, some previously free-riding consumers will choose SB products, increasing the profit of the B&M store.

4.5.6.2

Effects of Store-Brand Awareness

Store-brand awareness has been the focus of much research. To develop insight regarding the store-brand strategy, we pose the following research question: How does store-brand awareness affect the B&M store’s and the online store’s pricing decisions, profit, and demand? Proposition 4.6 Table 4.2 illustrates the impact of β on p, d, π .    ∂β < 0; (i) ∂ paA  ∂β < 0; ∂ prA  ∂β < 0; ∂ poA   (ii) ∂daA ∂β > 0; ∂drA ∂β < 0; ∂drA+a ∂β > 0; ∂doA ∂β < 0; (iii) ∂πoA ∂β < 0;    If 0 < t < (10α − 9) 16, ∂πrA ∂β > 0 when β ∈ (0, β ∗ ), ∂πrA ∂β ≤ 0 10α. when β ∈ (β ∗ , 1), β ∗ =   (16t + 9) If t ≥ (10α − 9) 16, ∂πrA ∂β > 0. Proof of Proposition 4.6. Proof of Proposition 4.6(i).  Taking of retail to β, we can get ∂ paA ∂β =   prices with Arespect   the firstAderivative −α 6 < 0, ∂ pr ∂β = −α 6 < 0 and ∂ po ∂β = −α 3 < 0. Proposition 4.6(i) is proven.

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Proof of Proposition 4.6(ii). Taking the first derivative of the demands with respect to β, we can  > 0, ∂doA ∂β = −α 3t < 0, ∂daA ∂β = obtain ∂drA+a ∂β=α 3t  α(4t +  + 2(1 − αβ)) 6t > 0.   As poA =(t − αβ) 3 > 0, we know t − αβ > 0. Therefore, ∂drA ∂β = −(2t + 2(t − αβ) + ) 6t < 0. Proposition 4.6(ii) is proven. Proof of Proposition 4.6(iii). Taking the first derivativeof online store’s profit with respect to β, we get  ∂πoA ∂β = −2α(t − αβ) 9t < 0. Taking  the first and second derivativeof physicalstore’s profit withrespect to β, (∂πrA ∂β = 16tα + α2 (9 − 10αβ) 36t,∂ 2 πrA ∂β 2 = −52 α 2 18t < 0), it shows that the first derivative decreases monotonically.   Moreover, we can easily see that ∂πrA ∂β (β=0) = 16tα + 9α2 36t > 0.   Further, let ∂πrA ∂β = 16tα + α2 (9 − 10αβ) 36t=0, we have the only  extreme point β ∗ = (16t + 9) 10α. We can easily prove  that: If t ≥ (10α − 9) 16,β ∗ ≥ 1 always holds, πrA increases with β over (0, 1); If 0 < t < (10α − 9) 16, 0 < β ∗ < 1 always holds, πrA increases with β over (0, β ∗ ) and then decreases with β over [β ∗ , 1). Proposition 4.6(iii) is proven. Proposition 4.6(i) indicates that as the store brand becomes better known, the prices for the NB products at both B&M and online stores decrease, and so does the price of the SB product. Proposition 4.6(ii) shows that demand for NB products at both the B&M and online stores decreases when store-brand awareness grows. Nonetheless, total demand for SB and NB products at the B&M store still increases, due to growing demand for the store brand. Proposition 4.6(iii) shows that the online store’s profit decreases with β. This is intuitive. Proposition 4.6(iii) also indicates that the impact of store-brand awareness on the B&M store’s profit depends on the hassle cost factor t. If the hassle cost factor is relatively small, the B&M store’s profit first increases with β, and then decreases. If the hassle cost factor is relatively large, the B&M store’s profit always increases with store-brand awareness. This means that the growth of store-brand awareness will not necessarily benefit the B&M store. The explanation may be that when the hassle cost factor is relatively small, the difference between utility values purchasing online and offline is getting smaller, which means the competition between B&M store and online store may be fierce. The B&M store’s profit increases with consumer store-brand awareness at the beginning due to the growing demand for SB product. However, when the store-brand awareness reaches a high level, the online store continues to lower the price of NB product to retain consumers. To gain competitive advantage, the B&M store will also lower the prices of both NB and SB products. In this case, although the demand for the SB product has increased, the reduced prices of both SB and NB products may lead to a decline in the B&M store’s total profit. We can also see some real-world observations. For example, most popular supermarkets (e.g. Walmart, Tesco) usually choose to retain the best-selling NB

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products while introducing their SB products. They will not use the home-court advantage to excessively expand store-brand awareness. In summary, a brand promotion strategy can be implemented for B&M stores. When the hassle cost factor is relatively high, raising the visibility of the store brand will increase the B&M store’s profit. Regular marketing campaigns, including advertising, promotions, and membership activities, can be used to increase store-brand awareness. However, when the hassle cost factor is relatively low, it is better for B&M stores not to promote store brands overly, as it may lead to excessive competition, causing damage to the total profit of the B&M store.

4.5.7 Conclusion The phenomenon of showrooming by consumers has posed a great challenge to B&M retailers. In this context, we consider the introduction of store brands as a strategy to help physical stores combat showrooming. For this purpose, we model a dualchannel supply chain consisting of a physical store and an online store and analyze the equilibrium pricing strategies for the two retailers. We first examine the effectiveness of the store-brand strategy. Then we investigate the ways in which nationalbrand product mismatch, both in breadth and depth, affects the B&M retailer’s and the online retailer’s pricing decisions, profit, and demand. To develop insight into the store-brand strategy, we extend the model by taking store-brand awareness into consideration. Finally, we propose a series of measures that B&M retailers may take to ensure that the strategy is implemented effectively to mitigate showrooming. Our main results are as follows. First, a store-brand strategy may be an effective means for B&M retailers to combat consumer showrooming. However, it may be better for them to introduce premium store brands to reduce the adverse impact of showrooming. Second, the B&M store’s profit increases with the breadth of nationalbrand product mismatch, while online store profits decrease. Thus, if the national brand is a mismatch for many consumers, a product positioning strategy for the store brand can increase the profits of B&M stores. Third, as national-brand product mismatch increases in depth, the B&M retailer’s profit again increases, whereas online store profit decline. If the mismatch is very deep—that is, if the national brand fails to meet many consumer expectations—a B&M retailer can implement a product differentiation strategy to promote product improvement and innovation, filling the gaps created by the depth of the national-brand product mismatch. Finally, the impact of store-brand awareness on the profit of a B&M store depends on the hassle cost factor. If the hassle cost factor is relatively small, the B&M store’s profit first increases, but then decreases, as store-brand awareness grows. If the hassle cost factor is relatively large, the B&M retailer’s profit always increases with storebrand awareness. This means that the expansion of store-brand awareness will not necessarily benefit the B&M store. Our study has several limitations. We assume that the B&M store and the online store make their pricing decisions simultaneously, which implies that the two retailers

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have the same pricing power. However, it is quite possible that the physical store has more pricing power. In this setting, the B&M retailer decides the prices of the storebrand and national-brand products first as a Stackelberg leader, and then the online store sets its price for the national-brand product. An interesting direction for further research would be to study how different power structures affect the equilibrium results. Another limitation is that we consider a relatively simple supply channel that consists of a B&M store and an online store. It would be interesting to analyze the case in which the B&M retailer also operates an online channel to compete directly with the online store. In such an extension, the consumer’s showrooming and the B&M retailer’s store-brand strategy would be more complex.

4.6 Summary In this section we examined the channel strategy and conflict resolution with and without the introduction of store brands. Specifically, we review the literature both in Chinese and English in four streams: channel competition between traditional manufacturers and retailers; channel competition with the consideration of store brand; channel coordination of traditional retailer and manufacturer, and channel coordination between retailers and manufacturers when store brands are introduced. We find that the introduction of store brands could bring channel competition to manufacturers and retailers: however, manufacturers and retailers also seek channel coordination through price discounts, revenue sharing, cooperative advertising, inventory transfer, and other methods to adapt to the changes. We conduct a study of pricing strategy for B&M stores in a dual-channel supply chain based on the hotelling model. In this study, the introduction of store brands is considered as a strategy to combat showrooming. As a result, this study contributes to the stream of channel competition in that it finds that the store-brand strategy is an effective strategy for retailers to compete with online stores by preventing showrooming. We also find that the B&M store’s profit increases with the breadth of national-brand product mismatch, while the online store’s profit decreases.

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Chapter 5

Supply Chain Coordination

The introduction of store brands by retailers has created a new fierce competition for manufacturers, but it is not uncoordinated. Through a specific coordination mechanism, the supply chain benefits can be maximized, and a win-win situation can be achieved. In terms of research methods, scholars generally adopt standardized mathematical models and use game theory to study the impact of store brand involvement on each member of the supply chain’s pricing and profit and propose a feasible coordination mechanism. In reviewing the research for this book, we conclude that supply chain coordination for store brands is principally divided into the following three categories: theoretical analysis based on game theory; coordination mechanisms, and competition and cooperation between retailer and manufacturer. Theoretical analysis based on game theory is mainly focused on three problems: the pricing and profitability problem; the strategic interaction between manufacturer and retailer, and the positioning of the store brand. In order to study the best pricing strategy and corresponding profitability of different members in the supply chain and profitability problem, researchers have considered many factors: market power structure; the price-sensitivity of consumers; promotion efficiency; information sharing rate; uncertain market information; spillover effect, and brand recognition, that give information to both retailers and manufacturers. In terms of the strategic interaction between manufacturer and retailer, Stackelberg game theory is generally adopted by researchers. Different assumptions shed light on varying equilibrium conditions in many aspects of the supply chain under different circumstances. Regarding the positioning of the store brand, researchers mainly focus on the quality of store brand products and find that the optimal strategy for different situations could be in complete opposition. In the research field of supply chain coordination mechanisms, they can be divided into the following five categories with different mechanisms: advertising strategy; channel coordination; rebate strategy; shelf allocation strategy, and contractual strategy. Regarding advertising strategies, most papers establish the evolutionary game model to analyze how separate or cooperative advertisements by suppliers and

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retailers affect the profit levels of both parties and channel profit levels. In addition, some papers have analyzed the gaps between store brands’ distribution and manufacturer brands’ distribution on the advertising leaflets used by retailers in store. Considering that improving the manufacturer’s brand reputation is beneficial to retailers, they are also willing to share some of the manufacturer’s brand advertising investment. By designing cooperative advertising contracts, dual-channel supply chain coordination can be achieved. In terms of channel coordination mechanisms, unlike previous mechanisms that were limited to online and offline dual-channel coordination, recent papers have examined omni-channel strategies, including direct marketing and delivery platforms. They analyze the complex differences in pricing, sales, and profit under the opening of different channels. In terms of discount strategies, this book defines rebates, discount sales, and promotions as discounts. In the early days of the market, to quickly occupy the market, store brands usually make massive discounts to attract new users. Later, store brands usually formulate a discount strategy corresponding to the manufacturer’s brand behavior to increase bilateral profits. On shelf allocation, researchers consider a retailer’s decision to develop a store brand version of a national brand and examine the role that its positioning strategy plays in appropriating supply chain profit. The retailer’s business can be regarded as selling to NB manufacturers the shelf space at its disposal. In studying the contract mechanism, the two parties comprising the supply chain form a consensus by formulating corresponding contracts, thereby obtaining an optimal balance of product quality, to the profit of all parties, and for the total profit of the supply chain, a consumer surplus and a measure of social welfare under each contract. Joint pricing, cooperative advertising, manufacturers providing store brands and other cooperation strategies, such as bundling sales, minimum order volume contracts, and channel collaboration have been researched and proved to be effective coordination mechanisms. There are more studies on channel coordination, such as advertising and contract mechanisms. There is less research on price discounts and shelf allocation. Later studies can be expanded to include these two aspects. In most analyses, it is assumed that the manufacturer is the dominant player in the game. With the development of store brands, the competitive position of the manufacturer is improved. Therefore, in later analyses, the game’s dominant player may need to be formulated according to more specific situations. Regarding studies on the relationship between retailers and manufacturers, they mainly study the relationship between the two parties in the existing store brand system and the changed relationships within the supply chain after the newly introduced store brand. In terms of supply chain coordination after the introduction of store brands, there is not much research on supply chain coordination after the introduction of store products. The studies that do consider complex channel structures are fewer for supply chain coordination strategy design, and there is less analysis of supply chain coordination for the introduction of store brands after consumer brand and channel selection leads to the impact of demand shift and change mechanisms. Research shows that when the demand function is more complicated, it is difficult to coordinate the supply chain by using extant common strategies alone. Following the introduction of store brands, the problem of supply chain coordination

