Emerging Market Firms’ Internationalization Pricing Strategies: The Role of Country of Origin and Organizational Learning

Abstract

Over the last decade there has been significant interest in international expansion of emerging market firms (EMFs) due to their distinctive patterns compared with developed market firms (DMFs). In this article, the authors develop an analytical model to generate prescriptive insights for pricing strategies when EMFs enter new foreign markets. Grounded in the international marketing and organizational learning literatures, the model accounts for local and multinational competition, the influence of country-of-origin effects, and the role of organizational learning in foreign markets. The results suggest that when an EMF enters a host market with a local competitor, it could generate higher profits even when charging a lower price than the local competitor. Additionally, the authors find that DMFs enjoy greater profitability than EMFs in foreign markets as a result of positive country-of-origin effects. Finally, the authors propose and validate the use of organizational learning as a process EMFs can use to surmount the negative impact of country-of-origin effects and achieve greater profitability than DMFs.

Keywords

emerging market firms pricing strategies country of origin organizational learning analytical models

There has been a long stream of research on the international expansion of firms from developed economies. Some of these firms have built global brands such as Coke, Pepsi, and Starbucks. Many more have been operating in a variety of international markets. Some of the firms originating from emerging markets (emerging market firms, or EMFs), such as Cemex of Mexico, Tata of India, and Beko of Turkey, have also pursued international expansion strategies successfully (Őzcan, Mondragon, and Harindranath 2018). Although studies have examined the internationalization of EMFs, prior research on pricing decisions of EMFs at the initial stage of market entry into a new foreign market has been sparse (Table 1). EMFs face a variety of challenges that firms from developed economies (developed market firms, or DMFs) did not experience in the earlier phase of their internationalization. EMFs that are at the early stages of their internationalization process have to compete with DMFs that have rich international experience and brand recognition. In many cases, these DMFs are market leaders in their industries, whereas EMFs must overcome the negative quality perceptions associated with originating from an emerging country, which are labeled as negative country-of-origin (COO) effects in the literature (Bartlett and Ghoshal 2000). In contrast to EMFs, DMFs, on average, enjoy more positive brand evaluations and COO associations (Guo 2013). Can EMFs be successful in international markets given the branding and COO challenges they face in the early stages of their internationalization process? If so, what is the underlying process that would enable EMFs to compete with DMFs effectively?

Table 1.

Illustrative List of Studies on the Differences Between EMFs and DMFs.

Study	Research Question	Key Arguments/Findings	Comparison of EMF with DMFs	Organizational Learning Studied	Learning to Improve Cost Efficiencies or Learning to Offer Value-Added Services	Normative Pricing Recommendations Informed by the Type of Competitor
Banerjee, Prabhu, and Chandy (2015)	The impact of EMFs’ indirect learning, through local competitors with developed market experience, foreign competitors in domestic markets, network partners, and executives’ experience, on their growth in developed markets	In a sample of firms in India, the firms that learn from their local competitors with developed market experience, global competitors in their home market, network partners with developed market experience, and leaders with developed market experience exhibit greater revenue growth in developed markets compared with the other firms from India that do not indirectly learn about developed markets. The authors do not find empirical evidence for the effect of indirect learning on revenue growth for firms based in the United Kingdom.	Yes	Yes	No (the article focuses on the impact of various types of indirect learning on revenue growth)	No
Assadinia et al. (2019)	The chain of effects from international experience to export learning, marketing strategy adaptation, and export sales growth	Export learning has a positive impact on marketing strategy adaptation for firms in China and the United Kingdom. For Chinese firms, firms that have longer international experience achieve greater export sales growth. In contrast, for the U.K. firms, firms with longer international experience have lower export growth.	Yes	Yes	No (organizational learning related to the export activities is measured)	No
Assaf et al. (2012)	The moderating role of firm age at entry to international markets, M&As, and home-country size on the relationship between retailers’ internationalization and performance	Using a sample of retailers from the United States and Europe, the authors find that the retailers that enter international markets at an earlier age perform better than the retailers that do not. The authors argue that the younger firms are more flexible in learning how to adapt their strategy to the market needs compared with older firms.	No	Yes	No	No
Tang et al. (2020)	The impact of exploratory versus exploitative internationalization strategies on firm performance	Based on a sample of firms from China, the authors report that in the short run both exploratory and exploitative internationalization has positive effect on firm value, but only exploratory internationalization has a positive impact on firm value in the long run.	No	Yes	No	No
Li, Yi, and Cui (2017)	The mediating role of organizational learning between the international activities in the home market and the expansion to foreign markets	Using a sample of firms based in China, the authors report that the firms that engage in more international activities such as partnering with foreign companies are more likely to develop organizational capabilities that in turn increase their international expansion activities, compared with the firms that do not engage in international activities.	No	Yes	No	No
Hu and Wang (2010)	The impact of sellers’ COO on the price dispersion of homogeneous products	The retailers from the United States enjoy a price premium compared with the retailers from other countries after controlling for product quality and trading risk. For some products, the retailers from the United States enjoy a price premium of 10% over the retailers from China and Hong Kong.	Yes	No	No	No
This study	Pricing strategies of EMFs versus DMFs at market entry	EMFs suffer from negative COO effects when competing with the DMFs. However, if EMFs can obtain greater learning benefits than the DMFs from entering a new foreign market, then the learning benefits can compensate for the negative effects of COO.	Yes	Yes	Yes	Yes

A stream of research on the international expansion of EMFs focuses on organizational learning as one of the mechanisms for EMFs to effectively compete with DMFs in global markets (Luo and Tung 2007; Mathews 2006). The key argument in this line of research is that successful EMFs are able to quickly learn from a variety of sources such as network partners, competitors, and customers (Mathews 2017). For example, Banerjee, Prabhu, and Chandy (2015) conclude that Indian firms that have been successful in international markets have been able to learn quickly to compete with the DMFs. Both DMFs and EMFs obtain new knowledge as a result of operating in international markets (Nelaeva and Nilssen 2022). The new knowledge can help the performance of EMFs and DMFs along two dimensions: cost efficiency and value enhancement. This operationalization aligns with the conceptualization of firms’ objectives and strategies in the prior research: low cost versus differentiation strategies (McAlister et al. 2016), and cost emphasis and revenue emphasis (Mittal et al. 2005). For firms competing in international markets, learning can enable them to charge higher prices by enhancing value-added offerings to consumers (e.g., Rodrigue and Tan 2019), or learning can enable them to lower their costs because of efficiencies in the supply chain (e.g., Hewett et al. 2022). Although the importance of organizational learning in international markets is discussed in the literature, prior research has not addressed the performance implications of learning related to value enhancement versus cost efficiency.

Furthermore, when an EMF enters a market, how the EMF should price its products is another complex decision because of the negative COO effects (Pant and Ramachandran 2012). If the EMF does not accurately take the COO effects into account when setting prices, consumers may perceive the price to be too low or too high (Ding, Ross, and Rao 2010). Along with the challenge of COO effects, EMFs face a wide variety of competitors ranging from small local firms to multinational enterprises from developed countries. How should EMFs set their pricing strategy depending on the type of competitors they will face in the foreign market? The current research on international marketing strategies either addresses such questions conceptually or infers these relationships piecemeal, without investigating optimal pricing strategies when the EMF faces various types of competitors with different COO effects (see Table 1).

To address these questions, we develop a game-theoretic model to examine the market entry decisions of EMFs. Building on previous research in the international expansion literature, we focus on how COO effects and organizational learning can work together to affect the firm's pricing decisions. We study these effects in the context of internationalization of retailers because their pricing strategy has a significant impact on their profitability and survival (Levy et al. 2004; Nath, Kirca, and Kim 2021).

