The literature on measuring open innovation implementation has attracted significant attention, as researchers aim to identify key variables that shape OII and its practices (refer to Table 1 for a literature summary). The distinction between viewing OII as an organizational transformational change (Boscherini et al., 2013) versus as an openness toward ideation and knowledge sources (Cui et al., 2018) depends on the organizational, managerial, technological, and contextual aspects of the measurement method being used.

In an effort to understand the transition from closed to open innovation, Chiaroni et al. (2010, 2011) investigated managerial factors such as organizational archetype, knowledge management systems, collaborative networks, and technology transfer process assessment. Boscherini et al. (2010) argued that these managerial factors should be considered during the realization phase of an open innovation pilot project, as they help firms adjust their internal structures and procedures to adopt open innovation approach and redefine their business processes.

Naruetharadhol et al. (2020) proposed that organizational archetype, knowledge management, and networks all serve as organizational and managerial means for assessing open innovation implementation. They also discovered that these subdimensions of OII positively influence both inbound and outbound innovation practices. This finding is crucial, as OII has the potential to offer small businesses a competitive edge in innovation.

The concept of organizational archetype refers to the underlying patterns of behavior, culture, and values that characterize an organization (Hill, 2021, p. 405; Rubio-Andrés & Abril, 2023). It reflects the organization's unique identity, which is shaped by its history, leadership style, and the beliefs and attitudes of its employees. SME archetype designs, with their ability to embed key behavioral routines supporting innovation, have primarily been categorized as innovation generators or adopters (Pérez-Luño et al., 2011). Recent research has indicated that decentralization, open communication, and employee empowerment are essential elements to the fostering of creativity and innovativeness within organizational archetypes (Çakar & Ertürk, 2010; Min et al., 2016). The decentralization of decision-making aligns with driving employee-driven innovation (Kesting & Ulhøi, 2010). Further, centralization may result in inefficiency in the context of knowledge transfer and sharing (Tsai, 2002), while the coordination and decentralization of opportunity exploitation increase the accessibility and deployment of external knowledge sources (Foss et al., 2013).

Liao et al. (2011) argued that firms can adopt a hybrid decision-making archetype to respond to market changes by utilizing centralization for financial decisions (e.g., capital budgeting and pricing policies) and decentralization for marketing decisions. Organizational design can help facilitate a firm's interactions with external sources, which implies that well-designed organizational archetypes are dedicated to incorporating new knowledge acquisition into the firm's innovation process and supporting communication among project collaborators (Chiaroni et al., 2011). In some cases, reorganization is necessary to pursue external paths to the market for internally developed innovation ideas (Boscherini et al., 2013; Naruetharadhol et al., 2020).

Organizational archetype design for open innovation should address (1) the roles of technological gatekeepers and shepherds in assembling internal and external knowledge and facilitating communication exchange between networks (Ter Wal et al., 2017); (2) the creation and development of innovation champions (Sergeeva, 2016); and (3) the provision of reward systems and incentives to support the new OI paradigm (Burcharth et al., 2017). Adopting a centralized approach and establishing a dedicated business development unit, Lichtenthaler and Ernst (2007) suggested that adequate skills and organizational capabilities are necessary for the effective external knowledge exploitation of proprietary technologies. This implies that SMEs need to ensure that revenues from external technology commercialization and the management of commercialization tasks still occur at the firm level. Thus, the choice of organizational archetype significantly impacts a firm's capacity to innovate and adapt to changing market conditions.

Knowledge is a crucial resource for innovation activities and serves as a form of capital; therefore, effective knowledge management is considered vital for a firm's ability to innovate (Gold et al., 2001). While there is no universally accepted measure of knowledge management for innovation in the literature, firms' focus on knowledge management activities (such as creating, sharing, utilizing (Liao et al., 2011), and retaining knowledge (Adamides & Karacapilidis, 2020)) has long been considered to be a suitable proxy. Other measures include knowledge management enablers and processes (Yeh et al., 2006). Lopes et al. (2017) found that knowledge management promotes open innovation, and Naqshbandi and Jasimuddin (2018) discovered that developing knowledge management capabilities encourages firms to continuously perform open innovation by supporting their infrastructure and processes.

To search for new ideas and technologies, Chiaroni et al. (2010, 2011) recommended that open innovation firms increase the breadth and depth of their innovation networks. However, the evidence regarding the effect of collaborative networks on open innovation is mixed. An empirical study of high-to-low technology firms found that the search breadth of external innovators is a key consideration in explaining the exploration process involved in knowledge learning. Nevertheless, firms’ acquisition and assimilation capabilities can begin to decrease due to overextended external partners (Ferreras-Méndez et al., 2016), leading to the so-called paradox of openness to collaboration (Laursen & Salter, 2014).

