Effective Implementation of the Enterprise’s Innovation Capacity

1. Introduction

The COVID-19 pandemic has shut down the world economy. The world’s largest economies developed the aid packages aimed to reduce the effects of an impending recession. Businesses, for example, received support in the form of loan guarantees and tax relief. However, aid packages did not finance innovations and start-ups. Furthermore, innovations and R&D were not the priority in government aid and relief programs up until now. Also, countries devoted large sums of money to the research and development of a coronavirus vaccine. In these circumstances, the impact of the COVID-19 crisis on innovation will depend upon business recovery approaches, innovation itself, and policy. Hence, it will be difficult for both private and public sector enterprises to find new sources of investment for their innovative activities, which aim to meet the actual needs of the digital economy (Wang et al., 2022).

According to the Global Innovation Index 2020 (GII) which ranks 130 countries as per their innovation capacity, Switzerland is the world’s most innovative country, followed by Sweden and the US. Among the CIS countries, Russia takes 47th place, Kazakhstan — the 72nd place, Azerbaijan — 82nd place, Uzbekistan and Kyrgyzstan — 93rd and 94th in the ranking (WIPO, 2020). The global challenges and trends in innovation and business discussed above unveil a need for a more systematic review of literature considering innovations and enterprise performance.

The research on innovation revolves around two domains: management and economics (Tagues et al., 2021). In his research, Ludeke-Freund (2020) discusses innovation as part of sustainable entrepreneurship. The author states that efficiency in business does not make innovation itself but depends on business models used for commercialization purposes. Therefore, commercializing an innovation involves a variety of challenges from defining customer segmentation to production scale-up. Such mechanisms benefit suppliers, co-operators, and competitors, not innovators. The researcher argues that the enterprise’s innovation capacity allows it to modify or create new value propositions in the business model. Ferreira et al. (2017) describe innovation as an entrepreneurial tool that is essential for effective performance and profitability in a highly competitive environment. The researchers argue that innovation helps enterprises respond effectively to diversified and ever-changing demands. As a result, it leads to profit efficiency. Innovation can be described as a driving force of competitiveness. Companies with innovations show better financial performance and economic growth than non-innovation organizations. The findings of Da Xu (2011) and Xu (2020) show that the main obstacle to harnessing the potential of Industry 4.0 is the lack of technological tools that address the challenges of the digital economy. Such technologies come from a variety of disciplines (Xu, 2020), including cyber-physical systems, Internet of Things, cloud computing, industrial integration, enterprise architecture, service-oriented architecture, business process management, industrial information integration, and others.

Overall, in modern enterprises, the common barriers to innovation are the lack of financial resources, technical skills, information updates, and “know-hows” (Tsindeliani and Mikheeva, 2021). Kuratko (2017) argues that entrepreneurial thinking is the main strategic objective of an enterprise that aims to achieve sustainable competitive advantage and improve its profitability. Sustainable competitive advantage inspires innovation as part of corporate entrepreneurship. In this case, corporate entrepreneurship can be defined as innovative behavior within established medium-sized and large enterprises. Today, there is no universally accepted definition of enterprise innovation capacity in the academic environment. Terebova (2021) claims that the concept of innovation capacity is too complex to be explained by a single, unified definition and offers three approaches to define it:

Terebova suggests that the mixed approach is the most effective among the three because it describes resources available to the enterprise and the rate of their effective follow-up. Based on the information mentioned above, the enterprise innovation capacity can be defined as the combination of resources and results of the enterprise’s innovative activities that interact with the external environment under certain organizational conditions to improve enterprise competitiveness and ensure sustained economic growth. According to Shamil and Almaz (2015), innovative products and commercialization are to be a base for innovation capacity. Knubley (2021) proposes to evaluate innovation ecosystems according to ten innovation indicators that give an idea of how well they create social and economic value both in qualitative and quantitative aspects. Here are these indicators:

The results of such evaluation will correspond with the real economic conditions and ecosystems. In turn, Bousmah and Branch (2021) claim that entrepreneurship development strategies can be seen as a part of an organization’s expansion or renewal process, as well as economic growth, which is often ignored by organizations. The researcher proposes evaluating an enterprise’s innovation capacity as means of descriptive statistics. Kultyn and Tsybuliak (2018) offer a similar approach based on a set of global index indicators. The authors suggest taking the methodology used to measure the innovation ecosystem on a national/regional level and applying it to an enterprise. Kalu and Okafor (2021) underline the importance of public policy that enables public and private sector enterprises to contribute to the economy, as it provides them with tools to mitigate business-related issues. These may include entrepreneurship education, additional business financing, development and delivery of business support services, and simplification of regulatory policies. In particular, Honorato and de Melo (2022) focus on the importance of enterprises taking responsibility for Industry 4.0 readiness to survive and grow in the global digital transformation. The researchers propose that enterprises implement a comprehensive maturity model assessment of Industry 4.0 based on the framework and expert opinion, using six dimensions of measurement:

The findings show that the top management makes decisions based on different approaches applied to research and evaluation of innovation capacity. However, running the business in an uncertain and post-industrial world requires business owners to search for valuation tools to consider the renewable resources of the enterprise striving for economic efficiency (Bhowmik et al., 2021). This study contributes to the current understanding of available business tools in the context of innovation capacity and their comparability in terms of enterprise profitability and market performance.

