Sarah Kelly

1 Running head: INTELLECTUAL CAPITAL AND BUSINESS PERFORMANCE The effects of Intellectual Capital on Business Performance in Information Technology industry: The mediating role of Innovation Truong Thu Thao National Taiwan Normal University, Taiwan Cheng-ping Shih National Taiwan Normal University, Taiwan Copyright © 2018 Truong Thu Thao & Cheng-ping Shih 2 Abstract In present fast-paced and competitive market, companies have to rely on the making of new, creative, and advanced products and services for their success. The rate of innovation has risen dramatically over the past decades, especially in information technology (IT) which has always been considered a knowledge-intensive industry. Intellectual capital (i.e. knowledge) is recognized as a critical driver of innovation, thus, the capability to translate knowledge into more innovative process and product will bring outstanding business performance to companies over the coming age. Despite that much of the success in IT business is due to knowledge-intensive innovation, little empirical evidence exists about how intellectual capital transfers its impact to business performance. The present study uses data obtained from various IT companies to examine the direct effect of intellectual capital on business performance as well as the indirect effect via innovation. Structural equation modeling’s estimation reveals the direct positive effects that intellectual capital has on innovation and business performance, as well as the importance of innovation as the mediating variable. Implications for effectively building on intellectual capital are discussed along with future research directions. Keywords: Intellectual Capital, Innovation, Business Performance 3 Introduction Organizations nowadays function in a dynamically competitive market which includes new challenges of the 21st century, namely, the emergence of millennials in the workforce, who have high expectations for meaningful jobs, and development opportunities, the changing nature of modern business that requires a more diverse workforce, the ubiquity of technologies such as smartphones, 3D printing, IoTs, 5G that is changing the way companies run business and manage people. These challenges demand organizations to acquire, accumulate valuable resources and constantly bring new, unique products to the market. Resources that are controlled by organizations are “assets”. Kaplan and Norton (2004) estimate that intangible assets account for more than 75% of the company’s value. The role of intangible assets or intellectual capital (the two terms are considered synonyms, see Brooking (1996), Guthrie (2001)), has long been recognized. In 2001, Thomas A. Steward, author of two famous books Intellectual Capital: The New Wealth of Organizations (1997) and Wealth of Knowledge: Intellectual Capital and the Twenty-first Century Organization (2001), exclaimed that intellectual capital were becoming America’s most valuable asset. Most recently, The World Intellectual Property Report 2017 (WIPO, 2017) provides data analysis on shares of value added by labour, tangibles, and intangibles in 19 manufacturing industries spanning various economies. From 2000 to 2014, the value added by intangibles accounts for around one-third of production value, and overall income from intangibles increases by 75%, amounting to 5.9 trillion US dollar in 2014. These numbers demonstrate the importance of intangible assets and direct managers’ focus to the need for converting these intangibles into innovation that benefits business. Innovation is the crucial factor for economic growth since it helps expand productivity by initiating effective approaches to the creation and delivery of goods and services. Unsurprisingly, investment in 4 knowledge has surpassed investment in machinery and equipment in some countries such as Finland and the U.S (OECD, 2005). Those statistics are in line with academic research in which knowledge and intellect have been recognized as the most valuable strategic assets (Grant, 1996; Bontis, 1998). IC is widely viewed as intangible assets that contribute to value creation in organizations (Brooking, 1996; Marr, Schiuma, & Neely, 2004). There is a good deal of research related to the relationship between IC and firm performance (Cheng et al., 2010; Mention & Bontis, 2013; Scafarto, Ricci, & Scafarto, 2016). Nevertheless, the relationship between IC and Innovation, and how they both impacts performance, are less studied. Although previous research has greatly enriched our knowledge of the field, there is very much left of the issue to investigate. To begin, there is not much empirical evidence on how IC contributes to performance through its effect on Innovation. In addition, as the scope of this research involves knowledge and innovation, knowledge-intensive industries provide ideal conditions for research. One particular industry that is classified as knowledge-intensive is IT industry in which the relationships amongst IC, Innovation, and performance are under inspected. In order to fill these research gaps, this study aims to empirically examine how IC affects business performance through its effect on innovation within the IT industry. IT services