BUSINESS INNOVATION AND ENTREPRENEURSHIP
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Contents
Abstract 4
CHAPTER ONE 6
Introduction 6
Background of the Study 6
Problem Statement 9
Objectives of the Study 10
Research Questions 11
Research Hypothesis 12
Limitations of the Study 13
Justification of the Study 13
CHAPTER TWO 15
Literature Review 15
Introduction: Overview 15
Definition of Open Innovation 17
Open Innovation Pillars 18
Users Innovation 18
Appropriation Regime of Open Innovation 19
The Absorptive Capacity 20
Strategic Alliances 21
The Idea of Open Innovation and Small and Medium Enterprises (SMEs) 22
Scholarly Review and Summary of Open Innovation Systems and SMEs 26
CHAPTER THREE 29
Research Method and Methodology 29
Introduction 29
Research Onion 29
Research Philosophy 30
Research Approach 31
Research Design 31
Variables 32
Independent variable 32
Dependent Variable 32
Data Collection Procedure 32
Data Source: Primary Data 32
Population, Sample and Sampling Technique 33
Data Analysis Process: Quantitative 34
Reliability Test and Validity of the Data 35
Advantages of Using SPSS, Ms. Excel and NVivo in The Present Research 36
Ethical Consideration in Data Collection 37
Limitations in Data Collections 37
Data Reliability 38
Time Constraint 38
Chapter Four: Results 39
4.1 Demographic Results 39
4.2 Reliability Test 43
4.3 Inferential Statistics 44
4.4 Discussion 48
Chapter Five: Conclusion and Recommendation 55
5.1 Conclusion 55
5.2 Recommendation 56
5.3 Recommendations for Further studies 56
References 57
Appendix 64
Questionnaire 64
SPSS Output 73
The Impact of Absorptive Capacity on Innovation in Small and Medium Enterprise in the Technology Industry in an Open Innovation System
Abstract
Absorptive capacity plays a critical role in understanding, determining, and implementing firms’ ability to acquire and apply innovations, especially in the technology industry. This is because the external knowledge gained is more often than not inferior to the organization’s needs. Thorough assessment enhances the way the new technology is integrated with the old methods and procedures of the business to improve the quality of output. Similarly, the present study has the objective to study the current analysis of the open innovation on SMEs to help integrate those using empirical evidence that is pointed on previous research studies. Many research findings are critically investigated using panel data and sophisticated statistical analysis packages such as MS. Excel and SPSS to derive the best outcome that will measure the impact of the research on the field of investigation. The literature in current research has shown that open innovation studies are done in Europe, Korea, and China. However, there are scant studies of the field in North America. Preliminary investigation has shown that there is an overall improvement of innovation performance of SMEs due to open innovation. However, the literature has established that relevant models and theories that are appropriate for managers in open innovation are not well established. The present study investigated the factors that affect the absorptive capacity of the SMEs that included relationship, governance, facilitators, and provision of resources, open innovation and cultural elements. Relationship factor, governance, facilitator, open innovation and cultural element were found to have a positive relationship with the impact of absorptive capacity of the firm. While provision of resources was found to have a negative effect with the absorptive capacity of the firm.
Keywords: Open Innovation, Absorptive Capacity, Small and Medium Enterprises (SMEs), Networking, Strategic Management, and Literature Review.
CHAPTER ONE
Introduction
Background of the Study
The world of business has experienced a dynamic and paradigm shift since the firm was invented with globalization, connectivity, and industrialization taking the lion share in the development of the arena (Pontiskoski and Asakawa 2009). Thus, many organizations are slowly trajecting from the traditional form of doing business to new methodology informed by superior technological innovation models and systems. In 2003, Henry Chesbrough developed an idea known as open innovation to enhance new modeling of business. Chesbrough (2012) asserted that this idea would allow small and medium enterprises (SMEs) have a share of the market, and similarly add novelty to the business arena. Ever since the open innovation was introduced, there have been many studies on the field analyzing the significance of idea. According to Chesbrough (2003), in a research conducted by Chesbrough (2012) defined open innovation as the process through which an organization purposively uses the inflow and outflow of external knowledge to enhance the internal experience and help in the expansion of markets for the organization to strengthen its external output or production.
According to Chesbrough, the significant driver of the business is not developed by the employees that the company has hired but rather the outside world that influences the performance of the organization (Pontiskoski and Asakawa 2009). Before the recent studies, a lot of research that seeks to evaluate the impact of open innovation has been focused on large organizations significantly leaving out small and medium enterprises that form the epitome of the business arena (Sari, Salmi, & Torkkeli 2007). Jesic, Kovacevic, and Stankovic (2011) asserted that the open innovation model assumes that a firm should use both external and internal forces, inventions, and analytical results to advance their production and technology. According to recent case studies and literature, various firms such as DuPont, IBM, Microsystems, and Intel have shown the successfulness of application of open innovation strategies and models (Sari, Salmi, & Torkkeli 2007).
Huizingh, (2011) observed that the main characteristics of open innovation are innovation entrepreneurship, innovation networks, innovation cooperation and partnership, innovation clusters and ecosystems. The key driving factor in open innovation is entrepreneurial innovation that covers all the phases and sections of system innovation process developing innovation in technology, market, management, social and giving innovative development of the society (Huizingh, 2011). While innovative collaboration comprises of participation in innovation process with use of various types of technology like information exchange, research and technology transfers. The creation of innovation cooperation is dependent on the formation and status of innovation ecosystem as the medium of open innovation determined by global elements.
Many studies have shown that small and medium enterprises (SMEs) lack the technical and managerial skills, and knowledge to effectively execute the proper running of the business entity (Rahman and Ramos 2010,). It was revealed that small firms are less attractive in the nature and conduct of business to professionals as compared to larger firms that have defined the structure of operations. This is attributed to factors such as strategic management, business culture, as well as the firm’s belief, enshrined in the proprietor of the organization rather than within the firm itself. Therefore, this makes it difficult for other people to get in line with the objectives and goals that are proprietor’s oriented (Rahman & Ramos 2010).
A study that was conducted by OECD revealed that significantly less than 20% of the SMEs are correspondingly utilizing open innovation models and approaches. According to Biachi and Guijarro (2010) and Colombo et al. (2014), research on open innovation in small and medium enterprises is fragmented. Some researchers assert that SMEs can benefit higher for the research on open innovation than other larger firms whose structure are rigid and engulfed by bureaucratic approaches if the SMEs are willing to indulge in risk-taking operations and are swift to adapt to the new technologies and adhere to the ever-changing business environment (Parida et al. 2012). Moreover, other research work shows that open innovation is a new phenomenon in the business arena with promising outcomes especially to SMEs to ensure that they overcome the business operation challenges and enhance the strategies to increase their marginal profits (Gassnann et al. 2010).
However, not all the organizations have the ability to adopt the open innovation systems to the existing systems, and they provide little evidence to show the acquisition and implementation undertaking. The little information provided in the literature work of other scholars has shown that it is essential to understand the determinant of the knowledge acquisition in small and medium enterprises (SMEs) to ensure the model is equitably and well absorbed to provide more significant development of the organization. Recently, it has been understood that research based on open innovation on SMEs have gained rapid growth and popularity. However, there has been a minimal understandable review of the field or research on the model. The present research aims to investigate the importance of absorptive capacity created by organizations’ research and development (R&D) and the accumulated human resource development of the field.
The methods that open innovation device can help overcome the problems that SMEs face if they are successfully implemented. Notably, it is also vital to identify that the adoption of open innovation is not adequate if the organizations fail to collect and integrate and arrange the fragmented literature on SMEs and innovation (Gassmann et al. 2010). Thus, the main objective of this dissertation is to evaluate the current studies on the impact of absorptive capacity on SMEs using empirical findings and integrate the outcomes to give out the best strategies of implementation and guide for further research studies.
Problem Statement
The paradigm of open innovation was discovered and launched by Henry Chesbrough in 2003. It has inspired many organizations to explore external factors of change to help the internal structures of the organization to overcome the problem of business operations, otherwise known as the Outside-In approach (Sari, Salmi, & Torkkeli 2007). In the past businesses were very rigid, enshrined in traditional methodology and very secretive in that many of their innovation were closed to ensure that the copyright of the change is not hackneyed by other organizations (Viskari, Salmi, & Torkkeli 2007). This made the research and development department of the organization compelling as it was its sole responsibility to devise new ways to run the organization.
However, as the world of businesses changed over time with the invention of new technologies from external forces the research and development (R&D) department slowly lost its powers and was integrated to new systems with acceptance of external innovations to improve the internal organizational strategies. This situation was forced by the demand by consumers and other external factors such as the ever-changing enterprise market, new opportunities, and human resource outsourcing strategies. This necessitated a change in the closed model of innovation to open innovation approaches (Wu, Tsai, & Chang 2011). The model of open innovation has received mixed reactions and understanding based on the vision that the business has to implement any change. For example, there has been a conflict in the universal definition by Chesbrough on open innovation. Other people have come up with their interpretation based on open source software of the business innovation. According to Raymond and St-Pierre (2010), this mixed reaction has made the adoption of open innovation strategies, methodologies, and models difficult, especially to small and medium enterprises. Therefore, this forms the base of the research, especially to investigate the impact of absorptive capacity on innovation in the small and medium enterprises in the technology industry in an open innovation system.
Objectives of the Study
The main aim of the current study is to measure the impact of absorptive capacity on innovation in the small and medium enterprise in the technology industry in an open innovation system. Similarly, it seeks to examine the current studies that have made an impact to the business frontier through the implementation of open innovations on small and medium enterprises (SMEs) and try to use the findings to integrate the systems to upcoming SMEs. However, it is critically hard for the present study to evaluate a general objective primarily based on the formulations and statistical measures that are available to examine the epistemological underpinnings of a research study. Therefore, to overcome this analytical problem, the study has devised three specific objectives give an informed analysis of the impact of absorptive capacity on open innovation in SMEs.
