The Journal of Applied Business Research January/February 2010 Volume 26, Number 1

Transcription

1 Product Dversfcaton In Compettve R&D-Intensve Frms: An Emprcal Study Of The Computer Software Industry C. Catherne Chang, Elon Unversty, USA ABSTRACT Ths paper studes the effect of dversfcaton nto dfferent product categores wthn compettve hgh-tech ndustres. Emprcal results obtaned usng the prepackaged computer software ndustry as the sample show that: (1) dversfed frms appear to be more proftable (have hgher returns on equty), attan a larger market share, enjoy hgher excess market returns, and have a hgher market-to-book rato; (2) from the nvestors perspectve, dversfcaton per se does not ncrease frm value. Realzed hgher values do not appear to come from more effcent use of assets. Rather, they stem from the hgher nnovaton effcency (hgher sales revenue per employee) and hgher levels of R&D spendng for frms that have dversfed nto dfferent product categores than for frms that confne development to a sngle product category. Keywords: R&D, Product Dversfcaton, Computer Software Industry INTRODUCTION D versfcaton has ntrgued the busness world and researchers for decades. It s often used strategcally to mprove frm performance and/or to reduce rsk. There are a number of dfferent forms that dversfcaton may take. Frms may dversfy nternally or through mergers and acqustons, and they may dversfy nto related ndustres or un-related ndustres, nto dfferent geographcal areas, and nto dfferent product categores wthn the same ndustry. In ths paper, my focus s partcularly on dversfcaton by expanson nto dfferent product categores wthn a technology-ntensve ndustry. I frst examne emprcally whether frms that dversfy nto dfferent product categores perform better than un-dversfed frms. I then nvestgate the potental source of the observed performance mprovement and consder whether nvestors attach a hgher frm value to dversfed frms due to dversfcaton alone, because of more effcent use of assets, or because R&D spendng s valued more hghly for dversfed frms than for un-dversfed frms. I use the prepackaged computer software ndustry as the sample to conduct the emprcal tests because (1) R&D s a crtcal factor for a computer software company s success; (2) the software market s known for ts great dversty of product offerngs; and (3) product-level data s commercally avalable. Unlke a cross-sectonal study, an ndustry-specfc study provdes natural control for the external compettve envronment (Sherer and Ross 1990) and for the technologcal condtons of the sample frms (Matolcsy and Wyatt 2008), both of whch are hypotheszed to affect frm performance. My emprcal results show that (1) dversfed frms have hgher sales revenue per employee and hgher returns on equty, attan a larger market share, and enjoy hgher excess stock returns above S&P market returns; (2) dversfed frms have a hgher market-to-book rato; (3) the superor performance of these frms s not explaned by more effcent use of assets, as ther average assets turnover rate s not better than that for undversfed frms; and (4) dversfcaton per se does not add frm value, wth R&D assets beng valued more hghly for frms that engage n multple-product categores development than for frms that confne development to a sngle-product category. 99

2 The next secton summarzes relevant pror research. I then present the pertnent hypotheses and outlne the nsttutonal settng of the computer software ndustry, followed by research desgn. Sample selecton and summary statstcs are reported and the emprcal results and ther mplcatons are dscussed. The fnal secton provdes a summary and conclusons. PRIOR RESEARCH Research on dversfcaton can be roughly dvded nto the followng threads. One thread centers on dentfyng the motves for dversfcaton. For example, t has been argued that frms dversfy to reap benefts from economes of scale by sharng resources, to ncrease market power, to prolong frm lfe, to reduce rsk of bankruptcy, and to take advantage of tax shelds (see Copeland, Weston, and Shastr 2004 for a survey of the relevant lterature). Another thread seeks to expose the frm-value dynamcs created by