The Effects of Knowledge Management Strategies on Innovation and Business Performance in Information Technology Industry

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(1)The Effects of Knowledge Management Strategies on Innovation and Business Performance in Information Technology Industry. by TRUONG THU THAO National Taiwan Normal University, Taiwan. A Thesis Submitted to the Graduate Faculty in Partial Fulfillment of the Requirements for the Degree of. MASTER OF BUSINESS ADMINISTRATION Major: International Human Resource Development. Advisor: Shih Cheng-Ping, Ph.D.. National Taiwan Normal University Taipei, Taiwan August, 2018.

(2) ACKNOWLEDGEMENT “Beliefs aren’t about truth. Beliefs are about believing. They are guides for our behaviours” Richard Bandler One of the things that I firmly believe is that I am blessed by life. After two years of studying in Taiwan, this belief becomes even more rooted. All of the good luck I have in my life are much owing to people that are always there for me. First and foremost, I would like to express my deepest gratitude toward my family members who have assisted me not only financially but also emotionally. They are my strongest and most loving support; without them, I wouldn’t even be able to be here. Secondly, I want to thank National Taiwan Normal University and IHRD program for awarding me a one-year scholarship that enabled me to pursue my master degree. Studying in Taiwan has brought me a lot of joys, valuable knowledge, and once-in-a-lifetime experience. Not everything is rosy and cheerful but through challenges and tough times, I have grown up so much, and love my improved-self so much more. My gratitude also goes to my advisor – Dr. Tony Shih who has led me on the right path to favorable end results, my committee members – Dr. Lee and Dr. Lu who have given me all the help I need and have been very kind to me, the director of our program – Dr. Yeh who has provided me with many good advices, insightful lectures, and (delightedly) compliments, other professors whom I have taken classes with, and all the precious staff of the IHRD office who have genuinely and eagerly helped me on every matter I have. Last but not least, thank you to all of the friends that I have here: Marta, Tumee, Debby, Edison, Jasmine, Karen, Derrick, Cindy, JoJo, Sally, Shun-Ching Yang and so many more; especially Marta, Tumee, Shun-Ching Yang, and Sally, you guys were there for me during the hardest time of my life when I was broken down so badly and thought that I wouldn’t survive the day. You guys are the reason I’ve got back on my feet and I will never forget the kindness and caring you all have given me. Thank you guys so so much. Everybody that I’ve just express gratitude toward in this short paragraph, you are the reason I am who I am, and achieve what I achieve today. I love you guys with all my heart!.

(3) ABSTRACT In a rapid changing economy where companies have to cope with many challenges to survive and stay ahead of their competitors, it is constant innovation that gives them the best chance at thriving in business. Knowledge converted into innovation has been acknowledged by both researchers and practitioners as the most critical factor in the process. However, the mere act of possessing knowledge is not enough. Knowledge Management (KM) has been proposed as the one discipline that helps organizations manage and access their full knowledge potential, especially regarding innovations. Questions exist regarding the impacts of KM on Innovation, and business performance. There has been research studying the effects of KM on Innovation or business performance. Nevertheless, the interrelationships among these three variables are still understudied, especially in IT industry setting. Thus, this study aims to examine how KM affects Innovation which, in turn, contributes to business performance. Structural Equation Modelling (SEM) is utilized for this study due to its capability of handling multiple variables simultaneously. Practical implications will be proposed for organisations to build proper KM strategies which will enhance innovation and business performance. Keywords: knowledge management, innovation, business performance, information technology industry. I.

(4) TABLE OF CONTENTS ABSTRACT ................................................................................................................................. I TABLE OF CONTENTS ......................................................................................................... II LIST OF TABLES ................................................................................................................... IV LIST OF FIGURES .................................................................................................................. V CHAPTER I INTRODUCTION ........................................................................................... 1 Background of Study................................................................................................................... 1 Purpose of the Study ................................................................................................................... 3 Questions of the Study ................................................................................................................ 3 Significance of the Study ............................................................................................................ 4 Delimitations and Limitations ..................................................................................................... 6 Definition of Terms ..................................................................................................................... 7. CHAPTER II LITURATURE REVIEW ............................................................................ 8 Knowledge Management Strategies and Business Performance ................................................ 8 Knowledge Management Strategies and Innovation ................................................................. 11 Innovation and Business Performance ...................................................................................... 14. CHAPTER III RESEARCH METHOD............................................................................ 18 Research Framework ................................................................................................................. 18 Research Hypotheses................................................................................................................. 19 Research Procedure ................................................................................................................... 20 Research Method ....................................................................................................................... 22 Data Analysis ............................................................................................................................ 24. CHAPTER IV FINDINGS AND DISCUSSION ....................................................... 27 Main Study ................................................................................................................................ 31 II.

(5) Findings Discussion .................................................................................................................. 37. CHAPTER V CONCLUSION AND RECOMMENDATION ................................... 40 Research Conclusions ............................................................................................................... 40 Research Implications ............................................................................................................... 40 Recommendations for Future Research .................................................................................... 42. REFERENCES ......................................................................................................................... 43 APPENDIX A RESEARCH QUESTIONNAIRE (ENGLISH & CHINESE)........ 50. III.

(6) LIST OF TABLES Table 3.1. Coding System Used for SPSS .................................................................................... 25 Table 4.1. KMO and Bartlett’s Test of Sphericity of Pilot Study................................................. 27 Table 4.2. Factor Loadings of the Pilot Study .............................................................................. 28 Table 4.3. Reliability and Validity Results of the Pilot Study ...................................................... 29 Table 4.4. The Square-root of AVE (in bold) and Correlations among Constructs of the Pilot Study ............................................................................................................................................. 30 Table 4.5. Hypothesis Testing Results of the Pilot Study............................................................ 30 Table 4.6. Sample Characteristics ................................................................................................. 31 Table 4.7. Descriptive Statistics.................................................................................................... 32 Table 4.8. KMO and Bartlett's Test of Sphericity ........................................................................ 33 Table 4.9. Factor Loadings ........................................................................................................... 33 Table 4.10. Reliability and Validity statistics ............................................................................... 34 Table 4.11. The Square-root of AVE (in bold) and Correlations among Constructs ................... 34 Table 4.12. Hypothesis Testing Results ........................................................................................ 36. IV.

(7) LIST OF FIGURES Figure 3.1. The Research Framework ........................................................................................... 18 Figure 3.2. The Research Procedure ............................................................................................. 21 Figure 4.1. Research Model. ......................................................................................................... 37. V.

(8) CHAPTER I INTRODUCTION The chapter presents an overview of the research. It includes background, purpose, questions and hypotheses, significance, delimitations, and limitations.. Background of Study For the last century, the world has swiftly moves from its industrial economic base, which mostly depends on tangible assets, toward a knowledge base which is tied to the capability of developing and managing knowledge resources. The knowledge economy is built on continuous dynamic value creation, and profits are increasingly coming from knowledge creation, integration, and system-solutions instead of from tangible assets. This change is uprising globally due to the increase of travellers, expenditures, immigration, and communication technologies, making the world more connected and interdependent. Globalisation has facilitated the exchange of goods, services, labour, information, and most importantly, the share of unique ideas and knowledge. Beside all the mutual gains brought by globalisation, competitiveness is accordingly increasing. Short product’s life cycles and a rapid change rate in customers’ needs and preferences are considered typical features as well as challenges of the current industrial paradigm. In such a competitive era, the top way to acquire sustained competitive advantages is continuous innovations, depending on different organisational, technological, and marketing abilities to effectively deliver a lasting flow of innovative products and services to customers (Teece, Pisano, & Shuen, 1997). The importance of innovation has been underscored by preliminary research stating that it can increase market share, production efficiency, productivity, and revenue (Shefer & Frenkel, 2005; Van Auken, Madrid, & Gracia, 2008). Tidd, Bessant, and Pavitt (1997) also marked that organizations which use innovation to differentiate their products are on average twice as profitable as other organisations. In this sense, a huge amount of research has been dedicated to dissecting innovation’s components. Different theories, namely, ‘Knowledge-based view’ or ‘Intellectual capital-based view’ (Reed, Lubatkin, & Srinivasan, 2006), have been developed in management literature. They declare that firms’ innovative capabilities rely largely on knowledge and intellectual assets that firms own and on their ability to utilize those assets (Subramaniam & Youndt, 2005).. 1.

