Open innovation,dynamic organizational capacities and innovation performance in SMEs: Empirical evidence in the Tunisian manufacturing industry

Abstract

In this paper, the main objective is to examine inbound open innovation adoption by exploring its antecedents in terms of dynamic organizational capabilities, its implications on innovation performance, but also its mediating role between these capabilities and Small and Medium Enterprise (SME) innovation performance. Therefore, a conceptual model was proposed and empirically tested on the basis of data extracted from a survey involving a sample of 228 Tunisian manufacturing SMEs and analyzed through the Structural Equation Modeling method. In doing so, this paper adds to the existing literature on open innovation in SMEs and fills a neglected theoretical gap by proposing a link between dynamic capabilities theory and the open innovation paradigm, two literatures that have little overlap, to improve current understanding of innovation performance in SMEs. Ultimately, by empirically confirming the significant relationship between dynamic organizational capacities and innovation performance through the mediating role of open innovation, our results are mainly relevant for entrepreneurs and innovation managers in SMEs who can find valuable guidance on how to strengthen innovation performance under the nexus between the dynamic capabilities perspective and open innovation by focusing on the development of three capabilities that are dynamic capabilities, absorptive capability, and appropriation capabilities.

Keywords

inbound open innovation dynamic capacities absorptive capacity appropriation capacities SME innovation performance

Introduction

Today, more than ever, the reality of organizations has been transformed. From changing market demands to globalization and the need for new knowledge, the economic landscape is constantly changing and companies, especially Small and Medium Enterprises (SMEs), must be able to keep pace with this evolution. In this context, a strategy based on innovation seems to be a factor of success for any company and a strategic element for maintaining and improving its competitive advantages while being exposed to the imperatives of current economic trends. Nevertheless, the question today is no longer to know the merits of innovation nor to identify the related risks, but rather to explain the conditions for its success as well as the indispensable mechanisms to stimulate it. With regard then to how companies organize for innovation management, the debate is still very topical. In recent decades, academics and professionals have agreed on an emerging trend towards openness in innovation strategy, particularly since the rise of the open innovation paradigm (Chesbrough, 2003). As a relatively young discipline of innovation management, but in a rapid phase of adoption by companies since the emergence of the concept in the early 2000s, open innovation has become an increasingly important instrument for innovation management attracting attention from both academics and managers (Bigliardi et al., 2020, 2021; Torchia and Calabrò, 2019). The willingness to explore the specific research field of open innovation leads us to give a great interest to a less investigated context, that of SMEs. Kraus et al. (2019: 3) proclaimed that “publications dealing with open innovation in SMEs as a core field are still limited in quantity”, in agreement with several other researchers (D’Angelo and Baroncelli, 2019; Torchia and Calabrò, 2019). Certainly, innovation in SMEs differs greatly from that in large firms, especially with respect to their specificities in terms of “smallness liability.” In SME context, there is ample evidence in the literature that open innovation practices are of great value in overcoming the lack of internal resources and leveraging access to new knowledge, skills, and technologies to drive the innovation development process and firm performance (Chabbouh and Boujelbène, 2020; Hervas-Oliver et al., 2021; Ovuakporie et al., 2021; Popa et al., 2017). Therefore, the study of innovation performance in light of open innovation is a major management concern and an active research topic.

Looking more closely at the existing literature, we note areas that have been little explored in the context of SMEs and that require further investigation, including the study of the antecedents and implications of open innovation on firm performance. While recent studies have provided some empirical insight regarding the determinants and effects of open innovation practices on SME performance (Chabbouh and Boujelbène, 2020; Popa et al., 2017), there is a shortage in understanding how to innovate with performance under the aegis of the open innovation model and the dynamic capabilities perspective. Certainly, some recent research (Bogers et al., 2019; Teece, 2020) has conceptually established significant links between dynamic capabilities¹ and open innovation, but there is a gap in empirical examination of the mediating role of open innovation in the relationship between organizational dynamic capabilities and performance in the SME context (Pundziene et al., 2021). This mediating role of open innovation has been neglected so far in the literature.

Thus, this study partially covers this gap by proposing to examine the antecedents of inbound open innovation and its impact on innovation performance to consequently show its mediating role in the relationship between dynamic organizational capabilities and innovation performance in SMEs. To do this, and given the importance of the research at hand, this study builds on the dynamic resource theory and the open innovation paradigm and focuses on three capabilities that are dynamic capabilities, absorptive capability, and appropriation capabilities to explain the links with open innovation and innovation performance in SMEs.

Hence, we present three objectives in this paper. The first is to explain the degree of openness towards inbound practices through the effect of dynamic organizational capacities. The second is to study the direct effect of open innovation on innovation performance in SMEs; and the third is devoted to examine the indirect impact of dynamic organizational capacities on innovation performance through the open innovation model. To meet these objectives, a conceptual model has been developed and empirically tested on the basis of a sample of 228 Tunisian manufacturing SMEs. Our methodological approach is quantitative using Structural Equation Modeling (SEM) method. In doing so, this paper adds to the existing literature on open innovation in SMEs and fills a neglected theoretical gap by proposing a link between dynamic capabilities theory and the open innovation paradigm, two literatures that have little overlap (Chabbouh and Boujelbène, 2020; Ovuakporie et al., 2021; Pundziene et al., 2021), to improve current understanding of innovation performance in SMEs. Ultimately, by empirically confirming the significant relationship between dynamic organizational capacities and innovation performance through the mediating role of open innovation, our study offers practical guidance on how to strengthen innovation performance under the nexus between the dynamic capabilities perspective and open innovation.

