Managing open innovation: the roles of strategic orientation and dynamic capabilities, evidence from Ethiopian SMEs

ABSTRACT

The focus of this study was to assess empirically the effects of strategic orientation open innovation through dynamic capabilities of small and medium-sized enterprises (SMEs). A cross-sectional field survey quantitative approach was applied to collect primary data from the study area. Based on the empirical data of 321 SMEs operating in the study area, the study employed structural equation modelling (SEM) to analyze the data and research hypotheses in the proposed structural regression model. The SEM analysis found that open innovation is associated with small and medium enterprises’ higher-level strategic orientation. Finally, the results indicated that dynamic capabilities mediated the path between strategic orientation and open innovation. Moreover, the study indicated that strategic orientation creates value through the dynamic capabilities of the SMEs. Hence, the ability to sense, seize, and transform external ideas, knowledge, and information along with internal resources is vital to small and medium-sized enterprises success.

KEYWORDS:

• Dynamic capabilities
• open innovation
• strategic orientation
• small & medium enterprise
• structural equation modelling

Introduction

Small and medium-sized businesses (SMEs) are a major source of employment, income, and innovation (Kiani, Mustafa, and Ahmad 2019; Matthew et al. 2020). SMEs’ innovation success determines small-to-medium-sized businesses’ contribution to national growth and development (Kamalaldin et al. 2021). Innovation is key to every country’s economic development since it fosters industry competition, significantly impacts business performance, and promotes national economic growth (Rasheed, Shahzad, and Nadeem 2020). For emerging economies to stay competitive, they must encourage innovation among their small and medium-sized enterprises (Akhtar et al. 2021).

Firm resources particularly strategic perspective and capabilities enhance innovation (Chatterjee, Chaudhuri, and Vrontis 2021; Kamalaldin et al. 2021). A sustainable business strategic orientation that deals with product and process innovation is necessary for ongoing innovation (Han and Zhang 2021). Carvache-Franco, Carvache-Franco, and Carvache-Franco (2022) study indicated that Knowledge, market information, and low capacity for innovation information factors determine the business’s innovation (Kim, Park, and Paik 2018). As a result, countries are looking for strategic and innovative ways to empower their SMEs to ensure economic sustainability (Yang et al. 2022).

The government of Ethiopia updated the main Ethiopian MSE Strategy in 2011. The strategy focuses almost entirely on urban youth-founded survivalist businesses that ensure food security in cities and help fight poverty. The MSE strategy has become outdated and unusable, in addition to being highly partial (Pinto 2019). However, the focus of recently amended policies has been restricted to manufacturing SMEs in a few priority areas, and support has been given with too little consideration for the role that medium-sized enterprises play in competitiveness, innovation, exports, etc. The government has made an effort to concentrate on the growth of SMEs, but because the focus is on start-ups, businesses that have advanced to the next level are not treated as such. Additionally, despite advancements, SME development strategies do not adequately explain the business strategies, appropriate conduct, and guiding principles of SMEs (Esubalew and Raghurama 2020).

The rapid growth of SMEs in Ethiopia is offset by a very high failure rate (Bokoro 2016; Page and Måns 2015). Furthermore, the risk of business failure is higher in the first 2–4 years of business operation (Woldehanna, Amha, and Yonis 2018). Prior researches show that Ethiopia’s SMEs have a very high failure rate despite the hope placed in them and the support methods and programs put in place (Belay, Teshome, and Minalu 2015; Hailu 2019; Tesfaye 2016). Hence investigation is needed to determine why micro, small, and medium-sized enterprises (SMEs) fail at such high rates and why SMEs are remaining status quo despite the country’s aspiration to become middle-income in the near future. Previous studies have demonstrated that a major issue is a lack of strategic thinking skills, business vision, and risk aversion among members, which is compounding the fall in MSE membership (Daba and Atnafu 2016; Laukkanen et al. 2013).

Beyond generating a variety of small business possibilities that serve as sources of revenue for the private sector and businesses, it is essential to understand the important long-term factors that influence business success in order to effectively support SMEs as the nation’s economy grows. Hence, SMEs are being forced to alter their business philosophies and consider new approaches to creating a strategic orientation for survival and competition. Therefore, in order to make meaningful and long-lasting contributions from SMEs, it is crucial to comprehend the strategic orientation and its influence on open innovation. The ability of business organizations to adapt and develop their strategic management processes is crucial to their survival and greatest success (Sulistiawan et al. 2022). Moreover, Previous study has demonstrated that both internal and external ideas, expertise, and procedures are essential to creative customer value (Lee et al. 2010; Naqshbandi and Jasimuddin 2022; Sok et al. 2017). The research gaps are identified as: First, strategic orientation affects innovation differently in different studies. Some studies have found that strategic orientation such as entrepreneurial, customer, competitor knowledge, information, and ideas are key determinants of innovation (Kim, Park, and Paik 2018; Naqshbandi and Jasimuddin 2022; Ramírez-Solis, Llonch-Andreu, and Malpica-Romero 2022; Tuominen et al. 2022), but others have found that market orientation does not influence innovation or performance, (Agus Zainul Arifin 2020). Moreover, some prior studies showed an insignificant influence of customer-oriented strategy on product innovation success (Abid and Gulzar 2018). Research results on the relationship between strategic orientation and innovative performance show conflicting results, how strategic orientation affects innovation is controversial and contradictory to justify generalizations.

Second, an empirical strategic orientation study advises adding contingency factors to explain the relationship between independent and dependent variables (Bing 2011; Lumpkin and Dess 1996; Maaodhah et al. 2021). A variable between the dependent and independent variables reduces the misleading effect and clarifies relationships. Consequently, it has been proposed that strategic orientation is a fundamental component influencing dynamic capabilities (Teece 2014; Tuominen et al. 2022). Furthermore, according to Bogers et al. (2019), dynamic capabilities encourage open innovation by strengthening detecting and seizing and transforming abilities. Hence, the mediator factors and dynamic capacities can reconcile inconsistent results and support mediation effects described by past research in strengthening the strategic orientation-open innovation link. Dynamic capacities mediating strategic orientation and open innovation are unstudied.

Lastly, previous research on strategic orientation and management of open innovation concentrated mostly on high-tech and big businesses. Additionally, the majority of research on these strategic factors and open innovation is descriptive and conceptual in nature and is based on in-depth interviews with large, high-tech companies that operate in developed countries as well as case studies (Hailu 2019; Kiiru 2015; Lee et al. 2010).

The objective of the study was to analyze empirically the missing link between strategic orientation and open innovation by showing that dynamic capabilities mediate the relationship between them; as a result, it is anticipated that it will contribute to both the theory and practice of the Strategic orientation framework. It implies that customer orientation, entrepreneurial, and competitor strategies would substantially improve SMEs’ management of innovation by harnessing dynamic capabilities. The key insights provided by the current work are to strengthen the strategic orientation that can promote dynamic capabilities, thereby binding the SMEs’ innovativeness.

2. Theoretical framework and hypotheses development

2.1. Definition of small enterprises in Ethiopian context

The most crucial variables for the definition of micro and small firms in the Ethiopian context are considered to be human capital and assets. The following definitions of SMEs apply to this study in accordance with Ethiopia’s Micro and Small Business Development Strategy, Delivery Framework, and Implementation Methods (FDRE 1997): (a) Industry (manufacturing, building, mining) the firm employs between 6 and 30 employees, and the SME’s total paid-up capital is between 100,000 and 1.5 million birrs. (b) Service industries (retail, transportation, hotel and tourist, ICT, and maintenance services) with six to thirty employees, a total of five hundred thousand birr or less in paid-up capital, or both. This study provided a business management model of the important factors that affect how successfully SMEs innovate.

