Association for Information Systems
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PACIS 2014 Proceedings
Pacific Asia Conference on Information Systems
(PACIS)
2014
From IT Investment to Firm Market Value: The
Mediating Role of Stock Analysts’
Recommendation
Xueming Luo
Temple University, [email protected]
Bin Gu
Arizona State University, [email protected]
Cheng Zhang
Fudan University, [email protected]
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Recommended Citation
Luo, Xueming; Gu, Bin; and Zhang, Cheng, "From IT Investment to Firm Market Value: The Mediating Role of Stock Analysts’
Recommendation" (2014). PACIS 2014 Proceedings. 352.
http://aisel.aisnet.org/pacis2014/352
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From IT Investment to Firm Market Value: The Mediating Role of
Stock Analysts’ Recommendation
Xueming Luo, Fox School of Business, Temple University, USA, [email protected].
Bn Gu, School of Business, Arizona State University, USA, [email protected]
Cheng Zhang, School of Management, Fudan University, China, [email protected]
Abstract
This study provides a new perspective to the long-standing debate on the business value of IT. Advancing
prior research on the internal perspectives of IT value creation, we take an external view and investigate
the role of stock analysts in facilitating the market evaluation of firms’ IT investments. Stock analysts
collect information about firms’ IT investments and provide informed recommendations to investors in the
financial market. As IT investments are known for their complexity and inherent risks, stock analysts can
reduce information asymmetry between the firm and investors in the financial markets, thus helping
discovering the business value of IT investments. On the basis of Fortune 1000 firms between 1996 and
2007, our analysis suggests that stock analysts play an intricate mediating role in the stock market
evaluation of IT investments. Analyst recommendations have a strong mediating role in the effects of
enterprise IT systems (ERP and CRM) on firm market value, but a weak to insignificant mediating role in
the effects of function IT systems (DSS, HR and AIS). In addition, we find that the mediating role of
financial analysts for IT investments is more salient when the firm’s market environments are more
uncertain and unpredictable. These findings suggest that analyst recommendations play a critical role in
the stock market valuation of firms’ IT investments in situations where the value of such investments is
difficult to assess.
Keywords: IT investment, stock analyst, market value
1. INTRODUCTION
IT investment is an important element in the firm value creation process and has grown to be the largest
category of capital expenditures in businesses. Research has extensively investigated the effect of IT
investments on firm performance (see Kohli and Grover (2008); Melville et al.(2004) for a review of the
related literature). A key insight of prior research is the “general purpose” nature of IT (Bapna et al. 2013;
Brynjolfsson and Hitt 2000) . As such, the value of IT investments depends on how firms leverage the
technology and cannot be assessed without a full understanding of a firm’s business strategies and
organizational resources. Substantial research has been conducted to look inside the firm to identify the
strategies, processes and resources that help generate value from IT investments.
What is often neglected in this internal view is that IT and its complementary strategies and
processes within a firm are difficult to assess for external investors who determine firms’ market value.
Echoing this, Brynjolfsson et al. (2002) lamented: “many firms do not report the IT intangible assets on
their balance sheets, forcing investors to rely on other sources of information to value these assets.” This
lack of observability creates significant information asymmetry in the financial market. Without bridging
this information asymmetry, it is challenging for the financial market to assess the value of IT
investments.
Thus, there is a demand for information intermediaries to aid the external financial market to better
understand firms’ IT investments and their value. A key information intermediary that affords such
responsibility is stock analysts. Stock analysts are employed by stock brokerages, banks and financial
consulting firms to compile information from various sources, examine firm fundamentals, provide
informed recommendations and forecast firm future earnings for institutional and/or retail investors
(Jegadeesh et al. 2004; Kimbrough et al. 2009). The stock analysts’ role in interpreting firm behavior
suggests that they could play an important role in facilitating and certifying the stock market evaluation of
IT investments.
The goal of our study, therefore, is to investigate the following three questions: (1) Do analysts help
the stock market discover the business value of firm IT investments, i.e., mediate the effects of firms’ IT
investments on stock market value? (2) What types of IT systems rely more or less on the medicating role
of stock analysts? And (3) is this mediating role more or less salient for firms in uncertain market
environments?
We assess this question empirically using IT assets and firm performance data of Fortune 1000 firms
between 1996 and 2007. The IT and firm performance data are obtained from Harte-Hanks and
Compustat respectively. Our analyses show that, stock analysts’ recommendation has a significant
mediating effect on the stock market value of IT investments. The mediating effect varies by IT
investment type. Analyst recommendations have a stronger mediating effect for firms investing in
enterprise IT systems (ERP and CRM) than those investing in functional IT systems (DSS, HR and AIS).