To the best of our knowledge, this article is the first to propose a theoretical model of these factors and their impact on pricing decisions. There have been calls for studies to focus on global firms and brands that are not from the “West” (Batra et al. 2021; Burgess and Steenkamp 2006; Mandler et al. 2021). To respond to these calls, we study the international expansion strategies of EMFs under the conditions of different types of competition (local vs. foreign), COO effects, and learning benefits. Specifically, we answer the following questions:

How should an EMF price its product when entering a market with only a local competitor?

In presence of negative COO effects, how should an EMF price its product when competing in a market with a DMF?

In presence of greater learning benefits than DMFs, can an EMF overcome the negative COO effects?

When is it more profitable for the EMF to focus on learning related to value enhancement versus learning related to cost efficiency?

We make the following contributions to the international marketing literature. First, we provide insights into the pricing decisions of EMFs and contrast them with those of DMFs. Using a game-theoretic framework, we compare the key challenges of COO effects with the benefit of learning from operating in a foreign market for EMFs and DMFs. More specifically, we provide insights into the relative profitability of the EMFs and the DMFs under different competitive scenarios, while taking COO effects and learning benefits into account.

Second, we provide insights into the impact of organizational learning on EMFs' performance in international markets. Prior research on EMF learning focused on the type of learning, such as indirect learning versus direct learning (Banerjee, Prabhu, and Chandy 2015) or exploratory versus exploitative learning (Tang et al. 2020). We focus on the domain of learning, namely, EMFs' learning about improving cost efficiency versus learning about how to offer value-added services. We focus on such a distinction because many EMFs begin their internationalization journey as a low-cost provider and over time learn to improve their value-added services or product quality (Kumar and Steenkamp 2013). This strategy has worked for many EMFs from different countries (e.g., expansion of Turkish firms in Eastern Europe). However, our model demonstrates that if EMFs can achieve a higher learning rate than DMFs and, hence, can provide a greater level of value-added services than DMFs, then they can achieve higher profitability than DMFs even if the DMF has greater international experience and enjoys positive COO effects in the host country. Although learning to achieve cost efficiency could enhance the profitability of EMFs compared with DMFs, to ensure long-term success of EMFs in international markets, learning to offer value-added services would help EMFs overcome the negative perceptions associated with COO effects. Furthermore, we provide insights into the profitability of EMFs under various scenarios of the knowledge gap and the rate of learning by modeling the differences in the stock of knowledge and the rates of learning of EMFs and DMFs.

Third, we make a methodological contribution to the international marketing research by using an analytical model to inform the pricing strategy of EMFs at the point of market entry. In our review of the international marketing and business research, we came across only one article that uses an analytical model to study an international business problem. Schmid and Kotulla (2011) focus on the adaptation versus standardization strategy in international markets. However, the authors do not investigate the challenges that EMFs face in international markets (e.g., COO effects). Prior research on the internationalization of EMFs has generated valuable insights into the process and the performance of EMFs using quantitative and qualitative methods (Cuervo-Cazurra and Genc 2008; Kaufmann and Roesch 2012). However, the data or information required to study the pricing strategies rigorously is either very difficult or impossible to obtain. For example, the complexities surrounding procuring data on the actual prices of products and services that business-to-business firms sell in international markets cause significant obstacles in the study of pricing strategies of business-to-business firms, especially those from emerging markets. For such international marketing research questions, analytical models offer a strategic avenue to investigate the impact of pricing strategies on profitability under various competitive scenarios.

Conceptual Background

The prevailing literature on firm internationalization has predominantly centered on the experiences of DMFs, owing to the predominant economic influence of developed countries on the global stage (Johanson and Vahlne 1977; Karhunen and Ledyaeva 2012; Li, Qian, and Yao 2015). However, the emergence of EMFs onto the international scene has ushered in a distinct avenue of research into firm internationalization, driven by the substantial disparities in internationalization trajectories between EMFs and DMFs (Luo and Tung 2007; Mathews 2006). These divergences stem from the distinct starting points and learning processes intrinsic to DMFs and EMFs, a topic we delve into in the subsequent sections.

The Impact of Brands in International Expansion

The internationalization process of firms is often preceded by building a strong presence in home markets. EMFs usually expand to foreign markets after establishing a strong presence in their home markets. Although EMFs enjoy brand loyalty in their home markets (e.g., Anadolu Efes in Turkey, Tata in India), their brands face significant challenges in international markets due to the negative COO effects associated with them. Scholars argue that EMFs have a liability of origin compared with DMFs because the home countries of EMFs are not as wealthy or advanced as the home countries of DMFs (Bartlett and Ghoshal 2000; Leonidou et al. 2022). Empirical findings support this argument. For example, according to a meta-analysis, on average, consumers have negative perceptions of quality associated with brands originating from emerging economies (Verlegh and Steenkamp 1999). Across product categories such as chocolate, TVs, watches, and cars, DMFs enjoy higher quality perceptions and more positive product evaluations compared with EMFs (Ettenson 1993; Guo 2013; Kaynak, Kucukemiroglu, and Hyder 2000; Ozretic-Dosen, Skare, and Krupka 2007). Consequently, in the context of a market-entry decision, a brand owned by a DMF has an advantage over a brand owned by an EMF due to the COO effects. To account for this difference, in our model we incorporate a positive COO effect for the DMF and a negative COO effect for the EMF.

Learning in International Markets

When EMFs start to internationalize, they face a steeper uphill climb than the DMFs experienced. EMFs must compete with experienced incumbents, so they need to learn quickly in order to compete with their DMF counterparts (Nelaeva and Nilssen 2022). DMFs are not under such pressure, as they are the market leaders with strong global brands across geographies.

In contrast to DMFs, theories focusing on the internationalization of EMFs discuss organizational learning as a solution for EMFs to overcome strategic asset deficiencies and time pressure to catch up with DMFs (Luo and Tung 2018; Mathews 2006). For example, based on the global expansion experience of large Asian EMFs, the linkage-leverage-learning framework proposes that EMFs overcome their deficiencies in global competition, such as deficiency in brand recognition or technological assets, by partnering with companies in more advanced markets (Mathews 2017). The key process here is the EMFs’ motivation and ability to learn from other companies, competitors, and foreign markets much faster than the DMFs in order to compete more effectively in international markets (Banerjee, Prabhu, and Chandy 2015). Anecdotal evidence suggests that EMFs are highly motivated to succeed in international markets. The CEO of a Chinese manufacturer of home appliances and consumer electronics stated, “If a country has no global brand, then it cannot stand on the top. Haier is willing to be the pioneer” (Kumar and Steenkamp 2013, p. 36). The Anadolu Group, a business group founded and headquartered in Turkey with operations in 19 countries, has the corporate vision of being “The star that links Anatolia to the world and the world to Anatolia,” underscoring the ambition of the EMF to succeed on the global stage (Anadolu Group 2023). The case studies of EMFs underscore the EMFs’ motivation to learn from their operations in foreign markets to improve their competitiveness in international markets (Ayden et al. 2021; Bianchi 2011; Klossek, Linke, and Nippa 2012).

