Hitotsubashi University
Institute of Innovation Research

THE IMPACT OF COLLABORATIVE
AGREEMENT ON FIRMS PERFORMANCESThe case of the IT industry in the 90 s
Olivier Mortehan
Bruno Van Pottelsberghe De La Potterie

IIR Working Paper WP#03-14

August 2003

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

THE IMPACT OF COLLABORATIVE
AGREEMENT ON FIRMS’ PERFORMANCESThe case of the IT industry in the 90’s
Olivier Mortehan
Bruno Van Pottelsberghe De La Potterie
August 2003
WP#03-14

THE IMPACT OF COLLABORATIVE AGREEMENT ON FIRMS’PERFORMANCESThe case of the IT industry in the 90’s

Olivier MORTEHANφ
Bruno VAN POTTELSBERGHE DE LA POTTERIEαABSTRACT: In the I.T. industry, which was confronted to a major transition phase during the 90’s,
partnerships became a strategic component of the new “divided technical leadership” which
emerged from the industry vertical disintegration. This paper attempts to evaluate the impact of
1676 partnerships on the financial performance (revenue and profit) and innovative efforts (R&D
intensity) of 14 large firms and 725 of their partners. On average, there is a positive impact ofcollaborative agreements on large incumbents and their partners’ financial performance (revenue
and income). These results vary according to the type, form and content of the agreement andaccording to the partner’s field of activity. Incumbents get the most benefit from broad scope
informal alliances while smaller and more hierarchical forms of partnerships (consortia, jointventures) do profit to their partners. For large incumbents, collaborative agreement are more
effective when performed with partners from the services industry than with partners from the I.T.
industry. The role of partnerships in such an industry transformation phase with heavy costpressure is confirmed by the negative impact of collaborative agreements on internal R&D
spending.
Keywords: collaborative agreement, R&D intensity, financial performance, alliance, partnership,
Information Technology industry.
JEL: C2, L11, L14

φSend correspondence to O. Mortehan, Solvay Business School, Brussels University (ULB), CP 135, 50 av. F.D. Roosevelt, B1050, Brussels ; email : Olivier.mortehan@ulb.ac.be.
αSolvay Business School, Brussels University (ULB), Centre Emile Bernheim and DULBEA, Solvay Chair of Innovation, email:bruno.vanpottelsberghe@ulb.ac.be. This research was partly performed when Bruno van Pottelsberghe was Visiting Professor at
the Institute of Innovation Research, Hitotsubashi University, from July to December 2003.

11.Introduction
Due to their proliferation, inter-firm relationships have received increasing attention from thescientific community during the last decade in order to explain their formation process or evaluate
their impact on firms’ performance. Most empirical investigations attempt to measure therelationship between partnerships and various financial performance indicators. In most cases,
such financial indicators are based on short term stock value evolution (in the few days following
the partnership agreement) and most empirical investigations focus on cross industry panel data.
The objective of this paper is to evaluate the impact of collaborative agreements in the I.T. industry
on firms’ financial performances and R&D investment. The contribution to this small but
burgeoning literature has several features:---by adopting business performance indicators such as firms yearly revenue andincome, it looks at the impact of collaborative agreements on partners financial
performance from a longer term perspective;by evaluating the impact of these agreements on both large incumbents and several
hundreds of smaller and more specialized firms, it shows the two faces of theeconomic implications of partnerships;
by analyzing the impact of these agreements on large firms’ propensity to invest in
R&D, it also looks et long term innovative efforts of large incumbents;
by investigating systematically whether the type of agreements, their content, andtheir importance do affect their impact;by focusing on a single industry, the Information Technology (I.T.) sector, confronted
to major technology and financial evolutions, it aims at identifying a link between
firms partnerships strategy and the evolution of their performance and positioning
during industry transformation phases.
By using an original database composed of about 1700 collaborative agreements
between 14 large incumbents and 725 small firms in the IT industry.

Such an approach has been chosen in order to test whether the growth of collaborative
agreements formed by I.T. industry leaders in the early 90’s can be interpreted as an attempt toabsorb a major industry shock characterized by vertical disintegration and the emergence of“divided technical leadership”.
The results show that the number of collaborative agreements does affect both financial and R&D
performances of large incumbents and their smaller partners. The impact of these agreements
depends on their size, type, content, and importance. These results lead to some important policy
and managerial implications.
The next section focuses on the review of the literature. We identify six broad hypotheses that are
to be validated in our empirical investigations. Section 3 concerns the empirical implementation. It
presents the econometric equations, explains the construction of the database and defines thevariables that are included in the model. The econometric results are interpreted in section 4 andsection 5 concludes.

