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Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2009 The Effects of Relationship Marketing on Brand Equity Jeremy S. Wolter Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF COMMUNICATION THE EFFECTS OF RELATIONSHIP MARKETING ON BRAND EQUITY By JEREMY S. WOLTER A Thesis submitted to the College of Communication and Information in partial fulfillment of the requirements for the degree of Master of Science Degree Awarded: Fall Semester, 2009 The members of the committee approve the thesis of Jeremy Wolter defended on September 18th, 2009. ______________________________ Steven McClung Professor Directing Thesis ______________________________ Gary Heald Committee Member ______________________________ Michael Hartline Committee Member Approved: _____________________________________________ Stephen McDowell, Director, School of Communication _____________________________________________ Lawrence C. Dennis, Dean, College of Communication and Information The Graduate School has verified and approved the above-named committee members. ii ACKNOWLDGEMENTS I would first like to thank my wife for supporting me through this process. She took the brunt of any frustration and desperation in the pursuit of this research. Without her love and support, I would not be anywhere near where I am today. I would like to thank Dr. Gary Heald for taking time to be a member on my committee. Dr. Heald’s remarks and criticisms tamed my seemingly wild interpretations of the data. In addition, he laid the groundwork for my understanding and use of SPSS and the exploration of new ways to tackle data analysis. I would also like to thank Dr. Michael Hartline for being on my committee. Dr. Hartline was my quintessential eye of the hurricane where calm resided despite uncontrollable forces raging just beyond arm’s reach. His remarks brought clarity where it had been lacking previously. I owe a special thank you to Dr. Vicki Eveland who helped me begin this process and whose initial guidance had a significant effect on the final direction. Her patient and timely advice assured me that the path I had chosen was the correct one. My future research as a doctoral student is indebted to her initial prodding. Finally, I would like to thank my chair, Dr. Steven McClung. Dr. McClung was the epitome of patience and kindness in the long path that was this research. He took over as my chair and helped me pursue my interest instead of forcing the research to fit his own interest. By allowing me to follow my desire in research and subsequently pursue a topic that became more marketing than communication, Dr. McClung had a profound effect on the direction of my future education. His guidance helped me work through the life altering event that is having children and laid the framework to undertake future research endeavors. For all of this, I cannot thank him enough. iii TABLE OF CONTENTS List of Tables ……………………………………………………………………………………....……... v List of Figures ………………………………………………………………………………………......... vi List of Abbreviations ………………………………………………………………………...………..… vii Abstract…………………………………………………………………………………//……………... viii INTRODUCTION ………………………………………………………………………………………... 1 1. LITERATURE REVIEW ………………………………………………….…………………………... 3 Branding and Brand Equity …………………………………………………...………………..... 4 Relationship Marketing ………………………………...……………………………………….. 10 Hypotheses …………….…………...………………………………………………………….... 12 2. METHODOLOGY……………………………………………………………….……………………. 16 3. RESULTS ……….…………………………………………...………………….……………………. 21 Cluster Analysis …………………………………………………...………………..................... 21 Validity Assessment ………………………………...…………………………………………... 30 Common Method Variance Assessment ……………………………………...……………….... 30 Hypothesis Testing ……………………………………………………………………………… 32 CONCLUSION ….…………...…………………………………………………….……………………. 41 Discussion …………………………………………………………………..………………...… 41 Limitations …………………………………………………………………..………………..… 42 Future Research………………………………………………………………..……………...… 43 Conclusions …………………………………………………………………………………...… 44 REFERENCES …………………………………………………………………………………………... 45 BIOGRAPHICAL SKETCH ……………………………………………………………………………. 51 iv LIST OF TABLES Table 1. Number of Responses and Average Scores on Brand Equity Component per Brand ….....…… 19 Table 2. Survey Items with Means, Standard Deviations and Cronbach’s Coefficient Alpha ………..… 20 Table 3. Agglomeration Coefficients from Clustering Procedure of Relationship Marketing Strategy Levels …………………………………………………………………………….…... 22 Table 4. Number of Brands Assigned to Clusters per Relationship Marketing Strategy Level ……........ 27 Table 5. Correlation and Covariance Coefficients Between All Constructs Variables ……………….… 29 Table 6. Correlation Coefficients Adjusted for Common Method Variance .……………….....………... 31 Table 7. Means, Standard Deviations, Skewness, Kurtosis, and Normality Test Among the Variables ... 34 Table 8. Average Brand Equity Component Scores per Relationship Marketing Strategy Level …..…... 36 Table 9. Box’s Test and Levene’s Test Statistics from the Multivariate Test ……………………….….. 37 Table 10. Results of the Multivariate Test Between Relationship Marketing Strategy Usage …..……… 38 Table 11. Results of the Univariate Test Between Relationship Marketing Level Two Strategy Usage .. 39 v LIST OF FIGURES Figure 1. Dendogram of Level One Relationship Marketing Clustering using Ward’s Method …........... 23 Figure 2. Dendogram of Level Two Relationship Marketing Clustering using Ward’s Method ..…….... 24 Figure 3. Dendogram of Level Three Relationship Marketing Clustering using Ward’s Method …........ 25 Figure 4. Scatterplots of RM Levels versus Brands and Cluster Solutions ……………………………... 26 Figure 5. Normality Plots of the Residuals of the Dependent Measures …………………………...…… 33 Figure 6. Mean Plots of Perceived Quality and Brand Awareness by Relationship Marketing Level Two Strategy Clusters ….………………………………………………………………......….…… 39 vi LIST OF ABBREVIATIONS CBBE customer based brand equity CMV common method variance df degrees of freedom MANOVA multivariate analysis of variance RM relationship marketing SD standard deviation vii ABSTRACT Relationship marketing practices have become ubiquitous in the consumer domain and their effects on various consumer behaviors are well documented. At the same time, branding strategies are widely utilized as well and their effect on consumer behavior is also well documented. What is not known is how relationship marketing strategies impact brand building activities or brand equity. This study addresses this gap in the literature by examining the brand equity scores of 46 brands collected from a convenience sample of 356 undergraduates at a Southeastern university. The effects of relationship marketing strategies on brand equity components are analyzed using a MANOVA technique. Findings provide mixed support for the hypothesized effects. Specifically, as a firm’s use of social bonding strategies increases, there is a significant and positive effect on the brand equity components of brand awareness and perceived quality. Price incentive strategies and structural bonding strategies had no significant effect, positive or negative, on any of the brand equity components. viii INTRODUCTION Brand equity and relationship marketing are two areas of research that have generated a large amount of interest in the marketing literature. Each respective research stream has shown to predict a multitude of consumer behavior outcomes with some overlap in areas such as loyalty (Chaudhuri and Holbrook, 2001). Despite this overlap, both areas remain distinct strategies for a firm to utilize in the quest for market share so that a firm may choose to employ one strategy singly or both strategies simultaneously. While research has been done on the drivers and outcomes of both brand equity (Aaker, 1991; Keller and Aaker, 1998) and relationship marketing (Berry and Parasuraman, 1991; Morgan and Hunt, 1994), in-depth studies into the impact of the two separate areas on each other have been missing. Instead, research has examined how well relationship marketing antecedents may crossover onto brand equity and studies have shown that these antecedents do contribute to the understanding of brand equity, either directly or indirectly (Chaudhuri and Holbrook, 2001; Delgado-Ballester and Munuera-Alema´, 2005). There exists then, the potential of a much larger framework as the two strategies are reconciled with each other. As Chaudhuri and Holbrook (2001) note, “these conceptualizations can be reconciled and integrated as crucial aspects in an overall process” ( p. 82). Such integration would not only allow for a fuller understanding of consumer behavior but it would give practitioners a more parsimonious model for basing their strategies. The true impact of carrying out relationship marketing strategies on brand equity is still unknown, despite the aforementioned research. Questions remain as to exactly how a relationship marketing approach may impact a brand’s brand equity components. It would seem to reason that if a firm were to offer price incentives for a brand as a starting point to their relationship marketing endeavors in an attempt to build loyalty (Berry and Parasuraman, 1991) then the perceived quality component of the brand equity could suffer (Aaker, 1991). However, some of the top brands in the world as listed by Interbrand such as McDonald’s and IKEA are known for their pricing incentives but still maintain their high brand value. Thus, the assumed negative effect of price incentives may be compensated by other relationship marketing strategies such as social bonding (Berry and Parasuraman, 1991). Since the build up of associations is another component of brand equity (Aaker, 1991), as the relationship marketing 1 strategy expands to social bonding the positive effect of the increased number of associations may overtake the negative effects of price incentives. Before a reconciliation of these two strategies can be completed, the full extent of the impact of each component on each other needs to be explored. This is the attempt of the present research. This study looks at how relationship marketing strategies affect the brand equity of existing brands. Specifically, the present study examines the effects of different relationship marketing strategies as demarcated by Berry and Parasuraman (1991) on the brand equity components of brand awareness, brand associations, perceived quality, and brand loyalty as outlined by Aaker (1991). These strategies of relationship marketing are conceptualized along three levels with the first level representing pricing incentives, the second level representing social bonding, and the third level representing structural bonding (Berry and Parasuraman, 1991). 