CC BY
100
ПРИКЛАДНАЯ ИНФОРМАТИКА / JOURNAL OF APPLIED INFORMATICS
_ /Том 10. № 6 (60). 2015 /
V. Nissen, Ilmenau University of Technology, Germany, [email protected] A. von Rennenkampff, Ilmenau University of Technology, Germany, [email protected]
Measuring and managing IT agility as a strategic resource — examining the IT application systems landscape
A company's ability to change increasingly depends on the ability to change its IT, something referred to as «IT agility» here. High IT agility can contribute to increased business agility and thus create a competitive advantage. In this paper we look at which factors influence IT agility and how the IT agility can be increased. The main body of the paper, however, is devoted to the research question how IT agility can be measured and actively managed. Here, the focus is on the IT application systems landscape, a resource of significant importance for the IT agility and competitiveness of a company.
Keywords: IT agility, IS architecture metrics, enterprise architecture management, IT value contribution, design science research.
1. Introduction and Motivation
The business of companies and thus their business processes and products are changing over time. These changes almost always have an impact on the company's IT in the sense that IT systems need to be adapted. Frequently, two problems occur:
1. The change of IT systems can be realized only with great delays; in extreme cases some requirements are infeasible.
2. The planning and implementation of changes in the IT systems cause major financial and human efforts.
The consequence of these circumstances is that
• the business must absorb with a lot of manual work the period until the IT side has implemented the change,
• revenues are lost, for example in sales required changes are implemented too late,
• corporate IT lacks behind the business changes and thereby often a change backlog forms so that the problems described above amplify (snowball effect).
At the same time, IT penetration of the core business processes in companies in recent decades has increased continuously. There is hardly a company which can survive long without IT. Thus, the change ability of enterprises increasingly depends on the ability to change the IT [8].
Surveys in recent years among IT managers show that a key requirement for the IT organization is the ability to adapt to the needs of the professional business. In 2012 Capgemini questioned 156 CIOs of large and medium-sized enterprises about the most important issues for the coming year [15]. In nine of the fourteen most frequently mentioned aspects change and the ability to change the IT plays a key role, such as in the topics «Business Process Improvement» and «Supporting Business Change». The importance of IT agility is also underlined by statements of leading market research companies, for example, Gartner [23] or Forrester [50]. Despite this high practical relevance Gronau states, from a scientific perspective, that the adaptability of the IT architecture is treated in business informatics only to a small degree. Moreover, as far as mutability of IT is concerned, the focus lies
mainly on the IT organization and not on the IT architecture [25, p. 217]. Thus, both IT agility as well as the management of the IT application systems landscape represent current and relevant topics of business informatics.
2. Research Question and Objective
As one of the main causes of the lack of IT agility in companies the complex, over many years uncontrolled grown IT application systems landscapes can be identified [51, pp. 5-6; 57, pp. 140-142; 20, pp. 65-67; 33, pp. 9-11]. The (nonexistent) IT architectures of obsolete application landscapes prevent easy maintenance and lead to low IT agility. However, the agility of the IT application systems landscape is «absolutely essential» for the future ofthe business [20, p. 68].
Both in science and practice IT agility receives more and more attention. However, IT agility is still not clearly defined [46, p. 59]. Also, no method is known to measure the IT agility and especially the agility of the IT architecture based on objectively observable factors. From the problems described the following research question is derived:
How can the agility of the IT application systems landscape as an important part of the IT agility of a company be measured and actively
managed, based on objectively observable characteristics?
The aim of this study is thus the development of a key figure system for measuring the agility of the IT application systems landscapes. A key figure system is a set of indicators that can be structured to statement areas by order and aggregation [27, pp. 351-353].
3. Subject Area IT Application Systems Landscape (Application Landscape)
In order to structure the enterprise architecture at a higher abstract level layer models have established themselves in business informatics. Based on a layer model, inspired by Winter and Fischer [72, p. 3], the object of observation of this work, the architecture of the IT application systems landscape, is defined (see Fig. 1).
This layer model differentiates five layers:
• On the corporate strategy layer artifacts are assigned that describe the company's strategic objectives, the market segmentation, the services provided and relations with suppliers and customers.
• The organizational and process layer contains artifacts that describe the organizational structures, business processes, roles, responsibilities and information flows.
Corporate Strategy Layer
Organizational and Process Layer
Integration Layer
Software Layer
Infrastructure Layer
Architecture of IT Application Systems Landscape
Applications Interfaces Domains Functions Information Objects
Fig. 1. Layer model of the enterprise architecture (adapted from [72, p. 9]) and location of the architecture of the IT application systems landscape
• The integration layer includes artifacts that describe the elements of the IT application systems landscape, their grouping and their relationships.
• The software layer contains the artifacts to describe individual application systems and data structures in the company.
• The infrastructure layer includes the artifacts to describe hardware and network components.
Between the organizational and the software layer the integration layer is situated. At this level and at the interfaces to the layers above and below artifacts are located that describe the elements of the IT application systems landscape and their relationships. The elements of the IT application systems landscape are the application systems (applications and associated data), their interfaces as well as the domains and functions of a company. The architecture of the IT application systems landscape therefore describes the application systems, their relationships and their structure based on business-related criteria (domains and functions).
4. Methodology: Design Science Research
The design science research approach as a methodological framework seems very well suited to answer the research question described. On the one hand the lack of measurability of IT agility and thus the agility of IT application systems landscapes is a relevant issue that comes from the business practice. On the other hand, the development of a key figure system for mea-
suring the agility of application landscapes is a design activity. Peffers et al. define design as «the act of creating an explicitly applicable solution to a problem» [47, p. 47]. Hevner describes design as a process as well as a product (artifact) [28, p. 78].
The specific sequence of the research activities in our investigation is shown in Figure 2. It is based on the popular Design Science Research Methodology Process Model of Peffers et al [47, p. 54].
The motivation of our research and the problem to be solved is the lack of ways to measure and actively manage the IT agility (not only) in the field of the IT application systems landscape. This problem is of considerable practical importance across industries and regions, since the IT agility can be seen as an integral part of the value contribution of IT in the enterprise [62]. Therefore, a management instrument for this area should be created. This we want to develop based on a hierarchy of objectives, in the form of a key figure system for measuring the agility of application landscapes. A key figure system is an artifact in the sense of Hevner [28].
From a scientific point of view, our contribution is the developed hierarchy of goals (and associated key figures) as the first comprehensive model to explain the relationships between architecture principles and the agility of IT application systems landscapes. The corresponding key figure system is the tool for measuring and actively managing the IT agility in this area of the company. This paper describes the essen-
Process Iteration
Fig. 2. Research process in this investigation (following Peffers et al [47])
- [ 7 ]
tial components and results of the corresponding design process.
The applicability and usefulness of the key figure system was tested in several case studies in practice. For the case studies, the approach followed recommendations ofYin [73, pp. 114-122] and Benbasat et al. [7, pp. 369-386]. In selecting the companies studied emphasis was placed on finding relatively different companies in order to examine a wide range of possible scenarios for the key figure system.
In order to ensure the validity and reliability of the case studies, several different data sources were used in the company, such as architecture data bases, architecture graphs, architecture concepts and interviews with enterprise architects. In addition, a database has been set up for each case study that includes, separated from the raw data, a comprehensible analysis and calculation of key figures.
It turned out that through the developed indicators the agility of the IT application systems landscape can be measured and controlled over time. In addition, the key figure system and the goal hierarchy provide scientists with a new basis for the continuing development of the concept of IT agility in the IT architecture of organizations.
After the construction and demonstration of an artifact the next step in the design process is the evaluation [35, p. 726]. The evaluation should prove the usefulness, quality and effectiveness of the artifact [28, p. 85]. To take account of the rigor of the evaluation, a multi-perspective approach is chosen. Here, several different methods for evaluation are used in parallel.
In this paper two qualitative methods, expert interviews and case studies, are combined to evaluate the performance measurement system. This combination of evaluation methods is frequently used in application-oriented work when no similar model exists, which can be compared with the model developed in a kind of «benchmark» [36, p. 798].