are multifaceted, and involve complex technology-related tasks. These services are developing, combining and applying various types of knowledge about technologies to specific problems, issues and contexts of their clients (EMCC, 2005). Due to that fact, the industry requires a substantial amount of knowledge, which makes research on IC and Innovation within the context both relevant and exciting. This study contributes to literature in several ways. First, it is one of only few studies focusing on IT setting, thus, exploring new perspectives on the matter. Second, by extending 5 preliminary investigation on the same subject, the results broaden a set of evidence that further supports generalizability. Last, it seeks to make both theoretical (by constituting a model for assessing relationships amongst the variables) and managerial contributions (by providing empirical results that support managers on forming the appropriate strategies). Data was collected from various IT companies and statistically analyzed by Partial Least Squares (PLS). The remainder of this paper is structured as follows: Literature review provides basis for the hypotheses by looking at relevant literature; the Research method delineates data collection, measurements, and statistical procedure; the Results, and Discussion illustrate and discuss the empirical findings; the Implications demonstrates the study’s contribution, and the final section provides research limitations and future directions. 6 Literature review Intellectual capital The concept of intellectual capital has been extensively researched, thus, evolved from different perspectives, namely, the Resource-based View (Wernerfelt, 1984; Barney, 1991), the Knowledge-based View (Kogut & Zander, 1992; Nonaka, 1994). Therefore, its definition varies from study to study. Brooking (1996) considered IC as intangible assets of market, human, infrastructure, which enable the company to function. Edvinsson and Sullivan (1996) referred to IC as a stock of knowledge that can be converted into value for companies. Stewart (1997) defined IC as collective brainpower or packaged useful knowledge that can be put to use to create wealth. This study uses the definition adapted from Steward (2001) stating that IC is the sum of an organisation’s processes, employees’ knowledge, skills, experience, technologies, information about customers and suppliers. These exist in the form of procedures and networking – internally and externally – with business partners (Botha, Kourie, & Snyman, 2008). Along with various definitions, there is a plethora of IC classification in the literature. For example, Sveiby (1997) classified IC’s components into employee competence, internal structure, and external structure. Stewart (1997) identified three components of IC as human capital, structural/organizational capital, and customer capital. Later, he added to the idea, stateing that IC takes just two forms:  the semi-permanent body of knowledge (the expertise, the know-how of people, of organizations). This body may consist of communication or leadership skills, knowledge of a particular industry, experience with the organisation’s processes, culture, etc. 7  the tools that facilitate the body of knowledge. They make accessible the data or information, and deliver them when needed. In other words, they help leverage the available expertise. Standfield (2001) elaborated on Steward’s (1997) classification to rephrase “customer capital” to “relational capital” and “social capital”. Despite the divergence of opinions as to what IC encompasses, a concensus emerges on the fact that it is a multidimensional concept. Most of researchers have agreed on three dimensions of IC: human, structural/organizational and relational captial (Stewart, 1997; Bontis, 1998; Sullivan, 1999; Mention & Bontis, 2013). These three sub-dimensions refer to the knowledge resides in an organisation’s employees, rountines, and network relationships respectively. Additional dimensions of IC have also been proposed and researched, such as innovation capital (Chen, Zhu, & Xie, 2004; Tseng & Goo, 2005), social capital (Hsu & Sabherwal, 2011), and renewal capital (Kianto, Hurmelinna, & Ritala, 2010). Within the scope of this study, IC will be viewed as consisting of human capital, organizational capital, and relational captial because these three are well-known dimensions which were validated, thus, ensuring their credibilities in representing the latent variable. Moreover, as stated above, IT industry is a knowledge-intensive sector, the three dimensions under investigation are deemed to well represent different sources of knowledge that every company has. Human capital Human capital (HC) comprises the knowledge, skills, experiences, and abilities of an organization’s members (Roslender & Fincham, 2004). Bontis et al. (2002) referred to human capital as the individual knowledge stock of an organisation as represented by its employees. Due 8 to its nature, human capital is neither owned nor fullly controled by the firm (Edvinsson & Sullivan, 1996). Human capital is of importance because it is the source of innovation and strategic renewal (Bontis, 1998). Organizational captial Organizational capital (OC) or