Brand Valuation Model: a Shareholder Value Approach

BRAND VALUATION MODEL: A SHAREHOLDER VALUE APPROACH

A dissertation submitted to the Kent State University Graduate School of Management In partial fulfillment of the requirements for the degree of Doctor of Philosophy

Hyunjung Lee

April, 2012

ACKNOWLEDGEMENTS

I would like to thank my dissertation advisor, Dr. Michael Hu, and my committee members, Dr. Murali Shanker and Dr. Tuo Wang for their support and guidance and confidence in me. I would also like to thank Dr. Sungha Jang for his assistance with statistics and SAS programing. I am very grateful for the support from my friends and colleagues in the doctoral program. Last, but not least, I would like to thank my dear husband, Brian and beloved daughter,

Sidney. Without a doubt, I would not be able to finish my dissertation without them and their support.

iii

TABLE OF CONTENTS

Acknowledgements iii Table of Contents iv List of Figures vi List of Tables vii

Chapter 1. Brand Value 1 Introduction 1 Literature Review 4 Brand and Brand Value 4 Brand Value Measure 5 Brand Value and Firm Value 21 Brand Value in M&A 23 Brand Valuation Model in M&A 24 Summary 25 Problem Statement 26 Purpose of Dissertation 27 Dissertation Outline 28

Chapter 2. Brand Value in Mergers & Acquisitions 29 Introduction 29 Brand Value Measures in M&A 30 The Acquirer Perspective 30 The Shareholder Perspective 31 Brand Valuation Models in M&A 33 Summary 35

Chapter 3. Methodology 36 Introduction 36 Sample 36 Data Source 39 Measures 40 Model Overview 43 Acquirer Perspective Brand Valuation Model 44 Shareholder Perspective Brand Valuation Model 46

Chapter 4. Data Analysis and Results 51 Introduction 51

Acquirer Perspective Brand Value and Valuation Model 51 Shareholder Perspective Brand Value and Valuation Model 57 Comparison of Brand Value Measures and Valuation Models 62

Miscellaneous 63

Chapter 5. Discussions and Conclusion 68 Introduction 68 Summary of Purpose 68 Summary of Results 69 Acquirer Perspective Brand Value and Valuation Model 69 Shareholder Perspective Brand Value and Valuation Model 73 Summary 77 Managerial Implications 78 Limitations and Future Research 79

References 81

v LIST OF FIGURES

Page Figure 1. Marketing, Brand Equity, Brand Value, and Firm Value 91 Figure 2. Brand Value Metrics 92 Figure 3. Brand Value in M&A 93 Figure 4. Target Abnormal Return 94 Figure 5. Acquirer Abnormal Return 95

LIST OF TABLES

Page Table 1. Marketing and Shareholder Value Metrics 96 Table 2. Target Firm Value and Brand Value in M&A 99 Table 3. Target Distribution by Industries - Population Data Set 100 Table 4. Target Distribution by Industries - Initial Sample 101 Table 5. Target Distribution by Industries - Final Sample 102 Table 6. Acquirer Distribution by Industries - Population Data Set 103 Table 7. Acquirer Distribution by Industries - Initial Sample 105 Table 8. Acquirer Distribution by Industries - Final Sample 107 Table 9. Description of Variable 109 Table 10. Brand Value to M&A Deal Value Distribution - Final Sample 110 Table 11. Brand Value to M&A Deal Value Distribution - Reduced Sample 111

Table 12. Mean Comparison Between BVA>0 Group and BVA=0 Group 112

Table 13. Cross Tabulation of Target Industry Type and BVA Group 113

Table 14. Cross Tabulation of Acquirer Industry Type and BVA Group 114

Table 15. Cross Tabulation of Strategic Motive and BVA Group 115

Table 16. Cross Tabulation of Combined Industry Type and BVA Group 116

Table 17. Acquirer Perspective Brand Valuation Model 117 Table 18. Acquirer Perspective Brand Valuation Model-Descriptive Statistics 118 Table 19. Target AR & CAR Descriptive Statistics and Simple T-Test Result 119 Table 20. Acquirer AR & CAR Descriptive Statistics and Simple T-Test Result 120 Table 21. Distribution of Acquirer CAR [0,2] 121

Table 22. Descriptive Statistics – Target Firm Value by BVA Group 122

Table 23. Target Firm Values Mean Comparison by BVA Group 123

Table 24. Mean Comparison Between BVSH>0 Group and BVSH≤0 Group 124

Table 25. Cross Tabulation of Target Industry Type and BVSH Group 125

Table 26. Cross Tabulation of Acquirer Industry Type and BVSH Group 126

Table 27. Cross Tabulation of Strategic Motive and BVSH Group 127

Table 28. Cross Tabulation of Combined Industry Type and BVSH Group 128

vii Table 29. Shareholder Perspective Target Brand Valuation Model 129 Table 30. Shareholder Perspective Target Brand Valuation Model-Descriptive Statistics 130 Table 31. Descriptive Statistics - Firm Value and Brand Value 131

Table 32. Target Brand Valuation Model Comparison - Two Perspectives 132

Table 33. Mean Comparison Between BVT=HIGH Group and BVT=LOW Group 133

Table 34. Cross Tabulation of Target Industry Type and BVT Group 134 Table 35. Target Perspective Target Brand Valuation Model 135 Table 36. Target Perspective Target Brand Valuation Model-Descriptive Statistics 136

viii CHAPTER 1

BRAND VALUE

Introduction

“The effective dissemination of a new method of assessing marketing

productivity to the business community will be a major step toward

marketing’s vitality in the firm, more important, toward raising the

performance of the firm itself” (Rust, Carpenter, Kumar, & Srivastava, 2004)

There is an increasing demand to demonstrate the financial contribution of marketing to shareholder value. With rising global competition, recession, and stock market pressure, marketing productivity has become an important issue for board members as marketing-related expenditure accounts for 20 to 25 percent of overall corporate budget (Lehmann & Reibstein,

2006; Stewart, 2009). Marketing executives are under increased pressure to account for marketing expenditure in financial terms, a commonly used language in the boardroom to denote revenue generated by marketing expenditure. To guarantee marketing expenditure in the firm’s overall budget, marketing executives must literally translate marketing expenditure into the firm’s net profit or into stockholder value for board members who lack marketing experience.

Failure to clarify in financial terms the value created by marketing expenditure, including advertising, promotion, and research and development, increases the likelihood of its removal from the firm’s overall budget as these activities do not appeal to the CEO or CFO as essential in upholding stockholder value.

In response to this demand, marketers have started using the language of finance in the boardroom, referring to “the number”, which denotes marketing accountability. Marketing accountability is not a new concept in marketing; in fact, marketing metrics have been utilized for decades to illustrate marketing performance (Lehmann, Keller, & Farley, 2008).

Traditionally, marketing performance is measured by brand performance in the marketplace. Examples of traditional marketing metrics include brand awareness, preference, purchase intention, share of wallet, customer satisfaction, loyalty, and brand market performance metrics (i.e., sales, cash flow, and net profit). These measures are designed to evaluate various aspects of consumer beliefs, attitudes, and behaviors toward brands and short-term performance of brands; typically, a summary of these metrics is provided to senior management to monitor marketing productivity because (Kaplan & Norton, 1992). Although useful, the utility of these traditional marketing metrics for senior management is limited because their connection to financial consequences is not well established (Srivastava & Reibstein, 2005; Stewart, 2009). In response to this issue, the Marketing Science Institute (MSI) has prioritized the research topic,

“Accountability and ROI of Marketing Expenditure”, to encourage investigation of this issue since 1997. This project has encouraged marketers to start using firm financial market performance metrics to measure marketing performance.

Most marketing expenditure is used to create brand equity, which is defined as what the brand means to the consumer; brand equity is then utilized to increase brand value, which is defined as what the brand means to a focal firm (Raggio & Leone, 2007). Therefore, it is reasonable to measure brand value to demonstrate marketing accountability, as the change in brand value is an outcome of marketing activities. However, there is no consensus in the literature on how to measure brand value.

Practitioners have proposed a number of proprietary brand valuation techniques generating numerous brand value measures, but no consensus brand value measure has emerged from these efforts. Similarly, academics have shied away from developing a standard brand value measure (Fischer, 2007; Salinas & Ambler, 2008; Simon & Sullivan, 1993). In the absence of a well-established brand value measure, these brand value estimates have unclear credibility. As a result, firms do not report their brand value in financial statements, even though a successful brand is one of most valuable assets a firm can possess. Furthermore, firms cannot capitalize marketing expenditure used to increase value of the assets that have a long-term effect on firm performance (Hanssens, 2009). This marketing-unfriendly accounting practice makes boardroom members consider marketing as an expense rather than a long-term investment.

Consequently, marketing budget cuts become a first choice for board members when making decisions regarding cost reduction. It is contingent on marketing managers to demonstrate that marketing expenditures are used to increase the value of the brand, which ultimately is a valuable asset, crucially tied to firm market and financial performance. The lack of an accepted brand value measure complicates this responsibility of the marketing manager when trying to convince finance-oriented board members not to cut marketing budgets.

In this dissertation, I address this gap by developing a brand value measure that is based on shareholder value and by developing a brand valuation model. Shareholder value is a well- established objective measure of firm market value in finance. This measure is developed within the context of mergers and acquisitions (M&A). This newly proposed brand value measure may appeal to boardroom members whose ultimate goal is often shareholder value maximization. A firm valuation model from the accounting literature is applied to develop brand valuation models in the context of M&A.

A literature review focusing on brand value follows in the next section. Then, this chapter provides a problem setting to be studied and defines the purpose of this study. This chapter closes with an overview of succeeding chapters.

Literature Review

Brand and brand value.

Brand. In contemporary marketing, a brand is defined as “a distinguishing name and/or symbol (i.e., logo, trade mark, or package design) intended to identify the goods or services of either one seller or a group of sellers and to differentiate those goods or services from those of competitors” (Aaker, 1991, p. 7). A brand is the essence or promise of what will be delivered to or experienced by a buyer. Importantly, brands enable a buyer to identify the offerings of a particular company easily. Therefore, firms with good brands attain a competitive advantage in the marketplace, because a good brand is created through excellent product quality or non- product-related means, such as distinct brand image and brand personality (Keller, 1998). Brand is a marketing asset that drives current and future earnings for a firm (Keller, 1998; Mortanges &

Reil, 2003). As a result, building and managing brands has become a priority for firms of all sizes in a wide variety of industries to maximize this significant intangible asset value (Lehmann et al., 2008).

Brand value. There is a certain amount of terminological confusion between the concepts of brand value and brand equity, where these entities are erroneously treated as the same construct (Raggio & Leone, 2007). Keller (1993) defined brand equity as the different effect of brand knowledge on customer response to the marketing of the brand. Simon and

Sullivan (1993) defined brand equity as the incremental discounted future cash flow that results

4 from a product having its brand name in comparison to cash flow that would be accrued if the same product did not have a brand name. In MSI’s definition, brand value is the strong, sustainable, and differentiated advantage with respect to competitors that leads to a higher volume or a higher margin for the company compared with the situation it would have without the brand (Fernandez, 2002).

With this confusion in definitions, researchers started to distinguish brand value from brand equity. Kerin and Sethuraman (1998) defined brand value as the financial impact of brand on the firm and brand equity as the effect of the brand on consumers. Chou (2002) identified two categories of brand equity definition in the marketing literature: customer-based brand equity (Keller, 1993) and financial brand equity (Simon & Sullivan, 1993). Brennan (2004) defined brand value as the extra financial value accrued to a firm due to its ownership of a successful brand and brand equity as the successful brand’s effect on consumer behavior.

Raggio and Leone (2007) proposed integrated definitions of brand value and brand equity.

Brand value is what a brand means to a focal firm, whereas brand equity is what the brand means to the customers. It seems that researchers agree that brand value reflects the firm financial perspective, whereas brand equity reflects the consumer perspective. Therefore, in this research

I will adopt the definition of brand value as the financial value of brand to a focal firm that is an outcome of customer based brand equity.

Brand value measure.

Introduction. Measuring brand value in financial terms is the first step marketers have to take to be accountable for their marketing expenditure, because most marketing expenditure is used to increase brand value. This first step has been a challenging task for marketers for decades. Even though there are at least 39 proprietary brand value measures that directly

5 measure brand value, no one brand value measure is generally accepted by brand value measure users (Salinas, 2009). As a result, marketers are reluctant to use any of these brand value measures.

Since there is no standard direct brand measure, marketers began using well-established firm value measures from accounting and finance to measure brand value indirectly. These measures are indirect, as these firm value metrics are operationalized at the firm level and these firm level metrics are used to measure brand value that should be operationalized at the brand level. The rationale for this approach is that marketing activities create brand value and that brand value will eventually increase firm value (Kerin & Sehturaman, 1998; Madden, Fehle, &

Fournier, 2006). Because of the difference in operationalization level, these firm-level measures may not measure brand value accurately. However, considering the circumstances (e.g., lack of brand value measure and lack of brand level data), a firm value measure is a reasonable surrogate for a brand value measure.

As depicted in Figure 1, marketing activities create customer-based brand equity which then creates brand value; both of these in turn increase firm value (Srivastava, Shervani, &

Fahey, 1998). Logically, brand value should be measured by brand performance, as firm value is measured by firm performance. However, marketing researchers have been using firm performance measures from accounting and finance to measure brand performance due to the lack of widely accepted brand performance measures. So in reality, brand value is measured indirectly through firm value measures with which boardroom members are concerned the most.

In the following section, I review direct and indirect brand value measures identified in the marketing literature. In the direct brand value measure section, I review three classic approaches to measure brand value and illustrate examples for each approach. In the indirect

6 brand value measure section, I review firm value measures that marketers utilize to measure brand value from the accounting and finance literature; examples are provided for each measure.

Direct brand value measure. Brands are the primary marketing asset that firms create through various marketing activities. Therefore, valuing the brand is the most rational approach to measure marketing performance. Brand value also can serve other purposes, such as trading brands, managing taxes, and justifying share prices (Salinas & Ambler, 2008). Brand value can justify share price by explaining the increasing discrepancy between firm stock price and tangible assets, as the discrepancy is attributed to intangible assets (Lev, 2008). Brand value is essential when firms buy or sell brands. As a result, a number of proprietary brand valuation techniques have been proposed in both the private sector and academia. However, there is no well-accepted or standard brand valuation model, likely because determining brand value is a complicated task (Salinas, 2009).

Proprietary brand valuation techniques take three approaches (Cravens & Guilding, 1999;

International Valuation Standards Committee, 2003): cost-based, market-based, and income- based approaches. In a cost-based approach, the brand value is equal to the historical cost of creation or what it might cost to create a similar brand (Anson, Suchy, & Ahya, 2005; Boos,

2003). This approach can sometimes provide a minimum value of the brand, but it is not a good future indicator of brand value.

In a market-based approach, the brand value is calculated by comparing recent transactions involving similar brands in similar markets or by referencing to comparable market multiples (Ambler & Barwise, 1998; Anson et al., 2005; Smith, 1997). This approach is useful only when there is sufficient comparable data.

In an income-based approach, various metrics have been proposed to measure brand value. It includes price premium (Ailawadi, Lehman, & Neslin, 2003; Ambler & Barwise, 1998;

Smith & Parr, 2000), demand drive/brand strength analysis (Brand Economics, 2002; Brand

Finance, 2000a; Brand Metrics, 2004), gross margins (Smith, 1997), royalty relief (Aaker, 1991;

Ambler & Barwise, 1998), excess cash flow (Fernandez, 2001), residual approach (Ambler &

Barwise, 1998; Anson et al., 2005), excess margin (Andriessen, 2004; Pratt, 2002; Smith, 1997), marginal cash flows (Lamb, 2002; Smith, 1997), core brand value plus the value of other related assets (Anson et al., 2005), and customer lifetime value (Fischer, 2007). The income-based approach is the most popular and many practitioners take this approach (Salinas & Ambler,

2008).

However, the variability among these estimates of brand value calculated using these approaches is large as the number of brand value measures increases. As a result, firms do not adopt these proposed brand value measures even though they want to measure brand value to manage their brands better (Gunther & Kreigbaum-Kling, 2001).

In academics, a few brand value measures have been proposed. Examples include Simon and Sullivan’s (1993) brand value measure utilizing stock price movement, Srivastava, Shervani, and Fahey’s (1998) measure based on the Capital Asset Pricing Model (CAPM), and Fischer’s

(2007) measure using customer lifetime value. These academic approaches are not well accepted by practitioners because they are complicated and require detailed information at the brand level

(Salinas & Ambler, 2008).

Indirect brand value measure. Figure 2 represents indirect brand value measures from the accounting and finance literature. In the accounting literature, firm market performance measures are used to value firms, whereas in the finance literature, firm financial market

8 performance measures are used to value firms. Firm market performance measures include sales, cash flow, and earnings, while firm financial market performance measures include shareholder value measures related to stock price.

Measuring brand value using firm market performance measure.

- Sales. Sales are the most popular brand value measure used in the marketing literature

(Aaker, Carmen, & Jacobson, 1982; Bass & Pilon, 1980; Davidson, 1999; Dekimpe & Hanssens,

1995; Doyle & Saunders, 1985). Lodish (1986) suggested that sales is an ideal measure reflecting marketing productivity and Shimp (2000) further argued that any metric not including potential sales is limited.

Abundant empirical research reports that marketing activity has an impact on short-term sales. Examples include the impact of marketing activities on brand switching (Bass & Pilon,

1980; Kumar & Leone, 1988), advertising impact on sales in different cereal brands (Aaker et al., 1982), and the lead effect of sales promotions (Doyle & Sanders, 1985).

While the short-term effect of marketing activities on sales has been consistent in the literature, the long-term effect of marketing on sales has not been consistent. Clarke (1976) reported that 90% of the advertising effect on sales disappears in a few months, whereas

Broadbent (1993) reported that the advertising effect on sales is more than short-term. Dekimpe and Hanssens (1995) reported a strong “trend setting effect” of marketing activity on sales and argued that long-term effects of advertising were underestimated because of inappropriate research methodology. They also reported a positive short-term but not a long-term promotion effect on sales. This finding is different from what Blattber, Briesch, and Fox (1995) and Lodish

(1997) found. In addition to these findings, Ehrenberg, Hammond, and Goodhardt (1994)

9 reported that even the short-term promotion effect on sales does not exist for well-established brands.

- Cash flow. Cash flow equals cash receipts minus cash payments over a given period of time. In economic theory, a firm’s cash generating ability determines firm performance

(Rappaport, 1986). As a result, abundant research has been carried out concerning cash flow in accounting and finance. In marketing, the importance of this measure has been documented by various researchers. Day and Fahey (1998) argued that if a goal of marketing is to help ensure business renewal and growth, then superior cash flow should be the outcome of winning customers in the market. Srivastava et al. (1998) also emphasized the importance of cash flow in firms as they stated that marketing adds value to firms when it creates assets that generate future cash flow with a positive net present value.

