Amsterdam, 23
rdJune 2017
MUSIC FESTIVAL-GOERS’ EXPERIENCE:
THE ROLE OF ARTISTS’ GEOGRAPHICAL ORIGIN
AND THE MUSIC FESTIVALS’ THEME
Davide Grigatti (11374225)
Supervisor: Dr. Frederik Situmeang
Master course:
Business Administration – EMCI
STATEMENT OF ORIGINALITY
This document is written by Davide Grigatti 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.
Acknowledgements
I would like to thank my supervisor Dr. Frederik Situmeang for the support he gave while I was writing the thesis.
I would like to thank my mother Ida, my father Riccardo, my grandmother Teresa and my grandfather Egidio for all the support and trust they have given me during this year.
I would like to thank Eugenio, Phill, Elli, Adri, Raghav, Conny and Alessandra for having always stood close to me, having been my family in Amsterdam.
Table of Contents
Abstract ... 1
1. Introduction ... 2
2. Theoretical Framework and Hypotheses ... 5
2.1. Customer Experience and Music Festivals ... 6
2.2. The drivers of music festival-goers’ experiences ... 8
2.3. The “Artists’ Origin – Festival’s Theme” Framework ... 10
2.3.1. The “Artists’ Origin” Component ... 10
2.3.2. The “Festival’s Theme” Component ... 11
2.3.3. Classification Gap and “Artists’ Origin – Festival’s Theme” Framework ... 12
2.4 Moderating Variables ... 14
2.4.1. Familiarity with the Artists ... 14
2.4.2. New Artists’ Appreciation ... 15
2.5 Control variables ... 16 2.6 Hypotheses Summary ... 17 3. Methodology ... 17 3.1. Study design ... 17 3.2. Sample profile ... 18 3.3 Measures ... 19 3.3.1. Dependent Variable ... 19
3.3.2. Independent Variable ... 20 3.3.3. Moderating Variables ... 20 3.3.4. Control Variables ... 21 4. Results ... 22 4.1. Descriptive Statistics ... 22 4.2. Analytical Strategy ... 25 4.2.1 Recoding ... 25 4.2.2. Missing Values ... 26 4.2.3. Reliability ... 27 4.2.4. Correlations ... 27 4.3. Hypotheses Testing ... 28
4.4. Impact of Control Variables ... 33
5. Discussion and Implications ... 34
5.1. Theoretical Implications ... 35
5.2 Managerial Implications ... 36
6. Limitations and Future Research ... 37
References ... 39
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MUSIC FESTIVAL-GOERS’ EXPERIENCE:
THE ROLE OF ARTISTS’ GEOGRAPHICAL ORIGIN
AND THE MUSIC FESTIVALS’ THEME
Abstract
Over the last decade, music sales have constantly been decreasing due to the introduction of digital and streaming formats. Simultaneously and consequently, live music has obtained an absolute relevance, being the unique means able to counterbalance these losses. As a direct consequence, the level of competition between music festival organizations has increased. For dealing with this new scenario, music festival managers can leverage some elements that lower customers’ informational asymmetries and enhance their experiences. The thesis focuses on the geographical origin of the artists, considering it as an element characterised by these features. The aim is to study how geographical origin, interacting with the theme of a music festival, affects attendees’ experience. This analysis implies considering how two opposite scenarios arising from this interaction affect music festival-goers’ experience, and which strategies managers can adopt for moderating their effects. A quantitative research method, based on a survey, was adopted. 102 interviewees filled out the questionnaire. The results coming from this were analysed using a statistical linear mixed-effects method. The first contribution brought by the thesis coincides with the contextualization of the customer experience construct in the music festival’s field. The second coincides with the broadening of the cases in which a classification gap arises. This thesis also describes how music festival managers can intervene if a classification gap takes place. This coincides with the introduction of artists perceived by consumers as surprises.
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1. Introduction
The evidence and the relevance of the economic and cultural impact of music festivals on local and regional economies have been widely demonstrated by different studies (Leenders, van Telgen, Gemser, & Van der Wurff, 2005; Gazel & Schwer, 1997; McIntyre, 2003; Bracalente, et al., 2011). Focusing on the Netherlands and on its music festival industry, it is possible to see how this last presents a prominent position in the European landscape. The Netherlands are the motherland of some of the oldest and most relevant music festivals in the international scene. Pinkpop, Lowlands, North Sea Jazz, Dekmantel, ADE, Eurosonic Norderslag are some of these. Every year, this industry attracts millions of tourists, precisely 12.8 million of visitors in 2013 (VVEM, 2014; Kuijken, Leenders, Wijnberg, & Gemser, 2016). It also generates millions of revenues. In 2013, solely the tickets’ sales produced a total of 119.7 million euros (VVEM, 2014; Kuijken et. al, 2016). Due to the introduction of download and streaming technologies, music organizations have been experiencing declining revenues from recorded music sales since the end of the 20th century. For counterbalancing these losses, live performances and music festivals have obtained a constantly increasing relevance (Leenders et al. 2005). Inevitably, the level of the competition has intensely risen (Leenders et al. 2005). Vilet (2016) reports in “Festival Atlas 2015” that in 2015 the Netherlands hosted 924 music festivals - excluding the jazz and classical music ones. This signifies that on average just in 2015 the Netherlands held more than two festivals per day. In the light of this impressive information, it is easily understandable the necessity of developing efficient, competitive strategies able to guarantee distinctiveness, and therefore success and survival. As Lendeers et al. (2005) underline, music festival managers have tried to differentiate their festivals but have focused on obtaining this solely by leveraging different dates and different artistic content.
Looking at the business literature, a trending topic of the last years is represented by customer experience. Customer experience is defined as “a multidimensional construct focusing on a customer’s cognitive,
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emotional, behavioral, sensorial, and social responses to a firm’s offerings during the customer’s entire purchase journey” (Lemon and Verhoef, 2016; p. 71). The relevance of speaking about customer experience and customer journeys is introduced by Edelman and Singer (2015). They state that “by making the journey a compelling, customized, and open-ended experience, firms can woo buyers, earn their loyalty, and gain a competitive advantage” (Edelman et al, 2015; p. 5). Therefore, by developing an appealing and enduring customer experience, music festival organizers can differentiate themselves from their competitors and gain a competitive advantage.
This study aims to link the creative industries, and especially music festivals, to the customer experience construct. There is a twofold reason for doing so. First, the focus of the customer experience literature (i.e. Lemon et. al, 2016; Richardson, 2010; Edelman et. al, 2015) is just on search products - “goods that consumers can evaluate by specific attributes before purchase” (Cui, Lui, Guo, 2012; p. 42) – and not on experience ones - products “[…] such as movies and books, [that] require feeling or experiencing, are more difficult to describe using specific attributes, and may render varied experiences across consumers” (Cui, Lui, Guo, 2012; p. 42-43). This is the first literature gap the thesis aims to investigate. It does so providing an example relative to how the customer experience construct functions applied to experience products and the creative industries.
