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J. of the Acad. Mark. Sci. (2018) 46:147–167 DOI 10.1007/s11747-017-0559-0

ORIGINAL EMPIRICAL RESEARCH

The impact of retail format diversification on retailers’ financial performance Yuying Shi 1 & Jeremy M. Lim 2 & Barton A. Weitz 2 & Stephen L. France 3

Received: 31 May 2016 / Accepted: 4 July 2017 / Published online: 22 July 2017 # Academy of Marketing Science 2017

Abstract For retailers, format portfolio management is a core marketing operation, but has received little attention in the marketing literature. This study analyzes the relationship between format diversification and retailer performance in a global setting, where retailers as part of their geographic expansion process often employ format diversification. The dual strategies of geographic diversification and format diversification substantially complicate the diversification-performance relationship. Using a six year panel data set for leading global retailers, we find a positive impact for geographic diversification, a negative impact for format diversification and a negative interaction for the dual strategies, supporting a single focus diversification strategy. We further show the consistency of our findings using a series of model robustness checks.

Shrihari Sridhar served as Area Editor for this article. * Yuying Shi [email protected] Jeremy M. Lim [email protected] Barton A. Weitz [email protected] Stephen L. France [email protected] 1

Department of Marketing and Business Analytics, Texas A & M University-Commerce, Commerce, TX 75428, USA

2

Warrington College of Business Administration, University of Florida, Gainesville, FL 32611, USA

3

School of Business, Mississippi State University, Mississippi State, MS 39762, USA

Keywords Retail format . Financial performance . Tobin’s Q . Diversification portfolio . Dynamic panel model

Introduction For a retailer, expanding into a new market is not an easy proposition. International expansion is fraught with difficulties, both seen and unseen. For example, the UK retailer Tesco expanded into the U.S. using a convenience store format called Fresh & Easy. Tesco utilized small neighborhood stores in walkable neighborhoods, but this format did not entice U.S. consumers accustomed to bulk shopping in larger stores. After losing $1.6 billion dollars (Sonne and Evans 2012), Tesco withdrew from the U.S. market in 2013. Conversely, the American retailer T.J. Maxx has been very successful in exploiting international opportunities. T.J. Maxx’s Boff-price^ format strategy of selling aspirational fashion at low prices, combined with functional midsized stores, has tapped into a global segment of aspirational but price-conscious consumers (Loeb 2016). In both of the above examples, the success or failure of the retailer’s strategy depended on both its format strategy and its geographic internationalization strategy. In fact, most global retailers face strategic decisions at the intersection of these two areas. The major purpose of this paper is to provide a comprehensive examination of how the interplay of format and geographic strategies affects retailer financial performance. Format diversification refers to the variety of formats that retailers utilize to offer goods or services to their target market(s). Launching a new format enables retailers to target different consumer segments (Gauri et al. 2008; Gielens and Dekimpe 2007; GonzálezBenito et al. 2005). For example, Walmart in Mexico operates the Sam’s Club, Supercenter, and Supermarket formats for more affluent consumers, and its convenience store format called Bodega Aurrera and the Mini Bodega format for less affluent

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consumers (Corstjens and Lal 2012). However, the operation of each format requires a different focus on core skills. Expertise in fashion and merchandise budget planning is a key performance driver for retailers operating apparel specialty stores, while expertise in supply chain management and cost control is a performance driver for hypermarket retailers. Each area of expertise requires specialized know-how, and it may take a retailer a long time to develop and realize its core competency (Teece et al. 1997). In adopting a new format, retailers frequently have to resort to experiences from other specialized firms. For example, in their expansion to China, Walmart acquired an interest in Trust-Mart, a Chinese hypermarket retailer, to help develop the hypermarket format, a format rarely used by Walmart in its home country. From a marketing perspective, Bformat^ is essentially a problem of consumer choice. The literature on retail format strategy includes extensive studies on how elements of retail format, such as location, assortment level, and product categories affect consumer choice (e.g., Bhatnagar and Ratchford 2004; Ganesh et al. 2007; Waller et al. 2010). While these studies provide information regarding individual consumers’ choices, which would be ultimately reflected in firms’ financial performance, few studies explore the impact of retail format portfolio management on financial performance at the firm level. This gap in the literature can most likely be attributed to the difficulty of operating a portfolio of formats. As a result, only large retailers usually have the resources to operate a portfolio of different formats. Even for leading retailers, of those who operate in a single domestic market, around 70% will still stick to one dominant format (Deloitte 2015). As most local retailers cannot afford to operate a large number of formats, the question of format diversification in domestic markets has not gained enough attention in the literature. The situation is different in a global context. Internationalization has become a major revenue source for retailers. More than 65% of leading retailers have expanded to foreign countries (Deloitte 2015). As the retail business is by nature highly sensitive to local institutional norms, keeping the original retail format without any adaptation in a new market is quite rare, especially when expanding to developing countries (Akehurst and Alexander 2013; Goldman 2001; Rosenbloom et al. 1997). In fact, the failure of an international expansion plan is often related to format (Goldman 2001). For example, the hypermarket retailer Carrefour failed in the U.S., as its traditionally employed hypermarket format was too large and too overwhelming for American customers (Keegan and Green 2005). According to Deloitte’s Global Powers of Retailing report, more than 70% of global retailers operate between two and eight formats. With current trends toward both globalization and retail format diversification, there is an imperative need for work on the impact of different retail format portfolio management strategies on retailer performance. The two diversification strategies involve different benefits and operational risks. Hence, the major questions addressed in this

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research are: How do the dual diversification strategies affect the financial performance of retailers? Do certain combinations of markets and format portfolios yield higher financial performance than others? We address these questions by compiling a comprehensive panel dataset, comprised of a six-year record for 172 global leading retailers and supplemented with individual country- and firm-level data from a variety of other sources. We analyze the data utilizing a dynamic panel model and conduct a series of model robustness tests. Our study makes the following major contributions: First, to our knowledge, this study is among the first to investigate the direct effect of global format portfolio management on retailers’ financial performance over time. The dual strategies of format diversification and geographic diversification are analogous to operating different business models and make the retailers’ internationalization process more complex. Our findings provide empirical evidence supporting a single-focus strategy for retail diversification. Second, we show that geographic diversification benefits retailers, while format diversification is risky and generally has a negative impact on retailers’ performance. Third, despite extensive academic work on firm internationalization, previous studies have mostly dealt with manufacturing firms. Due to significant differences in the operating characteristics of retailers compared to manufacturing firms (Dawson 2007; Wrigley et al. 2005), researchers have questioned the generalizability of the extant manufacturing-dominated research and have called for industry-specific studies (e.g., Assaf et al. 2012; Ekeledo and Sivakumar 1998). Thus, our findings complement the previous internationalization literature.

Conceptual framework and hypotheses development We summarize recent empirical retail diversification literature chronologically in Table 1. The table summarizes the diversification type (geographic, channel, or format), the dataset used for the research, major findings, the firm/format types studied, the outcome, and the expansion markets covered. There is little broad-scope research on retail geographic and format diversification. Most of the studies focus either on one or two format types (e.g., Assaf et al. 2012; Gielens and Dekimpe 2007) or on a few formats in limited markets (e.g., Goldman 2001). Additionally, of the few diversification related studies, several are channel expansion studies (e.g., Geyskens et al. 2002; Homburg et al. 2014). The process of format diversification is more complex than that of channel expansion. For example, apparel or cosmetics retailers can expand their channel through opening an online specialty store on an ecommerce platform, such as Amazon. However, this expansion does not create an entirely new online format. In addition, in the listed papers, there is no study on both geographic diversification and format diversification in a

Format

Channel

Geographic

Geographic

Channel

Goldman (2001)

Geyskens et al. (2002)

Gielens and Dekimpe (2007)

Assaf et al. (2012)

Homburg et al. (2014)

Geographic & Format

Major findings related to retailing diversification

Format transfer strategies are affected by economic dissimilarities and targeted market segments. Emerging market firms grow in Announcement of an developed markets through indirect internet channel learning from market leaders, expansion for 98 competitors, and interfirm networks. newspapers. Expansion data for the top A firm’s decision to enter a country is 75 European grocery both deterred and encouraged by retailers. same format rivals. Expansion data for 43 The relationship between European supermarket internationalization and cost efficient chains. performance is U-shaped. Establishment of a new channel Announcement data on positively influences firm value, but channel expansions for an increase in distribution intensity big firms in three reduces firm value in highly countries. turbulent markets. Mall intercept survey for Hypermarkets are preferred over retailers in Germany and discounters in emerging markets due Poland. to prestige sensitivity. Panel data for the top 172 Format diversification negatively global retailers. moderates the geographic-diversification relationship.