Structual capital is defined as the knowledge stored in organizational infrastructure such as information systems and databases, routines, procedures, processes that support employees’ productivity (Bontis, 2001). Unlike human capital, organizational capital elements can be owned and traded by firms since they can be legally protected (Scafarto et al., 2016). Relational captital Relational capital (RC) encompasses the knowledge embbeded in all the relationships with external stakehholders of an organisation, namely, its customers, its suppliers, its competitors, the gorvernment, etc. (Bontis, 1999). As compared with other IC elements, relational capital more directly impacts a firm’s bottom line because it plays the role of a bridge in converting IC into market value and thereupon business performance (Chen et al., 2004). Relational capital has been consistenly found to positively affect performance. Cheng et al. (2010) found that the higher the cost of maintaining customer relationships, the higher the good impact on corporate performance. Tseng and Goo (2005) also acquired a direct and positive influence of Relational capital on companies’ market value based on survey data from Taiwanese manufacturers. Although IC definitions may vary to some degree, all of them emphasize the central role of knowledge-based capital and its relation to value creation. Research on perfomance improvement has emphasized the importance of leveraging knowledge for better organizational 9 performance and competitiveness (Marr et al., 2004). Over the past decades, the relationship between IC and performance has been extensively studied across industries and regions such as banking (Mention & Bontis, 2013), pharmaceutical (Sharabati, Jawad, & Bontis, 2010), IT (Huang & Liu, 2005), wood industry (F‐Jardón & Martos, 2009), etc. Based on preliminary research, we hypothesize that: Hypothesis 1: Intellectual capital postively affects business performance Although the basic relationship between IC and performance on the whole sounds persuasive, more remains to be investigated about how IC transfers its effects to performance. Some studies have included mediators in their research models to explain the relationship. Multiple mediators through which IC exerts its influence have been introduced, such as knowledge management capabilities (Hsu & Sabherwal, 2011), management accounting practices (Taylers, Pike, & Sofian, 2007), organisational learning (Mahmood et al., 2017), etc. Innovation has also been proposed as a mediator that can translate the effects of IC into performance. Innovation The current literature recognises innovation as a multi-aspect concept, as Crossan and Apaydin (2010) claimed “Innovation is a broad term with multiple meanings; it draws on theories from a variety of disciplines and has been studied using a wide range of research methodologies”. However diverse the concept, most of existing research was conducted on the notion that “innovation implies conceiving and implementing something new” (Sáenz & Aramburu, 2010). The classification of Innovation to a certain extent is summarized in the work of BuenecheaElberdin (2017). Basically, the author classified innovation based on how it is measured, by the results of innovation, the process of innovation, the degree of radicality, the innovative character, 10 or by innovativeness. Innovation concept in this study will be measured as the results of innovation (new products and processes). The effects of IC on Innovation are somewhat backed by preliminary emperical findings. Carmona-Lavado, Cuevas-Rodríguez, and Cabello-Medina (2013) acquired a positive impact of human capital on service innovativeness in software and computer program industry. Agostini and Nosella (2017) found that OC and RC totally mediated the effects of HC on radical innovation. Notably, Subramaniam and Youndt (2005) discovered a link between social capital and firms’ innovative capabilities. In addition, institutionalised knowledge (organizational capital) is found to directly and positively impact incremental innovations. Based on the theoretical foundation of the Resource-Based view and preliminary empirical findings, we hyphothesize that: Hypothesis 2: Intellectual capital positively affects Innovation Innovation has always been recognized for its importance, and thus, enterprises have strived to develop innovation in various aspects such as organisational, production, or marketing to increase competitiveness. Due to its significance, innovation has been considered as a mediator that can explain the relationship between IC and business performance. The theoretical foundation of Innovation’s mediating role stems from the centre of IC literature which sees knowledge as an asset “when it can be deployed to do something that could not be done if it remained scattered” (Stewart, 1997). In other words, knowledge is valuable only when contributing to value creation or providing competitive differentials (Edvinsson & Sullivan, 1996; Sveiby, 1997). As innovation is seen as results rather than processes in this study, it is the value created from knowledge. Also, the Resource-based view suggests that organizations’ internal features and resources have major impact