MASTER THESIS

How to understand brand equity, involving the network of strong, favourable and

unique brand associations, by using visual user-generated content as source of information?

“Visual user-generated content as a measure for brand equity”

A case study on the brand Iamsterdam

University of Amsterdam

Faculty of Economics and Business

Master of Science in Business Studies

Track: Marketing

Under supervision of: B. Rietveld

Student: Sissy Zwienenberg

Student Number: 10003088

Date of submission: 24 June 2016

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Statement of originality

This document is written by Student Sissy Zwienenberg 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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TABLE OF CONTENTS

1. INTRODUCTION ................................................................................................................ 4 LITERATURE GABS AND RESEARCH QUESTION ......................................................................... 5

SCIENTIFIC AND MANAGERIAL CONTRIBUTION ....................................................................... 6

2. LITERATURE REVIEW .................................................................................................... 9 CUSTOMER-BASED BRAND EQUITY .......................................................................................... 9

Brand awareness .............................................................................................................. 11

Brand image ..................................................................................................................... 12

MEASUREMENT MATTERS...................................................................................................... 13

THE IMPACT OF E-WOM ON BRANDING ................................................................................ 17

VISUAL USER-GENERATED CONTENT AS A MEASURE FOR BRAND EQUITY .............................. 19

Measuring brand equity from a consumers’ perspective ................................................. 20

Measuring associations .................................................................................................... 20

Measuring the consumers’ associative network structure ............................................... 21

Measuring the behavioural component ............................................................................ 22

The measure is quick to conduct ...................................................................................... 22

The measure is objective .................................................................................................. 23

CBBE is measured in an unconscious and un-verbalizable manner................................ 23

The measure is easily applicable on a large sample size ................................................. 23

The measure can measure brand equity of 'non-monetary’ brands ................................. 24

Measuring the social component ...................................................................................... 24

CONCLUSION ......................................................................................................................... 25

3. THE CASE OF IAMSTERDAM ...................................................................................... 26 AMSTERDAM MARKETING ..................................................................................................... 26

THE CITY AS A BRAND ........................................................................................................... 27

BRAND EQUITY OF A CITY ...................................................................................................... 28

RELEVANCE OF THE CASE ...................................................................................................... 30

4. RESEARCH DESIGN ....................................................................................................... 31 DATA COLLECTION PROCEDURE ............................................................................................ 31

REDUCING THE DATASET TO A SUBSET .................................................................................. 32

CONCEPTUAL FRAMEWORK AND OPERATIONALIZING OF THE VARIABLES ............................. 33

5. ANALYSIS AND RESULTS ............................................................................................. 35 DESCRIPTIVE STATISTICS ....................................................................................................... 36

Associations ...................................................................................................................... 36

Strength ............................................................................................................................ 38

Favourability .................................................................................................................... 39

Uniqueness ....................................................................................................................... 41

MAPPING CONSUMER-BASED BRAND EQUITY ........................................................................ 42

6. DISCUSSION ..................................................................................................................... 50

7. CONCLUSION ................................................................................................................... 54

8. LIMITATIONS AND SUGGESTIONS FOR FUTURE RESEARCH ......................... 56 LIMITATIONS ......................................................................................................................... 56

SUGGESTIONS FOR FUTURE RESEARCH .................................................................................. 57

REFERENCES ....................................................................................................................... 59

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Abstract

Understanding brand equity involves identifying the network of strong, favourable, and

unique associations in a consumers’ mind. This research introduces a methodology, based on

visual user-generated content, for eliciting brand associations and measuring and mapping

brand equity. In this research a model is proposed, that uses visual and textual data from

visual user-generated content, to understand brand equity. It is argued that this new

methodology remedies problems that occur with exciting brand equity measurements. To

illustrate the new method, city branding has been used as an application by measuring brand

equity of the brand Iamsterdam. The analyses show that brand associations and their strength,

favourability and uniqueness are measurable by investigating visual user-generated content.

Based on the conducted analyses, and consequently the mapping of these results, valuable

brand related information can be provided to marketing practitioners.

1. Introduction

Brand equity is regarded as a very important concept in business practice as well as in

academic research. Brand equity is about identifying the value of a brand and therefore it is an

important measure of the brand and a marketeers’ performances. From a consumer’s

perspective, Keller (2003) states that brand equity occurs when the consumer is familiar with

the brand and holds some favourable, strong and unique associations in mind. This network of

associations plays an important role in successfully differentiating a brand and securing

competitive advantages (Aaker, 1991; Keller, 1993, 2003). Many researchers have studied the

impact of brand equity on business performances. They state that high brand equity increases

shareholder value (Madden, Fehle & Fournier, 2006), profit and share prices (Till, Baack &

Waterman, 2011), margins (Keller, 1993), and returns (Barth, Clement, Foster & Kaszkik,

1998). Furthermore, brand equity facilitates price premiums (Starr & Rubinson, 1978), raises

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levels of customer satisfaction (Pappu & Quester, 2006), improves the efficiency of marketing

efforts (Smith & Park, 1992) and increases loyalty (Taylor, Celuch & Goodwin, 2004),

Several measures of brand equity exist, however considering a dynamic business

environment, new research techniques and new information sources available, it is important

to constantly review existing measures and look for improvement. This study aims to propose

a new measure for brand equity and thereby remedying some of the problems that existing

measures do coop with. One of the main issues with the current survey and interview based

approaches is that these methods are not standardized which makes this type of measurement

prone to subjectivity. Furthermore, most measures are time consuming to conduct, they do not

investigate brand associations as a network and assume that brand equity is formed in

isolation.

The method proposed in this research, aims to understand brand equity, involving the

network of strong, favourable and unique brand associations, by using visual user-generated

content (VUGC) as source of information. In order to illustrate the measure, city marketing

has been used as an application by measuring the brand equity of the brand Iamsterdam. This

research has three general objectives namely (1) investigate a new measure of brand equity,

(2) apply this measure by investigating the brand equity of the brand Iamsterdam and (3)

provide an effective and useful way to map the outcomes of this measure.

Literature gabs and research question

In the literature review in section two, it is outlined what brand equity is and why it is

valuable for companies to get insight in the brand equity of their brand(s) and the associations

that consumers have towards these brand(s). Several brand equity measurements exist, but

this research shows that all of these measures have one or more shortcomings. Therefore, this

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research proposes a new measure which can remedy some of the problems that exist with

current measures.

By studying visual user-generated content posted on Instagram, this study aims to

investigate a new way of measuring brand equity. It suggests a quantitative approach, which

is undertaken in a standardized, simple and quick manner. Instead of gathering new data,

visual user-generated content that has already been made available by users of the social

media network, is used as source of information in an unconscious manner. To illustrate the

measurement, it is applied by investigating the brand equity of the brand Iamsterdam.

Furthermore, this research provides an effective and useful way to map the outcomes of the

measurement.

With the recent emergence of social media networks, it has become popular to

indirectly leverage user-generated data on online communities. Beneficially, the resources on

such social media are obtainable instantaneously and inexpensively from a large crowd of

potential customers. However, no study has explored visual user-generated content as source

of information when measuring customer-based brand equity. This altogether results in the

following research question: “How to understand brand equity, involving the network of

strong, favourable and unique brand associations, by using user-generated content as source

of information?”

Scientific and Managerial contribution

By developing a new method for measuring brand equity, applying this method to measure

the brand equity of the brand Iamsterdam and finally mapping the results of this measure, the

outcomes of this research have significant scientific and managerial implications.

The new method of measuring brand equity makes a contribution to the literature

regarding earlier identified ways of measuring brand equity by overcoming limitations that

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existing measures do coop with. Brand equity, from a consumer viewpoint, it is traditionally

measured by analysing consumer responses to a survey. A contribution of this research is to

introduce a novel source of data, namely that of visual user-generated content.

The ubiquitous, dynamic and real-time interaction, enabled by social media, has

changed the landscape for brand management and substantially influenced the performance of

brands (Gensler, Völckner, Liu-Thompkins & Wiertz, 2013). Due to the new dynamic

networks of consumers, and the ease of sharing brand experience in such networks,

consumers have become authors of brand stories and brand managers have lost their pivotal

role as author of these stories (Gensler et al., 2013). This means that brand equity is being

shaped and sculpture by social media more and more. User-generated content now shapes

what a large mass of consumers think about a brand. These customer-generated brand stories

are able to determine a brands image and general associations (Gensler et al., 2013). When

knowing this, it is important to no longer ignore consumer-generated brand stories as source

of information when investigating the value of a brand.

Rather than thinking of consumers as passive absorbers of brand information and

brands as controllable knowledge structures, it is important to understand brands as a

“repository of meanings for consumers to use in living their own lives” (Allen, Fournier &

Miller, 2008, p. 782). Therefore, all stakeholders of the brand, including consumers, are active

co-creators of brand knowledge and brand meanings. The construction of brands can thus be

seen as a collective process involving several brand authors/stakeholders who all share their

brand stories (Gensler et al., 2013).

Corresponding with the second motivation of Keller (1993) as stated later (“a

strategy–based motivation to use the outcome as a strategy to improve marketing

productivity”), marketers are continually under pressure to justify the impact of their

marketing activities. Therefore, there is renewed interest in measures of marketing

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performance (O’Sullivan & Abela, 2007). The current research aims to show a new way to

give brand managers and marketers strategic, comprehensive and consumer-driven insight

into their brand associations and associative network. By identifying the strength,

favourability and uniqueness of the brand associations, guidelines are provided to

(brand)managers about how to maintain a brands strong image. The measure provides

information about which core associations should be protected from erosion or dilution.

Marketers can exploit this new measure for a variety of purposes. First, it can be used

diagnostically to check brand meaning and the brands overall health. Second, managing the

strength of key associations can also help to ensure that the brand sustains and achieves its

desired position in the competitive market. Third, the measure provides scope to monitor the

effectiveness of communication activities and their impact on how consumers perceive the

brand. Fourth, it is suggested that insights into the consumers’ brand associations can help to

determine the strength of the negative impact on a brand following a crisis situation (Dawar &

Pillutla, 2000). Finally, managers can exploit the insights provided by this new measure to

maintain or adjust their brand management strategy accordingly.

In order to provide a useful and attractive way to present a brand’s brand equity to

marketing practitioner and other stakeholders, the outcomes of the research are visualized.

This visualization includes mapping of the brand associations and the consumers’ associative

network structure. Furthermore, visualizations are created for all the different dimensions of

brand associations as indicated by Keller (1993). Altogether, these insights into the

measurement, and improved understanding of brand associations and their network, strength,

favourability and uniqueness, represents a significant contribution to brand equity literature

and managerial practice.

This research is divided into eight sections. The next section is the literature review

which concludes by outlining why visual user-generated content functions as good source of

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information when measuring brand equity. The third section introduces the case of

Iamsterdam, which is used to applicate the measure. The fourth section proposes the research

design and the fifth section elaborates on the analysis and results. In the sixth section results

are discussed, the seventh section provides the conclusion of the research and in the last

section, limitations and suggestions for further research are given.

2. Literature review

In order to structure this literature review, it is divided in five parts. The first part of this

literature review provides a comprehensive elaboration about what brand equity is and its

different components. The second part elaborates on brand equity measurement and compares

the different methods. The third paragraph explains the strong impact of eWOM on branding.

The fourth part explains, according to different desiderate for brand equity measurement, why

visual user-generated content serves as good source of information when measuring brand

equity from a consumers’ perspective. The final part provides a summary of the literature

review and gives a sub-conclusion of this research.

Customer-based brand equity

Brand equity is considered as a critical part of brand building (Keller, 1993) and therefore it is

an important concept to outline when doing research in the field of marketing and branding.

Though the terms ‘customer-based brand equity’ and ‘brand equity’ have been used

interchangeably, the present research focusses on customer-based brand equity (CBBE).

Measuring CBBE differs from other type of brand equity measurements since it does not

investigate financial assets or a products performance in the market place. Instead, it involves

investigating customers’ reactions to an element of the brand. So when measuring customer-

based brand equity, managers are able to track brand equity at a customer level.