Frank recommends the use of expert interviews on the evaluation of hypotheses derived
beforehand from literature studies and for which a «substantial theory» is missing [22, p. 42], as is the case in this study. Expert interviews have two major advantages. First, the experts can be used as «auditors». Their «operating knowledge» is used to validate the developed hierarchy of objectives and related indicators or falsify them [39, pp. 75-77]. Second, by the interviews one has access to «exclusive, detailed and compre-hensive» knowledge of the area of interest [49, p. 113]. Based on feedback of the experts, final adjustments to the goal hierarchy and associated indicators can result, which is in line with the iterative process of design science research.
Semi-structured interviews were chosen to communicate with the experts. They are composed of open and closed questions and follow an interview guide. They will not, however, as in the standardized interview, proceed question for question. This method has the ability to respond to certain answers in more detail, ask more questions to gain a deeper understanding and, thus, generate more valuable information. Through the interview guide it could be ensured (standardization) that important aspects of the research topic are covered [37, p. 66]. The findings of the previous literature review formed the basis for a first version of the guide. This was subjected to a pre-test in order to optimize the comprehen-sibility of the questions. The interviews were recorded on the basis of notes and tape recording. The analysis of the data from the interviews was carried out by transcription of the key messages, summary, comparison and generalization of data following Meuser and Nagel [39, pp. 83-91]. More details on the schedule of the expert interviews are included in [52, pp. 220-234].
Hevner et al. emphasize the iterative nature of the evaluation step in the discipline of Design Science. This means that findings from the evaluation flow back into the design and the artifact is incrementally modified [28, p. 85]. In this work the expert interviews and case studies were carried out at different times. Therefore, after the round of interviews, it was possible to incorporate the feedback received in the key figures
before they were used in the case studies. The feedback from the case studies was also eventually incorporated in the performance measurement system, so that the indicators described represent the final version after the evaluation rounds. However, a comprehensive evaluation of the proposed performance measurement system must be reserved for future research.
This contribution finally is our attempt to make the core results of our research available to a wider audience in science and practice. We now provide some necessary background information and define basic concepts in order to prepare for the design of our key figure system (the artifact).
5. Background and Basic Concepts 5.1. What is IT-Agility?
Agility was identified already in 1967 by Ackoff [1] as an important company property, but neglected by the scientific community for a long time. In the nineties, the term was especially popular in the context of production research and process management. Consequently, Nagel and Dove mean by «manufacturing agility» a production system with the ability to quickly recognize and fulfil market needs [41]. Sharifi and Zhang [60] complete the definition with the strategic aspect. For them «agile manufacturing» is a strategic approach to production, able to respond to expected and unexpected changes and take advantage of them. Warnecke [68] formulated the concept of «agile management» in response to the rise in market requirements. Necessary were «structural flexibility» and «operational flexibility» of companies. The claim thus formulated of a highly dynamic company was further sharpened later with the concept of the «real-time enterprise» [31]. This focusses on business process management «in real time».
In German «Wirtschaftsinformatik» (business informatics) the term agility is in use only in the last 5-10 years, mainly through «Agile Software Development». In American information system (IS) research publications can be
identified from the past 20 years that use of the term in different contexts, for example «IT agility», «IS-agility» or «organizational agility». To date, however, there is no generally accepted definition of IT flexibility and IT agility [67]. One can, however, find frequently used elements of the definitions that enable a basic characterization of the terms. Frequently mentioned elements in definitions of flexibility are:
• speed, rapid response to change
• scope, high number of options to respond to change
• efficiency, low implementation costs for changes
Definitions of agility often include the following elements:
• large, massive, significant change
• unforeseen, uncertain, unknown change
• use of new, emerging opportunities for business
• strategic approach
• proactive activities in order to be able to better respond to future changes
In this paper IT agility is defined as the ability of a company's information processing function to respond very quickly (preferably in real time) to changing capacity demands and changing functional requirements, and be able to use the potential of information technology in such a way that the business scope of action of the company is extended or even redesigned.
IT flexibility is considered here as part of IT agility. IT agility is a broad term and can first be further differentiated in capacitive and functional components. The capacitive IT agility can be assigned properties such as scalability, i.e. the ability of IT to respond to growing business volumes, or performance, i.e. provide a constant response time even with changing demand volumes. When it comes to a functional change (features, products, processes), we speak of functional IT agility.
When dealing with potential changes two archetypal forms can be distinguished: reactive and proactive. Many contributions in the IS literature define a passive coping with change
as reactive, whereas an active, internally driven change intention is defined as proactive. Some authors consider the adaptation to unexpected changes as proactive (e.g. [57]). Capacitive and reactive IT agility address mainly the operational handling of existing IT systems within the IT organization. These necessary skills are only the foundation for proactive IT agility which additionally requires strategic skills to anticipate changes and to actively shape them in the business through changing or newly developing IT systems.
5.2. What is the Strategic Value of IT and IT-Agility?
The resource-based (RBV) [6; 48; 70] of strategic management places the heterogeneous equipment of companies with internal resources as a source of competitive advantage in the center. It is argued that not the sole possession of these resources is the cause of the success of a company, but additionally appropriate employee skills and management skills are needed who know to take advantage of the potential of resources [29, pp. 998-999], which can be understood as a refinement process.
Following Schneider [58, p. 60] strategic resources within the meaning of RBV can be defined as: production factors purchased in markets, altered or enhanced by able management, employees or external specialists to create company-specific characteristics of competitiveness. While production factors can be bought by all competitors in markets, resources embody specific tangible and especially intangible assets of a firm. Their main characteristic is a more difficult acquisition by competitors.
For characterization of (strategically relevant) resources, various properties were defined in the course of academic discourse. According to the VRIS-framework of Barney, a resource is valuable, rare (or even unique), inimitable and non-substitutable, i.e. cannot be replaced by other equivalents [6, p. 105]. Other authors have varied these properties and added in particular the usability and immobility aspects [66].
Applying the principles of RBV on the subject field of IT, so it can be stated that the mere possession of IT does not lead to competitive advantage, but this can be achieved only through its effective and efficient use. Therefore, a systematic planning and designing of the IT function within the responsibility of strategic IT management is required.
Looking at the information infrastructure of enterprises, it can be said that not all of its components are equally sources of competitive advantage. In particular, hardware and standard software, which are available on the market, are no strategically relevant resources. Carr has described this situation very striking, in which he referred to the IT as a «commodity» without strategic relevance [16]. This can be explained with his narrow viewing angle to pure technique. However, if the entire IT application systems landscape on the one hand and the individually developed or configured IT application systems of the company on the other hand are considered, the criteria of the resource-based approach can be met:
• Valuable: The individual application systems and the application landscape support a company's business processes and have thus a positive impact on efficiency and effectiveness. If the IT architecture is designed to be flexible, additional value is created by the ability to quickly implement changes [34, pp. 935-937; 54, pp. 237-239].
• Rare: Individual elements of an application landscape, in particular standard software and hardware are not scarce. However, their specific connection and usage in enterprises and the resulting IT architecture are unique. Thus, the set of all application systems in a company forms a unique resource [9, p. 172; 11, p. 158].
• Usable: Only the actual use of the IT application systems landscape and thus the exploitation of its capabilities enable competitive advantages. Because the application landscape is geared to the business processes, it is well usable by the staff. A prerequisite is that the application landscape is designed with foresight in order to
implement changes quickly. Otherwise, it can also have the opposite effect and inhibit the development of competitive advantage.
• Inimitable: IT application systems landscapes often evolve over many decades individually within companies. They are the result of many individual decisions and therefore hardly reproducible [11, p. 160].
• Not substitutable: For substitutability a similar logic to rareness applies. Individual elements of the application landscape are easily substitutable. Also, the entire structure of the application environment can be substituted up to a certain degree. For example, similar strategic objectives can be achieved with standard as well as individual software. A competitor could therefore build a similar application landscape and thus jeopardize competitive advantage. However, from a practical viewpoint this is only possible to a very limited extent due to the high temporal and financial expenses that are necessary for setting up an application environment using a predefined IT architecture.
• Immobile: Some elements of the application environment, in particular hardware, are very mobile. The IT application systems landscape as a whole, however, is highly immobile, as it is unique and tailored to the respective companies. A complete application landscape can be purchased by competitors hardly in full.
The mere presence of application systems and infrastructure is therefore not sufficient to generate a competitive advantage. It is the combination of its elements to create a unique company-specific IT architecture and its effective and efficient usage that are sources of sustainable competitive advantage.
