Innovation strategies and performance distribution of ICT-industry’s companies

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Innovation strategies and performance distribution of ICT-industry’s companies

Author’s name: Maksym Feshchuk

Stockholm Business School

Master’s Thesis (two years) 30 HE credits

Study Program in Operational Management and Control, 120 HE credits Spring semester 2017

Supervisor: Alexander Chakhunashvili

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ABSTRACT

In ICT industry, firms’ financial prosperity and growth are highly depended on innovation. The innovation provides competitive advantage and high performance for the ICT companies. The purpose of the paper is to study effect that the companies’ internal resources, experience on the market, investments in innovation and business environment have on their performance. The sample includes 22 ICT companies from North America, EU and Asia region. The examined data cover period from 2011 to 2015. The companies’ annual reports and USPTO were used as the sources of the data. The mixed research method is chosen to reach the study’s objectives. The study is analysed with help of DEA and Balanced scorecard performance measurement methods.

Organizational learning theory and Resource based theory are applied to answer the research questions in the study. The outputs of performance are categories such as assets and

competencies, innovative activities, suppliers and customer relations, and employees and management. These categories are presented by a variety of indicators such as assets turnover, labour productivity ratio, debt ratio, SG&A costs ratio and number of published patents. The factors such as companies’ size, age, investments in R&D and business environment have significant impact on the performance of innovative activities. The results suggest that the large established ICT companies are less efficient in studied categories than the smaller and younger ICT firms. Moreover, the younger smaller companies show higher efficiency even in terms of such outcomes as R&D intensity and number of published patents. The results are consistent with the findings attained in previous studies from the same scientific field.

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1. INTRODUCTION

1.1. BACKGROUND

Information Communication Technology (ICT) industry connects many sectors such as computer hardware and software, electronics, semiconductor, Internet, telecom equipment, e- commerce and computer services. There is a growing research interest to the application of the innovation in the context of ICT-industry. The investigations about innovation in ICT firms are rare (Mainardes, et al., 2016). The innovation influences the performance by improvement of the existing or creation of new processes, products and services. An innovation strategy has a crucial impact on the performance of the companies (Ezzi & Jarboui, 2016). The efficiency is a very important concept for companies, because it provides ability to achieve the desired objectives with as low as practicable costs. High efficiency is connected to the good management of the resources available for the companies. The most efficient companies are characterized by the experienced managers, skilled employers and growing financial resources. Innovation can be viewed as an important part of performance measurement (Ivanov & Avasilcăi, 2014). The explicit aim for the paper is to investigate relationships between innovation and ICT companies’

performance. The paper reconsiders influence of innovation efficiency on the performance in the context of ICT-industry. It examines whether the companies’ internal resources and context affect their innovation and business strategies.

The previous studies in the field of relationships between companies’ innovativeness and performance come to some interesting findings. They found that innovation is important for companies’ performance but the measurement indicators is a challenging issue. Estimation of innovation efficiency of companies is a pressing issue of modern economy (Teplykh, 2016).

There exist many studies that focus on investigation of high-tech companies’ overall performance in relation to innovation performance indicators. But, the measurements of

indicators are not universal and differ from one study to another. The concept of measurement of innovative performance is unclear due to variety of samples, measurements, industries and country settings (Hagedoorn & Cloodt, 2003). The indicators depend on type of industry, number of competitors, financial resources available for the companies, experience on the market, investments made in innovation, employers’ skills, and quality of management. Variety of the performance indicators allows researchers to investigate the problem from different angles and collect comprehensive data in this area. The researchers argue that examining of the known regularities should be tested in different contexts for validating of the objective laws. They require continuation of the research in the field of measurement of how companies’ innovation strategy affects their performance. It allows to minimize errors in the projecting of innovation and business strategies and to test the results of the previous studies in the scientific field.

The companies’ size, age, financial resources, business environment and external relationships have significant impact on innovativeness and performance. There is a relationship between the companies’ internal resources and effectiveness of innovative activities (Norek & Costa, 2014).

ICT industry has specific features that impact the companies’ business strategy as well as innovation strategy. The specific for the ICT industry is that every company proposes a wide variety of products and services for the customers. Categories of the products and services of an ICT firm are presented in different sectors of the industry. There, the companies compete with

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advantage is the internal resources available for the companies. The internal resources include financial resources, human capital, previous experience and knowledge, and relationships with customers and other key stakeholders. The size of the companies represents their internal resources available to invest in innovation and business activities. The managers’ plan and implement the strategies in relation to the resources available.

There is a constantly renewed interest of exploring relationships between innovation and performance from various of perspectives such as measurement and control of resources allocation. The selection and implementation of the performance indicators have been an increasingly important matter in recent years (Ezzi & Jarboui, 2016). The dominant idea of measurement of how the innovative strategy affects performance is to become more effective in responding to changes. A company can reduce costs, make new innovative products or services, and improve processes by choosing of a correct innovating strategy. For example, employees can be more effective if they can reduce the time of decision-making and the costs of the process to adopt valuable information quickly. Eliminating changes early means being proactive and responsive to business conditions and avoid failure. Since, innovative activities provided today will pay off in the future. An innovative strategy is a part of a company’s business strategy.

The current research is based on the results of the previous studies in other sectors of the high- tech industry and appropriateness of their results will be tested here. The study reviews the relationship between innovation strategy and performance from the prism of ICT-industry’s companies with taking into consideration their size, age, investments in research and

development (R&D) activities and environment in which the companies operate. The key stakeholders’ requirements, competitors and market aspects are integrated in the evaluation of the companies’ innovation strategies design. The efficiency measurement of performance is used as a method of evaluation of ICT-companies’ innovative activities. The appropriate variables for measurement are specified based on recommendations from the previous studies.

The innovation and performance issues are discussed from the point of view of the primary disciplines concerned with these terms such as management, accounting, sociology and marketing. The variety of economic and financial measurement variables are included for assessment of ICT companies’ innovative efficiency. The concept of action efficiency is often applied in relation to economic science (Norek & Costa, 2014). The investment in research and development (R&D) is equal to innovation strategy in this study. Innovation means the activities that the studied ICT companies provide in the aim to improve their processes, products or services. Innovation efficiency represents level of performance obtained in relation to the investments made in innovation. Performance is an achieved efficiency of the ICT companies compared to the maximal available efficiency. The performance outcomes are evaluated in relation to investments made in innovation and the ICT companies’ internal resources. In addition, the performance outcomes are represented by the drivers of performance adapted from the Balanced Scorecard.