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CBBE can be defined in terms of the marketing effects that are uniquely attributed to

the brand (Keller, 1993). Furthermore, customer-based brand equity is the differential effect

of brand knowledge on consumer response to marketing of the brand. Consequently,

understanding the structure and content of brand knowledge is important because this

influences what comes to a consumers’ mind when they think about a brand. The consumer

response to marketing is defined in terms of consumer perceptions, preferences and behaviour

arising from marketing mix activity. Keller (1993) states that building customer-based brand

equity requires the creation of a familiar brand that has favourable, strong and unique brand

association (see figure 1).

Figure 1: Dimensions of Brand Knowledge (Keller, 1993, P. 7)

Another important element in Keller’s (1993) study is understanding the structure of

brand equity. He outlines a node based memory approach, meaning that (brand) knowledge

consists of a set of nodes and links. Nodes are associations stored in memory that are

connected by links that vary in strength. Consequently, the strength between the activated

node and all linked nodes determine the extent of ‘spreading activation’ (Keller, 1993, p. 2),

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which is a consumers’ retrieval from memory. The same associative network memory model

as outlined by Keller (1993), serves as the basis for determining the consumers’ associative

network according to the new method proposed in this research.

Brand equity is an important and frequently studied subject, which is not strange if one

considers the consequences of the creation of a strong brand. Dacin and Smit (1994) argue for

example that a brand is one of the firms most valuable assets. Also Keller (2003) listed many

benefits of having a strong brand such as (increased) customer loyalty, increased marketing

communication effectiveness, and being less vulnerable to marketing crises. Furthermore, the

creation of a valuable brand can strongly influence a company’s bottom line result. Fehle,

Fournier, Maddon and Shrider (2008) provide empirical evidence for the fact that strong

brands cause its stock value to increase and therefore brand value may be a useful tool for

fundamental stock analyses. Moreover, Keller (1993) outlines two general motivations for

studying brand equity namely (1) a financially based motivation, to estimate the value of a

brand more precisely for accounting purposes and (2) a strategy–based motivation to use the

outcome as a strategy to improve marketing productivity.

According to Keller (1993), brand equity is conceptualized along two dimensions

which are brand awareness and brand image. The current research mostly focusses on brand

image, nonetheless, for the comprehensive understanding of band equity, both concepts are

outlined in the following sections.

Brand awareness

Brand awareness can be described as the strength of the brand node in memory. It reflects the

consumers’ ability to identify the brand under different conditions (Rossiter & Percy, 1987).

Brand awareness of a brand name relates to the likelihood that the name will come to mind

and the ease by which it does so.

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According to Keller (1993), brand awareness can be sub-divided in two concepts,

namely brand recognition and brand recall performances of consumers. Brand recognition

can be conceptualized as ‘Have you ever heard of brand x?’. Brand recognition requires that

consumer correctly discriminate the brand as having seen or heard of before. Brand recall is

the consumers’ ability to retrieve the brand when the product category is given. This means

that it requires of consumers to correctly generate the brand from memory.

Brand image

Brand image is the set of ideas, beliefs, and impressions that a person holds regarding an

object (Kotler, 1997, p. 607). Therefore, brand image refers to the set of associations linked

to the brand that consumers hold in memory (Keller, 1993). Understanding this mental picture

is important because people’s attitudes and actions towards a brand are highly conditioned by

this image (Kotler, 1997, p. 607; Jaffe & Nebenzahl, 2006, p. 15). Understanding and

measuring brand image, as the most important determiner of brand equity, is the main focus

of this research.

The key components of brand equity are the associations consumers have with the

brand (Aaker, 1991; Keller, 1993). Brand associations have been called “the heart and soul of

the brand” (Aaker, 1996, p. 8), and “fundamental to the understanding of customer-based

brand equity”. The central role of brand associations in the creation and maintenance of

brand equity is widely accepted (e.g. Hsieh, 2004; Walvis, 2008; Wansink, 2003). Practically,

high equity brands are more likely to have positive brand associations (i.e. brand image) than

low equity brands (Krishnan, 1996). Brand associations serve to differentiate a brand and to

create meaning for brands. These associations (both intended and unintended) give meaning

to the brand and are an important component of brand equity. Therefore, a better

understanding of brand associations is a fundamental role of brand managers (Till et al.,

2011).

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Brand associations can be captured on different relevant dimensions. Keller (1993)

distinguishes between the following dimensions of brand associations: favourability, strength

and uniqueness. When brand associations are favourable it means that the consumer believes

that the brand has benefits and attributes that satisfy his/her wants and needs in a way that a

positive overall brand attitude is formed (Keller, 1993, p. 5). When a consumers’ brand

associations are mostly favourable, they are more likely to form overall positive brand

judgements. Secondly, strong brand associations can be characterized by the connection to the

brand node. Strength is a function of both the quantity and the quality of the associations

(Keller, 1993, p. 5). A brand association will be stronger, the deeper a consumer thinks about

the association and is able to relate them to existing brand knowledge. Finally, unique brand

associations are associations that are not largely shared with competing brands. Building

brand equity is about positioning the brand in a way that it has unique selling points and

thereby a competitive advantage over the long run (Keller, 1993, p. 6). Strong and favourable

brand associations are critical for brand success, however when these associations are largely

shared with competitors, the brand is not likely to gain a competitive advantage from these

associations.

Measurement matters

Brand equity has become more important over the last 15 years as the key to understanding

the mechanisms, objectives and net impact of the integrated impact of marketing (Reynolds &

Phillips, 2005). Knowing this, it is not surprising that measures capturing brand equity, or

certain aspects of brand equity, have become part of a set of marketing performance

indicators (Ambler, 2003). Therefore, many academic research has been conducted on

identifying measures for brand equity and brand image (e.g. Aaker, 1996; Lasser, 1995).

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These studies have offered relevant insight into the processes of consumers evaluating and

choosing brands within a given product category.

Academics and practitioners who gathered at an MSI (1999) workshop on brand

equity metrics, summarized the following purposes for measuring brand equity: (1) to guide

tactical decisions and marketing strategy, (2) to determine the extendibility of a brand, (3) to

evaluate the impact of marketing decisions, (4) to track the brand’s health compared with that

of competitors and over time, and (5) to assign a financial value to the brand.

In order to review the current measures of brand equity, use is made of Keller and

Lehmann’s (2001) division into three different categories. The categories between which

these authors distinguish are the following: (1) ‘product-market’, (2) ‘financial market’ and

(3) ‘customer mind-set’ measures. In the next part of this literature review, every category of

measurement is explained. Finally, a table is provided in which the strengths and

shortcomings per measurement are identified.

The first category are measures related to ‘product-market’ variables. These measures

conceptualize product-market-level brand equity as the incremental revenue that the brand

earns over the revenue it would earn if it was sold without the brand name (Ailawadi,

Lehmann & Neslin, 2002). The most common measure within this category is the price-

premium measure; that is the ability of a brand to charge a higher price than a private label

equivalent (Ailawadi, Lehmann & Neslin, 2003). Overall, measures in this category are

simple, intuitive, and easy to calculate from public sources. Moreover, they have a strong

construct and face validity. According to Ailawadi et al. (2003), product-market measures

offer an attractive middle ground between financial and customer mind-set measures in terms

of relevance to marketing and objectivity.

The second category of measures assesses ‘financial market’ variables. From the

perspective of financial markets, brand equity is the capitalized value of the profits that results

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from associating a certain brand name with particular services or products (Simon & Sullivan,

1993). These measures asses the value of a brand as a financial asset. These type of measures

focus on the outcomes or net benefit that a firm derives from the equity of its brands.

Measures in this category are easy and quick to conduct, however, only financial data is being

considered.

Measures in the final category ‘customer-mindset’ are focusing on assessing the

consumer-based sources of brand equity and investigate a consumers’ brand knowledge.

These type of measures are rich in the way that they asses several sources of brand equity

such as associations, attitudes and awareness. These type of measures can also be used to

predict the potential of a brand. However, these type of measures are mostly based on

consumer surveys. Meaning that these measures are time consuming to compute and they do

not provide an objective and simple measure of brand performance. Measures in this category

have been the focus of much academic research (e.g. Aaker 1991, 1996; Keller 1993, 2003).

In table 1, an overview is provided of brand equity measurements in the three different

categories. Examples within all different categories are given, according to measurement

methods that have been proposed in frequently cited articles. To provide a comprehensive

overview, also industry measurements such as Interbrand and Millward Brown are included.

Exact methodologies of the industry measurements are obviously kept confidential, but broad

parameters have been published. Interbrand’s methodology is based on the net present value

of the earnings the brand is expected to generate in the future (Kumar & Shah, 2015) and

Millward Brown’s method ‘BrandZ’ combines consumer research with financial analyses.

Both the academic and industry measures are rated according to a list of desiderata for

the ideal measure. The table provides further evidence for the idea that visual user-generated

content offers an attractive way of measuring brand equity in comparison to earlier identified

methods.

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Table 1: Overview of brand equity measurements

The impact of E-WOM on branding

Earlier in this literature review the concept of CBBE has been outlined and the importance of

this concept has been stressed. The existing (categories of) measurement methods of brand

equity have been reviewed and criteria have been stated which are important when creating a

new measure for brand equity. In this paragraph, the impact of electronic word-of-mouth

(eWOM) is discussed and the relationship between eWOM and branding is outlined.

Recently, a strong relationship has developed between electronic word-of-mouth and

branding. This because brand stories are no longer only told by the marketer but social media

gave a strong voice to consumers. Consequently, it is possible for consumers to share their

brand stories with peers. These stories can help to build awareness, comprehension,

recognition, recall, and to provide meaning to the brand (Singh & Sonnenburg, 2012, p. 189).

Firms need to pay attention to these consumer-generated brand stories to ensure a brand's

success in the marketplace (Gensler et al., 2013). The brand stories that are told by consumers

of a brand can determine a brand’s general associations and the image of the brand (Holt,

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2003). Because of this finding, there nowadays exists a critical question for marketers and

brand managers on how to successfully coordinate consumer-generated brand stories. To

highlight the important relationship between brand equity and eWOM; it is found that eWOM

is one of the most effective factors influencing brand image (Jalilvand & Samiei, 2012).

Peres, Shacher and Lovett (2013) further explore the relationship between branding

and (e)WOM by presenting a theoretical framework whose fundamentals are consumers and

what stimulates them to engage in (e)WOM. They find that brand characteristics, above and

beyond its category or product type, play an important role in generating (e)WOM. They

further argue that consumers spread the word on brands as a result of functional, social and

emotional drivers. On the contrary, this research resonates that this link exists in the opposite

direction since it is argued that eWOM properties make up CBBE.

User-generated content (UGC) has been, and will likely be, increasingly changing the

way that people search, find, read, gather, share, develop, and consume (brand related)

information. UGC is “the media impression created by the consumers, usually informed by

relevant experiences and shared or archived online for easy access by other impressionable

consumers” (Zheng & Gretzel, 2010). UGC may serve as the new form of word-of mouth

(Ye, Law, Gu, & Chen, 2011). Although UGC is closely aligned and often confused with

eWOM, the two differ depending on whether the content is generated by users (UCG) or the

content is conveyed by users (eWOM). To be successful, eWOM depends on the spread of

content, and UGC has less influence without eWOM (Cheong & Morrison, 2004, p.3).

Through the Internet, individuals can make their ideas and opinions more easily

accessible to other Internet users (Dellarocas, 2003). Furthermore, the majority of consumers

reported that they trusted the opinions which were posted online by other consumers (Gretzel

& Yoo, 2008). One of the reason, why this research proposes that UCG is very useful as

information source when determining the brand equity of a brand, is because of the strong

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influential and persuasive characteristics of UGC.

When talking about user-generated content, a distinction can be made between textual

(words) and visual (photographs, images and video) content. The combination of social media

with the integrated mobile technology makes the capturing of activities easier and more

enjoyable. Therefore, social platforms supporting visual content, such as Instagram

(http://instagram.com), Tumbler (http://tumblr.com), and Pinterest (http://pinterest.com) are

rising to the top of social media channels (Thomas, 2012). Since the current research focusses

on Instagram, the following numbers are useful to illustrate the impact of this specific social

media network: on average, 80 million pictures are posted per day and these pictures are

generating on average 3.5 billion likes per day (Our story, a quick walk through our history as

a company, 2016).

