Do social media marketing activities enhance purchase intention and customer equity? : an empirical study of the fine arts market

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Do social media marketing activities enhance

purchase intention and customer equity?

An empirical study of the fine arts market.

MSc in Business Administration – Marketing Track

Alexander Minniti, 11386673

Supervisor: Meg Lee

Academic year: 2016/2017

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Statement of originality

This document is written by Student Alexander Minniti 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.

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List of Tables and Figures:

Tables:

Table 1: Means, Standard Deviations and Correlations ... 22

Table 2: Means and Standard Deviations with and without SMMA’s ... 23

Table 3: Means and Standard Deviations with and without CC’s ... 24

Table 4: Frequencies of customer equity drivers with and without SMMA's 25 Table 5: Price premium and purchase intention frequencies with and without SMMA's ... 25

Table 6: Outer Loadings and Indicator reliabilities ... 27

Table 7: Chronbach’s Alphas and Composite Reliabilities ... 28

Table 8: Average Variance Extracted and Composite reliability ... 29

Table 9: Heterotrait-Monotrait Ratio of Correlations ... 30

Table 10: Path coefficients ... 32

Table 11: T-Statistics and P-Values ... 33

Table 12: Comparison of path coefficients and T-values between SMMA and no SMMA conditions ... 34

Table 13: Summary of Hypotheses ... 37

Figures:

Figure 1: Conceptual model ... 15

Figure 2: Design diagram ... 16

Figure 3: The Inner and Outer model in Structural Equation Modeling ... 21

Figure 4: Drawing the PLS model ... 28

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Abstract:

In light of a growing interest towards social media marketing activities in the world of hedonic products and fine art, this study attempts to discover effective ways in which promotional campaigns can lead to positive effects on customer equity and purchase intention. Inspired by a study of Kim and Ko from 2012, this research contributes to previous literature by examining past contributions on hedonic marketing and social media marketing in general, and offering a comprehensive framework that shows how the marketing of art can benefit from the promotion through visual social media channels, and can be taken as an example of how creative luxury brands can deepen and increase their purchase intention and customer equity through a wise use of social media.

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1. Introduction:

According to the report from TEFAFS of 2016, in 2015 the sales for the art market amounted to a total of $63.8 billion. This year was the first to seed a drop since 2011, and comes as the sectors of the fine art market, that were at the forefront of the rapid expansion, began to cool off. The report also includes data on online sales, one of the fastest growing sections of the art market: in 2015, sales of art online were estimated conservatively to have reached $4.7 billion, up seven percent year on year, and accounting for seven percent of all global art and antiques sales by value

(Artnet.com). Additionally, with the rise of social media, customers have begun to build more personal relationships with brands, creating deeper bonds with companies and increasingly including them in their daily life. This rapid uptrend in all online activities, driven by consumers increasing confidence towards digitalization, has led to significant changes in B2C relationships that have yet to be studied in depth. Little is known at the present about how the fine art sector should optimally react to these radical changes.

Thanks to a 2X2 factorial experiment completed by 200 art-oriented individuals, this study aims to explore these new dynamics, studying the impact of customer equity and purchase intention in the marketing of fine art through social media.

Research was conducted through Partial Least Squares Structural Equation Modeling (PLS-SEM). This method was chosen because of its capacity to analyze situations which common regression based models couldn’t tackle.

The thesis begins with a literature review in which the main existing theories are discussed and explored. The concept of art as a hedonic, creative, and also a luxury product, is outlined, social media marketing is explained and the remaining

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Next, the research question is formulated, the hypotheses are shaped and the conceptual framework is drawn to highlight the relationships.

The Method chapter is dedicated to explaining the design of the experiment, to outlining the sample and showing how the different variables were measured. The last part of the chapter is instead used to delineate the various steps in the statistical analysis procedure.

The following is the Results chapter, in which descriptive and inferential statistics are calculated and explained, the structural equation model is built and computed, the

T-values are estimated and the whole model is checked for both reliability and

validity. In the last part of the chapter the hypotheses are evaluated and weighed. In the Discussion chapter the significance of the results and findings are discussed and the research question is answered. The second part of the chapter is instead dedicated to explaining the main limitations and to tracing some opportunities for future research.

Finally, the conclusion section closes the study by summarizing the main findings and highlighting the research’s contribution.

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2. Literature Review:

The first part of this chapter is dedicated to examining the literature on which this research was based and exploring the existing theories. In the second part the general framework and hypotheses are outlined.

2.1 Social media marketing activities:

Social media can be defined as online applications, platforms and media, which aim to facilitate interactions, collaborations and the sharing of content (Palmer, Koenig, 2009). Their proliferation has created a new era for companies and brands, forcing them to seek innovative interactive ways of reaching and engaging their customers (Gallaugher & Ransbotham, 2010; Kozinets, de Valck, Wojnicki, & Wilner, 2010). This quickly expanding marketing channel, which already reaches more than two thirds of all Internet users, provides unparalleled opportunities for brand and

reputation building (Correa, Hinsley, & De Zúñiga, 2010; Spillecke & Perrey, 2011). Zhu and Chen (2015) identify two main kinds of social media: profile-based and content-based, depending on the nature of the connection and interaction.

The main focus of profile-based social media is on members and individuality, and on providing user related information, while the main objective is to encourage

connections and exchanges. Users are mainly interested in getting to know other people and expanding their relationships. Examples of this typology are Facebook, LinkedIn and Twitter. Content-based social media instead, focuses on the sharing of content and media. Its main objective is to transmit and share content, making it accessible to other users who then evaluate and review it through discussions and comments. These users interact with each other based on shared interests, and use the

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platform primarily as a creative outlet where they can share and spread their ideas. Examples are Instagram, YouTube and Pinterest (Zhu & Chen, 2015).

