Technological Innovation Outcomes: Does the Internal Ecosystem Play a Key Role?

Abstract

This study empirically investigates the causal relationship that exist between innovators and internal ecosystem factors driving technological innovation among small and medium-sized enterprises (SMEs) across the European region using the linear equation model. Our analysis is based on firm-level panel data from the European Innovation Survey conducted by the European Union from 2011–2018 with 296 SMEs across Europe. The results show that, human resources, research systems, financial support, firm investments, linkages, and employment impact was positively related to technological innovation performance. However, the outcome of an innovation-free environment and sales impacts as contextual factors had an insignificant influence on SMEs’ innovation performance. The results imply that effective human resource approach coupled with good governmental policies are important factors that can increase SMEs’ innovation performance. Based on this study results, SMEs’ managers can provide effective and sustainable policies that would provide both direct and support roles on the main drivers of regional and economic development. The outcome of this research indicates that firms need to pay greater attention to external factors such as governmental policies governing SMEs, considering customers’ needs, product specification and ensuring that they deliver quality product to satisfy consumers.

Keywords

Small and medium-sized enterprises technological innovation linear equation contextual factors ecosystem

Introduction

Technological innovation is when a firm introduces a new or improved product or process through simplified technology on the market (De Castro et al., 2010). It also comprises activities that result in new products or services, research, development or improving existing products and generating new technological ideas. Computer technology is quickly spreading to almost every sector of the economy worldwide. Although there has been an influx of advanced technology on the market in recent times, SMEs managers and researchers are worried about the slow adoption of technology by the SMEs sector (Aydalot & Keeble, 2018). Despite several efforts from SME operators to heavily invest in innovative technologies to fasten their work, they have been less than successful (Meijer et al., 2019). The introduction and survival of technological innovation in the current environment depend on internal factors contributing to its successful implementation. Understanding how internal factors influence innovation requires a comprehensive and systematic approach considering the nature of the environment and other forces that help with the interactions among components. Moreover, understanding the best ecological approach will help understand the successful practices other SME’s technology experts have adopted.

Concerns about the low adoption of technology by SMEs managers in Europe is not new (Damaskopoulos & Evgeniou, 2003). Many researchers from diverse backgrounds have studied the phenomenon through different approaches from Europe (Timmermans et al., 2020). These studies offer various accounts of why management of SMEs across the European region has been slow in adopting both product and process innovation. Embracing modern technology to its full potential could genuinely pave the way to regional and economic development in the European region. Horváth and Szabó (2019) assert that SMEs are disadvantaged in adopting smart technologies because they lack the necessary funding for this process. According to Grzelak et al. (2019), the main aim of these technologies is not to solve only a given problem but to provide a long-term solution for SME operators to ease the pressure and burden of producing more output given a limited quantity of inputs. However, SMEs management resist changes that are highly probable to pressure the old method of conducting business. In addition to this inherent resistance to change by SME managers, there has been an issue with infrastructure development as there is limited space for expansion in the ecosystem environs (Holzer et al., 2019). However, more complex issues go beyond technology or infrastructure, considering the conceptual structure in the establishment of SMEs. Recent literature affirms that unless SMEs leaders have a pre-requisite understanding of computer technology, they are unlikely to adopt new methods of process innovation (Al-Jayyousi, 2017; Odei et al., 2020). The evolution of technology in the SME sector has sprung up competition over the last decades. Technological innovation has compelled customers to choose a wide range of quality products and has attractive packaging. A more frequent factor affecting technological innovation in the ecosystem is internal contextual factors from SME managers and sustainable governmental policies.

This article attempts to identify the elements that can guarantee firms’ innovation capacity. Computer technology has been the significant driver of SME’s innovation performance. First, there are unsatisfactory remarks about the value of modern technology and conflicting advice about how technology adoption will lead to SME’s growth. Firms’ adoption of technological innovation leads SME owners to confusion as to which factors promote growth within their firm and the area that needs investment. Second, the rapid technological change has made it very tedious for SMEs managers to compete. With this aim, we focus on internal factors set by managers within firms that drive technological innovation and present some indicators that SME operators can consider key to development and efficiency. From a business perspective, we consider the outcome has important implications for SME management and highlight which determinant has a similar causal relationship across European economies.

This article is structured as follows. The next section reviews the key literature on SME innovation. The third section presents the methodology and data sources for the empirical analysis of innovation. The fourth section presents the empirical results and the robustness test using the ordinary least square (OLS). The fifth section considers the results in the light of previous studies and concludes with recommendations and suggestions for further research.

Firm-Level Factors Contributing to the Technological Innovation Ecosystem

Theoretical Background

Technological innovation is essential for economic growth and has been explicitly recognized in endogenous growth theory and Schumpeterian economics (Romer, 1994; Schumpeter, 2013). Another theory that recognizes technological innovation, research and development is Robert Solow’s theory of economic growth (Solow, 1956). The key factor identified in the literature to massively contribute to firms’ competitiveness is technological innovation (Gnyawali & Park, 2009). Technological innovation is significant for firms seeking higher sales turnover and remaining competitive in the market. However, SMEs have unique characteristics within their organizational structure, such as unwillingness to take a risk and lack of space for expansion which are significant considerations a firm needs to be innovative (Pianta, 2020). Therefore, it is relevant to access SME’s internal factors that are probable to influence SME innovators either with or without external support from the ecosystem. Drivers of innovation within SMEs can emanate from internal (human resources, funds, linkages, intellectual asset, sales impact, employment impacts) and other sources. Several studies with diverse backgrounds have identified various internal factors that are likely to influence SMEs to obtain external support from their environs and innovate.