dfferent knds of dversfcaton. The extant lterature ndcates that frm value s reduced for dversfcaton nto unrelated ndustres, whle dversfcaton nto related ndustres ncreases value (Berger and Ofeck 1995; Graham, Lemmon, and Wolf 2002), and that whle geographc dversfcaton adds frm value, ndustral dversfcaton decreases t (Bodner, Tang, and Wentrop 1998). A related lne of research nvestgates the effects of dversfcaton on frm performance. The results obtaned to date are nconclusve. For nstance, usng Rumelt s Specalzaton Rato to defne degree of dversfcaton, Pandya and Rao (1998) fnd that, although dversfed frms perform better n both rsk and return dmensons than do undversfed frms, domnant undversfed frms as a group have hgher average returns on equty that are accompaned by hgher varance when compared to dversfed frms. Stern and Henderson (2004) fnd that frms falure rates ncrease (decrease) wth prmary-product lne dversfcaton when the external envronment s (s not) very compettve. Other than the Stern and Henderson study, most of the extant lterature analyzes the mpact of crossndustry dversfcaton or cross-country dversfcaton rather than that of the cross-product dversfcaton wthn the same ndustry that s the focus of the present study. In both cross-ndustry and cross-country dversfcaton, t s dffcult to dscern how long t takes for the dversfcaton to have an mpact on frm performance. Consequently, researchers are often left to assume an mmedate effect and use msmatched performance measures n ther studes. Ths renders the resultng mplcatons nvald. In a market where technology advances rapdly, cross-productcategory dversfcaton has a drect and mmedate mpact on frm performance and market valuaton. Therefore, the assocaton between dversfcaton and frm performance can be dentfed wth greater accuracy. HYPOTHESES DEVELOPMENT AND THE SOFTWARE INDUSTRY In ths secton, I develop hypotheses that are pertnent to cross-product lne dversfcaton n compettve technology-ntensve ndustres. Gven ths context for the hypotheses, I frst revew the market structure of one such ndustry that can represent these ndustres as a whole, namely the prepackaged computer software ndustry (SIC = 7372). Ths s an ndustry that has experenced rapd growth from ts 24 frms n 1979 to 445 n Its fnancal mportance has also ncreased rapdly, wth the market value of the average company growng from $80.83 mllon n 1979 to $1.587 bllon n The ndustry s ntensvely compettve, wth the market share of an average frm beng lmted to about one per cent. 1 Software developers compete wth ther peers on prce, product functons, speed, compatblty, and ease of use, as well as through other product qualtes. What makes the compettve envronment even more challengng s that technologcal advances affectng computer hardware may change the compettve envronment vrtually overnght. 2 In addton, t s a market for dfferentated products, wth competng frms producng a number of smlar but not dentcal software tems. Consumers n ths market are aware of product dfferentaton and have vared preferences. To ensure that ther needs and preferences are met n a tmely manner and that the relevant 100

3 product dfferentaton s recognzable, frms must make huge nvestments n marketng surveys and advertsng campagns. For these reasons, software products have relatvely short economc lves. Consequently, patentng does not provde an effectve means of deterrng entry nto ths ndustry or of mantanng compettveness. Instead, hrng and keepng sklled workers to ensure frequent nnovatons and upgrades, as well as sustanng product dfferentaton, are all crtcal for a software frm s survval and success. Lebowtz and Margols (1999) attrbute the software market s unque structure to the followng two characterstcs: (1) nstant scalablty and (2) ndependence of product qualty and margnal cost. Instant scalablty refers to the fact that as the reproducton of a software product requres no more than a dsk/cd-rom duplcator, output can be expanded almost nstantly. Independence of product qualty and margnal cost refers to the fact that ncorporatng addtonal features nto a software product often requres only transportng/alterng program codes