To establish and profit from a value network, Lee et al. (2010) emphasized the importance of collaboration in both exploration and exploitation stages. They proposed a collaboration model using various combined networks, including the following: (1) supplier-customer relations as outsourcing; (2) strategic alliances as partnerships; and (3) interfirm alliances as networks. Furthermore, SMEs need to interact with a variety of stakeholders, including industry, research institutions and universities, suppliers, customers and users, community, and society (Yun & Liu, 2019). Nieto and Santamaría (2007) found that technological collaborative networks positively influence innovation novelty, providing a direct source of technological knowledge and skills with which to improve a firm's innovation process.

SMEs must position technology transfer as an implementation aspect of open innovation process that is used to evaluate project-based innovation for intellectual property (Drivas et al., 2018) and the commercialization of new technologies (Rahal & Rabelo, 2006). Technology transfer refers to the movement of technical knowledge, know-how, or technology from one organizational environment to another (Rambe & Khaola, 2022; Scarrà & Piccaluga, 2022). Huang et al. (2010) classify the technology transfer determinants into (1) having a technology transfer office; (2) cooperation and inventor involvement; (3) nature and sophistication of technology; (4) commercialization; (5) intellectual property exclusivity; and (6) university-industry technology transfer facilitation and organizational psychological factors. Hess and Siegwart (2013) proposed large corporate technology transfer models (e.g., corporate R&D, corporate venturing, and corporate mergers & acquisitions) for systematically scanning and monitoring the spectrum of technologies that are accessible in the external environment. These appear to be growing in importance in regard to the outside-in direction of open innovation, suggesting business value propositions for implementation. The literature also suggests that external exploitation alternatives such as selling patents, out-licensing deals, cross-licensing patents, and spin-outs are necessary (Hung & Chou, 2013). Yun et al. (2018) further indicated that the technological value of firms such as SMEs can only grow as a result of open innovation. Adamides and Karacapilidis (2020) highlighted the fact that information technology can support open innovation implementation by facilitating knowledge sharing, collaboration, and coordination among internal and external stakeholders. However, the beginning of the technology transfer process, as well as SMEs’ potential profits due to innovation, could be affected.

Based on all arguments, organizational archetype, knowledge management, collaborative networks, and technology transfer are essential in implementing the adoption of open innovation, which leads to the following hypothesis:

H1. Open innovation implementation (OII) is a second-order factor comprising the subdimensions of organizational archetype, knowledge management, technology transfer, and collaborative networks.

H1a. Organizational archetype positively facilitates open innovation implementation.

H1b. Knowledge management systems positively facilitate open innovation implementation.

H1c. Technology transfer positively facilitates open innovation implementation.

H1d. Collaborative networks positively facilitate open innovation implementation.

The current research considers open innovation through two relevant processes: (1) the process of implementation and (2) open innovation practices. Huizingh (2011) describes the first process as a paradigm shift toward open innovation; this is framed as the process of opening up (more) closed innovation processes. The OI process is referred to in this research as open innovation implementation (OII). The second process is concerned with open innovation practices: how can open innovation be conducted? Now that the first process of open innovation is set up and addressed in the prior section, this section proceeds to provide an understanding of open innovation practices for SMEs.

Small firms are more persistent and attempt to survive longer when they are open to new knowledge and able to adapt to new ideas of innovation development while being united by a shared goal. Innovation is a complex system; its success relies on striking a balance between exploiting knowledge resources and exploring new ones. Thus, the concepts of exploration and exploitation are necessary for innovation and the operation of new ventures, meaning that both exploration and exploitation may be the single most essential strategic task for any firm's management.

Ambidextrous innovation refers to a firm's ability to simultaneously pursue both exploratory and exploitative innovations (O'Reilly & Tushman, 2013). By adopting open innovation practices, SMEs can enhance their ambidexterity, leveraging external knowledge and resources to explore new opportunities while also exploiting their existing capabilities. These practices include collaborating with external partners, such as suppliers, customers, universities, and research institutions, to cocreate value and foster knowledge exchange. They also involve integrating various open innovation mechanisms, such as crowdsourcing, open source software, and open data, to facilitate knowledge sharing and ideation.

This concept of open ambidextrous innovation essentially refers to organizational ambidexterity, which is cited as a key antecedent of structure, context, and leadership in open innovation performance (Bodwell & Chermack, 2010). The exploration-and-exploitation model posits that within organizational change processes, firms adapt their organization to new requirements, such as open innovation from the firm environment, by altering their organizational, managerial, contextual, and technological drivers. Consequently, it is understandable why open innovation implementation is required prior to establishing open innovation practices.