First, the availability of effective human resources internally supports technological innovation within firms. This is based on absorptive capacity theory, which examines how a company can recognize new information and apply it towards achieving organizational goals (Cohen & Levinthal, 1990). The educational background of SMEs’ staff is crucial for them to innovate across the European region. According to Lenihan et al. (2019), firms’ capability to embrace innovation is directly connected with the educational background of employees. Unskilled management of the SME sector is unlikely to adapt to innovation strategies. Vocational education coupled with work experience is highly probable to generate the necessary absorptive capability to quickly assimilate knowledge, promoting small firms’ innovation (Odei, 2018). However, the academic background of SME managers is a significant factor for SMEs to innovate in Europe.

Second, using effective research systems internally contributes significantly to technological innovation within an ecosystem. The research system allows policymakers to identify the main drivers of innovation processes and discover new interventions to facilitate their business operation. SMEs adopt effective research systems through collaboration with academic institutions at a cheaper cost and improve existing products or design new products to ensure keen competition. The main role of the research system is to come up with new product designs and processes to ensure that the firm’s product is unique and quality to meet the demand of consumers. The outcome of an effective research system is software that enables firms to maintain technological advantage within competitive markets and realize a greater share in total sales through innovative products (Xie & Wang, 2020). However, fewer studies on SME innovations focused on investigating the relationship between firm performance and innovation (Gimenez-Fernandez et al., 2020).

Furthermore, an innovation-friendly environment supports technological innovation. An innovation-friendly environment means all forms of activities resulting in environmental protection since the world is corrupted with pollution using resources (Yurdakul & Kazan, 2020). Hence, going eco-friendly also improves the quality of life in terms of age, mortality, and diseases. Innovation is critical to the new world of sustainability and distinguishes between new or emerging firms and existing firms. Leading firms have realized their sustainability challenges and thus have been allowed to re-invent their product and services to achieve huge success on the market. According to Hofstede (1982), the innovation-friendly culture addresses the social and cultural determinants of technological innovation, which is key for economic development (Odei & Stejskal, 2018). Hofstede emphasized that focusing attention on the importance of culture in determining an innovation-friendly environment improves the ability to promote innovation activities within the environment (Hofstede, 1985). Hofstede (1982) considers innovation as a key component that drives economic growth, and he asserts the ability to orient a certain society towards a cultural environment that is innovation-friendly. Hofstede concludes in his theory that creativity and the culture of innovation are skills that we can translate into an institutional and political framework for the European region. They discovered that the relationship between innovation-friendly environment is higher individualism, willingness to take risks and ability to adapt to changes. New emerging SMEs firms benefit from innovation activities than already existing ones. Therefore, the flexibility of emerging firms to benefit from successful innovation depends on an enabling environment free of crime and theft. The flexibility of new emerging SME firms to adapt rapidly to their environments depends on how it adopts new technology to combat the influx of cybercrimes within the European region, which is likely to decrease sales output (Odei et al., 2021). However, achieving this result also requires good governmental policies against cybercrime or theft within the European region.

In addition to the above, the availability of finance and support internally contributes significantly to technological innovation. Again, financial support will also enable the company to acquire the services of expert scientists, which will train employees to acquire new knowledge, which in the long run help them improve on the traditional method of working. Again, internal support for the research department within the SME sector will motivate them to contribute to technological innovation (Odei & Novak, 2020). Providing financial support will serve as an incentive for technology transfer and promote research development activities within firms. Firms will be able to rebrand their product and improve their services to benefit the firm at large. A positive result of innovation was discovered when firms invested heavily in funds for institutional development (Elert et al., 2017).

Furthermore, the inclusion of firm investment has contributed to technological innovation. In recent times, the contribution of knowledge to the productivity of the SME sector has gained attention in the knowledge base society as a result of knowledge spillover and technological advancement, which has had a significant impact on regional development (Odei et al., 2020). Therefore, there is a need for SME firms to invest in human capital, which will also increase employees’ commitment and position them to provide the best performance. Companies training employees would bring success to their employers by contributing the knowledge acquired with the help of modern technology, which will contribute to greater sales output in the long run. Furthermore, investment can be made into research and development; patents, brand names and product quality generate future economic benefit. Although intangible assets are not captured on the balance sheet, they are beneficial for commercialization and product development—goodwill, an intangible investment that accounts for excess purchase price for firms. Brand recognition is also an asset for a company that is not easily quantifiable but has a significant impact on sales output.

In addition to the above, linkages externally contribute massively to technological innovation. Linkages with expert scientists to develop technological discoveries have contributed massively to SME development in an ecosystem (Sousa et al., 2020). Researchers explore new opportunities and build quality assurance through high-tech innovations to give them a competitive advantage over their rivals. Inability to link with research institutions can negatively impact the SME development process, disadvantaging it to compete globally. Again, companies can explore the market through research by engaging with design consultants. These design consultants conduct market research by factoring in product design that will appeal to consumers. According to Odei et al. (2020), using external consultants’ services will enhance innovativeness, improving the market performance. Collaboration with external consultants will determine which external inputs and the outcome of their research will be taken to the market.

Again, the intellectual asset is likely to influence technological innovation. The judiciary service in Europe has been reliable and provided legal protection against the infringement of intellectual property rights. It has implemented competition laws and enforced contractual obligations between parties, which is considered a stronger factor that gives businesses some protection (Farley & Isaacs, 2020). Most SMEs now are confident in dealing with third parties as they have massive support from the government to deal with any disagreement from consumers and intermediaries. Copyright protection laws have made intellectuals confident in delivering quality research as their innovation activities are protected under law.