Marketing activities, including advertising and product innovation, increase cash flow

(Krasnikov, Mishra, & Orozco, 2009; Srinivasan & Hanssens, 2009). Marketing assets reduce risk associated with firm future cash flow (Buffet, 1994; Reichheld, 1996). Customer satisfaction has a positive relationship with firm future cash flow, whereas negative word of mouth has a negative relationship with firm cash flow (Gruca & Rego, 2005; Luo, 2007; Morgan

& Rego, 2006).

When brand value is measured by cash flow, marketing activities and marketing assets have a positive impact on brand value. However, cash flow is a short-term oriented performance measure that measures short-term liquidity and indicates the immediate cash implications of expenditure (Hodgson, 2004). Cash flow treats investment and expenses the same, as deductions from cash flow for investment and for expense are considered as the same cash payment at the

10 time they are made. Brand is considered to be an asset, which has a long-term effect on firm performance. Therefore, using cash flow as a proxy for brand value may not be appropriate.

- Earnings. Earnings are the most popular metric to assess firm performance in accounting. Earnings are calculated by matching current expenses against current revenue. The board of directors and financial analysts are most interested in earnings when investigating the primary financial reports (i.e., Income Statements and Balance Sheets) because all else being equal, investors favor stable earnings over volatile earnings (Graham, Harvey, & Rajgopal, 2005;

Goyal & Santa-Clara, 2003). However, earnings can be manipulated by a firm through flexible accounting techniques, subjective judgment, and manipulative practices (Hodgson, 2004).

In marketing literature, earnings have been utilized as a measure of brand value to show marketing accountability. Advertising is significantly associated with earnings up to five years following the year of the expenditure and boosting advertising expenditure during recessions increases earnings (Frankenberger & Graham 2003; Graham & Frankenberger, 2000). Customer satisfaction is positively related with firm earnings (Anderson, Fornell, & Lehmann, 1994).

However, some studies found that marketing activities can even reduce earnings and recommended firms to decrease marketing activities (Abraham & Lodish, 1990; Doyle, 2000).

In conclusion, firm market performance mesures (i.e., sales, cash flow, earnings) are frequently used as measures of brand value because they are easily accessible and these accounting metrics can be calculated in brand or item-micro level at any time frame (Hodgson,

2004). However, firm market performance measures are neither long-term oriented nor forward- looking measures, while brand is a long-term oriented asset for companies. So, using firm market performance measures is not appropriate to measure brand value.

Measuring brand value using firm financial market performance measure.

As seen in Table 1, marketers adopted firm financial market performance measures that represent firm value as marketing performance metrics that represent brand value. It becomes a common methodology to adopt firm value metrics to measure brand value because of prolific value-based management (Brigham & Ehrhardt, 2005). Value-based management involves the systematic use of the firm value to evaluate a firm’s potential decisions. In this management practice, when executives make any decisions about firm strategy, the first consideration they take is the firm value (or shareholder value; I use firm value and shareholder value interchangeably). When they do not think that a strategy is going to increase firm value, they tend not to pursue the strategy. However, if they think that it is going to increase firm value, they often pursue the strategy. Since stock price changes represent the increase or decrease in firm value, stock price is a primary measure that boardroom members investigate before they make any decisions (Hodgson, 2004). Therefore, it is important for marketers to understand firm value measures related to stock price and the relationship between marketing and these measures.

In the finance literature, two major approaches are proposed for firm value: a valuation approach and a risk approach. Both approaches are based on the efficient markets hypothesis. In the efficient markets hypothesis, the stock price is the best estimate of firm value and is used to calculate firm market value. The valuation approach measures focus on the growth potential of the firm, while the risk approach measures emphasize the associated risks of the firm.

Valuation approach measures. Brand value measures in the valuation approach include market capitalization, stock return, market-to-book value, and Tobin’s q. Market capitalization is the market value of shareholder equity without assessing a deflator, whereas the other three measures reflect firm value while accounting for deflators. Stock return, Tobin’s q ratio and

12 market-to-book value reflect shareholder value, while deflating for the firm market value at time t-1, the replacement cost of the tangible assets and book value of equity, respectively.

- Market Capitalization. Market capitalization is a market estimate of firm value, based on perceived future prospects of economic and monetary conditions. It, based on firm equity, is measured as the product of stock market price and the number of common shares outstanding.

Market capitalization is calculated with the following formula:

Marketing activity has a positive relationship with firm value measured by market capitalization. Graham and Frankenberger (2000) reported that changes in advertising and R&D expenditure are significantly associated with changes in firm market capitalization while controlling for accounting factors. In their research they found that firm advertising expenditure has, on average, a 3-year effect on firm market capitalization. Joshi and Hanssens (2009) supported the positive relationship between advertising expense and firm market capitalization.

Marketing assets have a positive relationship with value (Fornell, Mithas, Morgeson, &

Krishnan, 2006; Ittner & Larcker, 1998). Fornell, Mithas, Morgeson, and Krishnan (2006) concluded that customer satisfaction, a marketing asset, explained an additional 9% of the variance in firm market capitalization than when considering only typical accounting measures.

- Stock Return. The stock return measure is based on the efficient market hypothesis that stock prices reflect all known information about the firm’s future earnings prospects (Fama &

French, 1992). Stock return assumes that investors view any new information about firms as a

13 signal for change in the future discounted cash flow of the firm. As the signaling changes, investors change their expectations of future cash flow based upon the new information. The changes in their expectations of future cash flow lead to changes in stock price. This change is reflected in short- or long-term stock price changes (Kimbrough, Mcalister, Mizik, Garnaise,

Srinivasan, and Hanssens 2009) Stock return is calculated with the following formula:

where, i refers firm I and t refers time t.

Stock return has been utilized to examine the market reaction to a dynamic process that occurs over a longer period of time and to a specific event that occurs on a given day (Mizik &

Jacobson, 2003). The examples of the dynamic marketing process include marketing strategic emphasis (Mizik & Jacobson, 2003), pioneering innovation and promotional incentives

(Srinivasan et al., 2009), brand equity (Aaker & Jacobson, 2001; Mizik & Jacobson, 2008), customer satisfaction (Luo & Homburg 2007, 2008; Morgan & Rego, 2006), and corporate social responsibility (Luo & Bhattacharya, 2009).

Examples of a specific event that occurs on a given day include company name change

(Horskey & Swyngedouw,1987), brand extension (Lane & Jacobson, 1995), celebrity endorsement (Agrawal & Kamakura, 1995), green promotion (Mathur & Mathur, 2000), new product announcement and preannouncement (Koku, Jagpal, & Viswanath, 1997), Internet channel additions (Geyskens, Gielens, & Dekimpe, 2002), quality award (Balasubramanian,

Mathur, & Thanur, 2005), and innovation (Sood, James, & Tellis, 2009).

- Market to Book Value. Market-to-book value measures firm value by assessing a firm’s potential growth opportunities (David, Hwang, Pei, & Reneau, 2002). It assesses a firm’s ability to achieve abnormal returns related to its investment base (= book value of firms). It is calculated by dividing firm market value by the book value of the firm. The market value

(market capitalization) is calculated by multiplying the value of the firm’s common stocks and the number of outstanding shares; the book value equals the difference between total assets and the sum of total liabilities and preferred stock.

where Market value of the firm = Common stock price Number of shares outstanding,

Book value of the firm = Total assets – (Total liability+ Preferred stock).

A firm with a higher market-to-book ratio is considered to have higher growth opportunity compared to a firm with a lower market-to-book ratio (Hovakimian, Opler, &

Titman, 2001; Pauwel, Silva-Risso, Srinivasan, & Hanssens, 2004). Daniel and Titman (2006) suggest that market-to-book ratio is a good proxy for the intangible asset returns. This ratio reflects management’s success in creating shareholder value, because it accounts for firm book value, which reflects historical accounting costs and net worth of the firm to shareholders. The difference between the market value of the firm and the book value of the firm is explained partly by off-balance-sheet assets (i.e., marketing assets and intellectual property) and by an excess or lack of investor enthusiasm about the firm. The measure is a useful indicator to

15 measure the strength of marketing assets and is used to identify under-valued or over-valued stocks (Srivastava et al., 1998, 1999; Rust et al., 2004).

Marketing activities and marketing assets have a positive relationship with firm value measured by firms’ market-to-book value ratios. New product introductions increase firms’ market-to-book value ratios (Pauwel et al., 2004). Joshi and Hanssens (2010) reported a long- term effect of advertising spending on firms’ market-to-book value ratios. Shin, Sakaribara, and

Hanssens (2008) utilized this measure to investigate the relationship between marketing and firm value.

- Tobin’s q ratio. Tobin’s q ratio is widely used firm value measure in the marketing literature. It is the ratio of the market value of the firm to the replacement cost of the firm’s tangible assets (e.g., property, plant, equipment, inventory, cash, stocks and bonds) (Tobin, 1969,

1978). Tobin’s q ratio has gained wide acceptance because it measures firm economic performance and is a good indicator of shareholder value (Morgan & Rego, 2006; Anderson,

Fornell, & Mazvanchery, 2004).

where Market value of the firm = Common stock price Number of shares outstanding.

Tobin’s q ratio is a forward-looking measure that provides a market-based view of firm future profit potential (Chauvin & Hirschey, 1993; Lee & Grewal, 2004; Rao, Agarwal, &

Dahldoff, 2004). Tobin’s q ratio is better than accounting measures at estimating firm performance because accounting measures are industry-dependent measure, whereas Tobin’s q

16 ratio is an industry-independent and risk-adjusted measure that can be used to compare performance of firms in different industries (see Smirlock et al., 1984, for a detailed discussion).

In the marketing literature, researchers advocate using Tobin’s q ratio as a proper measure to assess the impact of marketing on firm value (Day & Fahey, 1988; Srinivasan &

Hanssens, 2009). Traditionally, marketing activities and assets are not related to firm book value because firm book value is focused only on tangible assets of the firm, while marketing activities are majorly focused on intangible assets, such as brands. Since Tobin’s q isolates firm book value from firm market value, it captures the impact of marketing on firm value effectively.

Simon and Sullivan (1993) empirically measured brand value by using Tobin’s q ratio.

They asserted that the brand equity accounts a major component of a firm’s intangible assets and proposed a brand value measure that is grounded on financial information and intangible assets.

Marketing activities and assets are related to firm value measured by Tobin’s q ratio. Examples include brand portfolio strategy (Morgan & Rego, 2009), innovation strategy (Sorescu &

Spanjol, 2008), adoption strategy for new technology (Lee & Grewal, 2004), advertising strategy

(Luo & Donthu, 2006), and customer satisfaction (Anderson et al., 2004; Mittal, Anderson,

Sayrak, & Tadikamalla, 2005; Morgan & Rego, 2006).

There are, however, several notable challenges and limitations of the Tobin’s q ratio.

First, the replacement value of tangible assets, the denominator of Tobin’s q ratio, is complex and difficult to estimate (Anderson et al., 2004; Mittal et al., 2005). Second, estimates of replacement costs for assets do not include intangible assets in the computations even though intangible assets in addition to tangible assets contribute to the book value of a firm. As a result, a firm’s true Tobin’s q ratio is, sometimes, overestimated (Mittal et al., 2005). Third, Tobin’s q

17 ratio is typically available only on a quarterly or annual basis, because it requires the calculated replacement costs for assets that are reported only quarterly or annually by firms.

- Comparison between market-to-book value ratio and Tobin’s q ratio. Both market-to- book ratio and Tobin’s q ratio are commonly used to measure firm value in the marketing literature. The difference between these two measures is the size deflators. Market-to-book value uses book value of firm equity, while Tobin’s q uses replacement cost of firm asset.

However, there is no consensus on when to use the market-to-book value ratio as opposed to

Tobin’s q ratio (Mizik & Jacobson, 2008).

Linderberg and Ross (1981) stated that the market-to-book value ratio better reflects the future impact of intangible assets compared to Tobin’s q ratio because Tobin’s q is biased for firms with high R&D and advertising. Day and Fahey (1988), however, stated that Tobin’s q is preferred to market-to-book value because it avoids accounting complications regarding book value and accounts for inflation (Srinivasan & Hanssens, 2009). Varaiya, Kerin, and Weeks

(1987) argued that both Tobin’s q and market-to-book value ratios provide the same signals regarding shareholder value creation. Therefore, researchers should be careful about what metrics they use to measure brand value.

Risk approach measures. Brand value measures in the risk approach include systematic risk, unsystematic risk. The focus of risk approach measures is on risk associated with firms.

There are two distinct risks in finance that shareholders must face: (a) systematic firm risk and

(b) unsystematic firm risk. Shareholders can avoid unsystematic risk by keeping a diversified portfolio, but they cannot avoid systematic risk. As a result, many studies focus on systematic risk rather than unsystematic risk.

- Systematic Risk (β). Systematic risk measures the sensitivity of firm stock price to total stock market price movement. It consists of 20% of total stock risk (Srinivasan & Hanssens,

2009). A firm with high systematic risk means that the firm’s stock return has greater variance than stock market average return. It is measured as the covariance of firm returns with overall market returns. Systematic risk has an impact on stock price because shareholders pay a premium to avoid risk. Higher systematic risk is related to a number of factors, such as higher growth, higher leverage, lower liquidity, smaller asset size, lower dividend payout, and higher level of earnings variability (Beaver, Kettler, & Scholes, 1970; Farrelly, Ferris, & Reichenstein,

1985). Criticisms about the popularity of systematic risk in the finance literature have emerged because no relationship is found between firm systematic risk and stock return (Fama & French,

1992). Despite this well-known criticism, shareholders still pay a premium for stocks with lower systematic risk because they believe that firms with lower systematic risk have more market stability. Therefore, reducing systematic risk is beneficial for firms because it can reduce the cost of capital financing and increase firm value.

It becomes important to examine the factors affecting systematic risk so that firms can take advantage of low systematic risk. Marketing activities reduce firm systematic risk. High advertising activities decrease firm systematic market risk by influencing investor perception and behavior (Bharadwaj, Tuli, & Bondfrer, 2011; McAlister, Srinivasan, & Kim, 2007; Osinga,

Leeflang, Srinivasan, & Wieringa, 2011; Singh, Faircloth, & Nejadmalayeri, 2005). Investors are biased toward firms that they are familiar with when they make decisions about investment and advertising increases brand awareness and positive brand perception (Huberman, 2001;

Singh et al., 2005).

Marketing assets also decrease firm systematic risk (Bharadwaj et al., 2011; Veliyath &

Ferris, 1997). Bharadwaj, Tuli, and Bondfrer (2011) reported that unexpected changes in brand quality have a negative relationship with firm systematic risk. Veliyath and Ferris (1997) proposed that developing strategic differentiation could reduce firm systematic risk because firms positioned differently from other firms in the market place will be insulated from overall stock market fluctuation. So, they recommended that firms build brands to differentiate themselves from other competitors in the market place. Fornell et al. (2006) reported that customer satisfaction also reduces firm systematic risk because customer satisfaction has a positive impact on customer loyalty and usage behavior.

- Cash flow volatility. Cash flow volatility explains 80% of firm systematic market risk

(Srinivasan & Hanssens, 2009). It is a ratio of the firm cash flow coefficient of variation and the market cash flow coefficient of variation. If a firm has cash flow volatility greater than 1.0, then the firm’s cash flow fluctuates more than does the cash flow of the average market.

Gruca and Rego (2005) reported a positive aspect of customer satisfaction on firm cash flow validity. They found that customer satisfaction decreases cash flow variability, thus increasing firm value. Morgan and Rego (2009) also suggested that the brand strategy portfolio explains significant additional variance of cash flow variability. They reported that firms with large brand portfolios tend to have lower cash flow variability compared to firms with small brand portfolios.

- Unsystematic (Idiosyncratic) Risk. Unsystematic risk is a part of firm risk that cannot be explained by changes in average market portfolio returns. It consists of 80% of total firm risk on average and is driven by firm-specific factors such as managerial actions/signals and changes in marketing strategy (Luo, 2007; Srinivasan & Hanssens, 2009). Unsystematic risk has been studied less than systematic risk because it is considered to be avoidable through obtaining a well-diversified portfolio (Campbell, Lettau, Malkiel, & Xu, 2001). Recent developments in the finance literature, however, advocate that research investigating both systematic and unsystematic risks provides a better estimate of firm value because there are many investors who cannot avoid unsystematic risk through keeping a diversified portfolio (Srinivasan & Hanssens,

2009). Brown and Kapadia (2007) found that growth opportunities, profit margin, firm size, and industry composition are related to unsystematic risk.

Marketing activities and assets reduce firm unsystematic risk. Luo and Bhattacharya

(2009) reported that corporate social performance decreases firm unsystematic risk. Luo (2007) claimed that customer’s negative voice increases firm unsystematic risk. Consumer-based brand equity and unanticipated changes in brand quality are found to mitigate firm unsystematic risk

(Bharadwaj et al., 2011; Rego, Billet, & Morgan, 2009).

Brand Value and Firm Value.

Studies investigating the relationship between brand value and firm value are sparse.

Barth, Clement, Foster, and Kasznik (1998) reported that brand value is positively related to firm value and they also found significant additional explanatory power of brand value for firm value measured by share price and share return. They used brand value estimates published by

FinancialWorld (FW) since 1992. This brand value is estimated by a methodology developed by

Interbrand, Ltd, an established brand valuation consulting company.

In the same year that Barth et al. (1998) asserted the positive relationship between brand value and firm value in an accounting journal, Kerin and Sethuraman (1998) reported the positive relationship between brand value and firm value in a marketing journal. In their empirical research, a brand value estimate from FW is also used for brand value and a market-to- book value ratio is used for firm value. Both researches used a brand value from FW but used different firm value measures from the finance literature; Barth at al. measured firm value with stock return while Kerin and Sethuramna (1998) measured firm value with a market-to-book value ratio.

A few years later, Madden, Fehle, and Fournier (2006) provided more empirical evidence for the positive relationship between brand value and firm value. They also utilized the FW brand value estimate (also known as World’s Most Valued Brands; WMVB) for brand value measure and risk-adjusted stock return for firm value measure. They argued that brand value increases firm value through lowering risk on firm stock price. They compared monthly stock returns of firms in the WMVB list with that of firms not in WMVB and found that firms in the

WMVB list outperformed firms not in WMVB list.

It is not surprising to see only a few studies that provide empirical evidence for the positive relation between brand value and firm value because there is no well-accepted brand value measure. These three studies utilized FW’s brand value estimates for a proxy of brand value and tested its relation with firm value measured by firm financial performance measures; despite the few studies published on this matter, the results are consistent. Brand value has a positive effect on firm value measured by various firm value metrics.

Brand Value in M&A.

Most research on brand value is focused on brand value when there is no brand ownership change because the brand value is sufficient to demonstrate marketing productivity and to monitor brand performance within firms. What if brands are in the market for sales because a firm is engaging in M&A? How can brand value be determined when there are ownership changes of brands? Even though brand value accounts for a significant portion in

M&A deal value, empirical research on brand value in the context of M&A is exceedingly scarce, except Bahadir, Bharadwaj, and Srivastava’s work in 2008. The dearth of investigation on brand value in the context of M&A may be caused by the lack of brand value measure and the lack of brand value data in the context of M&A and the complexities of the M&A process.