2.2 Theoretical, empirical and conceptual hypothesis development

2.2.1. Resource based theory

According to resource theory, resources determine organizational performance (Peteraf 1993). Resource-based philosophy says companies compete on resources and abilities. Competitiveness depends on resources. SMEs’ human capital, skills, SME traits, knowledge, strategies, and expertise help to enhance performance (Barney 1991). Only resources don’t boost performance. Hence, resource management must be coordinated and integrated. The valuable, rare, inimitable, and irreplaceable (VRIN) resource model assists in identifying a company’s internal strengths and weaknesses and takes into account how each resource or capacity may help the organization become more competitive (Barney 1991). Resource-based approach focuses on valuable and rare resources to ensure competitive advantage (Khana, Talibb, and Kowangc 2020; Tsai and Wang 2017). RBV theory claims that market-oriented methods are rare, valuable, and hard to imitate. That’s a company’s own competencies that gave it a long-term advantage. RBV claims market orientation boosts organizational performance (Al Marzooqi and Abdulla 2020; Kiessling, Isaksson, and Yasar 2016).

Entrepreneurial strategy improves SME performance, supporting the resource-based perspective argument (Agus Zainul Arifin 2020). Hence, entrepreneurship boosts revenue, market share, and resource use. RBV principles state that entrepreneurship is a set of instruments used to generate new products in response to environmental trends (Jogaratnam 2017). Entrepreneurship enhances performance and shows a business’s direction (Adams, Bodas Freitas, and Fontana 2019). Barney (1991) and Kollmann and Stöckmann (2014) describe entrepreneurship as a valuable, unique, limited, and irreplaceable resources. Sonenshein asserts that an entrepreneurial approach helps a business achieve its objectives, hold onto its vision, and obtain a competitive advantage. Entrepreneurship boosts performance and maintains competitiveness, according to numerous studies (Imran et al. 2019).

2.2.2. Dynamic capabilities theory

Dynamic capability is an organization’s ability to integrate, develop, and restructure internal and external resources to adapt to rapid change (Teece, Pisano, and Shuen 1997). This is resource-based worldview development (Schilke 2014). Dynamic capacity theory says businesses with significant dynamic capacities perform better. Sensing, seizing, and transforming organizational processes promotes dynamic skills (Teece 2014). These three dynamic feature sets simplify open innovation for enterprises. Outside-in open innovation requires knowledge and market information acquisition, understanding, and filtering. Entrepreneurial managers create and seize possibilities and they require ideas and knowledge.

Bogers Sensing helps organizations find and assess external knowledge. Management links with universities, research institutions, and government officials foster inward open innovation, successful commercialization, and exploitation (Naqshbandi and Jasimuddin 2022. Open innovation demands internal and external knowledge. Outside-in, inside-out, and related actions using internal and external knowledge improve innovation (Chesbrough 2003). Additionally, ‘outside-in’ methods use customer, seller, and rival ideas to create new products. Inside-out operations must use inside information. Open innovation is a decentralized invention process that uses methods that fit the organization’s business model to exchange controlled information across organizational borders (Chesbrough and Bogers 2014). This study makes use of the Dynamic Skills Framework to help us comprehend open innovation and provide an explanation for its successes and failures.

2.2.3. The role of strategic orientation in the management of open innovation

Hakala (2011) defined strategic orientation as a set of strategic management principles that generate the behaviours of an organization intended to enhance its performance. Market, customer, competitor, and entrepreneurial orientation are just a few of the several elements that fall under the umbrella term of strategic orientation (Hakala 2011). Strategic orientation is a key determinant of achieving innovation and successful outcomes for a firm (Ozkaya et al. 2015). The prior study highlights that enhancing customer orientation (consumer-centred approach), competitor orientation (gaining, disseminating, and reacting to market-generated intelligence), and entrepreneurial orientation (exploring, shaping, and exploitation of market opportunities), harness SMEs-level innovation (Al Mamun et al. 2022).

Open innovation was initially characterized by Chesbrough (2003, 37) as a model that presumes organizations may and should exploit both internal and external ideas as well as paths to market. Moreover, businesses should use an outside-in-open innovation strategy to gain knowledge from external sources. According to prior research, customer orientation improves creativity and innovation in service and industrial firms (Wang 2016). Pundziene, Nikou, and Bouwman (2021) argue that outside-in innovation requires customer engagement, searching for customers’ needs, and new ideas that enhance open innovation. Customer-oriented businesses are better able to recognize the requirements and desires of their clients and approach them directly in order to satisfy them, which eventually leads to better customer value creation, delivery and increases businesses’ ability to compete (Baker and Sinkula 2009). Businesses obtain new customer ideas and integrate them with the existing internal knowledge, transforming them into new ones (Bogers et al. 2019).

Tuominen et al. (2022) indicated that firms can learn from customers and develop effective customer-centric strategies to spread the acquired information into internal decision-making as it contributes to firm innovativeness and business growth. Additionally, they add an understanding of the synergistic effects both of using customer information and developing deeper relationships on firm innovativeness and performance. Managers have an important role in building the customer orientation of an organization so that there is innovation within the SMEs. Innovation capabilities of SMEs can be improved by strengthening customer orientation such as giving more time to listen to customer opinions (Wahyono 2021).

Similarly, competitor orientation illustrates how organizations comprehend the strengths and weaknesses of their existing and potential rivals by periodically reviewing the competitive environment in the same industry to develop fresh ideas (Aydin 2020). Other studies demonstrate that externally available customer and competitor information and resources influence all innovation activities. Moreover, understanding rivals’ plans and strategies, responding to them with value, and mirroring their marketing initiatives are the main goals of competitor orientation (Zhou et al. 2005). Understanding competitors’ flaws and strengths, giving customers more value, and outperforming competitors are all examples of being competitive (Kumar, Raman, and Raman 2017).

A business is engaging in entrepreneurial activity when it behaves in a manner that actively searches for, makes use of, and pursues opportunities across borders (Knight and Cavusgil 2004). Moreover, businesses that have a greater emphasis on entrepreneurial orientation are more likely to make greater use of information obtained from external sources and to adopt fresh ideas, both of which may assist the companies in creating superior products for their respective consumers. Additionally, a more entrepreneurial mindset might also make inside-out open innovation activities viable (Cheng and Huizingh 2014). An optimistic approach, a willingness to take measured business risks, a concentration on creativity, and the creation of new firm concepts for the market are key entrepreneurial attributes (Covin and Miller 2014).

Hypothesis 1: strategic orientation is positively related to open innovation.

2.2.4. The influence of strategic orientation on dynamic capabilities

Dynamic capacities are made possible by three sets of organizational processes: sensing, seizing, and transforming (Teece 2014). The sensing component involves both, recognizing opportunities, and anticipating competitive threats (Helfat and Peteraf 2015). According to Wilden, Devinney, and Dowling (2016) study seizing thus means that market opportunities are successfully exploited and that threats are eluded. Seizing bridges external and internal information and knowledge, and it is closely linked with strategic decision-making, particularly regarding investment decisions. Seizing capacity starts from a strategy that enables the recognition of valuable knowledge. This evaluation is based on prior knowledge, and it results in a selection from a variety of strategic options. Seizing capacity within an organization is high if the organization is able to decide whether some information is of potential value, to transform valuable information into concrete business opportunities.

Transforming, includes enhancing, combining, protecting, and reconfiguring the business enterprise’s intangible and tangible assets, such that path dependencies and inertia are avoided (Teece 2007). That is, transforming refers to putting decisions for new business models, products, or process innovations into practice.

