2.1. Family-owned SMEs across generations

Family-owned SMEs are characterized by the coexistence of family and business systems, which may emerge a specific bundle of capabilities and resources [6]. In family SMEs, the family is at the heart of the operation, with family members connected by a sense of responsibility and loyalty and for the firm’s success [7]. When considering individual social networks as social capital, it is important to clarify their roles to a firm-level exploration network [8,9]. Otherwise, when multi-networks overlap, it results in multiplexity or paradox of openness, thereby causing tensions among actors in collaborative innovation [10]. There are no clear lines between the business and family systems, which makes multiplexity in family businesses a lot like living in a tight-knit community with strong norms, trust, and rules. Thus, family-owned SMEs may not be as flexible as non-family-owned SMEs [11]. Family businesses with a high degree of interdependence between family members are more likely to share information, work collectively, and offer support [6]. Thus, it can be summarized that there is a relationship between family involvement in the firm and family-to-business support, and this relationship’s effect may have on their business model innovation.

The theory of generational cohorts has made a few notable contributions to the field of business model innovation. As posed by Karl Mannheim in 1928 [12], the premise of generational cohort theory is that a generation of individuals who grow up in the same political, economic, and social environments will have a similar set of views, attitudes, and behaviors during the early stages of life experience [13,14]. Pure generational cohort theory says that a generation can be shaped by unexpected events like terrorist acts or war, and it can be shaped by new technology like digital media, which can change the way those generational cohorts think about things. Prior research by Kariv et al. (2014) finds that situations, difficulties, and resources encountered at each development stage of business model innovation will be interpreted differently among generational cohorts [15]. Thach et al. (2021) show that the events that take place in the year of their birth and the subsequent 10 to 20 years have an impact on each generational cohort [14]. We concluded that experience age plays a role in the management of business model innovation.

This current study segmented four main generational cohorts: Generation Z (between 1997 and 2009); Generation Y (between 1981 and 1996); Generation X (between 1965 and 1980); and Baby boomers (between 1946 and 1964). Please note that the year of birth relies on the arguments about generational differences suggested by Kotler et al. (2021) [16].

However, it is important build a better narrative about business that takes societal issues into account when it comes to its main business model. If there are leftover profits to be only addressed in the business model while viewing society as an add-on. This would not please either baby boomers or millennials (Generation Y) because both age cohorts want business to be a positive force in society. Both want business to work to build stronger communities, cleaner environments, and more meaningful and better jobs. It all signals that the role of generation matters. For example, in conditions in which family business owners are motivated to deeply create new business venture because the new venture creation pertains to new business unit as a result of cooperation that will directly affect them. When seeing the great benefits from open innovation cooperation, family business owners are more inclined to open up their innovation process/activities and develop new joint venture. This is how collaboration-based business model innovation takes place. The degree of open innovation cooperation to develop collaboration-based business model innovation depends on family business owners’ experience age. When they perceive that their experience age to share knowledge is lower, generational cohorts by experience age moderates the effects of cooperation on the propensity for collaboration-based business model innovation. We developed the following hypothesis:

1. H1: Relationships between open innovation cooperation and collaboration-based business model innovation will be different depending on generation cohorts.

They also influence what happens before an open innovation strategy is used, so the breadth and depth of openness for each generation are going to be different. Another hypothesis was proposed:

1. H2: Generational cohorts will have different effects on the breadth and depth of openness based on the antecedent factors of open innovation, which are made up of innovation capability, innovative human capital, network partnership benefits, and strategic agility.

2.2. Open innovation

The second stream of the literature is focused on the theory of open innovation. The term open innovation was first coined by Chesbrough (2003) in his book, which mentioned the new paradigm for creating and profiting from technological innovation [17]. This concept is opposite to closed innovation, which means firms believe that innovations are developed by them for their own benefit–no need for cooperation [18,19]. Many SMEs were struggling to adjust to and recover from the effects of a shortage of capabilities and resources [20]. Many resorted to open innovation, which is a collaborative approach that plays to the strengths of all key stakeholders and may offer innovative, unexpected solutions [21,22]. Open innovation is thus a type of cooperation that is worthwhile to engage in whether or not SMEs are in such short supply [23]. This comes to the reason why open innovation is required as a theoretical base.

