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COMPLEMENTARITY EFFECTS OF R&D AND
INFORMATION TECHNOLOGY ON FIRM MARKET
VALUE
INDRANIL BARDHAN SCHOOL OF MANAGEMENT, SM 41
THE UNIVERSITY OF TEXAS AT DALLAS RICHARDSON, TX 75083-0688, USA E-MAIL: [email protected]
VISH V. KRISHNAN RADY SCHOOL OF MANAGEMENT
UNIVERSITY OF CALIFORNIA, SAN DIEGO LA JOLLA, CA 92093, USA
E-MAIL: [email protected]
SHU LIN CRAIG SCHOOL OF BUSINESS
CALIFORNIA STATE UNIV, FRESNO FRESNO, CA 93740, USA
E-MAIL: [email protected]
DECEMBER 7, 2010
2
ABSTRACT
We empirically study the joint impact of R&D and IT investments on firm market value. The
mixed evidence in the prior literature on the impact of R&D and IT on firm performance brings
to mind new questions of whether R&D or IT alone are sufficient determinants of firm market
value. As IT plays an increasingly critical role in the execution of innovation-intensive
investments, it is important to understand the extent to which IT has transformed the execution
and delivery of R&D. We argue that complementarities between R&D and IT spending will
enable future growth options and allow innovation processes to become more effective. We
estimate the interaction impact of IT and R&D investments on Tobin’s q, a forward-looking
measure of firms’ market-to-book value. We test our empirical model on a recent multi-year,
firm-level, archival dataset that spans multiple industries. Our results suggest that the interaction
effect of R&D and IT on Tobin’s q is positive and statistically significant after controlling for
other firm- and industry-specific effects. Our study addresses a critical gap in the innovation
literature which has ignored the possibility of interaction effects between R&D and IT and their
impact on firm market value. Our results provide empirical support for recent anecdotal evidence
with respect to manner in which IT is revolutionizing R&D-intensive innovation processes.
Keywords: R&D investments, IT investments, firm performance, interaction, Tobin’s q.
1. INTRODUCTION
A large number of academic studies have focused on the relationship between research
and development (R&D) spending, productivity growth, and firm-level performance (Morbey,
1988; Cohen and Klepper, 1996; Cohen, Levin and Mowery, 1987; Ettlie 1998). While recent
studies suggest that R&D spending has a significantly positive impact on firm productivity, with
a rate of return that is somewhat larger that the rate of return on traditional investments (Lev and
Sougiannis, 1996), others have reported that R&D investments do not have a statistically
significant impact on financial measures such as stock returns (Chan et al. 2001).
The mixed evidence of the impact of R&D on firm performance brings to mind new
questions: “Is R&D alone a sufficient determinant to explain firm performance? Could other
factors moderate the impact of R&D on firms’ market value?” Recent anecdotal evidence
suggests that information technology (IT) plays an important role in enabling and enhancing the
productivity of R&D processes (Marwaha et al. 2007). Mittal and Nault (2009, p. 140) observe
that “… value from information technology (IT) arises not only directly through changes in the
factor input mix but also indirectly through IT-enabled augmentation of non-IT inputs and
changes in the underlying production technology….” Their empirical analyses of industry data
show that the indirect effects of IT predominate in IT-intensive industries while the direct effects
of IT dominate in non-IT intensive industries.
Although recently researchers have argued that IT is changing the nature of innovation,
there is a dearth of empirical evidence on how IT investments interact with R&D investments to
enhance overall firm performance and market value. In this paper, we focus on the role of IT in
moderating the impact of R&D investments on firm market value. We test our research
hypotheses on a unique and recent data set of firm-level, IT spending over an eight-year period
4
that allows us to trace the impact of IT investments on the relationship between R&D and firm
market value. Our study is the first attempt at examining the joint impact of R&D and IT on firm
value using recently available data from the Internet era that allows us to study the impact of
newer information technologies on the innovation-intensive processes.
Our results support our hypothesis that IT spending moderates the impact of R&D
spending on Tobin’s q, a ratio that measures the market-to-book value of a firm. Our results are
robust and consistent across a number of model specifications and econometric estimation
methods. While the focus of prior research has been on studying the direct effects of R&D and
IT investments, our results show that their joint (or interaction) effects are positive and
significant across firms from many industries spanning a long time period. Our results suggest
that the overall effects of R&D and IT cannot be simply captured by measuring only their
individual direct effects, but rather by accounting for their interaction effect on firm valuation.
The results indicate that Tobin’s q, a forward-looking measure of the stock market’s perception
of a firm’s future growth potential, is significantly influenced by the effectiveness of IT-enabled
R&D investments.
2. RELATED LITERATURE
There exists a large body of literature on the impact of R&D on productivity and firm
market value. However, much of this literature does not consider the role of IT investments
(Griliches and Mairesse, 1984; Englander, Evenson, and Hanazaki, 1988; Chauvin and Hirschey,
1993; Griliches, 1994; Hall and Mairesse, 1995). We review the related literature on the impact
of R&D and IT on firm performance as a prelude to developing a theory-based model which we
test in the next section.
2.1 IMPACT OF R&D AND INFORMATION TECHNOLOGY ON FIRM MARKET VALUE
A majority of the extant research on IT and firm performance has focused on relating the
impact of IT spending to accounting measures of firm performance. Most of these studies have
focused on productivity measures such as firm output (Brynjolfsson and Hitt, 1996; Hitt and
Brynjolfsson, 1996; Barua et al. 1991), while more recent studies have focused on firm
profitability measures. While early studies on the impact of IT on firm productivity were
equivocal (Rai et al. 1997; Carr 2004), recent studies have shown that IT plays a significant role
in improving firm and industry productivity growth (Brynjolfsson and Hitt, 1995; Breshnahan e
al. 2002). Recent studies have also demonstrated the impact of IT on firm profitability using
firm-level data collected during the last decade (Aral and Weill, 2007; Mithas et al. 2010).
However, as noted by Foray, Hall and Mairesse (2007) and Bharadwaj (1999), there are
several limitations in linking the impact of R&D and IT investments on accounting-based
performance measures. First, much like R&D, the true contribution of IT investments must be
considered based on their impact on long-run firm performance and risk avoidance. Due to the
learning curve and long implementation times associated with major IT projects, investments in
IT systems are likely to have a lagged impact on firm business value which is more likely to be
reflected in future cash flows. Second, many IT investments are made to manage or avoid
operational or regulatory/compliance risks. Risk avoidance remains an important objective of
many IT investments as borne by a spate of IT controls that firms implemented in the post-
Sarbanes-Oxley era to comply with new regulations related to COSO (REF). A recent study by
Arkali, Bardhan and Krishnan (2009) reported that IT investments are associated with lower firm
risk as measured by the volatility of stock market returns and earnings. For example, in 2003,
Wal-Mart initiated implementation of radio frequency identification (RFID) technologies within
6
its supply chain which allowed it to track inventory at any given time (Mendelson, 2007). The
RFID initiative resulted in reduced stockouts and lower losses (from theft and perishable items)
as products move across the supply chain, and provides an example of a firm leveraging its IT
investments to manage supply chain disruption risks, which may not be reflected in greater
profitability in the short run. Similarly, the impact of IT on other factors such as firm flexibility,
agility, and growth potential, may not be fully represented in accounting measures (such as
profitability) in studies linking the impact of IT investments to firm performance. On the other
hand, measures such as Tobin’s q reflect the ex ante market valuation of the level and risk of
future firm cash flows (Ben-Horim and Callen, 1989; Smirlock et al. 1986).