Organizational ambidexterity is defined as a company's ability to respond to market demands in an aligned and efficient manner while also being adaptable to changes in the environment (Chang et al., 2019; Hanifah et al., 2019; Kaur et al., 2019). Organizational ambidexterity necessitates the use of both exploration and exploitation techniques to foster open innovation. The ambidexterity model of exploration and exploitation relates to the open innovation process in the form of inbound, outbound (Hung & Chou, 2013), and coupled innovation activities (Cheng & Huizingh, 2014). In this research, a new form of ambidexterity is identified, which is referred to as open ambidextrous innovation. As in the work of Huizingh (2011), our terminology is used interchangeably with open innovation practices, but these are explored here in the context of ambidexterity. Exploration and exploitation efforts based on open innovation initiatives constitute open ambidextrous innovation. Firms' innovation ambidexterity results are derived from open innovation approaches, where firms are no longer restricted to their R&D departments in the exploration and exploitation of technologies. In constructing the idea of open ambidextrous innovation, we attempted to link organizational ambidexterity with open innovation practices. In return, this allows us to classify open innovation practices into three modes of ambidexterity: inbound (exploration), outbound (exploitation), and coupled (exploration and exploitation) open innovations. Gassmann and Enkel (2004) classified the open innovation approach into three core archetypes. Consequently, open ambidextrous innovation practices are defined in this study as an SME's ability to simultaneously pursue and balance both exploratory and exploitative innovation while leveraging external resources and collaboration. This approach enables companies to maintain a competitive advantage by fostering incremental improvements and radical breakthroughs in products, processes, or business models by leveraging open innovation strategies.

First and foremost, inbound open innovation refers to the inflows of technological or knowledge exploration that are related to innovation activities aimed at capturing and leveraging external knowledge sources to improve current technological developments (Burcharth et al., 2014). Second, outbound open innovation refers to the outflows of knowledge or technology exploitation through which a company may benefit from the sale of its intellectual property or the transfer of information and technologies to third-party organizations (Lichtenthaler & Ernst, 2009; Lichtenthaler & Lichtenthaler, 2009). Third, coupled open innovation refers to the integration of these two exploration and exploitation processes through dyadic collaboration (cocreation), cooperation with partners (through strategic partnerships and strategic networks with complementary assets), and collaboration with partners (through strategic partnerships and strategic networks with complementary assets) (West & Bogers, 2014).

In this research, the literature review reveals that Cheng and Huizingh (2014); Greco et al. (2016), and Naruetharadhol et al. (2020) tested the open innovation approach using a unidimensional construct and individual aspects. Our paper contends that each subconstruct of open innovation varies slightly in testing, reflecting the underlying dimensions of these previous studies. We argue that inbound, outbound, and coupled open innovations represent ambidextrous practices in the open innovation approach explored in SME studies, thus warranting empirical investigation. The rationale for this examination is the consideration of open ambidextrous innovation as a second-order factor. As previously mentioned, it appears that these three dimensions can be effectively combined and used to illustrate the concept of open innovation practices; thus, we hypothesize the following:

H2. Open ambidextrous innovation practice is a second-order factor comprising the subdimensions of inbound, outbound, and coupled open innovation activities.

H2a. Inbound open innovation positively facilitates open ambidextrous innovation practice.

H2b. Outbound open innovation positively facilitates open ambidextrous innovation practice.

H2c. Coupled open innovation positively facilitates open ambidextrous innovation practice.

Chiu and Lin (2022) argued that supply chain open innovation capabilities are essential for organizations aiming to achieve a competitive advantage in today's rapidly evolving business landscape. By implementing open innovation practices, firms can tap into external knowledge sources and resources, thereby enhancing their innovation capacity and accelerating new product development. Chiu and Lin (2022) highlighted the importance of understanding the underlying mechanisms that drive open innovation implementation within supply chains. By linking these findings to open innovation implementation and open innovation practices, it becomes evident that the research underscores the importance of understanding the interplay among knowledge creation, internal mechanisms, and technology within supply chains. By doing so, SMEs can effectively implement open innovation practices and enhance their innovation capabilities.

The necessity of formalizing the link between open innovation implementation and ambidextrous practices is a point of debate. Such a link can be used to form a theoretical framework, as illustrated in Fig. 1, which is tested in this study. Although some studies have already identified the importance of certain open innovation implementation activities (Bianchi et al., 2011; Chiaroni et al., 2011), there is still room for further examination of the specific link between open innovation implementation (OII) and open ambidextrous innovation. The connection between these two processes of open innovation is not yet fully understood, thus requiring further investigation.

Fig. 1.

Conceptual research model.

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To gain a comprehensive understanding of the implementation enablers, it is necessary to scrutinize the organizational, managerial, technical, and contextual factors within open innovation environments. Despite its importance, the open innovation literature (e.g., Aloini et al., 2017; Biagini et al., 2014; Bianchi et al., 2011; Boscherini et al., 2010; Chiu & Lin, 2022; De Oliveira et al., 2018; Naruetharadhol et al., 2020; Ukko & Saunila, 2020) has predominantly neglected to explore the influence of open innovation implementation on open ambidextrous innovation practices. Furthermore, a more profound understanding of the key implementers and the interplay between exploration and exploitation in the context of coupled open innovation is necessary. Given these gaps, our study is meant to provide valuable insights into the critical factors and mechanisms that contribute to the successful adoption of open ambidextrous innovation practices, thereby enriching the open innovation literature and guiding practitioners in their pursuit of effective innovation strategies. Given the gaps in the literature, the following hypothesis should be considered:

H3. Open innovation implementation is positively related to open ambidextrous innovation practices.