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strategy design after the process of competition and cooperation between retailers and manufacturers and other competing channels is complicated. In the supply chain of existing store brands, most research focuses on analyzing the effects of product differences, quality gaps, and store differentiation on the profit, sales, and system effectiveness of store and national brands. Research shows that different strategies can bring different results. In terms of research objects, most research analyzes the competition between one manufacturer and one supplier, but there is a lack of research that analyzes multiple competitor situations. Thus, the research methods may be summarized as follows: (1) There are two main types of demand function construction method. One is the traditional linear demand function on price. When studying issues such as advertising, goodwill, and shelf allocation, the impact of this decision variable on demand is added to demand function, constitutes the final linear demand function. Another way is to consider consumer heterogeneity, especially that which is based on brand preference and quality preference and build demand by constructing utility functions to conduct relevant research. (2) With the introduction of store brands, a layer of horizontal competition is added to the original vertical competition between retailers and manufacturers, making the competition coordination relationship of the original vertical supply chain more complicated. Taking the maximization of their respective interests as the basis for decision-making will inevitably bring about the ‘double marginalization’ problem that prevails in the supply chain. It is not easy to judge whether traditional supply chain coordination mechanisms, such as the original quantity discount, two-part pricing method, cooperative advertising, and contracts are still effective.

5.1 Supply Chain Coordination Based on Game Theory 5.1.1 Pricing and Profitability Raju et al. (1995) present an analytical framework for store brands and national brands to study the effect of cross-price sensitivity to potential market capacity on retail profit under the condition of selling national brands or introducing store brands. In a duopoly model, it is proven that the introduction of store brands could bring retailers more bargaining power and profit and decrease the wholesale price and profit of manufacturers. If the total category demand remain unchanged, the profit the retailers gain is higher than that lost by manufacturers (Narasimhan and Wilcox 1998; Mills 1995). Cohen (2013) investigate whether a retailer’s store brand supply source impacts vertical pricing and supply channel profitability. Cohen applies a random coefficients logit demand model, using a Bayesian estimation approach to chain-level retail scanner data. He applies Bayesian decision theory to select the best fitting pricing model. Results indicate that a vertically integrated retailer fares best

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by engaging in linear pricing for brand manufacturers’ products, while competing retailers make nonlinear pricing contracts with brand manufacturers for branded products and store brands. Zhao et al. (2018) investigate the product position and promotion investments of retailer’s store brands in a distribution channel composed of one manufacturer and one retailer. Results indicate that the basic demand of a store brand is a deterministic decision factor for retailers to introduce store brands. When promotion efficiency is smaller than the threshold value, promotion investments can improve the retailer’s profit and the total channel profits but will lower the manufacturer’s profit. The greater the difference between the store brand and the national manufacturer’s brand, the more the retailer’s profit and total channel profits increase, and the more the manufacturer’s profit reduces. Considering the fact that related market information is usually uncertain for retailers, Cheng et al. (2018) employ uncertainty theory to build three uncertain programming models under three channel power structures. Accordingly, they provide pricing decisions and corresponding equilibrium results, and thus shed light on how store-brand introduction affects all the supply chain players, and the whole channel, differently under different power structures. The results show that the more powerful the national brand manufacturers are, the more inclined they will be to suffer from the store-brand introduction; in contrast, retailers with different power positions always benefit from the store-brand introduction and may behave differently when pricing the national brand markups after the store-brand introduction. Zhang et al. (2017) discuss three different approaches to generate demand forecasting and pricing decisions for the mix of national and store brand products in the era of big data. They derive the equilibrium wholesale price and retail price for national brand products, and the equilibrium retail price for store brand products based on demand forecasts under three different information scenarios, including Non-information Sharing, Information Sharing, and Retailer Forecasting. Within the context that a store brand recognition is close to that of competing national brands, Yang (2018) constructs three models under different power structures: Manufacturer-led Stackelberg game model (MS), Retailer-led Stackelberg game model (RS), Vertical Nash (VN). She compares the optimal decisions of different members in supply chain in three models and finds that the price of store brands is highest in the VN model and is lowest in the MS model. For national brands, the wholesale price and retail price are both highest in the MS model while the retail price is lowest, and the advertising investment is highest in the VN model. Alan et al. (2019) study how a retailer’s category management strategy and interactions with its supply chain partners, in a setting which involved increasing the store brand market share in a focal category, improve the retailer’s overall profitability by creating demand spillover to other categories. They analyze a game-theoretic model with one retailer, one high-quality national brand manufacturer, and one low-quality national brand manufacturer. The result shows that overlooking store brand spillover can result in suboptimal assortment and pricing decisions, leading to financial losses for the retailer. Taking store brand spillover into account decreases the retailer’s category profit when the degree of store brand spillover is high. However, a low

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degree of store brand spillover may enable the retailer to simultaneously increase its category profit and store brand market share. Furthermore, store brand spillover is never beneficial for the low-quality national brand but may increase the high-quality national brand’s profit when the retailer removes the low-quality national brand from its assortment. Milberg et al. (2019) investigate the conditions under which a leading brand manufacturer would be better or worse off in terms of profitability by producing store brands for retailers. They calibrate the trade-offs between the shelf space devoted by the retailer to the manufacturer brand and the amount of profit required from supplying the SB necessary to counteract cannibalization and to generate profits for the manufacturer, under different levels of uncertainty in respect of the availability of alternative suppliers.

5.1.2 Strategic Interaction Between Manufacturer and Retailer Wu and Wang (2005) build a game-theoretic model that analyze the interactions among two national brand manufacturers and one common retailer, and find that the store brand mitigates the promotion competition between the two national brands and provides benefits for all three members in the channel. In addition, they discuss the optimal quality level of store brands. To challenge the notion that store brands hurt the manufacturers of competing national brands while benefiting retailers, Ru et al. (2015) study the impacts of a store brand when it is introduced by a power retailer and find that a store brand may benefit the manufacturer when the interaction between the manufacturer and retailer is modeled as a retailer-led Stackelberg game because the store brand changes the nature of the strategic interaction between the manufacturer and retailer. By constructing a game-theoretic model that incorporates the firms’ channel and brand strategies, Li et al. (2018a) investigate the strategic interplay between a national brand manufacturer and a retailer in introducing an online direct channel and a store brand. They find that at equilibrium, the store brand may be introduced but the online direct channel may or may not be introduced. Ma et al. (2018) construct a game-theory-based framework to model the strategic interaction between a leading national-brand manufacturer and a retailer. Besides the national brand, the retailer also has an option for its own store brand to compete with the national brand head to head. There are two choices for the store-brand production available to the retailer: a fringe manufacturer with low production efficiency or alternatively the nationalbrand manufacturer with high efficiency. They are able to show that, under certain conditions, there is a win-win situation for both the store-brand retailer and the national-brand manufacturer with the latter supplying the store brand. More interestingly, they find that the national-brand manufacturer supplying the store brand may lead to a higher likelihood of the store brand introduction. This finding explains

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why more national-brand manufacturers now supply store brands. To study the relationship between the introduction of store brand products and the choice of manufacturers’ sales modes, Cai and Nie (2019) conduct a research based on Stackelberg game model. The study find that the choice of the manufacturer’s sales mode is influenced by the introduction of a store brand strategy in e-commerce. When the commission ratio is too low or too high, the introduction of store brand strategy will not affect the choice of sales modes. At this time, the commission fee determines the sales mode selection; when the commission ratio is moderate, the manufacturer will introduce the agency mode under the introduction of store brands but will otherwise choose the distribution mode. The platform commission ratio and consumer recognition of store labels will affect the introduction of store brands. The higher the degree of consumer recognition prompts e-commerce not to introduce store brands. Aiming at a two-echelon dual-channel supply chain, Xu (2011) study the strategy to achieve win-win situations by using optimization theory, non-cooperative game theory and stochastic inventory theory. Focusing on the interaction between retailer and manufacturer, Li et al. (2016) study changes to different parties’ profits after the introduction of a store brand by a retailer and a direct channel by a manufacturer in contrast to their original states, and find that both parties may fall into either the prisoner’s dilemma or a win-win situation under different conditions. Many researchers have shown that, for a symmetrical information setting, introducing store brands can benefit the retailer and do harm to the manufacturer. Liu and Fu (2019) extend the problem of store brand introduction to the situation in which the retailer might be better informed than the manufacturer. They find that the retailer’s ordering decisions are different compared to the symmetrical information scenario. In particular, the retailer’s ordering quantity of national brand product may increase with the introduction of store brand, depending on production costs. An exploration of the two players’ performance with respect to relative profit improvements shows that they can reach a ‘win-win’ outcome under certain conditions. Examining an actual retail scenario, Cheng et al. (2020) employ game theory to model interactions between a national-brand manufacturer and multiple locally monopolistic retailers, one of whom has the capability and motivation to introduce a store brand. They build five Stackelberg games, investigating the following questions: how does the presence of the non-store brand retailers affect the store-brand retailer’s decision on and profitability in the store-brand introduction; how does the store-brand retailer arrange store-brand production and how can a win–win situation arise, where both the store-brand retailer and the national-brand manufacturer are better off with the latter producing the store brand? Considering a market where consumers are heterogeneous in their valuation of product quality, Nasser et al. (2013) propose a game-theoretic model to analyze a national brand manufacturer’s response to a store brand threat with the assumption that the valuation of product quality obeys a continuous distribution. To analyze the dynamic game and coordination strategy of the multichannel supply chain based on brand competition, two dynamic Stackelberg game models are built involving both a manufacturer and a retailer assumed to be the leader in order. In the two models, the manufacturer sells national-brand product to an independent retailer or directly

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to consumers through a direct channel while the retailers sell a store-brand product when they also sell the national-brand product coming from the manufacturer. The results show that the game leader has advantages when the market is stable, but it turns disadvantageous if the state falls into instability, as the game follower can quickly adjust their strategy to seize the market. The stable region within the system will be narrow since the market is sensitive to channel competition, brand competition, and advertising indifference (Chen et al. 2011). From the perspective of a manufacturer, Wang et al. (2020) examine the manufacturer’s channel strategy as it considers introducing an online channel to sell its own national brand (NB) product, when the brick-and-mortar retailer sells both the NB and its own lower-quality store brand (SB) product. The manufacturer is motivated to introduce an online channel when customers’ ‘hassle costs’ of shopping online are relatively low, and their transportation costs are relatively high. However, their results also demonstrate that when the online shopping hassle cost is high and the transportation cost is low, and even when there are very few sales in the online channel, the online-sales strategy can still contribute positively to the manufacturer’s profit. The introduction of the online channel by the manufacturer may result in a win-win situation for the manufacturer and the retailer.