on organizations’ capability to launch new products amd compete in disparate markets (J.Tvorik & McGivern, 1997). Hence, innovation is proposed to mediate the effects of IC on 11 business performance. In addition to a theoretical base, Innovation as a mediator has been previously tested. Empirical evidence has supported the effects of Innovation on business performance (McDermott & Prajogo, 2012; Greco, Grimaldi, & Cricelli, 2016) and the relationships amongst IC, Innovation, and performance (though much less frequently studied). Amongst a few is the study of Mathuramay (2012) where impact of IC on competitive advantages is mediated by innovative capability. Chahal and Bakshi (2015) acquired a nearly identical results in India banking sector as they found innovation fully mediates the effects of IC on competitive advantage. To test these relationships more deeply and in a different setting, we hypothesize that: Hypothesis 3: Innovation positively affects business performance, and in turn, mediates the effects of intellectual capital to business performance Business performance The fact that measurement of performance or business performance is diverse and multidsciplinary is proved by numerous approaches to and definitions of performance measurement system. A summary of these approaches and definitions can be found in the work of Tangen (2003) and Franco‐Santos et al. (2007). Due to this lack of concensus on a definition, to avoid complications, it is necessary to specify which business performance system is used in this study. While measurement systems initially contained only financial metrics, a more balanced and integrated approach had developed in the 1990s to adapt to the challenges of the fast changing environment, resulting in multi-dimensional models (Looy & Shafagatova, 2016). Amongst them, the Balanced Scorecard (BSC) developed by Kaplan and Norton (1996) is by far the most used performance measurement approach in business (Sullivan T. , 2001; Looy & Shafagatova, 2016). Considering the setting of the study is IT industry, BSC performance measurement system is a 12 good fit for the following reasons. First, it helps translate an organisation’s strategy into operational performance indicators and objectives for each of its four performance perspectives: (1) financial persepective, (2) customer perspective, (3) internal process perspective, and (4) learning and growth perspective. Second, BSC gives a more balanced view by capturing both leading (internal process, learning and growth) and lagging (customer satisfaction, financial metrics) performance measures. The leading measures are particularly important for IT companies because fast-changing and continuous evolving are features of the industry, which requires a quick and flowing internal process as well as constant learning and growth to adapt with environment changes. And last, the study considers Innovation as new products/services and the success of a new product or service is reflected in customer retention (customer perspective) and an increase in sales or market shares (financial perspective) (Pelham, 1997). When customers are pleased with a product, they may become loyal to the brand, purchase more and generate more revenues for the company (Wang & Wei, 2005). Accordingly, companies are dedicated to innovation to meet the needs of the market and their customers. Research method Data collection and participants Data was obtained from IT companies in various countries such as Taiwan, Vietnam, India, etc. An online self-administered questionnaire was used to collect the data. A total of 221 questionnaire responses were achieved. 35 responses had to be eliminated for various reasons. The sample finally included 186 responses from employees working in IT industry. As for the general features of the respondents, male respondents account for 72.6% of total, female respondents account for 27.4%; 65.1% of respondents are married, 34.4% are single, 0.5% are on other marital status. 13 Measurement Well-established measurement scales were used for the research. The four-item scale for Human capital (IC_H) was adapted from Youndt, Subramaniam, and Snell (2004) and represents the knowledge, skills, and abilities employees possess that bring economic value to firms. The fouritem scale for Organizational capital (IC_O) was adapted from Ling (2013) and refers to the institutionalized knowledge and codified experience embedded in organizational systems and processes. The four-item scale for Relational capital (IC_R) was also adapted from Ling (2013) and regards the knowledge embedded within a network of relationships with external stakeholders, such as customers or suppliers. Innovation consists of two constructs which are Product innovation (I_Pd) and Process innovation (I_Pc). I_Pd contains five items, while I_Pc contains four items. Both constructs were adopted from Prajogo and Sohal (2006). Product innovation is concerned with the creation of something new that is reflected in changes in the end product or service, while Process innovation attends to changes in the way companies produce products or services through the diffusion or adoption of an innovation developed elsewhere (Tidd, Bessant, & Keith, 2001). Business performance, the dependent variable, was measured using the famous balancedscorecard approach. Four sub-dimensions were integrated, namely, Internal process performance (BP_I), Learning and growth performance (BP_L), Customer performance (BP_C), and Financial performance (BP_F). The four-item scale for BP_I, the four-item scale for BP_C, and the twoitem scale for BP_F were all adapted from Gonzalez-Padron, Chabowski, Hult, and Ketchen Jr (2010). The two-item scale for BP_L focuses on the human aspect only and was taken from Hou’s (2016) study. 