Agile Information Systems for Collaborative Enterprises

Barbara Livieri

Department of Economic Sciences, University of Salento, Via per Monteroni, Lecce, Italy

Keywords: Enterprise Modelling, Collaborative Enterprises, Performance Measurement, Information Systems,

Ontologies.

Abstract: In the last years, new challenges have arisen for both business and technical aspects due to the movement

towards a collaborative-SMEs-driven society, where collaborative enterprises are used as catalysts of

competitive advantages. However, between 50%-70% of Collaborative Enterprises fails, due to the lack of

tools and methods to measure performance in an inter-organizational environment, where common

boundaries of firms fail. In this scenario, a new role has been recognized to Enterprise Information Systems.

As stated from FInES annual report, IS should “enable new forms of participation and collaboration,

catalyze further the formation of networked enterprises and business ecosystems. In this project, a possible

solution to face these challenges is offered. The general objective is to build a framework and a set of tools

to support the governance of CEs through monitoring and benchmarking. This can be enabled through a

comprehensive online service, based on enterprise modelling techniques, the creation of a collaborative web

application and of repositories, taxonomies and ontologies for CEs.

1 INTRODUCTION

In the last twenty years, organizational relationships

have moved from intra-organizational to inter-

organizational ones and are moving towards trans-

organizational relations, with a prediction of a speed

for value creation never seen before (Bititci et al.

2012). However, it is known that globally 50%-70%

of CEs fails, often due to the lack of a

comprehensive analysis that combine strategic goals

and KPIs (Kaplan et al. 2010; Bititci et al. 2008)

with a possible negative impact on component firms.

The risk of failure or low success for CEs “... is

mostly the avoidable result of inadequate

governance resulting in inadequate strategy

development and implementation” (Hoogervorst

2009). Indeed, performance measurement is a key

element in turning goals into reality (Popova &

Sharpanskykh 2010).

This has led to new challenges related to the

performance measurement in a collaborative-SMEs-

driven society for both business and technical

aspects.

Indeed, as stated from the “Future Internet

Enterprise Systems” annual report, Information

System (IS) should “enable new forms of

participation and collaboration, catalyze further the

formation of networked enterprises and business

ecosystems […] ushering in a new generation of

enterprise systems” (FInES 2010). Therefore, the

question is how to design and develop IS for CEs

and for networked SMEs. In particular, the

monitoring should be performed at two level of

granularity, which are the CE level and the firm

level, with a guarantee of comparability between

KPIs and perspectives of the two levels. More in

detail, at each level it is important to offer domain-

specific KPIs (Parung & Bititci 2006), which

depends on the type of the CE, on the maturity of the

collaboration and on the goals of the CE. In this

project, a possible solution to face these challenges

is offered.

The work is structured as follows: Section 2 is

for the outline of the research problems and Section

3 for the objectives of the research. In Section 4 an

analysis of the current state of research is presented.

In Section 5 is for the methodology and Section 6

for the stage of the research. Section 7 is for the

expected outcome.

2 RESEARCH PROBLEM

Performance measurement is a key aspect in the

Livieri B..

Agile Information Systems for Collaborative Enterprises.

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

management of all kinds of organizations, no matter

if the level of granularity is the individual enterprise

or a CE. In particular, through performance

measurement and KPIs analysis it is possible to

understand if the organization is achieving its

strategic goals. Indeed, monitoring and

benchmarking are essential in order to notice

promptly a gap between goals and achieved result

and to define which actions to undertake in order to

reduce the gap. However, performance management

has a high degree of complexity in inter-

organizational settings, due to the fail of traditional

organizational boundaries and it is not yet possible

to analyze in detail which costs and which revenues

of a firm are ascribable to the CE. Thus, currently it

is not possible to know in detail the impact of a CE

on components firms. Indeed, although several

authors (Caglio & Ditillo 2008) studied the role of

management accounting in inter-organizational

environments, no one applied these results in order

to quantitatively analyze the performance of CEs

and of involved firms (Bochicchio et al. 2013; Bititci

et al. 2012; Krathu et al. 2013). In addition, in

several proposals, the skills required for CEs’