The second reason for investigating the contextualization of the consumer experience construct in the creative industries field comes from another core characteristics of these industries: uncertainty. Peltoniemi (2015; p. 42) states that “there is extreme uncertainty regarding the success potential of any specific product because, prior to consumption, consumers cannot have complete information about the product (De Vany and Walls, 2007; Pratt, 2008; Towse, 2003b)”. Hence, uncertainty simultaneously affects consumers and producers. Since consumers cannot have objective information about the product and organizers are not able to predict the success of their offerings. For these reasons Peltoniemi (2015;
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p. 43) speaks about “nobody knows anything (Goldman, 1983)”. Contextualizing uncertainty in the music festivals’ field, on one hand, makes it possible to identify it in the sense that music festival-goers do not have any objective and reliable source of information relative to their experience at the music festival. On the other hand, this dynamic also affects music festivals’ managers, who are unable to understand how the demand will behave. Nonetheless, festival organizers have a series of silver bullets at their disposal. They can leverage elements that customers perceive as valuable and important for their experience. These elements are at the core of the analysis of this thesis. They can lower customers’ informative uncertainty and therefore enhance their experience. They are represented in here by the role of the geographical origin of the artists included in a festival’s line-up and what Leenders et al. (2005) call the theme of a music festival. Therefore, the second contribution to the customer experience and creative industries theories is represented by the analysis of how these elements affect the experience of music festival-goers. This topic will be considered introducing a new framework (“Artists’ Origin – Festival’s Identity” framework) and broadening the “Classification Gap” construct proposed by Kuijken et al. (2016).
The research conducted by this thesis is developed from the customers’ point of view, allowing for the possibility to directly perceive how their experience at a music festival varies depending on the relevance they give to a series of elements.
The question at the core of this research is the following: “How do artists’ geographical origin and the
music festival’s theme influence the experience of music festival-goers?”.
Looking at the managerial practice, the thesis aims to contribute to it by providing insights relative to how artists’ geographical origin and the festival’s theme influence the experience of music festival-goers. This coincides with the demonstration of the relevance of taking artistic decision that are coherent with customers’ perception of the festival’s theme. In addition, this thesis proposes two strategies music
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festival managers should consider in enhancing attendees’ experiences in the cases a classification gap appears or not. These strategies coincide with the introduction of artists that are perceived by customers as familiar or as a novelty.
This study evolves following the structure described in the next lines. Chapter 2 presents the theoretical framework at the core of the thesis. It introduces and explains each of the variables characterizing the conceptual model displayed by Figure 1. It also displays and describes the hypotheses that will be tested. Chapter 3 displays the characteristics of the quantitative research method adopted by this thesis for testing the hypotheses. Moreover, this chapter presents the features of the sample investigated by the thesis and all the metrics adopted for measuring the variables of this research. Chapter 4 introduces the effective testing of the hypotheses presented in Chapter 2. In its first part, it reports the features of the sample, the demographical characteristics of respondents that make it up, and the insights from the analysis of the insights coming from the descriptive measures. In the second part, it effectively tests the hypotheses, presenting in the conclusive section the analysis of the results obtained. Chapter 5 draws the conclusion of this study, elaborating also a series of managerial implications. Chapter 6 is the conclusive section of the thesis. It presents the limitations the researcher faced. In this chapter are also displayed the suggestions for further research.
2. Theoretical Framework and Hypotheses
To understand how certain elements influence the experience of music festival-goers, it is necessary to have an overview of the theories supporting the customer experience construct. Section 2.1 describes this construct, what are the phases making it up, what are the touch points, and therefore introducing the dependent variable named “Music Festival-Goers’ Experience”. Sections 2.2, 2.3, and 2.4 provide the definition of “Mismatch”, “Familiarity with the Artists”, “New Artists’ Appreciation”, the independent and the moderating variables in Figure 1. These sections explain the rationale for having chosen them
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and provides theoretical support to each of these variables. This chapter also introduces the hypotheses this research aims to study. The final part of Chapter 2 – sections 2.5 and 2.6 – describes the four control variables included – “Brand Equity”, “Online Interactions”, “Age”, and “Gender” – and sums up the hypotheses. Figure 1 displays the conceptual model of the thesis.
Figure 1 - Conceptual model
2.1. Customer Experience and Music Festivals
As already mentioned in the introductive section, Lemon et. al (2016; p. 71) define customer experience as “a multidimensional construct focusing on a customer’s cognitive, emotional, behavioral, sensorial, and social responses to a firm’s offerings during the customer’s entire purchase journey”. Looking to the idea of journey, Lemon et. al (2016; p. 74) state that a customer experience is a “journey with a firm over time”. This journey flows through three different phases. The first is the Pre-Purchase phase. It starts when consumers recognize they have a need and try to satisfy it through research. At this point, they get in touch with the organization and develop their decision-making process. This step lasts until when users understand they have identified a product or service able to satisfy their initial need. Consumers thus are close to take a decision. The Pre-Purchase phase can be divided into two steps, the Awareness and the Consideration ones (see Figure 2). The second phase, named the Purchase Phase, is the most time-restricted one and it is characterized by actions as taking a decision and purchasing. The third phase, the
Mismatch
Music Festival-Goers’ Experience
New Artists’ Appreciation Familiarity with the Artists
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Post-Purchase one, starts after the effective purchase of the product or service and lasts until when customers opt for a new purchase or the offering of another organization. As the Pre-Purchase phase, the Post-purchase phase is time-extended and can be divided into two steps: Consumption and the Advocacy/Retention ones (see Figure 2). In the latter, the organization develops the effective engagement with customers, and this is possible just if in the Consumption step customers result to be satisfied.
Figure 2. The customer experience construct’s phases
Particularly relevant in the customer experience construct - due to the technological evolution we are witnessing to - are the touch points. Richardson (2010) defines them as “any interaction point between the customer and your brand”. A touch point can take place in each of the three phases mentioned and presents different characteristics depending on where it takes place. It is relevant to speak about touch points due to the increased interaction possibilities between customers and organizations brought by web 2.0 and social media. Organizations, if interested in better interacting with customers during the different phases of their experience, must pay attention to touch points and the ownership of each of them. Lemon et. al. (2016) identifies four types of touch points. The first of these - Brand-owned touch points - represents all the interactions with customers that are directly designed and managed by the media and marketing channels of the organization. Partner-owned touch points – the second type of touch-points – stands for all the interactions with customers jointly managed by the firm and external marketing, media partners. Customer-owned touch points (the third category) are those directly owned by customers – i.e. “situations in which customers use products in ways not intended by the firm” (Lemon et. al. 2016; p. 1- Pre-purchase phase
a. Awareness step b. Consideration step
2- Purchase phase 3- Post-purchase phase
a. Consumption step
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78). Concluding, the fourth type corresponds to the Social/External touch points. They embody the power of other people interacting with the customer and influencing his or her experience.
2.2. The drivers of music festival-goers’ experiences
This paragraph is oriented to explain the rationale behind including “Mismatch” as the primary variable affecting “Music Festival-Goers’ Experience”. As above Figure 1 depicts, it is the independent variable of the thesis.