1. To evaluate the absorptive capacities of the SMEs to adopt new open innovations
2. To examine the impact of absorptive capacity of the adoption of new open innovations of the SMEs in the modern era
3. To determine the characteristics that differentiates SMEs & large firms.
When the research studies specific objectives, it is critical because the goals that will be achieved at the end of the study will enhance the examination of the general aim of the study. Seshadrinathan et al. (2010) assert that if the objective of the research study is precise and straightforward, it is easier to evaluate and examine. Consequently, this forms the base for the research questions and hypothesis testing parameters. In short, a research objective acts as the heartbeat that supplies the research with required nutrients to ease the analytical evaluation procedure (Seshadrinathan et al. 2010)
Research Questions
A research question is a fundamental and integral part of the research study since it forms the milestone for the review of the literature of the specific survey (Creswel et al. 2013). For instance, studies that base their findings on the impact of new model adaptability in the business, the research question will direct the researcher on the ideal forms and articles to review the existing literature of the study (De Massis, Frattini & Lichtenthaler, 2013).
However, like the research objectives, the study questions must be specific for quick evaluation while informing the topic of the research. For this reason, it is significant to develop research questions based on the problem under investigation. Thus, research questions are derived from research objectives. The idea that prompts the issues to be derived from objectives is that when the literature evaluates the shortcoming of the existing studies, and methodology informs the new research it will be answering the research questions and consequently notify the objectives (Graneheim and Lundman 2014). For instance, when research meets its ascertained objectives, it is termed as satisfactory and has enriched the study area with new empirical and analytical investigation coupled with supportive data analysis and methodology (Grbich, 2012). Therefore, the research questions for the current study will be;
1. What absorptive capacities do the SMEs have to allow them to adopt new innovations?
2. What are the impacts of the adoption of new innovations of the SMEs in the modern era?
3. How do SMEs and large firms compare in terms of likelihood of adoption of the new technology, especially the innovation that is fostered by external forces and factors such as business demand?
Research Hypothesis
A research hypothesis is a statement that the researcher believes to be true or false based on the study that the researcher seeks to investigate (Schmidt, 2016). The significance of the study is tested by statistical evaluation of the hypothesis. For instance, in a study of innovation and new technology, the researcher may believe that humanity is distorted by the adoption of the latest technology (Schmidt, 2016). Therefore; the researcher may use a new methodology and psychological underpinnings to investigate the belief. Hence, in the present study, it is believed that the reason that small and medium enterprises fail to thrive in the new trends of a business ecosystem is catalyzed by the reluctance and failure of adoption of open innovations.
Ho: Absorptive capacity does not significantly impact SMEs in an Open Innovation system
H1: Absorptive capacity have a significant impact on the SMEs in open innovation system
It is essential to note that a hypothesis cannot be tested if the research studies do not address the contingencies and contemporary market trends, especially in the industry field (Schmid, 2016). For instance, in the first phase of the 20th century, many countries embarked on manufacturing as a model of business development that may enhance the evolution of countries’ Gross Domestic Product and Produce (GDP). However, Sachs (2012) asserted that construction of the countries might not merely rely on manufacturing as the mode of production as the service sector had shown a significant input to the o utput of the state. At the beginning of the 21st century, it was revealed that environmental impact assessment had shown a considerable influence in the business frontier Gunasekaran & Spallanzani, 2012). Thus, many organizations shifted from development through manufacturing and service delivery alone and focused on sustainable development that evaluated the need of the production unit to cater for the current needs in recognition of the needs of the future generation and maintenance of the environment (Sachs 2012).
Limitations of the Study
There have been fewer research studies on the influence of open innovation of SMEs as likened to a relative survey of more substantial firms. The present study will be limited in several ways. For instance, the research will face a limitation of inadequate empirical literature that studies the open innovation adaptability in SMEs (Schmidt, 2016). Therefore, it will focus on the personal and analytical research of large firms about smaller enterprises.
Similarly, the research will be limited to the knowledge that correlates with opening external innovation that affects or influence SMEs. This limitation is attributed to the many SMEs that are rigid in their structure and solely depend on the understanding of the proprietor and customer demand to grow (Creswell et al. 2013). Lastly, the research will face non-flexibility, inadequate creativity, and low implantation of sophisticated managerial strategies that face SMEs. However, this is a structural limitation rather than an analytical limitation of the study (Gunasekaran & Spalanzani, 2012)
Justification of the Study
In any country, small and medium enterprises (SMEs) contribute significantly to the economy. Rahman and Ramos (2010) state that SMEs dictate more than 45% of the global market output index, with 65% of them based in third world countries where much of the economy is not exploited. Rahman and Ramos (2010) states that insignificant to ignore their impact in the marketplace because the overall performance demonstrated in the international market competitiveness of the firms is dependent on the overall effects of adoption on new technologies and the interrelationships that exist between the external forces and the research and development (R&D) of the SMEs. Therefore, it is justified to conduct the present study to help measure the capacity and capability of the SMEs to adopt and implement open innovation models and the effect of the adoption of the models.
CHAPTER TWO
Literature Review
Introduction: Overview
In the recent past, a research conducted by Teece (2010) stated that the world had witnessed a constant shift in the way that businesses do their research and development to increase their global competitiveness with recognition of technological advancement to enhance commercial activities. According to Chesbrough (2003) as quoted in Buckley and Casson (2010), some scholarly material state that many firms have augmented their absorptive capacity, innovation, performance and control of the market share through the acquisition of external development innovations by involving a multi-agency development corporation. These agencies include but not limited to customers, business innovators and competitors, supply chain developers, and management as well as varied research and development institutes (Chesbrough 2012). Also, most firms especially the manufacturing and production industries have externalized their research and development innovation team through the process of acquiring external materials and information and recruitment of external capacities to improve the performance of the organization. For example, before the introduction of open innovation systems in 2003, firms acquired external development through integration, consumer and market collaborative efficiency, in and out-licensing of companies (franchises), similar alignment of business models and management strategies among other forces (Marjanovic et al., 2012).
The term open innovation can be defined as a paradigm shift in business innovation from closed and in-house research and development to new products developed through internal and external collaborative ideas and knowledge to enhance commercialization of the business entities (Marjanovic et al., 2012). In its inception, the assumption was that open innovation was practically and categorically preserved for larger firms and the initial research practices focused on adaptability of the change in more giant multinational corporations such as the IBM (Dodgson et al., 2006). However, over the years or research, it has been revealed that open innovation modeling and approaches are slowly being adopted by small and medium enterprises (SMEs) across the globe (Wynarezyk, 2013). Despite the mushrooming and widespread open innovation around the world, scholars have suggested that open innovation of SMEs remain adversely scarce and this contributes to a more significant share of death of organizations before they celebrate their first anniversary (Marjanovic et al. 2012; Dodgson et al. 2006; Wynarezyk 2013; Van de Vrande et al. 2009).
However, the present study has embarked on relatively independent exploration on open innovation and much importantly through the focus on SMEs and international studies and aim to disintegrate the challenges and opportunities that are associated with adaptability and incorporation of the new open innovation technologies. This overview provides the base and foundation as well as the background for the current research so that it can help identify the need gaps and challenges that the previous study has faced in trying to investigate the adaptability and impact of absorptive capacity on SMEs. The investigation begins with providing a critical definition of open innovation. It then examined the pillar theories of the field in which the study of paradigm shift of open innovation have taken and overall literature on SMEs adaptability and its basic outline. This will be followed by critical analysis of the weakness of the previous study that will be capitalized on to inform the impact of absorptive capacity on innovation in the small and medium enterprise in the technology industry in an open innovation system and provide insight for further research studies coupled with technical and analytical parameters.
Definition of Open Innovation
The popularity of open innovation system across the world has gone over-board since since the idea came to the fore in 2003 in a seminar of new business innovations and critical topic evaluation on management and business strategic planning (Chanal et al., 2011). According to Chesbrough and Bogers (2014), open innovation is the purposive use of inflows and business outflows activities and knowledge to influence the impact of change through the expansion of business market by study of external research, development, and innovation. Despite rigorous research on the field, scholars have not come to a consensus on the components of open innovation systems (Chesbrough & Bogers, 2014). However, the innovation has grounded itself as the key player that organization seeks to utilize in development and enhancement of supply chain, customer relation, private and public research influence as well as enhancement of competitive nature of the business in the changing business environment (Gassmann et al., 2010). To achieve this milestone, firms have enhanced innovation capabilities through internal and external competitiveness (Piperopoulos, 2016). This means that many organizations have been in constant engagement with other organizations, experts and varied players in the business market a strategy known as open innovation and digresses from the traditional closed development paradigm (Chesbrough & Bogers, 2014).
In the seminar it emerged that open innovation was viewed in two dimensions; that is, open inbound change that is concerned with the external acquisition of knowledge and technologies to enhance business operations (Christensen, Olesen & Kjær, 2015). For example, IBM uses R&D contracts, university research work on business development and in-licensing, as well as merger strategies to enhance its operations (Chesbrough & Bogers, 2014). Secondly, innovation is catalyzed by outbound open innovation; this means that a firm can transfer its internal technology to other organization and enhance other organizations improves its commercial activities (Chesbrough, 2012). For instance, DuPont has been keen on the provision of joint ventures activities, rigorous out-licensing, and joint venture spin-outs.
According to Enkel et al. (2009), open innovations make it possible for the firms to garner the opportunities and benefits of their innovative ideas and conceptualize the concept in an attempt to improve their products. In his, research Enkel suggested a third task force in the development of open innovation that he referred to as a coupled process. The coupling process ensures that the strategies in which firms combine inbound activities with outbound ones to enhance development, commercialize as well as improve co-capitalize innovation.
Open Innovation Pillars
According to current research, it has been revealed that the introduction of open innovation formed a new theory in the innovation industry of firms and research and encircled that paradigm with basic gist in which the approach sits on Enkel et al., (2009). Similarly, Giudici, Reinmoeller, and Ravasi (2018) argued that the establishment of these pillars had enhanced the long standing of these substructures of management and strategic research and development institute, entrepreneurship and innovation theories rely upon. Therefore, the present study seeks to evaluate some of the pillars that open innovation systems sit on and help in the improvement of business operations.
