New Evidence of the Impact of Innovative Capacity on Firm Employment

Abstract

The business behavior towards innovation and its impact on the creation of new jobs of 113 firms in the Valencian Community (Spain) were analyzed between 2014 and 2020. The sample included manufacturing, services and trading enterprises; technological and non-technological companies; micro-, small, medium, and large enterprises; and locations across the entire geographical extent of the Valencian Community. The firms were divided into quartiles based on their level of employment growth, linearly correlating this variable with 14 innovation indicators for each of the resulting four groups, reflecting the innovative capacity of these firms. It was found that the factor linked to innovation capacity that most favors or drives business employment creation is co-operation with other companies, as it had a direct and significant relationship with the two highest growth groups (quartiles 4 and 3), with no negative relationship with either of the two lower growth groups (quartiles 2 and 1). This suggests that the public administration should increase efforts to create spaces for the exchange of ideas between companies and organizations to reduce unemployment levels. Overall, this study provides new insights into the subject, and its findings lead to the conclusion that firms with higher innovative capacity create more jobs.

1. Introduction

The impact of innovation in companies on employment is a complex issue, which has been extensively studied to gather precise information on the topic. While some studies indicate a positive effect of innovation on employment, others suggest less optimistic outcomes (Dachs and Peters 2014; Harrison et al. 2014; Kunapatarawong and Martínez-Ros 2016; Zhu et al. 2021). Therefore, innovation is defined as the sum of necessary steps for the successful development and commercialization of new or improved products, as well as innovative processes or business concepts. This includes everything from the generation of new ideas to their effective implementation in the market. According to the OECD and Eurostat, innovation encompasses not only technical and scientific aspects but also commercial and financial ones (OECD and Eurostat 2005; Neely and Hii 2014).

A company’s ability to innovate, in turn, refers to its potential to develop, incorporate, or launch innovative outcomes. This capacity crucially depends on the resources and capabilities that the company can mobilize, including its organizational culture, the internal processes it adopts, and the external environment in which it operates. Most published studies predict a positive impact of innovation on employment, though there are notable exceptions.

For instance, Harrison et al. (2014), in a study conducted between 1998 and 2000 with a sample of around 20,000 companies from France, Germany, Spain, and the UK, observed that process innovation does not necessarily reduce the number of employees if old products are maintained and their sale promoted through a price reduction strategy. They argue that product innovation can create new jobs in the manufacturing of these products, offsetting job losses due to the replacement of old products. This finding applies to companies in both the manufacturing and service sectors. In contrast, Dachs and Peters (2014), in a similar study conducted between 2002 and 2004 on more than 64,000 companies in 16 European Union countries, found that process innovation and the resulting increase in productivity generally led to a reduction in employment. This phenomenon was more pronounced in foreign-owned firms than in national state-owned companies.

Another interesting case is the study by Kunapatarawong and Martínez-Ros (2016), conducted between 2007 and 2011 in Spain, with a sample of over 5000 companies. They concluded that green innovation, which involves new or modified processes, techniques, practices, systems, and products to avoid or reduce environmental harm, has a positive impact on employment. This relationship is even more significant when the innovation is introduced voluntarily by companies and when they report an increase in the importance they assign to it, especially in industries considered as highly polluting.

In contrast, Zhu et al. (2021) found that, in companies implementing product innovation, employment growth is hindered or impeded due to productivity effects.

From the above arguments we can derive the main research problem to be addressed and the research questions to be answered by this study.

The primary research problem addressed in this study is to identify the specific characteristics of intermediate development regions concerning their firm’s strategy towards innovation and their impact on their overall performance, measured in terms of employment.

Based on the results and conclusions of our empirical analysis, we will hopefully derive a set of proposals and recommendations to firms located in intermediate developed regions, as well as to identify the categories of firms most likely to obtain greater advantages and benefits from the innovations they develop. Additionally, we expect to contribute to the determination of what strategies concerning innovation should be prioritized in order to maximize their impact on job creation.

Research question 1 (RQ1)

Do highly innovative companies create more jobs than less innovative ones?

Research question 2 (RQ2)

Is innovation a determining factor for job creation in companies?

If so,

Research question 3 (RQ3)

In which company profiles does innovation have a greater impact?

Research question 4 (RQ4)

What factors related to innovation capacity favor or drive business employment creation?

Thus, we aim to analyze to what extent business behavior towards innovation affects the performance of companies, translated into employee growth, over a period of six years, from 2014 to 2020, in firms in the Valencian Community (Spain). This location is of special interest due to its intermediate position among European regions in terms of the innovation capacity indicator (Hollanders and Es-Sadki 2021). These companies were distributed in quartiles according to their level of employment growth. We seek to identify the indicators or factors related to innovation that drive or favor the employment growth of companies. To do this, we turn to the types of innovation outlined in the Oslo Manual of 2005 (OECD and Eurostat 2005). Finally, each group (quartile) was modeled using a linear correlation to carry out a qualitative analysis.