This paper is structured around three other parts in addition to this introduction. First of all, a review of the literature necessary for the development of the research hypotheses is conducted. Then, the empirical study is presented, specifying the methodology, the data analysis strategy, the interpretation and discussion of the obtained results. Finally, in conclusion, an overview of empirical results, the contribution of the research, its managerial implications and its limitations is presented, with recommendations for future research.

Literature review and research hypotheses

Since the seminal work of Chesbrough (2003), the concept of open innovation has garnered increasing attention from academics and practitioners (Bigliardi et al., 2021). The open innovation model is based on the premise that firms can and should use both ideas developed internally and those from other organizations, while considering new ways of commercializing and advancing their own technologies (Chesbrough, 2003). Based on a porous view of organizational boundaries, the open model promotes inward and outward knowledge flows that are inscribed in the literature in three dimensions: inbound, outbound and coupled (Chesbrough, 2003; Gassmann and Enkel, 2004). However, the literature shows a preponderance of research investigating the inbound process in comparison with other processes (Bigliardi et al., 2020, 2021; West and Bogers, 2014), particularly in the specific contexte of SMEs (Kraus et al., 2019). In fact, the question of the adoption of open innovation practices by SMEs remained unexplored until the publication of the first study by van de Vrande et al. (2009), which showed that SMEs are more inclined to approach unstructured open practices that do not require significant investment referring to the inbound dimension. By inbound dimension, scholars describe the practices that aim to explore the external environment to enrich the internal knowledge base and consequently advance the internal innovation process. These practices of internalizing external resources are grouped into three main practices that refer respectively to the practices of searching for external sources of information, the practices of collaboration with external partners and the practices of acquiring external resources and skills (Chabbouh and Boujelbène, 2020). In line with many researchers in open innovation paradigm, we are emphasizing this inbound dimension by trying to explore a new avenue of research susceptible to advance the state of knowledge in this field. Thus, we focus in this paper on the study of the degree of openness of SMEs towards inbound practices by analyzing its determinants in terms of dynamic organizational capacities and its relationship with innovation performance.

The impact of dynamic organizational capacities on inbound open innovation

Organizational capabilities are described as the ability of a firm to deploy its resources, tangible or intangible, to perform a task or activity to improve performance (Teece et al., 1997). When the environment is dynamic, organizational capabilities underpin firms’ competitive advantages and their ability to respond to internal and external change (Inan and Bititci, 2015). In the management of innovation, researchers agree that embracing open innovation requires certain dynamic organizational capabilities within the firm in order to ensure successful implementation and full benefit from openness practices (Hervas-Oliver et al., 2021; Lichtenthaler and Lichtenthaler, 2009; Pundziene et al., 2021, Teece, 2020). For the purposes of this paper, we highlight three capacities that are widely supported in the literature for exploration and integration of new knowledge from the external environment, namely dynamic capacities, absorptive capacity and appropriation capacities.

The impact of dynamic capacities on the degree of openness

Dynamic capacities refer to “firm’ ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments” (Teece et al., 1997:517). In reviewing prior studies on open innovation, we notice that scholars emphasized specific dynamic capacities to facilitate exploration and integration of external knowledge. Lee et al. (2010), for example, proclaimed that dynamic networking capacities are important to identify promising collaborative relationships and opportunities available in the external environment by fostering interaction and exchange between the firm and its external partners. Brunswicker and Ehrenmann (2013) also reported that firm requires additional managerial capabilities to successfully transform its innovation management process towards the open model. In addition, Theyel (2013) proclaimed that search and environmental scanning capabilities play a crucial role in fostering the openness of the innovation process. Grimaldi et al. (2013), for their part, explored the relationship between dynamic capacities and the degree of openness in the context of SMEs, based on Teece’s (2007) analytical framework, and demonstrated that the presence of dynamic capacities, in terms of sensing, seizing and reconfiguring, favors the openness of the innovation process. In this vein, Bogers et al. (2019) stated that outside-in open innovation requires the sensing, sense-making and the filtering of externally developed technologies. To successfully integrate and use knowledge from external sources, companies need also to employ various organizational practices such as extensive delegation, intensive lateral and vertical communication, and rewards for knowledge sharing, which often requires transforming capacity (Bogers et al., 2019). Moreover, the positive and significant relationship between dynamic capabilities and open innovation has recently been proven by the empirical study of Pundziene et al. (2021) which was based on the Teece et al. (2020) framework. Building on all this work, dynamic capabilities seem to be extremely important to support the strategic decision towards opening the innovation process and to successfully implement the associated practices. Thus, we attempt to test this link through the following hypothesis:

H1a: Dynamic capacities foster the degree of openness of SMEs towards inbound open innovation practices.