5.1.3 Supply Chain Coordination Based on Positioning of Store Brand By analyzing a game theoretic model composed of one or two national brand manufacturers and a retailer, Chung and Lee (2017) strategically examine the quality level(s) of the store brand(s) relative to the well-established national brand manufacturer position(s) to maximize their category profits. The research reveals that the nature of a retailer’s store brand quality positioning is remarkably sensitive to the distribution of consumers’ willingness-to-pay. The relative proportions of quality sensitive consumers and price sensitive consumers determine the balance of three key strategic forces: the market expansion force, the retail margin force, and the consumer profitability force, leading to different optimal product line designs for store brands across different category environments. Based on an evolutionary game model involving quality control of the store brand supply chain, Wang et al. (2019) study quality control issues related to store brands with the existence of a supervision and punishment mechanism and the introduction of an incentive mechanism. Their research firstly analyzes a quality control evolutionary game for the private brand supply chain under the supervision mechanism, and then compares the influence of the three incentives of quota award, cost sharing and revenue sharing on the quality control evolution. Focusing on the positioning problem of store brands, Sayman et al. (2002) conduct an empirical research based on game theory, finding that the optimal strategy for a retailer is to position their store brand as close as possible to the stronger national

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brand. Considering two forms of price leadership: Manufacturer-Stackelberg (MS) and Retailer-Stackelberg (RS), Liao et al. (2020) study a retailer’s store-brand qualitypositioning problem under three sourcing structures and two types of price leadership. The three sources are in-house (IH), a leading national-brand manufacturer (NBM) with a competing product, and a strategic third-party manufacturer (3M). The results show that the retailer must choose lower quality when sourcing from NBM, rather than from other sources to benefit from quality differentiation, as there are few other points of leverage. On the other hand, the retailer may choose a higher quality when sourcing from 3M vs. producing IH because, despite the double marginalization, a higher-quality store brand induces greater competition between 3M and NBM, which benefits the retailer.

5.2 Supply Chain Coordination Mechanism Many experts and scholars have conducted in-depth research into supply chain coordination mechanisms for store brands. This research can be divided into five main areas: advertising strategy; channel coordination; rebate strategy; shelf allocation strategy and contracts. There are many additional studies on supply chain coordination mechanisms for store brands, such as money-back guarantee (MBG) mechanisms and bonus-penalty models.

5.2.1 Advertising Mechanisms Karray and Zaccour (2005) study cooperative advertising that was introduced into the supply chain as a vertical coordination strategy. They consider cooperative advertising strategy under the competitive environment of store brands and national brands through differential game models. They wish to investigate if a cooperative advertising program could help the manufacturer to mitigate the negative impact of a new store brand introduction. Karray and Zaccour (2006) also consider the marginal profit of manufacturers and retailers only considering local advertising parameters, modifying their model to a static game. Their results show that store brand introduction is profit-improving for the retailer and the channel although it may harm manufacturer’s profits. The cooperative advertising plan is an efficient counterstrategy for the manufacturer that is acceptable to the retailer only if the national brand competes strongly with the store brand. Amrouche et al. (2008) characterize feedback-Nash pricing and advertising strategies and assess the impact of a store brand and a national brand’s goodwill stocks on these strategies in different settings. The main findings suggest first that investing in building up some equity for each brand reduces the price competition between them and propels market power for both. The relationship between the pricing and advertising decisions in a channel where a national brand is competing with a store brand was studied by Karray and Martín-Herrán

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(2009). Analysis of the obtained equilibrium Markov strategies shows that the relationship between advertising and pricing decisions in the channel depends mainly on the nature of the advertising effects. The retailer’s optimal reaction depends on two factors: the price competition level and the strength of the competitive advertising effects. Feature advertising is perceived to be the most cost-effective way to deliver information that may influence consumers’ store choice and is discussed in different contexts in terms of flyer space by Ieva et al. (2015). Their findings support the idea that customers’ characteristics are relevant as far as their memory for feature advertising is concerned. Retailers and manufacturers will therefore be well advised to segment their audience and target different segments with versions of the flyer that place different emphasis on NB or SB. From the perspective of theoretical research, Yan et al. (2016) examine the value of manufacturer’s cooperative advertising and its strategic influence on information sharing for manufacturer and retailer. Their results show that a manufacturer’s cooperative advertising strategy will coordinate dual-channel distributions effectively and help improve the channel performance under the environment of demand uncertainty. However, a manufacturer’s cooperative advertising may also stimulate a retailer to distort information, while the manufacturer has a motivation to understate its forecast. Hence, an advertising agency should be employed to verify the shared information and help make an optimal investment in advertising. In this way information distortion can be eliminated and optimum results achieved. From another aspect, Amrouche and Yan (2017) propose a novel utility-demand function that includes the consumer’s brand valuation, retail prices, and brand qualities. They investigate the effect of the NB local advertising strategy on supply chain players’ profits when either one of the players supports the advertising. They also explore the role of prior information about the manufacturer’s incentive function on supply chain players’ behaviors. Although the support for advertising from either the manufacturer or the retailer is Pareto improving, the manufacturer prefers to incite the retailer to invest in local NB advertising through profit sharing instead of using its money to counter the threat of the SB. The wholesale price incentive motivating a retailer to invest further in advertising is not preferred as might be expected, and all supply chain players are better off without prior information about the manufacturer’s behavior in the context of branding competition and advertising-level dependent incentives. From the perspective of game theory, a Stackelberg game model dominated by manufacturers is constructed by Zhao and Huang (2016), by analyzing the equilibrium game results of distribution channels in the two cases of store brand promotion and no-promotion, discussed the optimal positioning decision and promotion investment of a store brand by comparing the equilibrium results in the two cases. The research shows that within the range of promotion acceptable to manufacturers, the overall market share and profit level of retailers and distribution channels increases with the improvement of promotion efficiency, and with the increase of the difference between store brand and manufacturer brand. However, the market share and profit of the manufacturer’s brand decreases with the improvement of promotion efficiency, and the decrease level gradually attenuates with increasing difference between store

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brand and national brand. Another five scenarios are considered: Centralized case (CC); Stackelberg-manufacturer (SM) game; Stackelberg-retailer (SR) game; Nashmanufacturer (NM) game and Nash-retailer (NR) game. In the context of store brand advertising cooperation within a supply chain, Wang (2018) constructs an evolutionary game model to investigate a suitable advertising strategy for the store brand supply chain. The researchers derive an advertising cooperation mechanism through analysis the evolutionary game result. In order to investigate whether manufacturers can use the timing (sequence) of their pricing and advertising decisions to benefit from or to deter store brand introductions, Karray and Martín-Herrán (2019) develop and solve six sequential gametheoretic models for a bilateral channel where different timings for these decisions are considered before and after the retailer introduces a store brand. Comparisons of equilibrium solutions across games show that the sequence of pricing and advertising decisions in the channel significantly impact the profitability of a store brand entry by the retailer. Chen and Zhang (2019) research cooperative advertising in a dual-channel supply chain where the competition between different channels and different brands exists and built a stochastic differential game model. From this, they obtain the equilibrium advertising input and advertising sharing rate under centralized and decentralized decision settings, respectively. By designing an effective two-way cooperative advertising coordination contract, dual-channel supply chain coordination can be achieved. A fixed transfer payment value may need to be introduced to ensure that both manufacturers and retailers are willing to accept the coordination contract.

5.2.2 Channel Coordination Mechanism Channel coordination is a field that some retail experts have studied to achieve optimal outcome situations between channel participants. Occhiocupo and Hanke (2015) think consistency in the integration of omni-channel retail activities becomes paramount to the success of retailers operating across different channels. Integrating off-line and online activities seems to be beneficial for increasing the level and the frequency of consumer engagement in online communities, hence it sends to increase awareness of brands and their positioning. Zhang (2009) suggests that setting reasonable wholesale prices under the presence of price protection rate policies, optimal order quantity by the retailer can be achieved. Such policies optimize the total benefits of channel alliance and are most likely to achieve a win-win pattern and realize the most effective channel coordination. A supply chain where a supplier is considering whether to open a direct selling channel to complement a retailing channel—and in response, the retailer considers whether to introduce a store brand is explored by Huang et al. (2009). They model this relationship by a four-stage dynamic game. There exists an equilibrium solution for this game where the direct selling and store brand introduction are the dominant strategies for the supplier and the retailer, respectively. Discussing the effects of the

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product and channel substitutability on supplier and retailer’s profits they show that the higher the product substitutability, the higher the retailer’s profit will be; and the higher the channel substitutability, the higher the supplier’s profit will be. Chen et al. (2009) consider a regular marketing environment in which a manufacturer endowed with a branded product seeks to design a new product to resolve its retailer’s mistargeting problem and to optimally screen consumers’ responses. Assuming that only linear pricing schemes are available, and that the retailer learns the state of demand earlier than the manufacturer does, the results show that the presence of a store label always improves channel efficiency. Moreover, a store label is more likely to prevail when the existing branded product is a premium item. Aiming to present a novel, empirical analysis of the competitive battle between retailer-owned store labels and national brands in the online retail market, ArceUrriza and Cebollada (2012) investigate competition between store labels and national brands across online and offline retail channels using data supplied by a multichannel supermarket chain describing a full year’s purchase records for 2,742 households in 36 product categories. They analyze competition between these two types of brand by estimating the competition indicators of: market share, loyalty, and conquest power (a measure of the ability of a brand to attract new customers). The results indicate that, whereas both SB and NB increase their loyalty online (versus offline), only the SB increases market share and conquest power online. Several specific category-level effects are also found. Ding et al. (2016) consider a hierarchical pricing decision process and find the joint optimal strategy for three prices: the wholesale price, the retail price in the traditional channel, and the selling price in the direct channel. Their framework involves various operational strategies, e.g. dual channels; a single traditional channel; a single direct channel; an equal-pricing strategy (in which the wholesale price is equal to the selling price in the direct channel) and a price-matching strategy (in which the product is priced the same on the website and the retail store). They provide criteria to identify different operational strategies and compare the performance of those strategies. The results show that operating dual channels is optimal for the manufacturer only under some conditions, and equal-pricing strategy and price-matching strategy may not always be optimal for the manufacturer. The interaction between a manufacturer’s channel strategy and a retailers’ store brand decisions, under both a flexible wholesale price (FWP) scheme (where the manufacturer can charge different prices to the retailers) and a uniform wholesale price (UWP) scheme (where a uniform price should be offered) are studied by Jin et al. (2017). First, under the UWP scheme, fewer retailers should introduce a store brand regardless of its increasing competitiveness under certain conditions. Second, the retailer should decrease the price of the store brand when its base demand is large. For the enterprises in a supply chain, the choice of channels is a crucial business decision that relates causally to the competition in similar products and the success of an enterprise’s strategy. By using a consumer utility function model, Stackelberg and evolutionary game theory, Huang (2017) attempts to find the optimal decisions for supply chain channel selection. The study also analyzes the influence of the parameters on supply chain decision variables. The results show that in a single

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brand supply chain system, as for manufacturers, they can secure the highest profits from the dual channel mode and minimum profit from the traditional single channel mode. But for retailers, the single channel model is superior to the dual channel mode. How retailers strategically optimize the price and quality of store brands given customer tastes and the production costs of the store brands in order to make their store brands sustainable are investigated by Choi et al. (2018). They investigate a multibrand sustainable channel coordination problem where a national brand manufacturer sells a product through two local retailers, competing against their own store brand products, respectively. They find that retailers have an incentive to position their store brand far away from the national brand to maximize their own monopolistic power. Another empirical investigation by Chung and Lee (2018), analyzing a multi-product category retail database from a major grocery chain, which captured both a period before and a period after the introduction of a store brand in each product category. They show that store brand introductions frequently lead to price leadership changes, generally in a more favorable direction for the retailer than for the national brand manufacturer, evidenced by either the decay of the manufacturers’ price leadership or the rise of the retailer’s price leadership. However, such a change is not universal but tends to be concentrated among a certain quality tier of national brands, which is not always the low-tier, but sometimes the top-tier, despite the low-price low-quality position of the store brand. Instead of focusing on how a supplier can overcome channel inefficiencies stemming from misaligned pricing incentives, Inderst and Shaffer (2019) show that when an incumbent supplier faces competition from other suppliers to supply the downstream firms, it may want to create inefficiencies. Their analysis offers useful prescriptions for how incumbent suppliers should react to competitive threats by smaller competitors, how manufacturers should react to powerful retailers who can produce their own store-label brands, and how upstream firms should optimally treat downstream firms who may have different marginal costs of distribution.

