Estimating store brand shelf space: neural networks

Estimating store brand shelf space: neural networks and partial least squares Mónica Gómez1 Shintaro Okazaki2 Universidad Autónoma de Madrid

Abstract: Despite abundant research that examines the effects of store brands on retail decision making, little attention has been paid to the predictive model of store brand shelf space. This paper intends to fill this research gap by proposing and testing a theoretical model of store brand shelf space. From the literature review, eleven independent variables were identified (i.e., store format, reputation, brand assortment, depth of assortment, in-store promotions, leading national brands’ rivalry, retailers’ rivalry, manufacturers’ concentration, store brand market share, advertising, and innovation), and analysed as potential predictors of the dependent variable (i.e., store brand shelf space). Data were collected for 29 product categories in 55 retail stores. In designing the statistical treatment, a three-phase procedure was adopted: (1) interdependence analysis via principal component analysis; (2) dependence analysis via neural network simulation; and (3) structural equation modelling via partial least squares (PLS). The findings corroborate our proposed model, in that all hypothesized relationships and directions are supported. On this basis, we draw theoretical as well as managerial implications. In closing, we acknowledge the limitations of this study and suggest and future research directions. Keywords: modelling, neural networks, retailing, store brands, partial least squares, private labels

1

Contact person: Associate Professor of Marketing, Department of Finance and Marketing Research, College of Economics and Business Administration, Universidad Autónoma de Madrid, 28049 Cantoblanco, Madrid. Tel: 913974348; Fax: 913978725; e-mail: [email protected]. 2 Associate Professor of Marketing, Department of Finance and Marketing Research, College of Economics and Business Administration, Universidad Autónoma de Madrid, 28049 Cantoblanco, Madrid. Tel: 914972872; Fax: 913978725; e-mail: [email protected].

1

Introduction The role of the store brand is increasingly important in retail decision making. Also termed ‘retail brand’ or ‘private label product’, more and more firms offer the store brand as an alternative to a market-leading brand, offering consumers equal quality at a lower price. Store brand items are no longer considered as ‘white label’, and retailers are willing to sell them not only for their high profitability, but also for their clear brand identity. Retailers attempt to provide rigorous quality controls for store brands without increasing the final price. Prior research suggests that at least three factors contribute to a successful store brand strategy: the reduction of the gap or distance between the store brand and the national brand (Dunne and Narasimhan 1995); its favourable price (Laaksonen and Reynolds 1994); and a reduced number of brand options per shelf (Simmons and Meredith 1983; Fernández and Gómez 2003). In the European market, store brands are widely recognized as viable competitors to national brands, offering good value for the money (Baltas 2003), as store brand market share has drastically increased. At an aggregate level, as much as 23% of the total sales value now accounts for store brands, in comparison to 16% in North America. Furthermore, Europe exhibits greater growth in store brand market share. In 2005, store brand sales grew in Europe by 4%, while aggregated sales of national brands remained flat. Such robust growth indicates that store brand sales have not reached their peak in the European markets (ACNielsen 2005). European retailers tend to allocate larger shelf space in optimal positions for the store brand (Burt 2000). Shelf space has been considered as one of the most important assets for retailers. It is a limited resource that must be optimally divided among a diverse range of brands or product categories. Hence, the issue of shelf space allocation and its impact on retailers’ performance has attracted much attention from marketing

2

and operations research scholars (Amrouche and Zaccour 2005). Much research on store brands has been published since the beginning of the 1990s (for a comprehensive review, see Narashimhan and Wilcox 1998; Baltas 1999, 2003; Smeijn et al. 2004; Ailawadi and Keller 2004). However, research on the predictive model of store brand shelf space is relatively scarce, and few studies have examined the question of whether or not a firm’s store brand policy has any effect on national brands’ marketing mix. Furthermore, while most research primarily focuses on mathematical models that maximize the effectiveness of shelf space allocation policies for retailers, little attention has been paid to the structural relationship in store brand shelf space and its determinants. To fill this research gap, this paper aims to achieve two primary objectives: (1) to identify the underlying factors influencing store brand shelf space, and (2) to propose and test a conceptual model to determine the optimal level of store brand shelf space. To this end, this study uses three types of multivariate techniques, including two third-generation statistical analyses:

neural networks and partial least squares

(PLS). In what follows, we first review the relevant literature and establish a conceptual framework. On this basis, research questions are formulated. Next, the methodology is explained in detail, and two estimation methods are evaluated. After the results are described for each study, we draw theoretical as well as managerial implications. Finally, we recognise the limitations of this study and suggest future research directions.

Conceptual framework Both academics and retailers have shown increasing interest in the topic of shelf space allocation. Using the electronic database ‘ABI/INFORM’, Lim et al. (2002) identified over 500 references on this topic and numerous articles in trade journals.

3

However, as far as the academic literature is concerned, most of the references appear to be obsolete (Yang and Chen 1999); thus it is worthwhile to update our knowledge. In general, shelf space literature can be divided into two perspectives: the theoretical perspective, which focuses on modelling solutions to the shelf space allocation problem (SSAP); and the empirical perspective, which focuses on the relationships between inventory levels, customer service, and sales results. In the theoretical perspective, the basic objective of SSAP3 is to improve the financial performance of the retail store (Buttle 1984). In many models, the demand rate is proposed as a function of the shelf space allocated to the product and, sometimes, to the shelf space allocated to competing, substitute and/or complementary products. Recent studies consider the allocation problem within the framework of the strategic interaction between the partners in the marketing channel, typically formed by two competing manufacturers and a retailer (Martín-Herrán and Taboubi 2005; MartínHerrán et al. 2005). A major drawback of the theoretical perspective is that, in practice, there may be unacknowledged feedback effects. An item with a high sales level will be granted more shelf space in the store, thus resulting in more facings for the item. This feedback effect is neither included nor analysed in these models. Researchers may underestimate the impact of the feedback effect because of their reliance on static experimental data. The empirical perspective focuses on inventory management. In our view, there is a serious limitation to the empirical investigations: the exclusive attention to the supplier perspective, focusing on backroom inventory levels, thereby disregarding one

3

Theoretical models have been proposed by many authors: Anderson and Amato (1974), Zufryden (1986), Bultez and Naert (1988), Borin et al. (1994), Dreze et al. (1995), Urban (1998), Lim et al. (2002) and Nierop et al. (2003), among others.