Moreover, employment impact is likely to affect SME’s technological innovation. Employment positively impacts technological innovation because it will increase competition, and firms will have higher efficiency with quality products at a lower cost. Companies will be able to use most of their resources more efficiently with good employee training. A proactive approach gives the business a more significant attraction to customers and potential qualified, skilled employees. Staff would like to work in innovative and challenging jobs promoting team building and solving complex problems with improved technology. Employees are one of the most productive assets if an enabling environment is created through innovative thinking and action. This inspires employees to share experience, knowledge, skills, and recommendations. Management needs to reward employees for influencing the direction of their business as this compels employees to share ideas by working towards the success of their ideas and encouraging productivity. The SME market in an ecosystem receives a massive boost when recruiting skilled, semi-skilled and unskilled (Grugulis, 2018).

Lastly, sales contribute to technological innovation within an ecosystem. SMEs are increasing in revenue due to attractive product branding and packaging; this has compelled customers to buy more improved versions of existing or new products. The outcome of a successful innovation helps firms to gain a competitive advantage and maintain a technological advantage over their competitors in the market (Martin-Rojas et al., 2019). As a result, SMEs would realize a maximum output due to their innovated products in their total sales impact. Hence, sales impact is a significant factor in technological innovation within a given ecosystem.

Figure 1.

Source: The authors.

Research Question

European countries desire to gain different ideas required to sophisticate its economy. This involves investment in strategic sectors across different regions in Europe and adopts different technological innovations to enhance the smooth operation of businesses within the region. This research argues some useful determinants that result in technological innovation through research and development. However, this collaboration would involve strategic management decisions that encourage technological innovation. Therefore, this article answers the question: which factors play a key role in technological innovation?

Data and Methodology

Data for the empirical analysis was from the European innovation survey (EIS) conducted between 2011–2018. Panel data was used to analyse the innovation performance to overcome potential outliers that could affect the robustness of the data and the capture over a wide period the contributory variables and their impact in the technological innovation environment. The final sample involved 296 SMEs across the European regions. The EIS a database officially run by the European Commission and provides variables for comparative innovation measurement scores and analysis for all EU-28 member states. This data comprises an annual analysis of the SMEs sector across the European region and reveals other factors that drive innovation performance. The survey also collects information on sustainable policies that enhance innovation performance and inform policymakers on the rapid changes to technological innovation across the globe. These variables have been used by Azar and Ciabuschi (2017) to analyse technological innovation among Swedish export ventures. However, this study would assess 28 member states to identify how enabling an environment free from crime, theft, disorder across different countries would contribute to technological innovation outcomes. The study subsequently used the OLS to develop a relationship between innovators and the independent variables. The OLS is a linear model used to estimate the regression parameters in a linear model by minimizing the sum of the squared residuals, and its outcome is a linear function. The OLS is preferred because of the dependent variable’s continuous nature and the independent variables that represent the line of best fit. Innovators were posited as the dependent variable for all innovation classes for the analysis. Product, process, market and organizational innovation make up the composite of innovators. Several researchers have used these variables to study technological innovation in the past, including Azar and Ciabuschi (2017). The result is interpreted using the straight-line equation in case the relationship among the variables is linear. The OLS may also be used to test multiple independent variables and dependent variables. The Ordinary least square has been used by Cincera and Santos (2018) to investigate the drivers that stimulate eco-innovation and how European firms access finance for development. The model was chosen because of its continuous response variables and continuous independent outcomes that represent the line of best fit using the formula of a straight line provided by Craven and Islam (2011)

y = α + β x i + ε

(1)

Where, y = innovators α & β = constants

xi = Independent variables

ε = Error term

Estimation Methods and Identification

The instrumental variables (IV) estimation technique using the two-stage least squares (2SLS) is used to estimate the innovator’s outcomes to access the factors that result in technological innovation. First, simultaneity issues concerning the model are addressed using the instrumental variable approach. Finance is considered endogenous based on the extant literature on innovation. Second, it addresses the errors in measuring the variables (Zhou et al., 2014). In order to estimate instrumental variables, the instruments involved should be able to explain the disparities in the instrumented variables. However, there is an uncorrelation between the error term and the instruments variables involved (Maydeu-Olivares et al., 2020). Although the instrumental variable approach provides accurate estimates and solves simultaneity issues, omitted variable bias and measurement errors, its reliability depends on the validity and the choice of the instruments used (Güntner et al., 2020). One major issue in using the IV estimation is obtaining a strong instrument that explains the disparities in the instrumented variables but is uncorrelated with the error term. Bad instruments may result in unreliable estimates than the conventional OLS (Andrews et al., 2019).

Results

Furthermore, about 47% of these SMEs invested in marketing or process innovation. Regarding intellectual assets, around 35% of firms invested in activities such as technology acquisitions and product branding. The mean results for linkages, about 42%, show that SMEs collaborated with research and development experts from other institutions. Again, about 49% of these SMEs had a significant employment impact which contributed to innovators. Lastly, the sales impact of the SMEs involved in this survey was around 49% which had a huge impact on innovators.

Table 1.

Correlation Matrix.

	1	2	3	4	5	6	7	8	9	10
Human resources [1]	1.000
Research systems [2]	0.845	1.000
Innovation-friendly environment [3]	0.710	0.736	1.000
Finance and support [4]	0.730	0.781	0.702	1.000
Firm investment [5]	0.584	0.518	0.418	0.472	1.000
Innovators [6]	0.511	0.709	0.482	0.608	0.577	1.000
Linkages [7]	0.774	0.7551	0.666	0.722	0.686	0.635	1.000
Intellectual asset [8]	0.633	0.7054	0.586	0.589	0.509	0.551	0.572	1.000
Employment impact [9]	0.542	0.5213	0.394	0.405	0.386	0.258	0.410	0.469	1.000
Sales impacts [10]	0.360	0.4829	0.158	0.368	0.387	0.433	0.364	0.449	0.444	1.000

Source: The authors.