managers are far from those available in the largest

part of existing SMEs. In this context, firms and CEs

would benefit from methodologies and tools

allowing them to better link desired objectives and

achieved results in an inter-organizational

environment. In other words, firms could find useful

having more structured and rich information not

only on their own performance but also on how it

compares with partners and competitors (Parmenter

2011), even in different CEs, in order to understand

the drivers of CEs’ success and, thus, to enhance

their performance. Moreover, benchmarking within

a CEs, even with a comparison of synthetic data,

enable the analysis of benefits, of their distribution

among partners and of the performance drivers for

the CE. Indeed, firms are concerned both with

performance drivers and targets; therefore

benchmarking is relevant not only for KPIs

comparison, but also for the identification of the

“collaborative practices” that contribute to the

success of a CE (Simatupang & Sridharan 2004).

This implies, for managers, the ability “to

observe and evaluate”, the awareness of “being

observed and evaluated”, a stronger perception of

the “value of the collaborative enterprise”, the

personal consciousness of the “impact of CEs on

firms” (and vice-versa) and the knowledge of the

different meaning that performance indicators

assume in a collaborative enterprise. In practical

cases, this kind of interrelated performance

evaluation and comparison cannot be conceived and

realized without a set of suitable IS elements and

procedures, which becomes not neutral with respect

to the measured performance and to the style of

management adopted for modern CEs, as well as a

music instrument is not neutral with respect to the

played music. In this perspective, Information

Systems (IS) have to face the new challenge offered

by a networked society (FInES 2012). In traditional

control systems built for individual enterprises, there

is a clear-cut between external and internal

environment. Indeed, whilst for CEs it is possible to

use the same performance measurement frameworks

used for individual firms, it is still necessary to

structurally and operatively change the measurement

system (Bititci et al. 2004).

General Problem. In order to measure performance

in CEs there is the need to develop an agile

Information System built for inter-organizational

and changing environments and able to analyze the

phenomenon.

In particular, the same KPI can be calculated or

interpreted in several ways, making them not

comparable within a CE or among different CEs

(P.1). This problem concerns both financial and non-

financial KPIs and derives from the need to share a

common understanding of the domain (Bertolazzi et

al. 2001).

Problem 1. In order to monitor CEs and to perform

benchmarking within and between CEs and firms in

CEs, it is necessary to share a common language for

KPIs.

Moreover, benchmarking within a CE enable the

analysis of benefits, of their distribution among

partners and of the performance drivers for the CE.

Indeed, firms are concerned both with performance

drivers and targets; therefore benchmarking is

relevant not only for KPIs comparison, but also for

the identification of the “collaborative practices”

that contribute to the success of a CE (Simatupang &

Sridharan 2004). However, CEs are heterogeneous

clusters of partnerships among enterprises (FInES

2012). CEs can indeed be of different types (e.g.,

horizontal CEs, vertical CEs), be at different stages

of maturity and have different goals. In this frame, it

is obviously not enough to compare CEs only taking

into account the business sector or the size, but other

factors, such as the CE type, maturity, organizational

structures and goals, come into play.

Problem 2. There is the need to analyze and

understand CEs type, lifecycle, organizational

structures, roles and goals in order to comprehend

the phenomenon.

AgileInformationSystemsforCollaborativeEnterprises

Problem 3. CEs goals, types, structure, role and

maturity are relevant in order to perform an

effective and accurate benchmarking.

Moreover, different CEs types need for different

KPIs (Parung & Bititci 2006); therefore firms and

CEs have to understand which KPIs are relevant and

what a KPIs mean in a given firm or a CE with

defined goals. However, this kind of understanding

is not immediate, especially in several SMEs, which

lack of the know-how needed to perform this kind of

analysis and often choose the more “known” KPI,

instead of the more relevant one, with possible

negative effects on the CE equilibrium. Therefore,

CEs need to understand which KPIs are relevant for

them, considered their “type”, maturity, and “goals”.

Problem 4. Build domain-specific KPIs, which

means KPIs specific for the CE type, maturity and

goals.

Furthermore, CEs are a multifaceted phenomena,

that is sometimes difficult to analyze and to

comprehend in abstract ways. The analysis by itself

of CEs’ goals, CEs type and related KPIs could be

misleading for firms and CEs.