4

of the main sources of information for consumers in the store—displayed inventories (shelves). In order to overcome the limitations discussed above, we propose a new method, focusing on shelf space management. Our method is not based on the demand function, but rather on the space devoted to the store brand. We attempt to employ variables that can be observed on the shelves from a consumer’s point of view, along with additional variables obtained from secondary sources. In our view, developing a model that integrates the effects of some independent variables on the shelf management of store brands requires the following three factors: store differentiation, market structure and competitive strategy. Prior research can be classified into these three groups, where the independent variables that have an influence on store brand shelf space are grouped according to these factors. The first factor, store differentiation, is conceptualized as a joint effect of assortment (i.e., number of brands and their varieties), number of in-store promotions, store image, and store format. This is a strategic factor that allows retailers to differentiate from their rivals and enables them to build and maintain competitive advantage. There are two possible effects of the assortment on market share: the depth of the assortment (number of varieties and formats) and the brand variety (number of brands sold). Prior research indicates that the depth of assortment has a significant and negative effect on national brand performance (Dhar and Hoch 1997). Narrow assortments favour the store brand, and speciality items are more likely to be eliminated. Brand variety acts as a market entry barrier because market share is carved up into so many pieces. According to Hoch and Banerji (1993), the greater the variety in

5

a given category, the lower the share of store brands in that category, and therefore, the lower the store brand shelf space. Promotions may have a similar effect. The extent of promotion techniques employed in a given category may also have an impact on consumers’ intention to purchase store brands within that category. Prior research suggests that store brands are likely to end up with lower shares in a category whose promotional intensity is high (Lal 1990; Dhar and Hoch 1997). Regarding store image, not all retail chains (hereafter, chains) have the same prestige, based on the general confidence and image they convey (Cruz et al. 2007). Store image may influence the amount of shelf space devoted by retailers to their own brands. Theoretically, we can expect that the chains with maximum prestige and image will designate less space for their brands. Finally, the store format may also influence the amount of space devoted to store versus national brands (Fernández and Gómez 2005), and therefore, there is a need to control for inter-type differences (i.e., supermarket versus superstore). Second, regarding competitive strategy, manufacturers can combine different strategies to defend their brands against store brands (Verhoef et al. 2002; Oubiña et al. 2006). Strategies that give brands a favourable competitive position, specifically national brand differentiation (Hoch and Barneji 1993; Ashley, 1998) and innovation (Simmons and Meredith 1983; Hoch 1996) are the two most commonly recommended strategies. Manufacturers can defend their brand’s market share by means of an advertising strategy that improves consumers’ perception of the brands (Lal 1990; Dhar and Hoch 1997). Traditionally, a heavy advertising expenditure by national brands successfully builds customer awareness and loyalty (Harvey et al. 1998).

6

On the other hand, manufacturers’ differentiation and innovation could have a negative relationship with store brand market share (Putsis and Coterill 1999; Coterill et al. 2000; Messinger and Narasimhan, 1995). Both strategies could create significant barriers that inhibit the growth of store brands. Therefore, in general, the greater the market share, the more profitable the store brand will be for the retailer. Next, many product categories which receive more support on shelf display are also more profitable for retailers. However, there may be overly exposed product categories that receive special promotional attention, and therefore, disproportionate shelf space, although they are not necessarily profitable. This circumstance generates negative effects on the total profitability of the categories for the retailer (Agustin and Iniesta 2001). Third, in relation to market structure, there are three variables to consider: retailers’ rivalry, national brands concentration and national leading brands rivalry (Rubio and Yagüe 2005). Narashimhan and Wilcox (1998) contend that a high concentration of stores selling store brands may increase retailers’ power in the channel. The retailers can exercise their power to improve the quality and positioning of their own brands, and therefore, their space on the shelves. Furthermore, they can take advantage of economies of scale and scope resulting in concentration of store brands in their stores (Putsis 1997). In relation to the national brand concentration, basic economic arguments support the existence of a negative relation to store brand space. Thus, the greater the aggregate market share of a specific number of national brands, the smaller the space that store brands can have. As for competitive rivalry among national brands, store brand space tends to be smaller in a market where no strong leading national brand exists, and where various

7

national brands compete with each other for the leading position (Simmons and Meredith 1983).

Research questions and hypotheses Based on the preceding discussion of factors influencing store brand shelf space, we set forward the following research questions:

Q1: What are the most relevant explanatory variables of store brand shelf space allocation? Q2: What are the basic relationships between the explanatory variables and the dependent variables (i.e., store brand shelf space)? Q3: What are the structural relationships between the explanatory variables and the dependent variable (i.e., store brand shelf space)?

In addressing Q3, we posit that distributors’ store differentiation strategy for their outlets will have a direct but inverse relationship to manufacturers’ competitive strategy. This variable determines the market configuration in a way that the defence strategy of NB brand redefines the market structure, stimulating the concentration and rivalry of all market participants. This market structure in turn determines the store brand shelf space in a linear relation. Figure 1 shows our proposed model. H1:

The store differentiation will directly and positively affect the

competitive strategy. H2: The competitive strategy will directly and positively affect the market structure.