Considering the fact that valuing brands without brand ownership change is still evolving in the marketing literature, valuing brands while brand ownership is changing seems very complex.

However, the lack of brand value data is not an obstacle for marketers to investigate brand values in the context of M&A any more. In June 2001, the Financial Accounting

Standards Board (FASB) issued Statement No. 141, Business Combinations, and No. 142,

Goodwill and other Intangible Assets, which requires acquirers to report the monetary value of identifiable intangible assets acquired through business combination (i.e., M&A) in their SEC filings. A brand is recognized as an identifiable intangible asset apart from goodwill in

Statements No. 141 and No.142.

Brand value reported in the acquirer’s SEC filings is different from other brand value measures in the marketing literature. It is the first brand value measure that firms actually assigned financial value to a brand, whereas other brand value measures are surrogates for the financial value of a brand. This measure has several attributes to be a good measure for brand

23 value (Bahadir, Bharadwaj, & Srivastava, 2008): First, this brand measure is based on the acquiring firm’s expected cash flow through an acquisition of the brand. Therefore, it is a forward-looking measure. Second, it represents value that is attached only to the brand and nothing else. Third, this brand value measure is grounded on the acquirer’s and brand valuation expert’s comprehensive analysis. Last, this brand value measure is subject to scrutiny by the acquirer’s auditor and the SEC.

Brand Valuation Model in M&A.

Only one study investigated brand valuation model in the context of M&A. Bahadir et al.

(2008) examined brand value that the acquirer paid to the target and reported in its SEC filings after the M&A was completed. Their brand valuation model is based on resource based view and brand strategy literature. They argued that acquirer cash flow expectations from acquiring a target firm brand after M&A is a function of marketing capability of both acquirers and targets.

The marketing capability of the acquirer should determine how effectively acquirers generate cash flows from target brands. Acquirer brand portfolio strategy also determines future cash flow generated by acquired target brands. Acquirers with corporate branding strategies would have lower future cash flow generated by target brands compared to acquirers with house-of- brands strategy, because the acquirers with corporate branding strategies have much less chance to utilize target brands than acquirers with house-of-brands strategies.

Bahadir et al. (2008) found that acquirer characteristics have a significant impact on target brand value in the context of M&A. The marketing capability of the acquirer is positively related to target brand value. They also found that acquirer M&A strategy moderates the effect of the acquirer brand strategy on target brand value. When M&A strategy is synergistic, target

24 brand value decreases as the acquirer’s brand portfolio diversity (measure of brand strategy) increases. Conversely, when M&A strategy is non-synergetic, target brand value increases as the acquirer’s brand portfolio diversity increases.

In sum, Bahadir et al. (2008) conclude that target brand value in the context of M&A is a function of the acquirer’s marketing capability (A_Cap), branding strategy (A_BrnS) in the context of the given target marketing capability (T_Cap), and branding strategy (T_BrnS).

The study is focused on the acquirer characteristics as determinants of target brand value in M&A. They have samples from a wide range of industry backgrounds between 2001 and

2005. The time frame for the sample is in an early stage for the new accounting policy

(Statement No.141 and Statement No.142 were posted in June 2001) and the time frame of four years is a relatively short time period to examine general trends of brand value in the context of

M&A. Therefore, updated research with samples over a longer time period has potential for future investigation.

Summary.

An overview of brand value measurement and a brand valuation model in the context of

M&A are discussed in the previous section. Valuing brands is the most effective way to demonstrate marketing performance in the firm. However, there are no well-accepted brand value measures, making it difficult for marketers to be accountable for their marketing expenditure. Since boardroom members to whom marketers must demonstrate marketing

25 productivity are familiar with firm value and the firm value measures are well established in the finance and accounting literature, marketers began utilizing firm value measures for brand value.

This lack of brand value measure makes it difficult to demonstrate a direct relationship between brand value and firm value. The existing brand value measures in the marketing literatures are pertinent when there is no ownership change. What happens to brand value in the context of

M&A? Only one study has attempted to answer this question even though assigning financial values to brands in M&A became a necessary task for acquirers. The ultimate conclusion from the evidence compiled above is that a well-accepted brand value measure is direly needed, especially for the marketer who must justify the cost of marketing activities and for firms that plan to be involved in M&A transactions.

Problem Statement

As brand is one of most valuable market-based assets for a company, marketing activities are carefully developed to create consumer-based brand equity. To this end, the outcome of marketing activities is often measured by traditional marketing metrics, such as brand awareness and brand preference, which do not convey much meaning to boardroom members, often unfamiliar with marketing. As a result, marketing expenditure is often exposed to early cuts when cost reduction must take place.

In response to this trend, marketers have begun to use boardroom language, talking about financial contributions to firm value. More specifically, marketers have begun utilizing firm value measures with which boardroom members are familiar to measure brand value. Firm value measures exist at the firm level that can be far from brand value, especially when a company has a house-of-brand strategy (e.g., Procter & Gamble). Despite this limitation, utilizing firm value

26 metrics to measure brand value seems well accepted by researchers and practitioners (especially boardroom members).

Most existing brand value measures are used when there is no brand ownership change.

What happens to brand value when the brand is in the market for potential buyers? How is brand selling price determined? Only one study has looked at brand value in the context of M&A, perhaps because there is a lack of brand value measures in general and therefore a lack of brand value measure data in M&As in particular. Since FASB posed new regulations that require acquirers to report the financial value of brands they acquire through M&A, the brand values that acquirers have assigned to target brands have been available since 2001. This provides an opportunity to understand how brand value in M&A is determined and to develop brand value measures that marketers urgently need to gain greater status at the board level and ultimately to improve firm value.

Purpose of Dissertation

The purpose of this dissertation is to develop a brand value measure and brand valuation model. Increased pressures on marketers to demonstrate marketing expenditure accountability have compelled researchers to develop brand value metrics to measure marketing productivity and brand valuation models to understand how brand value is determined. In the context of

M&A, because of brand ownership change, valuing a brand becomes more complicated than if the firm and brand are just static. There are now two firms involved in the brand valuation process, the acquirer and the target. How the acquirer (a potential buyer) determine the financial value the target brand and how the target (a seller) decides the financial value of its brand may be quite different. In this dissertation, a novel brand value measure using shareholder value and a

27 brand valuation model using Ohlson’s valuation model (1995) in the context of M&A are developed.

Dissertation Outline

This chapter provides an overview of brand value, brand value measures, and brand value and brand valuation model in the context of M&A. This general overview and literature review is a foundation for Chapter 2, which proposes brand value measures and brand valuation models in the context of M&A. Chapter 3 defines the research methodology and the results are presented in Chapter 4. Chapter 5 discusses the implications of the results and potential future research.

CHAPTER 2

BRAND VALUE IN MERGERS & ACQUISITIONS

Introduction

Marketing activities are designed specifically to build and manage brands. With increasing economic pressure, there is growing demand for marketing executives to provide financial contributions illustrating marketing productivity. In response to this demand, academics and practitioners have begun investigating brand value measures and brand valuation models.

Since 2001, firms that acquire brands through M&A are required to report the acquired brand’s financial value in their SEC filings. With this new accounting regulation, the demand for a brand value measure and brand valuation model in the context of M&A is increasing.

When firms acquire or sell brands through M&A, they have to justify their brand values. The acquirer has to decide how much to pay for target brands and the target has to determine how much its brand is worth.

In this dissertation, brand value measures from two different stakeholders’ perspectives in the context of M&A are identified: acquirer and acquirer shareholder. An acquirer shareholder perspective brand value measure is developed based on the efficient markets hypothesis (Fama,

1970) in the finance literature. A brand valuation model in the context of M&A is developed based on Ohlson’s (1995) accounting valuation model.

Brand Value Measures in M&A

In the context of M&A, two distinct target brand value measures exist. As shown in

Table 2, the two target brand value measures are from two different stakeholder’s perspectives in

M&A: (a) the acquirer perspective and (b) the shareholder perspective.

The acquirer perspective.

Target firm value. When one firm is acquiring a target firm, the acquiring firm managers have to answer two major questions: first, how much would the target be worth after being incorporated into the acquiring firm? This may be quite different from the target’s current value, which does not reflect any post-merger synergies. Second, how much should the acquirer offer the target? From the acquiring firm’s perspective, a low price is better but a high offer price may scare off potential rival bidders. So, valuing target firm judiciously is critical for acquiring firms’ success in M&A.

There are four generally accepted methods to value target firms in M&A (Brigham &

Ehrhardt, 2005): (a) market multiple analysis, (b) the corporate valuation model, (c) the equity residual model, and (d) the adjusted present value model. The market multiple analysis is the simplest but generally the least accurate. The target firm value is computed by multiplying its earnings per share by the industry average price/earnings (P/E) ratio, which is called the multiple. Similarly, the multiple of sales, book value, or number of customers could be used.

This approach is easy to implement but it assumes that the target firm is similar to an average firm in its industry. Because of this assumption, the firm value estimated by this approach provides a best ballpark estimate.

In the corporate valuation model, target firm operating value is defined as the present value of expected free cash flow, discounted at the weighted average cost of capital. To estimate

30 the total value of a target firm, any non-operating assets are added to the firm’s operating value and any debts are subtracted from the firm’s operating value. In the equity residual model, target firm value of equity is defined as the present value of expected free cash flow to equity, discounted at the required return on equity. Conceptually, equity residual is the cash flow available for distribution to shareholders after the target has made all necessary payments. In the adjusted present value approach, the firm value is defined as the present value of the firm’s free cash flow, discounted at the unleveled cost of equity, and the present value of all of the interest tax saving, discounted at the unleveled cost of equity. This method is used when there is a capital structural change in target firm, whereas the corporate valuation model and equity residual model are used when there is no capital structural change in the target firm.

Target brand value. An acquirer estimates target firm value through systematic firm valuation models that were reviewed in the previous section. The price of the target firm is determined based on this target firm value estimation. Therefore, the acquirer perspective target firm value (FVA) is equal to the net M&A deal price, which is defined as total M&A deal value minus target liability (see Table 2). Following the same logic, acquirer perspective target brand value (BVA) is equal to the price that the acquirer paid to the target firm for the target firm brand portfolio.

The shareholder perspective.

Target firm value. According to the efficient market hypothesis in the finance literature, acquirer shareholders immediately evaluate the target firm by estimating future cash flow associated with the target firm within an acquiring firm after an M&A announcement is made.

As a result, the acquirer’s stock price, which is defined as the present value of firm expected cash flow, changes. When investors expect that acquisition of a target firm will increase acquirer’s

31 future cash flow, the acquirer stock price will increase because investors will buy the acquirer stock. When investors expect that acquisition of a target firm will decrease acquirer’s future cash flow, the acquirer stock price will decrease because investors will not buy acquirer stocks and acquirer shareholders will sell the acquirer stock.

When the acquirer stock price increases after the M&A announcement is made, shareholders experience a positive abnormal stock return. Following the same logic, when the acquirer stock price decreases after an M&A announcement is made, shareholders experience a negative abnormal stock return. Therefore, the shareholder perspective target firm value (FVSH) can be estimated from this positive or negative abnormal return that shareholders experience after an M&A announcement is made. The positive and negative abnormal return can be converted to shareholder wealth($) gain or loss.

Target brand value. Shareholder perspective target brand value can be estimated using a ratio of acquirer perspective brand value (BVA) to acquirer perspective firm value (FVA). It is rational to argue that the shareholder perspective target firm value includes shareholder perspective target brand value because brand is part of the target assets. To parse out the target brand value from target firm value, the ratio of acquirer perspective target brand value to acquirer perspective target firm value is used. Shareholder perspective target brand value measure is proposed as follows.

The shareholder value-based brand value measure proposed in this dissertation has several attractive aspects. This measure is based on strong theoretical and empirical foundations

32 in the efficient market hypothesis in the finance literature. The efficient market hypothesis predicts that in a well-functioning capital market, stock prices provide the best available unbiased estimate asset value (i.e., brands). Simon and Sullivan (1993) also advocated measuring brand value with stock price. This measure is intuitive and the unit of the measure is dollar so that it is easy to understand. It is based on shareholder value, with which boardroom members are concerned the most, so that this measure can help marketers communicate with boardroom members concerning marketing productivities. So far, I have discussed brand value measures from two different perspectives in the context of M&A. Now, I will discuss the brand valuation models that I develop in this dissertation.

Brand Valuation Models in M&A

As shown in Figure 3, the current value of target firm brand (= intrinsic value of the brand) can change when a target firm is involved in an M&A transaction. Target firm brand value in M&A has two parts: (a) target firm brand value before M&A (pre-M&A brand value) and (b) value that an acquirer can add to target pre-M&A brand value or value that an acquirer can detract from target pre-M&A brand value. Therefore, a brand valuation model in M&A should have two parts, as the brand value measure has two parts. In a target brand valuation model, it is logical to model these two parts separately and then combine them to explain the target brand value better.

In this dissertation, brand valuation models follow a three-step approach. First, the target brand value is modeled with only target characteristics to predict target brand value in M&A.

Second, the target brand value is modeled with only acquirer characters to assess acquirer

33 contribution (+/-) in predicting the target brand value in M&A. Last, the target firm brand value is modeled with both target and acquirer characteristics to predict brand value in M&A.

Target firm brand value in M&A = Target firm brand value before M&A ± Acquirer contribution to the target brands

To model the target brand value, a firm valuation model is adopted from the accounting valuation literature. The accounting valuation model is built on accounting measures. The

Ohlson valuation model (1995) from the accounting literature has high explanatory power. For instance, Frankel and Lee (1998), Hand and Landsman (1999), and Dechow, Hutton, and Sloan

(1999) all find that the Ohlson model explains 70-80% of variation in prices across stocks.

Lundholm and O’Keefe (2001) compared the RIM model with a discounted cash flow model from finance and reported that both models yielded identical valuations of companies.

Ohlson’s valuation model (1995) states the true value of equity as a function of its book value of equity and the excess equity returns that a firm can generate in the future. Applying this equity valuation model to the firm valuation model, Ohlson’s firm valuation model starts from the book value of the firm and adds the firm’s accrual earnings. The model includes a firm’s level factor that represents what a firm has now and a firm’s growth factor that represents the firm’s growth potential. Therefore, the brand valuation model in this dissertation also includes level factor and growth factor as Ohlson’s valuation model suggests.

My brand valuation model is different from Bahadir et al.’s (2008) brand valuation model in three ways. First, the brand valuation in this dissertation is based on an Ohlson’s (1995) accounting valuation model, which is based on accounting measures. I operationalized this

34 model at the brand level to develop the present brand valuation model. Second, the focus of

Bahadir et al.’s (2008) brand valuation model is on the effect of acquirer characteristics on target brand value in M&A, but the focus of my brand valuation model is on the effect of target characteristics on target brand value in M&A. Third, my brand valuation model in this dissertation follows a three-step approach: step 1 – target characteristics only model, step 2 – acquirer characteristics only model, step 3 – target and acquirer characteristics full model. This model is laid out in Chapter 3.

Summary

In the marketing literature, there is a comprehensive framework to link marketing and shareholder value. To test the theory empirically and to provide evidence to boardroom members, developing brand value measures and brand valuation models is necessary. In this dissertation, a shareholder value based brand value measure is developed based on the efficient market hypothesis in the finance literature and brand valuation models are developed using

Ohlson’s (1995) valuation model.

Chapter 3 discusses the methodology this research uses to develop a brand value measure based on shareholder value and brand valuation model based on Ohlson’s (1995) valuation model.

CHAPTER 3

METHODOLOGY

Introduction

In this research, a brand value measure and brand valuation models are developed in the context of M&A. First, an acquirer perspective brand valuation model is developed using the reported target brand value (BVA), which is an acquirer-assigned financial value of the target brand portfolio. Second, a shareholder perspective target brand value measure and a shareholder perspective brand valuation model are developed. Olson’s (1995) valuation model is adopted for the brand valuation models and an event study is conducted to develop shareholder perspective target brand value measure. The following section discusses the samples, data source, measures, and models.

Sample

The study sample includes all the completed M&A deals in which target firms are in consumer-related product and service industries regardless of the acquirer industry and both targets and acquirers are U.S.-based public firms for the period between January 1, 2001 and

June 30, 2010. As data were more likely to be complete for public firms, I focused on public firms in this analysis. The sampling period starts from 2001 because detailed reporting of the intangible assets, including BVA in M&A, was mandatory only after 2000. I used target firms only from the consumer-related product and service industry for two reasons: first, a company in the consumer-related product and service industry is more likely to attribute value to a brand than a company in an industrial product and service industry (Bahadir et al., 2008); second, the

36 size of consumer-related products and services is substantial. In the Bureau of Economic analysis in 2007, the consumer-related packaged goods industry exceeded $2 trillion (Bureau of

Economic Analysis, 2007; Sorescu & Spanjol, 2008).

Sampling Process. A total of 265 M&A deals that met sampling criteria were extracted from five source databases (see below). I eliminated 94 deals from the population dataset (n =

265) because they have the same target and acquirer as a result of an internal transaction such as a subsidiary acquisition. Such internal transactions do not have the brand ownership changes that are necessary for this study. This sample elimination reduced the initial sample to 171 deals.

To complete my analysis, it was necessary that BVA information is available in acquirer SEC filings after M&A was completed. A total of 98 deals met this requirement and the final sample includes these 98 deals.

- Target industry distribution throughout the sampling process. Table 3 shows target industry distribution in the population dataset (n = 265). All targets operate in the consumer- related product and service industry: Sixty-seven percent of target firms are in the consumer product and service industry (CPS) and thirty-three percent of target firms are in the consumer staple industry (CS). In the CPS industry, more than 50% of the targets are in professional services. In the CS industry, 50% of the targets are in the food and beverage business and 35% are in the textile and apparel business.

In the initial sample (n = 171), the target industry distribution is similar to that in the population dataset (see Table 4). Sixty-eight percent of target firms are in the CPS industry and thirty-two percent of target firms are in the CS industry. In the CPS industry, more than 50% of the targets operate in the professional services business, similar to what was seen in the

37 population dataset. In the CS industry, food and beverage businesses constitute 50% of target firms and textile and apparel businesses make up an additional 40%.

In the final sample (n = 98), target industry distribution did not change from the initial sample. As shown in Table 5, 67% of the targets in the final sample operate in the CPS industry and 33% operate in the CS industry. In sum, target industry distribution is consistent throughout the sampling process.

- Acquirer industry distribution throughout the sampling process. Table 6 displays acquirer industry distribution in the population dataset. Sixty-five percent of the acquirers operate in the consumer-related product and service industry: Thirty-eight percent of the acquirers operate in the CPS industry and twenty-eight percent of the acquirers operate in the CS industry. In the CPS industry, more than 50% of the acquirers are in professional services, similar to what was seen in the target industry distribution. In the CS industry, 40% of the firms are in the food and beverage business and 30% are in the textiles and apparel business, similar to what was seen in the target industry distribution.