Users Innovation
This theory was first used by Eric Von Hippel in 1988 and suggested that the consumers or the first adopters of new technology are the real creators of the latest innovation, a system known as development paradigm (Wynarczyk, 2013). In Hippel observation he realized that user innovation paradigm in business is enshrined in four external modulation features, that is, the suppliers and customers, government and university innovation developers, business competitors and influence of other nations.
The user’s innovation theory asserts that a more substantial proportion of the products and new services that firms provide in the new market are co-developed. The co-development strategy ensures that through refinement procedure undertaken by the production firms the quality of the products and services are improved to meet the demand and need that is aired by the consumers in the modern market (Greco, Grimaldi, & Cricelli, 2016). Also, Agarwal and Shah (2014) affirm that modern organizations continuously use alliances and networks through licensing agreement and joint ventures as well as informal business relationships. This incorporates external knowledge, skills, and expertise to improve the innovated process of the organization and production of tailor-made products based on consumer demand.
Appropriation Regime of Open Innovation
Giudici et al.,(2018) observes that a research that was conducted by Teece (1986) on the amount of profit that emanates from innovation shows that some changes may be so poorly in a particular company in that their imitators and competitors may copy the difference and gain higher economic returns than the innovative firm. This phenomenon has made many organizations that are initiators of some critical change die before they can realize the importance of the development that they initiated. It is because the appropriation regime of open innovation that is the environmental factors fails to govern the ability of the innovator to gain profits from the development. Also, the present study notes that the efficacy and efficiency of appropriation regimes of open innovation are separated between two legal mechanisms. For example, an arrangement that contains secure technology that is not easy to be stolen, reasonably easy to protect and a device that is weak and its technology is easy to reciprocate and very difficult to defend. That observation necessitates a strategic decision of the firm that is about suppliers contracting and integration of the company to commercialize the operations of the firm should be considered central to indicate whether the appropriation regime is weak or tight.
The Absorptive Capacity
The theory of absorptive capacity was introduced by Colen and Levinthal, and they defined it as the ability of an organization to recognize and endure the challenges on innovation from external innovators, assimilate it and use it for commercial gains (Cheng, & Chen, 2013). Colen and Levinthal viewed internal research and development on investment as a facilitator of the capacity of the fundaments factors that ease the process of acquisition of open innovation from external knowledge and technology (Helfat & Winter, 2011).
Similarly, Zhou and Wu, (2010) affirmed this theory by identifying and offering the specification of four distinct dimensions that an organization takes to enhance absorption capacity, that is, the acquisition, assimilation, transformation and exploitation dimensions. The development of private capital and human resources that is, internal research and development (R&D) capacity have been labeled as the critical component of the position of the firm to absorb innovations. This because of its ability to the impact it has on technological advancement, development, and change of new products as well as assessing and ability to utilize the knowledge from external innovators in the organization. A firm needs to engage in the unremitting process of learning and inflow of new information and ideas on its level and environment. Therefore, its research and development (R&D) department cannot be merely reliant on conducting development that emanates from inside with the already existing knowledge and expects the new outcome. The organization has to expand its wings outside so that it can be able to collect external information and incorporate it into internal development and enhance its productivity.
According to OECD (2005), acquisition of technology and knowledge comprises of the purchase of external information without actively cooperation with the source. The external knowledge may be part of the equipment or machinery that is purchased by the firm. It may also comprise of hiring workers who have the new knowledge or application of contract research or consultation services (OECD, 2005). The disembodied advancement comprises of other information like licenses, patents, software and trademarks. On the other hand, Zhou and Wu, (2010) observed that the assimilation of technology involves the process in the organization starting from the first awareness of the innovation to possible adoption and full deployment. The assimilation of the technology gives the management the steps that will take place to getting new technologies in the company.
Zhou and Wu, (2010) observed that transformation of technology is the full reassessment and overhaul of enterprise IT system with the aim of rising efficiency and delivery in the digital economy. IT evaluation develops the structure for digital transformation of the enterprise and is led by the business leaders. The exploitation of new technology is the utilization of new technology or scientific advancements to rise the performance of the products or manufacturing process.
Strategic Alliances
A firm is viewed as a joint resource force based on the strategic framework that the organization has developed (Munivenkatesh & Islam, 2010). This resource describes the best and effective business model that offers an opportunity for the organization to collaborate, share, and transfer funds and knowledge. Lodhia, (2015) argues that business partnerships and collaborative strategies and alliances offer the vital capacities of innovation, especially on small and medium enterprises to sustain the competitive business environment through the attraction of customers and like-minded investors. These alliances are crucial in the sector that offers capital and knowledge as well as fast-growing and changing technology. A criticism of the strategic partnerships states that organization with prominent business grounds dictate the operations of their new counterparts when they come in to contact with them. However, it is essential to understand that for small and medium enterprises to thrive; they must embrace the strategic alliances to adopt technologies from other developed firms. For instance, it was revealed that more than two-thirds of Japanese firms rely on a collaborative business model to acquire new technology and enhance their business operations. A similar observation was made on US firms and revealed that more than half of them participate in joint venture alliances primarily to access the latest business operation technologies (Un et al., 2010). In business environments that are complex and turbulent to conquer, many firms become dispersed and it is essential to embrace collaborative supply, as well as research and development to enhance quantity and management of the organization as well as improve market competitiveness through adoption of external resources and knowledge that prompt new synergies, access and transfer of ideas. Piperopoulos (2012) argues that firms that do not have cooperative undertaking do not exchange ideas, technologies, and knowledge and this reduces their long-term effectiveness or even lose the market capability to conquer new markets or even exchange of experience with existing firms.
The Idea of Open Innovation and Small and Medium Enterprises (SMEs)
Growth of any business is dependent on the level of innovative ideas and the level in which it embraces technological advancement. This way, the market will have the utmost required productive capacity as well as international competitiveness coupled with a high level of living standards and welfare of the company and its employees. In the recent years, many organizations have embraced innovation and technological advancement and have made them their focus of attention to the rapidly growing global as well as local competition from other related firms and information-based economies (Castells, 2014). In particular, research and development, as well as policy formulation, have become the focal points of SMEs as the vital source and economic drivers that enhance the production of new products by the organizations through the adoption and supply of new knowledge (Castells, 2014).
Today, it has been recorded that there are more than 30 million SMEs that operate in the European market. This number represents close to 94% of all the enterprises that operate in the European Union (EU) and provide jobs to more than 75million people across Europe and Eurasia (Europa, 2012). This makes open innovation on small and medium enterprises a pivotal part of the and component to enhance dynamism in which these businesses operate in local, regional and internationally competitive markets to strengthen economic development (Carcary, Doherty & Conway, 2014). There is a small proportion of SMEs that are responsible for the research and development as well as innovate ideas of the majority of other SMEs, and this has impeded the event of new products, advanced research, and development wealth creation as well as employment of people (Wynarczyk, 2013). Among the innovative SMEs, very few have the urge and capacity as well as the opportunity to actively and successfully participate in growth and development, diversification of products, expansion of business wings beyond their local markets. However, in this era of information and knowledge-based economy, SMEs are mired by their internal and external management structures, management skills and capacity, awareness of external innovation as well as a limited capital injection by other investors (Carcary, Doherty & Conway, 2014). Generally, SMEs have shown that they do not have sufficient internal adoptive capacity and human resources to be able to beat or overcome the challenges that current business environment provides and realize development through diversification of their products portfolio. Similarly, their research and development departments do not have the capability as well as adequate opportunities to form a partnership with external organizations R&D to enhance their commercialization as well as internationalization of their products (Van de Vrande et al., 2009; Wynarczyk, 2013).
The ration of adaptability and impact of open innovation systems differ significantly between the SMEs and larger firms due to factors such as organizations size, management structure as well as willingness to adopt new technology. Battarink et al., (2010) asserts that the relationship and collaborative effort between firms is not merely influenced by the fact that companies want to gain economic and financial mileage. It is instead geared to address business uncertainties, resource constraints as well as enhance managerial skills which have been identified as the biggest challenge and buffer to organizations’ internal and external growth. Open, innovative systems have provided alternative strategic management through which growth-oriented SMEs can collaborate with other firms at low cost and vividly address previous problems such as geographical location barrier, technological impediment, human resource incapacity and financial constraints that impede the development of new products and conquer new business markets (Batterink et al. 2010). Moreover, open innovation ensures that there is greater access of information by SMEs, sufficient research and development programs, technological exchange and advancement as well as the exchange of human resource capacities that impact directly to the business environment (Agarwal & Shah, 2014).
Initially, when open innovation was introduced it only focused on large firms and left out SMEs. Similarly, studies have shown that the focus was on high-technology multinational corporations, their market dominance and impact on the economy significantly ignoring the role through which the innovation will impact the national, regional, international firms’ performance and the influence of government policies (Van de Vrande et al., 2009). Additionally, the research on open, innovative systems was qualitative and failed to address the quantitative aspect of the innovation, the epistemological underpinnings of the change and multivariate approach that the innovation will be investigated through (Lee et al., 2010). Recently, the research has been diversified, and there have notable changes through which open innovation systems are evaluated. The difference in the type of study and research methodology employed as well as the industries that have been investigating has shown that many companies do not adopt open innovation with the fear of being expensive to implement (Wynarczyk, 2013). The present research has established that the existing literature on open innovation especially that pertains the SMEs adaptability have been limited with most of them focusing on the management of the SMEs rather than the impact of the versatility of open innovation systems.