Thus it can be stated that with the provision of an agile IT application systems landscape a strategic resource can be created that allows a sustainable competitive advantage. This may be more important, the more the company's business processes are IT-supported and the stronger the company's business is subject to change.
Since most business processes today operate IT-supported, there is a close relationship
between the agility at the business level and at the IT level. Different authors from science and practice believe that the architecture of the IT application systems landscape represents in fact the key differentiator between highly agile and less agile companies (e.g. [13; 38; 56]).
5.3. Status Quo and Demand for IT-Agility
In this context, we wanted to clarify the status quo regarding IT agility in companies and which sectors have a particularly high demand for IT agility. As to our knowledge, no corresponding evidence can be found in the current literature. The gap in research presented at this point required an exploratory approach [10, pp. 351-392]. Therefore, we conducted a semi-structured interview among 18 experts in the field [46; 52, pp. 100-106]. We interviewed both IT top managers as well as IT architects. Moreover, in order to complete the picture, and also to get an external IT view, managers from the business were included that have a strong link to IT. Finally, some IT-related business consultants were also interviewed.
When asked about the presence of IT agility in their IT organization, all but one participant said not to be sufficiently agile. Stopped or failed projects on the one hand and the inability to start new projects were cited as testimony of this situation. Often the business requirements could not be implemented on time or even not at all. However, some participants cited some positive examples in which the IT was a proactive driver of business innovation and thus generated competitive advantage. Overall, however, it was clear that the need for greater IT agility in the companies is high. Around two thirds of the interviewees (13 mentions) perceive an increasing need for IT agility in their business.
When asked about sectors with a high demand for IT agility companies in the service sector were mentioned most frequently. A representation of the responses of the participants can be found in Fig. 3.
Although this survey cannot be considered representative, an in-depth analysis of the fac-
Fig. 3. Sectors with a particularly high demand for IT agility (number of mentions)
tors influencing the demand for IT agility supports this result. Figure 4 shows the most commonly cited factors influencing the need for IT agility. The factors most frequently mentioned are of particular importance in companies of the service sector.
The most cited drivers for IT agility were the importance of IT for the core business and complex product features. Once the IT plays a key role in the core business processes, as is the case e.g. in a bank, the need for IT agility in-
creases. Product features such as short product life cycles, complex end products and a wide variety of products in the business portfolio require high IT agility because change requests from the professional business are frequent and IT must be prepared for this. Companies, whose business model is based on a high level of innovation, as is the case with e-commerce companies, also require a particularly high IT agility. Similarly, the demand for IT agility is high for companies that have a strong end customer
Fig. 4. Factors influencing the need for IT agility (number of mentions)
orientation, higher than for companies in B2B markets.
The competitive dynamics of an industry was seen as another important factor in the need for IT agility. Companies that are subject to stringent regulatory requirements, also require increased IT agility as necessary changes often emerge relatively unexpected and must be implemented quickly. The remaining factors of Figure 4 have rather secondary importance in practice according to our survey.
Afterwards, the participants were also asked about factors that impede an increase in IT agility in the enterprise (Fig. 5). As the largest preventer, participants named the outdated and complex structure of the IT application systems landscape and the application portfolio seen in their company (9 nominations). Outdated and uncontrolled grown structures make changes complex, expensive, risky and thus nearly impossible. Here are some statements from the expert interviews in support of this view: «Our IT looks like an inverted pyramid. We started small, always putting something on top later.» «We have plenty of architectural legacies, making changes very expensive and risky.» The terms «complex-ity» and «heterogeneous IT infrastructure» were often mentioned by the experts in this context.
In addition to the complex structure of the application environment, the lack of business-IT alignment has been named as a major pre-
venter of IT agility (7 nominations). The IT and business organizations have different objectives and do not communicate adequately with each other. This delays projects and leads to undesirable results.
In some cases, the existence of a «shadow IT» that exists parallel to the IT organization in the business departments has also been reported. The business departments directly operate application systems and mandate IT projects, thereby sidestepping the IT organization. This undermines the efforts of the IT organization to achieve a high level of agility, because of redundant application systems, which often fail to comply with the standards of the company and thereby result in complex integration scenarios. This in turn leads to an increasing complexity and decreasing consistency of the IT application systems landscape.
As an additional preventer, participants mentioned the unwillingness of IT staff to change (7 nominations). Even the skills of the IT staff have been perceived by participants as a preventer of IT agility (6 nominations). Finally, participants have seen the cost pressure on the IT organization as an additional preventer (6 nominations). IT agility costs money, and the willingness of corporate management must be present to support this additional investment. Even outsourcing can curiously have a negative impact on IT agility. On the one hand, a loss must
10
Outdated, Lack of Business -complex structure IT Alignment of Appl. Ld.scape
Attitude of Staff
Skill Level
Cost Pressure
Fig. 5. Preventers of IT agility (number of mentions)
9
8
7
6
5
4
3
2
0
be absorbed in internal IT expertise and on the other hand, external IT service providers may employ low skilled employees to optimize their costs. Overall, the in-depth analysis of the interview results confirmed the importance of the sector affiliation with regard to the need for IT agility in general, and the increased need for IT agility for service companies in particular.
5.4. Measures to Increase IT Agility
IT agility should be implemented due to rational considerations. As also became clear in the expert interviews, the demand for IT agility is not identical in all businesses and in all processes. Rather, this demand depends on a number of company-internal and external factors. Basically, an optimal IT agility for a company or its core processes could be determined, based on cost-benefit considerations. Due to internal or
external constraints, such as budget constraints, immature technologies or lack of availability of qualified personnel, this level of IT agility is often temporarily unattainable. Here, a maturity model of IT agility [43] could in the future support to plan a meaningful series of measures (Fig. 6).
Different fields of action for IT agility management can be differentiated that, based on the company's objectives, should be prioritized differently under a holistic strategy. We suggest four fields of action: IT organization, IT processes, IT architecture/infrastructure and IT staff/management. Table 1 contains a (non-exhaustive) list of possible measures to increase IT agility in these areas, taking into account evidence from the literature as well as proposals of our interviewees.
Due to the variety of potential measures to increase IT agility, it seems sensible to priori-
Subject Area of IT Agility Management
Maturity Model based on key figures of IT Agility
Fig. 6. Overview of IT agility management [43]
Tablel. Some exemplary measures to increase IT agility
Field of Action
Measures to increase IT Agility
IT Architecture
Systematic IT development planning, redesign of the application landscape, building a service-oriented architecture (SOA) elimination of redundancy (functions and data) configurable software, use of rules engines
early consideration of non-functional requirements such as performance investments in scalable, standardized hardware, building buffers in the infrastructure virtualization of storage and application systems create transparency about data and systems
standardization, harmonization and centralization of IT systems, creation of platforms
active management and reduction of IT complexity keep technology up to date
focus on communication standards for the integration of partners
IT Staff
broad professional and technical qualification of staff
promote understanding of the business and its external customers
promotion of new ideas and motivation for innovative, success-oriented thinking
IT Processes
standardization of IT service delivery processes (e.g. ITIL compliance and introduction of project management standards such as PRINCE2 or PMBOK) agile software development, professional release planning cooperation with the business at a strategic level
integration of internal customers (business departments), external customers and partners in the IT processes
innovation management: identify relevant trends systematically
IT Organization
clear IT governance structures and requirements, headquarter controls subsidiaries
IT is represented on a high management level
Align organizational structure of IT with business
Joint business and IT teams in projects
Separation of supply side and demand side IT
Simplify addition and disentanglement of partners in the IT value chain
Shorten decision-making processes
variabilization of IT fixed costs through outsourcing
tize the areas of action in the next step. To this end, the interviewees were asked about the importance of the areas of activity for the IT agility on the one hand and about the simplicity of the change in the respective fields of action based on the feasibility of the measures. Figure 7 illustrates the results.
The IT architecture has been attributed the utmost importance by the participants with regard to the IT agility (32%), followed by IT personnel (27%). The importance of IT processes (23%) has been estimated to be higher than that of the IT organization (18%). In the feasibility of the
measures, the IT organization is considered to be the most easily modifiable, followed by the IT processes. The changeability of the IT architecture is considered much more difficult, the IT personnel is considered relative to the other three areas of action as the most difficult changeable field.