from another exstng product. Improvng a product s qualty therefore adds lttle to ts cost. In searchng for the types of resources that can be most successfully transferred n the process of dversfcaton, Mata, Fuerst, and Barney (1995) dentfy those that are valuable, rare, and mperfectly moble across frms, most especally technologcal knowledge. Such resource shfts can lead a frm to a poston of both short-term and sustaned compettve advantage. The currently extant lterature confrms that product dversty can be attaned wth lttle margnal cost, that product output s nstantly scalable n compettve hgh-technologcal ndustres, and that the shared resources are both valuable, heterogeneous, and not easly moblzed n a cross-sectonal manner. It follows that frms n such ndustres can take advantage of dversfcaton by growng nto other product categores wthn the same ndustry so as to acheve performance levels that are superor to those that can be realzed by remanng undversfed. In realty, observatons also ndcate that frms that eventually enjoy large market shares are not necessarly the orgnal creators of a product/product dea, but rather those that can develop products wth the best desgns at lower cost. 3 Hence, the followng hypothess s pertnent n the present context: Hypothess 1: Frms that dversfy nto dfferent product categores wthn the same ndustry have superor proftablty and a hgher market valuaton than those that do not dversfy. Although frms that have dversfed nto dfferent product categores may enjoy superor proftablty and a hgher market valuaton, dversfcaton per se may not necessarly be the exclusve source of these outcomes. Two other factors may contrbute to the extent of frm value ncreases assocated wth dversfcaton n technologyntensve ndustres: (1) more effcent use of tangble assets and (2) hgher nnovaton effcency. Improved performance may be acheved by dversfcaton f a dversfed frm can take advantage of economes of scale through the deployment of ts combned tangble assets. However, effcent use of assets also reles on the poltcal envronment and the effectveness of the nternal organzatonal structure. For example, ROIbased compensaton for dvsonal managers n a dversfed frm s lkely to result n myopc manageral behavors and neffcent use of combned assets and resources. Because the poltcal and nternal organzatonal structure are hghly dosyncratc and frm-specfc, the effcency wth whch each frm uses ts assets s lkely to exhbt great varablty. Therefore, t cannot be expected that any frm group wll exhbt mproved effcency n assets use due to dversfcaton alone. For technologcally-ntensve frms, dversfcaton may have a postve effect on frm value va mproved nnovaton effcency. Teece (1980) contends that R&D dvsons can share ther technologcal nnovatons, takng advantage of economes of scale n the nnovaton effort and thereby achevng hgher nnovaton effcency. In the case of software development, a technologcal advance n one area can sgnfcantly beneft development n other areas. For nstance, advancement n the object-orented paradgm has been appled n the development of many software products such as databases, word processors, and web browsers. If nnovaton effcency can be acheved across products and dvsons, nvestments n R&D wll be valued more for a dversfed frm than for a nondversfed frm. Here the followng hypothess becomes relevant: 101

4 Hypothess 2: R&D spendng s more valuable for frms that dversfy nto dfferent product categores (wthn the same ndustry) than for those that do not dversfy. RESEARCH DESIGN Operatonal Measures Level of Dversfcaton. The modfed Herfndahl Index n Holthausen et al. (1995) s appled to measurng the level of dversfcaton, wth dversfcaton n the present study measured across product categores as follows. Each software product n the sample s classfed (by PC Data) nto one of sx categores: busness, educaton, fnance, games, personal productvty, and reference software. The total sales amount for each product category of each frm s frst summed and then squared. The sum of the squares for each category s then dvded by the squared total of frm sales to form the frm s modfed Herfndahl ndex. Wth sx categores under