In conclusion, it is proposed that eWOM is becoming one of the main drivers of

consumers (brand related) behaviour. This brand behaviour/purchase behaviour comes forth

from the consequences of the consumers’ brand knowledge which is the consumers’

associative brand network. Therefore, in a brand related context, eWOM is becoming more

relevant than ever.

Visual user-generated content as a measure for brand equity

In this paragraph it is proposed that using visual user-generated content as source of

information when measuring CBBE, fills up a large gap of important desiderata that current

measures do not address. The argumentation is structured according to the most important

desiderata as listed in table 1. In the final conclusion of this literature review, a summary and

conclusion is given about the idea that VUCG is a valuable, new source of information when

measuring CBBE.

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Measuring brand equity from a consumers’ perspective

Like all other measures in the category ‘consumer-mind set’, this method also aims to

measure brand equity from a consumers’ perspective. Measures in the category ‘customer

mind-set’ might be most appropriate when aiming to measure customer-based brand equity

because these measures investigate awareness, attitudes, associations, and loyalties that

customers have toward a brand. When measuring CBBE, it is believed the value of the brand

resides in the minds of the customers and not in the brand itself. Product-market and

financial-asset based measures are both outcome based measures, meaning that they are a

result of the knowledge structure and its impact on subsequent consumer behaviour. As a

consequence, these measures provide little diagnostic value. By using visual user-generated

content as source of information, the proposed method is able to identify the underlying

processes of consumer-based brand equity and has the ability to predict the brands potential.

Measuring associations

Brand associations can be anything which is seated in the consumers’ mind and related to a

brand. All brand associations together will form the consumers’ overall brand image. To

argue whether visual user-generated content is a good way to understand brand associations, it

first needs to be investigated how consumers actually learn brand associations. Van Osselaer

& Alba (2000) conducted a series of experiments to illustrate a learning process that enhances

brand equity at the expense of quality determining attributes. They argue that when the

relation between brand name and product quality is learned prior to the relationship between

product attributes and quality, inhibition of the latter may occur. This phenomenon is

described as ‘blocking’ of consumer learning. In other words, consumers value brand cues at

the expense of quality cues. This finding provides evidence for the idea that blocking of

consumer learning is a phenomenon that has implications for brand equity. The results of this

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research are in line with theories that view learning as a forward-looking process. It suggests

that learning associations is dominated by a satisficing process aimed at establishing accurate

prediction of future consumption benefits (van Osselaer & Alba, 2000, p. 13).

It is argued that associations measures can provide diagnostics to practitioners that

other (outcome) measures cannot. Only a few existing methods assess brand associations, all

by using qualitative research techniques. This research aims to introduce a measure that elicits

brand associations by using a standardized and quantitative method.

Measuring the consumers’ associative network structure

While actual purchase behaviour and financial assets may be used as a measure of equity,

delving into consumers’ knowledge structures may provide information on a brand’s potential

that may not be captured by past behaviours (Krishnan, 1996). Delving deeper into

consumers’ association structures may provide new information on the brand’s weaknesses

and strengths. Most of the current measures do not identify brand associations as a network,

which is one of the mayor shortcomings of these measures. Measuring the consumers

associative network structure is desirable since it is believed that consumers store information

in memory in the form of networks (Anderson & Bower, 1973).

Moreover, an influential network model is developed by Collins and Loftus (1975).

These authors provide evidence for the concept of spreading activation, meaning that when

someone is reminded of a stimulus, the activation of the nodes (associations) corresponding to

this stimulus occurs. Also this theory investigates the importance of analysing the consumers’

associative network structure, rather than investigating isolated brand associations.

Moreover, the hashtags used can be seen as the description and/or the categorization

of the content (Nam & Kannan, 2014). Therefore, user-generated content does not only give

insights into the brand associations as isolated concepts, it also allows to see relationships

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between these associations and gain information about the consumers’ associative network.

In this research, brand associations are analysed by the use of network analytics

techniques. It is believed that these analyses yield rich information about the linkages and

relationships between brand associations. A part of the analysis that is conducted determines

the centrality of associations within the network. Some nodes are more connected than others.

According to the spreading activation theory, the most connected nodes are the strongest since

these nodes facilitate a better spreading.

Measuring the behavioural component

It is argued that the new method is capable of measuring the behavioural component of

customer-based brand equity. Most methods only measure consumer attitude towards a brand

(i.e. ‘What do you think of a brand?’), and thereby not measuring actual consumer behaviour.

When using VUGC as a source of information, not only intentions but the actual behaviour of

consumers is measured.

The measure is quick to conduct

Surveys are time consuming to conduct and therefore the proposed method aims to provide a

quick alternative to elicit brand associations from a consumers’ perspective. Furthermore,

strength, favourability and uniqueness of the associations is also investigated by using

systemized, quantitative techniques. For most existing measures (especially in the consumer

mind-set category), it will cost additional time to enlarge the investigated sample. For the

proposed measure, this is not the case and the sample can be as large as all VUCG available.

Instead of obtaining new information for the purpose of brand equity measurement, the

proposed measure makes use of already available information. There is no need of collecting

new data, which makes the measure less time consuming to conduct.

23

The measure is objective

Almost all measures in the ‘consumer mind-set’ category use interviews and surveys to elicit

consumers’ brand associations. These techniques are sensitive to several types of bias due to

the social nature of these methods. Three major types of bias that are likely to occur are

interviewer bias (i.e. the interviewer can have prejudices), respondent bias (i.e. the respondent

may lie) and the actual interview situation (i.e. the social setting). The new measure, as

proposed in this research, is standardized and does not make use of any interview technique in

order to elicit brand associations. Therefore, the outcomes will not depend on the competence

of the interviewer which makes that the newly proposed is more objective than existing

‘customer mind-set’ methods, while still measuring brand equity from a consumers’

perspective.

CBBE is measured in an unconscious and un-verbalizable manner

Methods that measure brand equity in an unconscious and un-verbalizable manner all make

use of financial or product-market related data and therefore do not investigate brand equity

from a consumers’ perspective. However, measuring brand equity in an unconscious way is

important in order to overcome social desirable responses. Therefore, the proposed method

aims to measure brand equity from a consumers’ perspective in an unconscious way. The

VUGC is created without preliminary knowledge of the purpose of brand equity measurement

and therefore it investigates associations that are elicited by consumers in an unconscious

manner.

The measure is easily applicable on a large sample size

Another advantage of the new standardized approach is that it can make use of an extremely

large dataset in order to investigated brand associations. All brand related VUCG is taken into

24

account and used as source of information. Moreover, enlarging the sample will not cost extra

time but only extent the amount of information.

The measure can measure brand equity of 'non-monetary’ brands

Many of the existing brand equity measurements make use of a monetary approach by

investigating actual purchase behaviour or the company’s revenues and/or profit. It needs to

be realized however, that brands can be much more than selling branded products or services

at a (fixed) price. Think for example of personal brands (e.g. celebrities), place brands

(destinations), NGO’s or media brands. By investigating consumers brand knowledge

(structure) through VUGC, the proposed measure does not make use of any financial data and

can therefore measure brand equity of nearly every brand type.

Measuring the social component

Many existing measures assumes that brand equity is formed in isolation, meaning that

consumers are not socially influenced when determining their opinion about how much they

value a brand. The currently proposed measure rejects on this assumption since for VUCG on

Instagram it is proposed that the hashtags used and the content that is featured, reflect the

social interpretation of the brand (Fu, Kannampallil, Kang & He, 2010).

The description and/or categorization of user-generated content is filtered through the

lens of an individual user’s knowledge structure as well as through the lens of others’ social

tags. This process of assigning particular tags to brand related content provides a social

interpretation of the content. Therefore, it is argued that the hashtags and content of VUGC

provide insight into a person’s beliefs and will therefore be a good representation of the

favourability of consumers’ brand association and thereby of the consumers’ overall

evaluation of the brand (Nam & Kannan, 2014).

25

Nam and Kannan (2014) state that people have motivations in two categories when

engaging in social tagging, namely (1) content organization and (2) social communication.

This latter motivation shows that the social tagging processes provides a social interpretation

of the content which will in turn influence other users in forming their brand associations and

brand related knowledge structures.

Conclusion

This literature review started by explaining the concept of brand equity and the importance it

has gained as a key concept in understanding effects of marketing practice. Capturing and/or

measuring brand equity have become part of a set of marketing performance indicators and

therefore many measures of brand equity have been identified. An overview, including

strengths and shortcomings, is shown of the most important (categories of) brand equity

measurements. As a result of the increased impact of eWOM on branding and the

shortcomings that exist with current brand equity measurements, a new brand equity

measurement is proposed. This new brand equity measurement uses VUCG as a source of

information.

Both qualitative and projective measurement tools offer opportunity in the direction of

assessing brand equity. However, all these measures coop with a mayor downside since they

all make use of questionnaires, surveys, and attitude scales in order to elicit consumer-based

brand equity. This means that most of the time, these measures lack standardization and the

outcomes depend on the competence of the interviewer. Moreover, these methods require a

lot of time and resources, and are usually applied on small samples so the outcomes cannot be

generalized. Furthermore, these methods are measuring consumers’ attitudes instead of

consumers’ actual behaviour. Overall, attitude scales which are considered open methods, as

well as all the closed methods, assume that brand image is a conscious and fully verbalizable

26

construct (Cian, 2011). Consequently, they do not allow for a full investigation of the brand

image, which is in part un-conscious and un-verbalizable (Ballantyne, Warren, & Nobbs,

2006).

On the other hand, product-market and financial asset measurements are mostly

standardized, systemized and thereby more objective. Most of these measures also make use

of already available information which makes that these type of measures are less time

consuming conduct. However, a mayor short coming of these type of measures is that they are

all outcome measures meaning that they provide little diagnostic value from a consumers’

perspective and they do not provide valuable information regarding the brands potential.

The measure that is proposed in this research combines the strengths of all three types

of measurements (i.e. consumer mind-set, product-market and financial assets) by accessing

brand equity from a consumers’ perspective and using a standardized, quantitative approach.

3. The case of Iamsterdam

The new measure is applied and illustrated by measuring the brand equity of the brand

Iamsterdam. In this section, an introduction about Amsterdam Marketing and their mission

and vision is provided. Furthermore, it is described whether a city can be regarded as a brand

and consequently how brand equity of a city(brand) is formed. Lastly, it is elaborated on the

relevance of the case as illustration for applying the proposed brand equity measurement.

Amsterdam marketing

This study is undertaken in collaboration with ‘Amsterdam Marketing’, the city marketing

organization of the Amsterdam metropolitan area. Their ambition is to put the Amsterdam

metropolitan region on the map as one of the five most attractive metropolitan areas of

Europe and therefore they aim to positively influence the city’s public image internationally

but also for local residents, boosting their sense of civic pride and appreciation (Who we are,

27

2016). Recently, Amsterdam Marketing is moving from a ‘sales’ to a ‘guide’ function,

focussing on reputation management. This will enable them to emphases different aspects in

the city marketing activities that benefit the balance in the city. Amsterdam marketing helps

facilitating the distribution of visitors in time and space, with a focus on different type of

visitors (Strategic plan 2016-2020, 2016, p. 13). Amsterdam Marketing will guide residents

and tempt visitors to visit unknown neighbourhoods in the city and metropolitan area

(Strategic plan 2016-2020, 2016, p. 14). Amsterdam marketing owns the motto ‘Iamsterdam’

and uses this motto to brand the city.

The city as a brand

According to the definition of Hankinson and Cowking (1993) a brand is defined as ‘a

product or service made distinctive by its positioning relative to the competition and by its

personality, which comprises a unique combination of functional attributes and symbolic

values’. Furthermore, these authors state that the key to successful branding is to establish a

relationship between the brand and the consumer, such that there is a close fit between the

consumer’s own physical and psychological needs and the brand’s functional attributes and

symbolic values. Like brands, cities satisfy symbolic, functional and emotional needs

(Rainisto, 2003). Another comparison between cities and brands is that the attributes that

satisfy those previously mentioned needs, need to be orchestrated into the city’s unique

proposition (Ashworth & Voogd, 1990). Moreover, Morgan and Pritchard (2000) state that

‘the battle for customers in the tourism industry will be fought not over price but over the

hearts and minds — in essence, branding . . . will be the key to success’.