By exploiting these three dynamic feature sets, organizations can gain from strategic orientation. Entrepreneurial orientation plays a crucial role in developing capabilities by encouraging the values of receptiveness that make up innovativeness (Kiani, Mustafa, and Ahmad 2019). Being entrepreneurially minded necessitates being proactive about new opportunities and responding in creative ways (Tutar, Nart, and Bingöl 2015). It makes it easier for a company to choose the best resources for integration, which sparks creativity. According to Rasheed, Shahzad, and Nadeem (2020), SMEs’ top leadership entrepreneurial mentality significantly facilitates their participation at the process level of dynamic capability. In order to develop and capture chances, entrepreneurial activity needs to be connected to resources like knowledge and ideas that enhance transforming dynamic capability (Teece 2014).

Entrepreneurial managers not only allocate resources but also sense, select, shape, and synchronize opportunities (Bingham, Eisenhardt, and Furr 2007; Felin, Zenger, and Tomsik 2009). The sensing dimension is about spotting or envisioning market and technological opportunities within and outside an industry (Burgelman and Grove 2007; Klein 2008). Alert scanning and searching (Tang, Kacmar, and Busenitz 2012), and imagining (Felin, Zenger, and Tomsik 2009; Klein 2008) are important mechanisms for sensing. Sensing opportunities can come from employees, managers, and decision-making process of an organization. Shaping connotes the orchestrating of relationships among internally and externally available capabilities and resources for opportunity realization (Felin, Zenger, and Tomsik 2009). In order to develop and capture chances, entrepreneurial activity needs to be connected to resources like knowledge, and ideas that enhance transforming dynamic capability (Teece 2014).

Similarly, customer orientation can be thought of as a search procedure that takes into account customer expectations and preferences when developing new products and changing existing product offerings (i.e. innovativeness). Additionally, it encourages more direct communication with clients, boosting little adjustments that bring items closer to their ideal levels of features, quality, and price (Voss and Voss 2008). By getting ideas from customers and having access to information about how markets are growing, firms can enhance their ability and perform innovation activities (Walter, Thomas, and Georg 2001). Sensing, seizing, and transforming capabilities help a business use, locate, and evaluate crucial outside information, particularly customer information and forge partnerships (Bogers et al. 2019).

Consequently, it has been proposed that a fundamental component influencing dynamic capacities is the strategic orientation (Wilden, Devinney, and Dowling 2016). Strategic orientations that promote proactive deployment of opportunities by enhancing consumer involvement are required for effective opportunity discovery and exploitation (Payne and Frow 2005). Additionally, businesses can identify and create interorganizational collaboration with key stakeholders, such as research institutions and other technology companies, using their sensing skills.

Hypothesis 2: strategic orientation has a positive significant relationship with dynamic capabilities of SMEs in Ethiopia.

2.2.5. The effect of dynamic capabilities on the management of open innovation of the SMEs

One of the main themes of open innovation is the ability of enterprises to find or seek external sources of invention by interacting with a wide range of external stakeholders (Chesbrough and Bogers 2014; Naqshbandi and Jasimuddin 2022). Innovation success is positively connected with external knowledge-sharing, according to Ritala et al. (2015).

The dynamic capability view serves as the foundation for managerial competence (Teece 2007). For an organization to successfully react to shifting market conditions and provide value, it needs dynamic skills. These abilities assist businesses in developing new procedures, changing current ones, and identifying and seizing business opportunities, all of which boost open innovation (Bogers et al. 2019). In a similar vein, we argue that more management skills should allow providers to more effectively manage, bundle, and utilize different firm-level resources and capabilities through the production of beneficial synergy that leads to innovation.

Bogers et al. (2019), combined dynamic capabilities (sensing, seizing, and transforming) with Open innovation strategies. They asserted that sensing dynamic capabilities, which scan, identify, interpret, and select pertinent external information and technology, promote the open innovation strategy. Additionally, thanks to their sense-making abilities, businesses may recognize and establish cross-organizational collaboration with important stakeholders including research institutes, start-ups, and other technical businesses. Sensing capability enables cross-organizational communication and the discovery and evaluation of relevant external knowledge.

Externally generated information and market information must be acquired, comprehended, and filtered for outside-in open innovation. It’s crucial to gather several ideas, assess them, and choose the best one. Therefore, enhancing and applying dynamic capabilities are essential for open innovation. Businesses frequently restructure and incorporate outside ideas and information, which requires transforming capabilities. Dynamic capabilities allow businesses to change their resource base to improve open innovation, resource efficiency, and firm performance (Schilke 2014).

Hypothesis 3: The higher the dynamic capabilities, the higher open innovation of the SMEs.

2.2.6. The mediating role of dynamic capabilities on the relation between strategic orientation and management of open innovation

According to Teece (2007), sensing capability is the capacity of firms to constantly scan, spot, and explore opportunities across technologies and markets. In a fast-changing market, new information and knowledge can create opportunities for innovation. According to academics, firms must investigate, transform, and use both internal and external knowledge to better the outcomes of open innovation. In light of this, it is crucial for small and medium-sized businesses to be able to investigate, transform, and use these strategic resources, knowledge, and ideas in an open and integrated culture in order to capitalize on their innovative behaviour (Gao, Xu, and Yang 2008; Naqshbandi and Jasimuddin 2022).

Having customer information may drive consumer-focused innovation. In a dynamic environment, meeting consumer needs and expectations affects an organization’s innovation and performance (Zhou et al. 2005). Customer feedback can inspire new goods and procedures (Tuominen et al. 2022; Walter, Thomas, and Georg 2001; Zhou et al. 2005). Additionally, the authors found that consumer-focused, open organizations innovate more. Gathering, evaluating, and picking the best ideas need extensive market integration. It’s crucial to gather several ideas, assess them, and choose the finest. Therefore, understanding and application of dynamic capabilities are essential for open innovation. A firm can identify its competitors’ strengths and shortcomings by routinely reviewing the competitive environment in the same industry that strengthens innovativeness (Aydin 2020). Less entrepreneurial SMEs may not be as well-suited for outside-in and inside-out open innovation when developing market-focused products.

Consequently, it has been proposed that strategic orientation is a fundamental component influencing dynamic capabilities (Teece 2014; Tuominen et al. 2022; Wilden, Devinney, and Dowling 2016). Furthermore, according to Bogers et al. (2019), dynamic capabilities encourage open innovation by strengthening detecting and seizing and transforming ability (Figure 1).

Hypothesis 4: Dynamic capabilities mediate the relationship between strategic orientation and open innovation in SMEs.

Figure 1. Conceptual framework. Source: Modified by authors, 2023; From Bogers et al. (2019), Teece (2014) and Tuominen et al. (2022).

3. Methodology

3.1. Research design

A research design is a detailed strategy to direct the investigation (Collis and Hussey 2003). The choice of research design for this research study is justified based on the nature of research, a plan that incorporates the research problem, data collection methods, organization, and analysis techniques which form strong evidence of answers to the research problem (Asenahabi 2019; Creswell and Plano-Clark 2007). Research design is divided into three groups: quantitative, qualitative, and mixed method research design (Asenahabi 2019). A quantitative study design was applied for this study due to the nature and purpose of the study. According to Patel, Quantitative research is the logical investigation of the number or extent of the phenomenon and their connections.

Explanatory research, usually referred to as causal research, advances beyond descriptive study by explaining why or how occurrences occur rather than just reporting their attributes (Collis and Hussey 2003). Hence, the explanatory research design was applied to analyze the hypotheses in this study. Since the focus of this study is on how strategic orientations relate to other widely accepted categories of dynamic capabilities and open innovation, it is important to verify the conceptual model and hypothesis that has been developed (Baker and Sinkula 2009; Zhou et al. 2005).

A cross-sectional field survey approach was employed to investigate the proposed hypotheses. Self-administered survey is an efficient and effective tool for characterizing the features of many small and medium-sized businesses to establish empirically the conceptual framework (Erik 2008). Due to these factors, a direct questionnaire distribution strategy was used to collect data using a standardized structured questionnaire in order to analyze the effects between variables in this study.