The impact of absorptive capacity on the degree of openness

Absorptive capacity is linked to the capacity to recognize the value of external information, assimilate it and exploit it for the organization's purposes (Cohen and Levinthal, 1990). According to Zahra and George (2002), absorptive capacity is understood as an organizational process by which the firm acquires, assimilates, transforms and exploits knowledge from the external environment. In the field of open innovation, research has greatly emphasized the importance of absorptive capacity for the success of the inbound process by suggesting that a firm wishing to explore external knowledge must not only acquire it, but must also think about its effective integration into its internal knowledge base (Lichtenthaler and Lichtenthaler, 2009). In this vein, Benhayoun et al. (2020) stipulated that in order to effectively use externally accessible knowledge for innovation purposes, SMEs that are embedded in collaborative innovation networks, are required to deploy their absorptive capacity. In addition, Lu et al. (2021) stated that although open innovation provides a stream of external knowledge, it is not beneficial for all firms; as its effectiveness depends on absorptive capacity which determines the extent to which a firm is able to profit from open innovation. However, most studies carried out so far have addressed the concept of absorptive capacity primarily in a theoretical manner with only a few that have been shown empirically a positive relationship with open innovation (Kim et al., 2016; Scuotto et al., 2017; Spithoven et al., 2011; Zobel, 2017; Lu et al., 2021). Therefore, we join the theoretical and empirical literature and assume that absorptive capacity enables the firm to identify and recognize the value of external knowledge by referring to potential absorptive capacity (Zahra and George, 2002). In addition, the presence of the capacity to transform and exploit knowledge received from outside allows the combination of internal knowledge with that identified and assimilated from the outside according to realized absorption capacity (Zahra and George, 2002). Consequently, and as this multidimensional capacity represents a crucial determinant to stimulate inbound practices, we propose to verify this second hypothesis:

H1b: Absorptive capacity fosters the degree of openness of SMEs towards inbound open innovation practices.

The impact of appropriation capacities on the degree of openness

The appropriation capacity refers to the firm's ability to take full advantage of its strategic capabilities and knowledge and turn them into a competitive advantage (Cohen and Levinthal, 1990). In the open innovation paradigm, appropriation remains a central element for firms aiming to exchange and interact with their external environment. As such, Chesbrough and Brunswicker (2014) stipulated that the major challenge for managers in open innovation process lies in the protection of key competencies and the implementation of an intellectual property (IP) rights protection system. Indeed, there may be increasing tensions over knowledge exchange during the open innovation project, as openness practices carried concrete risks of knowledge leakage, encouraging free-riding behaviors (Marullo et al., 2020). According to Marullo et al. (2020), it was the knowledge paradoxes that involved the challenge of protecting internal IP assets, clearly challenging openness advantages. Laursen and Salter, (2014) have already invoked this paradox issue and concluded that a stronger appropriation capacity, using a variety of protection mechanisms ranging from patents to secrecy and speed to market, is associated with greater levels of openness. In this context, a recent study by Grimaldi et al. (2021) proposed a framework to characterize the different IP strategies in open innovation context by defining three strategies: defensive, collaborative and impromptu, which involve three IP protection mechanisms: formal, semi-formal and informal in order to obtain additional revenue streams, signal its competencies, and favor new partnerships. Grimaldi et al. (2021:157) concluded that “firms should be aware of their inside-out flows of knowledge and technology and protect their IP accordingly”, to prevent the risk of appropriation by competitors and to maintain and defend their competitive position in the market. Accordingly, firm should assess its degree of appropriation of its knowledge to know how to manage openness to the outside world. By doing, firms can decide which parts of the knowledge can be made open and which parts remain proprietary (Bogers et al., 2019: 81).

Therefore, the presence of appropriation capabilities reflecting the use of IP protection mechanisms may influence the degree of inbound open innovation and the level of interaction with external partners without fear of strategic knowledge leakage. However, empirical work addressing this relationship is, to our knowledge, non-existent in the SME context. In this paper, we wish to empirically verify the link between appropriation capabilities and open innovation by positing this hypothesis:

H1c: Appropriation capacities foster the degree of openness of SMEs towards inbound open innovation practices.

Inbound open innovation and innovation performance in SMEs

Research on open innovation in SMEs has been initiated since the first study by van de Vrande et al., in 2009 reporting the positive effects of different openness practices on innovation performance in these firms (Lee et al., 2010; Parida et al., 2012; Popa et al., 2017, Spithoven et al., 2013). Indeed, firms that adopt open practices are involved in a fertile environment for the development of innovations due to the multiplicity and importance of external sources of information and knowledge (Laursen and Salter, 2006; Lee et al., 2010, Chabbouh and Boujelbène, 2020). Likewise, engaging in collaborative relationships is a key factor in overcoming the lack of internal resources by taking advantage of the complementary resources dispersed in the external environment through various actors (Kraus et al., 2019; Torchia and Calabrò, 2019). Collaborating with a variety of external partners is also helpful to SMEs to generate new ideas, develop faster problem-solving skills, and perceive opportunities in rapidly changing environments (Lu et al., 2021). Hence, in a highly competitive and rapidly changing environment that makes innovation more difficult, costly, and risky, companies, including SMEs, are adopting the new open innovation approach to overcome all these challenges (Sağ et al., 2016).

