Strategic Management · of cloud computing: 1. “The set of disciplines, technologies, and...

Strategic Management International Journal of Strategic Management and

Decision Support Systems in Strategic Management

www.ef.uns.ac.rs/sm Publisher University of Novi Sad, Faculty of Economics Subotica Segedinski put 9-11, 24000 Subotica, Serbia Tel: +381 24 628 000 Fax: +381 546 486 http://www.ef.uns.ac.rs For Publisher Nenad Vunjak, University of Novi Sad, Faculty of Economics Subotica, Serbia Editor-in-Chief Jelica Trninić, University of Novi Sad, Faculty of Economics Subotica, Serbia National Editorial Board Esad Ahmetagić, University of Novi Sad, Faculty of Economics Subotica, Serbia Jelena Birovljev, University of Novi Sad, Faculty of Economics Subotica, Serbia Jovica Đurković, University of Novi Sad, Faculty of Economics Subotica, Serbia Nebojša Janićijević, University of Belgrade, Faculty of Economics Belgrade, Serbia Tibor Kiš, University of Novi Sad, Faculty of Economics Subotica, Serbia Božidar Leković, University of Novi Sad, Faculty of Economics Subotica, Serbia Vesna Milićević, University of Belgrade, Faculty of Organizational Sciences, Serbia Aleksandar Živković, University of Belgrade, Faculty of Economics, Serbia International Editorial Board Ilona Bažantova, Charles University in Prague, Faculty of Law, Czech Republic André Boyer, University of Nice Sophia-Antipolis, France Ivan Brezina, University of Economics in Bratislava, Faculty of Economic Informatics, Bratislava, Slovakia Ferenc Farkas, University of Pécs, Faculty of Business and Economy, Hungary Agnes Hofmeister, Corvinus University of Budapest, Faculty of Business Administration, Hungary Pedro Isaias, Open University Lisbon, Portugal Novak Kondić, University of Banja Luka, Faculty of Economics, Banja Luka, Bosnia and Herzegovina Mensura Kudumović, University of Sarajevo, Faculty of Medicine, Bosnia and Herzegovina Vujica Lazović, University of Montenegro, Faculty of Economics, Podgorica, Montenegro Martin Lipičnik, University of Maribor, Faculty of Logistics Celje-Krško, Slovenia Pawel Lula, Cracow University of Economics, Poland Emilija Novak, West University of Timisoara, Timisoara, Romania Elias Pimenidis, University of East London, England Vladimir Polovinko, Omsk State University, Russia Ludovic Ragni, University of Nice Sophia-Antipolis, France Kosta Sotiroski, University „ST Kliment Ohridski“ Bitol, Faculty of Economics Prilep, Macedonia Ioan Talpos, West University of Timisoara, Faculty of Economics, Romania Assistant Editors Marton Sakal, University of Novi Sad, Faculty of Economics Subotica, Serbia Vuk Vuković, University of Novi Sad, Faculty of Economics Subotica, Serbia Lazar Raković, University of Novi Sad, Faculty of Economics Subotica, Serbia English translation Željko Buljovčić Zora Trninić Prepress

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Decision Support Systems in Strategic Management ISSN 1821-3448, UDC 005.21 Strategic Management is a quarterly journal addressing issues concerned with all aspects of strategic man-agement. It is devoted to the improvement and further development of the theory and practice of strategic management and it is designed to appeal to both practicing managers and academics. Specially, Journal pub-lishes original refereed material in decision support systems in strategic management.

Thematic Fields Mission and Philosophy of the Organization

Culture and Climate of the Organization

Effectiveness and Efficiency of the Organization

Structure and Form of the Organization

Strategic Analysis

Aims and Strategies

Process of Strategic Management

Characteristics of Strategic Management in the New Economy

Contemporary Ontological, Epistemological and Axiological Suppositions on the Organization and its Environment

Analysis of the Organization and its Interaction with the Environment

Structure and Dynamics of the Organizational Environment

Uncertainty and Indistinctiveness of the Organizational Environment

Synchronic and Diachronic Analysis of the Organizational Environment

Analysis Techniques of the Organization

Business Processes, Learning and Development within the Context of Strategic Management

Evaluation and Measuring of the Potential and Realization of the Organization within the Context of Strategic Management

Strategic Control in Contemporary Management

Information Technologies in Strategic Management

Business Intelligence and Strategic Management

Decision Support Systems and Artificial Intelligence in Strategic Management

All scientific articles submitted for publication in Journal are double-blind reviewed by at least two academics appointed by the Editor's Board: one from the Editorial Board and one independent scientist of the language of origin - English. Reviewers stay anonymous. Authors will timely receive written notification of acceptance, re-marks, comments and evaluation of their articles.


Decision Support Systems in Strategic Management www.ef.uns.ac.rs/sm ISSN 1821-3448 UDC 005.21 2013, Vol. 18, No. 4

Contents Imre Petkovics, Jelica Trninić, Jovica Đurković The Role of Information Technology Support in Sustainable Development 3-13 Esad Ahmetagić, Blaženka Piuković A Business Processes Management Model Applicable in Public Water Supply Utilities 14-19 Natalia P. Leshchenko Crisis Management of Corporate Strategic Stability 20-26 Nenad M.Vunjak, Jelena Buha, Vedat Zulfiu, Penekeh Pechu TangiriAn Empirical Study on Efficiency, Effectiveness and Performance Measurement in Supply Chain Management 27-34 Michaela Chocholatá Stock Market Integration: A Case Study for Serbia and Slovakia 35-42 Vuk Vuković Business Software Testing Model Design: A Theoretical Framework 43-48

STRATEGIC MANAGEMENT, Vol. 18 (2013), No. 4, pp. 003-013 UDC 004.738.5:502.131.1

Received: April 10, 2013

Accepted: November 12, 2013

The Role of Information Technology Support in Sustainable Development

Imre Petkovics University of Novi Sad, Faculty of Economics Subotica, Serbia Jelica Trninić University of Novi Sad, Faculty of Economics Subotica, Serbia

Jovica Đurković University of Novi Sad, Faculty of Economics Subotica, Serbia

Abstract In addition to providing an opportunity to foster the development of functioning process and the sale of com-panies’ products and services, modern-day innovation in the area of information technology (IT) provides amodel of using IT for reducing start-up investment cost and optimise functioning costs. Two key paradigms ofmodern-day business operations will be focussed on in this article: cloud computing and Internet of Things(IoT). The objective of the analysis is to assess the possibility of exploiting the new development and function-ing opportunities, and establish the challenges of their implementation and functioning. Keywords Cloud computing, Internet of Things.

Introduction

As early as in its initial stage, the role of I was largely oriented to easier performance and auto-mation of routine, well-defined activities in orga-nisations. This task is still performed as a standard procedure very efficiently. It is irreplaceable in the storage, retrieval, procession and presentation of stored and/or processed data. Continuous func-tionality and availably of data not only of each individual process but also at the integrated level, high speed of transactional and other types of processing, and the short return on investment (ROI) cycle have resulted in the presence of tech-nology in all the spheres of society. Recently, the application of artificial intelligence, knowledge management, statistical analysis and data mining for discovering tacit knowledge, new laws, yet undiscovered correlations located in large data-bases (Petkovics, 2007) makes qualitative changes in the significance and role of applying IT in or-ganisation and contributes to their efficient and successful business.

This article analyses new trends or two new concepts in the IT area already available on the market and implementable by organisation. In addition to providing an opportunity to foster the development of functioning processes and the sale of companies’ products and services, these inno-vative technologies provide a model of using IT for reducing start-up investment cost and reaching an affordable levels of functioning costs.

This article addresses the issue of cloud com-puting and Internet of things, analysing the possi-bilities (i.e. advantages and risks) of their applica-tion in organisation, from the viewpoint of achiev-ing new possibilities in development and func-tioning, i.e. their contribution to companies’ sus-tainable development.

1. Cloud computing

The notion of cloud computing encompasses the use of IT resources (hardware, software platform, database server and applicative software (public, telephone, or any other already existing network)

4 Imre Petkovics et al. The Role of Information Technology Support in Sustainable Development

STRATEGIC MANAGEMENT, Vol. 18 (2013), No. 4, pp. 003-013

as a pay-as-you-go public service, billed based on the amount of used service (Petkovics & Tumbas, 2010).

Neither the idea, i.e. paradigm not the term “cloud” are of recent date. The word “cloud” itself was borrowed from telephone companies, where the term “cloud” is applied to a situation where the contracted bandwidth is not provided with specific wire pairs, but free phone lines are acti-vated at the time of the transfer to ensure guaran-teed bandwidth. The word “cloud”, therefore, was then applied to the state when it is still impossible to pinpoint the specific transmission wire pairs. The paradigm of cloud computing is even older (Armbrust et al., 2009; Wikipedia, 2013b; Foster, Zhao, Raicu & Lu, 2008). As early as 1961, McCarthy formulated the idea, which he an-nounced at MIT’s centenary celebration, that IT, i.e. "computation may someday be organized as a public utility" (Wikipedia, 2013a), similar to electric power supply.

To date, cloud computing is not an unequivo-cally defined concept. At the “Cloud Summit Ex-ecutive 2008” conference, the chairman of the opening session said that he had asked twenty experts how they would define cloud computing and got twenty-two different answers. The com-putation model by means of cloud computing is not quite unequivocally understood, so that three are different definitions, which still mostly over-lap, while all of them emphasise the fact that it is distribution of IT services by the supplier, i.e. its application and use (by the users) (Petkovics & Tumbas, 2010). Below are some of the definitions of cloud computing:

1. “The set of disciplines, technologies, and business models used to render IT capabili-ties as an on-demand, elastic, scalable ser-vice.” (Reeves, 2009).

2. “Cloud Computing refers to both the appli-cations delivered as services over the Inter-net and the hardware and systems software in the data centres that provide those ser-vices. The services themselves have long been referred to as Software as a Service (SaaS). The data centre hardware and soft-ware is what we will call a Cloud.” (Armbrust et al., 2009)

3. “Cloud computing is a new cost-efficient computing paradigm in which information and computer power can be accessed from a Web browser by customers.” (Xiong & Perros, 2009)

4. “…cloud computing is an emerging com-putational model in which applications, da-ta, and IT resources are provided as servic-es to users over the Web (the so-called “cloud”). (Fisher & Turner, 2009)

5. “…a computer cloud is a large cluster of machines located in a single or multiple da-ta processing centres, connected with fast network technology, enabling external clients to use computer resources and tools for on-demand storage of data from those machines.” (Ailamaki, Dush & Pantere, 2009)

6. “a style of computing where massively scalable IT-enabled capabilities are deli-vered as a service to external customers us-ing internet technologies.” (Gartner) (Dargha, 2009)

7. “a style of computing where massively scalable IT-related capabilities are provided ‘as a service’ across the Internet to multiple external customers.” Daryl Plummer, Gart-ner (Bozzabench, 2013)

8. “A pool of abstracted, highly scalable, and managed compute infrastructure capable of hosting end-customer applications and billed by consumption.” Forrester (Bozza-bench, 2013)

9. “Highly scalable, on-demand, web-accessed IT resources with major cost / cash and flexibility benefits due to standar-dization, modularization, and virtualization using scaling effects.” (Jaekel, Luhn, 2009).

10. Cloud computing architecture: services and data exist in a shared, dynamically scalable cluster of resources based on virtualisation technologies and/or scaled applicative en-vironments. (Intel’s definition (Bozza-bench, 2013))

A question arises: if the idea was known in the

1960s, why has cloud computing computation model not appeared earlier? One of the answers may be that there was no adequate infrastructure for providing such services. This answer, howev-er, is not quite true, as the Internet has existed for quite a long time. Another answer could be that it is due to the emergence of the latest global eco-nomic crisis in 2008. This answer is only partially true. Several factors causing the emergence of cloud computing are discernible, and the most compact formulation is the one by Garner. “Cloud computer, opines Gartner, is an ‘emerging phe-

Imre Petkovics et al. The Role of Information Technology Support in Sustainable Development 5


nomenon’ – in other words, a phenomenon ap-pearing at the moment when the conditions are favourable and when numerous factors allow it – in this case, service orientation, virtualisation and standardisation of computing by means of the In-ternet. In connection with the global economic crisis, cloud computing enables cutting some IT related costs.” (Bozzabench, 2013).

Cloud computing services are mostly used via the Internet, and the dynamics of users’ demands in the use is not limited, with the additional bene-fit that service scaling is executed automatically, without special intervention of the user and the provider. Costs are billed based on used services. Cloud computing is the highest step on the devel-opment ladder: time-sharing, grid computing, on-demand services, utility computing, software as a service, and finally cloud computing. The key characteristics can be formulated as follows (Wil-liams, 2012, COMING, 2013)

1. on-demand self-service 2. ubiquitous network access 3. location-independent resource pooling 4. rapid elasticity 5. scalability 6. low cost of use 7. reliability 8. safety

1.1. Cloud computing service offer (use model)

The categorisation of the cloud computing servic-es usually distinguishes between three layers of service (which could also be referred to as use models) (Bozzabench, 2013, COMING, 2013): Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Some categorisations, however, split the lowest layer of service (IaaS) into two levels: Hardware Infrastructure as a Service (HIaaS) and Software Infrastructure as a Service (SIaaS).

Hardware Infrastructure as a Service (HIaaS) provides access to the server or storage space on a virtual basis, so that the users do not need to pur-chase hardware, provide space for its accommoda-tion and execute a computer network; rather they can use it in the form of virtual service via the Internet, i.e. Rackspace Cloud Servers).

Hardware Infrastructure as a Service (HIaaS) provides basic software infrastructure (operative system, database management software, web browser, web hosting, messaging) suitable for work and adding the user’s own development en-

vironments and applications (Microsoft SQL Da-tabase, Amazon Simple Database).

Platform as a Service (PaaS) encompasses HIaaS and SIaaS, and also provides contracted (and, of course, licensed) development platforms and software systems for developers, managers and other users, thus sparing the user purchasing, installation, maintenance, licensing and managing those resources (Microsoft Azure, Amazon Elastic Compute Cloud – EC2, Google App Engine).

Software as a Service (SaaS) enables users to access specific (licensed) software solution, appli-cation they require and use as needed within the organisation’s operations (Salesforce CRM, Google Apps, Microsoft Exchange Online).

As it can be seen from the presented descrip-tion, each subsequent cloud computing service is an upgraded of the previously existing ones, and also contains resources required for functioning from the previous services (Figure 1 – Drue Reeves et al., 2009). There are, of course far more elaborate models of using cloud computing from the user’s point of view. The leaders in this area are IBM and Sun Microsystem’s models (Figure 2: Sun Microsystems, 2009) and Siemens (Figure 3: Jaekel & Luhn, 2009) are also shown below.

Figure 1 General computer cloud use model Source: Reeves, 2009

Figure 2 Sun’s detailed computer cloud use model Source: Sun Microsystems, 2009



Figure 3 Siemens’ cloud computing model Source: Jaekel & Luhn, 2009

2. Cloud computing model and execution method

The cloud computing execution has another as-pect, which can be termed as the cloud computing provider mode. It is a way, or possibility, of pro-viding all cloud computing services, whether a single provider pro all the services, or there may be a single independent provider for each service. This aspect is shown in Figures 4 and 5 (Reeves, 2009).

Figure 4 A single provider supplies a cloud. Source: Reeves, 2009

Figure 5 Three providers supply a cloud. Source: Reeves, 2009

From the viewpoint of development and ex-

ecution (the functioning model) of cloud compu-ting, there are as many as three (but not definitely exclusive) classifications: (a) publicity of usage, (b) locality of execution and (c) organization of maintenance.

1. From the aspect of publicity of usage, i.e. level of public access to services, cloud computing can be: a. private computer clouds, providing in-

dividual, dedicated services, reserved for a single user or selected group of users, available through the Internet or private networks (large organisations, universities, hospitals, etc.),

b. open-type public computer clouds offer-ing general services, and available to all interested users over the Internet (Ama-zon EC2, Microsoft Azure, Google App Engine, Salesforce CRM), and

c. Hybrid computer clouds, i.e. a combina-tion of the private and public.

2. As for locality of implementation (whether they are within the users’ premises or not), they can be a. internal computer clouds, physically lo-

cated within the organisations’ premis-es, protected by the organisations’ fire-walls, accessed through the intranet and used internally, and

b. external clouds, arranged outside the user organisation, by cloud providers within their environments.



3. Organisation of maintenance (in terms of whether the user maintains it or not) of a computer cloud is possible:

a. within the organisation, by the in-house maintenance team, or

b. by means of outsourcing, when another organisation (as a rule, a cloud provid-er) is in charge of maintenance.

Not all out of 12 possible combinations of op-

tions/elements of the above three classifications, are valid, of course. The two diametrically op-posed combinations are: (a) private internal com-puter cloud with in-house maintenance and (b) public external cloud with outsourced mainten-ance. Both options have both advantages and dis-advantages.

Private internal cloud computing (where type of maintenance is of secondary significance) is the only acceptable solution for companies involved in handling secret and/or confidential data, or data critical for the functioning of a particular enter-prise. In that case, the organisations themselves set the dynamic and workflow, and also provide their own safety measures related to the data secu-rity. This, however, results in loss in the cost-effectiveness of IT solutions, which is exactly what cloud providers offer.

A public external cloud computing includes outsourcing, in addition to maintaining the func-tionalities, delegation of resourcing, and adapta-tion of the users’ demands to a public computer cloud. In this case, however, the user may opt for one of the available alternatives for executing the implementation of cloud computing, but the cost-effectiveness of the implementation remains.

Recently, IBM offers hybrid computer clouds of specific execution, with applications located in a public external cloud, and the data in a private internal cloud. This type, i.e. execution of a hybr-id cloud could be popular among companies that cannot use public external clouds for safety and/or security reasons (Petkovics, 2010).

3. Internet of Things

Over the past few years, there have been more intensive considerations and executions of the idea of connecting all possible objects, appliances and devices through the Internet. Modern-day objects, appliances and devices, of course, cannot be directly connected to the Internet, as they lack “intelligence”, but the addition of “smart” devic-es, i.e. sensors, can easily help gathering data on

the state and functioning of these “things” (ob-jects, appliances and devices), and sending the collected data to a server (in a computer cloud, for instance, where useful analysis of these data could be performed. Such intelligent things could also communicate mutually, and resolve certain classes of problems controlled by a server from the cloud.

The above described idea, need or aspiration was named differently. According to some re-sources, the first name, “Internet of Things”, with such a vision was formulated by the British scien-tist Kevin Ashton in 1999 (Webmajstori.Net, 2013). Names for this trend are certainly numer-ous: Industrial Internet, Web of things, Machine-to-Machine (M2M), Intelligent Systems, etc. The objective of this concept is easier, better and faster monitoring and insight into relevant real-time processes in organisation, and making faster, more appropriate and better decision based on the results of analyses of gathered data. According to forecasts, a growth in the number of such intelli-gent devices is to be expected in the near future. This technology is ready and able to change al-most any aspect, every area of business, and con-sequently, the environment.

When asked how he would describe the Inter-net of Things in two sentences from the citizens’ point of view, Professor Rob van Cranenbroek said in his interview for B92: “The Internet of Things is a convergence of final and daily servic-es, creating a new ontology in which every object will be recognised in a unique way, one way or another. It is like the wind – you feel it, but you can’t see it or tell where it is coming from.” (B92 – Tehnopolis, 2012).

The same issue is worded differently by com-mon people, i.e. journalists: “The other prospec-tive task is connecting machines by means of the Internet, so that they can communicate indepen-dently to an extent. For most of us, it would mean that we can manage house appliances with our mobile phones, checking whether we have turned off the cooker, turned on the water heater, the air-conditioning, or something else. Another great thing is the system of sensors, by means of which our car could move safely, whether or not some-one is holding the steering wheel…” (Stanojević, 2013).