14 In total, 33 items (questions) were included in the questionnaire. Respondents were asked to rate the extent to which they agree or disagree with statements in the questionnaire. All items use a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). No reverse question was admitted. Statistical procedure The Partial Least Squares (PLS) was employed as data analysis method. PLS is a statistical technique for examining path models that involve latent variables (Fornell & Cha, 1994). Its objective is to maximize the explanatory power of a conceptual model. PLS is selected because of its ability to predict and explain the target constructs. The path loadings represent causal links from one construct to the other (Bontis, 1998). These paths can be interpreted as the standardized beta coefficients in regressions. Methodology consists of a confirmatory factor analysis and an estimation of structural equation model, using SmartPLS 3.0 package. First, the model’s measurement part (constructs and their indicators) was examined; then structural relationships in the model were assessed. Mediating effect of Innovation was tested through steps suggested by Nitzl, Roldan, and Cepeda (2016). Results The structural model is described graphically in Figure 1; circles represent latent variables. The results are divided into two sections: (1) an assessment of the measurement model, and (2) the estimation of the structural model (hypotheses testing). For descriptive statistics, correlations amongst constructs are shown in Table 3. Correlation helps to detect the presence of multicollinearity amongst explanatory variables, which occurs when correlations exceed 0.8 (Kennedy, 1985). As shown in Table 3, all correlations are smaller than 15 0.8 (the lowest value is 0.445), except for the correlation between I_Pd and I_Pc (0.818), which indicates a slight multicollinearity between the two constructs. Before examining the relationships amongst variables, it is crucial to validate the measurement scales so that the data is gathered by the most accurate method possible. A CFA was conducted to assess the measurement’s reliability and validity. To ensure the quality of CFA or to evaluate how suitable the data is for structure detection, Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) test and Bartlett's test of sphericity test were conducted. KMO is a statistical procedure which evaluates the quality of the correlations between variables in order to continue with factor analysis. Kaiser and Rice (1974) proposed the threshold of .50 which means any value under .50 is unacceptable. Table 1 shows the KMO values for every construct; all the values are acceptable. However, the BP_L construct and BP_F construct have the KMO of .50, which indicates that factor analysis is likely to be ineffective. Bartlett’s test of sphericity is a statistical test for the presence of correlations among variables, providing the statistical probability that the correlation matrix has significant correlations amongst at least some of variables. Small values (p < .005) of Bartlett’s test of sphericity indicate appropriateness to conduct factor analysis. All the constructs have the adequate p-values of Bartlett's test of sphericity as shown in Table 1. 16 Table 1. KMO and Bartlett's test of sphericity statistics Construct N of Items Bartlett's test of Variance sphericity Explained KMO IC_H 4 .843 422.890*** 75.83% IC_O 4 .805 317.166*** 68.69% IC_R 4 .751 354.143*** 69.03% I_Pd 5 .799 621.022*** 70.54% I_Pc 4 .826 672.097*** 83.78% BP_I 4 .807 335.187*** 69.26% BP_L 2 .500 252.813*** 93.23% BP_C 4 .813 407.182*** 74.28% BP_F 2 .500 232.657*** 92.38% Note. *p < .1, **p < .05, ***p < .001. IC_H = Human capital; IC_O = Organizational capital; IC_R = Relational capital; I_Pd = Product innovation; I_Pc = Process innovation; BP_I = Internal process performance, BP_L = Learning & growth performance; BP_C = Customer performance; BP_F = Financial performance Assessment of the measurement model After KMO and Bartlett's test of sphericity were conducted, a CFA was performed to evaluate the measurement model. 