(9) In accordance with the above notion, knowledge is of overriding importance to companies that achieve their competitive advantages through knowledge and innovation. Those companies, notably in high-tech industries, must develop management practices, organisational structures, and employees’ skills and capabilities to remain competitive. Take an example of Dell – a company famous for manufacturing and process innovation, is now shifting towards creating and providing knowledge-intensive solutions for businesses and individuals. This movement manifests the fundamental characteristics of what is needed to succeed in the rapidly changing industry. Controlling intellectual assets helps to increase firms’ possibilities of entering new markets, creating better products, and earning first movement advantages, especially in hightechnology stock (Hayton, 2005). As Drucker (1993) states that “knowledge is the only meaningful resource today […] And knowledge in this new sense means knowledge as a utility, knowledge as the means to obtain social and economic results” (p. 42, 44-45). The importance of knowledge has been recognized for a long time. Knowledge is the only source of superior power and the most crucial driver of force and wealth (Toffler, 1990). In business context, knowledge is the fundamental basis of competition (Zack, 1999). However, the sole act of owning knowledge itself does not warrant strategic advantages; companies must explicitly manage their intellectual resources (Zack, 1999). That leads to the need of Knowledge Management (KM). KM has been developing into big business and growing explosively. Corporates spend millions of dollars on KM projects and tools such as software, platform, search engines, and etc. KM conferences are held all around the world, there are journals dedicated to KM, and academic discussion about KM has always been on the table. With the increase of organisations’ attention to KM and Innovation, this research intends to investigate how these two elements affect each other and business performance. Consequently, suggestions for practitioners and researchers in the field can be obtained more comprehensively.. 2.

(10) Purpose of the Study In a fast-changing market, innovation is indispensable for companies to adapt and survive. Therefore, developing a KM strategy that will effectively facilitate firms’ innovation is crucial. Thus, many researchers have devoted to achieving a better understanding about the relationships among those variables. With the above assumptions, the purposes of this research are: 1. To analyze and examine how KM strategies affect Innovation. 2. To analyze and examine how Innovation affects Business Performance 3. To examine and analyze whether Innovation transfers the effects of KM strategies to Business Performance.. Questions of the Study The research questions are as follows: 1. Do different KM strategies (human-oriented and system-oriented) have effects on Innovation? 2. Does Innovation have effects on Business performance? 3. Does Innovation transfer the effects of KM strategies to Business performance?. 3.

(11) Significance of the Study Research in the Knowledge field is important because knowledge is the only key to coping with and surviving the rapid changing environment. As Don Tapscott articulated in his book – The Digital Economy (Tapscott, 1996): Today we are witnessing the early, turbulent days of a revolution as significant as any other in human history. […] The computer is expanding from a tool for information management to a tool for communication ... enabling a new economy based on the networking of human intelligence […] For individuals, organizations, and societies that fall behind, punishment is swift ... This is an age of networking not only of technology but of humans, organizations, and societies (p. 01). This trend of constant change requires businesses to transform themselves in order to adapt and thrive in the new demanding era. Transformation happens in the way jobs are designed, in the way people contribute to their companies, and in the way companies recognize and manage their vital assets – their knowledge resource. The role of knowledge resource has become widely recognized as successful organizations are those that constantly innovate, depending on technologies, and their employees’ knowledge and skills, rather than physical assets such as properties, buildings, or machinery (Guthrie, Petty, & Ricceri, 2007). Some of the companies that are famous for gaining huge success thanks to their nonstop innovation nowadays are Apple, Google Inc., Samsung, etc. Also, it is broadly recognized that the management of knowledge generates value; but not so obvious is the link between the management practice and organizational performance. Thus, literature has expressed the need to study KM in more depth. Mouritsen (2004) stated that companies struggle to specify how decisions can develop knowledge and translate it into desirable effects. That’s why KM strategies are demanded to be integrated. Nevertheless, building such a strategy is a challenging mission, considering KM system’s failure rates are over 80% for various causes (Storey & Barnett, 2000). Many managers are still unaware of what factors can enhance KM programs’ success (Moffett, McAdam, & Parkinson, 2003), and impacts of KM strategies on innovation and organisational performance have been seldom studied (Choi, Poon, & Davis, 2008), hence, there exists a research gap on which conditions help KM practices enhance business performance.. 4.

(12) Thus, the paper attempts to enrich KM literature in general by presenting hypotheses and testing a theoretical model links these streams to address the need for effective KM, and to examine how KM strategies lead to better organizational performance through increasing innovation. Both researchers and practitioners recognize that knowledge management requires the integration of IT systems and people who involve in the systems. Accordingly, the paper will examine links between two KM strategies (system-oriented and human-oriented) and innovation and business performance. Research in KM can take several different approaches. For example, basing on an established theory, or assessing organizational outcome levels (performance). They can also focus on strategic intent and firms’ choices regarding the development of management practices. This research addresses some of these avenues. For practitioners, the study’s findings may help aid the prediction about which KM interventions are likely to boost efficiency and profitability, thus, develop KM strategies that are effective on driving organizational performance to success. For example, organizations that put stress on tacit knowledge may rely on human-oriented rather than system-oriented KM strategy. Tacit knowledge has a personal quality that makes it formalization and communication difficult (Nonaka, 1994), thus, requiring interpersonal interaction and collaboration. Therefore, interventions that attempt to build better systems may generate little effectiveness, and are a waste of resources. In contrast, tasks that place heavy demands on the efficiency of system such as analysing or accounting will require organizations’ effort on building a strong system.. 5.

(13) Delimitations and Limitations Delimitations Delimitations of the study are its extent which is intentional to make the research feasible. With that intention, the study is delimited to information technology industry. The study will only examine the relationships among variables which are KM strategies, Innovation and Business performance. Data was collected only by online questionnaire.. Limitations Despite much of the researcher’s efforts, the study has some limitations that can be attended to by future research which are: research is conducted only among employees working for IT companies, and hence, research results cannot be generalized for other contexts; research data covers only a certain period of time the sample size is relatively small (N = 219), which may constrain the generalizability of the results; and only one KM strategies measurement, one Innovation measurement and only one business performance measurement were employed. Also, the measurement scales for these variables can be enhanced by including more indicators to better explain their variance. As the research’s extent mainly concerns maximizing its feasibility, this work can accept these limitations. Some strong points of the study that lend more credibility to the findings are the significance of all hypothesized relationships, thus, support the theoretical model, the use of SEM, the validation for the measurement scales’ quality achieved with various tests, and the details on direct, indirect, and mediating effects.. 6.