2 CHAPTER 1 LITERATURE REVIEW Branding and Brand Equity A brand has been identified as “a name, term, sign, symbol, or design, or combination of them which is intended to identify the goods and services of one seller or groups of sellers and to differentiate them from those of competitors” (Kotler, 1991, p. 442). This definition was representative of early conceptualizations of branding in that the components of the brand were tangible aspects that could easily be changed. With the introduction of brand equity in the early 1990s, the focus of branding started moving away from the branded product and into the mind of the consumer. It was this consumer-based perspective that Aaker (1991) and Keller (1993) based much of their research and developed what Keller termed as consumer based brand equity (CBBE). The advent of a consumer-based approach to brand equity established two distinct ways of conceptualizing brand equity, namely a financial, market-based method and a consumer-based method. The financially based concept was derived from the desire to bring brand equity into a financial perspective so it could be accounted for on a company’s balance sheet and subsequently leveraged for market capitalization. This financial driven method has resulted in the proposal of multiple options for measurement (Lassar, Mittal, and Sharma, 1995) and has been termed as brand value (Wood, 2000). The consumer-based approach is based on the consumer’s perception of the brand. Both conceptualizations take a long term approach to the return on brand building investments in the form of brand loyalty (Wood, 2000). Since this study is focused on the dual effects of brand equity and relationship marketing on the consumer, consumer-based measures as well as consumer-based conceptualizations will be utilized whenever the opportunity arises. The value of brand equity is in the behavior outcomes of consumers; brands with high equity obtain reduced marketing costs as well as greater market share, trade leverage, price premiums, and loyalty (Aaker, 1991; Keller, 1993; Park and Srinivasan, 1994). Consumer-based brand equity has also been linked to the overall financial performance of the firm (Kim, Kim, and An, 2003). In Managing Brand Equity, David Aaker’s (1991) seminal work on brand equity, 3 he defines brand equity as, “A set of brand assets and liabilities linked to a brand, its name and symbol, that add to or subtract from the value provided by a product or service to a firm and/or that firm’s customers.” ( p. 15). He then broke brand equity into five components: brand loyalty, brand awareness, perceived quality, brand associations, and other proprietary brand assets. Of these five components, most research has focused on the first four and deemed them to be more important for research purposes than the final component of ‘other proprietary brand assets’. Following the lead of such research, this study utilizes these four components at the exclusion of the final component (Atilgan, Aksoy, and Akinci, 2005; Pappu, Quester, and Cooksey, 2005). Keller (1993) built off of Aaker’s work and conceptualized CBBE as “the differential effect of brand knowledge on consumer response to the marketing of the brand” ( p. 2). This definition placed the concept of brand equity fully in the mind of the consumer and has led to the idea that if the brand has no meaning to consumers, it has no meaning at all (Cobb-Walgren, Ruble, and Donthu, 1995; Keller, 1993; Rio, Vazquez, and Iglesias, 2001). Brand knowledge is then broken down by Keller into two components, brand awareness and brand image, with brand image representing the set of consumer’s associations tied to the brand. The conceptualizations of Keller and Aaker are very similar but remain distinct. Both have been used in brand equity research extensively and have shown to be valid across multiple product categories (Cobb-Walgren, Ruble, and Donthu, 1995; Krishnan, and Hartline, 2001). This study will follow the work of Aaker for a couple of reasons. While Keller’s model would seem to be more parsimonious and therefore have greater extendibility, the antecedents in Aaker’s brand equity model have all shown to be distinct and valid. While some of the antecedents have equitable definitions across the two models, others do not. Keller’s conceptualization of brand equity seems to be an attempt to create a simpler and more extendable model. However, Aaker’s model contributes more to the study of the separate antecedents that create brand equity. Since this study’s purpose is to examine how the varying levels of relationship marketing will affect the brand equity components, Aaker’s (1991) model and conceptualization of brand equity will be used as a set of brand assets and liabilities linked to a brand, its name and symbol, that add to or subtract from the value provided by a product or service to a firm and/or that firm’s customers (p. 15). Brand Equity: Brand Loyalty 4 Aaker (1991) defined brand loyalty as the “measure of the attachment that a customer has to a brand” (p. 32). Brand loyalty is unique when compared to the other components in that it is both a driver of brand equity as well as a behavioral outcome. As Aaker (1991) states, “Note that brand loyalty is both one of the dimensions of brand equity and is affected by brand equity. The potential influence on loyalty from the other dimensions is significant enough that it is explicitly listed as one of the ways that brand equity provides value to the firm.” (p. 18) Because of this dualism, the practice of loyalty measurement has been split into primarily two areas as well, one that measures the intention of the consumer and one that measures the actual behavior. Aaker (1991) advocated both measures but researchers who have measured brand loyalty based on behavior claim that measuring intentions misrepresent loyalty. This argument is based on studies that have questioned the intention-behavior link of consumer behavior, especially concerning the multifaceted process of real-world decision making (Miniard and Cohen, 1979; Morrison, 1979). In his foundational study into developing brand loyalty, Tucker (1963) noted, “No consideration should be given to what the subject thinks or what goes on in his central nervous system; his behavior is the full statement of what brand loyalty is.” ( p. 32). A counter claim by those who study the consumer’s intention is that repurchase behavior does not necessarily mean the customer was loyal; measuring the resulting behavior does not conclude the reason behind the behavior. Repeat purchase behavior can be a result of simple habit and as such, this type of behavior has been termed ‘spurious loyalty’ (Knox and Walker, 2001). Also, the behavioral research practice has been plagued by defining the best method of operationalizing and measuring brand loyalty. In comparison, the measures used to determine the attitudinal loyalty construct are far more homogenous and therefore offer more external validity. In addition, new research has shown that attitudinal measures of loyalty can be reliably used to explain or predict purchase behavior (Bennett and Rundle-Thiele, 2002). To dispel the confusion behind the brand loyalty construct, there has now been a recent push to break the intentional aspect away from a single concept of brand loyalty and into its own measurement. Unfortunately the newly separated construct has already been identified with several different names, such as brand commitment (Warrington and Shim, 2000), true brand loyalty (Amine, 1998), attitudinal loyalty, and brand support (Bennett and Rundle-Thiele, 2002; Bloemer and Kasper, 1995; Chaudhuri and Holbrook, 2001; Knox and Walker, 2001). In an attempt to develop a perceptual measure of brand equity, Lassar, Mittal, and Sharma (1995) 5 determined that it was better to focus on commitment as a feeling since behavior was “to be a consequence of brand equity rather than brand equity itself” (p. 13). This break away of the attitudinal dimension has been further dissected into measures of a consumer’s propensity to be loyal and the attitude towards the brand or repurchase (Bennett and Rundle-Thiele, 2002). Interestingly, this thinking may give a reason why some researchers have deemed that brand loyalty cannot be measured from a universal set of research questions, but instead the questions must be product specific. If part of brand loyalty lies in a consumer’s propensity to be loyal, then obviously this variable could change between market segments and product categories. This study follows this line of reasoning by focusing on a consumer’s attitudinal loyalty towards a brand and leaving the behavioral loyalty as a consequence, similar to that of relationship marketing frameworks. Given that Aaker had allowed for such a possibility in his original conceptualization, we utilize his original conceptualization of brand loyalty as the “measure of the attachment that a customer has to a brand” (Aaker, 1991). Brand Equity: Brand Awareness Aaker (1991) defined brand awareness as “the ability of a potential buyer to recognize or recall that a brand is a member of a certain product category” ( p. 61). Brand awareness is an obvious necessity to building brand equity since a consumer would need to be aware of a brand before that brand could have any equity, but brand awareness can also be used solely by consumers for purchase decisions often in low involvement based purchasing situations (Broniarczyk and Alba, 1994; Hoyer and Brown, 1990; Macdonald and Sharp, 2000). From these findings, research has shown the worth of using advertising to build awareness since it can be used as a simple choice heuristic leading a consumer to choose a remembered brand over even a higher quality brand (Hoyer and Brown, 1990; Macdonald and Sharp, 2000). Aaker broke brand awareness into four levels representing a hierarchy of awareness within the consumer with each level using a different recall test. These levels ranged from the lowest level where the consumer is “unaware of the brand” to the highest level where the brand is “top of mind” (Aaker, 1991). This “top of mind” area has also been coined as brand recall as well as brand salience (Alba and Chattopadhyay, 1986). Keller (1993) broke brand awareness into two components, brand recognition and brand recall, that mimicked Aaker’s components but leaves off the two extremes. Brand recognition is correctly identifying the product category when 6 given the brand name and brand recall is recalling the brand name when given the product category. Keller then recognizes that the relative importance of each item depends on how the brand is purchased. For example, if the brand is purchased in a store, then brand recognition is more important (Keller, 1993). Most studies concerning brand awareness have centered on the process and measurement of recalling the brand. Recent studies show that the measures change