For understanding the reason why including “Mismatch” as the independent variable is relevant, it is necessary to mention what Lemon et al. (2016) define as key drivers of customer experience worthy of being investigated by further studies. They state: “We strongly recommend the researchers […] assess the combined effects of the elements that make up the “raw data” of the customer experience (e.g. service quality attributes, […], external environment” (Lemon et. al. 2016; p. 85). In contextualizing these two elements in the field of music festivals, this study adopts “service quality attributes” as the artist characterizing a music festival, and in terms of what Leenders et al. (2005) defines as the theme of a music festival. Meanwhile, the “external environment” will be the artists’ international or local reputation. These elements are what characterize the “Mismatch” variable and make up what is named as the “Artists’ Origin – Festival’s Theme” framework.
As already stated in the introductive section, the demand relative to experience products is highly uncertain, and consequently, it affects supply’s decisions – “nobody knows anything” (Peltoniemi, 2015; p. 43; Goldman, 1983). Nonetheless, to counterbalance this structural uncertainty, music festival managers can leverage the “Mismatch” construct and the “Artists’ Origin – Festival’ Identity” framework in a twofold way.
First, artists in a line-up, their geographical origin, and the festival’s theme are able to spread information about the music festival’s features, they generate awareness, and therefore they reduce customers’
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informative uncertainty. They can do so because they are perceived by customers as a signal, an element close to their understanding. Speaking about signals, it is relevant to present Taj’s (2016) and Connelly, Certo, Ireland, & Reutzel (2011). The first provides a definition of what a signal is - “signals are informational cues sent out by one party to another in order to influence desired outcomes” (Taj, 2016; p. 339). Looking at the theories backing the power of signals that can lower customers’ informative uncertainty, Connelly et al. (2011) present the Signalling Theory. They state that the “signalling theory is fundamentally concerned with reducing information asymmetry between two parties (Spence, 2002)” (Connelly et al. 2011; p. 40). As it is possible to see from this definition, informational asymmetry – and therefore informative uncertainty - has a central role in this construct. Stiglitz (2002; p. 470) states that informational asymmetries are present when “different people know different things”. Thus, if one of the desired outcomes of an organization is to reduce customers’ informational asymmetry about its offering (i.e. creating awareness in the Pre-Purchase phase of the customer experience), managers can send out signals to customers. This can be done, for example, by leveraging certain artists, their geographical origin and the theme of the festival.
The second way in which “Mismatch” and its components enhance the music festival-goers’ experience is by developing an effective emotional and ideological engagement between them and the organization. These elements can do so by providing a symbolic value to customers. Dolfsma’s (2004; p. 275) study on symbolic goods, defines them as “goods that people define themselves in terms of, goods the consumption and use of which helps constitute people’s identity, goods that communicate the kinds of commitments people have”. Moreover, Kruijken et. al. (2016; p. 3) argue that “music festivals can satisfy consumers’ needs for self-expression and can help in the development of subcultures (Saleh and Ryan 1993; Lash and Lury 2007)”. Therefore, the artists and the theme of a music festival can generate engagement by leveraging the customers’ willingness to display their identities and to participate in the community (i.e. supporting a local art scene or an international artistic movement). To further support
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the engagement developer function of this variable, Packer and Ballantyne (2014) affirm that “the music experience provides the common ground on which both the social experience and the festival experience are built, and facilitates a sense of connection among participants” (Packer et al. 2014; p. 67).
2.3. The “Artists’ Origin – Festival’s Theme” Framework
“Mismatch” is the unique independent variable of the model. This variable is made up of two components – “The Artists’ Origin” and “The Festival’s Theme”. The first of these analyses is the geographical origin of the artists. The second considers festivals’ tendency to support local or international artists. This thesis introduces a framework for simultaneously considering how the origin of artists and festivals’ propensity affect the experience of music festival-goers. Next paragraphs provide theoretical support to both components. The last part of this section will join these two, effectively modelling the “Artists’ Origin – Festival’s Propensity” framework and it will present hypothesis 1.
2.3.1. The “Artists’ Origin” Component
The focus on the geographical origin of the artists is motivated by the will of understanding what means the inclusion of international artists rather than local ones in a music festival’s line-up. This paragraph considers how an artist in a festival’s line-up differently influences the experience of music festival’s attendees depending on its geographical origin. The inclusion of an artist (either local or international) may be perceived by customers as a signal of quality, diversity and trust, resulting in a mean for easing their Awareness and Consideration steps. Moreover, attendees may appreciate the choice of including an artist, which may develop a feeling of engagement with the music festival – enjoying the decisions of the artistic direction – and with other attendees - supporting a “local scene” or a wider international movement. To provide a theoretical support to this geographical categorization of the artists in a music festival, Dubois (2010) and Magnusson, Westjohn, & Zdravkovic (2011) are considered.
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Dubois (2010; p. 29) distinguishes between recognition – “the reputation an artist enjoys within his or her original world of art” – and renown – “the extension of the artist’s reputation beyond his or her world of art”. Therefore, the artists defined as “international” are those that have transcended their national scene and are renown in the international landscape. Meanwhile, “local” artists are those that are recognized and have developed a reputation, in their “original world of art” (Dubois, 2010; p. 29). Looking at Magnusson et. al. (2011), these authors introduce the Country of Origin (COO) construct. The authors state that “consumers view extrinsic cues, e.g. brand name, price, retail outlet, and COO, as consistent and credible predictors of value and quality (Dodds, 1991; Kardes e. al, 2004)” (Magnusson et. al, 2011; p. 456). Therefore, contextualizing the COO construct in the music festival’s field, artists and their geographical origin result in being a signal understandable by customers in forming a product judgement (Bredahl, 2004).
2.3.2. The “Festival’s Theme” Component
The next lines will introduce the second element of the “Artists’ Origin – Festival’s Theme” framework. The festival’s inclination toward supporting local or international artists is what this this paragraph considers as the theme of a music festival. For providing a theoretical fundament to this construct, it is considered what Leenders et al. (2005) proposes as the “theme” of a music festival. They state that a festival has a theme “when it has been organized because of a certain subject or event or when it chooses to have a special topic” (Leenders et al. 2005; p. 152). Linking this construct with the above presented geographical origin of the artists, it is possible to understand what the thesis contemplates as the theme of a music festival. Therefore, the “Festival’s Theme” component represents the festival’s inclination toward supporting artists that have transcended their “original world of art” (Dubois, 2010; p. 29) - international artists – and those that have a reputation just in their local artistic community – local artists.
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2.3.3. Classification Gap and “Artists’ Origin – Festival’s Theme” Framework
For understanding the relevance of speaking about the “identity” of a music festival it is necessary to introduce another construct, the “Classification Gap” by Kuijken et al. (2016). It is defined as a situation where “consumers and producers have different perceptions about the categorical identity of the same products” (Kuijken et al. 2016; p. 4). A Classification Gap arises when there is a discrepancy between the meanings, values, and ideas the organization delivers through its offering and the customers’ perception of the organization’s offering. The inclusion of the “Classification Gap” construct in this thesis is due to the need of understanding what implies the presence of a certain kind of artist (local or international) in the line-up of a festival mostly oriented to promote the same or the opposite kind of artists.
