International Journal of Accounting Information Systems

International Journal of Accounting Information Systems 9 (2008) 154 174 Contents lists available at ScienceDirect International Journal of Accounting Information Systems Information technology, contextual factors and the volatility of firm performance Kevin Kobelsky a,, Starling Hunter b, Vernon J. Richardson c,1 a Hankamer School of Business, Baylor University, One Bear Place #98002, Waco, TX 76798-8002, United States b Carnegie Mellon University, Qatar, P.O. Box 24866 Doha, Qatar c Sam M. Walton College of Business, University of Arkansas, Fayetteville, AR 72703, United States article info abstract Article history: Received 1 June 2007 Received in revised form 29 January 2008 Accepted 5 February 2008 JEL classification: C2 M4 M19 O30 O33 Keywords: SSRN: Journals-Management Research Network MRN Information Systems & ebusiness Network Computers & Information Technology MRN Management Network IO: Productivity, Innovation & Technology This study uses previous theory developed in the IT implementation literature and the information processing view of the firm to empirically investigate the impact of IT investments and several contextual variables on the volatility of future earnings. We use InformationWeek 500 data on IT spending from 1992 1997 to find evidence that IT investments increase the volatility of future earnings but that this impact is highly contingent upon three firm level contextual factors sales growth, unrelated diversification, and size. These factors can lead to conditions in which IT increases or reduces earnings volatility. Taken together, these results may help explain what has recently been termed the new productivity paradox, i.e., the apparent under-investment in information technology despite evidence of highly positive returns for doing so, and suggests settings where managers may be under- or over-discounting returns on IT investments. 2008 Published by Elsevier Inc. 1. Introduction and motivation Evaluating information technology (IT) investments has been a central concern in information systems (IS) research and practice for decades. A substantial body of research has found that IT's average (Bharadwaj et al., 1999; Brynjolfsson and Hitt, 1995, 1996; Dewan and Min, 1997; Hitt and Brynjolfsson, 1996) and Corresponding author. Tel.: +1 254 710 1155; fax: +1 254 710 1067. E-mail addresses: kevin_kobelsky@baylor.edu (K. Kobelsky), starlingdavidhunter@gmail.com (S. Hunter), vrichardson@walton.uark.edu (V.J. Richardson). 1 Tel.: +1 479 575 6803; fax: +1 479 575 2863. 1467-0895/$ see front matter 2008 Published by Elsevier Inc. doi:10.1016/j.accinf.2008.02.002

K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 155 maximum (Banker et al., 1990) effects on financial performance are highly positive. This has generated a new twist to the so-called IT productivity paradox (Lichtenberg, 1995; Strassman, 1990). Given evidence of such high returns, firms would appear to be under-investing in IT. Financial management theorists have long maintained that returns should be evaluated relative to the risk associated with an investment. This raises an intriguing possibility: if investments in IT lead to increases in risk, i.e., in the volatility of future earnings, perhaps firms and their managers willingly forgo higher returns to avoid increased risk. Interestingly, the impact of IT investment on future earnings risk has yet to be addressed in theoretical or empirical research in the IS or accounting fields. There are very good reasons however why it should be. Computers, software and communications technology has been the largest category of corporate fixed investment since 1992 ($447 billion in the US in 2004) (Bureau of Economic Analysis, 2005) providing ample potential for a substantial impact. Research in accounting and finance investigating the consequences and determinants of firm earnings volatility is well-established. Earnings volatility can impose significant costs on the firm including: reduced market value (Barth et al., 1999); reduced ability of investors and owners to predict future cash flows (Barnea et al., 1976); increased cost of capital and reduced access to external capital markets (Badrinath et al., 1989); and, as a result, decreased discretionary investments (e.g. capital expenditures, R&D and advertising (Minton and Shrand, 1989). These consequences combine with the fact that top managers' compensation is often tied to firm performance to create strong incentives for managers to anticipate the impact of their decisions on the volatility of future earnings (Bartov, 1993) and take concerted action to reduce it (Badrinath et al., 1989; Beidleman, 1973). Research has yet to examine empirically whether investments in IT affect earnings volatility. Prior research suggests two possibilities. The first is that IT investments are not unlike other capital expenditures (e.g. R&D spending and advertising), that have been shown to increase earnings volatility (Kothari et al., 2002). IT investments are notoriously difficult projects to manage, sometimes failing spectacularly (Financial Times, 1998), often falling short of management expectations (Compass, 1999), and sometimes succeeding spectacularly. This leads to IT investments being associated with higher earnings volatility than a comparable investment in plant or equipment. We refer to this as the implementation risk view. A second view is that other characteristics of IT enable it to mitigate earnings volatility. According to the information processing view of the firm (Galbraith, 1977) IT acts as a coordination mechanism, providing information that enables the firm to better and more quickly respond to unexpected challenges arising from the business and competitive environment (Gurbaxani and Whang, 1991). This, in turn, reduces the bullwhip effect of these events on earnings (Lee et al., 1997), decreasing earnings volatility from what it would have been otherwise. An example of this is Dell's integrated sales order and factory floor systems. Spikes in obsolete inventory costs caused by new product introductions are reduced by its build-to-order factory systems and the ability to highlight those components with high inventory levels to the order system for aggressive promotion or price discounting. While the characteristics associated with these two views have opposite effects on earnings volatility, they are not mutually exclusive. Rather, they describe two different logics that may operate within the same organization or project. For example, a successful high-risk IT project may lead to a decline in earnings during implementation followed by a significant one-time jump in earnings, but thereafter reduces the volatility of earnings. Because these two views are not mutually exclusive theoretical alternatives and prior empirical research addressing their relative salience in earnings volatility is lacking, there is no basis for prematurely truncating theoretical discussion by selecting one over the other. The empirical analysis provides the basis for assessing which view is most descriptive, i.e., does IT increase earnings volatility, indicating dominance of implementation risk view effects, or does IT decrease volatility, indicating dominance of the information processing view effects? Central to both of these views, and the focus of this study, is the moderating effect on IT's impact of uncertainty arising from the organizational context. Higher levels of contextual uncertainty both amplify implementation risks and provide more opportunity for IT's coordinating capabilities to be exhibited. Below we identify four firm- and industry-level sources of uncertainty and develop hypotheses concerning their positive moderating effect on the relationship between IT and earnings volatility. The baseline empirical analysis ignoring contextual variables suggests that IT increases earnings volatility. When contextual moderators are added to the model we find that IT's effect is highly conditional, increasing volatility in some contexts, decreasing it in others, and having no significant