in usefulness over a brand’s lifetime, with unaided tests showing more receptiveness to change in an older brand and aided recall tests showing more receptiveness in a newly launched brand (Laurent, Kapferer, & Roussel, 1995). The tricky nature of using aided recall tests has also been an interesting area. Specifically, aided recall can actually inhibit the recall of competing brands that are not shown (Alba and Chattopadhyay, 1986). The size of the effect was related to both the number of brands given as an aid and the time of exposure (Alba and Chattopadhyay, 1986). Other potential inhibitors of recall are the product and category cues that are used (Nedungadi, Chattopadhyay, and Muthukrishnan, 2001) as well as the presence of “market leaders” (Laurent, et al., 1995). Significantly, recall can correlate with brand attitude depending on what attributes are actually recalled (Chattopadhyay and Alba, 1988). The simplicity of the aforementioned awareness definitions has recently been contested; with researchers noting a memory network in play with the brand name and product category as it has been noted with brand associations. This line of research questions the usefulness of employing a one cue test (product category) when the actual process used by consumers utilizes far more cues (Romaniuk and Sharp, 2004). In some cases it has been shown that a brand can have a strong awareness but be in a weak or relatively unknown product category (Nedungadi, Chattopadhyay, and Muthukrishnan, 2001). To give credence to the complexity of the recall function, Romaniuk and Sharp (2004) proposed to separate brand salience away from brand awareness and its conception as a “top of mind” ranking to an operationalization as “the propensity of the brand to be thought of in buying situations”. These findings give more credence to the depth of brand awareness, but for the sake of simplicity and parsimony, awareness is operationalized using Aaker’s (1991) original definition, as “the ability of a buyer to recognize or recall that a brand is a member of a certain product category” ( p. 61). Brand Equity: Brand Associations 7 While researchers have postulated that brand associations are the driving force behind brand equity (Chen, 2001), Aaker (1991) himself stated “The underlying value of a brand name often is its set of associations” ( p. 110). There are various ways that brand associations contribute value to both the consumer and the business and these include helping to process and retrieve information, differentiation, reason-to-buy, the creation of positive attitudes, and a basis for extensions. Aaker (1991) defined brand associations simply as “anything linked in memory to a brand” but then went on to also state that “the association not only exists but has a level of strength (p. 109). Keller (1993) similarly defines associations as the other informational nodes linked to the brand node in memory and containing the meaning of the brand for consumers. He states that associations can vary in strength, favorability and uniqueness and these variations thereby hold the various associations’ strength and “play an important role in determining the differential response that makes up brand equity” (p. 3). Aaker (1991) classified brand associations into an expansive eleven categories. Keller’s (1993) classification has fewer categories but is actually wider in scope with just attributes, benefits, and attitudes. This classification increases in abstraction from attributes and attitudes with benefits representing the personal value consumers attach to the product and attitudes representing their overall evaluation of the product. The nature and further classification of brand associations has been the focus of much of the research on brand associations. The first categorization was between attributes and nonattributes, effectively distinguishing between informational and emotional associations (Park and Srinivasan, 1994). Recent research has identified another set of associations as organizational associations, which has also been linked to a new area of branding research, that of organizational branding (Aaker, 2004). This new category of associations has been broken down into corporate ability associations and corporate social responsibility associations (Keller and Aaker, 1998) with additional research attesting to the insignificance of the corporate social responsibility associations for predicting consumer behavior (Chen, 2001). The abstractness of an association has been shown to be a predictor of the strength of the association as well as an indicator of how long the memory or association will stay in the consumer’s mind (Alba and Chattopadhyay, 1986; Keller, 1993). A standard measurement of associations has been elusive. One line of research combines brand associations with brand awareness, even though they are conceptually different (Egan, 8 2003; Yoo and Donthu, 2001; Yoo, Donthu, and Lee, 2000). Other research, though, has shown that these two areas are distinctive dimensions (Sinha, Leszeczyc, and Pappu, 2000; Sinha and Pappu, 1998). A separate line of research has shown that different goods and services will require different measures (Low and Lamb Jr., 2000) and those different associations can have varying effects on the accepted consumer behavior outcomes of brand equity (Broniarczyk and Alba, 1994; Rio, et al., 2001). Rather than determining a product specific scale, the pattern of Cobb-Walgren, Ruble, and Donthu (1995) was followed. Thus, the present study utilizes a more universal scale for brand associations that will allow for the comparison of brand associations across different product categories. As such, the concept of brand associations is operationalized simply as the summation of anything linked in memory to the brand. This operationalization allowed for the measurement of the favorability as well as the strength of the associations by considering the number of associations since Aaker (1991) deemed that associations “not only exist but have a level of strength” and that an association will be stronger “when it is supported by a network of other links” (p. 109). Brand Equity: Perceived Quality Perceived quality (PQ) can almost be viewed as another brand association since it is “a perception of customers” rather than the actual product quality (Aaker, 1991). The importance of perceived quality has been attested to by research that has linked it to brand equity responses such as willingness to pay a price premium, brand purchase intent, and brand choice as well as accept brand extensions (Aaker, 1996; Keller, 1993). The exact nature of the perceived quality component antecedent has been debated. Perceived quality has been changed into a perceived performance variable (Lassar, et al., 1995) as well as incorporated into a grouping with “perceived value for the cost” (Netemeyer, et al., 2004). An interesting note about perceived quality is the relationship marketing literature’s use of the construct as service quality. The long use of customer satisfaction in relationship marketing caused a debate regarding the difference between these two constructs once service quality was introduced. This debate gave further clarity to service quality, and thereby perceived quality, namely that service quality can be from an outsider’s perspective where information is learned primarily second-hand (Storbacka, Strandvik, and Grönroos, 1994). The second-hand 9 nature of service quality relates to research borne out on perceived quality, specifically that advertising spending has been linked to perceived quality as well as the perception of a marketing campaign’s expense (Moorthy and Zhao, 2000). The fact that perceived quality has shown to be significant in both relationship marketing as well as brand equity gives credence not only to the similarity of these two paradigms but also to the possibility that more antecedents are extendable between the paradigms. To operationalize PQ, we look to Netemeyer et al.’s 2004 study, which operationalizes PQ as “the customer’s judgment of the overall excellence, esteem, or superiority of a brand (with respect to its intended purposes) relative to an alternative brand” (p. 210). Relationship Marketing The advent of relationship marketing pushed marketers to look beyond discrete transactions or the transactional view of marketing, which focuses on single transactions, to a broader scope of relational exchanges made up of multiple transactions (Dwyer, Schurr, and Oh, 1987; Grönroos, 1994). The long term view that is implicit in such a paradigm shift is similar to that of brand equity since the CBBE components are built up over time. Relationship marketing was conceptualized as marketing to existing customers and therein was the value to firms since it was deemed that marketing to existing customers would be cheaper than attempting to obtain new customers (Berry and Parasuraman, 1991). From the onset, customer loyalty was the primary behavioral goal when relationship marketing strategies were considered. The focus on loyalty is one of the main areas of overlap between the relationship marketing and brand equity strategies. This is not a new discovery since even Jacoby and Chestnut realized that, “brand loyalty is essentially a relational phenomenon” in their original brand loyalty research (1973). The relationship marketing research and application initially lay in the realm of businessto-business marketing (Morgan and Hunt, 1994) and the service industry (Egan, 2003) but received additional interest when researchers started extending it into the consumer goods market. This widened view of exchange led to the definition of relationship marketing by Morgan and Hunt as “all those market activities directed toward establishing, developing, and maintaining successful relational exchanges” (1994). It was here, in the study of consumer- 10 organization and consumer-brand relationships, that one of the key features of relationship marketing became evident: exchange between organizations and consumers are not just limited to economic boundaries but can encompass emotions as well (Bolton and Bhattacharya, 2000). Hence, part of the increased value generated by relationship marketing to the consumer is an emotionally anchored one, similar to some of the benefits of brand equity as discussed before. Increased value is also possibly generated by the reduction of choices from ongoing loyalty. This seemingly counter intuitive method of value creation has several theoretical foundations such as task simplification, expertise development, risk reduction, and cognitive consistency (Sheth and Parvatiyar, 2000). Researchers have shown that this particular value component is not as straightforward as some have stated since engaging in relationships with companies can provide consumers more choices in their particular