Case study of 27 retailers expanding into China.

Data

Tobin’s Q

JAMS

Worldwide

JM

All formats

None

JR

Germany and Poland JAMS

Abnormal stock returns

All types of firms

Europe and the U.S.

Central and Eastern JM European countries

Discounters and hypermarkets Store preference

Cost performance

Entry speed and entry size

JM

Stock market return None

Journal

JR

Expanded market

China

Format change decision

Outcome

Supermarket

Grocery

Six formats including supermarket and apparel specialty. Online retailing

Firm type

Homburg et al. (2014) include all types of firms. Journal Abbreviation: Journal of Marketing (JM), Journal of the Academy of Marketing Science (JAMS) and Journal of Retailing (JR)

This study (2017)

Zielke and Komor (2015) Format

Diversification

Selected studies on retailing diversification

Author

Table 1

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synthesize this information and discuss how these theories inform the interplay between costs and benefits in a retail context. These insights are used to develop our conceptual framework.

global context. Whether the dual strategies should be employed extensively or with caution is unknown. We present our conceptual framework in Fig. 1. This framework posits that a retailer’s financial performance is affected by its level of geographic diversification and level of format diversification. The format types within the retailer’s portfolio affect this diversification–performance relationship. This relationship is also inevitably affected by the retailers’ international market portfolio characteristics (dissimilarity), domestic country characteristics, and the retailer’s own characteristics. The dashed feedback paths in the framework represent a feedback loop in the retailer’s decision making process. The retailer’s financial performance affects its diversification decisions, while diversification affects future financial performance.

Diversification benefits Expansion to other countries brings firms many exploration and exploitation benefits. According to the resource-based view, diversified retailers can have greater access to both internal and external sources of financing (Lang and Stulz 1994; Kozlenkova et al. 2014), and this expanded access provides diversified retailers with privileged access to less costly financing (Froot et al. 1994; Peteraf and Barney 2003). In addition, diversification can lower costs by enabling arbitrage of differences across input and output markets (Peteraf and Barney 2003). According to FDI theory, global expansion brings economies of scale. By expanding operations to new markets, a retailer can reduce risks by spreading the effects of nonsystematic fluctuations over more business units (Palich et al. 2000) and can spread its fixed costs across multiple markets (Berry 2013; Caves 1996). This risk reduction further produces a feedback effect affording retailers the opportunity to better realize external

Geographic diversification The literature on the impact of geographic diversification is extensive. We will discuss the benefits and costs of diversification, utilizing several international marketing and management theories given in the retailing context. We then

Fig. 1 Conceptual framework of the diversification and performance relationship Format Type Price-oriented retailer High-end retailer Hypermarket Other formats

Geographic Diversification

Retailers’ Financial Performance

H1 H3 H2

Format Diversification

Control Variables: Retailer Portfolio Characteristics • •

Economic dissimilarity Cultural dissimilarity

Home Country Characteristics: • • •

Urban Population Prop. Population Density Economic Size

Firm Level Characteristics: • • •

Sales Competition Brand

(Tobin’s Q)

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financing terms and to improve borrowing ability (Goetz et al. 2016; Kuppuswamy and Villalonga 2015). Organizational learning theory offers another perspective. This theory posits that a firm’s knowledge is a basic asset and that global expansion creates important learning opportunities (Ruigrok and Wagner 2003). The benefits of globalization can be seen in terms of gained knowledge and the ability to spread and apply this knowledge (Barkema and Vermeulen 1998; Kogut and Zander 1993; Ruigrok and Wagner 2003). Given greater variations across international markets than in the relatively standardized domestic markets and large consumer and economic differences across international markets, expansion to other counties creates more opportunities for organizational learning. The enhanced knowledge base and the ability to effectively use it has a positive influence on future performance. Diversification costs Diversification incurs two types of costs: internal governance costs and external challenge costs. The internal costs come from the inefficiencies brought about by the expanded organizational structure. Transaction cost theory suggests that expansion to other markets increases governmental and transaction costs (e.g., Erramilli and Rao 1993; Johanson and Vahlne 2009; Steenkamp and Geyskens 2012). As retailers diversify, their management hierarchy expands and they encounter greater difficulties in disseminating information to business units, resulting in transmission and coordination inefficiencies and the loss or distortion of information (e.g., Contractor et al. 2003; Gomes and Ramaswamy 1999). Furthermore, the large hierarchical structure of diversified retailers is conducive to employee shirking, thereby further incurring costs associated with either heightened monitoring or decreased labor productivity (Garicano 2000). Agency theory (e.g., Hoenen and Kostova 2015; O’Donnell 2000) discusses the incongruences between a principal and the delegated agency in their respective interests. Specifically, managers have an incentive to over-diversify, for their compensation is often tied to the revenues they generate (e.g., Bergstresser and Philippon 2006; Jensen and Murphy 1990) and their promotion opportunities can be enhanced due to new positions created by revenue growth. This tendency to over-diversify is further exacerbated in mature firms with substantial Bfree cash flow^ (Jensen 1986; Rajan et al. 2000). In contrast to internal costs, external costs come from inefficiencies from entering a new market (e.g., Johanson and Vahlne 2009; Lu and Beamish 2004). These inefficiencies result from a lack of market reputation, limited knowledge about the new environment, and increased uncertainty from operating in a complex and unfamiliar environment (e.g., Barkema et al. 1996) characterized by a different set of economic, political, and legal factors (e.g., Carpenter and Fredrickson 2001). Even with extensive internationalization studies, the relationship between diversification and performance is still unclear. Findings are mixed on whether the relationship is

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positive or negative (Hitt et al. 2006). It is generally agreed that the relative magnitude of the various costs and benefits determines this relationship and that the magnitude of relative costs and benefits varies across firms’ different internationalization stages (e.g., Gomes and Ramaswamy 1999; Lu and Beamish 2004). For example, Gomes and Ramaswamy (1999) hypothesize that as firms become more involved in globalization, their diversification costs outweigh benefits due to a failure to adjust existing processes and structures to the global environment. Thus a firm’s performance increases up to a certain level of diversification and declines thereafter. However, Assaf et al. (2012) argue that as the globalization process proceeds further, retailers can improve their performance through organizational learning. They learn to adjust their operational systems to a global environment and can achieve economies of scale with further expansion. We lean on the side of the positive argument. Leading retailers have been operating globally for decades. It is expected that these already strongly internationalized retailers have gained enough knowledge in dealing with internal governance costs and external newness costs. At a certain stage of expansion, their accumulated resources can help them to sustain any temporary losses. In addition, they are more likely than other less internationalized retailers to enjoy the benefits arising from economies of scale. Retailers in their initial expansion stage, usually take a conservative approach by expanding to markets in which they feel comfortable, usually in countries that share similar economic status or cultural values (e.g., Assaf et al. 2012; Zielke and Komor 2015). A European retailer is more likely to expand to nearby European countries. A U.S. firm will often expand to Canada first. For example, Carrefour started its internationalization in Belgium, while Home Depot and Target started in Canada. When retailers operate in a limited set of markets, these markets mostly fit their competencies. In this case, the governance and coordination costs may not be a central concern, for the organizational structure is still relatively streamlined. Retailers can enjoy the benefits of expansion, while having sufficient slack to manage the governance issues in a limited set of markets. Furthermore, given technological advances and changes in consumer behavior, expansion to new markets is easier than in the past. Firms have access to more actionable information regarding the global business environment (Leidner 2010) and there have been far-reaching advances in global sourcing (Ganesan et al. 2009). While cultural differences between markets still exist, there has been a convergence of consumer preferences at the macro/country level (De Mooij and Hofstede 2002). Thus, we expect to see a positive relationship between geographic diversification and retailer financial performance. In line with the above argument, we propose that: H1: Retailer financial performance is positively affected by geographic diversification.