As social media use increases exponentially, not only existing social networkers, but also business firms and governmental organizations are joining, and starting to use it as a communication tool. Unlike individual social networkers, these entities actively make use of the media for advertising and marketing purposes, taking advantage of these new platforms to perform integrated marketing activities with much less effort and cost than before. However, consumer motivations to purchase widely diverge with different kinds of products, deeply impacting the required marketing activities. Broadly speaking, consumer choices are always driven by utilitarian or hedonic evaluations, making it necessary to make a separate distinction between utilitarian and hedonic products.

2.2 Utilitarian vs hedonic products:

Although the consumption of many goods involves both dimensions to varying degrees (Batra and Ahtola 1991), most products can be considered either as primarily hedonic or as primarily utilitarian. Utilitarian goods are ones whose consumption is more cognitively driven, instrumental, and goal oriented and accomplishes a functional or practical task (Strahilevitz and Myers 1998). Their consumption has little to no cultural or social meaning attached, and is solely driven by necessity. Hedonic goods are instead defined as goods whose consumption is primarily

characterized by an affective and sensory experience of aesthetic or sensual pleasure, fantasy, and fun (Hirschman and Holbrook 1982). Thus, hedonic products elicit an emotional arousal (Mano and Oliver, 1993), and are primarily consumed for sensory

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One of the most valued and unique hedonic products, that has up to now received very little attention and study from an economical perspective, is fine art.

2.3 Fine art as both a cultural and luxury good:

Fine art is often defined as one the highest manifestations of cultural product. Cultural products are experience goods containing a considerable amount of creative elements that mostly serve as identity building and social display, and have an aesthetic rather than utilitarian use. Fine arts products commence with a creative idea, which can be articulated through various types of art such as a painting, sculpture or photograph. This artwork is usually made accessible to the general public through art galleries, exhibitions and auctions. (Stegemann and Thompson, 2011).

The phenomenal growth of such a niche market (as stated by the figures in the introduction), and the increasingly high prices fine art is often sold for, have contributed to increasingly overlap art with the luxury market, and have led it to be more and more often, not only identified as a one of the most refined cultural products, but also as one of the top-range luxury goods.

A luxury good can be defined as a prestigious good having excellent quality, high experiential value, distinctiveness, exclusivity, and craftsmanship (Kapferer & Bastien, 2009). Consumers buy luxury products for two main reasons: their own pleasure and as symbols of success. Kapferer and Bastien (2009) maintains that the success of luxury brands depends on finding a balance between these two

motivations. Despite the global economic stagnation in recent years, the luxury goods market has been growing in terms of sales revenues. The number of luxury consumers has also been increasing (Kim and Joung, 2016). Following the expansion of the market and of the customer base, luxury companies have started looking for new ways

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to reach their younger customer groups. For these reasons, the market has shifted away from its original inaccessibility and now focuses on delivering exclusive hedonic experiences of the highest quality, through highly accessible communication mediums such as social media. The market for fine art has also followed this trend, and is increasingly migrating its sales and promotions towards digital mediums.

2.4 The digital art market:

With the unstoppable rise and growth of the Internet the younger generations are increasingly working, starting businesses and making their fortunes online, and have started to recognize social media, and the Internet in general, as an art-trading vehicle. In fact, after a slow start and quite a lot of resistance, mainly due to the fact that old-fashioned prospective buyers wanted to personally inspect the works they bid for, online auctions and art-selling websites have become increasingly used and popular, and are now frequently accessed from both fixed and mobile devices.

Past studies have demonstrated that marketing through social media plays a crucial role in determining brand success, especially in luxury brand’s advertising strategies (Kim & Ko, 2012; Phan, Thomas, & Heine, 2011; Chevalier, & Gutsatz, 2012). Some social media platforms, especially the more visual ones that support communication techniques (specifically the content-media platforms mentioned above), are optimal for the promotion of both creative and luxury brands. According to Kapferer & Bastien (2009) luxury should be vague rather than specific when

communicating to the medium, leaving customers with the possibility of creating their own individual luxurious dream. In addition, hedonic products depend more on

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to contextualize this research, as its high visual impact makes it optimal for hedonic marketing activities. Instagram is an online, mobile phone photo-sharing, video-sharing, and social network service (SNS) that enables its users to take pictures and videos, and then share them on other platforms. Since its launch, it has gained a lot of young customers, and now has approximately 700 million monthly users, with 95 million new photos uploaded every day (Omnicore agency, 2017). It is currently the fastest growing social network site globally (Wagner, 2015). Instagram functions as a virtual gallery, online meeting space and pictorial narrative platform and brands are increasingly adopting it as a mean to enhance their customer equity and exposure.

2.5 Customer equity:

Since marketing has shifted from product-centered to being more customer-centered, customer equity began to be considered one of the most important, if not the most, determinants of long-term value for companies. Customer equity is usually defined as the discounted sum of customer lifetime values (CLV), where each customer’s

lifetime value results from the frequency of category purchase, average purchase quantity, and brand-switching patterns, combined with the firm’s contribution margin (Rust, Lemon & Zeithaml, 2004). This means that the value a customer brings to a firm is not limited to the profit from each transaction, but is the total profit the customer may provide over the duration of the relationship with the firm (Kumar & George, 2007). However measuring Customer Equity through the CLV formula has often proven complicated and time-consuming, and as the measure is extremely long-term, so more short-term measures were sought for the purpose of this research: Price premium quantifies the extra amount that a customer is willing to pay for his/her

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traditionally been considered as an optimal representative of brand equity. However it was recently also identified as a proxy to quantify short-term customer equity (Mark et al., 2013) and so was adopted in this research as a substitute of the CLV formula.