The practical contribution of the study is to provide a cost-effective tool for measuring the innovation potential of an enterprise for the development of economic clusters in the digital economy.

The paper is organized as follows. The next section introduces the research methodology and describes the procedure of the data analysis. The third section interprets the research findings. The fourth section looks at the related work and background of the algorithms used. The fifth section, the summary of the findings, also draws application procedures and implications for future research.

The research objective is to compare qualitative and quantitative tools for measuring the effective use of the enterprise’s innovation capacity. The objectives of the study are to:

2. Materials and Methods

The study uses a mixed approach to compare tools for assessing innovation capacity within the enterprise efficiency through qualitative and quantitative variables. A systematic approach to qualitative assessment is used to examine the structure of innovation capacity, and a comparative approach is used to consider tools for measuring innovation capacity in enterprises. The qualitative analysis is based on the data collected by the quantitative analysis applied to measure the profitability of assessment tools within the enterprise and their efficiency for the market. The methods include analysis and synthesis, comparison, and systematization. The financial data came from international reports (WIPO, 2020), the official website of the Russian Statistical Service (Rosstat, 2020), and information from Elsevier and GoogleScholar on corporate governance (Bousmah and Branch, 2021; Honorato and de Melo, 2022; Kuratko, 2017; Shamil and Almaz, 2015), business development (Ferreira et al., 2017; Ludeke-Freund, 2020; Kalu and Okafor, 2021) and innovation (Huseinova, 2021; Knubley, 2021; Terebova, 2021). The research evaluates the tools based on the criteria of practicality and cost, according to the methodology proposed by Demand Driven Institute (2021).

The study consists of three phases. The first phase is reviewing the case studies (Ferreira et al., 2017; Kuratko, 2017; Terebova, 2021) to describe each component within the structure of the enterprise’s innovation capacity and explain their role in enterprise efficiency. Their results are used in the development of a concept in which innovative activities of the enterprise are carried out to achieve economic efficiency (Table 1).

The second stage discusses tools for innovation capacity assessment and addresses the enterprise activities as a whole. For this, the methodological framework for evaluating innovation capacity within an enterprise was analyzed using a comparative approach. The analysis shows that the assessment of the innovation capacity includes the following techniques: the balanced scorecard, multilevel clustering, world indices or descriptive statistics, mixed approach, and statistical method. Business planning and investment design systems make use of the techniques mentioned above. Table 2 describes the methodological approach and provide information about the author.

The third phase builds on the previous two phases. The qualitative assessment identifies five key tools for measuring innovation, such as the balanced scorecard, multilevel clustering, world indices or descriptive statistics, mixed approach, and statistical method to determine the most cost-effective tool. They were compared based on the criteria of practicality and cost (Demand Driven Institute, 2021).

The following comparison criteria were used here:

Each technique was assigned with points; based on the number of points, the tool was considered meeting the criterion at high (2 points) and low (1 point) levels. The only exception is the cost criterion: 1 point — costs for hiring a software developer, 2 points — zero costs for a software developer. The tool that scores the highest can be used to enhance enterprise competitiveness in global markets and promote sustainable economic growth. The paper explains why the measurement tools used in markets with different stages of development were proposed.

2.1. Limitations

The study examines various tools for measuring the enterprise’s innovation capacity and gives an idea of how to interpret the obtained results. Thus, possible challenges that the innovation enterprise may face are not considered.

3. Results

The enterprise’s innovation capacity is the key to innovation market development at a macro level. Its main components are research and development, human resources, logistics, and corporate entrepreneurship (Table 1). The outputs of the innovative activities are new products and technologies, competitive advantage, employment opportunities for a productive population, and the growth of financial capital.

In this study, the enterprise innovation capacity is defined as the innovation-driven growth that increases the level of enterprise efficiency in a globalized world and improves the well-being of the population through the provision of goods and services. The innovative activity of the enterprise exists in four contexts: maintenance of competitiveness, capacity building, financial capital raising, and market expansion.