If a festival includes in its line-up artists that are not perceived by customers as aligned with the knowledge and imaginary they have about festival’s theme, there may be the appearance of a classification gap. For example, a music festival, appreciated and known by its customers for being strongly oriented to valorize local artists, then designs a line-up highly characterized by the presence of international artists. What does the presence of a classification gap imply? If the organization is not able to communicate the program’s features correctly, or to convey the proper signals, customers may face issues in evaluating the offering, so even quitting the decision-making process - the Pre-Purchase phase. Kuijken et. al. (2016) report the negative consequences connected to the rise of a classification gap. The most important one is represented by the arise of “feelings of mistrust” in customers referred to the organization - “consumers feel that the producer provided them with incorrect information (Geyskens, Steenkamp, & Kumar, 1998; Sirdeshmukh, Singh, & Sabol, 2002)” (Kuijken et. al, 2016, p. 9). A further consequence coincides with the complication of the decision-making process – in the case of customers receiving unexpected or not-easily-understandable information (Kuijken et al, 2016).
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Thus, considering that in this study both “Artists’ Origin” and “Festival’s Theme” can assume two values – “Local” or “International”, their interaction can generate four different combinations (see Figure 3). This thesis hypothesizes that these combinations can positively and negatively affect the dependent variable. Figure 3 depicts the possible combinations and effects.
Figure 3 – “Artists’ Origin – Festival’s Theme” framework
As already stated, the interaction between “The Artists’ Origin” and “The Festival’s Propensity” generates four different outcomes with two opposite effects. The top-left and the bottom-right frames depict the cases of a match between “The Festival’s Propensity” and “The Artists’ Origin”. These represent the scenarios in which a music festival with a strong inclination toward supporting a kind of artists (local or international) designs a line-up fitting with the developed image of itself. These cases are named “match”. On the other hand, the top-right and the bottom-left frames present the cases in which a
Local Theme International Theme
Loc al Artist s Local Artists; Local Theme + Local Artists; International Theme - Inte rna ti ona l Artist s International Artists; Local Theme - International Artists; International Theme + Music Festival’s Theme
G e ogr ap h ical O ri gi n of t h e A rti sts
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classification gap arises. These are named “mismatch”, and they take place when a festival with a strong local identity (international identity) structures a line-up mainly oriented towards international artists (local artists). This thesis, therefore, hypothesizes that if a “mismatch” scenario takes place, it has an adverse effect on the experience of music festival-goers, due to the appearance of a classification gap. Otherwise, if a “match” scenario appears, it has a positive effect on the experience of music festival-goers.
The independent variable presented by this chapter is included as a dummy variable, and it is named “Mismatch”. This is due to the will of focusing on the effect of a classification gap appearing from the “Artists’ Origin – Festival’s Theme” framework. Therefore, the null hypotheses (H1) presented below studies how a “mismatch” scenario affects the experience of music festival-goers. This implies that the alternate hypothesis considers how the other scenario arising from the “Artists’ Origin – Festival’s Theme” framework – a “match” - affects the dependent variable.
H1: A mismatch between the festival’s propensity and artists’ origin implies a negative effect on “Music festival-goers’ Experience”.
2.4 Moderating Variables 2.4.1. Familiarity with the Artists
“Familiarity with the Artists” is one of the two moderators characterizing this thesis. Its inclusion is due to the will of studying how customers’ familiarity with the artists in the line-up affects the interaction between “Mismatch” and the experience of music festival-goers. For providing a theoretical support to this variable, Campbell & Keller (2003) and Kent & Allen (1994) are taken into consideration. Starting from the last of them, the brand familiarity construct is defined by Kent et al. (1994; p. 98) as a “variable that reflects a consumer's level of direct and indirect experiences with a product (Alba & Hutchinson, 1987)”. Campbell et al. (2003; p. 293) affirm that “brand familiarity captures consumers’ brand
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knowledge structures, that is, the brand associations that exist within a consumer’s memory. […] Familiar and unfamiliar brands differ in terms of the knowledge regarding the brand that a consumer has stored in memory”. Therefore, hypotheses two studies if a certain level of attendees’ familiarity with artists moderates the effect of “match” or “mismatch” cases on music festival-goers’ experience.
H2: The relationship between “Artists’ Origin - Festival’s Propensity Framework” and “Music festival-goers’ Experience” is moderated by “Familiarity with the Artists”. This relationship is more positive in the presence of a higher level of customers’ familiarity with the artists.
2.4.2. New Artists’ Appreciation
“New Artists’ Appreciation” is the second of the moderators included in this study. This analysis moves from the will of studying how the inclusion in a festival’s line-up of an “unexpected” artist affects the interaction between the “Mismatch” and the experience of music festival-goers. For providing theoretical support to this, Kim & Mattila (2013) are considered. The focus of these authors is on studying how surprise strategies influence consumer expectations. Their study is taken into consideration by this thesis because it furnishes a definition of what is a surprise element and directly links it to how to satisfy consumers. A surprise occurs when “a person encounters an unexpected element; hence, [when] he or she experiences a discrepancy in his/her schema (Elkman & Friesen, 1975; Vanhamme, 2008)“ (Kim et al. 2013, p. 362). The second contribution brought by Kim et al. (2013) is represented by their idea of customers’ satisfaction, what they name as “delight”. This is defined as “a function and a combination of two emotions: surprise and joy” (Kim et al. 2013; p. 362). Linking surprise to the topic of this thesis, it is possible to see how an “unexpected” element could be represented by the insertion of an artist in the line-up of a music festival. Hence, “New Artists’ Appreciation” considers if a certain level of “delight” – appreciation – of an “unexpected” artists in a festival’s line-up may moderate the effect of “match” or
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“mismatch” scenarios on the experience of music festival-goers. Hypothesis 3 is oriented to study this moderated interaction.
H3: The relationship between “Artists’ Origin - Festival’s Propensity Framework” and “Music festival-goers’ Experience” is moderated by “New Artists’ Appreciation”. This relationship is more positive in the presence of a higher level of customers’ satisfaction for having seen new artists.
2.5 Control variables
This paragraph presents the variables included in this study as control variables and the reasons for controlling them. These are “Brand Equity”, “Online Interactions”, “Gender”, and “Age”.
Lemon et al. (2016) suggest a series of “key drivers” of customer experience worthy of being investigated by further studies. Paragraphs 2.2 already takes in analysis “service quality attributes” and “external environment”. Nonetheless, Lemon et al. (2016) make mentions of the necessity of investigating also how a “brand” affects the consumer experience. Furthermore, Lemon et. al. (2016) stress the need of using more and more data analytics with the aim of shaping customers’ experiences. Therefore, this thesis takes into consideration two variables linked to these topics, respectively naming them “Brand Equity” and “Online Interactions”. The reason for including these as control and not as independent variables, is given by Leenders et al. (2005) and Hudson, Roth, Madden, & Hudson (2015). These demonstrate that in the field of music festivals, the brand (Leenders et al. 2005) and the online interactions with brands (Hudson et al. 2015) have an impact on customers’ satisfaction, on the word of mouth generated, and on customers’ emotional attachment. Therefore, having these demonstrated that “Brand Equity” and “Online Interactions” can create awareness and engagement, and can satisfy music festival-goers, this thesis includes these two variables solely for controlling their general effect on music festival-goers’ experience.