A concise description of the technology of the Internet of Things can be uncomplicated: “It is not difficult to explain a concept by which appliances will be designated with their URLs, enabling each of us to manage the technology we are already



using. At first glance, this is only a technological improvement, acceleration and raising cost effec-tiveness. The second and subsequent glances, however, disclose that this is not only a new, uni-versal remote control device.” (Rodić, 2012)

3.1. Motivation for introduction

The following advantages can be stated as motiva-tion for implementing the Internet of things in an organisation:

a. real-time gathering and storing data moni-tored by intelligent devices,

b. more efficient management of the net-worked resources provided with sensors,

c. processing and analysing data from intelli-gent sensors located in data bases or ware-houses in an organisation’s computer cen-tre or a computer cloud,

d. relevant data and their timely processing and analysis in the form of required reports serve as a basis for making good business decisions, and valid decisions provide functional efficiency, which contributes to the organisation’s sustainable development,

e. such an execution of monitoring the work of appliances relevant for an organisation’s function enables operational cost cutting, and

f. faster development of products and servic-es due to connections and communication between the appliances, etc.

Motivation for individuals (physical persons)

to use the Internet of things:

a. monitoring the vital functions of the human organism,

b. monitoring the parameters of an illness, with the opportunity to gain information, and possible intervention,

c. curbing and controlling the electric power and heating energy consumption, etc.

Motivation for society and the government:

a. reduced electric power and heating energy consumption due to reduced greenhouse ef-fect, as the Kyoto Protocol imposes a bind-ing target of reducing household energy consumption by 20% in the EU by 2022 (Rodić, 2012).

3.2. The functional characteristics of IoT

Owing to their own intelligence, or the intelli-gence of their immediate environment (compris-ing intelligent sensors that may have some or even all the elements of microcomputers: processor, memory, external memory, input-output devices, operative system, applications etc.), entities, i.e. object, appliances and devices become nodes in the Internet of things. Due to this, practically any object may became and IoT node: a lamp post in the street, an air conditioner, a blood sugar meter, a coffee maker, a car, a piece of agricultural ma-chinery, or a cow battery. Essentially, it is essen-tial for these entities to be directly or indirectly connected, either in a local area network or trough cables or wireless links to a remote server (per-haps within a cloud).

Local connection between IoT nodes and the server (or a device connected to the Internet) can be executed by means of cables (Ethernet, Ho-mePlug, HomePNA, HomeGrid or LonWorks networks) or wireless appliances (WiFi, Blu-etooth, Bluetooth Low Energy, RFID, NFC, Xbee, Zigbee, Z-Wave, Meter-Bus, or Wireless M-Bus). Already existing mobile networks (GSM, 3G, LTE, WiMAX, GPRS, SIGFOX, or Neul-NET) and satellite connections are used for net-works covering a broader geographic area.

Devices for executing asynchronous commu-nication based on scanning local devices by local servers are used where synchronous communica-tion between the IoT appliances and the server is not necessary. Some modular executions of intel-ligent sensors may contain modules for local and wide-area communication with synchronous and asynchronous mode (Figure 6 – McLellan, 2013).

In some executions of IoT installation (i.e. au-tomation of a home or office), it is recommenda-ble to use local servers or gateways (Figure 7 – Mc Lellan, 2013), which can perform real-time data analysis, and are, as a rule, connected to a server in a cloud.

According to estimates, the number of data generated by the IoT nodes will be several de-grees higher than human-generated data. Record-ing, transmission, storage and processing those data will require strong support which will, most probably, be provided by cloud computing.



Figure 6 Intelligent sensors with synchronous and asynchronous modes Source: McLellan, 2013

Figure 7 Server/gateway IoT installations

Source: McLellan, 2013

According to the above descriptions of indi-

vidual elements in the execution of an IoT net-work, the entire architecture of IoT service, re-gardless of the specific function it performs, looks as shown in Figure 8 (Mc Lellan, 2013). In specif-ic executions, depending on the type of task, some parts of the entire IoT network “anatomy” do not appear, of course.

Figure 8 The entire architecture of IoT service regardless of the specific function it performs

Source: McLellan, 2013

3.3. The role of cloud computing and the Internet of things in sustainable development

Regarding the role of the computer clouds and Internet of things, it can be generally said that they contribute to the increase in the operative efficiency of organisations with reduced function-ing and maintenance costs of hardware and soft-ware support. Thus saved funds can be invested in product and service development, or marketing activities.

The application of computer cloud technology brings about three novelties from the aspect of hardware usage: (a) without purchasing of IT equipment, cloud computing meets hardware needs even in the case of extreme and very fast changes in demands; (b) without start-up invest-ment in IT, organisations enter business with low costs, and the costs of the computer cloud will grow in proportion with the development of their undertakings; and (c) owing to the pay-as-you-go billing of the used resources, cloud computing is a cheaper solution even than hiring hardware equipment and software solutions.

The Internet of Things was discovered by or-ganisations in search of innovation and new busi-ness models, under pressure for higher productivi-ty and profit. Low-cost data capture and transmis-sion by IoT and their integration into the data sys-tem in an enterprise enables improvements of the existing processes, services and products. IT costs, and consequently, IoT costs (implementa-tion and maintenance) keep falling, and IoT can contribute to efficient and simple monitoring of resources, plus reduced operating costs. With the support of intelligent systems, IoT solutions can



automatically initiate the decision making process for achieving the organisations’ set objectives, and moreover, even faster than the staff them-selves.

3.4. The possibilities of applying cloud computing and the Internet of things in organisations

IBM has devised a system for monitoring the use of computer clouds in the form of innovation por-tals, where the suppliers and users (voluntarily) record all the executions of cloud computing in the world. All the experiences and problems of executed systems are described here, from an in-cubator software farm in the city of Wuxi outside Shanghai to counselling system integration Sogeti or university solutions (joint execution by IBM and Google it MIT, Berkeley, and Seattle (Cappos, Beschastnikh, Krishnamurthy, & Anderson, 2009), etc).

Organisations, particularly SMEs, can provide IT support without initial investment, which is cost-effective and safe, with guaranteed and dy-namic performance. As well as fast and efficient help in case of any failure or malfunction, func-tion support is provided by professionals.

Public utilities and administration use a more complex usage model, including a public and a private cloud, as private application they also have public services available to the general popu-lation in addition to protected ones.

Software companies and other application suppliers prefer to use computer clouds for pre-senting and testing their products, and also for training users, as they needn’t worry about the necessary hardware and installed applications. The same advantage exists in the case of using, removing errors and further development of ap-plication (i.e. change management).

IT support in the form of computer cloud is the best possible solution for undertakings and events of limited duration: in addition to cloud services, all that is required is power supply and internet connection, with the inevitable existence of mini-mum computer infrastructure (laptop, printer, etc.)

Generally speaking, the Internet of things is a step further in the application of cloud computing, regardless of the fact that IoT solutions can be executed and used within the organisation itself, or through its communication lines or private network. The very philosophy of IoT systems im-plies using internet for connecting nodes and cen-tres for processing data gathered by intelligent devices.

In the application of IoT in logistics and trans-port, vehicles, containers, pallets or parcels (prac-tically, any type of product packaging units, or even individual products) can be tracked real-time in the transport process, providing a higher degree of protection, security and reliability in transport. Tracking the means of transport, i.e. vehicles, provides the possibility for toll calculation and collection management.

IoT executions can play a vital role in health-care systems: real-time monitoring of patients’ vital parameters, dispensation of medicines to patients and reminding patients of dosages and times of medication. Should the monitored para-meters deteriorate, healthcare professionals can intervene and recommend patients or their care-givers what to do in order to improve the emerged situation.

Intelligent measuring instruments are easily applicable in public utilities, for measuring the consumption of electrical power, gas, water or heating, also providing information on interrup-tions, peak demand and delivery, and the delivery pathways of these energies. This information, used by users as information to public utilities, are a basis for planning and billing their services.

Retailing is currently undergoing a transition process. Setting up automated outlets, mobile shops and pop-up stores, even without staff, is quickly feasible by means of IoT, and all it takes is a few minutes for execution, if power supply, i.e. electrical socket is provided, and if there is a strong enough telephone provider signal (a wire-less router resolves the issue of communication. Tracking the proper and safe functioning can be executed by real-time video-surveillance, paying bills by POS terminals, and emerging problems and help to customers can be solved by real time video connection.

A detailed overview of the possibilities of us-ing IoT across the sectors of economy, by applica-tion groups, locations and devices, is given in Figure 9 (Beecham, 2013).

4. The risks of applying the analysed IT techniques

There is still scepticism, especially in our culture, towards cloud computing. This negative attitude generates different approaches and interpretations of cloud computing in general. As well as aggres sive advertising campaigns lead by cloud provid-ers, there are variously formulated models of use and functioning. The disadvantages have been pointed out several times in several places



(Armbrust et al., 2009; Reeves, 2009; Soat, 2010), and the most notable that can be highlighted in-clude

1. Cloud computing performance defined in Software Licence Agreements (SLA) can-not always be delivered by providers, as the access, speed of information exchange between the user and the cloud depends on the functioning and performance of the In-ternet network, and, on the other hand, due to complexity of cloud computing services, the provider is more likely to malfunction than an organisation’s in-house system.

2. The limited transferability of services be-tween providers due to lack of standard re-sult in high level of the users’ dependence on their cloud providers.

3. The shortcomings and inconvenience of SLAs on the one hand and the stability and reliability of providers on the other (as seen on the example of dotcom boom) prejudice favourising renowned large cloud providers (who can be significantly more expensive than the other.

4. Privacy and security of data can also be disputable and problematic.

5. Short-term investment in cloud computing (at present conditions and rates) is accepta-ble and appealing, but there are no guaran-

tees as to how long such a state will remain on the market.

EU legislations pertaining to cloud computing

includes the following directives and regulations:

1. The EU Electronic Commerce Directive 2000/31/EC pertains to cloud providers’ Iaas, PaaS and SaaS.

2. The EU Directive on Privacy and Electron-ic Communications 2002/58/EC, amended in 2011, regulates interception and surveil-lance of cloud providers.

3. The Data Retention Directive 2006/24/EC defines providers’ obligation to routinely capture and archive data on the use of their services.

4. The Data Protection Directive 95/46/EC regulates the degree of protection and modes of processing of the personal data of the EU citizens.

These regulations formulate the five key legal

issues defining the following five areas of risk in cloud computing: (a) data protection; (b) confi-dentiality; (c) intellectual property; (d) profes-sional negligence and (e) outsourcing services and amendments in the mnitoring.

Figure 9 Application possibilities of IoT

Source: Beecham, 2013



The greatest problem related to the use of the Internet of Things is achieving satisfactory safety levels, i.e. protection of data and devices in unse-cured environments such as retail outlets and mo-bile shopping counters, where the risk of network, physical and software break-ins (through cellular networks), is the highest. Communication net-works are critical for the functioning of IoT, so that robust, durable and reliable appliances (from the functioning point of view) need to be used, with remote operation and supervision.

Similar to cloud computing services, IoT, i.e. applications based on intelligent devices cannot be provided by a single provider due to the hete-rogeneity of the task itself: there are network (cel-lular) operators, suppliers of hardware, intelligent devices and/or sensors, and application integrating software companies (Lohman, 2013).

Conclusion

The article analyses two paradigms in the devel-opment of information technologies: a new para-digm of using computer technology and computa-tion (cloud computing), and the paradigm of con-necting the necessary objects of an indefinite number from the human surroundings (Internet of Things), and their impact on and contribution to the sustainable development of organisations. The authors also point to the possible areas of using cloud computing and Internet of Things in organi-sations regardless of the fact that these two para-digms are still not fully defined and matured.

Cloud computing is at a more mature stage than IoT, there are much more providers and us-ers, with a further growing trend, but at the begin-ning of its introduction, there were the same con-cerns as with the IoT now: reliability of function-ing, data security and the privacy of users and their data. If the business community is willing to accept these new paradigms (obviously having accepted the first one, i.e. cloud computing), cost cuts and advances in business operation will con-tribute to the acceptance of the Internet of Things, and in return, the experts’ continuous work will improve their characteristics to users’ general sa-tisfaction.

One of the disadvantages from the aspect of use of IoT – the potentially huge number of intel-ligent devices, addressability or address space has been removed by the introduction of the IPv6 standards, thus making 3.4*1038 addresses availa-ble, which is quite a sufficient number for intelli-gent objects in the foreseeable future. Standardisa-tion of hardware components and communication

are under way, and the protection of data and networks must be worked on on a daily basis (as it is the case already).

Forecasts point to the growing trends of both cloud computing and the Internet of things, re-gardless of the fact that their introduction entails certain risks. This growth will bring about a change in the manner of functioning and operating of organisations in general, first in the West, and then this wave will spread rapidly. Our organisa-tions should certainly become acquainted and ready for applying these technologies in order to be competitive and survive on the international, i.e. global market. SM

References

Ailamaki, A., Dash, D., & Kantere, V. (2009). Economic Aspects of Cloud Computing. Retrieved March 4, 2013 from Flash informatique: http://flashinformatique.epfl.ch/IMG/pdf_fi-8-9-page45.pdf

Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., et al. (2009, February 10). Above the Clouds: A Berkeley View of Cloud Computing. Retrieved March 3, 2013 from EECS: http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf

Beecham Research Ltd. (2009). M2M/IoT Sector Map. Retrieved March 6, 2013 from Beecham Research: http://www.beechamresearch.com/article.aspx?id=4

Bozzabench. (2013). Cloud Computing - mit ili revolucija u elektronskom poslovanju. Retrieved March 6, 2013 from Bozzabench: http://www.bozzabench.com/Tests/TestView/tabid/95/id/43/Sta-je-e2809eCloud-Computinge2809c.aspx

Cappos, J., Beschastnikh, I., Krishnamurthy, A., & Anderson, T. (2009). Seattle: A Platform for Educational Cloud Computing. The 40th ACM Technical Symposium on Computer Science Education SIGCSE 2009 (pp. 111-115). Chattanooga: ACM SIGCSE.

COMING. (2013). Ciljne grupe korisnika Cloud Computing usluga. Retrieved March 03, 2013 from COMING: http://www.coming.rs/cloud/ciiljne_grupe

Dragha, R. (2009, April). Cloud Computing-Key Considerations for Adoption. Retrieved March 6, 2013 from Infosys: http://www.infosys.com/cloud/resource-center/documents/cloud-computing.pdf

Fisher, F., & Turner, F. (2009). Cloud Computing as a Supply Chain. Retrieved March 4, 2013 from http://www.cu-0xff.de/downloads/FrankFi_CloudComputingSupplyChain.pdf

Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008). Cloud Computing and Grid Computing 360-Degree Compared. Grid Computing Environments Workshop, 2008. GCE '08 (pp. 1-10). Los Alamitos: IEEE.



Jaekel, M., & Luhn, A. J. (2009). Cloud Computing – Business Models, Value Creation Dynamics and Advantages for Customers. Retrieved March 4, 2013 from Digitale Bibliothek Braunschweig: http://digisrv-1.biblio.etc.tu-bs.de:8080/docportal/servlets/MCRFileNodeServlet/DocPortal_derivate_00027466/Beitrag253.pdf

Jeste li čuli za "Internet stvari"? (2012, February 29). Retrieved March 6, 2013 from B92: http://www.b92.net/tehnopolis/intervjui.php?yyyy=2012&mm=02&nav_id=586817

Lohman, T. (2013). The business benefits of M2M, u The Executive’s Guide to the Internet of Things. Retrieved March 5, 2013 from TechRepublic: http://www.techrepublic.com/whitepapers/the-executives-guide-to-the-internet-of-things/32603017

McLellan, C. (2013). The tech behind M2M and the Internet of Things, in The Executive’s Guide to the Internet of Things. Retrieved March 5, 2013 from TechRepublic: http://www.techrepublic.com/whitepapers/the-executives-guide-to-the-internet-of-things/32603017

Petkovics, I. (2007). Tehnologije poslovne inteligencije za strategiju odnosa sa kupcima. Unpublished doctoral dissertation. Subotica: Faculty of Economics in Suboti-ca

Petkovics, I. (2010). CRM in the Cloud. 8th International Symposium on Intelligent Systems and Informatics (SISY 2010). Los Alamitos: IEEE.

Petkovics, I., & Tumbas, P. (2010). Felhő alakban kísért a múlt – Cloud Computing. Informatika Korszerű Technikái Konferencia 2010 (pp. 190-199). Dunaújváros: Dunaújvárosi Főiskola, Informatikai Intézet.

Reeves, D. (2009, December 3). Cloud Computing: Transforming IT. Retrieved March 06, 2013 from Educase: https://net.educause.edu/ir/library/pdf/ECR0901.pdf

Rodić, V. (2012). Kuda ide Internet. Retrieved March 6, 2013 from Top Srbija: http://www.topsrbija.com/index.php?option=com_content&view=article&id=2929:vojislav-rodi-kuda-ide-internet-&catid=310:najava&Itemid=400

Soat, J. (2010). The Cloud’s Five Biggest Weaknesses. Retrieved March 5, 2013 from InformationWeek: http://www.informationweek.com/cloud-computing/blog/archives/2010/06/the_clouds_five.html

Stanojević, Z. (2012, January 10). Godina za spavanje. Retrieved March 6, 2013 from Vreme: http://www.vreme.com/cms/view.php?id=1092035

Sun Microsystems. (2009, Jun). Introduction to Cloud Computing architecture. Retrieved March 4, 2013 from TechRepublic: http://www.techrepublic.com/resource-library/whitepapers/introduction-to-cloud-computing-architecture/

Webmajstori.Net. (2013). SteelMaiden:Internet of Things (IoT). Retrieved March 6, 2013 from Webmajstori.Net: http://www.webmajstori.net/forum/category/12-ostalo/

Wikipedia. (2013). John McCarthy (computer scientist). Retrieved March 4, 2013 from Wikipedia: http://en.wikipedia.org/wiki/John_McCarthy_%28computer_scientist%29

Wikipedia. (2013). Računarstvo u oblaku. Retrieved March 5, 2013 from Wikipedia: http://sr.wikipedia.org/wiki/Ra%C4%8Dunarstvo_u_oblaku

Williams, H. (2012). Spatial Cloud Computing (SC2). Retrieved March 5, 2013 from SKE: http://www.skeinc.com/pages/Downloads/SC2_White_Paper_August_2012.pdf

Xiong, K., & Perros, H. (2009). Service Performance and Analysis in Cloud Computing. Retrieved March 5, 2013 from CiteSeer: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.149.5001&rep=rep1&type=pdf

Correspondence

Imre Petkovics

Faculty of Economics Subotica Segedinski put 9-11, 24000, Subotica, Serbia

E-mail: [email protected]

STRATEGIC MANAGEMENT, Vol. 18 (2013), No. 4, pp. 014-019 UDC 005.5:334.724 ; 007:658]:004

Received: March 15, 2013

Accepted: July 17, 2013

A Business Processes Management Model Applicable in Public Water Supply Utilities

Esad Ahmetagić University of Novi Sad, Faculty of Economics Subotica, Serbia Blaženka Piuković Ministry of Finance - Tax Administration Subotica, Serbia

Abstract Process orientation is a relatively new approach to researching the structure and functioning of a company.Unlike classical functional approach, process-based approach is focussed on the market, that is, on client andconsumer demands. Processes that result in a product or a service are defined - and are managed by follow-ing technological or business hierarchy and activity content. Management (the so-called “business process owner”) is assigned more obligations and activities of all functional areas in the company, that is, other busi-ness processes are subordinated to these key processes. Research of business processes was performed inpublic utility companies for water supply in Vojvodina and an adapted to generate a viable managementmodel. Keywords Business process, business process owner, process management model, business process management,modelling.

Introduction

When highlighting the key features characterising the process-based approach, i.e. process orienta-tion, the primary issues to point out undoubtedly include horizontal communication, ownership of processes and allocation of an organisation’s ob-jectives and resources across functional units by means of business processes.

Horizontal communication is highly efficient in overcoming problems that emerge in cases of lack of the required communication between par-ticular organisational segments of an organisation. Coordination and better communication between the employees results in easier accomplishment of organisational objectives, and cross-functional interaction is a vital element of business process.

In addition to horizontal communication as an essential feature of process based approach, the other two significant factors are business process ownership and allocation of an organisation’s ob-jectives and resources across functional units by means of business processes. The process owner’s task and responsibility is to keep the system func-

tioning as a whole (Bosilj, Hernaus, & Kovačić, 2008). Rentzhog (2000) argues that it is not enough for the process owner to provide for effec-tive and targeted process, adding that the owner must also manage processes in a manner that en-tails constant process improvement. In addition to the above, another stated task of the process owner is the obligation to manage development and consumer oriented processes, through daily collaboration with the staff executing individual process tasks.

