Selling opera:
The effect of repertoire type and presence of a famous star
soloist on consumer demand
Master thesis
Christiaan Uytdehaage 11131780
March 12th, 2017
MSc. Business Administration: Entrepreneurship and Management in the Creative Industries University of Amsterdam
First supervisor: Dr. B. (Balazs) Szatmari Second supervisor: E. (Erik) Dirksen, MSc.
This document is written by Christiaan Uytdehaage who declares to take
full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original
and that no sources other than those mentioned in the text and its
references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the
supervision of completion of the work, not for the contents.
The information contained in this document about Dutch National Opera
is strictly confidential and is intended for the addressee only. The
unauthorized use, disclosure, copying, alternation or distribution of this
Table of contents Page
1. Abstract 4
2. Introduction 5
3. Literature review 8
3.1. Consumer demand and buying process – traditional goods and services 8 3.2. Consumer demand and buying process – experiential goods and services 9
3.3. Willingness to buy 10
3.4. Buying risk 11
3.5. Consumer persuasion 11
3.6. Familiarity 12
3.7. Celebrity endorsement 12
3.8. Opera as experiential good 14
4. Hypotheses building 14
4.1. Opera and consumer demand 14
4.2. Repertoire type and a famous star soloist 15
4.3. Liking and familiarity 19
5. Method 22
5.1. Measurement remark 22
5.2. Research design and procedure 23
5.3. Variables 25
6. Results 27
6.1. Demographics 27
6.2. Normality, homogeneity, and outliers 27
6.3. Control variables 28
6.4. Two-way ANOVA and independent T-tests 29
6.5. Correlation 31
6.6. Regression and moderation 33
7. Discussion and theoretical implications 35
7.1. Discussion of results 35 7.2. Theoretical implications 36 7.3. Managerial implications 38 7.4. Limitations 38 7.5. Further research 39 8. Conclusion 39 9. References 40 10. Appendix 48
1. Abstract
The goal of this study is to expand the literature on consumer demand for opera productions by investigating the effect of the repertoire type and the presence of a famous star soloist on consumers’ willingness to buy a ticket. Research is not clear about these relationships, while the current decrease in government funding for opera companies makes it important to increase box office revenues. In collaboration with the audience of Dutch National Opera an in-between experimental design is used measuring consumers’ willingness to buy for classic and modern types of productions. Furthermore, the presence of a famous star soloist is used to see whether this moderates consumers’ willingness to buy for both repertoire types. Results show that consumers are more willing to buy a ticket for a classic type of opera production, compared to a modern type of opera production. When a famous star soloist is shown to be present in an opera production, consumers are significantly more willing to buy a ticket for modern type of productions. In order to check if consumers’ willingness to buy a ticket is indeed caused by the repertoire type and the presence of a famous star soloist, we run a regression analysis on the relationships between liking the repertoire type and liking the famous star soloist and levels of willingness to buy. Moreover, these relationships are checked for levels of familiarity, to see whether familiarity moderates the relationships. Results show that the more consumers like the repertoire type and the more familiar consumers are with the opera, the higher their willingness to buy a ticket will be. Moreover, results show that the more consumers like the famous star soloist, the more they are willing to buy a ticket. However, there was no significant effect found for a moderation effect by familiarity.
2. Introduction
Opera companies face difficult times when it comes to funding. Recently, governments in Europe decreased the size of their funding budgets for opera companies (Fonds Podiumkunsten, 2016; Bogaart & Van der Horst, 2011). This decrease in opera companies’ funding income, requires an increase in revenue coming from other sources, such as opera companies’ box office revenue. Box office revenue is, after government funds, the second major source of revenue for opera companies (Towse, 2011; Auvinen, 2001; Nationale Opera & Ballet, 2016). Box office revenue is inherent to consumer demand: assuming constant prices, the more people buy a ticket the higher the box office revenue will be. Therefore, strategies on consumer demand for opera productions should 1) encourage current visitors to attend more frequently and/or 2) enhancing audience diversification by attracting new consumers (Tajtáková, 2006). However, literature shows that opera consumers face difficulties during their buying process (Berning & Jacoby 1974; Hirschman & Holbrook, 1982; Lévy-Garboua & Montmarquette, 1996). Levels of perceived risk can be high due to a lack of sufficient information about the opera. For example, the quality of the singers can be difficult to check for consumers in advance. Marketers of opera companies can reduce such levels of perceived risk by using the right strategies (Petty, Cacioppo & Schumann, 1983). Therefore, the repertoire type and the presence of a famous star soloist will be researched in combination with familiarity and celebrity endorsement to see how this affect consumer demand for opera.
Outcomes on consumer demand for different repertoire types provides mixed results (Laamanen, 2013; Towse, 2011; Da Silva, 1998). Some researchers state that consumer demand for classic types of operas is significantly higher than for modern types of operas (Towse, 2011; Throsby, 1994). On the other hand, researchers found evidence that consumer demand for modern types of operas is higher (Laamanen, 2013; Voss & Voss, 2000). At the moment of writing, the classic opera repertoire is about operas written before the year 1900 (being the ‘common-practice’ period which includes baroque, classical and romantic composers) and the modern opera repertoire is about operas written after the year 1900 (being the ‘20th century’ period which includes modern and contemporary composers) (Thorsby, 1990; Laamanen, 2013; Opera Europa, 2016; Taruskin, 2010). Marketing literature suggests that perceived levels of familiarity influences the consumer buying process (Petty, Cacioppo & Schumann, 1983). Therefore, this research combines the repertoire type and familiarity in order to see how these will affect consumer demand for opera. Secondly, the relationship between repertoire type and consumer demand will be checked by the use of celebrity
endorsement, being a famous star soloist present in the opera production and on advertisements for the opera production to see whether this strengthens or weakens the relationship. Literature on celebrity endorsement shows positive effects on consumer demand (Erdogan, 1999). Opera companies often pay high prices for famous star soloists and currently intensified the use of famous star soloist in opera productions hoping to increase consumer demand. However, the effect of the use of famous star soloists on consumer demand for opera is not clearly proven. For example, Karniouchina (2011), Laamanen (2013) and Elberse (2007) did find evidence for higher consumer demand when famous star soloists were present in the opera production, while Litman (1983) and Da Silva (1998) did not find significant evidence for this relationship. Therefore, further research is necessary to give more insight into this relationship and will help marketers of opera companies to increase consumer demand for opera productions.
Thus, this research gives answer to the question: what is the effect of the repertoire type
on consumers’ willingness to buy a ticket and how does the presence of a famous star soloist moderates this effect? Willingness to buy will be measured prior to the opening night. By doing
this, we exclude the effect of information becoming available after opening night, which will blur the results by possible inclusion of external effects (Morwitz, Steckel, & Gupta, 2007; Kotler & Keller, 2015)
The first contribution of this research is that it gives more clarity on the relationship between repertoire type and consumers’ willingness to buy at ticket. The second contribution will give more clarity on the relationship between the presence of a famous star soloist and consumer’s willingness to buy a ticket. Moreover, according to Kindem (1982), research often does not isolate consumer demand for stars as an independent variable, which makes it difficult to properly measure the significant construct of consumer demand for stars when a star is present. This research does isolate the construct.
