Modelling the Dynamics of Trust across a Cloud Brokerage Environment

Information Resources Management Journal, 28(1), 17-37, January-March 2015 17

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ABSTRACTThe globalised nature of cloud computing presents us with new challenges towards the development of effective business relationships across a dynamic service ecosystem. While availing of additional service capabilities, organisations are tasked with managing unfamiliar relationships with unfamiliar cloud service providers to generate increased business value. This calls for more attention towards the concept of trust within a cloud service environment. Cloud computing presents new economic and flexible business and technological models which supports the co-creation nature of service quality and ultimately business value. This research examined various methods to assess service quality and service capability assessment. During the course of this work, the author has identified the need to revisit the concept of ‘trust’ within a cloud computing context and prescribe a method to model its complexity. The objective of this paper is to argue that, while cloud computing allows organisations to avail of increased service capabilities; it challenges the concept of trust. To support this argument the author presents the Cloud Services Trust Model to explain the dynamics of trust. In doing so, it introduces a notion of a distributed relational structure in service value co-creation. The paper also draws on theoretical developments to highlight the fundamental changes in the nature of service provision and how they impact on the assessment of service value and service quality. The author supports the need for greater transparency in the move towards greater accountability in the cloud ecosystem. The paper applies social network analysis (SNA) to model the trust relationships of a cloud brokerage environment.

Modelling the Dynamics of Trust across a Cloud Brokerage Environment

Noel Carroll, Department of Management and Marketing, University of Limerick, Limerick, Ireland

Keywords: Cloud Brokerage, Cloud Service Trust Model, Service Capabilities, Service Quality, Social Network Analysis, Trust

1. INTRODUCTION

Cloud computing present’s new economic and flexible business and technological models. In essence, cloud computing is a form of outsourcing whereby organisations typi-cally outsource data, software, infrastructure,

business and platform services. As a result, organisations place significant trust in other organisations to deliver a service. The explo-sive uptake of dynamic service solutions has fuelled the growth of cloud services. How-ever, to support cloud providers and users, it is critical that decisions regarding sourcing

DOI: 10.4018/irmj.2015010102


18 Information Resources Management Journal, 28(1), 17-37, January-March 2015

service capabilities are informed through some system of cloud service analytics. One critical element of decision-making is based on trust. The nature of risk and reward may differ within a cloud computing context (Car-roll and Helfert, 2015) when compared to the traditional outsourcing environment (Lacity and Hirschheim, 1993). While risk has been well documented throughout outsourcing lit-erature, for example, using frameworks such as COBIT 5, trust has not received similar attention within a cloud computing context. Trust is generally concerned with an assessed outcome of risk associated with a relationship. Cloud brokerage can support companies to sustain business relationships. Cloud broker-age comprises of a third-party business which acts as an intermediary between the purchaser of a cloud computing service and the sellers of a particular service. Considering that cloud brokerage is a relatively new service model, trust can play a vital role in the transition towards cloud services. Trust encourages us to rely on a person or entity to generate some concept of value and sustains a relationship. In this paper, the author examines the concept of trust within a cloud context from a conceptual perspective. The paper demonstrates how so-cial network analysis (SNA) presents us with an innovative technique to model trust across cloud brokerage networks. SNA allows us to reconsider how we design and model cloud service networks with particular interest in introducing service metrics to visualise and measure the value of cloud brokerage networks. Nair et al. (2010, p.192) explain that a cloud service broker “creates a governed and secure cloud management platform to simplify the delivery of complex cloud services to cloud service customers”. This emphasises the need to understand the concept of trust and model the factors which enable customers to realise the full potential in availing of newfound cloud services. It also supports companies to enforce the correct service level agreements (SLAs) and IT policies between cloud provid-ers and cloud service consumers. The literature

suggests that there are many concerns about weak trust relationships across a service value web especially with regard to ‘on-demand’ service models which can have unforeseen consequences on their business activities (for example, Pearsons, 2011; Capgemini, 2012). The contribution of this work is the application of SNA to model trust relationships across a cloud brokerage service network. In doing so, the author introduces the Cloud Service Trust Model to explain the dynamics of trust. This work contributes towards our understanding of cloud service dependability and the establish-ment of cloud-specific service quality metrics.

It is worth noting that we should recog-nise that there are different aspects of trust in a human sense and in a technical sense when we consider cloud computing. Trust has two main core focuses of which the first one is human trust that forms exclusively inside a person driven by largely sub-conscious urges. That conforms fully with the axiom of human action. Thus, human action is fully served and accurately so with delivering information that allows for background checking using mashups in an electronic collaboration set-ting as, e.g., in a cloud-broker situation. The second trust-dimension is a somewhat “new” as it refers to artifacts in clouds such as, e.g., stateful (i.e. preserve their state for long run-ning or distributed transactions) or stateless (i.e. responding to requests) services and their internal properties. Here, the meaning of trust focuses on how dependable and secure is an artefact, i.e. the properties of dependability and security (Avizienis et al. 2004). Thus, in a socio-technical collaboration setting using service-oriented cloud computing, those two orthogonal trust-dimensions must be consid-ered in their distinct correlation. Note that hu-man action may be considered in mathematical terms in a sense that it coincides with discrete system behaviour, for example, workflow nets. For the purpose of this paper, the author provides a conceptual scenario and begins the discussion on how we can model trust networks across a cloud service ecosystem.