Problem 5. Reduce the complexity of the analysis

and of the monitoring of CEs performance, through

graphical representations.

Moreover, in order to “track” and store KPIs

large enterprises usually benefit from internal

control systems (Enterprise Information Systems),

whilst SMEs perform, whenever that even happens,

a manual analysis of their financial statements and

compare their values with those of similar firms, by

means of public databases of financial statements.

The choice is often due to the high costs and the

complexity of EIS.

Problem 6. Build Information System suitable for

SMEs, that means more user-friendly.

Finally, firms who cooperate need to exchange

information (e.g., on their transactions, goals), since

this can increase their performance (Essa et al.

2013). Also, in case they decide to share more data

not only with partners but also with other firms, this

can increase the effectiveness of benchmarking.

Problem 7. Enable information sharing with

partners or with other firms and CEs.

3 OUTLINE OF OBJECTIVES

Aim of the project is to build a framework and a set

of tools to support the governance of CEs through

monitoring and benchmarking. The sub-objectives

include: (a) the definition of a shared knowledge on

KPIs formulas, rationales and explanations; (b) the

classification of CEs types, lifecycle, organizational

structure and firms role in order to perform an

effective benchmarking; (c) the analysis of the

linkage among KPIs, CEs goals, type, maturity and

structure; (d) the analysis and use of graphic tools to

facilitate the comprehension of CE-related

phenomena; (e) the design, prototyping and testing

of an online service suitable for CE-oriented SMEs

and for information sharing among partners.

4 STATE OF THE ART

At the best of my knowledge, there are no tools or

conceptual framework offered as a means of

operatively manage and quantitatively analyze

collaborative enterprises. Therefore, in this

paragraph is presented a short analysis of the

literature on performance measurement and

enterprise modelling for collaborative enterprises, on

enterprise ontologies and on cross-organizational

Information Systems, which are necessary for

enabling performance measurement. For each topic,

the current state of research, the existing gap and the

prospective of future research are analyzed, thus

outlining how these topics have to evolve in order to

face the new challenges deriving from the changes

in society.

4.1 Performance Measurement

Performance management and performance

measurement have a key role in the assessment of

CEs and of how the CE is affecting firms, according

to the principle of “if you cannot measure it, you

cannot manage it” (Kaplan & Norton 1996; Parung

& Bititci 2006). Indeed, several authors (Caglio &

Ditillo 2008) have analyzed control mechanism in

inter-organizational environments, such as

management accounting. In particular, in CEs the

monitoring can operate on three layers: a) firm; b)

effects of the CE on the firm; c) CE. For sub-c)

researchers and practitioners propose several

guidelines, performance and cost management tools

(e.g., modified Balanced Scorecard and scorecards) (

Fayard et al. 2012; Kaplan et al. 2010) and

enforcement methods, such as Open Book

Accounting (Caglio & Ditillo 2012; Romano &

Formentini 2012; Agndal & Nilsson 2010). In

particular, Open Book Accounting (OBA) allows

firms of a network to share accounting information,

which enable an improvement in the decision

ICEIS2014-DoctoralConsortium

process (Caglio & Ditillo 2012). However, many

firms are reluctant to disclose these data, because

OBA is sometimes seen as formal control

mechanism that damages trust (Windolph & Moeller

2012). Moreover, while there is a consolidate

literature on sub-a), there are still few works on how

to measure the effects of networks on firms (sub-b))

(Dekker 2003), and even in those there is no focus

on quantitative aspects (Bititci et al. 2012;

Bochicchio et al. 2013). Furthermore, there are few

works that takes into account both CEs and SMEs

(Pittino et al. 2013; Lee 2007). Nonetheless,

performance management and performance

measurement have a key role in the assessment of

the achievement of CE goals and of how the

partnership is affecting firms. Therefore, whilst we

are going towards a network-SMEs-driven society,

new challenges arise for performance measurement

system, since they have to be developed and used

across the traditional organizational boundaries. The

question is how to manage both the performance of

CEs and of firms for SMEs (Bititci et al. 2012). In

order to face this question, it is necessary to modify

existing tools for inter-organizational settings,

overcoming the clear-cut between external and

internal environment. Indeed, whilst it is possible to

use the same performance measurement frameworks

used for individual firms, it is still necessary to

structurally and operatively change the measurement

system (Bititci et al. 2004).