Thirdly, this research particularly distinguishes itself by the use of an in-between field experiment. Seaman (2006) notices the lack of adequate data in most of the studies on consumer demand for the performing arts and underlines the demand for empirical research. Existing empirical research is often based on historical sales data, only a few studies used surveys. The problem with historical sales data is the inclusion of many other internal and external elements such as ticket price discounts and critical reviews, which can blur consumers’ initial preferences. In this research, initial preferences are measured by gathering data from consumers via surveys, while controlling for other influences.
The structure of this paper is as follows. It starts with the literature review. After that a deep dive will be taken into existing literature about consumer demand, willingness to buy,
familiarity, celebrity endorsement, repertoire type and the presence of a famous star soloist. When hypotheses are formed, the methodology section will follow. Using the results of an in-between experimental design and questionnaires, a two-way ANOVA test, independent t-test, correlation analysis, regression analysis and moderation analysis is presented. After reporting the results, a discussion about the findings and some managerial implications is presented. Lastly, recommendations for further research are given.
3. Literature review
3.1. Consumer demand and buying process – traditional goods and services
Consumer demand comes from a desire to fulfil a need and in order to fulfil the need, the consumer will follow a buying process (Kotler & Keller, 2015). Peter and Olson (2008) define the consumer buying process as the cognitive process by which consumers interpret product information and integrate that knowledge to make choices among alternatives. One of the most important models on the consumer buying process comes from Kotler and Keller (2015). Kotler & Keller (2015) constructed a five stage model of the consumer buying process, explaining how consumer demand can result in buying behaviour (see table 1). In the model by Kotler and Keller (2015), the first step of their five stage model is the problem recognition stage. When a consumer is aware of a problem or a need, he or she wishes to solve or fulfil this problem or need. Therefore, after recognizing the problem or need, the consumer will start searching for information, being stage two of the consumer buying process. When searching for information, the consumer will gather information about the possible options. Often, the consumer does not know all the options available but will at least find out some of the options, the so called ‘awareness set’. At this point, the consumer moves into the third stage in which he or she will make a choice by evaluating the possible options of his or her awareness set. Here, the consumer tries to satisfy his or her need or demand and evaluates the benefits from the optional products or services. The consumer will perceive a product as a bundle of attributes with varying abilities to satisfy his or her need or demand and to deliver benefits. The consumer will weight his or her positive and/or negative beliefs about the possible options to finally come to the best choice. Once the initial buying intention is created, the consumer will evaluate the following aspects in stage four; which brand to buy, which dealer, what quantity, when to buy, and which payment method to use. In stage five, the post purchase behaviour, the consumer evaluates the purchased product or service and considers if he or she is satisfied or not. If being satisfied, the consumer is likely to purchase this product or service again, if necessary. If not satisfied, the consumer is unlikely to buy the product or service again and will talk negatively about the product or service, which is bad marketing publicity for the brand and the company.
Table 1 – The Five-Stage model of the Consumer Buying Process
Stage 1: Problem recognition Stage 2: Information Search
Stage 3: Evaluation of alternatives Stage 4: Purchase decision
Stage 5: Post purchase behaviour
Based on the theory of Kotler & Keller, 2015
3.2. Consumer demand and buying process – experiential goods and services
The consumer buying process as explained above is, according to some researchers, not complete (Holbrook & Hirschman, 1982; Pine & Gilmore, 1999). Adjustments are suggested in order to incorporate more types of products and services. Holbrook and Hirschman (1982) argue that the five-stage model of the consumer buying process must be expanded by incorporating the ‘experiential search’, focusing on pleasure-seeking. The experiential search for pleasure-seeking aims for a quality of experience arising from certain patterns of sensation, stimulating emotional experiences (O’Shaughnessy & O’Shaughnessy, 2002; Holbrook and Hirschman, 1982). Such experiences come from experiential goods and services. For example: seeing a stand-up comedian performance or doing a skydive are typical examples of an experiential good or service. However, buying a can of peanut butter or let an accountant do your tax return are typical examples of traditional goods and services.
Experiential goods and services are being consumed differently compared to traditional goods and services. Experiences are personal and subjective, involve emotional, intellectual, spiritual and physical attributes (Schmitt, 1999; Pine & Gilmore, 1999). Consuming the same type of experience, each person will perceive the experience differently. For example, when reading a book, each person undergoes a different emotional and intellectual reaction, hence, ‘consuming’ the same book differently. Experiential goods and services are not considered by the sum of their attributes, but as the potentials arising from their combination. If the components of a painting are combined differently, while not changing the single elements, the overall experience will be different while the sum of the attributes has not changed. Moreover, experiential goods and services are more based on consumption rather than on purchase, that is, on the psychological reaction induced by using the product (Schmitt, 1999; Pine & Gilmore, 1999). Also, experiential goods and services last longer than traditional goods and services. For example: seeing an impressive movie, the experience of seeing that movie can give long lasting feelings of fulfilment. While on the other hand, when you empty a can of peanut butter, you will start at stage one of the consumer buying process again; creating a new need for a new can of peanut butter.
Experiential goods are often compared with service goods, being intangible, inseparable of production and consumption, heterogenic and perishable (Berry, 1980; Lovelock, 1981; Holbrook & Hirschman, 1982). However, the information search for experiential goods is more complex than for traditional goods; consumers find it more difficult to form precise predictions about the experiential good than for the traditional good. Forming precise predictions is difficult for consumers since the evaluation of what the experience contains and what the quality of the experiential good will be can best happen after usage. In other words, consumers have limited tangible cues about the experiential good and its quality in advance and therefore consumers can experience feelings of risk (Abbé-Decarroux, & Grin, 1992; Lévy-Garboua & Montmarquette, 1996; Derbaix, 1982). In these situations of perceived risk, consumers tend to rely heavily on psychological inputs such as expectations by word of mouth marketing and evaluations of prior experiences (Berning & Jacoby; Hirschman & Holbrook, 1982; Lévy-Garboua & Montmarquette, 1996). Because of this, the purchase decision stage for experiential goods and services depends more on time constraints, individual characteristics, social and cultural contexts. Moreover, heuristic techniques are used during the purchase decision stage for experiential goods and services since the purchase is often influenced by personal memories and experiences of similar previous experiences and other peoples’ opinions (Neelamegham & Dipak, 1999). While for traditional goods and services the strategy of the consumer is more based on maximizing utility, for experience goods and services the choice criterion differs.
The consumer buying process by Kotler and Keller (2015) is now discussed with the inclusion of experiential goods and services which shows to evoke feelings of risk (Hirschman & Holbrook, 1982; Lévy-Garboua & Montmarquette, 1996). In order to diminish these levels of risk, it is necessary to dive deeper into the literature on buying intentions, willingness to buy, and buying risks. In other words, how can we influence consumer’s willingness to buy in situations in which the consumer perceives feelings of risk?
