Willingness to Accept for Instagram Accounts. First Empirical Evidence

Textbook, 2016, 92 Pages

Business economics - Marketing, Corporate Communication, CRM, Market Research, Social Media

Excerpt

List of Figures

Figure: Title:

Source:

Page:

Overview of research fields

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Illustration

Relative and absolute growth of members/users

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Illustration 10

Characteristics of network industries applied to

Instagram

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Comparison of takeover prices within the industry

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Illustration 14

Formula to determine the optimal price for spon-

sored content

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Incentive compatibility

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Illustration

Question 1

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Measuring Consumer's WTA

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Validation process of account performance indica-

tors

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Snowball Effect of Survey Responses Own

Illustration

Anonymity of the survey

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12 Variable:

user

Frequencies

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Illustration 37

Figure 13: Variable: sex Frequencies Own

Illustration

Variable: sex chi square test with Gondorf

(2015)

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Variable: age descriptive statistics

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Age pyramid of frequencies

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Variable: country bar chart of frequencies

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Variable: marital frequencies

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Variable: education frequencies in comparison

between the UK

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Variable: income frequencies

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21 Key

Findings

Demographic

Variables

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Illustration 44

Variables: followers, followings, posts - frequen-

cies

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Variable: category frequencies and percentage

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Variable: business_income frequencies and per-

centage

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Key Findings: Account Metrics and Business

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26 Variable:

wta

descriptive

statistics

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Illustration 49

27 Variable:

wta_2

frequency

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Illustration 49

Variable: wta_2 Kolmogorov-Smirnov Test

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Key Findings Key Findings: WTA

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Variable: wta Pearson correlation with age

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Variable: wta Spearman correlations with demo-

graphic variables

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Key Findings: Demographic Correlations

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Variable: wta Pearson correlation with follower,

followings and posts

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Variable: wta Spearman correlations with ac-

count metrics

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Key Findings: Account Metrics Correlations

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36 Regression

Analysis

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Illustration 54

Linear Regression Graph

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38 Final

formula to determine the optimal price

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Illustration

List of Abbreviations

Random Bid

Bid of the Seller

BDM

Becker DeGroot Marschak (Becker et al., 1964)

Expected value

HTML

IQR

HyperText Markup Language

Interquartile Range (box plot)

Number of Instagram Followers

p* Optimal

price

PSM

Price Sensitivity Meter (van Westendorp, 1976)

RP Risk

Premium

RQ Research

Question

x;y

Correlation between x and y

Derived Utility of the Seller

True Value of the Possessed Good (WTA)

WTA

Willingness to Accept

WTP Willingness

Pay

Change in two Figures

Profit

1 Introduction

The radical novelty of the Web 2.0 has shifted focus from website administration

generated content to user generated content and allowed for the emergence of user

generated businesses in the same movement. Whilst a vast majority of users can be

described as "lurkers" and passive users, following the 1% rule, only one in one

hundred people actually actively participated in creating content on the internet ten

years ago (van Mierlo, 2014). Fast forward 10 years, the share of active users post-

ing videos, sharing pictures, writing blogs has now risen to 9% in 2009. The emer-

gence of social networks, allowing for more enticing opportunities to interact with

other users or brands and low barriers to sharing content over 3G and now 4G tech-

nology can be held accountable for this development. In the same period the first in-

cidences of social media entrepreneurship emerged and allowed users to gain reve-

nue from social media sites in return for various forms of advertising and related in-

come sources. The suddenly arising business-critical issues of privacy concerns

(e.g. losing the account over unauthorised access or technological errors) and tech-

nology based questions have been researched in a platform specific manner, with

most research revolving around Twitter and Facebook users. Despite its rapid

growth and recent overtake by Facebook, reliable economic studies on social pic-

ture sharing service Instagram are not represented in contemporary research.

Therefore, this study should serve as the first foundation in this area and analyse

the monetary value behind Instagram accounts. By closing this existing research

gap, insurances may be able to expand their current range of services and insure

Instagram related business models. At the same time, Instagram receives the op-

portunity to launch a fee-based premium version of their existing platform, which fur-

ther secures the value and privacy of one account, in the form of a Freemium model.

The third beneficiaries of the study are Instagram users, who derive information on

how to find the optimal price for sponsored content on the network.