Table 2 describes the tools for measuring the innovation capacity and their effective implementation. The balanced scorecard combines the enterprise’s strategy and operational activities. The multilevel clustering consists of multiple features with one or more top indicators. The world indices or descriptive statistics consider the reality of the country in which the enterprise operates. The mixed approach links resources, outcomes, and enterprise management. The statistical method provides a set of indicators adopted for a specific geographic area.

Table 2 presents a detailed description of five tools used to assess the innovative activity of an enterprise. They analyze the enterprise’s innovation capacity under these criteria: the ability of the enterprise to innovate, innovative product quality, human capacity, and the development level of internal business processes.

Based on the results presented in the comparative table above, conclusions are made. Each of the tools is assessed in terms of profitability and cost to ensure efficient resource management. Table 3 sums up the data collection results. Furthermore, it compares the five tools used for innovation capacity assessment.

The analysis shows that the balanced scorecard (total score, 10) has a high degree of reliability (Table 1). Due to the transparent and simple set of indicators involved in the balanced scorecard analysis, it takes 3–5 days to set up this method. This requirement is adequate. The analytical information is presented as a structured report, which anyone with analytical skills can create. This tool does not require expert support, external data sources, and a software developer. It is cost-effective if the enterprise has limited resources. The coronavirus pandemic is far from over and it is forcing enterprises to make difficult decisions to adapt to the crisis.

The multilevel clustering (total score, 5) has a low degree of reliability. The probability of errors may be considered rather high depending on the way the factors are selected. Multilevel clustering takes longer to implement. In this case, an enterprise needs expert support, external data sources (accounting system, Google documents, etc.), and a software developer.

The world indices or descriptive statistics (total score, 9) have a high degree of reliability because the input data is stored in the databases of international organizations. Several users can get access to reports via the Internet. The implementation period is short because of the transparent structure of the final assessment. The analytical information can be presented with the aid of the Report Designer, a tool that any analyst working at an enterprise can use. This approach does not require expert support and a software developer. Hence, the accounting data of the company may be required.

The mixed approach (total score, 7) is based on three blocks and four components of the enterprise’s innovation capacity. It has an average degree of reliability because of a large number of formulas and a need to work with the internal files of the company. This process is time-consuming and requires a user to perform many actions, possess mathematical skills, and gain access to data on the enterprise’s activities. Consequently, the adoption process will be slow. In this case, expert support is not needed. The user can act as an expert himself. At the same time, it requires external data sources that can be accessed through the company’s web services. An expert will need knowledge and skills in standard computer programs such as Microsoft Word and Excel. A software developer is not needed.

Similar to world indices or descriptive statistics (total score, 9), the statistical method builds on core indicators from national statistical services. The difference is that there is no need to transfer indicators to the enterprise level because the assessment takes place at the meso-level initially.

The balanced scorecard scored the highest among the examined tools (Table 3). The world indices/descriptive statistics and the statistical method have equal scores. The mixed approach and the multilevel clustering received the lowest total scores. Therefore, balanced scorecard may be considered a cost-effective tool to measure innovation capacity. Enterprises may use it to make rational decisions while building a strong organizational focus. The balanced scorecard is good for countries with the developed digital economy, where capitalization and corporate entrepreneurship are the main efficiency criteria (for instance, in the United States). This approach is less effective within the European Union Single Market (EU) as it focused on sustainable business only. For the EU countries, the effective techniques are multilevel clustering, world indices/descriptive statistics, and the statistical method. At this stage, the mixed approach suits best the emerging models of corporate governance, and those enterprises located in the CIS countries. This tool has advantages over the above-considered tools (probabilistic, expert, and statistical): it helps enterprises to avoid complex mathematical calculations and thus minimize errors. It also helps to select an optimal set of indicators to measure innovation within an enterprise.