1.2. PROBLEM FORMULATION

The analysis of the innovation efficiency is related to the business strategy selection problem (Ezzi & Jarboui, 2016). Therefore, the effect of innovation on companies’ financial position,

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relationships with customers, internal processes, learning and growth are the key issues in this research. The internal resources and external environment moderate the type of innovation and business strategies in other sectors of the high-tech industry. The previous studies in the field suggested that the large established and small younger companies have different efficiency output in relation to inputs made in innovation. This paper aims to measure innovation efficiency of the studied ICT companies to examine relationship between their internal resources,

innovation strategy and performance. The research question is:

How do ICT-companies’ internal resources and business environment affect their innovation strategies and performance?

The specific research sub questions addressed in the study are:

• How do the internal resources influence the innovative strategies of the ICT companies?

• How do the business environment influences the innovative strategies of the ICT companies?

1.3. HYPOTHESES

The ICT companies’ innovative strategies are evaluated with consideration of their internal capabilities and business environment in which they operate. The study explores how frequently the smaller younger and large established companies prefer efficiency or willing to risk-taking in context of the ICT industry. The hypotheses of the study aim to connect findings of the previous studies in the scientific field with the results obtained in this paper. It is expected that the

correlation between the studied variables will be positive to the previous results. The hypotheses of the study are:

The previous studies suggested that the smaller companies focus more on innovation efficiency than the large companies. Coombs, et al., (1996) suggested that R&D expenditures and

innovative capacity are increasingly concentrated in the large international firms. According to Satta, et al., (2016), the larger companies are expected to apply for more patents than the small companies in high-tech industry. Which allows them to generate additional profits and hold an innovation competitive advantage over the small companies. For Ivanov & Avasilcăi, (2014), R&D departments of the large organizations are involved in innovation on bigger and constant basis. Baker, et al., (2016) found that the innovation performance relationship is more

pronounced in the small firms than in the large firms. Huang, et al., (2017) concluded that the state owned Chinese enterprises have a resources and capital advantage but less motivation to promote the technological mercerization, which largely depresses innovation efficiency.

Hypothesis1: There are differences between innovation efficiency between the large established, and the smaller and younger ICT companies.

The previous studies found that the business environment has a significant impact on a

company’s innovativeness and performance. According to Xue, et al., (2012), the environmental factors have strong influence on companies’ R&D activities. Ivanov & Avasilcăi, (2014)

suggested that there is strong connection between innovation and changes in the economic environment. For Satta, et al., (2016), firms’ competitive advantage is ensured in highly-

competitive business environment by novel knowledge. Mainardes, et al., (2016) found that ICT firms are sensitive to the environment in which they are embedded. García-Piqueres, et al.,

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(2016) reflected that innovation is considered as a crucially valuable creation factor and one of the most important competitive advantages in the highly competitive environments.

Hypothesis 2: The business environment affects innovation and performance of the ICT companies.

1.4. PROPOSAL

The modern reality creates a real need for understanding of the fundamental ways in which innovation impacts companies’ performance. How much and what types of innovation are expected by the companies? Toward this aim, the study employs the mixed method to uncover core relationships between innovation efficiency and performance. The previous studies found that internal resources and business context moderate the innovation and performance

relationships. Therefore, the innovative activities more strongly relate to company’s competitive ability and financial prosperity. A company’s innovativeness help to solve problems connected with changes in the business environment and sustain competitive advantage.

Most of the previous researches were focused on how internal resources and business context influence innovation and performance within a range of industries. The findings make it clear that to find appropriate measures for evaluating of innovation on organizational performance is a challenging issue. Researchers concluded that older and bigger companies in a competitive environment are more likely to invest in innovation to improve their performance. Another relevant aspect is that the small companies are more effective in their innovative activities in comparison with the big companies. The paper takes one step further by investigating the effect of innovation efficiency on performance in context of ICT industry. It is supposed that ICT companies’ internal resources and environment have an influence on the selection of innovation strategies that effect their performance.

The results of this study examine the results from the previous studies in the field of innovation and performance. It allows to make a theoretical contribution in investigation of firms’

innovation strategies in the ICT industry. The paper makes also contribution to generalization of knowledge in the overall understanding of connection between innovation and performance on the companies and industries level. Some managerial implications may be possible to make from the study. Managers will be able to understand better how they can consider environment in which they operate. And align their assets portfolio with investments in innovation and

efficiency measures of the performance. Thus, the managers will more accurately account such factors as internal resources and environmental context for designing of the innovative and business strategies. It will lead to reduced operating costs and secured profits.

1.5. PERSPECTIVE

The study is focused on the evaluation of ICT companies’ innovation performance during timeframe 2011- 2015. The innovation investments and performance outputs relationships are measured with the use of data collected from the firms’ annual reports. In overview, the research has two main parts. The first part is the mathematical evaluation of innovative efficiency of 22 companies from USA, EU, Japan, Korea and Taiwan that represent the ICT industry. In the

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second part, similarities and differences of the companies’ innovation strategies with taking in consideration their size, age, investments in R&D and industry environment are qualitatively evaluated. The companies’ innovative efficiency is evaluated from managers’ perspective. The U.S. market is chosen as a place there the companies provide their business activities. As the focus of the study is on companies from ICT industry, the results of the research are provided from this perspective. The study’s results are limited by the most innovative companies related to the different sectors of the industry.

1.6. METHODS

The previous research in the field has been mainly quantitative and related on regression

methods such as OLS regression analysis. In this paper, the mixed method is used to answer the research question. The data in the quantitative research is analysed with help of DEA model.

DEA provides a comprehensive analysis of relative efficiencies for multiple input-multiple output situations (Wu, et al., 2006). The DEA model is used for estimation of how investments in innovation affect ICT companies’ performance. The Balanced Scorecard model focuses to align every part of the organisation on improving and aligning its strategy (Ivanov & Avasilcăi, 2014). The Balanced Scorecard model is used for qualitative assessment of innovation strategy taken by ICT companies’ in relation to their business environment.

The content analysis of a documentation survey is applied in the literature study. The literature studies provided the knowledge from the previous research in the field. In DEA, the efficiency measurement inputs are organized into three categories: size, age and R&D intensity. And outputs indicators are organized according to organizational performance classified by Balanced Scorecard: financial, clients, internal processes and learning and growth. As the result, the DEA model provides calculation of the ICT companies innovative efficiency. The patterns of

commonness and pattern of phenomena of efficiency presented by the ICT companies are analysed in relation to their business environment: market segment, competitors and customers.