The dominant methodology to determine price preferences, analogous to Facebook

or Twitter research, is to statistically determine a users willingness to pay (WTP,

purchasing price) to keep and use the existing account, through a survey. Willing-

ness to accept (WTA, selling price) as the slightly higher counterpart mechanism is

expected to complement the survey over the endowment effect. The effect postu-

lates that there is a "sentimental" non-rational value attached to the account, overes-

timating its actual value. Yet the overestimation may drive the price a customer is

willing to pay for an insurance and privacy protection (WTA > WTP).

To allow incentive compatible

measurement of the WTA (instead of the WTP), the

renowned Becker-DeGroot-Marschak mechanism is modified and applied to suit the

study requirements. Following data collection all established demographic variables

such as age, gender and education and key account metrics such as followers,

number of posted pictures shall then be used in a second step to define statistically

significant correlations to further break down and define WTA composition.

The scientific approach in measuring account values and metric will allow insight for

three main stakeholders of this study: Instagram, may now act as provider of addi-

tional security through a Freemium model and thus may be able to secure additional

revenue, aside from advertising. Knowing a close to accurate value of a profile in-

surance companies will be able to insure users and corporate accounts against loss

of business and the intellectual property created. Professional social media users,

who sustain a significant proportion of their monthly income through Instagram, will

be able to determine an adequate market price for advertising opportunities, taking

into account positive and negative changes in followers.

In order to diligently and systematically address the below research question this

dissertation will be divided into the following conceptual chapters. An analysis of key

findings in current literature will allow us to establish an up to date understanding of

network industries generally with special focus on social networks, their characteris-

tics, pricing options and applicable mechanism in Chapter 2. Chapter 3 will then jus-

tify and design an appropriate research plan, touching upon ethical issues and po-

tential risks and their mitigation during the data collection. When analysing sample

data, focus will be set on demographic variables in descriptive statistics and correla-

tions with key account metrics and socio-demographics to develop a round under-

standing of the topic. Concluding, all findings will be discussed in regards to limita-

tions, implications and potential further research questions.

For the individual to disclose their true preferences and values

Explore the following research questions:

RQ1 Mathematically develop an incentive compatible method to expose user's

willingness to accept (WTA) for existing Instagram accounts.

RQ2

Calculate descriptive statistics of the willingness to accept (WTA) for existing

Instagram accounts.

RQ3 Find significant correlations (to different levels) of the willingness to accept

(WTA) with demographic variables and key account metrics in order to inves-

tigate the current level of development of businesses on Instagram.

Figure 1: Overview of research fields (own illustration)

2 Literature

Review

2.1

Alignment and Social Networks

Past years have seen a trend in the usage of social media platforms, where users

are able to interact by self or peer- generated content in a virtual environment

(Kaplan and Haenlein, 2010). Traditionally, information in the online context has

been shared unidirectional from few content creators to attract are large number of

recipients by using simple HTML web pages (Cormode and Krishnamurthy, 2008).

Guided by several technological changes and increasing information processing ca-

pacity ("Moore's law") of servers since 2004 this proportion has progressively shifted

towards the number of content creators (Fox and Pierce, 2009, Kambil, 2008,

O'reilly, 2007). In the so-called Web 2.0 era content and information flows differ from

previous times as users may contribute to or customise an online service or platform

using a profile or personal user account (O'reilly, 2007, Weinberg, 2009). This sub-

stantial and on-going change also empowered millions of people to start online

businesses that create value by using social media platforms, blogs, video sharing

platforms or other web applications characterised through significantly lowering set

up costs and barriers to enter an online market than previous offline markets

(O'Reilly, 2005). Generally, social media platforms feature the highest diffusion rates

among content creators as they aim to target a broad audience with strategic focus

on direct network effects (Kietzmann et al., 2011, Lin and Lu, 2011). While "older"

networks such as Facebook (established in 2004) and Twitter (established in 2006)

have frequently been addressed in studies in the field of social, business and eco-

nomic science, the novel social picture sharing service Instagram (established in

2010) has been underestimated in its growth potential by researchers in the past.

The current growth rates support this hypothesis (Figure 2) and further indicate a

knowledge gap in regards to the social network Instagram. In addition, the rise in the

Alexa ranking, which can be seen as the dominant web analytical service, of 31

places to position number 26 (19.05.15) highlights the rapid diffusion of Instagram at

the moment (Alexa, 2015, Lo and Sedhain, 2006). Bearing that in mind, Instagram

may aptly be described by the "Early Majority" phase of the diffusion process,

whereby Facebook and Twitter are allocated in the "Late Majority" or in the turning

point of Rogers (2010) renowned classification.