3.2. Target population of the study

The entire population of SMEs operating in the study area is the study’s target population. This study focuses on the business activities of small and medium enterprises in Hawassa City Administration-selected sub-cities. Therefore, the target population of this study was the MSEs of the manufacturing industry and service sub-sectors. The institution that was engaged in supporting the MSEs was used as references to get information for the study. According to the inventory report of Hawassa City Job and skill development department, 1623 SMEs were established and put into operation in manufacturing and service sectors during the year. The study focused on businesses that had been in operation over the past three years. Therefore, its target population is 1,623 SME managers or owners.

3.3. Sampling frame, sample size, and sampling technique

Yamane (1967) suggested another simplified formula for the calculation of sample size from a population. According to him, for a 95% confidence level and p = 0.5, the size of the sample should be $n={\displaystyle \frac{N}{1+N(e^2)}}$where n = sample size, N is the target population size and its estimate = 1,623,

e is the level of precision = 0.05.

Hence, n = 1,623/ 1 + (1,623 × 0.05 × 0.05) = 1,623/5.0575 = 321.

Apply the above formula; the sample size was 321, total of 321 respondents were selected proportionally from both manufacturing and service sectors.

A multi-stage sampling is used for the survey. Accordingly, in the first stage, Hawassa City was selected conveniently since the researcher knows very well about the current existing problems with regard to MSEs business performance in their engaged activities. In the second stage, the purposive sampling method was used to select the 4 sub-cities namely, Menaheria, Tabor, Addis Ketema, and Haykidar sub-cities among eight sub-cities of the Hawassa city administration. The selection criteria are based on the high density of small to medium enterprise locations in Hawassa City according to the data of inventory report, Hawassa City (2022). In the third stage, the manufacturing and/ or service sectors of enterprises in each selected sub-city were selected on purposive bases since these sectors had a deep-rooted constraint to be focused on than others regardless high emphasizes being given by the government. In the Fourth stage, the sample size was distributed to all selected Sub-Cities and selected sectors based on the probability proportional to the size method.

3.4. Data collection methods

Both primary and secondary data gathering techniques were used to get the data. The data used to test the hypotheses was gathered from sampled SMEs with a survey questionnaire. SMEs owners and/or managers in registered SMEs make up the population frame and the sample covered 321 SMEs. Questionnaires and interviews were employed to gather primary data. Questionnaires are an effective data gathering tool, according to Sekaran and Bougier. Ten data collectors with a minimum degree and fluency in the local language were hired prior to the data gathering process. Fieldworkers were supervised by researchers and got a day’s worth of training on critical fieldwork duties such as mapping, respondent management, data quality assurance, and time management. The tool was then pretested on 15 managers of businesses in one of the administrative sub-cities of Hawassa in order to ascertain its validity, the typical time required to complete the questionnaire, and the capacity of the data collectors. After the pretesting was finished, the final version of the questionnaire incorporated the comments from the pretesting and was edited before being deemed duplicate-ready.

3.4.1. Measurement of variables and instruments

The main method for gathering information from the sampled respondents was a questionnaire. Many closed items can be found in a questionnaire. A structured questionnaire that included questions about the respondent’s profile, questions about the internal dynamics of the organization, and questions or structures measuring four latent variables was used to gather the data. Variables that are measured during the data collection process are known as observed variables. Because it cannot be assessed directly, a latent variable is one that is measured via association with an observed variable. A survey questionnaire was employed for this study. The remaining three latent variables (Strategic orientation, dynamic capabilities, and open innovation), whose constructs were based on the framework of components (disaggregated for analytical purposes) proposed by the majority of well-known researchers in the field, were also evaluated using the five-point Likert scale.

For instance, Henry Chesbrough (2003, 37) defined open innovation as a paradigm that presupposes businesses can and should use both internal and external ideas as well as paths to market, and two-dimension measurements of variables: outside-in, inside-out were explained from the theoretical and empirical context and five items identified.

Strategic orientation was measured using a nine-item scale by the three dimensions. Customer orientation in this study focused on how SMEs understand customers’ needs through the collection of information, dissemination of customer-focused strategies, and responsiveness to the potential market (Jeong, Pae, and Zhou 2006). The indicators of customer orientation (three items) were derived from Tuominen et al. 2022 and Yang et al. (2022). The scale items measure the firm’s objectives, commitment, and strategy towards customers’ needs and customer satisfaction. Furthermore, the indicators of competitor orientation (three items) were modified from Kiiru 2015. Next, the indicators of entrepreneurial orientation (three items) were adopted from Herlinawati et al. 2019. To capture dynamic capabilities, 8 items were identified from Teece (2014). Despite this, respondents were expected to be able to choose from the list of options given to them when answering closed-ended questions. Likert’s five-scale approach (strongly disagree (SDA), disagree (DA), practically agree (PA), agree (A), and strongly agree (SA)) as well as other closed forms were utilized in the questionnaire. Such a questionnaire will be used since it offers a high degree of regularity in responses. After the completed surveys were reviewed for quality and neatness, the data were entered into the Statistical Package for Social Sciences (SPSS 26 and Amos 23 software).

3.5. Data analysis

This study used structural equation modelling to examine the linkages and magnitude of major influences between SMEs’ strategic orientation and open innovation through the mediation of dynamic capabilities. Structural Equation Modelling (SEM), which consists of a measurement model and a structural model, is a statistical approach for estimating numerous regression equations in a single framework, according to Civelek (2018). In order to determine the sufficiency of the construct validity of scales, a measurement model assesses how effectively the hidden variables are represented by the observable variables. Therefore, it will not be worthwhile to test the structural model if the measurement model fit indices are poor (Dursun and Kocagöz 2010). In order to ascertain the precise assumptions of the studies offered, this SEM application integrates regression and factor analysis, according to (Afthanorhan, Nazim, and Ahmad 2014; Kero and Sogbossi 2017).

The capacity to measure both direct and indirect influence between causal variables in a single model is the main benefit of this statistical method (Meydan and Şen 2011). According to Civelek (2018), structural equation modelling is distinct from other multivariate statistical methods in that it verifies that the relationships in the theoretical model are supported by the data. Furthermore, by simulating complicated interactions between a large number of observable and latent variables, structural equation modelling enables the testing of research hypotheses in a single process. However, only observable variables can be included in multiple regression analysis, and only direct effects can be examined. Therefore, structural regression models are appropriate for data analysis based on the data’s type.

4. Results of the study

4.1. Reliability test of a construct

According to Hair et al. (2021), reliability is a concept used to evaluate the quality of research and the level of consistency of a measure in data collection and processing. The reliability of the latent constructs was assessed with Cronbach’s alpha, inter item to total correlation, and composite reliability and reported.

Cronbach’s alpha value, which should be greater than .7, is one of the most often used measures of internal consistency (Hair and Ringle 2012). As a result, the internal consistency of customer orientation, competitor orientation, entrepreneurial orientation, sensing capabilities, seizing capabilities, transforming capabilities, outside-in, and inside-out open innovation are evaluated and reported in Table 1. As a result, Cronbach alpha values are significantly higher than the benchmarks of 0.70, with the inside-out open innovation achieving the lowest Cronbach alpha score of 0.876 (Chin 2010). A scale with high Cronbach’s alphas can be considered to measure a construct as a coherent whole when its elements show strong intercorrelations.

Table 1. Displays each construct, and its associated reliability coefficients.

