Is advertising for losers? An empirical study from a value creation and value capturing perspective

European Management Journal xxx (2016) 1e9

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Is advertising for losers? An empirical study from a value creation and value capturing perspective Koen Tackx a, *, Sandra Rothenberger b, Paul Verdin b, c a

Vlerick Business School, Bolwerklaan 21, B-1210, Brussels, Belgium Solvay Brussels School of Economics and Management, Av. Franklin Roosevelt 42, CP114/01, B-1050, Brussels, Belgium c Harvard Kennedy School Mossavar-Rahmani Center for Business and Government, 79 J F Kennedy Street Box 83, Cambridge, MA, 02138, USA b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 October 2015 Received in revised form 6 July 2016 Accepted 30 July 2016 Available online xxx

Does advertising lead to higher profits? This question has preoccupied company executives and academic researchers for many decades. Arguments have been put forth in both directions, and evidence is mixed at best. In this article, we re-examine the question from a value creation and value capturing perspective, which allows us to re-interpret and reconcile the different views and empirically validate the resulting hypotheses. Using a database of the top 500 brands of established companies during the 2008e2015 period, we find that advertising spending has no significant impact on profitability, while both brand value and research and development (R&D) spending have a clearly positive effect. In addition, we observe a positive interaction effect between advertising spending and R&D spending and a negative interaction between brand value and R&D spending on profitability. These findings corroborate the view that advertising in and of itself does not improve profitability; rather, its effect is positive only when it acts in support of customer value creation as a result of R&D. © 2016 Published by Elsevier Ltd.

Keywords: Advertising effectiveness Brand value Research and development Profitability drivers Value creation Value capturing

1. Introduction Does advertising increase or decrease company profits? For decades, marketers and financial analysts in both academia and the corporate world have asked this question (e.g., Rust, Ambler, Carpenter, Kumar, & Srivastava, 2004). Marketing directors often express their lingering doubt with the popular conundrum that “though roughly half the marketing budget might be wasted, they do not know which half.” Rarely have alternative views on the relationship between key economic and business variables been so divergent, on both theoretical and empirical levels. More than 50 years ago, the Journal of Marketing published an article titled “What about the Relationship among Sales, Advertising, and Earnings?” (Twedt & Knitter, 1964); yet, so far, a universal, crystal clear, and undisputed answer has not been achieved. During the years, the discussion on the effectiveness of advertising has become rather polarized, with the two opposing views

* Corresponding author. Vlerick Business School, Bolwerklaan 21, B-1210, Brussels, Belgium. E-mail addresses: [email protected] (K. Tackx), sandra.rothenberger@ulb. ac.be (S. Rothenberger), [email protected] (P. Verdin).

labeled “advertising as market power” and “advertising as information” (e.g., Mitra & Lynch, 1995; Wilcox, Kang, & Chilek, 2015). The latter view treats the customer receiving or seeing the advertisement as the main beneficiary because advertising provides knowledge about the company offering and increases price sensitivity by stimulating competition; conversely, for the former, the beneficiary is the company spending the money because advertising persuades customers to consume more products or services from that company while decreasing their price sensitivity (Wilcox et al., 2015). Most prior research has focused on developing one of these views, rather than building bridges between them (e.g., Bahadir, Bharadwaj, & Parzen, 2009; Erickson & Jacobson, 1992; Taylor, 2013). Some studies have tried, at least indirectly, to combine both, by considering the effect of advertising and brand value on firm performance, because “brand equity represents the added value the product garners as a result of past investments in the marketing activity for a brand” (Eng & Keh, 2007, p. 92). Other studies suggest that the diverging results are the product of different research purposes and methodologies used (Peterson & Jeong, 2010). In an attempt to bridge the gap, we analyze the issue from a different angle, more specifically by distinguishing between the concepts of value creation (for the customer) and value capturing