156 K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 effect at average levels of the contextual variables. Though IT's effect on earnings volatility is significant, its direction and significance depend upon the rate of sales growth, level of unrelated diversification and firm size. The remainder of this study is organized as follows. In the next section we draw from prior research on system development risk and the information processing view to develop several hypotheses concerning the effect of IT investments on the volatility of future earnings and the moderating effect of context on this relationship. We focus on how uncertainty varies with each of the contextual variables, and how uncertainty moderates IT's impacts. The two sections that follow contain a description of our research method and data. The next section describes the results of the empirical analysis. We conclude with a discussion of the contribution of this study to the body of knowledge on the IT-firm performance relationship. 2. Literature review and hypotheses development IT investments may have distinctly different impacts on earnings volatility than other kinds of investments and resources. As previously described, on the one hand, IT investments have significant implementation risk, increasing volatility. On the other hand, IT can enhance information processing capability, enabling the firm to better and more quickly respond to contextual uncertainty, reducing volatility. In the remainder of this section we develop several hypotheses regarding IT investments' direct and moderated effects on earnings volatility. IT's direct effect apart from moderating effects is the focus of hypothesis one. Next, hypotheses are developed for the moderating effects of contextual variables based on the underlying principle that greater uncertainty from competition, firm sales growth, product market diversification, and firm size amplifies both implementation risks and the salience of information processing capability. These are presented in turn for the each view (H2a e and H3a e, respectively). The model developed in the hypotheses is illustrated in Fig. 1. 2.1. H1: IT's net direct effect In the moderated effects model depicted in Fig.1 it is important to clarify what is meant by direct effects. In a traditional direct effects only model one hypothesizes concerning the average effect of the independent variable(s) on the dependent variable over all values of the other independent variables. In a moderated model, direct effects hypotheses address the effect of an independent variable on the dependent variables at one particular level of each of the moderating independent variables (Jaccard et al., 2003). Below we hypothesize concerning the net effect of the two views of IT investment impacts at the average level of the contextual factors. This addresses the central question of what the effect of IT on earnings is after controlling for contextual moderating effects. This provides the most general finding concerning the effect of IT investments on earnings risk and whether this contributes to the new productivity paradox (i.e. managers' apparent under-investment in IT). Fig. 1. Model of IT and contextual effects on earnings volatility.

K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 157 2.1.1. The implementation risk view Large scale studies of IT returns indicate that they vary widely (Brynjolfsson and Hitt, 1996). Given the magnitude of IT projects and their impact on a firm's operations, they can have a substantial effect on earnings. Anecdotal evidence suggests that IT projects can be very difficult to manage, sometimes failing spectacularly (Financial Times, 1998) and often falling short of management expectations (Compass, 1999). Firms have also used IT to generate spectacular operational and strategic benefits (Kraemer et al., 2000). Previous research suggests several sources of risk that affect IT returns (Benaroch, 2002; Clemons and Weber, 1990; Lyytinen et al., 1998). Many of these relate to implementation: IT projects may require technology that is not available (technical risk); they may overwhelm the skills of the company's IT staff (project risk); they may involve revisions to processes that overwhelm the operations staff (operational risk) or undermine internal vested interests and in response are fought by them (internal political risk). Finally, the inability of management to provide a reasonable estimate of implementation costs and the time required to implement (management optimism risk) can lead to project failure (McFarlan, 1981). Benaroch (2002) also describes the technological market risk associated with newer, superior technologies rendering a project obsolete. Although it is possible to diversify some of these risks via a portfolio of IT projects, many of these risks are non-diversifiable. These risks are quite salient for IT projects. The Standish Group (2004) reports that a large majority of IT projects have experienced some form of failure. Its 2004 survey of 9236 projects finds that 18 percent of projects are not completed (cancelled prior to completion or delivered and never used), 53 percent are challenged (late, over budget and/or with less than the required features and functions) and 29 percent of all projects succeeded (delivered on time, on budget and with required features and functions). One third of chief executives surveyed in 1999 believed that IT's contribution to firm performance was low (Compass, 1999). The seminal large scale studies of IT returns indicate that these returns vary widely (Brynjolfsson and Hitt, 1996); however, these studies did not examine IT's effects on earnings volatility. Contemporary with this study, Dewan et al. (2007) find evidence of IT having effects on earnings volatility that are consistent with the implementation risk view. Their study finds that IT capital is associated with higher standard deviations of return on assets and daily investor market returns. It does not consider the potential for information processing effects, investigate potential contextual moderating effects, or otherwise address the underlying industry differences that might drive these contrasting results. 2.1.2. The information processing capability view IT investments can reduce volatility by acting as a coordination mechanism, enabling the firm to better and more quickly respond to external and internally-sourced uncertainty (Galbraith, 1977; Gurbaxani and Whang, 1991; Hitt, 1999). The bullwhip effect provides an example of how costly unplanned variability can be in supply chains, and of how IT can resolve this problem through information sharing (Lee et al., 1997). In uncertain contexts (those with higher levels of unplanned variation), IT-enabled responsiveness can reduce the cost and revenue impacts of unforeseen events, reducing the volatility of earnings. 2 For example, Dell's on-line sales system provides updates to manufacturing several times per day, enabling the company to exploit its JIT inventory system with suppliers more effectively, reducing costs associated with unexpected changes in sales levels and mix (Kraemer et al., 2000). These stabilizing effects of IT are conditional: they are only expected to be salient when both a high level of uncertainty exists and IT facilitates information sharing. Absent changes in the firm and its environment, there is no change-related information to communicate, precluding IT from reducing volatility. There does not appear to be a theoretical basis for hypothesizing whether implementation risk or information processing capability effects will be larger at average levels of the four contextual factors. Dewan et al.'s (2007) concurrent study finds evidence consistent with the implementation risk view, though it does not investigate contextual moderating variables. Based on this empirical evidence we expect the following. H1. The greater a firm's spending on IT, the greater its volatility of earnings, after controlling for contextual factors' direct and moderating effects. 2 It is conceivable that IT could be used to take advantage of transitory opportunities that would otherwise be missed to an extent equal to bullwhip effects avoided, with no net effect on the volatility of earnings. Given the pervasiveness of bullwhip effects and efforts to avoid them, we expect these reductions to dominate transitory effects in a large sample of firms over time.

158 K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 2.2. The moderating effect of context according to the implementation risk view In the remainder of this section we develop hypotheses concerning how contextual variables moderate the effect of IT implementation risk. The unifying principle uniting these variables' effect on volatility is as follows: The specific sources of uncertainty examined are suggested by previous research in information system, organizational theory and accounting, as described below. These include both environmental (industry competition) and firm (sales growth, product market diversification, and firm size) sources of uncertainty. In the implementation risk view, more uncertain settings are expected to exacerbate the risks associated with IT projects, amplifying the positive direct effect of IT on earnings volatility (Fig. 1). IT implementation risks are expected to be greater in firms having more competition, more growth, more related diversification, less unrelated diversification or smaller size (Fig. 2, left column). These are explained in turn below. Fig. 2. IT's direct and moderated impacts on earnings volatility under the two views. For implementation risk view, H2a, b, c and H2d, e have opposite signs due to opposite effects of variables on uncertainty i.e., high unrelated diversification, large size reduce uncertainty faced by the firm. The same applies for H2a, b and H2c, d, e for information processing view.