Additionally, it was established that the SMEs that have adopted open innovation is highly influenced by government policies and larger firms through collaborative, innovative strategies and degree of the economic impact of the SMEs (Higgins et al., 2010). The current study observes that one of the essential features of open innovation systems is the collaborative feature between the firms and the research institutes such as universities. Other functionalities of the system include the knowledge-based collaborative feature and research and development collaborative feature in which organizations share the knowledge of adoption of the open, innovative technologies. According to Higgins et al., (2010), universities are considered and vied as the hub through which innovation, research, and development are made through fundamental discoveries for business needed innovation drive.
Many scholars have argued that SMEs need to work collaboratively with universities to ensure that they adopt open innovation through transfer of knowledge and skills and enhance their commercialized appropriation joint venture business approaches (Igartua, Garrigós & Hervas-Oliver, 2010). In contrast, Bruneel et al., (2010) assert that many SMEs are focused on profit making business undertaking and focus less on the transfer of knowledge and technology in the development of the products that they manufacture and also services that they provide.
In the United Kingdom, investment in research and development in businesses, especially in small and medium enterprises has been termed as a priority through the government policy agenda. In this regard, the government of the United Kingdom introduced several schemes aimed at implementing the technological, strategic board remit to ensure that many SMEs are encouraged to invest in research and development (R&D). In the policy formulation, the government made it mandatory that SMEs should engage in collaborative open innovation schemes with Higher Education Institute (HEI) sector, and also research and development tax credit as well as collaborative research and development with smart programs (Department of Business Innovation, 2012). However, despite these government interventions, the level of uptake and inclusivity of SME remain significantly low compared to the expectation of the government. For instance, in the 2010 financial year, only two percent of the SMEs were recorded to have received relief from the tax credit scheme (HM Revenue & Customs, 2012). According to the report, non-government affiliated SMEs only account for less than three percent of the total research and development of business activities in the United Kingdom (UK).
Scholarly Review and Summary of Open Innovation Systems and SMEs
There have been assorted cases of research that has been undertaken to determine how adsorptive capacity of SMEs are impacted in open innovation. The research has selected a few research articles that have studied the field and compiled the report in the form of a factual summary to enable the researcher to develop a measure that is adequately important to investigate this field further. The previous research studies aimed to explore the vital issues such as opportunities and challenges that open innovation systems have brought in absorptive capacity of small and medium enterprises (SMEs) business arena. One of the critical issues that have been constant in all the previous studies is the extension of traditional boundaries of the open innovation systems on SMEs from a mere focus on research and development (R&D). For example, a study that was conducted by Theyel, (2013) confirmed the importance of open innovation archetype for small and medium enterprises (SMEs) in absorptive capacity of technology. In his study, Theyel (2013) used quantitative analysis to collect a data of more than 293 participants from Small and medium enterprises (SMEs) from across industries that are located in more than Five East Coast states of the United States and states that it was evident that open innovation depends significantly on the practices that the organization takes and the collaborative partner.
Similarly, other scholarly research shows a variety of approaches that the SMEs have adopted to improve their adsorptive capacity of new technology. For instance, Roper and Hewitt-Dundas (2010) advanced the research on absorptive capacity construct by investigating and exploring the role of the government in stimulating the innovation through the publicly-funded investment and the benefits of its connectivity. The exploration was funded and facilitated by a real-time monitoring exercise to enhance the development pattern of connectivity for a group of 18 publicly-funded established since 2002. The research used mixed research methods and approach to collect and analyze the data: the analysis provided and new insights to inhibitors and enablers of the innovation for public funded SMEs. In the study, it was revealed public research that is supported by the university centers establish a better collaborative approach for the adaptability of SMEs than company-funded research studies; similarly, the research is more likely to be interactive and enhance the productivity of the small organizations (Roper and Hewitt-Dundas, 2010).
A study conducted by Hervas-Oliver, et al., (2012) stated that collaborative innovation on small and medium enterprises should include a social networking platform and perspective so that it can enhance interaction among the users. In another study, Melendez et al., (2013) addressed the social networking perspective in innovation, its role on capital building and enhancement of knowledge transfer between HEIs and Supplement in SMEs. He observed that for the SMEs to convert knowledge into regional innovation systems, the small firms have to embark on social interaction through social networking strategies. The research revealed that informal and formal relations are essential in that they enhance the transfer of knowledge, skills, and ideas that involve HEIs and spin-off SMEs. Other studies that were conducted earlier presented evidence for the relevance and the level of prevalence of open innovation systems among the small and medium enterprises (SMEs). A study conducted by Oakey, (2013) warned against treating all SMEs inhomogeneity form, and they are not ready to accept the innovation. Oakey (2013) validate his claim by referring to the case study of a specific small firm, with a high affinity of acceptance of modern technology. This critique showed that first, there is a high tendency for an organization to overstate the association with the closed innovation system and the benefits that are aligned with the innovation. Secondly, the research revealed that although open change has been presaged as a contemporary business phenomenon, it has been existence before Henry Chesbrough coined the phrase; therefore, it is not a new business phenomenon. Finally, the research revealed that it appears that open innovation systems do not fit in the confidentiality agreement of many associations, especially those that are required in high-technology small and medium enterprises development process. Therefore, high-technology SMEs do not indulge or engage with the advancement of technology through open innovation to safeguard their strategic operation procedures.
CHAPTER THREE
Research Method and Methodology
Introduction
This chapter presents each step of the methods and materials that will be used to understand the impact of open innovation adaptability of the SMEs in the technological advancement business world. The methodology used will help in obtaining and establishing the following steps to organize the data and the outcome of the desired objectives of the study. It will also help understand the effectiveness of open innovation systems with small and medium enterprises in a competitive business arena. The chapter will also elaborate on the different methods that the research seeks to use and their influence in the research process. The standard code of research ethics and study limitations will also be highlighted in the process, and most importantly the methods will emphasize on the need to answer the research questions that are highlighted in chapter one of the present study.
Research Onion
The research onion is an essential tool that the research methodology employs to evaluate the specific set of organized plans that will ensure that the researcher can analyze the research topic from various analytical perspectives and enhance the credibility to meet the research objectives. According to Saunders et al., (2009) a research onion has six differentiated categories, that is, the philosophy of the study, the choice of methodology, the time’s zone of the research, approach, strategic organizations and procedure of undertaking the survey. Therefore, to achieve the desired outcome of the study, each layer of the research onion is analytically followed and evaluated to ensure that the data collected is representative of the study from the population. The diagram below represents a critically evaluated research onion that most research seeks to follow to achieve its objectives.
Source: Research methods for business students, Page 52, 5th ed.
Research Philosophy
Veal (2010) states that the most crucial epistemology of the research study and the methodology and empirical analysis is positivism. The methods that are employed in the research enable the research to investigate the relationship between research variables critically. Similarly, data collection tools and the data collected will allow the researcher to deduce information from the interpretation of the data and understand the inter-relationship that exists between the variables. The present research, therefore, will employ positivism approach to enhance the dissection the study and answer the underlying study questions. Cameron (2009) supported the philosophy of the study by stating that it increases the generation of the results of the studies that have a direct impact on the field of business.
Research Approach
An approach is a planned and organized way in which the research will follow to achieve its objectives. The method can be either inductive or deductive, depending on the complexity of the research topic and research questions. An inductive research approach enables the researcher to gain the relevant empirical information that helps in building or distorting the existing theories based on the field, for instance, the current methods on the field of small and medium enterprises (SMEs). Deductive research approach, on the other hand, is used to investigate the practicality of the theories (Finn et al., 2010). It presents more specifications and conceptual framework of the analysis of data that the research will choose to build on the research topic. Based on the above information, the current study will use both inductive and deductive research approach to investigate the validity of the existing literature and evaluation of the hypothesis of the present study.
Research Design
A research design is a model that decides the methods of data observation, the kind of data to collect and how the data collection will be conducted, that is, it explains the characteristics of the data to be received about the research (Easterby-Smith, 2012). The most used research designs are explorative, explanatory, and descriptive research designs. For the current study, the researcher will use both descriptive and explorative designs. The graphics research design will enable the research to synthesize the physical characteristics of the investigation, that is, through demographic analysis of the features of the data, for example, age, income, sex, education background of the respondents among other demographic factors. Since it is not a control experiment research, an explanatory research design will not be used. Instead, exploratory research design will be employed to explore the impact of the open innovation systems on small and medium enterprises (SMEs).
Variables
A variable is a unit of analysis or an element that is bound to take many changes due to environmental, socio-economic, or political factors (Cameron, 2009). There two types of variable, independent variables and dependent variable. An independent variable is that which is not influenced by factors other than itself, for instance, age; it is not influenced by external factors while a dependent variable is that which is controlled by external factors, for example, income, it can be controlled by age, education level, experience among others. Therefore, the present study will have two variables; independent or dependent variable
Independent variable
Knowledge and implementation of open innovation by SMEs, attitude, size and years of operation
Dependent Variable
Factors that affect the absorptive capacity of SMEs in open innovation
Data Collection Procedure
Data Source: Primary Data
The data that was collected tends to answer the questions that; what absorptive capacities do the SMEs have to allow them to adopt new open innovations? What are the impacts of the adoption of new innovations of the SMEs in the modern era? How do SMEs and large firms compare in terms of likelihood of approval of the latest technology, especially the change that is fostered by external forces and factors such as business demand? The data collection model that of the present study will involve the creation of networks. The networks will enable the research gain access to the firms and select the sample that will be used in the research study. The questionnaire will be revised to remove mistakes that may have arisen while developing it and enhance generation of accurate data from the field.
It is important to note that the essence of focusing on primary data is to get the raw form of information that is not tampered with other research studies (Secondary evidence) to be able to analyze the impact of open innovation at the grassroots level of the SMEs critically. In the first part of the data collection, the researcher will focus on the demographic distribution of the population that is under investigation. This distribution gives an insight into the nature of the business, the kind of people who run the activities, and the level of knowledge acquisition, acceptance, and implementation strategies. For instance, if the demographic distribution shows that majority of the people who operate a specific business do not have a formal education; it gives a slight hint that they are unlikely to adopt new technologies due to limited information about the technology. The second part of the research will focus on the opinion of the people based on open innovation systems and their impact on the businesses that they operate (Batterink et al., 2010). Belief in research studies is measured using Likert Scale tools, for current study, the device will employ five points in which 1-will represent disagreement strongly to the research question and 5-will constitute a firm agreement of the research question.