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(for the shareholder), also referred to as value appropriation or value claiming as introduced in the strategic management field (e.g., Aspara & Tikkanen, 2013; Bowman & Ambrosini, 2000; Lepak, Smith, & Taylor, 2007) and, to a lesser extent, in marketing literature (e.g., Mizik & Jacobson, 2003; Kotler & Armstrong, 2013). We develop and test a model that analyzes the influences of advertising, brand value, and research and development (R&D) on profitability. The advantage of this angle is that the different beneficiaries of the traditional views are reunited into one model. The value creationevalue capturing approach thus allows us to reconcile some of the apparent contradictions in previous research and to develop relevant and better-specified hypotheses that are empirically supported. By explicitly distinguishing the value creation and the value capturing dimensions, we also attempt to address previously stated concerns, such as that of Eng and Keh (2007, p. 91), who observe “While brand value creation is generally regarded as a ‘good thing’, we need to have more concrete measures of brand value appropriation.” The structure of this article is as follows: We begin with a review of the literature, after which we develop our hypotheses, provide the model to be tested empirically, and describe the data in detail. Then, we report the empirical results. We conclude with implications for theory and practice, research limitations, and avenues for further research. 2. Literature review and hypotheses development 2.1. Background Stemming from industrial organization theory, two major “schools” of thought on advertising spending emerged in the second half of the 20th century. First, the “power school” treats advertising as a tool to increase market power (Comanor & Wilson, 1972) by convincing customers to choose a certain brand, increase loyalty, and reduce price elasticity (Wilcox et al., 2015). In line with this view, advertising is considered a tool to shift market share among existing competitors while creating barriers to enter for new competitors (Carlton & Perloff, 1990). Recent research even shows that in mature markets, the revenues of the firm drive the advertising spending, rather than the reverse (Darrat, Wilcox, Funches, & Darrat, 2016). If this notion is valid, just the mere size of a company and its total advertising spending should yield economies of scale because of fixed costs, access to more effective media, and the impact of repetition. Marshall (1919, p. 199) described the third point as follows: “The chief influence of such advertisement is exerted, not through the reason, but through the blind force of habit: people in general are, for good and for evil, inclined to prefer that which is familiar to that which is not.” Thus, it is no surprise that followers observe several benefits of this school, including increased sales and market share (Bahadir et al., 2009), increased profits (Eng & Keh, 2007), and increased market value (Erickson & Jacobson, 1992). The benefits of advertising also reach beyond the boundaries of the firm in that “advertising can also act as a signal of financial well-being or competitive viability of the firm” (Joshi & Hanssens, 2010, p. 22) and, as such, can increase the salience among investors (Srinivasan & Hanssens, 2009). The second major school, called the “information school” (Nelson, 1974), refers to the benefits that advertising can generate for consumers. Advertising informs consumers about (new) products and services, reduces search costs, and, as such, expands demand (at the brand or category level) but also stimulates competition within the industry (Ali Shah & Akbar, 2008). Moreover, research predicts that advertising facilitates entry of new competitors (Taylor, 2013). Because advertising information also

contains pricing data, customers become more price sensitive, and prices are lowered. Part of the advertising industry (e.g., Deloitte, 2013), as well as some academic literature (e.g., Taylor, 2013) embraces this school of thought. The positive effect of advertising on total demand in established markets is questioned when it leads only to a shift in market share from one player to another (Wilcox et al., 2015). Overall, conclusions of the research stemming from both the first and second schools have rarely led to a uniform answer, and the outcomes depend largely on environmental variables at the market and brand levels (as well as the particular data sets used). Recent research concludes that in mature markets, the main purpose of advertising is to defend or increase market share rather than stimulate overall demand (Darrat et al., 2016), which is in line with an alternative school of thought on which we focus in this article. The alternative school of thought, which has emerged in the past decade, is based on the distinction between value creation and value capturing and is further referred to as VC2 (Hawawini, Subramanian, & Verdin, 2004; Verdin & Tackx, 2015). This view derives less from the advertising domain and more from the strategic management and marketing fields. According to this view, to create long-term shareholder value (or value capturing), a company's primary focus should be on developing a compelling and valuable offer to customers (or value creation), resulting in the socalled value proposition. It is then up to customers to decide whether the proposed offer creates value for them and thus is worth paying for (as the basis for the appropriate pricing or value capturing). Companies following this approach need to offer (consistently better) value to customers (consistently lower prices or consistently better products and services), for which both R&D and advertising (to publicize these offerings to existing and potential customers) are necessary. This framework deems R&D an important basis for value creation, because its outcome is superior products or services and distribution processes (Mizik & Jacobson, 2003), despite uncertainty and risks (O'Brien, David, Yoshikawa, & Delios, 2013). If offerings are not compelling enough, companies may resort to advertising spending in an attempt to “compensate” for the lack of attractiveness (Larreche, 2008). Conversely, companies whose products or services are convincing in their own right may be able to decrease their advertising spending and capture superior value for shareholders. Table 1 summarizes the different views of the impact of advertising according to the different schools and serves as the cornerstone for hypotheses development. 2.2. Advertising The traditional view (as represented in the power school) goes as follows: a company takes an action (advertising) that has an impact on the customer (change in perception of needs and/or expectation), such that he or she takes a subsequent action (purchase) that modifies the firm's position in the market (e.g., increased market share; Chaudhuri, 2002), thus affecting its financial metrics (i.e., profit; Sriram & Kalwani, 2007) and, in turn, eventually influencing the value of the firm (Rust et al., 2004) and reducing its systemic risk (McAlister, Srinivasan, & Kim, 2007). Attempts to quantify and measure this process, however, have faced the major challenge of various intangible factors (Mittal, 1999) that influence the overall customer perception. At the end of the process, it is up to customers to judge the usefulness of the communication (Mittal, 1994), but marketing activities are supposed to increase the performance of the firm (Peterson & Jeong, 2010). Advertising can play a key role in value creation and capturing.