2.6 Drivers of customer equity:

Rust, Lemon & Zeithaml (2004) also define three main kinds of equity as the key drivers of customer equity: value equity, relationship equity, and brand equity. Value equity is the objective assessment of the utility of an offering/brand, based on the perceptions that arise from the tradeoff of costs and benefits. It is determined by attributes such as quality, price and convenience.

The second driver, relationship equity, is defined as the tendency of the customer to stick with the brand, independently from her/his subjective value of the brand. The main factors influencing relationship equity are loyalty programs, special recognition and treatment, and knowledge programs.

Brand equity is instead the customer’s subjective assessment of the brand, above its objectively perceived value, and is driven by brand awareness, brand attitude and image, and corporate ethics.

2.7 Purchase intention:

Purchase Intention is defined as the probability that a given consumer will purchase a particular product (Grewal, Monroe, and Krishnan 1998). In an online environment, consumers are influenced on these purchasing decisions by the available information, which usually comes under the form of ratings and comments or marketing activities (Mangold and Faulds, 2009). In addition, Hoy and Milne (2010) discovered in their

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studies that the intentions to purchase are strongly driven by social media marketing activities. These intentions may also be affected by the amount of time a person spends online while exposed to a certain promotional content, and so be influenced by the intensity of content consumption.

2.8 Content consumption:

As just stated, content consumption refers to the quantity of time spent consuming a given content. Nowadays people spend more and more time browsing through their social media and by doing so are repeatedly exposed to marketing activities. Repeated exposure is a common practice in marketing which has been extensively studied (Cacioppo & Petty 1979, Campbell & Keller 2003, Nordhielm 2002), and an

increased level of repetition is generally believed to positively influence persuasion. However, other studies disagree with this theory (Batra & Ray 1986, Belch 1982, Burke & Srull 1988), supporting the fact that a repeated exposure in an everyday context can easily be ignored and so produces no effect. Furthermore, when advertising luxury and hedonic products an excessive amount of exposure may actually have a negative effect, harming the exclusiveness of the brand and reducing its desirability (Yoganarasimhan, 2012). In addition, as the consumption of art is of a visual nature, it could be somewhat replaced by the consumption of images of the same art on a visual social media such as Instagram, thus partially satisfying the consumer’s needs and decreasing his/her propensity to purchase. So, when speaking of fine art, a large amount of content consumption is actually expected to decrease the probability of it being purchased.

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2.9 Research gap and hypotheses:

Little is known at present on how the marketing and promotion of the fine arts industry will adapt to the fast and drastic changes of our digitalized society; and specifically, on how it will manage to fit in on the social media platforms that dictate today’s advertising world. The following research question and hypotheses were developed, based on the dynamics previously discussed in this chapter, with the purpose of filling this research gap, deepening existing knowledge and shedding light on such interesting and uncharted topics:

Do social media marketing activities towards hedonic products enhance purchase intention and customer equity?

• H1: Social Media Marketing activities have a positive effect on Brand Equity. • H2: Social Media Marketing activities have a positive effect on Value Equity. • H3: Social Media Marketing activities have a positive effect on Relationship

Equity.

• H4: Brand Equity has a positive effect on Purchase Intention. • H5: Brand Equity has a positive effect on Customer Equity. • H6: Value Equity has a positive effect on Product Consumption. • H7: Value Equity has a positive effect on Customer Equity. • H8: Relationship Equity relates positively to Purchase Intention. • H9: Relationship Equity relates positively to Customer Equity.

• H10: Content consumption negatively moderates the relationship between Brand

Equity and Purchase Intention.

• H11: Content consumption negatively moderates the relationship between Value

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2.10 Conceptual model:

The conceptual model (shown in figure 1) was developed to study the hypotheses and explain the relationships.

Figure 1: Conceptual model

3. Method:

The first part of the chapter describes the experiment design, the chosen sample and outlines the measurement of the different variables. Subsequently the statistical procedure is outlined and explained.

3.1 Design:

A quantitative study, by means of an experiment, was performed online thanks to a survey on Qualtrics with the aim of providing answers to the research question and

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hypotheses. The experiment was structured as a 2X2 factorial design, where every respondent was randomly assigned to one of the four treatments: SMMA (yes vs. no) X Content Consumption (yes vs. no). The participants were either exposed to an Instagram post with marketing, or to an Instagram post without marketing and were then either subject to a heavy repetition of these posts by completing tasks on an Instagram page (engaging in a high content consumption), or were sent to complete some similar tasks on a news website (not involving any content consumption). The experiment design is shown in figure 2:

3.2 Sample:

The population of interest for this study comprised art-oriented individuals who make frequent use of social media. Respondents were personally contacted online (on Facebook, Instagram and via email), and also sourced relying on self-selection. Out of the 328 people that started the questionnaire 200 of them completed it (the response rate was of 61%), thus every one of the four treatments was made up of 50

respondents. 53% of the respondents were Italian, 18% Dutch, 10% came from the United Kingdom and the remaining 19% came from other countries. Their age ranged from 19 to 69.

Social Media Marketing Activity Yes No Yes Content Consumption No N=50 N=50 N=50 N=50 Figure 2: Design Diagram

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3.3 Measurement of variables:

Social Media Marketing activity:

As Instagram is one of the fastest growing social media platforms, and one of the most apt mediums to convey visual content, a visual stimulus reflecting fine art social media marketing on Instagram was used. The streetartist Banksy was chosen as an example of artist because of his widely known style and broad international reach. Two different Instagram settings were developed, one with a high level of marketing activity and one with a low level of marketing activity, and each participant was randomly assigned to one of the two at the beginning of the survey. The two

treatments were represented by a dummy variable with value 1 = presence of SMMA, and 0 = no SMMA. Images of the two different treatments can be found in the

Appendix.