17 Construct reliability Evaluating reliability means evaluating the within-scale consistency (internal consistency) of the responses to the items of the measure. A reliable measurement will produce consistent results over time and across situations. There are two popular methods to assess internal consistency: Cronbach’s alpha and Composite Reliability. Cronbach’s alpha assumes that factor loadings are the same for every item of a factor. In contrast, Composite Reliability takes into consideration the differences of factor loadings. Thus, the more factor loadings vary amongst items, the higher the discrepancy between the values of Cronbach’s alpha and Composite Reliability. The acceptable value of reliability is .7 or greater (Nunnally & Bernstein, 1978). Cronbach’s alpha and Composite Reliability values for every constructs are reported in Table 2. Cronbach’s alpha ranges from .84 to .93, Composite Reliability ranges from .89 to .96, which exceeds the cutoff value of .7, thus, ensuring the reliability of the measurements used. 18 Table 2. Reliability Statistics Cronbach's Composite Average Variance Alpha Reliability Extracted IC_H .89 .92 .75 4 IC_O .84 .89 .68 4 IC_R .84 .89 .68 4 I_Pd .89 .92 .70 5 I_Pc .93 .95 .83 4 BP_I .84 .89 .69 4 BP_L .92 .96 .93 2 BP_C .88 .92 .74 4 BP_F .91 .96 .92 2 Construct N of Items Construct validity Construct validity determines whether the items (questions) used to measure a given construct actually measure that construct. Campbell and Fiske (1959) suggested two aspects to assess the construct validity: (1) Convergent validity: is the extent to which the items correlate with each other within their parent construct (factor). (2) Discriminant validity: is the extent to which the items do not correlate with other items of a different construct. 19 To establish convergent validity, three criteria should be considered: (1) Composite Reliability (CR), (2) Average Variance Extracted (AVE), and (3) factor loadings of items. CR value should exceed .7, AVE value should be more than .5, and factor loadings should be higher than .7 (Hair Jr, Hult, Ringle, & Sarstedt, 2016). Table 2 shows CR and AVE values for all constructs. All values exceed the cut-off criteria, which is adequate for convergent validity. Discriminant validity can be assessed by using cross-loading of items or Fornell & Larcker criterion. By looking at the cross-loading, factor loadings of items on the given construct have to be higher than all loadings on other constructs. They also need to satisfy the .7 cut-off value. All of the items in the study have high factor loadings on its parent construct and lower cross-loadings on other constructs. The items also exceed the value of .7 except for item BP_I3 with loading of .687. However, the value is very close to .7 and the deletion of the item wouldn’t significantly contribute to an increase in CR or AVE, thus, the item was retained. Another criterion for evaluating discriminant validity is Fornell & Larcker criterion. This method compares the square root of AVE with the correlations of latent constructs. A given latent construct should explain better the variance of its own indicators than the variance of other latent constructs’ indicators. Thus, AVE value of a construct should be higher than the corresponding correlations with other variables. However, for variance-based SEM (e.g., PLS), square-root value of AVE is advised to use for comparison (Hair Jr et al., 2016) because variance-based SEM tends to overestimate indicator loadings (Hui & Wold, 1982). Table 3 presents the square-root value of AVE of each construct (in the diagonal) and the correlations between latent constructs (offdiagonal). 20 Table 3. The square-root value of AVE values (in bold) and inter-correlations between constructs AVE OC HC RC I_Pd I_Pc BP_I BP_L BP_C OC .75 0.829 HC .68 0.590 0.871 RC .68 0.678 0.492 0.829 I_Pd .70 0.564 0.613 0.594 0.840 I_Pc .83 0.651 0.663 0.650 0.818 0.915 BP_I .69 0.568 0.557 0.602 0.773 0.772 0.832 BP_L .93 0.678 0.555 0.666 0.566 0.665 0.661 0.966 BP_C .74 0.533 0.471 0.685 0.637 0.625 0.713 0.542 0.861 BP_F .92 0.499 0.372 0.478 0.490 0.489 0.572 0.445 0.760 BP_F 0.961 As the results show, the square-root of AVE value of each construct (in the diagonal) is higher than its correlations with other constructs (in the corresponding rows and columns), thus, establishing discriminant validity. Hypotheses testing The estimated parameters for the hypotheses are shown in Table 4 and Figure 1. In Figure 1, each path way represents the hypothesis associated with it as well as the estimated path coefficient and the t-value in parentheses. T-values of path coefficients greater than 1.96 are significant at p < 0.05 (indicating moderate relations), greater than 2.58 are significant at p < 0.01 (indicating strong relations). The structural model explains 69.5% of the variance in Business performance (R2 = 21 0.695) and 58.4% in Innovation (R2 = 0.584). All path coefficients (β-path) are substantial and significant at p < .001 level, thus, supporting the relationships between variables. Table 4. Hypotheses testing results β-path (direct Path Hypothesis t-value Direction effect) IC  BP H1 .421*** 6.905 + IC  Innovation H2 .764*** 23.442 + Innovation  BP H3 .466*** 7.698 + Note. *p < 0.05, **p < 0.01, ***p < 0.001 22 Innovation (R2 = .584) - Product .948*** (93.946) - Process .955*** (129.172) .466*** (7.698) .764*** (23.442) H3 H2 Intellectual Capital - Human Capital .806*** (22.349) - Organizational capital .890*** (46.525) - Relational capital .856*** (35.541) .421*** (6.905) H1 Business performance (R2 = .695) - Internal performance .882*** (47.273) - Learning performance .798*** (24.250) - Customer performance .884*** (37.569) - Financial performance .792*** (27.301) Figure 1. Research model According to the results, Intellectual capital was found to positively influence Innovation (β = .764, p < .001, Hypothesis 2 supported) and directly affect Business performance (β = .421, p < .001, Hypothesis 1 supported). The indirect effect of Intellectual capital on Business performance is .356, while the total effect is .777. Innovation was also found to positively impact Business performance (β = .466, p < .001). Thus, all of the research hypotheses (H1, H2, H3) are supported (all path coefficients are statistically significant, t > 2.58). To test mediating effect of Innovation as hypothesized, the steps suggested by Nitzl et al. (2016) were followed. 