In addition, we find that the mediating role of analyst recommendations for IT investments is more salient
when the firm’s market environments are more uncertain. Our analyses suggest that stock analysts
provide a critical intermediary function in helping financial markets discover the business value of IT
investments, especially for investments in sophisticated enterprise IT systems and for firms operating in
uncertain market environments.
2. LITERATURE REVIEWS
The study is related to several streams of literatures. The first stream is on the business value of IT. This
stream of literature has extensively analyzed the internal processes that relate a firm’s IT investments to
its market performance (Barua et al. 1995). For example, Devaraj and Kohli (2003) posit and prove that
the actual usage of the technology is a key variable to explain the impact of IT investment on
organizational performance. Zhu and Kraemer (Zhu and Kraemer 2005) further point out that IT usage
breadth and depth are critical internal mechanisms that link IT investments to firm performance.
Similarly, IT-related business processes and dynamic capabilities are documented as important
intermediary outcomes between IT investments and organizational performance (Melville et al. 2004).
Complementing this internal process-oriented view of IT investment, our study takes an external processoriented view of IT valuation. We propose that stock analysts play a critical role in facilitating external
investors to understand and assess the value of IT investments.
Prior literatures of business value of IT have also considered specific IT applications, including
enterprise resource planning (Hitt et al. 2002), customer relationship management (Hendricks et al. 2007),
knowledge management systems (Sabherwal and Sabherwal 2005), e-commerce (Dewan and Ren 2007),
supply chain management systems (Dehning et al. 2007), and other IT infrastructure applications
(Chatterjee et al. 2002). These studies reveal significant variations in the market value of these IT
applications. We contribute to this stream of literatures by offering a new explanation of the phenomenon.
We propose that the mediating effect of stock analysts varies with the complexity of IT applications. As a
result, the market valuation of enterprise IT applications relies on not only IT applications themselves but
also financial analysts’ recommendations. This dependency on external financial analyst amplifies
variations in the market valuation of enterprise IT systems.
Our study is also related to prior IS studies that use stock price-based measures to assess the business
value of IT investments (Dewan and Ren 2007). The underlying assumption of these measures is that the
stock market has complete information and is able to accurately assess the value of IT investments. Our
study extends the literature by pointing out that stock analysts play an important role in facilitating stock
market investors to understand the complexity and inherent risks and benefits of IT investments in the
valuation processes of IT investments. If a firm is not or barely covered by stock analysts, market-based
measures may not accurately reflect the business value of IT for these firms.
Finally, our study also adds to emerging studies that examine stock analysts’ information-giving
behaviors on market information environment and market valuation (Xu and Zhang 2012) and use their
recommendation as risk measures (Dewan and Ren 2007; Dewan and Ren 2011). We extend these studies
by incorporating stock analyst recommendation as a mediator between IT investment and firm stock
market value, i.e., highlighting the role of stock analysts in discovering the value of IT investments.
3. RESEARCH FRAMEWORK AND HYPOTHESIS DEVELOPMENT
In this section, we present our research framework. Our framework hypothesizes that analyst stock
recommendations mediate the relationship between IT investments and firm stock market value, as the
mediating effect of financial analysts are driven by the degree of information asymmetry between firms
and investors with regard to firms’ IT investments. Two key factors influence the degree of information
asymmetry of IT investments made by a firm – the nature of the IT investments and the nature of a firm’s
business environments. So we consider the nature of IT investments and the nature of a firm’s business
environments as moderators of the mediational relationship.
3.1. Mediating Role of Analyst Recommendations for the Value of IT Investments
The information intermediary functions of stock analysts are particularly important for IT
investments for several reasons. First, as IT spending, like R&D and advertising expenditures, directly
influences firms’ asset, debt and cash flow (Bharadwaj et al. 1999), it motivates stock analyst’s to watch
IT-related expenditures carefully and advise investors on the firm’s accounting profitability accordingly.
Although on average IT spending has proven to increase firms’ productivity, the effect may not
necessarily lead to corresponding profitability increase to offset the cost of IT investments (Hitt and
Brynjolfsson 1996). Stock analyst can analyze whether benefits of IT investment outweigh the costs and
whether it helps firms improving financial performance with new information collecting, processing and
analytical capabilities. It is therefore the analysts’ additional value to deliver their understanding about the
IT costs and benefits trade-offs to the stock market and investors, whose buy and sell decisions
subsequently influence the stock market’s valuation on the firm.