No	Variables	NO. of items	Correlation (r)	Item- total corr.	Cronbach Alpha
I. Strategic Orientation
1	Customer Orientation	3	0.865	0.335 0.919
2	Competitor Orientation	3	0.845	0.327	0.910
3	Entrepreneurial orientation	3	0.871	0.337	0.928
III. Dynamic capabilities
1	Sensing	3	0.896	0.342 0.944
2	Seizing	3	0.852	0.325	0.913
3	Transforming	2	0.875	0.334	0.926
II. Open innovation
1	Outside-in	3	0.815	0.504 0.897
2	Inside-out	2	0.802	0.496	0.876

Source: Author’s data analysis, 2023 from SPSS output.

Furthermore, for data analysis that shows how strongly each item connects with the overall score, inter-item to total correlation values of 0.3 or higher are appropriate (Julie 2005). A substantial inter-correlation between the items and their constructs is indicated when an item’s correlation with the total of all respective constructs is greater than 0.3. This calls for the application of factor analysis (Field 2013; Julie 2005).

4.2. Composite reliability

Similar to Cronbach’s alpha, composite reliability (sometimes referred to as construct reliability) is a metric for scale components’ internal consistency. According to Brunner and Süß, it is equivalent to the entire amount of actual score variance in relation to the total scale score variance. It can also be referred to as an ‘indicator of the shared variance among the observable variables utilized as an indication of a latent construct’ (Fornell and Larcker 1981). A popular statistical software program called confirmatory factor analysis can be used to assess composite dependability.

As a result, composite reliability, a more trustworthy internal consistency measure, was also used. According to Hair et al. (2016), composite dependability ranges from 0 to 1, with higher numbers suggesting better levels of reliability. In explanatory research, composite values of 0.6–0.7 are considered acceptable, whereas in later stages of the research, 0.70–0.90 are considered satisfactory. For this study composite reliability coefficients of the three factors are above 0.7. The strategic orientation, dynamic capabilities, and open innovation factors’ composite reliability in this investigation were 0.89, 911, and 872 respectively, which were higher than the acceptable range needed given the explanatory nature of the study. Composite reliability was calculated by using standard estimates and variance of error term from Amos, confirmatory factor analysis. Hence a measurement for scale components’ internal consistency of the three latent variables is adequate and reliable (Table 2).

Table 2. Composite reliability (using AMOS).

No	Variables	Standardized Loadings(r)	error term (e) = 1-(r)	∑(ri) i = 1_n	∑(ei) i = 1_n	Composite reliability
I. Strategic Orientation
1	Customer Orientation	0.781	0.219
2	Competitor Orientation	0.727	0.273	2.346	0.654	0.894
3	Entrepreneurial orientation	0.838	0.162
II. Dynamic capabilities
1	Sensing	0.869	0.131
2	Seizing	0.738	0.262	2.424	0.576	0.911
3	Transforming	0.817	0.183
III Open innovation
1	Outside-in	0.85	0.15	1.616	0.384	0.872
2	Inside-out	0.766	0.234

Source: Authors’ calculation, 2023 from SPSS and Amos output.

Each construct’s Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) values were examined, and they all scored higher than the required minimum of 0.50. This study’s factor analysis revealed that the KMO value of 0.807 indicates adequate sampling. If the sample is sufficient and appropriate for factor analysis (Field 2013; Hair et al. 2016), Bartlett’s test of sphericity should be significant (p less than 0.05). All constructs are significant for this investigation.

4.3. Convergent validity

The degree to which elements that should be conceptually connected are really related is referred to as convergent validity. For each of the item scales, factor analysis was employed to whittle down the overall number of items to a manageable factor. As a result, Table 3 below displays the findings of the current final investigation. All items have loadings for the anticipated construct that are more than 0.50, demonstrating a strong link between prospective items and their constructs.

Table 3. Convergent Validity based on loading factors and AVE on constructs (Using SPSS).

Loadings	AVE	VIF
Customer Orientation 0.828	0.610	2.56
Competitor Orientation 0.826	0.529	2.12
Entrepreneurial orientation 0.847	0.702	3.36
Sensing Capability 0.800	0.755	4.08
Seizing Capability 0.719	0.545	2.20
Transforming Capability 0.853	0.667	3.00
Outside-in open innovation 0.702	0.723	3.61
Inside-out open innovation 0.726	0.587	2.42

Source: Authors’ calculation, 2023 from SPSS output. Extraction Method: Principal Component Analysis Varimax rotation with Kaiser normalization.

When all latent constructs’ average variance extracted (AVE) values were over the 0.50 cut-off, convergent validity was achieved (Hair et al. 2019). Moreover, an AVE of 0.50 or higher indicates the construct explains 50 percent or more of the indicators’ variance that make up the construct (Hair et al. 2022). In Table 3 below, the findings of this study also indicated that all AVE values were above 0.50. Hence, convergent validity was discovered for the data in both circumstances. This demonstrates the accuracy of the measurements in the measurement model. Lastly, the variance inflation factors were used to estimate multicollinearity concerns (VIF). Table 3, also indicates each factor’s VIF score is less than 4.1, indicating that there was no significant collinearity or issue (Chin 2010).

4.4. Discriminant validity

The discriminant validity study examines whether the constructs capture various facets of strategic orientation, dynamic capabilities, and open innovation factors. The average variance extracted (AVE) is also used to evaluate the constructs’ discriminant validity. A construct must have greater variation with its indicators than with other model constructs in order to meet this requirement. In other words, the AVE > r 2 technique is more advised (Fornell and Larcker 1981; Hair et al. 2019). The Fornell–Larcker Criterion was appraised by taking the square roots of the average variance extracted (AVE) of the construct and must be greater than the corresponding correlation coefficient to establish the discriminant validity. This criterion was based on the idea that a construct shares more variation with its indicators than with any other construct. Latent variable constructs are guaranteed to be distinct from one another by discriminant validity.

Using AMOS software, we evaluated the discriminant validity of each construct. The outcomes of the current study are described in more depth in Table 4 below. All values of the average variance derived in Table 4 are higher than the corresponding square of correlations. This information indicates that the discriminatory validity is good. In other words, the validity of discrimination is not violated.

Table 4. Discriminant Validity (using AMOS).

Fornell–Larcker criterion	CuO	CO	EO	Sens	Seize	Trans	Out-In	In-Out
Customer Orientation	0.781
Competitor Orientation	0.567	0.727
Entrepreneurial Orientation	0.653	0.611	0.838
Sensing	0.367	0.278	0.386	0.869
Seizing	0.307	0.356	0.370	0.655	0.738
Transforming	0.317	0.267	0.305	0.715	0.571	0.817
Outside-In	0.344	0.328	0.381	0.447	0.436	0.467	0.850
Inside-Out	0.277	0.271	0.272	0.390	0.343	0.543	0.651	0.766

Source: Author’s data analysis, 2023 from SPSS and Amos output. CuO, customer orientation; CO, competitor orientation; EO, entrepreneurial orientation; Sens, sensing; Seiz, seizing; Trans, transforming; Out-In, outside-in; In-Out, inside-out open innovation. The off-diagonal values in the Fornell–Larcker Criterion matrix are the correlations between the latent constructs, and diagonal are square root values of AVEs.

4.5. Model specification and identification

Model specification is building a statistical model by selecting an appropriate functional form for the model and choosing which variables to include. The study will apply structural regression models consisting of measurement and structural models. Incorporating the measurement model and the structural model allows the inclusion of measurement errors so that more accurate results can be obtained. In other words, confirmatory factor analysis and multiple regression analysis coexist (Civelek 2018, 28). The base model consisted of three exogenous latent variables (strategic orientation) and two endogenous latent variables (dynamic capabilities and open innovation) in direct and indirect relationships, and latent variables further sub-constructs with eight dimensions and twenty-two items.