The previous mentioned definitions of branding and brands outline a strong

comparison between branding versus the goals of city marketing and managing the city’s

image as identified in the literature (e.g. Ashworth & Voogd, 1990, 1994; Kotler et al., 1999).

28

Therefore, it is stated that branding provides a good starting point for city marketing (Kotler

et al., 1999) and as a consequence, cities can be considered as brands.

Brand equity of a city

In the previous paragraph, it is established that there exists a strong overlap between

product/service branding and city branding. Because of this, it is accepted to consider a city as

a brand, and cities or destinations as the product category. Consequently, it is stated that like

it is possible to measure the brand equity of a product/service related brand, it is also possible

to measure the brand equity of a city or city brand (e.g. Iamsterdam).

Initially, brand equity was conceptualized as consisting of consumers’ brand

associations that include brand awareness, knowledge and image (Keller, 1993; 2003). From

all these elements, a crucial role within the city marketing mix is played by image formulation

and image communication (Kavaratzis, 2004). This because it is accepted that encounters

with the city take place through images and perceptions. The probability of including and

choosing a specific destination in the process of decision making will get higher when people

have a strong positive image about the destination (Alhemoud & Armstrong, 1996). City

image is consecutively the starting point for developing the city’s brand (Kavaratzis, 2004).

The approach of image marketing in the field of city branding also emerges from the

realisation that images can be effectively marketed while the products to which they relate

remain vaguely delineated (Ashworth & Voogd, 1994).

In order to communicate a city’s image and evoke brand associations, Kavaratzis

(2004) outlines three forms of communication, namely primary, secondary and tertiary

communication. Primary communication relates to the communicative effects of a city’s

actions (actions in four areas namely: (1) landscape (2) structure (3) infrastructure and (4)

behaviour). Secondary communication is the formal, intentional communication that most

29

commonly takes place through well-known marketing practices like indoor and outdoor

advertising, public relations, graphic design, the use of a logo etcetera (Kavaratzis, 2004, p.

68). Finally, tertiary communication refers to word-of-mouth.

Figure 2: Image communication (Kavaratzis, 2004, p. 67)

Whereas primary and secondary communication are both partially controllable by

marketeers, tertiary communication is not. As can be seen in figure 2, the entire branding

process, including the two controllable types of image communication, have as goal to evoke

and reinforce positive tertiary communication. Frequently, tourists trust more in the images

and opinions of other tourists than in those provided by companies and destinations

management organizations (Tussyadiah & Fesenmaier, 2009).

Previous findings further stress the importance of word-of-mouth when determining,

measuring and/or influencing a city’s image. Traditionally, destination management

organizations and private sector businesses controlled the formation and dissemination of a

desired destination image (Lo, McKercher, Lo, Cheung, & Law, 2011). However, through the

internet, destination management organizations and industry marketing bodies are now also

being advised to consider the implications of Web 2.0 and independent user-generated content

created on their activities. The rapid grow of the use of social media networking creates a new

30

phenomenon in promoting and creating awareness on the existence of the destination (Hanan

& Putit, 2013).

Relevance of the case

It is proposed that this new measure of brand equity can be used for measuring brand equity

of nearly every brand in every product category as long as consumers engage in creating

visual brand related content. However, in this section it is outlined why the new measure is

particularly relevant in the case of destination/city branding.

Destination photography has a strong influence on potential visitors and residents of a

city and therefore it is valuable to study the brand image that is created by visual user-

generated content. Especially in tourism, visual content (i.e. photography) plays a dominant

role because tourism is a uniquely visual experience (MacKay & Fesenmaier, 1997).

Moreover, photos have the ability to ‘tell’ desired stories about a place or destination

(Jenkins, 2003). The development of marketing through posting a photo on a social media

network influences (new) interests to social media users to travel to the specific destination

(Hanan & Putit, 2013). The advent and rise of a range of online photo and image sharing

media has democratized the image creation and dissemination process of destinations and

places (Lo et al., 2011). Destination management organizations must now compete with a

wide range of non-commercial content, posted by users. These information providers are now

felt to exert a significant influence on the tourist’s decision-making behaviour (Akehurst,

2009).

For example, Instagram, the photo sharing social media network which is the focus of

this research, initially only served as a medium for online photography. Recently it evolves

effectively in advertising, promotion, marketing, distributing ideas/goods and providing

information services fast, precise and accurate (e.g. Doolin, Burgess & Cooper, 2002;

31

Sweeney, 2000). The function of the postcard as the traditional tool of marketing continuous

by photo sharing on Instagram, where the traveller can easily upload pictures and share the

experience on the place of interest that lead to the development of express marketing for the

tourist destinations (Hanan & Putit, 2013, p. 472). The contribution of user-generated content

shows an increasing tendency in shaping a destination brand (Bronner & de Hoog, 2011).

4. Research design

In this section it is first outlined how the data collection is undertaken. Thereafter, it is

explained how the total dataset is reduced to a representative subset to conduct analyses.

Finally, the conceptual framework of this research is proposed and subsequently, the variables

of the framework are operationalized.

Data collection procedure

In order to obtain data for the research, information is extracted from the social media

network Instagram. It is chosen to make use of the Social media network Instagram since this

network incorporates only visual content, mostly in combination with meta-data such as a

caption, (a) hashtag(s) and a location. Moreover, in contrary to for example Facebook, many

users have a public profile, meaning that the content is publicly accessible and therefore

available to use for the purpose of this research. When analysing the content posted on

Instagram, both visual and textual information are taken into account, meaning the picture

itself (i.e. what is in the picture?) and the hashtags that users use to describe and categorize

the content they post.

All pictures posted on Instagram, referring to the investigated brand by being tagged

with the brand name, serve as the population to make inferences about. The population that is

investigated for this research are images, which are tagged using (at least) the hashtag

32

#iamsterdam. This implicates that the total population of the current research consists of more

than 400.000 pictures.

Data mining techniques have been used to extract the needed content from Instagram.

Once this gathered data is transformed into an understandable structure, visual and textual

analyses are conducted in order to obtain information about the brand associations, the

associative network structure of the brand and the favourability, strength and uniqueness of

the identified associations. Please see table 2 below for an overview of the used methods and

corresponding data collection procedures.

Analysis Method Why?

Data mining Use the Instagram API in order to

extract content from Instagram and

transform this data into an

understandable structure

To collect visual user-generated

content posted on Instagram,

featuring at least the hashtag of the

brand name (#iamsterdam in the

case of the current research)

Concept

analysis

Use a publicly available concept

detection API called ‘ImageNet’

To determine which concepts are

featured in the pictures

Embedding

analysis

Use a tool called ‘word2Vec’ to

analyse the embeddings between the

identified associations

To investigate relationships between

associations and to consequently

determine the consumers’

associative network structure

Sentiment/

favourability

analysis

Use a publicly available sentiment

detection API developed by

‘SentiBank’ to determine the

favourability of the visual

information and ‘SentiText’ is used

to determine the textual favourability

To analyse the favourability of the

brand associations.

Hashtag

analysis

Use a script in order to determine

which hashtags have been used in the

researched dataset

To determine the most frequently

used hashtags in the researched

dataset Table 2: Overview of data collection procedure per method

Reducing the dataset to a subset

The entire dataset consists of more than 400.000 images, therefore, it needs to be reduced in

order to obtain a subset from which analyses can be conducted, which is still representative

for the complete dataset of ‘#iamsterdam-images’ and that aims to measure the behavioural

component of brand equity. Furthermore, SPAM posts (e.g. advertising/promotions) need to

33

be reduced from the dataset, since this content is not considered as being user generated. As a

consequence, the dataset is reduced based on the following two criteria:

(1) Only considering pictures posted between 01/01/2015 – 31/12/2016. The analysis

incorporates pictures posted throughout one year in order to overcome a bias related to

different seasons. However, since this research aims to measure the behavioural

component of brand equity, only the most recently posted pictures are taken into

account.

(2) SPAM posts (advertising, promotions etcetera) are deducted from the dataset. In order

to do so, 2500 images have been categorized manually to indicate whether they are

considered to be a ‘user-post’ or a ‘SPAM-post’. Based on this information, an

algorithm is created to exclude SPAM-posts from the dataset. Approximately 2.5% of

the entire data set is considered to be a SPAM-posts.

When the entire dataset of more than 400.000 pictures has been reduced according to the two

criteria above, a subset containing 88.137 posts is left. This set of 88.137 posts is used for the

purpose of conducting analyses for this research, as described in the next section.

Conceptual framework and operationalizing of the variables

Below, the conceptual framework is proposed. The starting point of the analysis is visual

user-generated content. Both textual (hashtags) and visual (the picture) information is

extracted from the content. This data is used to analyse the different parameters that

determine customer-based brand equity.

34

Figure 2: The conceptual framework

As mentioned throughout this research, Keller (1993; 2003) states that brand equity occurs

when the consumer is familiar with the brand and holds some strong, favourable and unique

associations in mind. In order to measure brand equity according to these variables, the

variables are operationalized/measured according to the following descriptions:

0. Familiarity/awareness: this research proposes that everyone who is using the hashtag

of the brand name (#Iamsterdam), has (good) knowledge of the brand. It is assumed

that the users who post content, and simultaneously using the hashtag of the brand

name, are recognizing the brand and associate it with the right product/service.

Meaning that they are familiar with the investigated brand.

1. Brand associations: in order to determine the brand associations that are evoked by the

investigated brand, the text and picture of the brand related content is analysed. It is

investigated which hashtags consumers are using and which concepts are detected in

order to see which brand associations consumer have.

1a. Consumers’ associative network structure: the relationships between the

35

associations are investigated. Associations are being processed with the

purpose of grouping vectors of similar associations together in a vector space.

Conducting this analysis shows which associations are grouped together and do

consequently determine the consumers’ associative network structure.

2. Strength: The strength of brand associations is determined by analysing most

frequently used hashtags and most frequent identified concepts. Although consumers

may identify many things with a brand, it is the core brand associations that should be

the focus of management efforts (John, Loken, Kim & Monga, 2006). Moreover, the

strength of the associations can also be identified within the consumers’ associative

network structure by investigating the associations’ centrality.

3. Favourability: In order to determine the favourability of brand associations, textual

and visual sentiment analyses are conducted. The visual analysis identifies which

concepts are present on the picture and connects these concepts with the

corresponding sentiment. The textual analysis looks for words in the metadata that

express emotions and feelings (i.e. favourability).

4. Uniqueness: The uniqueness of the brand associations is defined by comparing brand

associations of the investigated brand with associations consumers have with another

brand in the same product category. In this specific case, brand associations of another

destination brand are investigated in order to compare brand associations and

consequently find unique brand associations for the researched city brand.

5. Analysis and results

In this section, the different analyses are conducted and consequently the results of these

analyses are demonstrated. Outcomes of the different analysing techniques are given in order

to provide information regarding the brand equity of the brand ‘Iamsterdam. First the

36

descriptive analyses are being discussed and second the mapping of consumer-based brand

equity is outlined.

Descriptive statistics

Associations

First, as what is earlier explained as the essence of brand equity, the brand associations of the

brand Iamsterdam are investigated. Brand associations need to be investigated first, in order

to later determine whether they are strong, favourable and/or unique. Brand associations are

investigated according to the analysis of the different hashtags used and the concept analysis.

All posts in the researched dataset featured a total number of 1.455.755 hashtags,

meaning that on average users use about 16 hashtags to describe the content they post. Only

103.666 of these hashtags are unique, meaning that the group of 1.352.089 hashtags is

compost of hashtags that are used at least two times. In order to determine the strongest brand

associations, a top 100 is defined for the most frequently used hashtags. In total these top 100

hashtags are being used 654.632 times, explaining that 44.47% of all the hashtags used, is one

of the hashtags that is in the 100 most frequently used hashtags. This finding provides

evidence for the idea that every brand has strong, core association which are shared by a large

group of consumers. The top 100 of most frequently used hashtags can be found in column A

of appendix A.