A seven-point Likert scale was employed in the questionnaire design, with response options ranging from 1 to 7 indicating significant disagreement or agreement. Because the seven-point scale is the most accurate and representative reflection of a participant's assessment (Finstad, 2010), it tends to include a broader variety of response options, increasing the probability of aligning well with participants' objective reality. Appendix 1 contains the specifics of all major structures and questions (with minimal modifications) from prior research. The (re)definitions of each construct as constructed to fit the research context are presented in Table 2.

The latent exogenous variable, open innovation implementation, represents the degree to which SMEs implement open innovation internally, particularly in within the dimensions of organizational archetype, knowledge management, technology transfer, and collaborative networks. To measure the construct of open innovation implementation, four latent endogenous variables are used in this study as follows: organizational archetype, knowledge management, technology transfer, and collaborative networks.

The term organizational archetype refers to a pattern that characterizes the structure, behavior, and operational features of a firm, with the aim of achieving a balance between maintaining optimizing efficiency within existing operations and fostering innovation by investing in new ideas or technologies. The organizational archetype concept is derived from four 7-point rating items adapted from Chiaroni et al. (2011) and Liao et al. (2011), which are used to measure SMEs’ behavior (OA1) emphasizing new product or service introductions, (OA2) establishing knowledge network integration, (OA3) creating a gatekeeping role, and (OA4) being open to change.

The term knowledge management can be defined as a crucial process involving creating, sharing, utilizing, and retaining knowledge within an organization to enhance its innovation capabilities. Knowledge management is measured using four 7-point rating items as adapted from Chiaroni et al. (2011), Liao et al. (2011), and Naruetharadhol et al. (2020), which are used to assess firm activities such as (KM1) creating new knowledge to improve the firm's level of innovation, (KM2) sharing useful knowledge both within firm boundaries and with external partners, (KM3) engaging in knowledge utilization processes to develop scalable solutions, and (KM4) maintaining a knowledge database within firm boundaries.

Technology transfer can be defined as the movement of technical knowledge, know-how, or technology from one organizational environment to another, which is a critical aspect in implementing an open innovation process. Technology transfer is measured using four 7-point rating items adapted from Yun et al. (2018) and Chiaroni et al. (2011), which is used to assess firm activities such as (TT1) the evaluation process of technological knowledge acquisition and licensing (e.g., patents, industrial design rights, copyrights) for creating or improving (new) innovations based on existing resources, (TT2) preparing for intellectual property registration; (TT3) evaluating knowledge assets for commercialization in the market; and (TT4) implementing an IT skill base and IT infrastructure.

Collaborative networks can be defined as interconnected relationships among various stakeholders, such as industry, research institutions, universities, suppliers, customers, and users, that open innovative firms establish to search for new ideas and technologies. A four 7-point rating item scale, based on Laursen and Salter (2014) and Ferreras-Méndez et al. (2015), is used to measure potential collaborators, including (NWK1) partner firms (e.g., competitors, enterprises from the same industry or business group, software or material suppliers, or other large corporations), (NWK2) clients or customers, (NWK3) higher education institutions or universities, and (NWK4) public or government institutions.

The latent exogenous variable, open innovation practices, represents the degree to which SMEs use collaboration strategies and processes with their external partners, such as customers, suppliers, universities, and research institutions, to generate, share, and apply knowledge for the development of new products, services, and processes. To measure the construct of open innovation practices, this paper utilizes three latent endogenous variables: inbound open innovation, outbound open innovation, and coupled open innovation.

Three items were adapted from Hung and Chou (2013) and Cheng and Huizingh (2014) to assess respondent perceptions of agreement and disagreement concerning their potential inbound open innovation activities. These activities include (IOI1) engaging with external partners, such as consultants, research institutes, government, customers, competitors, universities, or suppliers in innovation projects; (IOI2) acquiring licenses, intellectual property, or know-how from external sources, such as R&D-related services, technological knowledge, or online technical course platforms; and (IOI3) utilizing the internet to explore new trends or technologies. In this study, inbound open innovation can be defined as inflows of technological or knowledge exploration that relate to innovation activities. These activities aim to leverage external sources of knowledge to improve current technological developments.