(14) Definition of Terms Knowledge Management Strategies Modern KM is an emerging discipline and still in an immature state – with many concepts and ideas still evolving. The lack of clear definition can be quite confusing for observers, practitioners, and academics. In order to better comprehend the notion of KM many researchers have approached it from different perspectives such as conceptual perspective, process perspective, implementation perspective, management perspective, etc. With different perspectives come different definitions. In this research, KM strategies will be defined as the overall approach an organization intends to take to align its knowledge resources and capabilities to the intellectual requirements of its strategy (Zack, 1999).. Innovation Innovation will be defined as a structure or administrative system, a policy, a plan or program, a production process, a product or service that is new to the company, which has been acquired or generated internally (Daft, 1982; Damanpour & Evan, 1984).. Business Performance The term ‘business performance’ has been commonly used by both academics and practitioners in all business-related areas, especially in strategic management area. In general, performance can be understood as an evaluation, quantitative or qualitative, of those that are produced as a result of an intended and planned activity (Yildiz, 2010). BP in this research will refer to economic aspects of organizational performance. Hence, BP was measured by outcomebased financial indicators. Measuring BP helps to ascertain whether organizations meet the needs of their clients, discover what they know and do not know about their activities to have a general vision of how successful they are. It also helps to make sure that organizations’ decisions are not given out of emotions or assumptions but based on real data, and to determine areas that can be better developed.. 7.

(15) CHAPTER II LITURATURE REVIEW In this chapter we will review preliminary research on KM, innovation, their definitions, adaptation, various perspectives and approaches. We further discuss how these variables interact with each other as well as relate to business performance.. Knowledge Management Strategies and Business Performance Interest in knowledge management (KM) has risen steadily since the 1990s and nowadays, many organizations have relied on KM strategies/practices as a key element of their operations. KM has been accepted as an integral part of management practice which builds methodologies through models, frameworks, and approaches that are objectively via rigorous studies (Wickramasinghe, Bali, & Geisler, 2007). The core of KM is knowledge but there are various terms and perspectives around it. Since Peter Drucker coined the term “Knowledge worker” (Ellingsen, 2003), researchers have defined and redefined the notion as well as how to best manage those workers (Kwon & Watts, 2006). Despite extensive research, we haven’t been able to build a commonly accepted approach towards the KM paradigm. Though, it has at least been acknowledged that KM is indispensable, and hence, there is a need for developing KM strategies. KM strategies refers to the overall approach an organization intends to take to align its knowledge resources and capabilities to the intellectual requirements of its strategy (Zack, 1999). Bierly and Daly (2002) similarly asserted that the set of strategic choices addressing knowledge creation in an organization comprises the firm’s KM strategy. Nevertheless, KM strategy is often embraced unconsciously (Garavelli, Gorgoglione, & Scozzi, 2004). To grow and stay competitive, organizations must have a consistent vision and deliberately manage its knowledge by implementing appropriate KM practices. Preliminary research has provided a multitude of approaches to managing knowledge. Gillingham and Roberts (2006) stated that KM field focuses on three key components: people, process, and technology. From simple technologies such as email, web blog, forum to more complex ones such as AI, data mining tools, etc., all belong to the technology component of KM. Technology is the least important to KM and fails to contribute unless the other two elements are properly aligned (Alan & Lorna, 2011). Process regards to internal systems that organizations have established over years of running business. It includes best practices for a specific organizational environment and embeds in it what is good and what is practicable for the organization. People 8.

(16) are the most fundamental component in KM, driving the other two components. KM is effective only when KM principles are accepted by individuals, which will later promote quick KM exploitation. Nevertheless, the focus on the people component does not necessarily mitigate the significance of the other two components. In contrast, a balance among all three components would provide the best KM practice. Another popular approach for developing KM practice is based on the categorization of knowledge. Polanyi (1966) initially classified knowledge into two categories: tacit and explicit. Explicit knowledge is easily captured and documented to share while tacit knowledge resides in human mind, behaviors, and perception, hence, it is hard to be formalized and communicated (Nonaka & Takeuchi, 1995). It emerges from people’s interactions, requires skills and practices (Mårtensson, 2000). Similar to explicit- and tacit-oriented types, Hansen, Nohria, and Tierney (1999) categorized KM into codification and personalization strategies. In the codification strategy, individual knowledge is combined, put in a cohesive context, and made accessible to members of the organization via databases. It is a document-to-person approach under the condition that knowledge can be extracted and codified. This KM strategy is highly structured as compared to the personalization approach that is semi-structured. The personalization strategy exploits the tacit aspect of knowledge and presumes that knowledge is shared chiefly through direct personal interactions (Desouza & Evaristo, 2003). From the same perspectives, Choi and Lee (2003) classified KM into four styles: dynamic, passive, system-oriented, and human-oriented. Companies of dynamic styles emphasizes both tacit- and explicit-oriented methods while companies of passive style don’t put emphasis on KM. System-oriented style focuses on codifying and reusing knowledge (Choi & Lee, 2003). Codifiability is increased through ITs (Shani, Sena, & Stebbins, 2000), thus, the difficulty of accessing and utilizing knowledge is decreased (Hansen, Nohria, & Tierney, 1999). Knowledge is managed and distributed in a formal way, through procedures, codes, documents, and working manuals. On the other hand, human-oriented style puts emphasis on obtaining and sharing tacit knowledge through interpersonal contacts. Knowledge is formed within informal social networks, thus, human elements are important for effective KM (Lang, 2001). This research chooses to study the KM strategies typology by Choi and Lee (2003), specifically, on system-oriented strategy and human-oriented strategy. The reason is that their typology is based on classical and well-known research in KM field which relate to difference 9.

(17) between tacit and explicit knowledge. Moreover, the terms of system-oriented and humanoriented strategy are also aligned with concepts about KM’s core elements in previous research, and are widely accepted by both academics and practitioners. The contention that KM affects multi aspects of organizational performance has been supported by findings from previous research. KM has been a crucial strategic resource due to its uniqueness and non-substitutability. KM is positively linked to enhanced competitiveness and organizational performance, as well as a major driver behind lasting business success (Schulz & Jobe, 2001). KM strategies has impacts on both financial outcomes (Vaccaro, Parente, & Veloso, 2010) and non-financial outcomes (e.g. quality, innovation, productivity) (Forcadell & Guadamillas, 2002; Mukherjee, Lapré, & van Wassenhove, 1998). When studying impacts of KM on organizational effectiveness, Mohrman, Finegold, and Mohrman Jr. (2003) examined 10 companies and found a weak positive correlation between the degree to which the organizations generated, utilized knowledge and overall organizational performance, including financial dimensions. On the other hand, by using both financial and non-financial measures, the strength of the correlation may be accentuated. Among many dimensions of organizational performance that are influenced by KM strategy, this paper will focus on a narrower domain reflecting business performance which represents economic aspects of organizational performance. The reason for this choice is the magnitude of business performance. That magnitude includes three dimensions – namely, theoretical, empirical, and managerial (Cameron & Whetten, 1983). Theoretically, business performance is at the center of strategic management. It is the time test of any business strategy (Schendel & Hofer, 1978), including KM strategy. Empirically, a dominant portion of strategy research employs business performance dimensions to assess a variety of strategy content and process problems; the central role of business performance can be clearly seen in many prescriptions proposed for performance improvement (Venkatraman & Ramanujam, 1986). In this study, business performance will be measured by both non-financial and financial criteria, including (1) customer performance, and (2) financial performance. Since KM strategy has impacts on various dimensions of organizational performance, it follows that KM strategy positively contributes to business performance.. 10.