K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 159 2.2.1. H2a competition Economic theory predicts that as the number of firms in an industry increases, the intensity of competition also increases. This increases uncertainty arising from competitive IT risk, the uncertainty about whether a competitor will make a preemptive move, or simply copy the investment and improve on it (Benaroch, 2002). This competitive pressure in turn makes it more likely that IT projects will be subject to significant scope and function changes during implementation, increasing the likelihood of IT project failure and amplifying the effect of IT investment on volatility. H2a. The level of competition positively moderates (increases) the effect of IT spending on earnings volatility. 2.2.2. H2b sales growth The risks associated with IT can be exacerbated during periods of high sales growth. Growth is a nearly universal goal of firms (Dalton and Kesner, 1985; Whetten, 1987) and is widely perceived as a positive signal (i.e., that the firm's strategy is working successfully) (Grant, 2001). That said, the pressure that firms often face to grow quickly poses several important challenges and opportunities for organizations and their managers (Penrose, 1959). Prior research has found rapid organizational growth to be associated with an increase in organizations' assets, and to help coordinate those assets, the adoption of new organization forms (Shane, 1996). These forms as well as entry into new markets often lead to the adoption of new technologies (Ohmae,1989; Scott-Morton,1991). In periods of slow growth, investments in assets and capability build upon strengths developed in previous periods and provide a sound basis for growth in subsequent ones (McGee and Thomas, 1994). During periods of rapid growth, two problems can arise from uncertainty: first, the conditions under which such decisions are made are subject to rapid change (Eisenhardt,1989); and second, such decisions are only partially reversible (Ghemawat and Costa, 1993) and the assets inwhich the firms and the capabilities developed to deploy them are frequently found to be overly-specific (Williamson, 1975) or insufficiently dynamic to meet changing market demands (Pettus, 2001). These effects are expected to be particularly salient for IT investments, which according to the implementation risk view, are difficult to design and implement. Thus, IT investments made during periods of high sales growth are more likely to fail, increasing earnings volatility. H2b. The level of sales growth positively moderates (increases) the effect of IT spending on earnings volatility. 2.2.3. H2c,d related and unrelated diversification of product markets Firms operate in diverse product markets in order to lever firm-specific advantages across these related markets. Previous research indicates that managing the interdependence of operating units serving related markets creates uncertainty that in turn demands higher levels of coordination. Dewan et al. (1998) empirically investigate this effect on IT spending, arguing that IT is used to facilitate coordination across related product markets. They find that related diversification is associated with higher IT spending levels. The complexity associated with developing information systems that support multiple related product markets (defined here as selling in different 4-digit SIC industries sharing the same 2-digit SIC) results in increased IT implementation risk. Such complex projects hold significant benefits but are even more challenging to implement than traditional business silo IT projects, as evidenced by the spectacular successes (Kraemer et al., 2000) and failures (Financial Times, 1998) of enterprise resource planning systems. Under the implementation risk view, the higher benefits of success and costs of failure associated with such projects would result in higher earnings volatility. H2c. The level of related diversification positively moderates (increases) the effect of IT spending on earnings volatility. In contrast, diversification into unrelated markets does not require increased coordination because operations supporting them are much less interdependent (Hill and Hoskison, 1987; Dewan et al., 1998). Unrelated diversification (defined here as selling in industries with differing 2-digit SICs) is therefore not expected to be associated with uncertainty and coordination requirements. Dewan et al. (1998) confirm this in finding that unrelated diversification had no impact on IT capital levels. This means that firms operating in several unrelated product markets will tend to have several smaller, less complex systems rather than one larger, more complex system for a single market firm having the same total sales, reducing implementation risk.

160 K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 A complementary arithmetic portfolio effect is expected to arise from unrelated diversification. As a firm diversifies, its IT projects are conducted in a wider variety of business and process settings, reducing overall portfolio uncertainty even if average individual project risk levels were to remain constant. To the extent implementation risks differ and therefore are not correlated across business units, IT's impact on earnings volatility must decrease. H2d. The level of unrelated diversification negatively moderates (decreases) the effect of IT spending on earnings volatility. 2.2.4. H2e firm size Firm size has two competing effects on implementation risk. An arithmetic portfolio effect reduces overall implementation risk, consistent with the arguments advanced for unrelated diversification in H2d. As firm size increases the number of projects increases, leading to a reduction in the proportionate size and earnings impact of any one IT project, even if IT projects on average are increasing in absolute size. If earnings effects of the incremental projects have a correlation with other projects of less than one, IT's impact on earnings volatility will decrease. An opposite effect is consistent with the related diversification arguments made for H2c. The larger the firm, the greater the likelihood that a portion of IT spending is for large IT projects. Large firms will often try to centralize at least some functions of the company (accounting, human resources, etc.) to develop synergies and economies of scale and this generally will lead to larger rather than smaller projects. The larger the scale of the process to be supported with IT, the greater the demands of the project, and the generally greater the complexity associated with that project (Martin et al., 2005). Greater size and complexity increase the likelihood of spectacular success or failure, increasing earnings volatility. Since the reduction effect is arithmetic and unconditional, while the opposite effect is conditional on management initiatives, we expect the reduction effect will dominate. H2e. Firm size negatively moderates (decreases) the effect of IT spending on earnings volatility. 2.3. The moderating effects of context according to the information processing capability view Under the information processing view, more uncertain and demanding settings provide the volatility necessary for IT's coordination capabilities to become salient. The more uncertain the setting, the greater the opportunity for coordinating mechanisms to share information and mitigate earnings volatility so that it is lower than it would have been otherwise. More uncertain contexts amplify IT's ability to reduce volatility (Fig. 1). We focus on IT use within the firm rather than between firms in a supply chain for two reasons. First, managers can create incentives for information sharing within the firm more easily than across firms in a supply chain, making this view more descriptive of behavior. Second, cross-sectional data on IT spending is available at the firm level but not at the supply chain level. The information processing capability view suggests IT's effect on volatility is always negative (depicted in Fig. 2's right column by having all lines below the X-axis). This is distinct from contextual factors' marginal effects on IT's relationship to volatility (i.e. the slope of the lines in Fig. 2). 2.3.1. H3a competition According to the information processing capability view, greater competition makes the firm's earnings more uncertain and therefore volatile. IT investments that would play a significant role is detecting a competitor's moves (e.g., a system that flags changes in soda market share by neighborhood) and in responding to them quickly (facilitated by production planning, purchasing and manufacturing systems) have more information to respond to, and therefore have a larger mitigating impact on volatility. H3a. The level of competition negatively moderates (decreases) the effect of ITspending on earnings volatility. 2.3.2. H3b sales growth Under the information processing view, IT could be used to gather and process information to help mitigate the uncertainties associated with sales growth for non-it assets when facing the challenges described in H2b. Higher IT investments would lead to lower levels of earnings volatility.