Since 2003, the literature shows us an evolution of open innovation theory. Open innovation involves the business management model for innovation that promotes collaboration with organizations and people outside the firm [24]. This current study agreed that understanding and mastering each of the innovation evolution stages is what leads to the very best open innovation has to offer. In the early era, Open Innovation 1.0 was focused on cross-functional approaches, cross-licensing, and value networks. These triple helix innovation models are developed by academia (universities), industry, and government to promote economic and social development. Following the quadruple helix model of innovation, open innovation evolved into a more customer-centric formula. Thus, in this Open Innovation 2.0 era, it is centered around understanding customer segmentation, innovation ecosystem, cross-fertilization, orchestration, and value constellation. However, SMEs that share knowledge must keep in touch with a lot of partners to come up with new ideas. From a birds-eye view, those SMEs act like a swarm, looking for a win-win situation because they work well together in networks, communities, and so on. Thus, the era of Open Innovation 3.0 emerged. In this way, collaborative SME clusters and networks are formed with communities that are both flexible and stable enough to store and use collective learning in multi-agent systems. We also call this era Enterprise 3.0. The rise of the trend towards automation and data exchange in manufacturing technologies and processes (i.e., industrial internet of things, etc.) changed the industrial path to interconnection. Thus, the quadruple helix model is worth keeping an eye on the fact that some of the stakeholders in innovation are now interacting with public relations, customers, and the media, while others are interacting with the environment. The open innovation notion has started to move to more digital and sustainable regimes and implement Open Innovation 4.0. This era is also given importance in Industry 4.0. The rise of Sustainable Development Goals (SDGs) has become a rather hot potato. The new attention is given to human, social, economic, and environmental sustainability. It was suddenly shifted to Society 5.0. In other words, Open Innovation 5.0 will be the university-industry-government-community-environment integration to provide social solutions. The quadruple and quintuple innovation helix frameworks are also activated. This era has great concern about how technology can empower and enhance sustainability. Thus, we take the importance of the environment and public society into account. The current research views the interactions among quadruple and quintuple innovation helix stakeholders as the knowledge base to conceptualize open innovation in terms of breadth, depth, and cooperation plan. They will be used in the next section to shape open innovation breadth and depth before influencing the open innovation cooperation plan.

2.3. The antecedent factors

Innovation capability The capacity to innovate is a predictor of firm-level innovation [34]. The previous definitions of innovation capability are featured as follows. Laforet (2011) defines innovation capability as the potential or ability to create new ideas or innovations [35], while Martínez-Román et al. (2011) define it as internal capability, especially related to the creation and appropriation of organizational knowledge [36]. Thus, this research defines innovation capability as the capability of innovating firms to integrate the firm’s core skills and resources to generate innovation. Carrasco-Carvajal & Garciá-Pérez-De-Lema (2020) pinpoint that innovation capability plays an enabler role in fostering open innovation [37]. They further illustrate that (1) innovation capabilities enable key stakeholders such as customers to interact with the innovations they develop, and (2) such a firm with knowledge-sharing capabilities would be better at sharing existing knowledge with its network partners [37]. Therefore, this research implies that innovation capability has a positive relationship with the success of collaborative innovation. Ahmad et al., 2020 argued that when family involvement in business increases, there may be an impact on the capability to innovate, thereby impacting its strategic perspective performance [2]. Innovation capability is one predicter of how firms assess their internal capabilities to open up their innovation process. This is how firms use innovation capability to improve their business innovation performance. As a result, this leads to the following hypothesis:

1. H5: Innovation capacity positively affects open innovation breadth and depth.

Innovative human capital McGuirk et al. (2015) divide human capital into tangible elements (e.g., education and training) and intangible elements (e.g., job satisfaction and willingness to change) [38]. It is re-titled as innovative human capital. They further addressed that these elements play an important role in fostering firm-level innovation [38]. Chabbouh and Boujelbene (2020) argued that human capital elements (e.g., strategic vision, managerial skills, social networks, and R&D capacity) appear to affect the degree of openness to innovation, further affecting firms’ innovation performance [5]. Human capital is also important when it comes to internal innovation capacity. This internal (open) innovation capability is as the cultural openness to innovation [39]. Open innovation in the sense of Lichtenthaler and Lichtenthaler (2009) is often linked to R&D personnel and the presence of technical talents inside the firm [5,40]. Through their technical and scientific skills, SMEs would be able to absorb new knowledge from external sources, attract network partners, and thus exploit new opportunities for collaboration [41,42]. Hossain and Kauranen (2016) suggest that human capital in terms of knowledge depth at individual level are necessary for open innovation in the setting of small and medium-sized enterprises (SMEs) [43]. It can be concluded that human capital is likely to influence open innovation, thereby impacting firm performance. Hence,