Second, R&D and IT investments provide firms with significant growth options that are
typically not accounted for in present returns. While past studies have primarily focused on the
cost-reduction and transactional automation benefits associated with IT investments, more recent
studies have explored the revenue growth potential associated with IT investments, especially in
the Internet era (Bardhan et al. 2004; Sambamurthy et al. 2003). New types of web-based
systems are expected to have far greater transformational potential compared to their predecessor
systems (Aral and Weill, 2007; McAfee and Brynjolfsson, 2008; Mithas et al. 2010). In this
respect, both R&D and IT investments are associated with significant intangible value in the
form of future growth options that can be enabled by these investments (Brynjolfsson, Yang and
Hitt, 1999). Just as R&D expenditures have been treated as an important determinant of a firm’s
intangible assets, in terms of creating superior knowledge, innovation, and technological
capabilities, researchers have increasingly started to pay more attention to the soft benefits
associated with IT investments such as improvements in quality, customer service, and strategic
flexibility associated with IT infrastructure (Bharadwaj et al. 1999; refs). A financial measure
such a Tobin’s q which measures the value of a firm based on its future earnings relative to
current book value is a better indicator of the growth options associated with IT spending. We
argue that the association between IT investments and firm intangible value, with respect to its
enablement of technology-enabled growth options, supports our rationale for using Tobin’s q as
a measure of firm performance in this study.
In their seminal work on the impact of IT assets on firm performance, Bharadwaj et al.
(1999) show that IT expenditures accounted for a significant portion of the variance in Tobin’s q
based on their analyses of firm-level data from 1988 to 1993. Their results suggest that the IT is
positively associated with Tobin’s q, even after controlling for firm-specific factors such as R&D
and advertising expenditures, and firm size, and industry-specific factors such as industry
structure, capital intensity, and regulation. Their work provides a foundation for our research on
the interaction effects of R&D and IT, in the context of a significant shift in the nature of IT-
enabled innovation in the Internet era.
2.2 COMPLEMENTARITY EFFECTS OF IT AND R&D
A review of the prior literature shows that the impact of R&D and IT on firm market
value has been treated separately in a vast majority of the studies. To the best of our knowledge,
none of these studies have explored the complementary effect of IT and R&D investments on
firm market value. We believe that, while the initial focus on measuring the direct impact of IT
in the initial stages of industry-wide computerization in the 1990s was useful, it is even more
important to develop a more nuanced understanding of the impact of IT investments by virtue of
their complementarities with discretionary investments in other processes. R&D represents one
such business process which entails making complementary investments in information
technology.
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We seek to understand specific mechanisms through which IT can impact firm value
through improvements in the effectiveness of innovation or R&D processes. The knowledge-
based view of the firm suggests that innovation processes are critical to generate new knowledge
in the execution of R&D projects (Kogut and Zander, 1992; Nonaka and Takeuchi, 1995). In
order to leverage tacit and explicit knowledge that resides within and outside firms’ boundaries,
firms must build extensive capabilities in identifying and processing the information that resides
within the workplace and can frequently involve external partners (Sakakibara, 2001; Cohen and
Levinthal, 1990). Research on organizational learning suggests that this knowledge can be
captured through IT-enabled routines which allow firms to leverage into the knowledge-base of
its partner network (Nicholls-Nixon and Woo, 2003). IT can help firms build “high bandwidth”
channels with their partners and customers to sense tacit and emerging customer/supplier
information as a feedback loop into their R&D processes. For example, until recently, P&G used
physical mockups of products on shelves when they engaged consumer focus groups or retailers
in the development of new products. With the advent of new virtual reality and simulation tools,
P&G now leverages its technology centers to provide three-dimensional views of the storefront
which allows customers to provide immediate feedback on product placement and packaging
decisions. Implementation of these IT solutions has reduced the time to create a product mockup
from six weeks to a few days, and is now used in almost 80% of P&G’s R&D initiatives (Bloch
and Lempres, 2008).
This example shows how IT complements investment in R&D processes and improves
the execution of R&D projects. An alternate pathway of measuring the impact of IT is to focus
on its role as an enabler of knowledge-generating R&D processes. New types of IT, such as
product lifecycle management (PLM) software, enable product design teams to collaborate
across inter-organizational boundaries, gather and share design requirements, conduct design
iterations, verify and test product designs, and facilitate final design hand-offs to other
departments (Adler, 1995; McGrath and Iansiti, 1998). Such web-based tools provide an
information rich medium that supports collaboration by facilitating synchronous communication
within and across R&D teams (Bardhan 2007). These tools also provide efficient data storage,
electronic retrieval and reuse of product designs, and allow R&D teams to compress the overall
product development time by reducing latency. Improvements in design quality arise from the
ability to share design ideas between R&D team members electronically and conduct real-time
version control, which enables engineers to track design defects and implement design changes
more efficiently (Banker, Bardhan and Asdemir, 2006).
A recent case study by Mendelson (2007) describes how technology developments are
fueling the life sciences industry through an information revolution with significant information
processing, data analysis, and storage requirements. New types of information technologies can
increase the efficiency and effectiveness of R&D in the life sciences industry in many ways.
While IT can accelerate drug discovery and development through productivity improvements, it
can also significantly improve value chain optimization by enabling new development processes
and organizational changes. Mendelson (2007, page 20) suggests that effective use of
information for decision making is an important driver of value chain optimization since
increasing the success rate of drug discovery and testing can have substantial benefits. For
example, by integrating information on the toxicity of potential leads while a drug target is being
evaluated, an effective decision support system can screen potential targets out of consideration
which would have otherwise progressed only to be rejected in a later testing phase. By enabling
information exchange across different parts of the drug development value chain, and managing
10
the coordination of workflow, IT supports better decision-making capabilities across the entire
value chain rather than within individual silos.
High-performance computing infrastructure can also support high-throughput screening
wherein lead-target drug pairs can be analyzed simultaneously, thereby compressing the
biological and chemical phases of the drug discovery value chain into one parallel phase. This
example provides one instance of the use of IT to support and improve the effectiveness and
efficiency of R&D processes within the pharmaceutical industry which can reduce overall
development times and lead to higher success rates in drug discovery and development. Our
observations on the effect of IT on innovation are based on a field study that we conducted with
a bio-pharmaceutical firm to motivate our hypotheses regarding the role of IT in improving the
effectiveness and productivity of R&D investments (see Appendix).
A recent study by McKinsey reports that pharmaceutical companies, which use IT in
clinical trials processes increased their overall productivity by improving the speed, quality and
costs associated with these processes (Marwaha et al. 2007). Estimated savings from IT-driven
initiatives that improve the overall efficiency of clinical trials is estimated to be in the range of
$50 million to $100 million. Table 1 provides a useful perspective on the role of IT in four areas
of R&D related to drug discovery and development during clinical trials: improving resource
allocation for integrated, enterprise-wide planning of clinical trials; better data management
through electronic case report forms; enabling easier access to researchers though electronic data
capture tools by providing standardized interfaces; and providing greater visibility across the
clinical trials process by eliminating bottlenecks. Advances in development of high-throughput
screening and simulation software and development of unified IT systems have greatly improved
the efficacy of the drug discovery and development process (Mendelson, 2007).
