HOW TO ENABLE CLOUD SUCCESS? UNVEILING THE ... · HOW TO ENABLE CLOUD SUCCESS? UNVEILING THE...

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HOW TO ENABLE CLOUD SUCCESS?

UNVEILING THE MULTIDIMENSIONAL NATURE OF

CLOUD SERVICE VIABILITY

Abstract

Providers of consumer cloud services face enormous challenges in developing a

sustainable market position in this highly competitive market. Emergent trends like

consumerization lead to high growth rates and extend the reach of these services

far into the enterprise sphere. Using a freemium model, many providers focus on

establishing a large customer base quickly. Unfortunately, they often lack a strategy

to generate revenue streams in the long run. Based on a sample of 596 actual

users, our study examines how consumer cloud services can become viable, i.e.,

being self-sustainable on the basis of the user base and revenue streams they

generate. The results indicate that focusing on a single performance indicator is not

sufficient to understand the viability of cloud services. We conceptually differentiate

between different types of willingness to pay that have been used synonymously in

previous studies and provide empirical evidence that their drivers differ

fundamentally. Thereby, we establish the multidimensional nature of cloud service

viability as a promising perspective to study cloud service scenarios. The key

findings are used to derive specific recommendations for three generic strategies

Authors / Viability of Cloud Services

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that cloud providers can apply to become viable in their competitive market

environment.

Keywords: Cloud computing, viability, online consumer behavior, cloud services,

willingness to pay, freemium, business models, word-of-mouth.


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Introduction

The consumer cloud service market is highly competitive and IT providers face

enormous challenges in positioning their services (Rossbach & Welz, 2011). In

contrast to enterprise services, many providers focus on establishing a large

customer base quickly. In the long run however, these services often lack a strategy

to generate sufficient revenue streams (Needleman & Loten, 2012). Others charge

users immediately or after a trial phase, but don’t manage to generate enough

growth. The difficulty of cloud services to become viable – i.e., being self-

sustainable on the basis of both the user network and revenues it generates – shall

be illustrated by a few cases. Successful examples for cloud services are

Freshbooks, a cloud-based accounting service, Mailchimp, a cloud-based marketing

service, or Evernote, a service for note taking and archiving. All of those offer limited

free versions of their services but charge users for advanced features or

requirements, also referred to as freemium model (Teece, 2010). The revenue

model has also been widely adopted by a variety of social networking or news

platforms (Niculescu & Wu, 2014). While growing fast, all these services managed

to continuously increase their number of paying users (e.g., Chestnut, 2010).

However, the transformation of free users into paying consumers can also fail (S. S.

Kim & Son, 2009). Experts estimate that - on average - only 2% of the users pay for

freemium-based cloud services (Needleman & Loten, 2012). Cases where a focus

on creating a large user base (instead of focusing on generating revenues) led to

problems are manifold. For instance, Chargify LLC, a provider of billing-

management software, was also motivated to use the freemium model by the


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seemingly low marginal costs of a cloud-based service. When Chargify started out in

2009, they were very successful in generating users (Needleman & Loten, 2012).

However, the company was soon on the path to bankruptcy because users never

became paying customers. They decided to change the focus of their strategy from

generating users to preliminarily generating revenues. Accordingly, they put up a

paywall for all users at the cost of lower growth rates but became self-sustainable

with more than 900 paying customers in 2012 (Needleman & Loten, 2012). A similar

turn was necessary for Ning, a platform to create social websites (Rao, 2010). The

firm also discontinued the free service and successfully introduced a small fee for all

users. In contrast, other companies such as iOctocat, a GitHub application, were

initially driven by revenue generation, but had to switch to a freemium model to grow

faster (Reimann, 2013). In summary, the examples highlight, that both, a focus on

growth and a focus on revenues, can have dramatic consequences for cloud service

providers. Thus, we suggest that a variety of performance indicators and their

interrelationships must be considered to understand the rise or stagnancy of cloud

services. In this article, we address theoretically and empirically how and why cloud

services become viable, i.e. become self-sustainable on the basis of the user base

and revenues they generate.

A service is viable if it is “capable of succeeding” (Merriam-Webster dictionary).

Therefore, viability does not refer to the long term success of a business. Rather, it

can be described as a customer-oriented configuration that provides the capabilities

for succeeding. If a service is supposed to be successful in the long run, it has to be

viable. However, if it is not viable, it can never be successful. Hence, viability is a

necessary building block for successful cloud services. In this study, we examine


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five customer-related key performance indicators (KPIs) that measure the viability of

a cloud service, namely customer satisfaction, loyalty, word-of-mouth, willingness to

pay for retention (WTPR) and willingness to pay for an upgrade (WTPU) with

respect to their main drivers and interrelationships. As we discuss in the following,

these KPIs indicate to what extent a cloud service is able to build and retain a solid

user base and transform users into paying customers.

The study focusses on consumer IT services for several reasons. While enterprise

services have been addressed by many researchers in IS, technological

advancements have driven the individuation of IS (Baskerville, 2011). This trend is

especially prevalent for cloud services, that eliminate an up-front commitment by the

users, allowing them to start small and increase or reduce computing resources as

needed (Armbrust et al., 2010). Two further prevalent developments drive the need

to understand the viability for consumer cloud services beyond the case of individual

consumers themselves: First, more and more gadgets find their way into the

workplace (Harris, Ives, & Junglas, 2012). Employees introduce services that they

use privately into their work environment and thereby undergo centrally provided

solutions. This development is supposed to drive innovation, productivity and

satisfaction (Harris et al., 2012). Thereby, business IT becomes more and more

similar to consumer IT and the lines between these two categories begin to blur.

Second, a current survey among 1,000 executives and IT decision makers revealed

that enterprise buyers are more and more mimicking consumers in their behavior

(avanade, 2013). Consequently, not only do the consumer services become more

important, the influence of individuals on the technological decisions increases due

to consumerization and changed buying processes. The market for the consumer


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cloud services in this study is therefore not limited to individual users but reaches far

into the enterprise sphere.

The expected contributions of this paper are threefold. First, our goal is to nudge

research away from focusing solely on one single KPI towards a theory that explains

the multidimensional nature of cloud service viability. Our results reveal complex

interrelationships between the different sub domains which need to be incorporated

when studying cloud service viability. Conversely, focusing on one performance

indicator can lead to decisions that harm other performance indicators such as

certain types of revenue streams. Second, our goal is to show that it is necessary to

differentiate between two different types of consumers’ willingness to pay. Although

paying for an existing service (that has been free before) is obviously largely

different to paying for a service upgrade, studies have equally referred to both types

as “willingness to pay”, without making an explicit statement what willingness to pay

refers to. Since our results highlight that these two revenue sources have different

antecedents, cloud services have to apply different strategies for improving these

KPIs. Third, our goal is to highlight the importance of relational factors for

understanding user behavior in the context of cloud services. As cloud service

relationships are characterized by information asymmetries, users’ uncertainty

perceptions have to be taken into account when studying user behavior in the cloud

context.

The remaining parts of the paper are structured as follows. In the next section, we

formulate the focus of our theoretical analysis. We then apply this perspective to

prior research on post-adoption and post-consumption phenomena to develop a

model explaining and predicting the viability of cloud services. Section three


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introduces our survey research methodology followed by a presentation of the

results in section four. Finally, section five discusses implications and avenues for

future research.

The Viability of Cloud Services

Cloud computing can be seen as evolution of IT service provisioning with respect to

both the underlying technology and the business models for delivering IT-based

solutions (Iyer & Henderson, 2010; Venters & Whitley, 2012). We define cloud

computing as a virtualization-based style of computing where IT resources are

offered in a highly scalable way as a cloud service over the internet (Armbrust et al.,

2010). Cloud services can be classified according to their deployment, service, and

revenue model.

We can distinguish two basic models of cloud service deployment, namely private

and public clouds. Private clouds are devoted to a single company only. They may

be built, owned, and managed by the organization or by a third party (Mell &

Grance, 2011). While they offer the highest degree of control over performance,

security, and reliability, they are often criticized for being similar to traditional

proprietary data centers without the typical advantages of clouds like no-up front

capital costs (Q. Zhang, Cheng, & Boutaba, 2010). Public cloud services are

available to the general public. They are owned, built, and managed by third parties.

While there is no fundamental difference in the technical realization compared to

private clouds, the consumer’s control over data, network and security is limited (Q.

Zhang et al., 2010). Within our study, we focus solely on public clouds built, owned,

and managed by an external cloud provider.