8

H3:

The market structure will directly and negatively affect the store

brand shelf space. Methodology Primary data source Our empirical data were collected in Madrid, Spain, from (1) 23 wholesale superstores pertinent to four national chains (i.e., Alcampo, Carrefour, Eroski and Hipercor); and (2) 22 supermarkets pertinent to seven national chains (i.e., Caprabo, Champion, Sabeco, Mercadona, El Corte Inglés, Consum and Supersol). The data collection lasted over one month. For 29 product categories based on the ACNielsen’s classification, we estimated the shelf space of each brand sold on the stores (Table 1). In total, we obtained data from 1305 cases (i.e., 29 categories x 45 stores). The store brand shelf space was assessed using a method commonly adopted by professional research firms such as Nielsen (Space Monitor), Sesta (Hyper-eye) and Planesic. The following variables were examined and recorded: (1) the number of national brands, (2) the number of store brands, (3) the number of product variants, (4) the type of promotion, and (5) the space occupied by distributor and national brands. The space was obtained by counting the number of facings. Subsequently, the percentage of store brand shelf space with respect to the total number of brands on the shelves was calculated. The shelf is the length of product display in the outlet. It can be measured either in metres, in facings or space for product units to be presented. In offthe-shelf space management the number of facings is defined as the packages that sit on the shelf. Every package that is presented full-face is considered a ‘facing’ in this study.

9

Secondary data source This study also used secondary data sources with regards to (1) the store brand market share by stores; (2) the market share by observed categories; (3) the chain’s reputation; (4) the retailers’ rivalry; (5) the concentration of national brands; (6) the national leading brands’ rivalry; (7) the manufacturers’ differentiation; and (8) the manufacturers’ innovation. Table 2 shows the detail of all the variables used in the study. Data analysis procedure We analysed our dataset in three steps, in each of which different multivariate techniques were used to improve our understanding of the nature of the relationships. First, in Step 1, along with descriptive analysis (means, standard deviation and ANOVA), preliminary explorations of the dataset are made with principal component analysis (PCA). Next, in Step 2, we aim at addressing our research questions by using neural network analysis (ANN). Finally, in Step 3, we attempt to test the proposed model and hypothesis by applying PLS.

Estimation of store brand shelf space Step 1: Preliminary explorations In Step 1, we first performed some data purification in order to eliminate inconsistent data as well as extreme outliers. To this end, we have eliminated cases with missing values and standardised the data by eliminating those values with +/-4. (Please see the detailed procedure of the data standardization in Hair et al., 2006). With data purification, the sample size for study 1 and 2 is formed by 808 valid cases. There is not a great change in the measure of dependent variables (mean=0.3051; SD = 0.1919 in the original sample and mean = 0.3034; SD = 0.1934 in the purified sample).

10

Table 3 summarises the means for the observed variables in each category. ANOVAs reveal statistically significant differences for the store brand shelf space in 19 product categories; for the number of brands in 25 product categories; for the store brand market share in 18 product categories; for the number of variants in 14 categories; and for the promotion variable in 21 categories. Next, the space devoted to store brands and their market share are compared in order to reach a conclusion about retailers’ support of their own brands. In 10 out of the 29 categories observed, the space occupied by store brands was higher than their market share. Therefore, retailers are not disproportionately supporting their brands. They support some categories, and these are not necessarily the most profitable ones. Therefore, retailers are aware that they are reaching a so-called ‘maximum shelf point’ with their brands. If they over-sell their own label, this fact does not necessary mean that they will gain profitability. In order to verify the relationships among metric variables proposed in the model, the first step is to analyse the correlation between the explanatory variables and the dependent variable (Table 4). As seen in Table 4, some of the variables are highly and significantly correlated. The results from PCA with eleven variables indicate that retailers’ rivalry has a very low communality (0.24). Moreover, the goodness of fit when this variable is removed improves from 61% to 67%. Therefore, we have eliminated this variable. The final results indicate that all the relationships and signs proposed in the model are as expected (Table 5). The first factor (explaining 26.8% of the variance) is comprised of innovation, advertising and store brand market share. This last variable has a negative

11

sign. They comprise the factor we call ‘competitive strategy’. The second factor, ‘store differentiation’, accounts for 20.77% of the explained variance. It is composed of number of brands (assortment), number of promotions and number of varieties (depth of assortment). Finally, the last factor, ‘market structure’, explains 19.5% of the variance. It is formed by manufacturers’ concentration and national leading brands’ rivalry. Based on the preceding results, we now put forward an analysis based on neural networks. Step 2: Neural networks A neural network consists of multiple layers of light bulbs (neurons) that are connected to each other by numerous pathways of varying strength. The net is composed of three layers (input, hidden and output layers) connected by nodes. Development of a network requires many iterations before reaching the acceptable model, that is, one with the ability to generalise the relationship between the inputs and outputs and predict an outcome with reasonable accuracy. During the learning process, the weights are modified based on the learning algorithm used and the desired output for a given input (Mann 1997). Many researchers see these models as a ‘black box’ where there is no control over the model structure. Actually, the nodes and weights are connected during the learning process, but this structure provides considerable flexibility. Neural network systems may show very complex non-linear relationships, a very difficult task to reproduce by using multivariate methods. Additionally, they allow great predictive accuracy in comparison with other statistical methods. In general, ANNs use more parameters than their classical counterparts and are thus more prone to over-fitting problems (Hair et al. 1999). There is an additional

12

practical problem with ANNs: the difficulty in interpreting the model structure (Hill et al. 1994). However, the literature review shows that ANN analysis is weak in some areas (Gorr et al., 1994; Refenes 1994; Peacock 1998). Specifically, ANN-models have not performed as efficiently in the area of optimisation as traditional statistical models (Krycha and Wagner 1999). We have tested the relationship between shelf space and the independent variables through the network developed in DYANE (Santesmases 2005). The model is as follows: the network is a supervised forward propagation perception. It has been estimated through a sigmoid function. The synaptic weight is the numerical number used to weight the stimuli (oi) received from a neuron. The weight of a connection between the neurons i and j is identified as wij. The whole number of inputs weighted linearly and received by a neuron j is the total input for this neuron:

n

Inputj =

o

ij

wij + j

i=1

j is a limit value that modifies the effect of the synaptic weight.