The originality of this study lies in its multidimensional approach to assessing innovation, using a broad set of indicators that cover product, process, organizational, strategic, and marketing innovations. Unlike previous studies that focused on a single type of innovation, this work examines how these dimensions, both individually and collectively within a company, affect employment growth. Moreover, the study considers co-operation between companies and with research centers as key facilitators. Additionally, the aim of this study is to determine whether companies with greater innovation capabilities actually generate more employment and under what conditions. To achieve this, the study analyzes the correlation between innovation indicators and employment growth, categorizing companies into quartiles based on their performance. This approach allows for the identification of specific mechanisms through which innovation drives employment, offering both theoretical and practical perspectives on business development in regions with intermediate innovation capacity, such as the Valencian Community and many others located in Southern Europe.

In order to ensure a sound basis, the 2005 Oslo Manual is used, which defines four types of innovation: product innovation, process innovation, organizational innovation, and marketing innovation (OECD and Eurostat 2005). Product innovation (X1) refers to the introduction of a new or significantly improved product to the market in terms of technical characteristics or established uses. On the other hand, process innovation (X2) involves the implementation of new processes or significant changes in techniques, equipment, and/or software, while organizational innovation (X3) refers to changes in business practices, workplace reorganization, or changes in a company’s external relationships. Marketing innovation (X4) essentially involves the implementation of a new marketing method or strategy. Beyond the 2005 Oslo Manual, a fifth type of innovation is presented as strategic innovation (X5), which could be defined as a company’s ability to create and revitalize the business idea and concept (Drejer 2006). We hope that the proposed methodology is also applicable to other European regions with a medium position in terms of economic development and indicators of research, development, and innovation (R+D+I). We also hope that the results and conclusions contribute to increasing knowledge about the studied topic.

2. Theoretical Review on Innovation and Its Impact on Companies

Innovation is essential for the success and sustainability of companies in today’s competitive environment. This theoretical review examines the impact of innovation on firms, focusing on key variables and their effects, based on various studies.

2.1. Introduction to the Resource-Based View (RBV)

The Resource-Based View (RBV) posits that companies can achieve and sustain a competitive advantage by acquiring and exploiting resources that are valuable, rare, inimitable, and non-substitutable (VRIN) (Barney 1991). These resources include both tangible and intangible assets, such as innovation and co-operation, which can provide firms with a unique strategic market position (Barney et al. 2021).

Recent extensions of the RBV emphasize that, in dynamic environments, organizational capabilities like continuous innovation and interorganizational collaboration are essential for adaptation and survival (Felin and Powell 2016). Furthermore, dynamic capabilities—defined as the ability to integrate, build, and reconfigure internal and external competencies to address changing environments—are recognized as crucial for sustaining a competitive advantage over time (Teece 2018).

2.2. Innovation as a Strategic Resource and Capability

Innovation is considered a crucial strategic resource, as it can be uniquely and effectively leveraged by firms with the requisite skills, knowledge, and a culture oriented toward innovation (Crossan and Apaydin 2010). Innovation capabilities not only lead to the development of new products and services but also enhance internal processes and organizational structures, creating barriers for competitors and contributing to sustained competitive advantage (Ferreira et al. 2020).

Recent studies have indicated that innovation in firms extends beyond new product creation (product innovation) to include process, organizational, and marketing innovations. These types of innovation interact to create complex innovation ecosystems that are difficult to replicate (García-Morales et al. 2012). Building innovation capabilities requires a combination of technological knowledge, managerial expertise, and an organizational culture that promotes continuous learning and creativity (Nieto and Santamaría 2010).

2.3. Interorganizational Co-Operation as a Strategic Resource

Interorganizational co-operation has emerged as a key strategic resource that enables firms to access complementary external resources, share risks and costs, and facilitate knowledge transfer (Lavie 2006). Firms that develop collaborative capabilities can build networks of relationships that not only create economies of scale but also cultivate capabilities that are difficult to imitate.

Recent research highlights that strategic alliances, joint ventures, co-creation agreements, and other forms of co-operation enable firms not only to access new markets but also to acquire new knowledge and innovative technologies (Kale and Singh 2007). Additionally, interorganizational co-operation can result in the creation of relational and social resources that are challenging for competitors to replicate, thereby generating a sustainable competitive advantage (Nieto and Santamaría 2010).

2.4. Synergy between Innovation and Co-Operation: Impact on Business Performance

The recent literature underscores that the synergy between innovation and interorganizational co-operation enhances firms’ capacity to create value and achieve sustainable competitive advantages (Foss and Saebi 2017). Firms that simultaneously develop innovation capabilities and establish co-operative networks gain access to complementary resources, emerging technologies, and external knowledge, which enhance their internal capabilities and promote superior performance (Santoro et al. 2018).

Studies show that organizations combining innovation with collaborative strategies can better adapt to market changes, allowing them to respond more quickly to opportunities and threats (Foss and Saebi 2017). Strategic co-operation allows firms to share financial and technological risks, while innovation ensures market differentiation, creating a strong competitive advantage (Zeng et al. 2010).