5.2.3 Promotion and Rebate Strategies Mechanism Large retailers once used simple price reduction and promotion methods to compete. Vicious price competition and long-term promotional costs damaged the interests of suppliers and had a negative impact on the long-term development of retailers. Therefore, many large retailers have adopted their own product strategy, trying to avoid competition by looking for new profit growth points (Nie 2008). With the development of store brands, supermarket store brands have become an important development strategy. Dai (2001) analyzes the advantages of store brands in terms of price, promotion, and depletion demand. He believe that the raise of proper brand strategy and the establishment of production bases for brand positioning are the key to its development. Zhang and Fu (2016) analyze three retail booms in Japan after the 1960s, with the expansion of large-scale comprehensive supermarkets, store brands developed rapidly. They believe that perfecting the development system and

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producing commodities with stable quality were necessary conditions for SBs to develop, and that conveying development ideas and product value to consumers is important for the success of SB development. Over the past years, rebates have been increasingly used by national brand manufacturers to avoid the substitution effect of price promotion on brand value. Game theoretic models have been developed to characterize channel dynamics (Liang et al. 2013), involving consumers’ knowledge levels of the present-biased preferences, the models reveal multiple insights. First, a positive slippage rate does not necessarily benefit the NB manufacturer if the rebates fail to expand the demand of the NB. Second, the retailer’s commitment to the original NB price plays a positive role in improving the equilibrium profits of both parties. Third, for loss-averse consumers under preference uncertainties, the NB manufacturer and the retailer prefer a low redemption cost, contrary to the conventional idea that sellers take advantage of consumers with high redemption costs. Luo et al. (2019) consider how manufacturers implemented consumer rebate promotions for well-known brands, and separately studied the balanced strategies of retailers providing and not providing consumer rebates for well-known brands in the supply chain structure led by manufacturers and retailers. They discuss the parameter conditions for retailers to provide rebates for well-known brands. Their research shows that in a supply chain led by manufacturers, retailers are more likely to implement rebate promotions for well-known brands, and that such rebates benefit both players.

5.2.4 Shelf Allocation Strategy Mechanism Researchers have studied shelf allocation mechanisms from the perspective of game theory. A game theoretic model is proposed by Amrouche and Zaccour (2007), in which one national-brand manufacturer, acting as a leader, maximized their own profit; and one retailer, selling the national brand and their store brand, acting as a follower, maximized their category profit. They characterize the resulting Stackelberg equilibrium in terms of the amount of shelf space allocated to these brands, as well as their prices. The results suggest that the allocation of shelf space depends on the quality of the store brand. Ter Braak et al. (2013) estimate that a selection model based on a sample of 450 manufacturer-category combinations from two leading discounters (Aldi in Germany and Mercadona in Spain), show that store label production is indeed rewarded: national brand manufacturers that are involved in such practices have a higher likelihood of procuring shelf presence for their brands. While powerful manufacturers are intrinsically more likely to obtain shelf presence with soft discounters, manufacturers with less power can compensate for this by producing store labels. No such dependence on power exists for hard discounters. Since the business of the retailer can be regarded as selling the shelf space at its disposal to NB manufacturers, Kuo and Yang (2013) formulate a game-theoretical model involving a single-retailer, single-manufacturer supply chain, where the retailer can decide whether to launch its own SB product and sells scarce shelf-space to a competing

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NB in a good consumer category. As a result, the most likely equilibrium outcome is that the available selling amount of each brand is constrained by the shelf-space available for its products and both brands coexist in the category. Motivated by the significant influence of trade allowance on store operation and the efficiency of shelf space allocation, Tsao et al. (2014) develop a model for optimizing category-level shelf-space management. A category shelf-space allocation framework with trade allowance is presented and a multi-player retailer Stackelberg game is introduced to model the interaction between retailer and manufacturers. With this framework, a retailer maximizes profit by taking the manufacturers’ trade allowance responses into account, which provides a realistic approach of simultaneously determining both the promotion level and trade allowances.

5.2.5 Contracts Mechanism From supplier Stackelberg, retailer Stackelberg, and Nash game theoretical perspectives, Cai et al. (2009) find that scenarios with price discount contracts can outperform non-contract scenarios. They also show that consistent pricing schemes can reduce the channel conflict by inducing more profit to the retailer. Qian and Tang (2009) attempt to address this issue by modeling vertical information transmission in a supply chain consisting of a retailer and a manufacturer. They try to find whether a wholesale-price contract or a two-part tariff contract can facilitate the manufacturer in identifying the demand type. The results show that the two-part tariff contract is always effective in realizing information transmission if the retailer’s reserve profit remains within a reasonable range. In the field of food retail, four case studies related to food retailers in Italy are analyzed to identify changes in transaction characteristics, costs, and governance(Banterle and Stranieri 2013). Variations in transaction characteristics determine changes in transaction costs. These changes led to new hybrid forms of transaction governance, namely dyadic contracts, and centralized organization of vertical relationships. From another perspective, when there are two channels, the reason why quantity discount contracts can not achieve channel coordination is that order quantity is based on the degree of competition. In the fashion industry, department stores normally trade with suppliers of national brands by markdown contract whilst developing store labels with cooperative designers by profit sharing contract. Shen et al. (2014) study a single-supplier single-retailer two-echelon fashion supply chain selling a short-life fashion product of either a national or store brand. They find the analytical evidence that there is a similar relative risk performance but a different absolute risk performance between the national and store brands. Based on the frequent quality problems with store brands, the performance of the product quality collaboration under the three contracts of the wholesale price contract, revenue-sharing contract and cost sharing contract is studied by Jiang and Chen (2015).In order to obtain the optimal equilibrium of product quality, profit of participants, total profit of supply chain, consumer surplus and social welfare under the three quality contracts the study show that: (1) the revenue-sharing contract gets

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the best results; (2) the performance of the revenue-sharing contract and the cost sharing contract is not always good for consumers, since it may drive up prices; (3) the quality cost structure of the store brand should be considered comprehensively when improving and selecting collaboration contracts. Mai et al. (2017) analyze three different extended warranty contracts for revenue transferred from the retailer to the manufacturer: fixed fee, proportional sharing, and manufacturer direct. Their results show that all three contracts provided incentives for the manufacturer to improve the product quality. In the numerical analysis, they compare the performance of the three extended warranty contracts with the baseline case, where no extended warranty is offered, which shows that a manufacturer-direct contract achieves the highest quality improvement, and the highest profit among the three contracts.

5.2.6 Rest of the Mechanisms Additional literature has studied the effect of price, money back guarantees (MBG) and bonus-penalty models on supply chain coordination. Kumar et al. (2010) divide customers into two segments: a quality sensitive segment (high type) and a price sensitive (low type) segment. They show that a retailer will choose a national brand manufacturer to supply the store brand when (a) the size of the high type customer segment is large relative to the low type customer segment; (b) the valuation of the high type customer segment is large relative to the low type customer segment, and (c) the retailer’s margin requirement on the store brand is not very high. Overall, these results suggest that retailers who serve a bigger sized quality (price) sensitive clientele will have the national brand (independent) manufacturer supply the store brand. Desmet (2014) studies the impact of MBGs on consumers. An analysis of their experimental design with a national sample of consumers shows that compared with a simple MBG, a double MBG do not further increase the relative preference for a retailer brand over a national brand. Another game-theoretic model of price competition between a national brand manufacturer and a retailer that also sells its store brand is built by Choi (2017). They reinforce the argument that building brand premium should be the first line of defense for a national brand instead of aggressively cutting wholesale prices. It can induce retailer cooperation, which is essential for a successful strategy in the distribution channel. Li et al. (2018b) examine the retailer’s decision on returns policies for two brands (either Money Back Guarantee or No Refund) and the effects of returns policies on the competition between the two brands. They identify the condition when the retailer should offer MBGs for both brands and they show that MBGs mitigated price competition between the two brands. MBGs are found to enhance the retailer’s profit and reduce the NB manufacturer’s profit. They also examine coordination mechanisms and found that a centralized supply chain intensifies the competition and pushes the NB to reduce its retail price. A simple coordination contract that can achieve supply chain coordination to ensure a win-win for both the retailer and the NB manufacturer was proposed. An OEM

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(Original Equipment Manufacturer) supply chain that consisted of a brand manufacturer and an OEM is considered by Yin and Luo (2019). In addition to traditional OEM production cooperation, the OEM can also be used to introduce a store label to sell through an online direct channel, based on product technology sharing by the brand manufacturer. Based on a sales charging mechanism and a fixed charging mechanism, the value of a product technology sharing strategy is studied to ascertain whether the implementation of this strategy can solve the conflict of interest between the two supply chain members and promote the Pareto improvement by the online direct selling of an OEM store brand. The results show that the sales charging mechanism cannot achieve a win-win situation for the supply chain members, but that the fixed charging mechanism can.

5.3 Supply Chain Competition and Cooperation Between Retailers and Manufacturers Most research aims to show how to achieve a win-win situation or enable one party to gain an advantage over another by the coordination of store and national brands. Some studies focus on the coordination strategies of supply chain competition and cooperation between store brands and national brands, where both already exist in the supply chain. Other studies focus on the impact that the introduction of new store brands have upon supply chain competition and cooperation.