consderaton, the modfed Herfndahl ndex should fall between and 1 (nclusve). A score of 1 ndcates a frm s non-dversfcaton status. The closer a score s to 0.167, the more dversfed the frm s. The Herfndahl ndex s ultmately converted to a dchotomous measure, wth dversfed frms (score <1) takng D=1 and the non-dversfed group (score=1) takng D=0 or nto three groups. Other performance measures. Return on Equty s net ncome dvded by common equty value. It measures the amount of net ncome earned by common shareholders for each dollar of nvestment. Assets Turnover s calculated by dvdng net sales by total assets. It measures how effcently assets are used to produce sales. The Gross Margn Rato s gross proft (net sales mnus cost of sales) dvded by net sales. Hgher gross margn ratos ndcate lower costs per dollar of sales. Market Share s the sum across 12 months of sales provded by PC Data dvded by the total ndustry sales. Excess Market Returns s measured as the stock prce apprecaton of a gven year mnus the S&P- 500 returns for that year. It provdes the nvestors perspectve of frm value and may ncorporate forward-lookng nformaton beyond the hstorcal nformaton presented n accountng reports. The Market-to-Book Rato s the market prce of equty dvded by the book value of equty. It s hypotheszed to measure, nter ala, rsk, market strength, and growth potental. To test hypothess 2 on the heterogenety of cross-sectonal market valuaton of R&D, the followng returns regresson model apples: R 0 1( RD 2 ( RD 3( D 4 ( D )( RD 5 ( E 6 ( E )( D 7 ( E 8 ( SIZE Where R t s the annual common stock return per share, accumulated begnnng nne months before to three months after the fscal year end; RD t s the R&D expendture for frm n year t; n ths study t ncludes R&D expense and captalzed R&D; RD t s the change n annual R&D spendng per share; E t s the annual earnngs per share before R&D for frm ; s the change n earnngs per share before R&D for frm n year t; E t SIZE t s the natural log of the market value of equty. All varables except the ntercept, R t, and SIZE are scaled by the market value at tme t-1. D = 1 for dversfed frms; D = 0 for non-dversfed group. In the above model, the group that s assgned D = 0 s the base group. A sgnfcantly nonzero shows 3 how much the group wth the characterstc D = 1 dffers n ts average annual stock returns from the base group after controllng for other factors. 4 A sgnfcantly nonzero would valdate the heterogenety hypothess. For the 4 hypothess relatng to product dversfcaton, a sgnfcantly postve would ndcate that R&D s valued more 4 102

5 for dversfed frms than for non-dversfed frms. A predcton for s not avalable because the sgn of 2 should 2 be consdered along wth the sgn of 4. A postve would suggest that R&D spendng s prced as for assets n 1 general. However, f s non-postve but 2 s postve, t would suggest that ncremental R&D spendng s prced 4 as for assets only n the case of dversfed companes. The other predctons are that 5,, 6, the coeffcents of earnngs change, the nteracton between 7 earnngs change and the dummy, and earnngs level, respectvely, are ndetermnate. The reasons for these results are dscussed below. Several returns-earnngs relatonshp studes (for example, those of Easton and Harrs 1991 and of Al and Zarown 1992) have shown that both the current earnngs level and the earnngs change varables are postvely assocated wth stock returns. Aboody and Lev (1998) decompose net ncome nto R&D components and net ncome before R&D and stll fnd a sgnfcantly postve assocaton between the stock returns and the net ncome before R&D n the pooled regresson. However, Black (1998) argues that earnngs are not value-relevant for start-up and growth frms because earnngs persstence s lower for these frms and a large porton of ther value conssts of ther growth opportuntes. Hs emprcal fndngs are consstent wth hs predcton n showng that earnngs of start-up and growth frms are not postvely correlated wth market value. Hayn (1995) fnds that reported losses are less nformatve concernng a frm s future prospects and exhbt a weaker assocaton wth returns than wth profts. Snce the prepackaged computer software ndustry