Figure 2: Relative and absolute growth of members/users in own illustration

(Alexa, 2015, GWI, 2014)

Two papers shall especially be highlighted at this point to reflect the current state of

knowledge in regards to account value in social networks.

First of all, the publication of Schreiner and Hess (2013a), which can be seen as the

staring point of the dissertation, successfully applied the van Westerndorp's Price

Sensitivity Meter (PSM) (1976) to investigate the willingness of Google Plus and Fa-

cebook users to pay for privacy-enhancing additional features. The research aim

was to statistically examine whether a so-called Freemium model, an omnipresent

pricing structure in online businesses that consists of a free and a premium version,

could be applied to the two social networks. Thereby, the paper serves as a perfect

example of accurate data collection through online surveys and application of a rec-

ognised and advanced technique to estimate willingness to pay. Despite the recog-

nition of the well-outlined and integrated PSM method, it must be emphasised that

the chosen approach is considered to be lacking incentive compatibility by state of

the art literature (Kim et al., 2012, Miller and Hofstetter, 2009). Beyond the lack of

theoretical grounding, the PSM mechanism is rather

difficult to explain and to illus-

trate in graphs. Another weakness of this paper is the relatively small sample size of

160 participants and non-representativeness concerning age and educational back-

ground (primarily students), which further limits the possibility to draw general impli-

cations. Those weaknesses, especially in regard to the used mechanism, directly

impact the dissertation as they provide profound insight into the major difficulties in

identifying consumer's monetary preferences, as they usually don't want to disclose

it (Wertenbroch and Skiera, 2002).

Secondly,

the paper of

Bauer et al. (2012), which

explores the perceived value of

actively as well as passively uploaded information on the social networking platform

Facebook, is of utmost importance. In the course of their study, the authors tested

several variations of the Becker-DeGroot-Marschak mechanism on a large sample

of 1045 participants in form of an online survey-based experiment. However, a seri-

ous weakness of the study is the distorting influence of external stimuli (e.g. free

iPhones), which had a significant correlation with the stated WTP (Bauer et al.,

2012). In other words, the WTP was fluctuating depending on whether a low- or

high-value external incentive was used.

Moreover, the study is completely lacking a

control group in order to make statistical adjustments

retrospectively. As a result,

the authors regression model to identify driving factors for participants WTP only has

a "small explanatory value of 14.5%" and consequently "couldn't explain one's pro-

file valuation" (Bauer et al., 2012, p. 8)

In the further evaluation, the

study outcomes are

mainly transferred to the context of

online privacy by the authors, but are also dependent on the perceived value of the

overall online profile (WTA of the profile subtracted by the social surplus), which is

one of the research questions of my dissertation.

Interrelation under the assumptions of rationality

(own illustration calculated accord-

ing to (Neus, 2013)):

2.2

Characteristics of Network Industries

In order to appropriately analyse the value of an Instagram account in later steps,

the social network Instagram has to be set into the right context. This guarantees

correct assessment of the special environmental circumstances in a network indus-

try, where the focus shifts from valuing an entity instead of valuing its connection to

other entities.

Figure 3: Characteristics of Network Industries applied to Instagram (own illustra-

tion)

Concerning the distorting influence of external stimuli on the stated WTP

Formula demonstrates the connection between the study of Bauer et al. and my research

question.

Figure 3 analyses the social network with respect to the dominant theoretical

frameworks of network industries by using four characteristics that distinguish those

industries from ordinary markets for commodities or stocks (Gabel, 1994, Majumdar

and Venkataraman, 1998, Shy, 2001). The first and eponymous factors in such in-

dustries are direct and indirect network effects, which may also be regarded as posi-

tive externalities (Akerlof and Kranton, 2000, Katz and Shapiro, 1986). In the case of

Instagram, direct networks (within one side) are present between users of the plat-

form. Moreover, they can also be seen as the driving force that enabled Instagram

to quickly establish a critical mass of users (tipping point), where the process of

gaining new users becomes self-sustaining, with no marketing costs or other incen-

tives needed (Markus, 1987, Valente, 1995). A large and growing body of literature

label this unique process as the "bandwagon effect" (Farrell and Saloner, 1985). In-

direct network effects on the other hand, are considered to be present at a minor ex-

tent at the moment, but may rise with the gradual implementation of sponsored con-

tent in 2014 and 2015 (Instagram, 2014). However, the key problem is that it may be

hard to distinguish in reality whether a network effect is truly direct or indirect since

an exact demarcation between the same and a different entity or side is impossible

The second characteristic focuses on (technical) complementarity to other products

or services, which often requires a compatible standard resulting from a negative

cross elasticity in demand (Kuraitis, 2009). In the case of Instagram, the application

requires a smartphone or tablet with Android or iOS software to operate. Limited ac-

count management functions or non-interactive Web 1.0 viewers are available via

PC.

