1
The effects of channel context and brand type on the
importance of brand strength
MSc. in Business Administration – Digital Business Track University of Amsterdam - Amsterdam Business School
Student name: Maria Kolokytha Student Number: 11374888
Supervisor: Kristopher Keller Date of submission: 22/06/2017
Version: Final Version
2 Statement of originality
This document is written by Student Maria Kolokytha who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
3 Contents Statement of originality... 2 Abstract ... 5 1 Introduction ... 6 2 Theoretical Background ... 8 3 Conceptual Model... 11 4 Hypotheses... 12 4.1 Channel Context ... 12 4.2 Brand type ... 13
4.3 The moderating effect of brand type ... 15
5 Research Method ... 16
5.1 Measures... 18
6 Data analysis ... 22
7 Descriptives ... 25
8 Results ... 25
8.1 Extension: The underlying role of information availability ... 33
9 Discussion ... 35
9.1 Theoretical Implications ... 36
9.2 Managerial implications ... 37
9.2.1 Implications for retailers ... 37
9.2.2 Implications for national brand managers... 38
4 References ... 40 Appendix A: Pre-Test ... 46 Appendix B: Experiment ... 47
5 Abstract
FMCG online sales are on the rise and both private label and national brand managers try to mitigate their branding efforts online to capture the lion share of the expected growth. But is the brand strength equally important to the decision in online and offline contexts? Moreover, are there differences in this effect between private labels and national brands? This study examines the differences on the importance of brand strength across channel contexts and across different brand types. Using a rating-based conjoint analysis on an experimental setting this study finds that brand strength becomes more important to the decision online than offline. Further, this effect is driven by the information asymmetry in the online context. Still, the results vary across brand types. The importance of brand strength in the online context is even greater when consumers consider national brands than private labels. Private label managers benefit less from their brands’ strength online as consumers consider it as a less relevant attribute to their decision.
6 1 Introduction
The rise of electronic commerce (e-commerce) represents one of the most significant consumer market trends in the business field. A recent study conducted by Kantar Worldpanel (2016) predicts online sales of Fast-Moving Consumer Goods (FMCG) to reach the $150 billion until 2025 in global level. In South Korea alone, the online value share was 16.6% in June of 2016 and it is expected to surge to 25% the next decade, while China in 2025 will be responsible for the amount of $36 billion of this global growth in FMCGs (Kantar Reports, 2016).
However, this trend provides also a challenge for marketers because trading online is different from traditional brick and mortar stores (Alba et al., 1997). One key difference includes the potential of retail channels to provide diagnostic information attributes (Alba et al., 1997; Degeratu et al., 2000; Danaher et al., 2003). When there is asymmetry in information, brand strength can be an important source of information for consumers about the credibility of the product (Erdem and Swait, 1998). Therefore, the importance of brand strength online and offline may vary due to information availability in each context. This is particularly important for both managers and retailers because differences in the importance of brand strength across channel contexts can change how strong and weak brands compete from offline to online contexts.
Another trend that adds weight to this challenge is the importance private labels have gained in the FMCG industry. Unlike the generic offerings of the past, private labels now come to compete national brands by being developed as real branded offerings with certain brand position and principles. According to Private Label Manufacturers Association (PLMA’s International Private Label Yearbook, 2016) the market share of private labels in volume in 2015 was at least 50% in Switzerland and Spain, at least 40% in Belgium, Germany and United Kingdom, and up to 29% in Netherlands.
7 Albeit, both national brands and private labels build their brand strength, brand types may differ on how they are assessed by consumers. The importance weights that consumers assign to the separate pieces of available information when evaluating a product may not be the same when one considers different brand types. Extant literature has for long examined the factors that might affect the proneness to buy either private labels or national brands (Livesey and Lennon, 1978; Baltas, 1997; Batra and Sinha, 2000; Liu and Wang, 2008). However, these studies mostly focus on factors that related to consumer characteristics (exception: Batra and Sinha, 2000). Yet, there is still scant evidence on whether different product attributes are valued differently across brand types. That is, brand strength may not be equally important as a factor on the decision between private labels and national brands. This is particularly important since a strong private label compared to a strong national brand might differ in their potential to affect choices.
Therefore, the current study addresses the following research questions: (1) How the importance of brand strength changes between online and offline contexts? (2) Are there differences in this pattern across private labels and national brands?
This research contributes to the existing literature in several ways. First, this study addresses the call for experimental control on this issue (Degeratu et al., 2000). Whereas previous studies (Degeratu et al., 2000; Danaher et al., 2003; Chu et al., 2010; Arce-Urriza and Cebollada, 2012) have provided important insights on whether importance of brand strength differs across channel contexts, as observational studies fail to find the phycological reasons for the examined effects (Saini and Lynch, 2016). This study, through causal inferences, finds support on whether the importance of brand strength differs across channel contexts, and finds out whether information availability is the underlying reason that drives this difference. Next, although Saini and Lynch (2016) yield through their experiment some useful initial insights to the previous question, their study focuses only on the differences of channel contexts, disregarding
8 the differences that might exist across brand types. This research sheds light on whether product attributes – and especially brand strength – of private labels and national brands are considered as equally important to the decision by consumers. Consequently, this study determines whether researchers should consider these brand types together when examining branding effects or should treat them apart.
From a managerial point of view, the findings of this study will offer several implications relevant to both retailers and manufacturers. Knowing the potential of each brand to affect choices across channel contexts, retailers will be able to optimise their assortment in both online and offline contexts such as to maximize their revenues. Next, the findings will yield useful implications for managers of both strong and weak brands, concerning whether there is a channel context where they could compete with greater potential for success. To this end, the results will reveal whether managers of national brands can still base their efforts on their brands’ strength to compete private labels in either channel context.
The purpose of this study is to identify potential differences on the importance of brand strength across different channel contexts and over different brand types. To accomplish this, the study defines the effect of channel context on the importance of brand strength and postulates that this effect is moderating by brand type. The results of this study are estimated by conducting a rating-based conjoint analysis through an experiment with four different conditions: private labels in the online context, private labels in the offline context, national brands in the online context and national brands in the offline context.
2 Theoretical Background
Extant literature has defined brand strength as the differential effect of brand awareness and brand image on perceptions, preferences, and responses to the marketing activities of a brand (Keller, 1993; Hoeffler and Keller, 2003; Datta et al., 2017). Datta et al. (2017) suggest that
9 there are two broad measurement approaches of brand strength, namely consumer-based brand strength and sales-based brand strength. The former measurement based on the common premise that brand strength lies on consumers’ feelings and thoughts about the brand (Keller, 1993; Leone et al., 2006; Datta et al., 2017), whereas the latter based on the manifestation of these perceptions in marketplace reactions (Keller, 1998; Leone et al., 2006; Datta et al., 2017). Illustrating further the concept of consumer-based brand strength, literature suggests that customers react more favourable to the marketing activities of a strong brand than they would react to the same marketing efforts of an unnamed version of the product or service (Keller, 1993; Hoeffler and Keller, 2003). This study focuses on consumer-based brand strength, and therefore when it refers to the construct of brand strength considers the consumer-based brand strength.