s relatvely young, the expectaton here s that the majorty of sample frms wll be n the start-up and growth stages. I also expect the sample frms to report more losses than do frms n more mature ndustres. If the earnngs effect of start-up and growth frms and/or the effect of losses domnate, 5 and 6 may not be postve n equaton (3). Thus, no sgn s assgned to the predcton for earnngs. In addton, two separate regressons wll be conducted for the loss group and for the proft group n order to control for losses. The extant lterature reveals that larger frms have smaller earnngs response coeffcents than smaller frms. In the R&D context, t s argued that centralzaton s necessary for large frms to control employee managers and that a more centralzed frms tend to nhbt nnovaton due to ther more bureaucratc control mechansms (Holthausen et al. 1995). To the extent that frm sze s a proxy for centralzaton, frm sze could show a negatve correlaton wth the market valuaton of R&D spendng. However, as frm sze s also related to the avalablty of fnancal resources, then f such avalablty can facltate nnovaton effcency, frm sze wll be postvely correlated wth the market valuaton of R&D. Snce the sze effect on the market valuaton of R&D nvestment s ambguous, but the effect of sze on frm valuaton s well documented, frm sze s used only as a control varable n the present study. DATA In short, the predctons are: 1 0, 4 0 Sample Selecton. Sample selecton set out from the frms lsted as beng n the computer software ndustry n Yahoo! Fnance as of February 20, A frm n the fnal sample must satsfy the followng selecton crtera: (1) ts SIC code s 7372 (prepackaged computer software ndustry), (2) ts relevant data for the fscal years 1993 to 1998 must be avalable n the 1998 COMPUSTAT fle and the 1998 CRSP daly and monthly fles, (3) data about ts monthly sales of each retal software product for the years 1994 to 1997 must be avalable from the PC Data Retal Sales Report, 5 and (4) ts number of employees must be avalable through ts 10-K reports. The fnal sample conssts of 176 frm-year observatons across 44 software developers durng the years from 1994 to1997. Summary Statstcs The summary statstcs for the software frms used n testng are provded n Table 1. Two sample statstcs 103

6 reveal the mportance of R&D n the prepackaged computer software ndustry: (1) on average, three out of every ten employees n these companes worked n the software development dvson, and (2) the average frm n the sample spends 28% of ts net sales on R&D (a measure of R&D ntensty). The R&D ntensty ranges from less than 1% to more than 300%. The average frm ncreases annual R&D spendng by $23.6 mllon durng the sample perod. The annual operatng ncome of the sample frms ranged from a loss of $64 mllon to a proft of $7.468 bllon. Market captalzaton of the frms ranged from $6.588 mllon to $ bllon, wth a medan of $309 mllon. Except for Computer Assocates, whch was traded on the New York Stock Exchange, all other frms were traded on the Natonal Assocaton of Securtes Dealers Automated Quotaton System (NASDAQ) durng the sample perod. Table 1: Summary statstcs for the software frms used n emprcal testng For the Years 1993 to1998 (n thousands of dollars except for percentages) N* Mean Medan Std. Dev. Mnmum Maxmum Total Assets ,622 86,790 2,332, ,357,000 Market Value of Common Equty 217 4,757, ,399 23,038,895 6, ,836,744 Operatng Income ,854 10, ,171-64,467 7,468,000 R&D Spendng ,642 20, , ,502,000 Change n R&D Spendng ,608 4, , ,680 1,085,000 R&D Intensty (=R&D Spendng/ Sales) % 19.09% 35.50% 0.09% % % of techncal employees ( )** % 30.61% 13.74% 9.62% 75.68% Annual Stock Returns % 16.67% % % 1,047.6% *Expanded samples are used when data s avalable. **Percentage of techncal employees based on an expanded sample of 386 observatons ranges from 4% to 90.9%, wth a medan of 28% and an average of 31%. The sample statstcs for the market structure varables are presented n Table 2. Evdence of dssmlar market shares and market domnaton are clearly present. Between 1994 and 1997, the aggregate market share of the sample frms ranges from less than 0.001% to 22%, wth a mean of 1.33% and