Rentzhog (2000) states that the characteristics of horizontal organisation were considered and researched, among others, Byrne (1993), Ostroff and Smith (1992) and Stewart and Jacoby (1992) and that, according to them, three central charac-teristics of a horizontal organisation can be de-duced. First, that process-oriented observation should permeate the whole organisation. The sec-ond characteristics points to the fact that consum-ers stimulate work in all activities. The third char-acteristic refers to authorised flow teams becom-ing central organisational entities.

Esad Ahmetagić et al. A Business Processes Management Model Applicable in Public Water Supply Utilities 15


Process-based approach is a convenient tool for connecting functions within an organisation, such as production, finance, marketing activities, and supplier relations. This approach is also ap-plicable on a broader plane, such as supply chains or on the micro level, such as a particular job in an organisation. The process-based approach em-phasises the fact that any organisation is a set of interconnected processes, and introducing proc-ess-oriented structure should result from the ef-forts of all its individual process participants. In addition, customer awareness is fully involved in this approach, taking up the prime position in it (Anupindi, Chopra, Deshmukh, Mieghem & Ze-mel, 2012).

There are several developed models for busi-ness process management, which we shall present below. Our research subject covers the business process in water supply public utilities in the Autonomous Province of Vojvodina, and includes all public utility companies in this region.

1. Representative business process management models

Business process management is a comparatively new discipline resulting from a merger of diverse fields of knowledge and practical experience of world renowned theoreticians, analysts and con-sultants.

Over the period from 1995, when Rummler and Brache launched the first business process management process to date, we have been wit-nessing the emergence of multiple business proc-ess management processes. These models are of great importance for the theory and practice of business process management, and their contribu-tion to the development of building business proc-ess management is evident.

Transformation of the classical organisational structure into business process oriented in a water management public utility requires the application of appropriate methodology.

The presentation of diverse approaches, i.e. methodologies given below will enable water supply public utilities to understand the transfor-mation process more easily, but they will also get an opportunity for comparison analysis, insights into some adjustments brought about by some specific features, and use several demonstrated and applied methodologies to visualise more eas-ily the method of creating a new model that will be tailored to their needs. In view of the above, some of the representative business process mod-els will be presented below.

The models presented below differ in terms of focus, as some of them are more focussed on the issue of organisation transformation through man-agement structure, cultural changes, and environ-ment factors, whereas others are more focussed on the field of business process enhancement. How-ever, the common denominator for all approaches is that they advocate the bottom-up philosophy, emphasising the support of top management and comprehensive organisational planning. On prin-ciple, there are two categories of transformation methodologies: conceptual methodologies and specific methodologies. Conceptual methodolo-gies include models that view the transformation much more broadly, considering numerous inter-nal and external relationships and elements that must be taken into account in the transition to a new organisational solution. Unlike these, rather than being so strategically oriented, specific methodologies are more focussed on operative steps leading through the transformation process (Hernaus, 2006).

There is a large number of methodologies, but a selection was made for the purpose of this pa-per, so that the subsequent section will present four methodologies of implementing organisa-tional transformation.

(a) The Rummler-Brache process management

model

Rummler and Brache created the first process management model as early as 1990, but did not update it until 1995. This model is a specific methodology including the following stages:

Phase 0: Performance improvement planning Phase 1: Project definition Phase 2: Process analysis and design Phase 3: Implementation Phase 4: Process management These authors pointed out that process man-

agement results in systematic improvement of both process performance and the entire organisa-tion’s performance.

In their methodology, they place emphasis on the dilemma how to structure processes and ac-tivities so that they guarantee for the organisation the efficient work of all the organisation’s mem-bers. Strategy is used for developing improvement plan, whose phases depend on the process and its efficiency, and possibly present problems and their impact on the execution of the process. If the process contains problems, it is necessary to carry

16 Esad Ahmetagić et al. A Business Processes Management Model Applicable in Public Water Supply Utilities


out a detailed analysis and design, followed by the implementation of the chosen solution. Having completed process improvement, the next stage is continuous improvement of the process activity by way of process management (Hernaus, 2006).

(b) The BPM methodological framework

The BPM methodological framework is a spe-cific approach, primarily aimed at improving business processes, but it is also very convenient for business process introduction. It is the result of work and research of the Canadian consultancy Process Renewal Group. The model comprises four basic components, and is represented with eight phases.

As adapted from Burlton (2001), this model includes:

1. Strategic segment: - Business Context

- Architecture and Alignment 2. Design - Process Vision

- Understand - Renew

3. Execution - Develop- Implement

4. Operative segment - Nurture and Continuously Improve

Within the strategic segment, the Business

Context includes the analysis of the environment, beneficiaries or users, the organisation’s poten-tials, stakeholders and competition. Based on a detailed analysis of this, the desired status is de-fined and priorities set. The Architecture and Alignment includes identification of all business processes and selection of design priorities, and the criterion for this selection should be the con-tribution achieved for beneficiaries and other stakeholders. This phase should also include the required technology, human capabilities, educa-tion requirements, etc.

Design comprises the phases of Process Vi-sion, Understand and Renew. The priority task to be performed in the Vision phase is setting the desired performance for each individual identified process, which contributes to understanding the overall business vision. The scope of the project, strategies, alternatives, communications, roles and project plan finalisation are defined for each iden-tified process. An AS-IS view of the existing processes is developed, the workflow is decom-posed and analysed and oversights detected, fol-lowed by considering the possibilities for over-coming the detected flaws. Then follows the Re-new phase, perform by the design team, aimed at

redesigning the processes and removing all the errors detected in the Understand phase. The TO-BE process is then modelled based on creativity and new ideas, accompanied by implementation strategy.

Execution comprises the Develop and Imple-ment phase. In the development phase, certain measures are taken and required changes are made so as to provide appropriate support for the execu-tion of the TO-BE process. The following phase is Implement, which is very difficult and time con-suming, so that it can result in a certain amount of frustration and fatigue, with possible adverse ef-fects. This phase includes manager education and pilot tests, and the final activity is the implemen-tation of the projected solutions. A predefined performance measurement system will be an indi-cator whether the organisation has progressed in comparison to earlier state.

The operative segment is represented by the Nurture and Continuously Improve phase. This is the phase of considering performance achieved and that projected by TO-BE processes. This is the avenue for obtaining feedback and new knowledge, which should be the springboard for finding possibilities for further improvements (Zakić, 2009).

(c) J.R. Galbright’s Star Model

J.R. Galbright developed what is referred to Star-Model, which is classified into conceptual methodologies. It is used for defining the ele-ments of the organisation, whose harmonisation stimulates business success, meet consumers’ de-mands more intensely, and increases the staff’s productivity.

The rules and policies in the Star Model are divided into five categories (Galbraith, 2012; Galbraith Management Consultants, 2013):

1. (Managment) Strategy; 2. (Organisational) Structure; 3. (Business) Processes; 4. (System of) Rewards; and 5. People (staff capabilities). Strategy is placed at the top of these model

elements, defining the direction in which the or-ganisation will move. The organisation’s structure is of key importance for choosing between several different organisational solutions, as it sets the objectives to be reached, and the values that must be considered. Business processes, classified into key and auxiliary business processes, are of great



importance for the organisation, as they represent the core for performing tasks in the organisation. Formal authority, i.e. decision making powers in the organisation are determined based on the or-ganisational structure. The organisational struc-ture is closely linked to the management proc-esses, as the latter establish how the objectives in the organisation are set, as well as the issues re-lated to decision making; organisational objec-tives are harmonised with the employees’ individ-ual goals, and the accomplishment of strategic goals is contributed to. The model is completed with the staff’s competencies, where it must be pointed out that it is essential to assess which competences are expected from the staff so that they can be ready for the forthcoming changes (Hernaus, 2006).

(d) Becker et al.’s model

This model has resulted of many years of ex-perience and research of Becker, Kugler and Rosman, with the collaboration of experts from German universities, and industrial managers. Their process management model was published in 2003, and their methodology is classified as specific approach, consisting of seven phases (C.H.BECK, n.d.):

1. Preparation of process modelling; 2. Strategy and organisational frame; 3. As-Is modelling and process analysis; 4. To-Be modelling and process analysis; 5. Design of a process-oriented organizational

structure; 6. Process implementation, - process roll-out;

and 7. Continuous process management. Among other elements, preparation for proc-

ess modelling includes creating a process-based organisational structure, selection of design tech-niques and tools, creation of appropriate docu-mentation, business simulation, benchmarking, etc. This is followed by the second phase, i.e. strategy and organisational frame. The authors of this model emphasise the significance of organisa-tional frame design. This phase includes the iden-tification of primary business processes, manage-ment processes and support processes. In the As-Is modelling and analysis phase, an As-Is model is created and analysed so as to establish the strengths and weaknesses of both the workflow and the other components. The following phase is To-Be modelling and optimisation, which implies

activities related to the preparation, design and documentation of the To-Be model. The authors especially recommend performing a simulation in order to evaluate the created model. This phase is completed with the optimisation and consolidation of redesigned processes, in order to create a co-herent model. As a separate and complete phase of their model, the authors single out the design of a process-oriented organizational structure. They point out that process efficiency is the pivotal point of a process oriented organisation. Accord-ing to the authors, in the process implementation phase, it is necessary to perform simultaneous implementation of the process and the process-adjusted structure. They argue that separation of process and structure implementation in time can result in a range of problems in the relation be-tween the old process and the old organisation. The continuous process management is the con-clusive phase of this model. During the execution of the process, the management’s task is to moni-tor, take corrective action if necessary, and take preventive action if possible. This stage refers to activities related to the execution, analysis, goal redefinition and change implementation (Zakić, 2009).

2. The concept and methodological framework of a possible business process management model in a water supply public utility.

Based on research conducted in 22 water supply public utilities, subsequent analysis and process-ing of questionnaires, and personal contact with managers in these enterprises, a conclusion was reached that this group of public utilities is almost completely functionally organised, with the initial steps of BPM introduction in only a few of the enterprises. Actually, most of these companies have a function-based organisational structure focussed on a standard product, the staff are ori-ented to mere fulfilling the orders of functional segments, and the structure reflects fragmented interests of individual functions and incomplete understanding of the organisation’s missions and objectives.

The conducted research leads to a conclusion that process orientation is still not fully compre-hended in water supply public utilities, as the definition of the key business processes must flow together with the alteration in the organisation management method. This is essential in order to avoid the emergence of discord between horizon-

18 Esad Ahmetagić et al. A Business Processes Management Model Applicable in Public Water Supply Utilities


tal processes and the traditional management sys-tem, for, in the majority of cases, this tends to result in a multitude of conflicts and failure.

The above presented representative business process management models are generalised, i.e. widely applicable and unrelated to program tool-kits, methods or features specific to a particular industry or business activity.

For this reason, this article will present a model aimed at maximised adaptation to water supply public utilities, i.e. their development level. It is a complex model requiring maximum dedication, education, time and resources, but primarily the organisation’s willingness and deci-sion to carry out the required organisational changes. Some of the water supply public utilities have manifested the absence of will to accept the innovation and business process management, or it is not taken seriously enough. It is, however, highly significant to make it clear to managers of these enterprises at the very beginning that busi-ness process management is not appropriate for organisations seeking partial solutions and over-night ways of improving their performance with-out essential changes in business processes and other related components. If the water supply pub-lic utilities’ objective is a long-term one, it can only be achieved if these enterprises succeed in continuous improvement, innovation and imple-mentation of their business processes.

Managers in these enterprises must keep de-veloping their management skills, given that it is knowledge that is the most important factor for the application of business process management. Developing the knowledge of business processes and process management in contemporary busi-ness conditions is any company’s competitive advantage.

If business process management is to be intro-duced in water supply public utilities, it is essen-tial to form a consultancy team of experts in proc-ess management, change management, and change and project management, and also hire experts of other profiles as well.

The business process management model given as a model for water supply public utilities includes both the conceptual and the specific di-mension of methodology, and emphasises the es-sential elements that need devoting appropriate attention when conducting the transformation it-self. Therefore, it features as a framework for conducing transformation in water supply enter-prises, modelled after N. Zakić’s (2009) business process management sample and T. Hernaus’

(2006) generic model of process transformation, and also based on conducted research and identi-fied specifics in water management public utili-ties. It comprises four stages, further divided into stages:

▪ Planning preparation for modelling and strategic

analysis identification and definition of business

processes development of process-oriented culture

▪ Process analysis and design creation and analysis of the current As-

Is processes vision and benchmarking modelling and simulation of (targeted)

To-Be processes designing organisational components

▪ Implementation planning and developing implementa-

tion execution of implementation

▪ Continuous process management supervision, performance measurement,

reconsideration and continuous process improvement

process management at the organisation level

Conclusion

Given the importance of drinking water and the issues related to supply, we conducted research into the success and modalities of managing water supply public utilities.

Research and analysis conducted in 22 water supply public utilities in Vojvodina show that the business process management concept is not im-plemented anywhere, but there are some initial initiatives in this direction. All water supply pub-lic utilities in Vojvodina have a classical function-based organisational structure and corresponding function-based management concept.

The article present representative business process management concepts (Rummler-Brache, 1995, Becker, 2003, etc), and a research-based adaptive model (according to Zakić, 2009 and Hernaus, 2006). In the authors’ opinion, this adapted model is applicable in business process management in water supply public utilities. SM



References

Anupindi, R., Chopra, S., Deshmukh, S. D., Van Mieghem, J. A., & Zemel, E. (2012). Managing Business Process Flows. New Jersey: Pearson .

Becker, J., Kugeler, M., & Rosemann, M. (2003). Process management (Eds.). Berlin: Springer Verlag.

Bosilj, V. V., Hernaus, T., & Kovačić, A. (2008). Upravljanje poslovnim procesima – organizacijski i informacijski pristup. Zagreb: Školska knjiga.

Burlton, R. (2001). Business Process Management:Profiting from process. Indianopolis: Sams.

Byrne, J. A. (1993). The Virtual Corporation. Business Week , 20 December, 98-103.

C.H.BECK. (n.d.). Table of Contents. Retrieved January 15, 2013 from C.H.BECK: http://www.beck-shop.de/fachbuch/inhaltsverzeichnis/9783642151897_TOC_001.pdf

Galbraith Management Consultants. (2013). STAR MODEL. Retrieved January 15, 2013 from Galbraith Management Consultants: http://www.jaygalbraith.com/index.php?option=com_content&view=article&id=11&Itemid=123

Galbraith, J. R. (2012). The Star Molel. Retrieved January 15, 2013 from Emergent Insights: http://blog.emergentconsultants.com/wp-content/uploads/2012/09/StarModelOverview.pdf

Hernaus, T. (2006). Transformacija klasične organizacije u organizaciju orijetiranu na poslovne procese. Unpublished Master’s thesis. Zagreb: Ekonomski fakultet, Sveučilišta u Zagrebu.

Ostroff, F., & Smith, D. (1992). The horizontal organization. The McKinsey Quarterly, 1 (1), 148-167.

Rentzhog, O. (2000). Temelji preduzeća sutrašnjice. Novi Sad: Prometej.

Rummler, G. A., & Brache, A. P. (1995). Improving performance: How to manage the white space on the organizational chart, Second edition. San Francisco: Jossey-Bass.

Stewart, T. A., & Jacoby, R. (1992). The Search for the Organization of Tomorrow. Fortune, 125 (10), 92-98.

Zakić, N. (2009). Inovacije i menadžment poslovnih procesa. Belgrade: Zadužbina Andrejević.

Correspondence

Esad Ahmetagić



STRATEGIC MANAGEMENT, Vol. 18 (2013), No. 4, pp. 020-026 UDC 005.334

Received: March 21, 2013

Accepted: October 10, 2013

Crisis Management of Corporate Strategic Stability

Natalia P. Leshchenko F.M. Dostoevskiy State University, Omsk, Russia

Abstract The present article deals with the outcomes of the fact that, under the conditions of declining external environment and factors, aggressively affecting the organization, an increasing amount of attention is devoted to stability of systems. Goals of organization are not always aimed at maintaining the stability of activities.Stability of organizations depends on financial, technologic and management factors. The crisismanagement of strategic stability has two key preconditions: resources and competencies. Keywords Crisis management, system stability, interaction, corporative management, system integration.

Introduction

For a long time an approach has been used in management considering organizations as sys-tems. The traditional definition of a system is “an assembly of elements, being in relations and link-ages against each other in a certain way and form-ing some holistic unity” (Ostreykovsky, 1997, p.10). Organization as a system includes general notions, characterizing its construction and func-tioning. A system, facing production challenges, requires: availability of main components and links between them, integrative properties, integ-rity, structure’s inner regularity, guided goal and criteria for assessment of its activity, managing and control arrangement, system boundaries, part-ing it from external environment, with the sys-tem’s ability to interact with it, as well as special characteristics of elements, with which they form a system (Sachko, 1997, p.6). Under the conditions of declining external environment and factors, aggressively affecting the organization, an increasing amount of attention is devoted to stability of systems. The stability of the economic system in a general sense it is denotes its ability for continuous and undisturbed performance of its activity. Stability is characterized by a set of variables, describing

system conditions defined by the goals of organization. Herewith, any dynamic system uses interval meaning of effective stability indexes. Generally in the process of management, as the stage of setting activity goals or making decisions, the organization may be assigned with parameters of operational, tactical or strategic system conditions in future. Goals, defined by management or proprietors of organization, are not always aimed at maintaining the stability of activities that is connected with acceptance of risky investment projects, errors in estimation of external environment, inductive to incorrect decisions and negative results. Consequently, the necessity arises to review different types of organization stability. Operational, tactical and strategic stability are dealt with in professional and academic literature. 1. The fundamental tenets of organization stability

Prior to viewing organization stability in the context of available classification, it is necessary to draw attention to the fact that stability in this article denotes the potential of organization, realized in the process of performing of production and replenishment functions. The

Natalia P. Leshchenko Crisis Management of Corporate Strategic Stability 21


organization’s production function includes processes of main activity and supporting processes; replenishment function includes actions and projects, oriented to the future. Stability is viewed as ability of organization not only to function in stable way, but in a broader sense, to promptly react to internal and external disturbances, turning to specified (goal) indexes of activity. Thus, the stability of processes is viewed as negative trend of constriction of fluctuation range of organization system that may cause loss of stability during non-routine situations.

Stability of organizations depends on factors such as financial, technologic and management aspects. At the same time, the mentioned approach, in the author’s opinion, is not an exhaustive one. The essence of system stability is more completely revealed in the viable system model (VSM), developed by S. Beer. The model defines principles of “survival” of organizations, which includes: manageability, educability, the ability to adapt, and development (Warner, 2001, p.150]. There is another approach, viewing stability of socio-economic systems in the context of active systems theory, in the scope of which system stability is a characteristic of a socio-economic system, defining its ability to secure realization of the goal function in case of modification of its functioning conditions. (Samosudov, 2008) Zubanov (2001) holds similar views on stability of organization concerning its identified goals. He views stability concerning the identified goals as “inherent to any organization attribute, consisting in capacity to accomplish identified goals at unpredictable impacts of external environments”.

Strategic stability covers tactical and operational stability defined by target functions of organizations in terms of timing. Discrepancies between the goals of the organization, the management and owners cause the appearance of gaps in operational and tactical stability, which reduces strategic potential of organization. Consequently, strategic stability is a potential, accumulated by organization during its existence, determining its market power, available resources for realization of main processes, as well as reserves for further development.

The target function of a system, determining the necessity of consolidating a certain market position, allowing receiving specified profit rate, sets the system in motion. Herewith, if the system realizes a standard program, but accumulation of

strategic potential does not happen, it shows only current or operational stability of organization. To achieve the strategic stability of the system, the organization should support first-level potentials (resource, technologic and market) and form second-level potentials (intellectual, investment and innovative).

2. Corporate competitive capacity

Regardless of the approaches to describing the stability of organisations, all authors agree that one of the most important characteristics of strategic stability is the market competitive capacity of organization.