With the expansion of open innovation research field, the literature has recently recognized additional empirical work examining the relationship between open innovation and SME performance. For instance, D’Angelo and Baroncelli (2019) revealed that the inbound innovation process within SMEs has a positive influence on the firm's product innovation. Lu et al. (2021) showed that open innovation strategies, in terms of the breadth and depth of external information sources, are positively associated with the SMEs’ innovation performance, as they help firms to obtain useful external knowledge and reduce the costs of firms’ innovation activities. In addition, Hervas-Olivier et al. (2021) investigated the relationship between inbound open innovation and the type of innovation pursued by SMEs through an empirical analysis of how different inbound open innovation sources, are associated with technological innovation strategies (process vs. product). Ovuakporie et al. (2021), for their part, showed a positive relationship between open innovation and innovation performance in service firms, confirming that when firms expand their internal knowledge base to allow the inflow of ideas from external sources and external collaborations, they expand their innovation opportunities pool, leading to increased innovation performance. Furthermore, systematic literature reviews on open innovation (Bigliardi et al., 2020, 2021; Kraus et al., 2019; Torchia and Calabrò, 2019) revealed that SMEs could benefit enormously from the dynamics of open innovation to improve innovation performance. Based on these findings, we attempt to verify the positive relationship between the degree of openness to inbound practices and innovation performance in SMEs through the following hypothesis:

H2: The degree of openness towards inbound open innovation practices improves innovation performance in SMEs.

The mediating effect of open innovation in the relationship between dynamic organizational capacities and innovation performance in SMEs

The literature review reports a consensus on the importance of the concept of dynamic capabilities as resources that generate competitive advantage by viewing it as a mechanism through which the firm learns and accumulates new skills and capabilities to deploy and coordinate different resources both internally and externally (Teece, 2007; Teece, 2020; Teece et al., 2020). Lately, the recent work of Teece (2020) has been illuminating in showing that strong dynamic capabilities are required to turn open innovation into a source of competitive advantage. As proclaimed by Bogers et al. (2019: 84), “dynamic capabilities can help companies effectively reap the full benefits of open innovation”. Empirically, the literature presents the last year of some studies linking dynamic capabilities, open innovation and firm performance. For instance, Kashosi et al. (2020) revealed the joint effect of openness strategies and firms’ absorptive knowledge capacity in improving SMEs’ innovation performance. In addition, Milan et al. (2020) showed through an explorative study that absorptive capacity positively affects the productivity of innovative activities and makes firms innovate faster in the light of open innovation practices. Likewise, Chabbouh and Boujelbène (2020) conducted an empirical study in Tunisian manufacturing SMEs to show partial mediating effect of inbound open innovation in the relationship between managerial and R&D capabilities and firm performance. Similarly, Pundziene et al. (2021), based on the most recent version of dynamic capabilities classification provided by Teece et al. (2020), found that dynamic capabilities strongly affect open innovation performance, which in turn affects the competitive performance of the firm. Furthermore, their results showed that open innovation partially mediates the path between dynamic capabilities and competitive firm performance.

Therefore, the resource-based approach and dynamic capabilities constitute, according to Hervas-Olivier et al. (2021), the basis of a framework to understanding how firms create and configure internal and external activities to build their capabilities to innovate. Thus, given an insufficient resource base, SMEs can be more effective in developing their innovation performance when they strengthen their dynamic organizational capabilities to successfully open up to the outside environment in order to identify, evaluate, and exploit new complementary resources and business opportunities that are considered value generators. This study regards the innovation performance of SMEs as the result of a consolidation of resources and competencies from internal and external sources, provided by a combination of dynamic organizational capabilities and the virtues of open innovation. However, we note, consistent with Pundziene et al. (2021), that so far there has been little research on assessing the impact of dynamic capabilities on firm performance through the intermediate role of open innovation. We therefore consider it interesting to examine the following mediating relationships:

H3a: The degree of openness towards inbound open innovation practices has a mediating effect on the relationship between dynamic capacities and the innovation performance in SMEs.

H3b: The degree of openness towards inbound open innovation practices has a mediating effect on the relationship between the absorptive capacity and the innovation performance in SMEs.

H3c: The degree of openness towards inbound open innovation practices has a mediating effect on the relationship between the appropriation capacities and the innovation performance in SMEs.

Presentation of the empirical study: methodology, analysis and discussion of results

Based on the above developments, we propose the empirical study of the degree of openness of companies towards inbound open innovation practices by the effect of dynamic organizational capacities and its relationship with innovation performance in SMEs. Our conceptual model is presented in Figure 1.

Figure 1.

Conceptual model.

Research methodology

In this paper, we adopt a quantitative methodological approach. In the paragraphs below, we specify the data collection and sampling procedure, the measurement of variables and the statistical methods used to test our hypotheses.

Data collection and sample design

The data used in this research are primary data, collected through a questionnaire. Thus, we took care to formulate questions in an unambiguous manner so that they would be easily understood by respondents. We also considered it relevant to test our questionnaire. The assistance of academic professors and professionals was of considerable importance in improving the relevance and quality of our survey.

