Does advertising spending improve sales performance?

International Journal of Hospitality Management 48 (2015) 161–166

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International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman

Does advertising spending improve sales performance? A. George Assaf a,∗ , Alexander Josiassen b , Anna S. Mattila c , Ljubica Kneˇzevic Cvelbar d a

Isenberg school of Management, University of Massachusetts-Amherst, USA Copenhagen Business School, Denmark c School of Hospitality Management, The Pennsylvania State University, USA d Faculty of Economics, University of Ljubljana, Slovenia b

a r t i c l e

i n f o

Article history: Received 29 January 2015 Received in revised form 2 April 2015 Accepted 29 April 2015 Keywords: Advertising spending Sales performance Size Star rating Dynamic frontier modelling

a b s t r a c t Hotel managers and investors commonly analyze the impact of advertising spending on firm performance. This paper investigates such an impact using a comprehensive framework incorporating the moderating effects of hotel size and star ratings. We estimated sales performance via dynamic, stochastic frontier modelling. Using longitudinal data from a sample of Slovenian and Croatian hotels, we demonstrate that advertising spending has a positive impact on hotel sales performance, and that the relationship strengthens for larger hotels and hotels with higher star ratings. Theoretical and managerial implications along with directions for future research are also discussed. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction Advertising is at the core of attracting new customers, and accordingly hotel companies allocate large amounts of money to such marketing campaigns. In Hong Kong alone, advertising spending in the hotel industry reached HK$49.03 million in 2011, up 228% from HK$14.94 million in 2010 (Perez, 2011). Across many leading hotel chains such as Marriott, Starwood, and Intercontinental, there is increased focus on boosting advertising budgets. In a recent survey of 600 leading international hotels, more than half (58.8%) increased their marketing budgets in 2013 (TravelClick, 2013). The recent economic crisis increased the need for higher advertising budgets to counter slowing sales and profit growth. Whether all firms realise higher sales performance from increased advertising remains controversial in the literature. Over the last decade, marketing scholars focused particularly on assessing the value of marketing actions such as advertising spending on sales performance and shareholder return. In several of these articles, advertising had no impact on sales performance (Kihlstrom and Riordan, 1984; Milgrom and Roberts, 1986). History contains many examples of companies in which CEOs and boards of directors were more disappointed with contributions from marketing

∗ Corresponding author. Tel.: +1 4135454192. E-mail addresses: [email protected] (A. George Assaf), [email protected] (A. Josiassen), [email protected] (A.S. Mattila), [email protected] (L. Kneˇzevic Cvelbar). http://dx.doi.org/10.1016/j.ijhm.2015.04.014 0278-4319/© 2015 Elsevier Ltd. All rights reserved.

departments than from other departments in the firm (Srinivasan and Hanssens, 2009). Tenures of CEOs in the industry are short in comparison to other executives in the company (Nath and Mahajan, 2008) since many companies cannot either demonstrate or realise expected outcomes from advertising (Luo and de Jong, 2012). Despite being one of the largest non-departmental costs in the hotel industry, only a handful of studies have investigated the impact of advertising spending on sales performance (O’Neill et al., 2008). For example, Chen and Lin (2013) show that advertising spending is linked to room revenues in the context of Taiwanese hotels. The present paper offers three contributions to the extant literature. First, we test both the nature and shape of the advertising-sales relationship. Although a linear relationship is commonly assumed between the two, many reasons exist to suggest the relationship is not linear, but follows a concave or Sshaped relationship (Little, 1979; Vakratsas et al., 2004). In other words, advertising returns change with varying degrees of advertising spending. Hence, for hotel operators, it is essential to assess the shape of the relationship this has important implications for allocation of resources to advertising budgets. Second, we consider moderators that influence the advertising–sales performance relationship. Luo and de Jong (2012) emphasise the importance of a contingency approach when assessing the impact of advertising on firm performance. The relationship is complex, and assessing it directly might be misleading. Since most studies focus on whether the influence is significant, little is known about the underlying mechanisms affecting the relationship. We focus on the moderating role of

