Felipe Luís

Bilecki
email: luis.bilecki@gmail.com

Adriano Fiorese
email: adriano.fiorese@udesc.br

A Confidence Indicator Model for Virtual Organization Creation in Cloud Computing Environment

Keywords: Virtual Organization, Confidence, Cloud Computing

A Virtual Organization (VO) is a form of organization where a set of independent entities share resources, skills and risks in order to attend a collaboration opportunity. The integration between VO's concept and cloud computing seems promising since VO participants use or provide their services in a cloud computing environment. Nevertheless, in this context of integration, the problem of trust between VO partners and cloud providers is highlighted. This is noteworthy since it's necessary to select a reliable cloud provider for a VO business partner launch its service. Therefore, this work presents a confidence indicator model for cloud computing providers using the Data Envelopment Analysis (DEA) method. For the evaluation of this work, we used Quality of Service indicators of public cloud service providers. Results presented and discussed show the generated confidence indicator can be a good criterion to help the VO's manager to select reliable Cloud Computing providers to host VO partners during the VO creation process.

Introduction

In recent years, an increasing variety of collaborative networks have arisen because of the socio-economic challenges faced by society, such as globalization, competitiveness, variety of products and services, low cost, etc [START_REF] Esposito | Investigating virtual enterprise models: Literature review and empirical findings[END_REF]. The companies (entities) have been specializing and collaborating with others in order to survive to the competitiveness imposed by other members present in market. In this regard, the collaborative process has became easier due to the advances provided by the information and communication technology (ICT), providing alternatives to deal with the geographical arrangement of the entities [START_REF] Camarinha-Matos | Collaborative networked organizations -Concepts and practice in manufacturing enterprises[END_REF].

Considering the forms of collaboration among enterprises, Virtual Organization (VO), is advantageous to incorporate flexibility and dynamism to handle the demands of the market [START_REF] De Lemos | Using data envelopment analysis and fuzzy logic as intelligent risk-based decision making support for virtual organizations[END_REF]. A VO is the temporary alliance of autonomous, heterogeneous and geographically dispersed entities sharing skills and resources to attend a collaboration opportunity (CO). Given that, the VO's life cycle presents the following phases: creation, operation, evolution and dissolution [START_REF] Camarinha-Matos | The Virtual Enterprise Concept[END_REF]. This article focuses primarily on the creation phase, which is seen as a critical stage to ensure the success of the VO's operation [START_REF] Winkler | Trust Indicator Modeling for a Reputation Service in Virtual Organizations[END_REF].

One of the problems observed during the creation of a VO is the necessary sense of confidence among the entities involved, particularly in the sub-process of search and selection [START_REF] Squicciarini | Trust establishment in the formation of Virtual Organizations[END_REF]. In a VO and Cloud Computing (CC) integration model, cloud computing providers can host VO members' services. In this sense, a VO depends on the cloud providers who host its members' services. Thus, comprising the integration of VO and Cloud Computing, confidence is a key concept to be taken into consideration during the cloud provider search and selection step in order to form a VO. Moreover, regarding this integration model, the Quality of Service (QoS) delivered by the cloud computing provider plays an important role in the confidence assessment. Therefore, confidence also can help to avoid the dissolution of the VO during the operation phase. Namely, it seems plausible that the greater the confidence level in a cloud computing provider the lower the risk level during the attendance of the collaboration opportunity. This claim is partially supported by results from multicriteria risk assessment works that take into consideration, among other indicators, the confidence one [START_REF] De Lemos | Using data envelopment analysis and fuzzy logic as intelligent risk-based decision making support for virtual organizations[END_REF][START_REF] Vieira | A Risk Analysis Method to Support Virtual Organization Partners' Selection[END_REF].

Therefore, this paper proposes a model for the generation of the confidence indicator, through the application of the Data Envelopment Analysis (DEA) decisionmaking method. Considering that there are multiple inputs represented by Quality of Service indicators for several cloud computing providers, the proposed method assists to generate a single value of confidence for partners (e.g., a cloud computing provider). The developed indicator will consist of QoS indicators, represented by Key Performance Indicators (KPIs), available in Garg et al. [START_REF] Garg | A framework for ranking of cloud computing services[END_REF] and Sun et al. [START_REF] Sun | Consumer-centered cloud services selection using AHP[END_REF]: availability, response time, stability, security and cost. Therefore the confidence value represents a level of trust in the service provided by cloud computing provider. Hence, a high level of confidence represents a greater probability of the service to be executed without failure during the several stages of VO's life cycle in the cloud. Thus, the proposed model, aims to support the decision process in several stages of VO integration with CC life cycle, such as the process of cloud computing providers' search and selection or during the operation phase. The generated indicator also can be used and integrated in a reputation service covering all the stages of the VO life cycle.

The remainder of this paper is organized as follows. Section 2 elaborates on the related work and concepts regarding the problem of trust concerning the various processes of a VO. Section 3 seeks to present the proposed model to generate the confidence indicator. Section 4 presents the results of implementation of the model. Finally, Section 5 presents conclusions and future work.

General Background

This Section presents related work, a definition of cloud computing concept and its relationship to virtual organization. It discusses the concepts of trust and reputation and methods of multi-criteria decision making, and it points out this work contribution regarding related works.

Related Work

Several works present approaches to support the decision making process to the VO Manager. In this regard, the process of search and selection of service providers based on competencies and candidates' skills occurs through different methods such as: multi-criteria decision making methods and others as optimization by swarm particles [START_REF] Junior | A KPI Model for Logistics Partners' Search and Suggestion to Create Virtual Organisations[END_REF][START_REF] Sadigh | A Multi-agent System Model for Partner Selection Process in Virtual Enterprise[END_REF][START_REF] Zhao | Particle swarm optimization algorithm for partner selection in virtual enterprise[END_REF]. These works present methods to enhance the partner's search and selection step during the VO creation, but they do not use or generate a confidence indicator based on Key Performance Indicators (KPIs) belonging to the analyzed partners.

A study presents methods for classification and selection of reliable services based on QoS indicators and user ratings [START_REF] Tang | Towards a trust evaluation middleware for cloud service selection[END_REF] returning a trust indicator. In other studies, services are evaluated according to the observations of users and also on a focused approach through the QoS indicators [START_REF] Noor | Cloud Armor: a platform for credibility-based trust management of cloud services[END_REF] as well as in satisfaction user ratings used for classification and selection of services [START_REF] Yau | QoS-based service ranking and selection for service-based systems[END_REF][START_REF] Liu | Service selection based on personalized preference and trade-offs among QoS factors and price[END_REF].