Second, despite the importance of IT investments, firms are not required to disclose to the public the
nature or the scale of their IT investments (Brynjolfsson et al. 2002). The lack of timely and public
disclosure creates information asymmetry and market inefficiency. Analysts can help mitigate the
information asymmetry through compiling information from multiple sources and presenting the
information in an integrated manner to investors (Kimbrough 2007). In this sense, analyst
recommendations may serve as an information mechanism for the stock market valuations of IT
investments. Echoing this perspective, IT investment can improve firms’ internal information
environment and results in better management forecasts (Dorantes et al. 2013). Therefore, IT investment
not only motivates stock analyst to pay more attention to it, but also affords firms to provide better
information to the analysts, thus allowing them to offer more accurate recommendations.
Finally, due to the “general purpose” nature of IT investments, investors need to know not only the
nature and scale of IT investments, but also firms’ investments in complementary resources and strategic
process changes related to the IT investments. A large amount of such information cannot be easily
accessed by investors. Analysts are able to mitigate this information asymmetry issue by obtaining firmspecific information through their connections within the firm and the industry to help investors gauge
future cash flows and financial value of the IT investments (Srinivasan and Hanssens 2009). Thus, the
forecasts and recommendations by stock analyst are more likely to reflect the true value of the IT
investments and be accepted by investors (Ivkovic and Jegadeesh 2004), allowing the stock market to
overcome the challenge of evaluating IT investments. In other words, there is a chained mediating effect,
i.e., the firm information is channeled through analyst recommendations, which influence investors’
evaluation of firms and determine firm stock market valuation. Thus, we posit that:
H1: Holding everything else constant, the effects of IT investments on firm stock market value are
mediated by analyst recommendations.
3.2. Moderating Effect on the Mediational Role of Analyst Recommendations
As the mediating effect of financial analysts is driven by information asymmetry between firms and
investors with regard to firms’ IT investments, we expect the mediating effect to vary with the level of
information asymmetry across firms. Two key factors influencing the level of information asymmetry of
IT investments are studied here: nature of the IT investments and market environment uncertainty.
Depending on the scope of the organizational functions involved, IT investments can be classified
into two important categories: functional IT and enterprise IT (also called cross-functional IT)
(Brynjolfsson and McAfee 2012; McAfee 2006). The former refers to IT systems that require the
involvement of one specific business function such as DSS, HR, and AIS, while the latter refers to IT
systems that require enterprise-wide, cross-functional collaboration such as enterprise resource planning
(ERP) and customer relationship management (CRM) systems (Nevo and Wade 2010).
We expect analyst recommendations to have a stronger mediating effect in firms with substantial
investments in enterprise IT than those with substantial investments in functional IT. First, firm’s
benefits from of enterprise IT are long-term and strategic. Enterprise IT often change the way
complementary organizational resources operate, and the ultimate outcomes are difficult for external
investors to forecast (Dos Santos et al. 2012; Mithas et al. 2012; Tambe and Hitt 2012). Furthermore,
while enterprise IT provides long-term value for future strategic initiatives, it usually requires significant
learning and incurs high short-term costs (Melville et al. 2004). For example, consider a firm that is
planning to implement a CRM system in order to better understand consumer preferences. Although such
a system can favorably affect a firm’s strategic position in the market, it is difficult for investors to value
its impact as its success requires substantial changes to the existing business processes and demands
collaborations among marketing, sales, operations and finance functions (Hitt and Brynjolfsson 1996).
Thus, the evaluation of enterprise IT systems requires substantial inside knowledge on firms’ operations.
In such cases, stock analysts could contribute significantly to the value discovery of enterprise IT
investments through their industry connections and their experience in evaluating similar investments,
thus reducing information asymmetry between firms and investors.
On the other hand, functional IT investments (DSS, HR, and AIS) are different from enterprise IT
investments. It is intended to solve firms’ specific challenges and needs in a given business function. Its
implementations take less time than enterprise IT and the benefits can be realized in a short period of time.
Given the specific objective and the relatively short implementation schedule, its value and costs are
easier for investors to estimate than enterprise IT without analyst recommendations. Further, with a
narrow scope, firms’ other business units are rarely affected by functional IT. Consequently, information
needed to value functional IT is more straightforward to collect and process. The above discussion
suggests that the expertise and experience of stock analysts are less important for the value discovery
process of functional IT investments but more important for the value discovery process of enterprise IT
investments. Therefore, we posit that:
H2: Analyst recommendations have a stronger (weaker) mediating role in the effects enterprise IT
investments in ERP and CRM (functional IT investments in DSS, HR and AIS) on firm stock market
value.