Content Consumption:

Content consumption was hypothesized to moderate the relationships Brand

EquityàPurchase Intention and Value EquityàPurchase Intention. It was designed to fix the level of interaction intensity respondents would have on social media and see if it had any influence on their purchasing decisions. This was done by creating two Instagram profiles, one for the SMMA condition and one for the no SMMA condition, and giving people different tasks to complete on the pages (for example like 5 pictures and comment on 2 pictures). The content on the SMMA profile comprised pictures of paintings for sale and a pronounced promotional activity, while the no SMMA profile focused more on the lifestyle of the artist and didn’t in any way try and directly sell the paintings. A no social media consumption condition was also created in which

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participants were asked to freely browse a news website and take notes of the titles that struck their interest. Thus, content consumption was set as a dummy variable with three possible values: 1=Content Consumption with SMMA, 2=Content Consumption with no SMMA, and 0 = no Content Consumption. Representations of the two

different profiles can be found in the Appendix.

Brand Equity:

Brand equity represents the customer’s subjective assessment of the artist or brand. To measure it a brand equity scale from Dwivedi and Johnson (2013) was used. The scale consists of 3 items and has a Chronbach’s α of .82. An example item is “I can identify myself with the artist”.

Value Equity:

Value equity was chosen to measure the respondent’s objective evaluation of the offering’s utility. The scale consists of 3 items, has a Chronbach’s α of .7, and was sourced from the studies of Yuan, Kim and Kim (2016). One item is “the art from this artist is excellently designed”.

Relationship Equity:

The relationship equity measures were also obtained from the study of Yuan, Kim and Kim (2016), and comprised 3 items with a Chronbach’s α of .82. Relationship equity was measured to quantify the respondent’s tendency to return to a brand irrespective of both objective and subjective brand assessments, as defined by Lemon, Kurtser, & Grossman (2001).

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Purchase Intention:

The purchase intention measure is used to predict if respondents plan to purchase the product in the future. The scale was adopted from a study of Lu, Zhao and Wang (2010) and consists of 3 items. The Chronbach’s α is of .9, and an example item is “given the chance, I would consider purchasing products from the artist in the future”.

Price Premium:

Price Premium was used as a proxy of Customer Equity. It is defined as the amount that a customer is willing to pay for his/her preferred brand over comparable/lesser similar brands (Aaker, 1996). The scale was made up of 3 items and was taken from a study of Netemeyer et al. (2003). The Chronbach’s α was .84, and the item “I am willing to pay ___% more for this artist over other artists” was recoded to fit a 5-point Likert scale after the termination of the survey.

All items used in the survey were in English, and all the validated scales were sourced from previous literature. They were measured on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

3.4 S

tatistical procedure:

All data was firstly analyzed thanks to Statistical software Package for Social Sciences (SPSS). The data was cleaned and all partial responses were identified and deleted, then any counter indicative items were recoded. Next, the descriptive

statistics and correlations were calculated. Descriptive statistics were measured thanks to measures of central tendency, dispersion and frequencies. Means were used to

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structural equation modeling was shaped and analyzed thanks to Smart PLS software. The Partial Least Squares Structural Equation Modeling (PLS-SEM) was chosen to conduct this research because of its capacity to analyze situations which common regression based models can’t tackle. Normal regression based techniques assume all models are simple and can’t properly analyze constructs with more than one

dependent variable. Specifically, SEM is a second-generation multivariate data analysis method often used in marketing research because of its ability to test theoretically supported linear and additive causal models (Chin, 1998; Haenlein & Kaplan, 2004). Haenlein and Kaplan (2004) outlined two methods of calculating SEM: The first is the covariance-based approach, using software packages such as LISREL and AMOS. The second approach, which was used for this research, is instead the variance-based approach, where PLS is the most common procedure. Every structural equation model is made up of two different sub-models: the inner model and the outer model. The inner model, or measurement model, highlights the

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relationships between the independent and dependent latent variables (and can be compared to the general conceptual framework of the model), while the outer model, also called the structural model, defines the relationships between each latent variable and its indicators (showing the items each variable is made up of). Figure 3 gives a clear example of the differences between the inner and outer model.

Both models had to be separately analyzed. Before doing so, reliability checks were computed, first by squaring the outer loadings to check indicator reliability and then by checking the Chronbach’s Alphas to measure internal consistency. After that the outer model was tested by analyzing its validity, this was done through the

measurement of convergent validity and discriminant validity. The inner model was instead tested by first running the SmartPLS algorithm, which resulted in the

calculation of the R2

s and path coefficients, and then by checking the significance of the results through a bootstrapping procedure leading to acceptance or rejection of the formulated hypotheses. The hypothesis regarding the effectiveness of SMMA’s on customer equity drivers were checked by running two separate SmartPLS algorithms and bootstrapping procedures, one in the marketing condition and the other in the no promotion scenario. Having done this, the results were compared and checked for significant differences.

The hypotheses regarding the possible moderating effect of content consumption on the relationships between brand equity and purchase intention, and on the relationship between value equity and purchase intention, were instead checked by using the SmartPLS algorithm and bootstrapping procedure.

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4. Results:

This chapter contains the results from the data analysis. The two main kinds of statistical analysis most commonly used for numeric data research are descriptive statistics and inferential statistics. Bhattacherjee (2012) defined descriptive statistics as a statistical description, aggregation and presentation of the constructs of interest or representation of associations between constructs, and the first part of this chapter is dedicated to their calculation and discussion. Inferential statistics are instead used to measure the relationships between variables; they are applied for testing reliability and validity hypotheses and are discussed in the second part of the chapter.

4.1 Descriptive statistics and correlations:

Table 1 outlines the general descriptive statistics and correlations. The first and most interesting observation that can be made from the table is that brand equity is

significantly negatively correlated to value equity, price premium, purchase intention and social media marketing activities, with its strongest negative correlation being with value equity.