Conceptually, therefore, it is expected that EMFs would have greater motivation to learn about competing in new markets, but is there evidence that these firms can learn and improve their international marketing expertise? Empirical evidence suggests that EMFs have indeed developed superior learning capabilities compared with DMFs because of operating amid challenging home country conditions, such as poor infrastructure, weak institutions, and instability of the business and political environment (Cuervo-Cazurra and Genc 2008; Guillén and García-Canal 2009). For example, Banerjee, Prabhu, and Chandy (2015) report that Indian firms learn from their competitors and network partners during the internationalization process, whereas they do not find such a learning effect for a sample of U.K. firms. More recently, Ozkan et al. (2022) find that during the 2010–2019 period, DMFs lost .029% in market share every year, whereas EMFs gained .063% in market share in the same markets every year. The authors discuss factors such as quick technology adoption and innovation, turning the disadvantages of operating in difficult environments to advantages to explain the relative success of the EMFs. The common theme in these factors is the superior learning capabilities of EMFs compared with DMFs. To capture the theoretical approaches and the empirical findings on the greater motivation and ability of EMFs to learn faster than established DMFs in international markets, we assume that EMFs’ learning benefits are higher than those of DMFs when they both enter a new market.

The Model

Conceptual Structure of the Model

To study the impact of COO effects and learning benefits on EMFs’ and DMFs’ profitability, we use the retailing sector as the context. Our model would also apply to manufacturing and other sectors in which firms sell undifferentiated products and compete on price. The retailing context enables us to focus on the relationships of interest (aka pricing strategies of the two retailers) without having to make further assumptions about firms’ technology and manufacturing processes to compete on the quality dimension. Furthermore, retailing is an appropriate context to study the internationalization of EMFs as emerging market retailers have been expanding to foreign markets (Bianchi 2011; Ozkan et al. 2022).

We study four distinct scenarios to elucidate the nuanced impact on the optimal pricing strategies of the EMF as varying factors come into play. We also provide an example market that each scenario would work for, to demonstrate model applicability across various host markets (emerging and/or developed), in Table 2.

Table 2.

Nature of Competition in Different Scenarios.

Competitive Setting in the Host Country	Additional Assumptions Within Each Case^a	Example Application to a Host Market
Scenario 1: Two local retailers competing	None	EM: In Brazil there is heavy competition among local smaller retailers/convenience stores. DM: Local retailers/convenience stores compete in Germany.
Scenario 2: EMF entering a host market with a local retailer	None	EM: An Indian retailer (EMF) could compete with the existing small local Brazilian grocery store. DM: An Indian retailer (EMF) could compete with the existing small local U.S. grocery store.
Scenario 3: EMF competing with a DMF	Yes; when an EMF competes with a DMF, the COO effects are made salient. Therefore, the EMF has a negative COO effect and the DMF has a positive COO effect in this case.	EM: An Indian retailer (EMF) could compete with a Japanese retailer (DMF) in Brazil. DM: An Indian retailer (EMF) could compete with a U.S. retailer in Germany.^b
Scenario 4.1: EMF providing a higher level of value-enhancing services to the consumer than a DMF	The EMF (DMF) continues to have negative (positive) COO effects. Further, we model offering greater value-added services that enhance consumer utility through two parameters: (1) the EMF's stock of international marketing experience/knowledge is smaller than the DMF's stock of international experience, and (2) we incorporate the higher (lower) rate of learning for the EMF (DMF).	EM: An Indian retailer (EMF) could compete with a Japanese retailer (DMF) in Brazil. DM: An Indian retailer (EMF) could compete with a U.S. retailer (DMF) in Germany.^b
Scenario 4.2: EMF incurring a lower level of cost than a DMF	The EMF (DMF) continues to have negative (positive) COO effects. Additionally, we incorporate the higher (lower) rate of learning related to cost efficiency that provides the EMF with a higher level of savings (than those accrued by the DMF).	EM: An Indian retailer (EMF) could compete with a Japanese retailer (DMF) in Brazil. DM: An Indian retailer (EMF) could compete with a U.S. retailer (DMF) in Germany.

Key assumptions across models: there are two competing retailers (of various types, as given by the subsequent cases); retailers compete in prices to gain market share; and consumers incur a transportation cost of getting to the store: the closer they are in location to the store, the lower the cost. Note that these are standard assumptions within the Hotelling framework, where the two competing retailers are at the ends of the unit line with a mass of consumers in between.

The results of the model remain the same if we replace the DMF with a local retailer that has strong brand equity.

Notes: EM = emerging market; DM = developed market.

Scenario 1: Only local competitors present

In this preliminary scenario, we examine the outcomes when a host market is dominated exclusively by local competitors; smaller cities in emerging markets or rural towns in developed markets would fit within this reality. Although this is a simplified case, it serves as a benchmark for gauging the market dynamics upon the EMF's entry in Scenario 2, shedding light on implications for both the EMF and the local retailer.

Scenario 2: An EMF enters a market with a local competitor (incumbent)

In this scenario we examine the profitability outcomes when an EMF enters a host market with only a local retailer as its competition. By investigating the profits of the local retailer across Scenarios 1 and 2, we gain insights into the impact of the EMF's presence on the local retailer.

Scenario 3: An EMF competes in a host market with a DMF

The third scenario examines the case in which an EMF opts to compete in a market against a DMF, incorporating COO effects in the process. The examination of this competitive landscape provides crucial insights into the strategic choices of the EMF in the presence of a significant disadvantage, that is, the COO effect.

Scenario 4: Incorporating firm learning in internationalization

In this final scenario, we introduce an additional dimension—learning—as a means for the EMF to better compete with a DMF in a host market. By incorporating the learning process, we enhance the comprehensiveness of our model and examine the implications of EMFs' adaptive strategies.

The Details of the Model

We model a mass of consumers on a unit Hotelling line (Hotelling 1929). The Hotelling framework has been used extensively to understand competitive strategies in retailing contexts (Pazgal, Soberman, and Thomadsen 2016; Sajeesh, Singh, and Bharadwaj 2022). Further, it has been used in the context of retailing in emerging markets (Jerath, Sajeesh, and Zhang 2016).

We consider two competing retailers located on opposite ends of a Hotelling line of unit length. Each retailer offers a similar product, denoted by retailer-specific subscripts. That is, the prices charged by the two retailers A and B are denoted by p_A and p_B respectively. We make the standard assumptions that (1) consumers’ valuation of their ideal product is V, (2) the transportation cost t is incurred when consumers travel to a store to familiarize themselves with it, and it depends on the location of the consumer, and (3) the variable costs associated with selling the product are zero. Note that while we make the standard assumption of variable costs to be nonexistent, we permit cost differentiation as a firm strategy through fixed costs. In other words, an important difference between EMFs and DMFs is that EMFs tend to be more efficient than DMFs through better and more efficient cost savings. We permit that by taking into account differential fixed costs in Scenario 4. We present the model notation in Table 3.

Table 3.

Model Notation.

Notation	Description
Retailer A_i, B	Incumbent local retailers, in case i {i = 1 or 2}
Retailer E_i	Emerging market firm, in case i {i = 2, 3, or 4}
Retailer D_i	Developed market firm, in case i {i = 2, 3, or 4}
t	Consumers’ unit transportation cost
V	Consumer valuation of the product
p_i	Price charged by Retailer i, $i \in {A, B, E, D}$
π_i	Profit of Retailer i, $i \in {A, B, E, D}$
δ	Additional valuation provided to consumer due to COO effects
B, b	Base (stock) of accumulated knowledge of DMF and of EMF, respectively
R, r	Rate of learning of EMF and of DMF, respectively
S, s	Cost savings accrued by EMF and by DMF, respectively

Before we model the entry of an EMF into a particular market, we first model a scenario with only two local retailers present. In this scenario (Scenario 1), consider a market dominated by local retailers without any EMFs or DMFs present. In Scenario 2 we study how pricing and demand changes when an EMF enters a market with a local competitor, not just for the EMF but for the local retailer as well.

