Influencing brand extension evaluations
The influence of construal level on perceptions of fit
Tamara van Zoeren 11886242 August 31, 2021
MSc. in Business Administration – Consumer Marketing ABS, UvA
EBEC Approval Nr.: EC 20210511120509 Supervisor: Dr. Karin Venetis
2 STATEMENT OF ORIGINALITY
This document is written by Tamara van Zoeren 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.
Over the years, it has been confirmed time and time again that perceived fit is one of the most important determinants of brand extension evaluations. Therefore, many studies have focused on narrowing down the processes behind perceived fit, and finding out how it behaves when combined with other factors. One of these factors are the psychological distances pertaining to Construal Level Theory (CLT), whose interactions with perceived fit appear to be contradictory. Therefore, this study aimed to test these different interactions against each other, focusing on temporal distance as the representation of CLT. These different interactions come in the form of the perception-of-fit hypothesis, the importance-of-fit hypothesis and the matching effect, where especially the importance-of-fit hypothesis and the matching effect partially contradict each other. In order to test these effects in the context of temporal distance, an online experiment was conducted with 168 participants. Results confirmed the overall relationship between fit and brand extension evaluations, but none of the hypothesized interactions between fit and temporal distance could be confirmed. Additional research is needed to further probe these effects.
4 TABLE OF CONTENTS
INTRODUCTION ... 6
LITERATURE REVIEW ... 11
Brand extensions... 11
Brand extension evaluations ... 12
Perceived fit... 13
Effects of perceived fit ... 15
Construal Level Theory ... 17
Psychological distance ... 18
The dimensions of distance ... 19
Construal level and fit perceptions ... 21
Perception-of-fit hypothesis ... 22
Importance-of-fit hypothesis ... 24
The matching effect ... 26
METHOD ... 30
Pretest ... 30
Selection of brand and brand extensions ... 31
Selection of launch dates ... 32
Sample and procedure ... 33
Measures ... 34
Results ... 35
Main study ... 41
Procedure ... 42
Measures ... 43
RESULTS ... 47
Scale reliability ... 47
Sample ... 48
Descriptives and correlations ... 48
Response analysis ... 51
Manipulation check ... 55
Assumptions ... 56
Hypothesis testing ... 56
Product-level fit, perceived fit and brand extension evaluation ... 57
Mediation of the importance-of-fit hypothesis ... 61
The matching effect ... 61
Additional analyses... 64
Temporal distance ... 64
Moderation via level of mental abstraction ... 65
Processing fluency ... 67
DISCUSSION AND CONCLUSION ... 70
Summary ... 70
The effects of fit ... 70
Temporal distance ... 72
The perception-of-fit hypothesis, the importance-of-fit hypothesis, and the matching effect... 73
Troubleshooting temporal distance ... 75
The manipulation check ... 75
There is no effect of temporal distance ... 76
Sample differences ... 77
Larger methodological issues ... 78
Practical implications ... 79
Suggestions for future research ... 79
Conclusion ... 81
REFERENCES ... 82
APPENDICES ... 93
Appendix 1. Pretest surveys ... 93
APPENDIX 2. Pretest sample information and pre-analyses... 141
APPENDIX 3. Main study surveys... 144
APPENDIX 4. Main study assumptions ... 230
APPENDIX 5. Main study sample analysis ... 234
APPENDIX 6. Mediation analysis importance-of-fit via level of mental abstraction ... 237
APPENDIX 7. Separate analysis of match ... 239
APPENDIX 8. Exploratory analyses ... 240
Imagine you are browsing the supermarket shelves, looking for something inspiring to eat today. You feel like making yourself some pasta, and make your way over to the aisle. As you reach out to grab some delicious spaghetti, you suddenly see it. Despite seeing advertisements for it, you would have never thought to come across it yourself. You recoil, suddenly not in the mood for pasta anymore. As you hurry towards the noodles and soy sauce, the only thing you can see in the back of your mind is the dreaded Colgate lasagna.
Despite being trending on Twitter for a while, the Colgate lasagna probably never existed, although similar Colgate Kitchen Entrees did (Harris, 2020). The scenario above is of course a dramatization, and not necessarily an accurate representation of what consumers feel while grocery shopping. However, it shows how brand extensions have no guarantee of being successful, and can even generate feelings of revulsion. Accordingly, a lot of brand extensions fail, with the more optimistic estimates displaying a failure rate of 30-35%, and pessimistic estimates dropping to success rates of only 2 out of 10 (Batra et al., 2010; Hem et al., 2003;
Miniard et al., 2020). Despite these figures, to many firms brand extensions are an attractive way to enter new markets, leveraging existing brands with established names and images (Aaker & Keller, 1990; Parker et al., 2018). With this reality in mind researchers have attempted to define the concepts most central to brand extension evaluations, while also offering advice to managers to mitigate the possibility of failure (Aaker & Keller, 1990; Pina Pérez et al., 2006;
Zhang et al., 2020).
In order to help managers increase the success of brand extensions, the focus of literature so far has mainly been on discovering how brand extension evaluations, also discussed as attitudes towards extensions, come to be (e.g. Aaker & Keller, 1990). From this research perceived fit has been identified as one of, if not the, most important factors that influence brand extension evaluations (Aaker & Keller, 1990; Bottomley & Holden, 2001; Bridges et al., 2000).
7 The perceived fit of a brand extension reflects the degree of similarity between an extension and its parent brand’s other products or services, personality, positioning, or any other salient and relevant association (Bridges et al., 2000; Parker et al., 2018). Researchers are still attempting to fully understand perceived fit, discovering that its effects are context-dependent (e.g. Czellar, 2003). This finding has led to an increased interest in what these contextual factors are and what the nature of their interplay with perceived fit is (e.g. Kim & John, 2008).
Extending this line of inquiry into context, there is conclusive evidence that construal level specifically tends to interact with, and influence the effect perceived fit has on extension evaluations (Huang et al., 2017; Kim & John, 2008). Construal level is a concept that belongs to Construal Level Theory (CLT), a theory focusing on the effects psychological distance has on thoughts, evaluations, and behaviour (Trope et al., 2007). According to this theory, increasing psychological distance leads construals to become abstract, with the reverse for decreasing psychological distance, making construals more concrete (Trope & Liberman, 2010;
Williams et al., 2014). The same goes the other way around, where thinking abstractly about something makes it seem more distant, whereas concrete thinking makes it seem closer (Bar- Anan et al., 2006). In CLT there are three forms of psychological distance, time, space, and social, with hypothetical distance being a tentative fourth (Calderon et al., 2020; Trope et al., 2007). For each of these types of distance, the concept is the same: as distance between the judge and the judged increases, construal level becomes increasingly abstract (Bar-Anan et al., 2006; Trope et al., 2007; Trope & Liberman, 2010).