The work proposed by Squicciarini et al. [START_REF] Squicciarini | Trust establishment in the formation of Virtual Organizations[END_REF] presents protocols for trust negotiation representing an effective way to select the best possible members during the several stages of the VO's life cycle. In Winkler et al. [START_REF] Winkler | Trust Indicator Modeling for a Reputation Service in Virtual Organizations[END_REF] it is proposed the generation of a confidence indicator for a reputation service, based on financial, organizational and operational indicators, among others. The developed confidence indicator has served as support for the decision-making tool for partner selection for the composition of the VO.

These aforementioned works seek to meet the purpose of supporting the VO manager in the decision making process in the VO creation phase, specifically in the partners' search and selection step. However, none of them provides an approach which defines a confidence indicator in the context of the virtual organization and cloud computing integration and also do not use this generated confidence indicator to support the several decision-making process presents in VO's life cycle, such as cloud computing partner search and selection and the VO's operation.

The application of confidence indicator on VO, during the integration with cloud computing providers, plays an important role, because the quality of service delivered by cloud providers is a key factor to ensure success in VO's operation. Therefore, the proposed model presents a confidence indicator based on QoS indicators for cloud computing providers, with the purpose to assist the several decision-making process presents in VO's life cycle.

Cloud Computing

The Cloud Computing (CC) paradigm provide access to computing resources (e.g., servers, networks, storage and applications) in a practical way and on-demand, where resources can be provisioned automatically to the user. For this purpose, the Cloud Computing Providers make available the necessary infrastructure so that users can run their services [START_REF] Mell | The NIST Definition of Cloud Computing[END_REF]. Also according to the National of Institute and Standards Technology (NIST) [START_REF] Mell | The NIST Definition of Cloud Computing[END_REF], CC has four deployment models (public, private, community, hybrid) and can provide three service models: Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). Finally, CC has five essential characteristics (Resource pooling, rapid elasticity, measured service, on-demand self service and broad network access).

Due to the benefits offered by cloud computing, many entities have used their services to implement their applications, so being able to respond quickly and nimbly to a collaboration opportunity [START_REF] Camarinha-Matos | Collaborative networked organizations -Concepts and practice in manufacturing enterprises[END_REF].

Despite the advantages that cloud computing offers, there are several concerns which fundamentally affect the service quality that providers offer to consumers. Some issues such as trust and security are defined as obstacles to the implementation of services delivered by the cloud computing provider [START_REF] Hwang | Trusted cloud computing with secure resources and data coloring[END_REF]. Although the information related to QoS indicators present in the Service Level Agreement (SLA) is crucial to establishing contracts it is not enough to establish a trust relationship between the provider and the consumer [START_REF] Tang | Towards a trust evaluation middleware for cloud service selection[END_REF], which in the context of VO and CC integration, the QoS indicators should be monitored, to ensure trust in VO's operation phase. Thus, in order to enhance the quality of the supporting mechanisms for trusting between the entities involved in the VO creation in a CC environment, it is necessary to develop a confidence indicator that can be automatically used by the business partners to make available their services through the resources provided by the cloud computing paradigm.

Particularly, during the several processes of a VO, this confidence indicator can be monitored and updated periodically allowing to identify whether a cloud computing provider continues meeting the needs of trusting regarding the established requirements by the collaboration opportunity. A reputation system using this confidence indicator can support and feed the whole trusting system [START_REF] Mashayekhy | A reputation-based mechanism for dynamic virtual organization formation in grids[END_REF].

Trust and Reputation

The trust concept originates from the social sciences, responsible for the study of the dynamic behavior of human society. Trust is a multidisciplinary concept, studied and applied in psychology, sociology, economics and technology. Psychologists study the trust as a mental attitude and brain behavior when the person trust or distrust someone. Sociologists claim that trust is the social relationship between people [START_REF] Cho | A survey on trust management for mobile ad hoc networks[END_REF].

In the information technology area, the trust management problem was introduced by Blaze [START_REF] Blaze | Decentralized trust management[END_REF] defined it is and as a collective study of security policies, credentials and trust relationships. The Blaze's approach has resulted in a system, called PolicyMaker that manages and controls access to distributed environments. This system have leveraged several approaches to trust management applied to technological concepts such as e-business environments (Business to Business (B2B), Consumer to Consumer (C2C), Business to Consumer (B2C)), P2P (Peer-to-Peer) and collaborative networks (virtual enterprises) [START_REF] Winkler | Trust Indicator Modeling for a Reputation Service in Virtual Organizations[END_REF][START_REF] Kerschbaum | PathTrust: A trust-based reputation service for virtual organization formation[END_REF][START_REF] Resnick | Trust among strangers in Internet transactions: Empirical analysis of eBay's reputation system[END_REF].

In addition to multidisciplinary, trust is also a complex concept that has no universally accepted definition. Each scientific area seems to have its own definition. In general, trust can be usually related with levels of confidence in something or someone. Therefore, when dealing with cloud computing paradigm, we can define trust as the level of confidence when using the services provided by the cloud service provider, from the consumer perspective [START_REF] Ko | TrustCloud: A framework for accountability and trust in cloud computing[END_REF]. It is worth noting that, in this work the cloud consumer perspective is associate with the companies that form a VO using cloud providers to host their services. Also, it is important to mention that other factors affect the trust of a cloud computing provider, quite often related to mechanisms for protect the user data and unauthorized access. Such factors can be summarized in: security, privacy, accountability and auditability [START_REF] Pearson | Privacy, Security and Trust Issues Arising from Cloud Computing[END_REF]. With the purpose of assisting the trust in a cloud computing environment and other online services the reputation concept can be applied.

The reputation is closely related to the trust. Reputation can be defined as a set of feedback (comments) about the past behavior of the participants of a community, helping in the selection of virtual partners that are considered honest in the real world [START_REF] Comput | Reputation systems : A survey and taxonomy[END_REF]. The reputation in VO context can be defined as a perception that each business partner has on each member of the VO. The perception is enhanced and developed based on past interactions and objective indicators used to evaluate the collaborative process. The reputation can be seen as a numerical value a participant A refers to a participant B. Thus, when considering that there is no past transactions, reputation may initially be defined as the value of the confidence indicator in the cloud computing provider.

Multiple Criteria Decision Analysis (MCDA)

Methods of multi-criteria decision-making have emerged as an important ally for solving problems presented by scientific, logistics, engineering and industrial areas. The MCDA can be defined as a collection of methods for matching, classifying and selecting multiple alternatives having multiple attributes whose application depends on the construction of a matrix called assessment matrix or payoff and can also be called scoreboard [START_REF] Rehman | Iaas cloud selection using MCDM methods[END_REF].