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Retail format diversification In contrast to geographic diversification, where the major revenue source comes from new geographic markets, format diversification in a market mainly serves to generate revenues from new customer segments. In addition, operating diversified formats can help retailers to increase market power. Larger, more diversified retailers can negotiate better terms with suppliers and can even demand exclusive relationships. They can engage in predatory pricing against competitors by threatening to respond with a price war (Berger and Ofek 1995) or can deter competitors’ entry into a new market (Gielens and Dekimpe 2007). However, a high level of format portfolio diversification entails substantial learning costs, increased fragmentation in the customer base, and high management costs. Distinct retail formats target distinct customer segments and thus require different skills and resources. For example, apparel specialty stores typically target fashion-oriented customers. Operating this type of store requires skills such as brand building, merchandise selection, pricing, store design, and visual merchandising. On the other hand, discount stores typically target value conscious customers. The management of this format requires skills that are related to cost control, such as supply chain management. However, format specific knowledge is not directly applicable across distinct formats. In addition, operating a different format is analogous to targeting a different customer segment. According to Tanriverdi and Venkatraman (2005), units Bwith dissimilar customer needs and behaviors have minimal opportunity to exploit crossbusiness customer knowledge synergies.^ The literature is replete with studies where mixing customer knowledge across unrelated units has resulted in decreased firm performance (Ramaswamy 1992). A high level of format portfolio diversification also substantially increases management costs. Compared with manufacturing firms, retailers typically interact with many more consumers and suppliers. Even a relatively small retail chain can operate stores in over five hundred locations, deal with a thousand suppliers, and sell to hundreds of thousands of customers (Alexander and Myers 2000). Due to the complexity of retail operations, launching a new format involves interactions with many different new customers and new suppliers, adopting new systems and implementing new promotional strategies, all of which substantially increase the complexity of management processes. In summary, although format diversification can bring new revenue sources and market power, the associated learning costs, increased fragmentation in the customer base, and high management costs are all risk factors that may counteract the positive effects of diversification. Hence a high level of format portfolio diversification will negatively impact a retailer’s performance, which may also be the

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reason that most leading retailers still stick to one dominant format. Thus, we propose: H2: Retailer financial performance is negatively affected by format diversification. Coupling format diversification with geographic diversification makes the diversification process more complex. The internal governance costs are further increased due to the operation of an expanded network that crosses country boundaries. The increased distribution intensity (increased stores/ employees) is found to decrease firms’ profitability due to added management complexity (Homburg et al. 2014). In addition to the increased management costs, the newness of foreign markets creates additional barriers. For example, store location is critical for the development of retailers. However, securing retail locations in foreign environments can be problematic for new entrants. Premium locations often are already taken by local competitors, leaving new entrants less optimal alternatives. In addition, sometimes local geography can restrict the development of a certain format. For instance, while Carrefour was successful in Mainland China with its hypermarket format, it had to exit Hong Kong because the small, densely populated territory could accommodate very few of its giant stores. Another issue in a new market is related to customer loyalty. Customer loyalty reflects a retailer’s competitive advantage from its reputation and success with its current customers. In its domestic market, when a retailer launches a new format without prior experience, it can at least rely on the loyalty of existing customers (Levy and Weitz 2011). In a new market, not only does such advantage disappear, but the formation of a loyal customer base may also take more time. To establish customer loyalty, a retailer has to tailor its products, services, and store environment to local customers (Srinivasan et al. 2002). The unfamiliarity with local norms due to the newness of the market can lessen the effectiveness of retailers’ marketing plans and weaken the opportunity to attract a new customer segment. Hence, with both increased internal governance costs and increased external costs coming from entering a new market, we propose: H3: The relationship between geographic diversification and retailer financial performance is negatively moderated by format diversification.

Data and methodology Data sources and data collection The sample of retailers used in this study consists of the world’s largest global retailers, as identified in the annual

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report Global Powers of Retailing provided by Deloitte Touche Tohmatsu Limited (DTTL). This report is published by DTTL annually in conjunction with STORES media and is considered an authority in the retailing industry. These reports provide data on the 250 largest retailers from 2002 to 2007. From 2008 through 2015, Deloitte stopped providing detailed format type information. Based on data availability, we build our model using data from a stable period, i.e., the period after the Asian financial crisis in 1998 and before the U.S.-originated financial crisis in 2008. We then provide descriptive statistics of Tobin’s Q and percentage of global retailers in the last six years (2010–2015) and discuss whether the pattern is consistent after the crisis. Some retailers in the Deloitte reports could not be included because market-based financial measures of firm performance were not available (i.e., they were not publicly traded or were part of larger diversified/holding firms). In addition, we also eliminated retailers that were either automobile, food (e.g., McDonalds), or hotel franchisors or franchisees, because these firms are not classified under the retail category by Standard Industrial Classification (SIC) or North American Industry Classification Standards (NAICS) due to different operating characteristics (Levy and Weitz 2011). We also eliminated retailers that had less than three years of records. These restrictions, along with firms entering and exiting the lists, resulted in a dataset composed of 763 annual observations for 172 retail firms for a six-year period. Diversification measures We used the diversification measure from Powell et al. (1996) in the context of format diversification. This measure describes the format diversification level (FormatDiv it ) for firm i at year t as follows: J

2 FormatDiv it ¼ 1− ∑ pijt ; j¼1

where j represents the jth format type and J is the aggregate format type up to year t. pijt = nijt/Nitrepresents the proportion of format type j out of total format types J for firm i at year t, where nijt is the number of format types at year t and Nit is the cumulative number of total formats in firm i’s format portfolio at year t. For example, if a firm up to year t has adopted 4 different types of formats, then the diversification measure at year t is equal to1 − 4∗(1/4) ^ 2 = . 75. The diversity measure is a continuous variable with a range [0, 1–1/J], with zero indicating only one format. This measure is equivalent to Blau’s index of heterogeneity (Blau 1977) and is essentially the same as the Hirschman-Herfindahl index (HHI) used in economics in the calculation of market concentration. The country diversification measure is calculated similarly.

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Financial performance We analyzed public companies so that we could utilize capital market–based financial performance metrics to measure retailers’ performance. Tobin’s Q is our focal measure. Tobin’s Q is the ratio of a public firm’s market capitalization to the cost of replicating its firm’s assets. Although many marketing studies employ accounting measures such as sales growth or cost index, the capital market based measure is more appropriate for our diversification study. Accounting measures may reflect the impact of past investment on current earnings, but they do not contain much information about the future value (Anderson et al. 2004). In our retailing context, a retailer’s decision to expand its portfolio (either geographic or format) will have a profound impact on its future market share. In addition, Tobin’s Q requires no risk adjustment when comparing across firms and is not subject to accounting conventions (e.g., Anderson et al. 2004; Germann et al. 2015; Srinivasan and Hanssens 2009). Therefore, Tobin’s Q is a preferred financial performance measure and is considered to be more s u i t a b l e t h a n a c c o un t i ng m e as u r e s f o r s t u dy i n g diversification-performance linkages (Palich et al. 2000). Tobin’s Q was operationalized as a retailer’s market capitalization at the end of the fiscal year divided by its total assets. Our computation follows the formula developed by Chung and Pruitt (1994), which is widely adopted in academia (e.g., Bharadwaj et al. 1999; Fang et al. 2008; Rao et al. 2004; Simon and Sullivan 1993): . Tobin’ s Q ¼ ðMVE þ PS þ DEBT Þ TA; where MVE is the product of closing share price at the end of the financial year and the number of common stocks outstanding, PS is the liquidating value of the firm’s preferred stock, DEBT is equal to the value of the short term liabilities net of the short term assets plus the book value of the firm’s long term debt, and TA is the book value of the firm’s total assets. Format type Following the Deloitte report classification and referencing Levy and Weitz (2011), we identified the following 13 format types: apparel specialty, catalogue or internet retailer, convenience store, department store, drugstore, electronic specialty, hard goods discounter, hypermarket, home improvement, non-apparel/other specialty, soft goods discounter, supermarket, and warehouse club. Each retailer falls into at least one format type category. We dummy coded each format type. The format diversification measure was calculated using the 13 format types. However, the inclusion of the format dummy variables together with additional interaction variables and other control variables was a major concern vis-à-vis power

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reduction and multicollinearity (Baltagi 1995), which will make the coefficient estimates highly sensitive to changes in the data. We thus organized the format types into smaller categories based on their major positioning in the market. The overall classification is summarized in Fig. 2. In categorizing the formats, we grouped them using three criteria: key positioning strategy, product characteristics, and sample size in the dataset. In selecting the positioning strategy and product characteristics, we focused on factors that are likely to be affected by retailing diversification. For example, price, in particular, has always been a key defining element for retailers’ positioning in the market (Tang et al. 2001). Price is particularly relevant in the context of retail diversification, as most expansion markets are emerging markets and the roles of price, prestige sensitivity, and price quality association are different for customers in emerging markets than for customers in developed markets (Zielke and Komor 2015). In line with our criteria, we grouped the 13 format types into four categories (Fig. 2). We next briefly discuss the major characteristics of each group. Price-oriented retailers Discount stores, supermarkets, and warehouse stores represent typical price-oriented retailers. These retailers carry customers’ frequent purchase items and are visited at regular intervals. They aim for a high volume/ low profit margin strategy and use price as a major marketing tool. With intense competition in their domestic markets, these retailers are expanding aggressively globally. Customers in relatively low income developing economies have higher