3.3. Willingness to buy
Willingness to buy in this research is best formulated as consumer’s buying intention, by measuring consumer’s willingness to buy an experiential good or service. Research on consumer’s buying intention originates from the marketing literature (Baker, Levy, & Grewal, 1992). Consumer’s buying intention can be formulated as the possibility that a consumer will buy a product or service. The higher his or her intention, his or her ‘willingness to buy’, the higher the possibility that he or she buys the product or service (Schiffman & Kanuk, 2000;
McKnight, Choudhury, and Kacmar 2002). Literature confirms the positive causal relationship between consumer’s willingness to buy and actual purchases (Lim, et al., 2006). Moreover, literature shows that consumer’s buying intention best predict actual buying behaviour if the intentions are measured for existing products, for specific brands or products, when the intentions are measured in a comparative mode, and if measured just prior to the performance of the buying behaviour (Morwitz, Steckel, & Gupta, 2007). Literature shows that in between the moment of being willing to buy and the actual purchase, several effects can influence the actual buying behaviour (Kotler & Keller, 2015; Morwitz, Steckel, & Gupta, 2007). However, before someone decides he or she is willing to buy the product or service, several perceived risks should be evaluated.
3.4. Buying risk
A consumer’s buying decision is heavily influenced by various types of perceived risk. For experiential goods, three types of buying risks are applicable: functional risk (the product or service does not perform to expectations, having not the preferable outcome), financial risk (the product or service was not worth the price), time risk (the failure of the product or service result in an opportunity cost of finding another satisfactory product or service) (Kotler & Keller, 2015; Roselius, 1971; Derbaix, 1982; Tam, 2012; Beneke, et al., 2013). Kotler and Keller (2015) state that the level of these perceived risks depend on the amount of money spent, the amount of uncertainty, and the level of self-confidence of the consumer. Moreover, research shows that the greater a consumer perceives levels of risk, the greater a consumer perceives the probability of a loss and the lower his or her willingness to buy will be (Stone & Winter, 1987). Therefore, a relevant question for marketers is how consumers can be persuaded in such a way that perceived levels of risk diminish in order to increase consumers’ willingness to buy. Hence, literature on consumer persuasion will be reviewed.
3.5. Consumer persuasion
According to Petty, Cacioppo and Schumann (1983), there are two ‘routes’ of persuasion in the consumer buying process; the central route and the peripheral route. The central route is based on consumer’s rational consideration of the most important product information. The central route helps consumers in decisions for traditional goods and services by outweighing the most important product information among consumer’s awareness set, to choose the best option. However, for experiential goods and services, the central route is more difficult to apply. As earlier mentioned, for experiential goods and services, consumers lack
having sufficient information about the good or service, because this information is not easy available (Berning & Jacoby; Hirschman & Holbrook, 1982; Lévy-Garboua & Montmarquette, 1996). In these situations, literature shows that consumers tend to follow the peripheral route (Petty, Cacioppo, & Schumann, 1983). Using the peripheral route, the consumer does not consider the positive and negative aspects of the good itself, but base his or her decision on positive or negative cues associated with the experiential good arising from extrinsic factors such as familiarity and celebrity endorsement. (Petty, Cacioppo, & Schumann, 1983). Therefore, literature on familiarity and celebrity endorsement will be reviewed.
3.6. Familiarity
Familiarity is shown to have an impact on consumer’s buying process and buying intention as it reduces the buying risk perceptions of the consumer (Gefen, 2000; Petty, Cacioppo, & Schumann, 1983; Baker et al., 1986). Alba and Hutchinson (1987) describe familiarity as consumer’s accumulated product, service and/or brand related experiences, in a direct and indirect manner. These are experiences such as advertising exposure, word of mouth marketing, trial and consumption experiences, and other sources of product, service and/or brand related experiences. Dick, Jain, and Richardson (1996) suggest that high levels of familiarity can result in high levels of perceived quality. These high levels of perceived quality due to familiarity can occur because of 1) enhanced identification with the product, service and/or brand, 2) increased inclusion of the evoked set, and because of 3) generating positive feelings towards the product, service and/or brand. Literature shows, the more familiar the consumer becomes with the product, service and/or brand in a positive way, the higher consumer’s level of trust will be, the lower consumer’s risk perception of the product or service, resulting in higher levels of willingness to buy and actual purchases (Gefen, 2000; Baker et al., 1986). Therefore, in situations in which a consumer has a set of risky alternatives to choose from, levels of familiarity can play an important role in consumer’s final decision (Mieres, Martin, & Gutiérrez, 2006).
3.7. Celebrity endorsement
Celebrity endorsement is also shown to affect consumers buying intention by reducing risk perceptions of the consumer (Petty, Cacioppo, & Schumann, 1983; Erdogan, 1999). According to Erdogan (1999), celebrity endorsement generates positive feelings and perceptions for the consumer. Celebrity endorsement increases attention for the product or service, polishes the image of the brand, introduces the brand, repositions the brand, and/or
underpins global campaigns. McCracken (1989) defines celebrity endorsement as any individual enjoying public recognition using this recognition on behalf of a good or service by being related with the good or service in marketing communication strategies. When a celebrity promotes a certain product or service and consumers have positive feelings and perceptions about the celebrity, consumers will generate positive feelings and perceptions about the product or service (Erdogan, 1999). There are three general ways of endorsement: 1) being a spokesperson; speaking on behalf of the product, service and/or company, 2) being an endorser in a testimonial way praising the product, service and/or company and 3) being a celebrity incorporated in advertisements (Erdogan, 1999). In this research, celebrity endorsement by celebrities being incorporated in advertisements will be used.
One could argue that a celebrity endorser is someone with status and reputation. Status is a sociological concept which captures differences in social rank, generating privileges or discrimination (Washington & Zajac, 2005). A famous celebrity has a whole different status than ordinary citizens. For example, celebrities are treated different in public, think of VIP treatments. Reputation, on the other hand, comes from the economic literature and is defined as the prestige someone has on the basis of how he or she has performed in particular events in the past (e.g. by receiving awards and positive critical reviews) (Wilson, 1995). Research shows that perceived levels of status and reputation of a celebrity, influences consumer’s willingness to buy in situations of high perceived risk about a product or service (Podolny, 2005; Jensen & Roy, 2008). For example, literature by Podolny (1994; 2005) and Jensen and Roy (2008) shows that consumers positively associate status and reputation of the celebrity endorser with high quality levels of the product or service. Therefore, in situations in which the quality of a product or service is hard to define, which is the case for most of the experiential goods, the affiliation of celebrities with status and reputation to the product or service will positively influence consumer’s buying decision. By the affiliation of celebrities with the product or service, research state that marketers can make the unobservable quality of the product or service observable (Podolny, 1994; Washington & Zajac, 2005).
However, the effectiveness of the celebrity endorsement depends on the credibility of the celebrity endorser (Caldwell and Nicholson, 2014; Erdogan, 1999; Hovland & Weiss 1951). Celebrity’s credibility is about his or her expertise and trustworthiness. Celebrity’s expertise comes from celebrity’s knowledge, experience and/or skills in the related field. Research shows that a celebrity who is an expert in his or her field is more persuasive and likely to generate higher levels of consumer’s willingness to buy a product or service (Aaker & Myers, 1987; Ohanian, 1991). For example; Lionel Messi is likely to be perceived as a more
credible endorser for football shoes than Mariah Carey. Moreover, Hovland & Weiss (1951) state that it is most important that the consumer believes the celebrity is credible, no matter if the celebrity is a real expert. For example, the president of the FIFA being an endorser for football shoes can feel as a credible endorsement, while the president of the FIFA may never use football shoes himself.