The level of information asymmetry of a firm’s IT investments is not only affected by the nature of
the IT investments but also the nature of the firm’s business environment. IT investments made by firms
in uncertain environments are more likely to create information asymmetry. This is because payoffs to IT
investments depend on a firm’s overall performance (Anderson et al. 2006). When a firm is facing
uncertain market demand for its product and services, the payoff becomes more uncertain
(Balasubramanian et al. 2000). This problem is further exacerbated by the high fixed cost nature of IT
investments. As market uncertainty for product or service (Dai et al. 2007) is often beyond the control of
a single firm (Beckman et al. 2004), greater uncertainties in demand increase the investors’ challenge of
assessing the return and risk of the IT investments (Bailey et al. 2003). Stock analysts provide investors
with valuable industry insights and their knowledge across multiple firms in the same industry enables
them to provide better forecast in uncertain environments (Beckman et al. 2004; Koopmans 1957;
Milliken 1987). Investors are thus more reliant upon stock analysts to assess the return to IT investments
when the firm is in more uncertain environments (Chang and Gurbaxani 2012).
H3: Analyst recommendations have a stronger (weaker) mediating effect for firms in a higher (lower)
market environmental uncertainty.
4. DATA AND MEASURES
To test the hypotheses, we collect data on IT investments, analyst recommendations, firm stock
market value, and a set of control variables. We collected data from multiple sources, including the HarteHanks CI database, Institutional Brokers’ Estimate System (I/B/E/S), the Center for Research of
Securities Prices (CRSP), and COMPUSTAT. After merging data from multiple sources and eliminating
missing values, we had a total of 3,236 firm-year observations between 1995 and 2006.
IT investment data. We obtained IT investment data from Harte-Hanks CI database. Harte-Hanks
conducts annual surveys of IT systems and IT assets of Fortune 1000 firms. The survey is conducted at
the establishment level with over 500,000 establishments contacted. It covers ten key IT areas, including
personal computing, systems and servers, networking, software, storage, managed services and others.
The survey also contains detailed information about the vendor and type of every IT system used by the
firm. Various versions of this database have been used in prior research in the IS literature (e.g. Chen and
Forman (2006); Forman (2005); Xue et al. (2008); Zhu and Kraemer (2002)).
We use the Harte-Hanks data to obtain measures of IT investments for each firm. We estimate the
total amount of IT investments for each firm based on the total number of PC, servers, and IT employees
reported by each firm. We follow the approach used by Hitt and Bryjolfsson (1996) that estimates a
firm’s IT investments as the sum of PC and server capital plus three times the IT labor costs. We obtain
yearly PC prices from Gartner Dataquest Global PC Annual Forecast, and yearly server prices from IDC
Worldwide Server Quarterly Tracker. We adjust the nominal prices to real prices using the Bureau of
Economic Analysis (BEA) price index for Computers and Peripheral Equipment (Lee and Barua 1999).
Industry-average labor compensation is obtained from occupational compensation data reported by the
Bureau of Labor Statistics (BLS), deflated by the Index of Total Compensation Cost. In addition, we
categorize IT systems into five types 1 : Enterprise Resource Planning (ERP), Customer Relationship
Management (CRM), Decision Support Systems (DSS), Human Resource Systems (HR) and Accounting
Information Systems (AIS). For each type of IT systems, we count the number of business
establishments of each firm that report using such systems and normalize the measure by the total number
of business establishments surveyed by Harte Hanks within the firm. These ratios measure the
percentages of business establishments that use a particular type of IT systems for a given firm and
provide an indicator of the prevalence of the particular type of IT systems in the firm.
Analyst stock recommendations. Data for analyst stock recommendations are obtained from I/B/E/S.
I/B/E/S provides comprehensive data on stock analysts’ recommendations on publicly traded firms,
earnings forecasts, and other financial indicator of the firm (Womack 1996). Though relatively new to the
IS discipline, I/B/E/S has been widely used in finance, marketing, and accounting fields (Bradshaw 2004;
Firth et al. 2013; He et al. 2013; Luo et al. 2010). I/B/E/S has data for over 45,000 companies from 70
stock markets worldwide. Because a company is often covered by multiple stock analysts, and each
analyst provides multiple recommendations for each company over time, we collected a total of 339,068
observations of analyst stock recommendations. I/B/E/S measures analyst recommendations as the
median consensus of buy-hold-sell recommendations provided by analysts (Howe et al. 2009). As the
raw data is in a reversed Likert scale (with 1 = strong buy, 2 = buy, 3 = hold, 4 = under-perform, and 5 =
sell), we transformed this reverse coding for ease of exposition (so 5 = strong buy). Because multiple
stock analysts cover the same company, we used the average of analyst stock recommendations for each
firm-year observation.