K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 161 H3b. The level of sales growth negatively moderates (decreases) the effect of IT spending on earnings volatility. 2.3.3. H3c,d related and unrelated diversification of product markets The arithmetic effect of portfolio diversification (related or unrelated) arises from the fact that if a firm's earnings in product markets are imperfectly correlated, increased diversification will result in total earnings sum exhibiting less proportionate volatility than the individual markets. This means increasing diversification reduces uncertainty: Firms with low levels of related diversification (RD) have higher levels of earnings volatility than high RD firms because of their lack of diversification. IT's coordination capabilities (which reduce volatility) will be less salient in high RD settings. H3c. The level of related diversification positively moderates (increases) the effect of IT spending on earnings volatility. The effect of unrelated diversification (UD) is expected to be similar in nature to that for RD, but with a stronger effect since the correlation of earnings in unrelated product markets is expected to be lower, reducing the volatility of high UD firms more than of comparable high RD firms. As in the RD case, firms with high UD have the lowest level of earnings volatility, reducing the negative effect of IT on volatility. H3d. The level of unrelated diversification positively moderates (increases) the effect of IT spending on earnings volatility. 2.3.4. H3e firm size Theoretical arguments for firm size parallel those for diversification. Size has a direct arithmetic portfolio effect that mitigates earnings volatility. This dampens the volatility impacts of IT's coordination capabilities. Small firms have higher levels of earnings volatility, so that IT investment can have a significant negative effect on earnings volatility. Large firms have lower earnings volatility, so that this same IT investment would have a less negative impact. H3e. Firm size positively moderates (increases) the effect of IT spending on earnings volatility. 3. Model and methods This section reports the results of regressions of future earning volatility on IT spending and the four contextual variables described in the hypotheses, and several other control variables suggested by previous research, most notably Kothari et al. (2002) which examines the effect of research and development expenditures on earnings volatility. We report results using the two measures of earnings volatility examined by Kothari et al. (2002): the standard deviation of earnings levels (SDE) and the standard deviation of the change in earnings levels (SDΔE). According to Kothari et al. (p. 375) If the time series properties of earnings suggest they are largely permanent, then the SDΔE is likely a better measure of earnings variability, whereas if considerably temporary components of earnings are non-trivial, then the SDE would be a better measure Since earnings contain both permanent and transitory components neither is unambiguously a superior measure of earnings variability. SDΔE largely eliminates the effects of earnings growth over the estimation period, while SDE increases with earnings growth. Following Kothari, we use the five year period after the year of IT expenditure to calculate our volatility measures. We believe this is appropriate to investigate the net effect of the implementation risk and information processing views for two reasons: First, recent empirical research indicates that IT has a significant positive impact on accounting performance levels within three years (Anderson et al., 2006; Kobelsky et al., 2008), indicating that the information processing benefits of IT have not only have begun, but exceed the expense associated with implementation by year three. Second, the analysis examines SDΔE, which controls for changes in the level in earnings over time. To the extent that information processing benefits result in persistent higher firm earnings beyond the five year period, they affect the level of SDE directly, but not SDΔE, since the latter controls for shifts in the level of earnings. We also control for the direct impact of several non-it variables on earnings volatility, including the direct effects of advertising expenditure, research and development expenditure, capital expenditure, leverage and size. Kothari et al. (2002) found that all of these have a direct impact on earnings volatility. As a

162 K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 first order control for the size of the firm, we deflate all financial variables by the market value of equity at the beginning of the current year. Market value captures the intangible information-based assets of the firm, which are particularly important both in systems implementation and the exploitation of benefits. Size is also included as an independent variable to test the second order effects described in the hypotheses. These adjustments are consistent with prior research. To enhance this study relative to Kothari et al.'s approach, we also control for earnings volatility in the five years prior to the IT investment. This allows us to control for the arguments implied in the information processing view: firms facing greater uncertainty and therefore experiencing greater earnings volatility may invest more in IT. Since past volatility is a predictor of future volatility (Table 2) IT might appear to cause volatility though it is in fact an effect of volatility. Following Kothari et al. (2002), we formally specify our linear models predicting earnings volatility in the following way: SD E tþ1;tþ5 ¼ α þ β1itt þ β2adv t þ β3r&d t þ β4capex t þ β5lev t þ β6size t þ β7sd E t 1;t 5 or þ β8comp t þ β9growth t þ β10rd t þ β11ud t þ β12it Comp t þ β13it Growth t þ β14it RD t þ β15it UD t þ β16it Size t þ e and SD ΔE tþ1;tþ5 ¼ α þ β1itt þ β2adv t þ β3r&d t þ β4capex t þ β5lev t þ β6size t þ β7sd ΔE t 1;t 5 þ β8comp t þ β9growth t þ β10rd: t þ β11ud t þ β12it Comp t þ β13it Growth t þ β14it RD t þ β15it UD t þ β16it Size t þ e where Standard deviation of earnings SD(E t +1, t +5 ) is calculated based on earnings before extraordinary items for years t +1 to t +5. Standard deviation of change in earnings SD(ΔE t+1, t+5 ) is calculated based on the change in earnings before extraordinary items over the previous year for years t+1 to t+5 (N=838). IT expenditure (IT) is the IT expenditures in year t according to the InformationWeek 500 annual survey. Advertising (Adv) is the advertising expense in year t. R&D is the research and development expenditures in year t. Capital expenditures (Capex) is the capital expenditures in year t. Size is the market value of common equity at the end of year t. Its natural log (LNSize) is used in subsequent analysis. Standard deviation of earnings last 5 years SD (E t 1, t 5 ) is calculated based on earnings before extraordinary items for years t 1 to t 5. Standard deviation of change in earnings SD (ΔE t 1, t 5 ) is calculated based on the change in earnings before extraordinary items over the previous year for years t 1 to t 5. Competition (Comp) is the percentage of sales in a four-digit SIC code held by the largest four firms in year t, coded as a negative value. Sales growth (Growth)=(Sales t 1 Sales t 5 )/Sales t 5. Leverage (Lev) is current and long-term debt divided by the market value of common equity plus current and long-term debt in year t. Related diversification (RD) is a measure of firm sales in industries having different 4-digit SICs, but the same 2-digit SIC (Dewan et al., 1998). Unrelated diversification (UD) is a measure of firm sales to industries in different 2-digit SIC codes (5). Industry and year dummy variables were created based on 28 two-digit SICs and 6 years in the sample. We do not propose that these models are exhaustive, and therefore our empirical models also control for other industry differences at the two-digit SIC level, as well as exogenous annual effects, though these are not shown in the interest of brevity. To provide the strongest test of the hypotheses, we examine the effects of the variables simultaneously rather than individually. The significance of coefficients in the models is tested by OLS regression analysis of the pooled observations. We first run the regression without contextual moderators to assess whether results are consistent with prior findings of direct effects (Dewan et al., 2007; Kothari et al., 2002). We then add the