1. H6: Innovative human capital positively affects open innovation breadth and depth.

Network partnership benefits A closer look at knowledge flows in family firms’ open innovation shows one factor that family-owned businesses should be concerned about—network partnership benefits. In this research context, it can be defined as firm partners that collaborate across borders to share the core values of the network in their innovation activities. Thus, network partnerships are like external knowledge sources [9]. Naruetharadhol et al. (2020) indicate networks as an implementer to foster inbound and outbound open innovation propensities [44]. Naruetharadhol et al. (2022) further find that collaborative networks positively explain the implementation of open innovation [18]. According to resource-based theory, when one network member contributes more resources, all network partners benefit. Collaboration in a business confederation may thus assist family SMEs in obtaining advantages such as finding new business and/or innovation prospects, gaining access to strategic expertise, and enhancing the entrepreneur’s professional profile [1,45]. In a value network, possible cooperation modalities include strategic alliances, R&D collaborations, and joining a network of other value networks [46]. Networks as a result of partnership collaboration may provide significant benefits to family businesses by assisting them in decoding and overcoming flows of limited source of internal knowledge, thus enhancing their competitive edge. Accordingly, we imply that there may be a possibility that the benefits arising from network partnership may positively influence open innovation in family firms. We assumed that:

1. H7: Network partnership benefits positively affect open innovation breadth and depth.

Strategic agility The term agile first came up in the business and innovation literature when it was used to describe flexible manufacturing systems (FMS) [47]. Agility and flexibility are often used together; the relationship between flexibility and agility is similar to the relationship between competence and capability [48]. Agility is a capacity that is outwardly oriented, while flexibility is a capability that is inwardly focused, and is thus an antecedent of agility [49]. The capacity to explore and exploit market arbitrage possibilities is one of the most distinctive characteristics of agility [50]. Agile firms must be used to efficiently obtaining resources and harmonizing capabilities, as well as to gathering the necessary assets, knowledge, and networks in a timely manner [51]. This perspective is consistent with not only the understanding of agility as a dynamic capability [50], but also an innovation paradigm for firms, which may require a group of meta-capabilities [52]. According to Lu and Ramamurthy (2011), organizational agility encompasses two distinct concepts: market capitalizing agility and operational adjustment agility [53]. Shin et al. (2015) argued that it appears that the strategic aim of SMEs toward agility can improve operational performance and customer retention, but not financial performance [49]. We first imply that it is possible that agility may affect firm performance. Liao et al. (2019) illustrate that when seeing inbound and outbound open innovations as strategic resources, it was found that openness affected firms’ dynamic capabilities in terms of market capitalizing agility and operational adjustment agility, thereby impacting business model innovation [54]. To differ from the study of Liao et al. (2019), we consider strategic agility as a capability antecedent. This is because open innovation (culture) requires sufficient flexibility and agility in a firm’s resources so that they can be used to support the innovation projects [39,55]. Thus, more flexible and agile, more open. It comes to the next hypothesis:

1. H8: Strategic agility positively affects open innovation breadth and depth.

3.1. Data collection and sampling

Primary data was collected in the form of a structured questionnaire survey design a as a technique for data collection. This is to examine the existing processes used by the theories suggested in the literature [58]. A self-administered questionnaire was used to deliver the survey through the internet. This study leveraged the number of SMEs from the database executed by the Office of SME Promotion (OSMEP) to determine the whole population (N) of 756,344 registered SMEs. According to Krejcie and Morgan’s premise (1970), the sample size for a known population was determined at approximately 384 SMEs, with a confidence level of 95 percent that the real value is within 5% of what was measured or surveyed and a level of accuracy for the sample of 50%. [59]. However, this study depends on multigroup structural invariance. There was a multi-sample group specification for family SMEs with multigenerational ownership, suggesting that each group needed to have at least 100 observations to perform the multigroup modeling [60]. Thus, this study set the minimum sample size required to gather data on four-generational ownership at 400.