consideration set through the reduction of unwanted choices (Peterson, 1995). The renewed research interest into research marketing was spearheaded by attempting to prove the validity of not only consumer-business relationships but also consumer-brand relationships. The conceptualization of a consumer-brand relationship has taken the form of an imagined relationship with the actual brand or real relationships within a brand sub-culture or brand community (Ambler, 1997; Fournier, 1998; Muniz and O'Guinn, 2001). Reducing technology costs and rising communication abilities has not only made it possible for manufacturers to engage in exchange directly with consumers, much like Dell, it has also allowed for the customization through technology, such as just-in-time inventory control, whereby the customer has now become a co-creator (Bolton and Bhattacharya, 2000; Sheth and Parvatiyar, 1995). When the customer is involved in such depth within the creation process, it is easier to imagine how a brand-consumer relationship would develop. The benefit of a brand-consumer relationship is anchored in the continued relationship or ongoing series of transactions with the consumer that can be quantified in a probable lifetime value or increased customer retention rate and thereby reduced marketing costs (Reichheld and Teal, 1996). Whether a lifetime value can actually ever be counted on has been questioned with researchers noting that the expectation of an exclusive relationship might be a fallacy (O'Malley and Tynan, 2000). This has led to considerable debate as to the applicability and feasibility of a relationship marketing framework to the consumer level. Research has borne out that businessto-consumer exchange can be either relational or transactional, depending on the consumer or the 11 product category (Garbarino and Johnson, 1999; Szmigin and Bourne, 1998). Even the lifetime probable value of a customer has been called into question (Sheth and Kellstadt, 2002) with research revealing that some customers are not profitable in a long term setting (Reinartz & Kumar, 2000) and that relationship marketing can have diminishing returns (Hibbard, Brunel, Dant, & Iacobucci, 2001). The reassessment of relationship marketing’s value has also led to a reassessment of the conceptualization. It was initially conceptualized as a marriage, which researchers noted was flawed due to the expectation of fidelity by the consumer (O'Malley and Tynan, 2000). A more recent conceptualization of the relationship is that of a ‘good friend’, where the consumer has many relationships but a few are considered privileged (Szmigin and Bourne, 1998). This less exclusive social metaphor has gained ground as the number of studies specifically focused on the benefits of relationship marketing to the consumer has increased (Fournier, Dobscha, and Mick, 1998; O'Malley and Tynan, 2000). Hypotheses Despite the extensive amount of research that has been done on relationship marketing and branding, there is no research that attempts to explain how these two areas interact. The question of how relationship marketing strategies impact brand equity has gone unaddressed. This is particularly perplexing considering that both strategies are long-term focused and therefore should have considerable overlap if a company pursues or attempts both strategies at the same time. As mentioned previously, the focus of the present study is to attempt to understand how the two strategies of relationship marketing and brand equity interact. Since branding and brand equity has been established longer and therefore used by practitioners for a longer length of time, the present study studies the effects of relationship marketing strategy on brand equity rather than vice-versa. One possible reason for the aforementioned inattention by researchers into the interaction of relationship marketing and brand equity is that relationship marketing research has typically focused on services (Berry and Parasuraman, 1991) while branding research has typically focused on goods (Krishnan and Hartline, 2001). As mentioned previously, some of the criticism of relationship marketing has been leveled at the application of it at the business-consumer 12 relationship, especially considering mass-produced non-durable goods (O'Malley and Tynan, 2000). In the same vein, branding has been shown to be a less sufficient strategy for services (Krishnan and Hartline, 2001). However, both strategies have been used across product types (Wentzel, 2009; Sheth and Parvatiyar, 2000). In addition, the new marketing strategy of servicedominant logic conceptualizes all products as services (Vargo and Lusch, 2004). Thus, attention at how branding and relationship marketing might interact is warranted. Due to the above history of the two strategies, product type is controlled for in the hypothesis testing. Before hypotheses can be constructed, though, the various strategies need to be conceptualized. Many definitions of relationship marketing can still be considered too vague to allow proper testing or demarcation from marketing theory in general (Peterson, 1995). Berry and Parasuraman (1991) did create a demarcation of relationship marketing when they conceptualized three successive relationship marketing strategies. It is through these levels that the incremental effect of utilizing relationship marketing on the brand equity components can become apparent. A level one strategy is characterized by the primary use of financial incentives to capture “retention marketing”. The incentives can represent rewards programs, discounted services, and additional coupons to name a few. This level is the most prevalent of the three levels in the market place and the least effective in establishing a competitive advantage since it is easily copied (Berry and Parasuraman, 1991). More recent research has shown that a customer’s shopping pattern before the loyalty program influences their long term behavior (Yuping, 2007). Since it is known that price can be used as an extrinsic cue for perceived quality (Aaker, 1991), it is expected that a brand employing level one strategies will suffer a reduction in the perceived quality component of brand equity. The ease of copying and ubiquitous presence of the strategies from level one means that there should not be any unique associations created by the strategies alone. Thus, it is also expected that such strategies will have no effect on brand associations or brand awareness. However, since these strategies are aimed explicitly at increasing loyalty, it is expected that brand loyalty will increase as a firm employs level one relationship marketing. It is then we hypothesize: H1) Brands that engage in more relationship marketing level one strategies will have (H1a) lower perceived quality, and (H1b) higher brand 13 loyalty when compared to brands that engage in less relationship marketing level one strategies. Level two relationship marketing strategies are characterized by social bonding and the building of relationships (Berry and Parasuraman, 1991). It is at this level that much of the criticism of relationship marketing to consumers has been aimed (O'Malley and Tynan, 2000), especially in the consideration of goods. As mentioned before however, research has shown that consumers can establish relationships with brands (Fournier, 1998) and the increased communication that is a hallmark of this level is also possible in a mass audience context through database technology (Schultz and Schultz, 2003) and Internet communities (Andersen, 2005). Thus, the benefits from this level, that of increasing customer retention and allowing a company time to respond to mistakes before customer defections (Berry and Parasuraman, 1991), can be realized at the brand level. The effects of a level two strategy on brand equity can be postulated based on the increased communication and social bonding components. Increased contact between the brand and consumer should allow for increased brand associations and brand awareness since both of these components are memory and learning based. Similar to the last level, brand loyalty should also be increased. In consideration of these relationships, we hypothesize: H2) Brands that engage in more relationship marketing level two strategies will have (H2a) increased brand awareness, (H2b) increased brand associations, and (H2c) higher brand loyalty when compared to brands that engage in less relationship marketing level two strategies. Level three relationship marketing is the solidification of relationships through structural bonds (Berry and Parasuraman, 1991). These bonds are unique and are usually built into the ‘system’ of the product thereby allowing the relationship to be based on the offering of the brand rather than depending on marketing communications or front line employees (Berry and Parasuraman, 1991). This strategy was conceptualized within a service context but an example of this strategy from a goods perspective is that of high customization such as utilized by Dell and Nike, both of whom allow their customers to customize products within the purchasing process 14 on their respective websites. Since some brand-relationship research has specifically touted the increased possibility that a brand relationship can form through such means (Bolton and Bhattacharya, 2000), it is reasonable to expect this level of relationship marketing to have an increased benefit on the brand equity components. However, it still remains that building structural bonds can be problematic for brands of nondurable goods such as Coca-Cola. The difficulty of creating structural bonds gives increased benefits to the firms who can employ such strategies. These benefits include increased switching costs for customers as well as increased value that is not anchored in price which in turn results in insulating customers from competitors pricing incentives (Berry and Parasuraman, 1991). The uniqueness of an offering by a brand that employs structural bonding should increase brand associations and brand awareness. Such a strategy should also increased perceived quality since additional features can be seen as a signal that a brand understands the needs of their customers (Aaker, 1991). Similar to the last two levels and indicative of relationship marketing in general, brand loyalty can be expected to increase as a brand practices level three relationship marketing. Thus, H3) Brands that engage in more relationship marketing level three strategies will have (H3a) increased brand awareness, (H3b) increased brand associations, (H3c) higher perceived quality, and (H3d) higher brand loyalty when compared to brands that engage in less relationship marketing level three strategies. The relative strength of each relationship marketing strategy compared to each other is not as straightforward as the effects of each strategy alone. Even more difficult is the consideration of comparing firms that are practicing multiple strategies. For example, what would be the difference in brand equity components between a firm practicing level one and level two strategies together compared to a firm practicing level one and level three strategies. We leave these differences to be deductively found through the analysis with the hopes of informing future research. 