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There are essentially three service models for cloud-based solutions which offer

different levels of abstractions: infrastructure as a service (IaaS), platform as a

service (PaaS) and software as a service (SaaS) (Mell & Grance, 2011). IaaS refers

to the provision of hardware resources like processing, storage and networks. PaaS

solutions provide - in addition to the infrastructure level - a cloud software

development environment which can be used to develop SaaS solutions. SaaS

offers complete applications running on cloud infrastructure which is completely

managed and controlled by the provider (Benlian, Koufaris, & Hess, 2011). As they

are hosted on the internet, they are accessed through a web browser or a thin client

instead of being deployed on the user's computer. Within our study, we focus on

SaaS solutions.

The most common revenue model for cloud services is the freemium model that

implies offering basic functions for free but skimming off profit for advanced features.

Since many internet customers expect basic services to be free of charge, the

freemium model has gained enormous popularity and has also been adopted by a

variety of social networking or news platforms (Niculescu & Wu, 2014). Cloud

markets are characterized by the effect that the best providers capture a significantly

large share of the rewards with remaining competitors being left with little. In these

types of markets, cloud providers have to balance two goals, building and retaining

a large customer base and skimming customers’ willingness to pay. Viable cloud

services are successful in balancing these often divergent goals.


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Key Performance Indicators of Viable Cloud Services

Based on marketing and practitioner literature, we identified five consumer-related

KPIs – customer satisfaction, loyalty, word-of-mouth (WOM), willingness to pay for

retention (WTPR) and willingness to pay for an upgrade (WTPU) – that measure the

viability of a cloud service, i.e., its ability to build and retain a solid customer base

and transform users into paying customers. We excluded other customer-related

outcomes from literature such as repurchasing intentions or complaining behaviors

(Gustafsson & Johnson, 2004; M. D. Johnson, Anderson, & Fornell, 1995; Luo &

Homburg, 2007; Szymanski & Henard, 2001) since they are either not applicable to

cloud services or they have no influence on the services’ viability as defined above.

Therefore, we conducted an extensive, cross-disciplinary literature review (Webster

& Watson, 2002) to establish a detailed overview of the studied relationships,

contexts, examination objects and the domains of the previously specified

performance indicators (cf. Appendix A). We make use of these insights to inform

our hypothesis building and to discuss our findings in the light of previous research.

In the following, customer satisfaction, loyalty, WOM, WTPR and WTPU are clearly

defined and their commercial desirability is highlighted.

Customer satisfaction represents an important cornerstone for customer-oriented

businesses since it drives strategically important outcomes (Szymanski & Henard,

2001). In a recent survey, customers of cloud providers declare that contentment

with the services is the main reason why they have not changed their provider

suggesting that customer satisfaction is a key performance indicator for viable cloud

services (Redshift Research, 2012). Customer satisfaction furthermore is a core

construct in information systems (D. J. Kim, Ferrin, & Rao, 2009; S. S. Kim & Son,


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2009) and marketing research (Homburg, Koschate, & Hoyer, 2005; S. O. Olsen,

2002). With respect to scope and level of abstraction, two general types of customer

satisfaction are distinguished in the literature, namely transaction-specific customer

satisfaction and cumulative customer satisfaction (M. D. Johnson et al., 1995). While

transaction-specific satisfaction deals with the ex-post evaluation of a particular

product or service experience, cumulative customer satisfaction is a more abstract

construct that describes customers' total performance experience of a service

provider to date (Gelbrich & Roschk, 2011). Since we aim to study customer

satisfaction in the context of an ongoing cloud service experience, we adapt the

cumulative conceptualization in the following (R. L. Oliver, 1980). Accordingly, we

define satisfaction as customers’ subjective judgment resulting from positive and

negative observations of a cloud provider’s performance (R. L. Oliver, 1993).

Customer loyalty is a customer’s or user’s overall attachment or deep commitment

to a product, service, brand, or organization (R. L. Oliver, 1999). The concept is

described as a customer’s intention to continue using (continuance) a product in the

IT innovation literature (e.g., Cyr, 2008) or as repeated patronage in the marketing

literature (e.g., Lam, Shankar, Erramilli, & Murthy, 2004). Transferring this

conceptualization to the context of cloud services, we define loyalty as a customer’s

affective commitment to continue using the cloud service of a given provider.

Customer loyalty is an important indicator for the viability of cloud services because

it determines how well the current customer base can be retained. Also cloud

practice suggests that cloud providers need to become better at holding on to

customers since the “payoff takes longer—and because it is easier for customers to

switch providers” (Bain, 2012, p. 8).


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WOM is a “dominant force in the marketplace” (Mangold, Miller, & Brockway, 1999,

p. 73) and an “effective mean to increase the revenues and profits of firms” (S. S.

Kim & Son, 2009, p. 50). The growing presence of the internet is even expanding its

importance for the market success of IT services (Brown, Barry, Dacin, & Gunst,

2005). Compared to traditional software products, cloud services are often promoted

by a “word-of-mouth model” (Deloitte, 2009, p. 55). WOM refers to “informal

communication between private parties concerning evaluations of goods and

services” (Anderson, 1998, p. 6) which can be either positive, neutral or negative.

The additional benefit of an increasing customer base for the individual user resides

in improved opportunities of file sharing or – in some cases – the earning of more

storage. In line with previous research, we use positive WOM behavior – referring to

the customer intention to spread favorable information about the service provider

and its service among peers (Maxham III & Netemeyer, 2003) – as a proxy for

estimating the potential increase of the customer base. Regardless of the channel

through which WOM activities are distributed, we believe that any positive WOM

activity contributes to the viability of a cloud service because it influences how easy

and effective the network externalities inhibited in cloud services can be exploited by

the cloud provider.

Customer’s willingness to pay (WTP) is very valuable information necessary to

formulate a business strategy. Therefore, the challenge of its determination has long

been in focus of research and practice (Miller, Hofstetter, Krohmer, & Zhang, 2011).

For cloud providers, mostly using a freemium revenue model, this question is even

more important since they depend on customers who upgrade their service. In the

IS literature, WTP has been accordingly defined as a customer’s willingness to pay


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a small fee for advanced features of a service currently available for free (S. S. Kim

& Son, 2009). However, a second possibility to generate financial earnings is often

either ignored or used synonymously: the willingness to pay for retention, defined as

the willingness to pay for the same service currently available for free (Vock, van

Dolen, & de Ruyter, 2013). We differentiate these two types of WTP in our study.

The importance of WTP of any kind as an indicator for the long term viability of cloud

services is unquestioned. It determines how well current customers using the free

version can be converted into paying customers who actually generate revenues.

Drivers of Customer Satisfaction

Prior research suggests that customer satisfaction can be well explained by using

metrics derived from the technology acceptance model (Devaraj, Fan, & Kohli,

2002). While we expect these well tested relationships to also hold in our context,

we introduce perceived uncertainty as an important new driver of satisfaction for

cloud services. More particularly, we propose that low levels of uncertainty

perceptions are a premise for higher levels of customer satisfaction. The usage of

cloud services is accompanied by a loss of control by the user (Armbrust et al.,

2010). This loss of control can lead to perceived uncertainty. Uncertainty

perceptions are based on information asymmetries and might include users’ privacy,

security or availability concerns (Trenz, Huntgeburth, & Veit, 2013). In particular, it is

difficult for the consumer to judge whether his data is not misused, but stored

securely, and whether the necessary resource buffers are provided before capacity

overload incidents occur. Accordingly, we argue that the ongoing information

asymmetry in cloud user-provider relationships causes uncertainty to be also crucial

for customer satisfaction:


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H1: Consumers’ perceived uncertainty of using the service is negatively associated

with their level of satisfaction.

Drivers of Customer Loyalty

We find compelling evidence in the literature for the relationship between customer

satisfaction and loyalty (Cyr, 2008; D. J. Kim et al., 2009; J. Kim, Lee, Han, & Lee,

2002; Lam et al., 2004; Oliva, Oliver, & MacMillan, 1992; S. O. Olsen, 2002; Otim &

Grover, 2006). Other studies argue that although loyal customers are mostly

satisfied, the opposite does not have to be true (R. L. Oliver, 1999). However, such

inconsistencies can be largely explained by definitions and scopes of satisfaction

and loyalty that differ from our conceptualization (Han, Kwortnik, & Wang, 2008).

Consistent with the expectation-confirmation paradigm, we argue that satisfaction

with a cloud service is a key to building and retaining a loyal base of long-term

customers. In contrast, when customers become dissatisfied with the service they

will less likely continue using the service any longer (R. L. Oliver, 1980):

H2: Consumers’ level of satisfaction with the service is positively associated with

their loyalty.