This theoretical review reveals that both innovation and co-operation are strategic resources that meet the VRIN criteria of the Resource-Based View, making them fundamental for enhancing business performance. The synergy between these elements provides a sustainable competitive advantage, offering unique capabilities that are difficult to replicate, and highlighting the importance of integrating these capabilities to maximize their impact on business performance.

3. Materials and Methods

This study has a qualitative approach and seeks to answer research questions RQ1, RQ2, RQ3, RQ4, within the Spanish context, between the years 2014 and 2020. This was carried out through the empirical linear modeling of significant independent indicators or variables for each of the 4 groups or response variables, contrasting the results with the findings in the literature (Dachs and Peters 2014; Harrison et al. 2014; Kunapatarawong and Martínez-Ros 2016; Zhu et al. 2021).

In this study, rigorous precautions were taken to ensure ethical integrity, adhering to the guidelines of the institution’s ethics committee. Before data collection, each participant was thoroughly informed about the objectives of the study, providing the necessary information to ensure a clear and complete understanding. Informed consent was obtained from all participants, ensuring their willingness to participate and their right to withdraw from the study at any time without adverse consequences.

Furthermore, strict procedures were established to ensure the confidentiality of the collected information. The data were anonymized and securely stored to prevent any identification of the participants. This approach complies with ethical regulations and promotes transparency and respect for the privacy of the individuals involved, thus strengthening ethical trust in the research process.

3.1. Study Variables

3.1.1. Employment Growth

Table 1. Dependent variables (growth in the number of employees between 2014 and 2020) and independent variables of the study.

Dependent variables
Yi	Nombre		Nº
Y1	Companies with growth within quartile 1		28
Y2	Companies with growth within quartile 2		29
Y3	Companies with growth within quartile 3		28
Y4	Companies with growth within quartile 4		28
		Total	113
Independent variables
Xi	Nombre
X1	Average predisposition for product innovation
X2	Average predisposition for process innovation
X3	Average predisposition for organizational innovation
X4	Average predisposition for marketing innovation
X5	Average predisposition for strategic innovation
X6	Obstacle: cost and investment
X7	Obstacle: lack of knowledge
X8	Obstacle: current market situation
X9	Impact on product
X10	Impact on productivity growth
X11	Impact on cost reduction
X12	Impact on compliance with legal and sustainable aspects
X13	Cooperation with R&D centers
X14	Cooperation among companies

presents the dependent variables (Y_i), which are related to the levels of employment growth in the companies under study. Each level, or dependent variable, represents a quartile, therefore, companies with very low employment growth are included in quartile 1 (Y₁), while companies with moderate growth are included in quartile 2 (Y₂), and companies with the highest growth are included in quartiles 3 and 4 (Y₃ y Y₄, respectively).

3.1.2. Types of Innovation

The Oslo Manual of 2005 defines four types of innovation: product innovation, process innovation, organizational innovation, and marketing innovation (OECD and Eurostat 2005). Product innovation (X₁) refers to the introduction of a new or significantly improved product into the market in terms of technical features or established uses. Process innovation (X₂), on the other hand, involves the implementation of new processes or significant changes in techniques, equipment, and/or software, while organizational innovation (X₃) refers to the modification of business practices, workplace reorganization, or changes in a company’s external relationships. Marketing innovation (X₄) is essentially the implementation of a new marketing method or strategy. Outside the Oslo Manual of 2005, a fifth type of innovation is presented as strategic innovation (X₅), which could be defined as the ability of a company to create and revitalize the idea and business concept (Drejer 2006). These five indicators are presented in Table 1 as independent variables and are dichotomous, taking the value of 1 when there is evidence of their introduction into the company during the established study period (average), and 0 in contrast.

3.1.3. Obstacles to Innovation

Obstacles measure the perception of a hindering factor to innovation in the company (de-Oliveira and Rodil-Marzábal 2019; Arza and López 2021). There are three obstacle indicators proposed in this case study, and they align with those proposed by the Oslo Manual of 2005. They are presented in Table 1 as independent and dichotomous variables, taking the value of 1 when the company declares the existence of such a barrier, and 0 in contrast.

3.1.4. Innovation Impact

In the scope of this study, the impact of innovation allows the specification of how the effort to innovate affects a key variable of business performance. The use of this indicator is incipient (Bustinza et al. 2010), and the impacts considered relevant in this work are presented as independent variables in Table 1. As in the previous cases, this indicator is dichotomous, taking the value of 1 when the declared effect is positive, and 0 in contrast.

3.1.5. Innovation Co-Operation

This refers to the establishment of partnerships with external entities, with the aim of accessing additional knowledge needed for innovation, without the need to internally develop the required strengths (Ryzhkova 2015; Un et al. 2010). In this study, two indicators are used to cover this field, presented in Table 1, with an equally dichotomous character, acquiring a value of 1 when it occurs, and 0 otherwise.

Table 2. Dependent variables (growth in the number of employees between 2014 and 2020). Independent variables of the study.