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Table 1 Summary of the impact of advertising on corporate performance (based on prior literature as explained previously and authors’ assessment).

The information aspect as described previously informs customers about new or changed offerings1 or reminds them of existing ones. Only if customers appreciate the value of this offering, however, will the brand value of the company increase. By contrast, advertising as such should not make a difference for products or services known to the public. In this case, the main role of advertising is to protect value by establishing a barrier to entry for competitors (Sutton, 1991) or by compensating for weaknesses in the product or service offering (Larreche, 2008). If we take these different observations into account, four financial consequences of advertising can arise. The first is a direct negative impact on profits, because advertising is an expense. The second is a direct positive impact on sales due to the information aspect. This effect has been subject to extensive research with different results (see Erickson & Jacobson, 1992; for an overview). In developed markets, an important goal of advertising is to move market share between existing players. As such, advertising can reach rather homogeneous levels within an industry (Mauri & Michaels, 1998). This type of advertising should not create value in the long run, because this kind of “shouting out loud” can be imitated by competition. It actually may increase the cost and thus decrease margins for all. The third consequence is an indirect negative impact on profit due to the compensation effect, as companies with shortcomings in their offer may try to “compensate” by advertising more. The fourth is an indirect positive effect on the value of the company due to the branding aspect. With regard to the third and fourth points, we hypothesize that the third will outweigh the fourth point, because brand advertising without a good enough underlying value offering is not sustainable. Taking these points together, we posit the following:

1 When referring to the offering, we do not limit this to the physical characteristics of the product or service, but also include the way it is brought to customers, such as through packaging, distribution, pricing, experience, and servicing.

Hypothesis 1. Advertising spending has a negative impact on firm profitability. Advertising spending is considered a discretionary flow of costs. Thus, in the next section, we focus on its possible impact on the stock of value that resides in the brand value. 2.3. Brand value Brand value (or brand equity) is the term used to measure financial value of the brands owned by a company (Kirk, Ray, & Wilson, 2013) and may be the most valuable intangible assets of all (Keller & Lehmann, 2006). Brand value is an indicator of the customer value the firm has created over time, and therefore, the firm does not need to depend on a short-term indicator or measure because brand value is the result of long-term efforts (“stock” or “strategic resource” concept instead of “flow” or “current expense”). Brand value reflects the “additional value (i.e., discounted cash flow) that accrues to a firm because of the presence of the brand name that would not accrue to an equivalent unbranded product” (Keller & Lehmann, 2006, p. 745). Many different methodologies can measure brand value, with some focusing on “the effects of brand equity on the demand and supply functions, in order to determine the influence of the brand in the decision making process” (Salinas & Ambler, 2009, p. 46). Such a methodology largely corresponds to our customer-oriented view on brand value, and we refer to this methodology in the empirical part of this study. According to prior studies, a strong brand drives much of the benefits that were historically attributed to advertising by the power school: ability to charge a price premium (Bick, 2009), lower price elasticity (Keller & Lehmann, 2006), a way to attract new customers (Bick, 2009), lower sensitivity to competitors’ prices (Keller & Lehmann, 2006), and higher barriers to entry (Eng & Keh, 2007). Considering these positive elements, it is no surprise that brand value has a positive impact on profitability (Eng & Keh,