The Multiple Criteria Decision Analysis methods can be subdivided into two categories: Multiattribute Utility Theory (MAUT) and Outranking Methods. The MAUT looks for a function that reflects the usefulness of a particular alternative, where each action is assimilated with a level of utility, where the real number represents a preference level of such action. The outranking methods decide which alternative is most appropriate through a pairwise comparison [START_REF] Whaiduzzaman | Cloud service selection using multicriteria decision analysis[END_REF].

Among the various MCDA methods presented in Velasquez and Hester [START_REF] Velasquez | An Analysis of Multi-Criteria Decision Making Methods[END_REF], the Data Envelopment Analysis method (DEA) will be used because it is capable of handling multiple indicators as cloud providers inputs and outputs and the efficiency of this providers can be quantified and analyzed. DEA is a linear mathematical programming technique for production monitoring, which takes a group of companies and its production indicators (input and output) to assess the productive efficiency of each company decision unit (DMU). Furthermore, it focuses on measuring the efficiency of multiple DMUs in a multiple input and output units environment [START_REF] Chen | A unified model for detecting efficient and inefficient outliers in data envelopment analysis[END_REF] The Banker, Charnes and Cooper Data Envelopment Analysis (BCC-DEA) model was designed to measure the efficiency of units with variable returns to scale, i.e., increasing the input values of a unit does not affect proportionally the output values [START_REF] Banker | Some models for estimating technical and scale inefficiencies in data envelopment analysis[END_REF]. For this reason, the Banker, Charnes and Cooper (BCC) model is suitable for the problem in question and also to the proposed confidence indicator generation model presented in Section 3. Unlike other usual methods to assess confidence, our approach uses DEA method because we claim the existence of a connection between efficiency and confidence.

The Proposed Model

Currently, cloud providers with similar characteristics and different prices have been emerging, eventually due to the competitiveness in the market [START_REF] Garg | A framework for ranking of cloud computing services[END_REF]. In addition, given the diversity of services offered by cloud computing providers, there is a challenge for consumers and, particularly, to the VO Manager, to select a reliable cloud computing provider thereby not causing problems during VO operation. Thus, the proposed confidence indicator can assist the decision making process regarding the search and selection as well as other steps of the VO life cycle.

For this purpose, in the proposed model, Data Envelopment Analysis (DEA) is used to compose a confidence indicator that can be used as a criterion to support VO's manager decisions. Performance indicators that represent Quality of Service will be used for composing the confidence indicator. These indicators belonging to the cloud computing providers will be applied to DEA, which will generate an efficiency level. This efficiency level is reported as the confidence level of that cloud provider. Therefore, the proposed method associates confidence with the Quality of Service delivered by the cloud computing providers.

The rationale relating these concepts is summarized as the higher the efficiency of a cloud provider, the lower the possibility of offering a service that cannot be trusted, given that quality of service indicators will be used, the confidence is related to the quality of service provided by cloud provider. Thus, a high confidence level represents a greater probability of service does not fail, during the VO's operation stage. If the service provided by a cloud computing provider does not attend the need required by the VO's manager, this provider will be replaced preventing issues during the collaboration opportunity attendance. • Confidence Indicator: The result of the application of the inputs in the DEA method is a confidence indicator of the cloud computing provider. This indicator can be integrated to the subprocesses present in the VO's creation phase, such as the search and selection step [START_REF] Squicciarini | Trust establishment in the formation of Virtual Organizations[END_REF]. In that subprocess, the VO manager needs to select a reliable cloud computing provider for the business partner to make available its service in the cloud environment and thus compose the VO. We also foreseen that in addition to providing support in the VO creation phase, the confidence indicator can be applied in the VO operation phase, where through a reputation system, it is possible to check if the confidence level of a cloud provider is maintained at an acceptable limit, allowing to perform the providers' change (VO evolution phase) in case the confidence level drops below that limit. In order to explain the proposed model, Section 3.1 presents the Quality of Service indicators used as input and finally, Section 3.2 details DEA method.

Fig. 1. The proposed model for the confidence indicator generation

Key Performance Indicators (KPIs)

Cloud computing providers can be evaluated using qualitative and quantitative indicators. Qualitative indicators are directly related to the user experience about a service provided. Quantitative indicators can be measured through hardware and software monitoring tools [START_REF] Garg | A framework for ranking of cloud computing services[END_REF].

In order to calculate the confidence level to identify the most suitable service to the user, according to Tang et al. [START_REF] Tang | Towards a trust evaluation middleware for cloud service selection[END_REF] the QoS properties are exploited, which are a set of non-functional attributes of services such as response time, stability, availability, security, etc [START_REF] Garg | A framework for ranking of cloud computing services[END_REF] , and these attributes are applicable to all cloud computing service models.

The indicators that will be used to generate a confidence level of a cloud provider are presented in [START_REF] Garg | A framework for ranking of cloud computing services[END_REF][START_REF] Sun | Consumer-centered cloud services selection using AHP[END_REF]:

• Response Time: In the case of services provided in IaaS model, the response time refers to the time that a service is available for use. For instance, the user requests a virtual machine (VM) to the provider, the time is calculated from the moment of the VM creation until it is available for use. • Stability: The stability of a service is defined as the variance in service performance. According to Garg et al. [START_REF] Garg | A framework for ranking of cloud computing services[END_REF], for cloud storage service, stability is the variance in average time of read and write operations. For computational resources, it is the deviation of performance level specified in SLA, i.e., ∑

- n = i i sla i avg n T ρ ρ 1 . .
, where ρ is defined as resource unit (network, storage or computational resource); is the promised values in SLA; T is the service time and n is the total number of users;

• Security: The security level of a service is a major concerning factor for the protection of security and privacy. It is a qualitative indicator evaluated by service user, which is defined by a numerical value on a scale of 1 to 10 ; • Availability: It represents the fraction of the time that the service is available to attend requests. Configured as a percentage of service access,

given by time service total e unavailabl time service total -time service total .

• Cost: Depends on two attributes: acquisition and on-going. Acquisition cost is used for SaaS services where users sign a service, paying monthly, weekly, among others forms. On-going cost refers to cost of resource usage (cpu, ram, bandwidth or storage) in a unit of time. However, in IaaS providers, cost can be defined as the amount charged for the time granted to use the resources of a VM.

DEA Application

Considering the number of service providers such as n (candidates with their level of confidence to be calculated) and the performance indicators used to evaluate the services, this section present the mathematical model used to calculate the confidence ratio.

Each cloud computing provider is modeled as a Decision Making Unit (DMU). The confidence measure takes into consideration the ratio between the input and output data set, where each input and output is multiplied by its corresponding weight.