Branding literature has long emphasized the importance of studying brand strength since it is considered one of the most important intangible assets of firms as it generates many marketing advantages such as greater price premiums (Aaker, 1992; Agarwal and Rao, 1996; Hoeffler and Keller, 2003), lower price elasticities and better effectiveness of communications (Hoeffler and Keller, 2003). Brand strength also has a meaningful impact on customer acquisition, retention, and profitability (Leone et al., 2006; Stahl et al., 2012).
Erdem and Swait (1998) support that when there is asymmetry in information, brand strength can be an important cue for consumers about the credibility of the product. Consumers when evaluating a product, search for four different types of product attributes, namely brand name (and therefore brand strength), price, sensory attributes1 and non-sensory attributes2, and
integrate this available information into overall product preferences (Degeratu et al., 2000).
1 Sensory attributes that are searchable prior to purchase are those attributes that can be determined through senses (touch, sound and smell) before we buy and experience the product (like taste).
10 Anderson (1981) developed the concept of Information Integration Theory, which describes how individuals integrate the available information to make an overall judgement. Degeratu et al. (2000) argue that based on the conception of this theory, customers assign importance weights to product attributes for which information is available at the time of the decision. The theory also suggests that if any factor (information attribute) relevant to the decision is removed, then the importance weight of one or more of the remaining factors will increase. Therefore, based on the information attributes that are available each time the importance of brand strength in the decision can be multiplied or diminished. For managers, it is important to know under which conditions the importance of their brands on the decision is stronger or weaker, because this affects their ability to leverage the advantages deriving from their brands’ strength.
Companies use different channels to sell their products, as do consumers for their shopping (Arce-Urriza et al., 2017). Channel contexts are defined as the ways that retailers can sell their products and consequently the ways that consumers can buy from retailers’ assortment. Today, FMCG retailers can trade their products either through online contexts with the establishment of e-tail stores, or through offline contexts with the operation of traditional brick and mortar stores. Alba et al. (1997) suggest that the potential of retail channels to provide information of attributes linked to consumption benefits differs greatly across contexts. Consequently, it is expected under different channel contexts the importance of brand strength to differ. Both retailers and brand managers should be able to understand how this difference of channel contexts affects brand choices because this impacts profitability (Aaker, 1992; Kapferer, 1997; Keller, 1998; Danaher et al., 2003).
Following this challenge, the importance of brand strength on the decision may also vary across different brand types. As brand types are considered the classification of brands into private labels and national brands. Private labels or store brands are lines of products that owned,
11 managed and branded by a particular retailer, while national brands are brands that owned and managed by manufacturers. Yet, literature supports that while private labels now build their own brand strength, still brand types differ on how they are perceived by consumers (González Mieres et al., 2006). Consequently, the importance of brand strength on the decision may also differ across brand types, while at the same time the differences between private labels and national brands may also moderate the effect of channel context on the importance of brand strength. As private label managers have started to invest into building their brands’ strength, they should be aware whether there is a channel that amplifies or defuses their marketing efforts and hence their investments.
3 Conceptual Model
The questions of this study are depicted in the below conceptual model.
H2 Channel Context (Online/
Offline Context)
Importance of Brand Strength
Brand Type (private label/ national brand)
H1
12 4 Hypotheses
4.1 Channel Context
Channel contexts are likely to differ on the information amount they provide. Offline contexts are more likely to provide sensory information. Physical contact with products is an important source of information to evaluate different alternatives and make choices (Peck and Childers,
2003a and 2003b; Yazdanparast and Spears, 2013; González-Benito et al., 2015). Some
products provide several diagnostic cues through touch and smell that can be relevant to the decision and help consumers to assess whether the products fulfil their needs. Based on the Information integration theory (Anderson, 1981), the evaluation of these products will be a consolidation of the utilities of this sensory information and other product attributes that are available each time. Consequently, in the offline context, the importance of brand strength will be constrained by the coexistence of the other information attributes such as sensory cues. On contrary, sensory attributes are not available when buying from the online context. Brand strength, price, and other non-sensory information are easy to be uncovered both online and offline, but it is difficult to convey product’s sensory attributes in online contexts (Chu et al., 2010). Based on the information integration theory and for products that sensory features are important factors on the assessment, the impact of one or more of the remaining product attributes in the evaluation will increase.
Chu et al. (2010) show that consumers of FMCGs are less price sensitive in the online context than in the offline context. They also find that the effect is greater for sensory goods. Degeratu et al. (2000), indirectly support that price sensitivity is smaller online than offline. Subsequently, it is expected for sensory products, price to become a less relevant attribute online and hence the weights of one or both of the remaining factors – namely brand strength and non-sensory features – to increase.
13 Consumers tend to rely their attributes assessments on the overall attitude toward the product than discriminate and evaluate carefully conceptual distinct product attributes (Leuthesser et al., 1995). This systematic bias referred to as “halo effect” (Thorndike, 1920; Leuthesser et al., 1995). Leuthesser et al. (1995) suggest that if any relevant information to the evaluation is missing, people may “substitute” this absence by relying their evaluation on their overall attitude toward the brand. Therefore, in the online context where sensory information is not available and for products that sensory attributes are relevant to the decision, under the “halo effect” argument, it is expected the importance of brand strength to increase.
Combined all the above, it is expected the potential of a strong brand to affect consumers’ attitudes to be greater in the online context than the offline context.
H1: Online context positively affects the importance of brand strength such as the importance of brand strength is greater in online than offline contexts.
4.2 Brand type
While both private labels and national brands make efforts to build their strength, brand types may differ on whether their brand strength can be an important factor in the decision.
In general, brands build their strength by managing both their quality and imagery positioning. Quality is related to the brand performance and the intrinsic properties of the brand, and consequently refers to how the product attempts to meet the functional needs of customers (Keller, 2001). Recent literature supports that private labels can be classified into three tiers based on their quality positioning (Geyskens et al., 2010; Gielens, 2012; Ter Braak et al., 2013). Geyskens et al. (2010) suggest that private labels can be introduced as economy, standard or premium private labels, which in turn provides a wider range of product differentiation in terms of quality and product features (Ter Braak et al., 2013). This distinction across private label
14 offerings facilitates their assessment based on the strength of private label brands, as the advantages of stronger brands (i.e. higher quality) are easily perceived by consumers. Compared with private labels, national brands have not had a clear distinction in terms of quality differentiation across different levels of brand strength, which makes their assessment based on their brand strength more difficult.