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2007). Following that logic, we posit the following: Hypothesis 2. Brand value has a positive impact on firm profitability. In linking the first two hypotheses to the three schools as described previously, we contend that the power school follows the logic of advertising's capability of increasing brand value and thus customer loyalty, both of which should drive profitability. According to the information school, advertising stimulates competition, lowers prices, and increases demand. The impact on the profit level depends on the price elasticity. This school is not very explicit on the effects of branding; rather, it conceptualizes branding's main function as protecting innovation (Taylor, 2013). Finally, the VC2 contends that strong advertising for established companies2 is a sign of compensating weaknesses in offerings, thus decreasing profits. Conversely, it maintains that brand value is the way to induce customers to appreciate offerings. That is, strong brand value leads to higher profitability.

that pharmaceutical firms in addition to high R&D spending also should focus more on advertising spending to amplify profitability. Therefore, we propose the hypothesis that advertising spending amplifies the impact of innovation (R&D spending) on profitability: Hypothesis 4. There is a positive interaction effect between advertising spending and R&D spending on firm profitability. Branding has an important impact on the innovation capability and success (Jha, Bose, & Ngai, 2016). In addition, brand reputation positively affects the purchase of new products (Gielens & Steenkamp, 2007). In a similar vein, Brexendorf, Bayus, and Keller (2015, p. 548) conclude “Firms rely on strong brands and product innovations to gain competitive advantage and fuel growth.” Therefore, a stronger brand value can protect innovation efforts (as measured by R&D spending) and positively affect profitability. As the interrelationship between brand value and innovation is still relatively under-researched (Brexendorf et al., 2015), we posit the following:

2.4. R&D spending

Hypothesis 5. There is a positive interaction effect between brand value and R&D spending on firm profitability.

While the role of advertising is still under discussion in the literature (e.g., Taylor, 2013), there is broad consensus that innovation (as often measured by spending on R&D) is an important driver of value creation (e.g., Larreche, 2008; Mizik & Jacobson, 2003), as it leads to higher sales, higher market share, higher sales growth, increasing profitability, and market value (Rubera & Kirca, 2012). Because the effect of R&D can last over time (Rubera & Kirca, 2012) and competitors are eager to copy successful innovations, capturing the value of innovations becomes a key priority for competitors (Mizik & Jacobson, 2003). Regarding the impact on profitability, we therefore posit the following:

The literature review and research on value creation and value capturing identify three dominant views of the performance drivers of companies: (1) the power school, (2) the information school, and (3) the VC2 school. As our main focus in this article is on the VC2 view, we aim to test which marketing drivers truly influence the profitability of specific companies. Therefore, we develop our theoretical framework by proposing that only brand value and R&D spending have a significant impact on profitability, while advertising spending has a negative impact on performance. We add various control variables related to firms and the industry for model completeness and to observe interactions. Fig. 1 depicts our research idea and framework.

Hypothesis 3. R&D spending has a positive impact on firm profitability. To capture the value of innovations, customers need to be informed and convinced of the benefits. Firms can leverage this value either by brand strength (e.g., strong brands do not need to invest in advertising or obtain substantial media coverage for a new product launch) or by advertising support. Communicating the benefits of product innovations enhances their value, and this effect is stronger for pioneering innovations, such as revolutionary products, than for product improvements, such as evolutionary products (Srinivasan, Pauwels, Silva-Risso, & Hanssens, 2009). The interaction between product innovations and communications creates the ability to increase prices (Cassiman & Vanormelingen, 2013). It is crucial for customer and innovation assets to be “optimally configured to both generate and appropriate value” (Fang, Palmatier, & Grewal, 2011, p. 598). This notion is in line with recent analysis showing that it is not sufficient to create unique assets to capture additional profits (Costa, Cool, & Dierickx, 2013). To create unique assets, advertising and communication might play an important role confirmed by Rubera and Kirca (2012) that “Advertising should help consumers and investors realize the benefits of radical innovations while reducing the associated risks” (p. 143). Luo and de Jong (2012), separating in their sample low and high advertising companies, found that advertising spending has a nonsignificant (albeit positive) incremental return for the former group, while it generates a positive and significant return for the latter group. This seems in line with Nord (2011) who suggested

2 A clear distinction should be made between established and new companies/ products/markets, because the information effect of advertising is more relevant for the latter group.