The context analysis and hermeneutic classificatory content analysis are used for explorative validation of the empirical data. The cross-sectional analysis is used for description of the study results.

1.7. THEORIES

The theories in the field of innovation and organizational performance are found during the literature studies (pre-research). The main theories that explain the effect of innovation strategy on business performance are organization learning theory and the resource-based view theory (Ezzi & Jarboui, 2016), and theory of innovation potential (Norek & Costa, 2014).

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1.8. EMPIRICAL SETTINGS

The primarily literature in the study includes data collected from peer reviews, course books and scientific reports. The appropriate theories and the researches related to the current paper are defined from the primary literature. The articles in newspapers, appropriate information from the companies’ websites and annual reports are used as the secondary literature. The numerical accounting and financial data are collected from the published ICT companies’ annual reports.

1.9. STUDY DESIGN

The study will be started with introducing the concepts of innovation, theories, Balanced

Scorecard, inputs and outputs variables, data sources, ICT industry’s description and the research methods. It will give a good foundation for the rest of the report. After that, the results of the quantitative analysis will be presented and qualitatively analysed, discussed, and concluded. In the end of the study, managerial implications, limitations, and perspective for the future

researches will complete the report.

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2. LITERATURE REVIEW AND THEORETICAL APPROACH

2.1. PERFORMANCE AND EFFICIENCY

Organizational performance is an analysis of a company’s performance compared to goals and objectives. Investments in R&D have significant effect on the companies’ performance (Ezzi &

Jarboui, 2016). Within corporate organizations, there are three primary outcomes analysed:

financial performance, market performance and shareholder value performance. In some cases, production capacity performance can be analysed. Efficiency is a performance capacity or an aspect of achievement how the results are obtained. It is crucial to understand mechanism of efficiency of innovation (Norek & Costa, 2014). Efficiency in economy is relations between inputs in companies’ business activities and achieved business results. In any business

environments, a company’s innovation potential can be evaluated by measuring its operational efficiency. Operational efficiency and innovation measures of performance are aligned with the business strategy and industry context of the organisation (Xue, et al., 2012). The innovativeness of a firm is interconnected with its internal resources and business environment. There is a relationship between companies’ internal resources and the effectiveness of innovative activities (Norek & Costa, 2014). The internal tangible and intangible resources have significant impact on firm’s innovation strategy, planning and operational implementation. A company that is able enough to develop and use its skills and strategic resources is more efficient than the one that is unable to manage its skills and resources (Ezzi & Jarboui, 2016).

2.2. INNOVATION

Innovation is not a single act but a total process of interrelated sub-processes. Innovation is a main engine of economic growth (Trott, 2012), important part of the daily activities of the organisations (Ivanov & Avasilcăi, 2014), mechanism for achieving financial performance and social performance (Ezzi & Jarboui, 2016). Innovation includes theoretical conception,

technological invention and commercial exploitation. The overall innovation process is a complex set of communication patches over which knowledge is transferred. These patches include internal and external linkages that link together product innovation, process innovation, competitive environment and organizational structure. Changes are placed in the middle of the process and caused by decisions that people made. The industry, products and services are together determining the innovation process that need to be managed by firms. During the four last decades, ICT have been among the sectors that present highest growth in the amount of innovations (Mainardes, et al., 2016).

Teplykh (2016) concludes that stages of innovation process include:

• The creation of new knowledge

• Development of new product or software

• Direct commercialization of this knowledge, leading to an increase in sales of new products or reduction of the company’s costs

Each of these stages are characterised by high uncertainty about:

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• Size of the effect they will have

2.3. TYPES OF INNOVATION

According to Trott (2012), four types of innovation are incremental, modular, radical and

architectural innovation. In this paper, we will focus on incremental and radical innovations. The determinants that underlying incremental and radical innovations are different (Teplykh, 2016).

Incremental innovation has continuous or evolutionary character and consists of incremental improvements in products and processes. Incremental innovation allows a firm to start with an innovators product to take a more efficient process and less risks. Firms focus on improving of their existing products and processes to pursue efficiency in their operations. Incremental

innovation is based on informal studies and the adaptation of technologies from other companies (Teplykh, 2016). The incremental innovation process allows the companies to support innovative performance without significant investments in R&D projects. Innovation capacity is the

combination of technological and human factors (Trott, 2012). The employees’ creativity and engagement is associated with a work environment that encourages innovative thinking. The continuous communication between employers and managers ensures employees trust and motivation to innovate. It is especially important for companies with limited financial and nonfinancial resources available.

Radical innovation has a discontinued character and is often disruptive and new. Disruptive innovation leads to creation of new services and business models, dramatic shifts in technology, science or business models. Discontinued innovation can lead to new technologies, requires frequently changes in thinking and behaviours and requires more from customers. Radical innovation mainly arises from complex knowledge, in the course of firms’ innovative activities (Teplykh, 2016). The previous studies found that radical innovation is the base for competition in dynamic industries (Xue, et al., 2012); can significantly increase the total factor productivity (Teplykh, 2016); stronger for international firms (Coombs, et al., 1996); not easily adopted by market (Trott, 2012); and required top knowledge provided by universities (Satta, et al., 2016).

The investments in radical innovation are riskier because innovation introduced on the market often don’t bring expected benefits. Customers experience difficulties to comprehend and evaluate radical innovation due to its newness of technology and benefits.

2.4. EXPLOITATION AND EXPLORATION

Organisational learning theory perspective suggest existence of exploration and exploitation processes (Xue, et al., 2012). The basic problem confronting an organization is to choose between exploitation and exploration business strategies. Exploitation answers for increasing productivity, control, certainty and variation reduction. The exploitative innovation involves incremental improvements in existing products and processes (Xue, et al., 2012). The exploitative innovation refers to efficiency. Exploration answers for search, discovery,

autonomy, innovation and embracing variation. Exploratory innovation is aimed at developing and entering new product market domains using radical innovation that involve acquiring and development new knowledge (Xue, et al., 2012). A firm’s business environment has influence on adoption of exploitation or exploration strategies.