K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 163 moderating contextual variables and evaluate whether IT's direct effects on volatility remain significant after these moderating effects are considered. Further, following the approach of Schoonhoven (1981), IT's effect on earnings volatility is calculated along the entire observed range of each contextual variable and illustrated graphically, as in Fig. 2 to determine whether contextual effects are monotonic (i.e., have no effect on the sign of the IT coefficient), or non-monotonic (cause the IT coefficient to switch signs). Next, the significance of the IT coefficient at the minimum and maximum values of the contextual variable is tested (Jaccard et al., 2003). This analysis allows us to determine two things: first, the sign of the moderation effect tells us whether the implementation risk view or the information processing view dominates in explaining the effect of each contextual variable on IT's relationship with earnings volatility. Second, examining the level of IT's effect (i.e. whether the IT coefficient is less than, equal to, or greater than zero) allows us to see which of the two views best describes the effect of IT on earnings volatility in a particular context. The signs of the direct and moderation effects need not be the same. If IT's net effect on earnings volatility is positive (negative) this means implementation risk effects dominate (are dominated by) information processing capability effects, regardless of the sign of the moderation effect. Note that a negative IT coefficient cannot arise from the implementation risk view: If IT assets were deployed in setting with no implementation risk, this would merely drive IT's positive effect on volatility to zero. Conversely, a positive IT coefficient cannot arise solely from the information processing view: If IT assets were deployed in perfectly stable settings having no uncertainty for IT to mitigate, this would drive IT's negative effect on volatility to zero. 4. Data The source of IT investment data is InformationWeek. We use IT budget for the current survey year as a measure of IT spending. This includes both capital expenditures and expenses for hardware, software and IT personnel. InformationWeek and ComputerWorld provide IT-related data such as IT budgets, number of IT employees and other IT-related information as part of an annual published survey. The data from both sources has been used extensively in other similar studies (Bharadwaj et al., 1999; Brynjolfsson and Hitt, 1996; Kudyba and Vitaliano, 2003; Lichtenberg, 1995). Lichtenberg (1995) provides evidence that there is a high correlation between the estimates of IT data from both of these public sources suggesting that either source would be reasonable. Since InformationWeek has IT budget data for a broader set of firms than ComputerWorld, it is used as our source of IT spending data over the 1992 to 1997 sample period. Starting in 1998, InformationWeek stopped publishing IT budget data in their public InformationWeek 500 reports. All data for other measures come directly or are calculated from the Compustat database. Industry concentration is used as a proxy for competition and is measured using the combined market share for the top four firms in each four-digit SIC code (Besanko et al., 2001). To minimize the potential effect of outlier observations on the results, variables are winsorized by adjusting all values in the top and bottom percentiles to be equal to their largest 2nd and lowest 99th percentile values (Kothari et al., 2002). After eliminating observations missing future earnings (131 most likely due to subsequent mergers) and capital expenditures (127), there were 1170 firm-year observations for standard deviation of earnings, and 1168 for standard deviation of changes in earnings. To provide the most reliable measure of competition, we use industry concentration multiplied by 1. This is calculated based on 4-digit SICs. Only firms coded by Compustat as being in a 4-digit rather than 1-, 2-, or 3-digit SIC are included in the sample. Calculation of industry concentration ratios for firms in such artificial 4-digit SICs (e.g., 2000, 2800, and 2820) would include only other firms sharing the same 4-digit SIC, and exclude firms in true 4-digit SICs that would be competing with them. This would lower these concentration ratios by a factor of 4, since 72% of observations (640) are in true 4-digit SICs, rendering them unreliable. Using only firms in true 4-digit SICs reduces the sample in the earnings volatility analysis by 28%, from 1170 (1169 for changes in earnings) to 840 (838). Adding the excluded observations back into the sample does not qualitatively change the results. Related and unrelated diversification are measured using the firm sales entropy measure employed by Dewan et al. (1998). Related diversification is calculated based on a firm's sales to industries with a different 4-digit SICs but sharing the same 2-digit SIC. Unrelated diversification is calculated based on sales to industries with different 2-digit SICs. The scales are positive with a minimum of 0. A firm can operate in a single 4-digit SIC, in which its RD and UD score would both be zero (e.g. JC Penney), or be diversified across

164 K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 4-digit SICs sharing the same first 2 digits (e.g. Harley Davidson). This firm would have high RD but zero UD. The opposite is also possible (e.g. 3M), as is a high score on both (e.g. General Electric) (Dewan et al., 1998). In this sample there is no significant correlation between the two measures (r=.051). Research and development (R&D) expenditure must be disclosed under US generally accepted accounting principles. Firms not reporting R&D expenditures were assumed to have a level of zero, consistent with Kothari et al. (2002). Table 1 provides the descriptive statistics for the dependent and winsorized independent variables. The ranges of the variables are high in relation to their means, facilitating detection of effects on the dependent variable. The mean for standard deviation of earnings is lower than the standard deviation of change in earnings, indicating that firm-specific earning patterns are less significant than regular de-trended earnings fluctuations in explaining earnings volatility for the sample. For analysis all independent and control variables in the model except year and industry are meancentered. This allows interpretation of the IT direct effect coefficient as its effect when all other moderator variables are at their mean. This is possible because in a moderated regression Y=b 1 X 1 +b 2 X 2 +b 3 X 1 X 2, b 1 is calculated as the effect of X 1 conditional upon X 2 =0, removing the effects of the b 3 moderator term. Without mean-centering, b 1 would reflect the impact of IT when all moderator variables are equal to zero. This would Table 1 Descriptive statistics Description Mean SD Minimum Maximum SD of earnings next 5 years 406.72 691.69 3.52 9932.80 SD of change in earnings next 5 years 480.89 866.51 3.31 11,509.80 IT expenditure 221.82 450.55 1.00 5105.01 Advertising 119.74 411.26 0 4100.00 R&D 228.99 728.43 0 8900.00 Capital expenditure 767.52 1897.87 0 31,393.00 Leverage.299.208 0.92 Size 10,114.05 16,966.45 4.48 162,602.45 SD of earnings last 5 years 222.79 406.09 1.98 5168.76 SD of change in earnings last 5 years 259.15 455.11 2.04 3858.71 Competition.62.23 1.00.22 Sales growth % last 5 years.50.78.57 4.38 Related diversification (RD).17.30 0 1.13 Unrelated diversification (UD).25.36 0 1.23 Standard deviation of earnings is calculated based on earnings before extraordinary items for years t+1 to t+5. Standard deviation of change in earnings is calculated based on the change in earnings before extraordinary items over the previous year for years t+1 to t+5. IT expenditure is the IT expenditures in year t according to the InformationWeek 500 annual survey. Advertising is the advertising expense in year t. R&D is the research and development expenditures in year t. Capital expenditures is the capital expenditures in year t. Size is the market value of common equity at the end of year t. Its natural log (LNSize) is used in subsequent analysis. Standard deviation of earnings last 5 years is calculated based on earnings before extraordinary items for years t 1 tot 5. Standard deviation of change in earnings is calculated based on the change in earnings before extraordinary items over the previous year for years t 1 tot 5. Competition is the percentage of sales in a four-digit SIC code held by the largest four firms in year t coded as a negative value. Sales growth= (Sales t 1 Sales t 5 )/Sales t 5. Leverage is current and long-term debt divided by the market value of common equity plus current and long-term debt in year t. Related diversification (RD) is a measure of sales across industries having different 4-digit SIC codes, but the same 2-digit SIC codes (Dewan et al., 1998). Unrelated diversification (UD) is a measure of sales across industries in different 2-digit SIC codes (5). Industry and year dummy variables were created based on 28 two-digit SICs and 6 years in the sample. Notes: N=840 except for standard deviation of change in earnings (N=838). All financial variables are in $millions. In subsequent analysis, all financial variables are deflated by market value of the firm at end of year t 1 (beginning of current year), and independent variables are mean-centered.