5.3.1 Supply Chain Competition and Cooperation Based on Store Brands Manufacturers may obtain more information that is useful to them by widening the quality gap between a national and a store brand, and various other nonlinear pricing measures, such as coupon redemption programs Mills (1995). Store brands not only enable retailers to gain more share in channel benefits with higher retail profits, but also enable retailers to benefit from the sales growth of national brands. Bontems et al. (1999) present a model of retailer-manufacturer interaction that focuses on retail competition between national and store brands that have been positioned by retailers to compete. They suggest that the wholesale price of branded goods may increase as store brand goods becomes closer substitutes for the national brand. Morton and Zettelmeyer (2004) study how to coordinate supply chains and achieve win-win situations between supply chain members where traditional retail and electronic direct sales coexist, by using optimization theory, non-cooperative game theory and stochastic inventory theory. Choi and Fredj (2013) study pricing strategies in a market channel composed of one national brand manufacturer and two retailers that each carry their own store

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227

brands and national brand products, extending the existing literature in main three directions: differentiation of products and stores, the use of different price-led structures in their own brand models, and the price-led model of retailers at the competition level. Their results suggest that to be more successful and reap higher profits every retailer should pursue greater store differentiation relative to other competing retailers and less brand differentiation relative to the national brand to provide relatively homogeneous brands in a well differentiated store. How buyers of one store label in a product category also cross-purchase the store labels of competing retailers in the same category is examined by Dawes and Nenycz-Thiel (2013). A higher level of SB cross-purchasing indicates heightened competitive intensity among the SBs of rival retailers. Results across 27 categories indicate that SBs compete against national brands within-store, but also compete against the SBs of other retailers across stores. Heightened competition among the SBs of different retailers occurs in categories with higher purchase frequency, in which the average SB price is well below the average NB price; and in categories with higher levels of manufacturer brand price promotions. Shindo and Matsubayashi (2014) study a retailer’s strategic decisions about outsourcing the production of such types of store brands to national brand manufacturers. The wholesale price of a NB is assumed to be set by the manufacturer, while that of the SB is assumed to be set by the retailer. The results show that the optimal strategy for the SB retailer is sensitive to the degree of differentiation between the SB and the NB. If both products are less well differentiated, the retailer benefits from offering only the SB, and, in such a case, the retailer should state its wholesale price, after the manufacturer sets the NB wholesale price. Furthermore, it was shown that the optimal strategies of the retailer are socially efficient if, and only if, the SB and the NB are sufficiently differentiated. Dobson and Zhou (2014) considere the competition effects of lookalike products, which seek to mimic the packaging, design, and appearance of leading brands. Such products, most notable in the fast-moving-consumer-goods (FMCG) sector, are particularly associated with items promoted by retail organizations as part of their store label programs. The market power and control over the supply chain that the major retailers now enjoy means that by developing lookalike products they may have the opportunity to exploit unfairly and anti-competitively the image and goodwill that brand manufacturers have developed through careful and continual product and marketing investment. This, in turn, could distort the way and the extent to which manufacturers compete, and enhance retailer control over the supply chain. In the process, this could undermine manufacturer branded goods which smaller retailers traditionally rely on, thus weakening their competitive position and resulting in further concentration of retail markets and less choice of store types and product varieties for consumers. The continuing absence of a rapid and effective legal remedy to prevent the rewards from brand investment being misappropriated by imitators means that such action will likely continue, with the upshot that manufacturer and retailer competition may be distorted to the detriment of consumer welfare and the public interest. Some research investigates the benefit of using national brand’s advertising (the aggressive strategy) that hurts a store label’s demand over using national brand’s revenue sharing (the partnership strategy) that fosters collaboration between

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the retailer and the national brand’s manufacturer. Amrouche and Yan (2015) find that when a national brand’s revenue sharing strategy is implemented, the manufacturer and the whole chain gain compared to the benchmark case. The retailer always loses in benchmark case. Thus, a profit-sharing mechanism is needed to split the increased profits and achieve a Pareto result for all channel members. Although the strategy of a national brand’s advertising is always beneficial to the manufacturer, it is not necessarily beneficial to the whole channel when the store label has low differentiation. Aiming to investigate the effect store brands have had on the relationship between the supermarkets (buyers) and their suppliers, Sutton-Brady et al.’s (2017) study used in-depth, high engagement interviews with a range of suppliers. An extensive data analysis process is carried out to ensure coding of the key insights into themes. This paper highlights the ability of supermarket chains to increase existing dominance by using their ever-increasing private label brand portfolio. Their findings indicate an uncertain future for food suppliers, with the situation likely to continue to worsen further as the supermarkets continue to exercise and abuse their power. A framework for supply chain collaboration between manufacturers and retailers is developed by Costa et al. (2017) which include managerial, behavioral and technical issues. From the different elements included in the supply chain collaboration model, the results seem to indicate that the ones used most are related to the design of the collaborative initiative and the behavioral aspects related to inter-organizational relationships (trust and mutuality) and human resources (longevity and informal cross-functional team working). Mills et al. (2018) think the competition between national brands and store brands has evolved from the mere imitation of physical characteristics to what we now call affect-based competition and the existence of a positive affect transfer from NBs to SBs through store image and vice versa. With more equal negotiation power, retailers are no longer just channel partners but rather business partners with whom to build business-to-business relationships. Aiming to examine empirically whether and under what organizational design conditions retailers can benefit from store brand (SB) merchandising improvements, Kim and Takashima (2019) test hypotheses using a structural equation model and data obtained from general merchandise managers at 190 supermarket retailers in Japan. Their results reveal that both centralized merchandising authority and store cooperation between merchandising and store divisions motivate SB merchandising improvement, which strengthens SB competitiveness. In addition, outcome-based merchandiser control strengthens the positive relationship between store cooperation and SB merchandising improvement. However, regarding centralized merchandising authority, they find that outcome-based control has no significant moderating effect.

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5.3.2 Supply Chain Competition and Cooperation with the Introduction of Store Brands Store-brand products are of increasing importance in many retail categories. While national brand products are designed by the manufacturer and sold by the retailer, the positioning of store-brand products is under the complete control of the retailer. Heese (2010) consider a scenario where products differ in a performance quality dimension and he analyze how retailer–manufacturer interactions in product positioning are affected by the introduction of a store brand product. He find that a manufacturer can derive substantial benefits from considering a retailer’s store-brand introduction when determining the national brand’s quality and wholesale price. If the retailer has a significant cost disadvantage in producing high-quality products, the manufacturer does not need to adjust the quality of the national brand product but should offer a wholesale price discount to ensure its distribution through the retailer. If the retailer is competitive in providing products of high-quality, the manufacturer should reduce their wholesale price discount and increase the national-brand quality to mitigate competition. Retailers are more randomized in formulating their own brand introduction strategies when customers have strong store preference and retailers’ own brands are similar to suppliers’ brands in terms of Customer Valuation and production costs (Groznik and Heese 2010). While an ability to commit clearly has value for the manufacturer, such commitment might also benefit the retailer. In fact, the benefit to the retailer (from the manufacturer’s long-term pricing adjustments) might exceed the potential benefits otherwise associated with a simple retailer-led SB introduction. A manufacturer’s ability to commit to wholesale discounting potentially benefits both channels. These results imply that the use of long-term contracts to enable wholesale price commitment can lead to improved supply chain efficiency, especially when focusing on the introduction of premium store brands (PSB) through strategic collaboration between a retailer and a national brand manufacturer (NBM). In order to determine how chains of modern international retailers can achieve a competitive advantage (CA) by introducing store labels in the organic category can, in turn, stimulate the consumption of food produced with respect to sustainability principles, Gorska-Warsewicz et al. (2018) use a qualitative approach involving two steps. First, they select retailers with organic store brands (OSBs) and producers delivering products under those brands and conducted in-depth semi-structured interviews with representatives from the management boards of 17 enterprises. Second, they analyze the assortment-based competitive advantage of the OSBs of 8 enterprises in depth. For retail chains, it is found that the introduction of OSBs was the source of CA via six contributors, namely: price; range of assortment; type of SBs; image of the retailer; sustainability and specific processes, and the product-related attributes of organic food. Extending offers with organic store branding makes it easier for consumers to buy organic food at more affordable prices and follow the principles of proper nutrition and a sustainable diet with low environmental impact. At the same time, the international retailers can position themselves as chains contributing to more sustainable consumption. Zhou et al. (2019) investigate the impact of in-store

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promotion and its spillover effect on store brand introductions. Their study involve different retail supply chain scenarios where the retailer carrying a national brand may introduce its own store brand product and promote either the national brand or the store brand inside the store. When the spillover from national brand to store brand is high, the retailer prefers to promote the national brand product. When the spillover from store brand to national brand is high, promoting the store brand product can also benefit the national brand manufacturer. With symmetrical spillover rates, the national brand manufacturer can still benefit from the store brand introduction, as long as the retailer promotes the national brand product, the horizontal competition is not intense, and the store brand product quality is sufficiently low. Xu (2019) aims to investigate the national brand manufacturer’s ability from corporate social responsibility (CSR) innovation as a counterstrategy against the private label by a retailer. By constructing a model of manufacturer–retailer interaction, he attempts to analyze that the national brand manufacturer’s decision on the CSR innovation and the effect of such innovation on the retailer’s motivation of launching the private label. The author find that the CSR innovation can indeed restrict the retailer’s incentive to launch the store brand. The results of the theoretical model can be applied by the actors in supply chains in making decision on CSR innovation and the launch of a new brand.

5.4 Supply Chain Advertising Decisions with Competition between National and Store Brands The growing market share of retailers’ store brands has gradually become a focus issue for retailers and manufacturers (Amrouche et al. 2008). According to the statistics of the American Private Label Manufacturers Association, in 2004, one out of every five commodities sold in American supermarkets is a store branded item. In some big supermarkets, it is not difficult to find growing numbers of store brand products from CenturyMart, Carrefour, CR Vanguard, Walmart, Tesco etc., ranging from food to paper products and other fast-moving consumer goods, expanding their position. Store brand products in the retail industry are quietly upgrading their position, fighting a “dark war” with suppliers. Many studies have shown that advertising campaigns that promote the goodwill of the manufacturer’s national brand are decisive for the manufacturer’s victory over the retailer’s store brand (Richardson 1997; Ailawadi 2001). Advertising not only increases consumer loyalty and creates positive brand associations but is also a strategic decision area for manufacturers to compete with retailer’s store brand products. To analyze the competition between manufacturers and retailers’ own brand products that are promoted by advertisement in the supply chain, retailers’ advertising decision-making must be considered. In recent years, researchers of domestic and foreign supply chain advertising have mainly focused on cooperative and competitive advertising. Little of the literature considers the situation of vertical cooperative and competitive advertising in

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231

the supply chain despite this phenomenon being common in practice. For example, Staples, the Beijing Olympic Games sponsor, a global top 500, public goods retailer, sells Staple branded printers and HP printers, and Watsons personal care retail stores sell Watsons skincare products and Olay oil skincare products. In these two examples, the retailer sells both national brand products and store brand products that have a competitive relationship, and both parties use competitive advertising investments to increase their goodwill and sales. At the same time, retailer advertising has also increased retail customer traffic, and to a certain extent promoted the sales of manufacturers’ products, so manufacturers are willing to share the advertising investment costs for retailers, namely by cooperative advertising. In this context, this article considers the situation in which brand retailers sell their store brand products in addition to national brand products. Manufacturers and retailers conduct advertising investment promotions and accumulate goodwill separately, and manufacturers and retailers share the advertising costs of retailers. Manufacturer advertising alone has a negative impact on the retailer’s store brand product sales, and retailer advertising alone has a negative impact on the goodwill of a manufacturer’s brand. A methodology of differential games is often adopted to investigate the optimal advertising strategies of the manufacturer and the retailer in Stackelberg leader-follower games under dynamic circumstances. The following sections set out our research methodologies, processes, and results.