2. THE CLOUD COMPUTING ECOSYSTEM

Cloud computing extends services by the way in which IT enables greater business value through increased technological capacity and capabilities (Armburst et al. 2010; Carroll et al., 2014). As businesses subscribe or rent ad-ditional service capabilities, IT capabilities are extended on an ‘on-demand’ basis, ranging from business processes (Lynn et al. 2014), service application, to additional storage. There are numerous definitions of cloud computing, but one of the most widely accepted definitions comes from Mell and Grance (2009) at the National Institute of Standards and Technology (NIST) where they define cloud computing as a “model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interac-tion.” This suggests that cloud computing allows users to utilise IT resources and capabilities when required. The fundamental benefit of cloud computing is its ability to share resources on-demand at considerably reduced costs to that of a traditional IT infrastructure asset invest-ment. This has led to the explosive uptake of cloud computing. According to the latest Cisco report, “cloud is now on the IT agenda for over 90% of companies, up from just over half of companies (52%) last year” (Cisco CloudWatch, 2012) which has increased in more recent years. However, as more and more organisations begin to establish themselves in the cloud ecosystem, establishing trust and dependability regarding the value of cloud service capabilities can become a very complex challenge (Pearson, 2011). According to a recent Capgemini report (Capgemini, 2012), only 56% of organisations surveyed stated that they trusted the cloud with their data. Data is the life source of any organisa-tion. Capgemini (2012) report that trust is one of the main factors which impacts on the speed of cloud adoption. As a result, organisations opt to take a slow step-by-step approach to cloud

migration often hindered by uncertainty which is rooted in mistrust of service provision. Ex-amining the complexity and value of ‘the cloud’ offers immense opportunities through service analytics (i.e. measuring performance) and de-sign guidance. Thus, understanding how cloud resources are exchanged for on-demand services requires a method to assess and visualise the resource exchange process within the cloud ecosystem. The author describes this process as a key factor within the cloud brokerage system. The benefits of assessing the cloud brokerage system include the ability to demonstrate trust networks based on reliability and capabilities of exchange frequency between particular service nodes or actors. Modelling the dynamics of trust networks also supports organisations (cloud service providers and users) ability to optimise IT usage, improve quality of information, im-prove decision-making, increase revenue, and improve the ability to justify the adoption of cloud services. For many, cloud computing is the next big step for IT strategy. However, the paper draws some balance and discusses how cloud computing is the next big step to transforming the business landscape and the realisation of business value through cloud service trust and dependability modelling.

2.1. Cloud Brokerage

A cloud brokerage environment acts as a criti-cal intermediary link within a cloud comput-ing ecosystem. Cloud brokerage enables an organisation to consume and maintain cloud services. This is particularly important nowa-days considering the potential range of multiple cloud service providers. Many organisations outsource specific processes of their service value chain using cloud services, for example, shipping, payroll, and hosting. Working with numerous cloud providers adds greater com-plexity to SLAs and ultimately, relationship management. Thus, introducing methods to add greater service transparency and efficiency, while reducing service costs, has become a core focus of cloud brokers. As part of the contract negotiation process there are many factors



which contribute towards the establishment of a business relationships including, payments, service quality, data management, and trust. Therefore, cloud brokerage is of significant importance in a cloud service environment. It acts as a third party who co-creates value on behalf of the cloud consumer and cloud provider and leverages services technologies to enhance capabilities and business value. Thus, as a cloud service consumer employs news cloud solutions, the cloud broker becomes an integral part of the organisations evolutionary developments. Therefore, trust is an important characteristic of the cloud service environment.

3. DEFINING TRUST

While cloud computing allows organisations to avail of increased service capabilities; it challenges the concept of trust. To support this argument the author presents the Cloud Services Trust Model to explain the dynamics of trust. It is important to get a better understanding of the concept of trust. The concept of trust has been well documented throughout the social science and business literature (for example, Rousseau et al. 1998; Bachmann et al. 2001; Child, 2001; Jones 2002; McEvily et al. 2003). For example, Rotter (1967, p. 651) examines interpersonal trust and defines it as “an expectancy held by an individual or a group that the word, promise, verbal or written statement of another individual or group can be relied upon.” This implies that there is a personal connection and agreement in the formation of trust. Much emphasis has been placed on the human elements of trust. For example, Farris et al. (1973, p. 145) defines trust as “a personality trait of people interacting with peripheral environment of an organisation”. In later years, Rotter (1980) revisits the concept of trust and defines it as “believing others in the absence of clear-cut reasons to disbelieve”. This suggests that trust requires a ‘leap of faith’ in believing that one will reach their desired outcomes. In addition, Mayer et al. (1995, p. 729) explain that considering “trust is a willing-ness to be vulnerable, a measure that assesses

that willingness is needed.” This is particularly true within a cloud service context. Similarly, Moorman et al. (1992, p. 315) argues that both “belief and behavioural intention components must be present for trust to exist”. Their study also concludes that and that trust influences relationship processes. Ultimately, these works indicate that trust is the basis of forming a partner relationship – such as the case within a cloud brokerage relationship.

According to Barney and Hansen (1994), trust is “the mutual confidence that one’s vulner-ability will not be exploited in an exchange.” This suggests that there ought to be some benefit from interaction which is governed by trust. One example of this is captured in Robinson’s (1996; p. 576) definition of trust: “trust is a person’s expectations, assumptions, or beliefs about the likelihood that another’s future actions will be beneficial, favourable, or at least not detrimental to one’s interests.” Within a business context, in many cases the reward is often financial gain. Trust is typically required in high risk environments such as cloud computing. For example, Schlenker et al. (1973; p.419) explain that trust may be defined as “a reliance upon information received from another person about uncertain environmental states and their accompanying outcomes in a risky situation.” Thus, the exchange process is one of the key factors in sharing the expectation and risk (Zucker, 1986) of any interaction such as a cloud service. The author recognises how trust is a “property of a system or system resource that ensures that the actions of a system entity may be traced uniquely to that entity, which can then be held responsible for its actions” (Huff, 1981). From a cloud computing viewpoint, the author identifies the need for the ‘traceability’ of cloud capabilities being exchanged in order to model trust and dependability within a more transparent cloud brokerage ecosystem. Consid-ering that trust implies some level of interaction, this presents a relationship or linkage between cloud service parties which may be examined by its structure, pattern, cause, and consequences. These characteristics have specific properties



within a network science such as transactional, nature of links, and structural characteristics to assist us examine trust which is examined later in this paper. These interactions are critical to support trust and value co-creation within a cloud ecosystem.