To constitute our sample, the analysis unit chosen is an SME operating in the Tunisian manufacturing sector and carrying out an innovative activity. The selection of companies to be interviewed was based essentially on the examination of the database of the Agency for the Promotion of Industry and Innovation, which includes manufacturing companies in Tunisia. However, this database does not provide information on innovation activities in companies. For this reason, we have undertaken additional investigations by visiting the locations of several companies to ascertain the innovation component within them. In addition, we consulted the database of the National Institute for Normalization and Industrial Property, the organization responsible for the protection of innovations to obtain a list of SMEs that have deposited innovation patents in Tunisia. However, despite all these efforts to constitute our target population, it was impossible to have an exhaustive list of innovative Tunisian manufacturing companies that can serve as the basis for a probability sampling method. For this primary reason, we considered a non-probability sampling method to constitute our sample. Among the non-probabilistic methods, we chose the convenience method. This is a sampling technique in which the sample is composed of all the companies that the analysis considers involved and voluntary, i.e. those that have agreed to participate in the study at a given time. We regard the application of this technique as advantageous in our case, since it enabled us to incite companies to participate in the survey in order to have a representative sample. Finally, and following the convenience sampling method, we succeeded in collecting 228 questionnaires well completed using multiple modalities of data collection (electronic, postal, telephone and face to face) to optimize the response rate which was nearly 43%.

Measures of the variables

For the measurement of research variables, we propose multi-item instruments chosen based on a thorough literature review and assessed using 5-modality Likert-type scales. A detailed presentation of the measurement scales is provided in the appendix.

Firstly, dynamic capacities were measured drawing on a number of works (Chabbouh and Boujelbène, 2020; Grimaldi et al., 2013; Lee et al., 2010, Teece, 2020) by means of a six-item scale measuring the capabilities necessary for successful open innovation adoption. Absorptive capacity was measured through a four-item scale referring to Zahra and George’s (2002) operationalization which proposed to focus on the organizational routines and processes that the firm engages in to acquire, assimilate, transform and exploit external knowledge. Appropriation capacities were measured based on several studies (Grimaldi et al., 2021; Laursen and Salter, 2014) using a three-item scale evaluating the extent to which internal knowledge is valuable as a source of competitive advantage and the extent to which a variety of formal and informal mechanisms for protecting innovation are used. For measuring inbound open innovation , a ten-item scale was used based on the three main inbound practices (search for external sources of information, collaboration and acquisition of external resources and skills) that are widely reported in previous empirical work (Chabbouh and Boujelbène, 2020; Laursen and Salter, 2006; Ovuakporie et al., 2021; Parida et al., 2012; Popa et al., 2017). Finally, innovation performance was operationalized using an indicator reflecting the degree of satisfaction of firms with nine items, chosen from pioneering empirical work in the specific area of open innovation (Chabbouh and Boujelbène, 2020; Ovuakporie et al., 2021; Popa et al., 2017; Pundziene et al., 2021; Zobel, 2017) and measuring overall innovation performance. We also introduce in our model two control variables commonly used in these empirical studies. These are the size and age of the SME. The size of the SME was measured by referring to the number of employees in the firm. The age of the SME was measured by the number of years of activity during the year in which the survey was conducted.

Data analysis strategy

In this paper, we use three statistical methods to analyze the data. Firstly, we perform a factorial analysis through a principal component analysis with Varimax rotation in order to verify the unidimensionality of the variables. Bartlett's test of sphericity, which must be statistically significant (p < 0.05), and the Kaiser Meyer and Olkin (KMO) test, which must be greater than 0.5 (Hair et al., 2006), are used to assess the factorability of variables. Subsequently, we conduct a reliability analysis of the measurement scales by studying Cronbach's alpha coefficients. To confirm the internal consistency of the measurement scales, a value greater than 0.7 is acceptable.These first analyzes were performed using SPSS software. Finally, we test our conceptual model using SEM with AMOS software by adopting the maximum likelihood estimation procedure.

To test for indirect relationships and the presence of mediation, we follow the approach recommended by Baron and Kenny (1986), which is used in several empirical studies. This approach stipulates four successive steps:

Step 1: Verify that the independent variable (Xp) has a significant impact on the dependent variable (Y). The significance of coefficient (c) should be confirmed.

Step 2: Verify that the independent variable (Xp) has a significant effect on the mediator variable (XM). The significance of coefficient (a) should be confirmed.

Step 3: Verify that the mediator variable (XM) has a significant impact on the dependent variable (Y). This involves regressing (Y) on (XM) and (Xp). By controlling (Xp), the coefficient (b) between (XM) and (Y) must remain significant.

Step 4: To prove the existence of a full mediation by (XM), the coefficient (c’) that links (Xp) and (Y) must decrease to null value, when controlling for (XM). It is required to assert that (c’) = 0 in the presence of the mediator variable (XM), otherwise the mediation will be partial.

Results analysis and hypotheses verification

Study of the dimensionality of the variables and reliability

The results in Table 1 show that all variables are unidimensional by providing a single factor for each variable with a relatively high proportion of the total variance explained. Indeed, the KMO indices and Bartlett's sphericity tests are all significant, indicating the validity of the factor structure. No item retained in the analysis was excluded, especially with regard to the reliability studies of the measurement scales. Thus, all Cronbach's alpha values show very satisfactory internal consistency indices ranging from 0.876 to 0.934.

Table 1.

Results of principal component analysis and reliability.