a medan of 0.15%. Only one frm has an aggregated market share of more than 10%. As expected, Mcrosoft s ths domnant software developer and has a market share (22% n 1994) that dwarfs that of the other players n the retal market for all four years tested. The modfed Herfndahl ndex measures the degree of dversfcaton of a frm across dfferent product categores. As dscussed n Secton IV above, the ndex should fall between and 1 (nclusve). As shown n Table 2, the medan of 1 suggests that more than 50% of the sample frms only produce tems n one product category. The mean of 0.89 mples that most frms are not very well dversfed among the sx product categores defned by PC Data. Table 2: Sample statstcs for product market structure varables Sample perod: 1994 to 1997 Varable N Mean Medan Std. Dev. Mn. Max. Aggregate Market Share % 0.15% 3.56% <0.0001% 22.1% Modfed Herfndahl Index Age of the Sample Frm s Average Product on the market snce ts frst release (n months) Rato of New Products to Total Products on the Market Aggregate Market Share of a frm s the frm s total sales dvded by total ndustry sales. Weghted Average Market Share of a frm s the sum of ts market share n a product category multpled by the percentage of ts revenue derved from that product category. Modfed Herfndahl Index, used as a dversfcaton measure n ths study, s the sum of the squares of a frm s sales n each product category dvded by the squares of ts total sales. 104

7 Statstcs on the software products age and on the percentage of new products on the market reveal the speed of product nnovaton n the computer software sector. For the average frm, the products on the market have an average age of around 16 months. Where the average age s one month, ths mnmum ndcates that ether some new company has just rolled out ts new products or some frm has just entered the retal market. No frm sells products that average more than three years old. The average frm has equal numbers of new products and old products on the market (50%) wth more than 20 frm-year observatons havng 100% new products on the market. RESULTS The Wlcoxon Test was used to examne the dfference between the frms performances n the dversfed and undversfed groups because normalty tests results (not presented here) show that the sample s not normally dstrbuted. The frst three rows of Table 3 show that the dversfed and undversfed groups do not dffer, at least statstcally, n terms of frm sze as measured by net sales and total assets. They are also smlar n terms of R&D nvestment per dollar of net sales. In terms of proftablty, though, dversfed frms enjoy a 5% greater return on equty than do undversfed frms. Dversfed frms also acqure more than 120 tmes the market share of that avalable to undversfed frms and delver 30% more n excess market returns than do undversfed frms. The market-to-book rato s hgher for dversfed frms than for the undversfed group as well. However, the gross margn rato of the undversfed group s 20 ponts hgher than for dversfed frms, suggestng that frms n the sngle-product category acheve relatvely lower costs of sales, perhaps due to the expertse they developed n the specfc product categores. The dfferences between these two groups are all statstcally sgnfcant at the 5% level, suggestng that frms that dversfed nto dfferent product categores, although smlar n sze and R&D ntensty, have hgher proftablty, a larger market share, and a hgher market valuaton than do undversfed frms. Table 3: Wlcoxon two-sample test for equalty of sample medan Non-dversfed (n=76) Dversfed (n=68) Wlcoxon statstc (One-sded P-Value) Sales Assets R&D Intensty 18.53% 19.06% Sales per employee < Return on Equty 10.11% 15.73% Gross Margn 84.50% 66.44% < Market Share < Excess Market Returns % 15.93% Market-to-Book Assets Turnover The asset turnover ratos, however, do not show any statstcally sgnfcant dfference between the dversfed (1.037) and the undversfed groups (0.942), mplyng that the mproved proftablty and market valuaton acheved by expandng nto dfferent product categores are not assocated wth effcences n usng tangble assets. Table 4 reports the results for the hypothess relatng to the market valuaton of R&D expendtures usng the returns regresson. The hypothess predcts that R&D spendng s more valuable for a dversfed frm than for a non-dversfed frm. The coeffcent of the nteracton term of change n R&D expendture and the dversfcaton measure s , sgnfcant at the 10% level, whch suggests that R&D expendture contrbutes around 12% more to annual stock returns for dversfed frms than for non-dversfed frms. The coeffcent of the R&D ncrement s not sgnfcant, mplyng that ncreases n R&D spendng only add to frm value for dversfed frms and not n the case of non-dversfed frms. 105