Brand imagery refers to the extrinsic properties of a product, which extrinsic properties are the ways the brand attempts to fulfil more abstract, social needs of customers (Keller, 2001). Literature suggests that “private label imagery is intimately tied to the store’s imagery, which by definition will always have to be very broad and bland in comparison” (Thain and Bradley, 2012). Consequently, private labels do not differentiate greatly in terms of their imagery positioning, which in turn means that customers do not perceive great differences between strong and weak private labels. This incommodes the assessment of private labels based on their brand strength. In contrast, national brands that satisfy the same functional need may differ greatly in their brand imagery. As Kumar and Steenkamp (2007) recognise, “two brands may be quality equivalent, but if one brand is stronger on image, it will generate higher utility”. This further suggests that customers perceive the differences between strong and weak images of national brands, which in turn facilitates the assessment of the alternatives based on their brand strength.
The aforementioned opposing arguments form an empirical issue that can be reconciled by the fact that the evaluation of private labels is driven primarily by the extrinsic cues of the products rather than their intrinsic characteristics (Richardson et al., 1994). Therefore, customers when considering private labels base their evaluation less on intrinsic cues, which facilitates the assessment of the products based on their brand strength. Consequently, it is expected consumers to base their decision less on the importance of brand strength. Overall, it is
15 expected the importance of brand strength to be a less relevant attribute for private labels compared to national brands.
H2: National brands positively affect the importance of brand strength such as the importance of brand strength is greater for national brands than private labels.
4.3 The moderating effect of brand type
In the online context, the importance of brand strength it is expected to be greater than offline because of the information asymmetry. However, the extent to which brand strength is important online on the decision may also depend on the brand types. González Mieres et al. (2006) suggest that while retailers make efforts to convert their private labels into real brands, this type of brand is still perceived as riskier than national brands. Extant research has supported that the perceived risk is a vital determinant in private label evaluation (Bettman, 1974; Richardson et al., 1996; Batra and Sinha, 2000). González Mieres et al. (2006) find that the perceived financial risk has a significant effect on private labels purchase decision, as well as that the perceived financial risk is greater for private labels compared to national brands. The authors demonstrate that even with the low prices of private labels and the high prices of national brands the former are still perceived riskier than the latter. This induces that consumers are more price conscious when buying private label brands compared to national brands. Online contexts due to the absence of sensory information attributes are characterised by information asymmetry which in turn causes the online buyer to have a higher perceived risk in online contexts than in offline ones (San Martín and Camarero, 2009). Consequently, when someone considers private labels online the perceived financial risk should be greater than in the offline context and hence the consumer will be more price sensitive online. This implies that for private labels, price remains a relevant attribute in the online context which in turn,
16 based on the Information integration theory (Anderson, 1981), limits the importance of brand strength in the decision. On the other hand, as financial risk – and hence price – is not relevant for national brands, following the argumentation of the first hypothesis about price sensitivity, it is expected online, prices for national brands not to be a relevant factor in the decision. Therefore, for national brands the importance of brand strength should be greater than for private labels in the online context.
H3: The positive effect of the online channel on the importance of brand strength is amplified when consumers consider national brands than private labels.
5 Research Method
To empirically test the hypotheses, this study uses a 2x2 between-subjects factorial design that combines two treatments – (1) online versus offline context, and (2) private labels versus national brands – and hence four experimental conditions. This experimental design allowed to manipulate the amount of information that is available across channel contexts, while at the same time allowed to examine the differences between brand types. This study manipulated information availability by allowing only respondents of the offline condition to physically assess the sensory attributes of products. A conjoint analysis was designed to determine how consumers value the different attributes (brand strength, price, sensory attributes and non-sensory attributes) of a product alternative across the different experimental conditions. Participants of each condition rated 16 product alternatives on their likelihood to purchase. The empirical context of this study is the body wash crème category. This is a suitable category as it features both scent and other functional attributes that are both diagnostic attributes for consumers (Mintel CDC, 2013). Scent is particularly important for this study as it accounts for the sensory information attribute of which physical assessment is possible only in the offline
17 condition. As it is impossible to physically assess scent online, participants of this condition suffered from information asymmetry and it is expected this to have changed how they valued the remaining product attributes.
Dutch population is considered as a suitable population for this study. First, the internet penetration in Dutch market is among the greatest in Europe, 95% (Nielsen, 2015). Additionally, Netherlands in 2014 have the fastest growth in online FMCG sales, with 55% growth from 2013 (Syndy, 2015). Since the invasion of the internet is high in this market this population is proper to test the hypotheses for the online context. Second, in Netherlands, the market share of private labels amounts to 29% of the total market (PLMA, 2016). This is an indication of the maturation of private labels in this market hence the probability the participants to be familiar with the products is higher and therefore the results are going to be more accurate.
The sample consists of 61 respondents. Dutch students in the University of Amsterdam are considered a representative sample of the population for two reasons. First, students as a sample are common in this kind of researches (Laroche et al., 2005; Yazdanparast and Spears, 2012 and 2013; González-Benito et al. 2015). Following, students are part of the population that is more likely to have experience with online shopping (Laroche et al., 2005; González-Benito et al. 2015).
The responses for every condition were collected through non-probability convenience sampling technique where participants were asked to complete a digital form. As far as the first treatment is concerned, the collection of data for the online context conditions was administered online through email addresses, whereas for the offline context conditions participants were reached through physical contact in the university facilities. The difference
18 between the two channel context conditions was that offline participants had had the opportunity to interact physically with the products (touch, scent etc.).
On the brand type treatment, participants of both channel contexts were randomly assigned in one of the two brand type conditions. However, the assignment of participants was controlled in the extent to which it was ensured that the size in each condition was similar. Overall, the sample sizes of each condition are portrayed in Table 1.
At the beginning of the survey, every participant was treated with instructions to simulate that he/she shops either online or offline based on which sampling group he/she was assigned.
Table 1*
Channel Context
Online Context Offline Context
B ran d T yp e Private Label Condition 1 N=16 Condition 2 N=15 National Brand Condition 3 N=15 Condition 4 N=15 * Descriptive Statistics 5.1 Measures
The importance of brand strength is approached indirectly through the partworth utilities of a rating-based conjoint analysis. Participants were exposed to four different factors which correspond to four product attributes: price of the product, brand, scent and whether the product is organic. Each attribute was a two-level factor, formulating a total of 2x2x2x2 = 16 profile alternatives. The responders were asked to indicate the likelihood to purchase each profile alternative on an adapted version of Juster’s probability scale (1966) from 0 to 10 (0= Would definitely not buy it, 10= Would definitely buy it).