It is not surprising that the IT architecture and IT staff seem to be the two more important areas of activity. The CIOs surveyed made it clear that in their view the IT «is a people business», while the IT architects have pointed out the special significance of the structures of the IT application
34
32
in
ld 30
eli
Fi n io 28
ct Acf 26
о e c 24
ant 22
ΠE 20
e tla 18
el ее 16
\o IT Architecture
IT Staff
•
IT Processes
IT Organization
Fig. 7.
of the
Ease of Change in Action Field
(1 - difficult ... 4 - easy)
Relative importance and changeability action fields of IT agility management
systems landscape in companies. These two areas of activity are the foundation for an agile IT. The IT processes and IT organization were, however, considered to be much more easily modifiable. This also explains why so many projects in companies address these two areas of activity, such as ITIL introductions, introductions of agile development methods, governance and organizational redesign, standardization of service delivery processes etc.
6. Measuring and Managing the Agility of the IT Application Systems Landscape
6.1. Development of a Goal Hierarchy
For a meaningful management of IT agility it needs to be measurable. Both the practice and the literature emphasize the importance of IT agility for the company's success. Yet so far there is no performance measurement system for IT agility. As one of the main causes of the lack of agility in IT organizations, the complex IT application landscapes that have often grown uncontrolled over years have been identified (see for instance [51; 57]. Consequently, the aim of this paper is the construction of a performance measurement system for measuring the agility of IT applica-
tion systems landscapes. Thereby, measures to increase IT agility in this area in a timely and focused manner and with reasonable effort will be supported.
To collect the data, aggregate it and calculate key figure values, all tools for recording and processing IT architecture data, both standard products and custom-developed applications, are fundamentally suitable. For large application environments Enterprise Architecture Management (EAM) standard products are recommended. These tools enable the profile-like documentation of application systems including their interfaces, infrastructure components, technical elements such as domains, functions etc.. EAM-tools provide additional benefits, such as interfaces for automated search of reference data from other application systems, such as an ITIL Configuration Management Database.
First the relevant parameters were determined for the design of the performance measurement system, i.e. those properties of IT application system landscapes that have an impact on the IT agility. This was done through a structured literature review following Webster and Watson [69]. Then the influencing variables along a hierarchy of objectives were brought into context. The resulting hierarchy of objectives was then discussed within a series of interviews with experts. Suggestions obtained therefrom were incorporated into the final design of the goal hierarchy and associated indicators. The literature review revealed 29 relevant papers from which design input could be derived for measuring the IT agility of application systems landscapes [52, pp. 122-135]. Of particular interest for this work are those papers that describe objectives in the context of IT flexibility and IT agility, to be achieved with suitable measures. These are listed in Table 2.
In the following paragraphs we derive goals in the context of the IT agility of application landscapes from the identified scientific literature. This is done in order to produce a reliable and consistent correlation between the IT
ПРИКЛАДНАЯ ИНФОРМАТИКА / JOURNAL OF APPLIED INFORMATICS
_ /Том 10. № 6 (60). 2015 /
Table 2. Goals and design input mentioned in the literature in the context of IT agility for IT application systems landscapes
Paper Goals Design Input
[2] Sustainability, flexibility Modularization, loose coupling, standardization, interoperability
[3] Flexibility, speed Standardization, interoperability, parameterization
[4; 5; 26] Adaptability Scalability, modularity, mobility, interoperability, redundance, self-organization, self-similarity
[12] Flexibility Connectivity, modularity, compatibility, data transparency
[14] Complexity reduction, simplification Standardization, economization
[17] Complexity reduction Low dependence, similarity of application systems, small number of application systems
[18] Flexibility Tight cohesion, loose coupling, low component complexity
[19] Quality of architecture, sustainability, flexibility Standardization, service orientation, low redundancy, functional completeness, process-oriented integration
[20] Ideal application landscape (correct, cost-efficient, effective, responsive, innovative) Component orientation (information hiding, separation of concerns, tight cohesion and loose coupling, category purity, no circular dependencies, unambiguous assignment of components to domains, content-based component design)
[24] IT agility Standardization
[32] Ability to accommodate frequent business changes Standardization, reusability, multi-tier architecture (presentation, application logic, data logic and storage)
[40] Flexibility, agility, adaptability, maintainability Tight cohesion, loose coupling
[42] Flexibility Modularity, internal change capability, consistency
[51] Agility, flexibility Multi-tier architecture (user interface, business rules, application logic and data management), standardized integration of legacy application systems
[53] Adaptation to a rapidly changing environment, high quality of architecture Consistency, portability, standardization, level separation of data/functions, minimum redundancy
[55] Quality, quick implementation Reuse
[59] Agility of information systems, complexity reduction Loose coupling between domains, reuse, low redundancy, consistency, few technologies
[61] Adaptability, expandability, changeability Reusability, standardization, modularity, coordinated model levels, loose coupling, minimal functional and data redundancy
[63] Flexibility, agility Modularization, standardization
[64] Consistency, agility, interoperability Reuse, service orientation, low data-redundancy, standardization
[65] IT agility, Business-IT alignment Modularity, loose coupling, standardization
[71] Adaptivity, agility Modularity, simplicity, SOA-integration, standardization
agility and its influencing factors. The goals are then arranged in the form of a goal hierarchy [52, pp. 141-173]. A goal hierarchy is a system that arranges upper and lower targets treelike. For the construction of tree-like structures, the MECE (mutually exclusive and collectively exhaustive) principle has been established. It demands that a tree structure at every level is complete and without overlap.
In this paper, the completeness, consistency and non-overlap of the goals is ensured by the derivation from the literature and the subsequent assessment within the expert interviews. The operational usability and feasibility are ensured by the case studies conducted.
The goal network in Figure 8 shows the goals that are often discussed in the analyzed literature relating to the agility of the IT application systems landscapes. For reasons of clarity, only the positive goal relations have been included. The bold relations are dominant. These are discussed frequently in the literature and have been further confirmed in the expert interviews.
Along the dominant goal relations a goal hierarchy is constructed starting from the top goal «agility of the IT application systems landscape». The goals connected by relations are adopted in the goal hierarchy. In addition, goals are further sharpened or detailed (Fig. 9). Some (few) elements from the literature have not been incorporated in the goal hierarchy, since they do not satisfy one or more of the required quality criteria. One example is the goal of high parameterization of the application landscape. Parameterization is the ability of an application system to implement a business change without programming. For this goal the necessary data can in practice not to be collected with reasonable effort. In both the expert interviews and the case studies, the goal and possible, corresponding indicators were discussed and found to be unworkable. Therefore, the goal has not been considered, although parameterization has been recognized as influencing factor of IT agility [3, pp. 1470-1474].
The goal hierarchy consists of five levels. On the first level is the overall goal «high agility
Parameterization
Interoperability
Legend:
Positive Relation
Dominant Positive Relation
Fig. 8. Goal relations from the analyzed literature relating to the agility of the IT application systems landscape
of the IT application systems landscape.» This is divided on the second level into the goals «high functional agility» and «high capacitive agility». The process is continued to the fifth level containing nine elementary goals. These elementary goals are briefly characterized below. Each of the elementary goals (level five) is subsequently assigned an indicator (key figure). The indicators are described further down.
Low Connectivity:
When looking at the connectivity, the entire IT application systems landscape is regarded as a network of connected application systems. It makes no difference whether two application systems are connected by one or more interfaces. The goal of «low connectivity» requires that in an application landscape as few application systems are interdependent as sensible.
Adequate Coupling:
If the application systems are coupled with each other in an application landscape, then it is necessary from the viewpoint of agility that the
coupling is adequate. Adequate means that domains are internally closely and externally loosely coupled. This requires that application systems within a domain (internal) must be more tightly linked than they are with application systems from other domains (external).
Homogeneous Technology:
The uniform application of one or few, dominant technologies facilitates the portability of application systems and also their compatibility. Data can be easily exchanged between application systems and functions reused, which enables changes to the existing application landscape and thus contributes to agility.