3. Methodology In this section, we will describe how the different variables are constructed and integrated, as well how the data are collected. 3.1. Dependent variable The dependent variable is firm profitability in a given year as measured by net profit divided by total assets. This measure is an appropriate proxy to determine how much an investment (in assets) generates, and as such, it is viewed as a measure of how much value is captured for the shareholder. Doing so has the disadvantage that we cannot correct for the direct effects of R&D and advertising on profit because of uncertainty about how much tax benefits these costs generate. Although (early) literature on advertising effectiveness has focused on market share and sales

Fig. 1. Conceptual framework.

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rather than profit, it is increasingly clear that financial measures related to firm value are necessary to examine performance (Lehmann, 2004) and product-market data are difficult to compare (Katsikeas, Morgan, Leonidou, & Hult, 2016). We also considered total shareholder return, but this measure could be subject to factors that are not linked to firm performance (Katsikeas et al., 2016), and it might be ambiguous because it might be expected that the market only rewards investments in R&D and advertising if these generate profits (Erickson & Jacobson, 1992). Following the VC2 view, we focus on how much value is captured for shareholders from operations (without any financial leverage effect) as reflected in net profit/assets. 3.2. Independent variables The three independent variables we analyze are advertising spending, R&D spending, and brand value. For advertising and R&D, we use the expenditures that were reported in firms’ financial statements in a given year. Brand value reflects the discounted excess value a brand generates for the company (Doyle, 2009), and we use the values as calculated by Brandfinance (2015). Brandfinance uses a royalty relief methodology as described by ISO 10668. Such a methodology has several advantages, including taking into account industry-specific valuations, and is accepted by fiscal authorities (Salinas & Ambler, 2009). The brand value obtained through this methodology comes from the “brand strength index,” a royalty rate of the revenues attributed to the brand. Underlying the brand strength index are 30 attributes that represent different stakeholders (e.g., customers, staff, financial, and external). 3.3. Control variables and moderators To increase the relevance of our model and to accommodate the possible interactions, we include five variables: industry, debt level, age, size, and year. First, we observed important differences between industries in terms of advertising and R&D spending (e.g., Mauri & Michaels, 1998). Industry effects accommodate the differences between business-to-business and business-to-consumer, technology and nontechnology (Homburg, Klarmann, & Schmitt, 2010), and product and service industries (Bick, 2009). As there are clear overlaps between these categorizations, we include only a single industry variable to encompass the distinction. Second, companies with a higher debt level likely have stronger desires to control expenses, as such debt levels might hamper spending in both R&D and advertising (Erickson & Jacobson, 1992) and the success of diversification is lower for firms with higher debt levels (O'Brien et al., 2013). However, companies can obtain tax benefits from deducting debt as well; hence, we do not put forth an expectation here. Third, older firms may rely more on their reputation, while younger firm's likely need to invest more in advertising to make their products known to the market (Bahadir et al., 2009). In addition, the effectiveness of product innovations is higher for younger firms (Cassiman & Vanormelingen, 2013). Fourth, economies of scale can decrease the unit cost of advertising (Levitt, 1983, pp. 92e102), and perceived globalness can increase the brand value (Steenkamp, Batra, & Alden, 2003). Thus, we introduce revenues as a variable to measure firm size. Fifth, because both the general economic climate and firm expenditures might affect consumers’ confidence in brands, we include the years 2008e2015 as a variable. At the time of analysis, these were the most recent data available. We describe the hypotheses tested as well the impact of the control variables as follows:

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Pit ¼ b  ait þ b1  (ADV)it þ b2  (R&D)it þ b3  (BV)it þ b4  (ADV  R&D)it þ b5  (BV  R&D)it þ [b6  (IND)it þ b7  (D)it þ b8  (A)it þ b9  (Y)it þ b10  (S)it] þ e where “P” is profitability, “a” is the intercept, “b” to “b10” are the regression coefficients, “ADV” is advertising spending, “BV” is brand value, “R&D” is spending on research and development, “IND” is the industry effect, “D” is the debt level, “A” is age, “Y” is the year effect, “Size” is firm size (measured by revenues), and “e” is the error term. For all the variables, “i” is the company and “t” is the period from which we obtained the data. To test the hypotheses, we built a database that collects financial, advertising spending, and brand data. To do so, we began with the 500 largest brands in the world by using brand value as calculated by Brandfinance (2015) for 2008e2015. We selected brands that were among these largest brands, as our focus was on established companies rather than start-ups, whose advertising spending effect we deemed as fundamentally different. We excluded companies that had multiple brands in this database in any given year. For financial data, we referred to the Thomson Reuters database, which collects data of companies that publish their financial results. As such, we excluded private companies from the data set. If reported currency was other than US dollars, we translated figures into US dollars by means of Thomson Reuters EIKON using the fiscal year-end date exchange rate. As an important filter, we included only companies that reported both R&D and advertising spending in a particular year. In the end, we withheld 511 observations of 133 companies. These observations represented in total US $20.4 trillion in revenue, US $4.4 trillion in brand value, US $713 billion in spending on advertising, and US $1.5 trillion in spending on R&D. We use a 1-year time lag between the brand value and the financial data. The logic behind this approach is that we can compare the value created at the beginning of the year with that captured throughout the year. As Table 2 shows, our sample was fairly diverse across industrial segments: consumer discretionary sector (18%), consumer staples (17.8%), healthcare (5.1%), industrials (11.6%), information technology (28.6%), materials (1.8%), telco services (9.8%), and utilities (3.5%). The average age of the 133 companies was 52.19 years, which confirms our focus on established companies. We included other control variables in our analysis such as debt ratio and size. 4. Results To measure the strength of the linear multiple relationships between the normally distributed variables, we used Pearson's correlation. Table 3 shows the results of all pairs of variables. As the most relevant insight from our research framework (Fig. 1), the table shows that brand value (0.210, p < 0.001) and R&D spending (0.132, p < 0.01) positively influence profitability. By contrast, advertising spending is nonsignificantly associated with profitability (0.032). By analyzing the interaction effect of advertising spending and brand value on R&D spending and profitability, we observe that R&D spending is positively correlated with both advertising spending (0.482, p < 0.001) and brand value (0.591, p < 0.001), which lends support to the idea that there must be an interaction effect. However, by combining independent R&D spending with the two interaction variables (advertising spending and brand value), we show that the moderating effect of advertising spending on R&D spending and profitability is nonsignificant (0.081), whereas brand value on R&D spending and profitability (0.159, p < 0.001) has a positive, significant correlation. Of the control variables, Table 3 shows that industry is positively correlated with R&D spending (0.136, p < 0.01) and brand value (0.179,

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Table 2 Sample firms: data and descriptive statistics (n ¼ 133). Variables

Advertising spending (k$)

Brand value (k$)

R&D spending (k$)

Profitability (return on assets %)

Age (years)

Debt ratio %

Size (revenues k$)

Mean SD Industrial sector

1395.29 2287.96 Consumer discretionary sector 18

8647.18 12,504.79 Consumer staples

3025.79 4571.23 Healthcare

6.29 0.071 Industrials

52.19 34.09 Information technology

13.06 660.98 Materials

39,850.77 40,635.04 Telco services

Utilities

17.8

5.1

11.6

28.6

1.8

9.8

3.5

%

Table 3 Bivariate correlations for all pairs of variables. Variables

1

2

3

4

5

6

7

8

9

10

11

1. Profitability 2. Advertising spending 3 R&D spending 4. Brand value 5. Industry 6. Debt ratio 7. Age 8. Size 9. Year 10. R&D spending  Advertising spending 11. R&D spending  Brand value

1.000 0.032 0.132** 0.210*** 0.020 0.160*** 0.160*** 0.088** 0.071 0.081 0.159***

1.000 0.482*** 0.324*** 0.089** 0.017 0.074 0.390*** 0.061 0.821*** 0.501***

1.000 0.591*** 0.136** 0.015 0.033 0.486*** 0.076 0.609*** 0.721***

1.000 0.179** 0.012 0.122** 0.620*** 0.160*** 0.407*** 0.866***

1.000 0.044 0.252*** 0.091** 0.066 0.010 0.076

1.000 0.038 0.229*** 0.006 0.050 0.102**

1.000 0.029 0.036 0.002 0.004

1.000 0.000 0.415*** 0.532***

1.000 0.076 0.161***

1.000 0.677***

1.000

Pearson's correlation is significant at levels: *p < 0.05, **p < 0.01, and ***p < 0.001.