We have now discussed two strategies to diminish consumers’ perceived levels of buying risk for experiential goods: familiarity and celebrity endorsement. The focus of this research will go further with one type of experiential good, namely: opera.
3.8. Opera as experiential good
The performing arts are a typical example of experiential goods. Opera is, like all other live performing arts, an experiential good of which the production and consumption happens simultaneously and which includes intangible and hedonic attributes (Towse, 2011; Seaman, 2006). Consumers of opera search for an experience on the emotional and intellectual level (Cooper-Martin, 1991). Consumer’s decision to buy the experiential good ‘opera’ is mainly directed by its intrinsic value, seeking for pleasure, for a hedonic experience (Boorsma, 2006). Moreover, the consumer buying process of opera can be accompanied with levels of risk. As mentioned earlier, the evaluation of the experiential good, in this case opera productions, can only happen after usage. Therefore, consumers can perceive feelings of risk during their buying process for an opera due to a lack of sufficient information about the opera in advance and this can negatively influence consumers’ willingness to buy for the opera production (Berning & Jacoby; Hirschman & Holbrook, 1982; Lévy-Garboua & Montmarquette, 1996). Currently, opera companies focusing on increasing consumer demand and therefore prefer to mitigate consumer’s feeling of risk (Boorsma & Chiaravalloti, 2010; Santana, 2009; Towse, 2011). In this research, feelings of risk are tried to diminish by the use of two peripheral cues; familiarity and celebrity endorsement. These cues will be tested to see if and how they influence consumer demand for opera.
4. Hypotheses building
4.1. Opera and consumer demand
In the 17th century, running an opera company was a fruitful business since there was no lack of financial supplies (Bianconi & Walker, 1984; Towse, 2011). Nowadays, the majority of opera companies have difficulties to survive without government funding (Towse, 2011;
Auvinen, 2001; NOS, 2016). As mentioned before, governments in Europe recently decreased the size of their funding budgets for opera companies (Fonds Podiumkunsten, 2016; Bogaart & Van der Horst, 2011). This decrease in opera companies’ funding income, requires an increase in revenue coming from other sources, such as opera companies’ box office revenue. Box office revenue is, after government funds, the second major source of revenue for opera companies (Towse, 2011; Auvinen, 2001; Nationale Opera & Ballet, 2016). Box office revenue is inherent to consumer demand: assuming constant prices, the more people buy a ticket the higher the box office revenue will be. Therefore, strategies on consumer demand for opera productions should 1) encourage current visitors to attend more frequently and/or 2) enhancing audience diversification by attracting new consumers to the art form (Tajtáková, 2006).
However, increasing consumer demand is shown to be a struggle for many opera companies balancing between the contradiction of ‘arts for arts sake’ and commercial goals (Bogaart & Van der Horst, 2011; Auvinen, 2001; Turbide & Laurin, 2009). Literature state that opera is not made mainly because there is demand for it; it is made for the sake of art and consumer demand is of subordinate priority (Lawrence & Phillips, 2002; Colbert, 2003). On the other hand, in order to create artistic excellence and the freedom to make ‘arts for art sake’, opera companies need to have a continuous flow of financial resources and thus also need to focus on their financial performance (Bourdieu, 1993). However, marketing is often feared for what it might do with the arts. On the one hand marketing can raise consumer demand, but on the other hand a marketing focus by opera companies is feared to result in repertoire standardisation around solely popular opera’s, the so called ‘box office’ repertoire, which means the end of repertoire innovation (Martorella, 1977; Tajtáková, 2006). Therefore, this research will also test how the repertoire type and the presence of a famous star soloist influence consumer demand for opera.
4.2. Repertoire type and a famous star soloist
The first part of this study contains an in-between groups experiment in which consumers’ willingness to buy, for different repertoire types, with and without the presence of a famous star soloist, will be measured (see figure 1).
Generally speaking, there are two types of repertoire: classic and modern (Thorsby, 1990; Bianconi & Walker, 1984). In literature, there is no strict rule about which operas can be labelled as classic or as modern types of operas, luckily authors often agree on the same measurement. Operas written before 1900 are classic types of operas (being the ‘common-practice’ period which includes baroque, classical and romantic composers) and operas written
after 1900 are modern types of operas (being the ‘20th century’ period which includes modern and contemporary composers) (Thorsby, 1990; Laamanen, 2013; Opera Europa, 2016; Bianconi & Walker, 1984). Existing research on the repertoire types in relation to consumer demand shows mixed outcomes. For example, in the handbook of cultural economics by Towse (2011) is stated that the classic types of operas significantly sell better than modern types of operas. However, Laamanen (2013) found in his research that audiences prefer modern types of operas over classic types of operas. Throsby (1994) shows, like Towse (2011), that consumer demand is higher for classic types of operas than for modern types of operas. Research by Sorjonen and Uusitalo (2005) states also that modern types of operas are negatively related with consumer demand. Abbé-Decarroux (1994) state that the chosen repertoire type has no significant effect on consumer demand. Therefore, this research tries to clarify this relationship.
Often, opera companies have only a few titles in their repertoire that are expected to result in high attendance figures (Miller, 1995). These operas are performed to compensate for the lower attendance figures of the more innovative programming. Data of Opera Base shows that the most popular and frequent programmed opera productions by all opera companies in the world are mostly classic types of productions (Opera Base, 2016). As mentioned before, consumers of experiential goods, such as opera, lack having sufficient information about the product or service. Not all aspects of an opera can be observed prior to the performance, thus consumers will perceive levels of risk prior to consumption (Berning & Jacoby; Hirschman & Holbrook, 1982; Lévy-Garboua & Montmarquette, 1996). Literature shows that in situations in which consumers lack sufficient information, consumers tend to use the peripheral route in order to make their buying decision (Petty, Cacioppo, & Schumann, 1983). When considering the literature on the peripheral route, one could argue that the risk consumers perceive when buying a ticket for an opera can be mitigated by levels of familiarity (Gefen, 2000; Baker et al., 1986). For example: modern types of operas often consist of unknown work,beingopera productions new to the world. Therefore, the audience is expected to be unfamiliar with the opera and levels of perceived risk can rise (Pierce, 2000). This is different for the classic types of operas. For example, the programming of opera companies often shows similarities since only a small amount of all written operas is regularly played, being mostly classic types of operas (Towse, 2011, Opera Base, 2016). Moreover, opera companies regularly reproduce an opera more than once. Therefore, one can argue that opera consumers know most of the frequent programmed classic operas and their stories and probably have seen versions of them earlier, suggesting a level of familiarity. For example, one can be familiar with the music, the story, and/or remember earlier versions of the production. Research shows that consumer’s
preference is accompanied by a sense of familiarity and that the probability of someone attending the theatre (again) increases by the extent he or she enjoyed what he or she saw before (Pierce 2000; Berning & Jacoby; Hirschman & Holbrook, 1982; Lévy-Garboua & Montmarquette, 1996). Therefore, one can expect that in situations in which a consumer perceives a high risk when looking for an opera production, levels of familiarity mitigate the perceived risk by buying the more familiar classic types of operas instead of the more unfamiliar modern types of operas.