15 CHAPTER 2 METHODOLOGY The options for testing brand equity theories involve either using real brands or creating fictitious brands. Fictitious brands would be good for measuring how the antecedents of brand equity are built but not for the measurement and comparison of these antecedents as has been proposed. It would be difficult at best to attempt to create rich brand associations that mimic the depth of associations some real world brands have created. As stated before, the purpose of this study is to study the impact of relationship marketing on brand equity. To study this phenomenon, 50 of the top 100 brands were used as delineated by Interbrand and reported by BusinessWeek (BusinessWeek, 2008). Since the nature of this study is a further attempt to reconcile two marketing areas whose distinct origins lay in either the service or packaged goods industry into one workable framework, a large range of existing brands that span across goods and services were used. This will allow for post hoc tests regarding the difference between goods and service brands. The literature states that generally, brand equity is more important where goods are concerned (Cobb-Walgren, et al., 1995; Krishnan and Hartline, 2001) but that brand equity can contribute to a richer understanding of the services market. The brand equity of these products was calculated similar to (Chaudhuri and Holbrook, 2001) in that the brand equity score for each brand was elicited from a sample that was then summed to create the score for each brand equity component. A convenience sample of students was utilized for the brand equity scores which is a common practice in branding research (Washburn and Plank, 2002; Yoo and Donthu, 2001). The 50 brands were chosen from the list of 100 based on their relevance to the sample. Thus, financial investment brands (ex. GoldmanSachs), luxury brands (ex. Rolex), and gender specific brands (ex. L’oreal) were not chosen in favor of automobile brands (ex. Ford, BMW), retail brands (ex. GAP), as well as food and fast food brands (ex. Coca-Cola, McDonalds). Most of the financial investment brands were eliminated due to inapplicability to the sample so two local bank brands were added (Wachovia and Bank of America) since relationship marketing has often been characterized as being very useful for financial clients (Berry and Parasuraman, 1991).A list of brands used in the study can 16 be seen in Table 1 (Four more brands are removed from the analysis after a cluster analysis as explained later bringing the total number of brands used to 46). Data collection consisted of the distribution of an anonymous online-based survey through the website Qualtrics.com. A basic marketing class of 507 undergraduates was given the URL of the survey and offered extra credit for taking the survey. A total of 356 unique responses were collected giving a response rate of 70%. Each participant was shown a brand at random with three iterations giving a total of 1,068 observations across the 50 brands. Since the brands were shown randomly, the number of responses for each brand varied from 13 (Nike) to 32 (Ford). The complete listing of the number of responses is shown in Table 1. The responses were reviewed for any brands that elicited an abnormally low awareness score and two brands were removed (HandM and Nescafe). The brand equity measures were either taken from pre-existing methods and scales or were developed based on the work of Aaker (1991). All items from the survey showed acceptable reliability and are included in Table 2 along with the reliability scores. CobbWalgren, Ruble, and Donthu’s 1995 study into brand equity provided measures of awareness and associations. The awareness measure is from an unaided recall question, from which are counted two separate scores: 1) the number of mentions and 2) the number of top-of-mind mentions. Two Likert scale items were also included for awareness to increase the total number of items above three to assess their reliability. All scores for the awareness construct were standardized and then summed together to create a brand awareness index. The brand association measure was also measured with an unaided recall type question. The participants were asked to write down any associations they had for the chosen brand. An association was explained as “anything that you think of when you think of the brand (ex. lumpy, foreign, nice). An association can be positive, negative or neutral.”Five different items were obtained from the listing: 1) the total positive associations, 2) the total negative associations, 3) the total number of times the first association mentioned was positive, 4) the total number of times the first association mentioned was negative, and 5) the total number of associations. The associations were assessed as being positive, negative or neutral by two separate judges. The reliability of the judgments was then assessed with Krippendorf’s alpha (Krippendorf, 1980) as outlined by Hayes (2007) utilizing syntax to be run in SPSS. The smallest computed alpha was .85 showing moderate agreement. Each judges’ count was averaged together to create the five 17 separate counts as outlined above. Each of the positive and negative counts were divided by the total number of associations since the brands had a different number of responses, thus each count became a percentage of that brand’s total associations. The negative association counts were then subtracted from one to make sure all of the counts were scaled in the same direction. Thus, the two negative counts actually became the percentage of the total associations that were not negative. All four of the counts were then combined together to create a final brand association scale. The only item that had to be deleted to increase the reliability of any scale was the count of the total number of associations. In fact, this item made the Cronbach’s alpha score for the brand associations construct .17 before the deletion. This is most likely because the positive and negative counts would have shown similar variance since they can be thought of as opposites of each other whereas the total number of associations would have been high with either negative or positive and therefore would not have varied at the same rate. A four measure scale for brand loyalty was adapted from Aaker’s description of the construct (Aaker, 1991). The measures for perceived quality were taken from Netemeyer et al. (2004). All of the relationship marketing scales were developed based on the description of each level from Berry and Parasuraman (1991). Each level contained three questions. All scales were created by averaging the related measures together. Only the brand awareness scores were standardized before being summed due to using scales and counts together. 18 Table 1 Number of Responses and Average Scores on Brand Equity Component per Brand (standardized) BRAND Adidas American Express Amazon Apple Bank of America BMW Budweiser Canon Coca-Cola Colgate Dell Duracell Disney Ebay FedEx Ford GAP Gillette Google Gucci Honda Hewlett Packard Hyundai IKEA Intel Kellogg’s Kentucky Fried Chick. McDonald’s Microsoft Nike Nokia Nintendo Pepsi Panasonic Pizza Hut Samsung Shell Sony Starbuck’s Toyota UPS Visa Volkswagen Wachovia Xerox Yahoo # of responses 22 17 24 14 17 19 27 23 13 20 23 22 23 19 17 32 27 21 20 26 19 22 28 28 19 22 13 17 15 13 20 24 21 21 18 28 16 25 15 20 19 23 17 17 15 23 Brand Awareness .23 05 -.56 -.14 .41 -.20 .80 .46 1.18 .47 -.17 .71 .79 .93 1.04 -.16 -.61 .84 .77 -.58 -.14 -.10 .71 -.85 -.24 .03 -.69 .82 .79 .71 -1.08 .06 .32 -.05 .53 -.37 -.40 .45 1.06 .26 .62 .85 -.78 -.64 -.58 -.06 Brand Associations -.33 .11 -.31 .09 .44 .68 -.21 1.16 .74 .41 -.98 1.15 .39 .89 .97 -1.10 -1.34 1.19 .88 -.92 .57 -.38 -.79 .89 .61 -.25 -.90 -2.67 -.86 .78 -2.10 .32 -.34 -.06 -.70 .32 -.54 .27 -.46 .50 .96 .21 .05 .03 .31 .31 19 Perceived Quality -.37 -.19 .30 .78 .61 .32 .13 .64 1.6 .22 -.48 .60 1.38 1.00 .97 -1.67 -1.34 .72 1.48 .45 -.66 -1.03 .49 -.40 .56 .90 -1.80 -2.10 .81 .92 -2.24 -.24 .04 -.29 -1.70 .05 -.40 .94 .94 -.26 .63 .53 -1.55 -.80 .05 -.53 Brand Loyalty .06 -.79 .06 .34 .96 -.13 .22 .64 1.37 -.05 -.44 .93 1.01 .75 .98 -1.77 -1.47 .69 1.84 -.14 -.50 -.82 -.34 .06 .49 .84 -1.50 -1.81 1.34 1.07 -2.52 .17 -.46 -.45 -1.29 .18 -.50 1.06 .91 -.07 .54 .78 -1.08 -.83 -.10 -.23 Table 2 Survey Items with Means, Standard Deviations and Cronbach’s Coefficient Alpha Construct (α) Measures Mean SD Count of number of brand mentions .67 .27 Count of number of times brand is mentioned first .33 .29 “I am aware of [brand X].” 4.75 .18 “I am familiar with [brand X].” 4.54 .31 α if deleted “List all brands that come to mind for the [product category X].” Brand Awareness “List any associations that come to your mind for [brand X].” [ PA=positive associations | NA = negative associations | TA = total assocations ] Brand Associations Perceived Quality (.98) Brand Loyalty (.96) PA/TA .44 .15 1– (NA/TA) .86 .11 PAtom/TA .38 .18 1- (NAtom/TA) .89 .15 Compared to other brands of [product category X], [brand X] is of very high quality. 3.90 .56 .98 [Brand X] is the best brand in its product class. 3.40 .62 .97 [Brand X] consistently performs better than all other brands of [product category X]. 3.40 .59 .97 I can always count on [brand X] for consistent high quality. 3.70 .56 .98 Overall, I have been satisfied with my usage of [brand X]. 3.90 .47 .96 In general, I have a positive feeling towards [brand X]. 3.90 .45 .95 I am committed towards [brand X] 3.20 .57 .93 I would choose to use [brand X] over other brands of [product category X]. 3.20 .63 .95 3.28 .35 .94 3.23 .34 .87 There are financial incentives for continuing to purchase [brand X] 3.20 .32 .95 I feel like I have a social bond with [brand X]. 2.95 .47 .88 [Brand X] attempts to establish a relationship with their customers. 3.48 .40 .78 3.20 .38 .85 3.56 .37 .90 3.27 .35 .93 3.46 .35 .82 [Brand X] has a rewards program that gives some kind of benefit for frequent or RM Level 1: Price Incentives (.95) RM Level 2: Social Bonding (.88) multiple purchases. [Brand X] offers price incentives to encourage me to continue to purchase their product if I have purchased it before. [Brand X] creates the sense of a relationship through direct mailings, email or other forms of direct and indirect contact. [Brand X] can be customized in some form to my personal tastes and/or choices. RM Level 3: Structural Bonding (.92) There are offerings available from [brand X] that are not readily available from somewhere else. There are offerings from [brand X] that are tailored to my tastes. The numbers in parentheses represent the Cronbach’s alpha score for that construct’s items. Items that were not based on counts were based on a 5 point scale that was anchored by strongly agree, neither agree or disagree, and strongly disagree. 