	Quartile	Yi	Number
Companies with growth within quartile	1	Y1	28
	2	Y2	29
	3	Y3	28
	4	Y4	28
		Total	113

presents the distribution of the firms of the sample in quartiles, according to their growth during the 6 years covered by this study. The levels of employment growth in said firms are considered the dependent variables (Y_i). Each level, or dependent variable, represents a quartile, therefore, companies with very low employment growth are included in quartile 1 (Y₁), while companies with moderate growth are included in quartile 2 (Y₂), and companies with the highest growth are included in quartiles 3 and 4 (Y₃ y Y₄, respectively).

The Oslo Manual of 2005 defines four types of innovation: product innovation, process innovation, organizational innovation, and marketing innovation (OECD and Eurostat 2005). Product innovation (X₁) refers to the introduction of a new or significantly improved product into the market in terms of technical features or established uses. Process innovation (X₂), on the other hand, involves the implementation of new processes or significant changes in techniques, equipment, and/or software, while organizational innovation (X₃) refers to the modification of business practices, workplace reorganization, or changes in a company’s external relationships. Marketing innovation (X₄) is essentially the implementation of a new marketing method or strategy. Outside the Oslo Manual of 2005, a fifth type of innovation is presented as strategic innovation (X₅), which could be defined as the ability of a company to create and revitalize the idea and business concept (Drejer 2006). These five indicators are presented in

Table 3. Independent variables of the study.

Name	Xi
Average predisposition for product innovation	X1
Average predisposition for process innovation	X2
Average predisposition for organizational innovation	X3
Average predisposition for marketing innovation	X4
Average predisposition for strategic innovation	X5
Obstacle: cost and investment	X6
Obstacle: lack of knowledge	X7
Obstacle: current market situation	X8
Impact on product	X9
Impact on productivity growth	X10
Impact on cost reduction	X11
Impact on compliance with legal and sustainable aspects	X12
Co-operation with R&D centers	X13
Co-operation among companies	X14

as independent variables and are dichotomous, taking the value of 1 when there is evidence of their introduction into the company during the established study period (average), and 0 in contrast.

Obstacles measure the perception of a hindering factor to innovation in the company (de-Oliveira and Rodil-Marzábal 2019; Arza and López 2021). There are three obstacle indicators proposed in this case study, and they align with those proposed by the Oslo Manual of 2005. They are presented in

Table 4. Basic descriptive data of the sample.

Category	Subcategory	Number	Percentage (%) *
Firms of	technology-based (TBCs)	32	28.32
	non-technology-based (NTBCs)	81	71.68
Firms that experienced, between 2014 and 2020,	a decrease in both revenue and employees	36	31.86
	growth in revenue and a decrease in employees	16	14.16
	a decrease in revenue and growth in employees	23	20.35
	growth in both revenue and employees	38	33.63
Firms that	had between one and ten employees (micro-enterprise)	67	59.29
	had between 11 and 50 employees (small enterprise)	13	11.50
	had between 51 and 250 employees (medium-sized enterprise)	19	16.81
	had more than 250 employees (large enterprise)	14	12.39
Firms in the sectors of	manufacturing	74	65.49
	trading	15	13.27
	services	24	21.24
Firms with an age of	6 years to 15 years	10	8.85
	16 years to 30 years	59	52.21
	more than 30 years	44	38.94
Firms located in	Valencia City	23	20.35
	Valencia Metropolitan	36	31.86
	Valencia Province	37	32.74
	Alicante-Castellón (rest of the Valencian Community)	17	15.04

* By category.

(X₆, X₇ and X₈) as independent and dichotomous variables, taking the value of 1 when the company declares the existence of such a barrier, and 0 in contrast.

In the scope of this study, the impact of innovation allows the specification of how the effort to innovate affects a key variable of business performance. The use of this indicator is incipient (Bustinza et al. 2010), and the impacts considered relevant in this work. As in the previous cases, this indicator is dichotomous, taking the value of 1 when the declared effect is positive, and 0 in contrast.

Co-operation refers to the establishment of alliances with external entities with the objective of accessing the additional knowledge necessary for innovation, without the need to develop the required strengths internally (Ryzhkova 2015; Un et al. 2010).

3.2. Sample

The target population of this study consisted of companies located in the Valencian Community, Spain, where sectors such as manufacturing, trade, and services were considered. With this, the companies varied in size from micro-enterprises to large corporations, selected to represent a diverse range of business profiles that served the purposes of this study.

A total number of 427 companies located in the Valencian Community (Spain) were considered for data collection. Financial information from the companies was extracted by 2014 from the Iberian Balance Sheet Analysis System (SABI 2020, 1 June). A survey was provided to these companies, and 201 of them completed it. The sample was finally reduced to 113 companies, consisting of those that remained active in 2020. In all of them, the SABI record in 2020, six years after the survey, and their economic data, were available in the SABI registry. The sample includes the three productive sectors (manufacturing, trading, and services); technological and non-technological companies; micro-, small, medium, and large firms, as well as locations throughout the geographical extent of the Valencian Community. Table 4 presents basic descriptive data of the sample. In this study, stratified sampling was used to ensure representativeness in terms of sector, size, and geographic location. Consequently, surveys were conducted using both online and face-to-face methods to maximize response rates. Firms participating in the study were selected in a way to ensure a relatively balanced proportion of firms in terms of sector, size, and location. As previously mentioned, 427 firms initially selected were contacted and invited. From them, 201 agreed and took part in the study. Moreover, the collected data included key innovation indicators and employment growth metrics, which were analyzed using linear correlation models to assess their impact on employment growth.