(18) Knowledge Management Strategies and Innovation Innovation concept has been studied in a good amount of research (Damanpour & Evan, 1984; Nonaka, 1994; Subramaniam & Youndt, 2005). Some define innovation as a structure or administrative system, a policy, a plan or program, a production process, a product or service that is new to the company, which has been acquired or generated internally (Daft, 1982; Damanpour & Evan, 1984). Work organization, introduction of changes in management, marketing systems, etc. can all be examples of innovation. Rogers (1998) defines innovation as the process of commercialising or deriving value from ideas. According to Cardinal, Allessandri, and Turner (2001), the innovating process emcompassess technical, physical, and knowledgebased activities that are essential to form product development procedures. As for Harkema (2003), innovation is a process aiming at generating new knowledge for the formation of commercial and feasible solutions. It is the process of obtaining, sharing, and absorbing knowledge in order to create new knowledge which is eventually converted into products and services. Although there are many definitions of innovation, most of them relate to knowledge which is exploited to create new products, processess, and services to increase competitive advantages and deliver to customers what they want (Metaxiotis & Psarras, 2006). Schumpeter (1934) identifies five types of innovation: . The launch of new products;. . New methods of production;. . The exploration of a new market;. . New source of supply, raw materials;. . New business organisations that create or break up a monopoly.. Within the context of this research, the type of innovation being investigated is new product (or service). Innovation process greatly relies upon knowledge (Gloet & Terziovski, 2004) as knowledge is generated and converted into products, services, and processes (Choy, Yew, & Lin, 2006). The importance of knowledge to innovation has been acknowledged by both scholars and practitioners. For example, the European Commission decided that Europe needed to elevate its creativity and innovation for both social and economic benefits. Hence, it declared the year 2009 as the European Year of Creativity and Innovation. In doing so, it recognized the need for better use of knowledge and faster innovation, and also acknowledged the need to broaden the creative 11.

(19) skills of the whole population (Alan & Lorna, 2011). As in research field, when analyzing the impact of knowledge and its features or typologies on innovation, du Plessis (2007) proposes that KM plays three roles in innovation. The first role of KM in innovation is to produce and sustain competitive advantages via utilisation of knowledge and combination of practices. The second role of KM is reducing complecation in the procedure, and managing knowledge as a major causal factor to innovation. As Cavusgil, Calantone, and Zhao (2003) noted, firms that produce, utilise knowledge quickly and effectively are capable of innovating quicker at higher rate of success than those who do not. This notion is strongly supported nowadays with the rise of many successful innovative companies like IBM, Sony, Alibaba, etc. The third role of KM is ensuring the consolidation of knowledge, both internal and external, by making it more accessible and ready at hand. Knowledge consolidation means that knowledge can be interchanged, shared, developed, elevated, and made accessible when needed. Knowledge can be integrated via KM platforms, tools, and processes to support organisational learning and innovation. Chen, Zhu, and Xie (2004) contended that without helpful information and KM to facilitate knowledge integration, which succeedingly boosts innovation, organizations might be under-exploiting knowledge as an innovative source. Beside these three roles, du Plessis (2007) also states a few critical contributions of KM to innovation, including: 1. KM allows the exchange and codification of tacit knowledge which is crucial for organisational innovation ability; 2. KM is a crucial for making knowledge explicit and amalgamating into new and innovative ideas. KM offers tools, mechanism, and platforms to assist the availability and accessibility of knowledge. 3. KM enables collaboration in innovation. Collaboration here is characterized as customers’, suppliers’, and employees’ ability to build knowledge sharing communities within and beyond organisational boudaries. These communities cooperate to accomplish a joint business goal, benefiting all community members. Cavusgil et al. (2003) suggested that collecting tacit knowledge from collaborative stakeholders is likely to lower risk and cost of innovation by securing a right-firsttime approach. This assists in cutting short development cycles and enhance the rate of successful innovation. 12.

(20) 4. KM with its various activities enhances organisational capabilities and opportunities (e.g. absorptive capability, transformative capability, potential learning opportunities, etc.) 5. KM is a part of the culture that is favorable for knowledge constitution, sharing, collaboration, and eventually, innovation. Knowledge’s constitution, exchange, and elevation develop employees’ skills that are specifically crucial for the innovation process. Through what du Plessis (2007) stated above, it is safe to say that KM directly contributes to innovation, which eventually contribute to an organisation’s successful business performance. More evidence on the notion can be found in an extensive amount of research in the KM and innovation field. For example, Gopalakrishnan, Bierly, and Kessler (1999) asserted that knowledge positively influences probability of innovation. Effective KM involves (1) identifying knowledge, (2) generating new knowledge, (3) developing competence, (4) effective management of innovation (Enkel, Gilbert, Makarevitch, & Vassiliadis, 2002). A KM system is conceded to facilitating the innovation process via faster access to new knowledge. As knowledge contributes to innovation, innovation is also seen as the best payoff from KM (Majchrzak, Cooper, & Neece, 2004). Darroch (2005) presents empirical findings to advocate the notion that firms with adequate are potentially more innovative as well. As innovation process is becoming more interactive, more reliant upon knowledge, KM is increasingly central (Swan, Newell, & Robertson, 2000). As this research studies KM strategies typology by Choi and Lee (2003) which are system-oriented strategy and human-oriented strategy, the fundamental basis of how these two strategies affect innovation is now discussed. Tapscott and Williams (2006) see a clear link between the role of knowledge employee and innovation in interacting with peers and with organisational KM resources, but believe that the nature of the interaction has become more advanced. They describe social media as tools that can initiate and enable more powerful forms of collaboration than before. They point out that knowledge employees rountinely participate in peer-to-peer knowledge exchange across organisational boudaries, forming networks of expertise in a more complex way than traditional communities of practices do. They do this by using a wide range of technologies such as wikis, social networking systems, blogs, search engines, video conferences, discussion forums, etc. Technology allows those workers to use a network to 13.

(21) exchange knowledge, which potentially lead to new knowledge creation. From their point of view, we see how the combination of human and system element plays a role in creating innovation. Similarly, Denning (2000), envisions two basic different approaches or mindsets relating to KM and innovation. The first one is called the Napoleonic or the engineering approach, which refers to the application of scientific discovery to practical invention. This approach assumes the existence of a controllable path – a process based on a series of linked tasks – from the formation of an idea to its exploitation. In Denning’s (2000) terms, the approach represents an effort to reduce all knowledge to a set of mechanistic propositions which he attributes to “…a continuing itch for reductionist simplicity” (p. XV). The other approach is called Tolstoyan or ecological approach. It is based on the creative chaos and freedom on which creativity thrives. It seeks to exploit the connections between things and people that are featured in the collaboration and social networks of KM. Denning (2000) maintained that human naturally connects things and can apply these connections in new and creative ways. This process is said to be faster and more reliable and rigid mechanistic processes of management, which tend to rely on analytical (rather than creative) thinking. Nevertheless, the integration of both approaches can be beneficial to organisations’ innovation performance. As discussed, KM strategy has been linked to business performance and innovation, preliminary research also declare that KM can enhance organizational performance and competitiveness indirectly via higher innovative capability (Braganza, Edwards, & Lambe, 1999; Gloet & Terziovski, 2004). Accordingly, innovation can be considered as a mediator between KM strategy and business performance.. Innovation and Business Performance In general, innovation refers to changing or creating new ways of doing things, new processes, products, and ideas. For business, innovation could mean implementing new ideas, improving an existing service or product, changing business models, and adapting to changes in the market. As previously mentioned, categories of innovations in this study are product innovations and process innovations, which are considered the most characterized classifications in the field (Šakalytė & Bartuševičienė, 2013). New products and processes are crucial for economic growth as they are usually better, faster ways of doing things or better meet the needs of society. Therefore, it is necessary to review the literature on the relationship between. 14.