20 CHAPTER 3 RESULTS Cluster Analysis In order to determine the differential effect of utilizing relationship marketing strategies on brand equity components, brands were grouped according to each level of relationship marketing strategy separately through cluster analysis. This method is preferable to utilizing a median split for two reasons. The first reason is that cluster analysis can be used as an exploratory measure to determine underlying similarities rather than making arbitrary cut off points as would be done through a split. The second reason is that cluster analysis can also be used to minimize the variance within the clusters and thereby increases the variance between the groups. Before the cluster analysis was computed, the brands were analyzed for outliers on the relationship marketing levels since outliers can have an overly powerful influence on clustering processes (Punj and Stewart, 1983). Four brands were shown to be outliers (3 or more standard deviations from the mean) on one or more of the levels and were removed (Heinz, Marlboro, Motorola, and MTV) leaving a total of 46 brands for the analysis. Following the work of Punj and Stewart (1983), a two-step methodology was employed. First, an agglomerative hierarchical cluster procedure was run with Ward’s method as an inductive technique to determine the number of clusters to be used for each level. This type of procedure was chosen to assess the natural underlying groupings rather than determine an artificial structure from the onset as would be required with the use of a nonhierarchical clustering method alone (Bailey, 1975). The method used for choosing the appropriate cluster number was the change in the agglomeration coefficient. An appropriate cluster is chosen based on the largest percentage change in the agglomeration coefficient as the number of clusters decreases. Interestingly, two of the three cluster procedures produced a strong case for a two cluster solution. Although a two cluster solution often produces a larger change in the agglomeration coefficient compared to a solution utilizing a larger number of clusters, the cluster analysis for the relationship marketing levels two and three all showed an abnormally large difference between three clusters as compared to two as shown in Table 3. Relationship 21 Table 3 Agglomeration Coefficients from Clustering Procedure of Relationship Marketing Strategy Levels RM Level 1: Price Incentives RM Level 2: Social Bonding RM Level 3: Structural Bonding 2 clusters 3 clusters 4 clusters 15.401 7.894 3.807 (195.10%) (207.35%)* (142.00%) 23.466 10.523 8.138 (223.00%)* (129.31%) (134.00%) 17.527 7.226 4.282 (242.55%)* (168.50%) (125.24%) The agglomeration coefficient is shown in the corresponding box for each RM level and cluster. Percentage change from the previous cluster solution (additional cluster) is shown in parenthesis. A star (*) represents the cluster solution used. 22 Figure 1. Dendogram of Level One Relationship Marketing Clustering using Ward’s Method 23 Figure 2. Dendogram of Level Two Relationship Marketing Clsutering using Ward’s Method 24 Figure 3. Dendogram of Level Three Relationship Marketing Clustienrrg using Ward’s Method 25 Figure 4. Scatterpl rplots of RM Levels versus Brands and Cluster ter Solutions 26 Table 4 Number of Brands Assigned to Clusters per Relationship Marketing Strategy Level Low Medium High TOTAL RM Level 1 Strategies 15 (2.9) 20 (3.2) 11 (3.47) 46 RM Level 2 Strategies 23 (2.9) - 23 (3.5) 46 RM Level 3 Strategies 27 (3.2) - 19 (3.8) 46 Note: Numbers in parentheses represent the mean of the corresponding averaged scale for the corresponding level of strategy (i.e. when referring to the top-left corner, there are 15 brands that are categorized as low in RM Level 1 strategies and their mean on the averaged scale for RM Level 1 Strategies is 2.9). 27 marketing level one showed the largest coefficient change at the three cluster solution. These differences are also apparent in the dendograms as shown in Figures 1, 2, and 3. Starting from the right, each horizontal line represents a cluster and the distance of the line represents the amount of change in cluster membership from breaking that cluster apart into two clusters. To use a dendogram for choosing a cluster solution, a vertical line is drawn through the point in the dendogram in which it intersects the largest horizontal lines possible. Each horizontal line it intersects represents an additional cluster. The longest horizontal lines that are apparent in the dendograms is at the far right for relationship marketing level two strategies (Figure 2) and level three strategies (Figure 3) and would therefore intersect only two lines. The vertical line for level one strategies (Figure 1) would be drawn more towards the middle and would therefore intersect three lines. Thus, a two cluster solution was chosen for the brands based on their relationship marketing level two and three strategies while a three cluster solution was chosen for the brands based on their relationship marketing level one strategies. The number of brands in each cluster for each relationship marketing strategy level is shown in Table 4. Once the correct number of clusters was determined, the second step in the clustering procedure was undertaken. This step consisted of utilizing K-means clustering as an iterative partitioning procedure to fully refine the clusters since an iterative procedure implicitly attempts to minimize the variance within each cluster (Punj and Stewart, 1983). The cluster centers from the hierarchical procedure were used as the initial centers for the K-means procedure. The final clusters were analyzed utilizing an adjusted Rand index to assess their similarities. The adjusted Rand index computes a score ranging from zero to one with a zero representing no similarity and a one representing complete similarity. The index can be calculated even if there are a different number of clusters as in this research (Rand, 1971). The clustering on relationship marketing level one showed a strong difference from the clustering on level two (AR=.08) and level three (AR=.16). The clustering on levels two and three however showed a marked similarity (AR=.47) compared to the other index scores. This would suggest that levels two and three have a higher than normal level of covariance. The scatterplots of the clustering solution on each independent variable are shown in Figure 4. Once the independent variables were demarcated, the construct correlations and covariances were assessed as shown in Table 5. While a clustering solution was used for the creation of the independent variables, each of the relationship marketing level items 28 Table 5 Correlation and Covariance Coefficients Between All Constructs Variables 1 2 3 4 5 6 7 - .15 .35 .36 .10 .25 .19 (2) Brand Associations .31* - .54 .56 .09 .35 .31 (3) Perceived Quality .59** .66** - .87 .31 .71 .61 (4) Brand Loyalty .63** .70** .95** - .28 .67 .60 (5) RM Level 1 .17 .10 .32* .30* - .63 .53 (6) RM Level 2 .41** .41** .72** .71** .63** - .88 (7) RM Level 3 .33* .37* .63** .68** .53** .88** - (1) Brand Awareness Note: Construct correlations are shown in the lower left triangle. Construct covariation based on standardized scores are shown in the upper right triangle. * p <.05 ** p < .01 29 were summed together to assess any potential problems with the data. The high degree of covariance between relationship marketing levels two and three that was suggested by the Rand index score is confirmed by the high correlation (r2 = .88). A large amount of multicollinearity among the dependent variables, particularly brand loyalty and perceived quality, is also apparent (r2 = .95). Validity Assessment Since the measures for all of the relationship marketing levels and brand loyalty were created based on previous conceptualizations rather than adapted from previous questions, the convergent and discriminant validity of these measures was assessed. The tool used for the assessment of convergent and discriminant validity was Pearson’s correlation coefficient since a confirmatory factor analysis was not possible due to sample size (Hair, Black, Babin, Anderson, and Tatham, 2006). For convergent validity the correlations of each of the individual items of a single construct with each other are expected to be high. For discriminant validity, the correlations of the summated scales with the summated scales of conceptually distinct constructs are expected to be small. There is no specific cutoff for the correlation coefficient for either validity assessment (Hair, et al., 2006). The within-construct correlations (between items of each construct) all were relatively high (r2 range of .39 to .94). The discriminant validity can be gauged by the correlation matrix shown in Table 5. It can be seen that two sets of constructs have discriminant validity issues. These sets are RM Level 2 / RM Level 3 (r2 = .88) and perceived quality / brand loyalty (r2 = .95). While quality and loyalty are known to be linked, a correlation of .95 would suggest that these items are not distinct. Subsequently, brand loyalty and relationship marketing level three was removed from the analysis. Due to the removal of relationship marketing level three from the analysis, all hypotheses regarding relationship marketing level three (H3a, H3b, H3c, and H3d) were not tested. Common Method Variance Assessment Common method variance was assessed since the data were collected at one time using a single instrument (Podsakoff, MacKenzie, Jeong-Yeon, and Podsakoff, 2003). The Lindell and 30 Table 6 Correlation Coefficients Adjusted for Common Method Variance (Lindell and Whitney,2001) 1 (1) Brand Awareness 2 3 4 5 6 7 - (2) Brand Associations .22 - (3) Perceived Quality .48** .62** - (4) Brand Loyalty .51** .67** .94** - (5) RM Level 1 .09 MV .24 .22 - (6) RM Level 2 .31* .34* .69** .68** .59** - (7) RM Level 3 .22 .30* .59** .64** .48** .87** Adjusted construct correlations are shown in the lower left triangle MV represents the correlation used as the marker variable for the adjustment of the other correlations * signifies significance at the .05 alpha level ** signifies significance at the .01 alpha level Double underlined correlations loss significance at the .05 alpha level after being adjusted. Single underlined correlations loss significance at the .01 alpha level but retained significance at the .05 alpha level after being adjusted. 