Table 2 Correlation matrix (1a) (1b) (2) (3) (4) (5) (6) (7) (8a) (8b) (9) (10) (11) Dependent variables (1a) SD of earnings next 5 years 1 (1b) SD of change in earnings next 5 years.468 a,d 1 Independent variables (2) IT expenditure.468 a.469 a 1 (3) Advertising.128 a.125 a.133 a 1 (4) R&D.399 a.421 a.345 a.084 1 (5) Capital expenditure.303 a.325 a.438 a.248 a.157 a 1 (6) Leverage.280 a.300 a.331 a.126 a.016.450 a 1 (7) Ln (Firm Size).409 a.413 a.377 a.039.136 a.234 a.260 a 1 (8a) SD of earnings prior 5 years.620 a.675 a,d.515 a.113 b.415 a.351 a.388a.419 a 1 (8b) SD change in earnings prior 5 years.573 a,d.625 a.482 a.110 b.408 a.310 a.367 a.398 a.942 a,d 1 (9) Competition.039.029.102 b.105 b.203 a.113 b.133 a.139 a.030.026 1 (10) Sales growth.042.071 c.030.001.046.075 c.165 a.049.200 a.238 a.120 a 1 (11) Related diversification.122 a.118 b.038.070 c.014.132 a.153 a.153 a.126 a.140 a.092 b.080 c 1 (12) Unrelated diversification.017.024.027.052.009.019.011.020.055.073c.239a.048.051 All financial variables are deflated by market value of the firm at end of year t 1 (beginning of current year). All independent variables are mean-centered. Legend: a pb.001, b pb.01, c pb.05. d These two variables are not analyzed simultaneously, but used in separate regression analyses of two different dependent variables (1a and 1b) therefore these correlations are irrelevant to the subsequent analysis. K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 165

166 K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 render the coefficient uninterpretable in this study because the Competition variable's range does not include zero (Table 1) (Hitt and Brynjolfsson, 1996). Table 2 provides Pearson correlation coefficients for the variables in the model. Since several of the variables' correlations are significant, variance inflation factors (VIFs) were computed to assess multicollinearity in the multiple regression analysis. Belsley et al. (1980) suggest that VIFs greater than 10 present a potential problem. The highest computed VIF in this sample was 3.34, leading us to conclude that multicollinearity is not a significant issue. The effect of multicollinearity is to create instability in parameter estimates, increasing their standard error and thereby reducing the statistical significance of variables having high covariance with others. Table 3 Results of pooled OLS regression of standard deviation of, and standard deviation of changes in, annual earnings on ITexpenditure and other variables (p values in parentheses) Dependent variables SD of earnings SD of changes in earnings Independent and control variables 1 2 3 4 5 6 7 8 IT expenditure (IT).411.229.104.501.269.006 (.001) (.001) (.210) (.001) (.004) (.954) Advertising (Adv).154.158.170.161.222.228.223.212 (.142) (.126) (.079) (.084) (.111) (.097) (.082) (.092) R&D.621.519.343.425.914.791.527.617 (.001) (.001) (.001) (.001) (.001) (.001) (.001) (.001) Capital expenditure (Capex).135.071.057.028.185.107.116.104 (.001) (.050) (.097) (.408) (.001) (.028) (.011) (.022) Leverage (Lev).064.051.001.014.114.098.032.045 (.001) (.001) (.953) (.338) (.001) (.001) (.105) (.021) Ln (Firm Size) (Size).018.016.010.011.024.021.013.015 (.001) (.001) (.001) (.001) (.001) (.001) (.001) (.001) SD of earnings prior 5 years.435.420 (.001) (.001) SD of change in earnings.546.438 prior 5 years (.001) (.001) Competition (Comp).052.065 (.001) (.001) Sales growth (Growth).013.002 (.001) (.626) Related diversification (RD).001.007 (.906) (.533) Unrelated diversification (UD).002.002 (.751) (.841) IT Competition (IT Comp).059.032 (.810) (.924) IT Sales growth (IT Growth).245.097 (.001) (.254) IT Related diversification.084.140 (IT RD) (.696) (.641) IT Unrelated diversification.403.488 (IT UD) (.014) (.027) IT Ln (Firm Size) (IT Size).120.290 (.004) (.001) Industry dummies (27) Significant Significant Significant Significant Significant Significant Significant Significant Year dummies (5) Significant Significant Significant Significant NS NS Significant NS df 802 801 800 791 800 799 798 789 Model F 15.71 16.72 21.38 21.00 18.17 18.95 24.15 22.18 Adjusted R 2 39.3% 41.6% 48.6% 53.4% 43.1% 44.9% 51.9% 54.8% SD E tþ1;tþ5 or SD ΔEtþ1;tþ5 ¼ α þ β1itt þ β2adv t þ β3r&d t þ β4capex t þ β5lev t þ β6size t þ β7sd E t 1;t 5 or SD ΔEt 1;t 5 þ β8comp t þ β9growth t þ β10rd t þ β11ud t þ β12it Comp t þ β13it Growth t þ β14it RD t þ β15it UD t þ β16it Size t þ e Non-standardized Coefficients, N=840 for SD of Earnings, 838 for SD of Changes in Earnings. All financial variables are deflated by market value of the firm at end of year t 1 (beginning of year). All independent and control variables are mean-centered.