These examples provide anecdotal evidence on how IT can be used to enable R&D
processes and improve the execution of R&D projects in several industries. In the next section,
we draw upon existing theory to develop research hypotheses related to the complementary
effects of R&D and It and their impact on firm market value.
3. RESEARCH HYPOTHESES
Prior work by economists has indicated that firms that invest more in their own R&D are
better able to exploit externally-generated knowledge compared to firms with lower R&D
expenditures (Mowery 1983, Evenson and Kislev, 1973). Cohen and Levinthal (1989) observe
that firms invest in R&D for two reasons: to generate new knowledge and develop “absorptive
capacity” which represents the ability to recognize, assimilate and exploit knowledge embedded
within a firm’s business processes and routines. While the economics literature has primarily
focused on the role of innovation in the theory of economic productivity growth and social
welfare, the finance literature has studied investor perceptions to R&D announcements and
expenditures (Chan et al. 2001; Hirschey and Weygandt 1985). Similarly, the accounting
literature is sparse and has primarily focused on investors’ cognizance of the capital aspects of
R&D with the notable exception of Lev and Sougiannis (1996) who studied the effect of R&D
capital on firm earnings and stock returns for the 1975-1991 period. In this study, we argue that
investments in R&D will be associated with a higher valuation on growth options that are created
through improvements in the product portfolio which allow firms to command greater market
value. These future growth options can be jointly enabled by prudent investments in R&D and IT
which provide the innovation capabilities necessary for sustained, competitive advantage.
Prior research on the impact of IT spending on firm performance has provided empirical
evidence on the relationship between IT investments and improvements in firm labor
12
productivity, total factor productivity, and output growth, using longitudinal data collected in the
late eighties through the mid-1990s (Barua et al. 1995; Brynjolfsson and Hitt 1996; Bresnahan et
al. 2002). More recent studies have focused on the pathways through which IT investments can
create value, using the resource-based view of the firm as the theoretical framework to study the
impact of IT-enabled capabilities on firm performance (Kohli and Devaraj, 2003; Banker et al.
2006; McAfee and Brynjolfsson 2008). A recent study by Aral and Weill (2007) has provided
empirical evidence of the link between strategic and infrastructure IT investments and various
measures of firm performance, such as net margin, new product innovation and Tobin’s q.
Mithas et al. (2010) show that IT investments are positively associated with firm profitability
through their impact on sales growth and cost reduction initiatives. However, these studies have
focused on the direct impact of IT investments without considering the potential
complementarities between IT and other discretionary investments such as R&D, and their joint
effect on firm performance.
An alternate pathway to measure the impact of IT is to focus on its role as a
complementary resource for innovation-centric R&D processes. In other words, can IT make
R&D investments more productive? An important issue in improving R&D productivity is the
capability to facilitate seamless communication among virtual product design teams (Loch and
Terwiesch 1998). New types of information technologies, such as product lifecycle management
(PLM) software and collaboration tools, provide an information rich medium that supports
collaboration by facilitating synchronous communication within and across R&D teams
(Bardhan 2007). Improvements in design quality arise from the ability to share design ideas
between team members electronically and conduct real-time version control, which enables
engineers to track design defects and implement design changes more efficiently. These
information technologies play a critical role in enhancing the productivity of R&D processes by
reducing the overall time to market and product development cost (Banker et al. 2006).
The ability to exploit and disseminate knowledge is an important component of
innovation. Cohen and Levinthal (1990, p. 128) coined the term “absorptive capacity” as “… the
ability to recognize the value of new information, assimilate it, and apply it to commercial ends
…” Their research suggest that absorptive capacity may be created as a results of a firm’s R&D
investments which allows the firm to assimilate and use new knowledge. However, absorptive
capacity refers not only to the acquisition and assimilation of new knowledge by a firm but also
to the firm’s ability to exploit it. Hence, an organization’s absorptive capacity depends not only
on its interface with the external environment (customers, partners, suppliers, etc.) but also on
the transfer of knowledge across and within organizational silos that may be quite removed from
the original locus of innovation. Cohen and Levinthal (1990) note that in order to understand the
sources of a firm’s absorptive capacity, it is necessary to focus on the structure of
communication between the external environment and the firm, as well as among sub-units of
the firm and the character and distribution of expertise within the firm.
We argue that IT plays a central role in improving a firm’s absorptive capacity for
innovation activities by providing a platform to exchange and disseminate information between
and across R&D teams that may be distributed across different functions of the organization. IT
mitigates the negative effects of distance by providing the richness of a communication medium
for project team members to collaborate with each other (Massey and Montoya-Weiss, 2006). In
the twenty-first century, global product development is characterized by distributed teams in
multiple countries that are supported by an IT-enabled digital infrastructure that supports a
collaborative R&D process (Banker et al. 2006). To mitigate the negative effects of distance and
14
time on team communication, companies have increasingly turned to IT tools. Based on their
study on the effect of computer-mediated communication (CMC) technologies across a cross-
section of 277 high-tech firms in the US, Song et al. (2007) reported that the CMC technologies
were associated with greater knowledge dissemination among R&D staff of product
development teams. IT investments help R&D managers improve the effectiveness of their
innovation processes by providing greater capabilities to harness the knowledge embedded
across firm boundaries and enable easier (and faster) access to critical product data that help to
reduce time to market and overall product development costs.
In a recent article, Brynjolfsson and Schrage (2009) describe how IT is revolutionizing
the way that innovation gets done. As a catalyst for complementary changes, Brynjolfsson
(2010) argues that “… IT is setting off a revolution in innovation on four dimensions
simultaneously: measurement, experimentation, sharing, and replication ….” By enabling all
four of these changes together, companies are using IT to create a new kind of R&D. For
instance, Amazon.com uses IT-based experimentation to conduct “A/B experiments” tests of its
web pages that deliver different versions of the same page at the same time to different visitors,
monitor customer experience, and follow through. Google runs about 200 to 300 experiments on
any given day to evaluate customer reaction to different types of online experiments which
allows them to conduct quasi-natural experiments on customer behavior which informs their
R&D processes. With respect to information sharing, Brynjolfsson (2010) highlights Cisco
where IT was used to develop a Wiki portal that provided their Macintosh users with innovative
ways to install new software and get their Macs to work with the company’s Linux printers.
IT also makes it easier to replicate and scale up innovations once they have been
identified (Brynjolfsson and Schrage, 2010). For example, CVS Caremark Corporation identified
a novel way to implement online prescription ordering at one of its pharmacies that resulted in a
significant jump in customer satisfaction. CVS was able to use IT to quickly scale up and
replicate this business process innovation by embedding it in an enterprise technology system
and replicated it to 4000 other pharmacies within one year. In this manner, IT can foster a culture
of experimentation and innovation where several ideas can be tested simultaneously to study
real-time changes in customer behavior. The knowledge gleaned from these experiments can be
used to design new innovations that can be scaled and replicated quickly across multiple
locations and functions through digital technologies.