3.1. Trust and Value Co-Creation

Based on the extensive research on the concept of trust, there is considerable diversity exhibited by definitions which are worth examining. For example, trust has been analysed in terms of a societal context, a psychological context, an organisational context, as a mechanism to deduce the complexity of a relationship through some behavioural expectation. When we apply this logic in a cloud computing context, one may summarise the main characteristics of trust across the literature as:

• The belief that another party in meeting a service expectation;

• An agreement that another party will fulfil an agreement without any evidence of otherwise;

• The intangible entity which assembles the formation of a partnering relationship;

• An expected commitment towards achiev-ing a desired performance level through another parties capabilities;

• A level of responsibility to oblige and meet a trustee’s set of specific requirements;

• An anticipated benefit of value that will be generated through value co-created form a capability exchange process;

• A level of cooperation which is satisfactory to all parties entering a relationship as set out in some form of agreement;

• A sense of confidence in a party to reach a desired outcome and deliver on an agreed set of terms, often built upon the reputation of a specific party;

• A predictable process which generates positive outcomes for all parties involved which is forecasted at the beginning of the relationship;

• A strong sense of integrity which instils greater consistency of actions, values, methods, measures, principles, expecta-tions, and outcomes within a relationship. This is often linked with confidence in a particular party based on their reputation.

From the list above, we can begin to char-acterise trust in terms of actions that conform to cloud consumer expectations, albeit to meet behavioural norms or contractual agreements. Often, the process of establishing trust goes through a number of maturity phases. However, unlike many other definitions of trust, such as Zaltman and Moorman (1988), this research does not define trust through the prediction that trust is ‘value free’. The author posits that relational structures that support resource ex-changes are built upon the basis of trust which presents an opportunity to establish a value network. The value network supports organisa-tions meet the interests of two or more parties of a cloud brokerage environment. The concept of trust suggests that there may be some level of expectancy in resource exchange as being an inherent property of trust, for example, qual-ity, commitment, reliability, and dependability of cloud services. In addition, Giffin’s (1967) work, suggests that there are several key fac-tors which are essential in describing trust. The author summarises them as follows:

• An actor is relying on something in order to improve their current situation;

• Something is relied upon (an object, an event, or a person) to perform some action;

• Something is risked by the trustee (although this risk may be reduced through relation-ship building);

• The goal outweighs the risk since the trustee hopes to achieve something important by taking a risk;

• There is a possibility of failure before the risk since the goal is not perceived as certain;

• The trustee must have some degree of confidence in the object of trust.



The list above summarises the key factors within the formation of trust which are appli-cable in a cloud brokerage context. Using these factors and the definitions described above, one can define trust within cloud brokerage as the property which supports an outcome through interactions which exchange resources and generates some belief that an action will be governed by a guarantee to perform at an expected service level to co-create value in a considered risky environment. Thus, the notion of ‘expectation’ must have some sense of realisa-tion through a set of pre-defined values which co-creates a reputation of reliance, regularity, and dependability. In a cloud service context, this is achieved through contractual agreements (e.g. a SLA) or internal audits of service provi-sion (e.g. a service catalog). This suggests that trust is an evolving intangible entity which goes through a number of phases to establish a busi-ness relationship. To summarise the key points discussed above, Figure 1 illustrates the phases throughout the trust value co-creation relation-ship within a cloud brokerage environment.

The phases are divided into four main quad-rants, each of which represents a transitional phase in establishing a trusting relationship within a cloud brokerage environment. At the beginning of the relationship, there is a sense of belief that a service provider will deliver the

required services although there is little fact to support this viewpoint which harnesses a negative impact on a high perception of trust. While the relationship begins to mature, there is an exploratory phase which allows parties to gather more facts (i.e. exchanging information) to reduce the risk element in service trustwor-thiness through negotiations. The third phase supports the establishment of trust and enters into a positive relationship development and a business relationship is formed. The final phase is where trust is established and allows the rela-tionship to grow through trust maturity where facts and experiences reduces the associated risk through a shared interest in service value co-creation. In the trust co-creation relationship cycle, it is understood that as the relationship matures, there is an increasing obligation to act in accordance to expectation or reliance as defined by roles, SLAs, and a service catalog. This suggests that there is a bi-direction trust relationship in value co-creation.

Thus, the concept of trust must involve a relationship where behaviour poses some un-certainty in the form of a risk to the failure to achieve a particular outcome (i.e. potential lack of value) from cloud services. Within a business and technological context such as cloud computing, trust must support a firm belief in the reliability, truth, or ability of someone or something to

Figure 1. Trust value co-creation relationship



deliver a service as promised without evidence or investigation. The cloud broker is tasked with removing elements of risk and providing a ser-vice matching process to best align cloud service customers with cloud service providers (Carroll et al. 2014). However, as a consumer, they may wish to visualise the current landscape on cloud brokerage and explore, for example, who are the dominant player within a cloud ecosystem.

In more recent years, emphasis has been placed on how we can trust technology to live up to our expectations. For example, Renzl (2008) examines trust in sharing knowledge within an organisation through technological means and explains how technology can dimin-ish the sense of individualistic value placed on people. In addition, Wu et al. (2010) discuss the importance of trust in a virtual commu-nity where they disseminate information and retain customers. They explain that shared values within a virtual community provides a positive impact on both trust and relationship commitment. However, although there have been some developments in an organisational context, trust remains an under-researched and poorly understood phenomenon within a cloud computing context. Few efforts have focused on the concept of trust although some

scholars identify the need to examine it (for example, Grefen, 2000; Wang and Benbasat, 2005; Santos et al. 2009; Armbrust et al. 2010; Khan and Malluhi, 2010; Zissis and Lekkas, 2012; Carroll and Helfert, 2015). The author recognises the importance of these works and the need to apply them in a cloud computing context to examine what trust means in terms of service quality. The literature suggests that there is some correlation between trust and success (as an outcome). Within an informa-tion systems context, the author is reminded of the work of Delone and McLean (2003) in the information systems (IS) success model. The author posits that there is a correlation between trust and IS success in terms of technological usage and benefits in cloud computing. This is further discussed in the next section.