Seven base capabilities promoting formation of competitive ability and stability of organization in long-run period are pointed out in this regard in literature (Baranenko & Shemetov, 2004, pp. 26-27). These include the ability to:

1. identify the needs of the market segment correctly,

2. organize the production of optimal products regarding price/quality ratio,

3. correctly promote products on the market, 4. achieve cost savings, 5. maintain technological superiority, 6. implement market and technological

strategies based on innovations, and 7. create and develop the human resources of

organization. Subject and management mechanism are

prominent within organizations as socio-economic systems, whose main condition is the accomplishment of the target function under constrained parameters of stability.

Organization management potential is realized through algorithms of impact, including such no-tions as technology and management mechanisms. In general and theoretical sense, management mechanism includes interrelations of rules, princi-ples, goals, procedures and management func-tions, characterizing variety of object-subjective linkages in management process.

On the one hand, management process com-bines general management functions; on the other, it is a dynamic characteristic, connected with changes, occurring under the influence of external and internal environment, influenced by control subsystem appended on controlled one in time. Therefore, management processes are subject to changes, which happen in the result of managers’ activity and interrelations of links of organiza-

22 Natalia P. Leshchenko Crisis Management of Corporate Strategic Stability


tional structure for meeting target goals. The re-sulting interrelation of general notions associated with management may be presented schemati-cally, as seen in Table 1.

Table 1 Interrelation of general management notions

Management system Statics Dynamics

Elements Interrelations

Direct linkage Inverse linkage Structure (people, subdi-visions, informa-tion, manage-ment technol-ogy, economic indexes)

Process (planning, organization, motivation, con-trol, coordination, management, deci-sion-making process, communications)

Structure of system man-

agement Management technology

Management mechanism

hierarchy, span of management, functional structure.

goals, strategy, tactics: specific

approaches, methods and ways of work

horizontal communica-tion in management system,

results and reactions,

material, information, human, financial flows.

Source: Leshenko, 2005

In view of the aforesaid management is a process of information impact, covering all direc-tions of organization activity. it is realized n prac-tice through setting goals, organization and regu-lation of systems, as well as through continuous alteration and development processes. Conse-quently, it is necessary to take devote significant consideration to description of composition and structure of elements (statics) and management impacts (dynamics). Thus, management is an or-ganizational and managing potential, which, as a set of instruments and recommendations, does not guarantee the achievement of the desired result, and only through purposeful work of managers, through their daily work, does it allow the realisa-tion of all plans and target objectives. Therefore, in the process of searching of effective ways of work of organizations, special attention should be paid to methods of management activity, perfec-tion of management impacts, tracing organiza-tional reactions, associated with influence of ex-ternal environment, and timely response to exter-nal “disturbances”.

To summarise, managing the strategic stability of organizations is viewed as target-driven management activity forming a complex potential, allowing organizations' timely response to disturbances of external environment and achieving strategic target goals on the basis of transformation of traditional capabilities of organizations in entirely new forms, and thus increasing the competitiveness of organization.

As mentioned before, indexes of organization stability may be changed under the influence of current activity. The main negative moment is that each of indexes should correspond to the industry average indexes of competitors, which is practically impossible due to lack of a comprehensive and complete information and statistical database.

Target-driven maintenance of organization stability gains prominence during crises. Herewith stability is viewed as one of the main goals of crisis management. It is necessary to note that, within the frames of crises management, one can talk about the stability of organization as its ability to change and transform, while keeping the integrity of the system’s boundaries in the process of achievement of target goals, but not as the instability of inner elements and rooting the “dynamic” stereotypes of behaviour and system management.

In such a way, crisis management is a holistic management system aimed at adapting the managed entity to any challenges, connected with changes of both its own elements and the external system whose element managed entity is and developing the managed entity rather than preserving it as it is (Ushanov, 2010, p. 69).

The question now arises of whether potential has a permanent character and if it will have stable indexes? By no means. Within the frames of crisis management, the task of organizations’ managers is to use available potential of their organizations in such a way as to achieve the set targets with optimum proportion of costs and results. In turn, another important characteristic is defined: a stable organization is an effective one, while, on the other hand effective organizations may not be characterised by stability.

Within the scope of crisis management, it is necessary to view factors characterizing current and strategic stability of the managed entity (see Table 2).



Table 2 Characteristics of organization stability in tactical and strategic terms

Factor Tactical stability Strategic stability

Finance

Balance liquidity indexes. Profitability and business activity indexes.

Indexes of organization market position (business reputation and market value of company’s shares)

Personnel

Labour productivity and number of mistakes. Adjustment period.

If employees do not have knowledge and experiment – adjustment and training during shift to another territory (adjustment period). Salary dynamics and profitability of staff unit.

Management

Timely reaction for current changes of factors of external and internal environment. Due process of activity

Collective management bodies, Availability of corporate information database. Operating efficiency.

Source: Baranenko & Shemetov, 2004 As a result of the above, changes in tactical

and strategic stability of organization may happen with various dynamics and speed. As a rule, a crisis situation in organizations in relatively stable external environment begins to form in latent form appearing at the middle level of management. The first low signals characterize increment of deviation of assets profitability indexes in relation to given values. Further the process starts affecting strategic resources, appearing in their shortage or incorrect distribution.

Crisis management, as science and practice viewing processes of system adaptation to internal and external changes, factors and consequences characterized by high level of uncertainty, allows using ways of organizational capacity stabilization, either ad hoc or over a longer period of time. The main difference between crisis management and strategic management is targeted orientation on adaptation to changes, as it happened during the evolution of living organisms.

Organization as a social organism is far more complex in adaptation and perception of changes; it is therefore able to respond to management impacts, predetermining possibility and necessity of crisis management.

Management process is a continuous process, and, in terms of timing, it is divisible into three periods: past, present and future. These timing periods are closely associated with cause and effect chain of appearance of results of any activities, particularly with the possibility of crisis occurrence in organizations.

Motion of a system or one of its elements may start as the result of one or another factor of influence: either management (oriented to a specific motion path), or external, that is, as a consequence of external environment changes. If it is not followed by correcting the impact of management, the situation gradually begins to get out of control. In turn, all elements in the system are interdependent, which drives the whole system. In such a way, management impact may be labelled as cause or impulse in organization activity that is, generally, considered in the past. This, however, does not mean that management activity is not performed in the current period, as the main idea is that managers’ tasks specify directions of development of one in another process in organization. Operational activity, which began as the result of management impact, effects the current capacity of the organization and creates opportunities for accumulation or loss of future potential, and achieving financial results.

Layering of management cycles simultaneously, i.e. during performance of crisis management, results in substantial gaps in tactical and strategic potential of organization during the performance of crisis management. Gaps, appearing at the moment of the beginning of the critical situation, as well as in the process of crisis resolution, should be neutralized and reduced. High priority measures and “growing points” are developed within the scope of anti-crisis measures for this purpose. As regards high priority measures, their main direction is in elimination of gaps in the current capacity, and special attention is paid to financial aspects, as more objective and easily controlled, as well as to maintaining business activity. These practices include the following measures:

▪ measures for removing social tension, ▪ debt restructuring, ▪ liquidation of “problem areas”, ▪ inventory and assets processing, ▪ closer attention to property safety and

informational security, ▪ regulation of purchases, ▪ liquidation of failures in critical processes,



▪ improvement of discipline and labour productivity.

Considering the above in terms of different

kinds of potentials, one can see that high priority measures are directed on maintenance of the first level: resource, technology, market.

Consequently, misbalance arises between tactical and strategic tasks set to organizations in crisis conditions. It is strategic measures or “growing points” that are in charge of preservation and development of organizational capacities in future. Their correct combination leads not only to the substantial impulse in organization development, but also to the fact that during that impulse no subsequent gap will happen, compounding critical situation. The growing points include: audit of assortment and innovation best practices, technical audit, selection of promising directions, and promotion of internal efficiency as direction of forming of second level potential and conditions to strategic stability of organization.

Within the scope of corporate management, interrelation of high priority and strategic measures assumes other scale. Corporate management in Russia has two historic stages: before 1917 and after 1986. The fundamental difference is that since the late 20th century Russian corporations speed up their transformation, in order to catch up worldwide leaders.

The peculiarities of Russian corporative management model have been forming during the past 20 years, including:

1. imperfection of legal basis and absence of the concept of corporation in the Civil Code of the Russian Federation,

2. presence of large majority shareholders (individual persons), as a general rule, who set management rules in corporations,

3. duplicity and non-transparency of financial and accounting reports of large corpora-tions,

4. high level of state influence on corpora-tions’ activity, as well as shares belonging to state,

5. high cost of attracting of borrowed and in-vestment capital, and

6. low level of confidence of investors and, as a consequence, preference of short-term and middle-term investments.

Russian corporations often use niche strategies for their potential development, and are therefore characterized by high level of concentration and integration of business units in closed-circuit process chains. At the same time, there are multi-industry holding companies at the market, with high level of diversification of business areas. Both the former and the latter types of companies are interested in increasing operational and strategic stability of corporations, which leads to a necessity to increase capitalization of companies. Capitalization of corporation depends on both of internal factors (structure of business units, technologies and management quality, adherence to standards of corporate management), and of external factors (relations of investors, state bodies, consumers, economic situation at the market). Consequently, one may speak about partial controllability on the part of interested parties, having various interests, at that evidence of corporate conflicts appears in a greater degree in crisis conditions. Areas of interested parties are given in Table 4.

Table 3 Stakeholders’ areas of interests

Managers Proprietors Investors

Analysis of production activity: profitability, comparative effectiveness. Resource efficiency: turnover, indebtedness, resources Profitability: rate of return, explanation of investments

Profitability:Earnings per share, value analysis of business in general. Distribution of profit: dividends per share, balance of dividends and assets. Market indicators: ratio price/earnings per share.

Liquidity: Indices of current liquidity, share of debt in assets, covering of bonds.

Source: Goncharuk, 2006

At the present day in Russia there are more

than 400 companies with fully or partially com-pleted corporative management, and some of them have sufficiently stable image on the international market. Herewith main external threat is in com-petitive expansion on the part of transnational companies. As to weakness of Russian corpora-tions, they include substantially smaller sizes or Russian corporations with resource-based charac-ter of business development (strong dependence on natural resources and unpreparedness for going public and entering t the IPO market. Table 4



gives data on the capitalization of the largest Rus-sian corporations. Data for 2009-2011 show that capitalization was increased several times. There-fore, operational stability of companies strongly depends on the national measures of the state.

Increase in capitalization of Russian corporations is observed in 2011. It is therefore necessary to note that industrial composition has not changed in the past three years, and the raw character of corporate structures is evident. (Table 5)

Table 5 Industrial composition of largest corporations of Russia, pcs.

Industry Number of corporations 2013 2011 2009

1. Oil and gaz 7 8 7 2. Finance 3 3 2 3. Metal industry 4 8 3 4. Energy industry 4 5 1 5. Chemistry 2 1 0 6. Retail sector 1 2 0 7. Telecommunication 2 3 2

Source: Rbc. Quote, 2013 The negative trend at the end of 2011 is the

decline in the capitalization of corporations to 23%, which may reflect fear of both investors and Russian companies, and oil price changes. Nevertheless, corporate actions directed on preserving the stability of Russian corporations are connected with major processes of restructuring conducted practically by all big players.

Main measures realized by Russian corporations in 2009-2011 include:

1. centralization of key functions and subdivi-sion of corporations,

2. restructuring of non-profile and auxiliary departments to outsource contracts,

3. redundancies, 4. rebranding.

Conclusion

Generally speaking in last three years quality of corporative management in Russia significantly improved, which is proven by capitalization indexes. Consequently, one can say that measures aimed at stabilization gave satisfactory results, and Russian corporations have a high level of current stability. To a great extent, this stability was achieved by means of first level potentials (resource and personnel).

But if one considers wider strategic stability, based on innovations, root competencies and high quality, corporative management is currently a matter of the future. The idea of competence-based strategies is weakly supported by Russian corporations, as evidenced by the poor rating of research and development costs. From the above-mentioned companies, the world rating on total research and development costs, made by EU joint research centre, includes JSC “Gasprom” (83 place) and “LUKoil” (632).

In conclusion, the crisis management of strategic stability of corporations has two key preconditions: resources and competencies. Although invariability of industrial composition of large Russian corporations is predicted for the forthcoming 10 years, multi-industry holding companies will more carefully approach the formation of root competencies as a factor of increase of strategic stability. SM

Table 4 Capitalization of the largest corporations of Russia, $ billion

Companies Industry 02.12.2013 2012 2011 2010 2009 2008

1. Gazprom Oil and gas 100 930 111 607 126 693 150 887 143 312 87 021

2. Rosneft Oil and gas 75 802 93 882 70 765 76 399 88 323 40 004

3. Sberbank Banks 66 572 65 831 53 529 74 077 59 705 16 899

4. Lukoil Oil and gas 52 112 55 815 45 185 48 805 47 641 27 939

5. NOVATEK Oil and gas 36 384 34 476 37 334 33 409 17 077 5 008

6. Surgutneftegas Oil and gas 29 311 31 412 28 206 37 951 31 674 20 124

7. Norilsk Nickel Metal industry 23 843 35 041 29 344 44 989 26 756 12 976

8. Gazpromneft Oil and gas 21 188 22 165 21 897 19 995 25 667 10 073

9. VTB Banks 18 098 18 391 19 176 34 800 24 003 7 644

10. Magnit Retail 26 004 14 849 7 853 11 957 6 360 1 346

Source: Rbc. Quote, 2013



References

Baranenko, S. P., & Shemetov, V. V. (2004). Strategic stability of enterprise. Moscow: CJSC Tzentrpoligraph.

Goncharuk, A. U. (2006). Crisis management and transformation of production systems: Methodology and Practice. Moscow: Economics.

Leshenko N.P. (2005). Development of integrated agricultural research units in market conditions. Unpbublished doctoral dissertation, Russian Academy of Agricultural Sciences, Novosibirsk.

Ostreykovsky, V. A. (1997). Theory of systems. Moscow: Vysshaya shkola.

Rbc.quote. (2013). Retrieved December 03, 2013 from Rbc.quote: http://quote.rbc.ru/exchanges/demo/micex.0/dail

Sachko, N. S. (1997). Theoretic bases of production organization. Minsk: Design PRO,Yu.

Samosudov, M. V. (2008). Механизмы управления системной устойчивостью компании. Retrieved August 12, 2013 from Корпоративный менеджмент: http://www.cfin.ru/management/strategy/holdings/sustainability_management.shtml

Ushanov, P. V. (2010). Crisis management as new paradigm of management. Effective crisis management, 14 (1 (60)), 66-79.

Warner, M. (2001). Classics Management. St Petersburg: Piter.

Zubanov, N. V. (2001). Анализ устойчивости относительно поставленной цели как один из подходов к описанию функционирования организации в условиях неопределенности. Retrieved August 12, 2013 from Бизнес-портал AUP.Ru: http://www.aup.ru/books/m66/

Correspondence

Natalia P. Leshchenko

F.M. Dostoevskiy State University Prospect Mira, 55а, 644077, Omsk, Russia


STRATEGIC MANAGEMENT, Vol. 18 (2013), No. 4, pp. 027-034 UDC 005.9

Received: January 12, 2013

Accepted: September 9, 2013

An Empirical Study on Efficiency, Effectiveness and Performance Measurement in Supply Chain Management

Nenad M. Vunjak University of Novi Sad, Faculty of Economics Subotica, Serbia

Jelena Buha University of Novi Sad, Faculty of Economics Subotica, Serbia

Vedat Zulfiu Dekor M&V-Preševo, Serbia

Penekeh Pechu Tangiri Kingstree Senior High School, Kingstree, South Carolina, USA

Abstract The aim of this paper is to identify metrics which companies focus on, in measuring the performance of asupply chain (SC) in terms of effectiveness and efficiency of SC processes. This research is a hypothetical-deductive study, where the results are based on a survey of 525 managers (with 101 responses) within theforestry, manufacturing and wholesalers/retailers industries, with more than 50 employees, from Statistics Sweden (www.scb.se) using the Swedish standard industrial classification (SNI) codes. Contrary to previousstudies, the paper has applied a broader, quantitative survey methodology, considering the entire SC ratherthan segments of the SC and hence provides deeper knowledge about the functionality of the system as awhole in terms of effectiveness and efficiency. Based on results, companies tend to focus on managing theiroperating margins and working capital (efficiency) paying little attention to strategies for sustainable growth (effectiveness), which in most cases leads to ephemeral profitability. Keywords Performance management, supply chain management; supply chain, effectiveness, efficiency.

Introduction

The pursuit of effective management of the SC has been of continuing interest to both practitioners and researchers in recent decades (Happek, 2005). Much of the effort to model the workings of SC's has been motivated by the belief that its underlying concepts are so logical that benefits are bound to follow successful implementation (Martin & Petterson, 2009). Although there is no direct relationship of performance measure and no significance difference in companies with a mismatch of

products and SC (Selldin & Olhager, 2007), there is an inescapable logic component that continues to prompt continuing and expanding research to measure performance progress in SCs in order to ensure that the benefits are realized (Martin & Petterson, 2009). Despite the huge investments made by companies to improve their SCs, Fisher, (1997) pointed out that the performance of most SCs has never been this worst with unprecedented costs rise.

According to Lord Kelvin (1824-1907), “When you can measure what you are speaking about, and express it in numbers, you know

28 Nenad M.Vunjak et al. An Empirical Study on Efficiency, Effectiveness and Performance Measurement in Supply Chain Management


something about it . . . [otherwise] your knowledge is of a meager and unsatisfactory kind…” (Mann, Murphy & Kumar, 2009); “if you cannot measure, then it does not exist” (Lebas, 1995). But it is however, interesting to note that 62% of the executives attending a SC seminar “thought that their performance measurement system measured the wrong things” (Morgan, 2003, p. 44 as stated in Mann, Murphy & Kumar, 2009). The authors argued that research that focuses attention on the entire value chain’s profitability demonstrates that each member of the chain is inescapably linked to the chain’s other members and impacted by their performance. Thus, for performance to be effectively and efficiently measured, the same measurement instrument(s) should be used by all members of the SC to ensure a common standard.

The need for an effective and efficient SC has compelled companies to review, evaluate, and consider the adoption of SC measurement techniques. In supply chain management (SCM), performance measurement provides feedback about whether the strategic objectives have been met, and informs management about which areas need improvement (Martin & Petterson, 2009). As, with all processes, performance measurement incurs cost and it is thus, imperative that the performance measurement system adds value, since managers at various levels spend substantial time in measuring performance, planning and implementing course corrections (Mann, Murphy & Kumar, 2009). Consequently, SC members should not only be efficient but also effective.

Although most companies prioritize financial metrics, they suffer from certain weaknesses, such as: lack of customers focus; inwards looking; are lagging indicators; fail to include intangibles; and they do not help managers to be proactive (Gunasekaran & Kobu, 2007; Mann, Murphy & Kumar, 2009; Martin & Petterson, 2009). According to Gunasekaran & Kobu (2007), performance measures and metrics are not just measuring the performance but are also embedded with politics, emotions and several other behavioral issues. This becomes more complex when performance measurement of SC functions in an organization is viewed to be the same as the performance measurement of the entire SC (Mann, Murphy & Kumar, 2009). Thus, this piece of research will evaluate how performance of the chain is measured in terms of efficiency and effectiveness. As shown in figure 1., the research focus is designed to have horizontal analysis and

consider the performance of the chain as a whole. For the sake of simplicity the existence of companies in more than one SC is being ignored and the survey is carried out from the perspective of the companies’ primary/most important SC as illustrated in figure 1.