In light of the complementary effects of IT on R&D innovations, we argue that IT
moderates the effectiveness of R&D by providing the technology infrastructure that allows better
coordination among innovation processes. In other words, IT enables digital capabilities that
have a positive interaction effect on the relationship between R&D and firm market value.
Hence, our sole research hypothesis can be framed as follows:
The interaction of R&D and IT investments has a positive impact on firm market value.
Our conceptual research model and hypotheses are described in Figure 1.
4. ECONOMETRIC MODELS
We estimate the impact of firm- and industry-specific factors on firm market value using
a series of hierarchical regression models. First, we estimate the effects of the firm-specific and
industry-level control variables using a baseline model which is specified in equation (1).
titititi
tititititi
INDCONCMKTSHARETOBINQSIZESALESCAPEXADVTTOBINQ
,,7,61,5
,4,3,2,10,
εααα
ααααα
+++
+++++=
− (1)
16
Firm-specific control variables include advertising intensity (ADVT), capital
expenditures (CAPEX), annual sales in dollars (SALES), and firm size measured as the
logarithm of total number of employees (SIZE). Industry-specific control variables include the
market share (MKTSHARE) and industry concentration (INDCONC) as measured by the
Herfindahl index. We also include the lagged value of the firm’s Tobin’s q ratio in the previous
year (TOBINQt-1) to control for the effect of prior year’s Tobin’s q on our estimation in year t.
Note that the model in (1) represents a baseline of the variance in firms’ Tobin’s q that is
explained by a combination of firm- and industry-specific control variables.
Next, we introduce firm-level R&D (RD) and IT intensity which represents R&D and IT
spending (in dollars) as a percentage of firm assets, respectively. Here, we estimate the impact of
firm R&D and IT spending on Tobin’s q, in addition to the effect of the control variables
estimated specified in equation (1). This model represents the main effects of firm-level R&D
and IT intensity and is specified in equation (2) as follows:
tititititi
titititititi
INDCONCMKTSHARETOBINQSIZESALESCAPEXADVTITRDTOBINQ
,,9,81,7,6
,5,4,3,2,10,
εββββββββββ++++
++++++=
− (2)
Next, we estimate the interaction effects of the R&D and IT on firm Tobin’s q. With the
addition of the cross-product of the R&D and IT variables to the main effects model, the
interaction model is specified in equation (3) as follows.
tititititi
titititititititi
INDCONCMKTSHARETOBINQSIZESALESCAPEXADVTxITRDITRDTOBINQ
,,10,91,8,7
,6,5,4,,3,2,10,
εδδδδδδδδδδδ
+++++
++++++=
− (3)
In multi-period panel data analyses, it is well known that the standard errors of ordinary
least squares (OLS) estimation can be biased when the residuals are not independent, resulting in
either over- or under-estimation of the true variability of the coefficient estimates. The possible
dependence among the residuals may result from within-firm autocorrelation in the errors (time-
series dependence) or from within-year, across-firm correlation in the errors (cross-sectional
dependence). To account for time-series as well as cross-sectional dependence of the residuals,
we compute standard errors that are robust to two-way clustering along both dimensions of year
and firm as described in Cameron, Gelbach and Miller (2006) and Petersen (2009). Following
Cameron, Gelbach and Miller (2006), we separately estimate three OLS variance matrices each
with one-way clustering by firm, year, and the intersection between firm and year, respectively.
We then compute the variance matrix which is robust to two-way clustering of firm and year by
adding the first two variance matrices and subtracting the third one. The cluster-robust standard
errors of the regression are the square root of diagonal elements of the resulting matrix.
Following Cameron, Gelbach and Miller (2006), the cluster-robust variance matrix of
regression coefficient estimators is computed as
(4)
where each of the three components is estimated separately using the SAS statistical software.
The cluster-robust standard errors of the regression are the square root of diagonal elements of
this matrix. The first component is the OLS variance matrix estimate computed by clustering on
firm, where is an N x N indicator matrix with ijth entry equal to one if the ith and jth
observation belong to the same firm, and equal to zero otherwise. The second component is the
OLS variance matrix estimate computed by clustering on year where is an N x N indicator
matrix with ijth entry equal to one if the ith and jth observation belong to the same year, and equal
18
to zero otherwise. The third component is the OLS variance matrix estimate computed by
clustering on the intersection between firm and year where is an N x N indicator matrix
with ijth entry equal to one if the ith and jth observation belong to the same firm and year and
equal to zero otherwise. This two-way clustering procedure provides unbiased estimates of the
standard errors of our regression models that account for firm- and time-specific effects
(Peterson, 2009) in our econometric estimation.
We note that Tobin’s q measures the market value of a firm based on its stock price at the
end of each of the years in our eight-year sample period, while decisions regarding discretionary
investments such as R&D and IT spending are typically made when annual budgets are
determined at the beginning of the year. It is unlikely that such investments may be co-
determined with firms’ market valuation leading to the problem of reverse causality between our
independent and dependent variables. In other words, since IT and R&D investment decisions
are made at the beginning of the year (or in the prior year in some cases) while Tobin’s q
measures the firm market value at the end of the year, the potential for endogeneity is minimal.
Furthermore, by incorporating a lagged value of the dependent variable (i.e. TOBINQt-1) as an
independent variable in our model specification in equations (1) through (3), we explicitly
account for the possibility of reverse causality. That is, what if firms with greater market value
are more likely to make greater levels of R&D and IT investments which may lead to higher
Tobin’s q in the current year? Our specification of a lagged dependent variable controls for the
impact of TOBINQt-1 on TOBINQt , and our coefficients of R&D, IT and their interaction effect
are estimated after accounting for this possibility.1
1 We also used an instrumental variable approach to estimate our models using two-stage least squares (2SLS) regressions following the approach models specified by Lev and Sougiannis (1996). Our coefficient estimates are similar to the results reported in this paper and suggest that endogeneity is not of significant concern.
In addition to the two-way robust clustering approach described earlier in this section, we
also deployed a random effects estimation model with autoregressive lag structure (i.e. AR(1)) to
estimate the main and interaction effects of R&D and IT on Tobin’s q. Random effects models
utilizie variation not only within each firm through time but also the variation between firms, and
provide more efficient parameter estimates while accounting for unobserved time-invariant
industry or firm-level factors if they are uncorrelated with explanatory variables in the model.
We estimate our models in equations (1) thru (3) using random effect estimation techniques
which views firm-specific constant terms as randomly distributed across cross-sectional units as
in our panel data (Baltagi 2001; Greene 2000, pp. 469-470). We estimated the random effects
models with AR(1) errors using the AR(1) XTREG procedure in the STATA statistical software.
We performed diagnostic checks to ensure the stability of our results and did not detect any
significant problems (Belsley, Kuh and Welsch 1980). We also checked for multi-collinearity in
our models and ascertained that variance inflation factors were within the acceptable threshold.