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price sensitivity than those in developed economies (Zielke and Komor 2015). Given price advantage coupled with moderate quality products, these stores are expected to find their niche in international markets. High-end retailers Department store retailers and apparel/ footwear specialty retailers focus less on price. Much of the merchandise carried by these stores is hedonic (Rintamäki et al. 2006) (e.g., high-end cosmetics) or premium brands. Their key competitive advantage lies in unique merchandise, appealing shopping environments, and experienced sales representatives. With changing preferences toward these features, sales for these retailers in developed markets have stagnated over the past several decades. But these features may be more desirable in developing markets. With the permeation of Western culture into these markets, Western products and stores are perceived to offer high quality and good service (e.g., Gielens and Dekimpe 2007; Zielke and Komor 2015). These retailers may have better performance in global markets. Hypermarket The hypermarket is unique in that this format carries both full-line groceries and general merchandise (e.g., consumer electronics and clothing). As this format carries both low price goods and premium branded products, it is perceived to be more upscale than the discount format, but it is less expensive than the department store and apparel/ footwear specialty store formats (Zielke and Komor 2015). Hence, we list it as a separate category. Although this format is rare in the U.S., it originated in Europe and has spread globally. It is considered modern and is especially appealing to consumers in emerging markets (Hassan et al. 2013). We expect it to have good performance in global markets. Others We grouped the remaining six formats into a category called Bother type.^ These formats generally have a relatively small sample size. The above three categories consist of seven format types, covering 60% of total format types. The remaining six format types each consist of 1% to 7% of the total sample size. Most formats in this category do not fall into the above three categories. For example, the key positioning strategy for the convenience store format is location proximity and speedy checkout. Although it carries daily goods, prices are perceived to be higher than those in supermarkets or discount stores. Effects of the characteristics of the international market portfolio

Fig. 2 Retail format categorization

The cultural and economic dissimilarity of the countries represented in a retailer’s diversification portfolio may impact financial performance. High levels of dissimilarity often impose additional difficulties for retailers’ operations (Leung

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et al. 2005; Craig and Douglas 2006). As most leading retailers originate in developed economies, a larger economic distance generally means a less developed expansion country, which is often characterized by relatively weak financial or legal systems, low per-capital income, and lack of public distribution systems (Reinartz et al. 2011). A high cultural dissimilarity is associated with the inconsistency in values between the home country and the expansion country. This incongruity can lead to many failures, such as operational failure due to different project priorities (Li and Guisinger 1992), communications failure with public authorities (Chua et al. 2009), or inappropriate promotional/advertising messages (Wan et al. 2014). These issues may prevent managers from fully taking advantage of economies of scale and scope compared to managers in more uniform environments (e.g., Brouthers and Brouthers 2001; Evans and Mavondo 2002). Hence, we expect both types of dissimilarity to have a negative impact on retailers’ performance. We used data from the World Bank Development Indicators and Hofstede scores to compute the two distance scores. Detailed descriptions and calculations for the two variables are presented in Appendix.

Other control variables We followed extant research by controlling for a retailer’s home country and firm-level characteristics. A firm’s expansion strategy is affected by its home country population demographics and level of economic development (Assaf et al. 2012; Chan et al. 2011; Keegan and Green 2005). We therefore included the urban population proportion, the population density, and the home country GDP (operationalized as logarithm of GDP per capita). We expect that firms from countries with a large, highly urbanized population will have better financial performance, as they can focus more on domestic growth opportunities, whereas firms from smaller economies will benefit more from geographic diversification because internationalization is more imperative for their growth (Assaf et al. 2012). In addition, we measured a firm’s size using its annual sales and capture the competition effect using the number of same format retailers in a market, following Gielens and Dekimpe (2007). A retailer’s expansion strategy is expected to be positively affected by its brand equity. We captured the brand effect at a firm level by building a brand equity index using data gathered from Google Trends (Du and Kamakura 2012). The calculation of the index is described in Appendix. All control variables vary across time. The means, standard deviations, and correlation matrix for the variables are shown in Table 2. All of the data sources are described in Web Appendix.

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Model specification In this section we discuss several potential models and the associated identifying assumptions. Starting with the simplest model, we specify a retailer i‘s performance (Tobin’s Q) at time t as follows: Qit ¼ β 0 þ DIV i;t−1 * βDIV þ Control it * βCtrl þ εi;t

ð1:1Þ

The DIVis a data matrix that captures the two diversification variables and their interaction variable. To account for the feedback between decision making in the prior period and current performance, we lag all of the diversification variables by one year. The control variables include cultural and economic dissimilarity, home country level, and firm level characteristics, as well as year dummy variables and time effect. All control variables are assumed to be exogenous. This simple model suffers from omitted variable bias. A retailer’s performance can be driven by many factors not included in the model. These factors can affect the diversification decision, resulting in a correlation between an omitted factor and a diversification variable. For instance, a retailer can choose from an array of different organizational structures. Whether the chosen structure is centralized or decentralized affects the retailer’s financial performance and its diversification strategy. A model without these omitted factors can lead to potential endogeneity. A primary motivation for using panel data is to address the omitted variable problem (Wooldridge 2002) and to ensure that the model captures unobserved factors. Hence, we add the unobserved effect δi into the following model: Qit ¼ β 0 þ DIV i;t−1 * βDIV þ Control it * βCtrl þ δi þ εi;t ð1:2Þ The model accounts for the impact of omitted variables, but the endogeneity issue is still present. Estimating this model requires the assumption of strict exogeneity, i.e., each εi , t should be uncorrelated with all of the explanatory variables Xit in all time periods (current, past and future periods). A retailer’s diversification decision and subsequent financial performance consist of a feedback loop, i.e., diversification can improve a firm’s performance, which in turn may generate more resources that result in expansion to new markets and the launch of new formats. This feedback loop results in a correlation between the diversification variables and εi , t, violating the strict exogeneity assumption. A common approach to address the violation of the strict endogeneity assumption is to find instrumental variables (IV) and then to apply two stage least squares (2SLS). The IVs are expected to be correlated with the decision variables, but are uncorrelated with the error term (Davidson and MacKinnon 1993; Wooldridge 2002). Theoretical and empirical rationales drive the selection of IVs. Using variables from peer firms is a commonly accepted approach (e.g., Sridhar et al. 2016;

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Germann et al. 2015). We use the level of geographic expansion and business/operation segment data from peer firms to compute the IVs. We define the peer firms as firms of similar size to our focal firms, but under different SIC codes. We compute the geographic diversification measure and the business/operation segment diversification level (a surrogate measure of format diversification, (Morgan and Rego 2009)) of the peer firms. Since the peer firms are of a similar size to our sample retailers, theoretically we expect them to share similar diversification expectations (Germann et al. 2015). Empirically, the diversification level of peer firms is positively correlated with the diversification level of our focal firms (all with ρ ≥ .15 and p < .5). We also need to establish the exclusion restriction. Conceptually, these IVs are unlikely to relate to the omitted variables in our focal firms. For example, the organizational culture of our focal firms is unobserved. The IVs of a peer firm are unlikely to correlate with this cultural factor because it is difficult for a peer firm to act on a focal firm’s culture, such as to measure, quantify, or even replicate such culture (Germann et al. 2015). Given the above discussion, the simple model suffers from the omitted variable bias and the unobserved effects model requires the employment of IVs to address the endogeneity issue. In addition, our conceptual framework reflects a common assumption that a firm’s past performance affects its current performance. The previous models do not account for this lagged performance. To capture this effect, we include the lagged dependent variable Qi , t − 1into Eq. 1.2. The complete model is specified as follows:

Table 2

FC þ Dis*β DIS þ HC*β HC n þ FC*β k

þ Year*Dummy þ ω*Time þ δi þ εi;t ;

ð1:3Þ

where Qit DIV Inter HC

Tobin's Q for firm i at time t, Diversification measures, is Formant type interaction, Dis is Dissimilarity, is Home country characteristics, FC is Firm characteristics, Diversification measures = (ci,t-1, fi,t-1, ci,t-1*fi,t-1), is Geographic diversification, f is Format diversification,  c F 0 β0 ; β0 ; γ ;