Due to the importance of perceived fit in determining brand extension evaluations, construal level is equally important to examine, as it has the potential to either change or strengthen the effects of perceived fit. Furthermore, as construal level is part of the context in which consumers experience or see a brand extension, it is something that can be influenced through marketing communication (Huang et al., 2017). As marketing support and framing are
8 another important set of determinants to brand extension evaluations (Völckner & Sattler, 2006), this will provide marketers with the ability to influence fit perceptions through communication about the extension.
Previous research indicates that the interaction between perceived fit and construal level has the propensity to take different forms. This research will focus on exploring these different forms, and finding out whether they can be reconciled. There are three different interactions that have been investigated so far: (1) the perception-of-fit hypothesis, (2) the importance-of- fit hypothesis, and (3) the matching effect (Huang et al., 2017; Kim & John, 2008; Monga &
First there is the perception-of-fit hypothesis, this is mainly concerned with the influence of construal level on the relationship between product-level fit and perception of fit, and its effects are mostly separate from the other two (Huang et al., 2017; Shan et al., 2017). The importance-of-fit hypothesis and the matching effect are conceptually closer to each other, with supposedly contradicting final effects. Both of these are concerned with the effect of construal level on the relationship between perceived fit and brand extension evaluation. However, they both claim different final states of brand extension evaluation. The importance-of-fit hypothesis states that abstract mindsets make final extension evaluations more extreme, meaning that poor- fitting extensions are evaluated even more poorly, while good-fitting extensions are evaluated even better (Meyvis et al., 2012). The matching effect on the other hand, states that when an extension is perceived to be a close or good fit, it is evaluated more favourably when consumers are close to the extension (concrete mindset) than when they are far from it (abstract mindset), with the reverse being true for poor-fitting extensions (Huang et al., 2017). There is an immediate discrepancy between these two explanations, specifically regarding the effects of abstract mindsets. The first theory states that abstract mindsets strengthen the effect of perceived fit on brand extension evaluations, making poor-fitting extensions be evaluated more
9 poorly, and good-fitting extensions even better (Kim & John, 2008). However, the matching effect states that extension evaluations increase when mindsets are abstract and fit is poor, with no effect for good-fitting extensions (Huang et al., 2017).
This discrepancy leads to the question of whether these two effects can be reconciled, or whether one or the other might not occur. For all of these effects, including the perception- of-fit hypothesis, there is limited evidence and some contradicting evidence (Huang et al., 2017;
Kim & John, 2008; Meyvis et al., 2012). So far these effects have mainly been studied separately, with only one study attempting to test all three (Huang et al., 2017). However, these studies have all tested the effects in different manners and focused on different aspects of them.
As perceived fit is the most important determinant of brand extension evaluations (Aaker &
Keller, 1990; Bridges et al., 2000), it is vital to figure out what the nature of its interplay with construal level is. Therefore, this study will focus on further exploring these effects in one model. This model will include proposed mediators for both the importance-of-fit hypothesis and the matching effect, in an effort to better understand their inner workings.
These three theories have all been researched in different contexts, with different manifestations of construal level (e.g. Meyvis et al., 2012). Nonetheless, most research has focused on the types of psychological distance (Huang et al., 2017; Kim & John, 2008). This research will test all proposed theories in the context of temporal distance, which is the interval between the moment of judgment and the occurrence of the judged event (Trope et al., 2007).
Temporal distance is of specific importance here, due to its real-world applications and relevance. Perceptions of time can be invoked in different ways in consumption situations with regards to brand extensions. Consumers can feel near or far from launch of the extension, but they can also feel near or far to the moment of purchase, or moment of consumption. In this case focus will be on the launch of the extension, after previous research (Kim & John, 2008).
10 If any, or all, of the previously found effects hold true for temporal distance, this means consumers will evaluate brand extensions differently as they feel temporally close or temporally far from it (Kim & John, 2008; Meyvis et al., 2012). Confirming this feat will mean marketing communication can play into this, and indirectly enhance brand extension evaluations by combining fit perceptions with the appropriate temporal distance. This will provide managers with further tools and knowledge, in order to optimize communications about brand extensions.
Summarizing, this research will focus on how construal level induced by temporal distance influences the relationship between perceived fit and brand extension evaluation, and the relationship between product-level fit and perceived fit. This will contribute to a better understanding of how these effects work and when they appear, if at all. The answers to these questions will add to existing theory on the influence of CLT on brand extension evaluations, especially its influence on the relationship between perceived fit and extension evaluations (Aaker & Keller, 1990; Huang et al., 2017). Additionally, it will contribute to the growing body of literature on appropriate marketing communication regarding brand extensions. Moreover, it will provide practitioners with more concrete insights about consumers’ evaluations of their brand extensions at different points in time. Finally, it will help practitioners adjust their communication depending on different contextual factors, for brand extension with both good and poor perceived fit, giving them more practical guidance.
The next section of this research will focus on explaining what brand extensions are and what type will be discussed in this research. Furthermore, it will dive deeper into the concepts of perceived fit and CLT. These will then come together in the three different interactions between fit and temporal distance, ending with a conceptual model showing the suggested hypotheses. Following this will be an overview of the experiment used to test the hypotheses.
Data from this experiment will then be analyzed in the results section, with its implications in the discussion, ending with a conclusion.
11 LITERATURE REVIEW
For the last decades, brand extensions have been tools of strategic growth for a large variety of firms (Aaker, 1990). Entering new markets is financially risky, and brand extensions are one way of trying to mitigate this risk (De Ruyter & Wetzels, 2000). Advantages of extending an existing brand are manifold, such as leveraging existing brand images to enhance the credibility and perceived quality of the extension, possibly enhancing the core brand (Aaker, 1990; De Ruyter & Wetzels, 2000), not having the costs associated with creating a new brand (Aaker &
Joachimsthaler, 2000), and being able to enter new markets more quickly (Hennigs et al., 2013).