Most Thai SMEs are family businesses, which embed strong family values, build trust, and give back to the community [18]. Philanthropy is at the heart of many high-quality family businesses around the world. They are a good model for the communities their innovation serves and also have a long-term impact on society as a whole through the values they contribute to. Consequently, family-owned business entrepreneurs were targeted for three key reasons. First, family SMEs fitted the research need for participatory innovation projects with their networking-partner role [61]. Second, they are both niche players and industry giants. They work on a wide range of innovation projects in a variety of industries, products, and businesses [62]. They may be able to draw on a much broader range of experience and collaborative R&D settings [63]. Both reasons carry a positive signal of open innovation. Third, according to the Thailand Family Business Survey 2019, it was found that 76% of Thai family-owned businesses have the next generation working in their businesses [64]. Furthermore, 96% of family businesses were expected to grow over the next two years, while 86% planned to pass on the baton to the next generation [64]. The impacts could be summarized as the role of generations impacting business model innovation performance. Hence, family SMEs’ business model innovation performance is worth investigating.

However, this current study was carried out in strict accordance with the recommendations in the Guide for human-subjects research. The protocol was approved by the Committee on the Ethics of Human Research of Khon Kaen University (Protocol Number: HE653009). All surveyed data was protected under the privacy and personal identity information of all respondents.

Although the SME information is available for search in Department of Business Development (DBD) Data Warehouse, probability sampling may not be applicable. This is because there is no completed list for random sampling. Purposive random sampling is applied. With this sampling technique, the sample selection depends on geographical area and inclusion criteria as follows:

• Must be over 18 years old
• Must be family-owned business only
• SME managers, business owners, entrepreneurs, and CEOs must be present. If none of the aforementioned people are accessible, they may send the questionnaire to anybody who works in a role that includes firm-level innovation only.
• Must identify the business’s location in terms of area and province.
• SMEs must be incorporated as legal entities.

The respondents will be excluded from quantitative study if one of these criteria is absent. The pilot questionnaire was sent out to 30 family-owned SMEs. This was a purposeful random sample as we needed to ensure that we sampled those SME managers, business owners, entrepreneurs, and CEOs from a variety of generational ownership. However, the comment was that some of the questionnaire content was difficult to understand; we decided to revise that content and re-distribute the survey. Please note that those 30 pilot samples will not be included in the final analysis.

To prevent a low return rate on the questionnaire, 800 questionnaires were sent out to a specific sample of family-owned SMEs for a six-month period (May–October 2021), where 200 questionnaires were targeted for each generation. Confidentiality has been assured in an online sample volunteer consent form, and respondents were provided with no identity for return. Table 1 lists the demographic profiles of all respondents grouped by generations.

• 111 were returned for those respondents in Generation Z (18–26 years old), accounting for 19.72%.
• 183 were returned for those respondents in Generation Y (27–41 years old), accounting for 32.5%.
• 163 were returned for those respondents in Generation Y (27–41 years old), accounting for 28.95%.
• 106 were returned for those respondents in Generation Baby Boomer (above 57 years old), accounting for 18.83%. It took almost four months to collect the data from this generation. 83 were available to fill out the questionnaire paper, and we went to meet them at their physical offices. There was a hybrid survey that happened for this generation.

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Table 1. Sample characteristics.

https://doi.org/10.1371/journal.pone.0265025.t001

Overall, 600 respondents successfully filled in the questionnaire, but only 563 respondents (111+183+163+106 = 563) were validated because data supplied by 47 respondents was unreliable as it contained missing information. The response rate was given at 93.83%. A sample of 563 is sufficient to test for validity and reliability in the step of data analysis.