Homogeneous Interfaces:
The contribution of uniform interface technologies to the agility of the IT application systems landscape is to reduce the complexity. Development resources can be easily moved as needed between projects, and data are available and therefore reusable across the application landscape. This is for example achieved by
Legend
Hierarchical Goal Relation
Goal from Goal Network
Detailed Goal
Fig. 9. Goal hierarchy for the agility of an IT application systems landscape
the introduction of an integration platform (enterprise service bus, middleware, etc.) because all interfaces must comply with the same rules and standards of the integration platform.
High Professional Modularity:
High professional modularity requires that the application systems of the application landscape of a company are clearly structured on the basis of professional business criteria. The structure of the application landscape of the company should be based on the company's business (processes and organization). The more two business processes, business functions or departments are linked together, the closer should be coupled the supporting application systems.
High Technical Modularity:
Each application system should have a clear unambiguous technical purpose. For this purpose Engels et al. differentiate four categories of software components [20, p. 161]:
• Interaction components: components of this category are used to interact with users or with other parts of application landscapes.
•• Process components: components of this category are used for mapping and managing business processes.
• Function components: Components of this category have an «algorithmic character» and implement the functionality.
• Inventory: Components of this category manage the databases and access to them.
High technical modularity requires that each application system of the application landscape can be assigned to precisely one of the four software categories.
Low Functional Redundancy:
The goal of «low functional redundancy» requires that, ideally, a professional function is implemented only at a single point in the application landscape. If this function is used elsewhere, it must not be re-implemented, but the existing implementation is to be reused. This has the advantage that multiple efforts for the implemen-
tation and for the ongoing maintenance can be avoided. Change requests are to be implemented only at a single location, and thus no inconsistencies by different versions of the function can arise.
Low Data Redundancy:
Databases should be managed each by a single application system and all application systems of the application landscape that require these data need to access the respective application system. Low redundancy of data is required at the logical level. At the physical level, data redundancy can exist for reasons of performance or data security without the agility of the application environment being compromised.
High Scalability:
Two perspectives can be identified in the literature regarding the scalability: an infrastructure and an application-system-related view [5, p. 72]. Therefore, in the evaluation of scalability both the ability of the application system for parallel execution of processing steps as well as the capability of the technical infrastructure to scale horizontally must be evaluated jointly.
The structure of the application landscape has only a supportive indirect influence on scalability. So it may be argued that application environments with a unified technology are more portable to powerful infrastructures. A high degree of modularity can also be helpful for scalability, as smaller modules can be more easily moved to a powerful infrastructure than large monolithic application systems. Finally, the actual direct factors affecting scalability can be found in the architecture of individual application systems as well as in the nature of the technical infrastructure.
6.2. Assignment of Key Performance Indicators (KPIs) to Goals
In order to manage the development of an IT application systems landscape towards a high agility, the attainment of the goals set out must be made measurable. For the design and implementation of performance measurement systems
ПРИКЛАДНАЯ ИНФОРМАТИКА / JOURNAL OF APPLIED INFORMATICS Table 3. Structure of key figure profile
KPI
Name of key figure and associated abbreviation Supported goal References
The associated goal of this KPI in the The most important sources in the analyzed literature that form the basis goal hierarchy of this goal and the KPI
Calculation logic of KPI Standardization of key figure values, based on a scale from 1 to 5, where
5 is the best value attainable
Data
Description of the input parameters for the calculation of the KPI; these may be other KPIs or basic data.
Kutz proposes a process consisting of two steps [30, pp. 44, 73]:
1. Definition of the goals, the task area and the user group: the goals to be achieved must be defined that will be measured by the performance measurement system; describe the task area, consisting of control object and control task, and identify the target audience and determine their information needs.
2. Definition of the indicators: selection and description of the measured variables and the performance measurement system
For the documentation key figure profiles are used. These follow the structure shown in Table 3. The complete performance measurement system is shown in Fig. 101.
The control task of the performance measurement system is to help change the agility of the IT application systems landscape actively. Thus, a company needs to set target values, in accordance with the importance of IT agility and after consideration of other strategic goals, and track the achievement of these target values. The performance measurement system can be used not only for measuring entire application landscapes, but also for parts of it. For clari-
1 Earlier versions of the performance measurement system can be found in [44-46].
ty, the figures are, however, always defined for the whole IT application systems landscape. If, for example, the KPIs should be used for the measurement of individual domains, the concept of the application landscape must be replaced with the appropriate domain name. The potential user base of the performance measurement system is the top leadership circle of the IT organization as well as the enterprise architects of a company.
Below the elemental KPIs are briefly described. The indicators were presented as part of the iterative design process in the expert interviews and case studies, and have been intensively discussed. To save space, only one KPI will be discussed in more detail by way of example to clarify the definition. For details about the other KPIs see [52, pp. 184-214].
Degree of Crosslinking:
With the degree of crosslinking, the degree of internal dependencies of the application landscape under consideration is measured. The actually existing links between the application systems are set in proportion to the maximum possible number of links. The degree of crosslinking measures the complexity of the application landscape based on the connections between the application systems. The more application systems
are connected together, the higher are the dependencies, and thus also the complexity of the application environment.
Degree of Coupling:
The application systems or sub-domains contained in a domain should be more closely linked than the domains are connected to each other. Interfaces of a domain can be measured by the «coupling ratio», weighted by the number of application systems. The coupling ratio for a domain determines the ratio between the number of interfaces within a domain and the number of application systems contained therein. The average coupling ratio of the domains is assessed in relation to the coupling ratio of the entire application landscape. The greater this ratio is, the stronger the domains are linked internally and the higher is the value of the degree of coupling.
Homogeneity of Technologies (Applications):
The homogeneity of application systems technology measures the complexity that arises because different technologies are used in parallel in the application landscape. In addition to the number of different technologies also their distribution in the IT application systems land-
scape is measured. A high number of frequently used technologies results in low technology homogeneity and thus leads to a high complexity of the application landscape.
Homogeneity of Technologies (Interfaces):
The technology homogeneity of interfaces measures the complexity that arises when different interface technologies are used in parallel in the IT application systems landscape. In addition to the number of different interface technologies also their distribution in the application landscape is measured.
Professional Modularity:
The professional modularity is the measure of the unambiguous assignment of application systems to domains. The professional modularity sets the number of domain overruns of application systems in relation to the number of all application systems of the application landscape. The fewer domain overruns exist, the higher the value of the measure.
Technical Modularity:
For this measure the full key figure profile is given in Table 4 as an example.
Fig. 10. Key figure system to measure the agility of an IT application systems landscape
Functional redundancy:
The functional redundancy measures how many business functions have been implemented several times (and to what extent) in different application systems. For this, the sum of all redundancies of functions in the application landscape is set in proportion to the total number of functions supported by IT.
Data redundancy:
The data redundancy measures how many data are maintained multiple times in different application systems and to what extent. For this, the sum of all redundancies of data stores in the IT application systems landscape is assessed in relation to the total number of data stores.
Scalability:
This KPI measures the proportion of the application landscape, which is designed scalable.
For this purpose, the proportion of the application systems is measured, where scalability was explicitly specified as a non-functional requirement in the implementation. This is then multiplied by the ratio of scalable infrastructure (hardware) components.
The interviewees considered the KPI scalability useful, but noted that the necessary data are rarely available in the required quality and completeness in practice. This has also been confirmed in the case studies conducted. The data for this KPI could not be fully identified or acquired with reasonable effort.
6.3. Aggregation of Key Figure Values
The hierarchical aggregation runs along the described goal hierarchy. Since the assessment of all key figures is carried out in an identical manner and standardized to the value range [1.. .5],
Table 4. Example of a key figure profile
How well can the application systems of the IT application systems landscape be assigned to a unique technical software category?
Description
The technical modularity is the measure of how well the application systems can be assigned to software categories. Software categories characterize different tasks (e.g. interaction, function or inventory) of application systems. Each application system should, ideally, play only one role and thus is assigned a unique software category. The technical modularity sets the number of software category overruns of application systems in relation to the number of all application systems of the application landscape. The fewer overruns exist, the higher the value of the
Calculation
Standardization
MODU
^(SK(a) -1) ' ELEM(AL)
5 — MODUtech < 0.2
3 — 0.35 > MODUtech > 0.25 2 — 0.5 > MODUtech > 0.35 1 — MODUtech > 0.5
Data
SK (a) = number of software categories that an application system «a» can be assigned to ELEM (AL) = number of application systems in the entire application landscape
4 — 0.25 > MODUtech > 0.2
the calculation is made simple. The lowest level consists of the already explained elementary KPIs. The aggregate indicators in the levels above are always put together by the same aggregation rule, a weighted additive aggregation of subordinated key figures.