This model comprises the parameters to indicate the relationships between the observable and latent variables and those between the latent variables. Directional effects, indirect effects, and variances are the three categories of parameters that will be stated. The structural model has three path coefficients and eight-factor loadings as its directional effects. Two error terms for the two unobserved endogenous variables, dynamic capabilities and open innovation, and indicator error related to the fifteen manifested variables were identified and estimated (Figure 2).

Figure 2. Structural regression model by AMOS (standardized estimates(r)). Source: Self structured, 2023.

4.6. Model estimation

The Maximum Likelihood Estimation (MLE) method was utilized in conjunction with IBM® SPSS® 26 and Amos 23 to estimate the parameters. Finally, analogous to the unstandardized weights and standardized betas in the regression analysis, AMOS presents both the unstandardized and standardized values of the estimations (Tables 5–7). The estimates show direct and indirect influence between causal variables in a single model (Meydan and Şen 2011). It verifies that the relationships in the theoretical model are supported by the data. Direct estimates of the unstandardized and standardized path indicated directional effects or influences between latent variables. Then, using the two-step procedure recommended by Civelek (2018), hypotheses were assessed utilizing direct and indirect estimates.

Table 5. Below Presents, the results of mediation conditions and Regression Weight estimates. Unstandardized Effects (ββ).

	Total Effects	Direct Effects	Indirect Effects	indirect/Total
Strategic Orientation→ Open Innovation	.48***	.21***	.27***	.27/.48 = .56
Strategic Orientation→ Dynamic capabilities	.53***	.53***
Dynamic capabilities→ Open Innovation	.50***	.50***

Source: Author’s data analysis, 2023 from Amos output. *** is significant at the p < 0.001 (2-tailed), n = 321.

Table 6. Below Presents, the results of mediation conditions and Regression Weight estimates. Standardized Effects (rr).

	Total Effects	Direct Effects	Indirect Effects	indirect/Total
Strategic Orientation→ Open Innovation	.50***	.22***	.28***	.28/.50= .56
Strategic Orientation→ Dynamic capabilities	.51***	.51***
Dynamic capabilities→ Open Innovation	.54***	.54***

Source: Author’s data analysis, 2023 from Amos output. *** is significant at the p < 0.001 (2-tailed), n = 321.

Table 7. Maximum likelihood estimates, regression weights (unstandardized and standardized).

Variables	Loadings	Variables	Unstandardized Estimate	P	Standardized Estimate
Dynamic Capabilities	<—	Strategic Orientation	0.53	***	0.512
Open Innovation	<—	Strategic Orientation	0.212	0.002	0.223
Open Innovation	<—	Dynamic Capabilities	0.499	***	0.542
Customer Orientation	<—	Strategic Orientation	1		0.781
Competitor Orient.	<—	Strategic Orientation	0.907	***	0.727
Entrepreneurial Orient	<—	Strategic Orientation	0.984	***	0.838
Sensing	<—	Dynamic Capabilities	1		0.869
Seizing	<—	Dynamic Capabilities	0.945	***	0.738
Transforming	<—	Dynamic Capabilities	1.055	***	0.817
Inside Out	<—	Open Innovation	1		0.766
Outside In	<—	Open Innovation	0.937	***	0.85

Source: Self structured 2023 from Amos output. Note: *** stands for values close to 0.001 for p- value/Probability.

4.7. Model evaluation

Model fitting’s primary objective is to reveal how well the model fits the data. The fit values suggested by Bayram (2013) and Hair et al., when the SMIN is divided by the degrees of freedom, this model produces a result of 3, which is equal to the required level of the upper bound of 3, suggesting a reasonable fit of the data to the model. GFI, which is similar to R2 in regression analysis, is an objective measure of model fit (Bayram 2013). To establish a satisfactory fit, a recommended value of 0.95 or higher should be attained, which our research model’s value of 0.956 did. CFI is one of the most widely used metrics for measuring incremental fit when contrasting an independent model with a particular research model (Kline 2011). According to Hair et al. values above 0.9 are generally considered to be indicative of a good model. In this investigation, the value was 0.965. A worse fit is indicated by a higher RMSEA value. The result of this investigation was 0.06 equal to the suggested cut-off value of 0.06, further supporting the model’s validity. After putting all the goodness-of-fit statistics together, we can say that the model does a great job of fitting the sample data.

4.8. Control variables in the model

There were inquiries on the respondents’ demographics in the survey questionnaire for the study. To examine the effect of demographic characteristics on research outcome variables, analysis of variance (ANOVA) and an independent sample t-test were used. To counteract any potential influence these variables might have on creative ability, this study included a number of control variables. To reduce the likelihood of confounding effects on the variables of interest, we accounted for this collection of variables in the model. The enterprises’ age, size, and working experience of the respondents had substantial mean differences in the case of managing open innovation, according to the results of the ANOVA and t-test, and were thus taken into account when evaluating the hypothesis.

4.9. Hypothesis testing

4.9.1. Findings and decision of the hypothesis test 1

The higher-level strategic orientation of SMEs is the higher open innovation of SMEs in Ethiopia. Strategic orientation has a positive significant relationship with the open innovation of SMEs in Ethiopia. From Table 5, the direct effect of strategic orientation on open innovation was (β = .21***). The direct effect (β = .21***), shows when strategic orientation increased by 1 units, open innovation increased by 0.21 units. Furthermore, the indirect effect of strategic orientation on open innovation was (β = .27***). The total effect of strategic orientation on open innovation was (β = .48***), which shows when strategic orientation increased by 1 Percent, open innovation increased by 48 Percent.

Moreover, the model includes the standardized estimates(r) for the causal paths for the direct effects (r = .22***), indirect (r = 28***) effects, and total effect (r = .50***) of strategic orientation on open innovation (Table 6). The total effect (r = .50***) shows when strategic orientation increased by 1 standard deviation, open innovation increased by 0.50 standard deviation. Hence, these results supported Hypothesis 1. Strategic orientation has a positive effect on open innovation. In general, the hypothesis was accepted as: Hypothesis: H1, Findings: Significant, Decision: Supported.

4.9.2. Findings and decision of the hypothesis test 2

Strategic orientation has a positive significant effect on the dynamic capabilities of SMEs in Ethiopia. Table 5, reveals the paths from the strategic orientation have the effect (β = .53***) on the dynamic capabilities. The direct effect (β = .53***) shows as strategic orientation increased by 1 unit, dynamic capabilities increased by 0.53 units.

Additionally, this model also includes the standardized estimates(r) for the causal paths for the direct effects (r = 0.51***) of Strategic orientation on the dynamic capabilities (Table 6). This direct effect shows when strategic orientation increased by 1 standard deviation, dynamic capabilities increased by 0.51 standard deviations. This indicates that strategic orientation has a significant positive relationship with dynamic capabilities, these results supported Hypothesis2. In general, the hypothesis was accepted as: Hypothesis: H2, Findings: Significant, Decision: Supported.

4.9.3. Findings and decision of hypothesis test 3

Dynamic capabilities have a positive significant effect on the open innovation of SMEs in Ethiopia. Table 5, reveals the paths from dynamic capabilities have effects (β = .50) on the open innovation. The direct effect (β = .50***), shows as dynamic capabilities increased by 1 unit, open innovation increased by 0.50 units.

Furthermore, this model includes the standardized estimates(r) for the causal paths for the direct/total effects (r = 0.54***) of the dynamic capabilities of the open innovation (Table 6). This direct effect shows when dynamic capabilities increased by 1 standard deviation, open innovation increased by 0.54 standard deviations. This indicates that dynamic capabilities have a significant impact on open innovation, these results supported Hypothesis 3. In general, the hypothesis was accepted as: Hypothesis: H3, Findings: Significant, Decision: Supported.