In the initial sample, acquirer industry distribution is similar to that in the population dataset. As displayed in Table 7, 48% of acquirers are in the consumer-related product and service industry, 25% of acquirers are in the CS industry, and 23% are in the CPS industry. In the CPS industry, more than 50% operate in professional services, similar to the population dataset. In the

CS industry, 43% of the firms are in the food and beverage business and 30% are in the textile and apparel business.

In the final sample, acquirer industry distribution is not much different from the initial sample. As shown in Table 8, 54% of acquirers operate in the consumer-related product and service industry: Thirty percent of acquirers operate in the CS industry and twenty-four percent

38 of acquirers operate in the CPS industry. Even though there is some difference in acquirer industry distribution across the sampling process, the majority of acquirers in the final sample still operate in consumer-related industries, as seen in the population dataset.

Data Source

The data were collected from five sources: Thomson ONE Banker Merger & Acquisition database, Standard & Poor’s COMPUSTAT database, the University of Chicago’s Center for

Research in Security Prices (CRSP), the U.S. Patent and Trademark Office’s (PTO) Trademark

Electronic Search (TESS), and the U.S. Securities and Exchange Commission (SEC) filings.

I began by collecting all M&A deals that have both U.S.-based acquirer and target firms in the CPS or CS industries in Thomson Financial proprietary macro-level industry classifications. To be included in the sample, the acquisition must have been completed between

January 1, 2001 and June 30, 2010 as identified in the Thomson ONE Banker mergers and acquisition database. I collected acquirer SEC filings and identified acquirers that reported the price that they paid to the target firm for the target firm brand portfolio in their SEC filings.

The PTO database was used to obtain data regarding the number of registered target firm trademarks and trade names. I searched trademarks and trade names registered under the target firm’s name. I excluded trademarks and trade names from the data if they were abandoned before the M&A effective date.

I collected data to calculate target sales growth (SALESg), target earnings growth

(EARNg), target market share growth (MSg), target industry sales growth (I_SALESg), target industry demand instability (I_INS), target industry concentration (I_HHI), target industry type

(T_TYPE), acquirer firm size (A_SIZE), acquirer financing consideration (A_CON), acquirer

39 leverage (A_LEV), and acquirer industry type (A_TYPE) from the Standard & Poor’s

COMPUSTAT yearly data file. The CRSP database was used to collect data to calculate stock return and abnormal return. The Fama and French (1992) factors, Treasury bond rates, and the momentum factor for stock return and abnormal return calculations are from Dr. Kenneth French

(http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).

Measures

Acquirer perspective brand value (BVA). BVA is measured by the financial value of the target brand(s) that the acquirer assigns to the target firm in the M&A transaction. An independent third-party valuator or appraiser who was hired by the acquirer determined the target brand value in M&A. Acquirer auditors then audited the determined value and this audited brand value was reported in the acquirer’s SEC filing after the completion of the M&A transaction. This singularly audited brand value was subject to a second audit by SEC.

Strategic motive (SM). The acquirer’s M&A strategic motive is defined as having synergy if the target and acquirer have the same primary four-digit SIC codes (Beckman &

Haunschild, 2002). I created a dummy variable as “0” if the acquirer’s primarily operating SIC was the same as the target’s; otherwise a “1” was coded.

Brand value to deal value (BVtoDV). The brand value to deal value ratio is calculated to reflect the importance of the brand in M&A deals. When the ratio of brand value to total M&A

40 deal value net liability is high, it can be assumed that transferring the ownership of the brand from a target to acquirer is a priority in the M&A deal. The following formula is used.

Target market share growth (MSg). Market share growth of a target is calculated by dividing target’s sales by target’s primary industry sales in the same year for the three years prior to the M&A transaction. Then I average them to get the target firm market share growth.

where Salest refers to the target firm sales at time t.

Target sales growth (SALESg). I calculate the sales growth of a target for the three years prior to the M&A transaction. Then I average them to get target sales growth.

where Salest refers to the target sales at time t.

Target earnings growth (EARNg). I calculate the earnings growth of a target for the three years prior to the transaction. Then I average them to get target earnings growth.

where Earningst refers to the target earnings at time t.

Target industry sales growth (I_SALESg). I calculate the sales growth of a target primary industry for the three years prior to the M&A transaction. Then I average them to get target industry sales growth.

where Industry salest refers to the target’s primary industry sales at time t.

Industry instability (I_INS). The coefficient of variation for sales in the target’s primary four-digit SIC is used.

Industry concentration (I_HHI). The Herfindahl-Hirshman Index (HHI) is used to measure industry concentration and is calculated by squaring the market share of each firm competing in the same industry and then summing the resulting numbers. Industry j concentration is calculated with the following formula.

where i refers to firm i in industry j.

Stock return. Stock return reflects the changes in the market’s expectation about a firm’s future cash flow through stock price that indicates all known information about the firm (Fama &

French, 1992). The following formula is used to compute stock return:

where SRit refers to stock return for firm i at time t.

Table 9 provides the description of the measures.

Model Overview

In this study, brand valuation models in the context of M&A are developed: (a) an acquirer perspective brand valuation model and (b) a shareholder perspective brand valuation model. Acquirer perspective target brand value is BVA, which was collected from acquirer SEC filings. Acquirer shareholder perspective brand value was calculated from cumulative abnormal returns that the acquirer had after the M&A announcement was made. An event study was conducted to calculate cumulative abnormal return, which represents the immediate reaction of shareholders to an M&A announcement.

In each valuation model, three sub-models are presented. Target brand value in M&A has two parts. One is the target firm current brand value and the other is the acquirer’s willingness or unwillingness to pay more than the current target brand value. If the acquirer is willing to pay more than current target brand portfolio value, then the target brand value in M&A will be higher than its current value. If the acquirer is not willing to pay more or wants to pay

43 less than the current target brand value, then the target brand value in the M&A will be lower than its current value. To incorporate this theory in the model, I used only target characteristic variables in the first sub-model to see how much target characteristics explain target brand value in M&A. In the second sub-model, I included only acquirer characteristics to see how much acquirer characteristics explain target brand value in M&A. In the third sub-model, I included both target and acquirer characteristic variables to model target brand value to see how target and acquirer characteristics interact when they explain target brand value in M&A.

Acquirer perspective brand valuation model. I used an Ordinal Least Squares (OLS) model to find factors that influence the acquirer perspective brand value in an M&A transaction.

I included level and growth data in the valuation models as Ohlson’s valuation model (1995) suggests. The level data included in the brand valuation model are the number of target trademarks registered (TMt-1), target salest (SALESt-1), target earningst (EARN t-1), target market sharet (MSt-1), target industry demand instability (I_INSt-1), target industry concentration

(I_HHIt-1), acquirer leveraget-1 (LEVt-1), acquirer financing considertationt-1 (FINt-1), acquirer firm sizet (SIZEt), target industry type (T_SEVt-1), and acquirer strategic motive for M&A (SM).

The growth variables in the model are target firm 3-year sales growth (SALESg), target 3-year earnings growth (EARNg), target market share growth (MSg), and target industry 3-year sales growth (I_SALESg).

I estimated three OLS regression models; Model 1-1 includes only target characteristic variables, Model 1-2 includes only acquirer characteristic variables, and Model 1-3 includes both target and acquirer characteristic variables. The target characteristic variables at time t (the year of M&A completion) had many missing values because targets do not make SEC filings after

44 they are acquired. As a result, I used target characteristic variables at time t-1, a year before the target was acquired.

Model 1-1.

Model 1-2.

Model 1-3.

- -

Shareholder perspective brand value and valuation model.

Shareholder perspective brand value.

- Calculating cumulative abnormal return. To calculate shareholder perspective brand value in an M&A context, cumulative abnormal return must be calculated. An event study is used to capture abnormal return as well as cumulative abnormal return. An event study, which measures the stock price reaction to an unanticipated announcement of an event, was introduced by Fama, Fisher, Jensen, and Roll (1969) and was further developed by Brown and Warner

(1980). This methodology is based on the efficient market hypothesis, which holds that financial markets are efficient and hence stock prices reflect instantaneously all the available information related to the profitability of the firm (Fama, 1970). Abnormal return occurs when shareholders perceive that the firm’s announcement of the event will have a positive (or negative) impact on the firm’s future cash flow, resulting in an immediate stock price increase (decrease) as shareholders buy (sell) stocks as they change their expectations about the firm’s future cash flow.

Event study has proven to be an appropriate research method on the valuation effects of a corporate event.

To conduct the event study, I first computed expected return and then calculated abnormal return by subtracting expected return from actual return. Expected return is assumed to be a function of the rate of return of the benchmark market portfolio for a certain time period

(Mizik, Knowles, & Issac, 2011). I computed abnormal stock return for firm i, day t as

where Rit is the raw actual return for firm i on day t and E(Rit) is the expected return for firm i on day t.

E(Rit) is computed by using the classic Fama and French (1992) multi-factor asset pricing model with momentum (Carhart, 1997). The model estimates the expected return as a function of risk factors that reflect the general stock market, size, the relative importance of intangibles

(book-to-market ratio), and momentum. Riskier stocks are characterized by higher returns; so smaller firms are expected to outperform larger firms. Stocks with higher book-to-market ratios are expected to outperform stocks with lower book-to-market ratios. Finally, stocks with higher momentum (e.g., high past return) are expected to outperform stocks with lower momentum.

The typical financial benchmark model for return is estimated as follows:

Equation 1.

- ( where Rft is the risk-free rate for the day t,

Rmt is the average market rate of return for the day t,

SMBt is the difference in returns between small and large firms for the day t,

HMLt is the difference in returns between high- and low-book-to-market ratio firms,

UMDt is the Carhart (1997) momentum factor, and ϵit is the error term.

I used a pre-event beginning 12 months (252 trading days) before and ending one month

(21 trading days) before the M&A announcement date. I first estimated the above 4-factor

47 financial benchmark model (Equation 1) for each acquirer i and each M&A announcement p in the [-252, -21] window preceding an M&A announcement. Next, I used the estimates of market

(β1,ip), SMB (β2,ip), HML (β3,ip), and UMD (β4,ip) risk factor loadings to compute abnormal return

(ARit) for each firm i and day t in the event window around the M&A announcement p. I then aggregated ARit over the duration of the event window to compute cumulative abnormal return.

- Converting Cumulative Abnormal Return to shareholder perspective brand value in monetary value. The unit of cumulative abnormal return was converted from % units to dollar units ($VCAR) because doing so gives an actual dollar value that is comparable with BVA and enables a more straightforward interpretation of the results. I converted abnormal return units from % to dollar by multiplying abnormal return on day 0 by the total market value of equity on day -1. I summed the dollar value of abnormal return ($VAR) from time t1 to time t2 to compute the dollar value of cumulative abnormal return ($VCAR). The dollar value of cumulative abnormal return is the shareholder perspective target firm value. The following formulas are used to calculate shareholder perspective target firm values.

48 where FVSH,i[t1,t2] refers to acquirer shareholder perspective target firm i value at time t,

$VAR,it refers to the monetary value of acquirer i’s abnormal return at time t when the M&A announcement is made.

The shareholder perspective target brand value is derived from the shareholder perspective target firm value. I utilized the ratio of acquirer perspective target brand value

(BVA) to acquirer perspective target firm value (FVA) to extract a shareholder perspective target brand value. The following formula is used to derive shareholder perspective brand value from shareholder perspective firm value.

where FVSH,i[t1,t2] refers to shareholder perspective target i firm value.

Shareholder perspective brand valuation model.

An OLS regression model was used for the shareholder perspective brand valuation model. Three sub-models were estimated. The first sub-model (Model 2-1) includes only target characteristics. The second sub-model (Model 2-2) includes only acquirer characteristics. The last model (Model 2-3) includes both target and acquirer characteristics in the model.

Model 2-1.

Model 2-2.

Model 2-3.

- -

CHAPTER 4

DATA ANALYSIS AND RESULTS

Introduction

In this chapter, results are presented in two major sections. First, the results from the acquirer perspective brand valuation model are presented with an overview of acquirer perspective brand value (BVA). Second, results from the shareholder perspective brand valuation model are reviewed with an overview of shareholder perspective brand value (BVSH) calculated through an event study. The chapter concludes with a comparison of brand value measures, BVA and BVSH, and brand valuation models from acquirer and shareholder perspectives in the context of M&A. The interpretations of the results, as well as managerial implications, are discussed in

Chapter 5.

Acquirer Perspective Brand Value and Valuation Model

Acquirer perspective brand value. On average, acquirer perspective target brand value

(BVA) accounted for 13.45% of the M&A deal value in the final sample (n = 98). Average BVA was $349.5 million with a highest value of $25 billion in the final sample. The average M&A deal value was $1.2 billion with the highest and lowest being $57 billion and $1.7 million, respectively. In the final sample, 65% of deals had more than $0 for BVA (see Table 10). When considering only deals that had a non-zero value for BVA, nearly 30% of the acquirers reported that BVA accounted for more than 30% of their M&A deal value (see Table 11). This indicates that brand value constitutes a significant part of an M&A deal value. For example, when

Energizer acquired Playtex, the BVA was 70% of the deal value (BVA = $1.31 billion); when

Whirlpool acquired Maytag, the BVA was 56% of the deal value (BVA = $ 1.46 billion); when

P&G acquired Gillette, BVA was 50% of the deal value (BVA = $25 billion).

Among deals that had zero value for target brand (n = 35), 30% of these deals had target firms that did not have any registered trademarks at the time of M&A transaction completion.

So, it is intuitive that these targets had zero value for BVA. However, why 70% of target firms that had registered trademarks at the time of M&A transaction completion did not receive payment for their brand(s) is still not answered. To determine what factors influence acquirers to pay targets for target brands, a two-group comparison analysis was conducted and then acquirer perspective brand valuation models were estimated. The results are presented in the following sections.

Comparison of BVA>0 group and BVA=0 group. I partitioned the final sample into two groups to examine which factors influenced acquirers to pay for a target brand portfolio. If a deal had an acquirer that reported BVA as above $0, the deal was placed in the BVA>0 group. If a deal had an acquirer that reported BVA as $0, the deal was placed in the BVA=0 group.

- The differences in target characteristics. An independent samples t-test was conducted to examine differences between the BVA>0 group and the BVA=0 group. Panel A in Table 12 contains the results of the tests. Means of 14 target characteristic variables were tested and seven target characteristics were found to be significantly different between the two groups. Not surprisingly, the number of trademarks that targets registered in the BVA>0 group was significantly higher than the number in the BVA=0 group (t-statistic = 38.08, p < .05). Targets in the BVA>0 group had eight times more registered trademarks compared to targets in the BVA=0 group. Targets in the BVA>0 group had almost six times higher salest-1 compared to those in the

BVA=0 group (t-statistic = 2.35, p < 0.05). Targets in the BVA>0 group had a mean of 9% market

52 sharet-1 and targets in the BVA=0 group had a mean of 4% market sharet-1. The group difference is significant (t-statistic = 1.78, p < 0.1). Targets in the BVA>0 group had six times more intangible assetst-1 than targets in the BVA=0 group and the group difference is significant (t-statistic = 3.39, p < .01). The mean of target R&D intensity in the BVA=0 group was nearly three times higher than that in the BVA>0 group and the difference is significant (t-statistic = 2.05, p < 0.05). The means of target adverting intensity between the two groups were not significantly different.

In target industry characteristics, the means of target industry sales growth between the two groups were significantly different (t-statistic = -2.52, p < 0.05). Target industry sales growth in the BVA>0 group was about three times higher than that in the BVA=0 group. A Chi- square test analysis was conducted to examine if there was a target industry type difference in the two groups. As shown in Table 13, targets in non-service industries tended to receive payment for their brand(s) more frequently than targets in service industry did (χ2 = 9.42, p < 0.05).

- The differences in acquirer characteristics. Panel B in Table 12 shows the results of an independent t-test for acquirer characteristic differences in the two groups. Five acquirer characteristics were tested and only one acquirer characteristic variable was significantly different between the two groups. Acquirer leverage in the BVA>0 group was 8% higher than that in the BVA=0 group (t-statistic = 2.38, p < .05).

A Chi-square test was conducted to examine if there was an acquirer industry type difference between the two groups. Acquirers in non-service industries tended to pay more frequently for target brands compared to acquirers in service industries. As seen in Table 14, the chance that an acquirer in a non-service industry would pay for a target brand was twice as high as the chance that an acquirer in a service industry would. However, the difference is not

53 statistically significant. As seen in Table 15, acquirer’s strategic motive for M&A was not different between the two groups.

- The difference in target and acquirer combined characteristics. Since target and acquirer industry type differences existed between the two groups, I combined these two variables into one variable and conducted a Chi-square test to understand the relationships among target brand value, target industry type, and acquirer industry type. I created a categorical variable by combining target industry type and acquirer industry type: I coded the variable as “1” when the target industry type was service and the acquirer industry type was service, “2” when the target industry type was service and the acquirer industry type was non-service, “3” when the target industry type was non-service and the acquirer industry type was service, and “4” when the target industry type was non-service and the acquirer industry type was non-service. As shown in Table 16, the combined industry categorization was different between the two groups

(χ2 = 10.08, p < 0.01). Targets in non-service industries tended to receive payment for their brands more frequently than did targets in service industries, regardless of the industry of their acquirers.

Thus far, my analyses have been focused primarily on comparisons of target and acquirer characteristic variables between the BVA>0 group and BVA=0 group. Now I move my attention to the brand valuation model to examine how acquirers value target brand in the context of M&A.

Acquirer perspective brand valuation models. Three OLS regression models were estimated: Model 1-1 includes only target character variables as independent variables to predict acquirer perspective target brand value (BVA). Target R&D intensity was excluded even though it seems to have an effect on target brand in the previous two-group comparison analysis due to a lack of data. Only 30% of the final sample had target R&D intensity data. Model 1-2 includes

54 only acquirer characteristic variables as independent variables. Model 1-3 includes both target and acquirer characteristic variables as independent variables. In order to compare three models, deals were excluded from this analysis if they had any missing variables. As a result, only 60 deals remained in the three valuation models. Table 17 reports regression results for these three models. The descriptive statistics and correlations between the independent and dependent variables can be found in Table 18. Due to the low correlations presented in Table 18, multicollinearity is unlikely to be a concern in the analysis.

In Model 1-1, the number of trademarks the target registered had a positive effect on BVA.

Surprisingly, target earningst-1 had a negative effect on BVA. In the previous two-group comparison analysis, target earnings were not significantly different in the two groups (MeanA>0

= 25.71, MeanA=0 = 3.86). Target market sharet-1 in the BVA>0 group was significantly higher than that in the BVA=0 group (MeanA>0 = 0.09, MeanA=0 = 0.04); however, it does not appear to be a predictor of BVA. As expected, target intangible assetst -1 positively contributed to BVA.

For target industry factor, target industry sales growth had a positive effect on BVA. It was consistent with the result of the two-group comparison analysis (MeanA>0 = 0.15, MeanA=0 =

0.05). Target industry type was a significant predictor for BVA. To test the effect of target industry type on BVA, a dummy variable was created for target industry type; “0” for a service industry and “1” for a non-service industry. The positive coefficient of target industry type in the regression model indicated that non-service industry target firms had higher BVA compared to service industry target firms. This was expected from the previous two-group comparison analysis that showed that four out of five target firms in non-service industries had non-zero BVA.