4.2 Enterprise Modelling

The research on enterprise modelling has three main

topics. Some authors focus on the analysis of

business processes (Comuzzi et al. 2012; Pan et al.

2004), others on the information architecture

(Kulkarni 2012) of firms and some others on the

modelling of strategic an organizational aspects as

well (Strecker et al. 2011; Frank 2012). In this sense,

a comprehensive research work in this field has been

performed at University of Duisburg-Essen

(MEMO: multi-perspective enterprise modelling).

For the purpose of this research project, MEMO and

MML (Meta Model Language) are relevant because

of their ability to model software engineering, social,

managerial and economic aspects of the firm

(Strecker et al. 2011).

In general, modelling has several benefits for

firms and for collaborative enterprises, such as:

understanding how a CE works, giving a starting

point for the re-arrangement, whereas needed, of the

CE, giving a starting point for the development of IS

for CEs and so on (Steen et al. 2002). In particular,

the need for EM is even more relevant in CEs, due

to the increase in complexity. However, still few

works exist on the subject. Therefore, the question is

how and in what measure current EM techniques

and tools can be used in order to face the issues

deriving from the inter-organizational setting.

A possible solution could come from the

principles adopted in Service Oriented Architectures

for service compositions, i.e. by adopting graph

based representations and graph-theory to represent

and manage the network of relationships in CEs and

among CEs. Other examples come from the

adoption of a graph-based notation for collaboration

contracts.

However, there is still much to do in order to

culturally change for manager the awareness of CEs

and to make possible for enterprise modelling to

have an active role not only in single firms, but also

in CEs.

4.3 Cross-organizational Information

Systems

Coordination among partners is a key factor in order

to achieve goals. This can result only from a flow of

information among and within organizations

(Eckartz et al. 2010), which can be assured by

Information Systems (IS) and, in more detail,

Enterprise Systems (ES) that takes into account the

inter-organizational setting. An IS is indeed made by

a set of applications which allow the collection,

elaboration and storage of information useful for the

decisional or operational processes (Laudon &

Laudon 2011, p.15; Bracchi et al. 2010, p.1).

However, according to the contingency theory, a

change in the organizational structure, imply a

change in the IS. Information Systems usually

distinguish and oppose relations within a firm, from

those across it. However, in an inter-organizational

setting it is necessary to broaden data sources so to

include partners as well and to consider them as a

beneficiary of the information (Håkansson & Lind

2004). While at the business level coordination

comes from coordination mechanisms, at the ES

level, it is performed through shared databases, data

warehouses, workflow management systems, web

services, service oriented architecture (SOA) or

cross-organizational ERP, which are used from

several independent firms whom cooperate in an

inter-organizational environment (value web)

(Daneva & Wieringa 2008). Although even

coordination mechanisms at the business level are

partially integrated with ERPs, however the use of a

cross-organizational ERP system can lead to a lost

AgileInformationSystemsforCollaborativeEnterprises

on flexibility because it implies processes

standardization and collaborative relations are now

always stable. This limit can be overcome through

customization; however it is usually very expensive

and, therefore, is out of the reach of SMEs.

Moreover, Information Systems represents only a

potentiality for change, but in order to fulfil it there

is the need for certain organizational characteristics

(Maraghini 2010). Therefore, the actual

implementation of cross-organizational ERP is not

suitable for CEs in the first stages of cooperation.

Moreover, most of IS adopted are not cross-

organizational; thus, “they focus on a single

enterprise with some supports towards sharing

performance information with external parties”

(Bititci et al. 2012). However, the key element in the

future seems to be “cooperation” (Missikoff 2012),

whilst IS should “enable new forms of participation

and collaboration, catalyze further the formation of

networked enterprises and business ecosystems […]

ushering in a new generation of enterprise systems”

(FInES 2010). Therefore, the question is how to

design and develop IS for CEs and for networked

SMEs. Nowadays, there is a lack of a model which

allows: a) in the pre-alliance phase, the opportunity

of engaging in a CE; b) in the operational phase, the

evaluation of goals achievements. In particular, the

monitoring should be performed at two level of

granularity, which are the CE level and the firm

level, with a guarantee of comparability between

KPIs and perspectives of the two levels.