4.9.4. Mediation tests, hypothesis 4

The variable starting the causality relation between the independent and dependent variables is called the mediator variable. The mediation effect analysis was done based on the hierarchical regression method introduced by Baron and Kenny in 1986. To verify the mediation effect, the following conditions must first be met (Baron and Kenny 1986): 1. The changes in the independent variable cause a change in the mediator variable; 2. Changes in the mediator variable cause changes in the dependent variable, 3. If the mediator and independent variables are included in the regression analysis, the effect of the independent variable on the dependent variable either falls or completely ceases (Civelek 2018).

As a result, all variables are first examined for correlation once the model has been constructed. This verifies whether the first two conditions put forward by Baron and Kenny are met by the model. A sample correlation table is shown in Table 8. Three distinct models and model coefficients are compared in a mediator analysis.

Table 8. Correlation coefficients sample.

	Variables of the Study	Strategic orientation	Dynamic Capabilities	Open innovation
1	Strategic Orientation	1
2	Dynamic Capabilities	.435**	1
3	Open Innovation	.393**	.528**	1

Source: Model Output (2023).

According to Table 9, model 1’s inclusion of the variables dynamic capabilities led to a drop in the coefficient of the connection between strategic orientation and open innovation. In this instance, we discovered that the dynamic capabilities mediator role was statistically significant (Civelek 2018). Table 9 also displays each model’s fit index. Additionally, these fit indices fell within allowable bounds for the objective verification of the function of mediator variable.

Table 9. Output of standardized effects.

Relations	MODEL 1	MODEL 2	MODEL 3
Strategic orientation → Open innovation	0.49***		0.22**
Strategic orientation → Dynamic capabilities		0.54***	0.51***
Dynamic capability→ Open innovation			0.54***
	χ2/df = 0.349 CFI = 1.000 GFI = 0.998 RMSEA = 0.000	χ2/df = 2, CFI = 0.990 GFI = 0.983 RMSEA = 0.06	χ2/df = 3 CFI = 0.965 GFI = 0.956 RMSEA = 0.08

Source: Author’s data analysis, 2023.

Further analysis was performed using AMOS to determine significance levels of mediation effects. Therefore, we analyzed the impact of mediator (dynamic capabilities) on the relationship between strategic orientation and open innovation. This study indicated the extent of the indirect effect over the total effect was 56 percent (Table 5). This result also shows that dynamic capabilities can significantly play a mediating role between strategic orientation and open innovation of Hawassa SMEs.

Moreover, the mediation model includes the standardized estimates(r) for the causal paths for the indirect effects (r = .24***), direct (r = 28***) effects, and total effect (r = .52***) of strategic orientation on open innovation (Table 6). The indirect effect (r = .24***) shows when strategic orientation increased by 1 standard deviation, open innovation increased by 0.24 standard deviations.

Finally, the mediational analysis illustrates that the influence of strategic orientation on enterprise open innovation is significantly mediated by dynamic capabilities. The findings support accepting H4. In general, the hypothesis was accepted as: Hypothesis: H4, Findings: Significant, Decision: Supported (Table 10).

Table 10. Summary of findings and decision of the hypothesis.

Hypothesis: H	Proposed hypothesis	Findings:	Decisions
Hypothesis: H1	Strategic orientation has a significant influence on open innovation of the Hawassa SMEs	Significant	Supported
Hypothesis: H2	Strategic orientation has a significant influence on dynamic capabilities of the Hawassa SMEs	Significant	Supported
Hypothesis: H3	Dynamic capabilities have positive significant effect on open innovation of Hawassa SMEs	Significant	Supported
Hypothesis: H4	Dynamic capabilities mediate the relationship between strategic orientation and open innovation of the SMEs.	Significant	Supported

Source: Author’s data analysis, 2023.

4.9. Discussion

Hypothesis 1 proposed a significant influence of strategic orientation on the open innovation of Hawassa SMEs in Ethiopia. The findings of this study also show how early customer feedback integration can boost an organization’s market competitiveness. Customer orientation encourages more direct communication with clients, boosting improvements that bring items closer to their ideal levels of features, quality, and price. The outside-in innovation process is thought to be significantly influenced by customer involvement as it can provide novel ideas that improve customer value. Additionally, the study indicated that understanding the capabilities and long-term strategies of both significant existing and potential competitors, helps SMEs to deliver higher value. This study also discovered that improving cooperation with stakeholders or strategic partners, such as research institutions, universities, pertinent government organizations, financial institutions, etc., is crucial for providing customers with added value. It has been demonstrated to be regarded as a crucial outside source of knowledge. These outcomes align with research by (Naqshbandi and Jasimuddin 2022; Pundziene, Nikou, and Bouwman 2021).

Exploring possible market opportunities, creating new business endeavours, and boosting usefulness all depend heavily on entrepreneurship. The value chain of a product starts with the supply of resources and proceeds through the stages of production, distribution, sale, usage, renewal, and repair. From the start of the chain of product value to the finish, deploying and exploiting business opportunities enable entrepreneurs to create new business ideas, and these new business concepts support open innovation. SMEs with an entrepreneurial orientation can develop ideas, draw on knowledge and ideas from outside sources, as well as apply their own ideas to ensure breakthroughs. Innovativeness, risk-taking, and proactivity are qualities that encourage SMEs to undertake proactive decisions and mindsets. According to Han and Zhang (2021) study, entrepreneurial orientation is a broad strategic perspective that spurs open innovation. Managers of SMEs must actively promote an entrepreneurial mindset in order to foster open innovation.

Hypothesis 2 proposed a significant influence of strategic orientation on the dynamic capabilities of Hawassa SMEs in Ethiopia.

The empirical results showed that strategic orientation has substantial effects on dynamic capabilities and that SMEs must advance beyond the level of resource acquisition and move to the level of capability transformation in order to stay competitive in a dynamic environment. This study also shows that in a business environment that is rapidly changing, these valuable and unique strategic orientations have the power to enhance an enterprise’s sensing, seizing, and transforming capabilities to perform better than average. These capabilities also help SMEs anticipating changes in the market and technology, as well as the reactions of rival businesses and consumers influence SMEs in developing these capabilities.

Customer orientation can be thought of as a search procedure that takes into account customer demands and preferences when creating new products and making changes to existing ones. Small and medium-sized businesses generate value by combining and transforming their internal and external resources ideas into new ones. Customers are an external source of knowledge that the company’s innovation processes may be able to use to their advantage through dynamic capabilities. Customers’ feedback should be incorporated as early in the reconfiguration process as possible to assist the business save time and improve its fit with the market.

Additionally, ‘how’ businesses compete, is intimately tied to strategy. Consequently, it has been indicated that a competitor orientation is a fundamental component of strategic orientation that encourages dynamic capabilities and then innovation. Deploying and exploitation of business opportunities enable entrepreneurs to create new business ideas. These sensed ideas must be integrates and transformed into new commercial ideas and value. The findings of this study are in line with the results of the prior researches: Strategic orientations that promote proactive deployment of opportunities by enhancing consumer involvement are required for effective opportunity discovery and exploitation capabilities (Payne and Frow 2005). The value of dynamic capabilities may be influenced by a variety of organizational factors, however, the author draws on previous research to emphasize the significance of strategic orientation (Wilden, Devinney, and Dowling 2016).

Hypothesis 3 proposed a significant effect of dynamic capabilities on open innovation. The innovation success of SMEs will be influenced by the deployment of methods to transform detected and sensed ideas into new goods, services, and processes through the use of well-constructed business plans. Additionally, businesses can identify and create cross-organizational collaboration with key players like colleges, universities, and other IT businesses using their sensing skills. It is essential to have dynamic capabilities that support the SMEs with respect to target markets for effective opportunity identification and exploitation. Developing and deploying dynamic capabilities of SMEs help to take advantage of SMEs’ open innovation management.