p < 0.001), age is negatively correlated with profitability (0.160, p < 0.001) and brand value (0.122, p < 0.01), and size has a main positive and significant impact on advertising spending (0.390, p < 0.001), R&D spending (0.486, p < 0.001), and brand value (0.620, p < 0.001). After examining the association among the dependent, independent, and control variables, we again focused on the impact of our conceptual framework (Fig. 1). Therefore, we applied a simple linear regression for the 8 years combined (2008e2015), whose results are presented in Table 4. Table 4 shows coefficients and associated t-statistics for Model 1, Model 2 with control variables, and Model 3 with control variables and interaction effects. We mean-centered the independent variables and created the interaction terms by multiplying these centered variables. The adjusted R-square for Model 3 is 0.185. The R-square change between Model 1 and Model 2 is 0.118 (p < 0.01) and that between Model 2 and Model 3 is 0.026 (p < 0.01). The results of this three-model linear regression analysis revealed the strength of our model and showed the impact of advertising spending, innovation, and brand value on profitability (see Hypothesis 1, 2, 3) and an interaction effect (see Hypotheses 4 and 5). Model 3 clearly shows a positive (increased) and significant impact of brand value (0.751, p < 0.001) and innovation (0.147, p < 0.05) on profitability, in support of Hypotheses 2 and 3. Hypothesis 1, which posits that advertising spending has a negative impact on the firm's profitability, is not confirmed as we found a nonsignificant, negative relationship (0.093) between advertising spending and firm profitability. The finding that there is no positive relationship between advertising spending and firm profitability corroborates the logic that advertising for established brands does not contribute directly to profit creation. In Model 3, we add two-way interaction terms for a test of Hypotheses 4 and 5. We can observe a positive and statistically significant two-way interaction effect between R&D spending and advertising spending on profitability (0.30, p < 0.01) supporting Hypothesis 4. Model 3 also tests the two-way interaction between

R&D spending and brand value. We see a statistically significant but negative interaction effect (0.54, p < 0.001) between R&D spending and brand value and therefore reject Hypothesis 5. To interpret these results and better understand the interaction effect of advertising spending and brand value on the relationship between R&D spending and profitability, we plotted the relationships between R&D spending and profitability for each of the four possible combinations of advertising spending (Fig. 2) or brand value (Fig. 3) by splitting the sample each time into two parts from the average. Figs. 2 and 3 highlight the moderating effect of our interaction analysis and confirm the results of Table 4. Fig. 2 shows not only the omnipresent positive effect of R&D spending on profitability, but it also shows that the effect of advertising spending on the relationship between R&D spending and profitability does vary depending on the level of R&D spending; thus, Hypothesis 4 is accepted. Fig. 3 shows that the effect of R&D spending on profitability is significantly different for companies with a low perceived brand value than for companies with a high perceived brand value. This graph clearly shows why Hypothesis 5 is rejected. The interaction effect of R&D spending and brand value on profitability is clearly different depending on whether a company has a strong or weak brand value. For companies with a weak brand value, R&D spending has a positive interaction effect, contrary for companies with a strong brand value. 5. Discussion and implications for management A debate that has remained unresolved for decadesdnamely, whether advertising spending is good or bad for profitabilitydseems to miss an important angle, as it is often framed mainly in terms of traditional industrial organization arguments. More recent views on the critical role of value creation show an alternative path to generating and explaining profitability: it is the sustained creation of customer value, achieved through innovation (as resulting from R&D) and brand value creation, that delivers

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K. Tackx et al. / European Management Journal xxx (2016) 1e9

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Table 4 Stepwise linear regression for advertising spending, innovation, and brand value on profitability. Model

Model 1

Independent variables

Unstandardized coefficient

Standardized coefficient

t-Value

Unstandardized coefficient

Standardized coefficient

t-Value

Unstandardized coefficient

Standardized coefficient

t-Value

1.68 (0.000) 5.61 (0.000) 1.18 (0.000)

0.05 0.04 0.21

1.08 0.62 3.82***

7.34 (0.000) 1.29 (0.000) 2.13 (0.000)

0.02 0.08 0.37

0.48 1.50 6.41***

2.90 (0.000) 2.30 (0.000) 4.29 (0.000)