4.4 Enterprise Ontologies

Nowadays enterprise are entities far more complex

than in the past; therefore, it is not easy to manage

them. In this frame, there was the need for a “…a

conceptual model [...that is…] coherent,

comprehensive, consistent and concise…” (Dietz

2006). Indeed, enterprise ontologies are developed

and used for several reasons linked with enterprise

modelling, such as the development of Management

Information Systems and strategic decision support

systems, Business Process Reengineering and the

construction of Virtual Enterprises. However, still

few enterprise ontologies have been developed and

use in productive settings, due to the complexity and

the novelty of the methods (Bertolazzi et al. 2001).

In more detail, there are two enterprise ontologies,

which are: a) the Enterprise Ontology developed

from the Edinburgh Group (Uschold et al. 1996) and

b) the Toronto Virtual Enterprise Project (TOVE)

(Fox et al. 1993). However, there is still a lack of

ontologies for CEs, which are entities more complex

than individual enterprises, or, more in general, for

KPIs and performance measurement. A first step

towards this direction if offered by a taxonomy for

CEs, developed by a FInES taskforce (FInES 2012).

5 METHODOLOGY

For the development of the research project, a

structured approach is adopted for all the four phases

here described.

In the first phase, a KAOS approach (Bresciani

et al. 2004) is used in order to elicit the requirements

of the service. In particular, an analysis of literature

on CEs is performed in order to outline the CE

lifecycle and to define the potential stakeholders and

their goals in each phase. Goals are then refined and

transformed in requirements. The output of this

phase is a requirements specification document

covering all phases of the collaborative enterprise

lifecycle. These requirements are used in the second

and in the third phase in order to design the system.

In the second phase, starting from this

preliminary analysis, CEs are modelled both from an

organizational and strategic point of view by means

of Meta Model Language (MML) (Strecker et al.

2011), and through ontologies, for the information

architectural part. Indeed, in order to properly model

and use KPIs, several layers of the CE have to be

taken into account.

 MML is used in order to describe the

organizational structure of the CE and the role of

each partner, e.g., how the decision power is

distributed, if there is a vertical structure, if there

is a focal firm and so on. This model should be

integrated with the ontology of CEs types.

 Ontology are used to semantically model CEs

(main ontology) with regards to the following

aspects which constitute the domain ontologies:

CEs’ lifecycle; CEs’ goals, CE’s type and KPIs.

CEs’ goals are modelled using the representation

of the strategic level as starting point. The

modelling of CEs’ type is based on the taxonomy

already elaborated by (FInES 2012), which will

be enriched with other classes and instances, with

the analysis of the relations among classes.

Finally, the ontology of KPIs the ontology has

the aim of representing a shared

conceptualization of the domain (Bertolazzi et al.

2001) and to allow for the aggregation of the data

of component firms and for the comparison of

the information among different firms and CEs.

For each KPI, informative contents are enriched

through literature references on the basic KPIs,

ICEIS2014-DoctoralConsortium

their rationales, formulas and “limit” values

(Strecker et al. 2011). KPIs are also linked with

CEs’ types, goals and lifecycle in order to

provide domain-specific performance indicators.

For the development of these ontologies OWL,

as language, and Protegé, as tool, have been

selected.

Indeed, ontologies are particularly useful in this

context because of the heterogeneity of data

connected to CEs, which are often stored in

documents like contracts, textual reports,

financial statements, web pages, and so on.

Therefore, for the processing of these sources the

use of Semantic Web techniques is suitable.

 Finally, information on CEs type, maturity,

goals, structures and KPIs will be represented

graphically by means of data and information

visualization tools, such as hypertrees.

In Fig.1 is visually represented the second phase.

In the left part, the objects of analysis are shown. In

the second column, each object of analysis is linked

with the method (except for the visualization tools,

which take into account the outputs). In the third

column the outputs of the use of methods on

information objects are represented. Finally, in the

right column the overall results are shown.

Figure 1: Second phase of the methodology.