DIVi,t-1 c βDIV =

(Pricefocusit, Highendit, Hyperit, Othersit),

Inter = βInt m

Int Int is an m×1 column vector with βInt m ¼ β m0 þ β m1 cit ; m ¼ 1; 2; …4; Dissimilarity measures=(Culture, Economic),  Dis Dis  0 ; βC ; βE

Dis βDIS = HC = βHC n FC = βFC k Year = Dummy

*

(Urbanit , Denseit , GDPit ), is an n×1 column vector with β HC n , n=1, 2, 3, (Salesit , Competitionit , Brand), is a k×1 column vector with β FC k , k=1, 2, 3, (year2, year3, year4, year5, year6), is a 5×1 column vector with Dt, t=1, 2,…5;

Means, standard deviations, and correlations of variables Mean

1 2 3 4 5 6 7 8 9 10 11 12 13

Qit ¼ β0 þ Qi;t−1 *α þ DIV i;t−1 *βDIV þ Inter*βInt m

Tobin’s Q Div_Country Div_Format Price Oriented High End Hypermarket Other formats Econ_Dis. Culture_Dis Urban. Prop. Pop. Density Econ. Size Sales (Billion)

14 Competition 15 Brand

SD.

1

2

3

4

5

6

7

8

9

10

11

12

13

1.00 0.05 0.52 0.11 0.06 0.03 −0.11 0.16 −0.19 0.16

1.00 0.08 −0.13 0.14 0.18 −0.18 −0.01 0.03 −0.05

1.00 0.31 0.12 0.19 −0.11 0.17 −0.31 0.23

1.00 0.03 0.09 0.11 0.10 −0.20 0.24

1.00 0.43 −0.03 0.11 0.06 −0.03

1.00 −0.12 0.11 0.01 0.08

1.00 0.23 1.00 0.15 0.02 1.00 0.09 −0.05 0.10 1.00

14

15

1.01

0.85

1.00

0.433 0.36 0.07 0.39 0.25 0.49 5.25 0.71 77.03 204.44 10.18 11.8

0.4 0.34 0.26 0.49 0.43 0.5 20.88 0.92 8.76 642.45 0.51 22.7

0.03 −0.26 −0.20 −0.18 −0.17 −0.07 −0.13 −0.11 0.24 −0.07 −0.02 0.12

1.00 0.18 −0.07 0.06 0.07 0.20 0.20 0.64 0.10 0.15 −0.07 0.17

1.00 0.52 0.37 0.63 0.47 0.14 0.21 −0.05 0.18 −0.32 0.19

12.12 32.09

9.98 23.27

0.04 0.30

0.02 0.02

−0.03 0.01 −0.03 −0.03 −0.01 −0.01 −0.01 0.10 −0.01 0.05 0.11 1.00 −0.23 −0.37 −0.04 −0.20 0.02 −0.10 −0.05 0.21 −0.19 0.30 0.33 0.08 1.00

Bold number indicates significance level at 5% level * Log GDP per capital

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Time δi εit

157

robustness check. We will discuss these models in the robustness check section.

the time trend, the unobserved random effects that are firm specific and time constant, the error term with mean of zero.

When a lagged dependent variable is included, the model is referred to as a dynamic panel model (Arellano and Bond 1991; Arellano and Bover 1995; Blundell and Bond 1998) or dynamic unobserved effects model (Wooldridge 2002). The lagged dependent variable can capture both the time varying and time invariant unobserved effects, hence mitigating the omitted variable problem (Germann et al. 2015). However, the inclusion of lagged dependent variables also causes the violation of the strict exogeneity assumption, as Qit becomes an independent variable at time t + 1and is correlated with εi , t. The commonly used approach to estimate the dynamic panel model is to eliminate δi using first order differencing, yielding the following differenced equation: ΔQit ¼ β 0 þ ΔQi;t−1 αi þ ΔX it Bk þ Δεi;t

ð1:4Þ

However, the endogeneity issue is still present because Qi , t − 1 is correlated with εi , t − 1 resulting in a correlation between ΔQi , t − 1and Δεi , t. The dynamic GMM approach provides a nice solution to the endogeneity problem by generating internal instrument variables through exploiting sample moments. In these models, lagged differences are used as the instruments for the level equation and the lagged levels are used as the instruments for the differenced equations (Blundell and Bond 1998). The joint estimation of level equations and differenced equations is referred to as the system of equations approach. As the instruments are generated internally, this approach does not require the employment of external IVs, the relevance and exclusion restriction of which are usually difficult to justify. The dynamic GMM approach enables researchers to utilize more instruments and hence increase estimator efficiency and the statistical power of the differenced equation (e.g., Mizik and Jacobson 2004; Roodman 2009). This dynamic model approach fits our data with Bsmall T, large N^ panels (Rego et al. 2013; Roodman 2009). In addition, our data does not meet the common assumption of homoscedasticity and independent errors. The reason is that the retail firms in our data have varying resources and characteristics, which results in heterogeneous outcomes and error terms. Our firms also demonstrate temporal dependence and variability, reflected as a first-order autoregressive process. The dynamic GMM requires no distributional assumptions and is useful in dealing with these estimation issues (Baum et al. 2003). The above discussion indicates that the dynamic panel model fits our conceptual framework and data characteristics and addresses the endogeneity issue with a set of more efficient instruments. We employ the dynamic panel model as our main model, but report the results of other models as a

Selection issue A selection issue arises in that firms operating certain types of format might be more likely to be included in the Deloitte and Touché top list due to high retail sales, the criteria used to rank the retailers. We find that hypermarket retailers and price-oriented retailers are more likely to be included in the list, for their average sales (above 13 billion dollars) are much higher than that of other store formats (ranging from around 3 to 11 billion dollars). We addressed the selection bias for these format types by employing the widely used Heckman (1979) two-step procedure. The estimation of the selection equation (the probability of observing a format type) should include all the control variables in the main equation and at least one additional instrumental variable that is correlated with the likelihood of observation, but not with Tobin’s Q (Liu et al. 2016; Seggie et al. 2013; Wooldridge 2002). We included the total assets of each retailer as an additional variable, as it is correlated with the likelihood of inclusion (all p < .5), but is not directly related to Tobin’s Q (ρ =-.023, p = .56). The probit selection model produces the inverse Mills ratio (IMR), which is included later in the main model as a correction parameter.

Results and discussion Model-free evidence We first present descriptive statistics. Table 3 reports the mean number of countries and formats for the top 250 retailers and top 50 retailers over time. We also report the number of countries in 2015 to view the recent changes. The level of geographic diversification increases steadily over time, with the mean number of countries in the 2015 showing a jump from the pre-crisis 2007 value. The level of format diversification is relatively stable, but the overall trend is upward. Domestic retailers on average employed a smaller number of format types than global retailers for both top 250 retailers (t = − 9.9, p < . 000) and top 50 retailers (t = − 8.67, p < . 000). Top 250 domestic retailers, on average, operated one or two format types. However, this number increased with expansion to other markets. The global retailers in the top 250 lists on average operated between two and three format types. Those in the top 50 lists operated between three and four format types. The top 50 retailers on average expanded more aggressively (t = 2.42, p =.03) and employed more format types (t = 10.783, p < . 000) than the top 250 retailers. These statistics indicate that a strategy

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J. of the Acad. Mark. Sci. (2018) 46:147–167

store formats are especially amenable to geographic diversification.

of operating in multiple countries and/or using multiple formats is prevalent among leading retailers. Figure 3a shows the average number of formats, the average number of countries, and Tobin’s Q for the top 250 retailers. The overall trend is increasing for all three variables. Figure 3b illustrates the interaction effect between the two strategies using a mean split. If retailers expand to a limited number of countries, employing more formats leads to better performance than employing fewer formats. The opposite holds for retailers expanding to many countries. A low number of formats results in better performance than a high number of formats. The low-low and high-high combinations are worse than either of the low-high and high-low combinations.

Other control variables

We present the results in Table 4. The following discussion is based on Model 1 (Eq. 1.3). All other models serve as robustness check. Consistent with H1 and H2, there is a positive main effect of geographic diversification (β c0 = .265, p < .000) and a negative main effect of format diversification (β0f = − .402, p < .000). The interaction between geographic and format diversification is negative, (γ = − .787, p< .000), supporting H3, i.e., format diversification negatively moderates the impact of geographic diversification. The negative interaction suggests a tradeoff between the two strategies.