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This thesis also controls for “Age” and “Gender”. The first of these two is introduced for investigating the differences between members of the same demographical cohort, the so-called “Generation Y” or “Millennials”. Lissitsa and Kol (2016; p. 304) state that the Generation Y “encompasses those born from 1980 to 1999 (Gurau, 2012) […]” and therefore it comprehends individuals aged between 18 and 37 years old.
2.6 Hypotheses Summary
H1: A mismatch between the festival’s propensity and artists’ origin implies a negative effect on “Music Festival-Goers’ Experience”.
H2: The relationship between “Artists’ Origin - Festival’s Propensity Framework” and “Music Festival-Goers’ Experience” is moderated by “Familiarity with the Artists”. This relationship is more positive in the presence of a higher-level of customers’ familiarity with the artists.
H3: The relationship between “Artists’ Origin - Festival’s Propensity Framework” and “Music Festival-Goers’ Experience” is moderated by “New Band”. This relationship is more positive in the presence of a higher-level of customers’ appreciation of having seen new artists.
3. Methodology
Chapter 3 introduces the research method supporting the thesis. Section 3.1 and 3.2 display the general features of this study and of the sample this study aims to analyze. Section 3.3 depicts in detail all the metrics used for measuring the variables displayed in the conceptual model in figure 4. Appendix A reports all the measures used for creating the questionnaire at the core of this research method.
3.1. Study design
For replying to the research question and thus for testing the hypotheses, an observational research will be conducted, supporting it with a quantitative research method. The primary data will be collected using
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digitally distributed surveys, via the website Qualtrics.com. Interviewees will be reached via Facebook and e-mails.
Figure 4 displays the conceptual model of the thesis. This considers how “Mismatch” – independent variable - influences “Music Festival-Goers’ Experience” – dependent variable. Two moderating variables are included. The first studies if customers’ familiarity with the artists in the festival’s line-up moderates the interaction between dependent and independent variables. The second considers if the presence of new affects the interaction between dependent and independent variables. “Brand Equity”, “Online Interactions”, “Age” and “Gender” are included as control variables.
Figure 4 - Conceptual model and hypotheses
3.2. Sample profile
The sample of reference will be represented by Dutch and international individuals that have attended a music festival in the Netherlands in the last year. Respondents will be collected using a non-probability, convenience sampling technique. Surveys will distinguish neither on the base of the gender nor on the base of education or employment. The minimum number of respondents required is 100. No identifying information will be collected. In the demographic section of the questionnaire, interviewees will be asked for their gender and their nationality.
Mismatch Music Festival-Goers’ Experience
New Artists’ Appreciation Familiarity with the
Artists H1 (-)
H3 (+) H2 (+)
19 3.3 Measures
In this thesis are included a series of descriptive measures. The ones relative to gender, age, and nationality, are nominal scales. In particular, the first mentioned is a dummy presenting to interviewees the possibility to choose between the “Male” and “Female” options. The second is a categorical variable asking interviewees to disclose their age by choosing one of the four options presented. The descriptive measure relative to the frequency of online interacting with the festival is an interval variable designed by the researcher. It asks interviewees to rate the frequency with which they interact online with the music festival organization on a scale ranging between 1 (“Never”) and 7 (“Always”). The following paragraphs and Appendix A present in detail these questions.
3.3.1. Dependent Variable
“Music festival-goers’ Experience” is the dependent variable of this thesis (named “MFGsX” in the statistics analysis’ results), and it represents the evaluation of the experience music festival-goers had attending a music festival. For measuring it, Lemon et al. (2016) are considered. These authors reviews and gathers the whole consumer experience literature, therefore also considering the state of the art related to measuring this construct. Speaking about how to measure the customer experience construct, Lemon et al. (2016; p. 81) state that “Recently, scholars and practitioners have started to measure the overall customer experience. This field is in its early stages of development, […] no strong customer experience scales have been developed […]”. Nevertheless, these authors state that the “Customer satisfaction has been the dominant customer feedback metric for years” (Lemon et al. 2016; p. 81), but practitioners have integrated this with the Net Promoter Score (NPS). This last was developed by Reichheld (2003; p. 48), it considers the number of consumers willing “to recommend a product or service to someone else”. The NPS corresponds to “the percentage of customers who are promoters of a brand or company minus the percentage who are detractors” (Reichheld, 2003; p. 53). Nonetheless,
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Lemon et al. (2016; p. 81) conclude stating that the Customer Satisfaction metric and the NPS metric “perform equally well” in measuring the experience of consumers. Therefore, this thesis for measuring “Music Festival-Goers’ Experience” adopts the Customer Satisfaction metric, considering redundant to develop this measurement also using the NPS metric. The Customer Satisfaction metric will be presented to interviewees asking them a single-item evaluated on a 10-point Likert scale, ranging between 0 (“Extremely dissatisfied”) and 10 (“extremely satisfied”).
3.3.2. Independent Variable
“Mismatch” is the independent variable of this study (see paragraph 2.3). It is a numerical variable made up of the combination of two measures. One measuring attendees’ preference toward international or local artists. The other measuring the festival’s inclination – in the eyes of respondents - toward supporting local or international artists.
The first of these is gauged using a dummy variable designed by the researcher. It presents to interviewees the following question: “Which is the geographical origin of the artists that motivated you to attend the
music festival?”. This variable assumes value 1 if interviewees reply with “International”, and 0 with
“Local”. The second measure making up the independent variable is a numerical variable. This is measured using the “Perceived Brand Globalness” scale by Steenkamp, Batra, Alden (2003). The original scale is a 3-item tool that here is adapted to a single-item one. It presents a .799 Cronbach’s alpha for American products and .785 for Korean ones. This measure is gauged using a 7-point Likert scale, ranging between 1 (“International”) and 7 (“Local”).
3.3.3. Moderating Variables
Customers’ “Familiarity with the Artists” is the first of the moderating variable of the model in Figure 4. It studies how the familiarity of attendees with the artists in the line-up moderates the interaction between dependent and independent variables. For measuring this variable, it is considered the “Brand
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Familiarity” scale by Kent & Allen (1994). These authors present a three-item metric, characterized by a 0.85 Cronbach’s alpha. It is adapted in here to a single-item tool, and it is measured using a “seven-point numeric format” (Kent et al. 1994; p. 99) ranging between 1 (“Unfamiliar”) and 7 (“Familiar”).
The second moderator included – “New Artists’ Appreciation” - is measured using the “Music Experience” metric by Packer & Ballantyne (2014). This is a four-item scale, where each item is rated on a seven-points scale, ranging between 1 (“Extremely dissatisfied”) and 7 (“Extremely satisfied”). The Cronbach’s alpha for this is .78.
3.3.4. Control Variables
The control variables included in this thesis are four: “Brand Equity”, “Online Interactions”, “Age”, “Gender”. Each of these will be considered in the following lines.