Drivers of Word-of-Mouth

The link between customer satisfaction and WOM has been under investigation both

empirically and theoretically (Brady, Voorhees, & Brusco, 2012; Chiou, Droge, &

Hanvanich, 2002; Gittell, 2002; Heitmann, Lehmann, & Herrmann, 2007; Hennig-

Thurau, Gwinner, & Gremler, 2002; M. W. Johnson, Christensen, & Kagermann,

2008). A key motivation for WOM is a consumer’s experience with the services. This

service experience produces “a tension which is not eased by the use of the product


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alone, but must be channeled by way of talk, recommendation, and enthusiasm to

restore the balance” (Dichter, 1966, p. 148). Thus, affective states of either valence

stimulate WOM transmissions (Westbrook, 1987) and satisfied consumers are likely

to engage in positive WOM (Gittell, 2002). We believe that in the context of cloud

services this relationship also holds and thus, propose:

H3a: Consumers’ satisfaction with the service is positively associated with their level

of word-of-mouth.

Customer loyalty has not only been discussed as a customer-related outcome of

customer satisfaction but also as a driver of WOM. In context of online services, Kim

and Son (2009) provide evidence that a person’s dedication with the service is a

necessary precondition for positive WOM. They argue that since referring a peer

puts the customer socially at risk, positive WOM does not occur without a high level

of loyalty and dedication for the service provider. We believe that this relationship

also holds in our context. Moreover, cloud services exhibit strong network effects,

i.e., the value of the cloud service for customers depends on the number of others

using it (Katz & Shapiro, 1986). When users are intending to continue using the

service, they also have an incentive to increase the customer base through positive

WOM:

H3b: Consumers’ loyalty with the service is positively associated with their level of

word-of-mouth.

Drivers of Willingness to Pay

Compared to loyalty and WOM, the relationship between customer satisfaction and

WTP has attracted less attention in the literature despite its importance as a key


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element of the profit equation and its link to profitability (Homburg et al., 2005).

Based on equity theory (Adams, 1964), if unequal outcomes of a transaction occur

between customer and provider, individuals try to change certain parameters of the

exchange and try to establish a balance. Accordingly, a higher level of satisfaction

with the service implies a higher outcome for the customer which should also relate

to a higher level of outcome, in terms of payment, for the seller. Empirical support

for this theoretical argument is provided by Homburg et al. (2005) in the service

context. Such considerations are supposed to be even stronger when the customer

feels dedicated to a particular firm. If customers feel loyal to the cloud service, they

prefer to deal with this vendor as opposed to another service provider and

accordingly are willing to pay more (Palmatier, Scheer, & Steenkamp, 2007).

Previous studies have addressed different benefits for which customers could be

charged. Kim and Son (2009) address the willingness to pay for the same product or

service. Vock et al. (2013) investigate willingness to pay for advanced features or

additional purchases. Although paying for an existing service (that has been free

before) is obviously largely different to paying for a service upgrade, studies have

equally referred to both types as “willingness to pay”, without making an explicit

statement what willingness to pay refers to. Furthermore, no research study has

investigated these two types of willingness to pay simultaneously (cp. Appendix A).

In the context of cloud services, we argue that it is necessary to distinguish these

two types of willingness to pay carefully, because they depict two different paths to

financial success whose connection to the other dimensions of cloud viability is not

identical. In the following, we refer to willingness to pay for an upgrade (WTPU) if we

are discussing the willingness to pay for advanced features of a service such as


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more storage or advanced security. Accordingly, we refer to willingness to pay for

retention (WTPR) if we are discussing consumers’ willingness to pay for keeping the

same service level that has been free before.

While both a high level of customer satisfaction and loyalty have been found to

positively influence classical WTP in previous research (Fullerton, 2003; Homburg et

al., 2005), we propose that in the cloud service context customer satisfaction does

not drive consumers to pay for advanced features (WTPU). The major driver of this

difference is the exploitation of the freemium model as explained in the following.

Basic functions are usually offered for free. Generally, consumers are willing to pay

more if they expect a higher utility from the transaction. However, if consumers are

highly satisfied with the current service level, the urge to change their present

service configuration is low. Accordingly, they derive less additional value from an

extended service than customers whose needs are currently not fully satisfied. This

difference in the valuation of a service upgrade is translated into differences in the

willingness to pay for advanced features. Customers who are satisfied tend to stay

with the present service configuration and feel no necessity to pay a fee for

advanced features. In contrast, customers with lower levels of satisfaction with the

basic service possess a comparably higher need for additional features and

therefore have a higher WPTU. Therefore, we propose:

H4a: Consumers’ satisfaction with the service is negatively associated with their

WTPU.

The opposite mechanism applies to the case of WTPR. WTPR is important when

the company decides to stop offering the current service level for free. Consumers

who are highly satisfied with the current service will perceive a higher financial or


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psychological loss in utility. This opportunity cost can be translated into a WTP for

retaining the current state. In contrast, it is easier for users with less favorable

feelings toward the service to leave the provider. Accordingly, their willingness to

procure money to preserve the current service level is lower. This argumentation is

in line with Homburg et al. (2005) who argue that consumers search a balance

between the outcome of an transaction (satisfaction) and the input of an transaction

(payment). Therefore, satisfied consumers should be willing to pay more for the

retention of the service than unsatisfied consumers:

H5a: Consumers’ satisfaction with the service is positively associated with their

WTPR.

A strong dedication for the service influences how consumers react when they have

the opportunity to intensify their relationship. Their dedication for the service makes

loyal customers more open for upgrading their service configuration even if they are

charged an additional fee by the provider (WTPU). Accordingly, they will refer to the

provider if they need additional features. Loyalty decreases consumers’ price

sensitivity (Krishnamurthi & Raj, 1991) and therefore increases the willingness to

intensify the relationship with the provider at a cost. Therefore, we assume that loyal

customers have a higher WTPU than consumers without a dedication for the cloud

service:

H4b: Consumers’ loyalty with the service is positively associated with their WTPU.

Loyal consumers are also willing to pay more for the retention of the service than

uncommitted consumers. Due to their high dedication with the service, they perceive

high costs to terminate this strong relationship with the service that can be described


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as types of switching costs (S. S. Kim & Son, 2009) or search costs (Reichheld &

Sasser, 1990). Therefore, they are willing to invest money to inhibit the occurrence

of these switching costs. However, consumers who are less committed do not

perceive this harm and therefore are willing to pay less for the retention of the

service:

H5b: Consumers’ loyalty with the service is positively associated with their WTPR.

Apart from these relationships with the other KPIs, the most common view to

establish willingness to pay is to focus on the utility that the customer derives from

the service (Miller et al., 2011). For the free service this utility directly derives from

the usefulness of the currently experienced free service. Accordingly, a higher

perceived usefulness of a particular service should lead to a higher WTPR:

H5c: Consumers’ perceived usefulness of the service is positively associated with

their WTPR.

In turn, for the premium service, this utility derives from the usefulness of the basic

service plus the perceived value of the premium services (Dodds, Monroe, &

Grewal, 1991). Accordingly, both perceived usefulness and perceived value of

upgrade are driving consumers WTPU:

H4c: Consumers’ perceived usefulness of the service is positively associated with

their WTPU.

H4d: Consumers’ perceived value of the service upgrade is positively associated

with their WTPU.

Figure 1 presents an overview of our research model.


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Research Methodology

The hypotheses derived in the previous section were tested using survey data from

an online questionnaire among actual users of cloud storage services. We focus on

cloud storage services because these services are widely adopted by internet users

(Zetta, 2010) and share the typical characteristics of other cloud-based services

(e.g., appearance of infinite computing resources available on demand, elimination

of an up-front commitment, ability to pay for use of computing resources, see

Armbrust et al., 2010). Moreover, they are characterized by very low marginal costs

Figure 1. Research Model and Proposed Hypotheses


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and therefore have to address the tension between growth and revenue generation.

Their cost structure and the highly competitive situation in growing cloud markets

provide incentives to offer basic functions like file-sharing, synchronization and a

certain amount of storage for free. However, cloud storage services also need to

identify ways to generate revenues. In the following, we describe our measurement

development as well as the survey deployment and data collection procedures.

Measurement Development

All measures used in our study were adopted from existing measures. However,

they were adapted to the context of our study. On grounds of the critique raised

about the validation of scales in the IS discipline (e.g., Boudreau, Gefen, & Straub,

2001; Scott B. MacKenzie, Podsakoff, & Podsakoff, 2011), we decided to re-validate

our constructs. This process included the definition and assessment of the domain

and dimensionality of the constructs using two sorting procedures (Moore &

Benbasat, 1991) and the assessment of content validity using a rating method

(Hinkin & Tracey, 1999; Scott B. MacKenzie et al., 2011). We pilot tested the

preliminary instrument 196 participants. After the pre-test, the respondents were

asked to give open feedback regarding composition of the survey, overall time, and

other issues they experienced. Following the pre-test, the instrument was shortened,

refined, and validated for its statistical properties. In the final survey, all principal

constructs were measured as first-order reflective constructs using three or more

indicators. An overview of all measures and their sources is given in appendix B.