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two hotel characteristics, namely hotel size and star rating. No study tests the moderating impact of these variables despite being known for their effects on the relationship. We offer theoretical arguments from both advertising and hotel literature to support moderation. Third, we offer a methodological extension to the hospitality literature by introducing a more robust measure of sales performance. We use the Bayesian stochastic frontier approach to (a) provide a benchmark of optimum sales performance value (i.e. the maximum sales performance that a company can achieve), (b) derive measures of sales performance relative to other competitors, and (c) allow sales performance to follow a dynamic framework. Although the approach has recently gained popularity in the hospitality literature (Assaf and Agbola, 2014; Chen, 2007; Brown and Ragsdale, 2002), most models are estimated statically. We argue that using a dynamic approach provides realistic measures of sales performance due the ability of firms to correct mistakes and manage advertising spending more effectively longitudinally. The paper proceeds as follows: next, we present the theoretical framework and hypotheses. We then discuss the data and the metrics used. We then present the analysis and directions, and finally we discuss the implications and provide direction for future research.

2. Theoretical framework and hypotheses The literature is rich with studies that compare the effects of advertising on sales performance. Consensus suggests advertising induces consumers to alter subjective valuations of a product; it changes consumers’ tastes and behaviors in favour of an advertised product, leading to a less-elastic demand. Evidence also suggests that advertising spending has a positive effect on firms’ market values (Joshi and Hanssens, 2010; Luo and de Jong, 2012). As Chauvin and Hirschey (1993) argue, advertising represents investment in intangible assets that results in higher future cash flows. Combined with a strong brand value, advertising also has a positive effect on a firm’s operating and market performance (Eng and Keh, 2007). Advertising drives sales performance in two ways. First by “making consumers interested enough in the focal product that they would seek information about it and, second, by converting information-seeking consumers into buyers” (Hu et al., 2014; p.300). By improving market awareness, advertising also improves the firm’s competitive position, increases customer preferences, and strengthens the brand image (Koslow et al., 2006; Kulkarni et al., 2003; Tellis, 2010). West et al. (2008) p. 35, emphasise that “the creativity in advertising is highly prized for its ability to gain consumer attention and bestow value to brands,” particularly in markets with intense competition. Despite these findings, the literature is inconsistent concerning the impact of advertising spending on sales performance. For example, research on hotels has found that “marketing expenditures have differential effects according to the type of hotel and the particular type of marketing expenditure” (O’Neill et al., 2008, p.1). In other words, there is no guarantee that marketing and advertising spending provide the same outcome for all types of hotels (O’Neill et al., 2008). In the business literature, research also indicates that advertising does not necessarily generate an expected sales return, rather it has a moderate influence on short- and longterm sales (Berndt et al., 1995; Kremer et al., 2008; Narayanan et al., 2004; Osinga et al., 2011). Some argue for a concave or S-shaped relationship between advertising and sales performance (Hanssens et al., 1999; Mesak, 1992; Simon and Arndt, 1980), suggesting that sales performance does not necessarily increase with greater advertising spending. For example, the S-shaped relationship indicates that initial spending on advertising has little impact on sales per-