8 Table 4: Product dversfcaton and market valuaton** R RD ( RD ( D ( D )( RD ( E ( E )( D ( E ( SIZE 0 ( D 1( D = 1 for frms whose Herfndahl Index <1 (Dversfed frms); n = 51 D = 0 for frms whose Herfndahl Index =1 (Non-dversfed frms); n = 59 Varable Predcted Sgn Parameter Estmate (P-value)* Parameter Estmate (P-value)* Intercept (0.042) (0.018) RD (0.063) (0.058) RD (0.004) (0.999) D (0.301) RD)( ) (0.071) (0.897) E (0.008) )( D ) ( E E (0.909) SIZE (0.036) Adjusted R % (0.488) (0.696) (0.024) 26.0% (N = 110) (N = 110) P-values reported are for the one-taled test f there s a predcton and for the two-taled test f no predcton s made. **Because the dversfcaton nformaton used n ths study s compled on the bass of software developers data for the retal market, the regresson results reported here are weghted by percentages of sales derved from retal sales. Notce that when the dversfcaton dummy s ncluded n the regresson, both earnngs level and earnngs change do not show any statstcal sgnfcance. Separate regressons for the loss group and the proft group (results not provded here) do not generate dfferent (n terms of the sgnfcance of the parameters) results wth respect to earnngs. Only when the observatons are equally weghted (that s, when regressons are not weghted by the percentage of retal sales) does the coeffcent of earnngs change become statstcally sgnfcantly postve at below 3% levels. Ths result mples that n terms of market valuaton, earnngs surprses are not as mportant as product dversfcaton for those frms wth sales domnated by retal sales. For frms that depend less on retal sales, earnngs changes are stll value-relevant. The results confrm that dversfcaton alone does not ncrease frm value and that ncremental R&D spendng alone does not add to frm value unless the frm has a dversfed product portfolo. R&D expendture level s statstcally sgnfcantly postve n all the returns regressons and ts coeffcent s around 4, suggestng that R&D s vewed as crtcal n the prepackaged computer software ndustry. SUMMARY AND CONCLUSIONS The paper studes same-ndustry dversfcaton among frms wthn the prepackaged software ndustry. My key argument s that because of the ease of transferablty of the knowledge developed n one product category to other product categores, frms that choose to dversfy by expandng ther product categores n the same ndustry can take advantage of economes of scale n nnovaton and acheve mproved proftablty and market valuatons. Emprcal tests demonstrate that dversfed frms on average have hgher returns on equty, hgher sales per employee, hgher market-to-book ratos, and hgher market shares than do undversfed frms. However, better performance through dversfcaton s not assocated wth the effcency of employng tangble assets. 106

9 I further test the hypothess n the regresson model usng accountng data, stock prce, and an extensve database that contans retal sales nformaton at a product level for the software frms between 1994 and Emprcal evdence, consstent wth my hypothess, suggests that R&D s more valuable for frms that are developng products n dfferent categores wthn the software ndustry than for undversfed frms. Test results are not senstve to how the varables are measured. AUTHOR INFORMATION Dr. Catherne Chang s an Assstant Professor of Accountng wth Love School of Busness at Elon Unversty, an AACSB accredted school. She receved her Ph.D. degree n Accountng from the Cty Unversty of New York, the Master of Accountng degree from UNC-Chapel Hll and Master of Scence n Management degree from NC State. Pror to jonng Elon, she has taught at CUNY- Baruch College, Greensboro College, and North Carolna Central Unversty. She has publshed artcles n Journal of Accountng and Publc Polcy, Revew of Quanttatve Fnance and Accountng, among others. Her research focuses on the captal market valuaton of accountng numbers, especally the valuaton of ntangble assets such as R&D. REFERENCES 1. Aboody, D. and B. Lev. (1998). The value-relevance of ntangbles: the case of software captalzaton. Workng paper (Unversty of Calforna at Los Angeles and New York Unversty). 