Population, Sample and Sampling Technique
A population is a complete set of elements either objects, people or animals that have or possess similar characteristics defined by the research study through sampling technique that is established by the researcher (Flick, 2018). In the present study, the population is defined as all the businesses whose capital and inventory are less than $100,000 and whose registration is limited to small and medium enterprise (SME).
A sample is a representation of the research study that is selected from the population through various sampling techniques. For instance, the sample from the current study will be members of the society that is representative and randomly selected to represent all the characteristics of the people in a smaller set of elements.
The following sampling method and formulae will determine the sample for the present study
n =Z2p(1-p)/d2
Where Z is the confidence level value (that is 1.96 at 95% confidence level)
d= is the significance level or the margin of error (0.05=5%)
p= is the estimated value from the sample that has the conditions that are similar to the populations, in this case, the conditions that are similar to SMEs that have or have not implemented open innovation systems, that is 60% as of 2016
Therefore, the sample size will be;
n= 1.962*(0. 6)(1-0.6)/0.052
n= 36.8
n=37
Data Analysis Process: Quantitative
A web-like questionnaire will be designed and used to collect the data from the field from various locations that the current study will decide. Statistical tools such as MS.Exel, SPSS, and NVivo will be employed to analyze the data. In this case, Ms. Excel will be categorically used to run the demographic distribution of the data collected and represent it. SPSS will be used to test the hypothesis, analysis of the Likert scale questions as well as Correlation, Analysis of Variance (ANOVA) t-tests, and Regression analysis. Lastly, NVivo will be used to compare the data from selected research abstract in the literature review and the analysis of the present study to broaden the theme of the research study.
The importance of using quantitative and explorative research method in the current study is to allow enable the research take a significant part in data collection and an opportunity to rectify errors that may emanate from commission or omission by the research participants (Saunder et al. 2009). While on the field, it is not possible to observe the impact of absorptive capacity as it is not a physical object but rather an effect that is felt by the business. Therefore, the explorative aspect of the study will enhance the research compare the company before and after the implementation of the open innovation systems, the capital, revenue, and profits generated before and after. Similarly, he will investigate the wing that the business was able to open before and after the implementation of the strategies. In so doing, the research will have the grounds to assess the impact of the innovation, whether it has a positive or negative effect both to the business and its surrounding environment.
Reliability Test and Validity of the Data
Cronbach’s Alpha test will be employed to test the reliability of the data (Bonett & Wright, 2015). This is done by analyzing the questions that are on the Likert Scale to assess whether they are unidirectional or multivariate based on the outcome of the Cronbach’s Alpha results. For instance, if a test is carried out and the results of the Cronbach’s Alpha test show that it is 0.78 or 78, it means that the research questions on the Likert Scale are accurately answered and are unidirectional and that indicates that the data is reliable. However if the Cronbach’s Alpha value falls below half, for example, 0.35 or 35% it shows that the questions were not accurately answered because they were not clearly understood and the data is referred to multivariate and is nor reliable for the research study, that is, it cannot inform research policies (Bonett & Wright, 2015).
On the other hand, the validity of the data is measured using other tests such as to a measure of the interrelation of the questions using Correlation analysis. For instance, if two issues in a questionnaire are analyzed using SPSS tools, and a researcher wants to investigate whether they are valid, a correlation test is conducted on the response of the two questions. If the correlation value between the two items is very high, it is said that the data is valid but is affected by multicollinearity problem. To rectify the problem, diagnostic measures such as removing one of the two questions is undertaken. Similarly, if the analysis shows a small correlation, then the research is labeled as validity with scientific parameters.
Advantages of Using SPSS, Ms. Excel and NVivo in The Present Research
In a research study, it is essential to note that more than one of the statistical tools is efficient in conducting research rather than over-reliance of one instrument. Therefore, one of the advantages of using the tools mentioned earlier is that they provide a wide range of statistical analysis models that makes it easy to analyze the raw data that is collected from the field. For instance, SPSS and MS. Excel, give broader and flexible analysis tools that enhance the analysis of dependent variable about the independent variable using a linear or log-linear regression model. The study of the linear and log-linear models makes it easy to test the hypothesis and later answer the research questions
Another advantage of these tools is that NVivo makes it easy for the researcher to compare his research outcomes with other outcomes from previous research studies and identify the problems that each of the studies in the field faced to give the recommendations that it provided.
Ethical Consideration in Data Collection
A research study is a scientific investigation of a social phenomenon that society is faced with in the intention of providing solutions to the problem through statistical surveys (Rovai, Baker & Ponton, 2013). Therefore, like any other scientific process, a research study is governed by ethical considerations, especially while conducting data collection process from the field and its analysis. The study is purposively enshrined in the investigation of the impact of adaptability of open innovation among the small and medium enterprises (SMEs) and, therefore, this means that the data will focus on people as the unit of participation in the data collection. Consequently, one of the ethical considerations while conducting such research is the confidentiality of the responses that will be gotten from the participants. An assurance of the information will be used for the study makes the participant more honest and open to answering the questions accurately without fear of exposure of their personal information. Another ethical consideration that the current study will focus on is the confidentiality of the financial reports of the SMEs. Many SMEs do not expose their financial reports because they do not generate large revenue and huge profits and therefore, their proprietors prefer to keep their financial report private (Rovai, Baker & Ponton, 2013). The present study understands that and therefore, it will ensure that they keep the financial report confidential and used purposively for the analysis of the impact of absorptive capacity on those particular SMEs.
Limitations in Data Collections
Any research study is faced with differentiated problems before it is completed. Therefore, the present study understands that there are two limitations that will be faced with in the process of data collection and analysis. Whereas some defects are humanly caused, the flaws in the present study are naturally created and cannot be avoided based on the ability of the research and its scope.
Data Reliability
Many small and medium enterprises are not exposed to research and development (R&D), and therefore, participants that will be selected may be fearful of the provision of the relevant data. This makes the reliability of the data collected relatively lower than the expected results, moreover if the research will focus on collecting data from junior employees who do not have the real information about the firm. Similarly, since the SMEs are similar, the data collected will have elements of biasness in terms of strategic implementation of policies, such as open innovation systems, therefore, limiting the reliability of the data.
Time Constraint
The research has been faced with time constraint in an in-depth analysis, dissecting, and comprehending the research topic. For that reason, the methods of data collected may not be critically evaluated to ensure that the data collected is bound by the time. This limitation will discourage the research from investigating many areas of the topic through cross-sectional data analysis and might give half-baked recommendations.
Chapter Four: Results
4.1 Demographic Results
Demographic results relate to the structure of the population. In the current study demographic results of the stud, participants are presented. Figure 1 shows the age distribution of the study participants. The demographic results included the age distribution, gender, work experience, and educational qualification. Those who were less than thirty years were found to be twenty accounting for 54.1% of the participants. The participants who were between the age of thirty-one to forty years were found to be eleven accounting for 29.7% of the participants. The participants between the age of forty-one to fifty were three accounting for 8.1% of the population. The respondent who was above the age of fifty years were three accounting for 8.1% of the participants.
Figure 1 Age Distribution
The study sought the gender of the respondents, as shown in figure 2 males were greater than females. It was found that there were seventy females accounting for 45.9% of the participants while males were twenty accounting for 54.1%.
Figure 2 Gender
The educational qualification of the respondents is shown in figure 3. It was found that most of the respondents had a Bachelor’s degree as the highest level of educational qualification. The number of respondents who had a high school as the highest level of education was five accounting for 13.5%. those who had a certificate as the highest educational qualification were four accounting for 10.8%. The respondents who had a diploma as the highest educational qualification were 4 accounting for 10.8%. Respondents with bachelor’s degree qualification were 19 accounting for 51.4% and post graduates five accounting for 13.5%.
Figure 3 Educational Qualification
Figure 4 shows the work experience of the respondents, it was found that most of the respondents had less than five years of work experience accounting for 45.9%. The respondents who were between six and ten years of experience were fourteen accounting for 37.8%. The respondents between eleven to fifteen years of experience were 3 accounting for 8.1%. The respondent who had more than sixteen years of experience were three accounting for 8.1% of the participants.
Figure 4 Work Experience
4.2 Reliability Test
A reliability test was conducted to determine the suitability of the data in determining the impact of adsorptive capacity on innovation in the small and medium enterprise in technology in an open innovation system. There were eight variables that were evaluated that included the impact of open innovation and factors that affect its implementation. The Cronbach’s Alpha was found to be 0.97. The value of Cronbach’s Alpha indicated that the data is suitable to predict the impact of absorptive capacity in the innovation of SMEs in the technology industry.
Table 1 Reliability Test
Reliability Statistics
Cronbach’s Alpha N of Items
.970 8
4.3 Inferential Statistics
In the current study inferential data analysis involved correlation, ANOVA and regression analysis. The correlation analysis involves the study of the strength of relationships between two quantitative variables. In the current study qualitative analysis was conducted between the impact of innovation and other variables. As shown in table 1, the correlation between the impact of innovation and relationship factors was 0.689 and was significant at p is less than 0.01. Correlation between the impact of innovation and characteristics of the people was found to be 0.575 and was significant at p is less than 0.01. Correlation between governance and the impact of innovation was 0.555 and was significant at p is less than 0.01.
Correlation between facilitators and the dependent variable was found to 0.687 while with the provision of resources was 0.826. Those correlations were significant at p is less than 0.001. Correlation between open innovation and impact of innovation was 0.943 while with cultural element was 0.908, they were significant at p is less than 0.01.