Figure 1: Focus of the research paper Source: Authors

1. Literature review 1.1. Efficiency and Effectiveness in Performance

Efficiency and effectiveness are central terms used in assessing and measuring the performance of organizations and both terms also apply to business arrangements such as strategic alliances, joint ventures, sourcing and outsourcing agreements (Mouzas, 2006). Drucker (1977) distinguished efficiency from effectiveness by associating efficiency to “doing things right” and effectiveness to “doing the right things” (Kumar & Gulati, 2010). Thus, by doing the right things wrongly, a company is effective but not efficient. Kumar & Gulati (2010) asserted that a measure of efficiency is not a measure of a success in the marketplace but a measure of operational excellence in the resource utilization process. Effectiveness, on the other hand, relates to company's own strategy to generate a sustainable business growth in its surrounding networks and the extent to which the policy objectives are achieved (Mouzas, 2006; Kumar & Gulati, 2010). These concepts can be extended to the SC based on the fact that SCM has a system approach to viewing the SC as a whole. As a result, efficiency will be seen as a cost-related advantage while effectiveness is an advantage of customer responsiveness within SC. Therefore, while trying to meet customers’ demand, companies in the SC have to optimize the use of their resource in order to maximize performance.

Nenad M.Vunjak et al. An Empirical Study on Efficiency, Effectiveness and Performance Measurement in Supply Chain Management 29


It is significant to note that though efficiency and effectiveness are two mutually exclusive components of overall performance measure yet they may influence each other (Kumar & Gulati, 2010) as shown in figure 2. Nevertheless, it is possible that a SC is efficient in utilizing the inputs, but not effective or vice versa. However, performance will be maximized by maximizing both efficiency and effectiveness of not only the individual companies, but all the companies in the SC. Thus, overall performance measure can be seen as a means of quantifying the efficiency and effectiveness of actions (Neely, Gregory & Platts, 1995). Hence, performance measure for an organization is a product of efficiency and effectiveness measures (i.e. performance = efficiency × effectiveness) (Kumar & Gulati, 2010).

Mouzas (2006) illustrated the effect of different levels of efficiency and effectiveness on the performance level of an individual organization. He noted that mainly focusing on efficiency and neglecting effectivenessresults in an ephemeral profitability. On the contrary, neglecting efficiency and focusing on effectiveness may result in an unprofitable growth. Thus, to ensure sustainable profitability companies need an equal emphasis on both high efficiency and effectiveness.

Given that efficiency and effectiveness are the central terms used in assessing and measuring the performance of organizations, irrespective of the organization or group of organizations, performance can be defined as an appropriate combination of efficiency and effectiveness (Kumar & Gulati, 2010). These authors noted that although most managers might use the terms as synonymous, each of them has its own distinct meaning.

1.2. Performance Management and Performance Measurement

Even though the concept of performance management is new, its meaning has been discussed under other labels, such as performance measurement systems, for a longer time (Forslund, 2007). Performance management creates a framework for, and measures for performance. Are there any significant differences between performance measurement and management? After thoroughly reviewing literature, Lebas (1995) concluded that measurement and management are inextricably linked and they work interactively. He concurred

that performance management precedes and follows performance measurement in a virtuous spiral and measures are used mainly to identify deviations from the expected results of the casual model. Hervani, Helms & Sarkis (2005) had also argued that performance measurement must evolve to performance management, where the organization develops the appropriate organizational structure and the ability to use performance measurement results to actually bring about change in the organization.

Performance management is one approach for measuring and improving performance in the SC, and can be seen as a process consisting of the following activities: selecting performance variables, defining metrics, target setting, measurement and analysis (Forslund, 2007; Forslund & Jonsson, 2007) and performance measurement is just a part of this process. According to Chan (2003), performance measurement describes the feedback or information on activities with respect to meeting customer expectations and strategic objectives. Therefore, performance measurement has many uses including the determination of the efficiency and effectiveness of an existing system or to compare competing alternative systems and is typically used to plan, design, implement and monitor proposed systems(Hervani, Helms & Sarkis, 2005).

1.3. Supply Chain Performance Measurement Metrics

There are several ways of describing and measuring the performance in a SC. Measuring means transforming complex reality into a sequence of limited symbols that can be communicated and that can be more or less reproduced under similar circumstances (Lebas, 1995). Hervani, Helms & Sarkis (2005) averred that the variety and level of performance measures depends greatly on the goal of the organization or the individual strategic business unit’s characteristics. In this light, Lebas (1995) argued that since the purpose of management is about creating and shaping the future of the organization and performance is not much about past achievements, as generally accepted, but about the future capabilities of the unit being evaluated. He asserted that performance is conceptual both in terms of users and in terms of purpose.

Beamon (1998) provided a basis for performance measurement within a company while advocating for the research need for more



holistic measurements of SC performance. This issue of how to measure SC performance was illustrated by many authors (Chan, 2003; Gunasekaran, Patel & Tirtiroglu, 2001; Gunasekaranet, Patel & Tirtiroglu, 2004; Folan & Browne 2005; Forslund & Jonsson2007).

Chan (2003) identified seven categories of performance measurement based on both quantitative and qualitative methods. Cost (inventory cost, incentives and subsidies cost, labor and machinery cost) and resource utilization (Labor, machine, capacity and energy) were the main focus under the quantitative category. Qualitatively, he identified quality (delivery time, customer response time, fill rate, etc); flexibility (material and process handling, delivery modifications, new product, expansion, etc.); visibility (time and accuracy); trust (consistency) and innovativeness (new product launch and new use of technology) as being vital to performance measurement. He pointed out that qualitative measurements are conceptual ideas and have no bases or standardized means of measurement leading to inconsistency, confusion and biased judgment. Overall, these difficulties in developing standards for performance measurement are traced to the various measurement taxonomies: management level to measure – strategic, tactical, or operational; tangible versus intangible measures; variations in collection and reporting; an organization’s location along the SC or functional differentiation within organizations (Hervani, Helms & Sarkis, 2005).

Drawing from the perspective of the balance scorecard, Folan & Browne (2005) presented an extended enterprise performance measurement system that incorporates internal, supplier, customer, and extended enterprise perspectives. They tested this in a case study in a first-tier supplier of chassis component products to leading automotive companies in the European automotive industry. They combined seven macro measures of performance on the vertical axis, with perspectives (internal, supplier, and customer) of the extended enterprise balanced scorecard on the horizontal axis, to create 21 different types of success factors.

Martin & Petterson (2009) conducted a survey that assessed the extent to which firms involved in specific agreements associated with SCM practice (organizational structure, partnering, supplier, and process improvement agreements) perceived their performance in terms of inventory, cycle times, and financial performance. They found out that

there were significant differences between firms that practiced SCM and those that did not although there was no significant difference in the financial performance. They concluded that while financial performance is ultimately important to each member of the SC, the statistical results indicate that financial measurements are appropriate for strategic decisions, but daily operational measurements might be supported better with non-financial measures.

Forslund & Jonsson (2010) pointed out that in order to be successful in performance measurement companies have to understand the importance of using validated, measurable and sufficiently detailed definitions of metrics, with clearly formulated targets or use standardized metrics that can be found in the SC operation reference (SCOR) model based on five standard SC processes: plan, source, make, deliver and return. In this light, Gunasekaran, Patel & Tirtiroglu (2001) had discussed a range of performance metrics and classified them into strategic, tactical and operational levels of management; financial and non-financial. Aligning them to the four basic links of the SCOR model, they constituted figure 4 with high performance metrics that targeted broader functional areas of SC as well as its total attributes. Considering the SCOR model, Gunasekaranet, Patel & Tirtiroglu (2004) designed a framework for performance metrics in the SC.

2. Methodology

In order to increase the respond rate, the authors used list, category, rating, ranking and matrix form of closed questions, making the questions as precise as possible. To ensure that the survey questionnaire operates and functions well as recommended by Bryman & Bell (2007), the authors reviewed the questionnaire several times with experts in this field. Also, a pre-test was then performed with experts and companies in order to avoid misunderstandings during the survey. Most questions used a six-point Likert scale (Not at all important to very important) for SC practices adapted from Keller, Savitskie, Stank, Lynch & Ellinger (2002). A six-point (rather than a five or seven-point) Likert scale was used to ensure that the respondent made an active choice. It was then transferred into a web-based questionnaire.

All companies were contacted by phone for four weeks and the top management (General Manager, SC, Logistics, Purchasing,



Sales/Marketing managers) were considered appropriate respondents because at least one of these positions exists in almost every company. E-mail addresses were obtained from the company website or from the responsible individual by calling through the switchboard number obtained through (www.scb.se) or company website. Each respondent received a personal e-mail with the web-based questionnaire link making it convenient for the respondent.

The authors selected the population from the list of forestry, manufacturing companies and wholesalers/retailers, with more than 50 employees, from Statistics Sweden (www.scb.se) using the Swedish standard industrial classification (SNI) codes. This ended up with a total of number of 2021 companies. To make the sample representative, the authors used stratified random sampling, selected the appropriate proportion from each of the stratum (manufacturing, forestry and wholesaler/retailers). Out of the 525 companies which were sampled, 101 responded giving a response rate of 19.2%.

3. Assessing Non-Response Bias

Despite the low response rate obtained in this study, low response rates alone do not necessarily suggest response bias. A test of non-response bias is another way of increasing the reliability of a study. According to Krosnick (1999) and Dillman (1991) stated in Sax, Gilmartin & Bryant (2003), when respondent characteristics are representative of non-respondents, low rates of return are not biasing. Non-response bias refers to a situation in which respondent who fail to return a questionnaire have opinions systematically different from those who return their surveys (Sax, Gilmartin & Bryant, 2003). There are two types of non-response bias: Total non-response which refers to individuals failing to return the survey at all and unit or item non-response bias which indicates that the survey was returned incomplete (Sax, Gilmartin & Bryant, 2003). Unit or item non-response bias could not be a problem in this research since the questionnaire could not be submitted without completing all items. As regard total non-response bias, it is customary to test for non-response bias by comparing the responses of those who responded to the first mailing of a questionnaire with those who responded to the second mailing. Those who return the second questionnaire are, in effect, a sample of non-respondents to the first and as such they are representative of that group. In this

survey, respondents were called and the questionnaire link was sent immediately. Nevertheless, a reminder was sent twice and in both cases, a total of 28 responses were received. This group will be considered as non-respondent since the questionnaire was anonymous.

Table 1 Statistically Significant Difference between

Response of First and Reminder e-mails

Source: Authors

In total, 6 variables out of 18 with statistically

significant (5% level) means between these two groups were identified as shown in table 1. However, in all these questions (appendix 1) respondents were asked to rate each of the issue on the scale of not at all important (1) to very important (6). Thus, a positive value indicates that more respondent saw the issue as important rather than an issue of non-response bias. Consequently, non-response bias in not a problem in this study and thus, providing a pre-requisite for validity.

4. Empirical findings

Performance metrics were divided into efficiency and effectiveness metrics. Efficiency metrics are those that are focused within the company and effectiveness metrics considers all members in the chain ability’s to develop sustainable growth (table 2). The efficiency metrics were sub-divided into three groups – cost and resource utilization; quality and visibility; and flexibility and trust. Cost and resource utilization had five specific metrics with an average of 80.41%; quality and visibility had four averaging 81.88%; and flexibility and trust three with an average of 73.00%. Customer satisfaction seems to be the highest concern of SC members (87.33%) followed by delivery time (85.00%). Although flexibility and trust has the least average amongst the three divisions of efficiency metrics, it should be noted that consistency and reliance on partners has an average of 77.17% indicating dependence and trust amongst SC partners.

Effectiveness metrics, which were divided into two sub-groups – customers’ responsiveness and innovation, had comparatively lower averages to efficiency metrics. Customers’ responsiveness had



an average of 68.95% while innovation averaged 71.29%. In customers’ responsiveness, the ability to provide rapid response to customers’ request had the highest score (76.67%) and identifying new markets was top on the list of innovation metrics.

Table 2 Scores and Percentage Efficiency and

Effectiveness Metrics

Source: Authors

Considering the fact that each company

measures performance and the objective of this study is to identify the focus, the results of the 101 respondents on the six-point scaled questions (not at all important to very important) were coded in two groups. The last four were coded as group 1, while the top two as group two. This was to examine if these metrics were actually a focus of the companies in theSC. Table 3 shows the frequency and percentages of the second group (the top two). Many companies focus on customer dissatisfaction (87.1%) and delivery time (81.2%), which are efficiency metrics.

Table 3 Frequency and Percentage of second group (top two of six point scale)

Source: Authors

In effectiveness metrics, identify new markets

(61.4%) was top followed by ability to provide rapid response to customers’ request as earlier seen. Although a number of efficiency metrics had percentages greater than even, only these two effectiveness metrics had percentages greater than 50.0%.

Bar charts were drawn to observe if the responses were influenced by a particular group (supplier, manufacturer or retailers) in the SC. But for delivery cost, customers’ dissatisfaction, degree to which products/materials are supplied to customers’ specific demand and identifying new markets, all other metrics contributed approximately the same to the averages. Retailers contributed comparatively higher to delivery cost and customer dissatisfaction, manufacturers to degree to which products/materials are supplied to customers’ specific demand and suppliers to identifying new markets.

Conclusion

The focus of this research which was aimed at identifying if companies in the SC equally use both efficiency and effectiveness metrics in measuring performance showed that there is an unbalance in their usage.

The analysis showed that out the 19 performance metrics, the first effectiveness metrics was eighth position. This implies that



companies focus on controlling their operating margins and working capital requirement (visible in cost and resource utilization metrics) than developing strategies for sustainable growth. However, the fact that in customers’ responsiveness, the ability to provide rapid response to customers’ request had the highest score (76.67%) and identifying new markets was top on the list of innovation metrics provides indication that SC members focus on offering value to the customers. These findings validate the results of Mouzas, (2006) who found out that companies rarely balance the achievement of efficiency and effectiveness simultaneously in performance. As illustrated in figure 2, this will lead to ephemeral profitability and consequently, preventing sustainable growth. As Lebal (1995) had indicated performance is about future capabilities, which is evident in high percentage of customers’ dissatisfaction metrics (87.33%) and identifying new markets (77.17%). The above explanation is consolidated in table 2, where the pattern of importance is still maintained after dividing the respondents in two categories.

This research also showed that companies focus on qualitative metrics which have no standard of measurement compared to quantitative metrics. It also provides evident that there is an increasing degree of trust amongst SC partners aiming at providing value to customers. The extended analysis to examine the particularly focus of each SC actor showed that delivery cost and customer dissatisfactionare significant to retailers and degree to which products/materials are supplied to customers’ specific demand to manufacturers. SM

References

Bryman, A., & Bell, E. (2007). Business Research Methods (2nd ed.). New York: Oxford University Press.

Chan, F. T. (2003). Performance Measurement in a Supply Chain. International Journal of Advanced Manufacturing Technology, 21 (7), 534-548.

Fisher, M. L. (1997). What is the Right Supply Chain for your Product? Harvard Business Review (March-April), 105-116.

Folan, P., & Browne, J. (2005). Development of an extended enterprise performance measurement system. Production Planning and Control, 16 (6), 531-544.

Forslund, H. (2007). The impact of performance management on customers’ expected logistics performance. International Journal of Operations & Production Management, 36 (8), 580-595.

Forslund, H., & Jonsson, P. (2007). Dyadic integration of the performance management process: a delivery service case study. International Journal of Physical Distribution & Logistics Management, 37 (7), 546-567.

Forslund, H., & Jonsson, P. (2010). Integrating the performance management process of on-time delivery with suppliers. International Journal of Logistics Research and Applications: A Leading Journal of Supply Chain Management, 13 (3), 225-241.

Gunasekaran, A., & Kobu, B. (2007). Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. International Journal of Production Research, 45 (12), 2819-2840.

Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87 (3), 333-347.

Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management, 21 (1), 71-87.

Happek, S. (2005). Supply Chain Strategy: The Importance of Aligning Your Strategies. Retrieved February, 2012 from TechRepublic: http://www.techrepublic.com/whitepapers/title/277342/?tag=wpbn

Hervani, A. A., Helms, M. M., & Sarkis, J. (2005). Performance Measurement for Green Supply Chain Management. Benchmarking: An International Journal, 12 (4), 330-353.

Keller, S. B., Savitskie, K., Stank, T. P., Lynch, D. F., & Ellinger, A. E. (2002). A Summary and Analysis of Multi-Item Scales used in Logistics Research. Journal Of Business Logistics, 23 (2), 83-281.

Kumar, S., & Gulati, R. (2010). Measuring Efficiency, Effectiveness and Performance of Indian Public Sector Banks. International Journal of Productivity and Performance Management, 59 (1), 51-74.

Lebas, M. J. (1995). Performance Measurement and Performance Management. International Journal of Production Economics, 41, 23-35.

Mann, I. J., Murphy, S. A., & Kumar, V. (2009). Unit of Analysis: A Case for Performance Measurement in Supply Chain Management. The IUP Journal of Supply Chain management, 6 (3&4), 41-56.

Martin, P. R., & Patterson, J. W. (2009). On Measuring Company Performance within a Supply Chain. International Journal of Production Research, 47 (9), 2449-2460.

Mouzas, S. (2006). Efficiency versus Effectiveness in Business Networks. Journal of Business Research, 59 (10-11), 1124-1132.

Neely, A., Gregory, M., & Platts, K. (1995). Performance Measurement System Design-A Literature Review and Research Agenda. International Journal of Operations & Production Management, 15 (4), 80-116.

Sax, L. J., Gilmartin, S. K., & Bryant, A. N. (2003). Assessing Response Rates and Nonresponse Bias In Web And Paper Surveys. Research in Higher Education, 44 (4), 409-432.

Selldin, E., & Olhager, J. (2007). Linking products with supply chains: testing Fisher’s model. Supply Chain Management: an International Journal, 12 (1), 42-51.



Correspondence

Nenad M.Vunjak



STRATEGIC MANAGEMENT, Vol. 18 (2013), No. 4, pp. 035-042 UDC 336.76(497.11) ; 336.76(437.6)

Received: May 15, 2013


Stock Market Integration: A Case Study for Serbia and Slovakia

Michaela Chocholatá University of Economics Bratislava, Faculty of Economic Informatics, Slovakia

Abstract The main aim of this paper is to analyse the stock market integration of two countries – Serbia and Slovakia –into the Western European stock market. The analysis was performed using the stock market index BELEX15for Serbia, SAX for Slovakia and the German DAX as a proxy for the Western European stock market. Thebivariate BEKK-GARCH(1,1) models, based on the daily data over the sample period May 5, 2008 –December 31, 2012, were used to assess the stock market integration before the start and during the currentfinancial crisis. A special attention was given to the development of the conditional correlation. The results show that, in the case of Slovak stock market, the conditional correlations were negative and therefore it is notpossible to speak about the stock market integration, but in the case of Serbian stock market the conditionalcorrelations varied around 0.2 during the whole analysed period, which can indicate the low degree ofintegration. The impact of the current financial crisis on the development of conditional correlation was notconfirmed. Keywords Stock index, BEKK-GARCH(1,1) model, conditional correlation, financial crisis.

Introduction

The investigation of mutual interdependencies between various stock markets has been attracting the attention of analysts for a long time. There are plenty of instruments which enable researchers to analyse and assess the degree of such integration. First of all, it is necessary to be aware of the fact that in the stock market analysis we have to deal with the financial time series. Although the information about the rate of return is important for investors, it is also necessary to take into account the risk of investment measured by volatility which has a tendency to cluster in periods, i.e. the large changes of volatility tend to follow large changes, while small changes have a tendency to follow other small changes. Changes in volatility can be caused by various factors inside the analysed country (e.g. political changes, changes in monetary or fiscal policy etc.) or by volatility development of some other market(s). The Engle’s (1982) ARCH (Autoregressive Conditional Heteroscedasticity) model or its

various extentions can be used (for an extensive survey of the ARCH-type models see e.g. Franses and van Dijk, 2000) to capture the characteristic features of financial time series

1 and to predict

volatilities in the future. As already mentioned above, an interesting

and challenging issue is to analyse the co-movements of financial returns from different markets, especially to study volatility spillover, i. e. if and how the shocks from one stock market influence the volatility development of the other market.

2 This issue has been investigated quite

frequently during the recent years using various 1 Time-varying volatility, leptokurtosis, skewness and heavy tails are some of the typical features associated with the financial return series. 2 Forbes and Rigobon (2002) distinguish the stock market co-

movement during the periods of stability and during the periods after a shock or crisis. They use the term contagion to define “a significant increase in cross-market linkages after a shock to one country (or group of countries)”. So, in case that the co-movement does not increase significantly after a shock or crisis, they speak about interdependence.