K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 167 We also performed various robustness tests including the use of different deflators (book value of stockholder's equity and sales), without industry and time indicator variables and the use of different winsorizing cutoffs (2 and 3%) and find qualitatively similar results throughout. To confront the possibilities of cross-sectional correlation and within-firm effects, we reran the analysis with one random observation per firm and found similar effects throughout. We also perform annual regression analysis and find qualitatively similar results throughout. 5. Results Table 3 presents the non-standardized coefficients for one moderated and three direct effects only OLS regressions for each of the two measures of earnings volatility. Direct effects only modeling was performed in a hierarchical fashion to enable results to be compared to prior studies. The first and fifth models contain the variables examined by Kothari et al. (2002) and provide a test of whether the data set generates results consistent with previous larger sample non-it studies. The second and sixth models add IT expenditure and provide initial support for H1. These models test also whether this more recent data set (1992 1997) generates results consistent with a concurrent study of an earlier time period (1987 1994) by Dewan et al. (2007). While the two studies address similar variables, there are significant differences beyond the time period: Dewan et al.'s study departs from the Kothari et al. method used here in three ways: it uses market value of IT hardware rather than total current period [IT] expenditures; it uses a mixture of asset book values and revenues to deflate variables rather than using market value consistently; and it examines the standard deviation of earnings but not the standard deviation of changes in earnings. The third and seventh models provide more robust support for H1 by controlling for the volatility of earnings in the five years prior to the IT investment. The moderated models (4, 8) investigate the contextual variables addressed in hypotheses H2a e and H3a e. 5.1. Direct effects only models 1 3, 5 7 comparison to prior research Models 1 and 5 (excluding IT) provide results that are consistent with Kothari et al. (2002) with the exception that advertising is not significant (p=.142, p=.111), though it has the expected positive coefficient. Adding IT expenditure in models 2 and 6 leads to findings that are virtually identical in significance and sign to those of Dewan et al. (2007). The lack of significance for advertising here and in the Dewan et al. findings may be due to differences in time periods and sample sizes. Kothari et al. examine 1972 1992, Dewan et al. examine 1987 1994 and this study examines 1992 1997. Kothari et al.'s sample is much larger, with 40,569 observations, in comparison to 4228 in Dewan et al. and 840 here. The confirmation of other studies' findings for IT and four of the five other variables suggests the data are suitable for further investigation of IT's impact on earnings volatility. Models 3 and 7 provide a more robust test of other studies' findings by controlling for previous earning volatility. IT's coefficient is reduced but retains its significance as do R&D, capital expenditure, and size. Leverage loses its significance, likely due to its significant correlation with previous volatility (.388 and.367 for SD of earnings and SD of changes in earnings, respectively). IT's positive coefficient suggests that in general the implementation risk view is more descriptive of IT's impact than the information processing capability view, and that managers are experiencing higher risks with IT investments. This may in turn lead them to demanding higher returns from IT and provide a partial explanation for the new IT productivity paradox. 5.2. Moderating effects models 4 and 8 tests of hypotheses These models find that IT's effect on earnings volatility is significant but highly dependent upon three dimensions of context: sales growth, unrelated diversification and firm size. All three variables demonstrate a positive marginal effect of uncertainty on IT's relationship with earnings volatility, consistent with the implementation risk view (H2b, d, e). At average levels of these variables IT has no net direct effect on volatility (H1 ns). Analysis of how the sign and level of the IT coefficient varies over the range of each of these contextual variables provides evidence of a more complex story: in some low uncertainty settings the information processing view better describes the net effect of IT on earnings volatility. Each of the hypotheses and its related findings are discussed in turn below.

168 K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 5.2.1. H1: IT's net direct effect For a firm with average levels of the context and control variables, IT's net effect on earnings volatility is not significant (b =.104, p =.210; b =.006, p =.954), indicating that a simple heuristic of demanding higher returns from IT to compensate for high earnings risk is inappropriate. Though the direct effects only models (2, 3, 6, 7) suggest that IT has a significant impact on earnings volatility, the moderated models' coefficients indicate that this is conditional on the context. The earnings volatility of a firm that invests more in IT is not significantly higher than a firm that invests less if they operated in settings with average levels of the contextual variables. The apparent positive impact of IT on volatility in the direct effects only models is a function of its effects in some contexts, but not an average context. It is possible that IT serves to decrease earnings volatility in some settings and increase it in other more common settings, so that its average effect over all contexts is positive. We investigate this below. 5.2.2. H2a/3a competition In Table 3 competition has a positive direct effect on earnings volatility (model 4: b=.052, pb.001; model 8: b=.065, pb.001) but does not moderate IT's effect (b=.059, p=.810; b=.032, p=.924). The Fig. 3. The moderating effects of three contextual factors on IT's impact on the standard deviation of earnings (regression coefficient for IT on SDE at varying levels of contextual variables). The dotted Average IT Impact line illustrates the impact of IT when all independent variables in the regression are at their average level (i.e., direct effect coefficient of Table 3 model 4).

K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 169 Fig. 4. The moderating effect of two contextual factors on IT's impact on the standard deviation of changes in earnings (regression coefficient for IT on SDΔE at varying levels of contextual variables). The dotted Average IT Impact line illustrates the impact of IT when all independent variables in the regression are at their average level (i.e., direct effect coefficient of Table 3 model 8). positive direct effect indicates that industries with fewer competitors have less volatile earnings, as predicted by economic theory. The lack of a moderation effect indicates that the implementation risk and information processing views either are not affected by competition, or affected relatively equally so that the two moderation effects offset. As a result neither H2a nor H3a is supported. 5.2.3. H2b/3b sales growth Model 4 indicates that in addition to a positive direct effect (model 4 b=.013,pb.001) sales growth has a strong positive moderating effect on the relationship between IT and earnings volatility (model 4 b=.245, pb.001). Model 8's coefficients are not significant, however this highlights a limitation of this alternate measure of earnings volatility rather than a substantive lack of evidence concerning sales growth impacts. The standard deviation of changes in earnings is based on changes in earnings and therefore is already adjusted for changes in sales, making it inappropriate to use in a test of the sales growth hypothesis. Fig. 3(a) depicts how IT's impact on earnings volatility for the subsequent five years varies from being insignificant for slow growth firms (b=.158, n.s.) to exceeding the amount spent on IT for the fastestgrowing firms (b =1.055, p. b.001). 3 This provides support for H2b and the dominance of the implementation risk view as an explanation for the effect of IT in high-growth environments. Sales growth exacerbates the uncertainties associated with IT project implementation. IT amplifies the potential earnings impacts of sales growth. 5.2.4. H2c/H3c, H2d/H3d related and unrelated diversification The results presented in Table 3 also show that while related diversification does not moderate IT's effect on earnings volatility (b=.084, p=.696; b=.140, p=.641), unrelated diversification has a strongly negative moderating impact on earnings volatility (b=.403, p=.014; b=.488, p=.027). The latter result provides support for H2d (unrelated diversification reduces uncertainty, mitigating the IT implementation risks): implementation risk is most salient in explaining how unrelated diversification moderates IT's impact on volatility. In order to assess whether the negative moderation effect merely mitigates IT's positive effect on volatility (a monotonic implementation risk view) or whether it reduces implementation risk effects sufficiently that IT's information processing/coordination capabilities become apparent, Figs. 3(b) and 4(a) 3 The coefficient of 1.055 is calculated as IT's average effect (.104)+its moderated effect (the moderator coefficient (.245) times the mean-centered value of sales growth (4.38.50=3.88)).