Number of

posts

Number of

hashtags

Average number of

hashtags per post

Unique hashtags Non-unique

hashtags

88.137 1.455.755 16 103.666 1.352.089

Table 3: Descriptive statistics hashtags analysis

In order to analyse the content of the pictures, use is made of two different types of

analysis, namely ‘ImageNet’ (Deng, Li, Do, Su, Fei-Feiand, 2009) and ‘SentiBank’ (Borth, Ji,

37

Chen, Breuel, & Chang, 2013). First the ‘ImageNet’ analysis is discussed. According to this

analysis, 15.293 ImageNet concepts, which can be present on the picture, are identified.

Therefore, the outcome of this analysis is an 88.137 (pictures) x 15.293 (concepts) matrix. For

all pictures, a nonnegative vector for each dimension is indicated. This value is a probabilistic

estimation of the concept presence in the picture. A script is built in order to identify the most

likely present concept in each picture. This results in 5289 unique concepts which are highly

likely to be on at least one of the pictures. From all these 5289 concepts, it is identified which

100 concepts are detected the most. This results in a top 100 of concept associations which

can be found in column A of appendix A.

A third way to identify brand associations is by making use of SentiBank, a novel

visual concept detector library that is used to detect the presence of 1200 adjective-noun pairs

(e.g. beautiful_flowers, disguising_food) (Borth et al., 2013). These adjective-noun pairs are

used to identify the favourability of the associations in the picture. However, the nouns

detected also provide an identification of the concepts present in the picture. A probability

score is given for the presence of each adjective-noun pair (ANP). A script is built in order to

identify the most likely present ANP. For the purpose of the association analysis, only the

nouns (concepts) have been taken in account. In total the analysis provides scores for 527

different nouns. A top 100 of the most likely present nouns is provided in column A of

appendix A.

Finally, it is proposed that this method is extremely valuable in identifying the

associative network that is in the consumers’ mind. This network is the relationship and

interaction between the different brand associations that consumers have. This network is

identified according to the investigation of the relationships that exists between the different

associations that are identified. Use is made of ‘Word2Vec’ software (Mikolov, Chen,

Corrado, & Dean, 2013). This system can make highly accurate guesses about a word’s

38

meaning based on past appearances. Those guesses are used to establish the relationship

between associations and consequently determine the consumers’ associative network

structure. The Word2Vec analysis is conducted for the 300 most frequent associations, based

on the hashtag, concept and ANP analysis. A score is indicated for the embedding between

one association and another. Later in this section, an elaboration is given on the consumers’

associative network structure by providing visualizations of this structure.

Strength

In order to determine the strength of the brand associations, a frequency analysis of the

concept, ANP and the hashtag analysis is undertaken. It is proposed that a brand association is

strong when the same brand association is highly repetitive in both textual and visual

analyses, and therefore shared by many consumers. From the analyses it results that 92% of

the hashtags used, and 91% of the objects identified, is not unique. Meaning that this brand

association is shared with at least one other user/consumer. When looking at the total dataset,

47% of the hashtags used is one of the hashtags that is in the top 100 most frequently used

hashtags, 43% of the most likely detected concepts is in the top 100 of most likely detected

concepts and 91% of the most likely detected noun is in the top 100 of detected nouns. These

numbers provide evidenced for the idea that certain brand associations are widely shared with

other consumers. For each association, a score for weight is indicated, based on the frequency

of this node. These numbers represent the strength of the individual brand node, relatively to

the other identified associations. An overview of these scores can be found in column C of

appendix A.

Another way of identifying the strength of the associations is within the consumers’

associative network. By investigating the centrality of the brand nodes it is argued which

39

associations are strong based on their central role within the network. The centrality score of

each association can be found in column I of appendix A.

Favourability

The favourability of the brand associations is investigated through the use of sentiment

analysis. The analysis of sentiment is conduct both visually and textually.

The visual content analysis of the pictures is undertaken by making use of SentiBank.

The advantage of using adjective-noun pairs (ANPs), is the capability to turn a neutral noun

into and ANP with a sentiment. This makes concepts more easy to detect and furthermore,

this method provides a useful analysis for the favourability of the concept. All the posts have

been analysed which resulted in an 88.137 (number of posts) x 1.200 (number of ANPs)

matrix. So each image is linked to a 1.200-dimensional vector in which each dimension

shows the probability of the ANP presence in the image. A high probability score therefore

means that the APN is highly likely to be on the picture, whereas a low score indicates that

the ANP is unlikely to be in the picture. For each image, the ANPs with the highest score is

identified and taken into account when conducting the analysis determining the top most

frequently occurring ANPs. From all these ANPs detected, the top 100 of overall highest

scoring ANPs is identified. Every identified ANP has a sentiment score based on the valence

of the ANP (e.g. happy and awesome have a positive score, dirty and bad have a negative

score). The sentiment value of each ANP is created by merging the adjective sentiment value

and the noun sentiment value together. The sentiment values are ranging from -2 (negative) to

+ 2 (positive). The total analysis results in 88.137 sentiment scores of which 32% is negative

(i.e. have a sentiment score below 0) and 68% is positive (i.e. have a sentiment score above

0). Furthermore, the sentiment score of the most frequently identified ANPs is determined.

The top 100 identified ANPs have an average sentiment score of 0,75. From these top 100,

40

69% ANPs have a positive sentiment and 31% a negative sentiment. Finally, the favourability

score of the top 100 nouns is calculated, the score per association (noun) can be found in

column D of appendix A. Please notice that the scores are rescaled to a range from -1 to 1 in

order to be comparable with favourability scores of the other associations.

Positive

sentiment (>0)

Negative

sentiment (<0)

Strong positive

sentiment (>1,5)

Strong negative

sentiment (<-1,5)

Average

sentiment

Total data set

68% 32% 38% 5% 0,66

Top 100 ANP

69% 31% 50% 5% 0,75

Top 100 Nouns

71% 29% 28% 3% 0,61 Table 4: Descriptive statistics visual sentiment analysis

Next to visual sentiment analysis, textual sentiment analysis using ‘SentiStrenght’

(Thelwall, Buckley & Paltoglou, 2012) is conducted. Textual sentiment analysis is a feature

to measure the sentiment evoked in the user posts, based on an analysis of the text from

hashtags associated with the images. The textual sentiment analysis indicates per post a

positive and a negative emotion rating. This because research has revealed that people process

positive and negative sentiment in parallel (Berrios, Totterdell & Kellett, 2015). The positive

emotion rating is indicated on a scale from 1 (neutral) to 5 (strong) and the negative emotion

rating is indicated on a scale from -1(neutral) to -5 (strong). The outcomes of the analysis are

scores for both negative and positive sentiment. Again, all the posts that have been posted

using at least #iamsterdam in 2015 have been analyzed. For all these posts together, an

average positive sentiment of 1.52 and an average negative sentiment of -1.10 is found.

According to textual sentiment analysis, in 68.88% of the pictures, no positive sentiment is

detected (positive sentiment is 1, is neutral) and in 91,99% of the pictures no negative

sentiment is found (negative sentiment is -1, is neutral). Moreover, when looking at the

strength of the sentiment, 21.31% of the posts is found to have a strong positive sentiment

41

(>2) whereas only 1.94% of the posts in found to have a strong negative sentiment (<-2). The

textual sentiment analysis is also conducted for the top 100 identified hashtags. Please notice

that a large amount of these hashtags have neutral sentiment. The sentiment score per hashtag

can be found in column D of appendix A. The score is rescaled to a range from -1 to 1 in

order to be comparable with the favourability scores of the other associations.

Positive

sentiment

(>1)

Negative

sentiment

(<1)

Strong

positive

sentiment

(>2)

Strong

negative

sentiment

(<-2)

Average

positive

sentiment

Average

negative

sentiment

Total data set

31,12% 8,01% 21,31% 1,94% 1,52 -1,10

Top 100 Hashtags

11% 0% 5% 0% 1,68% - Table 5: Descriptive statistics textual sentiment analysis

Finally, also the concepts detected in the picture are rated according to their

(un)favourability. Use is made of SentiWordNet (Esuli & Sebastiani, 2006), a lexical resource

for opinion mining. SentiWordNet assigns to each of the 15.293 concepts a sentiment score

which is neutral (0), negative (-1) or positive (+1). All the top 100 identified concepts have

been linked to the corresponding favourability score, which can be found in column D of

appendix A.

Uniqueness

The uniqueness of the brand associations is determined through comparison of the identified

associations with associations of another brand in the same product category. In case of the

current research, the brand association of the brand Iamsterdam are compared with the brand

associations of ‘Zaanse Schans’, another destination in the Netherlands. Of course any other

relevant benchmark can be used. The dataset that has been investigated in order to measure

brand equity of the brand ‘Zaanse schans’, contains 39.303 pictures featuring at least the

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hashtag #zaanseschans. The investigated posts for the brand ‘Zaanse Schans’ are also posted

between 01-01-2015 and 31-12-2015, to ensure overlap with the dataset that has been

researched for the brand Iamsterdam.

Brand associations of the brand Zaanse Schans are determined in order to compare

associations of the two brands, and consequently determine which associations are unique and

which associations not. Brand associations are again investigated by analysing the hashtags

that have been used, and by determining the concepts and nouns that are most likely to be

present in the picture. Again, a top 100 is determined for the three categories of associations.

Consequently, it is possible to indicate for each association of the Iamsterdam brand whether

the association is unique or not in comparison to the brand Zaanse Schans. Each association is

indicated with score representing the uniqueness of the association (0 = unique association or

1 = shared association). An overview of the uniqueness score of each association can be

found in column E of appendix A.

Mapping consumer-based brand equity

In order to visualize the findings of the research, the consumers’ associative network is

mapped. The network and visualization software ‘Gephi’ (Bastian, Heymann & Jacomy,

2009) is used to analyse the structure of the consumers’ associative network. The maps are

based on the 300 brand associations (nodes) as identified before, and their scores for

uniqueness, favourably, strength and embeddedness. When conduction the visualization, the

network is initially created with 300 associations (nodes) and 21115 linkages (edges). But in

order to ensure a meaningful visualization, the data is reduced according to the following

filters:

- Weight of the relationship: a value is indicated for the relationship between each of the

associations. However, this value is really low for some connections meaning that the

43

relationship between these two association is weak. Therefore, only the top 10%

strongest linkages are taken into account, meaning that the filter for edge weight is set

> 0.58.

- Weighted Degree: the number of degrees is the number of linkages an association has

to other associations. Emphasis is placed on the associations with the largest number

of linkages, since these associations are valuable for creating the consumers’

associative network. A filter is set for the value of weighted degree, which is the

number of degrees relatively to the relationship weight. The filter is set for a value >

12.53, meaning that only nodes with a weighted degree value larger than the average

weighted degree are taken into account.

Applying these filters results in a visualization including the 72 strongest associations (nodes)

and 981 linkages (edges). On this dataset, statistics have been run on modularity, degree,

weighted degree, eigenvector centrality, eccentricity, closeness centrality, betweeness

centrality, authority, hub, clustering coefficient and the number of triangles. These statistics

can be found in column F till P in appendix A.

To make brand equity more accessible to marketing practitioners and to provide

further insight in the associative network of brand equity, the data as describe above is

visualized by mapping the associations based on association type, modularity, weighted

degree and the three different dimensions of brand equity (i.e. strength, favourability and

uniqueness). The algorithm ‘ForceAtlas2’ is run to display the spatialization process, and

transforming the network into a map (Jacomy, Venturini, Heymann & Bastian, 2014). The

maps (graphs) all represent the consumer associative brand network based on visual user-

generated content.

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Graph 1: The consumers’ associative network structure according to type of association

The first graph presents the associative network structure based on the division between type

of association. As mentioned before, the associations result from three different type of

analyses, namely hashtags, and two different types of visual concept analyses. The three

different colours of the nodes all represent one type of association. The red nodes represent

the associations resulting from the ANP analysis, the green associations relate to the hashtag

analysis and the blue associations result from the ImageNet analysis.

45

Graph 2: The consumers' associative network structure divided by clusters

Graph 2 shows how well the associative network structure composes into modular

communities by measuring the strength of division of the network into modules. All the

different communities have been indicated with a different colour, resulting in 6 different

communities of which the ‘light blue’ (31%) and ‘green’ (29%) community are the largest.