formance, but with more spending, advertising begins to have an effect (Johansson, 1979). This incremental gain continues to a point, after which additional expenditures offer little or no returns. Hence, there is an advertising threshold that firms must exceed to generate strong sales performance (Hanssens et al., 1990). In view of contradicting findings in the literature, it is difficult to hypothesise the nature and the direction of the relationship between advertising spending and sales performance in the hotel industry. Following theoretical arguments found in the mainstream marketing literature (Osinga et al., 2011), we put forward the following hypothesis: H1. Advertising spending has a positive association with sales performance. In line with the objectives of this study, we also test two other different relationships between advertising and sales performance. For example, we also test whether advertising spending has Ushaped or S-shaped relationships with sales performance. 2.1. The moderating role of hotel size We argue that hotel size moderates the impact of advertising on sales performance. In most industries, firm size correlates with market size; the “larger the company, the wider is the area in which the products are being sold” (Peles, 1971; p.32). Most large companies are spread over various regions or markets, and small companies usually focus on local markets. Therefore, market size generates economies of scale from advertising (Porter, 1976). Accordingly, we suggest the positive impact of advertising on sales performance is stronger for large than small hotels. These hotels not only enjoy increased sales potential from increased size, the cost of advertising per customer is also lower. Since large firms are rarely niche players but have more diverse target markets, they reach a larger number of potential guests with the same advertising expenditure. In addition, as large firms can afford spending more on advertising, they create a reputation premium, which in turn allows them to charge higher prices (e.g. Erickson and Jacobson, 1992; Rubera and Kirca, 2012). Larger hotels also have more flexibility to advertise through arrays of advertising channels with larger circulations, resulting in lower average advertising cost per potential customer (Blattberg and Deighton, 1991). For small firms, advertising in widespread advertising channels creates risk of incurring higher advertising costs per customer since they do not have strong customer databases to justify such investments. There are also disparate rates of amortisation for advertising between small and large firms (Chauvin and Hirschey, 1993). For example, small firms usually face higher rates of amortisation since there traditionally exists higher customer turnover in the smaller markets in which they operate. High amortisation increases costs of advertising, and makes it less effective in terms of generating expected sales targets. Thus: H2. The impact of advertising on sales performance is stronger for larger hotels than smaller hotels. 2.2. The moderating role of star-rating Several industries have their own quality indication systems. Business schools are accredited by AACSB, AMBA, or EQUIS, while hotels are known for the international star-rating system. Star ratings signal quality (Fernández and Becerra, 2013), and support differentiation strategies (Bull, 1994) to promote sales, and third-party endorsements are a valuable quality cue. Research on desktop computers and auto insurance, for example, shows that with third-party endorsements, advertising increases quality perceptions (Dean and Biswas, 2001). Research also suggests that

A. George Assaf et al. / International Journal of Hospitality Management 48 (2015) 161–166 Table 1 Select hospitality industry indicators in 2012, Slovenia and Croatia.

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4. Methods

Hospitality indicators

Slovenia

Croatia

Number of hotels Number of hotel rooms Average number of rooms per hotel 5-star hotels (%) 4-stars hotels (%) 3-stars hotels (%) 1 and 2-star hotels (%) City hotels (%) Leisure hotels (%) Hotel net occupancy rate (%) Number of employees in hospitality industry

298 18.719 63 4 39 51 6 21 79 42.3 6.853

656 57.435 88 4 30 52 14 10 90 53.2 23.751

(Sources: SORS, 2014; SORC, 2014; Eurostat, 2014)

critical movie reviews are used often in advertising, and positive reviews affect box-office revenues (Basuroy et al., 2003). Like star ratings, advertising is an extrinsic quality cue (Milgrom and Roberts, 1986; Kirmani and Rao, 2000; Buil et al., 2013). It is reasonable to suggest a congruency effect between these two extrinsic cues. The congruency effect was first proposed by Osgood and Tannenbaum (1955). The theory is that congruent information is preferred because individuals dislike incongruent information. Congruent information will also be relied on to a greater extent. Congruent information is information that points in the same direction (Josiassen et al., 2008). Increased advertising points towards higher quality because of several effects. One of these is the mere exposure effect. The mere exposure which is a well-established and researched concept (Bojanic, 1991; Janiszewski, 1993) postulates that increased exposure in itself tends to promote an approach tendency while unfamiliar things promote an avoidance tendency (Fang et al., 2007). In terms of perceived quality, and based on the above arguments, high advertising spending therefore points towards high quality, and high star ranking points in the same direction. Specifically, positive advertising claims have the strongest effects on sales when backed by positive, third-party evaluations. Based on such a congruency effect between advertising spending and star-rating we argue that the relationship between advertising and hotel sales performance is higher with higher star rating. Thus, we expect star ratings to moderate the impact of advertising on sales performance positively. Specifically: H3. The impact of advertising on sales performance is stronger for hotels with higher star ratings than for hotels with lower star ratings.

3. Data This study tests the hypotheses on two popular hotel and tourism destinations, Slovenia and Croatia. Tourism’s contribution to both countries is large. Croatia’s GDP and employment in 2012 were 26.4% and 28%, respectfully, while the total tourism contributions to the country’s GDP and employment in 2012 for Slovenia were 12.8% and 13.1%, respectively. The Croatian hotel industry is large, having three times the number hotel rooms compared to Slovenia. Croatian hotels are, on average, larger, and have slightly lower star ratings. The majority of Croatian hotels are located by the sea in the Dalmatia Split and Dubrovnik regions, followed by the Kvarner and Istria regions. The majority of Slovenian hotels are concentrated on the seaside, in thermal-spa resorts, mountain resorts, and the capital, Ljubljana. Eurostat data show that in 2012 the average hotel occupancy rate was 53% in Croatia and 42% in Slovenia. Table 1 presents a comparison of industry statistics for Slovenia and Croatia.