3.3. Modeling

To determine the coefficients of the four linear models that describe the performance of firms (one for each level of employment growth), the least squares method was used (Gujarati and Porter 2010, pp. 97–101; dell’Olio et al. 2018). Calculation started with the 14 indicators and ended with those that achieved a significance level of 0.05, using the likelihood ratio test. For the linear model of the performance of companies grouped in the first quartile (Y₁), the coefficients of indicators X₇, X₈ y X₁₂ were significant. For the model of the second quartile (Y₂), the coefficients of indicators X₃, X₅ y X₁₃ were significant. For the model of the third quartile (Y₃), the coefficients of indicators X₅, X₆, X₁₃ y X₁₄ were significant. Finally, for the linear model of the performance of companies grouped in the fourth quartile (Y₄), the coefficients of indicators X₁, X₅ y X₁₄ were significant.

3.4. Statistical Analysis

An analysis of variance verified the significance of independent variables on the growth in the number of employees for each of the 4 groups. The adjustment was verified through the correlation coefficient, the coefficient of determination, the adjusted coefficient of determination, and the standard error of estimation. All of them collectively belong to the linearity criterion that every linear model should possess. Independence of residuals was verified using the Durbin–Watson statistic (Gujarati and Porter 2010, pp. 434–38); homoscedasticity of the four models was through a residuals plot and the Bartlett test; normality criterion was through frequency histograms, normal probability plots, and the Shapiro–Wilk test. Finally, no multicollinearity was verified through the correlation between the independent variables of each model and a residuals plot for each independent variable. These plots and statistics were generated by the SPSS program version 23, conducting linear correlation for each dependent variable.

4. Results

4.1. Firms with Employment Growth within Quartile 1 (Model 1)

It can be inferred that firms that experienced very low employment growth between 2014 and 2020 (Y₁) were positively affected by the obstacles related to current market conditions, which includes intense competition, economic slowdown, or fluctuations in market demand (X₈) (see Table 2). On the other hand, the obstacle to accessing relevant information or acquiring knowledge necessary for innovation or improvement in products or production processes (X₇), as well as the impact of compliance with legal and sustainable aspects by implementing product or process innovation (X₁₂), had a rather negative effect, leading to job destruction. This set of significant variables explains 60.7% (adjR² in

Table 5. Impact of innovation variables on employment growth between 2014 and 2020 for firms in the Valencian Community with growth within quartile 1 (Model 1 *).

Dependent Variables of the Model (Xi)	βi	s	Sβi	t	α
Obstacle: lack of knowledge (X7)	−0.268	0.109	−1.016	−2.455	0.021
Obstacle: current market situation (X8)	0.249	0.100	1.001	2.500	0.019
Impact on compliance with legal and sustainable aspects (X12)	−0.143	0.057	−0.721	−2.519	0.019
Model performance indices	R2			0.649
	adjR2			0.607
	D–W			1.745
	B			0.637
	S–W			0.5293

* X_i, independent variable i. β_i, coefficient corresponding to independent variable i. s, standard error of the coefficient. Sβ_i, standardized coefficient corresponding to independent variable i. t, Student’s t. α, significance level. R², coefficient of determination. adjR², adjusted coefficient of determination. SEE, standard error of estimation. D–W, Durbin–Watson statistic. B, p-value of the Bartlett test. S–W, p-value of the Shapiro–Wilk test.

) of the findings for this group of companies.

The negative effect of X₁₂ on job creation contradicts the impact of green innovation on employment growth found by Kunapatarawong and Martínez-Ros (2016), considering that X₁₂ implicitly includes the aspect of sustainability.

The fact that none of the five indicators representing the application of the five types of innovation (X₁, X₂, X₃, X₄, y X₅) resulted in a significantly direct relationship with the dependent variable does not necessarily imply that companies in this group of very low or decreasing employment growth have low innovative capacity. Instead, the results show that innovation capacity, in terms of a predisposition to innovate, are not related, are not a cause, or do not affect the decrease in employment experienced by this group of companies in the period 2014–2020. Therefore, the main conclusion of this model is that the decrease in employment is independent of the innovation capacity shown by these companies.

4.2. Firms with Employment Growth within Quartile 2 (Model 2)

Table 6. Impact of innovation variables on employment growth between 2014 and 2020 for firms in the Valencian Community with growth within quartile 2 (Model 2) *.