46

Graph 3: The consumers’ associative network structure according to the weighted degree of associations.

Graph 3 presents the consumers’ associative network based on the weighted degree of the

associations. The degree is the number of edges (linkages) a node (association) has. The

weighted degree of a node is based on the number of degrees, ponderated by the weight of the

edge. In this graph the weighted degree of an association is indicated by the size of the node;

the bigger the node size, the larger the weighted degree of the association.

47

Graph 4: The consumers’ associative network structure according to the strength of associations

Graph 4 presents the associative network structure based on the strength of the associations.

Based on frequency analysis, each association is indicated with a score for weight. The more

frequent the association is identified, the higher the score for weight. In this graph the strength

of the association is indicated by colour, the darker the colour of the node, the stronger the

brand association is.

Another measure for strength of a brand association is the centrality of an association

48

within the associative network. The score for centrality of the brand association is indicated

by the size of the node; the bigger the size the larger the score for centrality. The centrality is

computed by eigenvector centrality, a score that assumes that each node’s centrality is the

sum of the centrality of the nodes that it is connected to.

Graph 5: The associative network structure according to the favourability of associations

49

Graph 5 features the dimension of favourability of the brand associations. The colour of the

brand node represents whether the brand association is favourable (green), neutral (yellow) or

unfavourable (red). Most of the associations are neutral (64%), about a third of the

associations is favourable (26%) and a small part of the associations is unfavourable (10%).

Graph 6: The associative network structure according to the uniqueness of brand associations

Graph 6 maps the consumers’ associative network structure according to the uniqueness of

the associations. A green node colour indicates that the association is unique whereas the red

50

colour indicates that the association is shared with the brand that is used for benchmarking

purposes. From this visualization, it results that 42% of the associations is unique and 58% of

the associations is shared with the benchmark brand, which is ‘Zaanse Schans’.

6. Discussion

In this section, the results of the data analysis are being discussed and evidence is provided

for the idea that the new measure remedies shortcomings of the existing measurements.

The graphs representing the consumers’ associative network structure are created by

quantitative and standardized techniques, however, they do represent brand equity from a

consumers’ perspective. The method is capable of measuring brand equity from a consumers’

behavioural perspective since user-generated content is used as the information source. In

order to elicit brand associations, three different techniques have been used. Instead of

conducting surveys or interviews, associations are elicited from the brand related content

posted on Instagram and the corresponding meta-data. All three techniques are quantitative

and therefore providing a standardized and objective way of measuring brand associations.

Graph 1 shows that, after partitioning the associations by analysis technique, it is

found that the distribution between the three types of associations is almost even (43%, 31%

and 26%). Furthermore, the different colours are also evenly spread through the network.

These findings provide evidence for the suggestion that the different type of analyses do coop

with each other and together compose and reinforce the consumers’ associative network.

Whereas most qualitative measures result in only a few strong associations, this measure

easily identifies 300 associations.

Most existing measurements do not address brand associations as a network. The new

measure however, identifies linkages between the brand associations and can therefore

compose the consumers’ associative network structure as is visualized by the graphs. By

composing the consumers’ associative network structure, the method is able of identifying

51

strong associations within the network. Graph B1 visualizes the centrality of the associations

by indicating strong associations on the basis of their ability of spreading activation. When

analysing the consumers’ associative network, the new measure goes on step further by

identifying clusters (i.e. communities) within the network. In graph 2, six different

communities are found. The ‘light blue’ community (30,56%), the ‘green’ community

(29,17%), the ‘red’ community (20,83%), the ‘blue’ community (15,28%), the ‘yellow’

community (2,78%), and finally the ‘pink’ (1,39%) community. The first 4 clusters consist of

many associations, the last two (yellow and pink) can hardly be considered as a community

since they only consist of two and respectively one node. Interestingly, the two biggest

clusters overlap with the area’s of primary communication as proposed by Kavaratzis (2004),

earlier in this research. The light blue cluster has ‘landscape’ as an overlapping theme, while

the associations in the green cluster mostly relate to ‘infrastructure’. In branding terms, it is

proposed that complementarity exists between associations in the same cluster (Henderson,

Iacobucci & Calder, 1998, p. 319). When identifying clusters of associations, it is investigated

which complementary combination of associations can be leveraged in order to gain the

greatest success of the brand. To our knowledge, non of the existing measurements identify

clusters within the associative network.

Also graph 3 visualizes important information regarding the consumers’ associative

network structure. This graph indicates the weighted degree, which results in bigger node

sizes for associations with a high weighted degree. A high weighted degree means that these

associations have a lot of linkages to other nodes when also taking into account the weight

score of the embeddedings. Associations with a high weighted degree are found to be more

central in the consumers’ associative network structure. It is proposed that especially these

associations are of great value since they have the ability to stimulate many other brand

related associations. It is proposed by Aaker (1996, p. 234) that centrality is ideal for

52

uncovering the ‘driver’ of a purchase decision. Therefore, it is argued that the associations

central in the network are highly valuable to the brand, since they play a big role in driving

the overall consumers image and consequently valuation and purchase of the brand.

Especially this graph therefore provides high diagnostic value and helps to understand the

underlying process of consumer behaviour in comparison to existing measurement methods.

Besides analysing the associative network, graph 4 till 6 show that the current measure

is capable of identifying the 3 different dimensions of associations (i.e. strength, favourability

and uniqueness) as identified by Keller (1993).

Graph 4 indicates the strength of the association based on two different analyses,

resulting in a graph with different node sizes and different colours. The node colour indicates

the associations’ strength based on the conducted frequency analysis; the darker the colour,

the more frequent the association is identified. The size of the node indicates the centrality of

the node. The bigger the node size, the higher the centrality of the brand associations. This

measurement of centrality uncovers which brand associations are most pivotal to the overall

brand image. Consequently, nodes with both a big size and dark colour are uncovered as most

important in terms of strength.

Graph 5 indicates the consumers’ evaluative judgement per association by colour. The

different colours (yellow, green, red) indicate whether the association is respectively neutral,

favourable or unfavourable. An unfavourable brand association always negatively contributes

to the brands’ overall brand equity. However, the worst combination of dimensions for an

association is to be strong, unique and unfavourable. Therefore, it is suggested to identify the

favourability of nodes relatively to strength and uniqueness. These graphs are found in

appendix B1 and B2. Especially strong and unfavourable associations should deserve a

marketeers’ attention since these associations are harmful to brand equity. Strong and

favourable associations should be protected and supported since these associations make a

53

large contribution to the brands positive image formulation. When looking at appendix B1, it

is found that non of the unfavourable associations score high on centrality (strength), however

the unfavourable association ‘crowd’ and ‘cemetery’ score moderately high. The other way

around it, is advised to reinforce associations that are favourable and strong, such as the

associations ‘sun’ and ‘tourist’.

Graph 6 indicates by colours whether or not the association is unique in comparison to

the brand ‘Zaanse Schans’. As mentioned before, more than half of the associations are shared

with the brand ‘Zaanse Schans’. However, the central associations (e.g. street, city, museum)

are found to be unique. Furthermore, when looking at the graph in appendix B2, the

associative network is presented according to uniqueness and favourability. Combinations of

unique and unfavourable associations can harm the brand whereas a favourable association

can lose its contribution to brand equity when it is largely shared with competing brands. In

the graph in appendix B2, the colours indicate whether the association is favourable (green),

neutral (yellow) or unfavourable (red) and the size of the node indicated whether the

association is unique (big node size) or shared (small node size). It is found that, except for

one association which is ‘sun’, all favourable associations are shared with the benchmark

brand. Furthermore, the associations crowd, cemetery and graveyard are both unique and

unfavourable and therefore need to receive attention from marketing practitioners.

Whereas constructing the consumers’ associative networks structure, according to

graph 1 till 6, would be time consuming using survey and interview data, VUCG can compose

these maps using quick, standardized and objective techniques. Since no use is made of

financial data, the graphs can also be composed for ‘non-monetary’ brands which is the case

for the currently investigated brand ‘Iamsterdam’. Moreover, there is no need of collecting

new data for the purpose of measurement since publicly available content is used. Using this

data saves time and provides a large data-set. Whereas most measures are not very

54

informative when comparing CBBE over time, the created graphs provide important brand

information regarding the impact of planned (campaigns) and unplanned (crisis) events over

time. This because changes in the (visualized) network are detected easily. Moreover, the

outcomes of the new method provide important information to practitioners when determining

the extent to which they should focus on specific associations in their marketing activities.

7. Conclusion

The conducted research had three general objectives namely (1) investigate a new measure of

brand equity, using visual user-generated content as the data source, (2) applying this measure

by investigating the brand equity of the brand Iamsterdam and (3) providing an effective and

useful way to map the outcomes of this measure.

The research starts with the literature review in which the concept of brand equity is

explained. Capturing and/or measuring brand equity has become part of a set of marketing

performance indicators and therefore many measures for brand equity have been identified.

An overview is shown of the most important (categories of) brand equity measurements. As a

result, it is proposed that all of these measures have (several) shortcomings which can be

remedied by measuring customer-based brand equity according to visual user-generated

content.

In order to illustrate the measure in an actual branding application, the case of

Iamsterdam, the city marketing organisation of Amsterdam, is investigated. An introduction

to city branding is given and the relevance of the case is outlined. Furthermore. It is explained

how the Iamsterdam related VUGC is collected and how this data is analysed in order to

measure CBBE.

The introduced measurement method uses different quantitative analysing techniques

in order to determine brand association and the associative network that consumers have in

55

mind. Associations are analysed through the means of visual and textual analyses.

Furthermore, different techniques are used to determine whether these associations are strong,

favourable and/or unique. The proposed method provides a manner to unconsciously elicit

brand association, without making use of customer surveys and therefore not requiring a large

time commitment from respondents. Furthermore, the proposed method is easy to administer

and does not require a trained interviewer or any other time and/or resource consuming

investigation. The method is able to capture the network of brand association underlying

consumer perceptions of a brand. Moreover, the used approach measures consumer-based

brand equity using a structured and systematic procedure. Standardized measurements are

important for eliciting brand equity as they provide assurance that the method measures what

it intends to measure. The method is unique among other brand equity measurement

techniques by using visual user-generated content as the source of information and thereby

analysing brand equity from a consumers’ perspective, without making use of survey or

interviewing techniques.

In order to make brand equity measurement more accessible for marketing

practitioners and to allow for a holistic brand diagnosis, the brand associations of the focal

brand are mapped according to the consumers’ associative network knowledge. The

consumers’ brand network offers a rich opportunity for understanding brand equity and helps

to provide a clear understanding of the perceptions consumers have. The visualizations help to

get an immediate overview of the dimensions of brand equity which are strength,

favourability and uniqueness. Furthermore, the brand equity maps will help marketing

practitioners when determining the extent to which they should focus on specific associations

in their marketing activities.

Altogether, the VUCG method provides a new way of measuring brand equity. The

method provides added value by addressing shortcomings that existing measures do coop

56

with. The method delivers brand maps which identified core associations and the network of

these associations. Furthermore, it investigates the associations according to different

dimensions. The new measure combines the diagnosticity and investigation of a brands’

potential from the consumer mind-set approaches with the objectivity and standardizations of

the outcome measures.

8. Limitations and suggestions for future research

Although there remains work to be done in this area, it is believed that the new method of

measuring brand equity holds promise and is worthy of further research. This section

elaborates on the limitations of the research and suggestions for further research are given.

Limitations

The first and probably most important to understand is that the outcomes of this research are

all seen through the perspective of the social media network Instagram. This of course has

several advantages as described before, however it can also cause limitations. One of the main

disadvantages is that the users of this network are probably not representative for the average

world population, and consequently not for the (potential) consumers of the brand, which may

cause a respondent bias.

Second, the method cannot be used to investigate the brand equity for every single

brand. The proposed method requires that a brand is sufficiently present on Instagram. Brands

for which brand equity is probability not measurable through this method are less familiar

brands such as B2B brands and private label brands.