Variables analyzed	Number of items	Extracted Factors	KMO Indice	Total variance explained	Cronbach's Alpha
Dynamic Capacities	6	DYNC	0.919 (0.000)^a	83.712	0.934
Absorptive Capacity	4	ABSC	0.871 (0.000) ^a	91.327	0.897
Appropriation Capacities	3	APPRC	0.806 (0.000) ^a	81.632	0.902
Openness to inbound practices	10	OPEN	0.823 (0.000) ^a	79.896	0.876
Innovation performance	9	INOPERF	0.834 (0.000) ^a	80.629	0.889

Note: ABSC: absorptive capacity; DYNC: dynamic capacities; INOPERF: innovation performance. Bartlett Sphericity Test (Sig).

Verification of research hypotheses

For our model testing the direct relationships between the variables in our research, the results are shown in Figure 2 and Tables 2 and 3.

Figure 2.

Structural model testing direct effects.

Table 2.

Adjustment indices of the model testing the direct effects.

Absolute indices			Incremental indices			Parsimony indices
GFI	AGFI	RMSEA	NFI	CFI	TLI	CMIN/DF	AIC	CAIC
0.996	0.963	0.015	0.998	1.000	0.999	3.150/3 = 1.050 P = 0.369	53.150	163.884

Note: GFI: goodness of fit index; RMSEA: root mean square error of approximation; NFI: normed fit index; CFI: comparative fit index; TLI: Tucker-Lewis index; CMIN: Chi square statistic; DF: degrees of freedom; AIC: Akaike information criterion; CAIC: consistent Akaike information criterion.

Table 3.

Results of direct relationships by structural equation modeling.

Structural relations	Structural parameters
Structural relations	Direct effects	CR	Sig. P
DYNC→OPEN	0.495	9.703	0.000
ABSC→OPEN	0.144	4.200	0.000
APPRC→OPEN	0.235	5.971	0.000
SIZE→OPEN	0.175	4.066	0.000
AGE→OPEN	0.029	1.211	0.226
OPEN→INOPERF	0.788	16.400	0.000
SIZE→INOPERF	0.151	3.284	0.001
AGE→ INOPERF	−0.006	−0.207	0.836

Note: DYNC: dynamic capacities; ABSC: absorptive capacity; APPRC: appropriation capacities; INOPERF: innovation performance.

The results in Table 2 show very satisfactory indications of good model fit.

Looking at the third table, the examination of the obtained indicators shows that the degree of openness of SMEs is positively and very significantly influenced by the dynamic capacities (0.495; p = 0.000), the absorption capacity (0.144; p = 0.000) and the appropriation capacities (0.235; p = 0.000). Indeed, the impact of dynamic capacities is the strongest We therefore validate the first three hypotheses (H1a, H1b and H1c). Concerning the control variables, the results indicate a positive and significant relationship between the size of the company and the degree of openness. However, the effect of the age of the firm is not justified.

Moreover, the results of the SEM allow us to confirm Hypothesis H2 by arguing that the degree of openness of SMEs to external environment has a positive and significant influence on the firm's innovation performance; with respect to the coefficient obtained (0.788; p = 0.00).

In order to test the indirect relations in accordance with the approach of Baron and Kenny (1986), we tested a second model to estimate the direct and indirect relations between dynamic organizational capacities and innovation performance in the presence of the mediator variable relating to the degree of openness. Figure 3 presents our second model.

Figure 3.

Structural model testing the mediation of inbound open innovation.

In Table 4, we first present the model fit values measuring the mediating effect that validate the model's good fit to the data.

Table 4.

Adjustment indices of the model testing the mediation of inbound open innovation.

Absolute indices			incremental indices			Parsimony indices
GFI	AGFI	RMSEA	NFI	CFI	TLI	CMIN/DF	AIC	CAIC
0.994	0.910	0.084	0.997	0.998	0.979	5.191/2 = 2.595 P = 0.075	57.191	172.354

In Table 5, we summarize most of the tests on the direct and indirect effects of different organizational capacities on innovation performance through the mediating role of the degree of inbound openness.

Table 5.

Results of direct and indirect relationships by structural equation modeling.

Steps	Structural relationships	Direct effect	CR	P-value	Indirect effect	Total effect
1	DYNC → INOPERF	0.453	6.293	0.000	–	–
	ABSC → INOPERF	0.137	2.818	0.005	–	–
	APPRC → INOPERF	0.163	2.938	0.003	–	–
2	DYNC → OPEN	0.495	9.703	0.000	–	–
	ABSC → OPEN	0.144	4.200	0.000	–	–
	APPRC → OPEN	0.235	5.971	0.000	–	–
3 et 4	OPEN → INOPERF	0.726	9.092	0.000	–	–
	DYNC → INOPERF	0.195	2.967	0.003	0.360	0.555
	ABSC → INOPERF	0.027	0.602	0.547^NS	0.105	0.132
	APPRC → INOPERF	−0.016	−0.310	0.757 ^NS	0.171	0.155

Note: DYNC: dynamic capacities; ABSC: absorptive capacity; APPRC: appropriation capacities; INOPERF: innovation performance.