Dependent Variables of the Model (Xi)	βi	s	Sβi	t	α
Average predisposition for organizational innovation (X3)	0.054	0.022	1.294	2.422	0.023
Average predisposition for strategic innovation (X5)	−0.071	0.027	−1.569	−2.638	0.014
Co-operation with R&D centers (X13)	0.049	0.018	1.063	2.749	0.011
Model performance indices	R2			0.703
	adjR2			0.668
	D–W			1.266
	B			0.483
	S–W			0.5901

* X_i, independent variable i. β_i, coefficient corresponding to independent variable i. s, standard error of the coefficient. Sβ_i, standardized coefficient corresponding to independent variable i. t, Student’s t. α, significance level. R², coefficient of determination. adjR², adjusted coefficient of determination. SEE, standard error of estimation. D–W, Durbin–Watson statistic. B, p-value of the Bartlett test. S–W, p-value of the Shapiro–Wilk test.

reflects the results obtained for the group of companies with low employment growth (corresponding to quartile 2). Among the most notable results, it is worth mentioning that companies in this medium–low growth group show a greater tendency to co-operate with research and development centers (IV) (X₁₃) to improve their processes or products, and made changes in their structure, processes, or organizational culture (X₃). We also note that the development and implementation of new business strategies (X₅) by this group did not serve to boost employment growth but rather the opposite. This result contradicts to some extent what Research Question 1 expected.

The relationship among variables in Model 2 accounts for 66.8% (adjR² in Table 6) of the findings for this group of companies, a result above that obtained by the group of firms in quartile 1. Therefore, we observe that the innovation indicators more accurately explain the growth phenomenon in the quartile 2 compared to quartile 1.

4.3. Firms with Employment Growth within Quartile 3 (Model 3)

Here, the results for the group of companies corresponding to the third quartile, which experienced moderate employment growth, are analyzed. As shown in

Table 7. Impact of innovation variables on employment growth between 2014 and 2020 for firms in the Valencian Community with growth within quartile 3 (Model 3) *.

Dependent Variables of the Model (Xi)	βi	s	Sβi	t	α
Average predisposition for strategic innovation (X5)	0.086	0.040	0.470	2.127	0.044
Obstacle: cost and investment (X6)	0.068	0.032	0.375	2.140	0.043
Co-operation with R&D centers (X13)	−0.107	0.045	−0.536	−2.389	0.025
Co-operation among companies (X14)	0.112	0.040	0.650	2.818	0.010
Model performance indices	R2			0.891
	adjR2			0.878
	D–W			1.933
	B			0.0524
	S–W			0.7981

* X_i, independent variable i. β_i, coefficient corresponding to independent variable i. s, standard error of the coefficient. Sβ_i, standardized coefficient corresponding to independent variable i. t, Student’s t. α, significance level. R², coefficient of determination. adjR², adjusted coefficient of determination. SEE, standard error of estimation. D–W, Durbin–Watson statistic. B, p-value of the Bartlett test. S–W, p-value of the Shapiro–Wilk test.

, these companies with moderate growth (Y₃) reported perceiving obstacles to carrying out innovation or improvements in their products or production processes due to high costs or investments (X₆), and they declared having collaborated with other companies to improve their processes or products (X₁₄). They also developed and implemented new business strategies (X₅) and were characterized by a low propensity to co-operate with research and development centers (IV) (X₁₃).

The relationship of variables in this third group explains 87.8% (adjR², in Table 7) of the total variance. Therefore, it is confirmed that at the third level of growth, innovation variables explain more accurately the phenomenon of growth in employment.

4.4. Firms with Employment Growth within Quartile 4 (Model 4)

Finally, the results for the fourth group of companies, those in the quartile with the highest employment growth (high growth), are presented. As shown in

Table 8. Impact of innovation variables on the growth in the number of employees between 2014 and 2020 for firms in the Valencian Community with growth within quartile 4 (Model 4) *.

Dependent Variables of the Model (Xi)	βi	s	Sβi	t	α
Average predisposition for product innovatio (X1)	−1.116	0.305	−1.622	−3.660	0.001
Average predisposition for strategic innovation (X5)	0.990	0.259	1.444	3.824	0.001
Co-operation among companies (X14)	0.790	0.252	1.069	3.129	0.004
Model performance indices	R2			0.866
	adjR2			0.850
	D–W			1.534
	B			0.0523
	S–W			0.7981

* X_i, independent variable i. β_i, coefficient corresponding to independent variable i. s, standard error of the coefficient. Sβ_i, standardized coefficient corresponding to independent variable i. t, Student’s t. α, significance level. R², coefficient of determination. adjR², adjusted coefficient of determination. SEE, standard error of estimation. D–W, Durbin–Watson statistic. B, p-value of the Bartlett test. S–W, p-value of the Shapiro–Wilk test.

, high-growth companies (Y₄) actively collaborate with other companies to improve their processes or products (X₁₄) and have developed and implemented new business strategies (X₅). Surprisingly, the introduction of new or significantly improved products to the market (X₁) has a negative impact on job creation, becoming a factor in staff reduction in companies with a high-growth profile.