Three items were adapted from Hung and Chou (2013) and Cheng and Huizingh (2014) to assess respondent perceptions of agreement and disagreement regarding their potential outbound open innovation activities. These activities include (OOI1) selling licenses to other companies in the market for patents, copyrights, or know-how; (OOI2) operating divisions, product lines, and/or specialized business units (e.g., promoters, gatekeepers) with the purpose of commercializing knowledge assets, such as through spin-offs, cross-licensing patents, or selling; and (OOI3) actively participating in externally funded innovation projects. Consequently, outbound open innovation refers to the outflows of knowledge or technology exploitation, where a company benefits from the sale of its intellectual property or the transfer of information and technologies to third-party organizations.

Three items were based on Hung and Chou (2013) and Cheng and Huizingh (2014) and used to evaluate respondent perceptions of agreement and disagreement concerning their potential coupled open innovation activities. These activities consisted of (COI1) coordinating comarketing activities; (COI2) cooperating with partners to synchronize their information-sharing efforts; and (COI3) participating in collaborative R&D arrangements, such as R&D joint ventures or alliances. Coupled open innovation encompasses the integration of both inbound (exploration) and outbound (exploitation) processes.

In this study, firm size is considered to be a control variable used to examine differences among key variables while accounting for the potential influence of a company's size. Demographic indicators such as the number of employees and annual revenue or fixed assets can be used to measure firm size. In Thailand, the Office of Small and Medium Enterprises Promotion (OSMEP) defines these enterprises based on their number of employees (European Commission, 2015; OSMEP, 2019). Further, the terms SMEs and MSMEs are used interchangeably in this study. The classification of businesses into micro, small, medium, and large enterprises depends on various criteria, including the number of employees and the annual revenue or fixed assets of the firm. For instance, microenterprises have up to 5 employees or fixed assets of up to THB 5 million, while small enterprises have 6–50 employees or fixed assets between THB 5–50 million. In the manufacturing sector, medium-sized enterprises have 51–200 employees or fixed assets between THB 50–200 million. By controlling for firm size, the study isolates the effects of other variables of interest and makes more accurate comparisons across companies of different sizes. Consequently, there was no need to develop a separate hypothesis for firm size, as the main focus was to investigate the relationships between open innovation implementation and practices.

The use of bootstrap 95% confidence intervals can be a valid approach to estimating the precision of the parameter estimates and evaluating the fit of the model (Cheung & Lau, 2015). Fig. 2 illustrates the results of a structural model. We pose the following question: what are the primary characteristics of open innovation implementation (OII) and open ambidextrous innovation practices (OAIPs)? This result suggests that the variable organizational archetype has a significant positive effect (as represented by the standardized regression coefficient β = 0.858) on the open innovation implementation variable. In other words, an increase in the level of a firm's organizational archetype is associated with an increase in the level of open innovation implementation. A p value of less than 0.004 indicates that this relationship is statistically significant, and Hypothesis 1a is supported. Additionally, the R-squared value of 0.735 suggests that the organizational archetype variable can explain 73.5% of the variance in open innovation implementation. The result indicates that there is a positive relationship between knowledge management and open innovation implementation. The beta coefficient of 0.905 suggests that for every one-unit increase in knowledge management, open innovation implementation increases by 0.905 units. A p value of less than 0.004 indicates that the relationship between knowledge management and open innovation implementation is statistically significant, thus Hypothesis 1b is supported. The R-squared value of 0.82 suggests that 82% of the variance in open innovation implementation can be explained by knowledge management. Therefore, this result implies that organizations that prioritize knowledge management are more likely to successfully implement open innovation practices than those that do not. The result suggests that there is a significant positive relationship between technology transfer and open innovation implementation. Specifically, for every one-unit increase in technology transfer, open innovation implementation is expected to increase by 0.773 units. A p value of less than 0.004 indicates that this relationship is statistically significant, providing evidence to support Hypothesis 1c. The R-squared value of 0.597 indicates that technology transfer can explain 59.7% of the variance in open innovation implementation. A p value of less than 0.004 indicates that the relationship between collaborative networks and open innovation implementation is statistically significant. Therefore, Hypothesis 1d is supported. The beta coefficient of 0.863 suggests that for every one-unit increase in collaborative networks, there is an estimated 0.863-unit increase in open innovation implementation. The R-squared value of 0.745 indicates that collaborative networks can explain approximately 74.5% of the variance in open innovation implementation. The analysis confirms Hypothesis 1, which is the first research question, indicating that open innovation implementation (OII) is a second-order factor consisting of the subdimensions of organizational archetype, knowledge management systems, collaborative networks, and technology transfer. This is supported by the confirmation of H1a to H1d, which verifies that each subdimension has a statistically significant and positive influence on OII.

Fig. 2.

Baseline model (Aggregate group).