However, brand extensions also carry risks, such as dilution of the core parent brand (Keller &
Lehmann, 2006; Loken & John, 1993), forgoing an opportunity to establish a successful new brand (Aaker, 1990), and general failure, resulting in the loss of resources (Aaker, 1990). Thus, brand extensions have been the object of many studies, in order to better understand its processes and prevent failure (Viot, 2011).
When introducing a brand extension, marketers make use of a strategy that attempts to leverage well-established brands in order to promote their new offerings, being either goods or services (Aaker & Keller, 1990; Yang & Kim, 2019). Within this new launch, the name, logo, or any other image of the parent brand is linguistically or graphically tied to the extension, albeit in varying degrees (Ye et al., 2020). This definition can slightly vary, depending on the brand architecture employed (e.g. Hsu et al., 2016), or the context of the extension, being for example, a goods or a service extension (e.g. Ramanathan & Velayudhan, 2015), or the extension taking place in a B2B or a B2C context (e.g. Liu et al., 2018). Furthermore, brand extensions come in two distinctive forms, being either horizontal or vertical (Kim & Lavack, 1996). For horizontal extensions, brands are stretched to a new product class or category, this could be either similar or completely different from what the firm is already doing. In contrast, vertical extensions stay
12 within the same product category, but offer a different price point (Hennigs et al., 2013; Kim et al., 2001). For this particular research, the focus will be on horizontal brand extensions. With this definition in place, there are different facets of brand extensions worth studying. However, the focus of this study will be the way brand extensions are evaluated.
Brand extension evaluations
There has already been a large amount of research into how brand extension evaluations are formed. The most famous example of such a study is the one by Aaker and Keller. They theorize that extension evaluations are mainly influenced by how well the extension is perceived to fit the core brand, the perceived quality of the core brand and the extension, and finally, whether the extension is perceived as easy to make in terms of design or manufacturing (1990). This study has been replicated several times over the years following, with overall mixed results.
However, general consensus has been established on the positive influence of perception of fit (Bottomley & Holden, 2001; Chowdhury, 2007; Sunde & Brodie, 1993; van Riel et al., 2001). This effect will be the fundament of this research, used as a base to explore other effects.
A more detailed description of perceived fit can be found in the following section, however to already consolidate this framework, the first hypothesis is as follows:
H1: Perceived fit has a positive effect on brand extension evaluations.
With this basic framework in place, researchers started looking for other variables that could be of influence1. However one thing that has remained consistent, even with all the other research, is that perceived fit is consistently regarded as one of the, if not the, most important
1 Examples of these include the order in which different extensions are introduced (Keller & Aaker, 1992), the strength of the brand relationship quality (Kim et al., 2014), consumer innovativeness (Xie, 2008), and brand extension authenticity (Spiggle et al., 2012) to name a few. Furthermore, it was discovered that extensions can also influence parent brand perceptions, with research into the reciprocal spillover effects between the parent brand and the extension (Martínez Salinas & Pina Pérez, 2009, 2010; Ye et al., 2020), and the interaction between extensions and corporate image (Pina Pérez et al., 2006).
13 aspect(s) influencing brand extension evaluations (Czellar, 2003; Sattler et al., 2010; Sichtmann
& Diamantopoulos, 2013). Despite this reality, there is still a lot more to know about the influence of perceived fit on extension evaluations, especially in combination with other factors (e.g. Czellar, 2003). However, to examine relationships between perceived fit and other variables, it is important to know what exactly, perceived fit is.
As stated above, perceived fit is a crucially important determinant of a brand extension’s success (Ahluwalia, 2008; O'Reilly et al., 2017). In general terms, perceived fit can be described as the perception of the degree of similarity or proximity between a parent brand and its brand extension (e.g. Ahluwalia, 2008; Butcher et al., 2019; Czellar, 2003). Thus, it can directly be affected by the appropriate (or inappropriate) selection of the parent brand and extension product (Völckner & Sattler, 2006). This idea may seem rather simple, however, when diving deeper into the concept of perceived fit, one will find there is a lot more to say, and a lot more demarcation needed than ‘similarity’. Additionally, the conceptualization of perceived fit is not always consistent, (Dwivedi et al., 2010), therefore warranting further explanation.
The simplified definition of perceived fit in the previous paragraph was originally developed by Aaker and Keller (1990). Their view of similarity was specifically focused on overlapping features, meaning that the new product had to be similar to a product from its parent brand, making a product pair. In their view, there were three ways product pairs could exist, forming the dimensions of fit (Aaker & Keller, 1990). The first dimension of perceived fit is complement, which means that the new product should be complementary to the first product.
The new product would be considered complementary if they were used at the same time, to satisfy the same need (Aaker & Keller, 1990; Chowdhury, 2007). The second dimension is substitute, meaning the new product can replace the original one, satisfying the same need.
14 Finally, there is transfer, which is not so much about the product itself, but about the firm’s ability to manufacture it. Transfer applies when consumers feel that some of the capabilities the firm uses to produce the original product, can also be used to manufacture the new one (Aaker
& Keller, 1990; Chowdhury, 2007). This idea of perceived fit is now known as product-level similarity.
This definition of perceived fit was later slightly altered by Park, Milberg and Lawson, who contended that there were more ways for an extension to be similar to its parent, adding the idea of brand-concept similarity (1991). Their reasoning was based on the idea that people do not merely put two things in the same category because they are physically similar, but they might have other theories for why two things belong together. This led to the conclusion that an extension can still be similar to its parent without sharing physical attributes; as long as it is consistent with the parent’s brand concept or image (Bridges et al., 2000; Park et al., 1991).
However, there is still more to perceived fit than the distinction between product-level and brand-concept similarity. The final addition to the definition of perceived fit that will be used for this research was partially developed by Broniarczyk and Alba (1994), and later finalized by Bridges, Keller and Sood (2000). They believe that it does not matter inasmuch what type of association there is between a parent and an extension, but that this association should be salient and relevant. By salience they mean whether or not the association can be easily retrieved from memory, and with relevance they refer to the appropriateness and importance of the association (Batra et al., 2010; Bridges et al., 2000; Spiggle et al., 2012).
Thus, for the purpose of this research, perceived fit is the degree to which a consumer sees an extension as similar to its parent brand’s other products or services, personality, positioning, or any other associations that are salient and relevant (Aaker & Keller, 1990; Bridges et al., 2000;
Park et al., 1991). In order to influence perceived fit at a later stage of this research and combine
15 perceived fit with other concepts, product-level fit or similarity will be used as a way to reliably influence fit perceptions, hence, hypothesis 2 is the following:
H2: Product-level fit has a positive effect on perceived fit.