The results show that about 43% of these SME firms have the necessary human resources to facilitate the smooth operation of their business. Furthermore, around 40% of these SMEs within an ecosystem have a well-established research system that helps in providing new ideas for the firm’s survival. Again, about 43% of these firms operate in an innovation-friendly environment that addresses technological innovation’s social and cultural determinants. It can also be seen that financial support system which is about 42% is needed for the daily operation of the business.

Table 2.

Descriptive Statistics.

Constructs	Min	Max	Mean	Std. Deviation
Human resources	0.064	0.908	0.431	0.204
Research systems	0.026	0.983	0.403	0.262
Innovation-friendly environment	0.021	1.000	0.431	0.241
Finance and support	0.004	0.904	0.420	0.213
Firm investments	0.050	1.000	0.465	0.186
Innovators	0.000	0.996	0.500	0.237
Linkages	0.010	0.795	0.415	0.217
Intellectual assets	0.022	0.810	0.353	0.214
Employment impacts	0.000	1.000	0.498	0.220
Sales impacts	0.149	0.879	0.490	0.166

Source: The authors.

Table 3.

Results of the Regression Analysis.

Model	Unstandardized Coefficients		Standardized Coefficients	t	Sig.
Model	B	Std. Error	Beta	t	Sig.
1 (Constant)	.194	.033		5.793	.000
Human resources	–.585	.086	–.503	–6.768	.000
Research systems	.744	.076	.825	9.840	.000
Innovation-friendly environment	–.078	.058	–.080	–1.347	.179
Finance and support	.163	.067	.147	2.437	.015
Firm investments	.413	.064	.325	6.473	.000
Linkages	.156	.073	.143	2.124	.034
Intellectual assets	.059	.057	.054	1.042	.298
Employment impacts	–.166	.047	–.155	–3.548	.000
Sales impacts	.059	.065	.041	.909	.364

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.815^a	.664	.654	.139234

Source: The authors.

Note: ^aDependent Variable: Innovators.

Table 4.

Result of the Wu-Hausman Test for Endogeneity.

Innovators	Coef.	Std. Err.	z	P>\|z\|
Finance and support	0.299312	0.1806384	1.66	0.098
Human resources	–0.6555831	0.0917753	–7.14	0.000***
Research systems	0.6988547	0.1048967	6.66	0.000***
Sales impacts	0.0284203	0.0588731	0.48	0.629
Firm investments	0.470203	0.0573547	8.20	0.000***
_cons	0.1430487	0.0369677	3.87	0.000***
Model fit summary
R2	0.6746
Prob > F	0.000***
F	20.6381

Source: The authors.

Note: *** significant at 1%.

Wu-Hausman F (1, 289) = 0.390598 (p = 0.5325)

We anticipate that this result could change the outcome with a potential negative impact on the results. We therefore tested further which of the variables used can be considered endogenous to test for exogeneity. We ran an instrumented variables (IV) OLS model using Wu-Hausman test for potential endogeneity in our data. We used financial support as the dependent variable that shows the model’s predictive power is 0.675, which means that the combined effect of the covariates can predict the outcomes whilst all other variables used in Table 3 were instrumented.

Interestingly, the coefficient of all variables turns out to be highly significant and with a positive sign except for human resources and sales impact, which had a negative sign and not statistically significant, respectively. This result confirms the models using innovators during the seven years, although with lower significance.

The results of the OLS estimates are consistent, and all the instruments used are valid. Therefore, the Wu-Hausman test of endogeneity found that p >.53 is statistically insignificant at a 95% confidence interval. Hence, we declare that the study has no endogeneity issues, which strengthens the robustness of the models (Hult et al., 2018). Having confirmed the robustness of the data used for the analysis and its validity for the purpose of this study, the objective of the study can be fulfilled. The results of the Wu-Hausman test prompt some considerations regarding the possible impact of innovators in creating technological innovation on a long-term basis.

The results of the Table 5 show that the marginal effects demonstrate the average change in probability when the independent variables increase by one unit.

Table 5.

Fixed Effects Parameter Estimates.

Innovators	Coef.	Std. Err.	P>\|t\|	dy/dx	Marginal Effect Std. Err.	P>\|t\|
Human resources	–0.585	0.086	0.000***	–0.585	0.086	0.000***
Research systems	0.744	0.076	0.000***	0.744	0.076	0.000***
Innovation friendly environment	–0.078	0.058	0.179	–0.078	0.058	0.179
Financial support	0.163	0.067	0.015**	0.163	0.067	0.015*
Firm investments	0.413	0.064	0.000***	0.413	0.064	0.000***
Linkages	0.156	0.073	0.034*	0.156	0.073	0.034*
Intellectual assets	0.059	0.057	0.298	0.059	0.057	0.298
Employment impacts	–0.166	0.047	0.000***	–0.166	0.047	0.000***
Sales impacts	0.059	0.065	0.364	0.059	0.065	0.364
_constant	.1938039	.0334569	0.000***
Model Summary
R Square Observations P valueF(9, 286)	0.6642960.00162.93

Source: The authors.

Note: * significant at 10%. ** significant at 5%. *** significant at 1%.

Table 5 presents the results of the econometric model. First, Table 5 shows that the model’s predictive power is 0.664, which means that the combined effect of the covariates can predict innovators (dependent variable), which is used to measure technological innovation by approximately 66%. The model to have substantial predictive accuracy (see Cohen, 1988). The results show a negative but statistically significant relationship between human resources and innovators, with a coefficient of –0.585. There is a positive and significant relationship between the research system and innovators, with a coefficient of 0.744. Again, the result has shown a negative and insignificant relationship between innovation-friendly environment and innovators, with a coefficient of –0.078.