The weighting factors of the indicators have values between 0 and 1, and their sum in an aggregation is always equal to 1. As a result, the lower-level KPIs can be weighted relative to one another, and the result is always on a scale between the values 1 and 5. Through the weighting factors, companies can set priorities, if they consider parts of the goal hierarchy to be particularly desirable. For a benchmarking of various companies or parts of the same company, however, the weighting factors must be kept constant, otherwise the comparability of the results is not given.
6.4. Usability of the Key Figure System (Expert Interviews)
The experts interviewed were convinced of the practicality of the developed performance measurement approach. Figure 11 provides an overview of the assessment of the participants concerning possible uses of the key figure system [46; 52].
Most interviewees see the presented key figure system as an instrument for corporate benchmarking of individual business units or individual domains within one business unit. External
benchmarking is, however, considered to be critical, since the determination and collection of comparable data across companies appears much harder (e. g. due to a different understanding of what is an application system, a function, a data entity, etc.). In addition to the internal benchmarking the key figure system is seen as a suitable tool for a time series analysis. This allows the IT agility to be monitored and managed over time in a fixed range or across the enterprise.
Four participants would use the indicator system for the evaluation of alternative architecture scenarios, as in large implementation projects. Some interviewees saw the applicability in the context of discussions between IT and business departments only as of limited value, so that the purpose «justify decision» only received two nominations. Two participants saw yet another possible use of the performance measurement system as an evaluation tool of companies in merger & acquisition projects.
7. Case Study Automotive Industry
The presented case study was conducted at a large car manufacturer who solely in the IT organization has more than 4000 employees and more than100 000 employees in total. One ofthe key strategic objectives of the IT organization is to increase IT agility. This objective was formulated several years ago, but had not been oper-
Time Series External Evaluate Justify Decision Evaluation in
Analysis Benchmarking Alternative M&A Projects
Architectures
Fig. 11. Possible application areas for the key figure system (number of mentions, M&A = merger & acquisition)
10
9
8
7
6
5
4
3
2
0
ationalized. Therefore, based on the indicators proposed in this paper a way to operationalize IT agility should be tested.
The goal of the case study was to investigate multiple domains of the IT application systems landscape in terms of their current agility and make them comparable. The basis for measuring was the application landscape of the company at the beginning of 2013. At this time, it included more than 1000 active application systems and more than 12 000 interfaces. The application landscape is divided into eleven domains, derived from the business structure of the company. Each of the domains has evolved over several decades, so that today a variety of standard and custom-developed application systems is operated, partly based on heterogeneous technologies. When selecting the domains for the case study, it was important to find those that differ much in the perception of the enterprise architects with respect to the individual sub-goals of IT agility. As a result, the four domains of procurement, research & development, production and IT were chosen for the investigation. Figure 12 provides an overview of the quantity structure of the analyzed domains. The four domains under consideration include a total of 500 application systems and more than 600 interfaces.
In order to compare them on the basis of indicators for measuring the agility of application landscapes, data from the EAM database of the company was used for the calculation of key figures. Thereafter, the results were discussed with
experienced enterprise architects who are familiar with both the application environment and the supported business processes.
The calculation of the indicators was carried out using the calculation rules in the KPI definitions above. Of the nine proposed KPIs only seven were used and calculated due to incomplete data. The KPI «technical modularity» was not calculated because the information relating to the assignment of the application systems to the technical categories had not been well maintained and it was impossible to clarify this issue at a reasonable cost. For the key figure «scalability», the data base was incomplete and could not be supplemented with a reasonable effort.
Since this case study aimed for a corporate benchmark between multiple domains of the same company, the standardization of indicators took place not on an absolute level but relative to the best result achieved in any of the analyzed domains. Thus, for each key figure the domain with the best result determined the maximum value (5), and all other domains were normalized relative to this. This type of standardization was chosen because mainly the differences between the domains are of interest and reliable absolute reference values for indicators did not exist. Figure13 shows the calculation of the key figures for the four domains in the form of overlapping network diagrams. Thereby, differences between the domains relating to the IT agility can be visualized conveniently.
Application Systems
Interfaces
Fig. 12. Quantity structure of selected domains in automotive case study
Data
Redundancy
Functional Redundancy
Degree of Crosslinking 5,00.
Degree of Coupling
Homogeneity of Technologies
Professional Modularity
Homogeneity of Interfaces
Fig. 13. Overview of the calculated key figure values in this case study
It is striking that none of the domains occupies a dominant position in all key figures. If the areas of the graphs are considered for the respective domains, it can be noticed that they do not differ greatly from each other, with the exception of the R&D domain. Each domain has its strengths and weaknesses in other indicators. This result confirmed the preliminary assessment of the enterprise architects. A detailed analysis of the differences between the studied domains cannot be presented here for reasons of space. However, it is included in [52, pp. 250-265].
Absolute statements whether the domains have a high or low IT agility beyond corporate boundaries are not permitted. For such a statement more data sets of different companies would be required. Therefore, the representatives of the car manufacturer saw the benefits of the key figure system primarily in a relative comparison of different domains at one time or a single domain at different times. The second case, looking at one domain repeatedly over a period of time, has not been put into practice, yet. However, it is easy to imagine that such a cyclical measurement helps to track the effectiveness of measures taken to develop the agility of the IT application systems landscape.
The key figure system was assessed by the participating enterprise architects to be correct and helpful in measuring and managing IT agility. The practicability of the indicators is given, as seven of the nine indicators could be determined without additional data collection based on the existing EAM-database. The enterprise architects intend to use the key figure system as input for the development of EAM control in the future.
8. Conclusions and Future Research
IT agility is attributed a strong value proposition to corporate success, however, the required level of IT agility is seldom achieved in companies. The demand for IT agility is considered particularly high in companies with a strong IT penetration, complex and diverse products with short product life cycles, a strong end customer orientation and dynamic competitive environments. Based on design principles and with appropriate measuring instruments the IT agility can be improved in practice.
Although IT agility is not actively measured in companies yet, measures have been taken to increase IT agility. These can be assigned to the
action fields of IT architecture, IT staff, IT processes and IT organization. The IT architecture is assessed particularly important with regard to IT agility. In our research, for the first time a goal hierarchy and a derived performance measurement (key figure) system was developed to measure and actively manage the agility of IT application systems landscapes. This measurement model is scalable from the measurement of individual domains to the entire IT application landscape. It has demonstrated its practicality in the context of several case studies.
The key figure system was developed with a focus on the part of the application landscape that supports the core business processes of a company. In particular in the field of Business Intelligence, which often accounts for a significant portion of IT budgets, questions concerning the applicability of the suggested KPIs to the field of analytical applications may be of interest. Also interesting is whether the performance measurement system can be applied to enterprise networks (value chains).
The measurement system was developed in the context of a literature review and additional qualitative research. It was tested in several case studies. Methodically, it would be desirable to quantitatively evaluate the key figure system. For this purpose, however, a large number of participating companies is necessary. The cost of implementation of such a performance measurement system in the individual companies should not be underestimated. A quantitative survey would also enable the validation of the proposed indicators standardization (normalization of results).
Regarding the research methodology it can be stated that much of the work in the literature has chosen a behaviorist approach. This is suitable, provided that the aim of the work has a descriptive or explanatory nature. But when recommendations for the design of large application landscapes with respect to the construct of IT agility are sought for, a design science approach as in this research seems more appropriate.
The majority of the examined contributions focused on explanations of what makes up IT
agility. Here a large number of variables are presented. By contrast, very few contributions deal with the question of the demand for IT agility, and related influencing factors.
Also, the question of the cost of the build-up of IT agility is not yet considered. Only when this has been clarified, together with the demand question, a statement about the optimum or the possible level of IT agility in a company can be made. Furthermore, there is a lack of concrete proposals to operationalize IT agility throughout. While in this contribution the IT architecture was the focus, appropriate considerations are also required with respect to the other pillars of IT agility, namely the IT organization, IT processes as well as IT staff and management. This represents a current focus of our research with the ultimate goal to make IT agility measurable and manageable in all fields of action.