Third, only investigating images that are tagged using #[brand name], might not

provide a complete overview. It needs to be taken into account that some pictures are wrongly

tagged, so that the brand name does not relate to the picture and therefore noise is included in

the dataset. On the other hand, content might be omitted; filtering on hashtags might not

57

include all the brand related posts, since posts can be about the brand while the hashtag is not

included.

The fourth limitation of this research is that of external validity as the measure is not

compared to a secondary outcome measure (for example a brand ranking). Validation of the

research is difficult since this research is the first that uses VUCG as a source of brand equity

measurement and the associative network structure of the brand Iamsterdam has never been

investigated before.

Finally, this research assumes that the associative network created by VUCG equals

the associative network in the consumers’ mind. This assumption can be considered as a

limitation since the comparison between the two networks has not been investigated.

Suggestions for future research

First it would be useful to evaluate how well this measurement operates for different type of

brands. It is shown that this method is feasible for measuring customer-based brand equity of

a city brand, however it is suggested to also applicate this measure to other type of brands

such as a product-, person- or service brand. When the brand equity of a different type of

brand is measured according to the method, it can be stated whether the measurement

protocol is generalizable to other type of brands or not.

Second, it will be valuable to measure brand equity over time. As soon as a baseline is

created, practitioners will be able to see shifts in the consumers’ associative network

structure. This allows them to assess the impact on the structure of the consumers’ associative

network of planned (e.g. marketing campaigns) and unplanned (e.g. crisis) events.

Third, it is argued that brand equity is a pluralistic concept. Meaning that brand equity

and thereby brand associations, will differ for every individual consumer or consumer group.

Since effective brand communication is based on eliciting positive images of the brand, it is

58

valuable to assess the existing brand associations of the different target groups and highlight

the distinctive advantages of the brand. Visual user-generated content on Instagram can meet

the need of determining CBBE on an individual level since a personal profile of the Instagram

user can be obtained. This means that brand associations can be linked to characteristics such

as gender, age and country of origin. However, due to the limited scope of this research brand

equity is not investigated for different consumers (groups).

Finally, it needs to be recognized that techniques that have been used in this research,

such as visual concept detection, are relatively new. These techniques are constantly

improving and will provide even more accurate results in the future, and thereby improving

the measure.

59

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Appendix A

Associations Type Strength

(0 - 1)

Favourability

(-1 - 1)

Uniqueness Modularity

Class

Degree Weighted

Degree

Eigenvector

Centrality

Eccentricity Closeness

Centrality

Betweenness

Centrality

Authority Hub Clustering

Coefficient

Number

of

triangles

column A B C D E F G H I J K L M N O P

icerink Concepts 0,067 0,000 1 18 17 12,469 0,315 2 0,559 8,442E-04 0,009 0,009 0,733 88

tourist 0,058 -1,250 0 0 58 66,331 0,876 2 0,826 5,357E-02 0,030 0,030 0,388 620

waterfront 0,044 0,000 0 0 33 30,823 0,570 2 0,640 6,659E-03 0,017 0,017 0,641 318

ghat 0,035 0,000 1 18 14 11,170 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

palace 0,035 0,000 1 0 27 21,742 0,463 2 0,607 4,435E-03 0,014 0,014 0,646 210

bicycle 0,027 0,000 1 18 14 13,753 0,257 2 0,546 4,587E-04 0,007 0,007 0,756 59

canalboat 0,022 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

rowhouse 0,020 0,000 1 18 11 9,958 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

street 0,019 0,000 1 18 69 117,059 0,999 2 0,947 8,478E-02 0,035 0,035 0,345 786

city 0,019 0,000 1 0 70 114,527 1,000 2 0,959 9,236E-02 0,036 0,036 0,331 777

kremlin 0,019 0,000 1 30 14 10,412 0,255 2 0,546 2,350E-04 0,007 0,007 0,846 66

graffito 0,017 -0,125 1 18 15 11,201 0,205 0 0,000 0,000E+00 0,000 0,000 0,000 0

guard 0,017 0,000 1 1 5 3,120 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

lakefront 0,017 0,000 0 0 25 22,516 0,471 2 0,597 1,892E-03 0,013 0,013 0,772 213

crowd 0,015 0,000 1 14 22 26,198 0,357 2 0,582 4,041E-03 0,011 0,011 0,586 123

sister 0,015 0,000 0 2 14 9,163 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

towpath 0,015 0,000 0 18 28 23,018 0,478 2 0,612 5,462E-03 0,014 0,014 0,635 223

harbor 0,014 0,000 0 0 24 22,718 0,460 2 0,592 7,558E-04 0,012 0,012 0,854 216

fleetstreet 0,013 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

pushbike 0,013 0,000 0 18 13 11,529 0,200 0 0,000 0,000E+00 0,000 0,000 0,000 0

tenement 0,013 0,125 1 0 9 7,846 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

churchbell 0,013 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

guildhall 0,012 0,000 1 0 31 25,063 0,503 2 0,628 7,390E-03 0,016 0,016 0,570 248

bobsledding 0,012 0,000 1 8 3 2,512 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

lifepreserver 0,012 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

trailertruck 0,012 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

businessdistrict 0,011 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

watercourse 0,011 0,000 0 18 23 18,814 0,393 2 0,587 2,777E-03 0,012 0,012 0,680 157

brownstone 0,010 0,000 1 18 13 11,312 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

riverbank 0,010 0,000 0 18 36 32,041 0,626 2 0,657 8,872E-03 0,018 0,018 0,615 366

67

gondolier 0,010 0,000 1 18 23 18,088 0,433 2 0,587 1,450E-03 0,012 0,012 0,771 178

lock 0,009 0,000 0 3 2 1,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

skateboarder 0,009 0,000 1 18 13 9,939 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

bicyclerack 0,009 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

skilodge 0,009 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

rotatingmechanism 0,009 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

satellitetelevision 0,009 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

punt 0,009 0,000 0 4 2 1,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

billboard 0,009 0,000 0 18 14 11,611 0,217 0 0,000 0,000E+00 0,000 0,000 0,000 0

parisian 0,008 0,000 1 5 15 10,339 0,215 0 0,000 0,000E+00 0,000 0,000 0,000 0

marina 0,008 -0,125 0 0 31 29,779 0,540 2 0,628 4,935E-03 0,016 0,016 0,685 298

choochoo 0,008 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

bridge 0,007 0,000 0 18 49 61,521 0,784 2 0,747 2,593E-02 0,025 0,025 0,454 512

cinema 0,007 0,000 1 0 11 9,423 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

marcher 0,007 0,000 1 18 9 7,087 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

face 0,007 0,000 0 14 11 12,434 0,174 2 0,534 6,203E-04 0,006 0,006 0,622 28

waterside 0,007 0,000 0 0 29 24,898 0,529 2 0,617 3,344E-03 0,015 0,015 0,722 273

buddy 0,007 0,125 0 17 12 8,522 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

iceskate 0,007 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

tulipagesneriana 0,007 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

rink 0,007 0,000 1 18 13 11,282 0,203 0 0,000 0,000E+00 0,000 0,000 0,000 0

ferriswheel 0,006 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

goggles 0,006 -0,250 0 36 16 12,419 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

mountie 0,006 0,000 1 6 7 4,511 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

sluice 0,006 0,000 1 7 11 7,913 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

apartmentbuilding 0,006 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

displaywindow 0,006 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

bobsled 0,006 0,000 1 8 9 6,409 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

unicyclist 0,006 0,000 1 18 13 9,445 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

demonstrator 0,005 0,250 1 18 6 4,303 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

riverboat 0,005 0,000 1 18 20 16,910 0,381 2 0,573 8,149E-04 0,010 0,010 0,813 139

cloud 0,005 -0,125 0 14 7 6,753 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

gondola 0,005 0,000 1 18 22 16,919 0,395 2 0,582 2,246E-03 0,011 0,011 0,729 153

block 0,005 0,000 1 18 7 5,126 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

stage 0,005 0,000 1 14 9 6,603 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

cabincar 0,005 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

commuter 0,005 0,000 1 0 13 11,253 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

sunglasses 0,004 0,000 1 36 13 11,872 0,161 0 0,000 0,000E+00 0,000 0,000 0,000 0

banner 0,004 0,000 1 18 13 9,949 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

double 0,004 0,000 1 0 4 5,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

mortarboard 0,004 0,000 0 17 14 9,290 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

resort 0,004 0,000 0 0 16 13,143 0,266 2 0,555 9,137E-04 0,008 0,008 0,743 78

controltower 0,004 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

motel 0,004 0,000 1 0 20 16,949 0,335 2 0,573 1,790E-03 0,010 0,010 0,731 125

pedestriancrossing 0,004 0,250 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

68

lifejacket 0,004 0,000 1 18 10 8,090 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

chador 0,004 0,000 1 18 12 7,854 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

jinrikisha 0,004 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

containership 0,004 0,000 1 18 8 6,236 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

church 0,004 0,000 1 0 51 56,386 0,770 2 0,763 3,497E-02 0,026 0,026 0,407 498

chateau 0,004 0,000 1 0 23 16,710 0,400 2 0,587 2,694E-03 0,012 0,012 0,684 158

flowerbed 0,004 0,000 1 17 22 19,227 0,345 2 0,582 2,685E-03 0,011 0,011 0,648 136

fountain 0,004 0,000 1 18 27 21,950 0,472 2 0,607 3,879E-03 0,014 0,014 0,652 212

megaphone 0,004 0,000 1 14 5 3,861 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

florist 0,004 0,000 1 17 10 10,175 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

playground 0,004 0,000 0 18 21 16,562 0,332 2 0,577 3,278E-03 0,011 0,011 0,600 114

willow 0,004 0,000 0 17 12 9,068 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

triptych 0,004 0,000 1 17 12 8,076 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

trafficlight 0,004 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

station 0,004 0,000 1 0 12 9,523 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

schoolmate 0,004 0,000 1 9 8 5,570 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

freightcar 0,004 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

mascot 0,004 0,000 1 10 11 7,277 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

sun 0,004 0,125 1 14 46 52,464 0,715 2 0,724 2,797E-02 0,023 0,023 0,423 419

signboard 0,003 0,125 0 18 16 12,494 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

bicyclebuiltfortwo 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

lakeside 0,003 0,000 0 0 35 28,124 0,599 2 0,651 9,815E-03 0,018 0,018 0,581 326

scoreboard 0,003 0,000 1 14 12 9,003 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

pictureframe 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

dugout 0,003 0,000 1 14 12 8,447 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

iamsterdam Hashtags 0,211 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

amsterdam 0,150 0,000 0 14 12 8,280 0,198 0 0,000 0,000E+00 0,000 0,000 0,000 0

netherlands 0,045 0,000 0 14 11 8,056 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

holland 0,044 0,000 0 14 10 8,152 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

travel 0,031 0,000 0 17 15 12,178 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

igersamsterdam 0,019 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

rijksmuseum 0,018 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

amsterdamcity 0,017 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

europe 0,016 0,000 0 14 12 9,058 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

love 0,011 1,000 0 17 11 7,938 0,144 0 0,000 0,000E+00 0,000 0,000 0,000 0

instagood 0,011 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

instatravel 0,010 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

city 0,010 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

trip 0,010 0,000 0 17 16 12,276 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

dutch 0,009 0,000 0 14 10 9,175 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

picoftheday 0,009 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

architecture 0,009 0,000 0 0 30 30,912 0,512 2 0,623 6,257E-03 0,015 0,015 0,608 247

travelgram 0,009 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

wanderlust 0,009 0,000 0 11 4 2,496 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

holiday 0,008 0,000 1 17 18 15,862 0,271 2 0,563 1,420E-03 0,009 0,009 0,735 100

69

amsterdamworld 0,008 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

photooftheday 0,008 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

iloveamsterdam 0,008 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

nederland 0,008 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

canal 0,008 0,000 1 18 31 29,170 0,536 2 0,628 4,640E-03 0,016 0,016 0,680 296

igersholland 0,008 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

amsterdamlife 0,008 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

vscocam 0,007 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

eurotrip 0,007 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

art 0,007 0,000 1 0 14 10,981 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

museumplein 0,007 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

beautiful 0,006 1,000 0 17 19 15,359 0,359 2 0,568 8,368E-04 0,010 0,010 0,778 119