19 Conjoint analysis allows to calculate the partworth utilities of the four product attributes. More specific, the partworth utilities of the four factors correspond to the price sensitivity (price sensitivity is given by the parameter of price), the importance of brand strength (importance of brand strength is the parameter of brand strength), the utility of being an organic product (the utility of being an organic product is given by the parameter of organic variable), and the importance of scent (importance of scent is given by the parameter of scent). As these utilities will be calculated for each experimental condition, this will allow to test the hypotheses of this study (H1: Online context positively affects the importance of brand strength such as the importance of brand strength is greater in online than offline contexts; H2: National brands positively affect the importance of brand strength such as the importance of brand strength is greater for national brands than private labels; H3: The positive effect of the online channel on the importance of brand strength is amplified when consumers consider national brands than private labels).
The profile cards for both brand type conditions were randomly presented to respondents to avoid fatigue that could affect the ratings of the later presented cards (the profile cards are presented in Appendix B).
Brand strength. All participants came across with two brands, either private labels or national brands depending on which brand type condition they were assigned to.The brands of the study were selected based on their brand strength score (Yoo and Donthu, 2001). A pre-test survey was designed based on the customer-based brand strength scale of Yoo and Donthu (2001). A sample of 22 participants reached through non-probability convenient sampling technique and randomly assigned to answer questions either for four private labels (N=10) or four national brands (N=12). The participants saw 10 items for each brand, concerning the three dimensions of brand strength, namely brand awareness/ associations, perceived quality and brand loyalty,
20 and evaluated them on a five-point Likert scale (1=strongly disagree and 5=strongly agree) (Yoo and Donthu, 2001). The items are presented in Appendix A.
Each dimension of the pre-test has high internal consistency and scale reliability (brand awareness/ associations α = .90, perceived quality α = .84 and brand loyalty α = .91). The reliability of the revised construct of brand strength (10 items) is high with Cronbach’s α = .83.
The composite score of the means of each dimension was used to approximate the brand strength score of each brand. Figure 1 depicts the composite scores of all the tested brands. One strong and one weak brand 3 were selected for each brand type condition. Responders that were assigned to the private label condition saw products of Etos and Kruidvat, whereas responders of the national brand condition saw the brands Dove and Palmolive. Although Cien has lower brand strength than Kruidvat, the latter has been selected for the experiment as Cien and Etos do not have common scents.
Figure 1a4. Brand Strength Scores - National Brands
3 Strong brand is considered the brand with higher brand strength score and weak brand is considered the brand with lower brand strength score. i.e. Nivea and Dove are considered strong brands, whereas Fa and Palmolive are weak brands (see composite scores – Figure 1a). 4 Notes to Figure 1a & 1b: The brand strength score is the composite score of the means of three scales (brand awareness/ associations, perceived quality and brand loyalty). For instance, the brand strength score of Dove is calculated as: 4.5667 (the mean of brand awareness/ associations) + 4.5000 (the mean of perceived quality) + 3.5278 (the mean of brand loyalty)
12.5944 12.2472 8.3694 8.7361 Dove Nivea Palmolive Fa Figure 1a
21 Figure 1b. Brand Strength Scores - Private Labels
Scent. This attribute is considered the sensory attribute that because of its absence from the online context will cause information asymmetry and consequently the differences on the importance of brand strength in the two channel contexts. The selected scents were ones that were common between the two brands of each condition.
Price. Both in the private label and in the national brand condition, the prices were formed based on the lowest and the highest price of the market between the two brands of the condition for the month April of 2016. For instance, for the private labels condition the prices were 1.15 euro (lowest price at which the two private labels were offered) and 1.75 euro (highest price at which the two private labels were offered).
Organic. Organic information attribute represents the non-sensory attribute for this study. Schuitema and Groot (2015) support that green product attributes like organic ingredients do affect the intentions to purchase and are important determinants of purchasing behaviour. Information about the age, the gender and the educational background of each respondent was collected to control the effect of customer’s profile. The average age in the online condition for both brand type conditions is 24 years, whereas in the offline condition the average age for both brand type conditions is 22 years. In the online condition, 65% of the participants are women, while in the offline condition 67% of the participants are men. Finally, respondents in
11.5200 7.2633 12.1233 6.2800 Albert Heijn Kruidvat Etos Cien Figure 1b
22 the online context condition have acquired on average a bachelor’s degree, whereas respondents in the offline context condition have acquired on average some college degree or no degree.
To this end, because unfamiliarity of participants with the tested brands might have an effect on their ratings, two items for brand awareness (Yoo and Donthu, 2001) were used to ensure the familiarity of the responders with the tested brands (Appendix B). The responses were measured on a 5-point Likert scale (1= strongly disagree and 5= strongly agree).
6 Data analysis
The analysis begins by constructing a base model (Model 1) with the effects of the experimental conditions and the effects of the different product attributes on the likelihood to purchase. Subsequently, interactions are added to this base model on equations (2) and (3).
(1)𝐿𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 𝑡𝑜 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒
= 𝛽0+ 𝛽1𝐶ℎ𝑎𝑛𝑛𝑒𝑙𝐶𝑜𝑛𝑡𝑒𝑥𝑡 + 𝛽2𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 + 𝛽3𝐵𝑟𝑎𝑛𝑑𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ + 𝛽4𝑆𝑐𝑒𝑛𝑡 + 𝛽5𝑂𝑟𝑔𝑎𝑛𝑖𝑐 + 𝛽6𝑃𝑟𝑖𝑐𝑒 + 𝜀
where the output is the likelihood to purchase, an interval scale variable with values varying from 0 (would definitely not buy it) to 10 (would definitely buy it), 𝛽0 is the intercept term that is the purchase likelihood if the values of all the variables are zero (therefore the purchase likelihood of the parameters’ reference groups), and 𝜀 is the error term which is the part that cannot be observed and estimated by the model.
In Model 1, 𝛽1 is the contribution of buying through online channels (ChannelContext; 1 = Online) and 𝛽2 is the effect of being a private label brand (BrandType; 1 = Private Label). Next, the effects of products attributes are examined; 𝛽3 is the utility of strong brands and therefore
23 the importance of brand strength on the likelihood to purchase (BrandStrength; 1= Strong Brand), 𝛽4 is the contribution of scent (Scent; 1 = Coconut), 𝛽5 is the utility of being an organic product which is the non-sensory product attribute of the model (Organic; 1 = yes), and finally 𝛽6 is the effect of high prices on the likelihood to purchase and hence the price sensitivity (Price; 1 = High Price).
Equation 2 expands Equation 1 by adding the two-way interaction between the two experimental conditions and two-way interactions between experimental conditions and product attributes. The interactions with scent are not employed to the model as the physical assessment of scent is only available when someone purchases from the offline context.