Table 1: Means, Standard Deviations and Correlations

Number of items Variables M SD 1. 2. 3. 4. 5. 6. 7. 1. Brand Equity 3 3.2 .93 - 2. Relationship Equity 3 3.37 .96 -.08 - 3. Value Equity 3 3.42 .82 -.29** .27** - 4. Price Premium 3 2.62 .98 -.24** .23** .52** - 5. Purchase Intention 3 3.18 1.07 -.26** .30** .56** .67** - 6. SM Marketing Activities 1 -.15* -.04 .17* .13 .05 - 7. Content Consumption 1 -.02 -.03 .08 -.08 .01 .28** - Note: N=200.

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Value equity and relationship equity are instead strongly and significantly correlated between themselves, but also to both price premium and purchase intention. Value equity has also a weaker, but still significant, correlation with SMMA’s.

Price premium seems instead to be very strongly correlated to purchase intention (r = .67, p < 0.1). The last significant and positive correlation is between SMMA’s and content consumption. All the above correlations are positive, except from brand equity, which has only negative correlations and seems to move in the opposite direction with respect to the rest of the model.

As stated in the methodology, the experiment was structured as a 2x2 factorial design, so was made up of 4 different treatments. Table 2 compares how the means and standard deviations of the results vary in the two different initial treatments of the experiment, in presence of social media marketing activities, and when there are no social media marketing activities whatsoever.

Table 2: Means and Standard Deviations with and without Social Media

Marketing Activities

SMMA1 SMMA0

Mean Std. Deviation Mean Std. Deviation

Brand Equity 3.06 .9 3.33 .94

Relationship Equity 3.35 .94 3.4 .98

Value Equity 3.6 .82 3.3 .8

Price Premium 2.8 1.02 2.5 .93

Purchase Intention 3.23 1.14 3.13 1.01

Note: SMMA1= presence of social media marketing activities

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The measures of all the means, apart from brand equity and relationship equity, are significantly higher in the presence of SMMA’s. Relationship equity instead stays very similar, while brand equity is slightly lower in the no SMMA condition. Standard deviations provide a measure of the distribution of the answers around the mean, showing how close the scores cluster to the average, and thus giving a proxy of how much respondents give the same answers. There is no significant difference between the standard deviations between the two initial different treatments, which shows that respondents agreed about the answers they gave at approximately the same level.

In contrast to the significant differences between the first 2 treatments, the descriptive statistics of the last 2 treatments (presence of social media content consumption, and no social media content consumption), showed no significant differences between each other, as can be noted from table 3, and so their frequencies were not developed in the following pages.

Table 3: Means and Standard Deviations with and without Content Consumption

CC1 CC0

Mean Std. Deviation Mean Std. Deviation

Brand Equity 3.12 .94 3.2 .91

Relationship Equity 3.31 1.01 3.4 .94

Value Equity 3.53 .8 3.4 .82

Price Premium 2.53 .9 2.6 1.03

Purchase Intention 3.20 1 3.14 1.12

Note: CC1= presence of content consumption

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Tables 4 and 5 outline the frequencies of the responses in percentage with respect to the different possible answers along a 5-point Likert scale and confirm the insights given by Table 2. Analyzing Table 4 it can be seen that in case of Brand Equity the frequency of respondent’s answers are slightly lower (tend towards disagree) in presence of SMMA. Relationship Equity presents a rather uniform frequency of answers in both treatments, with a peak of 21% of respondents giving the answer number 4 (agree) in case of SMMA compared to the 13% in the no SMMA case. Value Equity instead does not present any significant differences in the different treatments. 0 10 20 30 40 50 60 70 80 90 100 1.00 2.00 3.00 4.00 5.00 P er ce n ta ge Likert Scale (1-‐5)

Table 4: Frequencies of customer equity drivers with and without

SMMA's

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Table 5 analyses the frequencies of responses in presence, and without, SMMA for Purchase Intention and Price Premium. No different trends can be observed between the 2 treatments, and the only notable difference is a 12% variation of Price Premium in answer number 1 (strongly disagree) between the two treatments (Price Premium SMMA1 = 21% and Price Premium SMMA0 = 33%).

0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 P er ce n ta ge Likert Scale (1-‐5)

Table 5: Price premium and purchase intention frequencies with and without

SMMA's

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4.2 Inferential statistics:

Before starting the calculation of the inferential statistics the outer and inner model had to be designed and built in SmartPLS.

The initial model was made up of five latent variables, represented by the blue circles shown in figure 4. The arrows between the latent variables represent the hypotheses outlined earlier in the previous chapter, while the remaining arrows connect the items to their respective indicators, with three indicators for each variable.

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4.2.1 Reliability:

Indicator reliability:

The outer loadings were calculated to check the correlation between each item and each latent variable and were subsequently squared to check indicator reliability. Hulland (1999) stated that values around 0.7 would be optimal, but values above 0.4 are also valid and accepted.

Table 6 shows the outer loadings and indicator reliability values. Henseler, Ringle and Sinkovics (2009) explain how the outer loadings represent the relationship between each latent variable and its respective items. Some researchers recommend

eliminating reflective indicators from measurement models if their outer standardized Table 6: Outer Loadings and Indicator reliabilities

Brand Equity Relationship

Equity Value Equity Purchase Intention Price Premium BE1 0.815 (0.664) BE2 0.870 (0.757) BE3 0.730 (0.533) RE1 0.671 (0.450) RE2 0.747 (0.558) RE3 0.799 (0.638) VE1 0.801 (0.642) VE2 0.889 (0.790) VE3 0.683 (0.466) PI1 0.916 (0.839) PI2 0.902 (0.814) PI3 0.938 (0.880) PP1 0.951 (0.904) PP2 0.946 (0.895) PP3 0.730 (0.533)

Notes: The first numbers in each row represent the outer loadings, while the numbers in ( ) are the indicator reliability values (the outer loadings²).