To summarize, considering existing literature, consumers’ higher level of familiarity with classic types of operas can reduce perceived levels of risk because the consumer knows better what to expect and is better able to create an expectation of the opera and its quality in advance. Therefore, one can imagine that in situations of which a consumer has a set of risky alternative operas to choose from, perceiving a high level of risk, levels of familiarity can play an important role in consumer’s final decision to buy a classic type of opera (Gefen, 2000; Mieres, Martin, & Gutiérrez, 2006). Therefore, the first hypothesis will be.
Hypothesis 1: A participant will perceive a higher willingness to buy a ticket for a classic type
of opera production in comparison to a modern type of opera production.
The second peripheral cue expecting to have an effect on consumer demand for opera productions is celebrity endorsement. Blaug (1978) was one of the first doing research on the presence of celebrities in opera productions. He found that international opera companies have the tendency to hire international famous star soloists and pay relatively high prices for them compared to the rest of the human capital in a production (Blaug, 1978; Towse, 2011).This means a greater part of company’s budget used to finance the star soloists. Today, opera companies intensified the use of famous star soloist in opera productions in the hope to increase consumer demand (Boorsma & Chiaravalloti, 2010; Santana, 2009). This is surprising since literature shows mixed outcomes on the relationship between the presence of a famous star soloist in opera productions and consumer demand. For example, Laamanen (2013) and Elberse (2007) show a higher consumer demand for productions with a famous star soloist in it. Karniouchina (2011) found in a research about the usage of famous stars in movies, that having a famous star does indeed help in creating a buzz, resulting in higher early ticket sales. However, the ticket sales on the later run was not guaranteed. Litman (1983) found no significant correlation between the presence of a famous star in a film and consumer demand.
Da Silva (1998) found that the presence of a famous star soloist is an important factor for public attention of the show, but is no significant predictor of higher consumer demand.
As mentioned before, consumers might experience levels of risk during the buying process for an opera by not knowing what the opera is about and whether the opera meets consumer’s quality expectations (Berning & Jacoby; Hirschman & Holbrook, 1982; Lévy-Garboua & Montmarquette, 1996). Literature on consumer persuasion shows that celebrity endorsement can help consumers during their buying process, positively affecting consumers’ willingness to buy (Petty, Cacioppo, & Schumann, 1983; Erdogan, 1999). Research shows that the association with the famous star soloist results in consumer’s conclusion that the production must be of good quality (Suarez-Vazquez & Montanes-Roces, 2015). Caldwell and Nicholson (2014) show that audiences are attracted to stars because of their expected high qualities. Consumers interpret the past success of a star as a good indicator of future success. Consumers ‘follow’ stars with good historical success, resulting in higher attendance figures which is the so called ‘bankability effect’ (Rein, Kotler, & Stoller, 1987). Moreover, stars are often referred to as symbols of success and beauty which attracts consumers (Streitmatter, 2004). Therefore, it is expected that levels of willingness to buy for an opera become higher when a famous star soloist is present.
However, the effect on levels of willingness to buy can be different for both repertoire types. One could argue that for modern types of operas of which perceived levels of risk are high, the presence of a famous star soloist can result in consumer’s decision to be willing to buy a ticket, while he or she was not willing to buy a ticket when the famous star soloist was not present in the modern opera production. On the other hand, for classic types of operas, consumer’s willingness to buy a ticket is already high due to earlier explained levels of familiarity and therefore the presence of a star soloist will not make a significant difference. Of course, one can be more enthusiastic about the classic opera now the star soloist is shown to be present, but the consumer already decided to buy a ticket for the opera. Therefore, the significant difference in levels of willingness to buy a ticket is expected for modern type of productions. This difference between not buying a ticket and buying a ticket for an opera is the crucial change marketers hope to achieve and is mostly expected to happen for modern types of productions in which a famous star soloist is present. Therefore, the second hypothesis will be:
Hypothesis 2: A participant will perceive a higher willingness to buy a ticket for a classic type
becomes weaker when a famous star soloist is shown to be present in the opera production in comparison to when there was no famous star soloist shown to be present in the opera production.
4.3. Liking and familiarity
If results show to support hypothesis 1 and 2, the results do not tell anything about, for example, why the willingness to buy a ticket of consumers is higher for classic opera productions than for modern opera productions, it only indicates the significant difference.
Therefore, the second part of this study will contain an analysis of the underlying constructs of the experiment. This analysis will be done to test whether the difference in willingness to buy for the different repertoire types and the presence of a famous star soloist is caused by consumers’ consideration of these elements. Literature states that many elements can influence consumer’s buying decision for an opera production. Internal factors such as ticket price, choir, soloist, directors, decor, and repertoire are shown to influence consumer’s willingness to buy a ticket. External factors such as seasonal effects, price for substitute entertainments, consumer income and education, weather, and reviews also have shown to effect consumer’s willingness to buy a ticket (Throsby, 1994; Tobias, 2004, Abbé-Decarroux, 1994; Lamaanen, 2013, Seaman, 2006). Moreover, opera is a complex art form having a large amount of interdependent elements and interplay presented during a performance and all these stimuli within the performance interact (Holbrook & Batra, 1987). Therefore, one can imagine that consumers also take other elements than the repertoire type and/or the presence of a famous star soloist into account during their buying process. While these other elements are interesting to consider as well, they are out of the scope of this research and therefore both constructs, repertoire type and presence of a famous star soloist will be isolated.
Research on hedonic decision making shows that for experiential goods, liking of the product or service, results in higher levels of willingness to buy and actual purchases (Peryam & Pilgrim, 1957; Hirschman & Holbrook, 1982; Baker et al., 1986). Voss and Voss (2000) state that frequent visitors are not interested in having their known preferences reflected in the programming, they rather want to visit what is innovative and new. Therefore, being known with an opera does not always mean that you like the opera. Therefore, in order to control for other elements blurring outcomes of this research, we will check whether consumer’s willingness to buy comes from his or her liking of the repertoire type and expect that the more a consumer likes the repertoire type, the more willing he or she is to buy a ticket. Therefore, hypothesis 3a will be:
Hypothesis 3a: There is a direct positive relationship between liking the presented repertoire
type and willingness to buy a ticket.
As mentioned before, levels of familiarity can positively affect levels of willingness to buy (Gefen, 2000). For example, consumers have seen the type of opera before and had a positive experience, therefore are better able to identify him or herself with the opera, and better able to create an expectation about the opera, generating positive feelings towards the presented type of opera (Dick, Jain, Richardson, 1996). Therefore, in the situation in which a consumer likes a certain repertoire type, but perceives feelings of risk due to uncertainty about the opera and its quality, levels of familiarity can mitigate these feelings of risk. Literature does show that higher levels of familiarity increases someone’s willingness to buy (Gefen, 2000; Baker et al., 1986). Therefore, one can argue that in situations in which the consumer indicates to like the repertoire type, levels of familiarity further strengthen the relationship with willingness to buy a ticket. Therefore, hypothesis 3b will be:
Hypothesis 3b: There is a direct positive relationship between liking the presented repertoire
type and willingness to buy a ticket and this relationship is moderated by familiarity, such that this relationship becomes stronger for higher levels of familiarity with the production and composer.