(2)𝐿𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 𝑡𝑜 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒 = 𝛽0′+ 𝛽1′𝐶ℎ𝑎𝑛𝑛𝑒𝑙𝐶𝑜𝑛𝑡𝑒𝑥𝑡 + 𝛽2′𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 + 𝛽3′𝐵𝑟𝑎𝑛𝑑𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ + 𝛽4′𝑆𝑐𝑒𝑛𝑡 + 𝛽5′𝑂𝑟𝑔𝑎𝑛𝑖𝑐 + 𝛽6′𝑃𝑟𝑖𝑐𝑒 + 𝛽7′𝐶ℎ𝑎𝑛𝑛𝑒𝑙𝐶𝑜𝑛𝑡𝑒𝑥𝑡 × 𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 + 𝛽8′𝑂𝑟𝑔𝑎𝑛𝑖𝑐 × 𝐶ℎ𝑎𝑛𝑛𝑒𝑙𝐶𝑜𝑛𝑡𝑒𝑥𝑡 + 𝛽 9′𝑃𝑟𝑖𝑐𝑒 × 𝐶ℎ𝑎𝑛𝑛𝑒𝑙 𝐶𝑜𝑛𝑡𝑒𝑥𝑡 + 𝛽10′ 𝐵𝑟𝑎𝑛𝑑𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ × 𝐶ℎ𝑎𝑛𝑛𝑒𝑙𝐶𝑜𝑛𝑡𝑒𝑥𝑡 + 𝛽11′ 𝑂𝑟𝑔𝑎𝑛𝑖𝑐 × 𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 + 𝛽12′ 𝑃𝑟𝑖𝑐𝑒 × 𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 + 𝛽13′ 𝐵𝑟𝑎𝑛𝑑𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ × 𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 + 𝜀′
Equation 2, accounts for the interaction effect between the two experimental conditions, and therefore the parameter 𝛽7′ shows the effect of private labels on the likelihood to purchase when consumers buy from the online than the offline context (ChannelContext x BrandType; 1 = Online, Private Label).
Next, Equation 2 is the model to examine the effects of product attributes over the different channel contexts and the different brand types. The remaining parameters that are added in this model represent the effects of the interactions between the experimental conditions and the product attributes. Specifically, 𝛽8′ shows the effect of organic products on the likelihood to purchase when consumers buy from online channels than offline ones (Organic x
24 ChannelContext; 1 = Organic yes, Online), 𝛽9′ is the price sensitivity when consumers buy online than offline (Price x ChannelContext; 1 = High Price, Online) and so on. Parameters 𝛽10′ and 𝛽13′ are particularly important for this study as with these parameters the effects of channel context and brand type on the importance of brand strength are examined. Specifically, the parameter 𝛽10′ examines the hypothesis H1, whereas the hypothesis H2 is tested by the parameter 𝛽13′ .
Equation 3 expands Equation 2 by adding the three-way interaction effect of brand strength with brand type and channel context, and the three-way interaction effect of price with brand type and channel context:
(3)𝐿𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 𝑡𝑜 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒 = 𝛽0′′+ 𝛽1′′𝐶ℎ𝑎𝑛𝑛𝑒𝑙𝐶𝑜𝑛𝑡𝑒𝑥𝑡 + 𝛽2′′𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 + 𝛽3′′𝐵𝑟𝑎𝑛𝑑𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ + 𝛽4′′𝑆𝑐𝑒𝑛𝑡 + 𝛽5′′𝑂𝑟𝑔𝑎𝑛𝑖𝑐 + 𝛽6′′𝑃𝑟𝑖𝑐𝑒 + 𝛽7′′𝐶ℎ𝑎𝑛𝑛𝑒𝑙𝐶𝑜𝑛𝑡𝑒𝑥𝑡 × 𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 + 𝛽8′′𝑂𝑟𝑔𝑎𝑛𝑖𝑐 × 𝐶ℎ𝑎𝑛𝑛𝑒𝑙𝐶𝑜𝑛𝑡𝑒𝑥𝑡 + 𝛽9′′𝑃𝑟𝑖𝑐𝑒 × 𝐶ℎ𝑎𝑛𝑛𝑒𝑙 𝐶𝑜𝑛𝑡𝑒𝑥𝑡 + 𝛽10′′𝐵𝑟𝑎𝑛𝑑𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ × 𝐶ℎ𝑎𝑛𝑛𝑒𝑙𝐶𝑜𝑛𝑡𝑒𝑥𝑡 + 𝛽11′′𝑂𝑟𝑔𝑎𝑛𝑖𝑐 × 𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 + 𝛽12′′𝑃𝑟𝑖𝑐𝑒 × 𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 + 𝛽13′′𝐵𝑟𝑎𝑛𝑑𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ × 𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 + 𝛽14′′𝑃𝑟𝑖𝑐𝑒 × 𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 × 𝐶ℎ𝑎𝑛𝑛𝑒𝑙𝐶𝑜𝑛𝑡𝑒𝑥𝑡 + 𝛽15′′𝐵𝑟𝑎𝑛𝑑𝑆𝑡𝑟𝑒𝑛𝑔𝑡ℎ × 𝐵𝑟𝑎𝑛𝑑𝑇𝑦𝑝𝑒 × 𝐶ℎ𝑎𝑛𝑛𝑒𝑙𝐶𝑜𝑛𝑡𝑒𝑥𝑡 + 𝜀′′
Equation 3 is the model to test whether the price sensitivity of private labels becomes stronger or weaker in the online context compared to the offline one. The parameter 𝛽14′′ is the effect of the three-way interaction between high prices, private labels and online context (Price x BrandType x ChannelContext; 1 = High Price, Private Label, Online). Moreover, equation 3 examines whether the importance of brand strength in the online context becomes stronger or weaker for private labels compared to national brands (Ηypothesis H3). Parameter 𝛽15′′ is the effect of the three-way interaction of strong brands with private labels and online context
25 (BrandStrength x BrandType x ChannelContext; 1 = Strong Brand, Private Label, Online), which tests the hypothesis H3.
7 Descriptives
As 61 participants participated in this survey and evaluated 16 alternatives each, 976 observations were generated in total. Table 2 provides an overview of likelihood to purchase ratings across the experimental conditions of this study and the t-tests results. In the offline context, consumers seem to be significantly (p < .10) more likely to buy national brands (5.30 ± 1.919, n = 240) than private labels (4.92 ± 2.307, n = 240). However, the phenomenon is reversed when one looks the online context (national brands: 4.99 ± 2427, n = 240; private labels: 5.48 ± 2.064, n = 256). In the online context, consumers are significantly more likely to buy private labels than national brands (p < .05).