The results of the second phase are: (a) the

development of reflective (Strecker et al. 2011)

domain specific KPIS, starting from the information

on CEs type, maturity, goals, structure and KPIs

types; (b) the modelling of the organizational and

semantic level of CEs; (c) the visual representation

of the elements. In the third phase, the collaborative,

cloud-based Information System is designed through

a structured approach based on HDM/IDM (for the

hypermedia design) and on UML (for all other

modelling aspects) and developed. The IS should be

composed by all the elements described in the

second phase. Indeed, ontologies can be easily

integrated in Java web application by means of tools

such as OWLAPI. The Information system has three

aims.

 Firms and CEs monitoring and benchmarking,

through the creation of personalized dashboards,

KPIs evaluation and information sharing. Using

the models and semantic tools developed in the

second phase, the IS should retrieve from

different sources information on component

firms, financial and non-financial data, contracts,

KPIs, etc. and store them in a central database.

The processing of data through the semantic

layer, enable the system to define which is the

type of the CE, its organizational structure. In

this way the IS can propose the use of relevant

KPIs, possible changes in contracts or in

structures and pertinent comparisons with other

firms and CEs.

 A repository of templates. Contracts or

agreements and organizational structures,

whereas available, can be furthermore processed,

in order to make available an online repository of

templates for CEs, such as those provided by the

Legal-IST project (www.legal-ist.org), for firms

that decide to formalize or change the

collaboration and organizational structures.

 Information sharing, in order to better collaborate

with partners and to have more detailed

benchmarks, with different level of privacy.

In order to achieve these goals, data mining and

semantic web techniques, Business Intelligence

tools, relational databases and a cloud architecture

will be used.

In the fourth phase, the validity of the approach

and of the system will be tested, with the analysis of

coherence with existing literature, of the usefulness

of the approach to firms and CEs and of the

performance of the IS. The coherence validation is

aimed at analysing whether the research contribution

is consistent with previous literature and can

therefore contribute to existing literature: this

analysis is particularly useful in the early-middle

stages of the research, when it’s not yet possible to

test the system with users. Moreover, the usefulness

of the approach will assess the contribution to

practice of the research and will be tested through

controlled tests with students purposely trained and,

then, with managers of CEs. Both test will be

performed in two phases. During the

tests/experiments, each participant will act as a

manager of a CE; therefore, individuals will receive

a case study of their CE, with a description of the

type, maturity, goals and participant firms. In the

first phase, each individual will choose a set of KPIs

that he consider more appropriate in order to

understand the performance of his CE. Individual

will be asked to make strategic decisions considering

AgileInformationSystemsforCollaborativeEnterprises

the values of the set of KPIs and results of the

choices will be evaluated. In the second phase,

another set of individuals will be provided with a

prototype of the system and they will be asked to

perform the same tasks of phase 1. At the end of the

second phase, the results will be compared with the

ones of the first phase.Finally, the performance test

will ensure that the system can be used by a large

number of users, with an adequate level of

performance.

The feasibility of the proposed approach is

supported by the joint effort of two research groups,

which cover the technical and business aspects of

the project. The specific contribution of the PhD

Student regards the requirements specification, the

development of the ontologies, the design and test of

the prototype of the online service and the overall

management of the project research aspects. The

contribution of the PhD Student, although

contextualized in a broader project, has an

autonomous scientific validity, whereas enterprise

modelling is a fast growing research theme, as well

as the design of IS.

6 STAGE OF THE RESEARCH

The first phase of the project is almost been

completed. The requirements specification document

is available, although it will be subject to change

whereas new interviews with firms and CEs will

highlight other key aspects. A preliminary version of

the approach and of the requirements has been

submitted to 26

International Conference on

Advanced Information Systems Engineering (CAiSE

Forum 2014). As for the second phase, the KPIs

ontology is alomost completed in its first version

and is now being formalized with OWL. The KPIs

ontology will be sumbitted to the Conference on

Business Informatics 2014. The CEs type ontology

has been drafted, and will be furthermore elaborated,

partly through student theses. It will be sumbitted to

the 15

IFIP Working Conference on Virtual

Enteprises (PRO-VE 2014); an extended version

will be submitted to EMISA 2014.