To test our hypotheses, we used a two-step approach. In the first stage, we estimated sales performance using the dynamic stochastic frontier approach, and in the second stage, we tested the effects of advertising and the two moderators on sales performance. We used a sample of 65 hotels from Slovenia and Croatia over a 6-year period (2007–2012), resulting in 390 cases. The study was conducted from October to December 2013. We created a brief online questionnaire in Slovene and Croatian languages, and distributed it to 205 hotel companies in Croatia and Slovenia. Questionnaires were sent to 205 marketing managers in Slovenia and Croatia, and 65 (32%) responded—25 from Croatia and 40 from Slovenia. Around 33% of the hotels in our sample are part of a chain with the majority being local hotel companies. Besides general questions concerning company ownership type and the hotel’s star rating, managers reported investments in advertising as a percentage of total sales for the last 6 years (2007–2012). Data were then matched with secondary financial data retrieved from the Agency of the Republic of Slovenia for Public Legal Records and Related Services (AJPES) and the Croatia Financial Agency (FINA). In 2012, firms in the sample generated nearly 32% of total hospitality industry sales in Slovenia (AIJPES, 2012), and 15% in Croatia. 4.1. Sales performance We used the Bayesian stochastic frontier approach to measure sales performance. This method has garnered increased popularity in the hospitality and marketing literature due to several advantages over simpler performance metrics (Barros and José Mascarenhas, 2005; Dutta et al., 1999; Krasnikov et al., 2009; Luo and Homburg, 2008). Extant studies that assess advertising–sales performance relationships often measure sales performance using total sales or longitudinal percentage-of-sale increases (Bass et al., 2007; Dekimpe and Hanssens, 1995). The stochastic frontier approach offers three advantages. First, it compares a company’s sales performance against its maximum potential sales performance (i.e. optimal performance).1 Second, it considers competition when measuring sales performance against optimum performance. Third, it makes it easier to impose a dynamic structure on the sale performance metric. The stochastic frontier uses the following econometric specification: qit = xit¢ b + vit − uit , i = 1, ..., n, t = 1, ..., T

, (1)

where qit is the observed output (total sales here), xit areinputs used to produce sales, vit is an error term distributed as N 0, v2 , and uit is a random term used to measure the gap between observed   sales and optimum sales performance, distributed as N + 0, u2 . We allow a dynamic structure on uit in line with Tsionas and George Assaf (2014). The argument is that firms learn from mistakes, and by accumulating experience, they manage advertising spending better. For specification of inputs (xit ) required for the estimation of Eq. (1), we followed the hotel literature (Assaf and Josiassen, 2012; Barros, 2005), selecting the number of hotel rooms (i.e. input), number of employees (i.e. input), cost of materials (i.e. input), and other operational costs, excluding labour (i.e. input).2 4.2. Measures of advertising, moderators, and control variables Advertising was measured as total annual advertising spending. We measured size as the log of total assets, according to the liter-

1 Using simple performance metrics (e.g. total sales), it is difficult to identify optimum performance (i.e. maximum sales a company could achieve). 2 As mentioned, the output in our model is “total sales”.

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Table 2 Correlation matrix and descriptive statistics.

Sales performance (%) Advertising spending (D ) Star rating Size Part of a group Customer satisfaction