Not everyone experiences the same feelings when a star soloist is shown to be present. One can imagine that a consumer perceives a negative feeling towards a celebrity. Literature states that attractiveness to the celebrity endorser has positive effects on consumer’s purchase intention (Baker & Churchill, 1977, Petroshius & Crocker, 1989). Therefore, one can be expected that he or she is more willing to buy a ticket for an opera production when he or she likes the celebrity endorser. For this research we are interested in the effect of the presence of a famous star soloist on consumers’ willingness to buy and therefore check whether someone’s willingness to buy is indeed caused by consumer’s liking of the presence of the famous star soloist. Therefore, the hypothesis 4a will be
Hypothesis 4a: There is a direct positive relationship between liking the presence of the famous
Hovland & Weiss (1951) state that it is most important that consumers believe the celebrity is credible to have positive effects on consumers’ willingness to buy, no matter if the celebrity is a real expert. Literature shows that consumers interpret the past success of a star as a good indicator of future success (Rein, Kotler, & Stoller, 1987). Moreover, literature on status and reputation shows that celebrity’s status and reputation can be a good indicator of the quality of the opera (Jensen & Roy, 2008). Status and reputation are shown to influence the buying decision process by eliminating alternative options due to the expected quality of the work from the famous star soloist (Jensen & Roy, 2008, Podolny, 2005). Being unknown with the famous star soloist in the opera production, one can imagine that it is more difficult to evaluate the status and reputation and the historical success of the famous star soloist. However, one can imagine, being familiar with the famous star soloist, feelings about the status and reputation of the famous star soloist will be confirmed by the presence in the upcoming opera and the opera will become more valuable for the consumer. Moreover, as mentioned before, levels of familiarity can result in higher levels of willingness to buy (Gefen, 2000; Baker et al., 1986). Therefore, it is expected that if someone likes the presence of the famous star soloist in the opera production, the effect on consumers’ willingness to buy will be even stronger when the consumer is familiar with the famous star soloist. Thus, hypothesis 4b will be:
Hypothesis 4b: There is a direct positive relationship between liking the presence of the famous
star soloist in the opera production and willingness to buy a ticket and this relationship is moderated by familiarity, such that this relationship becomes stronger for higher levels of familiarity with the star soloist.
Figure 1 – Conceptual model
In-between experiment || Regression analysis
5. Method
5.1. Measurement remark
Using quantitative measurements for analytical purposes in the arts and culture has always been accompanied with considerable tension (Schuster, 1997; Boorsma & Chiaravalloti, 2010). Literature states to be careful with measuring artistic success by quantifiable aspects such as audience numbers and financial results because it lacks a complete overview and variety of the stakeholders present (government, artistic goals, financial goals, private funders etc.) (Herman, 1990; Herman & Renz, 1999). However, there are plenty of reasons today to explain the use of attendance figures and financial results. The new situation of less governmental funding has made artistic companies alert to increase consumer demand, being an important source of revenue (Kolb, 2000; Burton 2003). Therefore, measuring consumers’ taste might be very helpful because in the end it is the consumers who buy tickets (Throsby, 1990). Surely, outcomes of these measurements should not become the only focus for opera companies and research has shown that artistic innovation stagnates when opera companies mainly focus on audience numbers and financial results (Martorella, 1977).
! ! Repertoire!type! [classic!/!modern]! Willingness!to!buy!a!ticket! Liking!the!presence!of!the! famous!star!soloist!! Liking!the!repertoire!type! Familiarity!with! famous!star! soloist!! Familiarity!with! composer!+! production!! Presence!of!a!famous! star!soloist! [present!/!not!present]! H1! H2! H3a! H3b! H4a! H4b!
Nevertheless, quantitative measurements on consumers’ taste should at least partially direct management of opera companies in their strategy, becoming more and more dependent on ticket sales. Therefore, for this research, consumers’ perception and their willingness to buy a ticket will be measured.
5.2. Research design and procedure
Using an experimental in-between design, data was gathered using visitors of Dutch National Opera (DNO). DNO is part of Dutch National Opera & Ballet. DNO is known as one of the leading opera companies in the world, being rewarded as ‘Opera Company of the Year’ in 2016 at the International Opera Award ceremony. DNO produces most of the opera productions in-house, some productions are produced in cooperation with other major international opera companies such as the Metropolitan Opera in New York. DNO’s director, Pierre Audi, is known for his adventures programming. His version of Der Ring des Nibelungen in 1997 is, according to some critics, one of the highlights in the history of opera (Spel, 2016). In order to create an experiment, a classic opera production, a modern opera production and a famous star soloist have to be selected. While there are plenty of operas written over the past hundreds of years, only a small amount of them is regularly played (Towse, 2011, Opera Base, 2016; Auvinen, 2001). The Opera Europa association gives us more insight into which composers belong to the different types of repertoire. Opera Europa is the professional association of opera companies and festivals in Europe. Working together with 166 opera companies in 42 European countries, having its headquarter in Brussels, Opera Europa is at the moment the main association when it comes to opera. According to their website; for classic types of operas one can think of composers such as: Monteverdi, Händel, Weber, Mozart, Wagner, Rossini, Verdi, Mussorgsky, Bizet and Puccini. For modern types of operas one can think of composers such as: Britten, Adams, Andriessen, Stockhausen, Saariaho, Glass, Schnittke, Berg, and Van der Aa (Opera Europa, 2016; Clements, 2011). For the classic opera repertoire, the list of opera productions contains at least productions such as: La Bohème (Puccini), Madama Butterfly (Puccini), Die Zauberflöte (Mozart), La Traviata (Verdi), Die Walküre (Wagner), Carmen (Bizet), L’Orfeo (Monteverdi), Guillaume Tell (Rossini), Boris Godunov (Mussorgsky), and Der Freischütz (Weber). The list of modern opera productions contains at least productions such as: Peter Grimes (Britten), Lulu (Berg), Light - The Seven Days of the Week (Stockhausen), La Commedia (Andriessen), Doctor Atomic (Adams), Waiting for the Barbarians (Glass), L’Amour de Loin (Saariaho), and One (Van der Aa) (Opera Base, 2016; Towse, 2011; Maddocks, 2011). Given names and titles should not be taken too
strictly. One has to take into account that above mentioned composers and productions are not officially ranked as the best composers or best productions in their repertoire and there are many more important composers and productions for the given repertoire types. For this research, composers and productions are chosen by looking at attention given by the opera company, reviews given by critics, and programming trends in the world.
DNO’s production of Die Zauberflöte in 2012 received enormous positive attention by critics (Schneeweisz, 2012; Nationale Opera & Ballet, 2013). Moreover, data shows that Die Zauberflöte is most often produced, after La Traviata, by opera companies in the world (Opera Base, 2016). Therefore, in this research, the classic opera production will be represented by: Die Zauberflöte – Wolfgang Amadeus Mozart.