Furthermore, there is a positive and statistically significant relationship between financial support and innovator, with a coefficient of 0.163. As shown in the Table 5, the results point to a positive and meaningful relationship between firm investment and innovators, with a coefficient of 0.413. Furthermore, there is a positive and significant relationship between linkages and innovators with a coefficient of 0.156. There is a positive but insignificant relationship between intellectual assets and innovators with a coefficient of 0.059. Last but not least, there is a negative but statically significant relationship between employment impact and innovators with a coefficient of –0.166. Lastly, there is a positive but statistically insignificant relationship between sales impact and innovators, with a coefficient of 0.059.

Discussion

The results of the empirical analysis have shown that the availability of an effective human resource supports technological innovation in an ecosystem. The study found that effective human resources contribute significantly to technological innovation, an internal decision within the firm. Human resources, not products, are the firm’s innovative major asset. Most innovative firms implement a mixed human resource management strategy with employees’ competent skills to boost technological innovation. Firms carefully access personnel needs and hire creative individuals to meet organizational goals set by management. SME operators put a good reward system and have an effective performance appraisal system that boosts employee creativity. Our result shows that skilled information communication technologists’ availability is an important factor that drives innovators. Our results have again shown that research scientists’ availability has sprung up new strategies and models in speedily conducting businesses. Competent employees through in-house training are likely to adopt new ideas that will contribute to the firm’s growth because knowledge acquired will be blended with traditional working methods which do not employ new technology. Furthermore, effective human resource support will have an additional marginal impact on innovation. This supports other research by Kanda et al. (2019), whose work concluded that internal activities by human resource supports technological innovation.

Again, this study found out that the research system has positively contributed to technological innovation within an ecosystem in the European region. The research system serves as an innovation hub for nurturing new innovators by providing a working office and incubation support for economic development. This means that an effective research system identifies problems in social science research and design the best tool for solving complex issues through simplified technology and building the right partnership for businesses. An effective research system comes up with a solution to known or sometimes to undefined needs of the company through brainstorming ideas, product development research and test concepts within a firm and its market. This helps boost sales and increase profitability among firms as they can develop products to suit customers’ specifications and needs. The research system also helps the company find new business partnerships, attract external finance, and access new supply chains. A company’s research system allows it to compete on the open new market—both in Europe and overseas; this enhances the firm’s reputation as an innovative business. If a firm invests heavily in its research system, it will have an additional marginal impact on innovation. Our result affirms a similar study by Muscio and Ciffolilli (2020), who discovered that research systems drive firms to adopt technology.

Furthermore, the study found a negative effect of an innovation-friendly environment on innovators. An innovation-friendly environment aims to protect innovation activities and improve environmental protection through new production processes, new management, and new products. Suppose a society avoids norms, procedures, and rules that promote innovation. In that case, there is an increasing probability that SME operators will not have the necessary conditions that can promote innovation activities. When oriented towards collectivism, society is more likely to promote inter-functional innovation and ensure cooperation among firms across different sectors.

The preference for innovation could only exist when there is a positive relationship between cultural values and collective orientation among societal members to boost cross-sector technological innovation through cooperation among firms. If a society does not accept the challenges that arise from the rules and norms during power distribution from authorities, this can limit innovation activities. Therefore, factoring cultural values and collectivism among societal members and SME managers throughout technological innovation’s lifecycle is key for development. In order to overcome the global sustainability challenges and make Europe leaders for promoting innovation-friendly trade more competitive, there is a need to follow clear guidelines with cultural values inclusion and implement sustainable policies of business trading. A firm could operate in an innovation-friendly environment but will not have an additional marginal impact on innovation. However, our results have shown that there can be a hostile environment with effective policies and cultural values to facilitate technological innovation. Still, if societal members and management fail to follow this, their efforts will be insignificant. Again, if a society fails to factor in the importance of rules and regulations in their daily operations, it will negatively affect an innovation-friendly environment. It could limit firms from investing heavily in their business operations. This result affirms research conducted by (Bitencourt et al., 2020), whose study found that non-friendly environment does not permit innovation.

The study found that financial support is also a key determinant for technological innovation. More funds are needed to purchase enough resources for expansion in technological innovation. Firms need funds to find innovative ways of solving policy issues and creating integrated communities. Financial resources enable the firm to recruit competent resource personnel to contribute their knowledge and expertise to its development and growth. Inadequate funds could hinder the firm from outsourcing qualified and competent resource personnel. Financial support is of crucial importance in the purchase of new softwares and sophisticated machinery for technological innovation. Trained employees and expert scientists use this sophisticated machinery during product innovation. Firms’ receiving financial support leads to higher sales turnover and productivity, and thus there is a need for firms to constantly collaborate with academic institutions and financial sectors to invest in technological innovation heavily. A company could have an additional marginal impact on innovation if it increases financial support effectively. However, our findings affirm a similar study conducted by Donald (2020), who affirms that funds are needed for technological innovation. Another study by Mina et al. (2021) affirms that funds from external sources have increased firms’ growth across Europe.

Again, this study found that firm investment plays a crucial role in technological innovation. Firms invest in innovation to reduce costs or gain greater market share to become more productive. For many SME firms, innovation is very important as consumer demand has become more complicated, and competition has been keen. Our results indicate that firms, on average, invest about 47% of their turnover on various innovation-related activities, and these firms benefit from public sector collaboration programs that promote innovation activities. This collaboration has made companies invest in human capital, research, and development to commercialize their invention from new ideas gained from training programs and research activities. The result of the marginal effect means that if companies increase firm investment, it will have an additional marginal impact on innovation. This buttress Aboal and Garda (2016), claim that firm’s investment in research and development are main drivers for technological innovation.