The controlling effect of “Brand Equity” is measured considering a series of metrics gauging brand identity and brand relationship. Starting from brand identity - customers’ awareness of the music festival’s brand - this thesis considers the “Brand Awareness” scale proposed by Girard, Trapp, Pinar, Gulsoy, & Boyt (2017). These authors measure it presenting to interviewees a 7-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). The “Brand Awareness” construct is a 5-item tool with a .86 Cronbach’s alpha. The brand relationship is measured using the “Brand Relationship Quality” scale presented by Smit, Bronner, Tolboom (2007). This is a “customer-based indicator of the strength and depth of the person-brand relationship” (Smit et. al. 2007; p. 627). These authors introduce it as an instrument of sixteen items (here adapted to a five-item tool), evaluated on a 7-point scale, where 1 stands for “not at all” and 7 for “very much so”, and it is characterized by a .95 Cronbach’s alpha. For measuring the controlling effect of Online Interactions on the above-described model, a series of different measures are taken into consideration. Two descriptive measures are used for asking to interviewees about their age and their frequency in online interacting with the music festival oganization.
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This last presents a list of social networks, video and music streaming services, and electronic devices, and asks interviewees their frequency level in online interacting with the music festival. Moreover, the two following scales are used. The first is a 4-item measure. It was obtained combining items from the “Relationship Building Value” metric from O’Cass & Ngo (2011) - it presents a .91 Cronbach’s alpha, and from the “Virtual Interactivity” scale and the “System Quality” scale by Barreda, Okumus, Nusair, Bilgihan (2016) – they present a Cronbach’s alpha greater than .70. The second is a 4-item measure. It joins other items from the “Relationship Building Value” metric by O’Cass et al. (2011) – it presents a .91 Cronbach’s alpha, and the “Psychological benefits” and from the “Brand attachment” (Barreda et al. (2016) - α > 0.70). These metrics are measured on a 7-point Likert scale, ranging between 0 (“Never”; “Strongly Disagree”) to 1 (“Always”; “Strongly Agree”).
“Age” and “Gender” have been measured using two items designed by the researcher.
4. Results
Chapter 4 presents the results of the statistical analysis of the thesis’ hypotheses. Sections 4.1 and 4.2 display the demographic features of the data gathered. Sections 4.3 and 4.4 describe in detail the analysis of the insights obtained from testing the hypotheses, and of the role of the control variables. For obtaining these results, it was used the statistical software “Statistical Package for Service Solutions (SPSS)” (version 24).
4.1. Descriptive Statistics
This section presents the results coming from the demographic questions presented to interviewees. These are displayed in the survey reported by Appendix A and correspond to the questions in the “Demographic Section”, to the first two in the “Online Interactions Section”, and to the first in the “Mismatch Section”.
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The survey was digitally distributed via Qualtrics.com between May 15th 2017 and May 29th 2017. 161 questionnaires were gathered. Of these, six correspond to pre-test individually run by the researcher, 47 represent not-filled-in surveys, and six were solely filled in the first section. These items were deleted, thus the final sample results of being made of 102 respondents. This is composed of 54 male individuals (52,9%) and 48 female ones (47,1%). Table 1 displays the values for each age class. As it is possible to see, the most represented age bracket is the 23-27 one, corresponding to the 76,5% of the sample. The second most represented group is the 18-22 one (15,7% of the sample). In Table 1, the 33-37 age bracket is not presented, implying that the questionnaire did not reach any respondent in this age bracket. The two values related to “Other” are referred to two individuals aged respectively 58 and 42.
Frequency Percent Valid Percent
Cumulative Percent Valid 18-22 16 15,7 16,3 16,3 23-27 78 76,5 79,6 95,9 28-32 2 2,0 2,0 98,0 Other 2 2,0 2,0 100,0 Total 98 96,1 100,0 Missing System 4 3,9 Total 102 100,0
Table 1 - Respondents in each age brackets – “In which age brackets are you?”
For what concerns the nationalities of the respondents undertaking the survey, the 91,2% of these (n=93) came from countries of the European Union (Austria, Bulgaria, Croatia, Czech Republic, France, Germany, Hungary, Italy, the Netherlands, Poland, Romania, Spain, United Kingdom). Just nine of the respondents in the sample were from Non-EU countries (Australia, Brazil, China, India, Mexico, Vietnam, United States). The most represented nationalities are the Italian (34 respondents – 33,3%) and the Dutch one (29 respondents - 28,4%). Table 2 displays this information.
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Frequency Percent Valid Percent
Cumulative Percent Valid American 3 2,9 2,9 2,9 Australian 1 1,0 1,0 3,9 Austrian 5 4,9 4,9 8,8 Brazilian 1 1,0 1,0 9,8 British 2 2,0 2,0 11,8 Bulgarian 3 2,9 2,9 14,7 Chinese 1 1,0 1,0 15,7 Croatian 1 1,0 1,0 16,7 Czech 2 2,0 2,0 18,6 Dutch 29 28,4 28,4 47,1 French 3 2,9 2,9 50,0 German 5 4,9 4,9 54,9 Hungarian 2 2,0 2,0 56,9 Indian 1 1,0 1,0 57,8 Italian 34 33,3 33,3 91,2 Mexican 1 1,0 1,0 92,2 Polish 2 2,0 2,0 94,1 Romanian 3 2,9 2,9 97,1 Spanish 2 2,0 2,0 99,0 Vietnamese 1 1,0 1,0 100,0 Total 102 100,0 100,0
Table 2 - Nationality of the respondents
Table 3 presents the results of a further demographic questions, studying interviewees’ frequency in online interacting with music festival organizations. This table displays the means and standard deviations relatively to the frequency of respondent in interacting with the festival. As depicted by paragraph 3.3, this descriptive question asked to respondents to rate this frequency on a scale ranging between 1 (“Never”) and 7 (“Always”). The highest means are those relative to the interactions using the website (3.56) and the Facebook account (4.35). On the other hand, the least used online platforms for interacting resulted to be the music festival’s Twitter profile (1.33) and the festival’s Vimeo account (1.42).
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N Mean Std. Deviation
Festival's website 101 3,56 1,452
Festival's Facebook page 101 4,35 1,533
Festival's Instagram profile 101 2,27 1,679
Festival's Twitter profile 100 1,33 ,900
Festival's Youtube channel 101 2,29 1,615
Festival's Vimeo account 99 1,42 1,135
Spotify's playlist on the festival's line-up
100 2,41 1,798
Soundcloud's playlist on the festival's line-up
101 2,11 1,726
Music festival's app 101 1,88 1,485
Technological support furnished by the festival [i.e. Wi-Fi, RFID
wristbands]
101 2,86 2,005
Valid N (listwise) 97
Table 3 - Frequency in Online Interacting
The last of the demographic questions presented to interviewees asked them to indicate which was the geographical origin of the artists motivating them to attend the music festival (see paragraph 3.3.2). The frequencies of the replies display that the 73,5% of the sample (n = 75) in analysis was motivated by international artists. Just 27 interviewees declared they attended the festival was motivated by local artists (26,5% of the sample)
4.2. Analytical Strategy 4.2.1 Recoding
The surveys’ results were recoded to make them correspond to individual measures. The means and standard deviations of each of them are exhibited below in Table 4. The independent variable (named “Match” in the SPSS output) is obtained recoding the 7-point Likert-scale “Perceived Brand Globalness” scale by Steenkamp et al. (2003) into a dummy variable. In a second step, this measure and the other presented by paragraph 3.3.2 were combined manually for obtaining “Mismatch”, the dummy variable
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adopted as the independent one in this study. This variable assumes value 0 when a “mismatch” scenario takes place (local artists – international theme; international artists – local theme). It assumes value 1 when a “match” scenario appears (local artists – local theme; international artists – international theme). As the next section explains “Music Festival-Goers’ Experience” and “Age” have been recoded for missing values.