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2.5. MEASUREMENT OF INNOVATIVE ACTIVITIES:

BALANCED SCORECARD

The development of the Balanced scorecard and strategy map offers managers the framework for a generic interactive system (Kaplan, 2010). Balanced scorecard (BSC) is the management practice that attempts to complement drivers of past performance (financial measures) with the drivers of future performance, such as customer satisfaction, development of human and intellectual capital, and learning. The balanced scorecard translates organisation’s mission and strategy to the set of performance indicators (Ivanov & Avasilcăi, 2014). The BSC presents nonfinancial measures that are used to motivate, measure and evaluate company performance.

The indicators represent the balance between external environment [indicators for stakeholders]

and internal indicators of the critical processes, development and innovation learning (Ivanov &

Avasilcăi, 2014). Figure 2.1 shows the structure of BSC.

Figure 2.1. Translation Vision and Strategy: Four Perspectives (Kaplan, 2010)

The idea of casual linkages among balanced scorecard objectives and measures led to the creation of a strategy map. The strategy map describes the casual relationships between the strategic objectives (Kaplan, 2010). Figure 2.2 shows the structure of strategy map.

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Figure 2.2. The strategy map links intangible assets and critical processes to the value proposition and customer and financial outcomes (Kaplan, 2010)

The previous studies stressed the importance of designing of the well-formed model for

measurement of performance. Ezzi & Jarboi (2016) proposed that the multi-criteria performance model of performance should take into account expectations of different stakeholders. Ivanov and Avasilcai (2014) suggested that a company can achieve competitive advantage by improving its talent to exploit intangible assets, focusing less on financial results. Franko and Oliveira (2016) states that companies should innovate in products and productions processes,

organizational structure, administrative processes and managerial practices. All these features are discounted in BSC, which is used as a part of evaluation of ICT-companies’ innovative

performance. Financial and accounting measurements for evaluating of firms’ performance are proposed by economic and financial literature. The financial measures can be used for evaluating of wide scope of innovation forms (Norek & Costa, 2014).

2.6. INTERNAL ORGANIZATIONAL INDICATORS 2.6.1. ORGANIZATION

The companies with good resources and skills choose the best strategies to create a competitive advantage and superior performance. Impact of organisational innovation on performance of companies is very important in productivity improvements in the service sectors of intensive knowledge (Simao, et al., 2016). The number of theories are of importance in relation to organisational resources and learning capabilities in relation to innovation. The resource based view of innovation assume that markets are dynamic and volatile and firm’s own resources provide a more stable context for innovation activities (Ezzi & Jarboui, 2016; Satta, et al., 2016).

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The theory of innovation potential is based on the concept of a company’s resources (Norek &

Costa, 2014). A company’s ability to create all the aspects of activities is closely related to the possess resources. Resources owned by a company determinate its innovative potential. The organizational learning theory and the cognitive theory take up the position that innovation requires a variety of competencies at key stages in the innovation cycle. Organization learning theory provides the context for generation of transformation of knowledge into innovation (Baker, et al., 2016; Xue, et al., 2012). The firm is able to learn and create knowledge. The innovation provides the means to achieving growth for companies whose objective is to grow the business in the long-term. The main factor of long-term economic growth is to create new

advantages based on knowledge and innovation (Norek & Costa, 2014). Organizations are open systems that affect and are affected by the environment. Innovation within requires a favourable context outside.

2.6.2. FINANCIAL RESOURCES

A company growth to maximize its financial performance and innovation is a tool for creating of firm’s profitability and sustainability. The established strategies aim to ensure company’s

growth, profitability and perennial (Ezzi & Jarboui, 2016). The relationship between a firm’s innovation and financial performance is significant and can be measured with help of financial indicators. García-Piqueres, et al., (2016) assumed that success of innovation processes produces profits. Satta et al. (2016) suggested that financial resources can play the role of predictor of firms’ innovation activities. It is preferably finance R&D activities by internal generated cash flows. Norek and Costa (2014) concluded that there is a relation with company’s internal resources and level of capabilities full innovative activities. Ivanov & AvasilcăI (2014) argued that financial resources lead to an organisation’s success by attracting and investing them to obtain good financial results. Thus, financial performance is an important indicator of a company’s innovative activities. The number of previous studies found that accounting and financial variables are practically useful for measuring of performance of firm’s innovation activities. They found also that financial indicators that are analysed differ from company to company.

2.6.3. DEBT RATIO

Financial resources have a positive impact on R&D activities in industries (Satta, et al., 2016;

Ezzi & Jarboui, 2016; Coombs, et al., 1996). Both retained earnings and availability of new debt or equity impact on: investment decisions, R&D projects and innovative activities. In addition, firms have varied capabilities to achieve efficiency and/or innovation. The debt ratio is debt and solvency measure that shows the proportion of assets bought from borrowing funds. The higher the debt ratio the greater financial risk the company is facing (Hitt & Zhou, 2002). The value of debt ratio is highly dependent on the industry that the company operating in. Total debt ratio or the long-term average ratio can be measured by using the financial leverage which resides in the total debt divided by the total assets. This measure is used also by other researches such as (Ezzi

& Jarboui, 2016; Xue, et al., 2012). Debt = (Total debt/Total assets) in percentage.

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2.6.4. MANAGERS

Innovation need to be viewed as a management process (Trott, 2012). Managers have the key position in creation of a firm’s innovation strategy. They play a key role in projecting of

innovative process that brings competitive success for a firm. Managers should identify how the company can make value for customers in the aim to choose right measures (Ivanov & Avasilcăi, 2014). As innovation leaders, they exercise their initiative and decide how assets portfolio can be allocated. Managers are willing to implement a better strategy to obtain competitive advantage and organisational performance. They have responsibility for assets and competencies available in company for strategic decisions. Accounting and financial measures guide managerial

decisions for evaluating investments in innovation. A firm’s innovative performance is strengthened by a higher managerial discretion (Satta, et al., 2016). The important managerial challenges are to maintain human resources, process improvement, design and product

development, creativity development, knowledge and technology management etc.

2.6.5. ASSETS TURNOVER RATIO

The operations and material flow within an organisation are indicative of managerial efforts.

Baker, et al. (2016) argued that management functions include general management, information technology, marketing and sales, customer service, manufacturing and R&D. For Xue, et al.

(2012), to handle aggressive competitiveness from competitors, the companies eliminate waste, reduce costs and increase efficiency of their operations to maintain profitability. The business performance is affected by operations that managers launch in the aim to improve innovative efficiency. Managers have direct responsibility for firm’s interlay focussed processes such as process, control, coordination etc. Assets turnover ratio shows how much revenues are generated for one dollar of total assets. The ratio is activity measure that displays how efficiently firms’

assets are used. High assets turnover ratio indicates high level of sales generated by total assets (Hitt & Zhou, 2002). In this paper, assets turnover equal to sales divided by account receivable.