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Furthermore, the results indicate that there is a statistically significant and positive relationship between inbound open innovation and open ambidextrous innovation practice. The β value of 0.91 indicates that for every one-unit increase in inbound open innovation, there is a 0.91-unit increase in open ambidextrous innovation practice. A p value of less than 0.004 indicates that this relationship is statistically significant, meaning that we can reject the null hypothesis and accept the alternative hypothesis that inbound open innovation positively influences open ambidextrous innovation practice. The R-squared value of 0.828 suggests that 82.8% of the variance in open ambidextrous innovation practice can be explained by inbound open innovation. Overall, these results support Hypothesis 2a. The results of the analysis support Hypothesis 2b, which proposes a positive relationship between outbound open innovation and open ambidextrous innovation practice. The beta coefficient of 0.867 indicates that outbound open innovation has a strong positive influence on open ambidextrous innovation practice. The R-squared value of 0.752 indicates that 75.2% of the variance in open ambidextrous innovation practice can be explained by outbound open innovation. Finally, a p value of less than 0.004 suggests that this relationship is statistically significant. Thus, the results suggest that there is a positive relationship between coupled open innovation and open ambidextrous innovation practice, and this relationship is statistically significant with a p value of less than 0.004. Hypothesis 2c is supported. The beta coefficient of 0.897 indicates that coupled open innovation has a strong positive influence on open ambidextrous innovation practice. The R-squared value of 0.804 suggests that coupled open innovation explains 80.4% of the variance in open ambidextrous innovation practice. Hypothesis 2 is thus confirmed by the analysis indicating that open ambidextrous innovation practice is a second-order factor consisting of inbound, outbound, and coupled open innovation activities. The confirmations of H2a through H2c provide support for this finding.

How does the OII process influence SME open innovation practices? To answer our second research question, the results indicate that there is a positive relationship between open innovation implementation and open ambidextrous innovation practices, and this relationship is statistically significant with a p value of less than 0.004. The beta coefficient of 0.545 suggests that open innovation implementation has a moderate positive influence on open ambidextrous innovation practices. The critical ratio of 0.297 is not commonly used as a measure of statistical significance, but it does suggest that the effect size is relatively large. Overall, Hypothesis 3 is supported by the analysis, which supports the idea that there is a positive relationship between open innovation implementation and open ambidextrous innovation practices, thus addressing the second research question. Table 7 shows the results of a statistical analysis conducted to examine whether the relationships among the variables used in the models are the same across different firm sizes. The size characteristics of SMEs, such as micro, small, and medium-sized enterprises, are also considered in the study. The results showed a significant and positive relationship between open innovation implementation and open ambidextrous innovation practices across all SME sizes. The beta coefficients of 0.464, 0.507, and 0.597 for micro, small, and medium-sized enterprises, respectively, suggest that open innovation implementation exerts a positive influence on open ambidextrous innovation practices across all SME sizes, as shown in Fig. 3. The R-squared values of 0.215, 0.257, and 0.357 indicate that open innovation implementation explains a significant portion of the variance in open ambidextrous innovation practices for micro, small, and medium-sized enterprises, respectively. The p value of less than 0.004 suggests that these relationships are statistically significant. The results of the multigroup moderation analysis shown in Table 8 indicate that there were significant behavioral differences among Thai microsized and small-sized enterprises regarding their organizational archetype, their knowledge management, and their coupled open innovation activities. This is supported by the critical ratio differences, which were found to be statistically significant (Z test > 1.96). This could be because microsized enterprises may have limited resources compared to small-sized enterprises, which could impact their ability to implement certain organizational archetypes or knowledge management practices.

Fig. 3.

Conceptual framework and the multigroup structural model.

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To understand how the OII process has an impact on SMEs’ open ambidextrous innovation practices, we explored these effects statistically using key item scale activities that are discussed in the next section.

Recent research findings demonstrate a positive relationship among organizational archetypes, knowledge management, technology transfer, and collaborative networks, as evidenced by the confirmation of Hypothesis 1. These primary latent constructs contribute to explaining the secondary latent formation of open innovation implementation. This discovery supports the findings of (Naruetharadhol et al., 2020); however, the unique contribution offered here concerns the role of technology transfer. The current research findings emphasize that technology transfer is one of the key components for implementing open innovation, as evidenced by the confirmation of Hypothesis 1c. Comparing this finding with those of Chiaroni et al. (2010, 2011), and Boscherini et al. (2010), who identified organizational archetype, knowledge management systems, evaluation processes, and networks as key components, it can be argued that technology transfer complements and interacts with these factors to promote open innovation. For instance, technology transfer is an essential component of open innovation implementation, as it involves the exchange of technological knowledge, expertise, and innovations between different organizations or stakeholders (Hess & Siegwart, 2013; Yun et al., 2018).