Moreover, network industries usually face immense economies of scale as the set

up costs (fixed costs) of a platform or software are assumed to be relatively high

(Shy, 2001). Due to the fairly low distribution and maintenance costs (variable costs)

in the case of electronic copies, firms in network industries have the opportunity to

achieve a fast degression of fixed cost, especially after reaching a critical mass

(Koshal, 1972). Instagram is no exception in regards to economies of scale and has

been able to significantly reduce the cost per new user since the launch of the plat-

form (TechCrunch, 2010).

Lastly, switching costs and lock-in effects, which occur if members of a platform try

to switch to a similar service, are also present on Instagram (Klemperer, 1987,

Kuraitis, 2009, Shy, 2001). They play a distinct role in the estimation of the value of

an account, as users may value their account higher as the rebuilding of an account

E.g. are business profiles on the same or on a different side in relation to a normal ac-

count?

may come with high or prohibitive costs. Shapiro and Varian (2013) provide a holis-

tic framework that further classifies various sources of lock in effects. In the case of

Instagram contractual-, information-, search- and especially data conversion costs

should be emphasised at this point. Shapiro and Varian (2013) didn't consider the

huge potential losses of connections in the network through switching (e.g. follow-

ers, followings, comments and likes), which might exceed the pure data transfer

costs by far.

All in all, the investigated pivotal characteristics of network industries can easily be

transferred to the social network Instagram. Hence, the above mentioned, underly-

ing theoretical models have to be taken into account in the methodology of the em-

pirical research. Questions have therefore been specifically designed to not only

survey the value of an account but also to reflect direct network effects that enable

the emergence of it. However, it still has to be emphasised that Shy's (2001) frame-

work of characteristics only reflects a small piece of digital reality and strategically

marginalises mixed forms of industries.

2.3

The Social Network Instagram

2.3.1 Historic

Development

After describing Instagram in the right business context, this section now targets the

social network itself from a microeconomic point of view, which enables a better un-

derstanding of the following chapters.

Instagram was developed by Kevin Systrom und Mike Krieger after working on their

HTML5-based check-in software burbn, which combines elements of Foursquare

and Mafia Wars (Systrom, 2010). The extended version of burbn, which allowed us-

ers to share, comment and like pictures was initially released on Apple's App Store

on October 6

2010 (Desreumaux, 2014). At the same time, the project was re-

named to Instagram, which is a portmanteau of "telegram" and "instant camera" and

according to the developers "better captured what you were doing" in the application

(Systrom, 2010). Their funding in the development phase from the 5

of March 2010

onwards, raised a total amount of 500.000 USD, including early, well-known inter-

net-investors such as Andreessen Horowitz and Twitter co-founder Jack Dorsey

(Siegler, 2010). From a computer science point of view mainly open source solu-

tions were used such as Ubuntu Linux, nginx-webserver, Django and PostgreSQL.

Similar to Foursquare and Dropbox, Instagram is hosted in an Amazon EC2 cloud,

which helped to reduce infrastructure and maintenance costs through economies of

scale (Bains, 2014, Armbrust et al., 2010).

In early 2011, the development team decided to integrate a "hashtag" function,

which is a method for content based filtering for microblogging services and social

networks. Although, hashtags were mainly used inside the boundaries of twitter

that point in time, they rapidly set out to be a useful tool to find photos with a specific

theme or content on Instagram. On the 3

of April 2012 Instagram launched their

services for Android based operating systems, which drastically increased global

dissemination at that time (Thomas, 2012). A version for personal computers is not

in the interest of the company and many key features are still only available via mo-

bile devices.

The same month, Facebook announced to take over the 13 employee strong Insta-

gram team for the price of one billion US dollars, which was equal to 1,592 billion

British pounds in 2012, in cash and stock and ranks at the highest takeover price in

the industry (Figure 4) (Facebook, 2012). After the deal was closed and approved by

the Federal Trade Commission in the U.S. in September 2012, Facebook decided to

keep the company independently managed. However, Facebook has a decisive in-

fluence on the company in relation to the monetisation of its business model and

launch of advertising services (Instagram, 2015a).