In Model 1-2, acquirer leverage did not appear to be a predictor of BVA even though the means for two groups (MeanA>0 = 0.23, MeanA=0 = 0.15) were significantly different in the two-

55 group comparison analysis. Acquirer firm size was found to contribute to BVA positively; the larger the acquirer, the higher the BVA. To analyze the effect of acquirer industry type on BVA, a dummy variable was created in the same way as for target industry type code: “0” for a service industry and “1” for a non-service industry. The positive coefficient of acquirer industry type means that acquirers in non-service industries tended to pay higher BVA compared to acquirers in service industries.

In Model 1-3, all the significant target characteristic variables in Model 1-1 remained significant while all the significant acquirer characteristics variables in Model 1-2 became non- significant. Target number of trademarks registered, target intangible assetst-1, and target industry sales growth were positively related to BVA and the coefficients were similar to those in

Model 1-1. Target earningst-1 were still negatively related to BVA. The target industry type did not appear to be a predictor of BVA. However, the coefficient of target earnings growth became marginally significant: the higher target earnings growth is, the higher BVA is. Target industry demand instability became a significant predictor for BVA: the higher target industry demand instability is, the higher BVA is.

The previous three regression models provided information about how acquirers determined target brand value (BVA) in M&A. The predictors in the regression models were very similar to what I observed in the two-group (BVA>0 and BVA=0) comparison analysis. The number of trademarks a target registered, target intangible assets, and target industry sales growth were significantly different in the two-group comparison analysis and these variables were significant predictors for BVA in the brand valuation model. The means of target salest-1 and the means of target market sharet-1 in the two-group model (BVA>0 and BVA=0) were significantly different in the two-group comparison analysis, but they were not significant

56 predictors for BVA in the brand valuation models. Target earning growth and target industry demand instability were not significantly different in the two-group comparison analysis, but they were found to be significant predictors for BVA in the brand valuation model. In sum, BVA is explained by target characteristics more than by acquirer characteristics. The model with only target characteristics variables (Model 1-1) has the highest f-value and the model with both target and acquirer characteristic variables (Model 1-3) has the highest R2 among three models.

However, R2 of the Model 1-1 is very close to R2 of the Model 1-3.

Shareholder Perspective Brand Value and Brand Valuation Model

Event study result. The event study was conducted to study the shareholders’ immediate market reactions to an M&A announcement. First, abnormal return for target and acquirer around M&A announcement date was calculated and then cumulative abnormal return for target and acquirer was calculated for several alternative event windows. Time 0 refers to an

M&A announcement day, time -1 refers to the day before the announcement (time 0), and time

+1 refers to the day after the announcement (time 0). As shown in Table 19 and Figure 4, target abnormal return at time -1, 0, and +1 are positive and statistically significant. The abnormal return is 1.90%, 18.74%, and 5.99% respectively. Consistent with past research, average cumulative abnormal return was 23.81% accruing to targets in the [-1, 1] event window at the time of the M&A announcement.

As shown in Table 20 and Figure 5, acquirer abnormal return at time 0, +1, and +2 are

0.75%, 0.50%, and 0.24% respectively, but are not statistically significant. However, acquirer cumulative abnormal return [0, 1] and cumulative abnormal return [0, 2] are 1.41% and 1.61% respectively and statistically significant. To evaluate whether a few outliers caused the positive

57 cumulative abnormal return for acquirers, I examined the distribution of cumulative abnormal return with an event window of [0,2]. As presented in Table 21, there is no extremely large cumulative abnormal return even though the distribution is skewed toward the right. Given the distribution of cumulative abnormal return and t-test results, I conclude that the positive cumulative abnormal return for acquirers occurred after an M&A announcement was made. I calculated shareholder perspective brand value using this significantly positive cumulative abnormal return [0,2] for acquirers. The positive cumulative abnormal return [0,2] was converted to a dollar value from a percentage to analyze its relationship with acquirer perspective target firm value ($) and with acquirer perspective target brand value ($).

As shown in Table 22, the mean of the dollar value of cumulative abnormal return [0,2] for acquires in the BVA>0 group is a profit of $9.57 million and that for acquirers in the BVA=0 group is a loss of $6.18 million. The correlation between acquirer perspective target firm value

(FVA) and shareholder perspective firm value (FVSH) in the BVA>0 group is not significant.

However, the correlation between acquirer perspective target firm value (FVA) and shareholder perspective firm value (FVSH) in the BVA=0 group is highly significant (r = -.81, p > .00). As shown in Table 23, the means of FVA between the two groups (BVA>0 and BVA=0) are significantly different (Mean A>0 = 709.78, Mean A=0 = 366.78, t-statistic = -1.74, p < .1). The means of FVSH between the two groups (BVA>0 and BVA=0) are also significantly different (Mean

A>0 = 40.89, Mean A=0 = -39.99, t-statistic = -1.74, p <. 1). Now, I examine how acquirer shareholders value target brand. The results of the two-group comparison analysis and the shareholder perspective brand valuation model are presented in the following section.

Shareholder perspective brand value. On average, shareholder perspective target brand value (BVSH) is $8.22 million. It is 11% of the brand value that an acquirer assigned as a

58 monetary value to the target’s brand portfolio (BVA). The shareholder perspective target brand value is correlated with BVA (r = .35, p < .01). To explore this new brand value measure based on shareholder value, a two-group comparison analysis was conducted.

Comparison between BVSH>0 group and BVSH≤0 group. I partitioned the sample into two groups to examine what factors have impacts on BVSH. I used the median value of BVSH to divide samples into two groups. If BVSH was higher than $0, the deal was placed in the BVSH>0 group. If BVSH equal to $0 or less than $0, the deal was placed in the BVSH≤0 group.

- The difference in target characteristics. An independent sample t-test was conducted to examine differences between the BVSH>0 group and the BVSH≤0 group. Panel A in Table 24 presents the results of the tests. Surprisingly, except for target intangible assets, all the tested target characteristic variables in the BVSH≤0 group had higher value, but none of them was statistically significant. A chi-square test was conducted to examine if there were target industry type differences between the two groups. The results are presented in Table 25. Targets in the

2 service industry tended to be in the BVSH≤0 group more frequently than in the BVSH>0 group (χ =

4.27, p < .01).

- The difference in acquirer characteristics. Panel B in Table 24 shows the results of an independent t-test for acquirer characteristic differences between the BVSH>0 group and the

BVSH≤0 group. The means of acquirer leverage and firm size in the BVSH>0 group were bigger than those in the BVSH≤0 group but they were not statistically significant. Acquirer R&D intensity in the BVSH≤0 group was higher than that in the BVSH>0 group but it was also not significant. As seen in Tables 26 through 28, the acquirer industry type, acquirer strategic motives, and acquirer and target combined industry type were not significantly different between the two groups. To understand how acquirer shareholders value target brand value, a shareholder

59 perspective brand valuation model was estimated and the results are presented in the following section.

Shareholder perspective brand valuation model. Three OLS regression models were estimated: Model 2-1 includes only target character variables as independent variables to predict shareholder perspective target brand value (BVSH). Model 2-2 includes only acquirer characteristic variables as independent variables to predict BVSH. Model 2-3 includes both target and acquirer characteristic variables as independent variables to predict BVSH. In order to compare three models, deals were excluded from this analysis if they had any missing variables.

As a result, 40 deals remained in the three valuation models. Table 29 reports the results of these three models. The descriptive statistics and correlations between the independent and dependent variables can be found in Table 30. Due to the low correlations presented in Table 30, multicollinearity is unlikely to be a concern in the analysis.

In Model 2-1, the number of trademarks the target registered, target earnings growth, target intangible assets, and target industry concentration were positively related to BVSH.

Surprisingly, target earnings growth had a negative effect on BVSH. Target industry type was a significant predictor for BVSH. A dummy variable was created for target industry type: “0” for a service industry and “1” for a non-service industry. The positive coefficient of target industry type in the brand valuation model indicates that non-service industry targets had a higher BVSH compared to service industry targets.

In Model 2-2, acquirer firm size was found to contribute positively to BVSH: the larger the acquirer was, the higher BVSH was. To analyze the effect of acquirer industry type on BVSH, a dummy variable was created for acquirer industry type code: “0” for a service industry and “1” for a non-service industry. The positive coefficient of acquirer industry type in the brand

60 valuation model means that acquirers in non-service industries tended to have higher BVSH compared to those in service industries.

In Model 2-3, all significant target characteristics were similar to Model 2-1, while all significant acquirer characteristics in Model 2-2 became non-significant. The number of trademarks targets registered, target earning growth, and target intangible assetst-1 were positively related to BVSH and the coefficients were similar to those in Model 2-1. Surprisingly, target earningst-1 and target market share were negatively related to BVSH in Model 2-3. Target industry concentration remained as a significant predictor for BVSH in Model 2-3: the higher target industry concentration was, the higher BVSH was. The target industry type did not appear to be a predictor of BVSH in Model 2-3.

The acquirer shareholder perspective brand valuation models provide insights about how shareholders determine target brand value (BVSH) in M&A. The predictors in the brand valuation models are somewhat different from the two-group (BVSH>0 and BVSH≤0) comparison analysis. In the two-group comparison analysis, means of all target characteristic variables except target intangible asset for the BVSH≤0 group were higher than those for the BVSH>0 group, but they were not statistically significant. In the brand valuation model, all significant regression coefficients were positive, except target earnings. In terms of acquirer characteristics variables, the positive effect of acquirer size was observed in both the two-group comparison analysis and the brand valuation model. This indicates that the bigger the acquirer is, the higher BVSH is. The brand valuation model with only target variables has the highest f-value (= 9.82) and adjusted R2

(= .73) among the three brand valuation models. In sum, shareholder perspective target brand value can be explained by target characteristics more than by acquirer characteristics.

Comparison of Brand Value Measures and Brand Valuation Models

As seen in Table 31, the mean of BVA is $72.2 million and the mean of BVSH is $8.22 million. The acquirer perspective target brand value (BVA) and shareholder perspective brand value (BVSH) in M&A are highly correlated (r = .35, p > .00). BVA is positively correlated with acquirer perspective target firm value (FVA) (r = .58, p > .00) and BVSH is positively correlated with shareholder perspective target firm value (FVSH) (r = .54, p > .00).

Table 32 represents the best target brand valuation models for BVA and BVSH. The brand valuation model with only target characteristics predicts both BVA and BVSH best. The valuation model for BVA has an f-value of 8.90 and explains 69% of BVA variance, while the valuation model for BVSH has an f-value of 9.82 and explains 81% of BVSH variance.

In the valuation model for BVA, the number of trademarks a target registered and target intangible assets have positive effects on target brand value while target earnings has a negative effect on target brand value. Two target industry factors are significant in the model; targets in a high sales growth industry have higher brand value than targets in a low sales growth industry and targets in a non-service industry have higher brand value than targets in a service industry.

In the valuation model for BVSH, target intangible assets, the number of trademarks a target registered, and target earnings growth have positive effects on target brand value while target earnings has a negative effect on target brand value. For target industry characteristics, target industry concentration has a positive effect on target brand value and targets in a non-service industry get a higher brand value than targets in a service industry.

In sum, the significant variance of target brand values in M&A is explained by target characteristics. Target earnings were significant in both valuation models (BVA and BVSH).

Target industry type is also significant in both valuation models. Both acquirers and

62 shareholders value target brands more highly when the target operates in a non-service industry than when the target operates in a service industry. The detailed interpretation of the results and discussion are in the following chapter.

MISCELLANEOUS

Target perspective target brand value was identified while conducting this study. Even though it was not in the scope of the dissertation, this perspective also provides interesting insight regarding brand value in the context of M&A. The conceptual model, data analysis results and discussions for target perspective brand value and brand valuation model are presented below.

Target Perspective Target Brand Value and Brand Valuation Model

Conceptual model.

Target brand value. Target perspective target brand value (BVT) is not available because brand value is not logged on accounting books with current accounting practices unless brands are acquired through business combinations such as M&A. However, it is reported that brand value comprises a significant portion of the intangible asset value (Anson, 2002; Simon &

Sullivan, 1993). So, I argue that target perspective intangible asset value is a reasonable proxy for target perspective target brand value.

BVT ≈ I

Target brand valuation model. An OLS regression model was used for a target perspective brand valuation model. Since target perspective brand value is not related to acquirer characteristics, I did not estimate any sub-models (Model 3) as I did in other perspective brand valuation models. The target perspective brand valuation model includes only target characteristics.

Model 3.

where BVT,i refers to target perspective brand value for target firm i.

Data Analysis and Results

Target perspective brand value. Target firm intangible assets value in a financial statement was used as a surrogate for target perspective brand value. On average, target perspective target brand value (BVT) was $130.8 million. It was 1.8 times more than acquirer perspective target brand value (BVA) and 16 times more than the shareholder value (BVSH). BVT was correlated with BVA (r = .70, p < .01) and with BVSH (r = .51, p < .01) even though the values were not close to either BVA or BVSH. To explore BVT, a two-group comparison analysis was conducted.

- Comparing between BVT=HIGH group and BVT=LOW group. I partitioned the sample into two groups around median value (= $27 million) of BVT. If BVT was higher than $27 million, the deal was placed in the BVT=HIGH group and if BVT was equal to or less than $27 million, the deal was placed in the BVT=LOW group. An independent samples t-test was conducted to examine differences between the BVT=HIGH group and the BVT=LOW group.

Table 33 includes the results of the t-tests. The number of trademark targets registered, target sales growth, target sales, target earnings, target market share growth, target market share, target advertising intensity, target industry sales growth, target industry sales, target industry demand risk, target industry sales growth, and target industry demand instability in the BVT=HIGH group had higher means compared to those in the BVT=LOW group. Targets in the BVT=HIGH group had 22% sales growth while targets in the BVT=LOW group had 8% sales growth. Target sales in the BVT=HIGH group were 14 times higher than in the BVT=LOW group. Target market share in the BVT=HIGH group was 2.5 times higher than in the BVT=LOW group. Target industry sales growth in the BVT=HIGH group was four times higher than in the BVT=LOW group. Target industry demand instability in in the BVT=HIGH group was about twice as high as in the BVT=LOW group. Interestingly, target earnings growth in the BVT=LOW group was higher than that in the

BVT=HIGH group but it was not significant (MeanLOW = 11%, MeanHIGH = -51%). As seen in

Table 34, target industry type was not related to BVT.

Target perspective brand valuation model. An OLS regression model was estimated.

Model 3 includes only target character variables as independent variables to explain BVT. Table

35 reports the results of the BVT valuation model. The descriptive statistics and correlations between the independent and dependent variables can be found in Table 36. Due to the low correlations presented in Table 36, multicollinearity is unlikely to be a concern in the analysis.

In Model 3, target sales growth, target sales, target earnings, and target market share had positive impacts on BVT. Targets with high sales and high sales growth tended to have high brand values and targets with high market share had high brand values. Surprisingly, the number of trademarks registered was negatively related to BVT. None of the target industry characteristics were found to be predictors for BVT. This model is significant (f-value = 8.16) and explains 65% of the variance of BVT.

Discussion

Overview of BVT. Target perspective brand value (BVT) was much higher than both acquirer perspective brand value (BVA) and shareholder perspective brand value (BVSH). This may indicate that target firms received less payment than they listed in their financial statements.

However, this result should be interpreted with caution because total intangible asset value that is used as a proxy for target perspective brand value includes not only brand value but also other types of intangible asset such as patents, while BVA and BVSH include only brand value. Even though there are big differences between BVT and BVA, and between BVT and BVSH, these three brand values are highly correlated, which could indicate that acquirer and shareholder brand values are influenced by target perspective brand value and target intangible asset can be a good surrogate for target brand value.

Target perspective brand valuation model. Target perspective brand value can be explained by a target’s market performance measures, such as sales, market share, and earnings.

Higher market performance is higher target perspective brand value is. Interestingly, target industry factors do not explain target perspective brand value. A possible explanation for this may be that when firms value their own intangible assets (i.e., brands), they have enough

66 information available to assess the value so that they do not have to consider macro environmental factors such as industry factors to value their own assets. The target perspective brand valuation model is significant and explains 65% of target brand value variance.

CHAPTER 5

DISCUSSION AND CONCLUSION

Introduction

This chapter integrates the purpose, findings, and implications of this dissertation. After the purpose of the dissertation is reviewed, managerial implications of the findings are discussed.

The chapter concludes with limitations and opportunities for future research.

Summary of Purpose

Justifying marketing expenditure in financial measures has been a challenging task for marketers for decades. Marketers have adopted firm performance metrics from accounting and finance to demonstrate marketing productivity. However, this approach seems not to measure marketing productivity as accurately as marketers wish, as there is a big gap between firm value measures that are based upon dollars and cents and marketing activities that are based upon consumer satisfaction and brands. Marketing activities are designed to build and manage a brand and are less focused on the overall status of the firm. Therefore, marketing productivity should be measured on the brand level. Unfortunately, there is no well-accepted brand value measure, making it difficult for the marketer to measure brand productivity with data at the brand level.

Developing a widely accepted brand value measure will help the marketer demonstrate the productivity of marketing activities. In this dissertation, a new brand value measure based on shareholder value is developed in the context of M&A and brand valuation models are developed to understand how brand value is determined in the context of M&A.

Summary of Results

Two brand value measures were identified in the context of M&A; acquirer perspective target brand value (BVA), and shareholder perspective target brand value (BVSH). Acquirers and shareholders in the context of M&A assess target brand value differently. The following sections discuss how these two brand value measures are different and how these two values are determined.

Acquirer perspective brand value and the brand valuation model.

Overview of BVA. On average, the ratio of BVA to M&A deal value is 13.45% in this dataset and it is higher than what Bahadir et al. (2008) reported. These authors found that BVA accounts for 7.3% of the transaction value. This discrepancy can be explained by the difference in sample. The previous work of Bahadir et al. (2008) consisted of samples from 2001 to 2005, whereas samples in this study are from 2001 to 2010. Furthermore, Bahadir et al. (2008) included samples from a wide range of industries while samples from the current study are only from the CPS and CS industries. This discrepancy suggests that the consumer-related product and service industry recognizes brands in M&A transactions more than other industries.

An interesting finding is that the brand value P&G paid for the Gillette brand was $25 billion and the Gillette brand value estimation from Financial World, a publisher of The World’s

Most Valuable Brand (WMVB) since 1992, was $16 billion in the year of the M&A. The brand value that P&G paid is 40% more than Financial World’s brand value estimation, which has been frequently used in the marketing literature to assess the effect of brand value on firm value

(Barth, Clement, Foster, & Kasznik, 1998; Kerin & Sethuraman, 1998; Madden et al., 2006).

This gap may indicate that Financial World’s estimated brand value is a poor proxy for brand value, even though it is frequently used in marketing literature, or that brand value changes in a

69 complex manner when brand ownership changes because WMVB estimations do not consider ownership changes in their brand valuation formula.