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Survey Deployment and Data Collection

We collected our data using an online survey, since the regular online access is a

prerequisite for usage of such a service. While little is known about which part of the

internet population is using cloud storage services, a representative set (with

respect to gender and age) of internet users that matches the general population in

Germany was pre-selected (cf. AGOF 2013) and subsequently, only those

participants of the survey were surveyed that use the market-leading cloud storage

service Dropbox - ensuring comparability of responses. Using these requirements,

a professional online panel has sent individual invitations to its members in the

period between 12th of November and 9th of December 2012. Overall, we received

2.011 valid responses of which 638 declared to use Dropbox. We further eliminated

responses of those Dropbox users that declared to use the premium service (42

premium users) - again to ensure comparability of responses. By the end, 596

responses were deemed useful for the subsequent analysis. Table 1 summarizes

the structure of the sample.


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Data Analysis and Results

We used partial least squares structural equation modelling (PLS-SEM) to validate

the structural model and test our hypotheses. Two different types of SEM

approaches exist, covariance-based SEM (CB-SEM) and partial least square SEM

(PLS-SEM), which differ in their underlying philosophy and estimation objectives

(Gefen, Rigdon, & Straub, 2011). On the one hand, CB-SEM emphasizes how well

the proposed research model accounts for measurement item co-variances, thereby

offering various indices how well parameter estimates match sample co-variances

Table 1. Years of Age and Gender Distribution among Respondents

Years of age/gender Representative sample of internet users

Users of free Dropbox version (our final sample)

Number Quota Number Quota

14-19/female 90 5% 41 7%

20-29/female 186 9% 105 18%

30-39/female 170 9% 33 6%

40-49/female 210 10% 24 4%

50-59/female 166 8% 19 3%

60-69/female 132 7% 14 2%

14-19/male 95 5% 53 9%

20-29/male 189 9% 113 19%

30-39/male 181 9% 75 13%

40-49/male 233 12% 54 9%

50-59/male 180 9% 36 6%

60-69/male 179 9% 29 5%

Overall 2,011 596


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(Chin, 1998). On the other hand, PLS-SEM uses the empirical data for estimating

relationships with the aim to maximize the explained variance in the endogenous

latent variable (Hair, Hult, Ringle, & Sarstedt, 2014). The primarily goal is therefore

to predict and explain variance.

There is an ongoing controversial debate about which SEM tool to use as well as

CB-SEM’s and PLS-SEM’s relative ability to support the empirical evaluation of

hypothesized relationships among variables (Goodhue, Lewis, & Thompson, 2006,

2012; Marcoulides, Chin, & Saunders, 2012). Two popular reasons for choosing

PLS-SEM were that it provides more accurate results for studies with small sample

sizes and non-normally distributed variables (Ringle, Sarstedt, & Straub, 2012).

However, this assertion has not been confirmed by previous comparison studies.

Rather, a recent Monte Carlo simulation-based study of Goodhue et al. (2012)

shows that CB-SEM and PLS-SEM provide consistent results regarding testing

relationships between variables:

“[…] if one is in the early stages of a research investigation and is

concerned more with identifying potential relationships than the

magnitude of those relationships, then regression or PLS would be

appropriate. As the research stream progresses and accuracy of the

estimates becomes more important, LISREL (or other CB-SEM

techniques) would likely be preferable” (Goodhue et al., 2012, p. 999).

To sum the discourse up, the choice of the SEM should primarily depend on the

research objective. Thereby, PLS-SEM is more suitable for exploratory and CB-SEM

is more suitable for explanatory evaluations of theoretical systems. Given the early

stage of this investigation, the exploratory character of the study and the primary


24

interest in identifying potential relationships between variables, we decided to use

PLS-SEM for evaluating the drivers and interrelationships of our KPIs. However,

consistent empirical results are expected when using CB-SEM.

Descriptive Statistics of Sample

Table 2 depicts the descriptive statistics of the surveyed Dropbox users. The

statistics highlight that the sample consists of heterogeneous sub-groups of low and

highly educated, employed and unemployed, low and high income as well as male

and female respondents. This provides evidence that no demographic group was

systematically excluded from the study.

Table 2. Descriptive Statistics of Dropbox Users (free version)

Education

No education

Secondary school

Higher education

Completed vocational

training

University degree

Doctorate degree

2 (0.3%) 129 (21.6%) 192 (30.0%) 108 (18.1%) 183 (30.7%) 4 (0.7%)

Income

< €500 €501-€1,500 €1,501-€2,500 €2,501-€3,500 > €3,500 Not specified

51 (8.6%) 121 (20.3%) 147 (24.7%) 92 (15.4%) 86 (14.4%) 94 (15.8%)

Occupation

In training

Employed Unemployed

or retired Not specified

214 (36.4%)

307 (51.5%) 72 (12.1%) 3 (0.5%)

Measurement Validation

For reflective measurement models in PLS-SEM, there are three criteria for

evaluating the reliability and validity of the measurement models, namely internal

consistency reliability, convergent validity and discriminant validity (Henseler,

Ringle, & Sinkovics, 2009). We checked internal consistency using composite

reliability scores (Table 3). All measurement models exhibit satisfactory composite


25

reliability values above 0.7 (Nunnally & Bernstein, 1994). At the construct level, we

assessed convergent validity based on indicator reliability and the average variance

extracted (AVE) (Henseler et al., 2009). All outer loadings were above the

recommended threshold of 0.708 suggesting reliable indicators. Moreover, all

constructs had AVE values above 0.5 suggesting that more than half of the item’s

variance is explained by the latent variable. We evaluated discriminant validity

based on the Fornell-Larcker criterion (Fornell & Larcker, 1981) as well as

comparing outer and cross loadings (Hair et al. 2011). According to Fornell-Larcker

criterion, the square root of each latent variable’s AVE must be larger than its

correlation with any other latent variable. This is the case for our measurement

models (cf. appendix C). Moreover, for each item the outer loading on its associated

latent variable was higher than the cross loadings on all other latent variables (cf.

appendix D). Since both criteria are fulfilled, one can conclude that the

measurement models exhibit discriminant validity.

Table 3. Measurement Model Results

Con-structs

Variable Name

Outer Loading

Items per Construct

AVE Composite Reliability

Mean Standard Deviation

Uncertainty UNC1 UNC2 UNC3 UNC4

0.8695 0.9005 0.9495 0.9413

4 0.84 0.95 3.48 1.51

Ease of Use PEU1

PEU2

PEU3

PEU4

0.9227 0.7195 0.8166 0.9162

4 0.72 0.91 5.48 1.16

Usefulness PU1 PU2 PU3 PU4

0.9289 0.9335 0.9133 0.9006

4 0.84 0.96 4.75 1.51

Value of Upgrade

PVU1 PVU2 PVU3

0.9584 0.9551 0.9584

3 0.92 0.97 2.67 1.85

Satisfaction SAT1 SAT2 SAT3 SAT4

0.9146 0.8953 0.9286 0.8492

4 0.81 0.94 5.76 1.11


26

WOM WOM1 WOM2 WOM3 WOM4

0.9183 0.8949 0.893 0.853

4 0.79 0.94 5.00 1.56

Loyalty LOY1 LOY2 LOY3 LOY4

0.8584 0.8552 0.9015 0.9285

4 0.79 0.94 5.24 1.43

WTPR WTPR1 WTPR2 WTPR4 WTPR4

0.686 (*) 0.8313 0.8944 0.8886

3 0.76 0.90 14.35 26.25

WTPU WTPU1 WTPU2 WTPU3 WTPU4

0.9216 0.9203 0.8272 0.8878

4 0.79 0.94 4.14 12.33

IT Experience

ITE1 ITE2 ITE3 ITE4

0.8678 0.9107 0.8851 0.8543

4 0.77 0.93 5.33 1.13

Internet Use IUSE - 1 - - 5.00 3.23

Cloud Knowledge

CK1 CK2 CK3 CK4

0.932 0.9458 0.9261 0.9141

4 0.86 0.96 4.27 1.46

Age AGE - 1 - - 33.19 13.88

Gender GEN - 1 - - 0.60 0.49

Income INC - 1 - - 4.61 2.10

Because data for each respondent was obtained using a single measurement

method, we applied the recommended procedural and statistical remedies as

proposed by Podsakoff et al. (2003) to minimize and control for common method

variance (CMV). First, we conducted the Harman’s single factor test using an

exploratory factor analysis in SPSS (Podsakoff et al., 2003). The unrotated principal

component factor analysis revealed eight factors with eigenvalues above 1,

explaining 80% of the variance. The most prominent component accounted for 34%

of the variance. Since neither a single factor emerged, nor one general factor

accounts for the majority of the covariance among the variables, evidence is

provided that CMV did not bias the results (Malhotra, Kim, & Patil, 2006). Second,

we used the marker-variable technique as proposed by Lindell and Whitney (2001)


27

to examine how a potential CMV biases the results. The marker-variable technique

controls for CMV by including “a measure of the assumed source of method

variance as a covariate in the statistical analysis” (Podsakoff et al., 2003, p. 889).