Firstly, we examined the direct relationships between the independent variables and the dependent variable without considering the mediator variable to validate the first step of the Baron and Kenny (1986) mediation approach. As a result of this analysis, we found positive and significant relationships between the three organizational capacities and innovation performance (Step 1). The second step of the mediation approach, which aims at assessing the relationship between the independent variables and the mediator variable, was well validated through the confirmation of hypotheses (H1a), (H2a) and (H3a). Going further, the test of the complete model presenting the direct and indirect relationships (Figure 2) shows that coupled open innovation remains interesting in the explanation of innovation performance, taking into account the three independent variables. This confirms the third step of the mediation process, which attests the presence of the mediator effect generated by the inbound open innovation. Lastly, it is interesting to assess the perfect or partial nature of this mediating effect. This evaluation is carried out by looking at the significance of the direct relationship between the dependent variable and the independent variable with and in the absence of the mediating variable. According to the results of the estimation of our complete model and in comparison with the estimation of the model testing the direct link in the absence of the mediator variable, the direct effect of dynamic capacities on innovation performance decreased while remaining significant in the presence of the mediator variable. The coefficient increased from 0.453 (step 1) to 0.195 (step 4). This finding reveals the partial effect of mediation in this relationship. Consequently, dynamic capacities have a direct positive effect on performance in the order of (0.195) and an indirect positive effect in the order of (0.360) reported by inbound open innovation. We can therefore confirm the hypothesis H3a through the presence of partial mediation.

However, the results of the full model show that the direct effect between absorptive capacity and innovation performance becomes insignificant in the presence of the mediator variable in step 4. This suggests that the mediation by inbound open innovation is perfect and thus generates an indirect effect of (0.105) between absorptive capacity and innovation performance. Similarly, the direct effect between appropriation capacities and innovation performance changed to insignificant in the presence of the mediating variable. Here, there is also a perfect mediator effect via the open innovation. Appropriation capacities have a positive indirect effect in the order of (0.171) via the inbound practices. Hence, we can confirm the hypotheses H3b and H3c through a perfect mediation.

Results discussion

Findings show that dynamic capabilities have a positive influence on the degree of openness to inbound practices. This result is consistent with previous works (Chabbouh and Boujelbène, 2020; Grimaldi et al., 2013) revealing that opening organizational boundaries requires managers with high dynamic capabilities to proactively act and respond to the opportunities existing in the external environment and also to manage the openness of the innovation process. Moreover, our results confirm, in line with several other studies (Kim et al., 2016; Lu et al., 2021; Scuotto et al., 2017; Spithoven et al., 2011; Zobel, 2017), that the presence of absorption capacity within companies promotes inbound open innovation by stimulating the exploration and integration of new knowledge from outside. Furthermore, this study sheds light on the relevance of knowledge appropriation capacities in boosting the degree of openness towards inbound practices. This confirms insights in the open innovation literature (Grimaldi et al., 2021; Laursen and Salter, 2014). Therefore, appropriation capacities are essential for companies to adopt a more open behavior in innovation management since they allow them to protect their basic knowledge and prevent its appropriation by others.

Our analyzes highlight the significant effect of inbound open innovation degree on innovation performance in SMEs. This supports well the existing empirical literature (Hervas-Olivier et al., 2021; Lu et al., 2021; Ovuakporie et al., 2021; Popa et al., 2017) and reveals that as firms expand their internal knowledge base through external sources of ideas and knowledge, they broaden their innovation opportunities pool, which leads to improved innovation performance. In addition, our research provides empirical evidence regarding the existence of mediating relationships through the effect of inbound open innovation in the linkage between dynamic capabilities and innovation performance in SMEs. In summary, our findings corroborate recent results obtained by Chabbouh and Boujelbène (2020) and Pundziene et al. (2021) and confirm that dynamic organizational capabilities, in conjunction with successful implementation of inbound open innovation, can help SMEs to advance their internal innovation process and, consequently, develop their innovation performance.

Conclusion

This paper proposes to study the determinants of inbound open innovation from the perspective of dynamic capabilities and its effect on innovation performance in SMEs. Our results confirm that organizational capabilities in terms of dynamic capabilities, absorptive capability and appropriation capabilities have positive effects on the adoption of inbound open innovation practices. Furthermore, analyzes show that open innovation has a significant impact on innovation performance within SMEs. Empirical evidence was also provided to support the mediating role of inbound open innovation in the relationship between different organizational capabilities and innovation performance.

Theoretical contribution and practical implications

The contributions of this research are twofold. In terms of theoretical perspective, our study proposes a link between the dynamic capabilities theory and the open innovation paradigm, two literatures which do not have much overlap (Chabbouh and Boujelbène, 2020; Ovuakporie et al., 2021; Pundziene et al., 2021), in order to improve the current understanding of innovation performance in the SME context. Indeed, our study enriches the previous works through the proposal and empirical validation of a conceptual model explaining open innovation through its determinants in terms of dynamic organizational capabilities and its implications on innovation performance in SMEs. Furthermore, analyzing open innovation as a mediator is considered an innovative insight and fills a gap in the open innovation literature, as to our knowledge, no study so far has examined the mediating role of inbound open innovation in the relationship between dynamic organizational capabilities and innovation performance in SMEs.