Effects of perceived fit
Now that it is clear what perceived fit is, attention can move to its effects on extension evaluations. Perceived fit can be anywhere on a continuum from high to low. When perceived fit is high, the extension is in some way perceived to be similar to its parent brand in a way that is relevant (Ahluwalia, 2008). For example, if Company A sells peanut butter, going into other bread toppings would likely be considered a good fit. On the other hand, if Company A were to go into hand sanitizers, that would likely be considered a poor fit. These examples are of course simplified, there may be associations linked to Company A that would make bread toppings an extremely poor fit, or hand sanitizer an extremely good fit. However, the idea of poor versus good fit transcends to more complex cases as well. Generally, it is believed that the higher the perceived fit, the more favourable the evaluation of the extension and the greater the probability of success (Ahluwalia, 2008; Kim & John 2008). This also means that in general, an extension with low perceived fit receives less favourable evaluations and is therefore less likely to succeed in the marketplace (Aaker & Keller, 1990; Ahluwalia, 2008).
The effects of perceived fit can be explained by a two-stage processing of extension evaluations. This is based on the two-stage processing of person impression formation, a theory based on social cognition. This process consists of a categorization and an evaluation phase (Kim & Park, 2019). In consumer memory, brands and product categories are envisaged as cognitive categories, all with primary and secondary associations, and an attitudinal component (Bridges et al., 2000; Chowdhury, 2007). When consumers encounter a brand extension, they first attempt to categorize this extension according to its similarity to the parent brand, using this as the basis of its evaluation (Sood & Keller, 2012). In other words, they try to identify
16 explanatory links that can connect the extension with its parent brand (Bridges et al., 2000). If this is successful, meaning the perceived fit with the parent brand is good, the evaluation phase begins (Kim & Park, 2019). According to categorization theory, categorizing the extension as part of the brand category in the consumer’s mind triggers the transfer of affect associated with the brand category to the extension. Even though this is generally beneficial, it is not always positive, as the process goes for both negative and positive affect (Chowdhury, 2007;
Dall’Olmo Riley et al., 2014; Kim & Park, 2019). However, if the extension is not a good perceived fit with the parent brand, and can thus not be categorized there, it can dilute the mental images held of the parent brand (Dall’Olmo Riley et al., 2014).
Thus, apart from negatively affecting evaluations of the extension, poor-fitting extensions can also damage and/or dilute the existing image of the core parent brand (Loken &
John, 1993; Parker et al., 2018). However, poor-fitting extensions are not inherently bad. It can be argued that the increased risk posed by a perceptually poor-fitting extension, can be offset by broadening the categories associated with a brand, tapping into new product markets, or moving into a category with minimal competition (Chun et al., 2015; Parker et al., 2018).
Despite these potential advantages, poor-fitting extensions still represent a significant risk. With this in mind, researchers have been looking for factors that can mitigate, or nullify the effects of poor perceived fit, or that can enhance the effects of high perceived fit.
When looking for other concepts that could interact with perceived fit, the first thing researchers noticed is that perceived fit is a very context-dependent concept (Meyvis et al., 2012; Zheng et al., 2019), being influenced by situational factors, external information, extension marketing strategy, available parent brand knowledge, and available knowledge of the extension category (Czellar, 2003). For example, Völckner and Sattler demonstrate that advertisements can increase the salience of important brand associations, making it easier for consumers to categorize the extension with the parent brand and thus increase perceptions of
17 fit (2006). Furthermore, research based on Construal Level Theory (CLT) has shown that thinking abstractly or concretely influences how consumers respond to variations in perceived fit (Kim & John, 2008; Meyvis et al., 2012). Combining these two insights suggests that appropriate use of insights from CLT in marketing communication can influence consumers’
fit perceptions. To further explore this proposition, it is necessary to first dive deeper into the premise of CLT.
Construal Level Theory
CLT is a cognitive framework that attempts to explain why people’s evaluation of, and behaviour towards certain objects, events, or situation changes under different circumstances.
Its focus is on construals, which are mental representations of real-life stimuli (Chang et al., 2015; Dhar & Kim, 2007). It is based on the idea that these stimuli can be mentally represented in many different ways (Eyal & Liberman, 2012). The main distinction made by CLT is between high-level construals and low-level construals. High-level construals are abstract, schematic, and decontextualized, representing the overall, superordinate features of stimuli.
They leave out any detailed information while still bringing across the essential characteristics of the object or event. Such features could be sites you visited during your holiday, or how long you stayed (Eyal & Liberman, 2012; Shan et al., 2017; Stephan et al., 2010). On the other hand, low-level construals are concrete and contextualized, including detailed, subordinate information of a stimulus. This could be the food you ate on your first day of vacation, or the prices of entry tickets (Eyal & Liberman, 2012; Shan et al., 2017; Stephan et al., 2010). The difference between high-level and low-level construal is thus its level of abstraction, leading the common denomination for the two levels to be abstract vs. concrete (Da Costa Hernandez et al., 2015, Reczek et al., 2018).
18 These two levels represent the extremes of a continuum, where moving from low to high (or from concrete to abstract) means increasingly leaving out more unique details, making implicit decisions about what information is most important (Trope et al., 2007). In simple terms, concrete construals represent how people do things, whereas abstract construals represent more why people do things (Dogan & Erdogan, 2020; Zhu et al., 2017). However, this does not mean that abstract construals are worse versions of concrete construals. Instead, they provide extra information about the value of the object or event, and the relation it has to other objects or events. Consequently, the increasing abstraction does not just mean losing detailed, unique information, it also means gaining information about the overarching meaning of a stimulus (Trope & Liberman, 2010).
This difference in mental abstraction does not only influence the way information is seen, but also the way decisions are made based on provided information. Research has shown that, when in an abstract mindset, people tend to make decisions based on abstract or general features (Dogan & Erdogan, 2020; Kim & John, 2008). The same principle applies when someone is in a concrete mindset, thus making decisions based on more specific, detailed aspects of a stimulus (Fujita, 2008; Kim & John, 2008). This means decisions about a stimulus can differ, based on the level of mental abstraction an individual is in (Fujita, 2008).