Scenario 1: Only local competitors present

In this scenario two local competitors (A and B) compete for market share and profits in a host market. They can typically be characterized as small retailers selling groceries that offer similar product offerings at similar prices (e.g., kirana stores in India). They are usually located close to their customers, who must walk a few hundred meters to make any purchase, and the transportation cost (indicated by t in the model) that the customers incur is significant in their choice of retailer (Child, Kilroy, and Naylor 2015).¹ We present a depiction of the Hotelling model in Figure 1 with the different scenarios described subsequently.

Figure 1.

Consumer and Retailer Locations in a Host Market.

Consider a consumer with valuation V, located a distance x₁ from the left end of the line. If such a consumer visits local Retailer A and purchases the item, the consumer will obtain a utility U(x₁) = V − p_A1 − tx₁, where p_A1 is the price at Retailer A in Scenario 1, t is the unit transportation cost, and tx₁ is the total transportation cost incurred by the consumer to travel to Retailer A from the consumer’s current location. The same consumer will enjoy a utility of U(x₁) = V − p_B − t(1 − x₁) if they choose to shop at local Retailer B instead.

The demand functions for the two retailers depend on the prices they offer and the consumer's location. Given the utility functions for the consumers (stated previously), we use the concept of the marginal consumer to derive demand for each retailer. We do this by setting the following equation to zero and solving for the marginal consumer (i.e., the x₁ term):

(V - p_{A 1} - {tx}_{1}) - (V - p_{B} - t (1 - x_{1})),

(1)

where p_A1 and p_B represent prices charged by the two competing local retailers respectively. Solving Equation 1, we obtain the indifference point (x₁), which represents the demand for Retailer A:

x_{1} = \frac{t - p_{A 1} + p_{B}}{2 t} .

(2)

This implies that the demand for Retailer B is (1 − x₁), which is given by

(1 - x_{1}) = \frac{t + p_{A 1} - p_{B}}{2 t} .

(3)

We now present the profits for the two local firms, which are simply the price multiplied by the demand and are given by the following:²

Π_{A 1} = p_{A 1} \times x_{1} .

(4)

Π_{B} = p_{B} \times (1 - x_{1}) .

(5)

Given that these two firms are local competitors (i.e., they exist in the market together), we solve their prices simultaneously. Solving the first-order conditions for profit maximization, the Bertrand–Nash equilibrium prices that are charged by each retailer are given by

p_{A 1} = p_{B} = t .

(6)

It is intuitive that in Equation 6 the prices are the same and equal to the transportation cost because these local stores have practically no differentiation and serve the same undifferentiated products. Customers decide to go to a particular retailer depending on whichever is close to them or which one they have the habit of going to (sometimes across generations). Optimal prices, demands, and profits for each scenario are presented in Table 4.

Table 4.

Model Results.

Scenario	Prices	Demand	Profits
1. Two local retailers competing	$p_{A 1} = p_{B} = t$	$x_{A 1} = x_{B} = \frac{1}{2}$	$π_{A 1} = π_{B} = \frac{t}{2}$
2. EMF entering a host market with a local retailer	$p_{A 2} = \frac{3 t}{2}$ $p_{E 2} = \frac{5 t}{4}$	$x_{A 2} = \frac{3}{8}$ $x_{E 2} = \frac{5}{8}$	$π_{A 2} = \frac{9 t}{16}$ $π_{E 2} = \frac{25 t}{32}$
3. EMF competing with a DMF	$p_{E 3} = t - \frac{2 δ}{3}$ $p_{D 3} = t + \frac{2 δ}{3}$	$x_{E 3} = \frac{1}{2} - \frac{δ}{3 t}$ $x_{D 3} = \frac{1}{2} + \frac{δ}{3 t}$	$π_{E 3} = \frac{{(t - \frac{2 δ}{3})}^{2}}{2 t}$ $π_{D 3} = \frac{{(t + \frac{2 δ}{3})}^{2}}{2 t}$
4.1. EMF providing a higher level of value-enhancing services to the consumer than a DMF	$p_{E 4}^{1} = \frac{1}{3} ((b + R) - (B + r) + 3 t - 2 δ)$ $p_{D 4}^{1} = \frac{1}{3} ((B + r) - (b + R) + 3 t + 2 δ)$	$x_{E 4}^{1} = \frac{(b + R) - (B + r) + 3 t - 2 δ}{6 t}$ $x_{D 4}^{1} = \frac{(B + r) - (b + R) + 3 t + 2 δ}{6 t}$	$π_{E 4}^{1} = \frac{{((B + r) - (b + R) - 3 t + 2 δ)}^{2}}{18 t}$ $π_{D 4}^{1} = \frac{{((B + r) - (b + R) + 3 t + 2 δ)}^{2}}{18 t}$
4.2. EMF incurring a higher level of cost savings than a DMF	$p_{E 4}^{2} = t - \frac{2 δ}{3}$ $p_{D 4}^{2} = t + \frac{2 δ}{3}$	$x_{E 4}^{2} = \frac{1}{2} - \frac{δ}{3 t}$ $x_{D 4}^{2} = \frac{1}{2} + \frac{δ}{3 t}$	$π_{E 4}^{2} = S + \frac{{(t - \frac{2 δ}{3})}^{2}}{2 t}$ $π_{D 4}^{2} = s + \frac{{(t + \frac{2 δ}{3})}^{2}}{2 t}$

Plugging the demands (Equations 2 and 3) and the prices (Equation 6) into the profit equations (Equations 4 and 5) provides the optimal profits for the two local retailers (Equation 7).

π_{A 1} = π_{B} = \frac{t}{2} .

(7)

Given that the retailers split the market in terms of the demand and their prices are the same, it is also intuitive that the profits of these two retailers are similar—this is a highly competitive market with undifferentiated products. We state the results from Scenario 1 in the following lemma:

Lemma 1:

When two retailers are local competitors, the prices charged, t, are the same and depend solely on the customer's transportation cost. Further, the two retailers split the market and obtain equal profits of t/2 each.

Scenario 2: An EMF enters a market with a local competitor (incumbent)

Next, we examine the case in which an emerging market retailer (denoted by E₂) is entering a market where there is a local retailer, Retailer A (denoted by A₂), already present. Given that the local competitor (A₂) already exists when the EMF is entering the market, we model the EMF’s entry as sequential. In other words, because the EMF is deciding what price to set in a market that already has an existing incumbent local competitor (who is charging a price), the EMF will consider the existing price when it determines its own price. The sequence of events is, therefore, sequential (EMF enters after the local retailer), unlike in Scenario 1, where price competition between the two local retailers was simultaneous (because both have existed for several decades in the local community). The sequence is formalized as follows:

The local firm, as the existing retailer, chooses its price (p_A2) first.

Taking the local firm’s price into account, the EMF entrant chooses its price in the same market (p_E2).

Customers make their store visits and purchase decisions.

As is the standard solution procedure, we adopt the notion of subgame-perfect Nash equilibrium and begin by developing demand functions at Stage 3.

Consider a consumer with valuation V, located a distance x₂ from the left end of the line (refer to Scenario 2 in Figure 1). If such a consumer visits the local Retailer A and purchases the item, the consumer will obtain a utility U(x₂) = V − p_A2 − tx₂, where p_A2 is the price charged by Retailer A in Scenario 2, t is the unit transportation cost, and tx₂ is the total transportation cost incurred by the consumer to travel to Retailer A from the consumer’s current location. The same consumer will enjoy a utility of U(x₂) = V − p_E2 − t(1 − x₂) if they choose to shop at the EMF (Retailer E₂) instead.