Table 2 Correlation
Impact of Innovation
Relationship factors Pearson Correlation .689**
Sig. (2-tailed) .000
Characteristics of people Pearson Correlation .575**
Sig. (2-tailed) .000
Governance Pearson Correlation .555**
Sig. (2-tailed) .000
Facilitator Pearson Correlation .687**
Sig. (2-tailed) .000
Provision of resources Pearson Correlation .826**
Sig. (2-tailed) .000
N 37
Open innovation Pearson Correlation .943**
Sig. (2-tailed) .000
Cultural element Pearson Correlation .908**
Sig. (2-tailed) .000
ANOVA analysis is a method of determining if the results of an experiment are significant. It helps to determine if the null hypothesis needs to be rejected or accepted. Table 2 shows the ANOVA analysis in the current study. It was found that the sum of squares for the regression was 29.02 with six degrees of freedom. The sum of squares for the residual was 1.667 with thirty degrees of freedom. The total sum of the square was 30.686 with thirty-six degrees of freedom. The mean square for the regression was 4.836 and for the residual was 0.056. The F-statistics for the model was 87.04 and was statistically significant at p is less than 0.01. Because the F-statistics is significant it indicates that the null hypothesis needs to be accepted
Table 3 ANOVA
Sum of Squares Df Mean Square F Sig.
Regression 29.019 6 4.836 87.037 .000b
Reidual 1.667 30 .056
Total 30.686 36
Regression analysis is applied to determine which among the independent variables is associated with the dependent variable and the kind of relationship. In the current study, the independent variable was the impact of innovation. On the other side, the independent variables included cultural element, governance, and provision of resources, open innovation, relationship factors, and facilitator. Table 4 shows the model summary of the regression. The value of R was 0.972, R-square was 0.946 and adjusted R-square was 0.935. The standard error of the estimate was 0.2357. The value of adjusted R-square indicates that 93.5% of the regression variables are within the regression line.
Table 4 Model Summary
Model Summary
Model R R Square Adjusted R Square Std. The error of the Estimate
1 .972a .946 .935 .23573
Table 2 shows that the coefficient of the regression line, the constant value was 0.001 with an error of 0.174. The coefficient for the relationship factor was 0.375 with a standard error of 0.208. The coefficient for governance was -2.370 with an error of 1.139. The coefficient for facilitator was 3.134 with 1.992 error. The coefficient for the provision of resources was -1.254 with an error of 1.027. Coefficient for open innovation was 0.673 with a standard error of 0.240 and for the cultural element was 0.422 with an error of 0.263. The regression analysis is summaries in equation 1 with dependent variable assigned Y and the independent X1 to X6.
Table 5 Coefficients
Coefficients
Variable B Std. Error
(Constant) .001 .174
Relationship factors .375 .208
Governance 2.370 1.139
Facilitator 3.134 1.992
Provision of resources -1.254 1.027
Open innovation .673 .240
Cultural element .422 .263
Y=0.001+0.375X1+2.370X2+3.134X3-1.254X4+0.673X5+0.422X6
4.4 Discussion
The current study found that an increase in the absorptive capacity of SMEs leads to increase profitability, employee motivation, competitive advantage, and increase efficiency in SMEs. A similar observation was made by Enkel et al. (2009), that absorptive capacity helps the firms to get benefits and opportunities of their innovative skills and conceptual the idea while trying to improve their products. However, a different observation was made by Marjanovic et al. 2012, it was observed that even with mushrooming and widespread innovation, and the SMEs do not have an absorptive capacity of the new technology leading to their death before the first anniversary. It shows that the absorptive capacity of the SMEs is key to ensure their progress.
The present study investigated the factors that affect the absorptive capacity of the SMEs that included relationship, governance, facilitators, and provision of resources, open innovation, and cultural elements. Relationship factor, governance, facilitator, open innovation and cultural element were found to have a positive relationship with the impact of absorptive capacity of the firm. While the provision of resources was found to have a negative effect with the absorptive capacity of the firm.
The constant value in the regression analysis was found to be 0.001, it shows that holding all the factors constant the absorptive capacity of the firm will increase by 0.001. The current study found a positive relationship between the impact of absorptive capacity and relationships factors that affect its implementation. The coefficient for relationship factor was 0.375 indicating that one unit increase in relationship factors increases the absorptive capacity of SMEs by 0.375. It indicates that the absorptive capacity of the SMEs in technology has a significant impact on their development and performance. Therefore, the null hypothesis of the current that the adaption of innovation has a significant impact on the growth of SMEs is accepted. It means that the proposed observation b the current study is correct. The relationship factors that were evaluated include collaboration, trust, and prior history of collaboration, openness, knowledge sharing and understanding partners.
In the current study, it was observed that collaboration is important to ensure that SMEs increases their absorptive capacity. It is because the firms are able to share ideas and acquire economies of scale. Collaboration between the firms increases trust between partners helping them to work faster and efficiently. A similar observation on the role of collaboration in absorptive capacity was made by Casson (2010), it indicated that firms had augmented their absorptive capacity, performance, innovation, and control of the market proportion by acquiring external development innovation by the involvement of a multi-agency development company. The agencies comprised of but not limited to business innovators, supply chain developers, customers, competitors, and various development and research institutes.
Higgins et al., (2010) also made a similar finding to the current study on the role of collaboration in improving the absorptive capacity of SMEs. That study observed that the main characteristics of open innovation are a collaboration between the firms and the research organization. Universities are important in open innovation because they are considered and viewed as the hub where research, innovation, and development are done by key discoveries for business needed innovation drive is conducted.
The current study instigated the role of knowledge sharing in improving absorptive capacity. It was found that knowledge sharing helps a firm to improve its open innovations and performance. A similar finding was made by Lodhia (2015), the study argued that collaboration of enterprises and partnerships gives the key capacity for innovation particular of SMEs. The collaboration enables them to maintain a competitive advantage by attracting clients and investors with a similar mind. Collaboration gives enterprises knowledge and capital in the fast-growing and changing technology environment. However, there is a criticism of knowledge sharing that indicates that the firm with a greater business ground dictates the conduct of their counterparts. However, for the SMEs to grow they should collaborate with large firms to gain knowledge about greater technology. Thus, collaboration enhances knowledge sharing that gives the firm a competitive advantage.
Higgins et al., (2010) observed that one the functionalities of the open innovation system are knowledge-based collaborative features, research and development collaboration where enterprises share knowledge of adoption of open innovative technologies. The finding by Higgins et al., (2010) is similar to the current study on the role of knowledge sharing in absorptive capacity. Among the relationship factors that were evaluated in the current study collaboration was found to be the foundation of relationship in the firm. Firms with proper collaboration are able to gain trust among each other and partners that enhance knowledge sharing in the firm.
The current study instigated the impact of characteristics of people involved in the absorptive capacity of the SMEs in technology. There were various characteristics of the people that were evaluated that included gender, age, education, commitment, altitude, personality trait, motivation and willingness to change. The gender of the people was found to have no impact on absorptive capacity; however, the age and education of the people were for to have an impact. It was found that the organization with young people and highly educated had the higher absorptive capacity.
It was found that the commitment of the personnel within an organization is key in improving the absorptive capacity of SMEs in technology. The committed personal are reliable, therefore the organization is able to have good teamwork. On the other side, the motivation of the workers was found to have a drastic effect on absorptive capacity because the highly motivated workers have a higher output. In addition, highly motivated workers are able to work effectively within a team. The motivation of the workers is affected by the attitude and personality of the workers. The workers with a positive attitude have a higher output.
The current study sought to determine the impact of governance structure on absorptive capacity of SMEs in technology. The coefficient of the governance structure was 2.37, indicating that one unit increase in governance structure leads to 2.37 increase in open innovation. The elements of governance structure that were evaluated include mechanism and structure, control and coordination, clear task distribution, clear decision making, good contract and use of external and internal measures of success. The current study found that the governance structure is important for open innovation for SMEs in technology.
Poper and Hewitt-Dundas (2010), observed that the government stimulate innovation by publicly-funded investment and the benefits of the connectivity. The finding indicates that the governance structure has a crucial role to play in improving the absorptive capacity of the SMEs. It was also found that publicly funded research enhance collaboration in innovation. The government helps to develop amenities for innovation and for the progress of the enterprise. The proper governance structure is essential to ensure that the firms improve their absorptive capacity.
The present study also investigated the role of facilitator on the absorptive capacity of SMEs in technology. The coefficient for the absorptive capacity was 3.134 indicating that one unit increase in facilitators improves the absorptive capacity of the firm by 3.134. The facilitators that were observed included innovation brokers, relationship managers, team training and coaching, intermediaries and collective research centers. According to Zhou and Wu (2010), the development of private capital and human resources are a key element of positioning the enterprise to absorb technology. It was also found that the enterprise needs to engage in the unremitting process of learning and inflow of new information and ideas in level and environment. The observation by Zhou and Wu (2010), is similar to the current study on the role of training and coaching in facilitating the absorptive capacity of the firm.
Chesbrough (2012) observed that a majority of the firms have externalized their research and development innovation by acquiring external materials and information and recruitment of eternal capacities to raise the performance of the enterprises. The observation by Chesbrough (2012) is similar to what was found in the present study on the role of collective centers in improving the absorptive capacity of the firms. The innovation brokers play a critical role in aiding the firm to achieve its potential. The relationship managers enable the SMEs to outsource functions thus improving their performance.
The provision of resources was found to have a negative relationship with the absorptive capacity of the firm. The coefficient of the provision of resources was -1.254 indicating that one unit increase in resources reduces the absorptive capacity by 1.254. The provision of resource factors that were evaluated includes personnel resources, balance, availability of resources and time. Resources are important to improve the absorptive capacity of the SMEs. However, without proper management of the resources, the firm cannot be able to increase its absorptive capacity thus the negative relationship.
The current study also sought to determine the relationship between open innovation and the absorptive capacity of the firm. It was found that open innovation has a positive relationship with the absorptive capacity of the firm. The finding of the present study is similar to what was found by Carcary, Doherty, and Conway (2014), that SMEs do not have the proper internal adoptive capacity and human resources to overcome challenges brought about by the present business environment and realize progress by diversification of their product mix. In such a situation open innovation plays a crucial role in the business to help it attain its potential.