2.6.6. EMPLOYEES

Individuals play a significant role within the industrial technological innovation process. All the results from customers, internal processes, and financial perspectives are strictly related to training and development of human resources (Ivanov & Avasilcăi, 2014). The competitiveness in the free market maximizes innovation and productivity. The labour productivity impacts efficiency of innovative activities. Firms require high-quality human-resources to transform new knowledge into economic benefits (Teplykh, 2016). Employees affect the corporate performance when the company is more efficient than its competitors. The labour productivity ratio measures the amount of sales generated per one employee. The ratio is profitability measure, high ratio indicates more productivity per employee (Hitt & Zhou, 2002). The higher value is the ratio the better.

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2.6.7. COMPANIES’ SIZE

Size is a proxy variable for more meaningful dimensions: economic and organizational

resources, including number of employees and scale of operations (Trott, 2012). Relationships between innovating firm size and propensity to innovate is a wide discussed topic in innovation theory (Franco & de Oliveira, 2016). According to previous researches, the size of a firm affects its financial policy. Larger companies have higher performance and are more investment in research and development than small and medium-size companies (Ezzi & Jarboui, 2016). Many of previous studies calculated firm size as logarithm of the number of employees in the firm (Doran and Ryan, 2016; Baker, et al., 2016; Bharadway et al.,1999; Xue et al., 2012; Coombs et al., 1996). In this study, the size of a company will be calculated as natural logarithm of the number of employees in the firm. Size = ln (number of employees)

2.6.8. COMPANIES’ AGE

The company age has a very significant effect on company’s performance (Ezzi & Jarboui, 2016) and innovation output (Doran & Ryan, 2016). The previous experience of successful R&D input increases the managers’ commitment to innovate. According to García-Piqueres, et al.

(2016), innovation activities required a long period to become profitable and previous innovation experience plays an important role. Norek & Costa (2014) argued that effectiveness of

innovation process may be studied with combining effectiveness of innovation processes with company size, length of operating on the market or organizational culture. Doran & Ryan (2016) assumed that size and age would be highly collinear with younger firms being smaller and older firms being larger. Authors concluded that size may also capture age effects. Ezzi and Jarboui (2016) expressed proxy of age by the natural logarithm of the number of working years. Age = ln (number of years). In this study, the variables of age is calculated as in the study provided by Ezzi & Jarboui (2016).

2.6.9. INVESTMENTS IN R&D

Innovation strategy or investments in R&D concentrates on such aspects as assets specificity and availability. Investments in R&D improves competitiveness and growth opportunities of a company. Huang et al. (2017) involved a survey of agglomeration effect of two important innovation determinants: R&D investments and R&D personnel. There are number of other studies that used investments in R&D as an input financial indicator for firm performance measuring (Hagedoorn & Cloodt, 2003; Xue, et al., 2012; Ezzi & Jarboui, 2016; Bharadwaj, et al., 1999; Satta, et al., 2016; Franco & de Oliveira, 2016). Xue, et al. (2012) argue that R&D investment not only reflects current input but also reflects the success of prior years’ R&D efforts. In this study, the R&D intensity is an input variable related to firm’s innovation strategy.

It is calculated as the ratio between R&D expenses and total sales. This measurement is also used in the study provided by (Ezzi & Jarboui, 2016). R&D intensity = R&D expenses/ Total sales

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2.7 EXTERNAL ENVIRONMENT INDICATORS 2.7.1 CUSTOMERS AND MARKETS

In considering highly innovative products, it is crucial to take into account the customers’ view and experience. Consumer trends can present both threats and opportunities for a wide variety of firms (Aaker & McLoughlin, 2010).Firms development depends to a great extent on products and services proposed to the customers.The customers arepositive in terms of quality, costs and distribution. Companies always seek to fulfil the satisfaction of their customers, employees and other key stakeholders (Ezzi & Jarboui, 2016). Personalized products and services satisfy the customers’ needs. Most companies from ICT sector hope their innovative activities will increase productivity of their customers (Mainardes, et al., 2016). The customers are also a source of knowledge and have a crucial role as generators for innovation. The lead users are basis for insights of the technology-intensive products and help with the diffusion process. Diffusion of innovations starts with implementation and continues with popularisation of innovative methods among different customers (Norek & Costa, 2014). Changing in customers thinking and habits may affect their willingness to embrace a new product. Marketing managers explore customers’

needs that plays an important role in new product development process.

Market based view states that the market conditions provide the context, which influences the extend of firm’s innovation activities (Trott, 2012). Demand of the market involved themselves in product developing. The companies strain to satisfy demand of their customers to turning innovation into profits. Further, the innovations outputs are marketed commercially which make it possible to evaluate innovation results and improve efficiency. Innovation becomes the main factor to boosting productivity (Mainardes, et al., 2016). Marketers look into future and form the products and services that will be successful. The relationship with the customers gives an

opportunity to establish robust communication between a firm and a market. The market value of a company is based on account of its tangible and intangible assets. For example, a company’s assets and competencies such as reputation and managerial skills are evaluated by the market. As the market learns about firms R&D activities, patents and new products introduction, it responds on this information through valuation of firms’ assets that are reflected in market value of the firms (Xue, et al., 2012). The company’s performance usually positive relate to its market share.

2.7.2 NUMBER OF PATENTS

The goal of the companies is to meet expectations of their customers. The company’s competitive advantage mainly lies in the value that the company can create for its customers (Ezzi & Jarboui, 2016). Mainardes, et al. (2016) concluded that the knowledge intensive business services companies can have innovative influence over their customers. For Baker, et al. (2016), frequency at which the firm introduces new products represent new ways to satisfy customers’

needs. The previous studies found that number of patents is an appropriate measure of firm’s innovative performance (Coombs, et al., 1996; Hagedoorn & Cloodt, 2003; Xue, et al., 2012;

Satta, et al., 2016; Teplykh, 2016). Teplykh (2016) declared that analysing of patent data identifies the relationships between innovation efforts and the creation of new knowledge.

According to Satta, et al. (2016), measuring of new patents registered provides evaluation of financial resources in shaping a firm innovative performance. Hagedoorn & Cloodt (2003) believe that a raw patent […] allows researchers to compare the inventive and innovative performance of companies in terms of new technologies, new processes and new products. For

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this study, patents activities are chosen as measurement consistent with the previous studies.