23 Step 1. Establishing an indirect effect First, indirect effects of IC on BP is established (0.356, as reported by SmartPLS 3.0). The value is equal to IC’s direct effects on Innovation (0.764) multiplies by Innovation’s direct effects on BP (0.466): 0.356 = 0.764 x 0.466 Step 2. Testing the indirect effect’s significance level After indirect effects of the independent variable on the dependent variable are constituted, the second step is testing the significance level of the effects. Results from SmartPLS 3.0 show that the indirect effects (.356) were statistically significant at p < .001, thus, confirming the existence of Innovation’s mediating effect. A mediating effect always exists when the indirect effect is significant (Nitzl et al., 2016). Step 3. Determining the type of mediation The last step involves defining the type of mediation. There are two types of mediation: full mediation and partial mediation. Partial mediation includes complementary and competitive partial mediation. Full mediation occurs when the direct effect c’ is not significant while the indirect effect a x b is significant, which means only the indirect effect via the mediator exists (Nitzl et al., 2016). In other words, full mediation happens when the effect of X to Y is completely transmitted by the mediator M. In contrast, partial mediation takes place when both the direct effect c’ and the indirect effect a x b are significant. Under partial mediation category, complementary partial mediation happens when the direct effect c’ and the indirect effect a x b point in the same direction (positive or negative). On the contrary, competitive partial mediation is present when c’ and a x b point in different directions. 24 In this study, the mediating effect via Innovation is complementary partial mediation because both the direct effect of IC to BP (β = .421, p < .001), and the indirect effect via Innovation (β = .356, p < .001) are significant, and point in the same direction (both are positive, β-path > 0). To provide more information on the mediating effect, the portion of mediation is also calculated using the ratio of the indirect-to-total effect, known as VAF (the variance accountant for) (Nitzl et al., 2016). If the indirect effect is significant but does not mediate much the total effect, VAF would be low. If VAF is less than 20 percent, there is nearly no mediation; if VAF is between 20 and 80 percent, a typical partial mediation is present (Hair Jr et al., 2016); and a VAF above 80 percent indicates a full mediation. Take note that VAF value can be greater than 1 if the total effect is smaller than the indirect effect; this is the case of a suppressor effect. VAF should be used with caution and researchers are advised to read the work of Nitzl et al. (2016) if intending to mention the ratio. In this case, the indirect effect of IC on BP is 0.356, the total effect is 0.777, accordingly: 0.356 VAF = 0.777 = 0.4581 The VAF is 0.4581, which means 45.81% effect of IC on BP was mediated by Innovation. A typical partial mediation is present. Discussion The purpose of this study is to examine the effects of IC on innovation and business performance of IT companies. There is strong support for the hypothesized relationships and mediating effects of innovation. The main finding of this study are that IC positively impacts both innovation and business performance, and the impact of IC on business performance is partially transferred through Innovation. When IC increases 1 Standard Deviation (SD), Innovation increases .764 SD. When innovation goes up by 1 SD, business performance is enhanced by .466 SD. IC comprises 25 human capital, organizational capital, and relational capital. Accordingly, when employees’ skills, knowledge, and experience are improved, companies better codify, store, and stream their knowledge assets as well as extract those assets from their relationship network with external stakeholders, innovation within the company increases, leading to positive effects on business performance. Companies need to know how knowledge can be turned into values. The mediating role of innovation emphasizes this aspect. The finding indicates that a substantial portion of knowledge (45.81%, as found in the study) is utilized to generate innovation in product or process, which helps to boost the firm’s business performance. Contributions of this study are expected to be timely since we are living in a time when “there were more unique inventions … over the past year than ever before in the history of humankind” (Reuters, 