Given the utility functions for the consumers, we use the concept of the marginal consumer to derive demand for each retailer. We do this by setting the following equation to zero and solving for the marginal consumer (i.e., the x₂ term):

(V - p_{A 2} - {tx}_{2}) - (V - p_{E 2} - t (1 - x_{2})) .

(8)

Solving Equation 8, we obtain the indifference point (x₂), which represents the demand for Retailer A₂:

x_{2} = \frac{t - p_{A 2} + p_{E 2}}{2 t} .

(9)

This implies that the demand for the EMF is (1 − x₂), which is given by

(1 - x_{2}) = \frac{t + p_{A 2} - p_{E 2}}{2 t} .

(10)

Profits for the two firms are given by

π_{A 2} = p_{A 2} \times x_{2}, and

(11)

π_{E 2} = p_{E 2} \times (1 - x_{2}) .

(12)

The solution procedure for equilibrium prices in Scenario 2 is as follows. In the spirit of backward induction, we first solve the first-order conditions for profit maximization for the EMF (because it is the later entrant), and then solve for the optimal price of the local retailer, A₂. This solution procedure accounts for the fact that the reaction function of the entrant (EMF) will take the existing retailer's (A₂) price into account when setting its own price. Therefore, the reaction function of the EMF is obtained by differentiating its profit function (Equation 12) with respect to the price (p_E2) after plugging in the demand (1 − x₂) from Equation 10 and is given by

p_{E 2} = \frac{p_{A 2} + t}{2} .

(13)

Equation 13 is then plugged into the profit function of the local retailer (Equation 11) and the first-order conditions are derived with respect to the price (p_A2), which results in the following equilibrium price for the local retailer:

p_{A 2} = \frac{3 t}{2} .

(14)

Finally, given that Retailer A₂ is already in the market when the EMF enters, A₂'s price is taken into account by the EMF when charging its own price. We derive the price charged by the EMF by plugging Equation 14 into Equation 13, and it is given by

p_{E 2} = \frac{5 t}{4} .

(15)

In comparing across Scenarios 1 and 2, several interesting observations emerge. First, prices in Scenario 2 are no longer the same as the prices in Scenario 1. This is because, in contrast to the simultaneous presence of local competitors in Scenario 1, in Scenario 2 the new market entrant (the EMF) arrives to the presence of an existing competitor in the marketplace (Retailer A₂). In this case the local firm (A₂) can charge a higher price than it could in Scenario 1 (i.e., the price in Equation 14 is greater than that in Equation 6), when it was competing with a local store that provided similar offerings. Further, it can charge a higher price than the EMF (i.e., the price in Equation 14 is greater than that in Equation 15) because it has the first-mover advantage, and the EMF is a later entrant into the host market.

Plugging the demands and the prices into the profit equations (Equations 11 and 12) provides the optimal profits for the local Retailer A and the EMF:

π_{A 2} = \frac{9 t}{16}, and

(16)

π_{E 2} = \frac{25 t}{32} .

(17)

Second, note that because of the lower price charged by the EMF, the EMF can gain a higher demand and take away more market share from the local firm, resulting in higher profits for the EMF in this case (compare Equations 16 and 17).³ This result is nonintuitive and different from the standard result where the first-mover firm has the advantage of higher profits. It arises because the model considers the higher price the incumbent charges and the fact that consumers prefer a cheaper alternative as a result of undifferentiated products and transportation costs. Therefore, the implication for an EMF entering a market with existing local competition is that entering the market as a second mover is advantageous because the EMF charges a lower price, enabling it to capture higher market share in such markets.

We now state the results from Scenario 2 in the following proposition:⁴

Proposition 1:

When an EMF enters a host market in the presence of a local competitor, (a) the prices charged by the local firm are higher due to first-mover advantage, and (b) the profits of the EMF are higher than those of the local firm because of higher consumer demand.

Does this mean that the local retailer (incumbent) is worse off with the EMF entrant than it is with a local competitor? Entry by an EMF permits the incumbent local retailer to charge a higher price than it could otherwise (i.e., the price of Retailer A is higher in Scenario 2 than the price in Scenario 1; i.e., p_A2 > p_A1). This is yet another interesting observation and challenges the existing narrative that foreign entry by a retailer is always bad for the existing local retailers. Specifically, comparing the profits of Retailer A in Scenarios 1 and 2 (Equations 7 and 16) demonstrates that while the profits of the local retailer are lower than that of the EMF, its profits are higher when it competes with an EMF than when it competes with local competition. This observation, that the entry of an EMF to a new market can increase the profitability of the local retailer in comparison to the case of only local retailer competition, is consistent with anecdotal evidence that in recent years, despite several EMFs entering the Indian market, the local kirana stores continue to thrive. Further, our results are also in agreement with the finding of a comprehensive study of developments in the Indian retailing sector in the past decade, which states that the local stores are able to differentiate themselves better when a new entrant arrives (Kohli and Bhagwati 2012). We state this result in a lemma:

Lemma 2:

The local retailer makes higher profits when competing with an EMF than when competing with another local firm.

Next, we consider the impact of COO effects when an EMF competes in a host market simultaneously with a DMF (Scenario 3).

Scenario 3: An EMF competes in a host market with a DMF

In this scenario an EMF and a DMF (E₃ and D₃) compete for market share and profits in a host market. This scenario would apply to most metropolitan cities in the world; for example, an EMF and a DMF exist simultaneously in Sydney, Australia.

Consider a consumer with valuation V, located a distance x₃ from the left end of the line (refer to Scenario 3 in Figure 1). If such a consumer visits the DMF retailer and purchases the item, the consumer will obtain a utility U(x₃) = V + δ − p_D3 − tx₃, where p_D3 is the price at the DMF retailer in Scenario 3, t is the unit transportation cost, δ is the additional brand value associated with a positive COO effect, and tx₃ is the total transportation cost incurred by the consumer to travel to Retailer D₃ from the consumer’s current location. The δ term captures the higher quality perceptions and more positive product evaluations that DMFs enjoy compared with EMFs.

The same consumer will enjoy a utility of U(x₃) = V − δ − p_E3 − t(1 − x₃) if the consumer chooses to shop at the EMF instead, where the negative valuation (δ) arises from the negative COO effects associated with purchasing from an EMF. Employing the same solution process that is outlined in Scenario 1 (i.e., taking the first-order conditions of the profit functions with respect to prices), the Bertrand–Nash equilibrium prices that each retailer charges is given by

p_{E 3} = t - \frac{2 δ}{3}, p_{D 3} = t + \frac{2 δ}{3} .

(18)

Similar to Scenario 1, prices reflect the transportation cost. In addition, however, and unique to Scenario 3, they are also a function of the value received (or lost) due to the COO effects. The negative brand image associated with the COO effects implies that the EMF must charge a lower price than the DMF can. Therefore, the profits of the DMF are higher than those of the EMF as follows:

π_{E 3} = \frac{{(t - (2 δ / 3))}^{2}}{2 t}, π_{D 3} = \frac{{(t + (2 δ / 3))}^{2}}{2 t} .

(19)

We now state the results from Scenario 3 in the following proposition:

Proposition 2:

When an EMF competes with a DMF and there are COO effects at play, the EMFs price is lower than that of a DMF and depends on both the customer's transportation cost and the additional (negative) brand value associated with the COO effects. This causes profits of the EMF to be lower than those of the DMF.