Table 4 displays the frequencies, the means and the standard deviations of the measures presented by paragraph 3.3.
N Mean Std. Deviation
SMEAN(Cx_Satisf) 102 7,475 1,6310
Mismatch 102 ,66 ,477
Familiarity with the Artists 101 4,40 1,692
New Artists’ Appreciation 101 5,53 ,996
Brand Equity 102 4,7960 ,88371
Online Interactions 102 4,3686 ,83076
Gender 102 ,47 ,502
SMEAN(Age) 102 1,918 ,5916
Table 4 - Means and Standard Deviations
4.2.2. Missing Values
All the previously described variables were checked for missing values. For each of them, a frequency test in SPSS was conducted. This revealed that the maximum amount of missing values is equal to 4,08%. This percentage is relative just to the control variable “Age” (n=98). Also the dependent variable “Music Festival-Goers’ Experience” presented missing values (n=99). The missing values from both were replaced using the “replace missing values” command in SPSS, therefore adopting a standardized mean for substituting these values missing.
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For checking the reliability of the variables displayed in Figure 4, a reliability check was run in SPSS. Most of these are dummy and categorical variables, characterized by a single-item measures (see section3.3). Therefore, it is not considered their reliability. These variables are “Music Festival-Goers’ Experience”, “Mismatch”, “Familiarity with the Artists”, “New Artists’ Appreciation”, “Age” and “Gender”. Instead, reliability was checked was for “Brand Equity” and “Online Interactions”, two of the four control variables. They respectively display a Cronbach’s Alpha of .840 and .844. The threshold for internal consistency is considered at the .70 level. Therefore, “Brand Equity” and “Online Interactions” are considered reliable measures.
4.2.4. Correlations
The correlations between the variables characterizing this thesis are displayed in Table 5. These results have been obtained using the “correlation” command in SPSS. The next lines present the most relevant and significant correlations characterizing the interaction between each of the variables.
The dependent variable “Music Festival-Goers’ Experience” – named in Table 3 as “SMEAN(Cx_Satisf)” – presents significant positive correlations with the moderating variables “Familiarity with the Artists” (r = .303; p < .05), “New Artists’ Appreciation” (r = .386; p < .05), and with the control variables “Brand Equity” (r = .438; p < .05) and “Online Interactions” (r = .222; p < .01). For what concerns “Mismatch”, it solely and negatively correlates with the control variable “Age” (r = -.200; p < .01). This represent the weakest of the significant correlations. The first of the moderators presented by Chapter 2, “Familiarity with the Artists” positively correlates with “New Artists’ Appreciation” (r = .213; p < .01), with “Brand Equity” (r = .360; p < .05), and with “Online Interactions” (r = .368; p < .05). It is negatively correlated with “Gender” (r = -.243; p < .01). The other moderating variable “New Artists’ Appreciation” displays strong and positive correlations with “Brand Equity” (r =
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.522; p < .05) and with “Online Interactions” (r = .515; p < .05). Concluding, the correlation between “Brand Equity” and “Online Interactions” is the strongest one in the model (r = .527; p < .05).
1 2 3 4 5 6 7 8
1 - SMEAN(Cx_Satisf) 1
2 - Mismatch ,059 1
3 - Familiarity with the Artists ,303** ,060 1
4 - New Artists’ Appreciation ,386** ,078 ,213* 1
5 - Brand Equity ,438** ,080 ,360** ,522** 1 6 - Online Interactions ,222* ,126 ,368** ,515** ,527** 1 7 - Gender -,064 -,105 -,243* ,007 -,098 -,033 1 8 - SMEAN(Age) ,079 -,200* ,028 ,144 ,120 ,121 ,028 1 Table 5 - Correlations 4.3. Hypotheses Testing
This paragraph presents what was obtained from testing the hypotheses characterising this thesis (see section 2.6). In its first part, it considers the interaction between the independent variable – “Mismatch” – and the dependent variable – “Music Festival-Goers’ Experience”, hence testing hypothesis 1. The second part of this paragraph presents the effects of the two moderating variables - “Familiarity with the Artists” and “New Artists’ Appreciation” – testing respectively hypotheses 2 and 3. The hypotheses testing is executed running a linear mixed-effects model in SPSS, using the mixed model command. A linear mixed-effects model was considered being more efficient in the presence of missing or repeated values, and more importantly, being “[…] often more interpretable than classical repeated measures” (Seltman, 2015; p. 358). Therefore, using a linear mixed-effects model gives to this study the possibility to deeply understand how the dummy, categorical and numerical variables interact with each other.
The model presented by Figure 4 - including also “Brand Equity”, “Online Interactions”, “Age” and “Gender” as control variables – is characterised by an R-squared value of .329. This implies that this model can explain the 32,9% of the variance of the dependent variable. The R-squared value is calculated using the process command by Hayes in SPSS and adopting the model number 2. It is also detected the
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presence of multicollinearity, by considering the Variance Inflection Factor (VIF) values (using the linear regression command in SPSS). These values range between a minimum of 1.010 and a maximum of 1.643. Being them lower than 2, they prove that in this model there is a low risk of having multicollinearity issues. Table 6 reports the results of the multicollinearity check.
Model Collinearity Statistics Tolerance VIF 1 Brand Equity ,715 1,398 Online Interactions ,720 1,389 SMEAN(Age) ,980 1,021 Gender ,990 1,010 2 Brand Equity ,609 1,643 Online Interactions ,607 1,648 SMEAN(Age) ,926 1,080 Gender ,930 1,075
Familiarity with the Artists ,793 1,260
New Artists’ Appreciation ,641 1,561
Mismatch ,929 1,076
a. Dependent Variable: SMEAN(Cx_Satisf)
Table 6 - Multicollinearity check
Table 7 and Table 8 gathers the results obtained from testing the hypotheses, being also comprehensive of the moderating and control variables described. Table 7 shows the general results of hypotheses testing, while Table 8 presents the detailed results about each meaning the dummy and the categorical variables can assume in the regression analysis.
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Source Numerator df Denominator df F Sig.
Intercept 1 84 4,605 ,035 Mismatch 1 84 4,348 ,040 Age 3 84 ,832 ,480 Gender 1 84 ,297 ,587 Br.Eq 1 84,000 7,562 ,007 On.Int 1 84 ,566 ,454 Mismatch * Cx.Fam 2 84 1,550 ,218 Mismatch * Sat_NewB 2 84 5,374 ,006
a. Dependent Variable: SMEAN(Cx_Satisf).
Table 7 - Type III Tests of Fixed Effects - General results of the Linear Mixed-Effects Model
Parameter Estimate Std. Error df t Sig.