This study found out that linkages with expert researchers drive technological innovation. From our results, R&D accounted for half of all innovation expenditure, but the share depends on the sector and firm size. Total expenditure on innovation includes intramural and extramural R&D, which results in the acquisition of patents, trademarks, and software needed to commercialize their innovation. The result has again shown that SME firms collaborating with academic institutions would benefit the business world as they will develop product development and new innovative technology that will aid in selling their products. Furthermore, if you increase linkages, it will have an additional marginal impact on innovation. This result affirms a similar study by Zygmunt (2019) that external linkages play a vital role in adopting technological innovation.

The result of the study again found out that intellectual asset does not contribute to SME technological innovation. Intellectual asset helps individual or firms that hold patent has the right to prevent others from imitating their innovation or importing their ideas. This creates an opportunity for researchers to sell or commercialize their inventions. However, our result has indicated that to obtain a patent, an inventor needs new technology and must comply with the national intellectual property (IP) laws which may differ from one country to another. Maintaining the IP laws and acquiring licensing for innovation are complicated, and certain guidelines and rules must be followed. However, our result is shocking as intellectual assets do not come up with new ideas through technology transfer. Intellectuals from both vocational and tertiary institutions will contribute their quota by providing new skills to aid firms in adopting new technology in the daily running of the firm. Again, if a firm increases intellectual assets, it will not have any additional impact on innovation. Most of the resource persons recruited would not have the needed skill to be innovative unless they are well trained for productivity and efficiency. This contradicts similar findings by Cunningham et al. (2020), whose results found that intellectual asset positively impacts technological innovation.

The study found out that employment impacts contributed to technological innovation. Studies have discovered that the influx of technological innovation has contributed to a decline in the manufacturing sector and led to unemployment in many advanced economies. Although process innovation can be job-destruction, it prevents companies from hiring new resource personnel because of the simplified technology used during process innovation. However, our results have demonstrated that process innovation requires the service of an expert scientist with adequate information about the business and well experienced enough to have an immediate impact on the successful operation of the business. The result means that unskilled training personnel and recruiting experienced resource personnel significantly influence technological innovation. When employees are constantly trained, new knowledge and ideas will be shared for the successful transformation of the business. Both skilled and unskilled resource personnel can contribute to technological innovation, and our result affirms this. The marginal impact on innovation means that if the company recruits competent and qualified employees, the firm will increase technological innovation. Similar research was conducted by Balsmeier and Woerter (2019), who found that employment impact had a positive influence on technological innovation. On the contrary, a similar study by Totterdill (2020) demonstrated that high levels of employee involvement impact business transformation.

Lastly, the study found out that sales impact negatively impacted technological innovation. Sales innovation describes tips and tricks that sprung up new ideas. Sales impact helps firms to break bureaucracy and rebrand new sales strategies. Firms can reinvest profit from sales into the acquisition of new machinery, assisting in process innovation. New technologies may track this, which will help customers provide constructive feedback, and management can also implement new strategies to improve their product. However, our results have demonstrated that firms utilizing technology are unlikely to impact sales output because technological innovation could result in product or process innovation. Customers will patronize a product not because of the product branding but the quality of the product and its benefit to the general public. The marginal impact of sales does not have any additional impact on technological innovation. However, Falk and Hagsten’s (2018) study contradicts that a massive impact on sales was technological innovation. Conversely, a study by Mahmutaj and Krasniqi (2020) affirms that sales impact results in the process, organizational and market innovation in a survey conducted in Kosovo.

Conclusion

This article’s main objective is to empirically investigate the causal relationship between innovators and factors driving technological innovation among small and medium-sized enterprises (SMEs). The most substantial positive influence on innovators is human resources, research systems, finance and support, firm investments, linkages and employment impacts. The least determinant factor which was insignificant was the innovation-friendly environment and intellectual assets. We found that human resources strategically build up firm internal capability by setting up new employees who are well trained. In addition, research systems attract external experts to undertake innovations through product marketing to increase firms’ total sales. Funding is key to encouraging innovative start-ups and enabling SMEs to go a long way to adopt technological innovation. Again, firm investment contributes to growth and development as it provides a competitive advantage over its competitors. Linkages will give innovative solid ideas and develop new product branding to compete across local and international boundaries. Given these factors the necessary attention, employment impacts will help transform the SME sector through effective recruitment, which would help strengthen the administration of the business. Innovation friendly environment and intellectual assets provide constructive feedback as to whether innovation can exist within an ecosystem with the help of intellectual assets.

These findings have significant implications for policy formulation. They argue that the central concern is the absorption and adaptation of the knowledge with the long-term aim to achieve technological innovation. Our findings support the idea that there is a need for European SMEs to promote learning and knowledge absorption through policy support with innovative activities. Learning technological innovation processes within the European context consists of interaction that strengthens the interrelationships among different sectors, including manufacturers and service providers. Europe is considered the hub for technological innovation. Therefore, forming an exclusive innovation policy with the help of academic institutions is paramount to facilitating innovation culture for promoting the firm’s competitiveness.

We recommend future research should explore some governmental policies that drive innovations. Again, this study can also explore funding policies and limitations in patent acquisition. Knowledge of this will broaden the understanding of why most firms are unwilling to shift from their traditional production method and adapt simplified technology.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to acknowledge the Internal Grant Agency of the Faculty of Management & Economics of Tomas Bata University in Zlin for providing financial support to carry out this research. Funding was extended through: TBU No. IGA/FaME/2021/004 and IGA/FaME/2021/006.

References

Aboal

, & Garda

(2016). Technological and non-technological innovation and productivity in services vis-à-vis manufacturing sectors. Economics of Innovation and New Technology , 25(5), 435–454. https://doi.org/10.1080/10438599.2015.1073478

Al-Jayyousi

O. R.

(2017). Integral innovation: New worldviews . Taylor & Francis.