The technique was used in a post-hoc manner by taking the second smallest

correlation among the variables to be the extent of CMV (0.027 between UNC

WOM, cf. Appendix C). We calculated the CMV-adjusted estimate and the test

statistic for each pair of the correlation matrix (using equations (4) and (5) in Lindell

& Whitney, 2001, p. 116). Because the CMV-estimate (second smallest correlation)

was close to zero, almost all previously significant correlations remained significant

(only UNCWOM turned insignificant). Thus, the marker-variable technique also

suggests that CMV did not bias the results. Third, we reconstructed the latent

variables and measures in AMOS 22 to include a latent general common method

factor that was allowed to load on every item in the research model (Podsakoff et

al., 2003). Since the common variance between the measures and the latent

general common method factor was also close to zero, we conclude that the path

estimates and significance levels are neither substantially inflated nor deflated by

CMV (S. B. MacKenzie, Podsakoff, & Paine, 1999). Overall, based on these three

statistical tests, we can rule out concerns that CMV biases the results.

Testing the Structural Model

Once evidence is provided that the measurement instrument is both reliable and

valid, the next step is to evaluate the path coefficients and their significance in the

structural model (Hair et al., 2014). As the PLS algorithm assumes that there is no

collinearity between the exogenous latent variables, one needs to ensure that this is

the case for each endogenous latent variable. Using the latent variables scores of


28

each variable, we ran a linear regression using SPSS and calculated the variance

inflation factor (VIF) based on the regression (Mooi & Sarstedt, 2011). The VIF

measures how much of the variance of an estimated regression coefficient is

increased because of collinearity, i.e., because two exogenous latent variables are

correlated. The VIF for all correlations was far below the recommended threshold of

5 (even all VIF were below 2.5) suggesting that multicollinearity did not bias path

coefficient estimations.

We included control variables into our structural model. Beyond age, gender and

income as demographic variables, we also included IT experience, cloud knowledge

and internet use as additional covariates into our model to check whether the effects

can be explained by differences in the users’ level of experience with technology,

computer or the internet. Based on 5.000 bootstrap samples, we found significant

effects of age (b=.082; p<.01) and gender (b=-.064 p<.05) on loyalty. Furthermore,

IT experience and cloud knowledge both had a strong effect on satisfaction. While

users with high knowledge about cloud tend to possess lower levels of satisfaction

(b=-.109; p<0.01), users with high experience with using IT are more satisfied with

the service (b=.133; p<.01). Also women were more satisfied with the service but

willing to pay less for retention than men (b=-.118; p<0.01; b=.136; p<0.01). Finally,

income had a significant effect on WOM (b=-.062; p<0.05) as well as on WTPU

(b=.108; p<0.01). Despite several significant effects, the control variables had no

effect on the conclusions drawn from the structural model evaluation, i.e., all

hypothesized effects remained within the same level and their significance was not

influenced by the control variables.


29

Regarding our hypotheses, we found that the negative relationship between

uncertainty and satisfaction was highly significant (b=-.16; p<.001; H1), over and

above the well-established constructs of perceived usefulness (b=.28; p<.001) and

perceived ease of use (b=.47, p<.001). Accordingly, all three exogenous variables

had strong loadings on satisfaction and explained 56% of its variance. Furthermore,

as hypothesized, satisfaction had a significant positive effect on loyalty with the

cloud service (b=.58; p<.001; H2). The variance explained for loyalty was 38%.

Regarding WOM, the relationship with satisfaction (b=.31; p<.001; H3a) was

confirmed as well as for H3b, claiming that loyalty increases word-of-mouth (b=.38;

p<.001; H3b). 41% of the variance of WOM was explained by our model. Regarding

WTP, our study shows that the connection of WTPR and WTPU to the other

dimensions of cloud viability is not identical. On the one hand, WTPU is significantly

negatively influenced by satisfaction (b=-.10; p<.01; H4a) and positively affected by

loyalty (b=.10; p<.01; H4b), perceived usefulness (b=.08; p<.01; H4c) and perceived

value of upgrade (b=.41; p<.001; H4d), explaining 23% of the variance. On the other

hand, WTPR is significantly influenced by loyalty (b=0.14, p<.01; H5b) and

perceived usefulness (b=.16; p<.001; H5c), while satisfaction has no significant

positive impact (b=.04; p=.271; H5a). Therefore, H5a was not supported in our

study. Figure 2 depicts the overall results of the structural model test.


30

Figure 2. Structural Model Results

Discussion

The objective of this study is to develop and test a parsimonious model that

examines the drivers of five KPIs of viable cloud services. In the emerging context of

cloud services, we combine established and new, cloud-specific drivers of each

performance indicator and investigate their influence on each other. Thereby, we

were able to resolve inconsistencies among previous studies regarding the

relationships between satisfaction, loyalty, WOM, WTPR and WTPU in this new

theoretical context. Overall, our findings provide strong support for our research

model. The three major findings that provide new insights on viable cloud services


31

are presented in detail in the following. Subsequently, the theoretical and practical

contributions as well as limitations of our study and future research are discussed.

Key Findings

The cloud-specific driver, perceived uncertainty is well-suited to explain customer

satisfaction in the context of cloud services, even after controlling for the well-

established drivers of perceived usefulness and perceived ease of use. This is

consistent with the expectation-confirmation paradigm where satisfaction is said to

be formed based on the expectations and experiences with the services

(Bhattacherjee, 2001; Hong, Thong, & Tam, 2006). Hereby, users’ believes about

the service (i.e., perceived uncertainty, usefulness and ease of use) are a function of

the expectations and subsequent experiences with the service (R. Oliver, 1977).

This study is the first to examine uncertainty perceptions as a major driver of

customer satisfaction in the context of cloud computing.

We find empirical support for loyalty as the strongest driver of WOM. This finding is

in line with the assumption that users are only willing to take the social risks of

recommending the cloud service when they are highly dedicated to the service as

highlighted by Kim & Son (2009) in their study of online services. However, we also

find strong empirical support for the positive relationship between satisfaction and

positive WOM (Brady et al., 2012; Heitmann et al., 2007; J. Zhang & Bloemer,

2008). This shows that satisfied customers have a tendency to share their positive

service experience with their peers (Arndt, 1967; Dichter, 1966). A possible

explanation for this strong relation between satisfaction and WOM is the nature of

the channel through which recommendations are distributed. The offline channel


32

usually provides a wealth of social bonding or personal fortitude among sender and

receiver. These opportunities are absent in the online channel through which most

cloud service referrals are distributed (Dellarocas, 2003). Here, WOM spreads much

faster, is less personal and thus, is putting the customer’s social image less at risk

than in offline scenarios (Reichheld, 2003). Moreover, the additional benefit of an

increasing customer base (improved opportunities for file sharing and – in some

cases – more storage as an incentive) which motivates WOM activities is not limited

to loyal customers but is instead a goal of all users positively experiencing the

service. Thus, both satisfaction and loyalty drive WOM for cloud services.

Our last set of performance indicators, WTPR and WTPU, are extremely important

for providers in the context of cloud services as revenues are generated based on a

freemium revenue model (Teece, 2010). The deviation from treating WTP as a one-

dimensional concept enables us to develop more fine grained insights on the

potentials for revenue stream generation. Unlike previous marketing research

(Homburg et al., 2005), our study shows that customer satisfaction has only an

indirect effect on customers’ WTP for retention in the context of cloud services. Few

previous studies also found no support for the direct positive relationship between

customer satisfaction and WTP, e.g., in the contexts of consumer goods (J. Zhang &

Bloemer, 2008) and travel services (Homburg, Wieseke, & Hoyer, 2009). However,

these contexts are hardly comparable to our study. Moreover, we find a significant

negative relationship between satisfaction and willingness to pay for upgrade. This

finding implies that a high level of satisfaction can even have negative

consequences for the firm’s revenue, especially in a freemium environment, since

consumers that are very satisfied with their current service level have little incentive


33

to invest financial means in additional features or capacity. The fact that we find no

effect of satisfaction on the second type of willingness to pay, WTPR, highlights the

importance to differentiate between the two. Overall, our findings indicate that for

cloud services, constructs other than customer satisfaction are needed to explain

revenue streams of cloud services. While previous research has mainly

concentrated on customer satisfaction as the central concept for increasing revenue

streams, our study reveals that loyalty is a key for cloud providers to yield profits.