Considering that the purpose of this model is the generation of the confidence indicator for cloud computing providers, the input data set corresponds to the Quality of Service indicators (response time, availability, security and stability) and the output data set is defined by the cost indicator. Therefore, we can define confidence as:

ν + X v = z m = i i i ∑ × 1 0 0 0 (1) Subject to, 1 = Y × u ∑ s 1 = j 0 j 0 j (2) 0 1 1 ≥ -∑ ∑ ν + Y u X v s = j jk jk m = i ik ik n} , { = k
1,2,3,... Equation 2 sets that the sum of weights multiplied to the value of the output indicator must be equal to 1 due to the BCC-DEA model transformation of fractional to linear, in which confidence value is inversely proportional to output. Inequality 3 defines the confidence value must be positive. Inequality 4 defines the weight of inputs and outputs must be strictly positive. Using the classic BCC-DEA model, the real scale factor in this case is not considered because it determines a return on the production if it is increasing, decreasing or constant to a set of inputs and outputs [START_REF] Souza | Performance Evaluation Methodology for Cloud Computing using Data Envelopment Analysis[END_REF]. Therefore, once calculated the weights, the Equation 5 results the confidence level of the Cloud Computing Provider o :

ν + X v Y u = Confidence m = i i i s j= j j o ∑ ∑ × × 1 0 0 1 0 0 (5)

Results

In this Section, the computation of cloud computing providers confidence indicator has been evaluated using QoS indicators of real public service providers: Amazon, RackSpace and Microsoft Azure, available at [START_REF] Garg | A framework for ranking of cloud computing services[END_REF] and used to validate the classification method of cloud computing providers using a MCDA method.

Considering the indicators cited in Section 3.1, Table 1 presents data related to services offered by the following cloud computing providers: Amazon EC2 (CSP1), Rackspace (CSP2) and Azure (CSP3), with their respective values of Quality of Service indicators, weight values related to each indicator for cloud service provider . The relative weights regarding input and output data sets coping with DEA must be set to define the preferences of the entity responsible for taking decisions regarding the indicators. In other words, weights should be as greater as they are judged as more relevant in the confidence analysis of the DMU. Therefore, only the weight of the cost indicator was set aiming to attend the restriction imposed by the Equality 02 present in the problem formulation and the other weights are randomly assigned to each QoS service attribute.

The application of the indicators in DEA method, returns for each cloud computing provider, the confidence level value related to presented indicators. In Table 2 are presented the results for the method application for each cloud computing provider. According the presented results it is possible to note that a higher value for response time associated with others indicators which represents Quality of Service, cause a decrease in the confidence level.

Considering the VO's and cloud computing integration, where it is necessary to attend a collaboration opportunity, higher response time associated with lower stability of the service provided by the cloud provider, can become issues during the VO's operation. These issues fundamentally affect the collaboration, when it is necessary to respond quickly to a collaboration opportunity in order to follow the market competitiveness.

Moreover, based on the generated confidence indicator, the VO's manager can establish an acceptable confidence limit and then select the most reliable cloud computing provider to host the VO's members services,

Conclusion and Future Work

This article presents a model to generate a confidence indicator for cloud computing providers based on Key Performance Indicators comprising Quality of Service measures. Also, this model can be adapted to several scenarios that do not use cloud computing providers, just changing the indicators' set. The generation of this confidence indicator is driven by the problem observed during the subprocesses of VO's life cycle, where is necessary the establishment of confidence between involved members, so that the confidence is a crucial factor in VO and Cloud Computing integration.

The presented results show that the proposed method has the potential to generate a confidence indicator that can support cloud computing providers' selection in the VO creation stage. The VO's manager can see which cloud computing providers are most reliable and appropriate to integrate VO, based on generated confidence indicator from Quality of Service values. Furthermore, the proposed method can be customized by adding more indicators to compose the confidence indicator. Also, the VO's manager can choose weight values, giving priority to the desired indicators for every VO Collaboration Opportunity.

In a further work, we intend to integrate this confidence indicator model in a reputation architecture, composed of a reputation system that will carry out the monitoring and evaluation of each entity present in the Virtual Organization composed by services host in cloud providers. Also, it is intended to investigate how this reputed confidence indicator can support the Virtual Organization Manager in the several stages of VO's life cycle.

Fig. 1

 1 depicts the proposed model. The proposed model consists of the following components: • Cloud Service Providers (CSP): Cloud computing providers that are input to the proposed model based on Quality of Service (QoS) indicators; • Service Directory: Component responsible for the list of potential candidates as well as the association with the indicators to be used for classification; • KPIs: Quality of Service indicators of the candidates; • DEA: linear programming technique which calculates the level of efficiency through candidate KPIs;

  Y are the input i and output j of the DMU k respectively, i v and j u are the weights; ν is a real scale factor; n are the number of cloud computing providers (DMUs); m and s , are the total number of input and output indicators, respectively.

Table 1 .

 1 Offered services, their Quality of Service indicators and relative weight

	KPIs	CSP1	W(1)	CSP2 W(2) CSP3 W(3)
	Response time	100ms	0,05	600ms	0,05	30ms	0,05
	Stability	38,5	0,02	43	0,02	49	0,02
	Security	4	0,05	8	0,05	4	0,05
	Availability	99.95%	0,7	99,99%	0,7	100%	0,7
	Cost	$ 0.68	1,47	$ 0.96	1,04	$ 0.96	1,04

Table 2 .

 2 Confidence indicator for the presented cloud computing providers

	Cloud Computing Providers	Confidence indicator
	Amazon EC2 (CSP1)	14,49 %
	Rackspace (CSP2)	3,13 %
	Azure (CSP3)	29,54 %


Hamideh Rezazade Mehrizi

Hodjat Soleimani Malekan
email: h.soleimanimalekan@uva.nl

Mohammmad H Rezazade Mehrizi
email: m.rezazadehmehrizi@vu.nl

Hamideh Afsarmanesh
email: h.afsarmanesh@uva.nl

Positioning Collaboration in Business Process Model Consolidation in VOs

Keywords: Business Process Consolidation, Merging organizations, Collaboration

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Companies select structured partnerships, such as Virtual Organization (VO) to reduce their costs, create innovative products, and provide optimized value-added services. The VO alliance represents a dynamic network, in which different companies share their knowledge, competences, and resources [START_REF] Afsarmanesh | A framework for management of virtual organization breeding environments[END_REF]. For instance, several travelling services can be integrated in a tourism VO, e.g. for buying the transportation tickets, reserving hotel rooms, reserving a rental car, purchasing travel insurance, etc., into a value-added service of a travel package.