In addition to estimating the interaction effect of RD x IT on Tobin’s q, using the two-
way clustered error regression and random effects with AR(1) estimation models described
earlier, we also estimate a first-difference model which incorporates the absolute values of the
dependent and independent variables at time t, as well as the change in these values between year
t and t-1. In other words, we estimate the impact of year-over-year change in our independent
variables, including R&D, IT and their interaction effect, on change in firm Tobin’s q. We posit
that the moderating effect of IT on change in R&D spending between years t-1 and t (i.e. ΔRDi,t
x ΔITi,t) will be associated with a significant impact on change in firm Tobin’s q (i.e.
Δ(TOBINQ)i,t).
20
(5) )_(
,,12
,11,10,9,8,7
,6,,5,4,3,2,10,
titi
tititititi
titititititititi
INDCONCMKTSHARETOBINQINDSIZESALESCAPEX
ADVTITxRDITITRDRDTOBINQ
εγγγγγγ
γγγγγγγ
++
+Δ++++
+ΔΔ+Δ++Δ++=Δ
(6) )_(
,,131,12
,11,10,9,8,7
,6,,5,4,3,2,10,
tititi
tititititi
titititititititi
INDCONCTOBINQMKTSHARETOBINQINDSIZESALESCAPEX
ADVTITxRDITITRDRDTOBINQ
εγγγγγγγ
γγγγγγγ
+++
+Δ++++
+ΔΔ+Δ++Δ++=Δ
−
Model (5) provides a simplified approach for estimating the year-over-year change in
Tobin’s q due to a corresponding change in R&D and IT intensities and the moderating effect of
IT on change in R&D spending (Hausman, Hall and Griliches, 1984). The coefficient γ5 provides
an estimate of the interaction effect of change in R&D and IT on change in TobinQt between
years t-1 and t. This approach explicitly accounts for bias due to missing variables in our
estimation models by modeling the effect of differences in the values of independent and
dependent variables over a multi-year time period (Forman, Ghose and Goldfarb, 2009). Our
estimation model in equation (6) is very similar to equation (5) with the only difference being
that we explicitly control for the effect of the lagged value of Tobin’s q in the previous year (i.e.
TOBINQt-1). This allows us to estimate the impact of the change in TOBINQ due to the
interaction effects of corresponding changes in R&D and IT, and after controlling for the impact
of the Tobin’s q in the prior year.
5. RESEARCH DATA
We use data from three sources in this study. First, we obtained multi-year, archival data
on firm-level IT spending from an international research firm that is well-known for its IT data
and research services. This proprietary database was obtained under a non-disclosure agreement
that protects the confidentiality of the data. The data was collected through an annual survey that
is administered to chief information officers (CIOs) and other senior IT executives of large,
global firms with the goal of collecting objective metrics on IT investments. The research firm
collects archival IT investment data, along with other IT investment-related information, as part
of its annual, worldwide IT benchmarking survey. IT investments include all hardware,
software, personnel, training, disaster recovery, facilities, and any other costs associated with
supporting the IT environment, including the data center, desktop/WAN/LAN server, voice and
data network, help desk, application development and maintenance, finance, and administration.
In this study, we restrict our locus of interest to the subset of firms for which firm-level IT and
R&D spending data are available for an eight-year span from 1997 to 2004 period.
Data on R&D investments as well as firm- and industry-specific financial and accounting
metrics were constructed from the Standard & Poor’s COMPUSTAT database. We note that the
advertising data maintained in Compustat was limited because several firms do not report their
advertising expenditures for a few years in our 1998-2004 panel. Hence, we supplemented this
data using data obtained from the TNS Media Intelligence database which collects firm-level
advertising data for the period from 2002 onwards. This database is widely used by researchers
in marketing since it tracks spending on new media including internet advertising expenditures.
Our panel data set consists of 208 firms for which we had firm-level data on our main
variables of interest, namely R&D, IT, and advertising expenditures, as well as firm assets. All
other variables including Tobin’s q, market share, and industry concentration were constructed
from available data in Compustat. Our initial sample contained 4,356 firm-year observations
from 567 firms collected in the years 1997 through 2004. Next, we remove: (1) 2,591
observations with missing IT spending in year t, (2) 926 observations with missing R&D
22
spending in year t, (3) 7 observations with missing data for total assets in either year t or t-1, (4)
50 observations with missing data for market value used in computing Tobin’s q in either year t
or t-1. The resulting sample contains 962 observations from 258 firms. We require a minimum
of ten observations per year for each two-digit NAICS industry. This constraint ensures that we
have enough observations per industry to compute industry-adjusted R&D and IT spending
measures. The size of our sample is reduced to 703 observations from 210 firms. Finally, we
remove industries in which all firms report zero R&D spending for year t. Hence, the final
sample consists of 693 observations from 208 firms spanning 8 years as shown in Table 2.
The firms in our data set span multiple industries which include NAICS codes 32, 33, 42,
44, and 51. Five industry categories represent a high proportion of the firms in our sample:
Chemical manufacturing, Metal and metal products manufacturing, Machinery manufacturing,
Computer & electronic products manufacturing, and Transportation equipment manufacturing.
Table 3 provides our sample distribution by industry.
Definitions of all model variables are provided in Table 4. We note that Tobin’s q, the
dependent variable of interest, is measured as “total firm liabilities plus total market value of
common equity, divided by total book value of assets of the firm” (Hall, 1999; Bharadwaj et al.
1999). We standardize R&D and IT intensity by two-digit NAICS by subtracting the industry
mean from firm-level R&D (and IT) spending and dividing by the industry standard deviation,
respectively. While we report both unstandardized and standardized values of R&D and IT
spending as a percent of firm sales in our descriptive statistics, we use only the standardized
values in our econometric analysis in the next section. Definitions of other independent and
control variables of interest, such as advertising, capital expenditures, sales, firm size, market
share, and industry concentration, are also provided in Table 4, and are consistent with their
measurement in the accounting and economics literature.
We present descriptive statistics on our model variables in Panel A of Table 5. The mean
and median values of Tobin’s q are equal to 2.35 and 1.67, respectively. Similarly, mean R&D
and IT spending intensities are equal to 4.8% and 2.6% as a % of firm sales. By definition, the
mean, industry-adjusted values of R&D and IT intensities are equal to zero. Similarly, mean
advertising expenditures are approximately 1.2% of firm sales, while capital expenditures
account for 5.4% of total firm assets and mean annual revenues are about 1.06 times the value of
total firm assets. Next, we present the Spearman/Pearson correlation matrix in Panel B of Table
5. The correlation coefficients between the independent variables are generally below 0.40 and
do not indicate the presence of multi-collinearity in our estimation models.
6. RESULTS
The estimation results of our baseline model in equation (1) are shown in the column
titled “Model 1” of Table 6. The results suggest that the lagged value of Tobin’s q in the
previous year is the only determinant of Tobin’s q in the current year. Firm-specific advertising
and capital expenditures and industry structure variables such as market share and industry
concentration are not significant determinants of Tobin’s q. Our estimation results of equation
(2), where we include our key variables of interest, are presented in the column titled “Model 2.”
We observe that R&D has a significant, positive association with Tobin’s q with a coefficient
value of 0.20 (p < 0.05). This result suggests that every 1% increase in firm R&D spending
above its industry mean is associated with a 0.2% increase in Tobin’s q. Similarly, we observe
that IT spending has a positive association with Tobin’s q with a coefficient value of 0.078 (p <
24
0.10). This result suggests that every 1% increase in firm IT spending above its standardized
industry mean is associated with a 0.078% increase in Tobin’s q.