(22) innovation and business performance in order to see how important innovation is in terms of values added to organizations. Nowadays, innovation is important for almost every aspect of society: it is an economic stimulus, a determinant of value creation and sustainability, social development, and a requirement for international competitiveness (Šakalytė & Bartuševičienė, 2013). However, the importance of innovation has not always been recognized. Dating back to the development of innovation concept in economic theories, adherents of classic economics did not consider innovation as a main factor behind economic growth and described its role mainly as a facilitator for other factors such as land, labor, capital. For instance, Adam Smith asserted that the allocation of labor in the economy was the driver of a country’s prosperity and inventions (mainly machines) were just a tool that made work more efficient and hence, allowing production at lower labor cost (Smith, 1776). Another economist, Jean-Baptiste Say, attended to the benefits of innovation in terms of introducing new machines that facilitated production and created new jobs. He emphasized the benefits of innovation for consumers mainly lying at lower prices of products. (Say, 1880). This stream of economic theory was criticized to be. disproportionately devoted to physical assets’ roles, while ignoring the role of intellectual assets (Lemanowicz, 2015). The importance of innovation was later recognized by the economist Joseph Schumpeter, author of the book The Theory of Economic Development. Since the book was published in 1934, Schumpeter has been reputed to be the “prophet of innovation” (McCraw, 2007). According to Schumpeter (1934), innovation is a critical dimension of economic change. He argued that economic change revolves around innovation, and technological innovation often results in temporary monopolies which brings in high profits for a period of time before competitors succeed in imitating or generating a similar innovation. These temporarily high profits are the incentive for companies to create new products and processes (Pol & Carroll, 2006). Innovation continued to receive more attention when the international OECD program called TEP (Technology/Economy Program) was established in 1988. Its publications focused on the application of technology, scientific research, and innovation for the economy and society. Thanks to the success of OECD program, economists started seeing knowledge and innovation as sources of competitive advantages and economic growth (Lemanowicz, 2015). In his 1954 book “The Practice of Management”, Peter Drucker also attended to the importance of 15.

(23) innovation, stating that the two most important functions of a business are Innovation and Marketing. As explained by Schumpeter (1934), theoretically, innovation helps business grow because new products initially face little competition, hence, creating temporary monopolies which generates abnormally high profits. Lieberman and Montgomery (1988) backed the notion, adding that continual product innovation is another key to maintain competitive advantages because pioneers that innovate to keep up with changing technologies and customer needs are able to secure available market niches, which makes them formidable rivals and excessively hard to win. They also contended that a series of monopoly profits can be maintained if companies continuously launch a line of new and innovative products. The theorized importance of innovation has later been validated by other researchers through several studies. Geroski, Machin, and Reenen (1993) observed a sample of 721 large, quoted firms in the U.K and concluded that innovating companies are likely to be faster, more adaptable, more flexible, and better at dealing with market pressures than non-innovating companies. These characteristics are especially crucial during tough times such as economic recessions. In his research in 1999, Peter Roberts examined 4914 drug products from different U.S pharmaceutical companies and found that innovative propensity affects the degree to which firms’ abnormal profit persist over time. Similarly, Cho and Pucik (2005) discovered a positive link between firm innovativeness and profitability, as well as growth, and market value. To answer the question “Are innovative companies more profitable?”, Minor, Brook, and Bernoff (2017) gathered five years of data from 154 companies that all used the same ideation management software. They were able to investigate correlataions between those companies’ commitment to innovation and their financial resutls like growth and profit. They found a significant correlation between the ideation rate and growth in profit or net income: the more ideas that were accepted, the faster the company grew. Companies that have the greatest level of employees’ participation in generating ideas are also the ones having best ideas and strongest profit growth. Not only scholars, real-life executives also demonstrate their strong interest and belief in the importance of innovation to their companies’ long-term success. According to the 2015 US Innovation Survey by Accenture, more than ever, US executives are higly committed to innovation and their organizations and believe that innovation is a critical tool for growth and market differentiation. Of 500 respondents surveyed, 26% asserted that their organizations extremely denpend on innovation for success, 16.

(24) 58% said “very dependent”, and 12% said “dependent”. These organizations focus on innovation because they expect it to help increase share in existing markets or enter new markets, add new value to a current product or introduce an entirely new product category, reduce the cost of the company’s products and services, change the company’s business model, etc. With the belief in innovation’s importance, companies are making big investments in innovation: 74% of the organizations surveyed have now established formal processes to approach innovation, 63% of the companies have appointed chief innovation officers, up to 90% apply emerging technologies to improve or add new features to products, services, and support the innovation process (Accenture, 2015). This strong belief and commitment that companies are demonstrating have resulted not only from favorable results in academic research but also from cases of real success based on innovation such as Uber, Airbnb, Snapchat, etc. The most prominent success is probably Apple’s iPhone as it is the most profitable product in history (Williams-Grut, 2015). Since its debut in 2007, iPhone has become a both cultural and economic phenomenon, replacing Blackberry as the most ubiquitous smart phone. In the last three months of 2014 alone, Apple earned US$ 51.1 billion and profitted US$ 18.1 billion from iPhones, making the company the most quarterly profitable of any public company ever. Another recent case of world-class success is Pokemon Go, a breakout-hit mobile game since its release in 2016. Within a few days, the augmented reality game has become a global phenomenon with millions of players chasing virtual Pokémons. Nintendo, the company that developed the game, enjoyed soaring profits as their stock price nearly doubled in the following week, closing at its highest level since June 2010 (Statista, 2015). Until February 2017, the game had 650 million downloads worldwide, and ranked at 9th in top 10 best-selling mobile games by revenue, scored US$ 890 million (Statista, 2017a, 2017b). Pokemon Go is the example of how Nintendo achieves massive success by reinventing what it has had, incorporating new technologies such as virtual reality, gamification, and commerce in a way that is incredibly lucrative. Besides the profit, customers’ expectation is also a driver behind companies’ efforts to innovate. Nielsen Global New Product Innovation Survey 2015 disclosed that 63% of customers say they are fond of manufacturers offering new products, and more than half of them (57%) say they purchased a new product during their last grocery shopping trip.. 17.

(25) CHAPTER III RESEARCH METHOD Quantitative approach is chosen for this research. This chapter seeks to elucidate the research method which comprises conceptual framework, research hypotheses, and research procedure. Data collection methods and instruments are also included.. Research Framework The research framework is based on the research purposes and the literature reviewed in chapter two. This research seeks to analyze the impacts of KM strategies on Innovation and business performance. As theoretical and empirical studies suggested, KM strategies are assumed to have effects on Innovation and Business performance (Darroch, 2005; Edvinsson & Sullivan, 1997; Vaccaro et al., 2010). H1. KM strategies. H2. - Human-oriented - System-oriented. Innovation - Product - Process. Figure 3.1. The Research Framework. 18. H3. Business performance - Customer performance - Financial performance.

(26) Research Hypotheses As being discussed in Chapter 2, according to Botha, Kourie, and Snyman (2008), there are two reasons for one company to outperform the other in the same industry with similar access to knowledge and information. The first one is embedding in its human, structural, and relational capital, not in its tangible assets. The second one is depending largely on how the company exploits or manages these intangible assets. Considering these two reasons, it can be proposed that KM strategies positively affect companies’ business performance. Also, innovation process greatly relies upon knowledge (Gloet & Terziovski, 2004), and firms embrace innovations to attain first and early advantages that will produce excellent performance (Damanpour, Walker, & Avellaneda, 2009), or to bridge a performance gap resulted from uncertainties in the external surroundings (Damanpour & Evan, 1984). Thus, the aim of this study is to investigate the interrelationships amongst the variables: KM strategies, Innovation, and Business performance. All the variables are defined and integrated in the literature review. Based on the established framework, three alternative hypotheses are assumed and detailed as follows: H1: Knowledge Management Strategies positively affect Business Performance. H2: Knowledge Management Strategies positively affect Innovation. H3a: Innovation positively affects Business performance. H3b: Innovation mediates the effects of KM Strategies to Business Performance.. 19.