Figure 4: Comparison of takeover prices within the industry (own illustration)

2.3.2 Distinct

Features

As established before, Instagram successfully convinced consumers since its launch

on October 6

2010 to adopt their new social networking platform. The creators

Kevin Systrom and Mike Krieger base their success on several key components.

One component was the strict implementation of a differentiation strategy, which

aimed to separate the network from their main rival at that time, Facebook. This was

mainly achieved through limiting the potential photo size to a square shape, similar

to Kodak Instamatic and Polaroid images, which improved clarity, simplicity and mo-

bile accessibility (standard scripts of transfer and substantially lower traffic of data).

Hashtags originated in twitter in 2007

Moreover, Instagram enables instant photo/video upload and editing by using 24

unique and patented filters. For the first time an online service made professional

photo adjustments options available for smartphones and tablets, which are other-

wise only available via desktop computers (e.g. Dodge and Burn technique). Like-

wise, the network also served as a useful add on for Twitter, which already featured

54 million active users in Q4 2010 (Statista, 2015c). This is mainly due to the fact

that Twitter did not feature an appropriate option to upload or rather edit photos from

mobile devices before sharing. For this reason, Instagram mainly served as an in-

termediate between mobile devices and Twitter in the start up phase of the network

by substantially lowering transfer costs of consumers (Manning et al., 1995). This

opportunity of convincing Twitter users to sign up for Instagram was strategically

used by the creators to overcome potential problems in the decisive early diffusion

process of the network (Rogers, 2010, Kwak et al., 2010, Valente, 1995, Valente,

1996). Consequently, from the start up of the network, photos could be shared par-

allel on other social networks with a single click (Instagram, 2015c). However, Twit-

ter realised the on-going cannibalisation and outmatch of Instagram in terms of

growth rates and advised it's high-profile users (e.g. celebrities) to stop sharing In-

stagram links and photos on Twitter (Fiegerman, 2015).

Further, the service is still available for free, making full use of the bandwagon effect

(increasing preference for a commodity due to direct network effects) and network

industry theory concerning consumer's adoption of new technologies. As a conse-

quence, the network did not have any significant source of income until early 2015,

when their advertising program was launched in the US (eMarketer, 2015). This

program is estimated to generate 0.59 billion in 2015, 1.48 billion in 2016 and 2.81

billion USD in 2017, due to new advertisement products for capital-intensive as well

as extensive advertisers, including the "Custom Audience" feature, which aims to

reduce scattering losses (Acar et al., 2015, eMarketer, 2015, Paul et al., 2015). Oth-

er e-commerce pricing strategies to further increase sales are still feasible and may

work alongside Instagram's advertising program (Lee, 2001, Turban et al., 2015).

Similar to the video game market, a free-to-play pricing strategy, based on the

Freemium software model, can be considered as suitable, as the incentive to join

the platform remains high (Hanner and Zarnekow, 2015, Alha et al., 2014). A possi-

ble implementation of this pricing strategy for Instagram will be discussed in Section

2.5.2 alongside with a feasibility analysis. Moreover, the outcome of this study is ex-

pected to directly influence the applicability of a Freemium model.

In contrast to free-to-play pricing, pay-to-play in general is not a suitable option for

social networks, aiming for a high amount of users in order to establish direct net-

work effects (Nojima, 2007). A large body of literature investigated the negative cor-

relation between high upfront fixed costs and speed of adoption (Barron and Torero,

2015, Ma et al., 2015, Shriver, 2015). Similarly, a part of existing users may not be

willing to pay for a service, which was previously for free.

2.4

User based advertising on Instagram

Instagram is increasingly described as a microblogging platform, which enables up-

load of user-generated content in smaller amount or file size than a normal blog

(Culloty, Leadbeater, Zappavigna and Zhao). The uploaded content itself are usually

named microposts and consist of short sentences, individual images, or video links

(Aichner and Jacob, 2015). As the similarity in the name microblog and blog would

already suggest, the associated revenue models are largely congruent, with the ex-

ception for revenue stemming from platform unique advertisement like Google Ad-

sense, banner advertisements or pop-ups (Lee et al., 2015).