The last column of Table 6 provides the estimation results of the interaction effects model
where we include the interaction term, “RD x IT”, in equation (3). The results indicate that is
R&D is not only significant at p < 0.01, but its interaction effect with IT is also significant with
an estimated coefficient value of 0.109 (p < 0.05). In other words, a 1% increase in the
interaction effect results in a corresponding 0.109% increase in firm Tobin’s q. These results
support our hypothesis with respect to the positive effect of the interaction of R&D and IT on
firm market value. We also observe that the increase in explained variance of the interaction
effects model is statistically significant and greater than the main effects model (F-statistic =
7.47; p < 0.01). We note that while the main effect of IT spending in Model 3 is positive, it is
not statistically significant at p-value < 0.10. All significant coefficients are also economically
significant based on the magnitude of their estimates.
Next we estimate the same models in (1) – (3) using random effects estimation
techniques with AR(1) standard errors. The results of the random effects estimation are shown in
Table 7 and are very consistent with our earlier results in Table 6. We find that the main effect of
R&D and the interaction effect of R&D x IT are both positive and strongly significant at p <
0.01. We also note that ADVT has a positive association with firm Tobin’s q in the main and
interaction effect models. Our panel data estimation using two types of estimation methods
confirm that the interaction effect of R&D and IT intensity has a significant and positive impact
on firm Tobin’s q.
Next, we estimate the first-difference models specified in equations (5) and (6) using
random effects estimation and present these results in Table 8. The estimated coefficients
represent the impact of a unit change in the difference between the values of independent
variables on the change in Tobin’s q between the years t-1 and t. The first column provides the
estimated coefficients of the first-difference model without controlling for the effect of Tobin’s q
in the prior year, while we explicitly control for TOBINQt-1 in the second column. Our results
suggest that the interaction effect of Δ(R&D) x Δ(IT) is positive and significant in both models
with a p-value < 0.01. When we control for the firm’s Tobin’s q in the prior year, we find that
the impact of a one unit change in the interaction effect of Δ(R&D) and Δ(IT) represents a
change of 0.204% in Tobin’s q between the years t-1 and t. Hence, our first-difference estimation
results support our earlier results (using the actual levels of IT and R&D intensity) with respect
to the moderating effect of on the relationship between R&D and Tobin’s q.
Next, in order to obtain a numerical sense of the impact of R&D and IT on Tobin’s q, we
conduct a simple, univariate analysis to estimate the differences in Tobin’s q across groups of
firms with different R&D and IT spending profiles. We present the results of our univariate tests
in Table 9. We classify firms as high and low spenders based on their R&D and IT investment
profiles, relative to their two-digit NAICS industry median value. Accordingly, Panel A of Table
9 compares the Tobin’s q for two groups of firms: “High R&D, high IT Spenders” versus “Low
R&D, high IT spenders.” We observe that the difference in Tobin’s q (z-value = 5.96) is
statistically significant at a p-value < 0.001. On average, our results indicate that, high R&D and
IT spenders outperform the latter group in terms of their market-to-book ratios. Similarly, in
Panel B, we compare the differences between two groups of firms: “High R&D, Low IT” versus
“Low R&D, Low IT” spenders. Again, the difference in Tobin’s q across these two groups of
firms is statistically significant with the former outperforming the latter. Panels A and B are
particularly interesting since they compare two groups that are characterized by differences in
26
their levels of R&D spending while holding IT spending constant. In both cases, we see that
higher R&D spending is associated with greater Tobin’s q.
Next, we contrast the differences in performance across firms in Panels C and D. The
primary difference between the two groups in panel C is due to Δ(IT) since both groups are high
R&D spenders. We observe that the difference in Tobin’s q (z-value = 4.79) is statistically
significant at p-value < 0.001. Similarly, the difference between firms in panel D is due to Δ(IT)
since both groups exhibit low R&D spending. Again, the difference in Tobin’s q (z-value = 5.90)
is statistically significant at p-value < 0.001. Panel E compares firms that are high spenders of
R&D and IT against firms that are low spenders. As expected the former outperforms the latter
in terms of Tobin’s q. Finally, in Panel F, we compare firms that are high R&D, low IT spenders
against firms that are low R&D, high IT spenders. Here, we observe that the differences are not
statistically significant since the effect of R&D and IT spending is mixed across groups.
Our univariate results support our observations from the regression estimation in terms of
the interaction effect of R&D and IT spending on firm Tobin’s q. We note that changes in R&D
spending while holding IT constant, and vice versa, are significantly associated with greater
Tobin’s q.
6.1 DISCUSSION
We now discuss the several implications of our research. First, R&D spending has a
significant positive impact on firm Tobin’s q which indicates its association with the market
expectation for future growth. This result is consistent with prior finance and accounting research
which shows that R&D spending is valued at a premium by stock market analysts in terms of
their contribution to firm market value (Lev and Sougiannis, 1996; Hall et al. 2007). Second, our
results suggest that IT spending has a marginal, positive impact on Tobin’s q when we only
estimate the main effects of R&D and IT. However, our results also suggest that after accounting
for interaction effects, the positive impact of IT spending on Tobin’s q disappears. Third, our
analyses suggests that IT moderates the impact of R&D spending on Tobin’s q, and this
moderation effect is positive and statistically significant as observed in our relatively large
sample of firms from a multitude of industries and a time period that spans the dot-com boom
and bust cycle, i.e. pre- and post-Y2K. While prior research has focused solely on the individual
effects of R&D and IT, our results reveal a new dimension by measuring the interaction between
these two types of complementary investments. Our results support our hypothesis with respect
to the positive, complementary effects of R&D and IT on Tobin’s q, and provide empirical
evidence to refute recent observations in the practitioner literature that question the value of
R&D investments (Kandybihn and Kihn, 2004).
Our results have several implications for practice and research. Our results imply that IT
and R&D investments must be well coordinated and are most effective for firms that adopt a lean
approach. The findings of our study imply that the IT can play a critical role in enhancing firms’
market value by improving the effectiveness and efficiency of R&D processes through their
effect as a lever to reduce coordination costs, increasing transparency, and make better resource
allocation decisions. From a managerial perspective, an important implication of our study is to
focus on the role of IT in improving innovation capabilities in R&D organizations.
Opportunities to use IT in all phases of the product innovation lifecycle should be explored. For
instance, IT systems should be deployed to identify and sense emerging customer needs which
will feed the product ideation phase. IT can be used to improve project governance through
creation of appropriate project management software and disciplined stage-gate processes that
28
streamline project workflows. From a research dimension, our study provides a fresh
perspective into the drivers of firm financial performance and provides a new causal path that
explains how IT can moderate the impact of R&D on firm market value. It addresses a gap in
the literature which has heretofore ignored the possibility of interaction effects when studying
the relative impact of IT and R&D on firm performance.