Patents assigned to each company in the period of five years. According to Satta, et al. (2016) it is one appropriate range for assessing the technological impact on the new inventions. The variable is calculated as natural logarithm of the number of patents. Number of patents = ln (number of published patents)

2.7.3 SELLING AND ADMINISTRATIVE COSTS RATIO

Managers have direct responsibility for firm’s long-term externally focused investments such as customer satisfaction. According to Ivanov & Avasilcăi (2014), it is a set of objectives that an organisation needs to achieve to satisfy its customers in the basis of their habits and values perceived in relation to the organisation. Administrative performance is an important part of assessment of company’s organizational performance. It is especially actual because of risks associated with innovative activities taken by a firm. Innovation activities are often inefficient (Norek & Costa, 2014). They require analysis of effectiveness of innovative activities taken by the companies. Improvement of internal processes within an organisation results in growth of financial returns. For Ivanov & Avasilcăi (2014), the focus on research and development projects allows an organisation to improve its current activities. Simao, et al. (2016) defined

administrative innovation as innovation of organizational structure on the principals of human resources. Coordinate activities with suppliers and customers are measured as selling and

administrative cost ratio. The SG&A costs ratio reflects the costs include in coordinate activities inside the firm and with suppliers and customers, and, thus, is an aggregate measure of

administrative costs (Xue, et al., 2012). Selling and administrative costs, measured as selling, general and administrative costs divided by sales.

2.8 INNOVATION IN ICT INDUSTRY

The ICT industry is characterized by high competitiveness in all sectors. ICT consists of permanent process of innovation (Mainardes, et al., 2016). There is a growing research interest to the application of the innovation in the context of ICT-industry. Previous studies found that industrial and sectorial environments have significant impact on firm’s innovative strategy.

Doran & Ryan (2016) concluded that sectorial characteristics are often linked with innovation performance. A number of theories support the argument. Industrial approach considers the effect of the industrial structure on the types of strategies adopted by the companies, which explain the business performance (Ezzi & Jarboui, 2016). Structure-conduct-performance paradigm stress that industry structure influences the behaviours of firms in relation to the specific industries (Xue, et al., 2012). From there, companies’ productivity and performance are influenced by their competitive environment. The relationship between business environment and specific nature of innovation is studied by (Hagedoorn & Cloodt, 2003; Xue, et al., 2012;

Doran & Ryan, 2016; Satta, et al., 2016; Huang, et al., 2017). The main findings of the studies are that environment moderates impact on efficiency and risks taking in connection to

innovation. For example, Satta, et al., (2016) stated that in high-tech industry environment, innovation and R&D activities are expensive and time-consuming.

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3. EMPIRICAL SETTING AND RESEARCH DESIGN

3.1 RESEARCH DESIGN

In different phases of the study, the qualitative and quantitative methods are employed for generating of data, empirical validation of the research questions, and analysis and explaining of the results. The purpose of the study is to verify the role of innovation strategy in performance. A content analysis in a documentation survey applies for processing of descriptive data from

literature studies and empirical data from companies’ annual reports. In the survey, continuous observations in the field are used as the method for detection of different nuances related to the research. Continuous observation of the field provides a basis on which, in a survey, the several waves are related or from which these waves are derived and shaped in the second design (Flick, 2009).

Figure 3.1. Continuous observation method (Flick, 2009)

Mixed method research is a growing area of methodological choice for many academics and researchers from across a variety of discipline areas (Cameron, 2008). Mixed methods research is a research design with philosophical assumptions as well as methods of inquiry. As a

methodology, it involves philosophical assumptions that guide the direction of the collection and analysis of data and the mixture of qualitative and quantitative data in a single study or series of studies. The mixed research is a mixture of qualitative and quantitative research methods (Flick, 2009). Its central premise is that the use of quantitative and qualitative approaches in

combination provides a better understanding of research problems that either approach alone. A lot of researchers, both quantitative and qualitative, take a pragmatist approach to research, using different methods depending on the research question they are trying to answer (Sukamolson, 2010). The study is conducted in mixed method research manner and takes a pragmatic approach to research methods.

Mixed-methodology approaches are interested in a pragmatic combination of qualitative and quantitative research (Flick, 2009). According to Flick (2009), a truly mixed approach

methodology (a) would incorporate multiple approaches in all stages of the study (i.e., problem identification, data collection, data analysis, and final inferences) and (b) would include a transformation of the data and their analysis through another approach.

Quantitative research is suitable to explain some phenomena and especially suited is the testing of theory and hypotheses (Sukamolson, 2010). The ultimate goal of any quantitative research is to generalize the “truth” found in the samples to the population. The ultimate goal of any qualitative research is to understand a certain phenomenon (Sukamolson, 2010). The data of qualitative research produce more context information about a single participant than

quantitative research. Qualitative research gives the data depth, breadth and a richness of

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interpretation and makes it possible to contextualise the phenomenon by contributing unique details and experiences (Murgado-Armenteros, et al., 2015).

In present paper, qualitative research supports quantitative research. Both methods are combined and provide a more general picture of the issue under study. The qualitative research is used for developing the research question, which are tested by quantitative approaches. Both areas of research are located at various stages of the research process.

3.2 PHILOSOPHY OF SOCIAL SCIENCE

Two main paradigms underlay the philosophies and worldviews of researchers. The qualitative and quantitative researches demonstrate two fundamentally different worldviews. The

quantitative view is described as `realist' or sometimes `positivist', while the worldview underlying qualitative research is viewed as being `subjectivist' (Sukamolson, 2010). For positivists, the researcher needs to be as detached from the research as far as possible, and use methods that maximize objectivity and minimize the involvement of the researcher in the

research. Qualitative researchers are subjectivists (Sukamolson, 2010). The subjectivists point to the role of human subjectivity in the process of research. Reality is constructed by a researcher and it changes and transforms by the process of our observations. Post-positivists believe that research can never be certain (Sukamolson, 2010). We should try and approximate that reality as best we can, while realizing that our own subjectivity is shaping that reality. The post-positivist social science focuses on confidence - how much can we rely on our findings? How well do they predict certain outcomes?