Users or microbloggers on Instagram generally have the opportunity to receive two

types of capital-forming payments from advertising companies. One is actual mone-

tary compensation in advance in exchange for favourable mentions of the respective

brand in hashtags and pictures in line with the company´s marketing communication

strategies among the users target group. In the second option users "only" receive

free items or gifts from their clients in return for their microblogs. It is often, however,

a mixture of both forms of compensation. Increasingly free products are also sent to

Instagram users with a big audience without prior contractual agreement on adver-

tisement, in the hopeful prospect that the recipient likes the respective products and

thus discreetly advertises over a shared picture. Bloggers and companies alike em-

bed Instagram as an integral part of their multichannel marketing campaigns

amongst platforms like Twitter and Facebook for either self-promotion or to increase

product sales or to enhance their brand reputation as an approachable and interac-

tive brand.

To determine the actual value and price of this form of picture based advertising and

to define the business foundation of digital social influence it is necessary to sys-

tematically examine the underlying variables of the account. It is to assume that

number of followers, as user who can share, like and comment on the picture, and

thus determine the outreach of a profile, is the most critical determinant in this case.

Still further interaction indicators and ratios, need to be taken into account to estab-

lish the value more accurately and determine profiles that may boast a large follow-

ing, but score low on interaction (e.g. over bought, inactive followers).

2.5

Significance of the study for professional practice

In general, four different stakeholders are involved in the social network, whereby

three of them (Instagram users, Instagram and insurance companies) could directly

benefit from the results of the study and the outcomes of the three research objec-

tives. The fourth stakeholder, companies that seek advertising space and sponsor-

ing partners on the platform are not targeted by this study. Though, important find-

ings may also be derived from this empirical research as firms may achieve a better

understanding of their contractual partners, especially their demographics. The ad-

vertising business model of companies is, however, widely regarded as analogous

to the ones offered on Facebook.

2.5.1 For Instagram Users

As outlined in Section 2.4, ordinary people enjoy the progressively decreasing start

up costs for building up a business using Instagram. However, the price of their

main source of revenue is still hard to assess for individuals. While some Instagram

users demand only a couple of pounds, others sell their advertising space in the

form of sponsored posts for thousands of pounds per photo. For instance, Danielle

Bernstein of the Blog "We Wore What " states to earn from 5,000 USD (£3,255) to

15,000 USD (£9,767) per sponsored post on her Instagram Account

"@weworewhat", monetising her audience of around 1 million followers (Kapadia,

2015). This uncertainty concerning the pricing of brand partnerships may be based

on three key factors.

Firstly, information about pricing of other Instagram users is scarcely available

online and outside the two partners of a market transaction. Likewise, it may not be

in the interest of one transaction partner to provide full information to the other party

(Ballwieser et al., 2012). This form of market failure, falls in line with classical micro-

economic and macroeconomic theory and results in a large amount of uncertainty in

the market for sponsored posts concerning pricing as well as the quality of the ser-

vice (Ottum and Moore, 1997). As a result of asymmetrically distributed information,

the supply side of a market transaction faces an incentive to offer low quality spon-

sored posts. However, rational advertising firms take this into consideration and pre-

fer sponsored posts with moderate or low quality due to risk aversion. In line with

Akerlof's theory of 1970, this ultimately leads to an equilibrium where mainly low

quality services are traded in the market (assumptions of the theory are reviewed in

Appendix 1). Namely, distribution platforms such as "buysellshoutouts.com" and

igshoutouts.com are affected and offer advertising space on inactive accounts.

However, critics of the model stem from fact that buyers can frequently seek ways to

prove the quality (e.g. by pre-testing, online user reviews or indicators for account

interactiveness).

Secondly, there is no consensus on which key figures of an Instagram account are

pivotal for its value, as no study previously investigated this topic. However, be-

cause the visible and predictable indicators of accounts are finite, they can be

shortlisted and tested for correlation with the overall value. Hence, it enables Insta-

gram to improve the relevant components, which increase the attractiveness of the

profile for sponsored posts and brand partnerships.