The study shows creating formal methods for disseminating fresh information about market trends in the form of documents like reports and automated data software aid to keep everyone in the business informed and to quickly discover solutions for their customers. Moreover, sensing ideas, knowledge, and market information, selecting the best ones, and transforming it into new or improved services or product enhance open innovation. The study indicated that businesses should prioritize the knowledge ideas and resources available at universities and research centers/institutions, leading to improved outside-in open innovation. These results are in line with the prior researches’ findings (Naqshbandi and Jasimuddin 2022).

Hypothesis 4, Dynamic capabilities play a mediating role between strategic orientation and open innovation of Hawassa SMEs in Ethiopia.

The study indicated that through dynamic capabilities, an organization’s strategic orientation defines its level of open innovation. The application and comprehension of dynamic capabilities demonstrated to be a powerful mediator of the small and medium business’ management of open innovation. Competition is a significant problem for small and medium-sized businesses, but it may be managed by putting the client first rather than the rivalry itself. Two competing businesses are in competition with one another when they both make an effort to draw clients by offering competitive prices, high-quality products, and excellent customer service.

Understanding and using strategic orientation to improve open innovation and the effective use of limited resources provides value, increases customer satisfaction, and benefits the owners of SMEs financially. The study discovers that market-focused SMEs can readily participate in open innovation activities and profit from exchanging market data, concepts, and expertise with clients and outside partners that can enhance open innovation outcomes. The ability to access outside ideas and to allow others to use their own ideas to generate or enhance the value chain of product/service innovation has also become a vital component. Therefore, new input types, new production processes, new product innovations, a new sales technique, and new distribution techniques (distribution methods) and are all examples of innovation types. Entrepreneurs can come up with concepts and then drive innovation and eventual commercialization by combining both internal and external information and ideas. Sensing, seizing, and transforming capabilities enhance searching and using these internal and external ideas, information, and knowledge. The results of this study demonstrate that strategic orientation generates value through dynamic capabilities and then enhance resource integration and efficiency of small and medium-sized businesses.

In order to show how SMEs might gain a competitive edge, this study uses dynamic capabilities as the mediating variable. The crucial idea is that strategic orientations complement one another, therefore combining these resources will give SMEs a better chance of success through dynamic capabilities and increase access to open innovation. This study offers empirical proof that one of the key frameworks for dynamic capabilities mediates the connection between SMEs’ strategic orientation and their management of open innovation. These results are essentially consistent with those of previous research that has examined the role of entrepreneurial orientation in the production of knowledge and innovation (Buccieri, Javalgi, and Cavusgil 2020; Freixanet et al. 2021). Accordingly, our findings showed that dynamic capabilities serve as a link between strategic orientation and open innovation.

5. Conclusions and recommendations

5.1. Conclusions

Small and medium-sized firms (SMEs) promote entrepreneurship and job creation, which makes them a significant source of employment, revenue, and development (Matthew et al. 2020). Additionally, they make a substantial contribution to the GDP and the fight against poverty, making it a crucial gateway to the labour market (ADB 2019; ILO 2015). Their importance is crucial for eradicating poverty and creating jobs in emerging economy. To make their contributions considerable and durable, it is crucial to understand the relationship and amount of significant influence between strategic direction and managing open innovation of SME, with the mediating roles of dynamic capabilities. The study focuses on the strategic perspective of SMEs and managing open innovation and how it may affect dynamic capabilities. This study used structural equation modelling (SEM) to examine the research hypotheses in the suggested structural regression model with a sample of 321 SMEs’ respondents in order to provide a deeper understanding of the complex relationships among these various elements. This analysis produced several interesting findings.

The empirical results showed that SMEs innovate openly and more efficiently when they have a higher degree of strategic orientation. Similarly, dynamic capabilities are significantly improved by strategic orientation. Additionally, dynamic capabilities have a considerable favourable impact on open innovation. Finally, the findings showed that dynamic capabilities mediated the relationship between a strategic perspective and managing open innovation. Understanding and utilizing strategic orientation (customer demands, competitive strategy, and entrepreneurial orientation) enhances open innovation and the effective use of limited resources, knowledge, and ideas, which adds value and benefits SMEs’ owners financially and in terms of customer satisfaction. Competition is a serious issue for small and medium businesses, but it can be handled by focusing on the customer rather than the competition itself. When two rival businesses compete with one another by offering low prices, high-quality products, and excellent customer service, they are actually competing with the customer.

5.2. Policy and theoretical implication

Theoretically, by elucidating empirically the gap between strategic orientation and open innovation. Additionally, the study adopts a sophisticated strategy by examining the mediating mechanisms of dynamic capabilities, which are frequently cited as significant enablers of open innovation. Furthermore, because the study examined various theories that had been proposed in the literature and produced conclusive results, it has significant ramifications for contributing new knowledge to the corpus of literature already in existence. In the way that they can improve the value of the management of open innovation, the paper conceptually adds to earlier conceptual talks in the study area. The result demonstrates that having a clear strategic orientation and understanding how to apply it improves open innovation through dynamic capabilities, which ultimately adds value and benefits SMEs’ owners financially and in terms of customer satisfaction. These findings have important management implications.

According to the study’s findings’ practical implications, managers of SMEs can acquire and utilize knowledge, concepts, information, and resources more effectively by building and fostering solid relationships with both internal and external sources. In order to guarantee creative value through dynamic capabilities and effective resource utilization techniques, managers can benefit from having a thorough awareness of their customers’ needs and the strategies used by their rivals. This can be accomplished by regularly asking customers for input, attending to their needs, and conducting surveys of customer happiness. To enhance customer service, SMEs are encouraged to use automated information communication and services more. All critical business activities should also be automated for increased SMEs efficiency, innovation, and control. Additionally, managers can identify and build cross-organizational collaboration with key players including colleges, universities, and other IT companies. The study also reveals that applying competitive business methods and market-focused business plans might help ideas turn into new goods and services. The practical application of this study is to give businesspeople in SMEs in Ethiopia and other developing nations insight and expertise to implement the idea of strategic orientation in relation to the deployment of innate innovative skills for superior value.

Policy implications: Given the importance of SMEs, the study also suggests that governments work to improve a consistent, trustworthy, and transparent information management system in order to comprehend the situation of SMEs and help them. A software program with a tiny data server must be used to set up and automate the information management system for SMEs. The study makes the suggestion that the Government of Ethiopia (agencies organizing and supporting SMEs) might use the study’s findings to build entrepreneurship and innovation training programmes that will improve SMEs’ performance and growth. The study also suggests that operating in priority and strategic business incentives will give a competitive advantage and long-term value addition to the developing economy. To help small firms prosper in the fiercely competitive economic environment, the government must improve the current business development services it offers. Furthermore, the study contends that improving an economy’s capacity for productive and value creation necessitates the transfer and mastery of innovative skills as well as a deliberate and thorough array of state-directed, synergistic interventions with a structural transformation as their primary goal. The private sector’s ability to facilitate and support proportionately more new jobs for educated and trained workers in priority economic activities will boost innovation.

5.3. Limitations and suggestions for further study

Overall, the study’s findings give the conceptual framework a lot of solid backing. Particularly, the outcomes show that strategic orientation is a potent tool that can both directly and indirectly result in successful product innovation. The study does, however, have some limitations, as do all studies. The fact that the current study relied on respondents’ opinions of their companies’ performance is one of its major limitations. If accurate records of financial growth indicators, such as sales and profit, in quantitative terms at various times could have been obtained, the analysis would have been more thorough. Another drawback is that future researchers should utilize a longitudinal strategy to compare any fluctuations in the outcomes because this study used a cross-sectional research design together with a quantitative research approach. Alternatively, qualitative studies could be conducted to supplement the quantitative findings because they help to describe the phenomenon in detail.

Disclosure statement

No potential conflict of interest was reported by the authors.