The cultural element in the organization was found to have a positive relationship with the absorptive capacity of the organization. The cultural elements that were considered include networking and knowledge, time spent in the field and helping workers to accept failure. Munivenkatesh and Islam (2010) observed that a culture of networking and knowing to share is important in helping a firm reach its absorptive capacity. Thus, positive culture within the organization is important to its growth. Each of the factors that affect the absorptive capacity of the firm is important to helping it achieve its potential.
Chapter Five: Conclusion and Recommendation
5.1 Conclusion
The absorptive capacity of SMEs in technology was found to have various impact on the performance of the firms. First, the absorptive capacity helps SMEs to increase their efficiency because they are able to adopt new technologies that are faster and efficient. The firms are also able to increase their profitability because the absorptive capacity helps them to reduce wastage of resources. The firms with good absorptive capacity were also found to have a competitive advantage over their rivals because the new technologies make it possible for them to innovate new methods of doing business.
The relationships factors were also found to have an impact on the innovative capacity of the firms. The enterprise with a good collaboration structure was found to have a higher absorptive capacity. It is because collaboration leads to the sharing of ideas among the players that makes it possibly easier for the firm to adopt ideas. The history of collaboration was found to affect the type of collaboration. The firm that has good collaboration was found to attack new firms. Knowledge sharing was found to have a drastic effect on absorptive capacity. The SMEs with a culture of knowledge sharing was found to have a greater absorptive capacity.
The characteristics of the people involved in innovation were found to affect the absorptive capacity of the firm. The firm with young and educated workers was found to have a higher absorptive capacity. It because young people have a greater capacity to innovate and are able to use the available resources to do greater things. The governance structure was found to have a positive relationship with the absorptive capacity of the firm. The firm with a good governance system is able to innovate more and acquire a greater market share. It is because the governance system creates the foundation for such innovations. Open innovation helps enterprises to improve their absorptive capacity by providing the knowledge that the firm requires for its innovation activity. Open innovation is an essential instrument to grow the capacity of the firm. The culture of the firm helped to increase its absorptive capacity and increasing attention between the parties.
5.2 Recommendation
• The SMEs in the technology sector show increase their collaboration with each other to increase their absorptive capacity.
• The SMEs should increase knowledge sharing among the organizations.
• The government should create an enabling environment for the SMEs to innovate.
• The SME should have a culture of knowledge sharing to enable them to achieve their potential.
• Open innovation should be enhanced to enable the SMEs to reach their full potential innovation.
5.3 Recommendations for Further studies
The current study analyzed how the absorptive capacity of SMEs affects their innovation. However, further study is required to determine the association between absorptive capacity and the type of innovation. It is important that there are various types of innovation that the SME can make. Some of that innovation may not be affected by the absorptive capacity. The future will seek to differentiate between the type of innovations that affected by the absorptive capacity of the firm.
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Appendix
Questionnaire
Questionnaire to investigate the impact of Absorptive Capacity on Innovation in Small and Medium Enterprise in the Technology Industry in an Absorptive capacity System
Part I: Introduction
This is academic research which investigates the impact of absorptive capacity on innovation in the small and medium enterprise in the technology industry in an Absorptive capacity system. Before complete, the questionnaire read the following.
• It will take up five minutes to complete this questionnaire.
• Please note that participation in this survey will not yield a direct return on you however it helps to improve overall performance in the industry.
• Please do not indicate your name or address.
• The data that you provide only be used for research purpose only.
• All the answers you will provide will be kept confidential. The data will be reported in a summary fashion and will not identify any individual person
• Please tick the consent form to show that you allow us to use the data provided for research.
Part 2: Consent Form
Please sign the form to indicate that you give the research consent to use the data you provide
Signature
Part 3: Demographic
i .Instructions .
Tick in the box below, each response as appropriate or specified by filling in the blanks provided.
1. What is your age?
Less than 30 Year ( )
31 -40 Years ( )
40-50 Years ( )
Above 50 Years ( )
2. What is your gender?
Female ( )
Male ( )
3. What is your highest educational qualification?
High School ( )
Certificate ( )
Diploma ( )
Degree ( )
Postgraduate ( )
4. How long have you worked in your organization
Less than 5 years ( )
5-10 ( )
10-15 ( )
More than 15 years ( )
Part 4: The impact of using Absorptive capacity on SMEs (dependent variable)
Does Absorptive capacity in your firm lead to an increase in the following factors
Factor Strongly disagree Disagree Neutral Agree Strongly agree
Increase profitability
Increase employee motivation
Competitive advantage
Increase efficiency
Part 5: The implementation of Absorptive capacity by SMEs (Independent variable)
1. The following relationship factors affect the Absorptive capacity of your organization
Factor Strongly disagree Disagree Neutral Agree Strongly agree
Nature of collaboration
Trust
History of collaboration
Smooth and continuous communication
Openness
Effective management of relations
Knowledge sharing
Understanding the distinctive characteristics of partners involved
2. The following characteristics of people involved in innovation affects the Absorptive capacity of your organization
Factor Strongly disagree Disagree Neutral Agree Strongly agree
Diversity in terms of gender, age and education
Committed
Altitude and personality trait
Motivation
Wiliness to change
Prepared for a willingness to develop new skills
3. The following governance factor has an impact on the Absorptive capacity on your organization.
Factor Strongly disagree Disagree Neutral Agree Strongly Agree
Mechanism and structure
Control and coordination
Clear distribution of tasks, roles, and responsibility
Dedicated project team
Clear decision making
A good contract that ensures agreement are met
Use internal and external measures of success
4. The following are the facilitators of Absorptive capacity in your organization.
Factor Strongly disagree Disagree Neutral Agree Strongly agree
Innovation brokers
Relationship managers
Team training and coaching
Intermediaries
Collective research centers
5. Provision of the following resources helps to increase Absorptive capacity of your organization
Factor Strongly disagree Disagree Neutral Agree Strongly agree
Personnel resources
First class personnel and equipment
Availability of time and resources
Balance of innovation and daily operation
6. Do you think the following factors about open innovation affects the implementation of Absorptive capacity?
Strongly disagree Disagree Neutral Agree Strongly agree
Understand the stages of the process
Understand the phases of the lifecycle of technology
Understanding the openness of innovation process
Performance
7. Do the following cultural element in your organization affect the Absorptive capacity of your organization?
Strong disagree Disagree Neutral Agree Strongly agree
Networking and knowledge sharing culture
A culture that helps workers to move from perceiving to the admission of failure
Time spent in the field
Thank you, for sharing your thoughts and agreeing to take this questionnaire
SPSS Output
GET DATA /TYPE=XLSX
/FILE=’C:\Users\user\Documents\Open innovation data.xlsx’
/SHEET=name ‘Sheet1’
/CELLRANGE=full
/READNAMES=on
/ASSUMEDSTRWIDTH=32767.
EXECUTE.
DATASET NAME DataSet1 WINDOW=FRONT.
FREQUENCIES VARIABLES=Age Gender EducationalQualification WorkExperience Part4a Part4b Part4c Part4d
/BARCHART FREQ
/ORDER=ANALYSIS.
Frequencies
Notes
Output Created 23-MAY-2019 11:35:41
Comments
Input Active Dataset DataSet1
Filter
Weight
Split File
N of Rows in Working Data File 37
Missing Value Handling Definition of Missing User-defined missing values are treated as missing.
Cases Used Statistics are based on all cases with valid data.
Syntax FREQUENCIES VARIABLES=Age Gender EducationalQualification WorkExperience Part4a Part4b Part4c Part4d
/BARCHART FREQ
/ORDER=ANALYSIS.
Resources Processor Time 00:00:07.58
Elapsed Time 00:00:04.31
[DataSet1]
Statistics
Age Gender Educational Qualification Work Experience Increase Profitability Increase employee motivation Competitive Advantage Increase Effeciency
N Valid 37 37 37 37 37 37 37 37
Missing 0 0 0 0 0 0 0 0
Frequency Table
Age
Frequency Percent Valid Percent Cumulative Percent
Valid Less 30 Years 20 54.1 54.1 54.1
31-40 Years 11 29.7 29.7 83.8
41-50 Years 3 8.1 8.1 91.9
Above 51 Years 3 8.1 8.1 100.0
Total 37 100.0 100.0
Gender
Frequency Percent Valid Percent Cumulative Percent
Valid Female 17 45.9 45.9 45.9
Male 20 54.1 54.1 100.0
Total 37 100.0 100.0
Educational Qualification
Frequency Percent Valid Percent Cumulative Percent
Valid High school 5 13.5 13.5 13.5
Certificate 4 10.8 10.8 24.3
Diploma 4 10.8 10.8 35.1
Bachelor 19 51.4 51.4 86.5
Post Graduate 5 13.5 13.5 100.0
Total 37 100.0 100.0
Work Experience
Frequency Percent Valid Percent Cumulative Percent
Valid Less than 5 Years 17 45.9 45.9 45.9
6-10 Years 14 37.8 37.8 83.8
11-15 Years 3 8.1 8.1 91.9
16 Years 3 8.1 8.1 100.0
Total 37 100.0 100.0
Increase Profitability
Frequency Percent Valid Percent Cumulative Percent
Valid Strong Disagree 4 10.8 10.8 10.8
Disagree 6 16.2 16.2 27.0
Neutral 16 43.2 43.2 70.3
Agree 11 29.7 29.7 100.0
Total 37 100.0 100.0
Increase employee motivation
Frequency Percent Valid Percent Cumulative Percent
Valid Strong Disagree 7 18.9 18.9 18.9
Disagree 2 5.4 5.4 24.3
Neutral 13 35.1 35.1 59.5
Agree 15 40.5 40.5 100.0
Total 37 100.0 100.0
Competitive Advantage
Frequency Percent Valid Percent Cumulative Percent
Valid Strong Disagree 3 8.1 8.1 8.1
Disagree 6 16.2 16.2 24.3
Neutral 12 32.4 32.4 56.8
Agree 16 43.2 43.2 100.0
Total 37 100.0 100.0
Increase Effeciency
Frequency Percent Valid Percent Cumulative Percent
Valid Strong Disagree 6 16.2 16.2 16.2
Disagree 3 8.1 8.1 24.3
Neutral 12 32.4 32.4 56.8
Agree 16 43.2 43.2 100.0
Total 37 100.0 100.0
Bar Chart
COMPUTE Dependablevariable=(Part4a+Part4b + Part4c + Part4d)/4.