Firm stock market value. We measure a firm’s stock market value using the abnormal returns of the
firm over and beyond what is expected after adjusting for market wide risk factors (Brown and Kapadia
2007). We obtained stock price data from CRSP to derive firm abnormal returns. To measure expected
return from the broad financial markets, we use the Fama-French-Carhart model (Carhart 1997; Fama and
French 1993) at the firm level as follows:
(1)
Rit  R ft  0i  1i ( Rmt  R ft )  2i SMBt  3i HMLt  4i MOMt   it ,
where Rit are returns for firm i on time t, Rm are average market returns, Rf is the risk-free rate, SMB are
size effects, HML are value effects, MOM are Carhart’s momentum effects, β0i is the intercept, and εit is
the model residual. We then calculate abnormal returns (ASRit) as the difference between the observed
returns and the expected returns (with an estimation window of 360 days, Luo et al. 2013):
(2)


ASRit  ( Rit  R ft )  ˆ 0i  ˆ1i ( Rmt  R ft )  ˆ 2i SMBt  ˆ 3i HMLt  ˆ 4i MOM t .
Data for Fama–French–Carhart factors and momentum (Rm, Rf , MKT, SMB, HML, and MOM) are
obtained from http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
Other variables. To rule out alternative explanations and robustly identify the hypothesized effects,
we include a comprehensive set of firm-, analyst- and industry-level control variables. We follow the
1
Our discussion with Harte-Hanks indicates that, during our study period, Harte-Hanks did not distinguish SCM systems from
ERP systems.
widely used models of financial analyst metrics (He et al. 2013; Jegadeesh et al. 2004) and firm stock
value in the extant finance and accounting literature (Firth et al. 2013; Lui et al. 2007) in our selection of
control variables. This allows us to calibrate the extent to which IT investments contribute new
information in explaining analyst stock recommendations and the mediating role of analyst stock
recommendations in channeling the effects of IT investments on firm stock market value. They are: Firm
profitability (ROA), measured as the ratio of a firm’s operating income (from Compustat) to its
book value of total assets; ROA variability, measured as the standard deviation of the prior five
years of ROA; R&D intensity, measured as research and development expenses divided by sales;
New product announcements, measured as the number of new products introduced by a firm and
collected from the Lexis/Nexis news search; Firm size, measured as the total assets of a firm;
Advertising intensity, measured as the firm’s advertising expenses divided by sales; Firm
financial leverage, measured as the ratio of long-term debt to total assets; Firm dividend, the
ratio of cash dividends to firm market capitalization; Firm liquidity, the current ratio of a firm.
Compared to fixed assets; Analysts’ earnings forecast errors, gauged as the differences (in
absolute values) between the latest analysts’ median consensus forecasts (MEDEST) before the
earnings announcements and the firms’ actual earnings per share scaled by stock prices; Analyst
coverage, measured as the number (in natural log) of stock analysts following or covering a firm;
Analyst expertise, measured as the averaged firm-specific experience of the financial analysts;
Institutional ownership, obtained from the Thompson Financial CDA Spectrum database of SEC
13F filings; Industry competition, measured as the inverse of the Herfindahl concentration index;
and Environmental uncertainty, measured as the variation of industry sales in the product market.
5. ANALYSES AND RESULTS
We use panel data models to control for a number of biases. Specially, to account for observable
heterogeneity, we have controlled for multi-level (firm-, analyst-, and industry-level) covariates. In
addition, to accommodate firm-specific unobservable heterogeneity, endogeneity, heteroskedasticity, and
serial correlation in panel data, we employ the generalized method of moments (GMM) for estimations.
Endogeneity may exist in that firms with more favorable analyst recommendations and higher stock
market value can afford more IT investments. To account for this endogeneity bias, we use instrumental
variables approach. Specifically, we use the lagged industry average IT investment after excluding the
firm (following the BLP approach to dealing with endogemeity). In addition, we use firm age, which is
exogenous to our modeling system in hypothesis testing, as another instrumental variable. Firm age can
be a desirable instrumental variable because firm age can be correlated with IT investments (because
younger firms tend to have more up-to-date IT infrastructures and IT applications) but not correlated with
error terms or firm stock market value (because old or young firms both can over-or under-perform
relative to the general stock market). To test the validity of these instruments, we conducted the Hansen
(1982) test. GMM also employs the White heteroskedasticity and autocorrelation robust covariance
matrix ̂ HAC :
(4)
T 1
T
ˆ
ˆ ( 0 )    k( j , q )( 
ˆ( j ) 
ˆ ' ( j )  , ˆ ( j )  1   Z '  ' Z  ,



HAC
t j t t j
t


T  k  t  j 1
 j 1


where ε = the vector of White residuals, k = the kernel, q = the bandwidth, and Zt = a k x p matrix in
GMM (see (Hamilton 1994): 409-22).