Another interesting finding is that there is an acquirer industry difference in terms of target brand value recognition in M&A. Acquirers in the CPS and CS industries tend to recognize target brand value and report the value in their SEC filings, whereas acquirers in the financial industry tend not to recognize target brand value. This difference also suggests that the service industry, into which the financial industry falls, may not appreciate brand value in M&A whereas the non-service industry, including most CPS and CS industries, may.

- BVA>0 group and BVA=0 group Comparison. Two group comparison analysis results provide several interesting insights into the difference between the BVA>0 group and the BVA=0 group. First, targets in the BVA>0 group have a significantly higher number of trademarks registered compared to targets in the BVA=0 group. This suggests that target firms must have their trademarks registered in order to receive payment for their brands in an M&A transaction.

Having registered trademarks is a necessary condition for targets to receive payment for their brands. Acquirers value target brands highly when the target has high intangible assets and sales. Targets that receive payment for their brands tend not to be R&D focused firms and operate in a fast sales growing industry.

As previously mentioned, target industry type and acquirer industry type play a significant role in determining whether an acquirer pays a target for a target brand. The chance to receive payment for their brands for non-service industry targets is 1.5 times higher than that for service industry targets. This is also true for an acquirer. The chance that non-service industry acquirers will pay for target brands is 1.5 times higher than the chance that service

70 industry acquirers will. The relationship is more noticeable in the results of target and acquirer combined industry type analysis.

From the target perspective, when a target is in a service industry, it does not matter what industry its acquirers are in because the chance to receive payment for its brand is similar regardless of the acquirer industry type. However, when the target is in a non-service industry, it has a slightly higher chance to receive payment for its brand when its acquirer is in a service industry. It is worth noting that the chance that non-service industry targets receive payment for their brand is 80% when their acquirer is in a non-service industry, while the chance is increased to 100% when their acquirer is in a service industry. This indicates that when service industry firms acquire non-service industry firms, acquirers tend to pay for target brand(s) or the target brand may be a reason for the acquisition. However, when non-service industry firms acquire service industry firms, the chance of the acquirer paying for target brands is 42%. This indicates that when non-service industry firms acquire service industry firms, acquirers tend not to pay for target brands or the target brand may not be a reason for the acquisition. In general, targets in a non-service industry have a higher chance of receiving payment for their brands than targets in a service industry. The chance of acquirers paying for target brand portfolios is higher when service industry firms enter the non-service industry through M&A than the chance when non- service industry firms enter the service industry through M&A.

In sum, BVA accounts for a significant portion of M&A deal value. Acquirers in the consumer-related product and service industry tend to recognize target brand more than acquirers in other industries. Targets in non-service industries receive payment for their brand more than targets in service industries. Brand is more important in non-service industries than in service industries.

Acquirer perspective brand valuation model. The three-step brand valuation model provides useful insight into how target characteristics and acquirer characteristics interact in acquirer perspective brand valuation models. Target characteristics explain the brand value more than acquirer characteristics do. The valuation model results are similar to the two-group (BVA>0 group and BVA=0 group) comparison analysis results. Targets that have a high number of trademarks registered, have high intangible assets, and operate in a high sales growth non-service industry tend to get paid well for their brands. The positive impact of the number of trademarks registered is consistent with research done by Krasnikov, Mishra, and Orozco (2009), who demonstrated that a brand’s protected names and symbols influence firm value. For acquirer characteristics, bigger acquirers in non-service industries tend to pay well for target brand value.

Interestingly, when both target characteristics and acquirer characteristics are in the model, the explanatory power of target characteristic variables outweighs acquirer characteristic variables’ explanatory power. In the target and acquirer characteristics combined model, all acquirer characteristics that were significant in the acquirer characteristic only model became non-significant, but target characteristic variable significance was not changed from the target characteristics only model. Another interesting finding is that target earnings growth became marginally significant and target industry demand instability became significant in the target and acquirer characteristics combined model. This suggests that acquirer perspective target brand value is predominantly determined by target characteristics.

In sum, acquirers pay for brands when the target operates in a fast sales growth industry with low industry demand instability and the target has a high number of trademarks registered, high earnings growth, and high intangible assets. This suggests that acquirers consider both firm growth factors and level factors when acquirers value target brand.

Shareholder perspective brand value and brand valuation model.

Overview of cumulative abnormal return. The results of the event study show that targets have 18% abnormal return on the day of M&A and 23.81% cumulative abnormal return on the event window [-1,1]. This result is consistent with previous research. Acquirer abnormal return on the day that the M&A announcement is made, however, is only 0.75%, which is not significantly different from zero. This result is also consistent with previous research. Acquirers often have zero abnormal return or slightly negative abnormal return after an M&A announcement is made (Agrawal, Jaffe, & Mandelker, 1992; Boone & Mulherin, 2007; Jensen &

Reback, 1983). Interestingly, I found that acquirers have 1.61% of cumulative abnormal return with the event window [0,2]. Even though this cumulative abnormal return is much smaller than target cumulative abnormal return, it is statistically significant.

Acquirer positive cumulative abnormal return, which is scarcely found in previous research, may be caused by sample industry distribution. My sample has targets from only consumer-related product and service industries and the majority of acquirers are from these industries. Swaminathan and Hulland (2008) also reported this industry difference in acquirer positive abnormal return after a merger announcement. They found positive abnormal return in the electronics and food industries but not in the chemicals industry. I found that firms acquiring targets in the non-service industry have, on average, 10 times higher cumulative abnormal return than firms acquiring targets in the service industry. Shareholders seem to prefer non-service industry targets. I speculate the reason why shareholders prefer targets in the non-service industry is that firms in the non-service industry have more tangible assets than firms in the service industry. When shareholders value a target firm after an M&A announcement is made,

73 they tend to place more value on target tangible assets than they would on intangible assets

(Brigam & Ehrhardt, 2005).

The comparison analysis between the BVA>0 and the BVA=0 group suggests that FVSH in the BVA>0 group is twice greater than that in the BVA=0 group; the mean of FVSH for the BVA=0 group is $9.57 million and for the BVA>0 group is negative $6.18 million. When a firm acquires a target without a brand, acquirer shareholders value the target firm very poorly. As correlation analysis for BVA=0 group revealed, when a firm acquires a target firm without a brand, FVSH decreases as the M&A deal value increases. This clearly suggests that shareholders react very negatively toward an acquisition that does not involve valuable brands.

In sum, an acquirer shareholder’s immediate reaction to an M&A announcement is lukewarm compared to target shareholders’ immediate reactions. The bigger the deal values are, the worse shareholders’ reactions are. Shareholder tendency of disliking a big-ticket M&A deal may be mitigated by acquiring targets in certain industries and by valuable brand acquisition.

Overview of BVSH. This research proposes a new brand value measure, which is a shareholder perspective brand value (BVSH) using cumulative abnormal return. BVSH is derived from FVSH which is shareholder wealth gain created from an M&A announcement.

Even though BVSH is correlated with acquirer perspective target brand value (BVA), there is much discrepancy between BVSH and BVA; BVSH is only 11% of BVA. This result suggests that acquirer shareholders do not value target brands as much as acquirers do. It is somewhat expected because shareholder perspective target firm value (FVSH), from which BVSH is derived, is much smaller than acquirer perspective target firm value (FVA). However, the gap between

BVSH and BVA is smaller than that between FVSH and FVA (

It is somewhat understandable that FVSH is much smaller than FVA because FVA is three times bigger than target firm net value, which is measured as net asset value in the target’s financial statement. This suggests that an acquirer over-paid for a target in the acquirer shareholders’ eyes. This difference can be explained by the different focuses in target firm valuation between an acquirer and a shareholder. When shareholders value a target firm, their focus is on the intrinsic value of the target firm, such as a target firm’s book value. However, when acquirer values a target firm, its focus is on the extrinsic value of the target firm within an acquiring firm and, thus, it becomes over-confident about target firms. In other words, an acquirer evaluates what benefits the target firm will bring into an acquiring firm, but this kind of internal information is not available to acquirer shareholders. As a result, acquirer shareholder target firm valuation is based solely on publicly available intrinsic values of targets.

The immediate ROI on M&A investment for acquirers is not favorable. Based on the ratio of FVSH and FVA, on average, the shareholder wealth gain created from acquiring a target firm is only 2% of the M&A deal value. This demonstrates that the positive acquirer shareholder reaction to an M&A announcement is not enough to cover the cost of the M&A.

In sum, shareholder perspective target brand value is much smaller than acquirer perspective target brand value. This difference clearly suggests that brand valuation differences exist between stakeholders in the context of M&A. Shareholders’ lack of information about the extrinsic value of a target firm within an acquiring firm can explain some of the difference. Even though an acquirer, on average, pays a target three times more than what the target is valued in the target’s accounting book (intrinsic value), the total intangible assets value the acquirer paid to targets is almost the same as what the targets had in their accounting book for their intangible

75 assets. This suggests that acquirers are very conservative with what they pay for target intangible assets, which include brand value.

Shareholder perspective brand valuation model. The three-step valuation model suggests a useful insight into how target characteristics and acquirer characteristics interact in the acquirer shareholder perspective brand valuation model. Target characteristics explain BVSH more than acquirer characteristics do. Shareholders value target brands highly when targets operate in highly concentrated non-service industries. Shareholders also value target brands highly when targets have a high number of trademarks registered, high target earnings growth, and high intangible assets. This result is very similar to what was seen in the acquirer perspective brand valuation model. The only difference is that the acquirer perspective brand value is influenced by target industry sales growth, while acquirer shareholder perspective brand value is influenced by target earnings growth. As expected, shareholder valuation is focused on target potential growth factors, such as intangible assets value, the number of trademarks, and earnings growth. The pattern that was seen in the BVA valuation model is also observed in the

BVSH valuation model. When both target and acquirer variables are included in the model, the explanatory power of target characteristic variables outweighs acquirer characteristic variables’ explanatory power. All acquirer characteristics that were significant in the acquirer characteristic only model became non-significant in the target and acquirer characteristics combined model. However, target characteristic variable significances remain the same as they were in the target characteristic only model.

Not surprisingly this accounting based Ohlson’s valuation model explains shareholder perspective brand value better than it does acquirer perspective brand value. R2 for the shareholder perspective brand valuation model is 81% (adj. R2 = 73%) while R2 for the acquirer

76 perspective brand valuation model is 69% (adj. R2 = 61%). As I briefly mentioned before, shareholders have limited information about target firms compared to acquirers. Thus, their valuation of target firms is based on the extrinsic value of firms that are seen in a financial statement. This can explain the higher R2 of the shareholder perspective brand valuation model compared to R2 of the acquirer perspective brand valuation model.

Summary.

The objectives of this research were two-fold: (a) to develop brand valuation models in the context of M&A and (b) to develop a shareholder value based brand value measure. I expected to find that target firm characteristics and acquirer characteristics would determine target brand value in M&A. Positive correlations and similar values between acquirers’ and shareholders’ perspective brand values in M&A were expected. The shareholder perspective brand value measure developed in this study can be utilized as another brand value measure in the context of M&A.

Surprisingly, target brand value is determined by target characteristics more than by acquirer characteristics regardless of from whose perspective they arise. Acquirer characteristics play a very limited role in brand valuation models in this study. Target growth potential variables, such as target intangible assets, the number of trademarks a target has registered, target earnings growth, and industry sales growth, are important to determine target brand value in the context of M&A. As expected, shareholder perspective brand value and acquirer perspective brand value are positively correlated, but their values are not similar; shareholder perspective brand value is much smaller than acquirer perspective brand value.

Managerial Implications

This study offers several important managerial insights. First, my findings should assist managers in the planning phase of an imminent M&A. Target firm managers who are cultivating firms for a potential M&A should focus on their current potential growth measures (i.e., sales growth, intangible asset). They should show the firm’s strong potential to grow in the future to receive payment for its brands because a firm’s performance is a proxy for brand performance to an acquirer. Target firms should recognize the significant role of the number of trademarks registered and the size of intangible assets in their balance sheet. It is a necessary condition that the target firm has registered trademarks to receive payment for its brands. Targets with higher intangible assets can charge a higher price for their brands because it assures a potential acquirer about the value of target intangible assets (i.e., brands).

Potential acquirer managers should utilize brand value to create a positive response from investors after an M&A announcement is made or, at least, mitigate a general negative response from investors. My findings suggest that acquirers should seek targets with valuable brands to mitigate the general negative response about M&A from their shareholders. Firms acquiring valuable brands through M&A are associated with superior financial market performance compared to firms acquiring no brand through M&A.

Potential target and acquirer can utilize the brand valuation models to predict acquirer perspective target brand value and acquirer shareholder perspective target brand value. The brand value measure developed in this dissertation can be utilized in other contexts, such as new product development.

Limitations and Future Research

Even though this study provides valuable implications, there are several limitations.

First, to be included in the final sample, both acquirer and target had to be in a U.S.-based company, have financial statements publicly available, and have a brand value that an acquirer paid to the target firm in acquirer SEC filings. These necessary sampling criteria could have eliminated acquisitions that involved a private company. Because of potential idiosyncratic industry-related properties of the data, the generalizability of the results needs to be assessed.

Second, this study used the most specific definition of brand value in the literature and operationalized it as the monetary value that an acquirer assigned to target trademarks and trade names in the context of M&A. The general definition of brand value is broader than value of trademarks and trade names. Brand value could include marketing related brand value (e.g., trademarks, trade name, Internet domain names, and noncompetition agreements), customer- related brand value (e.g., customer lists, customer contracts, and related customer relationships), and contract-based brand value (e.g., licensing, royalties, lease agreements, and franchise agreements). Some components of brand value that are not identifiable (e.g., customer equity) were eliminated from the overall value because only identifiable intangible assets that meet certain criteria can be recognized as an asset apart from goodwill (FASB Statement No. 141,

Business Combinations, and FASB Statement No.142, Goodwill and Other Intangible Assets,

2001). As a result, some unidentifiable brand value is included in the goodwill value.

Therefore, future research can compare goodwill (mostly unidentifiable brand value) and brand value (narrowest identifiable brand value-trademark and trade names) to explore their relationship. Future research can also study how different types of brand value (marketing- related brand value, customer-related brand value, and so on) interact with each other to have an

79 impact on firm value. It would be interesting to explore antecedents of different types of brand value.

Future research also can explore the effect of brand value acquired through M&A on a firm’s long-term financial performance. This research explored only short-term financial performance of brand value through event study. Since data about brand value acquired is available, the relationship between brand value and firm value can be investigated.

In brand valuation models, the dependent variable is the total value of brands that the target owns and the independent variables are firm-level data. Brand valuation models can be refined if brand level dependent variables and independent variables are used in the future when data becomes available.

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FIGURE 1

MARKETING, BRAND EQUITY, BRAND VALUE, AND FIRM VALUE

Theory

Marketing Consumer Activity Based Brand Value Firm Value Brand Equity

Measurement

Marketing Consumer Activity Based Brand Firm Brand Equity Performance Performance

FIGURE 2 BRAND VALUE METRICS

Brand Value Metrics

Market Performance Measures Financial Market Performance Measures (Accounting Measures) (Shareholder Value Measures)

Valuation Approach Risk Approach

SALES CF EARN MC SR MB TQ SYSTEMATIC UNSYSTEMATIC

FIGURE 3 BRAND VALUE IN M&A

Acquirer willingness to pay more

New Target Firm

Brand Value

Target Firm Brand Value

Scenario 1

Target Firm Brand Value

Acquirer Unwillingness Scenario 2 to pay

Before M&A

Target Firm New Target Firm

Brand Value Brand Value

In M&A

FIGURE 4 TARGET ABNORMAL RETURN

0 ARt-3 ARt-2 ARt-1 ARt ARt1 ARt2 ARt3

-5

FIGURE 5 ACQUIRER ABNORMAL RETURN

0.8

0.6

0.4

0.2

0 ARt-3 ARt-2 ARt-1 ARt ARt1 ARt2 ARt3 -0.2

-0.4

TABLE 1

MARKETING & SHAREHOLDER VALUE METRICS

Shareholder Value Metrics (Firm Financial Performance) Valuation Approach Risk Approach Marketing Activity/Asset Author Systematic Unsystematic MC SR TQ MB Risk (β) Risk Graham and Frankenberger (2000) √ Singh et al.(2005) √ Luo and Donthu (2006) √ Eng and Keh (2007) McAlister et al. (2007) √ Advertising Joshi and Hanssens (2009) √ Joshi and Hanssens (2010) √ Activity Shin et al. (2008) √ Srinivasan and Hanssens (2009) Osinga et al. (2011) √ √ Pauwels et al. (2004) √ Sales Promotion Luo and Donthu (2006) √ √ Joshi and Hanssens (2004) √ R&D McAlister et al. (2007) √ Shin et al. (2008) √

Shareholder Value Metrics (Firm Financial Performance) Valuation Approach Risk Approach Marketing Activity/Asset Author Systematic Unsystematic MC SR TQ MB Risk (β) Risk Sorescu and Spanjol (2008) √ √ √ √ Innovation Srinivasan and Hanssens (2009) Sood and Tellis(2009) √ Chaney et al.(1991) √ New Product Pauwels et al.(2004) √ Sorescu,Shankar and Kushwaha (2007) √ Company name change Horskey and Swyngedouw (1987) √ Celebrity Endorser Agrawal and Kamajura (1995) √ Brand Extension Lane and Jacobson (1995) √ Activity Green Promotion Mathur and Mathur (2000) √ Olympic Sponsorship Miyazaki and Morgan (2001) √ Internet channel add. Geyskens et al. (2002) √ Strategic Emphasis (SE) Mizik and Jacobson (2003) √ New Technology Lee and Grewwal (2004) √ Branding strategy Rao et al. (2004) √ Product Capital Sorescu et al .(2007) √ SE after M&A Swaminathan et al. (2008) √ Brand Portfolio Morgan and Rego (2009) √

Shareholder Value Metrics (Firm Financial Performance) Marketing Activity/Asset Author Valuation Approach Risk Approach Systematic Unsystematic MC AR TQ MB Risk (β) Risk Aaker and Jacobson (2001) √ Brand Attitude Mizik and Jacobson (2008) √ Bart et al. (1998) √ Kerin and Sethuraman (1998) √ Brand Value Madden et al. (2006) √ √ Chu and Keh (2007) √ Ittner and Larcker (1998) √ Anderson et al. (2004) √ Mittal et al. (2005) √ Gruca and Rego (2005) Morgan and Rego (2006) √ √ Asset Satisfaction Luo and Bhattacharya (2006) √ √ (Negative WOM) Fornell et al. (2006) √ Luo (2007) √ Luo (2008) √ Luo and Homberg (2008) √ Aksoy et al. (2008) √ Rego et al. (2009) √ √ Aaker and Jacobson (1994) √ Rust et al. (2002) √ Quality Balasubramanian et al. (2005) √ Tellis and Johnson (2007) √ 98

TABLE 2

TARGET FIRM VALUE AND BRAND VALUE IN M&A

Perspective Level Value Acquirer Shareholder

1) FVSH ≈ dollar value of AR on a M&A annoumenent day ($V ) or Target Firm FV =Total M&A deal value- AR Firm A 2) FV ≈ dollar value of CAR (FV) target liability SH around a M&A announcement day ($VCAR)