When competing with a DMF, the lower brand image of the EMF comes into focus (i.e., negative COO effects are salient). Because of the higher brand prestige associated with purchasing a brand from a developed country, the DMF can charge a higher price than the EMF and gain higher demand (see Table 4). Therefore, in comparing across Scenarios 2 and 3, it is evident that an EMF should prefer to compete in a market with a local competitor versus a market with a DMF, because profits of the DMF are always higher when competing with an EMF when accounting for COO effects. Therefore, to overcome these negative COO effects, the key process that we recommend that the EMF engage in is organizational learning. In the next section, we consider the impact of learning on an EMF's strategy by operationalizing learning in two possible ways.

It is important to note that in Scenario 2 we use sequential entry as the sequence, whereas in Scenario 3 we use a simultaneous sequence. We use the simultaneous sequence in Scenario 3 because we would need to make additional assumptions about the order of entry regarding the EMF and the DMF as well as the relative size of the first-mover advantage and COO effects if we used a sequential entry sequence.⁵ Our goal is to keep the model as generalizable as possible. Therefore, we use the simultaneous sequence in Scenario 3.

Scenario 4: Incorporating firm learning in internationalization

In the conceptual background section, we discuss EMFs’ greater motivation and ability to learn faster than DMFs in order to catch up with them (Banerjee, Prabhu, and Chandy 2015; Luo and Tung 2018; Mathews 2006; Ozkan et al. 2022). Prior research on organizational learning in international markets also highlights what the firms learn because of expanding to foreign markets. For example, Bianchi (2011) documents how the Chilean retailer Falabella has learned to be more cost efficient by opening a distribution center in Peru as well as opening an office in Shanghai to achieve better purchasing deals with suppliers. Khavul et al. (2010) emphasize the organizational learning related to customer support capabilities in international markets. In the process of internationalization, Nath, Kirca, and Kim (2021) argue that emerging market and developed market retailers learn about global brand management, which requires them to learn about delivering greater value to consumers in different foreign markets.

To capture these different types of learning, we distinguish between (1) learning about enhancing value to consumers and (2) learning to reduce costs to the firm. To compete in global markets, retailers can learn to reduce their costs by negotiating better contracts with vendors or increasing supply chain efficiencies (Hewett et al. 2022), or they can learn to enhance the value they offer to customers by providing an omnichannel shopping experience, higher service quality, or different store formats (Bonfrer, Chintagunta, and Dhar 2022; Timoumi, Gangwar, and Mantrala 2022). We base this distinction on the firm strategy literature, which suggests firms usually follow low-cost versus differentiation strategies (McAlister et al. 2016), or they have a cost emphasis or revenue emphasis (Mittal et al. 2005). We next model the two types of learning for an EMF competing with a DMF and identify conditions for when one type of learning may be preferred over the other.

Scenario 4.1: Value-enhancing learning

To incorporate the impact of learning related to enhancing value to consumers in the model, we update the consumer's demand function to incorporate the additional value received from the retailer. This additional consumer value could be derived from services that the retailer provides, such as a dedicated toll-free customer service line or the number of customer representatives at the store or enhanced ease of product returns. Here, similar to Scenario 3, an EMF and a DMF (E₄ and D₄) compete for market share and profits in a host market and the EMF (DMF) continues to have a negative (positive) COO effect. We extend Scenario 3 by incorporating learning benefits for both firms into the model.

Further, we model a nuance with respect to learning differences between EMFs and DMFs.⁶ While the case can be made that the rate of learning is steeper for EMFs than for DMFs, as done previously, we want to also account for the fact that DMFs will probably have a higher stock of accumulated learning due to their prior internationalization experiences. That is, we model these two separate aspects of firm learning with two separate parameters: R (r) is the higher rate of learning for an EMF (DMF), and B (b) is the higher accumulated knowledge base for a DMF (EMF) respectively (i.e., R > r and B > b). Over time, as the two firms compete, the higher rate of learning of EMFs will erode the accumulated knowledge base of DMFs, and learning could remain a key process that EMFs could use to overcome the COO advantage of DMFs.

Consider a consumer with valuation V, located a distance x₄ from the left end of the line (refer to Scenario 4 in Figure 1). If such a consumer visits the DMF and purchases the item, the consumer will obtain a utility $U (x_{4}) = V + δ + (B + r) -$ $p_{D 4}^{1} - {tx}_{4}$ , where $p_{D 4}^{1}$ is the price at the DMF retailer in Scenario 4.1 (value-added learning), t is the unit transportation cost, δ is the additional brand value associated with a positive COO effect, (B + r) is the added value the consumer receives from the DMF’s enhanced service provision as a result of its combined learning, and tx₄ is the total transportation cost incurred by the consumer to travel to Retailer D from the consumer’s current location. The same consumer will enjoy a utility of $U (x_{4}) = V - δ + (b + R) - p_{E 4}^{1} - t (1 - x_{4})$ if the consumer chooses to shop at the EMF retailer instead, where the negative valuation (δ) arises from the negative COO effects associated with a product from an EMF, while the positive valuation (b + R) denotes the enhanced services the customer receives at the EMF, which has achieved a greater learning rate than the DMF to improve its customer experience and provide its customers with additional value.

Employing the same solution process that is outlined in Scenario 1, the Bertrand–Nash equilibrium prices that each retailer charges is given by

\begin{aligned} p_{E 4}^{1} = & \frac{1}{3} ((b + R) - (B + r) + 3 t - 2 δ), \\ p_{D 4}^{1} = & \frac{1}{3} ((B + r) - (b + R) + 3 t + 2 δ) . \end{aligned}

(20)

When we compare the price charged by the EMF in Scenarios 3 and 4 (i.e., Equations 18 and 20), it is clear that the EMF's prices are higher in Scenario 4. This is the outcome of the learning effects and the additional value the enhanced services create for the consumer, which can then be captured back in terms of higher prices. Specifically, in contrast to Scenario 3, where the EMF's price as always lower than that of the DMF, in Scenario 4.1, as the gap in learning benefits increases, the EMF can charge a higher price than the DMF despite the existence of negative COO effects. Profits for the two retailers are as follows:

\begin{aligned} π_{E 4}^{1} = & \frac{{((B + r) - (b + R) - 3 t + 2 δ)}^{2}}{18 t}, \\ π_{D 4}^{1} = & \frac{{((B + r) - (b + R) + 3 t + 2 δ)}^{2}}{18 t} . \end{aligned}

(21)

We now state the results in the following lemma:

Lemma 3:

When an EMF provides a higher level of value-enhancing services to consumers (than a DMF) as a result of learning in the host market, the EMF’s price can be higher (than the DMF’s price) and offset the additional (negative) brand value associated with the COO effects. Further, the profits of the EMF can be higher (than in the case without learning benefits) despite the negative COO effects.

Scenario 4.2: Cost-efficiency learning

It is also possible to incorporate firm learning by focusing on the reductions in fixed costs because of improvements in operational efficiencies. This could, for instance, lower the total fixed costs associated with entering the host market and increase an EMF's profit.⁷ To model this scenario, consider a consumer with valuation V, located a distance x₄ from the left end of the line (refer to Scenario 4 in Figure 1). If that consumer visits the DMF, the consumer will obtain a utility $U (x_{4}) = V + δ - p_{D 4}^{2} - {tx}_{4}$ , where $p_{D}^{2}$ is the price at Retailer D in Scenario 4.2 (cost-efficiency learning), t is the unit transportation cost, δ is the additional brand value associated with a positive COO effect, and tx₄ is the total transportation cost incurred by the consumer to travel to the DMF from the consumer’s current location. The same consumer will enjoy a utility of $U (x_{4}) = V - δ - p_{E 4}^{2} - t (1 - x_{4})$ if the consumer chooses to shop at Retailer E instead, where the negative valuation (δ) arises from the negative COO effects associated with a product from an EMF. Employing the same solution process that is outlined in Scenario 1, the Bertrand–Nash equilibrium prices that are charged by each retailer are given by

p_{E 4}^{2} = t - \frac{2 δ}{3} {, p}_{D 4}^{2} = t + \frac{2 δ}{3} .