Lastly, many individuals, especially beginners, in the online advertising business are

not aware that the sale of advertising space also leads to variable costs for the indi-

vidual user. Every single post of sponsored content may reduce the number of fol-

lowers and thus one of the key figures of an account. This may on the one hand be

due to a deviation in content of sponsored posts, no longer matching the interest of

the person following or on the other hand due to increased post frequency, now an-

noying their followers. According to observation and empirical documentation the

loss in the amount of followers per sponsored post varies between 0.01% and 0.8%

(Appendix 2). With the help of the overall account value (WTA) and the correlation in

relation to the number of followers, the optimal price could be determined at which

sponsored posts should be offered (Figure 5). However, users may aim for a price

that exceeds the monetary compensation, due to volatility effects that requires a risk

premium

(RP) (Cunningham et al., 2005). Two examples, including the special

scenario, where the user increases the expected number of followers through selling

sponsored posts (e.g through increased reputation) are outlined in the Appendix 3.

Moreover, they show that, irrespective of the assumptions, a universally valid result

with the correct mathematical sign is generated by the formula.

Figure 5: Formula to determine the optimal price for sponsored content (own illustra-

tion)

Assumption: Instagram Users are risk-averse E(u(w)) < u(E(w))

All in all, advertising on Instagram should be viewed as a method of monetisation or

gradual sale of the respective audience. In order to determine the optimal price (p*)

for sponsored content, it is crucial to know the overall value of the profile (WTA) and

to the correlation with the number of followers (r

NF;WTA

). Both variables are part of

this work and had direct impact on the selection of the three research objectives.

Moreover, all variables in the formula are easy to obtain or to appreciate, and thus it

offers an easy way the approximate the optimal price, using the overall account val-

ue.

2.5.2 For Instagram in Terms of a Freemium Model

As mentioned in Section 2.3.2, a Freemium model could complement Instagram's

current pricing strategy and could provide additional features for a particular group

of users. Thereby, Freemium is a portmanteau and describes a combination of a

free (eng. Free) and a paid premium offering, which is widely used by software firms

and service providers in the Web 2.0 era (Bekkelund, 2011). For instance, in-

application purchases of Freemium applications resemble about 92% of revenues

on Apple's App Store (Schoger, 2013). They key concept was first characterised by

venture capitalist Wilson on his personal online blog in 2006, as a hybrid business

model, which offers basic function for free and offers users the possibility to sub-

scribe a premium version (Wilson, 2006). In the course of this, the standard version

does not usually contribute to the revenue of the company (Wilson, 2006, Lee et al.,

2013). However, as already emphasised, the launch of a premium version is not ex-

pected to hinder the parallel expansion of Instagram's ad program.

It is of vital importance to assess, which differentiation strategy is the most appropri-

ate in terms of expected revenue and applicability for the social network. Puyol

(2010) identified three independent, object related possibilities (by quantity, feature,

or distribution) that enable premium services to increase the derived utility of a sub-

set of users. Particularly, a premium version that enhances privacy and security of

user profiles in the shape of a value-added service, could be a promising approach,

as various studies find a existing market among users (Bauer et al., 2012, Schreiner

and Hess, 2013b, Schreiner and Hess, 2015). The practical implementation could

then be composed in the form of back-up solutions, geographically secured pass-

words and extended privacy settings. According to Puyol (2010) this form of differ-

entiation would be assigned by feature. Similarly, other authors describe the busi-

ness model as a performance-related price differentiation with self-selection (Simon

and Fassnacht, 2008). Thereby, the allocation to the premium version is not manda-

tory for certain individuals (Skiera, 1999). Rather, every individual assigns himself or

herself, based on his individual preference, to a service (free or premium version)

that maximises the respective personal utility (Skiera, 1999).

Recent evidence indicates three basic prerequisites for successful implementation

of a Freemium model, which shall be analysed in the following for Instagram

(Bekkelund, 2011).

In the first place, the company should hold the ability to offer its service at low varia-

ble costs, at least on the free version (Bekkelund, 2011). In the case of Instagram as

a business in the e-commerce sector, high fixed costs and low variable costs,

amounting to one USD per additional user can be assumed (Leber, 2012, Stähler,

2002). Furthermore, a large, established audience is necessary for advertisement

for the premium version, which is present in the case of Instagram (300m users).

Lastly, a sufficiently large proportion of the established user base should have a will-

ingness to pay for the premium service (Anderson, 2009, Bekkelund, 2011). As a

basis for this, user must assign a value to their own profile, which can be protected

by enhanced security mechanisms and privacy settings. This value shall empirically

be approximated in this study by using the willingness to accept to sell the respec-

tive profile (RQ2).