3.2. Instrumentation and variables

A survey instrument is comprised of two sections of questions as to demographics and variables of interest. We used seven-point Likert scales of agreement category to operationalize the research variables, ranging from strongly disagree (1) to strongly agree (7). Appendix 1 shows a list of questions for questionnaire development in this current study. However, the following variables were modified to fit the context of the research, with a partial adoption from the previous studies. Innovation capability was measured using the scale developed by Ahmad et al. (2020) [2], Liao et al. (2007) [65], and Lin (2007) [66] to ask the respondents about how family-owned SMEs are able to come up with new ideas and turn them into new or better products, services, or processes that benefit the firm. Those items included (IC1) constantly obtains new talents or resources to improve our firm’s innovation processes during the last five years, (IC2) try out new ideas or ways to innovate within our sources during the last five years., (IC3) have capabilities for R&D of new products or services at a certain level., and (IC4) increase the number of new products and services introduced to the market during the last five years. Innovative human capital was designed to capture manifestations of the underlying IHC construct by McGuirk et al. (2015) [38], Chabbouh and Boujelbene (2020) [5], and Vidotto et al. (2017) [67]. In terms of the understanding, behaviors, awareness, and development of tacit knowledge within individuals, the respondents were asked how family-owned SMEs embody knowledge, including (IHC1) increase in the level of skills necessary to carry out the specific job, (IHC2) encourage staff to join the training program during the last five years, and (IHC3) empower employees to be creative. Network partnership benefits were conceptualized using a variety of scale developed by Chesbrough and Appleyard (2007) [68], Lee et al. (2010) [69], and Del Vecchio et al. (2019) [1]. They were asked about how family-owned SMEs would think about the benefits arising from network partnership. The item scale included (NPB1) access to R&D partnerships and corporate collaborations, (NPB2) solve business problems, and (NPB3) create new revenue streams. For strategic agility, we used four-item scale based on Cepeda and Arias-Pérez (2019) [70], Panda (2021) [71], and Ashrafi et al. (2019) [72], asking them as to how family-owned SMEs are agile when developing new innovation or improving existing one. Those measures included (SA1) respond to and capitalize on general/disruptive changes, (SA2) effective IT-business support, (SA3) encourage quick internal adjustments whenever there is a shortage of resources (manpower, funding, etc.), and (SA4) quick decision-making in the face of market changes.

This research extracted measurement items of open innovation breadth and depth, and cooperation. Open innovation breadth and depth were focused on (OBD1) search for external knowledge sources (OBD2) identify key stakeholders (such as suppliers, customers, universities, rivals, laboratory, local community etc.), and (OBD3) select key stakeholders to partner with. Open innovation cooperation was measured using the scale by Kontinakis & Zhang (2018) [33] and Kontinakis et al. (2019) [73] and focused on (OIC1) explore relevant and feasible activities and project, (OIC2) consult local stakeholders to select what to propose, (OIC3) discuss their funding, timing and expected, (OIC4) allocate probable resources, and (OIC5) execute and manage activities, projects, and risks. This is because the contents of both constructs are different themselves although they are under the main concept of open innovation strategy execution. That is, open innovation as a cooperation plan is considered at the stage by which family SMEs promote joint innovation project strategies for cooperation and details the cooperation between two or more stakeholders [33].

Finally, the main idea we attempt to focus on is collaboration-based business model innovation performance. This is being developed to understand how collaboration-based business model innovation is likely to be managed and performed once open innovation cooperation has a positive-signal impact. As items were adopted from a mix of previous studies by Zhu et al. (2019) [31] and Kariv et al. (2014) [15], they included (CBMI1) scale up the collaboration-based business model, (CBMI2) enables demand aggregation, (CBMI3) incorporate transparency into the business model, (CBMI4) offer new solutions and combinations of products/services/knowledge, and (CBMI5) brings new collaboration stakeholders in the business model innovation. The data analysis will be discussed in more detail in the next section, which is concerned with operationalizing the variables of interest.

Step 1: Measurement model (CFA)

• CFA was used to confirm the constituent indicators in the measurement models of internal conformity, convergent validity, and discriminant validity, using the observed variables in the measurement model of ready components [76].
• GOF. The passed threshold levels, see Table 2: Goodness of Fit of the Structural Model, are consistent with the concept [76]. The values of CMIN/df (2.674), CFI (0.934), IFI (0.934) and RMSEA (0.055), and TLI (0.922) were an acceptable fit.
• Convergent validity. Cronbach’s alpha and composite reliability analysis exceeded the specified threshold of 0.70; therefore, this research is valid [76]. The data shows an excellent fit for the testing model, and the investigation is conducted to see how well the data fit the empirical study [77], through convergent and discriminant validity. The values of standard factor loading (see Table 3) were greater than 0.70 [76]. The composite reliability (CR) with a value greater than 0.70 showed that all model variables had high discriminative accuracy [78]. The average variance extracted (AVE) value was more than 0.50, indicating that the measurement model is a single latent construct [76].
• Discriminant validity. Discrimination has demonstrated a high link between reflecting structures and path model indicators [76]. In Table 4, the predicted Fornell-Larcker threshold value assures that the AVE square root (demonstrated through the estimated diagonal value) should be greater than the sum of all construct relationships (shown through the estimated off-diagonal value) [77].
• Table 4 shows the criteria result from the Heterotrait-Monotrait correlation ratio (HTMT). If the HTMT value is less than 0.90, discriminant validity across reflective constructs is detected [79].