(22)

In Scenario 4.2, we consider savings related to cost efficiencies for both firms. Whereas a DMF also has cost efficiencies (resulting in savings), we assume that the EMF has even lower costs (which translates to higher savings, S) compared with the DMF’s savings (s), when operating in the same market. The profits for the two retailers are as follows:

π_{E 4}^{2} = S + \frac{{(t - (2 δ / 3))}^{2}}{2 t} {, π}_{D 4}^{2} = s + \frac{{(t + (2 δ / 3))}^{2}}{2 t} .

(23)

We now state the results from Scenario 4.2 in the following lemma:

Lemma 4:

When an EMF has a higher level of savings accrued from cost efficiencies (than a DMF), the profits of the EMF can be higher (than those of the DMF) and offset the additional (negative) brand value associated with the COO effects.

When comparing the two types of learning strategies, in contrast to learning about enhancing value for consumers (Scenario 4.1), lowering the costs for the retailer (Scenario 4.2) would increase its profits without any substantive effect on consumer demand and the retailer's pricing strategy. Whereas learning that enhances the value the customer receives could then be captured back through higher prices, eventually resulting in higher profits (Scenario 4.1), learning to improve cost efficiency does not directly impact consumer demand (Scenario 4.2). Instead, in the second case, the increase in the firm's profits occurs as a result of lowering the associated costs of doing business in Scenario 4.2. Therefore, the EMF needs to strategically think through what it wants to accomplish when choosing a learning strategy.

While both types of learning will provide the EMF with a way to generate higher profits in the presence of negative COO effects, investing in value-enhancing services (as opposed to cost reductions) provides an additional benefit of improving customer brand perceptions, which would work to offset the negative brand image from the COO effects. In the literature, Akhter and Barcellos (2013) explore Brazilian domestic industrial company strategies in the context of internationalization strategies at the end of the 20th century and into the 21st. They find that to be able to compete in the new environment, these companies had to focus on quality improvement (aka value enhancement) rather than just reducing costs. Parnell (2011) find that Mexican companies performed better when they focused on innovation and product differentiation rather than cost minimization. However, in Batista et al.’s (2016) study of the competitiveness of Brazilian firms in the textile sector in the post-financial-crisis era, that is, after 2010, the authors find that companies succeeded by first getting their costs down and then by differentiating their products through offering superior quality. Therefore, this strategic choice of value enhancement versus cost reduction is an important and relevant decision parameter for managers and depends on each firm's unique situation.

Discussion

The international marketing and business research on international expansion of firms has focused on EMFs. While this stream of research has generated valuable insight into the international expansion of EMFs, most of these studies do not provide prescriptive marketing suggestions, such as setting prices in the presence of challenges EMFs face (e.g., negative COO effects) using normative theory (Li, Yi, and Cui 2017; Malik and Kotabe 2009). To address this gap, we develop an analytical model in the retailing sector to generate insights and prescriptive suggestions for EMFs and DMFs, providing them optimal pricing strategies when they enter foreign markets by considering COO effects and organizational learning.

Research Implications

International expansion of EMFs and DMFs: COO effects and learning benefits

The existing body of research regarding the international expansion of EMFs primarily revolves around conceptual discussions elucidating the comparative advantages and disadvantages of EMFs vis-à-vis DMFs or, alternatively, involves empirical comparisons between the performances of EMFs and DMFs (Cuervo-Cazurra and Genc 2008; Ozkan et al. 2022). Within this research trajectory, deliberation on the risks and the rewards that senior managers contemplate prior to market entry is prevalent; however, a quantitative analysis of the circumstances under which EMFs could outperform DMFs in a new foreign market is notably absent. Our endeavor is to model the distinction between EMFs and DMFs by simultaneously incorporating factors such as the nature of competition, the impact of COO, and the accrual of learning advantages.

Our central finding underscores that EMFs can potentially surpass DMFs in profitability if they achieve superior learning advantages, even in the presence of the adverse COO effects that EMFs encounter. Moreover, when competing within a market alongside DMFs, a strategic emphasis on learning benefits associated with the creation and provision of value-added services, rather than an exclusive focus on cost-efficiency-related learning benefits, empowers EMFs to mitigate negative COO effects and foster a more positive customer experience. A noteworthy implication for future empirical research is that researchers should consider the relative impact of COO effects and learning benefits under various competitive scenarios when studying the profitability of EMFs in new foreign markets.

Strategic learning of EMFs

In the international marketing and business research, scholars have emphasized the importance of learning to the performance of firms (Banerjee, Prabhu, and Chandy 2015; Gubbi et al. 2010). Researchers have investigated various aspects of learning in international markets, such as the importance of prior experience, the utilization of newly obtained knowledge from international markets on firm performance, and the type of learning, such as exploratory versus exploitative learning (Nelaeva and Nilssen 2022; Tang et al. 2020). With respect to the learning of EMFs, the research on the EMFs’ learning to change their strategy from being a low-cost provider to being a differentiated player is sparse. Prior research has investigated issues related to the acquisitions performed by EMFs in more advanced markets primarily motivated by their need to build their intangible asset bases (Dau 2013; Zhou, Xie, and Wang 2016). For example, Ülker, a manufacturer of chocolate and confectionary products based in Turkey, acquired the Godiva brand to compete effectively in global markets. While this is a viable strategy for some EMFs, it is not always possible for EMFs to find suitable brands or even have enough resources to acquire established brand names as a solution to the COO challenge. Therefore, it is important to study the factors that enable EMFs to learn how to move from a low-cost focus to a value-added focus with differentiated offerings. As our model suggests, such learning can enable EMFs to overcome negative COO effects by creating additional brand equity in international markets.

Research avenues for international marketing researchers

Our findings provide several future research avenues for international marketing researchers. First, we study various scenarios that generate optimal pricing strategies for EMFs under different conditions of competition, COO, and organizational learning. International marketing scholars could empirically study the positioning strategies of EMF brands with respect to price and quality. In prior international marketing research, scholars have discussed the experience of EMFs that implement various positioning strategies, such as starting at a low price point and then climbing the price and quality ladder in the host market (Ayden et al. 2021; Kumar and Steenkamp 2013). However, there is dearth of empirical research on the positioning strategies of EMF brands. Empirical international marketing researchers could study the following questions, which would enrich our understanding of the positioning strategies of EMF brands at the market entry phase and the evolution of this strategy over time: (1) Does the performance of EMF brands vary according to their positioning strategies at market entry? (2) Under which conditions (e.g., are they strong/weak local brands in the host market) do various positioning strategies lead to better performance outcomes for EMF brands? (3) Does the performance of EMF brands in the host market improve over time? (4) Is the improvement in the performance of EMF brands associated with changes in the positioning strategy of EMF brands? These questions could be studied in consumer packaged goods categories because of the availability of brand-level data in different countries.