4. Discussion

This study constructed an innovative activity framework for an enterprise using the components of innovation capacity such as research and development, human resources, logistics, corporate entrepreneurship, and a performance-based assessment. These innovative activities are closely connected with competitiveness, capacity building, financial capital raising, and expansion of sales markets, which is consistent with the findings of Batova et al. (2021). The researchers argue that the use of intelligent innovation provides an opportunity for the real sector of the national economy, especially in countries with emerging models of corporate governance, to implement intelligent automation of production systems to improve the competitiveness of enterprises, especially in the Asian markets of China, India, and Turkey. As in this study, Zhang et al. (2021) developed the structure of the enterprise innovation ecosystem that defined a value creation of enterprise based on system dynamics modeling. The enterprise innovation ecosystem in point comprises: (1) enterprises, governments, universities, research institutes, and scientific and technical institutes; (2) innovation factors (knowledge and skills of human capital); (3) revenue received from the commercialization of innovation; (4) innovation environment (market and bureaucratic management); and (5) behavioral models of technological innovation. The innovation factor provides the basis for measuring value creation and positively affects corporate performance, stimulating sustainable social and economic growth. The findings of this research coincide with Suhag et al. (2017) who claim that enterprise’s innovation capacity has a positive impact on productivity and is associated largely with the benefits of technology-driven innovation (Achi et al., 2016). It also provides a competitive advantage (Sherifi, 2020). Therefore, having innovation capacity measurement tools should be seen as a valuable competence of an enterprise oriented on innovation and intensive development, in particular, for emerging markets (Piperopoulos et al., 2018). Zaitsev et al. (2020) focus on the methodology for assessing enterprise innovation and believe that it should be adapted to the digital economy, automation and robotization. Therefore, a tool designed to measure the enterprise’s innovation capacity considers corporate entrepreneurship, human resources and new forms of cooperation between business and the state, partnership, and information exchange. The research found that the most cost-effective tool for an enterprise is the balanced scorecard. It reflects the actual consumer demand and covers the main components of innovation capacity, that is, operational, tactical and strategic components. Chen and Xie (2018) and van der Waal et al. (2021) propose to measure innovative activity of multinational enterprises using the patent application method. They found that most innovations were not related to sustainable development. Bruno et al. (2021) explained that the patent application method had limitations. As part of traditional innovation policy, it does not apply to non-technical innovations. The researchers concluded that uncertainty has influenced the assessment process greatly and advised that it should involve using the balanced scorecard and take into account the structure of the innovation capacity. In this study, the researchers did not consider the patent application method because it is based on measuring the results of enterprise innovation solely. The vision of this research is based on the results obtained by Janger et al. (2016). They claim that patents can be seen as a measure of innovative activity in contrast to a narrowly defined high-tech understanding of the innovation. It evaluates the amount of data, but not the result of the innovation activity. To measure the results of innovation adequately, it is necessary to take into account both the knowledge-intensive sectors and the actual knowledge-intensiveness of adjacent sectors, giving priority to a monitoring tool (Simyan, 2019). This approach covers all business sectors, not just a narrow subset of a predefined one. Frolova et al. (2021) admit that the model for assessing the possibilities of innovative activities is multidimensional and related to macroeconomic factors. Among them are the country’s competitiveness, favorable conditions for doing business and innovative activity of the enterprise. In their study, the researchers prioritize the global GII index and reveal that the higher the indicator of the country’s innovation capacity, the higher the degree of innovative activity at the meso-level. In addition, it contributes greatly to increasing the positive image of enterprises at the global level and attracting foreign investors. Le (2020) offers a framework that embraces innovation efforts of the enterprise and encourages the enterprise to participate in innovative activities. The results this author obtained coincide with this study. The author found that innovative activities in developing and developed countries differ. In this case, the analysis of innovation capacity should take into account the level of market development as well. The researcher found that measuring the innovation capacity for enterprises in developing countries had been effective within the framework of the mixed approach (a combination of indicators characterizing the resources of the enterprise and the process of implementing the final product), and it should be based on the following structure: enterprise size, market share, diversification, demand conditions, and technical capabilities. Therefore, the correct and complete measurement of innovation capacity helps to determine strategic directions, assess new ideas, and allocate resources (Chaudhuri, 2019; Nukusheva et al., 2021), which can also be a source of competitive advantage for enterprises (Tagues et al., 2021).

5. Conclusions

The research identifies the most effective tools to measure the enterprise’s innovation capacity. The ability of an enterprise to innovate depends upon four components: research and development, human resources, logistics and corporate entrepreneurship. The techniques used to measure the enterprise’s innovation capacity are the balanced scorecard, multilevel clustering, world indices or descriptive statistics, mixed approach, and statistical method. Given the wide range of tools, it is vital to evaluate their profitability and effectiveness. The mixed approach and statistical method both require a large set of data. The multilevel clustering requires expert support. The balanced scorecard appears as the most cost-effective tool for measuring business efficiency because it combines the enterprise’s strategy and operational activities. This tool assesses the enterprise at operational, tactical, and strategic levels. World indices or descriptive statistics are efficient and affordable. This approach is based on financial data available in the international reports and does not require specific skills. The results of the study can be used by enterprises, top management, and innovation policymakers in countries with the emerging models of corporate governance. Further research should focus on identifying constraints within those enterprises that have internal barriers to innovation and enterprises’ development.

Publisher’s Note

The copyright of this article has been transferred from “World Scientific Publishing Co.” to “School of Business and Management, Jilin University”.