In addition, to test the reverse causality direction from analyst recommendations to IT investments,
we conducted the Granger causality test (Granger 1969). The results confirmed the direction of influence
from IT investment to analyst recommendations across both total IT investment and all specific IT
investment dimensions (smallest FGranger test = 12.371, p < 0.01), except AIS. Furthermore, we tested
various model assumptions of normal distribution, auto-correlation, and heteroskedasticity of error terms
with the Jarque-Bera test, the RESET test, the Durbin-Watson and White’s test, the Davidson-MacKinnon
test, and the Breusch-Pagan test. None of the assumptions are violated in the results. Finally, the
multicollinearity problem is not a serious threat because all variance inflation factor results are less than
five and the condition indices are less than ten in our results.
5.1. Models Testing Moderated Mediation
Statistical testing a moderated mediation relationship requires a system of equations. Specifically, we
follow the widely accepted approach suggested by Muller et al. (2005), using the following regression
equations to test the moderated mediation effect. In the equations below, ITit refers to IT investment
(total or specific IT dimension) for firm i at time t, Recomit refers to the mediator variable of analyst stock
recommendations for firm i at time t, Valueit+1 refers to the abnormal return of firm i in the next time
period t, and Uncertaintyit refers to the moderating variable of market uncertainty.
(5)
Equation (5) models how environmental uncertainty moderates the effects of IT on firm stock market
value.
(6)
Equation (6) assesses how environmental uncertainty moderates the effect of IT on the mediator of
analyst recommendations.
(7)
Equation (7) assesses the degree to which environmental uncertainty moderates both the effect of analyst
recommendations on firm stock market value and the residual effects of IT on firm stock market value
after controlling for the direct effect of analyst recommendations. Muller et al. (2005) suggest that in
order to establish moderated mediation, three conditions must be satisfied. First, there should be a
significant effect of IT on firm stock market value (β11 ≠ 0). Second, the effects of IT on the stock analyst
recommendations should vary significantly with environmental uncertainty (β23 ≠ 0), and/or the effects of
the stock analyst recommendation on the firm stock market value shall vary significantly environmental
uncertainty (β35 ≠ 0). Finally, the residual effect of IT on firm stock market value shall be significant (β31
≠ 0), and controlling for the mediator, is moderated (β33 ≠ 0). That is, the mediational role of analyst
recommendation is moderated significantly (Rucker et al. 2011; Zhao et al. 2010).
5.2. Results for Analyst Recommendations as a Mediator between IT and Firm Stock Market Value
In H1, we expect that analyst recommendations mediate the associations between IT and firm stock
market value, i.e., ITanalyst stock recommendationsfirm value chain. Following Baron and Kenny
(1986) and Zhao et al. (2010), in order to establish mediation, 1) IT must affect analyst recommendations,
2) recommendations must affect firm value and 3) the inclusion of the mediation variable (i.e. analyst
recommendation) reduces the strength of the effect of IT on market value . We first presented the baseline
model (Model 1) without IT capital in Table 1. In Model 2, IT is included, and the coefficient is positive
and highly significant (p < 0.01). Models 4 and 5 are used to test whether analyst recommendations
mediate the effects of IT in a firm's market value. Model 5 results indicate that analyst recommendations
significantly affect firm stock market value (p < 0.05). Further, including stock recommendations as a
mediator in model reduces the strength of the effects of IT on stock market value (from p < 0.05 to p <
0.10). Collectively, the results suggest a significant mediating relationship.
M1 (IT total)
Analyst
Recom
-IT
IT x Market environmental -uncertainty
Mediation Effects
Analyst
Recommendations
(Recom)
Recom
x
Market
environmental uncertainty
Controls
Environmental volatility
-0.133**
R&D intensity
.035**
New product announcements .005
Advertising intensity
.031**
Firm size
.136**
ROA
2.055***
ROA variability
-0.212
Financial leverage
-0.005
Dividend
.521
Liquidity
.024
Analyst coverage
1.932**
Analyst forecast errors
-0.633**
Analyst expertise
.512*
M2 (IT total)
Analyst
Recom
.481***
M3 (IT total)
Analyst
Recom
.452***
M4 (IT total)
Firm Stock Market
Value
1.685**
M5 (IT total)
Firm Stock Market
Value
0.972*
--
.218**
0.703**
0.438*
--
0.266***
--
0.195**
-0.131**
.031**
.003
.029**
.135**
2.067***
-0.216
-0.004
.519
.022
1.917**
-0.615**
.509*
-0.185**
.025**
.002
.027**
.186**
2.219***
-0.175
-0.005
.536
.025
1.883**
-0.713**
.615*
-0.412**
0.172***
0.625**
0.191***
0.335**
2.036***
-1.808**
0.006
0.152
0.355*
0.468**
-0.051
0.237*
-0.523**
0.178***
0.633*
0.195***
0.316**
2.161***
-1.63**
0.003
0.155
0.387*
0.472**
-0.053
0.245**
Institutional ownership
1.686**
1.682**
1.557**
2.017**
2.152**
Industry competition
R-squared
Change in R-squared
F-statistic
N
-0.053
0.18
-0.072
0.24
0.06***
56.797
3,236
-0.073
0.28
0.04***
69.783
3,236
-0.352**
0.17
-0.319**
0.22
0.05***
23.082
3,236
38.089
3,236
18.845
3,236
Table 1: Results for CSP, Analyst Recommendations, and Firm Future Returns Note: * p < 0.10, p < 0.05,