The present study is grounded both on information from previous literature and the results are obtained from the present survey’s observations. The study provides by the mixed method for processing of the objective numerical data and constructing subjective suggestions. Mixed method research is a flexible approach where the research design is determined by what we want to find out rather than by any predetermined epistemological position (Sukamolson, 2010). The reasoning of the study is of post-positivists kind in aim to meet the objectives of the study.

Researchers need to make their epistemological position clear in order to make an appropriate evaluation of the research and its results possible (Flick, 2009).The questions related to validity, reliability and objectivity are equally important for qualitative as well as quantitative researches.

The quality assessment of the research depends on accounting of reliability and validity of the results. The reliability refers to the absence of differences if the study will be repeated. The researcher’s tools should be neutral in effect and return the same results next time an object is studied.

The validity is an extent to which the research findings accurately reflect the phenomena under a study. When a researcher utilizes several methods in the study and many sources of data are collected. The benefit with this is that many sources of data make better probability for quality in research. It makes a strong support for analysis if significance of meaning of the data to some extend correlate between the methods. A basic problem in assessing the validity of qualitative research is how to specify the link between the relations that are studied and the version of them provided by the researcher (Flick, 2009). Triangulation construes that researcher determinates an exact position through the lens of two or more coordinates. Triangulation means combining

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(Flick, 2009).This is conceived as the complementary compensation of the weaknesses and blind spots of each single method. On the level of data, the combination may be oriented to transforming qualitative data into quantitative data and vice versa.

Figure 3.2. Levels of triangulation of qualitative and quantitate researches (Flick, 2009)

Generalizability implies the extent to which the research findings can be extended. The

pragmatist approach will lead researchers to quantitative methods if they need to generalize the questions or test a theory mathematically, otherwise they will use qualitative methods. The problem of generalization in qualitative research is that its statements are often made for a certain context or specific cases and based on analyses of relations, conditions, processes, etc., in them (Flick, 2009). The degree of generalization striven for in individual studies should also be taken into consideration. The mixed method approach combining quantitative and qualitative methods may allow to reach at least an appropriate level of generalization (Sukamolson, 2010).

Objectivity is related to the researcher’s openness and obviousness about bias that without doubt exists. The researcher’s individual experience and values can influence the results of the study.

Post-positivists accept […] that the natural sciences do not provide the model for all social research (Sukamolson, 2010). The analyses of the quantitative data require a reflexive exposition for researcher’s subjectivism and its influence on a research. According to Flick (2009), if triangulate results from different researchers worked independently arriving at the same conclusions, they are objective and reliable.

The reasoning of the choice of methodology is based on several factors. Applying the mixed method approach may help to increase the validity, reliability and objectivity in the study. The literature studies help to get knowledge about the topic and support the design of the survey. The study is provided in the pragmatic fashion that gives both high reliability and validity in relation to the studies objectives. The researcher’s subjective analyzation is clearly visualized to allow readers to easily follow the reasoning.

3.3 DATA GENERATION METHOD

In the mixed method study, qualitative and quantitative phases can detect different nuances of the survey. While quantitative methods are better at looking at cause and effect (causality, as it is known), qualitative methods are more suited to looking at the meaning of particular events or circumstances (Sukamolson, 2010). The quantitative method’s analysis is made based on

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numerical representation. The numerical data is collected in the aim to explain the observations’

phenomena. This study is of explorative character and provides a quantitative research.

The qualitative methods analyse data in mainly textual form. As a method of research,the text becomes the central element for judging the translation of experiences into constructions and interpretations.In the social sciences, text as basis for interpretation is an epistemological instrument for mediating and communicating findings and knowledge (Flick, 2009). Research experiences should be transformed into texts and to be understood based on the texts. This study is of descriptive character and is used together with information from previous literature in the field.

Analysis of the data is carrying out qualitatively. The data for this research is combined from following primary sources. Regarding innovation strategy, the numerical accounting and financial data of the listened companies is collected to measure required inputs and outputs variables. 10-K reports are using for collecting firm-level data. The number of patents applied for each year are collected from the U.S. Patent and Trademark Office (USPTO) database. The final sample was composing of 22 firms over the 2011-2015 timeframe. These ICT-companies are selected because their R&D expenditures, patents and new products indicate high innovative activity. The companies are from USA, EU and Asian regions. Companies entered the sample if value of at list one of the variables used in the analysis has a value higher than zero. The data used in the empirical study is the secondary data collected from written documents such as companies’ annual reports and scientific databases. The survey includes reference period for innovation inputs and outputs.

The method chosen for gathering primary data is content analysis of peer reviews, books and online sources to gain required knowledge about the topic of the study. The literature pre-study provides the needed knowledge for designing of the study. The selected sources for searching through SU library were:

• Scientific business databases Scopus, SAGE journals Online, ScienceDirect,

SpringerLink, Emerald, Business Source Elite and Google Scholar with appliance of keywords or phrases (e.g. innovation OR performance efficiency AND accounting, finance, investments, patents, industry, ICT, high-tech, business strategy, measurement etc.)

• Exploration of studies that quoting fundamental works about innovation and performance efficiency measurement (e.g. a theory of oligopoly (Stigler, 1964); a general theory of competition resources (Hunt, 2000) etc.).

• Manual studies with references that citied in recently published peer reviews and studies that the databases included

• Similar publications from latest conference presentations (MEIDE, ANZAM etc.)

The timeframe of literature review is ranged from January to May 2017 to be updated about any new research realised before the end of the study. Some occasional searches are conducted during the study to get new insights in the areas of interest acquired. The selected articles are in English, Swedish or Russian without geographical limits.

The documentation survey is the basic method for economists and financial analytics. The data from companies’ annual reports and USPTO make a ground for the economic research. The collecting of numerical data in the study’s empirical part enables to provide basis for the

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interesting phenomena to confirm/deny the results obtained in previous studies or provide new previously unknown insights in the scientific field. Together with the pre-study, the data collecting method aims to reach specific conclusions in the survey. The main purpose of literature search is to collect as many of relevant items literature as possible to learn about previous methodologies used by other researchers. The peer reviews and relevant for the study online databases provided knowledge about the scope of searching and keywords. When specific of review is defined, the abstracts of articles are evaluated and list of references conducted for further reviewing.

3.4 DATA ANALYSIS METHOD

Qualitative and quantitative researches can be usefully combined in mixed methods designs (Sukamolson, 2010). The quantitative research aims to illustrate efficiency of innovative practices adopted by ICT-companies. The quantitative study consists of accounting of factors that are of interest with help of a mathematical statistical model. The DEA model presents efficiency of the ICT-companies based on the resulting efficiency scores. The ICT-companies are evaluated based on Balanced Scorecard performance criteria drawn from the literature study.