Regarding the third phase, a preliminary version

of the prototype of the system has been developed

and presented to itAIS 2013. The prototype is

currently able to store quantitative data on firms and

perform statistical analysis on financial statements.

The validation has been performed as coherence

with literature for phase 1 and 2 and as perfromance

and usefulness tests for phase 3. Each single

component (i.e., ontologies, taxonomies, etc.) will

be tested and verified with a small set of users or

simulated users (e.g. students trained to do so).

Finally, the system will be integrated and the system

test will be perfomed on a small group of final users.

7 EXPECTED OUTCOME

The expected outcome of the research project is the

design and test of the prototype of a comprehensive

online service, based on enterprise modelling

techniques, for CEs governance and analysis,

through the creation of a collaborative web

application and of repositories, taxonomies and

ontologies for CEs.

This system should offer a customized

monitoring and benchmarking platform with a

semantic layer able to analyze the CEs and to return

a classification and relevant KPIs and CEs for

benchmarking. Another expected outcome is the

development of a KPIs ontology, a CEs ontology, a

goals’ taxonomy and a lifecycle ontology.

Moreover, the online service should enable the

creation of templates for contracts and

organizational structures and the information sharing

among partners. The approach should facilitate firms

and CEs in the choice of which KPIs to include in

the dashboard, thus which KPIs are relevant for their

goals, CE type and maturity, therefore it should be a

suitable approach for SMEs which lack of the

financial and organizational resources needed for the

adoption of cross-organizational ERPs. The design

of the system will be based on a GORE analysis

approach.

REFERENCES

Agndal, H. & Nilsson, U., 2010. Different open book

accounting practices for different purchasing

strategies. Management Accounting Research, 21(3),

pp.147–166.

Bertolazzi, P., Krusich, C. & Missikoff, M., 2001. An

approach to the definition of a core enterprise

ontology: CEO. In OES-SEO 2001, Citeseer, pp. 14–15.

Bititci, U. et al., 2012. Performance Measurement:

Challenges for Tomorrow. International Journal of

Management Reviews, 14(3), pp.305–327.

Bititci, U. S. et al., 2008. Collaboration: a key competence

for competing in the 21st century.

Bititci, U. S. et al., 2004. Creating and managing value in

collaborative networks. International Journal of

Physical Distribution & Logistics Management,

34(3/4).

Bochicchio, M. A. et al., 2013. DsNA: a Database for

ICEIS2014-DoctoralConsortium

Strategic Network Analysis in Italy. In itAIS 2013.

Bracchi, G., Francalanci, C. & Motta, G., 2010. Sistemi

informativi d’impresa, Milano: McGraw-Hill.

Bresciani, P. et al., 2004. TROPOS: An Agent-Oriented

Software Development Methodology. Journal of

Autonomous Agents and Multi-Agent Systems, 8(3).

Caglio, A. & Ditillo, A., 2008. A review and discussion of

management control in inter-firm relationships:

Achievements and future directions, Elsevier Ltd.

Caglio, A. & Ditillo, A., 2012. Opening the black box of

management accounting information exchanges in

buyer–supplier relationships. Management Accounting

Research, 23(2), pp.61–78.

Comuzzi, M., Vonk, J. & Grefen, P., 2012. Measures and

mechanisms for process monitoring in evolving

business networks. Data & Knowledge Engineering,

71(1), pp.1–28.

Cooper, R. & Slagmulder, R., 2004. Interorganizational

cost management and relational context. Accounting,

Organizations and Society, 29(1), pp.1–26.

Daneva, M. & Wieringa, R., 2008. Cost estimation for

cross-organizational ERP projects: research perspectives.

Software Quality Journal, 16(3), pp.459–481.

Dekker, H. C., 2003. Value chain analysis in interfirm

relationships: a field study. Management Accounting

Research, 14(1), pp.1–23.

Dietz, J., 2006. Enterprise Ontology: Theory and

Methodology, Springer.

Eckartz, S., Katsma, C. & Daneva, M., 2010. The Inter-

organizational Business Case in ES Implementations :

Exploring the Impact of Coordination Structures and

Their Properties. CENTERIS 2010.

Essa, S.A.G., Dekker, H.C. & Groot, T., 2013. The

Influence of Information and Control on Trust

Building in Buyer-Supplier Negotiations.