7. CONCLUSIONS
One pathway through which the interaction of R&D and IT spending can impact firm
market value is through its impact on firm innovation capabilities. We argue that investments in
R&D and IT provide firms with greater absorptive capacity to generate future growth through
improvements in their product development portfolio. Such future growth options allow firms to
command greater market value. Considering that innovation has been a fundamental source of
technological change and productivity growth during the last two decades, there is no doubt that
R&D is a key driver of such productivity improvements in the economy. In this study, we focus
on the role of IT-enabled innovation and the importance of IT investments in moderating the
impact of R&D spending on firm Tobin’s q. Considering that quantum strides in computing
have been made during the IT revolution since the early 1990s, it is important to (a) understand
the specific role of IT in improving the innovation capabilities of R&D processes, and (b)
measure the joint impact of R&D and IT investments on firm market value. While the literature
has focused on the impact of R&D or IT spending separately, our study focuses on the critical
question: “Do complementarities between IT and R&D spending increase firm market value?”
We propose and empirically test our research model using a relatively recent data set that
reflects the significant technological changes in knowledge-based industries since the first wave
of Internet-based, commercial technologies in the late 1990s. Our study shows that R&D has a
significant, positive impact on firm market value as measured using Tobin’s q. Our results
indicate that IT spending moderates the impact of R&D spending on Tobin’s q. While R&D
investments are considered risky, smart IT investments may lower the volatility of these risks by
providing greater transparency into execution of R&D projects, and improving the coordination
across multiple projects. Future research will focus on evaluating whether these effects are
stronger among firms in IT-intensive industries compared to non-IT intensive industries.
Our study has several limitations. First, due to the secondary nature of the data, we do
not have insight into the specific types of IT investments that firms in our sample have made
during the time horizon in our study. Future research should focus on the types of IT
investments made including the allocation between various components of IT spending such as
software applications, consulting, and infrastructure investments. Second, since product
development times in some industries are long, it is possible that the time period in our study did
not allow us to observe the full effects of R&D and IT spending. Third, we use data on large,
global firms. This limits generalization of our findings to similar firms, and further exploration
with data from smaller firms is needed.
30
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Table 1. The Role of Information Technology in Drug Discovery and Development
Design,
Planning Start -Up Managing Trial Closeout Report
Key activities benefiting from IT
• Designing protocols • Creating regulatory documents
• Distributing drugs, information
• Data collection, Selecting site
• Monitoring progress, adverse events •Tracking patient enrollment •Tracking clinical response forms
• Entering, verifying data
• Processing clinical- resource forms
• Reconciling investigator’s queries
• Reporting Outcomes
IT Focus Clinical Data Management
• Database of investigators to support design of electronic forms
• Standard interface to integrate third party systems
• Greater computing power allows scientists to design randomized trials
•Automated data checks to minimize queries
Patient Safety
Document Management
Clinical Trials Management
• Modular design, construction of consent and case report forms
• System to convert study designs to electronic forms and database with minimal rework
• Electronic invoicing • Automated drugsupply work flow• Patient mgmt.
• More effective trials managed thru large-scale databases and simulation software
• Report builder
Project Management
• Standard data models to work with vendors
Source: Marwaha, Patil & Singh, 2007.
Real-time monitoring
Single data repository with version control, workflow management
34
Figure 1. Conceptual Research Model
Note: All model variables have been industry-adjusted by subtracting the industry mean and dividing by the industry standard deviation.
Table 2. Sample Selection Methodology Firm-Year
Observations Firms
Firm-year observations obtained from the original sample covering years from 1997 to 2004
4,536 567
After:
Removing those with missing IT spending data in year t 1,945 533
Removing those with missing R&D spending data in year t 1,019 274
Removing those with missing Total Assets in either year t or year t-1
1,012 273
Removing those with insufficient data for computing Tobin’s Q in year t or t-1
962 258
Requiring at least ten observations per year for each industry at two-digit sector level using NAICS codes
703 210
Removing industries in which all firms report zero R&D spending for year t
693 208
Table 3. Sample Distribution by Industry
3-digit NAICS Code Sector
No. of firms
No. of obs.
321 Wood Product Manufacturing 2 3322 Paper Manufacturing 10 31323 Printing and Related Support Activities 2 3324 Petroleum and Coal Products Manufacturing 5 23325 Chemical Manufacturing 31 155326 Plastics and Rubber Products Manufacturing 2 2327 Nonmetallic Mineral Product Manufacturing 3 3331 Primary Metal Manufacturing 7 23332 Fabricated Metal Product Manufacturing 6 39333 Machinery Manufacturing 19 63334 Computer and Electronic Product Manufacturing 44 155335 Electrical Equipment, Appliance, and Component
Manufacturing 10 24
336 Transportation Equipment Manufacturing 23 90337 Furniture and Related Product Manufacturing 4 7339 Miscellaneous Manufacturing 6 2942 Wholesale Trade 3 3423 Merchant Wholesalers, Durable Goods 3 3424 Merchant Wholesalers, Nondurable Goods 6 6442 Furniture and Home Furnishings Stores 2 2443 Electronics and Appliance Stores 1 1444 Building Material and Garden Equipment and Supplies
Dealers 1 1
445 Food and Beverage Stores 2 2446 Health and Personal Care Stores 4 4448 Clothing and Clothing Accessories Stores 1 1511 Publishing Industries (except Internet) 10 18518 Data Processing, Hosting, and Related Services 1 2 Total: 208 693
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Table 4. Variable Definitions TOBINQ Total firm liabilities plus total market value of common equity, divided by total
book value of assets of the firm.
R&D Firm-level R&D spending as a % of average total assets.
R&D (Industry-Adjusted)
Firm-level R&D spending as a % of average total assets (standardized at two-digit NAICS code level by subtracting industry mean and dividing by industry standard deviation).
IT Firm-level IT spending as a % of average total assets
IT (Industry-Adjusted)
Firm-level IT spending as a % of average total assets (standardized at two-digit NAICS code level by subtracting industry mean and dividing by industry standard deviation).
ADVT Advertising expenses divided by average total assets.
CAPEX Annual capital expenditure divided by average total assets.
SALES Annual sales revenue divided by average total assets.
SIZE Logarithm of total number of firm employees
ASSETS Logarithm of total assets.
TobinQ(t-1) Tobin’s q at the end of year t-1.
MKTSHARE Net sales divided by the total sales in that industry.
INDCONC Herfindahl-Hirschman Index equals the squared sum of the market shares of each firm in that industry.
Δ(TobinQ) Change in firm Tobin’s Q from year t-1 to year t.
Δ(R&D) Change in firm R&D spending scaled by total assets in year t-1.
Δ(IT) Change in firm IT spending scaled by total assets in year t-1.
Δ(Industry Q ratio) Change in industry mean Tobin’s q from year t-1 to year t.