3.2. Trust and IS Success

The Delone and McLean model examines the success of IS from a number of different perspec-tives and classifies them into six categories of success (Delone and McLean, 2003). The model adopts a multidimensional framework which measures independencies between the various categories (Figure 2) which are applicable to

Figure 2. IS success model (DeLone and McLean, 2003)



the examination of the relationship between service, trust and success of cloud services:

1. Information2. System quality3. Service quality4. Use (intention to)5. User satisfaction6. Net benefits

These dimensions suggest that there is a clear relationship between them which influ-ences the success of the IS and certain net benefits can be achieved which also supports the establishment of trust. The linkages between the six categories are of particular interest from a SNA perspective and how various resources are exchanged to derive net benefits. The net benefits influence user satisfaction and use of the information system which are also a result of trust being placed on a service provider.

Although the Delone and McLean model provides significant insight on examining IS success, this research examines trust and will develop a model which incorporates IS suc-cess and value co-creation of net benefits in a cloud computing context. This paper proposes a definition of trust and highlights the need to model trust across service networks to support design and analysis of cloud ecosystems. This work describes how trust plays a critical role as a scaffold to sustain accountability in cloud computing. One can define accountability as “the obligation to act as a responsible steward of the personal information of others, to take responsibility for the protection and appro-priate use of that information beyond mere legal requirements, and to be accountable for any misuse of that information” (the Galway Project, 2009). This is important as we try to explain the importance of trustworthiness to avail of service capabilities through some element of confidence and assurance through predefined conditions or duty from service supplier/buyer relationships. The author is particularly interested in the formation of trust and the important role it plays in establishing

business relationships. One should also identify this as a critical factor in the regulation and governance of cloud ecosystems. This work is a step towards supporting the development of dependability and accountability in cloud ecosystems to improve consumer trust and the complexity of compliance. Failing to deliver and meet those expectations implies some form of penalty or an expectation of protection in the trust is violated in some manner. Therefore, trust plays a critical role in the realisation of business value and ought to become a key component is service modelling.

4. CLOUD SERVICE MODELLING AND BUSINESS VALUE

This section discusses how trust adds value to organisations although there is a lack of meth-ods to model its potential impact or business value. Service modelling may be defined as (Räisänen, 2008; p. 6) “...the representation of relations between what is provided to custom-ers, the technical definition of the services, and the resources needed for operating the service”. This definition draws our attention towards the nature of relations which constitute as a service. The concept of IT value-creation continues to receive interest in understanding methods to explain the value of technological innovations (Willcocks, 1994; Mooney et al. 1995; Soh and Markus, 1995; Weill et al. 2002; Carr, 2003; Donnellan, 2011; Carroll et al. 2010; van den Hoof and de Winter, 2011; Chesbrough, 2011; Contractor et al. 2011). Normann and Ramirez, (1993) explain the concept of value constellation which focuses on the value-creating system. This is achieved through the reconfiguration of relationships and roles, the mobilisation of value in new forms, while improving the fit between competencies and the customer. Spohrer et al. (2010) explains that a service system can co-create value if resources are properly organised for value propositions which define the desired outcome. In fact, they argue that the foundations of a service system have the following charac-



teristics (p. 5). The author identifies that they present a significant overlap with the concept of trust and applicable in cloud computing:

1. A dynamic configuration of resources;2. A set of value co-creation mechanisms

between suitable entities;3. An application of competencies-skills-

knowledge any person(s) in job or stake-holder roles;

4. An adaptive internal organisation respond-ing to the dynamic external environment;

5. Learning and feedback to ensure mutual benefits or value co-creation outcomes.

Therefore, value is considered to be co-created through a combined effort of two parties or more across all service sectors and value is determined by the beneficiary, i.e. the client (Vargo et al. 2009). What is of immense importance here is to learn how the relational structure assembled to generate service value within a cloud brokerage environment. This encourages us to revisit the concept of value.

Value is widely discussed in business litera-ture (for example, in marketing) and supports various viewpoints of what may be described as organisational value. This research examines value from a consumer viewpoint of cloud ser-vices and therefore an individualistic realisation of value to support a specific business function, i.e. cloud users are consumers of cloud services. For example, value (i.e. perceived value) may be categorised as (Zeithaml, 1988):

• Value is low price;• Value as whatever the consumer wants in

a product or service;• Value is the quality received for the price

paid;• Value is all that is received for all that is

paid.

From this research stance, the author is interested in ‘value in use’ since the research explores the how one can model trust formation relationships within a cloud brokerage context.

Value is not only discernible in the customer’s outcomes, but is also, to a large extent depen-dent on the customer’s perception (i.e. how their interests are met). Thus, there is a clear relationship between what one can describe as trust (i.e. the expectation of an outcome) and value (i.e. the realisation of an outcome in the form of value). A service enables a two-way perceptive insight on what value is delivered and how an entity providing a service represents value. Therefore, value may be also defined by its utility (fitness for purpose for the customer) and warranty (fitness for use guaranteed by the organisation). The representation of value may be derived from the sustainability of a business relationship within a value network. One can describe a value network as a web of relation-ships that generates tangible and intangible value through complex and dynamic exchanges through two or more organisations. When we view trust co-creation and value co-creation as a network, we realise its suitable application for configuring value through a network of cloud services and process capabilities.