Target Brand BV : Price paid to target for Brand A BV ≈ BV × ( BV /FV ) (BV) target's brand portfolios SH FV A A

TABLE 3

TARGET DISTRIBUTION BY INDUSTRIES - POPULATION DATA SET

Industry n % Cumulative % Consumer Product and Service 179 67.5

Professional Service 102 38.4 38.4 Other Consumer Products 37 14 52.4 Employment Service 14 5.3 57.7 Travel Service 10 3.7 61.4 Educational Service 9 3.4 64.8 Home Furnishings 6 2.3 67.1 Legal Service 1 0.4 67.5 Consumer Staple 86 32.5

Food and Beverage 43 16.2 83.7 Textiles & Apparel 28 10.6 94.3 Agriculture & Livestock 5 1.9 96.2 Household & Personal Products 5 1.9 98.1 Tobacco 5 1.9 100 Total 265 100

100

TABLE 4

TARGET DISTRIBUTION BY INDUSTRIES - INITIAL SAMPLE

Industry n % Cumulative % Consumer Product and Service 117 68.4

Professional Service 68 39.7 39.7 Other Consumer Products 21 12.3 52 Employment Service 9 5.3 57.3 Educational Service 8 4.6 61.9 Travel Service 8 4.7 66.6 Home Furnishings 3 1.8 68.4 Consumer Staple 54 31.6

Food and Beverage 26 15.2 83.6 Textiles & Apparel 21 12.3 95.9 Household & Personal Products 4 2.3 98.2 Agriculture & Livestock 2 1.2 99.4 Tobacco 1 0.6 100 Total 171 100

101

TABLE 5

TARGET DISTRIBUTION BY INDUSTRIES - FINAL SAMPLE

Industry n % Cumulative % Consumer Product and Service 66 67.4

Professional Service 42 42.9 42.9 Other Consumer Products 13 13.3 56.2 Travel Service 5 5.1 61.3 Educational Service 3 3.1 64.4 Employment Service 2 2 66.4 Home Furnishings 1 1 67.4 Consumer Staple 32 32.6

Textiles & Apparel 14 14.3 81.7 Food and Beverage 11 11.2 92.9 Household & Personal Products 4 4.1 97 Agriculture & Livestock 2 2 99 Tobacco 1 1 100 Total 98 100

102

TABLE 6

ACQUIRER DISTRIBUTION BY INDUSTRIES - POPULATION DATA SET

Cumulative Industry n % % Consumer Staple 74 27.9 Food and Beverage 34 12.8 12.8 Textiles & Apparel 20 7.5 20.4 Agriculture & Livestock 9 3.4 23.8 Household & Personal Products 6 2.3 26.0 Tobacco 5 1.9 27.9 Consumer Product and Service 99 37.4 Professional Service 54 20.4 48.3 Other Consumer Products 27 10.2 58.5 Employment Service 6 2.3 60.8 Educational Service 5 1.9 62.6 Home Furnishings 4 1.5 64.2 Travel Service 2 0.8 64.9 Legal Service 1 0.4 65.3 High Technology 32 12.1 Internet Software & Service 8 3.0 68.3 Computers & Peripherals 7 2.6 70.9 Software 7 2.6 73.6 IT Consulting & Service 5 1.9 75.5 E-commerce / B2B 2 0.8 76.2 Semiconductors 2 0.8 77.0 Electronics 1 0.4 77.4 Financials 21 7.9 Insurance 11 4.2 81.5 Other Financials 4 1.5 83.0 Asset Management 3 1.1 84.2 Brokerage 3 1.1 85.3 Healthcare 14 5.3 Pharmaceuticals 6 2.3 87.5 Biotechnology 5 1.9 89.4 Healthcare Equipment & Supplies 3 1.1 90.6 103

103

Media and Entertainment 9 3.4 Broadcasting 3 1.1 91.7 Advertising & Marketing 2 0.8 92.5 Publishing 2 0.8 93.2 Casinos & Gaming 1 0.4 93.6 Recreation & Leisure 1 0.4 94.0 Real Estate 5 1.9 Real Estate Management & Development 4 1.5 95.5 Non Residential 1 0.4 95.8 Retail 4 1.5 Apparel Retailing 2 0.8 96.6 Discount and Department Store Retailers 1 0.4 97.0 Internet and Catalog Retailing 1 0.4 97.4 Telecommunications 3 1.1 Telecommunication Equipment 3 1.1 98.5 Industrials 3 1.1 Other Industrials 2 0.8 99.2 Transportation & Infrastructure 1 0.4 99.6 Energy and Power 1 0.4 Other Energy & Power 1 0.4 100.0 Total 265 100

104

TABLE 7

ACQUIRER DISTRIBUTION BY INDUSTRIES - INITIAL SAMPLE

Cumulative Industry n % % Consumer Staple 42 24.6 Food and Beverage 18 10.5 10.5

Textiles & Apparel 13 7.6 18.1

Agriculture & Livestock 5 2.9 21.1

Household & Personal Products 5 2.9 24.0

Tobacco 1 0.6 24.6

Consumer Product and Service 39 22.8 Professional Service 22 12.9 37.4

Other Consumer Products 11 6.4 43.9

Educational Service 4 2.3 46.2

Employment Service 1 0.6 46.8

Home Furnishings 1 0.6 47.4

High Technology 31 18.1 Internet Software & Service 8 4.7 52.0

Computers & Pripherals 7 4.1 56.1

Software 7 4.1 60.2

IT Consulting & Service 4 2.3 62.6

E-commerce / B2B 2 1.2 63.7

Semiconductors 2 1.2 64.9

Electronics 1 0.6 65.5

Financials 20 11.7 Insurance 11 6.4 71.9

Asset Management 3 1.8 73.7

Brokerage 3 1.8 75.4

Other Financials 3 1.8 77.2

Healthcare 14 8.2 Pharmaceuticals 6 3.5 80.7

Biotechnology 5 2.9 83.6

Healthcare Equipment & Supplies 3 1.8 85.4

Media and Entertainment 9 5.3 Broadcasting 3 1.8 87.1

Advertising & Marketing 2 1.2 88.3

Publishing 2 1.2 89.5

Casinos & Gaming 1 0.6 90.1

Recreation & Leisure 1 0.6 90.6

105

Real Estate 5 2.9 Real Estate Management & Development 4 2.3 93.0

Non Residential 1 0.6 93.6

Retail 4 2.3 Apparel Retailing 2 1.2 94.7

Discount and Department Store Retailers 1 0.6 95.3

Internet and Catalog Retailing 1 0.6 95.9

Telecommunications 3 1.8 Telecommunication Equipment 3 1.8 97.7

Industrials 3 1.8 Other Industrials 2 1.2 98.8

Transportation & Infrastructure 1 0.6 99.4

Energy and Power 1 0.6 Other Energy & Power 1 0.6 100.0

Total 171 100

106

TABLE 8

ACQUIRER DISTRIBUTION BY INDUSTRIES - FINAL SAMPLE

Cumulative Industry n % % Consumer Staple 29 30 Food and Beverage 10 10 10

Textiles & Apparel 11 12 22

Agriculture & Livestock 3 3 25

Household & Personal Products 4 4 29

Tobacco 1 1 30

Consumer Product and Service 23 24 Professional Service 11 12 42

Other Consumer Products 9 9 51

Educational Service 1 1 52

Employment Service 1 1 53

Home Furnishings 1 1 54

High Technology 19 19 Internet Software & Service 5 5 59

Computers & Peripherals 5 5 64

Software 4 4 68

IT Consulting & Service 3 3 71

E-commerce / B2B 1 1 72

Semiconductors 1 1 73

Electronics 0 0 73

Financials 4 4 Brokerage 2 2 75

Other Financials 2 2 77

Healthcare 8 8 Pharmaceuticals 3 3 80

Biotechnology 4 4 84

Healthcare Equipment & Supplies 1 1 85

Media and Entertainment 5 5 Broadcasting 2 2 87

Advertising & Marketing 1 1 88

Publishing 1 1 89

Recreation & Leisure 1 1 90

Real Estate 4 4 Real Estate Management & Development 3 3 93

Non Residential 1 1 94

107

Retail 2 2 Apparel Retailing 1 1 95

Internet and Catalog Retailing 1 1 96

Telecommunications 2 2 Telecommunication Equipment 2 2 98

Industrials 1 1 Other Industrials 1 1 99

Energy and Power 1 1 Other Energy & Power 1 1 100

Total 98 100

108

TABLE 9

DESCRIPTION OF VARIABLE

Variable Description Source Dollar value of the target firm's trademark and trade Target Brand Value (BV ) SEC Filing A name as reported by the acquirer firm Target Firm Number of Brand Number of trademark and trade name registered under USPTO TESS (TM) target firm's name in USPTO

Target Salest(SALES) Firm sales COMPUSTAT

Target Sales Growth Three year average of target firm annual sales growth COMPUSTAT (SALESg)

Target Marker Sharet(MS) Firm sales/total sales of four-digit SIC code COMPUSTAT

Target Marker Share Growth Three year average of target firm annual market share COMPUSTAT (MSg) growth

Target Earningst(EARN) Firm income before extra ordinary income COMPUSTAT

Target Earnings Growth Three year average of target firm net profit growth COMPUSTAT (EARNg) Target Industry Sales Growth Three year average of target industry sales growth COMPUSTAT (ISALESg) Target Industry Demand Coefficient of the variation of sales in the target firm's COMPUSTAT Instability (INS) primary four digit SIC code Target Industry Concentration Herfindahl concentration index (HHI) COMPUSTAT (HHI) (i.e. sum of squared shares of firms in the industry) 1 if target firm's primary four-digit SIC code starts with Service Industry (SVC) COMPUSTAT 1~3, 0 otherwise (service:0 and non-service:1) 1 if the target and acquirer are in the same four-digit M&A Strategic Motive (SM) COMPUSTAT SIC code, 0 otherwise

Acquirer Firm Leverage (LEV) Long Term Debtit-1 / Total Assetit-1 COMPUSTAT

Acquirer Financing Long Term Debt / Total Asset COMPUSTAT Consideration (FIN) it-1 it-1

Acquirer Firm size (SIZE) Total number of firm employees COMPUSTAT

109

TABLE 10

BRAND VALUE TO M&A DEAL VALUE DISTRIBUTION - FINAL SAMPLE

Brand Value to Deal Value n % Cumulative %

0% 35 35.7 35.7 above 0 % ~ 5 % 18 18.4 54.1 above 5 % ~ 10 % 11 11.2 65.3 above 10 % ~ 15 % 10 10.2 75.5 above 15 % ~ 30 % 6 6.1 81.6 above 30 % ~ 35% 5 5.1 86.7 above 35 % ~ 40% 3 3.1 89.8 above 40 % ~ 45% 4 4.1 93.9 above 45 % ~ 50% 1 1.0 94.9 above 50 % 5 5.1 100 Total 98 100

110

TABLE 11

BRAND VALUE TO M&A DEAL VALUE DISTRIBUTION - REDUCED SAMPLE

Brand Value to Deal Value n % Cumulative %

above 0 % ~ 5 % 18 28.6 28.6 above 5 % ~ 10 % 11 17.5 46.0 above 10 % ~ 15 % 10 15.9 61.9 above 15 % ~ 30 % 6 9.5 71.4 above 30 % ~ 35% 5 7.9 79.4 above 35 % ~ 40% 3 4.8 84.1 above 40 % ~ 45% 4 6.3 90.5 above 45 % ~ 50% 1 1.6 92.1 above 50 % 5 7.9 100 Total 63 100

111

TABLE 12

MEAN COMPARISON BETWEEN BVA>0 GROUP AND BVA= 0 GROUP

Group Mean Mean Sig. Difference BVA>0 BVA= 0 Panel A: Target Characteristics

Target Number of Trademarkt 43.31 (n=63) 5.22 (n=35) 38.08 ** Target Sales Growth 0.28 (n=59) 0.42 (n=32) 0.13

Target Salest 1,263.80 (n=60) 230.00 (n=33) 1,033.80 ** Target Earnings Growth -0.33 (n=60) 0.06 (n=34) 0.40

Target Earnings 25.71 (n=60) -3.86 (n=34) 29.58 t Target Market Share Growth 0.24 (n=58) 0.35 (n=32) 0.11

Target Market Sharet 0.09 (n=59) 0.04 (n=33) 0.05 *

Target Intangible Assett 268.00(n=38) 43.32(n=24) 224.7 ***

Target R&D Intensityt 0.05(n=21) 0.14(n=14) 0.08 ** Target Advertising Intensity 0.07(n=38) 0.09(n=10) 0.02 t Target Industry Sales Growth 0.15 (n=60) 0.05 (n=34) 0.10 ** Target Industry Sales 38,364.40 (n=60) 40,049.20 (n=34) 9,684.80 t Target Industry Demand 19.28 (n=60) 17.60 (n=34) 1.68 Instabilityt

Target Industry Concentrationt 0.31 (n=60) 0.32 (n=29) 0.00 Panel B: Acquirer Characteristics Acquirer Leverage 0.23 (n=63) 0.15 (n=35) 0.08 ** Acquirer Financing Consideration 0.01 (n=62) 0.02 (n=35) 0.01

Acquirer Firm Size 20.92 (n=62) 17.02 (n=35) 3.90

Acquirer R&D Intensity 0.04 (n=28) 0.11 (n=15) 0.07

Acquirer Advertising Intensity 0.03 (n=49) 0.03 (n=11) 0.00

*p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

112

TABLE 13

CROSS TABULATION OF TARGET INDUSTRY TYPE AND BVA GROUP

BVA group Target Industry Type χ2 BVA>0 BVA=0 24 23 Service 51% 49% 9.42*** 33 8 Non-Service 80% 20%

*p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

113

TABLE 14

CROSS TABULATION OF ACQUIRER INDUSTRY TYPE AND BVA GROUP

BVA group Acquirer Industry Type χ2 BVA>0 BVA=0 18 23 Service 43% 57% 1.88 33 14 Non-Service 70% 30%

114

TABLE 15

CROSS TABULATION OF STRATEGIC MOTIVE AND BVA GROUP

BVA group Strategic Motive χ2 BVA>0 BVA=0 17 9 Synergy 65% 35% 0.82 39 23 Non-Synergy 63% 37%

115

TABLE 16

CROSS TABULATION OF COMBIND INDUSTRY TYPE AND BVA GROUP

BVA group Target & Acquirer Industry Type χ2 BVA>0 BVA=0 T: Service & A: Service 24 20 55% 45%

T: Service & A: Non-Service 5 7 42% 58% 10.08*** T: Non-Service & A: Service 3 0 100% 0%

T: Non-Service & A: Non-Service 31 8 80% 20%

*p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

116

TABLE 17

ACQUIRER PERSPECTIVE BRAND VALUATION MODEL

Log (BVA)=α0 + β1T_TMi + β2T_SALESgi + β3T_SALESit + β4T_EARNgit + β5T_EARNit + β6T_MSgit + β7T_MSit + β8T_IAit + β9I_SALESg + β10I_INSt + β11I_HHIt + β12T_SEV + β13A_LVGt + β14A_FINt + β15A_SIZEt + β16A_SEV + β17A_SM + εit

Main Equation: Dependent variable=Log (Acquirer Perspective Target Brand Value) Only Target Variables Only Acquirer Variables Target & Acquirer Variables Variables Est. SE Sig. Est. SE Sig. Est. SE Sig Intercept 1.887 .545 .001 2.087 .737 .007 Target Number of Trademark .010 .004 .027 ** .010 .004 .033 ** Target Sales Growth 1.594 1.915 .409 1.554 1.89 .417 Target Salest -.000 .000 .491 -.000 .000 .476 Target Earnings Growth .084 .066 .208 .116 .067 .091 * Target Earningst -.006 .002 .043 ** -.005 .002 .064 * Target Market Share Growth -.414 1.820 .820 -.236 1.805 .896 Target Market Share -3.366 3.866 .388 -4.383 4.302 .314 Target Intangible Asset .006 .001 .000 ** .006 .001 .000 ** Target Industry Sales Growth 2.141 1.193 .079 * 2.623 1.199 .034 ** Target Industry Demand Instability -.036 .022 .109 -.049 .023 .039 ** Target Industry Concentration 2.206 1.52 .154 2.530 1.670 1.137 Target Industry Type 1.842 .473 .000 *** 1.287 .801 .115 Acquirer Leverage 1.318 1.880 .486 2.005 1.358 .147 Acquirer Financing Considerations 6.163 6.881 .374 -7.164 4.724 .137 Acquirer Firm Size .019 .009 .047 ** .003 .008 .704 Acquirer Industry Type 1.406 .646 .034 ** .531 .807 .514 M&A Strategic Motive -.553 .697 .431 -.607 .496 .227 n 60 60 60 f-value 8.11 2.38 6.38 R2 .69 .18 .73 Adj. R2 .61 .10 .62 *p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

117

TABLE 18

ACQUIRER PERSPECTIVE BRAND VALUATION MODEL - DESCRIPTIVE STATISTICS Descriptive Statistics (n=60)

Variables M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 Log_BV 2.36 2.59 1 A 2 T_TM 38.53 142.54 .49 1

3 T_SALESg .16 .31 .10 -.03 1

4 T_SALES 1103 3313 .43 .41 -.01 1 t 5 T_EARNg -.12 3.64 .06 -.01 .11 .12 1

6 T_EARN 25.80 236.68 .37 .88 -.08 .14 .08 1 t 7 T_MSg .09 .33 .01 -.05 .88 -.07 .06 -.07 1

8 T_MS .06 .10 .43 .67 -.10 .51 .02 .63 -.05 1 t 9 T_INTAN 175 328.23 .69 .60 -.02 .58 .01 .56 -.08 .68 1 t 10 I_SALESg .11 .27 .09 -.03 .15 .02 .13 -.01 -.10 -.11 .02 1

11 I_INS 16.23 15.74 -.03 -.09 -.00 .01 .02 -.08 -.23 -.04 -.00 .68 1

12 I_HHI .28 .16 .09 .04 .00 .01 -.00 .07 -.01 .13 .04 .10 .38 1

13 T_TYPE .51 .50 .33 .15 -.29 -.01 -.11 .15 -.23 .18 .07 -.21 -.09 .06 1

14 A_LEV .21 .17 .09 -.00 -.13 -.02 -.22 .00 -.15 .11 .06 .01 .21 .05 -.07 1

15 A_FIN .01 .04 -.14 -.00 -.02 -.06 .07 .02 -.00 -.05 -.03 -.02 -.08 .11 .10 -.06 1

16 A_SIZE 22.05 33.77 .25 .34 -.05 .20 -.01 .37 -.04 .51 .46 .01 -.00 -.06 -.03 -.06 -.00 1

17 A_TYPE .50 .50 .31 .15 -.29 -.02 .01 .15 -.24 .14 .04 -.21 -.10 .15 -.83 .07 -.16 .04 1 18 A_SM .68 .46 -.06 .05 .09 .16 .10 .19 .08 .08 .06 .11 -.00 -10 .05 .00 -.17 .08 .03 1