95% Confidence Interval
Lower Bound Upper Bound
Intercept 3,373932 1,672322 84,000 2,018 ,047 ,048336 6,699528 [Mismatch=0] -3,573175 1,713521 84 -2,085 ,040 -6,980699 -,165651 [Mismatch=1] 0b 0 . . . . . [Age=1] ,686706 1,119965 84,000 ,613 ,541 -1,540467 2,913879 [Age=2] ,536542 1,078575 84 ,497 ,620 -1,608322 2,681407 [Age=3] 2,000549 1,430481 84 1,399 ,166 -,844119 4,845216 [Age=5] 0b 0 . . . . . [Gender=0] -,160561 ,294752 84 -,545 ,587 -,746708 ,425586 [Gender=1] 0b 0 . . . . . Br.Eq ,563083 ,204766 84,000 2,750 ,007 ,155884 ,970282 On.Int -,173923 ,231201 84 -,752 ,454 -,633692 ,285846 [Mismatch=0] * Cx.Fam ,264933 ,157820 84 1,679 ,097 -,048910 ,578776 [Mismatch=1] * Cx.Fam ,074409 ,115831 84 ,642 ,522 -,155934 ,304752 [Mismatch=0] * Sat_NewB ,735606 ,226445 84 3,248 ,002 ,285295 1,185917 [Mismatch=1] * Sat_NewB ,249033 ,239840 84 1,038 ,302 -,227915 ,725981
a. Dependent Variable: SMEAN(Cx_Satisf).
b. This parameter is set to zero because it is redundant.
Table 8 - Estimates of Fixed Effects - Analytic results of the Linear Mixed-Effect Model
For understanding the results of testing hypothesis 1, it is necessary to look at Table 7 and focus on “Mismatch”. This represents the interaction between dependent and independent variables. It explains how the independent variable “Mismatch” affects “Music Festival-Goers’ Experience”. A .040 p-value
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characterises this interaction. Being this value below the significance threshold level of .05 implies that “Mismatch” has a significant effect on “Music Festival-Goers’ Experience” and therefore H1 is validated. Nonetheless, this is just a partial result. It is relevant to keep in mind that “Mismatch” is a dummy variable, and therefore it is needed to study which of the two meanings characterising this variable is significant. Table 8 explains in detail how differently these two meanings influence “Music Festival-Goers’ Experience”. As already stated in section 4.2.1, if “Mismatch” assumes value “0” it displays a “mismatch” scenario, if “Mismatch” assumes value “1” it represents a “match” scenario. The results presented in Table 8 show that “[Mismatch=0]” – “mismatch” – presents a p-value equal to .040, while “[Mismatch=1]” – “match” – being considered as the default value, displays no results. The “mismatch” scenario is significant, having a p-value lower than .05. It is possible to validate hypothesis 1 just for this case. This validation is also supported by the sign of its coefficient (-1,96). For a unit increase in the independent variable corresponds a -1.96 decrease in the dependent variable, therefore supporting the conjecture presented by hypothesis 1 about the detrimental effect of a mismatch scenario on attendees’ experience. For what concerns the “match” case, Table 8 describes its results as “redundant”. This because being “Mismatch” a dummy variable it can alternatively assume just one value. Therefore, if it assumes value “0” – “mismatch” – it cannot assume at the same time value “1” – “match” – and be also significant. This would imply validating at the same time the null and the alternate hypotheses.
Concluding and summing up, H1 was validated. The presence of a mismatch between the geographical origin of the artists and the festival’s propensity to promote certain artists has a negative effect on the experience of music festival-goers. On the other hand, a “match” scenario has no significant effects on the dependent variable.
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In the next lines, the effect of the two moderating variables is considered. At first, it is seen if “Familiarity with the Artists” moderates the interaction between dependent and independent variables. Later, it is examined how “New Artists’ Appreciation” affects this interaction.
Testing hypothesis 2 means studying if the higher the customers’ familiarity level with the artists, the more positive the interaction between “Mismatch” and “Music Festival-Goers’ Experience”. It is possible to see from Table 7 that the moderating effect of “Familiarity with the Artists” is not significant. A .218 p-value characterizes this interaction, resulting unquestionably greater than the .05 significance level. Therefore, H2 must be rejected. This implies that consumers’ familiarity with the artists in a music festival’s line-up has no relevant moderating effects on the relationship between the dependent and the independent variables of this study. For having a look in detail to if the consumers’ familiarity with the artists is significant in any of the meaning of the “Mismatch” variable, it is necessary to look at Table 8. The moderating effect of “Familiarity with the Artists” on the interaction between dependent and independent variables is relevant neither when “Mismatch” assumes value 0 – “mismatch” (p = .97), nor when it assumes value 1 – “match” (p = .522).
In the next lines, the focus is on testing hypothesis 3, thus on considering the second moderator of the model in Figure 4, “New Artists’ Appreciation”. Testing hypothesis 3 means considering if the relationship between “Mismatch” and “Music Festival-Goers’ Experience” is more positive in the presence of a higher level of customers’ appreciation for having seen new artists at the festival. This can be understood looking at first at the general results from Table 7 and to the more accurate ones from Table 8. Table 7 indicates that the interaction between “Mismatch” and “Satisf_NewBand” is significant. It displays a .006 p-value, lower than the .05 significance threshold. This indicates that a higher appreciation level for new artists generates a more positive interaction between dependent and independent variables. Hypothesis 3 is confirmed. Nonetheless, for understanding if this moderation has
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a positive or negative valence and in which case it is significant, it is needed to look at the insights coming from Table 8. When “Mismatch” assumes value 0 – “mismatch” scenario, its interaction with the dependent variable is even more positive if customers highly appreciate artists seen for the first time at the festival. This is demonstrated by the p-value and by the coefficient of this interaction. The first of these is equal to .002, thus lower than the significance threshold of .05. The latter, the “estimate” of “[Match=0] * Satisf_NewBand“ is equal to .74 and therefore describes a positive effect of the moderator on the interaction between dependent and independent variables. In the opposite case, when “Mismatch” assumes value “1” – “match” scenario, the moderating effect of “New Artists’ Appreciation” is not significant (p-value = .132).
4.4. Impact of Control Variables
In this paragraph, the focus is on considering the statistics analysis of the effect of the control variables presented in section 2.5. As it is possible to see from Table 7, “Brand Equity” is characterised by a .007 p-value, hence being significant. From Table 8 it is possible to see that this control variable has a positive coefficient equal to .56 and therefore “Brand Equity” has significant positive effect on the interactions with “Music Festival-Goers’ Experience”. Considering again Table 7, the focus will be on the other three control variables – “Age”, “Gender”, “Online Interactions”. The controlling effect of these is characterised respectively by .48, .59, and .45 p-values. None of them is significant at the .05 significance level. For what concerns “Age” and “Gender”, these results are in line with the absence of significant correlations explained by Table 5. In the case of “Online Interactions”, it is not possible to see the same congruence between the results of the correlation analysis and the ones of the regression analysis. This control variable results to be positively correlated with “Music Festival-Goers’ Experience” (r = .222; p < .01), but it presents a .45 p-value, displaying, therefore, a non-significant relationship between these two variables. Table 8 shows more in detail the results related to each of the possible meanings of “Age”