*** p < 0.01.
We conducted the Sobel test extended by the bootstrapping approach in order to assess whether the
indirect mediation effects are statistically significant (Sobel 1982). The Sobel test model is:
zvalue  ab / a 2 sb2  b 2 sa2  sa2 sb2 , where a and sa are coefficients and standard errors (from the
bootstrapping) for the impact of independent variables on mediators, while b and sb are coefficients and
standard errors for the impact of mediators on the dependent variable. We find that Sobel test results are
significant (zvalue = 6.029, p < 0.01) for the mediation effects of analyst recommendations, supporting H1.
5.3. Results on the Moderating Effect on the Mediating Role of Analyst Recommendations
H2 predicts that analyst recommendations have a stronger mediating effect on the stock market value
of firms with significant enterprise IT investments (ERP and CRM) than those with significant functional
IT investment (DSS, HR and AIS). Table 2 Panel A reports the results. The results show that enterprise
IT investments (ERP and CRM) significantly affect analyst recommendations (p < 0.01) and analyst
recommendations significantly influence stock market value. Further, including the mediator of
recommendations in the model reduces the strength of the effects of ERP and CRM-based IT investments
on stock market value. Sobel test again shows that the mediation effects are significant (zvalue = 8.386 for
ERP and 9.108 for CRM, p < 0.01), suggesting that analyst recommendations play a significant mediation
role in channeling the effects of enterprise IT investments (CRM and ERP) on firm stock market value.
M6 (IT M7 (IT CRM)
CRM)
Analyst
Analyst
Recom
Recom
Panel A
IT
.565***
IT
x
Market
environmental
-uncertainty
Mediation Effects
Analyst
Recommendations
(Recom)
Recom x Market
environmental
uncertainty
M14 (IT
DSS)
Panel B
Analyst
Recom
.408**
IT
IT
x
Market .130*
environmental
uncertainty
Mediation Effects
Analyst
Recommendations
(Recom)
Recom x Market
environmental
uncertainty
.538***
M8
(IT
CRM)
Firm Stock
Market
Value
1.806**
M9
(IT
CRM)
Firm Stock
Market
Value
0.956*
M10
(IT M11
(IT M12
(IT M13
(IT
ERP)
ERP)
ERP)
ERP)
Analyst
Analyst
Firm Stock Firm Stock
Recom
Recom
Market
Market
Value
Value
.525***
.542***
1.952**
0.903*
.267**
0.725**
0.442*
--
--
-M15(IT
M16
(IT
DSS)
DSS)
Firm
Stock Firm Stock
Market
Market
Value
Value
n.s.
n.s.
n.s.
0.715**
0.451*
0.272***
--
0.317***
0.205**
--
0.198**
n.s.
M21
(IT
AIS)
Firm Stock
Market
Value
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
--
n.s.
n.s.
n.s.
--
n.s.
n.s.
M17 (IT
HR)
Analyst
Recom
.277**
.318**
.105*
M18 (IT
HR)
Firm Stock
Market
Value
n.s.
M19
(IT
HR)
Firm Stock
Market
Value
n.s.
n.s.
M20 (IT
AIS)
Analyst
Recom
Table 2: Additional Results for IT, Analyst Recommendations, and Firm Stock Market Value. * p < 0.10,
p < 0.05, *** p < 0.01. n. s. = not significant.
In contrast, the effects of functional IT investment (DSS, HR and AIS) were not significant on firm
stock market value (p>.10) for all three types of IT systems, failing to meet the conditions for the
mediating effects. These findings support H2, suggesting that the analyst recommendations play a
significant mediating role for enterprise IT investments (ERP and CRM) but not for functional IT
investment (DSS, HR and AIS). We also tested the mediating models with all five IT investments
simultaneously (ERP, CRM, DSS, HR, and AIS in the same models) and found consistent evidence for
the mediational role of analyst recommendations for enterprise IT investments but not functional IT
investments.