Table 2: Descriptive Statistics for Likelihood to Purchase National Brand Private Label Mean SDa N Mean SD N t-test Online Context 4.99 2.427 240 5.48 2.064 256 -2.389†† Offline Context 5.30 1.919 240 4.92 2.307 240 1.957† † p < .10, †† p < .05, ††† p < .01 (two-sided) a Standard Deviation 8 Results
This study uses multiple linear regression to estimate the effects of the different constructs on the likelihood to purchase. Overall, to examine the hypotheses, this study runs three multiple linear regressions, based on the three equations that have been constructed. Table 3 reports the parameter estimates for the three models. This study uses one-sided tests for the directional hypothesized effects and two-sided tests for the nondirectional effects.
26 Table 3
Model 1 Model 2 Model 3
Variable Ha β
t-Value β t-Value β t-Value Cb
Intercept 4.787††† 26.087 5.147††† 19.951 5.263††† 19.091 √
Covariates
Channel Context (Online) 0.135 0.973 -0.871††† -2.831 -1.104††† -3.027
Brand Type (Private Label) 0.060 0.431 -0.087 -0.284 -0.320 -0.878
Brand Strength (Strong Brand) 0.650††† 4.694 0.265 1.110 -0.042 -0.151
Scent (Coconut) -0.031 -0.222 -0.031 -0.224 -0.031 -0.225 √
Organic (Yes) 0.330†† 2.384 0.246 1.032 0.246 1.034
Price (High Price) -0.371††† -2.680 -0.182 -0.763 -0.108 -0.393
Interactions with Covariates
Channel Context (Online) x Brand Type (Private Label)
- - 0.864††† 3.151 1.322††† 2.789
Interactions with Channel Context
Organic (Yes) x Channel Context (Online) - - 0.132 0.482 0.132 0.483
Price (High Price) x Channel Context (Online)
- - 0.006 0.022 -0.142 -0.363
Brand Strength (Strong Brand) x Channel Context (Online)
H1 (+) - - 0.995*** 3.629 1.608*** 4.121
Interactions with Brand Type
Organic (Yes) x Brand Type (Private Label) - - 0.032 0.117 0.032 0.117
Price (High Price) x Brand Type (Private Label)
- - -0.377* -1.376 -0.525* -1.345
Brand Strength (Strong Brand) x Brand Type (Private Label)
27 Interactions with Channel Context and Brand Type
Price (High) x Brand Type (Private Label) x Channel Context (Online)
- - - - 0.291 0.531
Brand Strength (Strong Brand) x Brand Type (Private Label) x Channel Context (Online)
H3 (-) - - - - -1.207** -2.205
N 975 975 975
R2 0.036 0.061 0.066
F-ratio; d.f.; p-value 6.016; 6, 969; .000 4.823; 13, 962; .000 4.536; 15, 960; .000
AICc 1511.822 1499.895 1498.680
Notes: All the variables are dummy variables and the parentheses in the table denote what the value of 1 is in each case. One-sided tests used for directional hypothesized effects, two-sided tests otherwise.
* p < .10, ** p < .05, *** p < .01 (one-sided) †
p < .10, †† p < .05, ††† p < .01 (two-sided) a Hypotheses, b Consistency
28 The first model is the base model that accounts for the effects of the two variables of the experimental conditions, the effects of the four product attributes on the likelihood to purchase and the intercept term that is the mean score of the likelihood to purchase for all predictors’ reference groups. For the Model 1, the percent of the variance in likelihood to purchase that can be explained from the predictors of the model is R2 = .036. The F-ratio is F(6,969) = 6.016, which is highly significant (p < .01) so it can be assumed that there is a linear relationship between the variables in the model.
This study finds that consumers seem to be more willing to buy online than offline (𝛽1 = .135) while at the same time they are more intent to purchase private labels compared to national brands (𝛽2 = .060). However, both effects are not significant which implies that channel context and brand type do not affect the likelihood to purchase.
Concerning the effects of product attributes on the decision, the results indicate that customers are more intent to buy stronger brands (𝛽3 = .650, p < .01), and organic products (𝛽5 = .330, p < .05). They are also less willing to purchase products on higher prices (𝛽6 = -.371, p < .01). However, likelihood to purchase is not affected by the scent as the parameter is not significant. This result is not in line with the earlier theorising that consumers when evaluating a brand, they value also sensory attributes to their final decision. For this reason, additional tests will be done to further examine the underline effect of scent on the decision.
To interpret the effects of channel contexts and brand types on different product attributes – and consequently on the importance of brand strength – the two-way interaction effects are required. The second model expands Model 1 by accounting also for the two-way interaction effect between the two experimental conditions (brand type, channel context) and the two-way interaction effects between the three information attributes (price, brand, organic) and the two experimental conditions (brand type, channel context).
29 The additional parameters in Model 2, help sufficiently in increasing the model fit, as the value of AIC (Akaike Information Criterion) is decreasing from Model 1 (AIC1 = 1511.822) to Model 2 (AIC2 = 1499.895), which indicates a better model fit. The proportion of the variance in the likelihood to purchase that is predictable by the variables of this model is R2 = .061, while the F-test supports the linear relationship between the variables of the model, F(13,962) = 4.823 (p < .01).
This study tests whether channel context and brand type interact. The results in Model 1 show that there is a positive but not significant effect of the online context on the likelihood to purchase (𝛽1 = .135). Accounting for the interaction effect between the experimental conditions in Model 2 shows that this positive effect of online context becomes even stronger and statistically significant when consumers buy private labels compared to national brands (𝛽7′ = .864, p < .01).
Similarly, the results suggest that the positive effect of organic products on the likelihood to purchase (𝛽5 = .330, p < .05) is positive but weaker when consumers buy from online channels than offline ones (𝛽8′ = .132) and when they buy private labels compared to national brands (𝛽11′ = .032). However, the interaction effects between organic attribute and experimental conditions are not significant.
Next, the negative effect of high prices (𝛽6 = -.371, p < .01), becomes much weaker and close to zero when consumers buy online (𝛽9′ = .006) than offline, however, the effect is not statistically significant. This is in line with the findings of Chu et al. (2010) who support that consumers of FMCG products are less price sensitive in the online context compare to the offline context. Following, the negative effect of high prices on the likelihood to purchase (𝛽6 = -.371, p < .01) becomes much stronger when consumers buy private labels (𝛽12′ = -.377, p <
30 .10) rather than national brands. In support of the earlier theorising, consumers are less likely to buy private labels at higher prices than they do for national brands.
Turning to the focal effects of this study, the positive effect of strong brands on the likelihood to purchase (𝛽3 = .650, p < .01), becomes much stronger when consumers buy from online contexts (𝛽10′ = .995, p < .01) compared to offline contexts. That is, in support of the first hypothesis, consumers seem to be more prone to buy strong brands in the online contexts than offline contexts. In line with previous studies, the importance of brand strength on the decision increases in the online context such as “strong brands do even better online than offline” (Danaher et al., 2003).