Sariaaho’s opera Only the Sound Remains was the opening of last years’ Dutch opera festival: Opera Forward Festival and received plenty of positive attention by critics (Schneeweisz, 2016). Opera Forward Festival is an annual festival of DNO which shows new work, new talents and new initiatives. Moreover, Sariaaho is ranked as one of todays leading composers by the Guardian (Clements, 2011). Since the production ‘Only the Sound Remains’ was on stage last year, it might be better to take a different opera by Saariaho to make sure people will not solely base their willingness to buy on that exact production. Therefore, in this research, the modern opera production will be represented by: L’Amour de Loin – Kaija Saariaho.
In search for an international famous star soloist, soloists were checked for being known, qualified and high in demand by opera companies. Eva Maria Westbroek is one of todays leading sopranos. Westbroek played lots of leading soloist roles at famous international opera companies like Covent Garden, Royal Opera House, Metropolitan Opera, Vienna State Opera, Deutsche Oper Berlin and more. She is often referred to as a ‘world famous star’ and critics are generally laudatory about her performances (Ramey, 2015; Opera News, 2015). Westbroek played several times at DNO, last performance being the ‘Jubileum Concert’ in honour of 50 years Dutch National Opera and Puccini’s Manon Lescaut (Opera Nederland, 2016). Westbroek is internationally famous for her quality and will represent the famous star soloist in this research.
Four types of conditions were distributed among the participants. For every condition a separate group was used, being an in-between design. The four conditions are:
Condition 1: Classic production / No star soloist present Condition 2: Classic production / Star soloist present Condition 3: Modern production / No star soloist present Condition 4: Modern production / Star soloist present
Conditions were presented using a poster in the style of DNO. To control for influences by visual aspects, the opera productions were shown in a similar manner, using the same font, size, and color. No images were used to prevent having side effects. Participants were randomly divided among the four conditions. In order to run an analysis, dummy variables were made for ‘repertoire type’ (classic = 0, modern = 1) and ‘presence of a famous star soloist’ (not
present = 0, present = 1). After seeing one of the four conditions, participants were asked to
fill in a short survey, which was the same survey for all participants. At first, the surveys were pre-tested to make sure all questions were clear to participants and not too difficult to answer. The pre-test was send out to 15 participants. Only one minor change was necessary. The final versions of the surveys were send out in Dutch language to 4000 visitors of DNO. The original items were translated into Dutch using a translation-back-translation procedure. This was done in cooperation with an advanced English speaker of Dutch origin. No correction was needed. As the population comes from the database of DNO, this is the sample frame. The research is conducted using a non-probability convenience sampling strategy as the participants could decide on their own (self-selection) to participate or not. Participants were reached via the official online channels of DNO.
Besides asking participants whether they were willing to buy a ticket for the shown opera production, participants were asked about their gender, age, income, level of education, frequency of opera visit, and preferred seating range. There is controlled for careless responding and socially desired answers. Careless responding is prevented by checking participating time and socially desired answers by informing participants about their anonymity within this research.
5.3. Variables Willingness to buy
Willingness to buy was measured using two items, both on a 7-point Likert scale (1=completely not, 7=definitely, with a ‘neutral’ option in the middle). The items asked participants whether they would buy a ticket for the shown opera production. One example item is: I am considering
purchasing a ticket for this opera production (Lim et al., 2006). The scale was highly reliable
(α = .929). A sum variable was made.
Liking the repertoire type
Liking the repertoire type was measured using two items, both on a 7-point Likert scale (1=completely not, 7=definitely, with a ‘neutral’ option in the middle). The items asked participants whether they liked the presented repertoire type (classic/modern). One example item is: How much would you say you like classic opera productions (Sen et al., 2001)? The scale was reliable (α = .813). A sum variable was made.
Familiarity with composer and production
Familiarity with the composer and the production was measured using two items, both on a 7-point Likert scale (1=completely not, 7=definitely, with a ‘neutral’ option in the middle). The items asked whether the participants knew the opera production and its composer. One example item is: I am familiar with Wolfgang Amadeus Mozart (Raju, 1977). The scale was highly reliable (α = .953). A sum variable was made.
Liking the presence of the star soloist
A star soloist was present on the poster or not. Participants were asked whether they feel a soloist is of importance in opera productions. This was done using a 7-point Likert scale (1=completely not important, 7=very important, with a ‘neutral’ option in the middle). The item was: Indicate whether the presence of a soloist is important for you when visiting an opera
production.
Familiarity with the star soloist
In the situation of the presence of a star soloist on the poster, participants were asked whether they know the star soloist. This was done using a 7-point Likert scale (1=completely not, 7=definitely, with a ‘neutral’ option in the middle). The item was: I am familiar with Eva Maria
6. Results
6.1. Demographics
The response rate was 26.8%, resulting in 1073 cases. Unfortunately, not everyone filled in the questionnaire completely. 67 cases had missing data and, because of the high N-size, were deleted out of the sample. A total of 1006 valid cases is used:
Condition 1: 255 valid cases Condition 2: 257 valid cases Condition 3: 253 valid cases Condition 4: 241 valid cases
The distribution of gender and age was almost equal among the four conditions (see table 2). More males participated in de study than females and the average age was 58 years. Only for condition 3, the females were less represented than in the other conditions.
Table 2 – distribution of gender and age
Male (in percentages) Female (in percentages) Age (Mean)
Condition 1 52.9 46.3 57
Condition 2 52.1 45.9 58
Condition 3 58.1 39.9 58
Condition 4 51.5 46.5 56
6.2. Normality, homogeneity, and outliers
The conditions were tested for normal distribution. The conditions are non normally distributed, showing a .00 significance level on the Shapiro-Wilk test for all variables. A transformation of data was done using Log10, however variables remained non normally distributed and therefore non transformed data is used.
Willingness to buy for condition 1 was highly negative skewed and had a low kurtosis. This indicate a long left tail since most of the participants indicated a high willingness to buy for the classic opera production without a star soloist present. This was shown by point 6 and 7 (indicating a high willingness to buy a ticket) of the 7-point Likert scale chosen by 40.7 percent of the participtants.
Willingness to buy for condition 2 was also highly negative skewed and had a low kurtosis. This also indicates a high willingness to buy for the classic opera production, but now with a star soloist present. Here, 45.1 percent of the participants chose point 6 and 7 of the 7-point Likert scale.
Willingness to buy for condition 3 was not skewed and had a high negative kurtosis. This is due to peaks of ‘not at all’ willingness to buy and a high number of participants chosen the option ‘neutral’ (Point 1 (‘not at all’); chosen by 17.5 percent and point 4 (‘neutral’); chosen by 19.8 percent and only 12.4 percent of the participants chose point 6 and 7 of the 7-point Likert scale). This indicates a low willingness to buy for modern opera productions without a star soloist present.
Willingness to buy for condition 4 was not skewed and had a high negative kurtosis. Same peaks as for condition 3 were visible; point 1 (‘not at all’) was chosen by 14.9 percent and point 4 (‘neutral’) was chosen by 26.6 percent. Moreover, point 6 and 7 of the 7-point Likert scale was chosen by 13.3 percent.