Andrews

, Stock

J. H.

, & Sun

(2019). Weak instruments in instrumental variables regression: Theory and practice. Annual Review of Economics , 11(1), 727–753. https://doi.org/10.1146/annurev-economics-080218-025643

Aydalot

, & Keeble

(Eds.). (2018). High technology industry and innovative environments: The European experience (Vol. 3). Routledge.

Azar

, & Ciabuschi

(2017). Organizational innovation, technological innovation, and export performance: The effects of innovation radicalness and extensiveness. International Business Review , 26(2), 324–336. https://doi.org/10.1016/j.ibusrev.2016.09.002

Balsmeier

, & Woerter

(2019). Is this time different? How digitalization influences job creation and destruction. Research Policy , 48(8). https://doi.org/10.1016/j.respol.2019.03.010

Bitencourt

C. C.

, de Oliveira Santini

, Zanandrea

, Froehlich

, & Ladeira

W. J.

(2020). Empirical generalizations in eco-innovation: A meta-analytic approach. Journal of Cleaner Production , 245, 118721. https://doi.org/10.1016/j.jclepro.2019.118721

Cincera

, & Santos

(2018). D3.3. Integration in the eco-system WP 3- innovation and access to finance . Deliverable 3.3. included in the project Investigating the impact of Innovation Union (I3U) [Mimeo].

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed). Lawrence Erlbaum Associates. http://www.utstat.toronto.edu/∼brunner/oldclass/378f16/readings/CohenPower.pdf.

10.

Cohen

W. M.

, & Levinthal

D. A.

(1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly , 35(1), 128–152. https://doi.org/10.2307/2393553

11.

Craven

B. D.

, & Islam

S. M.

(2011). Ordinary least-squares regression (pp. 224–228). Sage Publications.

12.

Cunningham

J. A.

, Romano

, & Nicotra

(2020). A European perspective on intellectual property and technology transfer. In Rooksby

J. H.

(Ed.), Research handbook on intellectual property and technology transfer . Edward Elgar Publishing.

13.

Damaskopoulos

, & Evgeniou

(2003). Adoption of new economy practices by SMEs in Eastern Europe. European Management Journal , 21(2), 133–145. https://doi.org/10.1016/S0263-2373(03)00009-4

14.

Donald

D. C.

(2020). Smart precision finance for small businesses funding. European Business Organization Law Review , 21(1), 199–217. https://doi.org/10.1007/s40804-020-00180-1

15.

Falk

, & Hagsten

(2018). Employment impacts of market novelty sales: Evidence for nine European countries. Eurasian Business Review , 8(2), 119–137. https://doi.org/10.1007/s40821-017-0098-0

16.

Farley

R. M.

, & Isaacs

D. H.

(2020). Patents for power: Intellectual property law and the diffusion of military technology . University of Chicago Press

17.

Ferreira

J. J. M.

, Fernandes

, & Ratten

(2019). The effects of technology transfers and institutional factors on economic growth: Evidence from Europe and Oceania. Journal of Technology Transfer , 44(5), 1505–1528. https://doi.org/10.1007/s10961-019-09730-3

18.

Gimenez-Fernandez

E. M.

, Sandulli

F. D.

, & Bogers

(2020). Unpacking liabilities of newness and smallness in innovative start-ups: Investigating the differences in innovation performance between new and older small firms. Research Policy , 49(10), 104049. https://doi.org/10.1016/j.respol.2020.104049

19.

Gnyawali

D. R.

, & Park

B. J.

(2009). Co-opetition and technological innovation in small and medium-sized enterprises: A multilevel conceptual model. Journal of Small Business Management , 47(3), 308–330. https://doi.org/10.1111/j.1540-627X.2009.00273.x

20.

Grzelak

, Guth

, Matuszczak

, Czyżewski

, & Brelik

(2019). Approaching the environmental sustainable value in agriculture: How factor endowments foster the eco-efficiency. Journal of Cleaner Production , 241, 118304. https://dx-doi-org.web.bisu.edu.cn/10.1016/j.jclepro.2019.118304

21.

Güntner

A. V.

, Klonek

F. E.

, Lehmann-Willenbrock

, & Kauffeld

(2020). Follower behavior renders leader behavior endogenous: The simultaneity problem, estimation challenges, and solutions. Leadership Quarterly , 31(6), 101441. https://doi.org/10.1016/j.leaqua.2020.101441

22.

Hofstede

(1982). Cultural pitfalls for Dutch expatriates in Indonesia . TG International, Management consultants.

23.

Hofstede

(1985). The interaction between national and organizational value systems[1]. Journal of Management Studies , 22(4), 347–357. https://doi.org/10.1111/j.1467-6486.1985.tb00001.

24.

Holzer

J. M.

, Adamescu

C. M.

, Cazacu

, Díaz-Delgado

, Dick

, Méndez

P. F.

, Santamaría

, & Orenstein

D. E.

(2019). Evaluating transdisciplinary science to open research- implementation spaces in European social-ecological systems. Biological Conservation , 238, 108228. https://doi.org/10.1016/j.biocon.2019.108228

25.

Horváth

, & Szabó

R. Z.

(2019). Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities? Technological Forecasting and Social Change , 146, 119–132. https://doi.org/10.1016/j.techfore.2019.05.021

26.

Hult

G. T. M.

, Hair

J. F.

, Proksch

, Sarstedt

, Pinkwart

, & Ringle

C. M.

(2018). Addressing endogeneity in international marketing applications of partial least squares structural equation modeling. Journal of International Marketing , 26(3), 1–21. https://doi.org/10.1509/jim.17.0151

27.