This input will be used by the activation function of the neuron. In our model, it is a logistic sigmoid function, with an interval between 0 and 1:

LF 

1 1  e x

X is the total input weighted by the neurons of the precedent node. The default learning index and moment have been used (1 to 0.7 and 0 to 0.3). The programme also splits the data into two random halves for (a) model building and (b) model testing.

13

We have tried several possibilities for the intermediate variables. Santesmases recommends the semi-sum of input and output variables. Therefore, the initial nodes are six. We have also proved with five and four hidden layers. The estimation with the highest goodness of fit is with five hidden layers. Figure 2 reproduces the final network obtained after 476 iterations. Table 6 presents the estimated network performance. The D.E. ratio shows the effective network performance. If 1 is subtracted from this value, the result is the explained variance, 61,3%. Another sign of the efficiency of the model is the correlation between the predicted data and the output variable, which is 92,2%. One of the main problems of estimation using neural network analysis is that the coefficients for the input variables cannot be obtained directly. We have used the procedure suggested by Bejou et al. (1996) to estimate the impact of the eleven explanatory variables for shelf space (although it was eliminated in PCA, we have included retailers’ rivalry). Each of the potential determinants of space is systematically removed from the training data, and a new neural network with only ten determinants is developed. The ‘trimmed’ network is then used to predict space. The prediction ability of the eleven trimmed networks and the full network (with all eleven determinants) is compared to determine how well a network can perform without any knowledge of one particular determinant. The predictive performances of the eleven networks are analysed by a statistical test. The randomised block two-way analysis of variance (ANOVA) without interaction is the appropriate statistical model.

14

The treatments are the elimination of each determinant from the network, and the blocking effect is due to the same set of data results being used. The data are imported into SAS and analysed by the ANOVA procedure to test the main effects. The ANOVA test resulted in a p-value less than 0.0001 for both treatments and blocking. This is strong evidence that the prediction performance of at least one of the twelve models is significantly different from the other models. Since the F-test found at least one model different, Duncan’s multiple-range test is used to discriminate among the different models. The results are given in Table 7. First, the most notable result is that only one model performs worse than the full model: the model with retailers’ rivalry. The network is actually able to better predict space when this variable is left out of the model. It may be contributing confusing information to the network, which hinders its ability to predict. This finding along with PCA shows that retailers’ rivalry is not a good predictor of shelf space and does not have a relationship with the other variables that comprise the “market structure” construct. Therefore, we will not include it in PLS analysis. Second, the models without the reputation and format predicted significantly worse than all other networks. When these variables are eliminated, network prediction performance is reduced. This is strong evidence that the reputation and format are very good indicators of shelf space. Third, manufacturers’ concentration, store brand market share, manufacturers’ rivalry, promotion, number of brands, number of variants, advertising and innovation are in the group that is significantly different for the full model and that has an impact on prediction ability of the network (although less of an impact than the reputation). The only drawback of using this procedure is that the sign of the coefficients cannot be

15

estimated. Consequently, the sign of the correlations (Table 4) complement the ANN results. The final step is to validate the solution to ensure that it is the global optimum and that it is able to be generalised. We have followed the procedures suggested by Hair et al. (1998). A validation sample has been created to provide an independent assessment of the model fit other than the calibration sample. The solution’s stability has been assessed by providing different starting points for the weights and rearranging the order of the cases in the calibration sample. We have also varied the number of nodes to ensure that a better solution is not possible. Step 3: Partial least squares (PLS) In Step 3, we attempt to test our proposed model by addressing hypotheses. The structural model consists of three exogenous factors composed of multiple variables, and one endogenous factor composed of a single variable. PLS was used to this end, because this method has been considered to be sufficiently robust against multicollinearity; and to be appropriate to develop a theory when a complex relationship exists among variables. Before applying the method, it is necessary to simplify the data since there are too many categories and stores. In order to do so, a non-hierarchical (k-means) analysis cluster is performed with SPSS 13.0. The variables used in the cluster analysis are all the metric variables (number of brands and varieties, promotions, innovation, advertising, store brands market share, manufacturers’ rivalry and leading national brand concentration). This results in a relatively homogeneous dataset, which is used in the final analysis. This new sample is mainly formed by superstores (79%). The categories in this sample occupy less space than the original dataset and the observed variables are also inferior in their magnitudes (less space, assortment and promotions).

16

Next, the proposed model and its associated hypotheses were tested using partial least squares (PLS) with SmartPLS 2.0 (Ringle et al. 2005). PLS was preferred over covariance-based structural equation modelling because it uses a least-squares estimation procedure, thereby avoiding many restrictive assumptions such as multivariate normality and residual distributions (Chin 1998). In addition, PLS is more appropriate for this study because it is primarily intended for predictive analysis in which (1) the explored problems are complex; and (2) both formative and reflective measures are used (Chin 1998). In total, 271 cases are used for the estimation. Chin (1998) recommends that a model based on PLS should be analysed in two stages of assessment: the measurement model and the structural model. First, the measurement model consists of the relationships between constructs and the indicators used to measure them. This involves the assessment of reliability and convergent and discriminant validity. The bootstrap sampling procedure was used to test the magnitude and significance of the loadings. Unlike structural equation modelling, PLS produces no specific fit index. Instead, the model fit is analysed by examining the loadings of the items with their respective constructs. After running a bootstrap sampling (n = 200), all the items loaded significantly onto the respective constructs. All the loadings were statistically significant, and the individual item reliability was thus considered to be sufficiently established. Next, we assessed construct reliability by calculating the composite reliability (CR). All the scores exceeded a generally recommended benchmark of 0.50, with an exception of store differentiation. Next, convergent and discriminant validity was assessed by comparing the square root of the average variance extracted (AVE) with the latent constructs’ correlations. Again, all the latent constructs met this condition, except