According to CLT, whether an object or event is construed as abstract or concrete depends on the perceiver’s psychological distance from it (Bar-Anan et al., 2006). Psychological distance can be defined as the subjective distance between a stimulus and the direct experience of the perceiver. In other words, whether an individual feels close to or distant from a stimulus (Bar- Anan et al., 2006; Bar-Anan et al., 2007; Liberman & Förster, 2009). Through influencing construal level, one impacts the way people think, the way they feel, and their motivations (Williams et al., 2014). The greater the psychological distance between the perceiver and their
19 target, the more abstract are the construals they form about it. This effect appears the other way around as well, meaning the more abstract the construals are about a stimulus, the more psychologically distant it is perceived to be (Liberman & Förster, 2009; Williams et al., 2014).
The logic behind this, is based around a zero-anchoring point, representing the direct experiences of the perceiver, the here and now. Some objects or events, however, are not part of this direct experience, and can thus only be mentally construed. These are stimuli that are impossible for the perceiver to experience, therefore, they can only imagine them (Bar-Anan et al., 2006; Bar-Anan et al., 2007; Ding & Keh, 2017; Stephan et al., 2010). The reason these stimuli are then construed more abstractly than stimuli in the perceiver’s direct experience, is that concrete aspects of distant stimuli are vague or unknown. The perceiver then relies on their knowledge of the overarching category to judge it (Bar-Anan et al., 2006; Bar-Anan et al., 2007;
Stephan et al., 2010). In addition, this association is overgeneralized, meaning people use concrete construals for close targets and abstract construals for distant targets, irrespective of the available amount of information about that target (Bar-Anan et al., 2006; Bar-Anan et al., 2007; Stephan et al., 2010; Trope & Liberman, 2010).
The dimensions of distance
Psychological distance comes in four different dimensions, three of which have been verified and accepted, with some doubt still surrounding the last one (Calderon et al., 2020; Trope et al., 2007). The first three dimensions are space, time, and social (Trope et al., 2007), with especially space and time frequently being the object of investigation (e.g. Huang et al., 2017; Zhang et al., 2020). The fourth dimension is hypothetical distance, referring to the likelihood of an event occurring (Trope et al., 2007). However, recently doubts have been cast on the reliability of hypotheticality’s effects on construal level (Calderon et al., 2020). Therefore, this study will not mention it from hereon.
20 The first dimension of psychological distance is space, referring to the physical or spatial distance between the perceiver and the stimulus (Bar-Anan et al., 2006; Trope et al., 2007).
This means that when a stimulus is physically farther away from the perceiver, it will be construed more abstractly (Trope et al., 2007). For example, you would describe the water bottle on the table in front of you in great detail, whereas you would describe the water bottle on your neighbour’s table much more abstractly. The second dimension is time, or temporal distance, being the amount of time that separates the perceiver from the stimulus, be it in the future or the past (Bar-Anan et al., 2006). Again the logic is that, the farther away in time a stimulus, the more abstractly the perceiver construes it (Trope et al., 2007). For example, describing an event that happened to you yesterday would be done in more concrete terms than describing an event that happened five years ago. Finally, social distance reflects the perceived distance between the perceiver and another person, where the close person is construed concretely, and the distant person abstractly (Bar-Anan et al., 2006; Trope et al., 2007). In this case, thinking about a close family member or a good friend will be more concrete, whereas thinking about a citizen in a neighbouring country will be more abstract.
Although these three dimensions have similar effects on construal level, they are by no means the same. It is not possible to say how far away in time an event should occur for it to match a specific physical distance (Liberman et al., 2007). For one, time is unidimensional and not under our control, time moves forward without us being able to influence that. Space on the other hand has multiple dimensions, and is much more under our control. Thus, despite the similar influence these dimensions have on construal level, they cannot be easily compared to each other (Liberman et al., 2007). Finally, it is important to realize that these dimensions interact with each other. The different types of distance, albeit not being equal, share the same meaning (Trope & Liberman, 2010). When confronted with one type of distance, people automatically access this meaning, and assign the other ones to the situation as well (Bar-Anan
21 et al., 2007; Stephan et al., 2010). This means that people have an automatic tendency to associate ‘tomorrow’ with ‘here’, and ‘long ago’ with ‘far away’, even when this is not necessarily related to their goals (Bar-Anan et al., 2007; Stephan et al., 2010).
CLT and its components have been found to have a profound impact on the way people perceive things, and the way they make decisions. It was shown that when people are in an abstract mindset, they prefer information that aligns with this mindset, and make decisions accordingly (Dogan & Erdogan, 2020; Eyal & Liberman, 2012; Kim & John, 2008). With this reality in mind, combining CLT with other theories and concepts can be a fruitful endeavour, since CLT influences an individual’s perceptions. In this light, the current research combines insights from CLT with the brand extension literature on fit. The effects of CLT on brand extensions will be further discussed in the following sections.
Construal level and fit perceptions
The use of CLT in the brand extension literature is not new, having been combined with various concepts already. There is existing research on the influence of self-construal (Ahluwalia, 2008), the combination with culture (Kim & Park, 2019), consumers’ natural predisposition for abstract or concrete construals (Kim & John, 2008), and construal level’s influence in combination with distraction (Zhang et al., 2020). However, most of these studies were not focused on finding a way to influence consumers’ evaluations of extensions through communication about it. This is where a different stream of research comes in.
Within this stream of research, the focus is on combining the construal levels from CLT with perceived fit. Here, the theories of CLT are used to influence perceived fit, thereby indirectly trying to exercise control over extension evaluations (e.g. Huang et al., 2017; Meyvis et al., 2012). Since perceived fit is the most important determinant of brand extension evaluations, being able to indirectly influence it can be very valuable. This line of inquiry has
22 yielded three different ways in which CLT can influence perceived fit, and the effects perceived fit has on extension evaluations. However, these effects are all very different, sometimes with different outcomes (Huang et al., 2017; Kim & John, 2008; Meyvis et al., 2012). This section will focus on exploring these different effects, trying to find out where the discrepancies between them are, and whether they can be united.
This study will apply the different explanations in the context of temporal distance.