The research findings show that organizational archetype exerts a positive influence on open innovation implementation, as evidenced by the confirmation of Hypothesis 1a. Although Chiaroni et al. (2010, 2011), Boscherini et al. (2010), and Naruetharadhol et al. (2020) emphasized the importance of organizational archetypes, this research suggests that Thai SMEs, particularly family-owned SMEs, should focus on addressing the underlying patterns of behavior, culture, and values that characterize an organization. These Thai SMEs should not concentrate solely on the formal arrangement of roles and responsibilities.

The research findings that show the positive influence of knowledge management on open innovation implementation (as evidenced by the confirmation of Hypothesis 1b) align with the findings of Chiaroni et al. (2010, 2011), Boscherini et al. (2010), Cui et al. (2018), and Naruetharadhol et al. (2020). These studies collectively underscore the importance of knowledge management, organizational archetype, and archetype in shaping an organization's approach to innovation and its ability to successfully implement open innovation practices. The results of this research suggest that knowledge management should (1) promote the efficient transfer of technological expertise and know-how, which in turn supports open innovation efforts, and (2) create an environment that promotes communication, collaboration, and knowledge sharing among different departments, teams, and external partners.

As evidenced by the confirmation of Hypothesis 1d, collaborative networks have a positive influence on open innovation implementation because they facilitate the exchange of ideas, knowledge, and resources among organizations, partners, and other stakeholders. These networks play a crucial role in creating connections that enable organizations to access external sources of innovation and expertise, thereby enhancing their ability to innovate and compete effectively in the marketplace. Studies such as Chiaroni et al. (2010, 2011), Boscherini et al. (2010), De Oliveira et al. (2018), Bianchi et al. (2011) and Naruetharadhol et al. (2020) have persistently emphasized the significance of collaborative networks in the successful implementation of open innovation. The positive relationship between collaborative networks and open innovation implementation suggests that the connections and interactions among organizations, partners, and other stakeholders are crucial for facilitating the exchange of ideas, knowledge, and resources. This finding can be further enhanced by adopting the quadruple helix model of innovation rather than using the triple helix model. The quadruple helix model emphasizes users or customers in the innovation process (Yun & Liu, 2019). By adopting the quadruple helix model of innovation, Thai SMEs can better involve their customers or users in the innovation process, which ensures that the needs and expectations of end users are better understood and addressed.

Hypothesis 2 recognizes the importance of open innovation in the success of modern organizations and suggests that an open ambidextrous innovation practice can serve as a framework for effectively implementing open innovation strategies. Hypothesis 2 proposes that an open ambidextrous innovation practice is a second-order factor comprising the subdimensions of inbound, outbound, and coupled open innovation activities. This leads to the support of Huizingh (2011) and Naruetharadhol et al. (2020). Huizingh (2011) identifies various open innovation practices, such as inbound open innovation (e.g., external knowledge acquisition), outbound open innovation (e.g., external knowledge exploitation), and coupled open innovation (e.g., collaboration with external partners). By considering open ambidextrous innovation practice as a second-order factor, a more comprehensive understanding of the complex dynamics of open innovation can be gained. The interplay between different open innovation activities is examined in this research and the most effective combinations of inbound, outbound, and coupled open innovation practices are identified.

The findings of the current research provide empirical evidence to support the theoretical framework outlined in the literature and further emphasize the importance of open innovation practices in promoting the ambidexterity of firms. While previous studies such as Cheng and Huizingh (2014) and Naruetharadhol et al. (2020) have focused on the individual dimensions of open innovation, this research highlights the importance of considering open innovation practices as a holistic approach of the second-order factor model to innovation that requires ambidexterity. Inbound, outbound, and coupled open innovation practices can be leveraged together to facilitate a firm's ability to explore and exploit new knowledge resources and effectively respond to market demands. As evidenced by the confirmation of Hypothesis 2a, inbound open innovation positively influences (explains) open ambidextrous innovation practice. This finding suggests that when small firms engage in inbound open innovation activities, such as collaborating with external partners or seeking external knowledge sources, they are more likely to take an open ambidextrous innovation approach. This is because inbound open innovation activities facilitate the acquisition of new knowledge and ideas, which can then be explored and exploited by the firm through ambidextrous practices. In other words, inbound open innovation provides a foundation that enables open ambidextrous innovation practices.

West and Bogers (2014) suggest that outbound open innovation can involve collaboration with partners through strategic partnerships and strategic networks with complementary assets. This indicates that outbound open innovation can facilitate the exchange of knowledge and resources with external partners, which can enhance a firm's ability to innovate and compete effectively in the marketplace. Therefore, the confirmation of Hypothesis 2b, which states that outbound open innovation positively influences open ambidextrous innovation practice, is supported by the idea that outbound open innovation can be a key factor in promoting collaboration and knowledge exchange with external partners that contributes to a firm's ability to conduct both exploration and exploitation activities in their innovation efforts. For example, Chesbrough (2003) argues that firms should look beyond their internal resources and capabilities and leverage external sources of innovation to enhance their competitive advantage. Similarly, Lichtenthaler (2009) suggests that firms that engage in outbound open innovation activities are more likely to achieve superior innovation performance. This research finding is consistent with the previous research of West and Bogers (2014) that emphasizes the importance of outbound open innovation in promoting firms’ innovative capabilities.