17

store differentiation, suggesting that this construct was somewhat in need of further improvement (Table 8). This should be noted as one of the limitations of the study. The structural model was assessed by examining the paths’ coefficients, and the variance explained (R-squared) in the endogenous variables. Following Chin’s (1998) recommendation, bootstrapping with 200 sub-samples was performed to test the statistical significance of each path coefficient, using t-tests. To complement the analysis of path coefficients, the variance explained (R-squared) in the endogenous variables was calculated as indicators of a model’s performance. While the size of Rsquare was fairly modest for all the constructs, the coefficients, standard errors, and tvalues showed solid evidence:

all the paths proposed in model were statistically

significant. In Hb1, we posit that store differentiation will have a direct but negative association with competitive strategy. Our findings indicate that this is indeed the case with the standardised effect greater than 0.50. Thus, H1 was supported. Next, our Hb2 hypothesizes that market structure will be directly and positively influenced by competitive strategy. This path in our model was statistically significant at p < .001 with a solid empirical evidence (standardised effect = 0.38). This rings true for H2. Finally, in Hb3, the direct and negative relationship was posited in the path from market structure to store brand shelf space. Our findings suggest that a statistically significant and negative relationship exists between these constructs. Therefore, H3 was supported. Table 9 summarizes the PLS estimation results. Limitations

18

To make our findings more objective, we should recognize two important limitations. First, because of the nature of the data available in the retailers’ industry, we cannot directly test the influence that the negotiation between chains and manufacturers has on the space devoted for store brands and national brands. This study did not include variables related to inventory and retailers’ decisions, which appear to be necessary to make a more complete prediction of store brand shelf space. Second, the composite reliability of store differentiation was low, and thus, future research should improve it by incorporating other variables that explain the greater portion of the variances. Discussion Shelf space is one of the retailers’ most important assets. In this research, we attempt to address three primary questions related to the strategic interaction between store brand strategies employed by the partners of the marketing channel and their results on shelf space. This study attempts to assess the rationale for the store brand shelf space and identifies the constructs that have an effect on store brand shelf space. This study makes significant contributions to the literature in several ways. First, we filled a research gap that has been neglected by prior research by creating a empirically verifiable theoretical model of store brand shelf space. We proposed new variables that had not been used before to predict optimum store brand shelf space. Second, this study is one of the first studies that simultaneously explores the relationship between store differentiation, competitive strategy and market structure in store brand shelf space estimation. Although prior research proposes several models based on mathematical algorithms in understanding the relationship between shelf space and different independent variables, little empirical effort has been made to incorporate other relevant variables. This study overcame such limitations by focusing on store differentiation and market structure strategies.

19

Third, the findings of this study help us (1) to understand retailers’ behaviour in their outlets and (2) to identify which factors influence their brands’ shelf space positioning. The primary–and rather straightforward—reason why retailers are promoting their store brands on the shelves is that in doing so they increase sales by satisfying a larger array of consumer preference. However, there is a second reason that accounts for the strategic role of the store brand: store brands increase the bargaining power of the retailer with respect to NB. Fourth, this study contributes to the store brand literature by taking the next step in analyzing the role of the store brand in the retailer-manufacturer relationship. We explain why assortment, promotions, store format and reputation have an influence on manufacturers’ differentiation strategies (advertising and innovation). This strategic reactions lead to different conditions in the market that finally affects shelf space allocation for the store brand. Finally, in terms of managerial implications, the findings of this study crystallize important issues from both retailers and manufacturers. With regards to store differentiation strategy, the direct and negative effect of this construct on competitive strategy shows that the retailers’ policies on the shelf (reduction of national brand assortment and national brand promotions) are affecting manufacturers’ strategies. However, retailers who seek to increase their store brand market share can penalize the global profitability of the category (store brand ‘over-merchandizing’). Retailers must realize that they might lose sales if they do not assign the necessary space to national brands, which also help to enhance store image. This situation is especially noticeable in those product categories in which one or more leading national brands hold great attraction for consumers.

20

In this study, we demonstrated that the marketing strategies commonly used by manufacturers (i.e., innovation and advertising) do strengthen their brands against the development of store brands. However, manufacturers should use a collaborative strategy rather than a confrontational one with retailers, especially in the introduction of new references on the shelves. These strategies are also changing market conditions. There is an increasing concentration of manufacturers. By doing so, they pretend to face store brands, gambling that the development of strong national brands will have a negative effect on store brand growth on the shelves. However, this strategy also affects retailers’ rivalry by helping to prevent distributors’ concentration. Future research suggestions First, future research should acquire more updated data from professional research firms (such as ACNielsen or IRI) or directly from retailers. Such data will allow us to formulate a more complete model with greater explanatory power. Second, it would be interesting to replicate the present study in other countries. Such replication would provide meaningful comparison in the model effects. Finally, a future extension should incorporate a wider range of predictor variables related to store brand shelf space. Specifically, the inclusion of mediating variables in the current structural model would enrich the predictive power and understanding of store brand shelf space estimation.