For the effect of brand types on the importance of brand strength, it is expected a positive relationship between national brands and importance of brand strength, such as the importance of brand strength to be greater for national brands than private labels. This study finds that the positive effect of strong brands (𝛽3 = .650, p < .01) on the likelihood to purchase, is weaker and negative for private labels (𝛽13′ = -.238) compared to national brands. This implies a positive effect for national brands. Although the direction of the effect is in line with the expectations, the effect is not significant and consequently, the hypothesis H2 is not confirmed. Turning to the nested parameters of Model 1 in Model 2, the effect of channel context on the importance of brand strength becomes negative and significant (𝛽1′ = -.871, p < .01), while the effect of brand type becomes negative but remain robust in level of significance (𝛽2′ = -.087, p > .10). Next, the effect of brand strength becomes weaker and not statistically significant (𝛽3′ = .265). Accounting for the interaction effect of brand strength (strong brand) with the channel context (online) on Model 2 shows that this positive effect of brand strength in Model 1 (𝛽3 = .650, p < .01), predominately comes from online context which further support the first hypothesis. Following, the effect of scent on the likelihood to purchase remains robust both in
31 sign and level of significance, while the effects of organic attribute and price become weaker and not statistically significant. Accounting also for the interaction effect of price (high) with the brand type (private label) on Equation 2 shows that this negative effect of high prices in Equation 1, is driven by high prices for private labels.
The third model has been constructed to interpret the three-way interaction effects of this study. Specifically, in this model, the interaction between brand strength and both experimental conditions, as well as the interaction between price and both experimental conditions are added. The value of AIC is decreasing for Model 2 (AIC2 = 1499.895) to Model 3 (AIC3 = 1498.680), which indicates better model fit for Model 3. The R2 for the third model is .066, while the F-ratio is F(15,960) = 4.536 (p < .01) which supports the linear relationship between the variables of the model.
The results demonstrate that the negative effect of high prices on the likelihood to purchase (𝛽6 = -.371, p < .01), is slightly stronger for private labels than national brands (𝛽12′ = -.377, p < .10), however, the stronger negative effect of private labels on higher prices is weaker, positive and not statistically significant for online contexts (𝛽14′′= .291) that offline ones. In other words, higher prices decrease the likelihood to purchase and this effect is even stronger when consumers buy private labels, however, when consumers buy private labels from online contexts price does not affect their likelihood to purchase. This result is in contrast to the predicted effect that for private labels online the consumers will become more price sensitive than offline.
Next, the moderating effect of brand type on the relationship between channel context and importance of brand strength is examined. This study expects the positive effect of the online context on the importance of brand strength – that observed in Model 2 – to become even greater and positive for national brands compared to private labels. The results demonstrate
32 that the positive effect of strong brands on the likelihood to purchase (𝛽3 = .650, p < .01), becomes stronger for online contexts than offline ones (𝛽10′ = .995, p < .01), however, the positive effect of the online context on the importance of brand strength is stronger but becomes negative for private labels compared to national brands (𝛽15′′ = -1.207, p < .05). This negative effect for private labels implies a positive effect for national brands. That is, in support of the hypothesis H3, while the importance of brand strength is stronger when consumers buy from online contexts than offline ones, this positive effect of the online channel on the importance of brand strength is even stronger when consumers consider national brands compared to private labels. This result supports the hypothesis that brand type moderates the relationship between channel context and importance of brand strength so that the positive effect of online channels on the importance of brand strength is amplified when consumers consider national brands than private labels.
Concerning the nested parameters of Model 2 in Model 3, all the parameters, except the brand strength, the interaction between price and channel context, and the interaction between brand strength and brand type, remain robust both in sign and level of significance from Model 2 to Model 3. Brand strength provides robust evidence for the significance level as it is not significant in both models, however, changes in sign and becomes negative (𝛽3′′= -.042). The effect of the interaction of price and brand type becomes negative (𝛽9′′ = -.142) but remains not statistically significant. Accounting also for the three-way interaction effect of strong brands, private labels, and high prices shows that the close to zero effect of high prices on the online context predominately comes from private labels. However, the effects are not significant which implies that both higher prices for private labels and national brands do not affect the likelihood to purchase in the online context. To this end, the interaction effect between brand strength and brand type remains not statistically significant but becomes positive (𝛽13′′ = .375), which means that accounting also for the three-way interaction of strong brands, private labels,
33 and online context, shows that this negative but not significant effect of strong private labels in Model 2 (𝛽13′ = -.238), comes from online contexts.
8.1 Extension: The underlying role of information availability
The results of the first model cast some doubts on whether information availability across channel contexts affects the importance of brand strength on the decision. Specifically, earlier theorising argues that because diagnostic sensory attributes (i.e. scent) are not available online, the importance of one or more of the remaining information attributes will increase. However, based on the previous findings scent is not significant on the likelihood to purchase which implies that scent is not a relevant attribute for the consumers.
To further diagnose the role of scent on the decision, two multiple linear regressions are used only for the observations that correspond to the offline condition. The first regression accounts for the Coconut scent that was common in both brand type conditions, and Vanilla that was available only in the national brand condition; the second regression employs Vanilla and Milk & Honey scents that were different for the two brand types. The results are presented in Table 4.
34 Table 4: Offline Context Cases Only
Coconut Other Scents
Variables β t β t
Intercept 4.898††† 13.118 5.365††† 21.032
Main Effects
Brand Type (Private Label) -0.233 -0.856 -0.233 -0.856
Brand Strength (Strong Brand) 0.146 0.756 0.146 0.756
Organic (Yes) 0.263 1.361 0.263 1.361
Price (High Price) -0.371† -1.923 -0.371† -1.923
Scent (Coconut) 0.467† 1.711 - -
Scent (Vanilla)a 0.292 0.756 -0.175 -0.642
Scent (Milk & Honey)a - - -0.467† -1.711
N 479 479
R2 0.027 0.034
F-ratio; d.f.; p-value 2.222; 6, 473; .040 2.222; 6, 473; .040 Notes: All the variables are dummy variables and the parentheses in the table denote what the value of 1 is in each case.
a Vanilla corresponds to National Brand condition, whereas Milk & Honey to Private Label condition
† p < .10, †† p < .05, ††† p < .01 (two-sided)
Indeed, coconut – which is the common scent between brand type conditions – seems to be a relevant attribute on the evaluation of an alternative (𝛽 = .467, p < .10). Coconut scent has a positive effect on the likelihood to purchase compared to Milk & Honey. This result provides an initial support that sensory attributes are important when someone considers a product in the offline context. Turning to the results of the second regression, Milk & Honey has a more negative effect on purchase likelihood (𝛽 = -.467) compare to Coconut and this is statistically significant at 10%. These results support that the scent attribute is relevant to the evaluation of the product.