The analysis phase is used for statistical evaluation to answer on the research question and sub- questions of the study. Together with differences, the statistical analysis provides ground of the study results’ validation and support importance of the findings. The qualitative exploration analysis takes to the next level the comparison and evaluation of empirical data with obtained in the pre-study’s phase theoretical data. The analyses of the quantitative research will be

visualized with help of graphs. The visualization technique employed is very important in order to achieve a good understanding and better interpretation of the output (Murgado-Armenteros, et al., 2015).

The data from the survey is analysed qualitatively to find factors of commonness and patterns of phenomena among the data. The phenomena will be found from the empirical study by

qualitatively reviewing and generalizing. The findings of the study are analysed in relation to the results obtained from the previous studies in the field. This analytical procedure is used to find factors that corresponds and differs in different parts of the study. The knowledge in the report is presented step by step in a fragmentary way (Flick, 2009). The data is structured in the analyses in the way that allows easily follow the reasoning. Images or drawings are exemplarily integrated in a published text (Flick, 2009). Some images and drawings are presented to visualise scientific facts and improve using of the results of the analysis. The results will be categorized and

presented in tables to provide an easy overview of the findings. In the interpretation step, the analyst looks to discover and extract useful knowledge that could be used to make decisions (Murgado-Armenteros, et al., 2015).The mixed method provides the massive data and a broad interpretation of the results. The final findings with explanations and descriptions are presented in the end of the study.

The data envelopment analysis (DEA) model is used for empirical studies in the research for evaluation of relationships between ICT-companies’ innovating strategies and overall

performance.DEA provides a better way to organize and analyse data which allows efficiency to change over time and requires no prior assumption on the specification of the best practice frontier (Wu, 2009). DEA can be used for modelling operational processes and its empirical orientation. The model has become a leading approach for efficiency analysis in many fields, such as supply chain management (Olson & Wu, 2011), banking industry (Wu, et al., 2006),

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business research and development (Olson & Wu, 2010) etc. for modelling operational processes.

Data envelopment analysis (DEA) is a nonparametric programming approach for evaluating the relative efficiency of DMUs with performance characterized by multiple attributes (Wu, 2009).

DEA is based on the ‘efficiency’ analysis of alternative objects, which are evaluated on benefit criteria (outputs) and cost criteria (inputs). DEA can hardly be used to predict the performance of other decision-making units (Wu, et al., 2006). DEA is used to establish the best practice group amongst a set of observed units and to identify the units that are inefficient when compared to the best practice group. The model also indicates the magnitude of the inefficiencies and improvements possible for the inefficient units.

DEA (CCR) is a quantitative model for efficiency analysis of decision-making units (DMU) (Olson & Wu, 2010). Input-oriented CCR model aims to minimize the expenses (inputs) subject to attaining the desired output levels. DEA evaluates alternatives by seeking to maximize the ratio of efficiency of output attainments to inputs, considering the relative performance of each alternative (Olson & Wu, 2010). The mathematical programming model creates a variable for each output (outputs designated by ui)and input (inputs designated by vj). Each alternative k has performance coefficients for each output (yik) and input (xjk). The classic Charnes, Cooper and Rhodes (CCR) DEA model is:

The primal form of CCR ratio model is:

In the present study, DEA CCR model have three inputs and five outputs. Every company’s efficiency is defined as the ratio of the weighted sum of its outputs (i.e. the performance of the

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performance of ‘efficient’ and ‘inefficient’ companies in the sectors of ICT-industry are tested so that the companies’ efficiency could be predicted and analysed. The efficiency scores for DMUs larger than 1 are not allowable in DEA context (Wu, et al., 2006). The efficiency score intervals can be evaluated as follows:

• (0.98, 1) are referred as ‘strong relative efficient interval’

• (0.8, 0.98) are referred as ‘relative efficient interval’

• (0.5, 0.8) are referred as ‘relative inefficient interval’

• (0, 0.5) are referred as ‘very inefficient interval’

Descriptive analysis is presented by descriptive statistic related to the factors such as the average innovation strategy, financial performance, ICT sectors and companies’ characteristics,

stakeholders, market conditions and risks. Thus, the descriptive statistics of all variables of interest used in this study in relation to the innovation efficiency. The cross-sectional analysis is the most appropriate method to analyse many variables and the possibility to identify hypothesis for the future research (Ivanov & Avasilcăi, 2014). The method is used to describe the things in their context: how they are connected and depend on each other. It provides analysis of sources and patterns of ICT-companies’ innovation efficiency changes across sectors. And explanations how the innovation types are distributed across sectors.

Empirical validation of the research hypotheses verifies the effect of innovation companies on the performance of the firms (Ezzi & Jarboui, 2016). The prior research, analysis and

conclusions of the survey is provided qualitatively. Qualitative data needs to be understood within a context. Hermeneutic classificatory content analysis method integrates ideas and procedures of objective hermeneutics into basically a quantitative content analysis (Flick, 2009).

Hermeneutic is a qualitative interpretation and understanding method of that no observations or descriptions that are free from the effects of the observer’s experiences, personal values and expectations.

The method of content analysis is used to find factors of commonness and factors of phenomena.

The information about the context is provided by the literature study. The casual conditions and their validity are presented as clear as possible. The conditions under which the phenomena and relations in study occur are controlled as far as possible. Most phenomena cannot be explained in isolation, which is a result of their complexity in reality (Flick, 2009). In this study, the context data will be analysed together with data from the statistical analysis to obtain information needed for discovering.

The study consists of data collecting method and results interpretation. The construction of the research mostly depends upon judgements made by the researcher. The techniques to achieve some consistency are following. The researcher identifies and classifies individually the sample of innovation with help of knowledge obtained in the pre-study. The cross-sectional data is transformed from different surveys into a simple pooled sample.

DEA model includes input variables that represent ICT-companies’ internal resources (size, age and R&D investments). The indicators reflect important aspects of innovative performance. The output indicators represent the companies’ overall performance indicators. The output indicators represent the four perspectives of Balanced Scorecard: financial, clients, internal processes and learning and growth. The balanced scorecard translates organization’s mission and strategy into the set of performance indicators (Ivanov & Avasilcăi, 2014). In the DEA model, organizational performance is represented as follows:

Innovation strategies and performance distribution of ICT-industry’s companies