Technological Innovation and the Market Value of Firms

Most previous research in this area has conceptualized innovation through one or more dimensions of a firm’s innovative capability such as R&D of a firm. The measurement of the construct has thus reflected this narrow conceptualization with a single measure such as R&D expenditure of a firm being the most often used proxy. This study utilizes a broader definition of organizational innovation capabilities that includes the generation, dissemination and strength of innovative activity in a firm. Such a composite measure is then used to predict market value of a firm. The unique features of this study are that it uses multiple indicators of firm innovation, it uses 10 year cross sectional, longitudinal data on a firm’s innovation profile along with lagged measures of market value. INTRODUCTION Knowledge and the innovative capability of firms driven by technology are considered key factors in internationalization and performance (Sher &Yang, 2005; Weikl & Grotz, 1999). Lately the “Knowledge Based View (KBV)” has emerged as a novel, if not radically new perspective to examine the nexus between innovation and firm performance (Gopalakrishnan et.al., 1999). Chetty & Campbell-Hunt, (2003) and Davenport, et.al., (2003) argued that technology driven strategy of firms help them leverage their strengths toward obtaining international significance. Other interpretations of the important role of innovation in determining a firm’s destiny include innovation as a barrier for entry and exit of competitors (Porter, 1983), as an instrument in increasing the market value of the firm (Toivanen et.al., 2002), as a strategic rent generating asset (Teece et.al., 1997) and as a tool for organizational change (Birkinshaw et.al. 2002). Innovation has been defined as an “internally generated or (externally) purchased device, system, policy, program, process, product or service that is new to the adopting organization” (Damanpour, 1991: p. 556). Thompson (1965) defined innovation as the generation of new ideas, processes and products or services. But according to Feeney and Rogers (2003) innovation is the application of new ideas to the products and processes of a firm’s activities and according to Amabile et al. (1996), it is the successful implementation of creative ideas within an organization. As can be seen there is fair amount of debate on what actually constitutes organizational innovation. We argue that a comprehensive definition of innovation should include all the above dimensions. The present study intends to address the above issues on a sample of 64 firms over a 9 year period by examining the relationship between a variety of innovation dimensions including generation, dissemination and strength of innovation that constitutes a firm’s “innovation capability profile” and, Market value of firms. The following hypotheses will be empirically verified. H1) A firm’s capacity for innovation generation is positively associated with its market value. H2) A firm’s capacity for innovation dissemination is positively related to its market value. H3) A firm’s strength of innovation is positively related to its market value. 2. DATA AND VARIABLES Data for the independent variables of Innovation capability profile were obtained from the patent database called “TECH–LINE® maintained by CHI Research. The use of patents and patent citations to measure innovation and knowledge of a firm has precedent in the studies by Jaffe (2000) and Fung & Chow (2003). Data for dependent variable market value were obtained from Research Insight® data base. Table 1 explains the variables and their operationalization. Table 1: Operationalization of Variables INNOVATION GENERATION Number of patent (PAT) Number of patents This identifies technologies receiving increasing emphasis and those in which innovation is slackening off. It also identifies companies increasing their technological development, and those whose R&D is played out. INNOVATION DISSEMINATION Current Impact Index (CII) The number of times a company’s previous five years of patents is cited in the current year, relative to all patents in the U.S. patent system. Indicates patent portfolio quality. A value of 1.0 Madan Annavarjula, Ramesh Mohan and Sam Beldona Communications of the IBIMA Volume 2, 2008 48 represents average citation frequency; a value of 2.0 represents twice average citation frequency; and 0.25 represents 25% of average citation frequency. In a Tech-Line company report, you can identify the technologies in which companies produce their best work. The CII has been found to be predictive of a company's stock market performance. INNOVATION STRENGTH Technology strength (TECH) Quality-weighted portfolio size, defined as the number of patents multiplied by current impact index. Using Technology Strength you may find that although one company has more patents, a second may be technologically more powerful because its patents are of better quality. EMPIRICAL MODEL For the panel data analysis, the data set consists of i = 1,.....,N cross sections (number of firms), and several points of time series for each firm t = 1,....,T(i), or a cross section of N time series each of length T(i). To break down the effect of R&D together with innovation generation, innovation dissemination and innovation strength, the following linear models is estimated: I) Ordinary Least Squares (OLS). FVit = λi + β’ Git + α’Dit + γ’Sit + φ’Xit + μit (1) where FVit is the firm value in firm i =1,......, N, year t = 1,....,T(i). Git is the vector of generation of innovation variables. Dit is the vector of dissemination of innovation variables. Sit is the vector of strength of innovation variables. Xit is the R&D expenditure. λi is the overall constant term, which is the same for all firms. μit is independently and identically distributed among firms and years. ii) Fixed Effects. FVit = η1 δ1it + η2 δ2it +..........+ β’ Git + α’Dit + γ’Sit + φ’Xit + μit (2) Where FVit is the firm value in firm i =1,......, N, year t = 1,....,T(i). Git is the vector of generation of innovation variables. Dit is the vector of dissemination of innovation variables. Sit is the vector of strength of innovation variables. Xit is the R&D expenditure. δjit is the firm specific year dummy variables. ηi is the individual specific constant or the firm effect. μit is a classical disturbance term with E[μit] = 0, var[μit] = σ 2 μ . EMPIRICAL RESULTS In Tables 2, 3 and 4 we present the model selection test (F-stat). In all the three cases, the F-statistics are significant indicating that fixed-effects model is preferred to pooled OLS. Thus, our discussion will be focusing on the fixed effects model. In Table 2, regression results of the R&D for 64 firms are reported. Parameter estimates of R&D were strongly positive and significant in the one year lag and three year lag. This confirms the expectation that R&D spending positively impact the firm value immediately and over time. To further investigate the impact on various components of R&D on the firm value, we regress various other independent variables. In the fixed effects model, the number of patent (PAT), a proxy for innovation generation, is positive but not significant in the one year lag. However, PAT is highly positive and significant in the three year lag. This indicates that patent applications impact the market value of firm with a time-lag. Table 2: Regression results of R&D Note: ***, **, * denote significance at 1%, 5%, and 10%,