118

TABLE 19

TARGET AR & CAR DESCRIPTIVE STATISTTICS AND SIMPLE T-TEST RESULT

Mean Median Min Max n SD t Sig. (%) (%) (%) (%)

Panel A: AR

AR-3 71 1.14 .03 .37 -7.89 15.59 2.54 **

AR-2 71 -.60 .05 -.30 -40.31 8.70 -.89

AR-1 71 1.90 .05 .98 -8.7 34.96 2.80 ***

AR0 68 18.74 .29 12.58 -64.08 160.10 5.20 ***

AR+1 71 5.99 .14 .65 -17.97 64.57 3.51 ***

AR+2 71 -.32 .03 -.18 -19.66 6.66 -.75

AR+3 71 -.04 .02 .02 -12.03 12.53 -.13

Panel B: CAR

CAR[-1,0] 68 18.74 .26 15.21 -64.73 158.85 5.80 *** CAR[ 0,1] 68 20.49 .29 21.13 -82.05 161.81 6.47 *** CAR[-1,1] 68 23.81 .30 22.30 -82.70 160.56 7.11 *** CAR[ 0,2] 68 23.47 .31 20.78 -101.71 160.67 6.17 *** CAR[-2,2] 68 25.55 .29 23.39 -111.90 158.97 6.44 *** *p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

119

TABLE 20

ACQUIRER AR AND CAR DESCRIPTIVE STATISTTICS AND SIMPLE T-TEST RESULT

Mean Median Min Max n SD t Sig. (%) (%) (%) (%)

Panel A: AR

AR 85 -.05 .02 .05 -11.04 6.18 -.21 -3 AR-2 85 .04 .02 -.16 -9.30 6.78 .17 AR-1 85 -.19 .02 -.15 -11.48 7.79 -.62 AR0 82 .75 .05 0 -13.71 23.59 1.25 AR+1 85 .50 .03 .52 -12.72 11.70 1.20 AR+2 85 .24 .02 -.12 -8.09 14.38 .79 AR+3 85 -.23 .02 -.13 -12.41 5.65 -.95

Panel B: CAR

CAR[-1,0] 82 .49 .05 -.34 -17.10 23.00 .76 CAR[ 0,1] 82 1.41 .06 .42 -10.40 19.33 2.09 ** CAR[-1,1] 82 1.16 .06 .61 -20.21 19.75 1.57 CAR[ 0,2] 82 1.61 .06 .71 -11.44 19.35 2.27 ** CAR[-2,2] 82 1.39 .07 1.03 -30.55 21.00 1.62 *p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

120

TABLE 21

DISTRIBUTION OF ACQUIRER CAR[0, 2]

Range of CAR[0,2] Cumulative Frequency % on announcement date % 15% < CAR[0,2] ≤ 20% 3 3.66 3.66 10% < CAR[0,2] ≤ 15% 6 7.32 10.98 5% < CAR[0,2] ≤ 10% 11 13.41 24.39 2% < CAR[0,2] ≤ 5% 11 13.41 37.80 1% < CAR[0,2] ≤ 2% 8 9.76 47.56 0.5% < CAR[0,2] ≤ 1% 4 4.88 52.44 0% < CAR[0,2] ≤ 0.5% 1 1.22 53.66 -0.5% < CAR[0,2] ≤ 0% 4 4.88 58.54 -1% < CAR[0,2] ≤ -0.5% 5 6.10 64.63 -2% < CAR[0,2] ≤ -1% 7 8.54 73.17 -5% < CAR[0,2] ≤ -2% 12 14.63 87.80 -10% < CAR[0,2] ≤ -5% 9 10.98 98.78 -15% < CAR[0,2] ≤ -10% 1 1.22 100.00 Total 82

121

TABLE 22

DESCRIPTIVE STATISTICS - TARGET FIRM VALUE BY BVA GROUP

Median Mean Variable SD 1 2 ($m) ($m) Panel A: BV group A> 0

1. FVA (M&A Net Deal Value) 285.35 709.78 954.18 1

2. FVSH (dollar value of CAR [0,2]) 9.57 40.89 173.03 .09 1 Panel B: BV group A= 0

1. FVA (M&A Net Deal Value) 98.89 366.78 809.19 1

2. FVSH (dollar value of CAR [0,2]) -6.18 -38.99 205.00 -.81 *** 1 *p<.1(two-tailed test).

**p<.05(two-tailed test).

***p<.01(two-tailed test).

122

TABLE 23

TARGET FIRM VALUES MEAN COMPARISON BY BVA GROUP

Group Mean ($m) Variable t Sig. BVA>0 BVA=0 Diff.

FVA (M&A Net Deal Value) 709.78 366.78 343 -1.74 *

FVSH (dollar value of CAR [0,2]) 40.89 -38.99 79.88 -1.74 *

*p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

123

TABLE 24

MEAN COMPARISON BETWEEN BVSH>0 GROUP AND BVSH≤0 GROUP

Group Mean Mean t Sig. Difference BVSH>0 BVSH≤ 0 Panel A: Target Characteristics Target Number of Trademark 20.97(n=35) 34.57(n=63) 13.60 .75 t Target Sales Growth 0.19(n=33) 0.41(n=58) 0.21 1.10

Target Sales 714.3(n=33) 997.4(n=60) 283.1 .60 t Target Earnings Growth -0.77(n=33) 0.12(n=61) .90 1.04

Target Earnings 12.36(n=33) 16.44(n=61) 4.08 .13 t Target Market Share Growth 0.21(n=32) 0.32(n=58) 0.11 .47

Target Market Share 0.07(n=32) 0.07(n=60) 0.00 .21 t Target Intangilbe Assett 278.8(n=22) 127.2(n=40) 151.6 -1.76 *

Target R&D Intensityt 0.03(n=8) 0.10(n=18) .06 1.90 * Target Advertising Intensity 0.04(n=15) 0.08(n=18) 0.04 1.31 t Target Industry Sales Growth 0.11(n=33) 0.12(n=61) 0.01 .31

Target Industry Sales 33,088.8(n=32) 48,054.1(n=52) 14,965.30 1.08 t Target Industry Demand 16.28(n=33) 19.96(n=61) 3.68 .29 Instabilityt

Target Industry Concentrationt 0.27(n=33) 0.34(n=61) 0.06 1.63 Panel B: Acquirer Characteristics Acquirer Leverage 0.23(n=35) 0.18(n=63) .05 -1.23

Acquirer Financing 0.01(n=35) 0.02(n=63) .00 .74 Consideration Acquirer Firm Size 25.59(n=35) 16.08(n=62) 9.51 -1.50

Acquirer R&D Intensity 0.02(n=11) 0.07(n=25) 0.04 .17

Acquirer Advertising Intensity 0.03(n=25) 0.03(n=28) 0.00 -0.32 *p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

124

TABLE 25

CROSS TABULATION OF TARGET INDUSTRY TYPE AND BVSH GROUP

BVSH group Target Industry Type χ2 BVSH>0 BVSH≤0 8 27 Service 22% 77% 4.27*** 15 17 Non-Service 46% 54%

*p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

125

TABLE 26

CROSS TABULATION OF ACQUIRER INDUSTRY TYPE AND BVSHGROUP

BVSH group Acquirer Industry Type χ2 BVSH>0 BVSH≤0 15 21 Service 42% 58% .03 19 29 Non-Service 40% 60%

126

TABLE 27

CROSS TABULATION OF STRATEGIC MOTIVE AND BVSH GROUP

BVSH group Strategic Motive χ2 BVSH>0 BVSH≤0 10 17 Synergy 37% 63% 0.02 25 46 Non-Synergy 35% 65%

127

TABLE 28

CROSS TABULATION OF COMBIND INDUSTRY TYPE AND BVSHGROUP

BVSH group Target & Acquirer Industry Type χ2 BVSH>0 BVSH≤0 T: Service & A: Service 14 30 32% 68%

T: Service & A: Non-Service 2 10 16% 83% 4.46 T: Non-Service & A: Service 2 1 67% 33%

T: Non-Service & A: Non-Service 17 22 43% 56%

128

TABLE 29

SHAREHOLDER PERSPECITVE TARGET BRAND VALUATION MODEL

Log(BVSH)=α0 + β1T_TMi + β2T_SALESgi + β3T_SALESit + β4T_EARNgit + β5T_EARNit + β6T_MSgit + β7T_MSit + β8T_IAit + β9I_SALESg + β10I_INSt + β11I_HHIt + β12T_SEV + β13A_LVGt + β14A_FINt + β15A_SIZEt + β16A_SEV + β17A_SM + εit

Main Equation: Dependent variable=Log (Shareholder Perspective Target Brand Value) Only Target Variables Only Acquirer Variables Target & Acquirer Variables Variables Est. SE Sig. Est. SE Sig. Est. SE Sig Intercept .151 .459 .744 .103 .723 .887 Target Number of Trademark .038 .007 .000 *** .034 .009 .000 *** Target Sales Growth -1.331 1.439 .363 -1.242 1.500 .416 Target Salest .000 .000 .888 -.000 .000 .878 Target Earnings Growth .138 .045 .005 *** .159 .048 .003 *** Target Earningst -.007 .002 .026 ** -.006 .003 .054 * Target Market Share Growth 1.795 1.373 .202 1.685 1.466 .262 Target Market Share -3.348 2.176 .135 -4.455 2.503 .088 * Target Intangible Asset .004 .001 .000 *** .004 .001 .000 *** Target Industry Sales Growth -.533 .806 .514 -.173 .892 .847 Target Industry Demand Instability -.008 .015 .565 -.022 .017 .224 Target Industry Concentration 2.07 1.195 .094 * 2.756 1.461 .075 * Target Industry Type .603 .327 .076 * .579 .524 .281 Acquirer Leverage 2.033 1.448 .169 1.546 1.035 .149 Acquirer Financing Considerations -.151 4.943 .975 -3.388 3.111 .288 Acquirer Firm Size .013 .007 .091 * .002 .005 .596 Acquirer Industry Type 1.240 .545 .029 ** .083 .522 .875 M&A Strategic Motive -.555 .604 .364 -.169 .438 .703 n 40 40 40 f-value 9.82 2.00 6.80 R2 .81 .22 .84 Adj. R2 .73 .11 .71 *p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

129

TABLE 30

SHAREHOLDER PERSPECTIVE TARGET BRAND VALUATION MODEL - DESCRIPTIVE STATISTICS

Descriptive Statistics (n=40)

Variables M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 Log_BV 1.18 1.79 1 SH 2 T_TM 15.87 23.79 .51 1

3 T_SALESg .08 .18 -.08 -.14 1

4 T_SALES 597.48 1082 .48 .02 -.08 1 t 5 T_EARNg -.44 4.22 .01 -.27 .21 -.03 1

6 T_EARN 10.43 75.09 .18 .04 .21 .50 .43 1 t 7 T_MSg .04 .19 .01 -.16 .71 -.07 .17 .20 1

8 T_MS .05 .08 .20 -.00 -.14 .44 -.00 .33 .02 1 t 9 T_INTAN 158.47 303.77 .65 .11 -.01 .85 -.01 .45 -.02 .45 1 t 10 I_SALESg .09 .25 -.17 -.04 .27 -.05 .11 .01 -.08 -.18 -.01 1

11 I_INS 16.33 15.46 -.23 -.23 -.02 -.07 -.04 -.12 -.31 -.01 -.04 .50 1

12 I_HHI .27 .15 .01 -.25 -.03 .06 -.00 .01 .05 .21 .00 -.00 .43 1

13 T_TYPE .50 .50 .35 .27 -.20 .06 -.18 -.08 -.10 .13 .06 -.24 -.07 .09 1 14 A_LEV .21 .18 .20 .19 -.26 .08 -.33 -.06 -.32 .21 .18 -.01 .25 .05 -.09 1

15 A_FIN .02 .05 -.06 -.07 .04 -.05 .08 -.02 .00 -.07 -.01 .02 -.06 .24 .13 -.12 1

16 A_SIZE 26.83 35.93 .20 .14 .19 .25 .01 .19 .19 .37 .34 .01 -.01 -.11 .09 -.09 -.01 1

17 A_TYPE .50 .50 .33 .29 -.16 .05 -.15 -.07 -.07 .09 .04 -.24 -.06 .08 -.80 .06 -.18 -.05 1

18 A_SM .70 .46 -.06 -.06 .09 -.02 .26 .31 .07 .14 .03 .15 -.03 -.27 -.10 .04 -.22 .12 -.10 1

130

TABLE 31

DESCRIPTIVE STATISTICS - FIRM VALUE AND BRAND VALUE

Variable Mean SD 1 2 3 4

1 FV 585.39 914.8 1 A

2 FV 16.39 185.8 -.21 * 1 SH 3 BV 72.7 224.0 .58 *** .18 1 A

4 BV 8.22 32.3 .08 .54 *** .35 *** 1 SH

*p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

131

TABLE 32

TARGET BRAND VALUATION MODEL COMPARISON -TWO PERSPECTIVES

Log(BVA)=α0 + β1T_TMi + β2T_SALESgi + β3T_SALESit + β4T_EARNgit + β5T_EARN it + β6T_MSgit + β7T_MSit + β8T_IAit + β9I_SALESg + β10I_INSt + β11I_HHIt + β12T_SEV + εit

Log(BVSH)=α0 + β1T_TMi + β2T_SALESgi + β3T_SALESit + β4T_EARNgit + β5T_EARNit + β6T_MSgit +β7T_MSit + β8T_IAit + β9I_SALESg + β10I_INSt + β11I_HHIt + β12T_SEV + εit

Perspective Acquirer =BVA Shareholder=BVSH Est. S. Est. SE. Sig. Est. S. Est. SE. Sig. Intercept 1.887 0 .545 .001 .151 0 .459 .744 Target Number of Trademark .010 .606 .004 .027 ** .038 -.135 .007 .000 *** Target Sales Growth 1.594 .184 1.915 .409 -1.331 -.135 1.439 .363 Target Salest -.000 -.138 .000 .491 .000 -.027 .000 .888 Target Earnings Growth .084 .121 .066 .208 .138 .327 .045 .005 *** Target Earningst -.006 .585 .002 .043 ** -0.007 -.294 .002 .026 ** Target Market Share Growth -.414 -.050 1.820 .820 1.795 .194 1.373 .202 Target Market Share -3.366 -.104 3.866 .388 -3.348 -.158 2.176 .135 Target Intangible Asset .006 .767 .001 .000 ** 0.004 .817 .001 .000 *** Target Industry Sales Growth 2.141 .230 1.193 .079 * -0.533 -.076 .806 .514 Target Industry Demand Instability -.036 -.209 .022 .109 -0.008 -.077 .015 .565 Target Industry Concentration 2.206 .128 1.52 .154 2.07 .182 1.195 .094 * Target Industry Type 1.842 .369 .473 .000 *** 0.603 .17 .327 .076 * n 60 40 f-value 8.90 9.82 R2 .69 .81 Adj. R2 .61 .73 *p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

132

TABLE 33

MEAN COMPARISON BETWEEN BVT=HIGH GROUP AND BVT=LOW GROUP

Group Means Mean t Sig. Difference BVT=HIGH BVT= LOW Target Characteristics

Target Number of Trademark 61.74(n=31) 13.19(n=31) 48.54 -1.37 t Target Sales Growth 0.22(n=22) 0.08(n=30) 0.14 -1.79 *

Target Salest 2,026.7(n=31) 160(n=31) -2.33 .02 ** Target Earnings Growth -0.51(n=31) 0.11(n=31) 0.62 -.68

Target Earnings 59.30(n=31) -7.57(n=31) 66.87 -1.13 t Target Market Share Growth 0.11(n=31) 0.07(n=30) 0.04 -0.53

Target Market Sharet 0.08(n=31) 0.03(n=31) 0.05 -2.08 ** Target R&D Intensity 0.04(n=13) 0.10(n=11) 0.06 1.45 t Target Advertising Intensity 0.06(n=17) 0.04(n=12) -0.01 -.73 t Target Industry Sales Growth 0.18(n=31) 0.04(n=31) 0.13 -2.03 ** Target Industry Sales 45,192.2(n=31) 26,964(n=31) -18,228 -1.36 t Target Industry Demand 20.35(n=31) 12.09(n=31) 8.26 2.16 ** Instabilityt

Target Industry Concentrationt 0.28(n=31) 0.28(n=31) .00 -.19

*p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

133

TABLE 34

CROSS TABULATION OF TARGET INDUSTRY TYPE AND BVT GROUP

BVT group Target Industry Type χ2 BVT=HIGH BVT=LOW 17 16 Service 51% 49% .06 14 15 Non-Service 48% 52%

134

TABLE 35

TARGET PERSPECITVE TARGET BRAND VALUATION MODEL

Log(BVT)=α0 + β1T_TMi + β2T_SALESgi + β3T_SALESit + β4T_EARNgit + β5T_EARNit + β6T_MSgit + β7T_MSit + β8I_SALESg + β9I_INSt + β10I_HHIt + β11T_SEV + εit

Main Equation: Dependent variable=Log (Target Perspective Target Brand Value) Only Target Variables Variables Est. SE Sig. Intercept 72.457 70.140 .306 Target Number of Trademark -1.142 .594 .060 * Target Sales Growth 437.705 244.235 .079 * Target Salest .051 .013 .000 *** Target Earnings Growth -13.562 8.533 .118

Target Earningst 1.048 .355 .005 *** Target Market Share Growth -381.040 234.577 .110

Target Market Share 952.076 459.217 .043 ** Target Industry Sales Growth -5.302 157.817 .973

Target Industry Demand Instability -1.21 2.892 .675

Target Industry Concentration -40.86 198.047 .837

Target Industry Type .698 60.607 .999

n 60 f-value 8.19 R2 .65 Adj. R2 .57 *p<.1(two-tailed test). **p<.05(two-tailed test). ***p<.01(two-tailed test).

135

TABLE 36

TARGET PERSPECTIVE TARGET BRAND VALUATION MODEL - DESCRIPTIVE STATISTICS Descriptive Statistics (n=60) Variables M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 1 BV 175.05 328.23 1 T 2 T_TM 38.53 142.54 .60 1

3 T_SALESg .16 .31 -.02 -.03 1

4 T_SALES 1103 3313 .58 .41 -.01 1 t 5 T_EARNg -.12 3.64 .01 -.01 .11 .12 1

6 T_EARN 25.80 236.68 .56 .88 -.08 .14 .08 1 t 7 T_MSg .09 .33 -.08 -.05 .88 -.07 .06 -.07 1

8 T_MS .06 .10 .68 .67 -.10 .51 .02 .63 -.05 1 t 10 I_SALESg .11 .27 .02 -.03 .15 .02 .13 -.01 -.10 -.11 .02 1

11 I_INS 16.23 15.74 -.00 -.09 -.00 .01 .02 -.08 -.23 -.04 -.00 .68 1

12 I_HHI .28 .16 .04 .04 .00 .01 -.00 .07 -.01 .13 .04 .10 .38 1

13 T_TYPE .51 .50 .07 -.15 -.29 -.01 -.11 .15 -.23 .18 .07 -.21 -.09 .06 1

* Correlations higher than .25 is significant .

136