Cultural dissimilarity has a significant negative impact on retailers’ performance (β DIS = − .103, with p < .05), consistent c with our expectation. However, economic dissimilarity is positive (β DIS ¼ .001, with p < .05). One possible explanation is e that a decision regarding globalization and format diversification already takes into account the host country’s economic conditions. The coefficients of urban population and population densiHC ty are all positive and significant (βHC 1 = .003 and β 2 = .000 respectively, with all p < .000), while the coefficient of per capital GDP is negative (βHC 3 = − .149 with p < .05). These findings are consistent with our expectations. In terms of firmlevel characteristics, the retail sales size effect is positive but insignificant (βFC 1 = .000, p >.05). The brand effect is positive, consistent with our expectations that higher brand reputation gives better performance (βFC 3 = .003, p .05). A possible explanation for the positive sign is that the presence of many same format retailers may imply that the market might be an appropriate place for a certain format type. The lagged Tobin’s Q is positive and significant (α = .693, p < .000), suggesting that retailers’ past performance is a reliable predictor of current performance.

Impact of format type

Robustness analysis

The interaction effects of format types with geographic diversification are all positive (β Int m1 = .050, .126, .255, all with p < .05) for price-oriented retailers, high-end retailers, and hypermarket retailers respectively, and all the main effects of these format types are significant. These results suggest that these

We conducted several model robustness checks. Table 4 presents the results from Model 1 to Model 3 (all using the dynamic panel GMM model). Table 5 presents the results from Model 4 to Model 6, with different model estimation procedures.

Impact of geographic and format diversification on performance

Table 3 Trends of country and format diversification

Report year

2002

2003

2004

2005

2006

2007

2015

Country (top 250) Format (top 250) Format (top 250 domestic) Format (top 250 global) Tobin’s Q (top 250)

4.97 2.31 1.67 2.64 0.99

4.97 2.27 1.64 2.57 1.05

5.34 2.53 2 2.79 1.04

5.12 2.37 2.02 2.56 0.91

5.31 2.42 1.97 2.66 1.04

5.47 2.44 1.95 2.69 1.11

10.69

Country (top 50) Format (top 50 domestic) Format (top 50 global) Tobin’s Q (top 50)

7.35 2 3.16 1.2

8.08 2 3.19 1.11

8.1 2.29 3.19 1.09

7.52 2.63 3.23 0.88

7.1 2.44 3.43 1.01

7.5 2 3.36 1.14

1.13 13.08

Domestic refers to retailers only operating domestically. Global retailers refer to retailers who expand to other countries. Tobin’s Q in 2015 is reported directly by the Deloitte report

J. of the Acad. Mark. Sci. (2018) 46:147–167 Fig. 3 a Retailer trends in diversification and Tobin’s Q. b The interaction of dual diversification strategies

159

a 6 5 4 Tobin's Q (top 250) 3

Number of Formats

2

Number of Countries

1 0 2002

2003

2004

2005

2006

2007

b 1.200 1.100

Tobin's Q

1.000 0.900 Low number of formats 0.800

High number of formats

0.700 0.600 0.500 Low Number of Countries

High Number of Countries

Note: Low number of countries5 Low number of formats3

As the dynamic panel GMM model requires the presence of first-order serial correlation (AR (1) should be rejected), but the absence of second order serial correlation (AR (2) should not be rejected), we employed the Arellano-Bond test on the first differenced residuals. We used the Hansen J-statistic for testing over-identifying restrictions. The Hansen-J statistic tests whether the model specification is correct and whether the instrument over-identification restrictions are valid. An insignificant test statistic indicates valid instruments.1 We also report the difference-in-Hansen C statistic, which is used to test the instrument validity for the changes-changes model specification. We used up to three period lags of Tobin’s Q as GMM style instruments. We do not include earlier lags, as more lags lead to a Hansen test with an implausibly good p value of 1. In addition, too many instruments can cause over fitting of endogenous variables (Roodman 2009). All results indicate an autocorrelation of order one (AR (1) statistics: z = − 4.57, −4.56, and −4.26 from Model 1 to Model 3 respectively, all with p < .000), but not of order two (AR (2) statistics: z = 1.56, 1.58, and 1.78 from Model 1 to Model 3 respectively, all with p > .05), satisfying the moment conditions. All the Hansen J and Hansen C statistics are insignificant, 1

We did not rely on the Sagan test statistic for testing the over-identifying restrictions because the test requires the errors to be independently and identically distributed, inconsistent with our error structure.

indicating the validity of the instruments. We also estimated variance inflation factor (VIF) statistics to confirm that multicollinearity is not a serious threat to our estimates (all VIF < 4). For the dynamic panel GMM model, we report the Wald χ2 statistic as an overall fit statistic. Model 2: nonlinear effect dynamic panel model Our model makes the assumption of linearity. However, nonlinear geographic diversification effects have also been observed in internationalization studies (e.g., Assaf et al. 2012; Lu and Beamish 2004). We specify a quadratic term for geographic diversification. The linear positive effect of geographic diversification is still sustained. The quadratic term is also positive, suggesting an increasing upward trend for firm performance with further geographic expansion. Other results are similar to those of Model 1. The nonlinear model fits less well than the linear dynamic panel model, suggesting that the linear model is more appropriate for the data. Model 3: dynamic panel model without interaction effect This model removes the interaction effects of format type with geographic diversification, allowing a focus on the dual strategies of format and country diversification. The results are consistent with those of Model 1. The fit statistics decrease progressively from Model 1 to Model 3, justifying the use of Model 1.

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J. of the Acad. Mark. Sci. (2018) 46:147–167

Table 4 Dynamic panel model results

Model 1 Beta

S.E.

Model 2 Beta

S.E.

Model 3 Beta

S.E.

Div_Country (H1: +) Div_Format (H2:-)

.265*** −.402***

.010 .012

.271*** −.427***

.012 .015

.237*** −.263***

.010 .007

Country*Format (H3: -) Div_Country Square

−.787***

.035

−.821*** .427***

.036 .053

−.248***

.036

Price-oriented retailer High-end retailer

.033* −.009*

.013 .004

.038** −.014**

.013 .006

.086*** .057***

.016 .005

Hypermarket

.050***

.008

.049***

.009

−.079***

.008

Other formats

.015

.014

.023***

.016

.061***

.013

Low price*Div_Country High end* Div_Country

.050*** .126***

.009 .016

.033* .110***

.015 .010

Hyper* Div_Country

.255***

.017

.243***

.015

IMR (Hyper) IMR (Low price)

.021*** −.012***

.006 .002

.017* −.010***

.007 .003

.031*** −.007**

.009 .002

Portfolio Characteristics Country level

Econ_distance Culture_distance Urban proportion

.001*** −.103*** .003***

.000 .004 .000

.000*** −.091*** .004***

.000 .004 .000

.000*** −.063*** .003***

.000 .003 .000

Characteristics

Pop. density Econ. size

.000*** −.149***

.000 .010

.000*** −.133***

.000 .011

.000*** −.162***

.000 .006

Firm level Characteristics

Sales Competition Brand Lagged Tobin Year dummy Time trend

.000 .000 .000 .002 .001

.000 .000 .002 .690*** Included −.037*** 7.91e + 08 27 87

.000 .000 .000 .003 .003

.000*** .000^ .002*** .712*** Included .016*** 3.98e + 07 23 81

.000 .000 .000 .003 .002

Wald X2 Parameters Instruments

.000 .000 .003*** .693*** Included −.040*** 4.19e + 09 26 88

AR (1)

−4.57***

−4.56***

−4.26***

AR (2) Hansen J Hansen C Sample (n)

1.56 143.01 50.93 591

1.58 142.93 51.72 591

1.78 136.29 69.31 591

Diversification

Format type

Lagged Q Year effect Time Model details

Model 1: Main model Model 2: Nonlinear model 3: No interaction with geographic diversification ^

p < 0.10, *p < 0.05; ** p < 0.01; *** p < 0.001

Model 4: OLS model Although the OLS model suffers from omitted variable bias, it serves as a basic model check. We use pooled OLS to account for the panel structure and assume that the error terms εi , t are uncorrelated with all independent variables at time t while allowing their correlations in different time periods. The main results are still maintained.

generalized least squares (GLS) framework (Wooldridge 2002). We hence employ the feasible generalized least squares (FGLS) framework by specifying a heteroskedastic and robust covariance estimator with an AR (1) error structure (Wooldridge 2002). The results of Model 5 are generally consistent with those in previous models.