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when eliminating indicators. Nevertheless all the indicators in the table score highly above the recommended threshold. The indicator reliabilities were subsequently determined by calculating the square value of each outer loading. They can be seen in () in Table 6 and are also all above the suggested value of 0.4.

Internal consistency:

Chronbach’s alpha is one of the most widely used measures of internal consistency. Values range from 0 to 1 and for it to be acceptable the value of each Chronbach’s alpha should be equal or above 0.7 (Mitchell and Jolley, 2012).

The closer Cronbach’s alpha coefficient is to 1.0 the greater the internal consistency of the items in the scale (Gliem and Gliem 2003). Table 7 outlines the values of each alpha, which had already been presented in the previous chapter. Every value is above the recommended value of 0.7. However, Chronbach’s alpha sometimes over- or under-estimates scale reliability. For this reason composite reliability is sometimes preferred as an internal consistency measure as it can lead to better estimates of true reliability (Hair et al., 2012).

Table 7: Chronbach’s Alphas and Composite Reliabilities

Chronbach’s Alpha Composite Reliability

Brand Equity 0.82 0.848

Relationship Equity 0.70 0.784

Value Equity 0.82 0.836

Purchase Intention 0.90 0.942

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According to Bagozzi and Yi (1988), composite reliability should be 0.7 or higher, even if values above 0.6 are also acceptable. Howbeit, all the values listed in Table 6 are highly above these recommended values so internal consistency is confirmed.

4.2.2 Testing the outer model:

The outer model, also known as the measurement model, is tested by analyzing validity. For the assessment of validity, two validity subtypes are usually examined: convergent validity and discriminant validity. Construct validity is a measure of how much sets of indicators, measuring the same construct, are related between each other (Henseler, Ringle and Sinkovics 2009), while discriminant validity instead measures if constructs that should not have anything in common are actually related.

Convergent validity:

In order to assess convergent validity the Average Variance Extracted (AVE) was analyzed, as suggested by Fornell and Larcker (1981).

A level of AVE of at least 0.5 is evidence of sufficient convergent validity, meaning that the factors should explain no less than half of the variance of their corresponding

Table 8: Average Variance Extracted and Composite reliability Average Variance Extracted

(AVE) Composite Reliability (CR) Brand Equity 0.651 0.848 Relationship Equity 0.548 0.784 Value Equity 0.633 0.836 Purchase Intention 0.844 0.942 Price Premium 0.778 0.912

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AVE measures are far higher than the minimum threshold of 0.5. Lastly, every measure of CR should be higher than the respective AVE. This requirement is also met and is illustrated in Table 8.

Discriminant validity:

In most studies, discriminant validity is established by comparing the square root of AVE to the correlation values among the latent variables (Fornell and Larcker, 1981). HoweverHenseler, Ringle and Sarstedt (2015) demonstrated that this approach often doesn’t detect a lack of discriminant validity and proposed a new way of assessing discriminant validity called the heterotrait-monotrait ratio of correlations (HTMT). The authors conducted various tests on this new procedure, comparing it to the Fornell-Larcker criterion and demonstrated this approach’s superior performance.

Table 9 presents the HTMT results. For discriminant validity to be met, each value has to be below 0.90. As all the values are bellow this limit discriminant validity is confirmed.

Table 9: Heterotrait-Monotrait Ratio of Correlations Brand Equity Relationship Equity Value Equity Purchase Intention Price Premium Brand Equity - Relationship Equity 0.156 - Value Equity 0.391 0.419 _- Purchase Intention 0.322 0.402 0.705 - Price Premium 0.310 0.330 0.668 0.756 -

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4.2.3 Testing the inner model

The inner model, or structural model, is first tested by analyzing the path coefficients and examining the R2s. This is done by running the PLS algorithm in SmartPLS. Subsequently a bootstrapping procedure is conducted (still thanks to SmartPLS), to analyze the T-values and check the significance of the structural paths.

In Figure 5 the numbers written inside the blue circles represent the variance of the target endogenous variables, the R2. In marketing research R2 is considered weak from 0 to 0.25, moderate from 0.25 to 0.50, and strong >0.50 (Wong, 2013). Observing the figure it can be seen that the three equity drivers moderately explain both Purchase Intention and Price Premium (P.I. R2 = 0.361, P.P. R2 = 0.310).

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research and embody the hypothesis outlined in the previous chapter. They can be observed in Table 10: Negative relationships can be identified twice: Brand Equity à Purchase Intention and Brand Equity à Price Premium with the corresponding values of – 0.113 and – 0.100. All the other relationships are positive with the highest values belonging to the relationships between Value Equity à Purchase Intention = 0.5 and between Value Equity à Price Premium = 0.488. The remaining relationships concern Relationship Equity and equal 0.150 for Relationship Equity à Purchase Intention, and 0.094 for Relationship Equity à Price Premium.

The significance of these positive and negative relationships can be determined by conducting the bootstrapping procedure in SmartPLS and analyzing the resulting

T-statistics: If the significance level of the two-tailed- t-test is set at 1%, any path

coefficient above 2.58 will be considered significant. While if it is set at 5%, any value above 1.96 will be accepted. According to Table 11, two relations don’t pass the significance test: Brand Equity à Price Premium and Relationship Equity à Price Premium.

Table 10: Path coefficients

Brand Equity à Purchase Intention - 0.113

Brand Equity à Price Premium - 0.100

Relationship Equity à Purchase Intention 0.150

Relationship Equity à Price Premium 0.094

Value Equity à Purchase Intention 0.500

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These relationships reflect hypothesis 4 and 8, so

• H4: Brand Equity has a positive effect on Purchase Intention. And

• H9: Relationship Equity relates positively to Customer Equity. Are both rejected.

All the other relationships are instead significant.