Trust impacts on value co-creation through a number of ways. For example, value co-creation is directly impacted through:

• Believability: If service credibility and commitments are tarnished – it impacts on the reputation of the service provider;

• Dependable: A service provider’s ability to commit to meeting service expectations;

• Preparing for the unknown in complex environments: An ability to reassure service provision as agreed, e.g. service catalog;

• Trust vs. control: Achieving a correct balance in service provision and control, e.g. service lock-in;

• Threat in service failure: A need for some level of guarantee to alleviate stress on service relationships and reinforces service commitment, reliability, and dependability;

• Competence: Ability to deliver expected results;

• Integrity: The integrity to deliver services as requested (“…as-a-Service”);



• Connected: Service providers must keep communication channels open;

• Reliability: Service providers must main-tain reliability and credibility for service provision;

• Transparency: Clarity of service opera-tions including pricing to engage in a busi-ness relationship.

All of these factors impact of trust and the realisation of specific values. The author applies this logic to a cloud service context to demonstrate the impact of trust on service value, i.e. an increase or a decrease in trust and how it impacts on outputs, performance, costs and stress. Obviously, organisations desire to achieve a positive outcome. However, the nega-tive outcomes also present other cloud service providers with opportunities to innovate and compete for business rewards by addressing their issues often rooted in mistrust, leading to increased costs and stress (creating a service learning environment). Cloud computing is a perfect example of where opportunities may exist to address these through the availability of increased service capabilities (Carroll et al. 2014). Thus, cloud services must reassure trust by, for example, listening to customer require-ments, confronting realities of service provision, adhering to commitments to service quality, maintaining open communication channels, increasing customer loyalty, promoting profes-sional behaviour, ensuring service responsibility and consistency. Therefore, to model trust, we must examine how one can build and repair trust networks. We can achieve this through the application of SNA. SNA provides us with a both a technique and vocabulary to examine trust across cloud computing brokerage envi-ronments.

5. MODELLING TRUST USING SOCIAL NETWORK ANALYSIS

This section discusses the suitability of employ-ing SNA to model the dynamics of trust on a cloud service network. The major characteristics

of service analysis imply that the unit of analysis is the individual actor (i.e. stakeholders) and the service variables which support our ability to describe the behaviour or relationship between the service network actors (e.g. organisations). Normann (2001) suggests that co-ordinating efforts of different actors towards a common whole are not novice. For example, he explains how economics describes the logic that leads to complementary specialisation as that of ‘competitive advantage’. Normann (2001) adds that what is new is not value co-production but rather the way it is now expresses itself in terms of role patterns and modes of interactivity and organically reshapes co-productive roles and patterns especially within service networks. This is also the case within a cloud brokerage envi-ronment. The new roles which result in service interaction defy what was once understood as the ‘value chain’ within the service-dominant mind-set and moves into a paradigm of service value networks or ‘service value webs’ (Ches-brough, 2011).

5.1. Social Network Analysis

The application of SNA may be simply described as an x-ray of a service network structure (Car-roll, 2012) which highlights the importance of relational structures to support cloud service performance. According to Tichy et al. (1979), network analysis is concerned with the structure and pattern of these relationships and seeks to identify both their causes and consequences (p. 507). Therefore, service networks can be viewed at an abstract level as social groupings with relatively stable patterns of interactions over time. These patterns also represent the established trust structures to support cloud brokerage.

The application of SNA allows us to explore the service system relational structures though a coherent framework and methods of analysis to capture both emergent process patterns between a specific set of linkages and their properties among a defined set of actors (Carroll, 2012). Tichy et al. (1979) provides an overview of network concepts and network properties as



summarised in Table 1. These are considered fundamental to service network dynamics and cloud service analytics. The properties of a network may be examined in three broad categories: transactional, nature of links, and structural characteristics. The transactional content explores what is exchanged by actors (e.g. information) during the formation of the network. The nature of the links considers the strength and qualitative nature of the relation between two or more nodes, while the structural characteristics examine the overall pattern of relationships between the actors, e.g. clustering, network density, and nodes on the network are all structural characteristics of trust. One can examine network properties and convert these into cloud service metrics, for example (Carroll et al. 2012):

• Transactional content: Expression of effect, influence, exchange of resources;

• Nature of links: Intensity, reciprocity, clarity of expression, multiplexity;

• Structural characteristics: Size, density or connectedness, clustering, openness, stability, reachability, centrality, star, liai-son, bridge, and a gatekeeper of a network.

These properties all form essential ele-ments of a network which can be extended to provide cloud brokerage metrics by providing mathematical representations of trust relation-ship formations. Watts and Strogatz (1998), report that real-world networks are neither completely ordered nor completely random, but rather exhibit properties of both. In addition, they claim that the structure of network can have dramatic implications for the collective dynamics of a system, whose connectivity the network represents, and that large changes in dynamic behaviour could be driven by even subtle modifications to the network structure. Therefore, the orchestration of structural rela-tions (emergent property of the connection, e.g. the exchange process) or attributes (intrinsic characteristics, e.g. value of an exchange) be-

Table 1. General properties of a network

Characteristic Description

Structure A collection of nodes and links that have a distinct format or topology which suggests that function follows form.

Emergence Network properties are emergent as a consequence of a dynamic network achieving stability.

Dynamism Dynamic behaviour is often the result of emergence or a series of small evolutionary steps leading to a fixed-point final state of the system.

Autonomy A network forms by the autonomous and spontaneous action of interdependent nodes that “volunteer” to come together (link), rather than central control or central planning.

Bottom-up Evolution

Networks grow for the bottom or local level up to the top or global level. They are not designed and implemented from the top down.

TopologyThe architecture or topology of a network is a property that emerges over time as a consequence of distributed – and often subtle – forces or autonomous behaviours of its nodes.