5.4.1 Model and Assumptions Consider a distribution system where a brand retailer sells both manufacturer’s products and store brand products. Assume that the goodwill and advertising investment of manufacturers and retailers not only have a certain impact on their own product sales, but also on the other party’s product sales. The manufacturer’s national advertising investment is I N (t); the retailer’s locality advertising investment is I S (t) and the rate of manufacturer’s share of the retailer’s advertising costs is dt , thus 0 ≤ dt ≤ 1. Suppose the advertisement cost functions of manufacturers and retailers are respectively C N (I N ) = μ2N I N2 (t), C S (I S ) = μ2S I S2 (t). Among them, μ N and μ S respectively represent the positive advertising cost factor of the manufacturer and the retailer, C N (I N ) and C S (I S ) respectively represent the advertising cost of the manufacturer and the retailer, and they are both convex functions related to advertising investment. Assume that manufacturers and retailers conduct advertising investment promotions and accumulate goodwill separately and retailer advertising has a negative impact on the goodwill of manufacturer’s brand. Manufacturers who invest in advertising to enhance their own brand image, increase consumer recognition of their products, and thus also increase consumer recognition of retailers selling that manufacturers’ products, thereby enhancing the goodwill of retailers as a by-product of the advertising. Set cumulative variables G S (t), G N (t) for the advertising investment of manufacturers to represent the image (goodwill) of the store brand and the national brand, G S (t), G N (t) change with time to satisfy the state differential equation:

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dG S = λI S + λI N − kG S dt

(5.1)

dG N = λI N + λI S − kG N dt

(5.2)

λ represents the degree of influence of manufacturers and retailers’ advertising investment on goodwill, and k represents the degree of attenuation of brand image. Q N (t), Q S (t) are immediate sales of national brand and store brand products: Q N (t) = α1 I N (t) + β1 I S (t) + θ1 G N (t) + ψ1 G S (t)

(5.3)

Q S (t) = −α2 I N (t) + β2 I S (t) − θ2 G N (t) + ψ2 G S (t)

(5.4)

where αi , βi , θi , ψi are all positive. α1 , β1 , θ1 , ψ1 represents the influence factors of manufacturer advertisements, retailer advertisements, manufacturer goodwill, and retailer goodwill on the sales volume of national brand products, and they are all positive effects. The retailer’s advertising investment and goodwill have a positive impact on the manufacturer’s product sales. The reason is that the retailer’s advertising investment and goodwill increases customer traffic for retail stores, which in turn promotes the sales of manufacturers’ national brand products. Assuming that for consumer products, consumers mainly buy them in retail stores in their city, a higher retailer’s goodwill often means that retailers can provide high-standard delivery and return and exchange (or assist in return and exchange) services with uniform standards across the store. At the same time buying from an established store suggests consumers that the products they sell will also have a higher quality guarantee, and these factors allow consumers to trust and buy their products with confidence. Therefore, consumers’ dependence on store brands is often not less than that for national brands. Assume that the impact of the retailer’s goodwill on the sales volume of the national brand products is slightly greater than or equal to the impact of the manufacturer’s goodwill on the national brand product sales volume, that is, ψ1 ≥ θ1 . −α2 , β2 , −θ2 , ψ2 represents factors of manufacturer advertising, retailer advertising, manufacturer goodwill, and retailer goodwill impact on store brand product sales. The manufacturer’s advertising input I N (t) and the manufacturer’s goodwill G N (t), respectively, have a negative impact on the store brand product sales. The reason is that the manufacturer’s national brand is competitive with the retailer’s store brand products, so the manufacturer’s higher advertising investment and goodwill will tend to make consumers buy the manufacturer’s products, which will negatively affect the retailer’s sales. Assume that manufacturers and retailers have the same positive discount rate ρ. The goal of both parties is to seek the optimal advertising strategy to maximize their profits in an unlimited time zone. The manufacturer’s marginal profit v N is constant, and the margins for the retailer selling national brand and store brand products are v S1 and v S2 respectively, both of which are constant. Then the objective functions of

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233

manufacturers and retailers are:   +∞ μN 2 μS d 2 exp(−r t) ν N Q N (t) − JN = I (t) − I (t) dt 2 N 2 S

(5.5)

0

  +∞ μ S (1 − d) 2 JS = exp(−r t) ν S1 Q N (t) + ν S2 Q S (t) − I S (t) dt 2

(5.6)

0

All the parameters in the model are constants that are not related to time, and in any period of an unlimited time zone, the participants are facing the same game, so the strategy can be limited to the static strategy, and its equalization is also static feedback equilibrium (t will be omitted later).

5.4.2 Differential Game Models Based on Stackelberg Game Theory Here, we assume that the manufacturer’s brand is a popular best-selling brand among similar products, and that there are fewer suppliers of the same level available to retailers in the market, such as the HP printers sold by office supplies retailer Staples, and Olay oil skin care products sold in Watsons stores. Therefore, consider the situation where the manufacturer is in a dominant position and the retailer is in a subordinate position. The problem evolved into a Stackelberg game with a manufacturer as a leader and a retailer as a follower. The manufacturer first determines the optimal national advertising input I N∗ and the sharing rate d ∗ . After observing the manufacturer’s strategic choice, the retailer determines the optimal advertising expenditure I S∗ . The optimal strategy combination of both parties is the static feedback Stackelberg equilibrium. Theorem 5.1 In the game situation where the manufacturer is the leader, the static feedback Stackelberg equilibrium strategy of the manufacturer and retailer are, respectively: I N∗

=

ν N α1 +

λν N (ψ1 +Θ1 ) ρ+k

μN

 1 ν S1 ν S2 β2 β1 + Is = + μS 2 2   2ν N (ψ1 − θ1 ) λ + + ν S1 (ψ1 − θ1 ) + ν S2 (ψ2 + θ2 ) 2(ρ + k) μS 

(5.7)

(5.8)

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⎧ ⎨ 2B < μ S A, d ∗ = 0 ⎩ 2B < μ S A, d ∗ =

λ [(2ν N −ν S1 )(ψ1 −θ1 )−ν S2 (ψ2 −θ2 )] (2−ν S1 )β1 −ν S2 β2 + ρ+k

(5.9)

λ [(2ν N −ν S1 )(ψ1 −θ1 )+ν S2 (ψ2 +θ2 )] (2−ν S1 )β1 +ν S2 β2 + ρ+k

where, A = vS1 β1 +vS2 βμ2S+λ(C2 −C1 ) , B = β1 + M1 λ − M2 λ, expressions of C1 , C2 , M1 , M2 are shown below. Proof First solve the retailer’s optimal control problem. The optimal profit function VS (G S , G N ) must satisfy the lower Hamilton-Jacobi-Bellman (HJB) equation: ⎫ ⎪ ν S1 [α1 I N + β1 I S + θ1 G N + ψ1 G S ]+ ⎪ ⎪ ⎬ ν S2 [−α2 I N + β2 I S − θ2 G N + ψ2 G S ] (5.10) ρVS (G S , G N ) = Max μ S (1−d) 2 ∂ VS (G S ,G N ) − 2 IS + (λI S + λI N − kG S ) ⎪ ⎪ ∂G S ⎪ ⎪ ⎪ ⎪ ∂ VS (G N ,G S ) ⎭ ⎩ + ∂G N (λI N + λI S − kG N ) ⎧ ⎪ ⎪ ⎪ ⎨

Retailers will decide the optimal advertising investment I S∗ . Assuming that the manufacturer has made the optimal decision I N∗ , d ∗ , then the best response of the retailer is the optimal decision I S∗ . The second derivative of Eq. (5.10) about I S is −μ S (1 − d) < 0. Therefore, solving its first-order partial derivative of I S and making it equal to zero results in I S∗ that maximizes the right side of the above equation: I S∗ =

∂ν S ∂ν S ν S1 β1 + ν S2 β2 + λ ∂G − λ ∂G S N

μ S (1 − d ∗ )

(5.11)

In the Stackelberg leader-follower game, the manufacturer as the leader will rationally predict that the retailer will decide I S∗ according to the above reaction function. Therefore, the manufacturer’s HJB equation can be expressed as: ⎧   μS d ∗2 μ N 2 ⎫ ∗ ⎪ ⎬ ⎨ ν N α1 I N + β1 I S+ θ1 G N + ψ1 G S − 2 I S − 2 I N + ⎪ ∂ VS (G S ,G N ) ∂ VS (G N ,G S ) ∗ λI + + λI − kG ρVN (G S , G N ) = Max N S S ∂G S   ∂G N ⎪ ⎪ ⎭ ⎩ −λI S∗ − λI N − kG N (5.12) Substituting (5.11) into (5.12), we can get:   ⎫ ⎧ ∂ν ∂ν ν S1 β1 +ν S2 β2 +λ ∂GS −λ ∂G S +θ1 G N +ψ1 G S S N ⎪ ⎪ ⎪ ⎪ ν N α1 I N + β1 ⎪ ⎪ μ S (1−d ∗ ) ⎪ ⎪ ⎪ ⎪   ⎪ ⎪ 2 ⎪ ⎪ ∂ν S ∂ν S ⎪ ⎪ μ S d ν S1 β1 +ν S2 β2 +λ ∂G −λ ∂G ⎪ μN 2 ∂ VS (G S ,G N ) ⎪ S N ⎬ ⎨ − − I + ∗ N 2 ∂G S )  ρVN (G S , G N ) = Max  ν β +ν β 2μs(1−d ∂ν S ∂ν S S1 1 S2 2 +λ ∂G −λ ∂G ⎪ N ,G S ) ⎪ S N ⎪ ⎪ λ + λI N − kG S + ∂ VS (G ⎪ ⎪ μs(1−d ∗ ) ∂G N ⎪ ⎪ ⎪ ⎪   ⎪ ⎪ ∂ν S ∂ν S ⎪ ⎪ ⎪ ⎪ ν S1 β1 +ν S2 β2 +λ ∂G −λ ∂G ⎪ ⎪ S N ⎭ ⎩ −λ − λI N − kG N μs(1−d ∗ ) (5.13)

5.4 Supply Chain Advertising Decisions …

235 μ2

The second derivative of Eq. (5.13) about I N is − 2N < 0. Therefore, let the first derivative of (5.13) about I N be 0, then the optimal I N∗ can be obtained. ∂ VN ∂ VN ν N α1 + λ ∂G − λ ∂G S N

I N∗ =

(5.14)

μN

Observing the Eqs. (5.10) and (5.13), we can speculate that VS (G S , G N ) = C1 G N + C2 G S + C3 , VN (G S , G N ) = M1 G S + M2 G N + M3 , the expressions of this set of solutions satisfy the Eqs. (5.10) and (5.13). Substituting these two expressions into (5.10), (5.13), we can get:   ρ C1◦ G N + C2◦ G S + C3 = (ν S1 θ1 − ν S2 θ2 − C1 k)G N + (ν S1 ψ1 + ν S2 ψ2 − C2 k)G S + (ν S1 α1 + ν S2 α2 + C2 λ + C1 λ) + (ν S1 α1 + ν S2 α2 + C2 λ + C1 λ)

∂ VN ∂ VN ν N α1 + λ ∂G − λ ∂G S N

μN ∂ VS ∂ VS ν S1 β1 + ν S2 β2 + λ ∂G − λ ∂G S N μ S (1 − d ∗ )

(5.15)

And C1 =

ν S1 θ1 − ν S2 θ2 ν S1 ψ1 − ν S2 ψ2 , C2 = ρ+k ρ+k

(5.16)

ν N ψ1 ν N θ1 , M2 = ρ+k ρ+k

(5.17)

M1 =

To find the best d ∗ , the first derivative of (5.13) with respect to d can be expressed as:   1 2B − μ S A μS A − 2μ S A 1/d − 1 (1 − d)2 v S1 β1 +v S2 β2 +λ(C2 −C1 ) , μS SA can get d = 2B−μ , 2B+μ S A

where, A =

(5.18)

B = β1 + M1 λ − M2 λ. Let the first derivative be

0 and we which is the only stagnation point of d in (5.13). To the right of the stagnation point, Eq. (5.19) is less than 0. On the left side of the SA , stagnation point, formula (5.19) is greater than 0. Therefore, when d = 2B−μ 2B+μ S A the maximum value of (5.13) can be obtained. By assumption, ψ1 ≥ θ1 , hence, SA A > 0, B > 0, 2B − μ S A < 2B + μ S A, 2B−μ < 1. But notice that d has 2B+μ S A 2B−μ S A ≤ 0 and 2B+μ S A 2B−μ S A ; if 2B ≤ μ S A, 2B+μ S A

a meaningful interval [0, 1], so we need to discuss the cases of 2B−μ S A 2B+μ S A 2B−μ S A 2B+μ S A ∗