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Table 2. Measurement model results.

https://doi.org/10.1371/journal.pone.0265025.t002

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Table 3. Convergent validity and reliability.

https://doi.org/10.1371/journal.pone.0265025.t003

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Table 4. Discriminant validity.

https://doi.org/10.1371/journal.pone.0265025.t004

Step 2: Structural model

All structures are developed according to the model presented in Fig 1. The results of the GOF values satisfied the requirements provided by Hu and Bentler after evaluating the structural model [80]. CMIN/df (3.023), CFI (0.918), IFI (0.918), RMSEA. (0.06), and TLI (0.905) were acceptable fit (Table 5).

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Table 5. Measurement invariance.

https://doi.org/10.1371/journal.pone.0265025.t005

Step 3: Multigroup structural invariance

Measurement invariance analysis provides a guideline to determine if the measurement models in the two groups are statistically different [81]. It was feasible to determine whether respondents from the two groups understood the underlying questions in the identical questionnaires. According to the CFA model, the measurement invariance technique conducts the following additions: assignment constant generation, constant metric generation, and scalar constant generation. When the values are within a certain threshold, the load factor can compare the two groups. Table 5 illustrates the continuous measurement invariance monitoring after the CFA model when the fit index is determined to pass the specified criteria.

5.1. Theoretical and practical implications

As a result of our present investigation, we feel that the findings are essential for both research and practice. In a theoretical perspective, this current research casts new light on the debate between the branch of literature hypothesizing that benefits from open innovation strategies are subject to collaboration-based business innovation. There are four key theorical implications proposed to the literature as follows.

1. First, this study presents the antecedent components of an open innovation approach that are based on resource and capability perspectives. The resource-and-capability antecedent factors of open innovation strategy are developed by incorporating innovation capability, innovative human capital, network partnership benefits, and strategic agility, all of which contribute to an emerging debate in the open innovation literature.
2. Second, open innovation strategy implementation includes both breadth and depth of strategic partner search, and cooperation with other open innovators. As the open innovation literature evolves to understand the concept of open innovation, such as Open Innovation 5.0, our findings show that open innovation as a cooperation plan should be taken into account by the innovation helix model to impact a broader range of stakeholders as new innovations are proposed to solve social issues.
3. Third, our current research provides a holistic approach to collaboration-based business model innovation. Our findings pay attention to the conceptualization of collaboration-based business model innovation which extends the insight into the open innovation literature. The findings show that there is an importance to business model innovation based on the collaborative network approach among generational cohorts of family-owned SMEs, especially Generation Z, Generation Y, and Generation X.
4. Fourth, we provide insights into the moderating role of generational cohorts by age in the business and innovation literature. Our findings contribute to the emerging literature which highlights the need for collaboration-based business model innovation as a family multigenerational-ownership SMEs in Thailand are more likely to work together, share resources, and use outside resources to come up with new solutions.

From a practical standpoint, our model gives us an insight of key resource-and-capability aspects of open innovation strategy and how these aspects relate to open innovation theory. However, our empirical evidence allows us to categorize our main findings into the following practical implications scenarios:

1. Our main findings reveal that innovative human capital and strategic agility appear to have the most powerful effect on the partner-search strategy of open innovation. This implies for SME practitioners that the value of human capital and the importance of strategic agility are necessary conditions for increasing business collaboration.
2. Among the respondents, three of four generations were associated with high tendencies for forming collaboration-based business model innovation. Therefore, business practitioners should be aware of their generational cohorts’ different shared value thoughts—especially zoomers, millennials, and busters—when coming to the formation of business model innovation through open innovation strategy.
3. Our findings suggest to business practitioners that they should implement open innovation, which begins with searching for, identifying, and selecting strategic partners. So, the breadth and depth strategy of open innovation will follow the general step of figuring out how SME collaboration actions will affect your stakeholders, which is a common step in the field of strategic management [85].
4. Moving to open innovation as a cooperation plan, our findings suggest that business practitioners should pay attention to the triple/quadruple/quintuple helix cooperation model. Our measures of open innovation cooperation further recommend the critical steps that SMEs should consider when they decide to cooperate with external knowledge partners. These steps include: (1) ensuring that innovation activities and projects are relevant and feasible; (2) consulting with local stakeholders to determine what should be proposed; (3) addressing funding, timing, and desired outcomes; (4) allocating resources to innovation activities and projects; and (5) executing innovation activities, projects, and risks. These key takeaways are sourced from OIC 1–5.