36 Michaela Chocholatá Stock Market Integration: A Case Study for Serbia and Slovakia


methodologies, e.g. cross-market correlation coefficients, cointegration techniques, Granger causality concept and impulse response functions, and various univariate and multivariate GARCH (generalized ARCH) models. In recent analyses, the dominant role is played by the use of multivariate GARCH models. Different types of multivariate GARCH models can be found in the literature, e. g. the VECH model of Bollerslev, Engle and Wooldridge (1988), the CCC model of Bollerslev (1990), the BEKK model of Engle and Kroner (1995), the GDC model of Kroner and Ng (1998), the DCC model of Engle (2002) and the AG-DCC model of Cappiello, Engle and Sheppard (2006).

3

The main aim of this paper is to study the stock market co-movements between the Western European stock market (represented by the German DAX) vis-à-vis Serbian and Slovak stock market based on BEKK-GARCH models

4. The

paper is organised as follows: Section 2 presents the brief survey of relevant literature dealing with Central Eastern and South Eastern European stock markets, Section 3 investigates the methodological aspects of multivariate GARCH models (VECH-GARCH and BEKK-GARCH), Section 4 describes the data used for analysis, Section 5 empirical results and Section 6 concludes.

1. Literature review

Concerning the subject of study, special attention in literature was devoted to the analysis of integration of emerging stock markets with developed stock markets. It is commonly known that the emerging stock markets are usually more volatile than the developed stock markets. There exist quite a lot of studies dealing with the co-movements of some Central Eastern European (CEE) stock markets known also as V4 countries stock markets (Czech, Hungarian, Polish and Slovak) and their integration with the Western European stock markets using various techniques. However, not so many studies dealing with co-movements and integration of the South Eastern European markets have been published. We will present here a brief survey of some relevant studies based on multivariate GARCH approach. 3 For an extensive survey of multivariate GARCH models see

e. g. Bauwens, Laurent and Rombouts (2006); Silvennoinen and Teräsvirta (2008). 4 For simplicity, we will consider only the GARCH (1,1)

models without further emphasising of this fact.

Kash-Haroutounian and Price (2001) analysed the volatility of the stock markets in V4 countries using daily data for the sample period June 1992 – March 1998 (Hungary, Poland) and April 1994 – March 1998 (the Czech Republic, Slovakia). They applied the several variants of univariate GARCH models and two types of multivariate GARCH models (CCC and BEKK). Based on the CCC model, they indicated significant conditional correlations between two pairs of countries: Hungary and Poland, and Hungary and the Czech Republic. The BEKK model showed evidence of return volatility spilovers from Hungarian to Polish stock market, but not vice versa. Égert and Kočenda (2007) studied co-movements between three developed (France, Germany, the UK) and three emerging (the Czech Republic, Hungary and Poland) European stock markets based on five-minute tick intraday stock price data for the period June 2003 – January 2006 applying the DCC-GARCH models. They detected very little systematic positive correlation between the Western European stock markets and the three CEE stock markets. Wang and Moore (2008) investigated the extent of integration of three CEE stock markets with the aggregate eurozone market based on daily data from April 6, 1994 to December 29, 2006 using the bivariate DCC-EGARCH model. They proved a higher level of the stock market correlation during and after the Asian and Russian crisis and also during the period after integration of the CEE countries into the EU. Baumöhl, Farkašovská and Výrost (2010) analysed the integration of the stock markets of V4 countries with the German market and also mutual correlations between the stock markets of individual V4 countries using the DCC-GARCH model. Their analysis was performed for daily data during the period November 19, 1998 and November 21, 2008. They confirmed that the correlations of the stock market indices of V4 countries (the only exception was the Slovak SAX) with German DAX became higher during the analyzed period. Higher rate of interdependence was also confirmed between the stock market indices of V4 countries (the results for the Slovak index SAX were also different). Kenourgios and Samitas (2011) investigated long-run relationships among five Balkan emerging stock markets (Turkey, Romania, Bulgaria, Croatia, and Serbia), the United States and three developed European markets (UK, Germany, and Greece). They used daily data during the period 2000-2009 and applied various methodologies

Michaela Chocholatá Stock Market Integration: A Case Study for Serbia and Slovakia 37


among them AG-DCC GARCH model. The results from AG-DCC GARCH model showed that the correlation became higher between Balkan and developed stock markets during the analysed period. Horvath and Petrovski (2012) analysed the stock market co-movements between Western Europe (based on STOXX Europe 600 index) and some CEE countries (the Czech Republic, Hungary and Poland) and also some South Eastern European countries (Croatia, Macedonia, Serbia). They made the analysis based on daily data from January 2006 till mid-May 2011 using the multivariate GARCH models. Their results show a quite high level of stock market integration between the analysed CEE countries and Western Europe (corresponding conditional correlations were around 0,6). In case of integration between Western Europe and the South Eastern European countries the results were different – in case of Croatia, the conditional correlation was around zero at the beginning of the analysed period, but during the analysed period (till the outbreak of the financial crisis) it increased to values similar to ones for the CEE countries. The conditional correlations for Serbia and Macedonia were on average zero during the whole analysed period indicating no integration with the Western European stock markets.

2. Methodology: Multivariate GARCH Models

It is commonly known that there exists a co-movement in volatilities over time across assets and markets. Using the multivariate GARCH models is therefore more advantageous and relevant than working only with individual univariate GARCH models. The key difference between univariate and multivariate GARCH models is that the latter also include equations which specify the development of covariances over time. As it was already mentioned in the introduction, nowadays there exist several various multivariate GARCH model specifications with certain advantages and disadvantages, and with different restrictions and specifications concerning the conditional variance. In order to investigate the market interdependence and volatility spillover, we will present here the bivariate BEKK-GARCH (1,1) model. Similar as Wang and Moore (2008), we use, the first order vector autoregressive (VAR) model of the following form to capture the dynamic relationship in returns (since it is a bivariate model, we deal with only two stock markets):

t1tttt εMrωεrr 1tE (1)

where tr is a 12 dimensional vector of

daily stock returns, 1tE tr is a conditional

mean 12 dimensional vector (ω is a 12 dimensional vector of constants, M is a matrix of dimension 22 the off-diagonal elements of which reflect the mean transmission between the analysed stock markets) and tε is a 12 dimensional vector of innovations (disturbances) conditional on information at time 1t . The conditional distribution of tε is assumed to be multivariate normal with the mean zero and the

22 dimensional conditional variance-covariance matrix tH , i. e.

tt Hε ,0~1 Nt . (2)

It is possible to specify tH in different ways.

Taking into account that tH is a conditional variance-covariance matrix, positive definiteness has to be ensured. We will concentrate on the BEKK-GARCH model in which the matrix tH can be received through the generalization of the univariate GARCH model of Bollerslev (1986). Since the BEKK-GARCH model of Engle and Kroner (1995) is a special case of the VECH model, we will briefly discuss both these models. VECH-GARCH and BEKK-GARCH Models

The VECH-GARCH model, originally proposed by Bollerslev et al. (1988), accounts for both varying correlations and changes in volatility. The general formulation for the multivariate analogue of the GARCH (1,1) for two stock markets is as follows (see e. g. Brooks, 2008):

)( .

)( .

1t

1t1tt

HBεεAcH

VECHVECHVECH ,

tt Hε ,0~1 Nt

(3)

where tH is a 22 dimensional conditional variance-covariance matrix, c is a 13 dimensional parameter vector, A and B are 33 dimensional parameter matrices, tε is a 12

dimensional vector of innovations and 1 t represents the information set at time 1t . VECH(·) denotes the operator applied to the upper



triangular elements of a symmetric matrix that stacks each element into a vector with a single column (see e. g. Brooks,2008; Gregoriou, 2009; Horvath & Petrovski, 2012). The bivariate VECH model can be also written as follows:

1,12131,22121,1111

1,21,1132

1,2122

1,11111,11

ttt

ttttt

hbhbhbaaach

(4)

1,12231,22221,1121

1,21,1232

1,2222

1,12121,22

ttt

ttttt

hbhbhbaaach

(5)

1,12331,22321,1131

1,21,1332

1,2322

1,13131,12

ttt

ttttt

hbhbhbaaach

(6)

It is evident from the presented model

specification that the conditional variances and conditional covariances depend on the lagged values of the conditional variances of both stock markets and conditional covariances between both stock markets, as well as on the lagged squared and cross-products innovations variables.

One of the disadvantages of this model is the high number of parameters to be estimated, e. g. the simplest bivariate VECH model requires the estimation of 21 parameters (3 parameters of vector c and 9 elements in each of matrices A and B). The diagonal VECH model can be used to reduce the number of estimated parameters the simplified version of the VECH model (see Bollerslev et al., 1988). Another important drawback of the VECH model presented above is that this model is not able to ensure the positive definiteness of the conditional variance-covariance matrix tH . The formulation of a special version of VECH model – the BEKK model, presented by Engle and Kroner (1995), ensures the positive definiteness of the conditional variance-covariance matrix tH . The matrix tHof the BEKK-GARCH(1,1) model has the following form:

BHBAεεACCH 1t1t1tt (7) where C denotes a 22 dimensional upper

triangular matrix of parameters and A and B are 22 dimensional matrices of parameters. We

will also have (similar as in (4)-(6)) three equations the right hand sides of which contain mostly quadratic terms and therefore the positive definiteness of the conditional variance-

covariance matrix is ensured (see Franses & Dijk, 2000; Horvath & Petrovski, 2012):

1,22

2211,121211

1,11211

21,111

211,11

2

tt

ttt

hbhbb

hbach (8)

1,222221,1222121,11

212

21,2221,112

222

212,22

2

ttt

ttt

hbhbbhb

aacch (9)

1,2222211,1221122211

1,1112112

1,2222122112112

1,21,12

1,112111211,12

tt

tt

tttt

hbbhbbbbhbbaaaaaa

aacch

(10)

There also exists the diagonal BEKK model

that restricts matrices A and B to be diagonals. The diagonal BEKK model is identical to the diagonal VECH model, where the coefficient matrices are rank one matrices, i. e.

1,112

112

1,111,11 ttt hbach (11)

1,22222

21,222,22 ttt hbach (12)

1,1222111,21,12211,12 tttt hbbaach (13)

Since the presented model is an extension of a

univariate one, the parameters can be similarly estimated based on the maximum likelihood method by replacing the one-dimensional function of the sample by its multidimensional counterpart (see e. g. Rachev, Mittnik, Fabozzi, Focardi, & Jasic, 2007; Brooks, 2008; Lütkepohl, 2005; Canarella, Miller, & Pollard, 2010).

3. Data

The analysis in this paper is based on daily closing values of stock market indices DAX for Germany, BELEX15 for Serbia and SAX for Slovakia. The source of data is as follows: German DAX – finance.yahoo.com, Serbian BELEX15 – www.belex.rs, Slovak SAX – www.bsse.sk. The analysis was based on daily data (closing values in local currencies) of stock indices during the period from May 5, 2008 to December 31, 2012 to overcome the infrequent trading on the one hand and to enable to analyse the impact of the current financial crisis on the other. The number of observations was different in individual cases, but only those data were used which were defined for each of the three analysed



indices, i. e. 1171 observations were used for analysis.

The whole analysis was carried out on first logarithmic differences of stock indices, i. e. on logarithmic return series. The individual logarithmic stock indices were via ADF (Augmented Dickey – Fuller) test identified to be non-stationary, the first differences of all analysed logarithmic stock indices, i. e. logarithmic stock returns, were already stationary.

5

Graphical illustration of the individual logarithmic stock index series (prefix “L”) and logarithmic returns (prefix “DL”) can be found on Figure 1. The logarithmic index series are clearly non-stationary, while the logarithmic returns are stationary with high time-varying volatility. In further analysis we will concentrate on modelling of logarithmic returns.

5 The results can be provided by the author upon request.

Figure 1 Logarithmic indices and logarithmic returns during the period May 5, 2008 -December 31, 2012

Source: Author's own calculation based on data from Yahoo Finance, 2013, Belex, 2013, and Burza cenných papierov v Bratislave, 2013

Descriptive statistics concerning the

logarithmic returns together with the values of Jarque – Bera statistics testing the normality are summarized in Table 1. The mean values vary around zero, concerning the values of standard deviations the German market is most volatile with standard deviation around 1.73 %, followed by the Serbian market with standard deviation 1.64 % and the Slovak market with volatility of around 1.61%. All the distributions are positively skewed and leptokurtic, the normality hypothesis can be rejected (see Jarque – Bera statistics and corresponding probability values).

Table 1 Descriptive statistics – logarithmic returns

DLBELEX DLSAX DLDAX Mean -0.000956 -0.000744 7.79E-05 Median -0.001023 0.000000 0.000613 Maximum 0.121576 0.279078 0.107975 Minimum -0.108614 -0.148101 -0.073355 Std. Dev. 0.016435 0.016067 0.017321 Skewness 0.267067 3.311192 0.181267 Kurtosis 14.06871 94.57488 8.006197 Jarque-Bera 5991.693 411304.7 1229.230 Probability 0.000000 0.000000 0.000000 Observations 1171 1171 1171

Source: Author's own calculation based on data from Yahoo Finance,

2013, Belex, 2013, and Burza cenných papierov v Bratislave, 2013

The unconditional correlations between pairs of analysed logarithmic returns are summarized in Table 2. It seems evident that the DLSAX stock returns are not correlated with DLDAX (negative values) and there is also almost no correlation with DLBELEX. The unconditional correlation between DLDAX and DLBELEX reaches 0.1762.

5.6

6.0

6.4

6.8

7.2

7.6

-.15

-.10

-.05

.00

.05

.10

.15

2008 2009 2010 2011 2012

LBELEX DLBELEX

5.2

5.4

5.6

5.8

6.0

6.2

-.2

-.1

.0

.1

.2

.3

2008 2009 2010 2011 2012

LSAX DLSAX

8.2

8.4

8.6

8.8

9.0

-.08

-.04

.00

.04

.08

.12

2008 2009 2010 2011 2012

LDAX DLDAX



Table 2 Unconditional correlation matrix – logarithmic returns

DLBELEX DLSAX DLDAX DLBELEX 1 0.0347 0.1762 DLSAX 1 -0.0168 DLDAX 1



4. Empirical results

Using the econometric software EViews, we estimated the parameters of the mean equation (1) and of the conditional variance-covariance matrix of the diagonal BEKK-GARCH (1,1) model

6 in

order to assess the existence of the co-movements between the German stock market vis-à-vis Serbian and Slovak stock market.

The estimated results are summarized in Table 3. The conditional mean of returns was dependent on its first lag in case of Serbia and Slovakia, but for Germany the results were different. Furthermore an interesting fact is that there exists the price spillover effect from Germany to Serbia, but not vice versa. The results for the pair Slovakia and Germany are quite surprising, since the price spillover effect was confirmed in unexpected direction: from Slovakia to Germany, but not vice versa. Concerning the conditional variance and covariance equations, all the parameters (with the only exception of the parameter 11a in Slovak – German model) were statistically significant on the 1 % significance level, i.e. we can conclude that the volatility generated on the German market was transmitted to both the Serbian and the Slovak markets and also vice versa.

Table 3 Estimation results

7

Serbia – Germany Mean equations

DLBELEXt = – 0,0004 + 0,2395.DLBELEXt-1 +0,0826.DLDAXt-1

(0,1035) (0,0000) (0,0000)

6 Since EViews does not enable direct estimation of the

general form of BEKK in which matrices A and B are unrestricted, we estimated diagonal BEKK that restricts A and B to be diagonals. 7 p-values for parameters in mean equations are in

parenthesis, in conditional variance-covariance equations the p-values for all parameters were 0.0000 (the only exception was parameter 11a in Slovak – German model, where the p-value was 0.9980).

DLDAXt = 0,0007 – 0,0011.DLBELEXt-1 – 0,0070.DLDAX t-1

(0,0969) (0,9623) (0,8163) Bivariate

8 diagonal BEKK-GARCH(1,1) model

1,112

1,16

,11 8078,02342,010.8,1 ttt hh

1,222

1,26

,22 9374,00610,010.8,1 ttt hh

1,121,21,16

,12 8702,01195,010.8,1 tttt hh

Slovakia - Germany Mean equations

DLSAXt = – 0,0002 – 0,2867.DLSAXt-1 + 0,0326.DLDAXt-1

(0,6437) (0,0000) (0,2964) DLDAXt = 0,0005 + 0,0538.DLSAXt-1 +

0,0005.DLDAX t-1 (0,0797) (0,0224) (0,9882)

Bivariate9 diagonal BEKK-GARCH(1,1) model

1,112

1,16

,11 8078,02342,010.8,1 ttt hh

1,222

1,26

,22 9374,00610,010.8,1 ttt hh

1,121,21,16

,12 8702,01195,010.8,1 tttt hh



After estimating of the above presented

bivariate diagonal BEKK-GARCH(1,1) models, we also estimated the conditional correlations in order to assess the stock market integration in the analysed set of countries. Conditional correlations between DLDAX vis-à-vis DLBELEX and DLSAX are displayed in Figure 2.

Conditional correlation

Serbia – GermanyConditional correlation

Slovakia - Germany

8 Index 1 is for Serbia, index 2 is for Germany.

9 Index 1 is for Slovakia, index 2 is for Germany.

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

2008 2009 2010 2011 2012



Figure 2 Graphical illustration of conditional correlations Source: Author's own calculation based on data from Yahoo Finance,


The conditional correlation between Germany

and Serbia varied around the value 0,2 during the analysed period, which can indicate the low degree of integration. Different results were received for the second group of countries – Germany and Slovakia, where the conditional correlation was negative, so it is not possible to speak about the stock market integration in this case.

Taking into account the approach of Wang and Moore (2008) concerning the examination of the impact of various crises (Asian and Russian) on the conditional correlation, we did a similar analysis. Although the direct impact of the current financial crisis on the development of conditional correlations is not clearly visible, we tried to deal with this issue. The first problem was to define the outbreak of the current financial crisis. It was done in two ways: by taking into account the first rapid fall of the main American stock indices (DJA Composite Average, NYSE US 100 Index, NASDAQ 100 and S&P 500) on October 6, 2008 and the minimum value of these American indices on March 2, 2009. Two dummy variables D1 and D2 were defined, taking the value 1 from October 6, 2008 onwards and from March 2, 2009 onwards, respectively. Subsequently follows the regression of the conditional correlations on individual dummy variables – the results are summarized in Table 4. In case of conditional correlation Serbia – Germany, no impact of current financial crisis was confirmed, i. e. no large regime shift in direction of integration was proved during this crisis. In Slovak – German case the conditional correlations were negative, so it is quite hard to speak about some stock market integration (although the results showed the

statistically significant decrease in conditional correlation during the crisis).

Table 4 Impact of the current financial crisis on the

conditional correlation10

D1 = since

October 6, 2008 D2 = since

March 2, 2009 Serbia –Germany 0.0454 (0.3381) 0.0208 (0.6363)

Slovakia –Germany -0.0190 (0.0000) -0.0378 (0.0000)



Conclusion

This study investigates the relationships of the stock markets in Serbia and Slovakia vis-à-vis a Western European stock market represented by Germany during the period May 5, 2008 – December 31, 2012. The VAR(1) models together with bivariate diagonal BEKK-GARCH(1,1) models provide evidence that there exists the price spillover effect from Germany to Serbia and from Slovakia to Germany but not vice versa. Concerning the conditional variance and covariance it was proved that the volatility generated on the German market was transmitted to both the Serbian and the Slovak markets and also vice versa.

Taking into account the development of conditional correlations, the low degree of integration was confirmed for Serbia and Germany and no stock market integration for Slovakia and Germany. Since the conditional correlation between Serbian and German logarithmic stock returns remained quite constant (varying around 0,2) and in Slovak – German case was negative, the impact of current financial crisis in these analysed cases was not confirmed.

Unlike our results, Kenourgios and Samitas (2011) proved in their study that the conditional correlation between the Serbian stock market and the German stock market was on average 0.162 during the stable period and increase on average to 0.203 during the financial crisis. On the other hand, e. g. Horvath and Petrovski (2012) identified the conditional correlation between the same stock markets to be around zero. Concerning the results for Slovakia, we can coincide with the concluding remarks of Baumöhl et al. (2010) that it is problematic to interpret the results for Slovakia because of low effectivity and restricted 10

p-vaules are in parenthesis.