The findings for the group of companies with high employment growth are in perfect agreement with what was reported by Zhu et al. (2021), which indicates that in companies where product innovation is applied, employment growth is hindered by the effect of productivity. On the other hand, it contradicts Harrison et al. (2014), which implies that the condition of substituting new products for old products was not met.

The list of variables of the fourth group of firms explains 85.0% (adjR², in Table 5) of the experimental data, a result quite close to group 3, which confirms that, the greater the growth in employment, the greater the precision with which the innovation variables explain this phenomenon.

4.5. Overall Performance of the Models and Goodness of Fit

The significant effect of the independent variables selected for models 1, 2, 3, and 4 on their corresponding dependent variables is validated by the low difference between R² and adjR², of only 4.2%, 3.5%, 1.3%, and 1.6%, respectively (Table 1, Table 2, Table 3, and Table 4, respectively).

No significance was found in the intercept coefficient (α = 0.05) for the models of any of the four groups, so they were eliminated. Likewise, the results of the statistical tools, presented in Table 1, Table 2, Table 3, and Table 4, show the goodness of fit for models 1, 2, 3, and 4, respectively, in terms of the linearity of the model, the independence and normality of the errors, as well as the non-multicollinearity of the independent variables.

5. Discussion

The main result derived from our study reveals that, in the territorial context of Valencia, a region of medium development and with innovation indicators located in the Spanish average, companies with greater innovation capacities create a greater number of jobs, with co-operation with other companies being the factor that favors the highest growth.

The impact of strategic innovation, in this context, varies depending on the growth in employment. It is not taken into account in companies with very low employment growth, while in companies with low growth the effect is counterproductive since it tends to eliminate jobs. In companies with moderate growth and in those with high job growth, the effect is positive towards job creation.

One of the most outstanding findings is the lack of impact of product innovation in the firm’s performance, regardless of its level of employment growth. The only confirmed connection with our analysis is the negative effect of product innovation on job creation precisely in the group of firms that has experienced the greatest growth in employment during the study period. Apparently, in the Valencian region, firms can increase, maintain, or reduce their employment levels, irrespective of the efforts and resources dedicated to developing innovation. This result stays in line with Zhu et al. (2021).

Although no straightforward answer to this unexpected finding can be provided, we believe it might be linked to the interpretation that most firms from the region assign to the term “innovation in product”. There is a general tendency to consider any minor improvement to existing products as genuine innovation. Consequently, the impact of these changes is minimal or even non-existent, as what most firms refer to as truly innovative products are often just slight modifications of existing products and do not warrant being called innovations.

Also significant is the stronger tendency to collaborate with other firms rather than with R&D centers, particularly in firms that show a faster growth in employment. In the Valencian region, the collaboration with the extensive network of publicly supported R&D centers is widespread and takes different modalities, beyond innovation development. Consequently, to acknowledge a relationship with these centers does not necessarily imply involvement in innovation-related activities. Conversely, co-operation with other companies has been shown to have a positive impact on employment in most of the companies under study. This finding suggests that the public administration should increase efforts to promote spaces for the exchange of ideas between firms and organizations, such as industrial fairs or the creation of hub-type clusters of companies which promote co-operation as something natural. Our study shows that broader co-operation does, indeed, lead to the growth of companies and the generation of more jobs.

6. Conclusions

This research study has reached the following conclusions:

(1)

Regarding research question RQ1, our study confirms that firms with a greater capacity for innovation are, indeed, able to create more jobs. Specifically, we base this conclusion on the fact that the absence of a relationship between types of innovation and growth occurs only in the lowest-growing business group (quartile 1). On the other hand, in the other three groups, low-growth, moderate-growth, and high-growth, at least some of the indicators that determine the implementation of innovations of different types did have significant direct effects on growth.

(2)

With regard to research question RQ2, it can be said that innovation is obviously a determining factor for job creation, and especially when the appropriate indicators of innovation are combined in business management.

(3)

Regarding research question RQ3, it can be stated that co-operation with research and development centers (IV), and the implementation of organizational innovations, have a greater and direct impact on companies with low employment growth. In addition, it is confirmed that co-operation with other companies and strategic innovation have a greater and direct impact on companies with moderate and high employment growth.

(4)

Regarding research question RQ4, our study confirms that the factor linked to innovation capacity that most favors or drives business job creation is co-operation with firms, since it is the factor with a direct and significant relationship on the two highest growth groups, with no negative relationship with either of the two lower growth groups.

The findings of this study should be understood within the specific socio-economic and cultural context of the Valencian Community in Spain, a region characterized by a medium level of economic development and innovation capacity within Spain. Thus, the interaction between innovation and employment growth in this region is influenced by local factors such as prevailing economic conditions, labor market dynamics, cultural attitudes towards innovation, and co-operation among businesses.