(27) Research Procedure The literature review on KM revealed several key topics commonly revolved around innovation and BP. Some articles concerning the significance of innovation has initiated an interest for the researcher to pursue studying. The literature also contained many studies related to KM and innovation that have been carried out in various fields and countries, but very few in the IT industry. This has prompted the researcher to focus on the IT industry. Although the topic was somewhat researched before, not all of the variables are investigated together and integrated into a theoretical model. This study will provide a more comprehensive perspective for KM strategies and innovation in order to help the business management field. This study was conducted by following the subsequent research process (see Figure 3.2). As illustrated in Figure 3.2, first, the researcher identified problems that were worth studying. Next, research subject and variables were generally established. After reviewing relevant literature, research questions, hypotheses, and research framework were constituted; methodology was chosen accordingly; measurement scales were searched for and later adapted from preliminary studies. Next, the measurements chosen underwent a strict review and translation (English to Chinese) from two separated translators and other two professors in the field. After the theoretical foundation of the study was well-researched, the first three chapters were completed for the proposal meeting. Pilot study was later conducted to detect necessary adjustments to the instruments. As the instruments were finalized, data for the main study was collected by online questionnaire. After that, data was coded and analyzed; results were produced, reported, and concluded. Thesis draft was completed for the final defense. Finally, feedback was provided for revision after the defense, and the thesis will be submitted after revision.. 20.

(28) Identify Problems. Conducting Pilot Study. Identify research subject. Review of Instrument. Review literature. Data Collection. Establish Research Questions and Hypotheses. Data Coding. Data Analysis Developing Research Framework. Report Results and Conclusions Developing Methodology Thesis draft Completion Finding Measurements. Final Defense Translation and Review of measurements Revision Proposal Meeting Thesis Submission. Figure 3.2. The Research Procedure. 21.

(29) Research Method The study adopts quantitative approach. It is selected because it provides basis for the researcher to construct a theoretical model that explains what is being observed (Neill, 2007). Also, a quantitative method approach facilitates the description, illustration and exploration of a given phenomenon. To explain the process through which variables interact with one another, as well as for the purpose of research validity, precise measurement is required. Quantitative approach allows researchers seek precise measurement objectively and analyze data gathered from questionnaires and surveys. Survey is to be used for two reasons. First, as surveys are structured around key items and topics, they are easily interpreted and analyzed. Second, as respondents’ confidentiality is ensured, respondents are more likely to provide honest feedback (Debowski, 2007). This study will be conducted in IT industry context, whilst the results cannot be generalized, they can provide directions for future research.. Data Collection The targeted population of the study is full-time employees working at IT companies. IT industry is a suitable choice for this research because of its fast-pacing nature, thus, innovation is considered a critical factor within the industry. Employees were randomly selected and contacted through email, personal contact, and social media sites. Employees with self-evaluation can provide a more realistic account of the organization they belong to. To gather data, an online questionnaire was utilized. The instruments were first tested on a small group of randomly selected employees. The sample size chosen for the pilot study is 50. Convenient sampling method was employed for both pilot and main studies.. Instrument The instrument has 22 questions in total that are divided into four parts: part I) KM strategies; part II) innovation; part III) business performance; and part IV) demographics. In part I, II, III, respondents were asked to rate every item in a 5-point Likert Scale (1 = strongly disagree, 2 = disagree, 3 = average, 4 = agree, 5 = strongly agree). For part IV, respondents were asked to provide some demographic information. In sum, 3 research variables are included in the questionnaire – KM strategies, Innovation, and Business performance. The measurements are described in the following: 22.

(30) 1. Knowledge Management Strategies (7 items): Adopted from (Choi & Lee, 2003), KM strategies consist of Human-oriented or KM_H and System-oriented or KM_S. Both are the approaches of knowledge management strategies based on whether knowledge is tacit or explicit. 2. Innovation (6 items): Innovation measurement is adopted from Bae and Lawler’s (2000) measurement of a firm’s product and service innovation. 3. Business performance (6 items): Adopted from Gonzalez-Padron, Chabowski, Hult, and Ketchen Jr. (2010), two dimensions of business performance are Customer performance (BP_C) and Financial performance (BP_F). Part IV of the questionnaire is intended to gather information on respondents’ jobs, gender, and marital status. Participants are advised to fill out all information.. Reliability and Validity Reliability represents the quality of the measurements. It is the degree to which a scale consistently measures what it is intended to measure. A Cronbach’s alpha of higher than 0.7 is the evidence of reliability (Nunnally & Bernstein, 1978). Validity of measurements is the extent to which scales measure what researchers claim they measure. Construct validity comprises convergent validity and divergent validity. Before establishing construct validity, researchers need to run two analyses and obtain required values. Convergent validity can be assessed using Composite Reliability and Average Variance Extracted values. Discriminant validity can be assessed by different criteria such as Fornell & Larcker criterion or Heterotrait-monotrait (HTMT) ratio of correlation (Henseler, Ringle, Sarstedt, 2015). The basic difference between convergent and discriminant validity is that convergent validity tests whether items that should be related, are related, and discriminant validity tests whether proposed unrelated items are, in fact, unrelated. The first test is the Kaiser-Meyer-Olkin (KMO). It measures Sampling Adequacy value. This value reveals how suitable it is to perform factor analysis on measurement scales. KMO value of lower than 0.5 is unacceptable, higher than 0.6 indicating mediocre fit, higher than 0.7 indicating middling fit, and higher than 0.8 indicating excellent fit, which means factor analysis is useful in this case. The closer the KMO value is to 1.0, the better the indicators of the construct explain the variance of the data. To improve KMO value (especially when the value is under 0.5), deleting bad items or adding items is necessary. KMO test were run by SPSS 23.0. 23.

(31) The second test is the Bartlett’s test for Sphericity. The test declares whether or not the correlations among variables happen by chance. For Bartlett’s test, the small p-value, which is under .05, confirms that factor analysis is eligible to conduct. After those tests are run, if all the values reach the minimum standards, factor analysis can be performed. Bartlett’s test for Sphericity value will be tested using SPSS 23.0 software. After the data passes requirements for KMO test and Bartlett’s test, it will undergo Confirmatory Factor Analysis (CFA) tests for factor loadings analysis, construct reliability, and construct validity. For factor loadings, if the item-to-total correlation score is below 0.4, the item is excluded from further analysis (Kerlinger, 1986). The reason is that the item may not be correlated with other items measuring the same variable. CFA value will be tested by using SPSS 23.0 software. CFA is also used to test hypotheses. Items’ factor loadings are observed. Structural equation modeling software is typically used for performing CFA. In this study, CFA value will be tested by using SPSS 23.0 and Amos 23.0 software.. Data Analysis Data collected for this study will be analyzed by three methods: Descriptive Statistics, Confirmatory Factor Analysis, and Structural Equation Modelling. Totally, 25 questions are coded by a 5- point Likert scale. Three demographic questions’ coding is given in Table 3.1 below:. 24.

(32) Table 3.1. Coding System Used for SPSS Coding. Variables. KMS. KM Strategies. KM_H. Human-oriented strategies. KM_S. System-oriented strategies. I. Innovation. I_PD. Product. I_PC. Process. BP. Business Performance. BP_C. Customer. BP_F. Financial. MS. Marital Status. Single. 1. Married. 2. Other. 3. Gender. Gender. 1. Male. 2. Female. 25.