Model 5: GLS model We estimate the unobserved effects model of Eq. 1.2 using a random effects model and assuming δi to be uncorrelated with all independent variables. If the errors in a random effects model are generally heteroskedastic and serially correlated, the model should be estimated in a

Model 6: random effects model with endogeneity correction The previous GLS estimation does not address the endogeneity issue. As noted before, we employ instrumental variables obtained from peer firms to correct for endogeneity.

J. of the Acad. Mark. Sci. (2018) 46:147–167

161

Table 5 Additional model results Model 4

Model 5

Model 6

OLS

GLS

RE

Beta Diversification

S.E.

Beta

S.E.

Beta

S.E.

Div_Country (H1: +)

.117*

.059

.067**

.021

1.100***

.200

Div_Format (H2:-)

−.179*

.087

−.089**

.028

−.656***

.047

Country*Format (H3: -) Price-oriented retailer

−.746* .051

.327 .055

−.272* .037^

.107 .021

−.675*** −.141^

.135 .076

High-end retailer Hypermarket

.021 −.020

.043 .039

−.010 −.032**

.013 .011

−.119*** .364***

.028 .072

Other formats

.019

.043

−.005

.016

−.380***

.044

Low price*Div_Country High end* Div_Country

.079 .085

.058 .112

.048** .069^

.017 .038

1.872** .097**

.639 .037

Hyper* Div_Country IMR (Hyper)

.037 −.015

.077 .021

.029 −.008

.028 .009

.202** −.277***

.063 .060

Portfolio Characteristics

IMR (Low price) Econ_distance Culture_distance

.018 −.001^ −0.005

.013 .000 .031

.021*** −.000 −.003

.124 .009 .009

.562*** .001** −.441***

.079 .000 .040

Country level Characteristics

Urban proportion Pop. density

.002 .000

.002 .000

.000 .000**

.000 .000

.011*** −.000***

.002 .000

Econ. size

−.110*

.052

−.072***

.013

−.108^

.060

Sales Competition Brand Lagged Tobin Year dummy

.000 .001 .000 .839*** included

.000 .001 .001 .026 –

.000 .000 .000 .902*** included

.000 .001 .001 .013 –

.000 .002** .001 – included

.000 .001 .001 – –

Time trend

.009 .824 591

.010

.011* 11,215.64 591

.004

−.002 17,665 563

.006

Format type

Firm level Characteristics Lagged Q Year effect Time Fit statistics

Fit Statistics: Model 4-R2 , Model 5 and Model 6- X2 ^

p < 0.10, *p < 0.05; ** p < 0.01; *** p < 0.001

We apply 2SLS on the random effects model and get consistent results for all focal variables. The unobserved effects model can be estimated using either a fixed effects model or a random effects model. We select the random effects model because of our data characteristics. Although the fixed effects model allows δi to be correlated with the independent variables, it will not work well when the within-group variation is small or when the variables change slowly over time. In our data set, the number of retail formats and number of countries change relatively infrequently, resulting in small withinfirm variations across years. A fixed effects model would barely capture these variations. In addition, fixed-effect models assume independence between individual firms. For our data, firms might share the same geographic location and accordingly share the same economic situation.

Many firms even inevitably share the same suppliers and consumers. Additionally, considering our data is drawn from a larger population, the random effects model is therefore more generalizable (Greene 1990; Mitra and Golder 2008).2

2 It should be noted that the Hausman test is usually conducted to test whether a fixed or random effects model should be used. Our Hausman test statistic does not support the use of the random effects model. However, the Hausman test has two caveats (Wooldridge 2002). First, the test requires a strict assumption of exogeneity. If there is a correlation between independent variables and ε for any time periods, then results for both the fixed effects model and the random effects model are inconsistent. Second, the test is usually implemented assuming constant variance and independent errors. As noted above, our data set has a heteroscedasticity and autocorrelation problem. The Hausman test is not appropriate for our data.

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J. of the Acad. Mark. Sci. (2018) 46:147–167 Elasticity and marginal effect

General discussion

Table 6

Implications

A: Elasticity Diversification

Our findings have important managerial implications. Retailers seek growth through two major diversification strategies. Format diversification in the retail industry is conceptually similar to the product diversity constructs used by other industries. If a company decides to stay in its domestic market, a natural growth strategy is to diversify its business/product lines. But when both strategies are available, the decision is not obvious. A retailer can choose either a conservative approach or an aggressive approach. The conservative approach focuses on one strategy, enabling firms to reduce risks by first sticking with current business practices and then expanding to new lines or new markets. An aggressive approach may bring more development opportunities but may induce big risks. Our study contributes to the literature by providing empirical evidence supporting the conservative approach, i.e., the single focus strategy. Theoretically, this approach is consistent with Porter’s (1985) business strategy dictum that a firm should stick to one single generic strategy to avoid being Bstuck in the middle.^ If a firm attempts to achieve a competitive advantage on all fronts, it may achieve no advantage at all. Intuitively, this single focus strategy should be consistent with a manager’s conjecture for most businesses, not just for retailing. To understand the impact of each strategy, we use the Model 1 parameter estimates to illustrate the magnitude of the impact for an average firm in our sample (at the mean level of each variable). We find that a 1% increase in geographic diversification increases Tobin’s Q by 16.37%, whereas a 1% increase in format diversification decreases Tobin’s Q by 26.55% (Table 6). The elasticity reflects both the main effect and the interaction effect of a single diversification strategy. We then analyze the marginal effect of each strategy when holding the other strategy at the 25%, mean, and 75% levels. We observe that the positive effect of geographic diversification decreases with the increase of the format diversification level. The negative format diversification impact worsens with higher levels of geographic diversification. These results clearly show a negative interaction effect between the dual diversification strategies. The negative impact of format diversification is consistent with our conceptual justification. The pursuit of format diversification opportunities is generally risky and often fails due to reduced competitive advantage when employing a new business model. These disadvantages can occur in a familiar domestic market and become more salient in a new market. For example, Tesco, when growing its convenience store business in the UK, encountered problems such as low barriers to entry for competitors on the high street, more complex logistics for smaller stores, and the problem of adapting a smaller range of products to local market

Geography Format

Mean Elasticity 16.37% −26.55%

B: Marginal Effects 25%

Mean

75%

Geography

66.60%

27.25%

14.11%

Format

−40.20%

−79.55%

−105.76%

conditions (Ruddick 2015). These problems were amplified when Tesco moved into the U.S., where it had little experience of consumer tastes or local conditions. When geographic diversification is used as a growth strategy in global markets, taking a conservative approach is the best way to reduce the negative impact of diversification. For example, T.J. Maxx has been successful rolling out its off-price format globally, making only minor format adaptions for different markets. These findings are consistent with current trends in retail diversification strategies. Despite some back and forth withdrawals in some markets for certain retailers, the globalization trend is non-revocable and is accelerating over time. Table 7 lists Tobin’s Q and the percentage of globalized retailers in the top lists from the Deloitte 2010 to 2015 reports. The percentage of globalized retailers increases every year. There are still many new opportunities in new markets and many domestic retailers are still waiting for the right time to expand (Deloitte 2015). Overall financial performance is still recovering from the 2008 global financial crisis. Tobin’s Q for all companies across all years is above 1 with an average of 1.11 , higher than the average of 1.02 in our main data period before the crisis. Also, the correlation between Tobin’s Q and the percentage of global retailers is .65 (p < .000), suggesting a positive relationship between globalization and performance. In contrast, the level of format diversification is relatively stable.3 This stability is not related to the current economic environment, but is related to the innate complexity of the format diversification process. From this perspective, this stability should hold unless the retailing business undergoes tremendous fundamental changes. Retailers are increasing employing scrambled merchandising, i.e., retailers add unrelated products that do not reflect their original focus (e.g., CVS sells grocery products). However, such blended product assortment does not change the original format type classification. In addition, we find that certain format types benefit more from geographic diversification than others. For example, our results indicate that high-end retailers can increase performance gains attributable to geographic diversification. The reason could be due to the unique 3 We kept track of the number of formats employed by the top retailers after the crisis. There is little change in the number of formats employed since 2007.