In terms of practical implications, our results are mainly relevant for entrepreneurs and innovation managers in SMEs who can find valuable guidance on how to address innovation challenges in their companies. Indeed, by orienting their strategy towards entrepreneurial practices based on proactivity and the search for new business opportunities, managers need to be aware of the importance of inbound open innovation as an effective way to overcome the difficulties and risks associated with innovation activities and to sustain their company's performance accordingly. Thus, it is essential for managers who are already adopting inbound practices or who intend to move towards them to recognize the key success factors of this approach in order to successfully plan and make the right decisions in managing innovation process. Obviously, developing dynamic organizational capabilities is a key prerequisite for the successful adoption of open innovation practices. In light of our results, managers should strengthen their dynamic capabilities related to the identification, evaluation and exploitation of business opportunities available in the external environment, and also those related to the implementation of new organizational practices due to the opening of the innovation process. Furthermore, innovation managers need to improve their absorptive capacity for new knowledge, which has proven to be extremely important for better combining internal and external sources of innovation. However, the company needs to be aware of its external knowledge flows to avoid losing its core competitive advantage when interacting with external environment. Therefore, capitalizing on their ability to appropriate internally developed knowledge is a very useful suggestion for managers to protect the knowledge base and reduce the risk of appropriation by competitors. Overall, the presence of all these dynamic organizational capabilities enables entrepreneurs and innovation managers to successfully implement inbound practices, marginalize risks and avoid failures to reap the full benefits by creating a combination of internal and external knowledge sources that strengthens innovation performance, widely considered a key driver of economic success.

Research limitations and future research directions

Despite its theoretical and managerial contributions, our research has some limitations. Indeed, only inbound dimension related to the open innovation model has been considered. Future studies should explore the other dimensions in the context of SMEs, such as the study of the combined effects of inbound and outbound processes on business performance. Furthermore, this paper examines the degree of openness only through the effect of three dynamic organizational capabilities and has not taken into account other potential factors that could also serve as antecedents of open innovation to enhance firm innovation performance. Thus, other factors, both internal and external, that may potentially influence the relationship between open innovation and firm performance still need to be explored. Although our paper addresses some important issues related to dynamic capabilities, open innovation and innovation performance in SMEs, further studies are recommended to understand the extent to which the nexus between the dynamic capabilities perspective and the open innovation approach represents a source of undeniable competitive advantage for SMEs in supporting their performance in an ever-changing open environment.

Footnotes

Declaration of conflicting interests

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

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Hajer Chabbouh

Notes

Appendix: Items used to measure research constructs

Variables	Some References
Dynamic capacities (Cronbach's Alpha = 0.934)
DYNC. I1: Capacity to identify business opportunities DYNC. I2: Capacity to evaluate and exploit opportunities and signals from the environment DYNC. I3: Capacity to pilot and organize change DYNC. I4: Capacity to adapt the organizational structure DYNC. I5: Capacity to reorganize staff to better respond to the challenges of innovation DYNC. I6: Capacity to improve human potential through continuous training programs	Lee et al. (2010); Grimaldi et al., (2013); Brunswicker and Ehrenmann (2013); Teece (2020); Pundziene et al. (2021).
Absorptive Capacity (Cronbach's Alpha = 0.897)
ABSC. I1: Capacity to identify and acquire new knowledge from external sources ABSC. I2: Capacity to interpret, analyze and understand new knowledge produced elsewhere ABSC. I3: Capacity to adapt, to combine new knowledge from partners with the internal knowledge base ABSC. I4: Capacity to incorporate and integrate the acquired, assimilated and transformed knowledge into the company's current operations	Zahra and George (2002); Spithoven et al. (2011); Kim et al. (2016); Scuotto et al. (2017); Zobel (2017); Milan et al. (2020); Kashosi et al. (2020); Lu et al. (2021).
Appropriation Capacities (Cronbach's Alpha = 0.902)
APPRC. I1: Capacity to appreciate the value of knowledge that forms the basis of competitive advantage APPRC. I2: Capacity to protect the development of innovations through formal instruments APPRC.I3: Capacity to protect the development of innovations through informal instruments	Laursen and Salter (2014); Marullo et al. (2020); Grimaldi et al. (2021)
Inbound Open Innovation (Cronbach's Alpha = 0.876)
OPEN I1: Search for information from market sources OPEN I2: Search for information from research sources OPEN I3: Search for information from generally available sources OPEN I4: Collaborating with customers OPEN I5: Collaborating with suppliers OPEN I6: Collaborating with end users OPEN I7: Collaborating with the academic world OPEN I8: Acquisition of knowledge or patents developed by other organizations OPEN I9: Acquisition of technological equipment indispensable for innovation OPEN I10: Recruitment of new skills	Laursen and Salter (2006); Van de Vrande et al. (2009); Lee et al. (2010); Dahlander and Gann (2010); Parida et al. (2012), Spithoven et al. (2013); Popa et al. (2017); Chabbouh and Boujelbène (2020); Ovuakporie et al. (2021)
Innovation Performance (Cronbach's Alpha = 0.889)
INOPERF I1: Volume of sales of new or significantly improved products INOPERF I2: Profitability of sales of new or significantly improved products INOPERF I3: Overall company growth of the firm INOPERF I4: Competitiveness of the firm INOPERF I5: Product and services quality INOPERF I6: Creation of new knowledge INOPERF I7: Technological skills and know-how of employees INOPERF I8: Reduction of time to market INOPERF I9: Overall efficiency of the innovation process	Spithoven et al. (2013); Popa et al., 2017; Zobel (2017); Chabbouh and Boujelbène (2020); Ovuakporie et al. (2021); Pundziene et al. (2021)

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