References
1. Ackoff R. L. Management Misinformation Systems. Management Science, 1967, no. 4 (14), pp. 147-156.
2. Aier S., Dogan T. Indikatoren zur Bewertung der Nachhaltigkeit von Unternehmensarchitekturen. In: Ferstl O. K., Sinz E. J., Eckert S., Isselhorst T. (eds.) Wirtschaftsinformatik 2005, Physica, Heidelberg, 2005, pp. 607-626.
3. Aier S., Schelp J. EAI und SOA — Was bleibt nach dem Hype? In: Proceedings Multikonferenz Wirtschaftsinformatik 2008, GITO, Berlin, 2008, pp. 1469-1480.
4. Andresen D., Gronau N. Die Gestaltung von Unternehmensarchitekturen — Wieviel Flexibilität ist notwendig? ERP Management, 2005, no. 1 (1), pp. 30-32.
5. Andresen K., Gronau N., Schmid S. Ableitung von IT-Strategien durch Bestimmung der notwendigen Wandlungsfähigkeit von Informationssystemarchitekturen. In: Ferstl O. K., Sinz E. J., Eckert S., Isselhorst T. (eds.) Wirtschaftsinformatik 2005, Physica, Heidelberg, 2005, pp. 63-82.
6. Barney J. Firm Resources and Sustained Competitive Advantage // Journal of Management, 1991, no. 1 (17), pp. 99-120.
7. Benbasat I., Goldstein D. K., Mead M. The Case Research Strategy in Studies of Information Systems. MIS Quarterly, 1987, no. 3 (11), pp. 369-386.
8. Berensmann D. IT matters -but Who Cares. Informatik Spektrum, 2005, no. 4 (28), pp. 274-277.
9. Bharadwaj A. S. A Resource-Based Perspective on Information Technology Capability and Firm Performance: An Empirical Investigation. MIS Quarterly, 2000, no. 1 (24), pp. 169-196.
10. Bortz J., Döring N. Forschungsmethoden und Evaluation für Human- und Sozialwissenschaftler. 4. ed., Springer, Berlin, 2006.
11. Broadbent M., Weill P., Neo B. S. Strategic Context and Patterns of IT Infrastructure Capability. The Journal of Strategic Information Systems, 1999, no. 2 (8), pp. 157-187.
12. Byrd T. A., Turner D. E. Measuring the Flexibility of Information Technology Infrastructure: Exploratory Analysis of a Construct. Journal of Management Information Systems, 2000, no. 1 (17), pp. 167-208
13. Byrd T., Turner D. An Exploratory Examination of the Relationship between Flexible IT Infrastructure and Competitive Advantage. Information & Management, 2001, no. 1 (39), pp. 41-52.
14. Capgemini (ed.). Application Landscape Report 2011 Edition, München, 2011.
15. Capgemini (ed.) IT-Trends 2012. Business-Alignment sichert die Zukunft. München, 2012.
16. Carr N. G. IT Doesn't Matter. Harvard Business Review, 2003, no. 5, pp. 41-51.
17. Dern G., Jung R. IT-Architektur-Governance auf Basis von Kennzahlen zur Komplexitätsmessung. Controlling, 2009, no. 12 (21), pp. 669-672.
18. Dreyfus D., Wyner G. Digital Cement: Software Portfolio Architecture, Complexity, and Flexibility. In: Proceedings of the Americas Conference on Information Systems (AMCIS 2011), Detroit, MI/USA, 2011, p. 62.
19. Durst M. Wertorientiertes Management von IT-Architekturen. Springer, Berlin, 2007.
20. Engels G., Hess A., Humm B., Juwig O., Lohmann M., Richter J. P., Voss M., Willkomm J. Quasar Enterprise. dpunkt, Heidelberg, 2008.
21. Ferstl O. K., Sinz E. J. Grundlagen der Wirtschaftsinformatik. Oldenbourg, München, 2006.
22. Frank U. Zur methodischen Fundierung der Forschung in der Wirtschaftsinformatik. In: Österle H., Winter R., Brenner W. (eds:) Wirtschaftsinformatik: Ein Plädoyer für Rigor und Relevanz. Infowerk, Nürnberg, 2010, pp. 35-44.
23. Gartner Agility Will Become the Primary Measure of Data Centre Excellence by 2012. Available: http:// www.gartner.com/it/page. jsp? id=535714 (Accessed 23.05.2015).
24. Glaser J. Creating IT Agility. Healthcare Financial Management: Journal of the Healthcare Financial Management Association, 2008, no. 4 (62), pp. 36-39.
25. Gronau N. Wandlungsfähige Informationssystemarchitekturen. GITO, Berlin, 2006.
26. Gronau N., Lämmer A., Andresen K. Entwicklung wandlungsfähiger Auftragsabwicklungssysteme. In: Gronau N., Lämmer A. (eds.). Wandlungsfähige ERP-Systeme. 2. ed., GITO, Berlin, 2007, pp. 45-66.
27. Heinrich L. J., Stelzer D. Informationsmanagement: Grundlagen, Aufgaben, Methoden. 10. ed. Oldenbourg, München, 2011.
28. Hevner A. R., March S. T., Park J., Ram S. Design Science in Information Systems Research. MIS Quarterly, 2004, no. 1 (28), pp. 75-105.
29. Hinterhuber H. H., Friedrich S. A. Markt- und Ressourcenorientierte Sichtweise zur Steigerung des Unternehmenswertes. In: Hahn D., Taylor B. (eds.) Strategische Unternehmungsplanung — strategische Unternehmungsführung. 7. ed. Physica, Heidelberg, 1999, pp. 988-1016.
30. Kütz M. Kennzahlen in der IT: Werkzeuge für Controlling und Management. 3ed. dpunkt, Heidelberg, 2008.
31. Kuhlin B., Thielmann H. (eds.). Real-Time Enterprise in der Praxis: Fakten und Ausblick. Springer, Berlin,
2005.
32. Lindström A. On the Syntax and Semantics of Architectural Principles. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences. Hawaii, Computer Society Press, Washington,
2006, p. 10.
33. Linthicum D. S. Enterprise Application Integration. Addison-Wesley Longman, Boston, 2000.
34. Lu Y., Ramamurthy K. Understanding the Link Between Information Technology Capability and Organizational Agility: An Empirical Examination. MIS Quarterly, 2011, no. 4 (35), pp. 931-954.
35. March S. T., Storey V. C. Design Science in the Information Systems Discipline: an Introduction to the Special Issue on Design Science Research. MIS Quarterly, 2008, no. 4 (32), pp. 725-730.
36. Maske P. Multiperspektivische Evaluation des integrierten, interdisziplinären Vorgehensmodells. In: Maske P (ed.) Mobile Applikationen. 2. ed. Gabler, Wiesbaden, 2012, pp. 797-939.
37. Mayring P. Qualitative Inhaltsanalyse. 8. ed. Weinheim, Basel, 2003.
38. Melarkode A., Fromm-Poulsen M., Warnakulasuri-ya S. Delivering Agility through IT // Business Strategy Review, 2004, no. 3 (15), pp. 45-50.
39. Meuser M., Nagel U. ExpertInneninterviews — vielfach erprobt, wenig bedacht. In: Bogner A., Littig B., Menz W. (eds.) Das Experteninterview — Theorie, Methode, Anwendung. VS Verlag, Wiesbaden, 2005, pp. 71-93.
40. Müller F. R. Anwendungsarchitektur — Beurteilung unter Berücksichtigung zu erwartender Flexibilität. In: Oberweis A., Weinhardt C., Gimpel H., Kos-
chmider A., Pankratius V., Schnizler B. (eds.) Proceedings 8. Int. Tagung Wirtschaftsinformatik. Universitätsverlag, Karlsruhe, 2007, pp. 89-106.
41. Nagel R. N., Dove R. 21st Century Manufacturing Enterprise Strategy: An Industry-led view. Lehigh University Press, Bethlehem, 1991.
42. Nelson K. M., Nelson H. J., Ghods M. Technology Flexibility: Conceptualization, Validation, and Measurement. In: Proceedings of the Thirtieth Hawaii International Conference on System Sciences, Hawaii, Vol. 3. Computer Society Press, Washington, 1997.