canals 0,006 0,000 1 18 24 22,307 0,432 2 0,592 1,647E-03 0,012 0,012 0,755 191

thankyouamsterdam 0,006 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

igers 0,006 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

instadaily 0,006 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

igamsterdam 0,006 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

vsco 0,006 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

traveling 0,006 0,000 0 17 12 9,503 0,164 0 0,000 0,000E+00 0,000 0,000 0,000 0

friends 0,005 0,500 0 17 15 10,971 0,191 0 0,000 0,000E+00 0,000 0,000 0,000 0

museum 0,005 0,000 1 0 23 19,310 0,399 2 0,587 1,978E-03 0,012 0,012 0,736 170

vacation 0,005 0,000 0 17 22 17,555 0,315 2 0,582 4,413E-03 0,011 0,011 0,610 128

summer 0,005 0,000 0 14 17 17,725 0,264 2 0,559 1,138E-03 0,009 0,009 0,750 90

amstergram 0,005 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

thenetherlands 0,005 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

fun 0,005 0,500 0 22 17 12,465 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

loves_amsterdam 0,005 1,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

vondelpark 0,005 0,500 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

nofilter 0,005 0,500 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

happy 0,005 0,500 0 12 8 5,308 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

travelling 0,004 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

igholland 0,004 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

holanda 0,004 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

bike 0,004 0,000 1 18 14 13,566 0,238 2 0,546 6,270E-04 0,007 0,007 0,731 57

instalike 0,004 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

tourist 0,004 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

instamood 0,004 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

weekend 0,004 0,000 1 14 15 14,702 0,201 2 0,550 9,103E-04 0,008 0,008 0,736 67

amazing 0,004 1,000 0 17 8 5,821 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

sky 0,004 0,000 0 14 39 46,149 0,624 2 0,676 1,708E-02 0,020 0,020 0,492 346

nature 0,004 0,000 0 13 6 3,816 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

autumn 0,004 0,000 0 14 37 43,322 0,513 2 0,664 2,124E-02 0,019 0,019 0,425 268

streetphotography 0,004 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

ig_amsterdam 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

instanetherlands 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

70

amsterdam2015 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

gramthedam 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

winter 0,003 0,000 1 14 16 15,269 0,263 2 0,555 4,599E-04 0,008 0,008 0,819 86

netherland 0,003 0,000 0 15 6 3,717 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

sun 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

ig_europe 0,003 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

holidays 0,003 0,000 0 17 14 13,060 0,189 3 0,542 3,936E-04 0,007 0,007 0,833 65

instapic 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

tbt 0,003 0,000 0 16 3 1,594 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

mokummagazine 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

water 0,003 0,000 0 18 19 17,427 0,360 2 0,568 5,972E-04 0,010 0,010 0,817 125

keukenhof 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

flowers 0,003 0,000 1 17 42 51,583 0,614 2 0,696 2,538E-02 0,021 0,021 0,433 355

instagram 0,003 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

street 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

selfie 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

vangoghmuseum 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

followme 0,003 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

boat 0,003 0,000 1 18 51 63,451 0,786 2 0,763 3,464E-02 0,026 0,026 0,415 508

wonderful_holland 0,003 1,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

photo 0,003 0,000 0 19 5 3,099 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

амÑ�Ñ‚ÐµÑ 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

streetart 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

redlightdistrict 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

igersnetherlands 0,003 0,000 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

landscape 0,003 0,000 0 0 14 12,332 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

sunset 0,003 0,000 1 14 60 67,750 0,896 2 0,845 5,788E-02 0,031 0,031 0,371 634

me 0,003 0,000 1 20 16 11,067 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

loveamsterdam 0,003 1,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

vangogh 0,003 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

tourism 0,002 0,000 0 0 14 12,463 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

ams 0,002 0,000 1 21 6 3,886 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

night 0,002 0,000 1 14 33 42,907 0,479 2 0,640 1,441E-02 0,017 0,017 0,504 250

blackandwhite 0,002 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

heineken 0,002 0,000 1 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

boat Nouns 0,093 0,662 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

city 0,082 -0,182 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

castle 0,077 0,716 0 0 36 28,199 0,595 2 0,657 1,053E-02 0,018 0,018 0,565 336

street 0,045 0,125 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

bridge 0,041 -0,610 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

market 0,332 0,224 0 0 5 4,550 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

church 0,022 0,982 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

architecture 0,021 0,770 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

food 0,021 0,707 0 17 11 8,792 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

girls 0,019 0,782 0 22 12 8,915 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

71

car 0,019 -0,621 0 18 16 14,270 0,266 2 0,555 1,283E-03 0,008 0,008 0,695 73

dress 0,018 0,751 0 17 11 7,938 0,135 0 0,000 0,000E+00 0,000 0,000 0,000 0

building 0,015 0,633 0 0 17 14,837 0,300 3 0,555 9,088E-04 0,009 0,009 0,742 89

river 0,014 0,328 0 18 31 30,715 0,554 2 0,628 3,740E-03 0,016 0,016 0,717 312

night 0,014 0,077 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

wedding 0,014 0,800 0 17 16 12,238 0,272 2 0,555 1,148E-03 0,008 0,008 0,705 74

face 0,014 0,104 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

baby 0,014 0,970 0 30 13 10,772 0,160 0 0,000 0,000E+00 0,000 0,000 0,000 0

house 0,013 0,659 0 23 24 21,273 0,405 2 0,592 3,920E-03 0,012 0,012 0,617 156

road 0,013 -0,560 0 18 17 15,440 0,288 2 0,559 1,882E-03 0,009 0,009 0,700 84

monument 0,000 0,855 0 0 27 24,206 0,460 2 0,607 3,738E-03 0,014 0,014 0,683 222

armory 0,000 0,694 1 0 12 8,522 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

party 0,000 0,952 0 0 11 8,111 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

cup 0,010 0,148 0 24 7 4,314 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

hair 0,010 0,282 0 14 16 11,593 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

team 0,010 0,755 0 25 6 3,588 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

flowers 0,010 0,855 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

smile 0,009 0,828 0 14 13 10,364 0,163 0 0,000 0,000E+00 0,000 0,000 0,000 0

dog 0,009 -0,365 0 30 13 10,533 0,132 0 0,000 0,000E+00 0,000 0,000 0,000 0

phone 0,009 -0,625 0 26 6 3,791 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

snow 0,009 -0,475 0 14 15 11,832 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

evening 0,009 -0,320 0 14 14 14,529 0,219 2 0,546 3,968E-04 0,007 0,007 0,808 63

festival 0,009 0,881 0 14 16 13,096 0,291 2 0,555 9,800E-04 0,008 0,008 0,714 75

train 0,008 -0,732 0 18 15 12,005 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

cake 0,008 0,829 0 17 13 10,186 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

trees 0,008 0,673 0 17 19 17,249 0,339 2 0,568 9,000E-04 0,010 0,010 0,765 117

drink 0,008 0,683 0 18 12 8,880 0,196 0 0,000 0,000E+00 0,000 0,000 0,000 0

wall 0,008 -0,423 0 18 15 11,861 0,196 0 0,000 0,000E+00 0,000 0,000 0,000 0

sign 0,008 -0,234 0 18 9 6,257 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

rose 0,007 0,803 0 27 3 1,676 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

hat 0,007 0,421 0 28 16 11,523 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

driver 0,007 0,809 0 18 8 6,122 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

crowd 0,007 -0,414 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

skin 0,007 0,555 0 14 8 6,581 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

space 0,007 0,273 0 0 8 5,893 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

coke 0,007 0,670 1 29 4 2,514 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

cat 0,007 -0,538 0 30 13 11,624 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

race 0,006 0,653 0 31 7 4,306 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

puppy 0,006 0,720 0 30 10 8,372 0,091 0 0,000 0,000E+00 0,000 0,000 0,000 0

autumn 0,006 0,664 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

band 0,006 0,870 0 14 13 9,591 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

tree 0,006 0,197 0 17 21 18,706 0,372 2 0,577 1,706E-03 0,011 0,011 0,716 136

garden 0,006 0,878 0 17 25 22,622 0,399 2 0,597 3,887E-03 0,013 0,013 0,612 169

scene 0,005 0,794 0 18 13 10,211 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

book 0,005 -0,166 1 32 2 1,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

72

statue 0,005 0,343 0 18 24 21,326 0,416 2 0,592 3,727E-03 0,012 0,012 0,668 169

glass 0,005 -0,586 0 36 16 11,407 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

cocktail 0,005 0,769 1 33 12 7,746 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

heels 0,005 0,735 1 34 7 4,654 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

office 0,005 -0,739 0 0 9 7,293 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

dance 0,005 0,740 1 18 15 10,593 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

home 0,005 0,308 0 23 16 12,434 0,247 2 0,555 1,393E-03 0,008 0,008 0,705 74

men 0,005 -0,141 1 22 8 5,431 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

king 0,005 -0,676 0 35 5 3,911 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

glasses 0,005 0,752 0 36 10 7,349 0,123 0 0,000 0,000E+00 0,000 0,000 0,000 0

body 0,005 0,685 0 37 10 7,671 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

drawing 0,005 0,448 1 38 6 4,043 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

tattoo 0,005 0,741 0 18 13 9,683 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

view 0,005 0,782 0 39 6 3,706 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

bird 0,005 0,504 0 30 16 11,906 0,223 0 0,000 0,000E+00 0,000 0,000 0,000 0

clouds 0,005 -0,068 0 14 10 9,747 0,137 0 0,000 0,000E+00 0,000 0,000 0,000 0

piano 0,005 -0,408 0 14 16 12,358 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

forest 0,003 -0,755 1 0 17 11,998 0,284 0 0,000 0,000E+00 0,000 0,000 0,000 0

pony 0,003 0,784 0 40 8 5,756 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

concert 0,003 0,897 0 14 16 12,515 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

room 0,003 -0,306 1 18 11 8,727 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

cats 0,003 0,523 0 30 12 10,406 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

kids 0,003 -0,584 1 22 14 10,481 0,172 0 0,000 0,000E+00 0,000 0,000 0,000 0

hotel 0,003 0,343 0 0 23 19,564 0,384 2 0,587 3,045E-03 0,012 0,012 0,680 157

paintings 0,003 0,767 0 17 12 9,879 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

sunset 0,003 0,827 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

commercial 0,003 0,716 0 41 5 3,217 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

shoes 0,003 0,485 0 36 16 11,690 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

santa 0,003 0,967 1 37 11 8,615 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

cemetery 0,003 -0,949 1 0 29 25,351 0,510 2 0,617 4,300E-03 0,015 0,015 0,659 249

beach 0,003 0,903 0 14 33 29,377 0,583 2 0,640 7,364E-03 0,017 0,017 0,633 314

sea 0,003 0,671 0 14 24 19,828 0,459 2 0,592 1,174E-03 0,012 0,012 0,802 203

meat 0,003 0,682 1 17 6 4,201 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

sky 0,003 -0,113 0 0 0 0,000 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

fan 0,003 0,835 1 14 7 5,252 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

fence 0,003 -0,690 0 18 16 12,514 0,233 0 0,000 0,000E+00 0,000 0,000 0,000 0

graveyard 0,002 -0,956 1 0 19 15,384 0,371 2 0,568 6,097E-04 0,010 0,010 0,797 122

waves 0,002 0,136 0 18 10 7,903 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

lighthouse 0,002 0,633 0 14 37 31,457 0,649 2 0,664 8,266E-03 0,019 0,019 0,625 394

flower 0,002 0,103 0 17 17 17,159 0,307 2 0,559 4,668E-04 0,009 0,009 0,817 98

doll 0,002 0,479 1 17 12 9,867 0,122 0 0,000 0,000E+00 0,000 0,000 0,000 0

farm 0,002 -0,390 0 42 14 9,485 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

loss 0,002 0,683 0 23 5 4,500 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

eyes 0,002 0,350 1 14 12 10,160 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

queen 0,002 -0,641 1 35 13 9,116 0,000 0 0,000 0,000E+00 0,000 0,000 0,000 0

Appendix B

Graph B1: The associative network structure according to favourability and strength (centrality)

Graph B2: The associative network structure according to favourability and uniqueness