PowerThe power of a node is proportional to its degree (number of link connecting to the network), influence (link values), and betweenness or closeness; the power of a network is proportional to the number and strengths of its nodes and links.

StabilityA dynamic network is stable if the rate of change in the state of its nodes/links or its topology either diminishes as time passes or is bounded by dampened alternations within finite limits.



come a central concept to analyse a networks structural properties of trust. SNA assumes that actors are interconnected, with real conse-quences for behaviour and dynamics similar to a cloud brokerage environment. Structures may be altered to optimise the networks outcomes through new cloud service relationships which present an opportunity to develop service net-work analytics. This is particularity important if we are to understand how trust is formed and maintained within cloud computing and the need to generate intelligence on how we assess cloud brokerage environments.

The nature of the links considers the strength and qualitative nature of the relation between two or more nodes. The structural characteristics examine the overall pattern of relationships between the actors, for example, clustering, network density, and special nodes on the network are all structural characteristics. The structure of a network can have dramatic im-plications for the collective dynamics. Changes in dynamic behaviour could be driven by even subtle modifications to the network structure. Therefore, the orchestration of structural rela-tions (emergent property of the connection, e.g. the exchange process) or attributes (intrinsic characteristics, e.g. value of an exchange) be-come a central concept to analyse a networks structural properties. This can also impact on cloud service dynamics. There are a number of key concepts which SNA examines.

SNA can be employed as a technique to graphically represent and visualise the rela-tional structures of a cloud brokerage system. More importantly, SNA is an approach and set of techniques which can assist in to study the exchange of resources and competencies (for example, cloud capabilities) between organisa-tions. The application of SNA is appropriate as Wasserman et al. (2005) discusses how we can identify the formal representation and model-ling of networks through SNA. It allows us to implement new modelling methods in cloud computing to uncover the correlation between social behaviour, dynamics and the outcome of economic performance. However, there are many difficulties in modelling the intertwin-

ing complexity and dynamic configuration of people, knowledge, activities, interactions, and intentions which creates and delivers value. Marsden (2005) explains that, as a technique, SNA data collection practices throughout literature typically involve survey methods. A common method in analysis has been on implicit or explicit snowball sampling. To develop an understanding of social networks, one must undertake a rigorous description of the relationship patterns of the research population as the starting point of analysis. Investigating the relationships which exist within a social network is a tedious task, for example, data gathering, analysis, manipulation, and calcula-tion using matrices to record data. SNA software is vital to support these tasks and to provide a visualisation which represents the relational descriptions (for example, UCINET). This al-lows us to mathematically represent the network data and learn of structural characteristics of the service network environment. Data which examines more than one set of relationships at various periods of time (i.e. to examine change) is described as two-mode (Wasserman and Faust, 1994). Therefore it is important to gain a sufficient sample size to examine the service network.

The concepts and network properties dis-cussed in this section offers us the opportunity to introduce new metrics to model trust (and other dynamic factors) within a cloud computing context which can be borrowed and possibly extended from SNA.

6. DEVELOPING TRUST INTELLIGENCE WITHIN A CLOUD CONTEXT

This section offers a discussion on how one can employ SNA to provide cloud brokerage metrics and support our ability to assess and visualise trust networks. The author offers a conceptual scenario to demonstrate the value of SNA as cloud analytics and explain how this research is in the process of applying this work to build on other developments such as the ITIL and



COBIT to include analysis on trust networks. As discussed in the previous section SNA offers us a technique which models the exchange of resources from one actor to another. We can identify how trust is influenced by the outcome of interactions which exchange resources (for example, knowledge or advice). The interac-tions generate some belief that an action will be governed by a guarantee to perform at an expected level to achieve a goal in a considered risky environment. We can explore how trust is established (see Figure 3) through the formation of relationships within a network from survey data on service interactions. For example, relationships may be assembled through the exchange of knowledge, money, or friendship of which trust becomes more established in the predictive behaviour of an actor (i.e. a cloud service provider). To model trust within a cloud brokerage sociogram (a graphic representation of interaction linkages) one can model trust based on the level of interaction and exchanged resources with particular nodes. One can make the assumption that greater interaction implies greater possibilities in establishing trust. The reverse is also true, few links indicate few interactions and therefore a lack of trust is as-sociated with these particular actors. Figure 3 illustrates two cloud service ecosystems. The various node shapes are different service opera-tors. The focus of this paper is to demonstrate that the relationships between cloud service providers can me modelled using SNA and thereby offer us a visual representation of the ‘trust network’ in a cloud service ecosystem. It becomes immediately evident through the SNA mapping technique (using UCINET in this example) which nodes have the greatest levels of interaction and which appear to be the most trustworthy and therefore dependable. This also supports our analysis on which actors may be considered the most/least reliable.

One can employ SNA concepts outlined in Table 2 to examine the change in the relational structure of the cloud brokerage network. One can summarise how some SNA concepts may

be introduced as service network analytics to examine change to service trust dynamics within a cloud network. This demonstrates how SNA may be employed as service network perfor-mance analytics to examine cloud brokerage. The relational structure indicates the trust placed in the brokerage system, for example:

• Number of ties: Indicates the level of interaction with an actor;

• Density: Indicates the level of trust place upon an individual actor;

• Distance: Indicates the level of cohesive-ness within the cloud brokerage system;

• Krackhardt GTD measures: Indicates the horizontal differentiation of the service structure to increase/decrease connectivity which indicates the reliability of an actor to provide a service;

• Hybrid reciprocity: The reciprocity of ties demonstrate the service efficiency level within the brokerage system;

• Degree (centralisation): Measures the level of cohesion and efficiency and actors dependency on other actors;

• Eigenvector centrality: Examines the cloud brokerage structures and actors at-tempts to adopt central positions (reflect-ing strength, reputation, reliability, and consistency of cloud service providers).