2. Al, A. and P. Zarown. (1992). The role of earnngs levels n annual earnngs-returns studes. Journal of Accountng Research 30: Berger, P. and E. Ofek. (1995). Dversfcaton s effect on frm value. Journal of Fnancal Economcs 37: Black, E. (1998). Lfe-cycle mpacts on the ncremental value-relevance of earnngs and cash flow measures. Journal of Fnancal Statement Analyss 4: Bodnar, G., C. Tang, and J. Wentrop. (1998). Both sdes of corporate dversfcaton: the value mpacts of geographc and ndustral dversfcaton. Workng paper (Unversty of Pennsylvana). 6. Copeland, T., F. Weston, and K. Shastr. (2004). Fnancal Theory and Corporate Polcy. Readng MA: Addson-Wesley Publshng. 7. Easton, P and T. Harrs. (1991). Earnngs as an explanatory varable for returns. Journal of Accountng Research 29: Graham, J., M. Lemmon, and J. Wolf. (2002). Does Corporate Dversfcaton Destroy Value? Journal of Fnance 57: Hayn, C. (1995). The nformaton content of losses. Journal of Accountng and Economcs 20: Holthausen, R., D. Larcker, and R. Sloan. (1995). Busness unt nnovaton and the structure of executve compensaton. Journal of Accountng and Economcs 19: Hosksson, R. E., M. A. Htt, and C. W. Hll. (1993). Manageral ncentves and nvestment n R&D n large multproduct frms, Organzaton Scence 4: Lebowtz, S. and S. Margols. (1999). Causes and consequences of market leadershp n applcaton software. A paper presented at the conference: Competton and Innovaton n the Personal Computer Industry, Aprl 24, Mata F., W. Fuerst, and J. Barney. (1995). Informaton technology and sustaned compettve advantage. MIS Quarterly 19(4): Matolcsy, Z. and A. Wyatt. (2008) The assocaton between technologcal condtons and the market value of equty. Accountng Revew 83: Pandya, A. and N. Rao (1998) Dversfcaton and frm performance. Journal of Fnancal and Strategc Decsons 11: Scherer, F. M. and D. Ross. (1990). Industral market structure and economc performance. Boston: Houghton Mffln. 17. Stern, I. and A. Henderson. (2004). Wthn-Busness dversfcaton n technology-ntensve ndustres. Strategc Management Journal 25: Teece, D. J. (1980). Economes of scope and the scope of the enterprse. Journal of Economc Behavor and Organzaton 1:

10 ENDNOTES 1 See Table 2. 2 For nstance, a software product desgned to mprove the runnng speed of a computer operatng system mght be rendered obsolete by a hgher-speed computer chp. 3 For example, the Graphc User Interface (GUI) computer operatng system was an dea created n the Xerox laboratory to make operatng systems user-frendly. Apple Computers adopted the GUI dea and appled t to ts Macntosh Computers. Macntosh Computers success n the consumer market n the 1980s can be largely attrbuted to ts GUI operatng system, the Macntosh OS. However, the OS that currently domnates the market for personal computers s Mcrosoft s Wndows, even though t was ntroduced fve years after the Macntosh OS. 4 Because the dummy may also nfluence how the market prces earnngs surprses, I add an nteracton term of change n E * D n equaton (4) to control for any effect the dummy may have on stock returns through earnngs and the dummy, earnngs. 5 PC Data was a market research company that collected monthly sales data from computer software wholesalers, dstrbutors, and retalers across the Unted States. It was merged nto the company currently known as Marketresearch.com. To comple ts Retal Sales Report, PC Data asked the contracted software retalers to provde nformaton about ther monthly sales n unts and n dollars for each software ttle. The market share of each product and the date of ts frst appearance were also collected n the report. 108