A potential longer-term cooperation in VOs however, can create the base for partners to provide more efficiently a wider range of services and a wider scope of operation. When a set of companies intend to participate in a VO to operate for a long time, some of their Business Processes (BPs) can be merged/consolidated to deliver more efficient and optimized services. For example, the partners of the tourism VO mentioned above can standardize and run their reservation BPs on a cloud environment. As such, to save total cost of operation at the VO level, instead of each keeping a "reservation" or "purchasing" BP variants, they can start consolidating these BPs into two standard optimized BPs that all organizations can use. The consolidation procedure however must be performed by the people at these organizations, agreeing on intention, semantics, etc. of those BPS, which will then result in developing a merged BP.

Although, the VO partners usually may not tend to consolidate their BPs, they can benefit from it in several different ways. First, the BP model consolidation assists companies to standardize their BP models, use each other's experiences, and produce a high-quality BP model. Second, in the case of shared BP model repositories, the reduced size of merged BP models saves some maintenance costs. Third, knowing about the others companies BP models can underpin further future alliances among the VO members.

The main aims of supporting BP consolidation in VOs are twofold: 1) Increasing the quality of the merged model that is the outcome of the consolidation process 2) Increasing the efficiency and effectiveness of the consolidation procedure However, because VO partners are highly heterogeneous, autonomous and usually geographically distributed, the BP model consolidation faces some difficulties and challenges: First, heterogeneities in the BP model representations, e.g. being graphically or textual, that can be found in different companies; Second, heterogeneities that can be found in the backgrounds and familiarity of the people who perform the consolidation job; Third, lack of a step-wise guideline for how to perform the consolidation. Consequently, a systematic approach is required to support and enhance the BP model consolidation procedure and products.

The existing approaches have mainly focused on merging graphical BP models, but lack the user centric perspectives [START_REF] Rosa | Business process model merging: An approach to business process consolidation[END_REF][START_REF] Gerth | Introduction[END_REF]. Therefore, the produced merged models are complicated to verify and difficult to understand by human users [START_REF] Dijkman | Managing large collections of business process models-current techniques and challenges[END_REF]. Additionally, the role of needed collaboration among organizations/agents in the BP model consolidation process is not addressed, while in practice, representatives from different companies typically gather together to decide how to combine their existing BP models and to develop merged models. Finally, no empirical study is performed to identify practical issues in the consolidation procedure, e.g. for identifying the importance of different business vocabularies used in the organizations for BP model consolidation in a VO context.

In this paper, we first describe and exemplify challenges in the BP model consolidation. We indicate the position of collaboration in increasing the efficiency of the consolidation procedure, as well as in enhancing the quality of the produced merged models. We then introduce a conceptual model with essential elements for successful establishment of collaboration in consolidating BP models in VOs. Then, we present the results so far achieved in our empirical experiment, which analyses our conceptual mode. Finally, the conclusions are addressed.

BP Model Consolidation in Practice

To develop an end-to-end business service, several BP models are used by VO members. This collection of BP models includes a number of BPs that are varied in different aspects, such as products they provide, purpose, locations, etc. For instance, two different insurance companies apply different "claim handling" BPs for liability or compensation cases. Thus, companies individually or collectively deal with their BP model variants [START_REF] Dijkman | Managing large collections of business process models-current techniques and challenges[END_REF]. Moreover, the proliferation of BP model variants in large collections and the need for developing inter-organizational BP models lead to an elimination of redundant and/or similar BP models through BP model consolidation.

In an inter-organizational BP model consolidation, the BP model variants of the involved companies are harmonized with their goals to produce added values. In other words, the BP model consolidation is about managing BP model variants [START_REF] Rosa | Business process model merging: An approach to business process consolidation[END_REF]. However, reflecting to heterogeneities in the BP model variants makes the BP models consolidation a challenging task. To introduce the practical challenges in the consolidation procedure, an example is provided.

A set of companies, including the two insurance companies, called "ABC" and "MTTM", constitute a VO to produce insurance and financial services. The companies work to establish a long time collaboration, thus, they decide to consolidate some of their BP models. As a first step, similar and/or equivalent BPs must be identified. Suppose that one of these services, agreed to be consolidated, is the claim handling service, for which two variants exist in the pool of BPs from the two companies. A merger team is then formed of the representative of the ABC and MTTM companies to follow the consolidation procedure and develop a merged BP model for the claim handling service. The members of the merger team play the role of BP modelers. A set of potential challenges posed in this BP model consolidation process are addressed below.

Different representation modes. Companies document their BP models in different representation modes. They use textual descriptions or graphical depictions to represent their BP models. The two textual descriptions for this BP from the two companies are showed in Table 1. Moreover, in Fig. 1, the graphical representations of BP models from the ABC and MTTM companies are illustrated in Business Process Model and Notation (BPMN) language [START_REF] Omg | Business Process Model and Notation (BPMN)[END_REF]. Selecting an appropriate modeling language for representing BP models requires a systematic method [START_REF] Malekan | Overview of Business Process Modeling Languages Supporting Enterprise Collaboration[END_REF]. The BP modeling language used by different companies involved in a VO might be different. Thus, frequently we face with the challenge of different representation modes when consolidating BPs from different organizations.

Beyond the representation modes, some other model related challenges can emerge during the consolidation procedure. Here, we explain some of these other challenges in the following subsections, as they are also exemplified by "a" to "e" in Fig. 1.

a. Different modeling conventions: The modeling conventions, including modeling methods and modeling styles, (i.e. the combination of the modeling language's constructs) are diverse. This is considered by merger teams, when two similar tasks are modeled in two different forms. In Fig. 1 (a), claim assessment and customer registration tasks are represented through a structured loop of exclusive gateways by the ABC company, while a parallel construct comprised of two inclusive gateways is applied by MTTM for illustrating the same tasks. Additionally, the steps followed by the merger teams that start to design (or redesign) a BP model are often emergent, i.e. decided between modelers rather than conducted by any instruction or guidelines.

b. Similar business rules: The business rules are business decisions that lead the flow of a BP through different alternatives [START_REF] Omg | Business Process Model and Notation (BPMN)[END_REF]. For instance, in Fig. 1 (b) both BP models follow same decision of either accepting or rejecting a claim that are preceded by assessing the claim. Although identifying similar business rules in the current BP models of companies may seem to be trivial, determining whether they are and to what extent they are similar is difficult [START_REF] Rosa | Business process model merging: An approach to business process consolidation[END_REF]. For instance, a business rule for accepting claims related to car compensation may differ from those for personal claims. This challenge further escalates when some modeling languages, such as the BPMN, have not provided efficient standard to accurately represent the business rules [START_REF] Recker | Opportunities and constraints: the current struggle with BPMN[END_REF].