0.09 0.15 0.75

1.23 2.43* 7.00***

0.00 1.59 0.00 6.51 0.00

0.04 0.15 0.15 0.37 0.01

0.98 3.61*** 3.56*** 6.74** 0.20

0.00 1.58 0.00 7.45 0.00

0.08 0.15 0.15 0.42 0.02

1.73 3.64*** 3.46** 7.61** 0.384

4.70 (0.000)

0.30

3.07**

H4

1.91 (0.000)

0.54

4.18***

H5

Main effects Advertising spending R&D spending Brand value Control variables Industry Debt ratio Age Size Year Interaction effects R&D spending  Advertising spending Brand value  R&D spending Constant R2 Adjusted R2

Model 2

0.053 (0.004)

13.300***

Model 3

(0.001) (0.000) (0.000) (0.000) (0.001)

0.472 (2.792)

0.046 0.041

0.169 0.172 0.159

(0.001) (0.000) (0.000) (0.000) (0.001)

H

0.971 (2.752)

H1 H2 H3

0.353 0.201 0.185

Standard errors are given in parentheses. *p < 0.05. **p < 0.01. ***p < 0.001. Notes: Dependent variable is profitability. Adjusted R2 for Model 3 ¼ 0.185. Adjusted R2 changed between Model 1 and Model 2 ¼ 0.118 (p < 0.01) and that between Model 2 and Model 3 ¼ 0.026 (p < 0.01).

long-term profitability. Our empirical results corroborate the view that advertising spending will only help in the process insofar as it supports genuine and sustained value creation; if not, advertising spending will have little or even a negative effect, because it risks wasting scarce resources that could have been better spent on the creation of customer value. Advertising seems useful to communicate information about the value created (value proposition); however, without such value creation, advertising in and of itself does not show potential to increase profitability. Therefore, we conclude that advertising is not a winning strategy but rather a losing strategy. It can facilitate value capturing if and only if it is supported by underlying customer value creation and, thus, it can only be effective (and add value) “for winners.” 6. Limitations and avenues for further research Fig. 2. Interaction effect of advertising spending on the impact of R&D spending on profitability.

Fig. 3. Interaction effect of brand value on the impact of R&D spending on profitability.

This article has several limitations that might lead to further research. First, the hypotheses are only valid for established brands and products. For new products or brands, the information school approach would likely lead to a stronger effect, as long as the value offer was compelling enough. To test Hypothesis 1, we assumed that all brands spend an amount of advertising above the “minimal” threshold to communicate their offering. Second, and perhaps more important, we did not take the quality or the content of the advertising into account, which at best is only (poorly) reflected in the amount of spending. In addition, spending levels are relatively easy to imitate, while the content, quality, and effectiveness of the advertising on the targeted customers may not be. The creative impact might be the real differentiator in terms of competitive advantage (Ericson and Jacobson, 1992). Most studies investigating the impact of advertising through financial measures implicitly assume that in the long run, all creations reach the same level of effectiveness. The same reasoning applies to R&D spending, as it is the result or impact that is most relevant. R&D spending mainly shows the firm's interest in and commitment to innovation. In other words, advertising and R&D

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K. Tackx et al. / European Management Journal xxx (2016) 1e9

spending are “input” measures, and thus, they omit the execution aspect and the resulting output or impact. McAlister et al. (2007) suggest using disaggregated measures to overcome this issue. Using survey methodology to ask people to assess the efficiency and effectiveness of advertising spending could be a solution, but this has the disadvantage of being less objective than the input figures we used. Third, we considered only mono-brand companies; however, prior research indicates that “a larger brand portfolio impacts positively on advertising efficiency” (Büschken, 2007, p. 68); therefore, multibrand companies might be considered as a further research track. Fourth, using data over a longer time horizon might provide insights into how the variables influence each other over time. This is particularly relevant as the effect of variables such as R&D and advertising might vary over time through the economic cycle, when taking into account macroeconomic conditions and the variation in effectiveness of advertising and R&D. For example, advertising effectiveness is different during recession periods (e.g., Graham & Frankenberger, 2011). Peterson and Jeong (2010) used a 17 year time horizon when evaluating the impact of advertising and R&D. Fifth, we only included “paid” advertising and, as such, did not include “earned” media such as press or social media, although these also might affect the firm's results (Stephen & Galak, 2012). A similar shortcoming is that online advertising might be relatively cheaper than offline advertising, and companies that switch earlier to online might benefit from this (temporary) price advantage.

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