31 - Whitney (2001) partial marker variable was used to ensure that common method variance did not overly influence the observed relationships between the variables. The partial marker variable method consists of using the smallest correlation between a marker variable and a primary variable to partial out the effects of common method variance. In effect, the smallest correlation is subtracted from the remaining correlations which are then assessed to see if the correlations lose significance. Since there was no marker variable used in the present study, the smallest correlation between two constructs based was used. This correlation is between relationship marketing level one and brand associations (r = .1). After partialling out the chosen correlation, four relationships were impacted. The brand awareness / brand associations, relationship marketing level one / perceived quality, relationship marketing level one / brand loyalty, and relationship marketing level three / brand awareness relationships all lost significance at the .05 alpha level after being adjusted as shown in Table 6. Of these relationships, the ones of most concern are those that might impact hypothesis testing. Since relationship marketing level three and brand loyalty were chosen for removal from the analysis due to lack of discriminant validity, the only remaining relationship of concern is between relationship marketing level one and perceived quality. The possible impact of a common method bias on the results will need to be taken into account if the data supports hypothesis one. Hypothesis Testing A MANOVA was used to test the hypotheses with the clusters as the independent variables and the brand equity components as the dependent variables. First the assumptions of MANOVA were assessed. The assumption of normality for the dependent variables was assessed by plotting the standardized residuals in a histogram as well as evaluating kurtosis and skewness statistics. As shown in Figure 5, the histograms suggest that the variables are normal with a slight negative skew. This departure from normality is expected since the brands that were used were from a list of the top 100 brands worldwide and would therefore group around the upper end of the scale. The skewness and kurtosis statistics were used to probe further into the normality of the variables as shown in Table 7. Brand associations and perceived quality both had a departure 32 Figure 5. Normality Plots of the Residuals of the Dependent Measures from normality since the skewness statistic of brand associations (-.85) was more than double the standard error of skewness (.35) and perceived quality (-.65) was close to this threshold as well. Finally, a Shapiro-Wilks test was assessed which showed that the distributions of perceived quality (p = .031) and brand associations (p = .046) were not drawn from a normal population (also shown in Table 7). In order to alleviate and problems from this divergence from normality, both brand associations and perceived quality were transformed by cubing the data (Hoyle, 1973). The statistics for the transformed data are also shown in Table 7. Hypothesis testing was performed twice, once with the transformed data and once with the original data. The 33 Table 7 Means, Standard Deviations, Skewness, Kurtosis, and Normality Test Among the Variables Mean SD Kurtosis (.69) Skewness (.35) S-W Brand Awareness .57 1.66 -.85 -.36 .078 Brand Associations 2.58 .50 1.00 -.85 .046 Perceived Quality 14.39 2.27 -.29 -.65 .031 Brand Loyalty 14.16 2.02 -.05 -.49 .41 Brand Associations Cubed 3,190 1,321 -.81 .15 .41 19 9 -.64 -.04 .44 Perceived Quality Cubed Standard error of the skewness and kurtosis is in parentheses. The S-W column represents the p value for the Shapiro-Wilk’s test 34 results of both tests were compared. Since there were no differences in the results as related to the testing of the hypotheses, the original data is used to shown the mean differences of the brand equity components across the relationship marketing strategies (Table 8) and plots of the mean differences (Figure 6) whereas the transformed data is used to present the test statistics used for hypothesis testing (Table 9 and Table 10). The assumption of multicollinearity is also not met since all of the dependent measures are highly correlated. However, this was assumed at the beginning of the research since the collinear nature of the brand equity components have already been established (Pappu, et al., 2005). The two highest correlated components, perceived quality and brand loyalty, have also been shown to be related in multiple studies and lines of research (Cronin, Brady, and Hult, 2000; Han, Kwortnik, and Wang, 2008; Lassar, et al., 1995). Thus, while the power of the overall test will be reduced, this is a necessity for the chosen area of study. The assumption of the equality of the covariance matrices was evaluated using Box’s test. As shown in Table 9, the F statistic was not significant at the .05 level; therefore the assumption was met. Levene’s univariate test of variance equality was then evaluated. All of the dependent variables met the univariate assumption as the smallest p-value was .54. Since relationship marketing has been said to be more beneficial to services while branding is considered better suited for goods, the type of product for each brand was intended to be used as a covariate in the analysis. However, since the product type did not correlate with any of the independent or dependent measures, it was not included. The results of the overall model are shown in Table 10. Hypothesis one stated that there would be a difference in perceived quality (H1a) and brand loyalty (H1b) for brands that used more relationship marketing level one strategies compared to those that used less. The data did not support hypothesis one at the multivariate level (Wilk’s λ = .12, p value = .99). Hypothesis two stated that there would be higher brand awareness (H2a), brand associations (H2b), and brand loyalty (H2c) for brands that used more relationship marketing level two strategies compared to those brands who used less. The data supported hypothesis two at the multivariate level (Wilk’s λ = 8.0, p value < .01). There were no specific hypotheses formulated for any interactions between the relationship marketing strategy levels. The interaction effect between relationship marketing level one usage and relationship marketing level two usage was not significant (p = .61). 35 Table 8 Average Brand Equity Component Scores per Relationship Marketing Strategy Level RM Level 1 Strategies RM Level 2 Strategies Brand Awareness Brand Associations Perceived Quality High 2.7 (.20) .67 (.10) 3.9 (.34) 23 Low 2.5 (.20) .62 (.14) 3.2 (.54) 23 2.6 (.24) .67 (.08) 4.0 (.35) 11 2.6 (.24) .67 (.08) 4.0 (.35) 11 - - - 2.6 (.20) .64 (.14) 3.5 (.52) 20 High 2.7 (.15) .66 (.13) 3.8 (.34) 8 Low 2.5 (.20) .63 (.15) 3.3 (.58) 12 2.6 (.22) .63 (.14) 3.4 (.65) 15 High 2.7 (.24) .67 (.13) 4.0 (.35) 4 Low 2.5 (.21) .62 (.14) 3.2 (.58) 11 High High Low Medium Low - N Note: Numbers represent the average of each brand equity component for that specific relationship marketing strategy (except for the far right column which represents the number of brands assigned to each category by the cluster analysis). Numbers in parentheses represent the standard deviation (i.e. when referring to the bottom-left number, there are 11 brands that are categorized as low in RM Level 1 strategies and low in RM Level 2 strategies and these brands have a mean of 2.5 and a standard deviation of .21 on the averaged scale for Brand Awareness). 36 Table 9 Box’s Test and Levene’s Test Statistics from the Multivariate Test Levene’s Test Box’s Test Brand Awareness Brand Associations Perceived Quality 1.30 .53 .79 .21 df1: 24 4 4 4 df2: 1005 41 41 41 .15 .71 .54 .93 F: p: 37 Table 10 Results of the Multivariate Test Between Relationship Marketing Strategy Usage Wilk’s λ P value Partial Eta2 Hypothesis Tested RM Level 1 Strategies .12 .99 .01 H1 – not supported RM Level 2 Strategies 7.97 <.01 .38 H2 - supported RM Level 1 * RM Level 2 .61 .61 .05 No hypothesized effect Each Relationship Marketing Level Strategy represents a categorical variable of brands that are ranked as either high or low (RM Level 2 Strategies) or ranked as high, medium, or low (RM Level 1 Strategies) 38 Figure 6. Mean Plots of Perceived Quality and Brand Awareness by Relationship Marketing Level Two Strategy Clusters Next, the impact of relationship marketing level two strategies was assessed for each brand equity component at the individual level as shown in Table 11. As postulated in hypothesis H2a, it was expected that brands that utilized more relationship marketing level two strategies would have higher brand awareness scores. The univariate test revealed that the data did support this specific hypothesis (F = 5.2, p value < .05).As stated in hypothesis H2b, it was expected that brands that utilized more relationship marketing level two strategies would have higher brand association scores. The data did not support this particular hypothesis (F = .3, p value = .40). The final hypothesis regarding relationship marketing level two strategies (H2c) could not be tested since brand loyalty was removed from the analysis. Though no effect was postulated, there was a significant difference in perceived quality (F = 16.6, p value < .01) between brands that utilized more relationship marketing level two strategies. To further assess the differences, all of the significant effects were graphed on mean plots as shown in Figure 6. The mean plots illustrate that as brands increase their level two relationship marketing strategies, their perceived quality and brand loyalty increase as well. The data thus supports hypothesis 2a regarding the positive effects of relationship marketing strategies on brand awareness but no effect was hypothesized for the effect of the level two strategies on perceived quality. 39 Table 11 Results of the Univariate Test Between Relationship Marketing Level Two Strategy Usage RM Level 2 Strategies F P value Partial Eta2 Hypothesis Tested Brand Awareness 5.2 .03 .11 H2a – Supported Brand Associations .73 .40 .02 H2b – Not Supported Perceived Quality 16.6 <.01 .29 No hypothesized effect Note: The statistics shown are based on the test of the difference between the brands ranked high in RM Level 2 Strategies versus brands that were ranked low in RM Level 2 Strategies on the brand equity components. 