SNA offers us a technique to visualise and assess how the trust properties are represented, utilised and expressed using this modelling tech-nique. As discussed in Section 5.1, to generate a SNA model, primary and/or secondary data must be gathered in order to collect informa-tion about the cloud service connections. For the purposes of SNA, this is typically achieved through surveys or in the case of cloud services it may be collected using process logs (i.e. process mining or workflow mining). The basic premise of this data collection for network analysis is to examine the relationship between two or more basic components (e.g. cloud service providers and consumers) and their ties (i.e.



Table 2. Social network analysis concepts and network properties (Carroll, 2012)

Property Explanation

Transactional Content

Four types of exchanges: Expression of effect (e.g. initiate a transaction) Influence attempt (e.g. negotiating software requirements) Exchange of information (e.g. terms and conditions) Exchange of goods and services (e.g. payment)

Nature of links:Intensity

Reciprocity

Clarity of Expression

Multiplexity

The strength of the relations between individuals (i.e. intensity of service interactions)

The degree to which a relation is commonly perceived and agreed on by all parties to the relation (i.e. the degree of symmetry)

The degree to which every pair of individuals have clearly defined expectations about each other’s behaviour in the relation, i.e. they agree about appropriate behaviour between one another (i.e. project contributions)

The degree to which pairs of individuals are linked by multiple relations. Multiple roles of each member (e.g. consumer, supplier, negotiator, etc.) and identifies how individuals are linked by multiple roles (the more roles, the stronger the link)

Structural Characteristics:Size

Density (Correctedness)

Clustering

Openness

Stability

Reachability

Centrality

Star

Liaison

Bridge

Gatekeeper

Isolate

The number of individuals participating in the network (i.e. GSD eco-system)

The number of actual links in the network as a ratio of the number of possible links

The number of dense regions in the network (i.e. network positioning or structural holes)

The number of actual external links of a social unit as a ratio of the number possible external links

The degree to which a network pattern changes over time (i.e. level of innovation)

The average number of links between any two individuals in the network.

The degree to which relations are guided by the formal hierarchy

The service with the highest number of nominations (staff performance)

A service which is not a member of a cluster but links two or more clusters

A service which is a member of multiple clusters in the network (i.e. a linking pin)

A star who also links the social unit with external domains (i.e. knowledge diffusion)

A service which has uncoupled from the network



their relationships or associations). There are a number of software tools which specialise in network analysis (UCINET was used for this simple demonstration). The author has piloted this work and it generates some interesting findings and is in the process of contextualis-ing and expanding upon and developing cloud service analytics. The statistics gathered at this initial stage which is applied to the network data are applied to symmetric data. Examining the distribution of relations among actors within a service network and the tie-strength, the mean or central tendency is proportion of all ties that are present is the “density.” One can also examine the distribution of relations among actors in a network, and central tendency which is indicated by the average strength of the tie across all the service relations. For example, if we examine the number of ties represented in the trust relationship we can examine when two nodes (customer and the cloud service provider) are connected and the degree to which they trust each other. In addition, we can see how trust supports a cloud service reputation and brand based on their centralisation within a cloud service network. All of this data collection and analysis can support our understanding of cloud service brokerage environments, visualise trust networks and ultimately, provide us with insights on service performance. The author briefly explores the relationship between trust and performance of cloud services and discusses how this influences the cloud analytics frame-work and later, the Cloud Service Trust Model.

6.1. The Economics of Trust

This section briefly discusses the need to in-corporate trust analytics to develop improved insights on how trust impacts on business value, for example, within a cloud computing broker-age context. Across cloud computing literature and conferences, one of the prominent issues which regularly crops up is the issue of security and trust as organisations are presented with the opportunity to avail of economies of scale in a considerably unknown territory. Trust is one of the main constraints which impact on the prom-ise of cloud computing. Thus, understanding and measuring the mechanisms of trust presents unique business opportunities to minimise risks associated with organisations participating in cloud brokerage environments to avail of and trade additional service technological capabili-ties. Employing SNA as a method of modelling trust to map the exchange of resources presents us with a unique approach towards our under-standing of the relationship between trust and relationships formations and how this influences cloud service economics using novel metrics. Understanding the economics of trust is a chal-lenging task, yet an exciting opportunity to examine the complexity of forming trust within cloud service relationships. The trustworthiness of a cloud service provider fosters a perceived quality about an organisation which influences decision-making tasks (for example, Bacharach et al. 2001; Guerra and Zizzo, 2004). This places a greater sense of control and predictability

Figure 3. Example of cloud brokerage ecosystem



about service behaviour and encourages repeat business based on service satisfaction and rat-ings (for example, trust marks). This has been evident in e-commerce business models and offers greater transparency to establish trust networks. This is more important in a cloud brokerage environment since organisations who adopt outsourced capabilities place significant trust that their business will not suffer as a result of failing to meet service requirements. As humans are non-trivial machines, contem-porary historical data about recent behaviour (for example, big data analysis) does not allow a statistically precise prediction about the next engagement. Such an assumption can only be explained by a misguided understanding of logical positivism. The only dimension of trust where we could consider a probability is one where we compare services in terms of discrete-systems behaviour. This places greater emphasis on incorporating cloud service analyt-ics to compare service activity and draw greater insights on where cloud consumers are placing more trust on certain cloud service providers. In addition, information regarding consumer decisions not to avail of certain services can be incorporated back into a cloud service strategy to improve service provision and improve service trust capabilities (for example, see Figure 4).