170 K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 Fig. 5. Net effect of implementation risk and information processing capability views. plot IT's impact over the range of unrelated diversification in the data set. The analysis indicates that IT's impact is non-monotonic: it shifts from being positive in firms operating in only one 2-digit SIC (b=.189, p=.044; b=.108, n.s.), to reducing earnings volatility at high levels of unrelated diversification (b=.307, p=.083; b=.492, p=.039). One possibility is that IT implementation risk has been reduced below that for non-it spending projects at these same firms. This would contradict the extensive findings in the implementation risk literature described earlier, and evidence in the popular press of challenges and risks faced by the highest IT spending firms regardless of their level of diversification. Fig. 5 illustrates how the reduction of implementation risk associated with diversification could combine with a fixed but lesser information processing capability effect to yield a non-monotonic net effect. This explanation suggests that in undiversified firms project-related risks dominate or equal IT's effects as a coordination mechanism, but as firms diversify their IT investment across unrelated industries, implementation risk impacts decline more rapidly than those of information processing capability, yielding a negative slope. As firms reach high levels of unrelated diversification, information processing capability effects exceed implementation risk impacts. This result suggests that firms that build diversified IT investment portfolios can reduce project risk so their IT investments enable them to react more quickly to environmental uncertainty, and have smoother earnings than comparable low-it firms. 5.2.5. H2e/H3e firm size Size has a strongly negative moderating impact on earnings volatility (b=.120, pb.004; b=.290, pb.001) consistent with H2e. Further analysis at Figs. 3(c) and 4(b) indicate that IT has a strongly positive effect on earnings volatility in small firms (b=.455, pb.001; b=.855, pb.001), but that it also has a strong negative effect in the largest firms (b=.226, p=.097; b=.793, pb.001). As for sales growth and unrelated diversification, implementation risk is most salient in explaining how differences in firm size affect IT's impact on volatility. Consistent with the results for unrelated diversification, the reduction in implementation risk at the portfolio level enjoyed by large firms is such that this source of risk becomes outweighed by IT's volatility reducing capabilities. Smaller firms are likely to have fewer IT projects, making the uncertainty impacts of individual projects more salient in earnings. Larger firms with more projects can exploit the averaging effect of a portfolio so that the effect of IT's information processing capability dominates, smoothing earnings. 5.3. Results summary Fig. 6 depicts the results for a firm examining risk return trade-offs when evaluating IT investments (based on prior research, it assumes positive mean returns to IT). In three of the six contexts, managers face

K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 171 Fig. 6. Moderating effects of context on risk return relationship for IT investment. a positive relation between IT-based risk and return, and must trade-off higher returns against higher risk levels associated with them (i.e., moving from the Starting Point to C), however in the remaining settings, IT's expected returns are either not associated with risk effect (moving to B) or negatively associated with risk (move to A). Firms in these three contexts (i.e. points A and B) enjoy risk-adjusted returns to IT that are higher than those moving to point C. 6. Conclusion This study investigates the impact of firms' IT investments on the volatility of subsequent earnings and the moderation of that impact by various organizational and environmental factors. Since IT spending outstrips all other types of corporate investment (Bureau of Economic Analysis, 2005), recognizing and understanding IT's impact on earnings volatility is an important complement to prior research on IT returns. This responds to recent calls for more research on how context moderates IT's use and impact (Orlikowski and Iacono, 2002). We believe that this study's findings make three important contributions to the understanding of the IT-firm performance relationship. First, the findings reported here help explain and deepen the new productivity paradox (Anderson et al., 2003), i.e., firms' apparent under-investment in IT given recent evidence of high average returns (Bharadwaj et al., 1999; Brynjolfsson and Hitt, 1995). The positive impact of IT hardware and software spending on earnings volatility in some settings indicates these firms have good reason to evaluate IT projects using returns that have been discounted for risk rather than nominal returns, just as they do for financial investments (Hoffman, 2002). This is consistent with Dewan et al.'s (2007) finding that IT hardware capital levels are associated with earnings volatility for the earlier 1987 1994 period. Second, the results indicate that a simple heuristic of discounting returns from IT investments for risk relative to other investments is inappropriate. At average levels of the contextual variables IT does not significantly increase risk. Further, firms that are large or have high levels of unrelated diversification can use IT to reduce earnings volatility. 4 To properly factor the benefits of this risk reduction effect into IT evaluation decisions, such firms should consider adding a premium to benefit estimates rather than discounting them. This raises an intriguing question: are these firms' nominal returns from IT investment comparable to other firms so that their risk-adjusted returns are even higher? Such behavior would deepen the productivity paradox. 4 This provides a possible explanation for the unexplained negative industry IT coefficients seen in four industries in Dewan et al. (2007).

172 K. Kobelsky et al. / International Journal of Accounting Information Systems 9 (2008) 154 174 The theoretical contribution of this study is its development and analysis of the relative importance of two models of how contextual uncertainty moderates IT's impact on the variability of firm performance. Existing research (Dewan et al., 2007) has focused exclusively on the implementation risk view. The positive relationships between IT's effects and contextual uncertainty indicate that the IT project implementation risk view better describes the marginal effect of differences in context-driven uncertainty. Further analysis indicates that though implementation risk may describe the effect of differences in uncertainty on IT's effect on volatility, benefits derived from information processing capability better describe the level of IT's impact in certain contexts having low levels of uncertainty (i.e. high unrelated diversification and large size). Firms evaluating IT investments should do so based on the level of uncertainty expected for that project; how this uncertainty might change in a different context is irrelevant unless it modifies the expected level for that project. This provides a new approach for valuing IT investments to complement traditional asset valuation and real options theory techniques. IT's coordination capabilities may have a direct impact of reducing operating costs and increasing revenues, but they also have an important risk-reducing impact: the smoothing of income. The value associated with this business risk reduction could be incorporated as an expected outcome in IT valuation techniques. This risk reduction premium is distinct from the uncertainty-reduction value arising from real options analysis. Fichman (2004) describes IT's value as a real option as follows: initial investments in new IT such as pilot projects, prototypes, or the first phase in a multiphase implementation create growth options These options confer the right, but not the obligation, to obtain benefits from future deployments of the technology The information processing view applies to the vast majority of implemented IT (excluding only pure automation such as robotics), not just pilot projects that defer or provide information to inform future deployments. For example, Dell's tightly coupled IT-based order and JIT factory production systems are coordination mechanisms that create business value not only by reducing absolute inventory levels and the level of personnel (traditional value drivers), but by improving responsiveness to change, reducing earnings volatility. The real options perspective is not descriptive of these benefits since these are not pilot systems being used to investigate the benefits of future technologies. These findings have important implications for managers. Prior research has identified IT capabilities as an important determinant of firm performance associated with IT investments (Bharadwaj, 2000). Our findings suggest knowing how to evaluate the impact of context on earnings volatility is a valuable IT managerial skill. There are limitations to this study. The estimates of IT spending in the InformationWeek data include IT spending by the corporate IS department, but usually exclude IT spending paid for directly by end-user departments from their own budgets. This would bias results if the two types of spending have different impacts on volatility but can't be assessed because end-user department spending estimates are not available. The analysis does not adjust IT spending amounts for declining prices. Although the IT variable could be transformed to reflect Moore's Law, the objective of the analysis relates to the effect of financial investments on earnings volatility. If transforming it were more appropriate, not doing so mitigates against finding significant effects. Further investigation of the three dimensions of context found to moderate IT's impacts present a significant opportunity for future research. Extending prior research of IT returns to examine these three contextual variables would allow analysis of the risk return trade-offs that managers have been making and an assessment of its consistency. One could use average firm returns on IT investment along with the measures of context to construct implicit risk return curves for each contextual factor. 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