The Levene’s Test showed a non significance of .538, by which we can conclude that the conditions are homogeneously distributed. Boxplots were created in order to detect outliers and no outliers were detected.
6.3. Control variables
Results show that most of the participants visit the opera regularly (3 to 4 times a year), being 36.4 percent of the total. For the rest of the participants, attendance was almost equally distributed with 22.3 percent going rarely (1 to 2 times a year), 20.6 percent often (5 to 6 times a year), and 20.8 percent going very frequently (7 or more times per year).
Participants showed a preference for the better seats in the theatre with 69.8 percent buying third to first range seats when they visit the opera. Only 5 percent of the participants chose the worst range.
Participants showed to be educated well. 61 percent of the participants finished university with a master degree. Only 8.6 percent did not attend academy or university after high school.
Income was skewed to the left, indicating a high average income. Using Dutch tax brackets as a reference; only 6.8 percent earn less than €19.923, - per year, 13.7 percent of the participants earn between €19.922 and €33.716, - per year, 25.5 percent between €33.715 and €66.422, - per year, and 28 percent earn more than €66.421, -. The amount of participants who
did not wished to answer this question accounted for 25 percent of the total amount of participants.
6.4. Two-way ANOVA and independent T-tests
A two-way ANOVA test was done (see table 3) since there were more than two conditions and more than one independent variable (‘repertoire type’ and ‘presence of a famous star soloist’). After, a correlation, regression, and moderation test was done.
Table 3 - Willingness to buy a ticket
Condition (N) M (SD)
1. Classic – no star present (255) 4.8412 (1.7443) 2. Classic – star present (257) 4.9767 (1.8935) 3. Modern – no star present (253) 3.4308 (1.7332) 4. Modern – star present (241) 3.8071 (1.7491) Total N = 1006
Results show that ‘repertoire type’ and ‘presence of a famous star soloist’ both had a significant impact on ‘willingness to buy a ticket’ (see figure 2). However, the interaction effect was not significant.
There was a significant main effect of ‘repertoire type’ on levels of ‘willingness to buy a ticket’; F(1,1002) = 131.703, p<.001, η=.116. The effect size was moderate. No post-hoc test was done due to less than three groups. An independent T-test showed us that participants’ willingness to buy a ticket for condition 1 [classic production / no star present] (N=255) was significantly higher M=4.8412 (SD=1.7443), t(506)=9.141, p<.001) than the willingness to buy a ticket of participants for condition 3 [modern production / no star present] (N=253) M=3.4308 (SD=1.7332). The assumption of homogeneity of variances was tested and satisfied via Levene’s F-test, F(506)=.230, p=.632.
There was also a significant main effect of ‘presence of a famous star soloist’ on levels of ‘willingness to buy a ticket’; F(1,1002) = 5.181, p<.05, η=.005. The effect size was low. No post-hoc test was done due to less than three groups. An independent T-test showed us that participants’ willingness to buy a ticket for condition 2 [classic production / star present] (N=257) was significantly higher M=4.9767 (SD=1.8935, t(496)=7.147, p<.001) than the willingness to buy a ticket of participants for condition 4 [modern production / star present] (N=241), M=3.8071 (SD=1.7491). The assumption of homogeneity of variances was tested
and satisfied via Levene’s F-test, F(496)=1.164, p=.281. Moreover, an independent T-test showed us that participants’ willingness to buy a ticket for condition 4 [modern production / star present] (N=241), was significantly higher M=3.8071 (SD=1.7491, t(492)=2.401, p<0.05) than the willingness to buy a ticket for condition 3 [modern production / no star present] (N=253), M=3.4308 (SD=1.7332). The assumption of homogeneity of variances was tested and satisfied via Levene’s F-test, F(492)=.050, p=.822. There was no significant effect on willingness to buy a ticket found between condition 1 [classic production / no star present] and condition 2 [classic production / star present], t(510)=.842, p=.400.
There was a non-significant interaction effect between ‘presence of a star soloist’ and ‘repertoire type’ on levels of ‘willingness to buy a ticket’, F(1,1002) = 1.147, p=.28, η=.001.
Figure 2 – Willingness to buy a ticket
4.8412 4.9767 3.4308 3.8071 3 4 5 6 no!star!present star!present
Willingness(to(buy(a(ticket
classic modern6.5. Correlation
A bivariate correlation test was done in order to check whether variables correlated or not (see table 4).
The correlation table shows that an increase in liking of the repertoire type is significantly related with an increase in willingness to buy (r=.572, p<.001). Familiarity with the composer and production and willingness to buy were also significantly positive related (r=.434, p<.001). Moreover, there was a significant relationship between familiarity with the composer and production and liking of the repertoire type (r=.626, p<.001).
There was a significant positive relationship between liking the presence of the soloist and willingness to buy (r=.098, p<.001). Familiarity with the soloist and liking the presence of the soloist were also significantly related (r=.304, p<.001). Familiarity with the soloist was not significantly related with willingness to buy. Furthermore, liking the presence of the soloist and familiarity with the soloist were both significantly related with liking of the repertoire type (respectively; r=.123, p<.001 and r=.125, p<.001).
Age was significantly related with all variables, except for level of education. Strongest results were the significant positive relationship between age and liking the presence of the soloist (r=267, p<.05). Age and familiarity with the soloist (r=401, p<.001). Age and frequency of visit (r=.200, p<.001) and between age and income (r=.259, p<.001).
Frequency of visit was also related with all variables, except for income. Strongest results were the significant positive relationship between frequency of visit and liking of the repertoire type (r=.175, p<.001). Frequency of visit and familiarity with the soloist (r=.380, p<.001) and between frequency of visit and age (r=.200, p<.001).
Level of education and income were both significant positively related with preferred seating range (respectively; r=.120, p<.001 and r=.238, p<.001). Level of education was also significant positively related with income (r=.183, p<.001).
Table 4: Means, Standard Deviations, and Correlations
Variables Mean S.D. 1 2 3 4 5 6 7 8 9 10 11
1 Willingness to buy 4.2734 1.8988 1
2 Liking the repertoire type 4.8439 1.6140 .572** 1
3 Familiarity with comp.
and prod.
4.2321 2.6601 .434** .626** 1
4 Liking the presence of the
soloist
5.4010 1.2245 .098** .123** .060 1
5 Familiarity with the
soloist
5.6165 1.9878 .052 .125** .059 .304** 1
6 Gender 1.4294 .5283 .034 .012 -.004 .080* -.067 1
7 Age 58.52 14.876 -.071* .111** .099** .267* .401** -.063* 1
8 Frequency of visit 3.40 1.051 .137** .175** .123** .127** .380** -.100** .200** 1
9 Preferred seating range 2.95 1.347 .019 .037 .020 .052 .074 -.079* .144* .069* 1
10 Level of education 7.12 1.310 .037 -.018 .008 -.046 .073 -.009 -.013 .092** .120** 1
11 Income 3.51 1.201 -.083** -.072* -.067* .070* .044 .006 .259** .028 .238** .183** 1
** p<.01 * p<.05