EXECUTE.
COMPUTE Relationshipfactors=(Part51a+Part51b+Part51c+Part51d+Part51e+Part51f+Part51g+Part51h)/8.
EXECUTE.
COMPUTE characteristicsofpeople=(Part2a+Part2b+Part2c+Part2d+Part2e+Part2f)/6.
EXECUTE.
COMPUTE Goverance=(Part3a+Part3b+Part3c+Part3d+Part3e+Part3f+Part3g)/7.
EXECUTE.
COMPUTE Facilitator=(Part4a_A+Part4b_A+Part4c_A+Part4d_A)/4.
EXECUTE.
COMPUTE Provisionofresources=(Part5a+Part5b+Part5c+Part5d)/4.
EXECUTE.
COMPUTE Openinnovation=(Part6a+Part6b+Part6c+Part6d)/4.
EXECUTE.
COMPUTE culturalelements=(Part7a+Part7b+Part7c)/3.
EXECUTE.
COMPUTE Openinnovation=(Part6a+Part6b+Part6c+Part6d)/4.
EXECUTE.
RELIABILITY
/VARIABLES=Dependablevariable Relationshipfactors characteristicsofpeople Goverance Facilitator
Provisionofresources Openinnovation culturalelements
/SCALE(‘ALL VARIABLES’) ALL
/MODEL=ALPHA.
Reliability
Notes
Output Created 23-MAY-2019 12:02:06
Comments
Input Active Dataset DataSet1
Filter
Weight
Split File
N of Rows in Working Data File 37
Matrix Input
Missing Value Handling Definition of Missing User-defined missing values are treated as missing.
Cases Used Statistics are based on all cases with valid data for all variables in the procedure.
Syntax RELIABILITY
/VARIABLES=Dependablevariable Relationshipfactors characteristicsofpeople Goverance Facilitator
Provisionofresources Openinnovation culturalelements
/SCALE(‘ALL VARIABLES’) ALL
/MODEL=ALPHA.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.05
Scale: ALL VARIABLES
Case Processing Summary
N %
Cases Valid 37 100.0
Excludeda 0 .0
Total 37 100.0
a. Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach’s Alpha N of Items
.970 8
CORRELATIONS
/VARIABLES=Dependablevariable Relationshipfactors characteristicsofpeople Goverance Facilitator
Provisionofresources Openinnovation culturalelements
/PRINT=TWOTAIL NOSIG
/MISSING=PAIRWISE.
Correlations
Notes
Output Created 23-MAY-2019 12:03:09
Comments
Input Active Dataset DataSet1
Filter
Weight
Split File
N of Rows in Working Data File 37
Missing Value Handling Definition of Missing User-defined missing values are treated as missing.
Cases Used Statistics for each pair of variables are based on all the cases with valid data for that pair.
Syntax CORRELATIONS
/VARIABLES=Dependablevariable Relationshipfactors characteristicsofpeople Goverance Facilitator
Provisionofresources Openinnovation culturalelements
/PRINT=TWOTAIL NOSIG
/MISSING=PAIRWISE.
Resources Processor Time 00:00:00.03
Elapsed Time 00:00:00.11
Correlations
Dependablevariable Relationship factors Characteristics of people Goverance Facilitator Provision of resources Open innovation Cultural element
Dependablevariable Pearson Correlation 1 .689** .575** .555** .687** .826** .943** .908**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 37 37 37 37 37 37 37 37
Relationship factors Pearson Correlation .689** 1 .931** .931** .897** .793** .707** .868**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 37 37 37 37 37 37 37 37
Characteristics of people Pearson Correlation .575** .931** 1 .997** .964** .825** .590** .807**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 37 37 37 37 37 37 37 37
Goverance Pearson Correlation .555** .931** .997** 1 .957** .802** .564** .803**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 37 37 37 37 37 37 37 37
Facilitator Pearson Correlation .687** .897** .964** .957** 1 .937** .657** .875**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 37 37 37 37 37 37 37 37
Provision of resources Pearson Correlation .826** .793** .825** .802** .937** 1 .777** .907**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 37 37 37 37 37 37 37 37
Open innovation Pearson Correlation .943** .707** .590** .564** .657** .777** 1 .904**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 37 37 37 37 37 37 37 37
Cultural element Pearson Correlation .908** .868** .807** .803** .875** .907** .904** 1
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000
N 37 37 37 37 37 37 37 37
**. Correlation is significant at the 0.01 level (2-tailed).
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Dependablevariable
/METHOD=ENTER Relationshipfactors characteristicsofpeople Goverance Facilitator
Provisionofresources Openinnovation culturalelements.
Regression
Notes
Output Created 23-MAY-2019 12:03:55
Comments
Input Active Dataset DataSet1
Filter
Weight
Split File
N of Rows in Working Data File 37
Missing Value Handling Definition of Missing User-defined missing values are treated as missing.
Cases Used Statistics are based on cases with no missing values for any variable used.
Syntax REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Dependablevariable
/METHOD=ENTER Relationshipfactors characteristicsofpeople Goverance Facilitator
Provisionofresources Openinnovation culturalelements.
Resources Processor Time 00:00:00.03
Elapsed Time 00:00:00.08
Memory Required 8144 bytes
Additional Memory Required for Residual Plots 0 bytes
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 Cultural element, Goverance, Provision of resources, Open innovation, Relationship factors, Facilitatorb . Enter
a. Dependent Variable: Dependablevariable
b. Tolerance = .000 limit reached.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .972a .946 .935 .23573
a. Predictors: (Constant), Cultural element, Goverance, Provision of resources, Open innovation, Relationship factors, Facilitator
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 29.019 6 4.836 87.037 .000b
Residual 1.667 30 .056
Total 30.686 36
a. Dependent Variable: Dependablevariable
b. Predictors: (Constant), Cultural element, Goverance, Provision of resources, Open innovation, Relationship factors, Facilitator
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) .001 .174 .005 .996
Relationship factors .375 .208 .305 1.797 .082
Goverance -2.370 1.139 -2.063 -2.080 .046
Facilitator 3.134 1.992 2.679 1.573 .126
Provision of resources -1.254 1.027 -1.130 -1.221 .231
Open innovation .673 .240 .676 2.808 .009
Cultural element .422 .263 .369 1.604 .119
a. Dependent Variable: Dependablevariable
Excluded Variablesa
Model Beta In t Sig. Partial Correlation Collinearity Statistics
Tolerance
1 Characteristics of people .b . . . .000
a. Dependent Variable: Dependablevariable
b. Predictors in the Model: (Constant), Cultural element, Goverance, Provision of resources, Open innovation, Relationship factors, Facilitator
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Dependablevariable
/METHOD=ENTER Relationshipfactors characteristicsofpeople Goverance Facilitator
Provisionofresources Openinnovation culturalelements
/RESIDUALS NORMPROB(ZRESID).
Regression
Notes
Output Created 23-MAY-2019 12:04:29
Comments
Input Active Dataset DataSet1
Filter
Weight
Split File
N of Rows in Working Data File 37
Missing Value Handling Definition of Missing User-defined missing values are treated as missing.
Cases Used Statistics are based on cases with no missing values for any variable used.
Syntax REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Dependablevariable
/METHOD=ENTER Relationshipfactors characteristicsofpeople Goverance Facilitator
Provisionofresources Openinnovation culturalelements
/RESIDUALS NORMPROB(ZRESID).
Resources Processor Time 00:00:00.53
Elapsed Time 00:00:00.61
Memory Required 8192 bytes
Additional Memory Required for Residual Plots 200 bytes
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 Cultural element, Goverance, Provision of resources, Open innovation, Relationship factors, Facilitatorb . Enter
a. Dependent Variable: Dependablevariable
b. Tolerance = .000 limit reached.
Model Summaryb
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .972a .946 .935 .23573
a. Predictors: (Constant), Cultural element, Goverance, Provision of resources, Open innovation, Relationship factors, Facilitator
b. Dependent Variable: Dependablevariable
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 29.019 6 4.836 87.037 .000b
Residual 1.667 30 .056
Total 30.686 36
a. Dependent Variable: Dependablevariable
b. Predictors: (Constant), Cultural element, Goverance, Provision of resources, Open innovation, Relationship factors, Facilitator
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) .001 .174 .005 .996
Relationship factors .375 .208 .305 1.797 .082
Goverance -2.370 1.139 -2.063 -2.080 .046
Facilitator 3.134 1.992 2.679 1.573 .126
Provision of resources -1.254 1.027 -1.130 -1.221 .231
Open innovation .673 .240 .676 2.808 .009
Cultural element .422 .263 .369 1.604 .119
a. Dependent Variable: Dependablevariable
Excluded Variablesa
Model Beta In t Sig. Partial Correlation Collinearity Statistics
Tolerance
1 Characteristics of people .b . . . .000
a. Dependent Variable: Dependablevariable
b. Predictors in the Model: (Constant), Cultural element, Goverance, Provision of resources, Open innovation, Relationship factors, Facilitator
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 1.2500 3.8992 3.0068 .89782 37
Residual -.64920 .38991 .00000 .21519 37
Std. Predicted Value -1.957 .994 .000 1.000 37
Std. Residual -2.754 1.654 .000 .913 37
a. Dependent Variable: Dependablevariable
Charts
SAVE OUTFILE=’C:\Users\user\Documents\Open innovation data.sav’
/COMPRESSED.