43. Nissen V. Grundlagen zum Management von IT-Agilität. Technical Report No. 2008-03. University of Technology Ilmenau. Institute of Business Informatics. 2008. Available: http://nbn-resolving.de/urn: nbn: de: gbv: ilm1-2008200054
44. Nissen V., Mladin A. Messung und Management von IT-Agilität. HMD Praxis der Wirtschaftsinformatik, 2009, no. 269, pp. 42-51.
45. Nissen V., v. Rennenkampff A., Termer F. Agile IT-Anwendungslandschaften als Strategische Unternehmensressource. HMD-Praxis der Wirtschaftsinformatik, 2012, no. 284, pp. 24-33.
46. Nissen V., v. Rennenkampff A. IT-Agilität als strategische Ressource im Wettbewerb. In: Lang M. (ed.) CIO-Handbuch. Vol. 2: Erfolgreiches IT-Management in Zeiten von Social Media, Cloud & Co. Symposion, Düsseldorf, 2013 pp. 57-90.
47. Peffers K., Tuunanen T., Rothenberger M. A., Chat-terjee S. A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 2007/8, no. 3 (24), pp. 45-78.
48. Penrose E. T. The Theory of the Growth of the Firm. Wiley. New York, 1959.
49. Pfadenhauer M. Auf gleicher Augenhöhe reden. In: Bogner A., Littig B., Menz W. (eds.) Das Experteninterview. Theorie, Methode, Anwendung. Leske + Budrich, Opladen, 2005, pp. 113-130.
50. Peters A. Alignment's Ailment: IT Strategy's Focus Shifts To Agility. Alexander Peters. PhD's Blog For Chief Information Officers. 2009. Available: http:// blogs.forrester.com/blog/203 (Accessed 23.05.2015).
51. Reddy S. B., Reddy R. Competitive Agility and the Challenge of Legacy Information Systems. Industrial Management & Data Systems, 2002, no. 1 (102), pp. 5-16.
52. Rennenkampff A. V. Management von IT-Agilität. Entwicklung eines Kennzahlensystems zur Messung der Agilität von Anwendungslandschaften. PhD Dissertation, University of Technology Ilmenau. Institute of Business Informatics. 2015 (to appear in German).
53. Richardson G. L., Jackson B. M., Dickson G. W. A Principles-based Enterprise Architecture: Lessons
from Texaco and Star Enterprise. MIS Quarterly, 1990, no. 4 14), pp. 385-403.
54. Roberts N., Grover V. Leveraging Information Technology Infrastructure to Facilitate a Firm's Customer Agility and Competitive Activity: An Empirical Investigation. Journal of Management Information Systems, 2012, no. 4 (28), pp. 231-270.
55. Rothenberger M. A., Hershauer J. C. A Software Reuse Measure: Monitoring an Enterprise-level Model Driven Development Process. Information & Management, 1999, no. 5 (35), pp. 283-293.
56. Sambamurthy V., Bharadwaj A., Grover V. Shaping Agility through Digital Options: Reconceptualization of the Role of Information Technology in Contemporary Firms. MIS Quarterly, 2003, no. 2 (27), pp. 237-263.
57. Schelp J., Winter R. Integration Management for Heterogeneous Information Systems. In: Desouza K. C. (ed.) Agile Information Systems: Conceptualization, Construction, and Management. Butterworth-Heinemann, Burlington, 2007, pp. 134-149.
58. Schneider D. Betriebswirtschaftslehre. Vol. 3: Theorie der Unternehmung. Oldenbourg, München, 1997.
59. Schwinn A., Winter R. Entwicklung von Zielen und Messgrößen zur Steuerung der Applikationsintegration. In: Ferstl O. K., Sinz E. J., Eckert S., Isselhorst T. (eds.) Wirtschaftsinformatik 2005, Physica, Heidelberg, 2005, pp. 587-606.
60. Sharifi H., Zhang Z. A Methodology for Achieving Agility in Manufacturing Organisations: An Introduction. International Journal of Production Economics, 1999, no. 1-2 (62), pp. 7-22.
61. Sinz E. J. Architektur betrieblicher Informationssysteme. Bamberger Beiträge zur Wirtschaftsinformatik. University of Bamberg. Technical Report, no. 40, 1997.
62. Termer F., Nissen V., Dorn F. Grundlagen zur Konzeption einer multiperspektivischen Methode zur Messung des IT-Wertbeitrags. In: Kundisch D., Suhl L., Beckmann L. (eds.) roceedings Multikonferenz Wirtschaftsinformatik (MKWI) 2014, Paderborn, pp. 2271-2283.
63. Terzidis O., Sure Y., Brelage C. SOA-Flexibility and Agility? Wirtschaftsinformatik, 2008, no. 1 (50), pp. 76.
64. The Open Group TOGAF Version 9.1. Van Haren, Zaltbommel, 2011.
65. Tiwana A., Konsynski B. Complementarities between Organizational IT Architecture and Governance Structure. Information Systems Research, 2010, no. 2 (21), pp. 288-304.
66. Wade M., Hulland J. Review: The Resource-based View and Information Systems Research: Review, Extension, and Suggestions for Future Research. MIS Quarterly, 2004, no. 1 (28), pp. 107-142.
67. Wagner D., Suchan C., Leunig B., Frank J. Towards the Analysis of Information Systems Flexibility: Proposition of a Method. Wirtschaftsinformatik Proceedings 2011, p. 34. Available: http://aisel.aisnet.org/wi2011/34
68. Warnecke H.-J. Komplexität und Agilität — Gedanken zur Zukunft produzierender Unternehmen. In: Schuh G., Wiendahl, H.-P. (eds.) Komplexität und Agilität. Steckt die Produktion in der Sackgasse? Springer, Berlin, 1997, pp. 1-8.
69. Webster J., Watson R. T. Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 2002, no. 2 (26), pp. 13-23.
70. Wernerfelt B. A Resource-based View of the Firm. Strategic Management Journal, 1984, no. 2 (5), pp. 171-180.
71. Wilkinson M. Designing an 'Adaptive' Enterprise Architecture. BT Technology Journal, 2006, no. 4 (24), pp. 81-92.
72. Winter R., Fischer R. Essential Layers, Artifacts, and Dependencies of Enterprise Architecture. Journal of Enterprise Architecture, 2007, no. 2 (3), pp. 7-18.
73. Yin R. K. Case Study Research: Design and Methods. Sage Publications. New York, 2009.
В. Ниссен, докт. экон. наук, профессор, Технологический университет Ильменау, Германия, [email protected] А. фон Ренненкампф, докт. экон. наук, Технологический университет Ильменау, Германия, [email protected]
ИТ-маневренность как стратегический ресурс — измерение и управление в контексте прикладных ИТ-систем
Способность компании к изменениям все более определяется ее способностью изменять свою ИТ-сферу, что мы будем далее называть «ИТ маневренностью». Высокая ИТ-маневренность может способствовать увеличению гибкости бизнеса и тем самым обеспечивать конкурентное преимущество. В этой статье мы рассмотрим факторы, влияющие на ИТ-маневренность, и способы ее повышения. Основные составляющие ИТ-мобильности могут быть измерены, и ими можно управлять. Здесь акцент делается в контексте прикладных ИТ-систем — ресурса, который имеет важное значение для ИТ-мобильности и конкурентоспособности компании.
Ключевые слова: ИТ-маневренность, метрики архитектуры ИС, управление архитектурой предприятия, значимость ИТ для предприятия, проектный подход к исследованиям.
About authors:
V. Nissen, Dr of Economy, Professor, Chair of Business & Information Systems Engineering in Services, University of Technology Iimenau, Faculty of Economic Sciences and Media, Institute of Business & Information Systems Engineering
A von Rennenkampff, Dr of Economy, Chair of Business & Information Systems Engineering in Services, University of Technology Iimenau, Faculty of Economic Sciences and Media, Institute of Business & Information Systems Engineering
For citation:
Nissen V., von. Rennenkampff A. Measuring and managing IT agility as a strategic resource — examining the IT application systems landscape. Prikiadnaya Informatika — Journal of Applied Informatics, 2015, vol. 10, no. 6 (60), pp. 5-30.