Kanda

, del Río

, Hjelm

, & Bienkowska

(2019). A technological innovation systems approach to analyse the roles of intermediaries in eco-innovation. Journal of Cleaner Production , 227, 1136–1148. https://doi.org/10.1016/j.jclepro.2019.04.230

28.

Lenihan

, McGuirk

, & Murphy

K. R.

(2019). Driving innovation: Public policy and human capital. Research Policy , 48(9), 103791. https://doi.org/10.1016/j.respol.2019.04.015

29.

Mahmutaj

L. R.

, & Krasniqi

(2020). Innovation types and sales growth in small firms: Evidence from Kosovo. South-East European Journal of Economics and Business , 15(1), 27–43. https://doi.org/10.2478/jeb-2020-0003

30.

Martin-Rojas

, Garcia-Morales

V. J.

, & Gonzalez-Alvarez

(2019). Technological antecedents of entrepreneurship and its consequences for organizational performance. Technological Forecasting and Social Change , 147, 22–35. https://doi.org/10.1016/j.techfore.2019.06.018

31.

Maydeu-Olivares

, Shi

, & Fairchild

A. J.

(2020). Estimating causal effects in linear regression models with observational data: The instrumental variables regression model. Psychological Methods , 25(2), 243–258. https://doi.org/10.1037/met0000226

32.

Meijer

L. L. J.

, Huijben

J. C. C. M.

, Van Boxstael

, & Romme

A. G. L.

(2019). Barriers and drivers for technology commercialization by SMEs in the Dutch sustainable energy sector. Renewable and Sustainable Energy Reviews , 112, 114–126. https://doi.org/10.1016/j.rser.2019.05.050

33.

Mina

, Di Minin

A. D.

, Martelli

, Testa

, & Santoleri

(2021). Public funding of innovation: Exploring applications and allocations of the European SME instrument. Research Policy , 50(1), 104131. https://doi.org/10.1016/j.respol.2020.104131

34.

Muscio

, & Ciffolilli

(2020). What drives the capacity to integrate industry 4.0 technologies? Evidence from European R&D projects. Economics of Innovation and New Technology , 29(2), 169–183. https://doi.org/10.1080/10438599.2019.1597413

35.

Odei

M. A.

, & Amponsah Odei

(2020). The mediating effect of firm’s R&D collaborations on their innovative performance. Ekonomski Pregled , 71(5), 493–511.

36.

Odei

M. A.

, & Novak

(2020). Appraisal of the factors contributing to European small and medium enterprises innovation performance. Problems and Perspectives in Management , 18(2), 102–113.

37.

Odei

M. A.

, Odei

S. A.

, & Novak

(2020). Demystifying the factors contributing to successful process innovations in the Czech automotive industries. Journal of Economic Studies , 29(1).

38.

Odei

S. A.

, Odei

M. A.

, & Anderson

(2020). Consultants and firm-level innovation performances: A doubly robust estimation approach. Eastern Journal of European Studies , 11, 288–311.

39.

Odei

M. A.

, Amoah

, & Jibril

A. B.

(2021, September). External factors influencing SME’s innovation outcomes in Visegrad countries: A document analysis. In European Conference on Innovation and Entrepreneurship (pp. 15–17). Academic Press Conferences International Limited.

40.

Odei

S. A.

, & Stejskal

(2018). The influence of knowledge sources on firm-level innovation: The case of Slovak and Hungarian manufacturing firms. Central European Business Review , 7(2), 61.

41.

Pianta

, Lucchese

, & Nascia

(2020). The policy space for a novel industrial policy in Europe. Industrial and Corporate Change , 29(3), 779–795. https://doi.org/10.1093/icc/dtz075

42.

Romer

P. M.

(1994). The origins of endogenous growth. Journal of Economic Perspectives , 8(1), 3–22. https://doi.org/10.1257/jep.8.1.3

43.

Schumpeter

J. A.

(2013). Capitalism, socialism and democracy . Routledge.

44.

Solow

R. M.

(1956). A contribution to the theory of economic growth. Quarterly Journal of Economics , 70(1), 65–94. https://doi.org/10.2307/1884513

45.

Sousa

M. J.

, Mercadé Melé

, & Molina Gómez

(2020). Technology, governance, and a sustainability model for small and medium-sized towns in Europe. Sustainability , 12(3), 884. https://doi.org/10.3390/su1203088

46.

Timmermans

, Blok

, Braun

, Wesselink

, & Nielsen

R. Ø

. (2020). Social labs as an inclusive methodology to implement and study social change: The case of responsible research and innovation. Journal of Responsible Innovation , 7(3), 410–426. https://doi.org/10.1080/23299460.2020.1787751

47.

Totterdill

(2020). Workplace innovation Europe CLG (pp. 26–29). https://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/lums/work-foundation/reports/PeterTotterdillFinalspring2020forpublication.pdf

48.

Xie

, & Wang

(2020). How can open innovation ecosystem modes push product innovation forward? An fsQCA analysis. Journal of Business Research , 108, 29–41. https://doi.org/10.1016/j.jbusres.2019.10.011

49.

Yurdakul

, & Kazan

(2020). Effects of eco-innovation on economic and environmental performance: Evidence from Turkey’s manufacturing companies. Sustainability , 12(8), 3167. https://doi.org/10.3390/su12083167

50.

Zhou

, Faff

, & Alpert

(2014). Bias correction in the estimation of dynamic panel models in corporate finance. Journal of Corporate Finance , 25, 494–513. https://doi.org/10.1016/j.jcorpfin.2014.01.009

51.

Zygmunt

(2019). External linkages and intellectual assets as indicators of firms’ innovation activities: Results from the Czech Republic and Poland. Oeconomia Copernicana , 10(2), 291–308. https://doi.org/10.24136/oc.2019.015