J. of the Acad. Mark. Sci. (2018) 46:147–167 Table 7 Tobin’s Q and the percentage of global retailers

Report Year

163

2010

2011

2012

2013

2014

2015

Percentage of Global Retailers

59.6%

59.6%

60.0%

62.4%

63.2%

65.6%

Tobin’s Q

1.033

1.006

1.060

1.115

1.297

1.130

merchandise they carry. These retailers carry many premium brands. Although these products can be relatively expensive, consumers’ price sensitivity varies across functional and hedonic products and customers in emerging markets are more likely to associate merchandise in high-price categories with high quality and attach higher prestige than customers in developed markets (Zielke and Komor 2015). Prestige-seeking behavior is a key factor in the consumption of branded products in emerging markets (Sharma 2011). As a result, many luxury brands that have stagnating sales in their domestic markets find their biggest market growth in these emerging markets (Sibony and Tochtermann 2014; Zhang et al. 2012). This implies that a retailer with a unique brand should either start to exploit cross-border opportunities or be more aggressive in its expansion. Managers can conduct marketing research to discover the perceived brand image or brand positioning in the expansion market. If positive, they can highlight their brand name in any subsequent marketing campaign. Retailers such as Zara and LVMH have followed this practice and their global expansion has been highly successful (de Jorge Moreno and Carrasco 2016; Sibony and Tochtermann 2014). Our results also indicate that price-oriented retailers and hypermarket retailers can benefit from global expansion. The success of price-oriented retailers may be attributable to the increasing price consciousness of global consumers. The huge success of German discount chains Aldi and Lidl is a pertinent example. Retailers can achieve price advantage by heavy promotion, supported by cross-subsidization from the parent company and utilization of bargaining power to elicit discounts from suppliers. The success of the hypermarket format could be attributable to the uniqueness of the format. It attracts price conscious customers with a variety of low priced daily goods and attracts prestige seeking customers with affordable luxury goods (Zielke and Komor 2015). In addition, the typical multiple-story giant hypermarket fills the multi-purpose roles of shopping, entertaining, sightseeing, and social gathering in Asian countries. The success of its major competitor Carrefour in China even inspired Walmart to operate several hypermarkets in Asia. These results suggest that retailers that need to pursue growth through format diversification in a new market can choose to either utilize corresponding marketing activities or expand to a locally preferred format or both.

Limitations and future research We acknowledge several limitations. These limitations present possible worthwhile future research directions. Our data are limited to the pre–financial crisis era. If both the pre- and postcrisis data were available, we could conduct a structural break test to verify the consistency of the trend. In addition, we lack detailed sales data by format type in each country. Such data would enable an entry study (e.g., Gielens and Dekimpe 2007) that could provide interesting insights into format choice decisions for specific retailers. Furthermore, our dataset includes only leading retailers. Smaller retailers, such as those in the top 500 lists (but not in the top 250), or those growing quickly from a small base, are not included. We believe that our general findings should hold for these retailers as well. In fact, if leading retailers encounter difficulties with multiple format global expansion, the effects may be amplified for smaller retailers, who have fewer resources. One important aspect of overall brand portfolio strategy is the actual branding strategy. Some retailers keep a consistent name throughout the globe, keeping all of their formats under a consistent brand umbrella (Erdem 1998). However, others will pursue a more mixed strategy. For example, Walmart has BWalmart^ branded stores in multiple countries, but it has not changed the names of some retail stores that it acquired by acquisition, for example Asda in the UK. Work exploring the interaction between brand strategy and portfolio strategy in a global context would be a useful addition to the global marketing literature. Another future direction is related to format definition. The Deloitte retailing report provides a set of consistent format definitions. However, conceptions of format may vary over time or by culture. For example, retail formats may be affected by the growth of blended (offline and online) omni-channel retailing (Verhoef et al. 2015) or by specific cultural adaptions. Thus, an analysis of retail format evolution over time and across cultures would be a valuable addition to the retailing literature. Furthermore, as our focal interest is in the impact of the dual diversification strategies, we do not specifically analyze all of the individual format types. The online format is particularly interesting. Internet retailers, such as Amazon, are uniquely equipped to take advantage of globalization opportunities. The instant connection between sellers and customers reduces technology and market uncertainty, and enables online retailers to gain global brand awareness quickly (Kotha

164

J. of the Acad. Mark. Sci. (2018) 46:147–167

et al. 2001). Thus, internet retailers maybe perceived to go global naturally and are expected to gain more from globalization. Although our study includes a non-store format, the non-store retailers categorized by the Deloitte report include both internet stores such as Amazon and catalogue retailers.4 Following the classification contained in the Deloitte report, we do not differentiate these in this study. Internet retailers have different operating characteristics from other retailers. The evolution of internet retailing in global markets deserves a separate study. In fact, in general, explaining how technology facilitates customers’ decision making and how the internet helps firms to better target their customers is a fertile area for future academic marketing research (Grewal et al. 2017). Acknowledgements The authors are very grateful to Jennifer Itzkowitz for her valuable help.

Appendix Calculation of metrics Cultural, economic, and physical distances Many different cultural dissimilarity measures have been proposed in the academic literature. We employ a traditional yet widely used approach. Our measure of cultural dissimilarity is based on the four cultural dimensions developed by Hofstede (1980): individualism, masculinity, power distance, and uncertainty avoidance. To calculate cultural dissimilarity, we created a composite index, which is a variant of the index used by Kogut and Singh (1988). We first divided the country rating for each of the four Hofstede dimensions by its variance. We then calculated the average dimension dissimilarity between all countries for each retailer’s portfolio. Next, we took the average of the squared average dimension dissimilarity across the four dimensions. We substituted missing values with the historical mean. If data were missing on all four cultural dimensions and there was no historical mean value to substitute, we dropped the observation. However, such cases requiring a complete drop were rare (less than 10 cases). Thus, this index is: Cultural dissimilarity !  . . 2 .  . 2 ¼∑ ΣΣ H i;k −H i; j nðn−1Þ 2 4; σi i

i k, j n

An economic dissimilarity index was developed using a similar approach to that employed for the cultural dissimilarity index, but using factors assessing the economic climate and technological development of the countries rather than Hofstede’s culture scores. The factor scores for the four dimensions (development status, infrastructure technological advancement and efficiency, international orientation, and market potential) were based on an exploratory factor analysis of 62 economic indicators from the World Bank report (2007). Calculation of Google Trends brand equity measure Google Trends has been utilized in the academic literature to analyze product trends by Du and Kamakura (2012). Google Trends provides an index of search volume over time for a given search term. It also allows for comparisons of multiple search terms, giving relative search volume (e.g., 54 vs. 65). However, in this previous work, Google Trends was used to gather longitudinal trends for a single product. Google Trends does not provide absolute value (e.g., number of actual searches) comparable across multiple Google Trends searches. In addition, Google Trends has low granularity, only reporting integer values between 1 and 100, so a comparison between a company with ultra-high global visibility, such as Amazon.com against a small domestic retailer would always give 100 for Amazon and 1 for the retailer. To get around these problems, the following procedure was used. i)

ii)

k< j

where: Hi,j 4 It should be noted that brick and mortar retailers with an addition of online channel are not considered to have an additional non-store format. Catalogue retailers also include stores such as JC Penny, who run a large separate catalogue section.

denotes the value of the ith Hofstede dimension for the jth country; 1… 4 denotes the ith original Hofstede dimension; 1… n indexes the country pairs in which the firm operates denotes the number of countries in the firm’s portfolio for that particular year.

iii)

Google search terms were gathered. For over 95% of the companies, Google had a search code specific to the company that would filter out non-relevant results. Where this was not available (a few small, defunct companies), the company search name was used in conjunction with the type of store. Given that Google throttles its Trends service, it would have been infeasible to compare each retailer with every other retailer. A set of 10 comparison retailers was selected from the overall list of retailers. The retailers were selected to be evenly spaced in terms of search volume, from Walmart at the top, to a small Wisconsin retailer (Roundy’s at the bottom), so that each retailer would have at least one non-dominating (e.g., not 100 to 1) comparison. A JavaScript program was written to grab the Google Trends data from 2004 to 2015 for sets of two retailers. The program was run for each combination of retailer

J. of the Acad. Mark. Sci. (2018) 46:147–167

iv)

and comparison retailer. The difference between the retailer and the comparison retailer was recorded for each time point.5 The difference data were aggregated by year for each combination of retailer and comparison. An overall index was calculated for each retailer by aggregating across the 10 comparisons. The retailers were then placed in rank order.

The overall index had strong face validity. Dell had the highest brand equity value from 2000 to 2009. In 2010 it was Walmart and then subsequently from 2011 to 2015, it was Amazon.com.

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