c. Conflicted Business Rules: In several cases the logical and temporal steps of performing a task is different, while the goals of tasks are the same. For example, in Fig. 1 (c) the activities that succeed rejecting claims are executed sequentially in the MTTM Company. However, ABC synchronizes both activities of "Updating customer's information" and "Inform customer", in order to perform the same task.

d. Different level of granularity: Some tasks are provided in more and some in less details, corresponding to the modeler's opinion. Different levels of granularity represent the specific way of performing a task in a company. In Fig. 1 (d), the compensation payment in the MTTM Company is depicted in one activity of "Initiate payment", however, the compensation payment task for ABC includes both "pay out" and "Issue financial documents" activities in parallel. The challenge here is to find the similarity threshold that specifies to what extent a BP model fragment, including one or more activities, are identical to the other one, in terms of fulfilling the same goal.

e. Different terminologies: Companies are communicating with their own business terminologies. Different companies need to converge their terms, business vocabularies, and semantics during the consolidation. In the represented BP models in Fig. 1 (e), both companies run their service satisfaction evaluation activities, called "customer satisfaction" and "quality of service assessment" in ABC and MTTM, respectively. Since BP models, and particularly the graphical models, are commonly understood from the labelling in the models, selecting a correct, concise, and unambiguous terminology is necessary for successful consolidation of BPs. In a similar manner, using different linguistic structures, such as words, verbs, sentences, and adverbs in a textual description imitates some of the same challenges as mentioned above. For example, the word "temporary customer" is used for new customers in the MTTM Company (step 4 of Table .1), while no specific word is used in the ABC company to address the customers.

In addition to the issues mentioned above, merger teams face another challenge, which is the heterogeneities among domain experts in practice. These are company representatives, with different backgrounds in modeling knowledge. Moreover, other roles such as consultant and mediator may be assumed to assist in BP consolidation procedure. Thus, the interactions and coordination of them are of importance to achieve best quality of consolidated BP model and efficient consolidation procedure. In brief, several challenges of BP model consolidation for companies involved in a VO are tackled in our research. To address the mentioned challenges, we introduce a conceptual model to be delineated in regard to the VO specifications. Recording the claim is the first step in the ABC Company.

2-

Then, the claim is assessed. If the claim needs more information, such as new customer's information, a message is sent to notify the customer for more complete information. If the claim is assessed completely then it could be accepted or rejected.

3-

The accepted claims are settled to pay. Afterward, both the pay out and issuing the related financial documents are done by ABC.

4-

In case of rejected claims, the customer would be informed through sending a letter and the customer's information are updated in the system, at the same time. Afterwards, a survey is conducted to assess the satisfaction of the customer with rejected claim.

5-

The process finishes after archiving the claim.

1-

In MTTM Company, the process starts when a new claim is received. Each claim has to be recorded with enough details.

2-

All claims are checked accurately. Simultaneously, the claims and information of customers are checked. If it is a claim from an existing customer, its information already exists in the system. Otherwise, the information about the new customer will be registered as the temporary customer.

3-

Based on the claim's checking results, it would be either rejected or accepted.

4-

For the accepted claims, the compensation is calculated for all customers besides, if the claim is from a temporary customer, then a new risk factor is also calculated. After the calculations, the payment is initiated.

5-

While, if the claim is rejected, only the customer information gets updated accordingly, and afterward a letter will be sent to the customer.

6-

The process ends by sending "quality of service" form to the customer.

A Conceptual Model for Positioning Collaboration in BP Model Consolidation

The BP model consolidation is a collaborative-based procedure per se. In other words, various participants work together on achieving a shared understanding and developing a consolidated BP model. To show the collaboration position in our research, a conceptual model is illustrated in Fig. 2. This model visually describes essential elements that have influence on consolidating the two heterogeneous BP input models (i.e., graphically depicted or textually described), collaboratively in a VO context.

In this regards, we address both the consolidation procedure (i.e., certain steps to consolidate BP models) and the consolidation product (e.g., a merged BP model). Meanwhile, other elements which are essential to be incorporated in the consolidation procedure, in order to achieve a high quality consolidated BP model, are delineated. Additionally, their kind of relations such as lead, support, enhance, and increase/decrease are specified in Fig 2 . Please note that the width of the arrow illustrates the strength of the relations, which we specifically address in this paper. Also, certain elements are specified from the insight we have gained from literature.

• Consolidation procedure:

The BP model consolidation is concerned with managing BP model variants. Depending on the number of BP model variants, five steps are often followed to consolidate BP models. First, step is devising the consolidation procedure, which specifies "what to do" and "how to do it". For instance, in the devise step, VO members decide on whether combining, synchronizing, or keeping their existing BP models. Formalizing of the existing BP models is the second step. Although the VO members work autonomously, the formalization is necessary to establish the communication among involved VO partners. However, the heterogeneities of the existing BP models are challenging for consolidation of BP models. The merging is the third step. In this step VO members, for instance combine their BP models into one merged model by identifying the similarities and resolving the conflicts. The fourth step is correcting the merged BP models, to assure that the merged BP model is in compliance with all syntax rules. Since during the merge steps, the BP models are restructured, there is a risk of some anomalies occurring, such as the deadlock and livelock, in the merged BP model. The last but not the least step is the coevolving step. This step is of great importance, because the BP merged models can be complex and large, therefore, managing their changes are critical in the shared repositories or even the individual collections of the companies' BP models.

The above recommended steps have to be considered by the involved companies, in order to avoid inconsistencies and to increase efficiency and effectiveness in consolidation procedure. This steps primarily enable collaboration among VO members during the consolidation of their BP models. • BP model representation: BP model representation modes, e.g. the graphical depiction, enable knowledge acquisition from the BP models. Nevertheless, heterogeneities in the representation modes of the current BP models in companies, pose challenges for BP consolidation projects. Several of these challenges are already addressed in Section 2. Today, different BP modeling languages are used to formalize BPs. The selection of a BP modeling language highly determines its capabilities and context. The textual descriptions and graphical depictions are identified as the two main representation modes used for BPs.

BP Model Representation

Guidelines

The textual documents, including reports, spreadsheets, and work instructions are the most dominant source of the business information. Additionally, the textual descriptions of BPs are easier for understanding by domain experts, since they usually do not require particular trainings or skills for their understanding by stakeholders. The textual representation can be (i) free-format, such as an English description of a BP, (ii) semi-structured, e.g. delineating a process steps in order of sequence, or (iii) structured texts, such as UML Use case descriptions. The textual descriptions of the two claim handling BPs are illustrated as examples in Table 1.