21

References ACNielsen. (2005) The Power of Private Label: A Review of Growth Trends Around the World. Executive News Report, ACNielsenGlobal Services. Ailawadi, K. & Keller, K. (2004) Understanding Retail Branding: Conceptual Insights and Research Priorities. Journal of Retailing, Special Issue on Retail Branding. 80 (4), pp. 331-342. Agustín, A. & Iniesta, F. (2001) Fidelización de los consumidores. Cómo pueden los fabricantes y los distribuidores fidelizar a través marcas y tiendas. Ed. Gestión 2000, Barcelona. Amrouche, N. & Zaccour, G. (2005) Shelf space allocation of national and private brands. Les Cahiers du Gerard. Gerard & Marketing Dept. HEC Montreal, G-2005-38. Anderson, E. & Amato, H. (1974) A Mathematical Model for Simultaneously Determining the Optimal Brand-Collection and Display-Area Allocation. Operations Research 22 (1), pp. 13-21. Ashley, S. (1998) How to effectively complete against private-label brands. Journal of Advertising Research 38 (1), pp. 75-82. Baltas, G. (1999) Understanding and managing store brands. Journal of Brand Management 6 (3), pp. 175-187. Baltas, G. (2003) A combined segmentation and demand model for store brands. European Journal of Marketing 37 (10), pp. 1499-1513. Bejou, D., Wray, B. & Ingram, T. (1996) Determinants of relationship quality: an artificial neural network analysis. Journal of Business Research 36, pp 137-143. Borin, N., Farris, P. W. and Freeland, J. R. (1994) A model for determining retail product category assortment and shelf space allocation. Decision Sciences 25 (3), pp. 359-384. Bultez, A. & Naert, P. (1988) S.H.A.R.P.: Marketing Science 7 (3), pp. 211-231.

Shelf Allocation for Retailers’ Profit.

Burt, S. (2000) The strategic role of retail brands in British grocery retailing. European Journal of Marketing 34 (8), pp. 875-890. Buttle, F. (1984) Retail Space Allocation. International Journal of Physics, Distribution and Material Management 14 (4), pp. 3-23. Chin W. W. (1998) The partial least squares approach for structural equation modelling. In: Marcoulides G.A., (Ed.), Modern Methods for Business Research. Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 295-336. Corstjens, J. & Corstjens, M. (1995) Store Wars: The Battle For Mindspace and Shelf Space. Chichester: Wiley. Corstjens. M. & Lal, R. (2000) Building store loyalty through store brands. Journal of Marketing Research 37 August, pp. 281-291. 22

Coterill, R. W., Putsis, W. P. & Dhar, R. (2000) Assessing the competitive interaction between private labels and national brands. Journal of Business 73 (1), pp. 109-137. Cruz, I., Oubiña, J. & Yagüe, M. (2007) Retail price dispersion: spatial and vertical competition analysis. Journal of Marketing Channels. Forthcoming. Dhar, S. K., & Hoch, S. J. (1997) Why Store Brand Penetration Varies by Retailer. Marketing Science, 16 (3), pp. 208-227. Dreze, X., Hoch, S. J. & Purk, M. E. (1995) Shelf Management and space elasticity. Journal of Retailing 70 (4), pp. 301-326. Dunne, D. & Narashinham, C. (1999) The new appeal of private labels. Harvard Business Review 77 (3), pp. 41-52. Férnandez, A., & Gómez, M. (2005) Shelf space management of private labels: a case study in Spanish retailing. Journal of Retailing and Consumer Services 12 (3), pp. 20516. Goor, W. L., Nagin, D. & Szczypula, J. (1994) Comparative study of artificial neural network and statistical models for predicting student grade point averages. International Journal of Forecasting 10, pp. 17-34. Hair, J., Anderson, R., Tatham, R. & Black, W. (1998) Multivariate Data Analysis. 5th ed. Englewood Cliffs, NJ: Prentice Hall. Harvey, M., Rothe, J. & Lucas, L. (1998) The trade dress controversy: a case of strategic cross-brand cannibalization. Journal of Marketing Theory and Practice 6 (2), pp. 1-15. Hill, T., Marquez, L., O’Connor, M. & Remus, W. (1994) Artificial neural network models for forecasting and decision making. International Journal of Forecasting 10, pp. 5-15. Hoch, S. J. (1996) How Should National Brands Think about Private Labels? Sloan Management Review 37 (Winter), pp. 89-102. Hoch, S. J. & Banerji, S. (1993) When Do Private Labels Succeed? Sloan Management Review 34 (Summer), pp. 57-67. Hoch, S. J. & Lodish, L. M. (1998) Store Brands and category management. Wharton School, University of Pennsylvania. Available at: http://citeseer.ist.psu.edu/cache/papers/cs/16476/http:zSzzSzfourps.wharton.upenn.eduz SzideaszSzpdfzSz98-012.pdf/hoch98store.pdf. Accessed on 11 May 2006. Krycha, K. & Wagner, U. (1999) Applications of artificial neural networks in management science: a survey. Journal of Retailing and Consumer Services 6, pp. 185203. Laaksonen, H. & Reynolds, J. (1994) Own brands in food retailing across Europe. Journal of Brand Management 2 (1), pp. 37-46. Lal, R. (1990) Manufacturer trade deals and retail price promotions. Journal of Marketing Research, 27 (4), pp. 428-444.

23

Lim, A., Zhan, Q. & Rodrigues, B. (2002) A heuristic for shelf-space decision support in the retail industry. SMU Conference Papers Series 6. Mann, L. (1997) Gaining global insights. Marketing Research: Management and Applications 9 (2) Summer, pp. 24-30.