The results of this research provide compelling evidence that coupled open innovation is a critical aspect of open ambidextrous innovation practices. The integration of both inbound and outbound open innovation activities is essential for successful innovation outcomes. This finding is consistent with those of the Cheng and Huizingh (2014) and Greco et al. (2016), which acknowledge the importance of coupled open innovation. Cheng and Huizingh (2014) proposed the concept of coupled open innovation, while Greco et al. (2016) emphasized the role of collaboration in open innovation. Both studies underscore the advantages of collaboration among different organizations and stakeholders in open innovation initiatives, which is a critical aspect of coupled open innovation. The current research expands on these findings by providing empirical evidence that coupled open innovation positively influences open ambidextrous innovation practice. The confirmation of Hypothesis 2c highlights the significance of integrating inbound and outbound open innovation activities through collaboration to achieve innovation success. This emphasizes the need for SMEs to engage in collaborative efforts to access external sources of innovation and expertise, promoting their ability to innovate and compete effectively in the market. Thus, coupled open innovation can be considered to be a strategic tool that enables firms to leverage external sources of innovation and internal knowledge resources to achieve a competitive advantage.

The results of this research support Hypothesis 3, which states that open innovation implementation is positively related to open ambidextrous innovation practices. This research provides evidence supporting the idea that open innovation implementation is positively linked to open ambidextrous innovation practices, as is hypothesized. This finding is consistent with those of previous studies such as Chiaroni et al. (2010, 2011), Boscherini et al. (2010), and Mortara and Minshall (2011), who have also found that implementing open innovation practices can enhance a firm's ability to engage in both exploration and exploitation activities. Mortara and Minshall (2011) highlight the importance of culture and skills for open innovation, which are key components of organizational archetypes and knowledge management systems.

Furthermore, our study adds to the literature by conceptualizing open innovation implementation as a second-order factor comprising four subdimensions: organizational archetype, knowledge management systems, collaborative networks, and technology transfer. This approach is in line with previous research by Naruetharadhol et al. (2020), who also emphasize the importance of considering multiple aspects in open innovation implementation.

Similarly, the findings support the idea that an open ambidextrous innovation practice is a second-order factor comprising the subdimensions of inbound, outbound, and coupled open innovation activities. This aligns with the work of Huizingh (2011), who first proposed the concept of open innovation as a paradigm shift toward more collaborative and open practices, and with subsequent research conducted by Naruetharadhol et al. (2020), who investigated the impact of open innovation implementation on small firms' propensity for inbound and outbound open innovation practices. The importance of both open innovation implementation and open ambidextrous innovation practices for firms seeking to effectively engage in innovation activities is clear. By recognizing the multidimensional nature of these concepts, Thai SMEs can better assess their innovation capabilities and design strategies that maximize their ability to explore and exploit new knowledge and technologies. Nevertheless, in comparison to Chiu and Lin's (2022) findings, which emphasize that developing open innovation capability in the context of supply chain management is crucial for supporting collaborative learning, idea generation, and problem solving among supply chain partners, our results demonstrate that open innovation achieved through ambidexterity is essential, as it can lead to a balance between exploitative and exploratory innovation activities, ultimately creating a supportive pattern for supply chain management.

The results of this research support the hypothesis that open innovation implementation is positively related to open ambidextrous innovation practices. The theoretical contributions of this study are noteworthy. First, it adds to the open innovation literature by examining how SMEs implement open innovation internally. The dimensionality of open innovation implementation (OII) and its effect on open ambidextrous innovation practices (OAIP) in six regions of Thailand were identified, contributing to the literature's identification of organizational archetype, knowledge management, and networks as key implementations for open innovation (Naruetharadhol et al., 2020). The conceptual development of open innovation implementation should also consider the role of technology transfer. Additionally, our emphasis on open innovation activities supported by the idea of ambidexterity is crucial, as ambidexterity is the ability to balance explorative and exploitative innovation processes. The direction of innovation exploration and exploitation is also a common element in defining open innovation practices (Hung & Chou, 2013). Another key theoretical contribution based on our empirical results is the new terminology of "open ambidextrous innovation practice," which incorporates insights from ambidexterity and open innovation practices. Our study's findings support similar arguments made by Huizingh (2011) regarding the relevance of these two open innovation processes.

The Thai government is committed to promoting integrated innovation strategies within the country. The first five years of the 20-year National Strategy (2017–2036) are outlined in the Twelfth Plan, and their aim is to:

“…encourage collaboration and partnerships among all stakeholders, including government, academia, industry, and communities, in order to create an environment and system that supports research and fosters innovation development” (NESDB, 2017).