H3 suggests that the mediating role of analyst recommendations in linking IT investments to firm
stock market value would be stronger in industries with higher environment uncertainty. To test this
moderated mediation hypothesis, we use equations 5-7 (Muller et al. 2005). Specifically, Table 1 Column
M3 shows that environmental uncertainty significantly moderates the effect of IT investments on analyst
recommendations (p < 0.05 in M3). Column M5 indicates that environmental uncertainty significantly
moderates the effect of analyst recommendations on firm stock market value (p < 0.05 in M5). After
controlling for the mediator of analyst recommendations, the residual effect of IT on firm value is
positively and significantly moderated by environment uncertainty (p < 0.05). Because both moderating
effects are positive, the mediating role of analyst recommendations between IT and stock market value is
stronger for firms facing higher environment uncertainty, thus supporting H3.
5.4. Robustness of the Results
We also conducted alternative measures of analyst stock recommendations by using upgrade
recommendations; employed different empirical models including fixed effects model (to rule out
alternative explanations due to firm-specific effects), random effects model (to rule out alternative
explanations due to firm-related random shocks), and bootstrapping methods for estimations (to rule out
alternative explanations due to unstable estimates); specified a hierarchical linear model (HLM) to
captures the potential heterogeneous industry-specific effects of IT investments; used instrumental
variables (IT investment of other firms in the industry except the focal firm) and control firm-level
variables to reduced endogeneity concerns; employed different measures of IT investment; tested the lags
in responses to IT timing; and conducted a falsification test to check the results. All results provide
consistent support for our hypotheses. Due to space limited, we don’t present details here.
6. CONCLUSIONS AND DISCUSSIONS
This research explores the value discovery role of stock analysts in facilitating the financial market’s
assessment of firms’ IT investments. Using twelve-year longitudinal data of IT investments, financial
performance, and analyst recommendations of Fortune 1000 firms, we find that analyst recommendations
play an important mediating role in linking a firm’s IT investments to its stock market value. Furthermore,
our analysis suggests that the mediating role is more significant for investments in enterprise IT systems,
and more salient for firms in uncertain environments.
The theoretical implications of this study are multifold. First, research to date on IT value has largely
overlooked the value discovery role of stock analysts. This study bridges the research gap and
demonstrates how analyst recommendations help financial markets evaluate firm IT investments. We
provide a new perspective that enriches the literature on the value creation process of IT investments.
Extant research on IT value takes an internal process-oriented view, focusing on identifying internal
capability factors that generate value from IT investments. This study takes an external process-oriented
view, focusing on how IT investments is understood and evaluated by the financial market. By examining
the mediating effect of stock analysts between IT investment and firm stock market value, we showed that
the value creation of IT investment is not an independent process insides the firm. Rather, it is intertwined
with the active engagement of stock analysts who interpret and assess firms’ IT investments for the
financial market. As such, our study broadens the scope of IT business value research by highlighting the
importance of understanding the external value discovery process in the evaluation of IT investments. To
the best of our knowledge, it is the first study that assesses the mediating role of stock analysts in market
evaluation of IT investments.
Second, this study further highlights the “general purpose” nature of IT and its implications on the
market evaluation of IT investments. Our finding suggests that, because IT is a “general purpose”
technology, its evaluation requires an in-depth understanding of firms’ internal operation, business
strategies, and complementary organizational resources. As such, different types of IT investments affect
the market evaluation of IT investments differently, depending on the complexity of the IT investments
and firms’ ability to make complementary investments. Our analysis suggests that investments in
enterprise IT, which are more intangible and strategic, benefit more through the mediating role of analysts’
recommendation than functional IT. One plausible explanation is that due to the intangible and long-term
nature of the business value of enterprise IT, the market relies more heavily on stock analysts who have
the expertise and information to assess firms’ business strategy, internal operations and complementary
resources in extracting the value from enterprise IT investments. Similarly, the market relies more
heavily on stock analysts when firms make IT investments in more complex and uncertain market
environments.
Third, this study provides a new explanation on variations in the market valuation of IT investments.
Our finding suggests that analyst recommendations could be a key factor that affects the market valuation
of IT investments. The amount of coverage a firm receives from financial analysts and their capability in
understanding and evaluating a firm’s IT investments affect the market evaluation of firms’ IT
investments. This finding has important implications for research that use market valuation to assess the
business value of IT. Our finding suggests that the financial market may not fully recognize the value of
a firm’s IT investments especially for investments in enterprise IT systems and for firms in uncertain
environments (situations in which the mediational role of analyst recommendations is much more needed
so as to assess and interpret the uncertain business value of IT investments).
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