∈ (0, 1). If 2B > μ S A,

2B−μ S A 2B+μ S A

∈ (0, 1), then d ∗ =

≤ 0, the interval making d meaningful is at right side of the stagnation point, so d = 0. Therefore,

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d ∗ = 0, 2B ≤ μ S A, SA , 2B > μ S A d ∗ = 2B−μ 2B+μ S A

(5.19)

Substitute (5.17) and (5.18) into A and B, and then substitute A and B into (5.20), we can get d∗ =

(2 − ν S1 )β1 − ν S2 β2 + (2 + ν S1 )β1 + ν S2 β2 +

λ [(2ν N ρ+k λ [(2ν N ρ+k

− ν S1 )(ψ1 − θ1 ) − ν S2 (ψ2 + θ2 )] + ν S1 )(ψ1 − θ1 ) + ν S2 (ψ2 + θ2 )]

(5.20)

Corollary 5.1 The manufacturer’s optimal sharing rate d ∗ is related to increasing v N , and related to decreasing v S1 , v S2 , β2 , θ2 , ψ2 . Proof

2(ψ1 −θ1 ) 2(ψ1 −θ1 ) 4B(ψ1 − θ1 ) ρ+k (2B + μs A) − ρ+k (2B − μs A) >0 = (2B + μs A)2 (2B + μs A)2 (ρ + k)

∂d ∗ = ∂v N

 ∗

∂d = ∂v S1 = ∂d = ∂v S2 =

λ(θ1 −ψ1 ) ρ+k



−4B 2β1 + 

∗

−2β1 +

λ(ψ1 −θ1 ) ρ+k

(2B + μs A)2 −β1 + 

λ(−θ2 −ψ2 ) ρ+k

−4B β2 +

  (2B + μs A) + (2B − μs A) −2β1 +

(2B + μs A)2



(2B + μs A)2



0 = ∂v S2 2 2(ρ + k) ∂ IS 2λ(ψ1 − θ1 ) >0 = ∂v N 2μ S (ρ + k) This means that the higher the retailer’s marginal profit from selling national brand products and store brand products is and the higher the marginal profit from manufacturers is the more inclined retailers are to increase advertising investment. The greater the positive impact of the manufacturer’s goodwill on the manufacturer’s

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product sales, the lower the retailer’s willingness to invest in advertising; the higher the degree of goodwill attenuation, the less willing the retailer is to invest in advertising. The higher the marginal profit of the manufacturer, the more the manufacturer tends to increase the advertising investment. The manufacturer’s advertising investment has nothing to do with the retailer’s marginal profit, which is different from the retailer’s situation. Manufacturers’ goodwill, advertising investment, and retailer’s advertising investment have an impact on the manufacturer’s sales. The greater the impact, the more willing the manufacturer is to invest in advertising. When the total investment of manufacturer and retailer has a greater impact on the retailer’s goodwill, and the difference between the manufacturer and retailer’s investment gap has a greater impact on the manufacturer’s goodwill, manufacturers and retailers are more inclined to increase advertising investment. This is also in line with the actual situation of the enterprise. Theorem 5.3 When ψ1 = θ1 , the optimal sharing rate d ∗ has nothing to do with v N , θ1 , ψ1 . Proof When ψ1 = θ1 , the expression of d ∗ can be simplified as ∗

d =

(2 − v S1 )β1 − v S2 β2 + (2 + v S1 )β1 + v S2 β2 +

λ [v ρ+k S2 (ψ2 λ [v ρ+k S2 (ψ2

+ θ2 )] + θ2 )]

From the above formula, we can see that when the retailer’s goodwill and the manufacturer’s goodwill have the same effect on the sales volume of the manufacturer’s products, they both have no effect on the optimal sharing rate d ∗ . Moreover, d ∗ has nothing to do with the marginal profit of the manufacturer.

5.4.3 Comparison with Nash Non-cooperative Game Under the Nash non-cooperative game, manufacturers and retailers make independent decisions. Let the first derivative of (5.10) about I S , first derivative of (5.12) about I N and d be zero, and get: I N∗ = I S∗ =

∂ VN ∂ VN ν N α1 + λ ∂G + λ ∂G S N

μN

∂ VS ∂ VS ν S1 β1 + ν S2 β2 + λ ∂G − λ ∂G S N

(5.23)

μS

(5.24)

d∗ = 0

(5.25)

Observing the best decisions under Nash non-cooperative games and Stackelberg leader-follower games, we compare formulas (5.11), (5.14) with (5.24), (5.25). We

5.4 Supply Chain Advertising Decisions …

239

find that if we let d ∗ in the optimal solution expression of the Stackelberg leaderfollower game be zero, the optimal I N∗ , I S∗ , d ∗ expressions are exactly the same as the corresponding solutions in the Nash non-cooperative game. Therefore, it is easy to compare the profits of manufacturers, brand retailers and systems under the two game decisions. Let d ∗ in (5.15), (5.16) be 0, they correspond to the optimal profit expression of both parties under the Nash non-cooperative game. Therefore, we only need to analyze the increase and decrease of d ∗ in Eqs. (5.15) and (5.16) to compare the profits of manufacturers, brand retailers and systems in the two cases. Theorem 5.4 Under the Stackelberg leader-follower game, the profits of manufacturers, brand retailers, and systems are not lower than the corresponding values of the Nash leader-follower game. Both the manufacturer and the retailer are inclined to make a Stackelberg leader-follower game decision. Proof The first derivative of (5.10) about d ∗ ,  ∂ρVS (G S , G N ) = ∂d ∗

∂ VS ∂ VS v S1 β1 + v S2 β2 + λ ∂G − λ ∂G S N

2

2μ S (1 − d ∗ )2 + (v S1 β1 + v S2 β2 + λC1 + λC2 )

∂ VS ∂ VS − λ ∂G v S1 β1 + v S2 β2 + λ ∂G S N

μ S (1 − d ∗ )2

∂ρVS (G S , G N ) >0 ∂d ∗ So (5.10) is the increasing function of d. Under the Stackelberg leader-follower equilibrium, from the Eq. (5.9), and the two cases we discussed, 0 ≤ d ∗ < 1; under the Nash non-cooperative game, d ∗ = 0. Therefore, under Stackelberg leader-follower equilibrium, the profit of retailers is not lower than that under Nash equilibrium. Now we compare the profits of manufacturers in two cases. As we have discussed before, in the two cases of formula (5.9), 0 ≤ d ∗ < 1. Therefore, the optimal profit of the manufacturer under the Stackelberg leader-follower game is not less than the optimal profit under the Nash non-cooperative game. The total profit of the system is equal to the sum of the profits of the manufacturer and the retailer, so the system profit under the Stackelberg leader-follower game is not lower than that of the Nash non-cooperative game. The above analysis shows that, compared with Nash non-cooperative games, both manufacturers and retailers tend to align with the Stackelberg leader-follower game outcomes.

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5 Supply Chain Coordination

5.4.4 Conclusion This section considered a retailer’s marketing channel that sells two types of competitive products at the same time, namely national and store brand products. Considering two theoretical differential games, the Stackelberg game decision that makes the manufacturer the leader and the retailer as the follower was studied first. In this game the manufacturer first determines the optimal national advertising input I N∗ and sharing rate d ∗ , and the retailer decides the best local advertising investment I S∗ . Analysis of the influence of the retailer and manufacturer parameters on optimal decision-making shows that the decision-making model is in line with the actual situation of the enterprise, so it has a certain reference value for the actual investment decision-making of the enterprise. Finally, it proves that for manufacturers and retailers, the Stackelberg leader-follower game decision outcome is better than the Nash non-cooperative game decision. Subsequent research may consider the optimal levels for the wholesale price of the manufacturer and the retail price for the retailer as the decision variables, and how the price variables influence sales volumes.

5.5 Competition Games for National and Store Brands with Advertisement Intervention The growing market share achieved by retailers’ store brands has gradually become a focus issue for manufacturers and retailers (Amrouche et al. 2008). The store brands products in supermarkets such as Century Lianhua and CR Vanguard are increasing, gradually expanding from the original food categories to fast-moving consumer goods such as paper products. When consumers buy products, they are often aroused when they are faced with high-quality and low-cost store brand products. In addition to price, an important factor that influences consumer purchasing decisions is advertising, which is also an important means to increase consumer loyalty and promote product sales (Richardson 1997; Ailawadi 2001). Price and advertising decisions have gradually become a vital aspects for manufacturers to compete with retailer’s store brands (Simon and Sullivan 1993; Yoo et al. 2000). Joint decision-making in supply chain advertising and price have become academic research hotspots in recent years. Research has expanded from single supply chain cooperative advertising decisions (Fu and Zeng 2007; Yue et al. 2006), to single product advertising and pricing joint decisions (Neyret 2003; Xie and Neyret 2009; Xie and Wei 2009), to joint advertising and pricing decisions based on manufacturers and retailers’ own brand competition (Amrouche et al. 2008; Karray and Martín-Herrán 2009) in this area.

5.5 Competition Games for National …

241

5.5.1 Models and Assumptions The literature above is based on the transparency of advertising cost information among members of the supply chain and does not involve game research based on the inaccessibility of manufacturer’s advertising cost information. The secrecy aspect of vertical cost information is very common among competitive enterprises (Ai et al. 2008). This article makes up for deficiencies within the above research. Assuming that the manufacturer’s advertising cost information is secret, consider the situation in which brand retailers sell their store brands in addition to national brands. Manufacturers and retailers conduct advertising investment promotions separately, and manufacturer advertising has a negative impact on store brands sales. For this scenario we construct a retailer-led two-stage Stackelberg game. Consider a brand retailer that sells both store and national brands. The two types of products are in competition. Suppose that the advertising investments of manufacturers and retailers have a certain impact not only on their own product sales, but also on each other’s product sales. The advertising investment of manufacturers and retailers are V1 , V2 , advertising costs are C1 (I1 ) = μ21 v12 , C2 (I2 ) = μ22 v22 . Among them, μ1 and μ2 represent the advertising cost coefficients of manufacturers and retailers, respectively, which are greater than 0. The profits of retailers and manufacturers are:   π2γ = (P1 − w) α1 − β1 P1 + γ1 (P2 − P1 ) + σ1 ν1 + σ2 ν2  μ2 2  ν + (P2 − w0 ) α2 − β1 P2 + γ1 (P1 − P2 ) − σ1 ν1 + σ 2 ν2 − 2 2   μ1 2 ν π1γ = w α1 − β1 P1 + γ1 (P2 − P1 ) + σ1 ν1 + σ2 ν2 − 2 1

(5.26) (5.27)

α1 , α2 represent the basic demand of national brand and store brand, assuming that they are sufficiently large relative to other parameters. Assume that the national brand is slightly stronger relative to the retail store brand, such as HP printers and Staples printers. Retailer advertising promotions promote the sales of both national and store brands because retailer advertising promotions increase customer traffic and purchases within retail stores. Retailer’s store brand sales have a stronger effect from the retailer’s advertising, so retailer advertising’s positive impact on retailer’s  store brand sales is greater than the national brand sales, σ2 < σ2 . As manufacturer brands are slightly stronger than retailers’ own brands, the positive impact of the retailer’s advertising on the national branded product sales is greater than a certain σ