Previous studies have tested their frameworks with varying manipulations of construal level, where most focused on one of the four types of distance (Huang et al., 2017; Kim & John, 2008). Thus, in order to provide as much theoretical relevance as possible, focusing on one of the four distances seems appropriate, as they are widely used (Trope et al., 2007). Furthermore, temporal distance has not been extensively researched in any of the three contexts. Thus, if any of the effects can be confirmed in this study, it will simultaneously be extended into a new context. Extending any or all of these findings into other dimensions of psychological distance will give practitioners the opportunity to exercise more control over the way their extensions are perceived, and especially improve the evaluations of poor-fitting extensions. Temporal distance in this context is especially relevant since it is relatively easy to adapt marketing communication in accordance with the launch of the new product.
The first application of combining CLT with perceived fit comes in the form of the perception- of-fit hypothesis. This states that increasing psychological distance, and thus level of abstraction, increases consumers’ perception of fit and therefore increases extension evaluations, irrespective of ‘actual’ fit or other explanations (Huang et al., 2017). There are several reasons this might be the case. First of all, when in an abstract mindset, people use fewer categories to classify stimuli, and are thus more likely to categorize an extension with its parent brand (Trope et al., 2007). This means fit perceptions will be higher in abstract mindsets than
23 they would normally be. This goes for those naturally dispositioned to use high-level construals, but also when abstract construals are induced (Kim & John, 2008; Shan et al., 2017).
Furthermore, it has been shown that abstract thinking enhances an individual’s ability to think creatively and make new connections. Therefore, they can find new, different ways of connecting an extension to its parent brand, leading to more favourable evaluations (Zhang et al., 2020). Concluding, this suggests that engaging in abstract thinking may increase extension evaluations, through increasing fit perceptions regardless of more objective fit.
This hypothesis has been both supported (e.g. Kim & John, 2008) and contradicted (Huang et al., 2017). However, in this research it is still expected to find this effect. Similar effects have been found previously with fit and holistic (abstract) versus analytic (concrete) processing styles (Monga & John, 2007). Furthermore, this research will specifically apply the hypothesis to the relationship between product-level fit and perceived fit. Contrary to this, research that did not find the effect applied it directly to perceived fit (Huang et al., 2017).
Therefore, hypothesis 3 goes as follows:
H3: Temporal distance moderates the relationship between product-level fit and perceived fit, such that increasing temporal distance leads to higher perceptions of fit between the extension and the parent brand.
This part of the model is displayed in Figure 1.
FIGURE 1. Perception-of-fit hypothesis
24 Importance-of-fit hypothesis
The second theory comprising CLT and perceived fit, is the importance-of-fit hypothesis.
Previous studies have confirmed that people who (naturally) construe their environment at a higher level place a greater importance on perceived fit than those who construe their environment at a lower level (Kim & John, 2008). Furthermore, it has been shown that activating a concrete mindset in consumers leads them to shift their focus from perceived fit, to perceived quality of the parent brand. This decreases the influence of perceived fit on extension evaluations, and shifts preference from extensions with a high perceived fit, to extensions and brands with a high perceived quality (Meyvis et al., 2012). With this in mind, the importance- of-fit hypothesis states that increasing psychological distance increases the importance of perceived fit, thus strengthening its influence on extension evaluations (Huang et al., 2017). This would mean that, since fit is more important when construals are abstract, good- fitting extensions would be evaluated even more favourably, whereas poor-fitting extensions would be evaluated even less favourably (Shan et al., 2017). Opposite to this, when construals are concrete, perceived fit becomes less important, so evaluations will not be based on fit (Huang et al., 2017; Meyvis et al., 2012).
Just like for the perception-of-fit hypothesis, there are contradicting findings regarding the importance-of-fit hypothesis, as the effect is not always found (Huang et al., 2017).
However, considering the previously discussed body of research that did find the effect, it is still very likely that the importance-of-fit hypothesis occurs to some extent. Thus, hypothesis 4 is the following:
H4:Temporal distance moderates the relationship between perceived fit and brand extension evaluations, such that increasing temporal distance leads to an increased weight on perceived fit, making extension evaluations more extreme.
25 As previously indicated, the explanation behind the importance-of-fit hypothesis relies on CLT.
In particular the part of CLT that says level of mental abstraction influences the way decisions are made. When in an abstract mindset, individuals are inclined to mainly use abstract features of a stimulus when making a decision (Fujita, 2008; Kim & John, 2008). It has previously been argued that perceived fit can be classified as an abstract, generalized concept (Kim & John, 2008; Meyvis et al., 2012). Therefore, following the previous logic, the level of mental abstraction influences the weight placed on fit perceptions when making a brand extension evaluation. Nonetheless, there is also literature that contests the claim that the psychological distances always influence construal levels, and thus level of mental abstraction (Huang et al., 2017; Williams & Bargh, 2008). In order to test the explanation, hypothesis 5 is offered:
H5: The moderating effect of temporal distance on the relationship between perceived fit and brand extension evaluation is mediated by level of mental abstraction.
A visual representation of the effects around the importance-of-fit hypothesis can be found in Figure 2.
FIGURE 2. Importance-of-fit hypothesis
Finally, there is one more plausible explored effect of CLT on the relationship between perceived fit and extension evaluations. It has been found previously, that combining CLT with other theories, where one condition of a concept can be seen as more distant than the other, is
26 prone to resulting in something called a ‘matching effect’ (Da Costa Hernandez et al., 2015;
Huang et al., 2017; Kim et al., 2019). This will be further explored in the following section.
The matching effect
The matching effect occurs when two concepts, possibly of different backgrounds, mentally match. This has also been called a congruency effect (Chang et al., 2015). For example, when you are provided information about a country in a benefit-based way (i.e. more abstract), and you are told that this country is very far away from you, these two things conceptually match (Da Costa Hernandez et al., 2015). This conceptual, or metaphorical matching, will lead someone to feel more positive towards the information they just read, independent of the contents of the information (Huang et al., 2017; Reber et al., 2004).
The matching effect has especially been applied to CLT, with it having been shown to occur in combination with information framing (Chang et al., 2015), appeal types (Da Costa Hernandez et al., 2015), regulatory focus (Lee et al., 2010), and image type (Kim et al., 2019).
The context for these studies was advertising, where making use of the matching effect enhanced the persuasiveness of the advertisements (Chang et al., 2015; Da Costa Hernandez et al., 2015; Kim et al., 2019; Lee et al., 2010). With these positive effects in mind, the idea was also introduced into the brand extension literature by Huang, Jia and Wyer Jr. (2017).