In the Valencian Community, co-operation among businesses, particularly within the manufacturing and services sectors, plays a fundamental role in driving employment growth. This study reveals that companies actively participating in collaborative efforts with other businesses, as well as with research institutions, tend to experience greater employment growth. In this way, the findings here align with other studies that emphasize the importance of co-operative strategies to foster innovation, especially in regions with similar economic profiles. In addition, our results can be applicable to other European countries with a level of innovation capacity qualified as moderate. In such territories, active collaboration across companies towards innovation development can be especially helpful to overcome market barriers and enhance businesses’ competitive advantage.

Moreover, the theoretical implications of this study contribute greatly to the discourse on innovation and employment growth, increasing the importance of this study for existing theories. While previous research often highlighted the positive correlation between innovation and job creation, this study has provided a broad view by examining different types of innovation and their varied impacts on companies with different growth trajectories.

Furthermore, this study contributes to the existing literature by offering new perspectives on the relevant relationship between innovation and employment growth in a medium-development region like the Valencian Community. Unlike previous studies, which often generalized the impact of innovation, our research distinguishes between different types of innovation and their diverse effects on companies with different growth trajectories. This differentiation provides a greater and more detailed understanding of how innovation can be strategically leveraged to promote employment, offering a unique contribution that was previously underexplored.

Additionally, this research is important because it contributes to the theory of economic development by demonstrating that the effects of innovation on employment are not uniform but context-dependent. For example, while strategic and organizational innovations were found to significantly influence job creation in companies with moderate-to-high growth, product innovation showed a counter-intuitive negative impact on employment in the highest growth quartile. These findings suggest that the relationship between innovation and employment is more complex than previously understood, especially in regions with intermediate innovation capacity like the Valencian Community. Our findings demonstrate that regions with intermediate development should be analyzed separately, as their innovation strategies and impacts differ from those typical of highly developed regions. Our study emphasizes that the regional context matters, and, therefore, territory should be considered a key variable in any study addressing innovation and firm performance.

Additionally, our results highlight the need to standardize the concept of innovation across studies. It is crucial to ensure that all firms participating in any empirical research fully understand the meaning, scope, and implications of each type of innovation as defined by the Oslo Manual OECD and Eurostat (2005).

The longitudinal nature of our empirical study brings a significant advantage on both the theoretical and methodological levels. Most studies in the literature are cross-sectional, which limits their validity over time. In contrast, our six-year longitudinal study allows us to obtain more precise and valuable insights into the actual impact of innovation on job creation, a key indicator of performance and competitiveness. Methodologically, our study is pioneering by dividing our database into four groups of firm based on their level of employment variation. This approach enables us to achieve more precise and focused results regarding our key performance variable: employment growth over time.

Lastly, the managerial implications derived from this study are directly linked to the specific findings, contributing to the importance of this study on the topic studied, providing actionable insights for managers in the Valencian Community and the business context. For instance, although training is a widely recognized managerial tool, our findings highlight the need for specific training programs that focus on enhancing co-operative innovation strategies. Managers can use these insights to tailor their training initiatives, ensuring they are aligned with the specific innovation capabilities that drive employment growth in their context.

More specifically, our results offer several valuable suggestions for managers. First, our study identifies the types of innovation that are more impactful in generating employment. Second, our results raise the importance of being more stringent in defining what qualifies as product innovation. Third, our study highlights that collaboration with other firms and R&D centers in developing new projects leading to innovations of any kind is particularly effective in fostering job generation. We believe these managerial implications add valuable insights to the field of innovation management, and they do not always align with the existing literature. Previous research has primarily focused on the impact of innovation on sales growth, with less emphasis on employment growth. In addition, unlike prior studies, our research categorizes firms into different groups based on their employment growth, ranging from those losing jobs to those experiencing rapid growth.

7. Limitations

In addition to the valuable insights this study offers regarding how innovation capacity influences employment growth in the Valencian Community, it is important to acknowledge limitations that may shape future research efforts.

Firstly, the focus on a single region, while providing depth, means that the findings may not be universally applicable. The Valencian Community has its own unique economic and cultural characteristics, and these results could differ in other regions or countries with different dynamics. Therefore, future studies could build on this work by testing similar hypotheses in various international settings. Doing so would not only validate the findings but also make them more relevant to a global audience, offering lessons that can be applied in different organizational contexts.

The main limitation of this study lies in the sample size, which was substantially reduced, from 2014 to 2020, because the sampled companies were required to remain active six years after conducting the survey. The years under study in this work were quite turbulent for the Spanish economy, which has surely impacted on the rate of company closure.

Similarly, while this study examined several types of innovation, there are other dimensions of innovation that were not covered, due to the delimitation made for this work to satisfactorily address the study’s objectives. For example, the growing importance of digital transformation and new technologies could be crucial factors in understanding how innovation drives employment and other outcomes. Expanding the research to include these elements could provide a richer understanding of how different types of innovation affect organizational success, especially in today’s rapidly changing global market.

As for future lines of research, we will intend to expand the database to other Spanish regions in order to incorporate the territory/region as an explanatory variable. We also consider using other statistical techniques such as binomial logistic regression. Finally, in order to better measure the impact of innovation, we are considering incorporating new innovation indicators that are not included in the Oslo Manual of 2005.