However, Brand Equity à Purchase Intention, in addition to having the lowest significant coefficient, have a negative relationship, which leads

• H5: Brand Equity has a positive effect on Customer Equity.

To also be rejected, for the hypothesis presumed a positive relationship between Brand Equity and purchase intention.

The remaining relationships: Relationship Equity à Purchase Intention, Value Equity à Purchase Intention and Value Equity à Price Premium are positive and

significant.

Table 11: T-Statistics and P-Values

T- Statistics P-Values

Brand Equity à Purchase Intention 1.801 < 0.10

Brand Equity à Price Premium 1.496 X

Relationship Equity à Purchase Intention 2.468 < 0.05

Relationship Equity à Price Premium 1.301 X

Value Equity à Purchase Intention 8.008 < 0.01

Value Equity à Price Premium 7.593 < 0.01

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The above leads to the fact that

• H6: Value Equity has a positive effect on Product Consumption. • H7: Value Equity has a positive effect on Customer Equity. • H8: Relationship Equity relates positively to Purchase intention. Are all accepted.

4.3 Social media marketing activity versus no social media marketing

activity:

The first three hypotheses regarded the possible positive effect of social media

marketing activities on brand equity, relationship equity and value equity, also known as the customer equity drivers. To test these three hypothesis respondents were

randomly sorted in two groups with two different visual stimuli, one presented strong SMMA while the other did not. The two groups were tested separately in SmartPLS to compare the SMMA condition to the no SMMA one and analyze the differences. The results, shown in Table 12, highlight the general positive effect SMMA have on Customer Equity drivers, in comparison to the no SMMA condition, and consequently on the relations they have with the dependent variables purchase intention and price premium. The strongest change in effect is seen regarding relationship equity: the T-values measures 3.900 and 2.304 (for REàPI and REàPP) in the SMMA1 condition, compared to the T-values of 0.443 and 0.350 of the no SMMA condition.

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This strong increase in effect, the positivity of the path coefficients and the 5% degree of significance means that

• H3: Social Media Marketing activities have a positive effect on Relationship

Equity.

is accepted.

Value equity’s T-values also significantly increase in presence of SMMA compared to the no SMMA condition: from 4.84 to 6.302 regarding VE à PI and from 3.086 to 6.143 regarding VE à PP and are both strongly significant at the 1% level.

Consequently

• H2: Social Media Marketing activities have a positive effect on Value Equity. is also significant and accepted.

Table 12: Comparison of path coefficients and T-values between SMMA and no SMMA

conditions Path SMMA Path no SMMA T-Values SMMA T-Values no SMMA Brand Equity à Purchase Intention -0.108 -0.156 1.210 1.674 Brand Equity à Price Premium -0.091 -0.165 0.939 1.595 Relationship Equity à Purchase Intention 0.257 0.045 3.900* 0.443 Relationship Equity à Price Premium 0.177 0.039 2.304** 0.350 Value Equity à Purchase Intention 0.489 0.483 6.302* 4.840* Value Equity à Price Premium 0.503 0.374 6.143* 3.086* Note: ** Indicates the correlation is significant at the 1% level (2-tailed).

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Brand equity has instead a negative relationship with both purchase intention and Price Premium, and although this relationship is mitigated and becomes less unfavourable in presence of SMMA in comparison to the no SMMA condition, showing that BE acqually somewhat benefits from the promotion, the relationship is still negative so

• H1: Social Media Marketing activities have a positive effect on Brand Equity. cannot be supported and so has to be rejected.

4.4 The moderating effect of content consumption:

Content consumption was hypothesized to negatively moderate the relationships between Brand Equity à Purchase Intention and Value Equity à Purchase Intention, meaning that an excessive content consumption would decrease the customer’s propensity to purchase the product. However, analyzing the content consumption variable in SmartPLS, the overall path coefficient turned out to be very close to 0 (-0.008), and was so far from any value that could lead to significance.

The individual moderating effects were both totally absent, with values of - 0.03 for Brand Equity à Purchase Intention and 0.046 for Value Equity à Purchase Intention (A figure representing these relationships can be seen in the Appendix).

The T-statistics were then checked as further confirmation and turned out to be 0.718 for Brand Equity à Purchase Intention and 0.455 for Value Equity à Purchase Intention, leading to the conclusion that both

• H10. Content consumption negatively moderates the relationship between Brand

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• H11. Content consumption negatively moderates the relationship between Value

Are rejected, meaning that content consumption actually had no significant

moderating effect on the relationships. The possible causes for this will be discussed in the conclusions.

Table 13: Summary of Hypotheses

H1: Social Media Marketing activities have a positive effect on Brand Equity.

X

H2: Social Media Marketing activities have a positive effect on

Value Equity. ✓

H3: Social Media Marketing activities have a positive effect on

Relationship Equity. ✓

H4: Brand Equity has a positive effect on Purchase Intention. X

H5: Brand Equity has a positive effect on Customer Equity. X

H6: Value Equity has a positive effect on Product Consumption. ✓

H7: Value Equity has a positive effect on Customer Equity. ✓

H8: Relationship Equity relates positively to Purchase intention. ✓

H9: Relationship Equity relates positively to Customer Equity. X

H10: Content consumption negatively moderates the relationship

between Brand Equity and Purchase Intention. X

H11: Content consumption negatively moderates the relationship between Value Equity and Purchase Intention.

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5. Discussion:

In this section the main findings outlined by the results are discussed. The first part is dedicated to the theoretical and practical implications, while the last two sections are used to outline the main limitations and give some suggestions for future research.

5.1 Theoretical and practical implications:

The study has examined the effect of social media marketing activities on customer equity and purchase intention regarding hedonic products, specifically fine art, highlighting the positive effects these promotions can have, and uncovering several ways in which artists can fine-tune their marketing activities.