Huang et al. focused their efforts on understanding and influencing perceived fit through the situational environment wherein consumers become acquainted with a brand extension, in order to affect their evaluations of said extension (2017). Since perceived fit is the most important determinant of brand extension evaluations, being able to indirectly influence it can be very valuable. In order to achieve this, they made use of the principles of the matching effect, to enhance evaluations for both perceptually good-fitting, and perceptually poor-fitting extensions (Huang et al., 2017). They showed that matching distance in perceived fit - where a
27 poor fit is distant, and a good fit is close – with spatial distance increased final extension evaluations. Specifically, they demonstrated that evaluations of perceptually good-fitting (close) extensions, improved when consumers were spatially close to it. At the same time, being spatially distant from a perceptually poor-fitting (distant) extension, also improved its evaluation. Spatial distance in this case does not just mean being physically removed from the extension, but the concept also applies to placement in images, for example in long-shot vs.
close-up shots (Huang et al., 2017).
This study will apply the logic of the matching effect to brand extensions in a similar fashion to Huang et al. (2017), but this time focusing not on spatial, but on temporal distance, as explained in the previous section about the other effects. Moving forward from this point, it will be assumed that the matching effect as previously found in many other cases (e.g. Chang et al., 2015; Da Costa et al., 2015), can also be applied to temporal distance and perceived fit.
Thus, the corresponding hypotheses are as follows:
H6a: A perceptually good-fitting brand extension will be evaluated more favourably when consumers perceive launch of the extension to be close, than when launch of the extension is perceived to be distant.
H6b: A perceptually poor-fitting brand extension will be evaluated more favourably when consumers perceive launch of the extension to be distant, than when launch of the extension is perceived to be close.
The premise of the matching effect is based around the idea of processing fluency (Da Costa Hernandez et al., 2015). Processing fluency reflects the ease with which we process information, nonspecific to the contents of that information. It occurs at various levels in the mind, where there is a general distinction between perceptual and conceptual fluency (Labroo
& Lee, 2006; Reber et al., 2004). Perceptual fluency concerns itself with physical representation and features. A brand or product that is perceptually fluent, is easily recognizable and identifiable. On the other hand, conceptual fluency is focused on mental processes and associations. When a brand or product is conceptually fluent, its associations and meaning come to mind easily (Labroo & Lee, 2006; Reber et al., 2004).
Processing fluency includes the speed and accuracy with which we process information (Da Costa Hernandez et al., 2015; Reber et al., 2004). It can also be enhanced when it is easy to interpret and construe the meaning of information about an object or event. When individuals experience processing fluency with regards to a certain stimulus, they evaluate it more favourably (Huang et al., 2017; Thompson & Hamilton, 2006). The idea here is that high processing fluency indicates a positive state, either in someone’s mind or in their environment (Reber et al., 2004). When concepts come to mind easily, it elicits these feelings of high fluency, which creates positive affect. This feeling of positive affect is then used in the evaluation of the stimulus itself (Huang et al., 2017; Lee & Aaker, 2004; Reber et al., 2004; Thompson &
In this context, processing fluency has been linked to both the matching effect (e.g. Da Costa Hernandez et al., 2015), and different types of fit effects (Huang et al., 2017; Lee et al., 2010). Within the matching effect, the matching itself facilitates processing fluency, and therefore creates more positive affect (Huang et al., 2017). As regards different types of fit effects, it has been found that messages fitting with the consumers’ mindset are easier to process. This high processing fluency then elicits more positive outcomes in judgments, similarly to the matching effect (Lee et al., 2010). In order to consolidate the matching effect between construal level and perceived fit in brand extensions, and verify the addition of temporal distance, this leads to the final hypothesis:
29 H7: The matching effect between temporal distance and perceived fit on brand
extension evaluation is mediated by processing fluency.
A visual representation of the hypotheses around the matching effect can be found in Figure 3.
Finally, the full conceptual model can be found in Figure 4.
FIGURE 3. Matching effect
FIGURE 4. Full conceptual model
In order to test the previously described hypotheses an online experiment was designed. The manipulations for this experiment were selected by means of a pretest. Both the pretest and the online experiment for the main study used fictitious extensions of a real brand. Using an existing brand enabled a more realistic recreation of the process consumers go through when normally judging an extension, improving ecological validity (Dwivedi et al., 2010; Till &
Priluck, 2000). Moreover, the core strategy in introducing a brand extension involves making use of existing brand equity, which is harder to replicate using fictitious brands (Aaker & Keller, 1990). In addition, common practice in the papers most relevant to this research is to use fictitious extensions of real brands (e.g. Huang et al., 2017; Kim & John, 2008; Meyvis et al., 2012). However, downsides of this practice include less control over experimental manipulations (Kirmani et al., 1999), and inconsistence in brand knowledge among participants (Lei et al., 2008). For this study, the disadvantages of using a real brand did not weigh up to the advantages, especially those relating to ecological validity.
A pretest was conducted to select appropriate brands, brand extensions and launch dates for the main study. The brand extensions and launch dates formed the manipulations of the main study, therefore a pretest was necessary to ensure they worked. For the manipulations in the main study, different combinations between good-fitting/poor-fitting extensions and close/distant temporal moments needed to be made. Therefore, the pretest focused on making appropriate selections for those four elements. The pretest consisted of two parts; part one focused on selecting appropriate brands and brand extensions, and part two focused on appropriate launch dates.
31 Selection of brand and brand extensions
The brands chosen for the pretest of the main study were Samsung and Nike, both based on previous research (Huang et al., 2017; Kim & John, 2008). Both of these are well-known brands from their respective industries, which was important to ensure participants possess enough brand equity to judge the extensions (Aaker & Keller, 1990; Tam, 2008). The brands also had to be well-liked, as transferring brand equity from a poorly judged brand to an extension would by definition harm the evaluation of the extension (Aaker, 1990). Brand attitude and brand familiarity were therefore further tested in the pretest. Apart from this, the brands were selected based on not being too broad nor too narrow, so that it would be possible to find both well- fitting and poor-fitting extensions for both of them. Finally, as previous studies in this domain all focused on more functional brands (as compared to luxury or prestige brands), it was elected to do the same here (e.g. Kim & John, 2008; Meyvis et al,. 2012). Based on the results of the pretest, one of the two brands would be used in the main study.