Mean

SD

0.55 1173774 3.54 14.62 0.26 5.55

0.39 3049401 0.61 2.64 0.44 0.76

Sales performance 1 −0.15 −0.05 −0.44 −0.07 0.10

Advertising spending

Star rating

Size

Part of a group

Customer satisfaction

1 −0.14 0.32 0.33 0.10

1 0.15 0.11 0.26

1 0.21 −0.05

1 0.06

1

ature (Assaf et al., 2012; Lu and Beamish, 2001). We added control variables type of ownership (i.e. independently owned versus part of a group), and degree of customer satisfaction (ranging from 1 to 7). Most performance studies include type of ownership (Assaf and Agbola, 2011; Pestana Barros and Dieke, 2008) due to its influence on performance. Customer satisfaction is also an important control variable and can impact the efficiency of advertising spending (Luo and Homburg, 2008; Assaf et al., 2015).3 5. Results Table 2 contains descriptive statistics and a correlation matrix for all variables; there were no serious correlational problems between them. To test our model, we used the sales performance gap (uit ) as a dependent variable instead of sales performance (exp (−uit )), and estimated our hypotheses simultaneously with the estimation of the stochastic frontier model in (1). Table 3 presents the results of our hypothesis testing. In line with our framework, the model we tested can be expressed as: SPGapit = ˇ0 + ˇ1 Advi,t−1 + ˇ2 Starit + ˇ3 Sizeit + ˇ4 Advi,t−1 2

×Starit + ˇ5 Advi,t−1 × Sizeit + ˙ Controlit + it

(2)

i=1

where SPGapit is the sales performance gap, Advi,t−1 is lagged advertising spending4 , Starit indicates star rating, Sizeit indicates hotel size, Controlit indicates the control variables, and it is error. Since our dependent variable is the sales performance gap (SPGapit ), a variable that has negative impact on SPGapit has a positive impact on sales performance (because the smaller the gap the stronger is sales performance). Table 3 reports results from four different models. We started with a simple model in which we estimated (2) without interaction terms (Model 1), and then added the moderating impacts (Model 2) and compared the difference in model performance. We also estimated two other models where we allowed for a squared and cubic terms of sales performance to test for the U-shaped (Model 3) and S-shaped (Model 4) relationships. We compare the performance of the various models using the Deviance Information Criterion (DIC), which is highly popular in Bayesian estimation (Tsionas, 2006). As it is clear from Table 3, by comparing the DIC of the various models we can see that our model proposed in (2) performs better than the simple model which does not include any interaction impact, providing hence support that the relationship between advertising and sales performance is complex, involving moderation. Model 2 performs better than Models 3 and 4. We can also see that the coefficient of the square term of advertising is not sig-

3 Customer satisfaction is comparable among hotels in our sample. To collect these data we asked managers to evaluate the overall customer satisfaction in their company on a scale from 1 to 7 (1-very unsatisfied; 7 very satisfied). In order to verify data reliability, we also randomly tested whether the satisfaction scores provided by managers are in line with the customers satisfaction scores available from secondary sources (primarily from booking.com). Overall we found high consistency between the two. 4 We use lag of spending since the effect is not immediate (Luo and de Jong, 2012).

nificant in both models. The cubic term of advertising is not also significant in Model 4. All these results confirm that Model 2 is the best fit and hence we use it for our hypothesis testing. The results from Model 2 demonstrate that advertising spending has a significant and negative influence on the sales performance gap (i.e. positive influence on performance), supporting H1. H3 suggests that the influence of advertising on sales performance is stronger for larger firms than for smaller firms. The coefficient of the interaction term between advertising spending and size is negative and significant (i.e. positive influence on sales performance) indicating that the relationship between advertising spending and performance becomes stronger for larger hotels, hence supporting H2. H3 suggests that the influence of advertising on sales performance is stronger for firms with higher star ratings. The coefficient of the interaction term between advertising spending and star rating is positive, supporting H3. 6. Sensitivity analysis For robustness, we conducted sensitivity analyses on the models, and compared findings. For example, in the context of our model and the Bayesian estimation, in general, prior distributions on parameters of the variance of the error term, it , in Eq. (2) should be evaluated carefully. We attempted three priors5 , estimating Model 2 without changing other assumptions. Results shown in Table 4 suggest the impacts of various priors were not excessive, and results appear to be largely consistent, supporting results reported in Table 2. 7. Theoretical and managerial implications This study investigated whether a hotel’s sales performance is enhanced by spending more on advertising. As discussed in our review of the literature, there are inconsistencies concerning the impact of advertising spending on firm performance. Some anecdotal evidence suggests that advertising spending may even be negatively related to sales performance. There is also some evidence that advertising spending is one of the first costs to be reduced when companies need to save money. The present study shows that increased advertising spending increases sales performance. Hence, this study demonstrates clearly that it is an error to consider advertising spending a cost, and it should be considered as an important investment. This in turn supports extant research (Luo and de Jong, 2012; Joshi and Hanssens, 2010) showing that firms trying to reduce spending by cutting advertising are less successful than their counterparts who opt for other types of budget cuts. The use of sales performance to evaluate the efficiency of advertising is congruent with the American Marketing Association’s (2012) call for research demonstrating marketing’s impact on company performance. Our finding that advertising spending enhances sales also allows hospitality marketers to discuss these

5 The variance is distributed as gamma (a and b). We tried priors the literature uses commonly: (0.01, 0.01), (0.01, 0.1), (0.1, 0.1).