a magazine of

Martin-Herrán, G. & Taboubi, S. (2007) Shelf space allocation and advertising decisions in the marketing channel: a differential game approach International Game Theory Review, Forthcoming. Martín-Herrán, G., Taboubi, S. & Zaccour, G. (2005) The impact of manufacturer’s wholesale prices on retailer’s shelf space and pricing strategies” Les Cahiers du GERARD, & Marketing Dept. HEC Montreal, G-2005-37 Messinger, P. R. & Narasimhan, C. (1995) Has power shifted in the grocery channel? Marketing Science 14 (2), pp. 189-223. Naresimhan, C. & Wilcox, R. T. (1998) Private labels and the channel relationship: a cross-category analysis. Journal of Business 71 (4), pp. 573-600. Nierop, E., Fok, D. & Franses, F. (2003) A Hierarchical Bayes Model for Shelf Management, Preliminary Draft, October, ERIM Research Series in Management, Erasmus University Rotterdam, ERS-2002-65-MKT. Oubiña, J., Rubio, N. & Yagüe, M.J. (2006) Relationship results of retail brand manufacturers depending on their competitive position. International Review of Retail, Distribution and Consumer Research 16 (2), pp. 257-275. Peacock, P. R. (1998) Data Mining in Marketing, Part. 1. Marketing Management 6 (4), pp. 8-18. Putsis, W. P. (1997) An empirical study of the effect of brand proliferation on private label-national brand pricing behaviour. Review of Industrial Organization 12 (1), pp. 355-371. Putsis, W. P. & Coterill, R. W. (1999) Share, price and category expenditure— geographic market effects and private labels. Managerial and Decision Economics 20 (4), pp. 175-187. Refenes, A. N. (1994) Comment on Neural networks: forecasting breakthrough or passing fad by C. Chatfield. International Journal of Forecasting 10, pp. 43-46. Ringle, C. M., Wende, S., & Will, A. (2005) SmartPLS 2.0. University of Hamburg. Available at: http://www.smartpls.de. Rubio, N. & Yagüe, M. J. (2005) Implementation of store brands and alternative strategies for manufacturer brands. UAM Marketing Research Paper Series 1, pp. 4459. Santesmases, M. (2005) Diseño y análisis de encuestas. Dyane 3, Pirámide, Madrid. Simmons, M. & Meredith, B. (1983) Own label profile and purpose. Journal of the Market Research Society 26 (1), pp. 3-27.

24

Smeijn, J., Van Riel, A. & Ambrosini, B. (2004) Consumer evaluations of store brands: effects of store image and product attributes. Journal of Retailing and Consumer Services 11 (4), pp. 127-258. Urban, T. (1998) An inventory-theoretic approach to product assortment and shelf-space allocation. Journal of Retailing 74 (1), pp. 15-35. Verhoef, P., Nijssen E. & Sloot, L. (2002) Strategic reactions of national brand manufacturers towards private labels: an empirical study in the Netherlands. European Journal of Marketing 36 (11/12), pp. 1309-26. Yang, M. & Chen, W. (1999) A study on shelf space allocation and management. International Journal of Production Economics 60/61, pp. 309-317. Zufryden, F. (1986) A Dynamic Programming Approach for Product Selection and Supermarket Shelf-Space Allocation. Journal of the Operational Research Society 37 (4), pp. 413-422.

25

Tables and Figures

Table 1 Stores selected Retailer chain

ALCAMPO

CARREFOUR

HIPERCOR

EROSKI

CAPRABO

CHAMPION

CONSUM

MERCADONA CORTE INGLES SABECO

SUPERSOL

Address

Store format

CTRA. NACIONAL II KM.34. CC. LA DEHESA AVDA./ GRAN BRETAÑA S/Nº. PARQUESUR AVENIDA PÍO XII, Nº 2 CAMINO DE VINATEROS S/Nº MONFORTE DE LEMOS S/Nº (MADRID 2) MONLEÓN, S/Nº PZA. JUAN CARLOS I. C.C. GETAFE 3 AUTOVÍA A-5 KM.15. P.ALCORCÓN C.C. LA VEGA. AVDA. LA GUINDALERA Nº 9 CTRA. DE LA CORUÑA KM.22 CTRA. MADRID-BURGOS KM.9 CRTRA. VILLAVERDE-GETAFE. C.C.BULEVAR AVDA./ DE ANDALUCÍA KM. 7,100 AVDA./ GUADALAJARA C.C. LAS ROSAS AVDA./ LOS POBLADOS S/Nº .ALUCHE GRAN VÍA DE HORTALEZA S/Nº CTRA. EXTREMADURA KM. 11,500 AVDA. PLZA DE TOROS C.C.VISTALEGRE AVDA./DE LOS ANDES. C. DE LAS NACIONES C.C. MENDEZ ALVARO (RETAMA, 8) C.C. ALCALÁ DE HENARES. LA GARENA AVENIDA ESPAÑA 17. C.C.LA GRAN MANZANA C.C. MADRID SUR. AVDA/ PABLO NERUDA PZA. CASTILLA ALFREDO MARQUERIE AV.BADAJOZ CONCHA ESPINA FELIX BOIX DOCTOR FLEMING LALATINA QUEVEDO COSTA BRAVA FRANCISCO SILVELA REYES MAGOS RIEGO, 14. ATOCHA BRAVO MURILLO FERROCARRIL GOYA GENERAL MOSCARDO CANILLAS HERRERA ORIA ORTEGA Y GASSET SINESIO DELGADO FERMIN CABALLERO

Source: ALIMARKET (2004)

26

Superstore

Supermarket

Table 2 Predictor and dependent variables

Variables Store brand shelf space

Abbreviation SPACE

Source Shelf observation

Description Percentage of space occupied by private label. Derived by dividing the number of private label facings by the total number of facings on the shelves in a category. The number of variants (formats, packaging, composition). Package size is not measured.

Depth of assortment

NUMVARS

Shelf observation

Assortment Promotions

NUMMARS PROMO

Shelf observation Shelf observation

The number of national brands in the category. The number of national brand promotions in a category.

Reputation

REPUT

OCU

Chain’s reputation. Retailers having higher reputation and higher level of service.

Store format

FORMAT

Dummy variable

Store format: 1 = superstore; 0 = supermarket

Retailers’ rivalry

RETAILRIV

Nielsen

Dispersion of volume market shares by retail formats that sell store brands (inverse indicator of the competitive rivalry).

National brands concentration

CR3MB

Nielsen

Aggregate volume manufacturers.

National leading brands rivalry

MANURIV

Nielsen

Dispersion of volume market shares for the top three national brands in the product category (inverse indicator of rivalry intensity).

National brands differentiation

ADVERT

Infoadex

Advertising expenditures for all national brands in the category in relation to total advertising expenditures for all national brands in the packaged products market.

Innovation

INNOV

IRI

Average number of product references in the superstore.

Market share

MSPROXY

Nielsen and IRI

Private label market share in the category.