Table 5. Spearman/Pearson Correlation Matrix and Descriptive Statistics of Model Variables Panel A: Spearman/Pearson Correlation Matrix TOBINQ R&D IT ADVT CAPEX SALES SIZE TOBINQt-1 MKTSHARE INDCONC
TOBINQ 0.514 0.225 0.218 0.198 0.080 0.038 0.848 -0.048 -0.163 R&D 0.401 0.189 0.064 0.083 -0.047 -0.192 0.514 -0.285 -0.308 IT 0.381 0.287 0.194 0.241 0.353 0.003 0.194 -0.061 -0.071 ADVT 0.242 0.191 0.216 0.115 0.077 0.094 0.206 0.198 0.230 CAPEX 0.229 0.122 0.238 0.081 0.274 0.151 0.225 -0.012 -0.093 SALES 0.164 -0.060 0.485 0.112 0.285 0.030 0.063 0.148 0.114 SIZE 0.040 -0.115 0.062 0.092 0.142 0.063 0.078 0.311 0.082 TOBINQt-1 0.836 0.421 0.344 0.235 0.302 0.187 0.083 -0.038 -0.171 MKTSHARE -0.036 -0.291 0.025 -0.026 0.083 0.243 0.368 0.002 0.781 INDCONC -0.247 -0.349 -0.021 0.004 -0.075 0.250 0.045 -0.237 0.728 p-value < 0.01; Italic - p-value < 0.05. Significant correlation values are shown in bold. Panel B: Descriptive Statistics of Model Variables (N = 693) TOBINQ R&D IT ADVT CAPEX SALES SIZE TOBINQt-1 MKTSHARE INDCONC Mean 2.354 0.048
(0.000) 0.026
(0.000) 0.012 0.054 1.066 3.057 2.405 0.275 0.239
Std. Dev. 1.917 0.050 (0.986)
0.028 (0.986)
0.028 0.037 0.550 1.310 1.958 0.270 0.242
Q1 1.318 0.015 (-0.671)
0.013 (-0.604)
0.000 0.030 0.740 2.437 1.326 0..065 0.039
Median 1.678 0.029 (-0.360)
0.021 (-0.151)
0.000 0.044 0.962 2.903 1.711 0.180 0.164
Q3 2.598 0.068 (0.405)
0.031 (0.412)
0.007 0.071 1.211 4.043 2.653 0.394 0.359
* Industry-adjusted numbers are reported in parentheses.
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Table 6. Two-way Clustered Regression Results of the Main and Interaction Effects on Tobin’s Q
Model 1 Model 2 Model 3 Predicted
Sign Coeff.
(t-stat.) Coeff.
(t-stat.) Coeff.
(t-stat.) Intercept
0.461 (2.57)
0.498 (2.83)
0.499 (2.86)
R&D + 0.200** (2.30)
0.191** (2.38)
IT + 0.078* (1.78)
0.066 (1.64)
R&D x IT + 0.109** (2.06)
Firm Specific Controls:
ADVT
3.873 (1.44)
3.173 (1.27)
3.553 (1.35)
CAPEX
-0.129 (-0.14)
-0.319 (-0.25)
-0.414 (-0.32)
SALES
0.096 (1.08)
0.066 (0.90)
0.072 (1.01)
SIZE
-0.048* (-1.95)
-0.019 (-0.78)
-0.026 (-1.08)
TOBINQt-1
0.813*** (7.93)
0.759*** (7.23)
0.758*** (7.21)
Industry Structure Controls:
MKTSHARE 0.097 (0.59)
0.211 (1.28)
0.214 (1.42)
INDCONC -0.355 (-1.63)
-0.246 (-1.20)
-0.263 (-1.34)
Adjusted R-Square 72.09% 72.89% 73.15% No. of firm-year obs. 693 693 693
The t-statistics are computed using cluster-robust standard errors with two-way clustering of firm- and year-specific effects. * p<0.1, ** p<0.05, *** p<0.01.
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Table 7. Random Effects with AR(1) Estimation Results of the Main and Interaction Effects
Model 1 Model 2 Model 3 Predicted
Sign Coeff.
(z-stat.) Coeff.
(z-stat.) Coeff.
(z-stat.) Intercept
0.707 (4.39)
0.732 (4.51)
0.718 (4.46)
R&D + 0.250*** (4.42)
0.239*** (4.24)
IT + 0.092* (1.85)
0.070 (1.40)
R&D x IT + 0.125*** (2.73)
Firm Specific Controls:
ADVT
5.817*** (3.04)
4.960*** (2.63)
5.371*** (2.73)
CAPEX
-0.335 (-0.25)
-0.513 (-0.39)
-0.621 (-0.48)
SALES
0.101 (1.17)
0.063 (0.71)
0.077 (0.87)
SIZE
-0.046 (-1.25)
-0.013 (-0.36)
-0.021 (-0.57)
TOBINQt-1
0.709*** (26.43)
0.652*** (22.34)
0.654*** (22.56)
Industry Structure Controls:
MKTSHARE 0.186 (0.58)
0.347 (1.11)
0.340 (1.09)
INDCONC -0.581* (-1.66)
-0.456 (-1.34)
-0.468 (-1.38)
R-Square 72.21% 72.98% 73.28% No. of firm-year obs. 693 693 693
* p<0.1, ** p<0.05, *** p<0.01.
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Table 8. First Difference Estimation Results using Random Effects Estimation: Effect of Changes in R&D and IT on Change in Tobin’s Q
Model 5 Coeff.
Model 6 Coeff.
(t-stat.) (t-stat.) Intercept 0.035
(0.20) 0.382 (2.16)
R&D -0.157*** (-3.02)
0.118* (1.95)
Δ(R&D) -0.041 (-0.84)
-0.004 (-0.10)
IT 0.074 (1.26)
0.045 (0.75)
Δ(IT) 0.064 (1.25)
0.092* (1.94)
Δ(R&D) x Δ(IT) 0.178*** (3.20)
0.204*** (3.89)
Firm Specific Controls: ADVT 1.554
(0.96) 3.938** (2.29)
CAPEX -1.112 (-0.83)
0.800 (0.58)
SALES 0.034 (0.28)
0.100 (0.81)
SIZE -0.021 (-0.56)
-0.006 (-0.16)
Δ(Industry TOBINQ) 0.478*** (7.23)
0.400*** (6.63)
TOBINQt-1
-0.249*** (-9.83)
MKTSHARE -0.242 (-0.94)
0.216 (0.79)
INDCONC 0.128 (0.44)
-0.306 (-1.00)
Adjusted R-Square 17.97% 29.00% No. of firm-year obs. 477 477
* p<0.1, ** p<0.05, *** p<0.01.
Dependent variable is Δ(Tobin Q). These results are qualitatively similar to a two-way clustered regression model (by year and firm) as deployed earlier..
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Table 9. Differences in Tobin’s q across Firms with Different R&D and IT Profiles.
Panel Median Tobin’s q
A High R&D, High IT (N=206) 2.56
Low R&D, High IT (N=141) 1.69
z-value 5.96
p-value <.0001
B High R&D, Low IT (N=140) 1.77
Low R&D, Low IT (N=206) 1.36
z-value 5.58
p-value <.0001
C High R&D, High IT (N=206) 2.56
High R&D, Low IT (N=140) 1.77
z-value 4.79
p-value <.0001
D Low R&D, High IT (N=141) 1.69
Low R&D, Low IT (N=206) 1.36
z-value 5.90
p-value <.0001
E High R&D, High IT (N=206) 2.56
Low R&D, Low IT (N=206) 1.36
z-value 11.44
p-value <.0001
F Low R&D, High IT (N=141) 1.69
High R&D, Low IT (N=140) 1.77
z-value 0.87 p-value 0.386