-.40

-.35

-.30

-.25

-.20

-.15

-.10

-.05

.00

2008 2009 2010 2011 2012



stock trading on the Slovak stock exchange market. This is also probably one of the main reasons why the studies dealing with the CEE countries usually exclude Slovakia from analysis. SM

References

Baumöhl, E., Farkašovská, M., & Výrost, T. (2010). Stock Market Integration: DCC MV-GARCH Model (In Slovak: Integrácia akciových trhov: DCC MV-GARCH model). Politická ekonomie, 4, 488-503.

Bauwens, L., Laurent, S., & Rombouts, J. V. (2006). Multivariate GARCH Models: A Survey. Journal of Applied Econometrics , 21 (1), 79-109.

Belex. (February 21,2013). Belgrade Stock Exchange. Retrieved February 21, 2013, from Belex: http://www.belex.rs/eng/trgovanje/indeksi/belex15/istorijski/3y

Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31 (3), 307-327.

Bollerslev, T. (1990). Modeling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. Review of Economics and Statistics, 72 (3), 498-505.

Bollerslev, T., Engle, R. F., & Wooldridge, J. M. (1988). A Capital-Asset Pricing Model with Time-Varying Covariances. Journal of Political Economy, 96 (1), 116-131.

Brooks, C. (2008). Introductory Econometrics for Finance. Cambridge: Cambridge University Press.

Burza cenných papierov v Bratislave. (February 21,2013). Burza cenných papierov v Bratislave. Retrieved February 21, 2013, from Slovenský akciový index - SAX: http://www.bsse.sk/Obchodovanie/Indexy/IndexSAX.aspx

Canarella, G., Miller, S. M., & Pollard, S. K. (2010). NAFTA Stock Markets: Dynamic Return and Volatility Linkages. New York: Nova Science Publishers, Inc.

Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric Dynamics in the Correlations Of Global Equity and Bond Returns. Journal of Financial Econometrics, 4 (4), 537-572.

Égert, B., & Kočenda, E. (2007). Time-Varying Comovements in Developed and Emerging European Stock Markets: Evidence from Intraday Data. Working Paper 861. Ann Arbor: William Davidson Institute.

Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50 (4), 987-1008.

Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20 (3), 339-350.

Engle, R. F., & Kroner, K. F. (1995). Multivariate Simultaneous Generalised GARCH. Econometric Theory, 11 (1), 122-150.

Forbes, K. J., & Rigobon, R. (2002). No Contagion, Only Interdependence: Measuring Stock Market Comovements. Journal of Finance, 57 (5), 2223–2262.

Franses, P. H., & Dijk, D. v. (2000). Non-Linear Time Series Models in Empirical Finance. Cambridge: Cambridge University Press.

Gregoriou, G. N. (2009). Stock market volatility. Boca Raton: Chapman & Hall/CRC Taylor & Francis Group.

Horvath, R., & Petrovski, D. (2012). (2012). International Stock Market Integration: Central and South Eastern Europe Compared. William Davidson Institute Working Papers Series, wp 1028. Ann Arbor: William Davidson Institute at the University of Michigan.

Kash-Haroutounian, M., & Price, S. (2001). Volatility in the transition markets of Central Europe. Applied Financial Economics, 11 (1), 93-105.

Kenourgios, D., & Samitas, A. (2011). Equity market integration in emerging Balkan markets. Research in International Business and Finance, Elsevier , 25 (3), 296-307.

Kroner, K. F., & Ng, V. K. (1998). Modelling Asymmetric Co-movements of Asset Returns. Review of Financial Studies, 11 (4), 817-844.

Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Berlin: Springer-verlag.

Rachev, S. T., Mittnik, S., Fabozzi, F. J., Focardi, S. M., & Jasic, T. (2007). Financial Econometrics. From Basics to Advanced Modeling Techniques. Hoboken, New Jersey: John Wiley & Sons.

Silvennoinen, A., & Teräsvirta, T. (2008). Multivariate GARCH models. In T. G. Andersen, R. A. Davis, J. P. Kreiss, & T. (Eds.) Mikosch, Handbook of Financial Time Series. New York: Springer.

Wang, P., & Moore, T. (2008). Stock Market Integration for the Transition Economies: Time-Varying Conditional Correlation Approach. The Manchester School, 76 (1), 116-133.

Yahoo Finance. (February 21,2013). Historical Prices. Retrieved February 21, 2013, from Yahoo Finance: http://finance.yahoo.com/q/hp?s=%5EGDAXI&a=04&b=5&c=2008&d=11&e=31&f=2012&g=d

Correspondence

Michaela Chocholatá

Faculty of Economic Informatics Department of Operations Research and Econometrics Dolnozemská cesta 1/b, 852 35 Bratislava, Slovakia


STRATEGIC MANAGEMENT, Vol. 18 (2013), No. 4, pp. 043-048 UDC 004.4:339

Received: June 7, 2013


Business Software Testing Model Design: A Theoretical Framework

Vuk Vuković University of Novi Sad, Faculty of Economics Subotica, Serbia

Abstract The article analyses the theoretical aspects relevant to designing business software testing models, describingthe evolution of software, and argumenting the case in favour of the need for continuous improvement of soft-ware testing. The results of prominent studies in the domain of software testing, featuring as the starting pointfor business software testing model design are analysed. The final part of the article defines a research model which will serve as a basis for research whose outcome will define the business software testing model de-sign. Keywords Software testing, business software, business software testing.

Introduction

In the current circumstances, production and busi-ness systems, such as research and development, distribution, after-sale support etc. directly de-pend, to a greater or lesser extent, on software system support. From this point of view, the im-pact of possible software errors on organisations’ operating results is evidently significant. Numer-ous practical instances indicate that failure to de-tect software errors may result in detrimental or even tragic consequences. A software error made at the Panama City National Cancer Institute in 2000 caused an incorrect calculation of the amount of irradiation to be used for treating pa-tients. Excessive amounts of radiation resulted in fatal outcome in eight patients, while twenty of them suffered serious health problems (Garfinkel, 2005). In the case of business software, the con-sequences are definitely not that tragic, but they are by no means negligible. EDS’s software sys-tem developed for UK Child Support Agency lead to remitting undue surplus payment to 1.9 million clients, whereas about 700 thousand were de-prived of due benefits in 2004 (Barker, 2007). This example illustrates short-term negative con-

sequences, such as financial loss in the case of the mentioned company, as well as the strategic det-rimental consequences reflected in damage to the company’s reputation among clients.

In order to reduce the incidence of the above mentioned instances, it is necessary for software developers and producers to arrange systematic software testing. The systematic approach primar-ily refers to the awareness that testing is nowa-days a separate and unified process rather than an ad hoc activity within the software development process. Varuious software testing models, such as ISO/IEC 29119, TMMi, TMap and others have been developed to this end. (International Organi-zation for Standardization, 2013; TMMi Founda-tion, 2010; Koomen, van der Aalst, Broekman & Vroon, 2006; Kasurinen, 2012). However, numer-ous organisations are unable to consistently im-plement in practice all the activities of the process models due to lack of time or human resources. This especially applies to small and medium sized software organisation. For this reason it is neces-sary to launch the design of a new process model for testing business software. This process model should eliminate the shortcoming of the above mentioned existing model, reflected in the com-

44 Vuk Vuković Business Software Testing Model Design: A Theoretical Framework


plexity of their practical implementation, and in-corporate necessary software product specific test-ing models. The aim of this research is to define research design that should provide preconditions for designing a process-based model for business software testing.

1. Research methodology

To complete business research as efficiently and effectively, a selection was made from relevant scientific and expert articles from journals and publications from scientific conferences, by searching the Web of Science and Scopus elec-tronic citation bases, and also the EBSCO scien-tific article electronic base. Key word combina-tion related to the issue, such as “software testing process”, “software testing model”, “improve software testing process” etc., were used to opti-mise the search results.

2. Theoretical basis

In the early stages of software engineering devel-opment and implementation, software testing used to be equated with debugging, i.e. removing unin-tentional errors in the code, or correcting identi-fied software errors, performed by software de-velopers themselves. Special testing resources existed in very rare cases, but even if they did exist, they were involved at very late stages of the development process, mostly after the software product had been coded – practically, completed and introduced into the working environment and exploitation.

1957. saw the first use of the words “testing” and “debugging” as two different concepts. Rather than correcting the identified errors, testing began to denote detecting potential, unknown software errors. Testing, however, was still performed as a post-development activity, enabling the partici-pants in software development to make sure that the software was functioning. Likewise, universi-ties did not pay significant attention to testing. Rather than on testing, computer science curricula were more focussed on developing numeric meth-ods and algorithms. Compilers, operative systems and databases were in the primary focus, but none of these were oriented to resolving open and es-sential problems and issues related to software testing.

The term “software engineering” started to be used with increasing frequency in the late 1970s, despite the lack of general consensus on what it actually represents and encompasses. The first

formal conference was held at the North Carolina University in 1972, followed by a series of publi-cations in the software testing area (Hetzel, 1973; Myers 1976, 1979). It was the publication of Glenford Myers’ The Art of Software Testing that made a great impact on software testing as a new discipline. Myers (1979) defined testing from a totally different angle in relation to the opinions valid at the time as “the process of executing a program with the intent of finding errors”. The key point he made was that, if the aim of testing was to demonstrate the absence of errors in the software, a very small number of errors will be detected. The establishment of Myers’ approach in the testing process was a major breakthrough and a new paradigm in comparison with the cur-rent practice, and enabled a further development of software testing as a new scientific discipline, relevant to the practice.

In the early 1980s, the notion of software qual-ity became a highly relevant and significant issue. Software testers became indispensable members of development teams. Groups were formed for creating international standards that are still in force, such as the IEEE (Institute of Electrical and Electronics Engineers) and the ANSI (American National Standards Institute) in the USA, and the ISO (International Standards Organization) in Europe. These documents contain very important guidelines providing valuable for providing soft-ware quality. In 1983, Microsoft officially em-ployed their first formally tested, and significantly increased the numbers of this staff as of 1985. The 1990s saw the emergence of software testing tools, which are currently one of the key compo-nents in software industry (Kit, 1999).

Over the past ten years, software testing has grown from only a stage in the software develop-ment process into a complete software testing process, which reflects the significance of this discipline.

Standish group has been conducting a research into the success of IT project implementation in the USA since 1994, when data was published revealing that only 16% of the total number of implemented projects were successful, i.e. com-pleted within the deadline, within the budget and with the achieved required characteristics and functionalities), 53% were unsuccessful (the pro-jects exceed the deadlines and allocated budget, and lacked the required characteristics and func-tionalities), and 31% failed, i.e. were not imple-mented. In 2009, the percentage of successfully implemented projects did double, but the percent-

Vuk Vuković Business Software Testing Model Design: A Theoretical Framework 45


ages of unsuccessful (44%) and failed (42%) were still intolerably high (Standish Group, 2009).

According to the study conducted by the U.S. Department of Commerce, software errors are estimated to cost the American economy up to 59.5 billion dollars annually (quoted from Everett & McLeod, 2007, p. 1), equalling 0.6% of the American gross domestic product.

The results of another study, entitled „The Economic Impacts of Inadequate Infrastructure for Software Testing“, initiated by the National Institute of Standards and Technology (NIST) and conducted by the Research Triangle Institute in North Carolina in 2002, show that over one-third of costs (22.5 billion dollars) can be eliminated by enhancing the infrastructure of the testing process, which would entail earlier and more efficient identification and elimination of software errors (Tassey, 2002).

Surveys for the above mentioned research study were conducted in two companies from the production sector (transport equipment) and two companies from the service sector (financial ser-vices). The NIST estimated that total costs result-ing from inappropriate software testing in the fi-nancial services sector amounted to 3.3 billion dollars. If the possible improvements in the test-ing process infrastructure had been made, the po-tential cost cuts would have reached 1.5 billion dollars. All the surveyed software engineers who had participated in developing the software for the financial services sectors agreed that it was neces-sary to improve the software testing system. Ac-cording to them, the improved software testing system should be able to track the error from the time and place of identification, with the possibil-ity of assessing its impact on the further process development.

Two earlier, but not less relevant studies in the software testing domain pointed to two essential facts that cannot be omitted when dealing with the software testing process, as highly significant ini-tial fact for research into the area. The first study, published by James Martin in An Information Sys-tems Manifesto (1984), confirmed that most soft-ware errors (56%) occur in inappropriately de-fined requirements (Figure 1).

Figure 1 Distribution of software errors across software

development lifecycle phases (Martin, 1984) Another study (Boehm, 1976), whose results

were slightly revised and practically confirmed twenty-five years later in an article entitled “Software Defect Reduction Top 10 List” (2001), shows that the software error correction is more cost-effective in the early phases of development (Figure 2). Detecting and correcting errors after the software has been delivered may cost up to 100 times as much as the same procedure applied during the system analysis and design (Boehm & Basili, 2001). In other words, if a software error occurs in the course of writing the program code, removing it requires correcting and recompiling the program code. If, however, the software error results from inappropriately defined user require-ments, and has not been detected prior to system testing, then it is necessary to re-record the re-quirements, redesign the functionalities, correct the database, rewrite the program code, test it, compile the user documentation and prepare the training materials. Repeating all these activities result in exceeding project timelines and financial framework (BenderRBT, 2009, pp. 2-3).

Figure 2 Error removal costs across software development phases

(Boehm, 1976; Boehm, 1987; Boehm & Basili, 2001) The above mentioned facts launched new and

significant aspects in the software development process. Reducing the time gap between the mo-ment of the error occurrence and the moment of



its detection directly influences the mitigation of project failure risk. The large time gap between the occurrence and detection is what characterised the parallel expansion of the project’s time and content dimensions in the structural approach. Iterative and incremental approach in software development overcame this problem by redeploy-ing the content dimension. This method resulted in dividing the problem into smaller segments in the early stage of the project, so that the time gap between the occurrence and detection of the error was reduced only to the development timespan of only a part of the problem.

However, despite the progress achieved by it-erative and incremental approach, software com-panies spend between 40% and 50% of their re-sources on subsequent removal of the conse-quences of problems that could have been pre-vented on time. These can also be detected and removed in earlier stages of software develop-ment, which would result in resource cost cutting, or even avoided completely. Practice has shown that about 80% of the problems preventable in the software development process stem from 20% of software errors, while 80% software errors stem from 20% of software modules (Boehm & Basili, 2001). By means of WER system and with the help of their users, Microsoft obtained data for its product Office XP that practically designed the Pareto chart, which confirms that 80% of the er-rors are located in 20% of the software code (Campbell, 2011). About 10% of software errors account for 90% of time delays in software pro-ject implementation. Moreover, analysis of nine large IBM’s software products has shown that only about 0.3% caused 90% of time delay in software project implementation (Boehm & Basili, 2001).

According to Caper Jones’ research (cited in Everett & McLeod, 2007, p. 21), 85% of all soft-ware errors occur in early development phases, before the software is delivered to users for use in the working environment. If, however, the soft-ware product is not completed, the question arises concerning the cause of such a high percentage of errors. The answer should be searched in docu-ments such as the users’ information require-ments, process design, data design, interface de-sign, database structure design etc.

3. Research results

Software systems are currently used in virtually all areas of people’s life and work. As a special type of software, business software has a high

participation in the software market. High-quality business software entails applying testing proce-dures before and during development, and after introducing software in its working environment. Numerous software system testing methods, types and techniques – more than 200 – are currently available (Vegas, 2002). Developing an appropri-ate testing approach implies its conformity with the specific features of the software tested. Ap-propriate testing methods and techniques should be chosen in accordance with the chosen ap-proach. Two key problems arise here. First, soft-ware engineers have a modest degree of knowl-edge of the available software testing techniques. This practically means that there are numerous techniques that an average software engineer is not familiar with. Second, currently available in-formation on the existing testing techniques are procedural, i.e. focussed on how the techniques are used), whereas there is very little or no practi-cally applicable information, focussed on the re-sults of applying these techniques. (Vegas, 2002)

In his endeavour to establish measurement cri-teria for evaluating software systems, Robert Grady (1992) classified the qualitative features of software systems. The qualitative features are ab-breviated in the acronym FURPS (Functionality, Usability, Reliability, Performance, Supportabil-ity), and serve as a good basis for testing business software. The features of Functionality, Usability, Reliability, Performance and Supportability can be used, as it were, as gateways that need to be passed in the business software testing process. When passing each of them, it is necessary to make an appropriate selection of available busi-ness software testing techniques so as to form an appropriate, i.e. active appropriate approach, fea-turing as a logically interconnected set of existing techniques. A simple choice of mutually inde-pendent techniques would be a passive and there-fore inappropriate approach.

The subject of research into testing methods, types and techniques is expressed in the form of the following research questions:

▪ Which testing methods, types and tech-niques are most frequently used in the business software development process?

▪ Which individual phases in the business software development process are most frequently used methods, types and tech-niques appropriate for?

▪ Are business software techniques in or-ganisations defined by testing models, strategies or standards?

Vuk Vuković Business Software Testing Model Design: A Theoretical Framework 47


Software testing theory and practice include various software testing models. In addition, there are various criteria for classifying these models. According to Misnevs & Daineko (2002), soft-ware testing models are classified in four groups:

a. software lifecycle oriented testing models, b. automation/maturity level oriented testing

models, c. product architecture oriented testing mod-

els, d. formal approach oriented testing models. The first group of models is “organically” as-

sociated with the software product lifecycle. One example of this group of models is the well-known V-model, based on the classical waterfall lifecycle model. Lifecycle models describe the interconnectedness of software development phases. Some of the most prominent ones include the waterfall model, the prototype model, iterative and incremental model, etc. Each of these models contains testing activities that can be represented by a specific testing model.

The second group of models is related to evaluation and support to the testing process, such as, for instance, based on the SEI CMM (Misnevs & Daineko, 2002). These models are referred to as process improvement approaches, and use a process model systematising and representing the best practices, defines metrics for evaluating process capabilities and provides a rational path to process improvement (Bueno, Crespo, & Jino, 2006). The TMM model, modelled upon the CMM but more interesting for the testing process, was modelled at the Illinois Institute of Technol-ogy. The main objective of the TMM is to identify the current condition of the state of testing capac-ity in an organisation and initialise the program improvement program (Misnevs & Daineko, 2002). In addition to the CMM and the TMM models, this group includes the ISO/IEC 12207 standard, the ISO/IEC 15504 model, the CMMI model (Bueno, Crespo, & Jino, 2006).

The third group includes models using the products' particular features for implementing the testing process. An example of models from this group could be a (UML-based) software testing model supporting object-oriented software prod-uct development.

The fourth group of models include various testing models using a fully formalised approach to testing process implementation. They can use various statistic model for developing test cases and planning, or formal languages for describing

specifications and generating tests. A notable model in this group is Software Testing by Statis-tical Methods. (Misnevs & Daineko, 2002)

In relation to the specific feature each of them possesses, business software products require cer-tain modification of the existing software testing models. In addition, due to limitations in their testing resources, many organisations developing business software products are unable to apply the entire range of activities of the existing software testing process models. To support this statement, Cruz, Villarroel, Mancilla, & Visconti (2010) ar-gue that, particularly, the TTMi testing model, primarily based on the CMMi, inherits most prob-lems related to applying such a type of frame-works (or models), in the small and medium-sized organisations environment.

The following research questions were formu-lated in order to obtain information on business software testing models and their characteristics.

▪ Is the testing strategy for business software defined in organisations prior to commenc-ing its development?

▪ Is the testing process in organisations de-veloping business software conducted ac-cording to a predefined testing model, and what are the features of the business soft-ware testing models in these organisations?

▪ How is the business software testing proc-ess performance managed in organisations developing business software?

As regards the defined research question, in

addition to theoretical questions, it is necessary to gather data from a sample of organisations devel-oping business software products, by means of appropriate research instruments. Answers to the posed research questions, obtained by means of both theoretical and empirical research, will be included in the research results of the defined re-search subject. In search of answers to the posed research questions, results will be obtained that will determine the construction of a business software testing strategy, design of a process model for business software testing, and business software testing process performance manage-ment model. Also, it is expected that, after the conducted research, new areas will emerge as a subject of future research.