2015). Unlike other industries, IT’s innovations advance particularly fast. The rate of innovations in IT industry proves innovations’ overriding importance. Thomson Reuters' 2015 State of Innovation report looks at patent-filing as a measure of innovation in various industries. Of more than 1.2 million patents filed across 12 industries in 2014, 30% were in the IT industry, far greater than any other industry. The top innovator in IT industry in 2014 was Samsung, with IBM — the only US company in the global top five. These statistics once again confirm the importance of innovations to the IT industry and the relevance of the study. Although these results are specific to IT industry, they are consistent with previous research conducted in other industries (e.g. Huang & Liu, 2005; Mathuramay, 2012; Mention & Bontis, 2013) and extend the literature on intellectual capital. 26 Implications This study provides useful implications for both academic research and practice communities. For academic research. Our work contributes to the literature supporting the notion that knowledge is important asset that directly affects innovation and firms’ business performance. Although there is a good deal of research on the relationship between IC and business performance, our contribution lies in the finding about the mediating role that Innovation plays in that relationship. As the results show, innovation mediates a large amount of effect between the two variables. Although there may be other mediators in the picture, innovation’s significance is supported by our empirical evidence. Our study is also one of the very few studies conducted in the IT industry context which is still under-investigated. For practice. Our results suggest a starting point for companies to improve their business performance by focusing on building their human capital, organizational capital, and relational capital. Specific strategies can be designed for each aspect. For human capital, companies can set up programs for building employees’ knowledge and skills, encouraging self-learning, selfimprovement, and funding individuals that wish to achieve higher education. To develop organizational capital, a good system for storing and streaming knowledge is of importance. The technology element should be cared for and managers can advocate for an environment that facilitates the flow of knowledge. As for relational capital, companies can train their employees to have a deep knowledge of customers’ demands, nurture high-quality relationships with customers and suppliers, implement practices that aid open communication and collaboration amongst external stakeholders whose knowledge may help to increase the potential of innovation. Also, as innovation plays the “middle man” role between IC and business performance, companies are 27 advised to focus their effort on how to better convert knowledge into innovation to strengthen the positive effects on performance. Managers in IT companies should be aware of the need for IT talents and be more proactive in searching as well as nurturing them. As the study points out the significance of Innovation, higher budget for R&D and tech is expected. Managers need to watch out for new inventions in the market that might disrupt their industry. Some practical steps to take include (1) forming a technology advisory team, (2) reading journals, newspapers that are not only about IT but every other industry, (3) frequently visiting technology centers and university research labs around the world, (4) identifying experts in different technology areas, (5) holding internal innovation challenges or conferences to share new ideas, (6) going where the change is by implementing new technologies such as AI, 3D printing, cloud-computing, etc. There are many more suggestions for managers in IT industry yet these proposed here mainly revolve around the innovation aspect and are hoped to be somewhat helpful. Limitations and Recommendations for future research Despite the careful conducting process, the study has several limitations that might be addressed by future research. First, we encountered some measurement issues for the financial dimension and learning and growth dimension of the business performance variable. There is a need for more research regarding the measurement of this variable. Second, due to the focus on the explanatory power of variables, the nature of the study, therefore, could not provide strong support for the goodness-of-fit of the model. Third, the sample size is relatively small (N = 186), which may restrict the generalizability of the results. Despite these limitations, that all hypothesized relationships are substantial and statistically significant backs the theoretical model. Some other strengths of the study that lend more credibility to the findings are the use of structural equation 28 modeling, the consistent results for the quality of the measurement scales under various tests, and the details on direct, indirect, and mediating effects. Further examination of business performance with different measures or with new and larger samples may provide deeper understanding of the hypothesized relationships. 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