(33) Descriptive Statistics Descriptive statistics are conducted to obtain general description of basic features of data, providing a summary of the sample. Descriptive statistics will help to identify which aspect of a variable that has the most impact within the population under study. The method also interprets data by calculating numerical indices such as Standard Deviation (SD) and Means (M).. Confirmatory Factor Analysis A confirmatory factor analysis (CFA) will be conducted to validate measurement scales and initially detect correlations among constructs. CFA is a statistical technique used to verify the structure of a set of observed variables (or indicators). CFA allows researchers to test whether a relationship between observed variables and their underlying latent construct exits. Multi-collinearity can also be identified through CFA. Multi-collinearity occurs when two variables are closely correlated to one another. This indicates that they are likely to measure the same construct. SPSS 23.0 and SmartPLS 3.0 will be used to perform this analysis.. Structural Equation Modelling Structural Equation Modelling (SEM) technique will be used to analyze structural relationships among variables. This technique is a multivariate statistical analysis technique that combines factor analysis and multiple regression analysis. There are two types of variables in SEM: endogenous (or dependent) variable, and exogenous (or independent) variable.. 26.

(34) CHAPTER IV FINDINGS AND DISCUSSION Pilot Study Pilot study was conducted before collecting the whole sample for the main study. The purpose of the pilot study is to validate the quality of selected measurement scales. The sample size for the pilot study is n = 50. Results of reliability, validity, dropped items (if any) and relationships among variables will be presented.. KMO and Bartlett’s Test of Sphericity The main purpose of KMO and Bartlett’s Test of Sphericity is to assess whether correlations exist among variables (or among indicators) so that a following factor analysis is applicable. KMO values range from 0 to 1. Values close to 1 indicate small partial correlation coefficients while values close to 0 imply that factor analysis is not a good idea. KMO value should be higher than 0.5 to be acceptable, higher than 0.6 to be mediocre fit, higher than 0.7 to be middling fit (Kaiser & Rice, 1974). For factor analysis to work, some correlations among variables are required. Therefore, a significant Bartlett’s test of sphericity is needed. Small pvalues (p < .05) from Bartlett’s test of sphericity indicate appropriateness to run factor analysis. Table 4.1 summarizes the results from KMO and Bartlett’s Test of Sphericity of the pilot study. All the KMO values are higher than 0.6, and all constructs’ p-values are smaller than .001, which is satisfactory. Table 4.1. KMO and Bartlett’s Test of Sphericity of Pilot Study Constructs. Number of Items. KMO. Variance Explained. Bartlett’s Test of Sphericity. KM_H. 4. .658. 62.09%. 82.280***. KM_S. 3. .703. 75.25%. 58.390***. I_PD. 3. .719. 74.67%. 53.820***. I_PC. 3. .760. 86.02%. 103.012***. BP_C. 3. .669. 64.22%. 28.513***. BP_F. 3. .725. 89.18%. 137.701***. Note. (n = 50) *** p < 0.001. 27.

(35) Factor Loadings Factor analysis was conducted using AMOS 23.0 to achieve factor loadings. Basically, factor loadings represent the relationship between a latent variable and its underlying factors. If a factor loading is below 0.4, the item should not be retained because it doesn’t well represent the latent variable. Accordingly, item km_h4 with factor loading of .336 may be dropped from further analysis. However, since the value is close to 0.4, the item may undergo factor analysis again in the main study. If its factor loading is still below 0.4 in the main study, the item will be dropped. Table 4.2. Factor Loadings of the Pilot Study Item. Loading. Item. Loading. km_h1. .861. i_pc2. .860. km_h2. .936. i_pc3. .940. km_h3. .671. i_pc1. .847. km_h4. .336. bp_c1. .693. km_s1. .908. bp_c2. .533. km_s2. .792. bp_c3. .800. km_s3. .666. bp_f1. .981. i_pd1. .663. bp_f2. .928. i_pd2. .920. bp_f3. .841. i_pd3. .734. Reliability Test There are two criteria to assess construct reliability: Cronbach’s alpha or Composite Reliability (CR). Cronbach’s alpha or CR value of 0.7 or greater is required to ensure internal consistency of measurement scales (Hair Jr, Hult, Ringle, & Sarstedt, 2016). Table 4.3 shows Cronbach’s alpha values of every construct before dropping items. All the values are satisfactory (higher than 0.7).. 28.

(36) Table 4.3. Reliability and Validity Results of the Pilot Study Number. Cronbach’s. Composite. Average Variance. of items. alpha. Reliability. Extracted. KM_H. 4. 0.78. 0.86. 0.62. KM_S. 3. 0.83. 0.90. 0.75. I_PD. 3. 0.83. 0.89. 0.74. I_PC. 3. 0.91. 0.94. 0.86. BP_C. 3. 0.72. 0.84. 0.63. BP_F. 3. 0.93. 0.96. 0.89. Construct. Validity Test Construct validity is tested on convergent validity and discriminant validity. Convergent validity is confirmed when CR is of 0.7 or higher, and Average Variance Extracted (AVE) is of 0.5 or greater (Hair Jr et al., 2016). Table 4.3 summarizes every CR (ranging from 0.86 to 0.96) and AVE (ranging from 0.62 to 0.89), which all satisfy the cut-off criteria. Hence, convergent validity is established. Discriminant validity can be examined by Fornell and Larcker criterion which compares the square-root of AVE (i.e. √𝐴𝑉𝐸 ) with correlations among latent constructs. A latent construct’s square-root of AVE should be higher than its correlations with other constructs to ensure discriminant validity. Table 4.4 shows that the square-root of AVE of each construct (in the diagonal) is higher than its correlations with other constructs (in the corresponding rows and columns), thus, confirm discriminant validity of measurement scales used.. 29.

(37) Table 4.4. The Square-root of AVE (in bold) and Correlations among Constructs of the Pilot Study KM_H. KM_S. I_PD. I_PC. BP_C. KM_H. .787. KM_S. .694. .867. I_PD. .461. .401. .862. I_PC. .529. .588. .685. .927. BP_C. .275. .343. .327. .587. .799. BP_F. .208. .210. .091. .329. .701. BP_F. .944. Hypothesis Testing of Pilot Study Results were obtained from an examination of the structural relation model. SmartPLS 3.0 was used in the pilot study due to the software’s ability to handle small samples effectively. Beta coefficients and their t-values are presented in Table 4.5 below. Besides p-values, t-values are also intended for determining the significance of path coefficients. T-values for two-tailed test higher than 1.65, 1.96 and 2.58 represent weak, moderate and strong relationships. The results suggest that KM does not have a significant effect on BP (β = .046, t < 1.65); KM has a positive, significant effect on Innovation (β = .606, t = 6.669); and Innovation has a positive, significant effect on BP (β = .497, t = 2.599). The fact that KM does not have a significant effect on BP is potentially because Innovation has fully mediated KM’s effects on BP, therefore, weakening the significance of the path coefficient from KM to BP (see Figure 4.1 for graphic illustration). The structural model explained 36.7% variance of Innovation (R2 = 0.367) and 27.6% of Business performance (R2 = 0.276). Table 4.5. Hypothesis Testing Results of the Pilot Study Hypothesis. β-path. t-value. Direction. Results. KM  BP. H1. .046. 0.198. +. Rejected. KM  Innovation. H2. .606***. 6.669. +. Accepted. Innovation  BP. H3a, H3b. .497***. 2.599. +. Accepted. Note. *** p < .001. 30.