40 CONCLUSION Discussion The purpose of this research was to make an attempt at the reconciliation of two distinct marketing strategies, that of relationship marketing and brand equity. The brand equity scores and perceived relationship marketing strategy usage for forty six different brands was captured by surveying college students regarding top performing brands. Using the forty six different brands, this study was able to show that certain relationship marketing strategies can impact the brand equity of a brand. However, of the nine hypothesized relationships, only one was supported by the data. The supported hypothesis pertained to the positive relationship between relationship marketing level two strategies and brand awareness. The fact that only level two strategies had a positive effect on the brand equity components suggests that only social bonding can add an additional contribution to brand equity. This finding is represented in the literature as well since other researchers have shown that brand personality (Pappu, et al., 2005) and brand trust (DelgadoBallester and Munuera-Alema´, 2005) can contribute to brand equity. Thus, it would seem that a brand’s social bonding strategy makes the brand more salient in the mind of the consumer. It could be that the additional increase in trust from personality factors and increased contact may be the drivers behind the increase in salience. The particular components of brand equity that were significantly different are interesting. While it was postulated that level two would result in increased brand loyalty, it was not expected for perceived quality. This result could be based on the apparent high collinearity between the brand equity components of perceived quality and brand loyalty. It would be wrong to assume that such high collinearity invalidates the findings since the components of brand equity are expected to be correlated (Yoo and Donthu, 2001). In fact, given that quality has been shown to lead to loyalty (Cronin, Brady, and Hult, 2000; Han, Jr., and Wang, 2008), the present collinearity is expected and possibly lends more credence to the findings. The insignificance of the main effects of level one relationship marketing is of particular interest. Given that much of the original research on brand equity (Aaker, 1991) as well as the 41 conventional wisdom as spouted by the popular press (Ries and Ries, 2002) states that pricing incentives will erode brand equity through perceived quality, the lack of findings in this area suggests a deeper look is warranted. It is already known that a customer’s previous shopping pattern can moderate the effect of level one strategies (Yuping, 2007). Perhaps there are other moderators that can increase the long term benefit of what is often decried as only short term gain. One such moderator may be age. College students may not have a negative perception of price incentives since their economic status would seemingly put them in a place to be more open to incentives. If age were a moderator, it would explain why the level one strategies had no effect in the present study since the sample was based on college students. Price based constructs such as price sensitivity may also moderate the negative effect of level one strategies on perceived quality. In the final analysis, social bonding strategies hold the most promise for marketing practitioners who are attempting to communicate the benefits of their brand. The difference in brand awareness and perceived quality between brands that were perceived to use more social bonding strategies compared to those that used less was substantial. This substantial difference is supported by two points of the study. The first point is regarding common method variance. Considering that an adjustment by a marker variable did not impact the correlation between relationship marketing level two strategies and the brand equity components, the impact of common method variance on the results can be ruled out. The second factor is how the relationship marketing level two variable was created. Once the cluster analysis was used to create the categorical variable, the difference between brands that were categorized as low in usage of relationship marketing strategies and those as high was rather small (on a 5 point scale, high usage was 3.5 and low usage was 2.9 as shown in Table 4). Thus, the difference in brand awareness and perceived quality between these brands is interesting. If brands were sought out that used no social bonding strategies then the difference in brand awareness and perceived quality should be even more pronounced. The lack of findings for relationship marketing strategies level one and three should not be used as a basis to spurn these strategies. There are several reasons why significant findings for level one strategies or discriminant validity for level three strategies may not have been found. These possibilities are further discussed in the subsequent limitation and future research sections. 42 Limitations Any conclusions that are drawn need to be tempered by the limitations of the study. One possible reason for the insignificance of many of the findings probably lies in the number of brands used. The final number of brands used after assumption testing and data cleaning were forty six. When these brands were clustered into groups, some of the groups had extremely small cell sizes, especially when considering interactions. The positive application of such a small sample size is that it gives testament to the significant findings of the study. Another possible reason for the insignificant findings may be based on the chosen brands. Since these brands were rated as some of the top brands worldwide, they had a negative skew in the distribution along the dependent variables. This skew was highest in the brand awareness component which also happened to contain some of the least significant main effects of the all the dependent variables. Another limitation is that of generalizability. Since the present study was based on a convenience sample of college students, the effects found may not translate to working adults or any other sample that excludes college students. A result of using college students was that some brands such as luxury brands and financial investment brands were excluded from the present study. It could be that the hypothesized negative effect of relationship marketing one strategies on perceived quality may be present for only certain brands such as luxury brands. Thus, the results of the present study are only generalizable to the types of brands that were used. A last limitation of the present research is the use of the participants’ perceptions for demarcating brands into relationship marketing strategies. While there are strengths to such a method over using external devices such as the assurance that the participants were witness to the strategies, it is possible that using a more objective approach could give different results. One way to do so in a “real world” setting would be to measure the brand equity components for brands during marketing campaigns that specifically utilized one of the relationship marketing strategies. Such a method would lend itself well to the use of longitudinal data that could be used to measure the incremental changes in brand equity as relationship marketing strategies are utilized. Future Research 43 Some future research opportunities present themselves based on the limitations of the present study as discussed above. Thus, using a larger sample of brands, a different sample of respondents, or measuring the effect of a relationship marketing campaign as it is being implemented may offer different results than those presently found. Other opportunities can be extrapolated from the findings or lack of findings of the present research. One such opportunity is to replicate the apparent relationship between relationship marketing level two strategies and perceived quality. Does this effect exist outside of the brand equity domain? Current research would tend to support the idea that affective responses can be used in place of cognitive evaluations (Homburg, Koschate, and Hoyer, 2006). However, the direct link between a social bond and higher quality perceptions needs to be established. An issue that still needs to be addressed is the impact of relationship marketing level three strategies (structural bonding) on brand equity. Though the variable was removed from the analysis, prior conceptualizations and research suggest that it is a distinct strategy and should therefore affect brand equity beyond social bonding strategies. One possible reason for the lack of discriminant validity between relationship marketing level two and three strategies is due to the sample. College students may not have been adequately aware of brands that used structural bonding strategies and therefore confused the strategies with social bonding strategies. Considering that structural bonding strategies should cost more and therefore require a higher price, a sample that is more likely to have the discretionary income to use such customization may be necessary to find any true effects. Conclusion In conclusion, it can be assessed that a firm is safe in employing relationship marketing strategies for brands when considering the potential impact on the brands’ brand equity since there were no negative effects found. In fact, utilizing social bonding strategies can create a synergy with branding strategies since the brand equity components of perceived quality and brand awareness can be increased. 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(2007). The lomg-term impact of loyalty programs on consumer purchase behavior and loyalty. Journal of Marketing, 71(4), 19. 50 BIOGRAPHICAL SKETCH Jeremy S. Wolter Jeremy S. Wolter was born in Crescent City, FL on July 27, 1976 and was raised in St. Augustine, FL. Jeremy received his Bachelors Degree from the University of Central Florida in Management of Information Systems and subsequently decided to not go into the IT industry as previously planned. After graduation, Jeremy married Katie Clifford and both moved to Tallahassee FL so that they could further pursue their educational goals. While Katie began her studies at the College of Medicine, Jeremy began a Master’s of Science in Integrated Marketing Communication under the College of Information and Communication. During Jeremy’s and Katie’s educational pursuits, Jude Wolter was born. Subsequently, Jeremy took a nine month break to stay at home with the new baby. Using this time to think and research thesis topics, Jeremy fully realized his interest in marketing. To pursue this interest, Jeremy began a Marketing PhD under the College of Business while finishing his Master’s degree. Jeremy is currently a second year PhD student in the marketing program at Florida State University. He currently has two children, Jude and Liam Wolter, who demand a lot of time but are worth every moment (no matter how many of those moments are spent awake rather than sleeping). He plans to continue his research into service marketing strategy with an emphasis on customer and marketing metrics. 51