What needs to be kept in mind: reaching customer loyalty is especially difficult for

cloud providers because they are hardly able to establish social bonding or personal

fortitude as common in offline service scenarios (R. Oliver, 1977). Therefore, when

loyalty becomes a key driver of such important business-outcomes like WTP, cloud

providers have to find alternative measures to generate revenue streams. One

starting point for providers is the customers’ usefulness perception of the service or

the upgrade option which is found to directly influence WTP consistent with previous

research.

Theoretical Contribution

Overall, our study makes three major contributions. First, our study shows that in the

context of cloud service, it is not sufficient to focus solely on one KPI. The complex

interrelationships between satisfaction, loyalty, WOM, WTPR and WTPU create the

necessity to move away from simple models focusing on single outcome variables

(cf. Appendix A). For instance, a focus on satisfaction as the main KPI would neglect

its divergent influence on customers’ WTP, which is an influential factor for viable

cloud services. This simplification would involve the danger of incorrect inferences

or policies. Our study implies that we need to develop theories that account for the


34

multidimensional nature of cloud service viability and incorporate the

interrelationships between the different dimensions. Moreover, the mediating effects

identified in our study (SATLOYWTPR) should encourage research to re-

examine our five KPIs together in other theoretical contexts in order to uncover

spurious relationships wrongly inferred in previous studies.

Second, we introduce a precise conceptual differentiation between willingness to

pay for retention and willingness to pay for an upgrade. We provide empirical

evidence for their different interrelations with other KPIs. While previous studies

have used both types of willingness to pay synonymously, the differentiation

between the two strategies to generate revenue streams for cloud services is

important because they relate to different strategies that cloud providers can

employ, i.e., charging existing customers for their current service level or generating

additional needs via the free services that customers are willing to pay for. By

showing that the two types of willingness to pay have different antecedents, our

results imply that their conceptual and empirical separation is crucial to understand

different paths to cloud viability.

Third, our study highlights the importance of incorporating relational factors for

understanding user behavior and users’ service experience in the context of cloud

services. Previous research on technology adoption or continuance has mainly

looked at the perceived characteristics of the IT artifact (e.g., performance

expectancy, effort expectancy) or the social environment of the user (e.g., social

influence, facilitating conditions). When using cloud services however, customers

are dependent on the provider over the whole life-cycle of the business relationship.

As their relationship is continuously characterized by information asymmetries, the


35

corresponding uncertainty has to be taken into account when studying customer

behavior in the cloud context. Our study shows that the characteristics of the cloud

provider-user relationship are increasingly shaping users’ experiences with the

service. These differ sharply from other service contexts, where the service

provisioning takes place within a limited time frame. Accordingly, our study

establishes users’ uncertainty perception as an important antecedent for explaining

user behavior in the context of cloud computing. We encourage future research to

be more strongly attentive to the characteristics of the cloud provider-user

relationship and study post-adoption phenomena using relational factors such as

users’ uncertainty perception.

Limitations and Suggestions for Future Research

First, cloud storage services were used as a study context for the evaluation.

Although cloud storage services are widely adopted by internet users and exhibit the

typical characteristics of cloud services, future research should re-examine viability

for other cloud services. Second, our study identified uncertainty as an important

inhibitor of satisfaction. Unfortunately the scope of our study did not allow us to

explain how the uncertainty connected to cloud services arises and how it could be

mitigated. However, this question should be addressed in further research because

it is of high theoretical and practical interest. Third, our findings regarding the effects

of satisfaction and loyalty on willingness to pay contradict previous studies. We

explain these findings by the unique characteristics of cloud services compared to

other contexts. However, this finding calls for further research challenging these

relationships in other scenarios and identifying contingency factors in order to create


36

a broader understanding of the development of willingness to pay in different online

service scenarios.

Implications for Practice

Based on our results, we derive recommendations for three generic viability

strategies that can be applied in practice: development, retention and habituation

(see Table 4).

Table 4. Strategic Implications for Cloud Service Providers

Viability Strategy

Recommendations

Development Effective versioning of services with a clearly identifiable added value of premium service

Make sure that free version is useful but does not fully address all needs of the user

Build a loyal customer base which is strongly committed to the service

Retention Make sure that free version is highly useful for users

Only switch to subscription model if you have a large number of loyal customers

Habituation Effective versioning of services with a clearly identifiable added value of premium service

Offer long-term free trials of premium service to get user accustomed to premium service

End trial period if individual user is committed to service

Cloud providers pursuing the development viability strategy mainly aim for

generating revenue streams based on transforming free users into paying

customers and for extending the user base through free service offerings. The goal

of these services is to develop free users to become premium users. Based on our

results, providers using the development viability strategy have to design the free

and premium versions of the service in a way that the premium service is clearly

distinguishable from the free version and whose identifiable added value is desirable

to a broad audience. Preferably, some advanced user objectives cannot be


37

achieved with the free version. This is for instance hardly the case for premium

services that offer an add-free interface. Beyond good versioning, providers have to

focus on building a loyal customer base with users that are unlikely to switch the

provider. Since there are typically very little points of contact with the customers,

these have to be exploited effectively (e.g., problem handling or requests using

social web technology) to tie users to the service provider. A good example of a

cloud service pursuing this “development” strategy is Prezi, a cloud presentation

software service for presenting ideas on a virtual canvas. The free version allows

users to create presentations that are publicly visible. Moreover, users are able to

collaborate and present on Prezi using a small amount of free cloud storage. While

the free version of Prezi is a useful tool for consumers, premium features like

privacy, editing presentations offline or more storage provide a clearly identifiable

added value for any customer. Moreover, the service has implemented various ways

to build a solid community of users around its application. Therefore, they are also

successful in building a loyal customer base.

Cloud providers pursuing the retention viability strategy mainly aim for generating

revenue streams based on switching to a subscription revenue model at an

opportune point of time. They use the free version to grow fast and monetize later.

Based on our results, providers trying to become viable using this strategy need to

make sure that the free version strongly increases the (work) performance of the

user. Moreover, providers should wait with switching to a subscription model until a

large number of users is affectively committed or faces high switching costs. A good

example of a viable cloud service that has switched to a subscription model after

having initially operated a freemium model is Chargify LCC as described at the


38

beginning of this article. Chargify LCC was successful in this transformation

because they had a considerably large number of customers who felt affectively

committed to the service and perceived a clear usefulness of the service Chargify

offered. Therefore, Chargify successfully turned into a viable cloud service.

Cloud providers pursuing the habituation viability strategy aim for generating

revenues by skimming both types of willingness to pay. Thereby, they offer each

individual user a very long, possibly hidden, trial phase which offers certain premium

features for free. At an opportune time, they end the trial phase and bet on

customers who adjusted their preferences or their habits towards the premium

features and are therefore willing to pay for keeping the same service level that has

been free before. At the same time, they keep effective versions of the free and

premium service and try to develop free customers to become premium users. Apart

from the guidelines for the other two strategies that the habituation strategy borrows

from, our results suggest that the length of the trial period should be extensive,

allowing users to become strongly accustomed to the service. A good example of a

cloud service pursuing the hybrid viability strategy is Dropbox. Dropbox offers a free

account with a set storage size and paid subscriptions for accounts with more

capacity. In 2012, Dropbox has launched the program “The Great Space Race” that

let college students gaining up to 25GB of free storage space for two years. The

program was meant to increase Dropbox’s market share among students but at the

same time intended to accustom these users to using more storage than the free

version offers. During that time, users might have unconsciously changed their

behavior in using Dropbox towards a higher level of (storage) requirements, e.g., by

synchronizing more files that they otherwise would or changing their sharing


39

behavior in collaborations. After the long period of two years, Dropbox can hope that

many trial users are willing to pay for keeping the same amount of storage capacity,

since their habits have changed and they do not want to remove files that they have

been able to access from everywhere or to end active collaborations with partners.

The habituation viability strategy therefore tries to combine the strength of the

retention and upgrade strategy.