The findings support the research question and are partially in line with the previous research by Kim and Ko (2012), but highlight a greater complexity of dynamics when dealing with fine art compared to general hedonic products.

The first important finding of this research is that it confirms most of the results of Kim and Ko (2012) regarding the effectiveness of SMMA’s on customer equity, its drivers, and purchase intention, and shows that SMM can often effectively contribute to the promotion of fine art as it does with other hedonic and luxury goods. Positive effects were found both on value equity and relationship equity, highlighting how this kind of marketing is good both for demonstrating the objective quality of the art for sale and at the same time for relationship building and retention of existing customers. Value equity was found to be the most significant driver of customer equity, on which it independently had strong effects with and without SMMA’s. The results showed it also has a positive effect on purchase intention, highlighting how, even in the fine art world, the rational evaluation of the product is still the main driver in deciding

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whether or not to purchase.

Relationship equity was instead the most affected by the presence of SMMA’s, varying from having close to no effect in absence of promotion to a strong significant effect both on customer equity and purchase intention in the marketing scenario. The reason for this lies in the fact that the scenario with no marketing did not create any connection with the customer, resulting very distant and preventing the initiation of any relationship between the parties. Instead in the SMMA scenario, by

communicating a promotional message the artist attempts to initiate a relationship with the perspective customer.

The second finding is that when speaking of fine art, brand equity building processes are complicated and need specially tailored marketing campaigns. While value equity refers more to the objective value the customer gives to the offering (the artwork), and can so be grown through promoting the artwork and encouraging transactions, brand equity instead represents the image and meaning a customer gives to the brand (which in the case of this research is the artist).

Brand equity showed negative results all round, but these results became less negative when supported by SMMA. This leads to believe that brand building can also be positively affected by SMMA, but this marketing activity should be specially designed for this purpose and fine-tuned to separately build the artist’s image focusing more on his/her story and less on merely commercial objectives.

This becomes even more important when the brand is an actual person, as is Banksy, the artist used for the purpose of this research. While companies strive to develop a strong personality to not stay anonymous, in the case of personal brands some kind of personality associations will always be created. If the personal brand’s promotion is absent or simply of a transactional kind these associations will tend to be negative,

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and the artist will either be seen as worthless, not having a distinct personality to express, or as money driven and only focused on selling artwork. In light of these findings, it is vital, especially for hedonic personal brands, to design separate marketing campaigns with the aim of developing strong and positive associations to consequently build brand equity.

Third, this research revealed how content consumption doesn’t actually moderate the relations between brand equity, value equity and purchase intention. The reasons for this could be more than one: Firstly, this could to be due to the fact that once people are exposed to a digital advertising stimulus, more or less consumption doesn’t actually make a difference. The marketing message is transmitted instantly so an absence of repeated exposure won’t influence the effectiveness of the promotional content, and on the other hand, regardless of how the perspective customer relates to it, a prolonged exposure won’t change her/his attitude towards the given message. Another possible explanation is that customers need a very long time, and so a long period of content consumption, to actually change their initial impressions and influence their attitudes, and so the time of exposure in this research was too brief to conspicuously influence their attitudes and give significant results.

5.2 Limitations:

The findings of this study are subject to a number of limitations.

The fist limitation regards the nature of the sample selection and data collection. Self-selection is a nonprobability sampling technique which can lead to self-Self-selection bias, as the participants in the experiment decide whether or not to take part in the survey they may not appropriately reflect the target population, thus reducing the

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Secondly, customer lifetime value regarding most fine art companies is quite low. A reason for this is the high price of art, which makes it inaccessible to many

consumers, rarely leads to customer retention and seriously limits repeated purchases. For this reason price premium was selected as a short-term proxy of customer equity, and although it has already been used as such, this is a not a common choice and its accuracy still needs to be explored.

Another limitation regards the nature of the content consumption variable in the experiment. The time respondents spent doing the survey was probably not sufficient to enable a high enough level of content consumption, which could effectively lead to a change in attitude.

Last but not least, as studied by LaPiere (1934), attitudes often differ from actual behavior so asking respondents to express their intentions regarding purchase

intention and price premium, especially on such exclusive purchases, may not provide realistic and accurate answers.

5.3 Recommendations for future research:

A number of interesting opportunities for future research are created by this study. Firstly, this research discovered that brand equity would benefit from separate promotional activities focused more on story-telling and identity-building than on actual selling. The design and application of such brand-building marketing

campaigns regarding hedonic products is largely unknown and poses an interesting opportunity for future exploration.

Another opportunity for further research is on the possible moderating effect of content consumption on customer purchasing behavior. Future studies should try

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experimenting with longer times of exposure to effectively conclude if a long and repeated social media content consumption actually affects the intention to purchase.

6. Conclusions:

Artists, galleries and managers of hedonic-oriented companies in general should keep in mind the importance of choosing a specific social media approach depending on the desired objectives: if the goal is primarily to sell the marketed product and to encourage transactional relationships a strong SMMA strategy should be adopted and pursued with the aim of enhancing the customer’s assessed value of the offering. Such a strategy will also have positive effects on relationship-building. Increasing value equity and relationship equity will consequently result in higher customer equity and a higher intention to purchase.

If the objective is instead to build positive brand associations in the mind of the consumer, priority should be given to a different kind of marketing campaign with a greater focus on giving insights on the story behind the artist and the message he/she wishes to convey through his/her art, thus using social media as an emotional story telling and relationship-building medium. Marketing is not just about selling any more, it has become the primary medium to develop a brand’s personality, a way to communicate an authentic identity that people will love and be passionate about (Bhargava and Kawasaki, 2008).

In conclusion, this study has shown how, if purposefully planned and designed, social media marketing activities regarding hedonic products, can have a positive impact both on customer equity and purchase intention.

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Do social media marketing activities enhance purchase intention and customer equity? : an empirical study of the fine arts market