Conclusive remarks

Despite the fact that modern software industry is recording a compelling development in the area of



software testing processes, there are numerous open, still unresolved problems. The business software developed and used nowadays is charac-terised by manifest complexity. This fact places organisations developing business software in an unenviable situation. That is to say, the scissors phenomenon, manifested through time and budget limitations, prevents companies to test business software properly, which is unfavourably, and in some cases utterly unacceptably, reflected on the quality of the delivered software product. To mitigate these consequences, the cumulative re-sults of research into the defined research subject will enable organisations producing business software to define the vision of testing in the or-ganisation, to test systematically, to apply appro-priate testing methods, types and techniques in implementing the testing process, and to manage the business software testing process perform-ance. SM

References

Barker, C. (2007). The top 10 IT disasters of all time. Re-trieved August 15, 2011 from ZDNet: http://www.zdnet.com/news/the-top-10-it-disasters-of-all-time/177729

BenderRBT. (2009). Requirements Based Testing Process Overview. New York: Queensbury.

Boehm, B., & Basili, V. (2001). Software Defect Reduction Top 10 List. IEEE Computer-COMPUTER, 34 (1), 135-137.

Bueno, P. M., Crespo, N., & Jino, M. (2006). Analysis of an Artifact Oriented Test Process Model and of Testing Aspects of CMMI. PROFES 2006 (pp. 263-277). Berlin: Springer-Verlag Berlin Heidelberg.

Campbell, J. (2011). Measure the Measurable: Improving software quality through telemetry a Microsoft case study. Better Software, 13 (1), 22-27.

Cruz, P., Villarroel, R., Mancilla, F., & Visconti, M. (2010). A Software Testing process for the reference model of Competisoft. XXIX International Conference of the Chil-ean Computer Science Society (pp. 51-59). IEEE Com-puter Society: Washington.

Everett, G. D., & McLeod, R. (2007). Software Testing: Testing Across the Entire Software Development Life Cycle. Hoboken: John Wiley & Sons.

Garfinkel, S. (2005). History's Worst Software Bugs. Re-trieved August 15, 2011 from Wired: http://www.wired.com/software/coolapps/news/2005/11/69355?currentPage=all

Grady, R. (1992). Practical software metrics for project management and process improvement. Upper Saddle River: Prentice Hall.

International Organization for Standardization. (2013). Part 2 - Test Process. Retrieved November 03, 2012 from ISO/IEC 29119 Software Testing: http://www.softwaretestingstandard.org/part2.php

Kasurinen, J. (2012). Software Organizations and Test Process Development. Advances in Computers, 85, 1-63.

Kit, E. (1999). Software Testing in the Real World. New York: Addison-Wesley.

Koomen, T., van der Aalst, L., Broekman, B., & Vroon, M. (2006). TMap Next. Hertogenbosch: UTN Publishers.

Martin, J. (1984). An Information Systems Manifesto. Upper Saddle River: Prentice-Hall.

Misnevs, B., & Daineko, D. (2002). Developing a New Test-ing Model. Transport and Telecommunication, 3 (1), 26-33.

Standish Group. (2009). Standish Group Chaos Report for 2009. Boston: Standish Group.

Tassey, G. (2002). The Economic Impacts of Inadequate Infrastructure for Software Testing. Gaithersburg: Na-tional Institutes of Standards and Technology.

TMMi Foundation. (2010). Test Maturity Model integration (TMMi) v3.1. Dublin: TMMi Foundation.

Vegas, S. (2002). Characterisation Schema for Selecting Software Testing Techniques. Unpublished doctoral disertation. Universidad Politecnica De Madrid, Madrid.

Correspondence

Vuk Vuković



Manuscript Requirements A paper must be written in text processor Microsoft Word. Paper size: A4. Margins: 3.0 cm on top and bot-tom, and 2.5 cm on left and right sides. As a guide, articles should be no more than 5.000 words in length. In case the paper exceeds the normal length, the Editors' consent for its publication is needed. Articles submitted for publication in Journal should include the research aim and tasks, with detailed methodology, presenting literature overview on the research object, substantiation of the achieved results and findings, conclusions and a list of references. Manuscripts should be arranged in the following order of presentation. First page: Title (no more that 10 words), subtitle (if any), autobiographical note (the author's full name, academic affiliation, telephone, fax and e-mail address and full international contact). Respective affiliations and addresses of co-authors should be clearly indicated. Please also include approximately 50 words of bio-graphical information on each author of the submitted paper. Second page: A self-contained abstract/summary/resume of up to 150 words, describing the research objective and

its conclusions

Up to ten keywords, which encapsulate the principal subjects covered by the article; and

A self-contained summary of up to 200 words, describing the article and its conclusions.

Subsequent pages: Main body of the text with headings, footnotes, a list of references, appendices, tables and illustrations. The paragraph parameters are: Font: Times New Roman, 10 pt, regular

Spacing: Before: 0, After: 0

Line Spacing: Single

Alignment: Justified

Indentation: Left: 0, Right: 0, Special: 0. Style: Normal (not Title, Heading1, Heading2,...,Body Text, etc!) Leave an empty line between paragraphs.

Headings: Headings must be short, clearly defined and numbered, except for Introduction and Conclu-sions. Apply at most three levels of headings. Please, leave two empty lines before headings and one empty line after. Font: Times New Roman, bold, 16 pt, centered. Section headings should be in bold with Leading Capitals on Main Words, Times New Roman, 14pt, bold, centered. Sub-section headings should be in italics, with Leading Capitals on Main Words, Times New Roman, 12 pt, bold.

All tables, graphs and diagrams are expected to back your research findings. They should be clearly referred to and numbered consecutively in Arabic numerals. They should be placed in the text at the appropriate paragraph (just after its reference). Tables should be centered. All tables must have captions. The title of your table should follow the table number. Tables should not be wider than the margins of the paper. Skip two lines before and after each table. Figures should be centered. All figures must have captions. The title of figures should appear immediately below the figure. The title of the figure should follow the figure number. Figures should not be wider than the margins of the paper. Skip two lines before and after each figure. Figures will not be redrawn by the publisher. Figures should be high-quality grayscale graphics (please, do not use colors): vector drawings (with text converted to curves) or 300 dpi bitmaps. Please do not supply any graphics copied from a web-site, as the resolution will be too low. In all figures taken or adapted from other sources, a brief note to that effect is obligatory, below the figure. One sentence at least referring to the illustration is obligatory. Mathematical expressions should be numbered on the right side, while all variables and parameters must be defined.

Copyright Articles submitted to the Journal should be authentic and original contributions and should have never been published before in full text, nor be under consideration for any other publication at the same time. Authors submitting articles for publication warrant that the work is not an infringement of any existing copyright and will indemnify the publisher against any breach of such warranty. For use of dissemination and to en-sure proper policing of use, papers and contributions become the legal copyright of the publisher unless otherwise agreed.

Proof Authors are responsible for ensuring that all manuscripts (whether original or revised) are accurately typed before final submission. One set of proof will be sent to authors, if requested, before the final publication, which must be returned promptly.

Referencing Guide The references should specify the source (such as book, journal article or a web page) in sufficient de-tail to enable the readers to identify and consult it. The references are placed at the end of the work, with sources listed alphabetically (a) by authors’ surnames or (b) by the titles of the sources (if the au-thor is unknown). Multiple entries by the same author(s) must be sequenced chronologically, starting from the earliest, e.g.:

Ljubojević, T.K. (1998). Ljubojević, T.K. (2000a). Ljubojević, T.K. (2000b). Ljubojević, T.K., & Dimitrijević, N.N. (1994).

Here is a list of the most common reference types: A. PERIODICALS Authors must be listed by their last names, followed by initials. Publication year must be written in pa-rentheses, followed by a full stop. Title of the article must be in sentences case: only the first word and proper nouns in the title are capitalized. The periodical title must be in title case, followed by the vo-lume number, which is also italicized:

Author, A. A., Author, B. B., & Author, C. C. (Year). Title of article. Title of Periodical, volume number(issue number), pages.

Journal article, one author, paginated by issue Journals paginated by issue begin with page 1 in every issue, so that the issue number is indicated in parentheses after the volume. The parentheses and issue numbers are not italicized, e.g.

Tanasijević, V. (2007). A PHP project test-driven end to end. Management Information Systems, 5 (1), 26-35.

Journal article, one author, paginated by volume Journals paginated by volume begin with page 1 in issue 1, and continue page numbering in issue 2 where issue 1 ended, e.g.

Perić, O. (2006). Bridging the gap: Complex adaptive knowledge management. Strategic Management, 14, 654-668.

Journal article, two authors, paginated by issue

Strakić, F., & Mirković, D. (2006). The role of the user in the software development life cycle. Management Information Systems, 4 (2), 60-72.

Journal article, two authors, paginated by volume

Ljubojević, K., & Dimitrijević, M. (2007). Choosing your CRM strategy. Strategic Management, 15, 333-349.

Journal article, three to six authors, paginated by issue

Jovanov, N., Boškov, T., & Strakić, F. (2007). Data warehouse architecture. Management Information Systems, 5 (2), 41-49.

Journal article, three to six authors, paginated by volume

Boškov, T., Ljubojević, K., & Tanasijević, V. (2005). A new approach to CRM. Strategic Management, 13, 300-310.

Journal article, more than six authors, paginated by issue

Ljubojević, K., Dimitrijević, M., Mirković, D., Tanasijević, V., Perić, O., Jovanov, N., et al. (2005). Putting the user at the center of software testing activity. Management Information Systems, 3 (1), 99-106.

Journal article, more than six authors, paginated by volume

Strakić, F., Mirković, D., Boškov, T., Ljubojević, K., Tanasijević, V., Dimitrijević, M., et al. (2003). Metadata in data warehouse. Strategic Management, 11, 122-132.

Magazine article

Strakić, F. (2005, October 15). Remembering users with cookies. IT Review, 130, 20-21. Newsletter article with author

Dimitrijević, M. (2009, September). MySql server, writing library files. Computing News, 57, 10-12. Newsletter article without author

VBScript with active server pages. (2009, September). Computing News,57, 21-22. B. BOOKS, BROCHURES, BOOK CHAPTERS, ENCYCLOPEDIA ENTRIES, AND BOOK REVIEWS Basic format for books

Author, A. A. (Year of publication). Title of work: Capital letter also for subtitle. Location: Publisher. Note: “Location" always refers to the town/city, but you should also include the state/country if the town/city could be mistaken for one in another country. Book, one author

Ljubojević, K. (2005). Prototyping the interface design. Subotica: Faculty of Economics.

Book, one author, new edition

Dimitrijević, M. (2007). Customer relationship management (6th ed.). Subotica: Faculty of Economics. Book, two authors

Ljubojević, K., Dimitrijević, M. (2007). The enterprise knowledge portal and its architecture. Subotica: Faculty of Economics.

Book, three to six authors

Ljubojević, K., Dimitrijević, M., Mirković, D., Tanasijević, V., & Perić, O. (2006). Importance of software testing. Subotica: Faculty of Economics.

Book, more than six authors

Mirković, D., Tanasijević, V., Perić, O., Jovanov, N., Boškov, T., Strakić, F., et al. (2007). Supply chain management. Subotica: Faculty of Economics.

Book, no author or editor

Web user interface (10th ed.). (2003). Subotica: Faculty of Economics. Group, corporate, or government author

Statistical office of the Republic of Serbia. (1978). Statistical abstract of the Republic of Serbia. Bel-grade: Ministry of community and social services.

Edited book

Dimitrijević, M., & Tanasijević, V. (Eds.). (2004). Data warehouse architecture. Subotica: Faculty of Economics.

Chapter in an edited book

Boškov, T., & Strakić. F. (2008). Bridging the gap: Complex adaptive knowledge management. In T. Boškov & V. Tanasijević (Eds.), The enterprise knowledge portal and its architecture (pp. 55-89). Subotica: Faculty of Economics.

Encyclopedia entry

Mirković, D. (2006). History and the world of mathematicians. In The new mathematics encyclopedia (Vol. 56, pp. 23-45). Subotica: Faculty of Economics.

C. UNPUBLISHED WORKS Paper presented at a meeting or a conference

Ljubojević, K., Tanasijević, V., Dimitrijević, M. (2003). Designing a web form without tables. Paper presented at the annual meeting of the Serbian computer alliance, Beograd.

Paper or manuscript

Boškov, T., Strakić, F., Ljubojević, K., Dimitrijević, M., & Perić, O. (2007. May). First steps in vis-ual basic for applications. Unpublished paper, Faculty of Economics Subotica, Subotica.

Doctoral dissertation

Strakić, F. (2000). Managing network services: Managing DNS servers. Unpublished doctoral disserta-tion, Faculty of Economics Subotica, Subotica.

Master’s thesis

Dimitrijević, M. (2003). Structural modeling: Class and object diagrams. Unpublished master’s thesis, Faculty of Economics Subotica, Subotica.

D. ELECTRONIC MEDIA The same guidelines apply for online articles as for printed articles. All the information that the online host makes available must be listed, including an issue number in parentheses:

Author, A. A., & Author, B. B. (Publication date). Title of article. Title of Online Periodical, volume number(issue number if available). Retrieved from http://www.anyaddress.com/full/url/

Article in an internet-only journal

Tanasijević, V. (2003, March). Putting the user at the center of software testing activity. Strategic Management, 8 (4). Retrieved October 7, 2004, from www.ef.uns.ac.rs/sm2003

Document from an organization

Faculty of Economics. (2008, March 5). A new approach to CRM. Retrieved July 25, 2008, from http://www.ef.uns.ac.rs/papers/acrm.html

Article from an online periodical with DOI assigned

Jovanov, N., & Boškov, T. A PHP project test-driven end to end. Management Information Systems, 2 (2), 45-54. doi: 10.1108/06070565717821898.

Article from an online periodical without DOI assigned

Online journal articles without a DOI require a URL.

Author, A. A., & Author, B. B. (Publication date). Title of article. Title of Journal, volume number. Retrieved from http://www.anyaddress.com/full/url/

Jovanov, N., & Boškov, T. A PHP project test-driven end to end. Management Information Systems,

2 (2), 45-54. Retrieved from http://www.ef.uns.ac.rs/mis/TestDriven.html. REFERENCE QUOTATIONS IN THE TEXT Quotations If a work is directly quoted from, then the author, year of publication and the page reference (preceded by “p.”) must be included. The quotation is introduced with an introductory phrase including the au-thor’s last name followed by publication date in parentheses.

According to Mirković (2001), “The use of data warehouses may be limited, especially if they contain confidential data” (p. 201).

Mirković (2001), found that “the use of data warehouses may be limited” (p. 201). What unex-pected impact does this have on the range of availability?

If the author is not named in the introductory phrase, the author's last name, publication year, and the page number in parentheses must be placed at the end of the quotation, e.g.

He stated, “The use of data warehouses may be limited,” but he did not fully explain the possi-ble impact (Mirković, 2001, p. 201).

Summary or paraphrase

According to Mirković (1991), limitations on the use of databases can be external and software-based, or temporary and even discretion-based. (p.201)

Limitations on the use of databases can be external and software-based, or temporary and even discretion-based (Mirković, 1991, p. 201).

One author

Boškov (2005) compared the access range…

In an early study of access range (Boškov, 2005), it was found... When there are two authors, both names are always cited:

Another study (Mirković & Boškov, 2006) concluded that… If there are three to five authors, all authors must be cited the first time. For subsequent refer-ences, the first author’s name will cited, followed by “et al.”.

(Jovanov, Boškov, Perić, Boškov, & Strakić, 2004).

In subsequent citations, only the first author’s name is used, followed by “et al.” in the introductory phrase or in parentheses:

According to Jovanov et al. (2004), further occurences of the phenomenon tend to receive a much wider media coverage.

Further occurences of the phenomenon tend to receive a much wider media coverage (Jovanov et al., 2004).

In “et al.", “et” is not followed by a full stop. Six or more authors

The first author’s last name followed by "et al." is used in the introductory phrase or in parentheses:

Yossarian et al. (2004) argued that…

… not relevant (Yossarian et al., 2001).

Unknown author

If the work does not have an author, the source is cited by its title in the introductory phrase, or the first 1-2 words are placed in the parentheses. Book and report titles must be italicized or underlined, while titles of articles and chapters are placed in quotation marks:

A similar survey was conducted on a number of organizations employing database managers ("Limiting database access", 2005).

If work (such as a newspaper editorial) has no author, the first few words of the title are cited, fol-lowed by the year:

(“The Objectives of Access Delegation,” 2007)

Note: In the rare cases when the word "Anonymous" is used for the author, it is treated as the au-thor's name (Anonymous, 2008). The name Anonymous must then be used as the author in the refer-ence list.

Organization as an Author

If the author is an organization or a government agency, the organization must be mentioned in the introductory phrase or in the parenthetical citation the first time the source is cited:

According to the Statistical Office of the Republic of Serbia (1978), …

Also, the full name of corporate authors must be listed in the first reference, with an abbreviation in brackets. The abbreviated name will then be used for subsequent references:

The overview is limited to towns with 10,000 inhabitants and up (Statistical Office of the Re-public of Serbia [SORS], 1978). The list does not include schools that were listed as closed down in the previous statistical over-view (SORS, 1978).

When citing more than one reference from the same author:

(Bezjak, 1999, 2002) When several used works by the same author were published in the same year, they must be cited adding a, b, c, and so on, to the publication date:

(Griffith, 2002a, 2002b, 2004) Two or more works in the same parentheses

When two or more works are cited parenthetically, they must be cited in the same order as they appear in the reference list, separated by a semicolon.

(Bezjak, 1999; Griffith, 2004) Two or more works by the same author in the same year

If two or more sources used in the submission were published by the same author in the same year, the entries in the reference list must be ordered using lower-case letters (a, b, c…) with the year. Lower-case letters will also be used with the year in the in-text citation as well:

Survey results published in Theissen (2004a) show that…

To credit an author for discovering a work, when you have not read the original:

Bergson’s research (as cited in Mirković & Boškov, 2006)… Here, Mirković & Boškov (2006) will appear in the reference list, while Bergson will not. When citing more than one author, the authors must be listed alphabetically:

(Britten, 2001; Sturlasson, 2002; Wasserwandt, 1997) When there is no publication date:

(Hessenberg, n.d.) Page numbers must always be given for quotations:

(Mirković & Boškov, 2006, p.12)

Mirković & Boškov (2006, p. 12) propose the approach by which “the initial viewpoint…

Referring to a specific part of a work: (Theissen, 2004a, chap. 3)

(Keaton, 1997, pp. 85-94)

Personal communications, including interviews, letters, memos, e-mails, and telephone conversations, are cited as below. (These are not included in the reference list.)

(K. Ljubojević, personal communication, May 5, 2008).

FOOTNOTES AND ENDNOTES

A few footnotes may be necessary when elaborating on an issue raised in the text, adding something that is in indirect connection, or providing supplementary technical information. Footnotes and end-notes are numbered with superscript Arabic numerals at the end of the sentence, like this.1 Endnotes begin on a separate page, after the end of the text. However, Strategic Management journal does not recommend the use of footnotes or endnotes.

CIP - Каталогизација у публикацији Библиотека Матице српске, Нови Сад 005.21 STRATEGIC managament : international journal of strategic managament and decision support systems in strategic managament / editor-in-chief Jelica Trninić. - Vol. 14, no. 1 (2009) - . - Subotica: University of Novi Sad, Faculty of Economics, 2009-. - 30 cm Tromesečno. - Nastavak publikacije: Strategijski menadžment = ISSN 0354-8414 ISSN 1821-3448 COBISS.SR-ID 244849927 Rešenjem Ministarstva za informisanje Republike Srbije, časopis "Strategijski menadžment" upisan je u regis-tar javnog informisanja pod brojem 2213, od 7. avgusta 1996. Rešenjem Ministarstva za nauku i tehnologiju Republike Srbije br. 413-00-435/1/96-01 časopis je oslobođen opšteg poreza na promet proizvoda kao publi-kacija od posebnog interesa za nauku.

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