The choice for a specific strategy depends on a market specific assessment whether

the drivers of our five KPIs can be successfully influenced or not. In any case, our

results provide specific recommendations that have been carved out through our

multidimensional conceptualization of cloud viability. These recommendations can

be used by cloud providers to develop a viable position in their particular competitive

cloud markets.


40

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Appendix A: Literature Analysis

Methodology of the literature review

We conducted a structured literature review to create a full picture of previous

literature on the relationships between our four performance indicators satisfaction,

loyalty, word-of-mouth and willingness to pay. Our search was conducted within the

AIS Senior Basket of top IS journals as well as the top journals in marketing and

service research.

Search space (in alphabetical order): European Journal of Information Systems

(EJIS), Information Systems Journal (ISJ), Information Systems Research (ISR),

International Journal of Research in Marketing (IJRS), Journal of AIS (JAIS), Journal

of Consumer Research (JCR), Journal of Information Technology (JIT), Journal of

Marketing (JM), Journal of Marketing Research (JMR), Journal of MIS (JMIS),

Journal of Service Research (JSR), Journal of Strategic Information Systems (JSIS),

Journal of the Academy of Marketing Science (JAMS), Marketing Science (MS), MIS

Quarterly (MISQ)

Selection criteria: At least two of our four major constructs are included in a

quantitative study.

Overall, 62 papers have been found. We used a concept matrix (Webster & Watson,

2002) to structure our findings. Besides the results of the tested relationships in

each paper, we line out type of satisfaction, reference point of loyalty, context and

examination object compared to our study in the table below.

56

Reference Journal

Relationships tested

WTP Type Loyalty Type Satisfaction

Type Context and examination object

SAT →

LOY

SAT →

WOM

LOY →

WOM

SAT →

WTP

LOY →

WTP

WTP →

WOM

Our Study JAIS +* +* +* -*/ns +*/+* Upgrade/ Retention

Vendor Cumulative B2C: Cloud storage services

Agustin & Singh (2005)

JMR ns n/a Vendor Cumulative B2C: Consumer Goods + Offline Service

Andreassen (1999) +* n/a Vendor Transactional B2C: Offline Service

Arens & Rust (2012) JAMS +* n/a Vendor Cumulative B2C: Service (not specified)

Beatty et al. (2012) JSR +* n/a Vendor n/a B2C: Service (not specified)

Blocker et al. (2011) JAMS +* n/a Product Cumulative B2B: ICT Service

Boenigk and Helmig (2013)

JSR +* n/a Vendor Cumulative B2C: Non-profit

Brady et al. (2012) JM +* +* n/a Vendor Transaction-specific

B2C: Offline Service

Brakus et al. (2009) JM +* n/a Vendor Cumulative B2C: Consumer Goods

Brown et al. (2005) JAMS +* +* +* n/a n/a Cumulative B2C: Consumer Goods + Offline Service

Note: * = significant relationship found; ns = relationship not significant; B2C = business-to-consumer; B2B = business-to-business; EJIS = European Journal of Information Systems; IJRM = International Journal of Research in Marketing; ISR = Information Systems Research; JAMS = Journal of the Academy of Marketing Science; JM = Journal of Marketing; JMR = Journal of Marketing Research; JSR = Journal of Service Research; MISQ = Management Information Systems Quarterly.


57

Reference Journal



Type Context and examination object

SAT →

LOY

SAT →

WOM

LOY →

WOM

SAT →

WTP

LOY →

WTP

WTP →

WOM



Chandrashekaran et al. (2007)

JMR +* n/a Vendor Cumulative B2B: Service (not specified)

Chiou & Droge (2006)

JAMS +* n/a Vendor Cumulative B2C: Consumer Goods

Chiou et al. (2002) JSR +* +* n/a Vendor Cumulative B2C: Offline Service

Chitturi et al. (2008) JM +* +* n/a Product Transaction-specific

B2C: Consumer Goods

Colgate & Danaher (2000)

JAMS +* +* n/a Vendor Cumulative B2C: Offline Service

Cooil et al. (2007) JM +* n/a Vendor Cumulative B2C: Offline Service

Cyr (2008) JMIS +* n/a Vendor Transaction-specific

B2C: Website

Davis-Sramek et al. (2009)

JAMS +* n/a Vendor Cumulative B2B: Industrial Goods

Dong et al. (2011) IJRM +* n/a Product Transaction-specific

B2B/B2C: Consumer Goods

Eisingerich et al. (2013)

JSR +* +* +* ns Classical Vendor Cumulative B2C: Banking

Evanschitzky et al. (2012)

JAMS +* +* Retention Vendor/ Program

Cumulative B2C: Retailing



58

Reference Journal



Type Context and examination

object

SAT →

LOY

SAT →

WOM

LOY →

WOM

SAT →

WTP

LOY →

WTP

WTP →

WOM



Fullerton (2003) JSR +* Retention Vendor n/a B2C: ICT Service

Ganesh et al. (2000) JM +* n/a Vendor Cumulative B2C: Offline Service

Garnefeld et al. (2010)

JSR n/a Vendor n/a B2C: ICT Service

Gittell (2002) JSR +* n/a n/a Cumulative B2C: Offline Service

Gong et al. (2013) JSR +* n/a Vendor Cumulative B2B: Employees of call centers

Gremler & Gwinner (2000)

JSR +* +* +* n/a Vendor Cumulative B2C: Offline Service

Gustafsson & Johnson (2004)

JSR * n/a Vendor Cumulative B2C: Offline Service

Han et al. (2008) JSR * n/a Vendor Cumulative B2C: Offline Service

Heitmann et al. (2007)

JMR +* +* +* n/a Product Transaction-specific

B2C: Consumer Goods

Hennig-Thurau et al. (2002)

JSR +* +* n/a Vendor Cumulative B2C: Offline Service

Homburg & Fürst (2005)

JM ns n/a Vendor Cumulative B2C/B2B: Not specified



59

Reference Journal




object

SAT →

LOY

SAT →

WOM

LOY →

WOM

SAT →

WTP

LOY →

WTP

WTP →

WOM



Homburg et al. (2005)

JM -* Classical Product Cumulative + TA-specific

B2C: Offline Service + Consumer Goods


JM +* ns Classical Vendor Transaction-specific



JAMS +* n/a Product Transaction-specific

B2B: not specified

Jha et al. (2013) JSR +* n/a Vendor Cumulative B2C: Banking Services

Jin & Su (2009) IJRM +* n/a Vendor Cumulative B2C: Consumer Goods

Keiningham et al. (2007)

JM n/a Product Transaction-specific

B2C: Service (not specified)

Kim et al. (2002) ISR *+ n/a Vendor Transaction-specific

B2C: E-Commerce

Kim et al. (2009) ISR +* n/a Vendor Cumulative B2C: E-Commerce

Kim & Son (2009) MISQ +* +* Ns n/a Vendor Cumulative B2C: Online Service

Lam et al. (2004) JAMS +* +* n/a n/a Cumulative B2B: Offline Service

Maxham III & Netemeyer (2003)

JM n/a n/a Transaction-specific

B2C: E-Commerce



60

Reference Journal




object

SAT →

LOY

SAT →

WOM

LOY →

WOM

SAT →

WTP

LOY →

WTP

WTP →

WOM



Maxham III & Netemeyer (2002)

JM n/a Vendor Cumulative B2C: Offline Service

Morgeson et al. (2011)

JAMS n/a n/a n/a B2C: Not specified

Nijssen et al. (2003) JAMS +* n/a Vendor Cumulative B2C: Consumer Goods + Offline Service

Nyer (1997) JAMS +* n/a n/a Transaction-specific

B2C: Consumer Goods

Oliva et al. (1992) JM +* n/a Vendor Cumulative B2C+B2B: Offline Service

Olsen & Johnson (2003)

JSR +* n/a Vendor Cumulative + TA-specific


Olsen (2002) JAMS +* n/a Product Cumulative B2C: Consumer Goods

Otim & Grover (2006)

EJIS +* n/a Vendor Transaction-specific

B2C: E-Commerce

Palmatier et al. (2007)

JMR +* Retention Vendor n/a B2B: Industrial Goods



61

Reference Journal




object

SAT →

LOY

SAT →

WOM

LOY →

WOM

SAT →

WTP

LOY →

WTP

WTP →

WOM



Raimondo et al. (2008)

JSR +* n/a Vendor Cumulative B2C: ICT Service

Ray et al. (2012) ISR +* n/a Vendor Cumulative B2C: Internet Provider Service

van Doorn & Verhoef (2008)

JM n/a Product Cumulative B2B: Offline Service

von Wangenheim & Bayón (2007)

JAMS +* n/a n/a Cumulative B2C/B2B: Offline Service

Walsh et al. (2010) JAMS Ns n/a Vendor Cumulative B2C: Offline + Online Service

Westbrrok (1987) JMR ns n/a n/a Cumulative B2B: Consumer Goods + Offline Service

Wieseke et al. (2012) JSR +* n/a Vendor Cumulative B2C: Travel agency service

Yim et al. (2008) JMR n/a Product Cumulative B2C: Offline Service

Zhang & Bloemer (2008)

JSR ns +* +* +* +* Retention Vendor Cumulative B2C: Consumer Goods + Offline Service


62

Appendix B: Measurement Items for Principal Constructs

Perceived Uncertainty (Pavlou, Liang, & Xue, 2007)

UNC1: I feel that using [cloud service] involves a high degree of uncertainty. UNC2: I feel the uncertainty associated with using [cloud service] is high. UNC3: I am exposed to many uncertainties if I am using [cloud service]. UNC4: There is a high degree of uncertainty when using [cloud service].