2.5.3 For

Insurances

A potentially high perceived and actual value (RQ2) could open up new target

groups for insurance companies. Especially, since some users generate significant

revenues through Instagram as a part time job or sometimes even as a profession

(analogous section 2.4). At the same time, users increasingly consider who may

have access to personal data and how data is used (Dinev and Hart, 2006,

Taddicken, 2014, Tucker, 2014). This form of privacy concern, can be subdivided,

according to Smith et al. (1996) into four dimensions ("Collection, Errors, Secondary

Use and Unauthorised Access to Information"). In particular degree, Errors and

Secondary Use shall be emphasised, as they may lead to deletion of the profile and

thus of the potential revenue stream.

The survey in this thesis is therefore designed to collect the main figures that could

help insurances to enlarge their on-going expansion in the e-commerce sector by

covering Instagram accounts (Gordon et al., 2003, Vaidyanathan and Devaraj,

2003). According to current information (August 2015) no English or German insur-

ance company provides aforethought insurance for Instagram accounts, although a

significant amount of users earn money using them. After an initial offering of ac-

count insurances, the insured sum could be based on key performance indicators, a

desired sum could (for example, the WTA) or be based on the perpetuity of account

revenues (Dufresne, 1990).

In summary, as a result of the existing gap in the market the expansion of digital

business models, it is only a matter of time until account values have to be covered

by insurance companies. Hence, the author and a team of two students are cooper-

ating with the entrepreneurship centre at the LMU Munich for further assistance and

development of a business plan, following on from this dissertation.

2.6 Conceptual

Framework

2.6.1 Willingness to Accept

In the context of quantifying the personal value of an account, the distinction be-

tween willingness to pay (WTP) and willingness to accept (WTA) is of great signifi-

cance for the outcome and is consequently highlighted in the conceptual framework

of the study (Horowitz and McConnell, 2002). Thereby, the WTA corresponds to the

monetary equivalent at which an individual is indifferent between keeping a good

and abandoning it (Zeiler and Plott, 2004). On the other hand, WTP, is usually de-

scribed as the maximum amount, an individual is willing to pay in order to transfer a

good into his own possession. Research findings regarding the amount of the WTA

have remained unchanged for the past 15 years and are aptly described by John K.

Horowitz et al. (2000, p. 1): "Previous authors have shown that WTA is usually sub-

stantially larger than WTP, and almost all have remarked that the WTP/WTA ratio is

much higher than their economic intuition would predict." The difference between

the two metrics is partially explained by the endowment effect

, a psychological ef-

fect that leads to higher perceived value of goods in own possession (Knetsch et al.,

2001, Kahneman et al., 1991, Zeiler and Plott, 2004). Other researchers, especially

in the online context, use Akerlof's (1970) assumptions regarding quality uncertain-

ties in order to explain the discrepancy (Horowitz and McConnell, 2000, Neus, 2007,

Pae, 2005). The concept refers to the uncertainty of the decision maker about the

value of the commodity. Decision makers are assumed to generally avoid having to

make a decision under (complete) uncertainty (Neus, 2007, Davis and Reilly, 2012,

Okada, 2010). Therefore, decision makers strictly prefer to delay the purchase situa-

tion until more information about the commodity is available or expertise is gained.

In the case of a decision maker not being able to defer the moment of decision to a

later moment in time, he will subsequently demand for an adequate risk premium (R

> 0)

to compensate for the presence of adverse information. However, it must be

Sometimes described as divestiture aversion

emphasised that this explanation can only account for imperfect markets, as it relies

on the existence of asymmetric information.

As uncertainty is expected to play a significant role when it comes to assessing the

value of an Instagram account, the used mechanism will be adjusted to mitigate a

possible discrepancy (Georgantzis and Navarro-Martínez, 2010, Isik, 2004, Zhao

and Kling, 2001). Current literature in the field, such as Bauer et al. (2012) often

failed to consider this significant difference and consequently received distorted re-

sults.

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Title

Willingness to Accept for Instagram Accounts. First Empirical Evidence

Author

Andreas Banzerus (Author)

Year

2016

Pages

Catalog Number

V336836

File size

15813 KB

Language

English

Tags

WTA Instagram Freemium model Becker-DeGroot-Marschak Mechanism Social network Empiric study WTP Willingness to pay Vickrey Auction

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Andreas Banzerus (Author)

, 2016, Willingness to Accept for Instagram Accounts. First Empirical Evidence, Hamburg, Bedey Media GmbH, https://www.anchor-publishing.com/document/336836

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