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165

Table 3 Impact of advertising spending on sales performance. Model 1

Model 2

Model 3

Model 4

Variable

Hypothesis

Coefficient

SD

Coefficient

SD

Coefficient

SD

Coefficient

SD

Advertising spending Hotel size Star rating Advertising spending × hotel size Advertising spending × star rating Part of group versus individual Customer satisfaction Advertisnig spending squared Advertisnig spending cubed Deviation information criterion (DIC)

H1

−7.82b −5.60b −7.79b

0.32 0.35 0.31

−7.78b −4.23b

0.32 0.41

−5.79b −1.07b −5.61b −0.97a −7.17b −5.62b 2.49b

0.40 0.31 0.61 0.49 0.43 0.60 0.21

−5.80b −1.11b −5.73b −1.07b −7.21b −5.73b 2.52b −0.38

0.39 0.31 0.62 0.50 0.44 0.61 0.22 0.21

−5.57b −0.47 −3.93 −1.26 −6.08b −3.94 1.95b −0.23 1.73 2889.84

1.18 0.79 2.71 2.02 1.63 2.72 0.73 1.12 1.61

H2 H3

4268.58

2778.98

2788.01

SD stands for standard deviation. a Indicates significance at the 10% level. b Indicates significance at the 5% confidence level. Table 4 Bayesian sensitivity analysis across various priors. Prior 1

Prior 2

Prior 3

Variable

Hypothesis

Coefficient

SD

Coefficient

SD

Coefficient

SD

Advertising spending Hotel size Star Rating Advertising spending × hotel size Advertising spending × star rating Part of group versus individual Customer Satisfaction

H1

−5.79b −1.07b −5.61b −0.97b −7.17b −5.62b 2.49b

0.40 0.31 0.61 0.49 0.43 0.60 0.21

−5.76b −1.05b −5.55b −0.92a −7.14b −5.56b 2.49b

0.45 0.34 0.75 0.52 0.52 0.75 0.24

−5.71b −1.02b −5.42b −0.83 −7.08b −5.43b 2.47b

0.51 0.38 0.97 0.58 0.63 0.97 0.29

H2 H3

SD stands for standard deviation. a Indicates significance at the 10% level. b Indicates significance at the 5% confidence level.

matters with finance executives, not only arguing that advertising increases customers’ positive perceptions, but also pointing to positive effects on sales performance. Moreover, our findings help to understand why advertising is sometimes less effective or even ineffective. The impact of advertising spending on sales performance appears stronger for hotels that are larger than for smaller hotels. The implication is that smaller hotels may not be able to reap the same benefits from advertising. We also contribute to the hospitality literature by suggesting that efficient advertising expenditures may require higher star ratings. The finding that a higher star rating enhances the positive effects of advertising spending on sales is critical to understanding the efficacy of advertising campaigns. Hotels should align their advertising spending with their star ratings; such that hotels with higher ratings could build on this strength in advertisements campaign, and hotels with lower ratings may need to focus on other cues (rather than star rating) on which customers can base decisions. The size of a hotel similarly determines benefits garnered from advertising, and could also influence the advertising content. In terms of future research, while smaller hotels in general cannot reap the same benefits from spending more on advertising as larger hotels can, they could perhaps form alliances to eliminate the small-size advertising jeopardy. Smaller hotels could form strategic alliances amongst each other or by collaborating with larger hotels in order to achieve similar benefits of larger hotels in terms of advertising–sales efficiency. It would be useful to test this hypothesis. We now know that sales performance is positively affected by increased advertising spending, and importantly we also now know how this relationship is influenced by two key firm characteristics. However, we still need to know more about whether, this relationship is influenced by consumer, media, and ad characteris-

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