(38) Main Study Descriptive Statistics The study was conducted within IT industry. A total of 230 responses were collected. Out of them, 219 were valid, representing 95% valid response rate. Characteristics of the respondents are listed in Table 4.6, which are divided into gender and marital status. In terms of gender, there is a higher percentage of male respondents (68.9%), female respondents account for 31.1%. In terms of marital status, there is a higher percentage of employees who are single (65.8%), married employees account for 33.8%, 0.5% of respondents selects ‘Other’ for Marital status. Table 4.6. Sample Characteristics Variable Gender. Marital status. Entries. Percentage. Male. 151. 68.9%. Female. 68. 31.1%. Single. 144. 65.8%. Married. 74. 33.8%. Others. 1. 0.50%. Note. n = 219 Items’ means and standard deviations are shown in Table 4.7 Table 4.11 contains correlations among variables, the highest correlation is between I_PC and I_PD (.782), none of the correlations exceeds 0.8 threshold (Kennedy, 1985), therefore, multi-collinearity is not present.. 31.

(39) Table 4.7. Descriptive Statistics Std.. Item. Mean. km_h1. 3.80. 1.015. km_h2. 3.75. 1.082. km_h3. 4.20. .842. km_h4. 3.15. 1.218. km_s1. 2.77. 1.114. km_s2. 2.89. 1.110. km_s3. 3.28. 1.114. i_pd1. 3.42. 1.180. i_pd2. 3.56. 1.169. i_pd3. 3.49. 1.159. i_pc2. 3.33. 1.174. i_pc3. 3.39. 1.165. i_pc1. 3.31. 1.143. bp_c1. 3.47. .973. bp_c2. 3.61. 1.055. bp_c3. 3.25. 1.047. bp_f1. 2.76. 1.105. bp_f2. 2.77. 1.106. bp_f3. 2.80. 1.086. Deviation. Confirmatory Factor Analysis KMO and Bartlett’s test of sphericity. Table 4.8 demonstrates KMO and Bartlett's test of sphericity values for every construct. All the values reach the cut-off criteria (KMO > 0.6, Bartlett's test of sphericity p-values < .001), thus, a following CFA can be conducted.. 32.

(40) Table 4.8. KMO and Bartlett's Test of Sphericity Construct. Number of items. KMO. Bartlett's test of sphericity. KM_H. 4. .738. 331.210***. KM_S. 3. .669. 200.760***. I_PD. 3. .740. 375.124***. I_PC. 3. .742. 614.044***. BP_C. 3. .728. 315.928***. BP_F. 3. .765. 786.047***. Note. *** p < .001 Reliability and validity. Factor loadings. So as to validate the instruments used, CFA was conducted. If factor loading is below 0.4, it means the indicator does not well represent its parent construct, therefore, should be withdrawn from further analysis. Table 4.9 shows factor loadings of all constructs. Different from the pilot study, in the main study, item km_h4 has factor loading of higher than 0.4, hence, the item was retained. Table 4.9. Factor Loadings Item. Loading. Item. Loading. km_h1. .913. i_pc2. .911. km_h2. .833. i_pc3. .957. km_h3. .605. i_pc1. .892. km_h4. .500. bp_c1. .844. km_s1. .832. bp_c2. .761. km_s2. .809. bp_c3. .861. km_s3. .585. bp_f1. .973. i_pd1. .847. bp_f2. .956. i_pd2. .911. bp_f3. .912. i_pd3. .793. 33.

(41) Construct Reliability. As shown in table 4.10, the lowest Cronbach’s alpha value is 0.78, and the lowest Composite Reliability value is 0.87, thus, satisfying the cut-off criterion of 0.7. Table 4.10. Reliability and Validity Statistics Number. Cronbach’s. Composite. Average Variance. of items. alpha. Reliability. Extracted. KM_H. 4. 0.80. 0.87. 0.63. KM_S. 3. 0.78. 0.87. 0.69. I_PD. 3. 0.88. 0.93. 0.81. I_PC. 3. 0.94. 0.96. 0.89. BP_C. 3. 0.86. 0.91. 0.78. BP_F. 3. 0.96. 0.97. 0.93. Construct. Construct Validity. CR values of the constructs range from 0.87 to 0.97; AVE values range from 0.63 to 0.93 (Table 4.10), which exceed the cut-off criteria of 0.7 and 0.5 respectively, which ensures convergent validity. Table 4.11 shows that the square-root of AVE of each construct (in the diagonal) is higher than its correlations with other constructs (in the corresponding rows and columns), thus, confirm discriminant validity. Table 4.11. The Square-root of AVE (in bold) and Correlations among Constructs KM_H. KM_S. I_PD. I_PC. BP_C. KM_H. .793. KM_S. .494. .834. I_PD. .381. .336. .904. I_PC. .424. .467. .782. .946. BP_C. .332. .439. .434. .479. .887. BP_F. .307. .377. .446. .456. .750. BP_F. .965. After the measurement scales’ reliability and validity are confirmed, the next step is using fit indices to evaluate the research model. The fit indices will help determine how adequately the model explains the data. The model fit statistics are summarized in Figure 4.1. Fit indices show that the model was adequate and reasonably fitted. Although the large value of chi-square (χ2 = 34.

(42) 342.688) suggests an inadequate fit, conclusion can’t be made without taking other fit indices into consideration. Moreover, researchers don’t favor chi-square because it is an absolute fit index that is affected by sample size and model size (Newsom, 2015). Chi-square values tend to be large as sample size is large (over 200) or model size expands (Newsom, 2015). Other fit indices such as Bentler’s Comparative Fit Index (CFI), Standardized Root Mean Square Residual (SRMR), Root Mean Square Error of Approximation (RMSEA) provide more details about the model and should be reported along with chi-square. According to Hu and Bentler’s (1999), Type I and Type II error are best minimized when using the combination of CFI and SRMR with values greater than .95 and smaller than .08 (or .09) respectively. The model’s CFI is .951, and SRMR is .053, thus, satisfying the cut-off criteria. A RMSEA cut-off value close to .07 is also recommended for a good fit of model. The model’s RMSEA is .065, indicating a good fit of the model. Summarized in Figure 4.1, the model’s fit indices (CFI = .951, SRMR = .053, RMSEA = .065) satisfy all the mentioned cut-off values, indicating that the model fits the data relatively well.. Hypothesis Testing Beta coefficients and their t-values are presented in Table 4.12 and depicted in Figure 4.1. The structural model explained 45.2% variance of Innovation (R2 = 0.452) and 42.2% of Business performance (R2 = 0.422). KM strategies has a significant and positive direct effect on Business performance (β = .411, t = 3.030). Also, the indirect effect of KM strategies to Business performance is at β = .193, the total effect is at β = .604. Thus, Hypothesis 1 is accepted. KM strategies has a significant and positive direct effect on Innovation (β = .648, t = 4.900). Thus, Hypothesis 2 is accepted. Innovation has a significant and direct effect on Business performance (β = .297, t = 2.712). Thus, the Hypothesis 3a is accepted. For Hypothesis 3b, the procedure adopted to test Innovation’s mediating effects was from Nitzl, Roldan, and Cepeda (2016). Innovation’s mediating effect is tested as follows: Step 1. An indirect effect of KM on BP was established (β = .193, reported by AMOS 23.0). The value is also equal to KM’s direct effects on Innovation (β = .648) multiplied by Innovation’s direct effects on BP (β = .297). 0.193 = 0.648 x 0.297 Step 2. The indirect effect of KM on BP is tested for significance. Bootstrap Maximum Likelihood technique was employed to calculate two tailed significant of the indirect effect. The 35.

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