35 Although it is not related to the underlying role of information availability, the results of these tests further clarify the finding that in the online context brand strength is more important to the decision than in the offline context.
9 Discussion
FMCG online sales are on the rise and both private label and national brand managers try to mitigate their branding efforts online to capture the lion share of the expected growth. Offline, strong brands have for a long enjoyed the differential effect of their branding knowledge on costumers’ preferences and reactions toward the brand. However, is brand strength important online, and if so, are there differences across channel contexts? Moreover, is the brand strength of private labels equally important on the decision as the brand strength of national brands? This study finds that indeed brand strength is more important in the online channel contexts than offline ones. Stronger brands perform better online as the brand strength becomes more relevant attribute on the decision when purchasing online. The results also support that the underline reason that drives this finding is the availability of diagnostic information attributes across channel contexts. Next, while the importance of brand strength is stronger when consumers buy from online contexts that offline ones, this effect is amplified when consumers consider national brands than private labels. That is, the importance of brand strength is greater for national brands than private labels in the online contexts.
The findings also demonstrate that when trading online, price becomes a less relevant attribute to the decision, which is in line with prior literature (Degeratu et al., 2000; Chu et al., 2010). Next, in the offline contexts, high prices have a negative effect on purchase likelihood of private labels, however, there is not such an effect for national brands.
36 9.1 Theoretical Implications
The findings of this study contribute to the existing literature of brand strength by demonstrating the differences of its importance across channel contexts through an experimental design. In line with previous studies, this research supports that well-known brands perform even better in online context than in the offline context (Degeratu et al., 2000; Danaher et al., 2003). However, these studies lack experimental control and hence cannot assign causal effects behind the observed phenomena. Such studies, support that the phenomena exist because of the differences in information availability across channel contexts. Specifically, Degeratu et al. (2000) suggest that brand names will be more important in categories where there are attributes that their evaluation is not easily accessible online. The setting of this research allows for causal inferences and supports the theorising of earlier studies that brand strength online becomes more important in categories where the evaluation of diagnostic attributes online is not possible.
Next, a key implication of this study is that different brand types may exert different effects on the importance of brand strength across channel contexts. Specifically, the findings concerning the moderating effect of brand type, demonstrate that in the online contexts importance of brand strength is greater when someone considers national brands than private labels. These results illustrate the importance of literature to account for both national brands and private labels and not aggregate the results across all brand types.
Summing up, this research holds three theoretical contributions. First, indeed after accounting for experimental control this study supports earlier findings that brand strength is more important online than offline. Second, the findings of this research support the underlying role of information availability in this effect. Finally, different brand types might exert different effects and therefore treating them as different conditions provides more useful insights.
37 9.2 Managerial implications
These results hold implications for both retailers and national brand managers. On the next sections, these implications will be discussed by classifying them into groups of interest.
9.2.1 Implications for retailers
Retail managers in the online contexts should take careful consideration of the assortment they carry in their online shops. Strong brands perform even better online and therefore retailers should be sure that they carry strong brands on their online shops to maximize their revenues. Retail managers of private label brands could also benefit from the insights of this study. As far as the differences between the brand types are concerned, the findings indicate that the importance of brand strength in the decision is greater online for national brands than for private labels. That is, the brand strength is a relevant attribute online, however, its importance is greater for national brands. Therefore, when trading their products online, private label managers in order to efficiently compete national brands should not concentrate their marketing efforts only on the brand itself, but more on other characteristics that might be more diagnostic for the customers. Literature suggests that the store aesthetics affect private label judgements offline (Richardson et al., 1996). Similarly, online store aesthetics on retailers’ online shopping platforms may have a positive effect on private labels proneness.
Next, the results indicate that offline higher prices on private labels are negatively related to the likelihood to purchase, however, this is not the case for national brands. This is particularly important, as private label managers should be very careful on the prices that they set in the offline contexts, as higher prices will negatively affect their private label sales. At the same time, it has been supported that in the online context, price is not a relevant factor on the decision, and higher prices of private labels do not negatively affect the likelihood to purchase.
38 Managers should apply higher prices online to counterfeit the negative effect of price in the offline context.
9.2.2 Implications for national brand managers
Managers of strong national brands should ensure their presence and enhance their marketing efforts online as their brands have even a greater potential online. The results indicate that the importance of brands strength is greater online than offline. This means that when consumers consider to purchase a product online, they value more a familiar brand than a less known product offering. Managers of strong brands should take the advantage of this phenomenon and improve their visibility in the online stores which in turn will increase their online sales. On the other hand, although the results indeed support further the conventional wisdom that managers of weak brands should make efforts to improve their brand strength in the long-term, managers of weak brands could also take useful insights from this study for their short-term tactics. The results support that consumers are less prone to buy weak brands online as well as that consumers are more price sensitive for national brands online. Consequently, managers of weak brands should put lower prices or provide better price discounts of their products online to effectively compete strong national brands.
The findings for the moderating effect indicate that in the case of the online context, national brands can still compete private labels through the differential effect of their brand strength on the decision. Brand strength is yet considered as a relevant, and even more important attribute when one evaluates national brands online. In conjunction with the finding that the effect is greater than this of private labels, national brands are less threatened by the brand strength of the private labels online. That is, managers of strong national brands can have an advantage when competing private labels online by simply promoting their brand name on their communication strategies online.
39 At the same time, the results demonstrate that in the offline contexts consumers are more price sensitive when they consider private label brands, while price is not a relevant factor for national brands in the offline contexts. National brand managers should take into consideration this finding as offline they can still benefit from price premiums compare to private labels.
10 Limitations and Directions for Further Research
This research has several limitations that offer avenues for future research. First, experimental designs that provide greater realism on the shopping conditions are needed to further validate the findings. The conjoint design of this study accounts for the utilities of four different product attributes and therefore provide good insights on which product attributes are relevant each time for the decision. However, in reality, people on their path to purchase may not be affected only by product attributes. Store aesthetics or promotional banners or even reviews of other customers may affect the final decision and therefore strengthen or weaken the importance of brand strength. Ho-Dac et al. (2013) have shown that positive online customer reviews on consumer electronics improve both the sales and the brand strength of weak brands, however, have no effect on strong brands. This might lessen the variation of the importance of brand strength among channel contexts. Further research should investigate these effects and particularly how the importance of brand strength responds to other information that is available on the environment.
Another limitation of this study is that it focusses only in one product category and only in the FMCG sector. Body wash crème products have been employed for this study and the results demonstrate that indeed for this product category the importance of brand strength is greater online than offline. Replicating the results on other product categories will generalize the findings. Alongside, both national brands and private labels are also present in other sectors