Second, as an extension of our research, international marketing researchers could study consumers’ updating of their evaluations of EMF brands. While the negative COO effects that EMF brands experience are well documented (Verlegh and Steenkamp 1999), there is limited research on consumers’ updating of their perceptions of EMF brands. As EMFs learn to offer more value-added services or higher quality products, it is important to understand the conditions under which consumers update their evaluations of EMF brands. For example, according to a study conducted by the Japanese agency Hakuhodo (2008), consumers lagged behind with respect to the ratings they gave to South Korean consumer electronics and car brands while these brands received high ratings from independent testing agencies. Thus, the study of the following research questions would enhance our understanding of consumers’ evaluations of EMF brands: (1) When an EMF brand is initially positioned at the lower end of the price continuum in a product category, which strategies (e.g., rebranding, certification in the host country) help improve the brand's image once it offers value-added services? (2) Under which conditions (e.g., product category involvement) do consumers update their evaluation of an EMF brand in parallel to improvements in the brand’s offerings? Do the updates of consumers’ brand evaluations match the EMF brand's performance in the market? (3) Are there systematic differences in consumers’ updating of their brand evaluations for DMF brands and EMF brands?

Managerial Implications

Our research offers several actionable managerial implications. First, our findings furnish EMFs with valuable guidance concerning the strategic allocation of their learning efforts, that is, whether to improve cost efficiency or to enhance value-added offerings. The decision to prioritize cost-efficiency learning can yield elevated profits, particularly if the magnitude of cost savings (S) is substantial. However, it is crucial to recollect that EMFs in the early phase of internationalization grapple with the challenge of negative COO effects, which could potentially impede their long-term global competitiveness unless they undertake significant investments in brand enhancement. Alternatively, channeling learning efforts toward value enhancement for consumers empowers EMFs to cultivate a favorable brand image through the provision of higher service quality.

Under what circumstances, then, might the pursuit of cost-efficiency learning be the preferred strategy over the emphasis on value enhancement for EMFs? We find that this would be beneficial if an EMF enters a market in which the firm does not face a negative COO effect (i.e., a much less developed country compared with the EMF's home country with only local retailers and no DMF presence). Alternatively, should the firm's strategic emphasis revolve around streamlining its supply chain operations, rather than augmenting consumer services, it could be judicious for the firm to prioritize cost-saving measures as its preferred approach.

Second, managers can use our modeling framework to undertake “what-if” analyses related to the assessment of profitability in new foreign markets by using different levels of COO effect, rate of learning, and stock of international knowledge under various competitive scenarios (e.g., with DMF presence). This is possible because the model considers COO and learning benefits in a competitive environment. Therefore, for example, by changing the level of COO effects or learning benefits, managers could identify the optimal pricing strategy to maximize their profits in new markets.

As an example of such a “what-if” analysis, in Scenario 3, managers could “assign” a value to the COO effects (δ), with possible values ranging from 0 (no COO effects) to 1 (extremely high COO effects) depending on the particular host market the EMF is planning to enter. This would provide the managers a specific optimal price to set, depending on the value of the COO effects (i.e., the δ term, $δ \in (0, 1)$ ). As presented in Table 4, the prices in Scenario 3 would be equal to p_E3 = t − (2/3) and p_D3 = t + (2/3) if the value of δ is equal to 1 (extremely high COO effects in the host market). However, the prices, demand, and profits in Scenario 3 would change to the values in Scenario 1 if the value of δ is equal to 0 (extremely low COO effects in the host market).

This form of “what-if” examination is applicable to other variables in the model as well, including the assessed learning value (e.g., magnitude of the learning gap, denoted as (b + R) − (B + r)) and the anticipated price sensitivity in the host market (i.e., t $\in (0, 1)$ ). For instance, in Table 5 we assume values for all three parameters to illustrate how optimal results from the model may be determined. We hold the values of δ and t constant at 1/2 and determine what the optimal prices, demand, and profits should be when there is a high versus low knowledge gap between the EMF and the DMF. These tables distinctly illustrate the value of “what-if” analyses for managerial decision making. They offer a straightforward means to ascertain optimal pricing within a market, while concurrently considering factors of competition, learning, and COO effects.

Table 5.

Optimal Prices, Demand and Profits When Knowledge Gap Is High Versus Low, t = (1/2), δ = (1/2).

Scenario	Prices	Demand	Profits
High Knowledge Gap (EMF’s Learning Is Five Times That of DMF)
4.1. EMF providing a higher level of value-enhancing services to the consumer than a DMF	$p_{E 4}^{1} = \frac{1}{6} + \frac{4}{3} (B + r),$ $p_{D 4}^{1} = \frac{5}{6} - \frac{4}{3} (B + r)$	$x_{D 4}^{1} = \frac{5}{6} - \frac{4}{3} (B + r),$ $x_{E 4}^{1} = \frac{1}{6} + \frac{4}{3} (B + r)$	$π_{E 4}^{1} = \frac{1}{36} (1 + 8 (B + r))^{2},$ $π_{D 4}^{1} = \frac{1}{36} (5 - 8 (B + r))^{2}$
Low Knowledge Gap (EMF’s Learning Is Two Times That of DMF)
4.1. EMF providing a higher level of value-enhancing services to the consumer than a DMF	$p_{E 4}^{1} = \frac{1}{6} + \frac{1}{3} (B + r),$ $p_{D 4}^{1} = \frac{5}{6} - \frac{1}{3} (B + r)$	$x_{D 4}^{1} = \frac{5}{6} - \frac{1}{3} (B + r),$ $x_{E 4}^{1} = \frac{1}{6} + \frac{1}{3} (B + r)$	$π_{E 4}^{1} = \frac{1}{36} (1 + 2 (B + r))^{2},$ $π_{D 4}^{1} = \frac{1}{36} (5 - 2 (B + r))^{2}$

Limitations and Future Research Directions

We develop EMFs’ learning process based on the literature on organizational learning and firm strategy (March 1991; Mittal et al. 2005). We assume that EMFs have greater motivation and ability to learn compared with DMFs in order to catch up with them (Banerjee, Prabhu, and Chandy 2015). In the longer term, as the firms accumulate knowledge in international markets, the returns on learning may diminish. However, we have not identified any prior research that provides evidence on the difference in decay rates of learning benefits for EMFs and DMFs. Future research could investigate whether the benefits of learning diminish differently for EMFs and DMFs. For some EMFs, the learning benefits related to cost efficiencies might diminish at a higher rate than the learning benefits related to enhancing value to consumers because EMFs, on average, have greater experience with achieving cost efficiencies in their home markets than with delivering greater value to consumers (Cuervo-Cazurra and Genc 2008). Therefore, international researchers could study whether the domain of the initial stock of knowledge of EMFs (e.g., cost-efficiency experience) could impact the returns on learning for EMFs in new foreign markets.

To isolate the impact of COO and learning effects, we assume the same product type is sold across the two retailers (i.e., horizontal differentiation model). As an extension of our model, future research could build a model with quality differentiation between products (vertical differential model). Such a model would be useful in studying the international launch of products with different versions and quality in emerging and developed markets.

Finally, whereas we consider a two-player game, with any two firms competing in the market, future research could examine the implication of having a third player in the model. For instance, we could determine optimal pricing strategies when there is a local competitor, an EMF, and a DMF all operating simultaneously in the market. Considering the actions of multiple players could provide interesting insights into profits and demands for these firms in the presence of COO effects and learning.

Footnotes

Appendix

Author Contributions

The authors contributed equally.

Special Issue Editors

Rajeev Batra, Kelly Hewett, Ayşegül Özsomer, and Jan-Benedict E.M. Steenkamp

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

S. Cem Bahadir

Notes

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