7. TOWARDS A CLOUD ANALYTICS FRAMEWORK

This work forms part of our overall cloud service analytics research in the development of a contingency model which supports the as-sessment of cloud composite capabilities and brokerage environments. Due to the increasing change in business in adopting cloud services there has been an inevitable change in how we measure service quality and performance. While we develop an understanding of the re-search gaps which exists throughout academic and industry literature, the contribution of this work is to develop a cloud service trust model (Carroll et al. 2013) in establishing a cloud assessment solution framework through the Cloud Service Index (CSI). Carroll et al. (2013) have developed the CSI in an attempt to define cloud service metrics from both a business and technological perspective. However, between each phase there must be some defined approach to establish trust, i.e. a trust maturity process. These metrics are being categorised into the service lifecycle phases illustrated in Figure 4 (macro viewpoint). In addition the author has attempted to sub-categories these measures to reflect their relevancy to Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS),

Figure 4. Cloud service index contingency model



Software-as-a-Service (SaaS), and Business Process-as-a-service (BPaaS) which supports the evolutionary growth of cloud comput-ing service models. This work feeds into the design science approach (Hevner et al. 2004) of the CSI as organisations are provided with a report which highlights areas within their cloud readiness assessment strategy which may warrant immediate attention prior to availing of additional cloud services. This work also indicates areas where organisations are strongest and examine which service functions may be of concern when compared to peer-organisations, i.e. benchmarking. The research described in this article contributes towards the CSI and highlights the need for greater visualisation of service brokerage through SNA. This also adds greater transparency on value co-creation particularly with regard to establishing trust. Through the development of the CSI, we can assist organisations improve their cloud services through the assessment of their cloud capabili-ties, quality and business performance using a Cloud Service Trust Model.

8. CLOUD SERVICE TRUST MODEL

This section presents the main contribution of this research. This study identifies the need to revisit the concept of ‘trust’ within a cloud computing context and prescribe the use of SNA as a method to model its complexity. Moreover, it argues that cloud computing challenges the concept of trust and introduces a notion of a distributed relational structure to service quality co-creation. The research also highlight the fun-damental changes in the nature of service provi-sion and how they impact on the assessment of service value and support greater transparency in the move towards greater accountability in the cloud ecosystem. This research demon-strates how SNA presents us with an innovative technique to model trust across cloud broker-age networks. The contribution of this work is both the application of SNA to model trust relationships across a cloud brokerage service

network and how the introduction of the Cloud Service Trust Model which resulted from the modelling task. Figure 5 encapsulates the main characteristics of both trust and IS success to illustrate the key factors in cloud service trust. It is intended to consolidate a trust model (by integrating trust in humans and systems). This is largely based on the theoretical discussions of trust and technological developments across cloud providers presented in this research. This also suggests the contribution of this model is to enhance business service quality and eventually business values in terms of trust. In addition to this model, the adoption of SNA as a method-ological approach, presents a suitable technique for trust network analysis (including structure, patterns, causes, and consequences). Figure 5 illustrates the maturity process in establishing trust and the relationship between trust and busi-ness values through establishing trust, quality of experience (QoE) and Quality of Service (QoS) which was influence by the CSI.

The Cloud Service Trust Model comprises of three main phases within a brokerage envi-ronment:

1. Exploring trust: Examining the likelihood that a desired service will be delivered by assessing factors of service quality;

2. Establishing trust: Establishing a relation-ship with a service provider and achieving some level of satisfaction to exchange resources and capabilities;

3. Promoting trust: Achieving the desired outcome and net benefits for all parties involved.

In addition, the model also proposes that these phases establishes service maturity and trust provides both the cloud service customer and cloud service provider with an enhanced brand and/or reputation status. This work feeds in to the CSI proposed by Carroll et al. (2013) as it allows us to identify what areas of compe-tence (between each of the cloud service cycle phases illustrated in Figure 4) the organisations needs to build on before they can enter a service



relationship and build a level of trust with the various business parties involved.

9. DISCUSSION AND CONCLUSION

Through the exploration of trust, it allows us to understand how relationships are sustained or dissolved within a cloud service environment. Thus, trust presents significant insights on business operations and business value. Trust may be considered to be a complex concept which has been extensively studied in many fields including social psychology, organisa-tion science, political science, and information systems. However, few efforts have attempted to highlight the importance of trust in the cloud service environment. In this paper, SNA has been adopted to provide an innovative meth-odological approach to understand service

relationships. SNA can also model the level of trust between cloud service customers and service provides.

This paper presents a contribution towards modelling trust across a cloud brokerage envi-ronment and discusses how it complements the research development of the CSI. The Cloud Service Trust Model provides a significant contribution to the cloud computing community and presents a new approach to examine the ma-turity process to establish trust and consequently business value. This research also highlights the suitability of SNA to model service exchanges and model trust based on service interactions. It also highlights how this work supports the CSI framework development by examining trust within the cloud lifecycle from a number of viewpoints. The Cloud Service Trust Model comprises of three main phases to establish a trust relationship within a cloud service envi-

Figure 5. Cloud Service Trust Model



ronment. The main focus is to identify ways in which we can re-examine how we can generate ‘value’ of cloud initiatives through the assess-ment of service capabilities, trust, and depend-ability within a cloud brokerage environment. Accountability can help us tackle the issues surrounding trust and complexity in cloud com-puting and establish cloud trust marks (or similar trust standards) which will support consumer confidence and enhance service standards. This work can also assist in clarifying legal situations in a cloud context (i.e. to enable transparency and enhance trust). The author advocates that there is significant potential for organisations to generate increased business value through cloud solutions which are governed by trust standards. This research highlights the need to introduce more innovative modelling tech-niques to map service interactions and examine the dynamics of trust using SNA. The CSI and Cloud Service Trust Model guide managers through the assessment and selection of cloud capabilities. This work also lends itself towards IT governance, reporting, and auditing of cloud ecosystems where one can geo-tag and track data transfer through the transfer of trust and accountability in accordance with regulation and standards throughout the cloud service lifecycle which forms part of our future work. As part of the future work, the author is also planning to implement these solutions across a number of industries and examine how we can apply other techniques to enhance other cloud computing properties while adopting a design science approach towards a customisable cloud brokerage solution.

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Modelling the Dynamics of Trust across a Cloud Brokerage Environment

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