Perceived Ease of Use (Davis, 1989; Pavlou et al., 2007)

PEU1: I find [cloud service] easy to use. PEU2: Using [cloud service] does not require a lot of mental effort. PEU3: I find it easy to get [cloud service] to do what I want it to do. PEU4: The use of [cloud service] is clear and understandable.

Perceived Usefulness (Davis, 1989; Pavlou et al., 2007)

Using [cloud service] enhances my effectiveness. Using [cloud service] enhances my productivity. Using [cloud service] improves my performance. Using [cloud service] enables me to accomplish tasks more quickly.

Perceived Value of Upgrade (Mukherjee & Hoyer, 2001)

An upgrade of %cloud service% (more storage and security) is likely...... PVU1: ... to offer me a lot of advantages. PVU2: ... add a lot of value. PVU3: ... increase the benefit of using the service.

Satisfaction (Lam et al., 2004)

SAT1: I am very contented with %cloud service%. SAT2: I am very pleased with %cloud service%. SAT3: Overall, I am very satisfied with %cloud service%. SAT4: Overall, the %cloud service% comes up to my expectations

Word-of-Mouth (S. S. Kim & Son, 2009)

WOM1: Ich werde meine Freunde einladen, [cloud service] zu nutzen. WOM2: Ich werde anderen [cloud service] empfehlen. WOM3: Ich werde Freunde und Bekannte zu [cloud service] einladen. WOM4: Ich werde meinen Kollegen [cloud service] empfehlen.

Loyalty (Ray, Kim, & Morris, 2012)

LOY1: It means a lot to me to continue to use [cloud service]. LOY2: I feel loyal towards [cloud service]. LOY3: I consider myself to be highly loyal to [cloud service]. LOY4: It means a lot to me to remain a customer of [cloud service].

Willingness to Pay for Retention (S. S. Kim & Son, 2009)

Imagine [cloud service] would no longer be freely available. How likely are the following statements? WTPR1: I am willing to pay € 0.50 per month for [cloud service]. WTPR2: I am willing to pay a one-time only fee of € 5 for [cloud service]. WTPR3: I am willing to pay an annual fee of €3 for [cloud service]. WTPR4: I am willing to pay a semi-annually fee of € 1.50 for this service.

Willingness to Pay for Upgrade (Vock et al., 2013)

WTPU1: I am willing to pay a premium for additional services of [cloud service]. WTPU2: I am willing to pay a premium for advanced features (e.g., more storage, better access) of [cloud service]. WTPU3: I am willing to pay a premium for advanced security of [cloud service]. WTPU4: I will upgrade to paid [cloud service] account soon.

Cloud Knowledge (Hess, Fuller, & Mathew, 2006)

CK1: I understand how cloud technology is functioning. CK2: I have a good grasp of how cloud technology works. CK3: I can easily describe the functionality provided by cloud technology. CK4: It is easy for me to recall how cloud technology functions.

IT Experience

ITE1: I know a lot about technology. ITE2: I know a lot about computers. ITE3: I know a lot about the internet. ITE4: I know a lot about cloud services.


63

Appendix C: Correlation Matrix and AVE

LOY PEU PU PVU SAT UNC WOM WTPR WTPU

LOY 0,89 - - - - - - - -

PEU 0,46 0,84 - - - - - - -

PU 0,56 0,58 0,92 - - - - - -

PVU 0,20 0,13 0,26 0,96 - - - - -

SAT 0,60 0,68 0,59 0,18 0,90 - - - -

UNC -0,29 -0,30 -0,22 0,06 -0,37 0,92 - - -

WOM 0,58 0,41 0,51 0,31 0,56 -0,23 0,89 - -

WTPR 0,25 0,16 0,26 0,44 0,21 -0,09 0,31 0,87 -

WTPU 0,16 0,04 0,19 0,42 0,07 0,01 0,21 0,44 0,89

Note: The diagonal elements (in bold) represent the square root of AVE.

Appendix D: Cross and Outerloadings

LOY PEU PU SAT UNC PVU WOM WTPR WTPU

LOY1 0.858 0.410 0.513 0.543 -0.238 0.177 0.567 0.240 0.156

LOY2 0.855 0.416 0.469 0.544 -0.330 0.132 0.431 0.206 0.128

LOY3 0.902 0.380 0.463 0.481 -0.235 0.175 0.471 0.210 0.136

LOY4 0.929 0.415 0.527 0.558 -0.259 0.195 0.570 0.261 0.152

WOM1 0.509 0.346 0.413 0.491 -0.180 0.312 0.918 0.318 0.214

WOM2 0.552 0.423 0.471 0.570 -0.267 0.250 0.895 0.270 0.178

WOM3 0.497 0.332 0.408 0.446 -0.154 0.276 0.893 0.278 0.197

WOM4 0.498 0.342 0.485 0.461 -0.191 0.298 0.853 0.262 0.193

WTPU1 0.164 0.039 0.187 0.086 -0.041 0.378 0.196 0.389 0.922

WTPU2 0.164 0.027 0.181 0.083 -0.026 0.382 0.204 0.406 0.920

WTPU3 0.117 0.051 0.172 0.050 0.093 0.412 0.211 0.411 0.827

WTPU4 0.133 0.010 0.152 0.046 -0.013 0.365 0.165 0.366 0.888

WTPR2 0.239 0.212 0.270 0.244 -0.095 0.363 0.293 0.831 0.364

WTPR3 0.223 0.104 0.234 0.182 -0.082 0.411 0.273 0.894 0.393

WTPR4 0.212 0.074 0.206 0.148 -0.046 0.398 0.253 0.889 0.401

PVU1 0.201 0.120 0.267 0.177 0.044 0.957 0.299 0.423 0.395

PVU2 0.175 0.140 0.285 0.181 0.047 0.954 0.323 0.445 0.433

PVU3 0.178 0.116 0.218 0.143 0.058 0.960 0.291 0.416 0.414

SAT1 0.541 0.625 0.520 0.915 -0.367 0.123 0.502 0.205 0.055

SAT2 0.6 0.608 0.579 0.895 -0.323 0.207 0.539 0.232 0.086

SAT3 0.566 0.622 0.545 0.929 -0.336 0.149 0.531 0.215 0.081

SAT4 0.434 0.577 0.424 0.849 -0.293 0.144 0.409 0.150 0.043

PEU1 0.394 0.923 0.524 0.639 -0.237 0.124 0.365 0.136 0.034

PEU2 0.253 0.720 0.242 0.425 -0.253 0.038 0.234 0.119 -0.013

PEU3 0.458 0.817 0.594 0.565 -0.230 0.146 0.399 0.145 0.055

PEU4 0.424 0.916 0.545 0.638 -0.268 0.121 0.366 0.136 0.037

PU1 0.535 0.556 0.930 0.568 -0.194 0.231 0.483 0.267 0.163

PU2 0.509 0.522 0.933 0.524 -0.198 0.279 0.468 0.260 0.201

PU3 0.495 0.528 0.913 0.539 -0.205 0.266 0.451 0.256 0.183

PU4 0.514 0.515 0.901 0.499 -0.200 0.210 0.435 0.231 0.170

UNC1 -0.264 -0.231 -0.189 -0.297 0.870 0.059 -0.186 -0.078 -0.020

UNC2 -0.247 -0.243 -0.147 -0.306 0.901 0.063 -0.180 -0.064 0.033

UNC3 -0.274 -0.284 -0.216 -0.366 0.950 0.045 -0.218 -0.101 0.009

UNC4 -0.303 -0.291 -0.232 -0.371 0.941 0.028 -0.234 -0.076 -0.002

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