To test the hypotheses in the main study, two different extensions were required: a good- fitting and a poor-fitting extension. Four options were presented for both brands, with two being arguably good-fitting and two arguably poor-fitting for each. As both brands were taken from previous studies, the most ideal situation was to use the same or similar extensions. However, it was soon found that the chosen good-fitting extensions (a smartwatch for Samsung and insoles for Nike) had already been made over the last years (Huang et al., 2017; Kim & John, 2008). As for the poor-fitting extensions, Kim and John used a moderate-fit extension for Nike, which could thus not be used in the present study (2008). Huang and colleagues used a collagen powder as a poor-fitting extension for Samsung, however, it was feared participants would not know what this product was (2017). Instead, one of the poor-fitting extensions for Samsung became a selection of vitamins, as a more general product within the vitamins and supplements category.
32 The above meant that extensions had to be found by researching both brands’
respective product assortments. An overview of the chosen extensions can be found in Table 1. The focus with these extensions was product-level fit, or product-level similarity. Therefore, for Samsung, the good-fitting extensions were both in the electronics category. The remaining poor-fitting extension (wine) was in the food- and drinks category, where Samsung is not active. As concerns Nike, both good-fitting extensions focused on sports, specifically on shoes, which is their main category.
The poor-fitting electronics were in the home electronics and beauty categories respectively, both categories Nike is not active in.
In order for the manipulation in the main study to be as controlled as possible, the aim was to have the extensions be as similar as possible on any dimensions other than perceived fit.
Therefore, all the extensions were also judged on perceived quality, as this is another key determinant of extension evaluations. Furthermore, measures for category familiarity and attitude were also included, for the same reasons as for brand familiarity and attitude (Czellar, 2003; Tam, 2008).
Selection of launch dates
Part two of the pretest concerned itself with suitable launch dates, of which a close and far one were needed for the main study. Based on previous studies, four possible launch dates were chosen to test, of which two were arguably close and two arguably far. Close dates were determined to be ‘tomorrow’, and ‘next month’ (Castaño et al., 2008; Zhang et al., 2020), while far dates were ‘in 6 months’ and ‘next year’ (Castaño et al., 2008; Kim et al., 2009; Zhang et al., 2020).
33 Sample and procedure
Data for the pretest was collected through surveys by ways of convenience sampling with participants from the same population as in the main study. Data consisted of twenty participants, of which three were eliminated due to wrongly answering the control question, leaving a sample of seventeen. There were no influential outliers, and missing data was under 10% for all variables (1 missing case was 5.9% due to the small sample size). Of the participants in this study, 58.8% was female. Age ranged from 20 to 80 years (M = 41.00, SD = 20.673), and all participants were Dutch. Further information on demographics can be found in Table 2.
TABLE 2. Demographics pretest
The survey was available in both English (Appendix 1A) and Dutch (Appendix 1B).
When starting the survey, participants were introduced to the research they would partake in. At this point, part one of the survey started with an introduction to Samsung and their desire to extend their brand into new categories. Participants then rated the fit and quality of each of the extensions, which were presented in random order. This process was then repeated for Nike. Part one was then concluded by asking
N = 17
Age Range (in years) 20 - 80
M age (SD) 41.00 (20.673)
Prefer not to say 0.0%
Less than high school 0.0%
High school graduate 17.6%
HBO Bachelor 17.6%
HBO Master 5.9%
WO Bachelor 11.8%
WO Master 17.6%
Working full-time 23.5%
Working part-time 11.8%
Unemployed and looking for work 0.0%
A homemaker or stay-at-home parent 0.0%
34 participants about their attitude on and familiarity with the product categories to which the extensions pertained, and the brands they were asked about.
Part two then started with some information on the fictional brand BottleCap, who they were told was looking for a suitable moment to launch their new brand extension. Participants were then shown the four possible launch dates in random order, and asked how far or close they perceived these moments to be. This finished up part two, after which they were asked for some demographics, debriefed and thanked for their participation.
The Inter-Nomological Network was used to find some of the scales and items for this research (Larsen & Bong, 2016). The survey started with measures for perception of fit and quality. Both of these were measured on 7-point semantic differential scales, with perception of fit being comprised of five items (inconsistent/consistent, illogical/logical, makes no sense/makes sense, inappropriate/appropriate, does not fit/does fit). These items were derived from two different scales, as scales for perceived fit are inconsistent across different researches (Batra et al., 2010;
Kim & John, 2008; Monga & John, 2007). Perceived quality was a scale with one item, being inferior/superior (Aaker & Keller, 1990).
Continuing, category attitude and brand attitude were measured on the same 7-point semantic differential scales. The scale was comprised of four items (very unfavourable/very favourable, extremely dislike/extremely like, bad/good, negative/positive). These items were derived from different scales, for the same reason as for perceived fit (Berger & Mitchell, 1989;
Faircloth et al., 2001; Mao & Krishan, 2006; Park et al., 2010). Category familiarity and brand familiarity were also measured on the same 7-point semantic differential scales. The scale consisted of two items (very familiar/very unfamiliar, very well known/completely unknown), a combination of two single-item scales (Aribarg et al., 2010; Tam, 2008).
35 The final key variable measured in the pretest was temporal distance, which was measured on a 7-point Likert scale (1 = Totally disagree to 7 = Totally agree). The scale included four items, examples being ‘This product launches in the near future’ (Kim et al., 2009), and ‘Launch of this product feels very far away’ (Gebauer et al., 2008). Finally, the survey was closed by questions on demographics, specifically, gender, age, nationality, education, and employment status.
After recoding reversed items2, items were compiled into scales (Table 3). The familiarity variables were unsuitable for reliability analysis as they consisted of only two items each, therefore Pearson’s correlation coefficients were calculated for these. All correlations were large and significant at either p < .05 or p < .01 (Appendix 2A). Finally, perceived quality consisted of only one item, therefore there is no coefficient for this variable.
Following this, the normality of the data was assessed through skewness and kurtosis (Appendix 2B). Based on the results from this, it was determined whether tests should be parametric (ratios between -2 and +2) or non-parametric (ratios smaller than -2 or larger than +2). This was necessary, as the sample size was too small to conduct parametric tests while violating assumptions of normality (Field, 2018). Perceived fit, perceived quality and product category attitude all had at least one variable that was non-normally distributed, meaning non- parametric tests were conducted. Temporal distance, and product category familiarity were approximately normally distributed, so they were suitable for parametric tests. Raw scores for brand familiarity and attitude were also normally distributed, however, as the suitable test for
2 Product category and brand familiarity were recoded as they were reversed. Temporal distance was recoded such that 1 = close and 7 = far.