On the other hand the graphical depiction is preferred by modeling experts, due to the ease of communication and execution. Nevertheless, using graphical languages needs training and understanding the modeling languages. The BPMN language is the de facto standard for graphical depiction of BPs. Despite the suitability of BPMN for specific contexts [START_REF] Malekan | Overview of Business Process Modeling Languages Supporting Enterprise Collaboration[END_REF], it still has certain problems, such as how it deals with ambiguities in some constructs, lack of formal semantics in its definition, and the arbitrary conventions that can be applied in modeling [START_REF] Recker | Opportunities and constraints: the current struggle with BPMN[END_REF]. Two example BPMN Diagrams for the claim handling process are also depicted in Fig. 1.

• Quality of consolidated BP Model

In a consolidation project, the involved companies aim at producing high quality consolidated BP models. For this purpose, three aspects of BP model quality are addressed: "syntactic" [START_REF] Dumas | A: Fundamentals of business process management[END_REF], "semantic" [START_REF] Dumas | A: Fundamentals of business process management[END_REF] and "innovativeness" [START_REF] Figl | Individual Creativity in Designing Business Processes[END_REF]. The syntactic quality aspect specifies to which extent a BP model adheres to the modeling language rules and grammar [START_REF] Dumas | A: Fundamentals of business process management[END_REF]. The semantic aspect of a BP model determines whether the existing model is consistent with what it intends to be in a real world context. In other words, it addresses how "good" the merged model is, in the existing context [START_REF] Dumas | A: Fundamentals of business process management[END_REF]. The innovativeness aspect however, reflects how well the model has chosen a convenient new way to express a BP model concept [START_REF] Figl | Individual Creativity in Designing Business Processes[END_REF].

The selection of above mentioned three aspects are due to the following reasons: (i) Achieving a correct merged model is challenging [START_REF] Schunselaar | Creating sound and reversible configurable process models using CoSeNets[END_REF] because the merged model is comprised of fragments of heterogeneous input BP models from different companies. Thus, there is the risk of using incorrect BP model fragments from the existing input models, as well as the error-prone configuration of the consolidated BP models, e.g., the threat of deadlocks occurring [START_REF] Dumas | A: Fundamentals of business process management[END_REF]. (ii) Having errors in a BP model makes the model difficult to understand and therefore difficult to share and reuse [START_REF] Dijkman | Managing large collections of business process models-current techniques and challenges[END_REF]. (iii) Fostering innovativeness directly enhances the quality of the consolidated BP models. However, introducing innovativeness can also cause the consolidated model to have more errors [START_REF] Figl | Individual Creativity in Designing Business Processes[END_REF], since creating new constructs beyond the existing structures pose the challenge of making mistakes or anomalies in the merged BP models.

Consequently, discussing the quality assurance aspects of the consolidated model are necessary for producing innovative and valid outcome in the consolidation procedure.

Producing high-quality consolidated BP models however is not the only challenging objective of the consolidation projects, but also reaching high impacts on two other desired effects in the VO context, i.e. producing reduced size of the pool of consolidated BP models, as well as increasing the satisfaction of VO members.

• BP model repository Managing a collection/pool of consolidated BP models itself brings new challenges, regardless of the size of the collections. For instance, a company, which is involved in the consolidation procedure, may demand for tractability of its BP model in the consolidated pool of BP models, for the sake of applying its internal changes in that model. The BP model consolidation, particularly by merging BP models, facilitates the maintainability of the BP model collections. Developing the consolidated BP model eliminates the collections redundancies. This is mainly because instead of the two BP model variants, one consolidated BP model is substituted and sustained/maintained.

It is noteworthy that consolidation, by focusing on similarities and reducing the differences, develops standardized BP models that impact on representation quality of the BP models in the VO.

• VO service satisfaction

The satisfaction of user is one aspect that is usually neglected in modeling tasks. The satisfaction of participants involved in consolidation procedures is defined as their attitudes toward factors that affect the consolidation procedure [START_REF] Wixom | A theoretical integration of user satisfaction and technology acceptance[END_REF]. User satisfaction is triggered by the system quality and information quality. In our proposed approach, we follow the satisfaction triggered by the quality of the consolidated BP model.

• Collaboration

In theory, Malone and Crowston have introduced a multi-level collaboration on modeling tasks in [START_REF] Malone | The interdisciplinary study of coordination[END_REF]. At the base level of collaboration, especially through social interactions, "perception of common objects" is achieved by various actors. For example, defining shared tasks to achieve a common goal. At the next level, "communication" is established by a common language to facilitate sending and receiving common messages among the actors. Making "decision by group" is the next level, where actors in a collaboration have a set of alternatives aligned with their goal and make explicit choices, e.g. by voting or making a consensus. When a group of actors identifies their goals, schedule activities and allocate resources, they accomplish the "coordination" level. Through addressing collaboration in BP modeling, all levels mentioned above get behind the collaboration process in developing consolidated BP models, nevertheless organizing the collaborative tasks, such as "making consensus" is of critical importance We distinguish between "emergent" and "systematic" collaboration procedures, more specifically, in the BP merging step. Systematic collaboration is associated with two or more individuals that are involved in a coordinated consolidation of BP models, when it is predefined beforehand. But emergent collaboration describes the situation when two or more individuals from different companies themselves define and follow their merge efforts process while it is happening [30]. More specifically, the systematic approach aims at drawing the consolidation team´s attention into predefined important issues, by which the collaboration can be facilitated; whereas in the emergent approach, the consolidation process is led by the emerging consensus of the team members. Moreover, collaboration in either of its forms, facilitates the innovativeness due to asymmetry in the knowledge of the members of the merger team [START_REF] Figl | Individual Creativity in Designing Business Processes[END_REF].

• Understanding Understanding in the modeling process generally, and in the BP model consolidation specifically, refers to ability of knowledge acquisition from a BP model. The grammar of BP modeling assists modelers to better understand the content of a BP. In principal, modelers perform two major tasks: "reading" and "writing" [START_REF] Rosa | Business process model merging: An approach to business process consolidation[END_REF]. When modelers read a model, they obtain information about its BP domain, while during the writing stage, a BP model is created to represent a real-world BP. Moreover, the collaboration and understanding of the BP models bilateral affect each other [START_REF] Bernhard | Preliminary insights from a multiple case study on process modelling impact[END_REF]. Furthermore, the quality of the merged model is increased, when the understanding of its modelers increases [START_REF] Dumas | A: Fundamentals of business process management[END_REF].