Fayard, D. et al., 2012. Effect of internal cost

management, information systems integration, and

absorptive capacity on inter-organizational cost

management in supply chains. Accounting,

Organizations and Society, 37(3), pp.168–187.

FInES Research Roadmap Task Force, 2010. Future

Internet Enterprise Systems (FInES)- Research

Roadmap.

FInES Task Force on Collaborative Networks and

SOCOLNET, 2012. Taxonomy of Collaborative

Networks Forms,

Fox, M.S., Chionglo, J.F. & Fadel, F.G., 1993. A

Common-Sense Model of the Enterprise. In

Proceedings of the 2nd Industrial Engineering

Research Conference.

Frank, U., 2012. Multi-perspective enterprise modeling:

foundational concepts, prospects and future research

challenges. Software & Systems Modeling.

Håkansson, H. & Lind, J., 2004. Accounting and network

coordination. Accounting, Organizations and Society,

29(1), pp.51–72.

Hoogervorst, J. A. P., 2009.

Enterprise Governance and

Enterprise Engineering, Springer.

Kaplan, R. S. & Norton, D. P., 1996. Using the balanced

scorecard as a strategic management system. Harvard

business review, 74(1), pp.75–85.

Kaplan, R. S., Norton, D. P. & Rugelsjoen, B., 2010.

Managing Alliances with the Balanced Scorecard.

Harvard Business Review, (February 2010), pp.114–121.

Krathu, W. et al., 2013. Identifying Inter-organizational

Key Performance Indicators from EDIFACT

Messages. 2013 IEEE CBI.

Kulkarni, V., 2012. Modeling and Enterprises–the past,

the present and the future. and Empirical Studies in

Software Modelling, p.63.

Lankhorst, M., 2013. Enterprise architecture at work:

Modelling, communication and analysis, Springer.

Laudon, K. C. & Laudon, J.P., 2011. Management

Information Systems: Managing the Digital Firm,

Prentice Hall.

Lee, C.-W., 2007. Strategic alliances influence on small

and medium firm performance. Journal of Business

Research, 60(7), pp.731–741.

Maraghini, M. P., 2010. Sistemi informativi per l’azienda

“collaborativa”: il caso Pelletteria Il Ponte. Controllo

di gestione, (4), p.57.

Missikoff, M., 2012. The future of enterprise systems in a

fully networked society. In Advanced Information

Systems Engineering. Springer, pp. 1–18.

Pan, Y., Tang, Y. & Gulliver, S.R., 2004. Artefact-

Oriented Business Process Modelling An Ontological

Dependency Approach.

Parmenter, D., 2011. Key Performance Indicators:

Developing, Implementing, and Using Winning KPIs,

Wiley.

Parung, J. & Bititci, U.S., 2006. A conceptual metric for

managing collaborative networks. Journal of

Modelling in Management, 1(2), pp.116–136.

Pittino, D., Angela, P. & Mazzurana, M., 2013. Alliance

Governance and Performance in SMEs : Matching

Relational and Contractual Governance with Alliance

Goals. , 3(1), pp.62–83.

Popova, V. & Sharpanskykh, A., 2010. Modeling

organizational performance indicators. Information

Systems, 35(4), pp.505–527.

Romano, P. & Formentini, M., 2012. Designing and

implementing open book accounting in buyer–supplier

dyads: A framework for supplier selection and

motivation. International Journal of Production

Economics, 137(1), pp.68–83.

Simatupang, T.M. & Sridharan, R., 2004. Benchmarking

supply chain collaboration: An empirical study.

Benchmarking: An International Journal, 11(5),

pp.484–503.

Steen, M. W. A. et al., 2002. Modelling Networked

Enterprises.

Strecker, S. et al., 2011. MetricM: a modeling method in

support of the reflective design and use of

performance measurement systems. Information

Systems and e-Business Management, 10(2), pp.241–276.

Uschold, M. et al., 1996.

The Enterprise Ontology,

Windolph, M. & Moeller, K., 2012. Open-book

accounting: Reason for failure of inter-firm

cooperation? Management Accounting Research,

23(1), pp.47–60.

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