σ





value, assuming σ2 > 22 . To sum up, 22 < σ2 < σ2 . Assume that there is a linear relationship between price and demand (Raju et al. 1995). P1 and P2 are the prices of the national brand and store brand products, w represents the internal transaction price of both parties, and the maximum internal transaction price that the retailer can accept is w, ¯ regardless of the manufacturer’s production cost. The direct influence factors of national brand and store brand product prices on their respective demand are both β1 . The price difference causes consumers to switch brands, and γ1 represents the

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5 Supply Chain Coordination

coefficient of substitution effect of two brand products. The direct impact of national brand and store brand products on their respective needs is greater than the impact of the price difference between them on their respective needs, namely β1 > γ1 . Manufacturer’s advertising investment v1 has a negative impact on the store brand product sales, because the national brand and store brand product are competitive, so the manufacturer’s higher advertising investment will make consumers tend to buy the national brand products, resulting in the loss of retail sales, the impact factor is σ1 . The manufacturer’s advertising investment v1 has a positive impact on the store brand product sales, assuming that it has the same effect on the national brand and store brand product sales, which is also σ1 . Retailer’s store brand products are produced under their OEM brand manufacturer contracts, which is common in retail industries such as Staples, Watsons, etc. We assume that the retailer purchases products from the authorized OEM manufacturers at a unit price of w0 , where w0 is a constant. Consider that manufacturers only adopt one traditional form of advertising, that is to use TV advertising. In the case where the advertising cost information is secret, the manufacturer only informs the amount of retail advertising, not the breakdown for specific advertising media, the choice of time period, and other relevant advertising cost information, but the retailer can estimate the distribution of the manufacturer’s advertising cost coefficient, which is subject to uniform distribution on the interval (μ¯ 1 − ε, μ1 + ε), the mean is μ¯ 1 , and ε is the estimated deviation. According to statistics from China Advertising Network, corporate brand strength is closely related to the choice of TV advertising media. CCTV is a national television station with strength to purchase television program resources. It has an authoritative role that cannot be replaced by the satellite stations and regional stations in establishing a brand image and corporate status. Large (strong) brands such as international famous brands or national famous products that pay attention to brand image will choose to place advertisements on CCTV (prime time) platforms, such as HP’s TV advertisements. In addition, the provincial satellite stations with particularly outstanding ratings, such as Hunan Satellite TV, have higher advertising fees than other satellite TV stations. They are also hot spots for big brand merchants to compete, such as the advertisements by the Johnson & Johnson Group. It can be seen from the above that the choice of TV advertising media for large-brand merchants is relatively narrow, so in respect of large-brand manufacturers, retailers can rely on there being small deviations in their advertising cost coefficient estimates. The advertising cost of CCTV’s prime time is three times that of the corresponding time of a local TV station. Therefore, small, and medium-sized brand merchants with weak finances generally take into consideration the cost issue and generally adopt the advertising strategy of provincial-level satellite stations or other provincial-level classified channels. This will not only save costs, but also maximize the strengths of the highly targeted characteristics of a province’s satellite stations. For example, television ads that can be used for cosmetics are used interchangeably with Shanghai Television Station and DRAGON TV and have achieved great success. Small and medium-sized brand merchants can choose to advertise at various provincial satellite stations and other classified channels across the country. The choice is relatively large. Therefore, small, and medium-sized brand retailers, may have relatively large deviations in their advertising cost coefficient

5.5 Competition Games for National …

243

estimates. There are no advertising cases for new products. Therefore, relative to mature products, the estimated deviation for the advertising cost coefficient of new products is also relatively large. Consider a scenario where a manufacturer and a retailer are related in two-stage Stackelberg games and consider the manufacturer’s advertising cost information μ1 as the optimal decision under both shared and secret conditions. The decision sequence is as follows: the manufacturer’s advertising cost information μ1 is shared (or secret), based on the principle of maximizing its own profits to decide whether to shareits advertising cost information  with  retailers, and determine the wholesale price w w  and advertising input V V1 ; The retailer determines the price of its own brand products, the price of the manufacturer’s brand products, and the level    of advertising input P1 , P2 , v2 (P1 , P2 , v2 ) according to the manufacturer’s advertising cost information sharing decision. The difference between the two types of decisions is whether the manufacturer chooses to disclose advertising cost information and how it will affect both the expected profits of both parties and the retailer’s optimal decision-making process. We analyze and prove the conditions for sharing advertising information by manufacturers and the nature of the optimal decision solutions for manufacturers and retailers.

5.5.2 Stackelberg Games with Shared or Secret Information of Manufacturer’s Advertising Costs (1) Consider the scenario where manufacturers and retailers share advertising cost coefficient information. ➀ The first stage decision-the manufacturer’s decision v1 , w. The manufacturer decides the best v1 , w. Find the derivatives of Eq. (5.2) about v1 and w respectively, and we get w=w ν1 =

wσ1 μ1

(5.28) (5.29)

where w is the largest internal transaction price (w > 0) that the retailer can accept. ➁ The second stage decision-the retailer’s optimal decision P1 , P2 , v2 . Substitute (5.3) and (5.4) into (5.1) and find the first derivative of (5.1) about v2 , P1 , P2 . We can get: V2 =

σ2 (P1 − w) μ2

(5.30)

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5 Supply Chain Coordination

P1 =

wγ1 − α2 +

σ 2 σ2 w μ2

+

2γ1 +   2 2γ1 + σμ2 σ2 P1

 β1 wσ21 −w(β1 + γ1 ) − α1 − (β1 +γ − 1 )μ1   2 σ 1) 2(β1 + γ1 ) + μ22 − (β1γ+γ 1

(β1 +γ1 ) γ1 σ2 σ 2 μ2

wσ22 μ2

  σ21 σ2 σ 2 + c(β1 + γ1 ) P2 = + α2 − w γ1 + + 2(β1 + γ1 ) μ1 μ2   β1 wσ21 (β1 +γ1 ) w(β1 + γ1 ) − α1 − (β1 +γ )μ σ2 −wγ1 − α2 + γ1 1 1   · ν2 = (β1 +γ1 ) σ2 σ 2 σ2 σ 2 μ2 2(β1 + γ1 ) + μ2 2γ1 + μ2 − γ1

 (5.31)

(5.32)

(5.33)

(2) The manufacturer does not inform the retailer of the advertising cost factor information. ➀ Manufacturer’s decision. The decisions of w and w1 are the same as (5.3) and (5.4). w  = w, ν1 = ν1 . ➁ Retailer’s decision. The retailer’s expected profit is: ⎧

Eπ2N

⎫   wσ  ¯ 2 P1 − w¯ α1 − β1 P1 + γ1 P2 − P1 + μ11 + σ2 v2 ⎬ = ∫ f (μ1 )dμ1    wσ    ¯ 2 μ1 −ε ⎩ + P  − c α2 − β P  + γ1 P  − P  − μ11 + σ2 v2 ⎭ 2 1 2 1 2 μ1 +ε ⎨

(5.34) 

Find (5.9) the first derivative of v2 , P1 , P2 separately. σ2 (P1 − w) μ2   2 β1 wσ21 1) −w(β1 + γ1 ) − α1 − (β +γ wγ1 − α2 + σ μσ22 w + (β1γ+γ − )μ 1   1 1 1 P1 = 2 2 σ 1) 2(β1 + γ1 ) + μ22 2γ1 + σμ2 σ2 − (β1γ+γ 1 V2 =

(5.35) wσ22 μ2



(5.36) P2 =

 2γ1 +

σ2 σ μ2

 2

2(β1 + γ1 )

P1

  σ1 σ2 σ 2 + c(β1 + γ1 ) + α2 − w γ1 + 2 + μ2 μ1

(5.37)

Substitute (5.11) into (5.10).   β1 wσ21 (β1 +γ1 ) 2 −wγ1 − α2 + w(β1 + γ1 ) − α1 − (β +γ γ σ )μ 1 1  1 1 V1 = · (β1 +γ1 ) σ2 σ 2 σ2 σ 2 μ2 2(β1 + γ1 ) + μ2 2γ1 + μ2 − γ1 where, u 1 =

2ε u +ε I n u 1 −ε 1

.

(5.38)

5.5 Competition Games for National …

245

Property 5.1 μ¯ 1 decreases with respect to ε. 

Proof d

μ +ε

I n μ1 −ε



1

ε

=

dε

1+

μ1 +ε μ1 −ε

ε(μ1 + ε)

−

I n μμ11 +ε −ε ε2

When ε = 0, 1+

ε > 0,

μ1 +ε μ1 −ε

>

ε(μ1 + ε)   1 + μμ11 +ε −ε ε(μ1 + ε)

I n μμ11 +ε −ε ε2  >

ln

μ1 +ε μ1 −ε ε2



This can prove: 1+

μ1 +ε μ1 −ε

ε(μ1 + ε)

>

ln

μ1 +ε μ1 −ε ε2

Therefore:  d

ln

μ1 +ε μ1 −ε



ε

>0

dε  d

 2ε ln

μ1 +ε μ1 −ε

dε

u 1 , when the estimated deviation ε of the retailer is large, that is, when the national brand product is a big brand or a new product, the manufacturer tends not to share the advertising cost information. Proof The difference between the optimal profit π1Y of the manufacturer’s advertising cost information sharing and the optimal profit π1N in the case of secrecy of information: ⎧ ⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪  ⎬ 1⎨ 2 2σ2 β1 1 wσ2 1 1   1− · − π1γ − π1N = σ2 2 ⎪ μ2 γ1 μ1 μ1 (β1 +γ1 ) 2(β1 +γ1 )+ μ2 ⎪ ⎪ ⎪ ⎪ ⎪ 2 σ2 σ 2 ⎩ ⎭ 2γ1 + μ2 − γ1 (5.39)

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5 Supply Chain Coordination

2γ1 −

2γ 2 (β1 + γ1 ) · 2(β1 + γ1 ) (β1 + γ1 ) · 2(β1 + γ1 ) = 1 − σ2 >

  σ2 σ2 2γ1 μ22 (β1 + γ1 ) μ22 σ2 σ 2 − 2σ2 σ2 σ 2 σ2 σ 2 − < − = u 1 , π1 − π1 < 0. The larger the estimated deviation ε of the retailer, that is, when the product is a big or mature brand product or a new product, the manufacturer tends to choose not to share advertising cost information.

5.5.3 Analysis of the Property of the Optimal Solution of the Game Property 5.3 If the manufacturer’s optimal decision is to share or not share advertising cost information, then the retailer’s optimal price and advertising investment decisions are lower than those without sharing or no sharing information. If μ1 < u 1 , the manufacturer chooses to share advertising cost information by Theorem 5.4.   β1 wσ21 1 1 − γ1 (β1 +γ1 ) μ1 μ1   0. 

Property 5.4 P1 or P1 increases with respect to w. ¯ If w, ¯ μ2 , σ2 is unchanged,     v2 or v2 increases with respect to P1 or P1 . P1 , P1 , P2 , P2 decrease with respect to σ1 . Proof ∂ P1 = ∂ w¯

γ1 +



σ2 σ2 μ2

+

2γ1 +

 β1 σ12 −(β1 + γ1 ) − (β1 +γ − 1 )μ1   2 σ 1) 2(β1 + γ1 ) + μ22 − (β1γ+γ 1

(β1 +γ1 ) γ1 

σ2 σ2 μ2

σ22 μ2



By assumption, β1 > γ1 > 0, 2γ1 =

2γ21 (β1 + γ1 ) · 2(β1 + γ1 ) < γ1 γ1

  σ2 σ2 2γ1 μ22 (β1 + γ1 ) μ22 σ2 σ 2 − 2σ2 σ2 σ 2 σ2 σ 2 − < − =