• Guidelines At the heart of the BP consolidation approach there is a set of guidelines that support and enhance its different elements. The guidelines specify certain configurations that facilitate the collaboration positioning in a consolidation procedure.

The guidelines suggest solutions in regard to the type of collaborations and the modes of input. Further, it investigates the role of the outcome representation modes, e.g. producing a graphical or textual merged BP model. These guidelines are not only applicable to companies for setting up their consolidation procedures, but also assist companies with increasing the quality of their consolidated BP models.

• Consolidation tool

Considering the correctness aspect of consolidation, a semi-automated tool is required to assure their correctness and generating anomaly-free merged BP models. The BP model anomalies, such as deadlocks and/or ambiguous constructs (e.g. ORjoin patterns) cause difficulties in executability of the consolidated BP models [START_REF] Van Der Aalst | Business process management: A comprehensive survey[END_REF]. Thus, the consolidation tool addresses the deficiencies and corresponding solutions, in order to provide a robust execution infrastructure for the consolidated BP models.

Additionally, two graphically depicted BP models from the two different companies were provided to the participants in study group "A" and "B" (See Fig. 1). Similarly, the equivalent textual descriptions of BP models were provided to the participants of the study Group "C" and "D" (See Table .

1).

Then, all teams were asked to develop two consolidated BP models, including one graphical and one textual merged BP model. In total, we have received 60 merged BP models, half graphical and half textual.

This experiment has produced a valuable large set of data, which we have so far partially analysed applying the fuzzy set Qualitative Comparison Analysis (fsQCA) method. A set of promising results related to the BP consolidation approach in VOs are already induced and being gradually represented in a format similar to Fig 3.

The presented results are used to evaluate the success and failure in achieving our desired outcomes. For instance, Fig. 3 represents some statistics about the results related to Syntax Structural Correctness of the produced merged BP models by teams. Please note that the headers in the first 4 rows in Fig. 3 indicate the four criteria considered in this analysis. Also for each row, two values are considered, as mentioned in the header, and the solid circle and crossed circle respectively indicate these values for that row. The fifth row indicates the success or failure of the produced results in relation to the Syntax Structural Correctness. Please also note that each column represents one combination of cases that with a high percentage has produced either a success or a failure result.

Achieving the syntax correctness -as a quality aspect of -a merged BP is represented by SSCS1, and the syntax failure by SSCS2. Each of these solutions may include a number of partial solutions. For instance, SSCR1 and SSCR2 are two partial solutions for having merged models with high level of syntactical correctness, i.e. the SSCS1 solution. Each of these solutions, as indicated by its respective column is due to a configuration of values for the four criteria represented by the rows.

For example, in the configuration of the SSCR3 partial solution, (i) given textual input BP models, (ii) while requiring the team to produce a graphical outcome, (iii) having low modeling knowledge of the participants, and (iv) applying an emergent consolidation procedure, the produced result is highly consistent, it represents an 89.4 percent of failures to achieve high quality merged BP model. Moreover, this configuration covers 12.8 percent of solutions that results in having syntactical errors in their merged BP model.

Similarly in SSCR2, we observe that if (i) the input is graphical, (ii) the team members have high modeling knowledge, (iii) regardless of the type of outcome (i.e., graphical or textual merged BP model) and (iv) regardless of consolidation procedures (i.e., systematic or emergent) the syntactical quality correctness have high consistency and acceptable coverage. It is noteworthy that, the fsQCA is not symmetric, e.g. in case of the SSCR2, having textual input BP model and low modeling knowledge, does not necessarily ends in merged BP model with low syntactical quality.

Conclusion and Future Works

The presented research addresses the main challenging practical aspects of the BP consolidation in a VO context. Moreover, it positions and further conceptualizes the role of collaboration in the BP merge process. An empirical study is presented to investigate and validate the consistency of the introduced conceptual model, which can be used to provide a set of solutions for the VO partners involved in BP consolidation.

The results of this experiment will be applied to developing a set of guidelines for the process of for BP consolidation among companies, aimed both to increase the quality of the merged BP models, as well as for automating a part of the consolidation procedure in the VO context. At present we are in the process of analysing results from the empirical experiment. In the next step, we aim to study the quality aspects of the consolidated BP models, i.e. the syntactic, semantic, and innovativeness aspects.

Fig. 1 .

 1 Fig. 1. Example of the graphical BP models in BPMN

2 :

 2 Positioning Collaboration in BP Consolidation Model

Table 1 .

 1 Example of textual description for two BP models in Fig.1.

	ABC Company	MTTM Company
	Claim-Handling Procedure:	Claims Handling Services
	1-

Experiment

In order to validate the inter-relations defined in the conceptual model described in Section 3, we have performed an empirical experiment with a group of students. The entire experiment is also recorded on videos.

The experiment was designed to study the following two main aspects: a) The joint effect that the "consolidation procedures" (i.e., systematic collaboration versus emergent collaboration) as well as the "input representation modes" (i.e., the textual BP model description or graphical BP model depiction) can have on increasing the quality of the produced merged BP models. b) The importance of the role played by the BP modeling knowledge of the collaborating partners, as the moderators from the two companies, in the consolidation procedure.

For the purposes of empirically investigating the two main issues mentioned above, 78 participants were recruited. The participants were students from the Faculty of Economics and Business Administration at the VU University. These were selected since in real-world companies, typically the staff with business administration degrees are assigned by companies to perform the tasks of BP modeling and/or BP merge. Moreover, short training in BP modelling is a part of the procedure that we apply for BP model consolidation, and using background expertise is not recommended in our experiment, e.g. for transferring between graphical and textual BP model representations [START_REF] Burton-Jones | Guidelines for empirical evaluations of conceptual modeling grammars[END_REF], since the experiment could then be biased by the background knowledge.

The participants formed teams of two. Each set of two members worked together, each as the representative of one of the two companies in a merger team. We also divided the participants into four separate conceptual study groups, and assigned some of the teams into each of the following study groups.

• Study Group A: Systematic collaboration-graphical BP model inputs For the systematic collaboration, the study groups "A" and "C" were suggested to fully adopt the following set of guideline steps:

(i) Reaching a mutual understanding of both input BP models.

(ii) Finding any and all similar fragments in the two input BP models.

(iii) Resolving any and all differences/conflicts over dissimilar parts of the merged BP model, with the original two BP models.

(iv) Verifying the correctness of the merged BP models.

On the contrary, the teams in the emergent study groups "B" and "D" did not receive any guidelines.

Outcome: Syntax Structural Correctness (SSC) of the Merged Model

Success Failure