27

market

share

for

the

top

three

Table 3 Categories means and statistical significance Product category OLIVE OIL RICE YOGURT INSTANT COFFEE ROASTED COFFEE SHAMPOO CANNED TUNA CANNED PINEAPPLE CANNED ASPARGUS DETERGENT BISCUITS SHOWER GEL MILK LEGUMES BLEACH MAYONNAISE MARMALADE SLICED BREAD DIAPERS ALUMNINUM FOIL TOILET PAPER PASTA PREPARED DISHES FEMININE HYGIENE PAPER FOIL NAPKINS FABRIC CONDITIONER TOMATO SAUCE JUICES

Space 30.92% 27.77% 24.51% 24.30% 27.62% 29.89% 28.11% 37.11% 29.57% 24.56% 27.37% 23.41% 22.19% 30.86% 31.72% 24.26% 30.66% 32.13% 29.48% 42.94% 38.63% 37.29% 27.76% 30.02% 36.48% 37.34% 32.87% 33.23% 28.41%

* * ** ** ** ** ** ** ** * **

** * * ** * ** **

**

Market share 33.40 38.30 23.50 24.90 22.90 14.80 30.20 58.70 53.90 22.90 23.30 21.00 25.30 45.80 23.20 23.20 41.90 33.80 21.50 54.50 48.70 38.60 41.20 20.30 44.40 44.40 44.10 44.60 36.70

* * * * * * * * * * * *

* * * * * *

Number of brands 10.04 *** 5.15 *** 3.26 *** 4.30 *** 6.32 *** 8.84 *** 14.65 *** 6.73 *** 13.84 *** 8.04 *** 5.76 *** 22.63 ** 8.83 6.86 *** 4.24 6.45 *** 7.22 4.54 *** 3.97 *** 2.89 *** 3.65 *** 3.65 10.17 *** 5.08 *** 3.41 *** 3.21 *** 4.45 *** 5.50 ** 7.73 ***

28

Depth of assortment 4.11 12.20 12.59 3.73 * 7.35 13.60 *** 19.89 * 6.76 16.75 ** 5.40 *** 6.63 16.76 *** 9.91 7.39 6.00 4.41 13.07 6.65 *** 4.24 1.37 2.80 *** 10.97 *** 22.39 9.06 *** 3.19 ** 2.93 8.17 * 3.26 * 16.04 *

Promotions 1.17 2.09 0.91 1.23 2.69 3.02 5.48 1.15 2.68 4.39 2.02 5.89 2.96 1.26 1.80 1.67 1.08 1.00 1.80 0.78 0.78 1.41 2.73 1.76 1.23 0.45 1.28 1.17 1.67

*** * *** *** *** * ** ** *** ** *** ** ** * ** ***

*** *** ** * *

Table 4 Correlation matrix Variables 1. Depth of assortment 2. Number of brands 3. Number of promotions 4. Retailers’ rivalry 5. NB concentration 6. Brands rivalry 7. Advertising 8. Innovation 9. SB market share

1 -.13 -.18 -.16 -.07 -.07 .10 -.15 -.12 .14

2 ** ** ** * * ** ** ** **

.36 .24 .11 -.10 -.20 .05 .30 -.25

3 ** ** * ** ** ** **

.36 .05 -.20 -.20 -.03 .09 -.08

4

5

6

7

8

9

** ** ** ** **

.06 -.05 -.16 .06 .09 -.10

* ** * ** **

** Significant at 1%; * significant at 5%

29

.31 .03 .29 .22 -.26

** ** ** **

.37 .34 .07 -.32

** ** * **

.08 .01 .00

** .63 -.48

** **

-.55

**

Table 5 Principal component analysis Variables Innovation Advertising SB market share Number of brands Number of promotions Number of varieties Manufacturers’ rivalry NB concentration Explained variance

Factor 1 Competitive strategy 0.89 0.85 -0.75

Factor 2 Store differentiation

Factor 3 Market structure

0.78 0.73 0.64

26.8%

20.73%

30

0.88 0.80 19.5%

Table 6 Neural network analysis Indexes

Results

Estimation errors mean

-0.12

Estimation errors standard deviation

0.51

D.E. ratio (errors/output)

0.37

Correlation estimation/output

0.92

31

Table 7 Duncan’s multiple range test Group

Mean

Reputation

51.28

Format

50.36

Manufacturers’ concentration

36.28

Store brands market share

35.24

Manufacturers’ rivalry

33.46

Promotions

32.76

Assortment (number of brands)

31.27

Assortment (number of variants)

30.12

Innovation

27.35

Advertising

28.00

Full model

22.64

Retailers’ rivalry

17.10

32

Table 8 R-squared, composite reliability, and average variance extracted Store differentiation Competitive structure Market structure

R2 n.a. 0.29 0.14

Type of construct Reflective Reflective Formative

33

CR 0.02 0.63 n.a.

AVE 0.35 0.52 n.a.

Table 9 Structural model results

Store differentiation → Competitive strategy Competitive strategy → Market structure Market structure → SB shelf space

Standardized effects -0.54 0.38 -0.19

Note: One-tailed test; ** p < .05; *** p < .001

34

Standard error 0.24 0.10 0.10

t-value 2.25 3.80 1.94

** *** **

Figure 1 Research model STORE DIFFERENTIATION Number of brands Number of varieties Promotions Store image Store format: superstores

(-)

COMPETITIVE STRATEGY Advertising Innovation

(+)

MARKET STRUCTURE NB concentration LNB rivalry

(-)

STORE BRAND SHELF SPACE

Figure 2 Artificial Neural Network INPUTS

Market share

INTERMEDIATE UNITS

OUTPUTS

V1

Assortment V2 Depth Assort.

Shelf space

V3 Reputation Store format Retailers’ rivalry

V4

V5

NB concent.

NB Rivalry NB Differnt. Innovation Promotions

35