22. Setting the hypotheses
According to Dussauge and Garette (1995), measuring the performance of an alliance is acomplex task. Multiple factors contribute to performance. Among these factors they identified thenumber of partner firms (suggesting that performance decreases when the number increases), thedegree of formalization of the alliance (supporting the idea that performance increases whenequity or legal form is involved), the relative position of partners (a strong asymmetry would reduce
the probability of a success), or the scope of the alliance (R&D, manufacturing, marketing).
The other dimension of the complexity of measuring the financial performance of alliances resides
in the choice of the performance variables: should it be based on short term indicators such as
stock price variations in the next days following the partnership or should it be made of longer term
parameters such as yearly revenue, gross margins, income, ROE, productivity per employee etc.
Opting for short-term indicators can only be justified in efficient financial markets where the initialstock market response to a key event positively correlates to the long-term performance and valueof the event. Kale et al. (2002) provide some empirical support for this efficient markets hypothesis
by establishing a correlation between both short and long term parameters.
Going to the core subject of measuring the impact of partnerships on the financial performance offirms, most of the authors find a positive relationship between both variables. Based on 345
alliances in the I.T. industry, Chan et al. (1997) show that non equity alliances add value to thestocks in the few days after announcement. Baum and Oliver (1991) and Mitchell and Singh(1996) treated firm mortality as the performance variable and showed that alliances raisedorganizational survival rates. Powell et al. (1996) find that companies which had formed many
alliances experienced accelerated growth rates. According to Stuart (2000), the greater therevenues of a high-tech firm’s alliance partners, the higher the rate of sales growth of that firm.
Sarkar et al. (2001) suggest that alliance proactiveness and firm external embeddedness lead tosuperior market-based performance, and that this effect is stronger for small firms and in unstable
market environments. It is during times when environmental changes are threatening to makeexisting sources of competitive advantage obsolete, when competitive landscapes are beingtransformed, customer demands are being redefined, and the value of existing competencies
being questioned, that proactive alliance formation is likely to create greater value.
The first objective of the present empirical investigation is to test the hypothesis of a positive
relationship between partnerships and firms financial performance in the context of the I.T.
industry during its transition phase in the 90’s.
Hypothesis 1: Collaborative agreements have globally a positive impact on the financial
performance (revenue and income) of large I.T. manufacturers and their partners during the 12 to
24 months following the partnership signature.
However, some authors find that such a positive effect is not homogenous for all types ofpartnerships. According to Hagedoorn and Schakenraad (1994) and Berg et al. (1982), short-term
negative effects can be expected from knowledge acquisition oriented joint-ventures (“technology
partnerships”) because of the investments involved, while production and marketing ventures are
expected to have short term positive effects.

3Meanwhile, the short term impact on the financial performance of firms is not enough to evaluate
the final outcome of technology alliances versus sales and marketing partnerships. Anand andKhanna (2000) stress that learning effects should be stronger in R&D partnerships than in othercategories (production or marketing) and might therefore counterbalance the short term negative
effects.
Hypothesis 2: When splitting agreements by content (sales, technology and mixed), their impact
on large I.T. manufacturers financial performance (revenue and income) might differ substantially.
Collaborative agreements might also have different impacts in function of the partner industry.
Chan et al. (1997) and Vanhaverbeke and Duysters (1997) show that partner industries influence
the outcome of partnerships differently according to the agreement content: in technology
agreements, the outcome is only positive when partners are from the same industry, while in sales
it is just the opposite; the outcome is only positive when partner industries differ.
Hypothesis 3: Agreements with partners from the I.T. industry have a negative impact on thefinancial performance (revenue and income) of large I.T. manufacturers, while agreements withpartners from the services industry have a positive impact.
Some authors compared the impact of different forms of partnerships on firms’ financial
performance and identified major differences between informal alliances and more formal forms ofpartnerships (alliances with equity participation, joint-ventures, mergers and acquisitions).
According to Harrigan (1985), rapidly changing technological developments in sectors of industry
induces the formation of somewhat more informal forms of cooperation such as non equity
agreements. On the opposite, Allen and Philips Gordon (2000) observe that block equity
purchases accompanied by agreements, alliances or joint-ventures result in significantly larger
excess stock returns compared to block purchases without partnerships. Target firms increase
investments and exhibit gains in operating cash flows after such block purchases. When block
purchases are associated with an alliance or joint-venture, those increases in investments andgains in cash flow are even larger. All these effects are bigger when target firms are in industries
with high R&D and advertising expenses.
Hagedoorn and Duysters (2000) find that mergers and acquisitions positively influence
technological performance. The more the firms are close in size, the worse the increase oftechnological performance. Restricting their analysis of partnerships impact on firm performance toa single form of collaborative agreement, joint-ventures, many authors underline their negative
impact.
Kogut (1989) observes that there is strong evidence that joint-ventures are highly unstable. Amongthe causes of instability is a lack of reciprocity between partners which are mutual hostages
through their equity contribution into the joint-venture. Geringer and Hebert, (1991) showed thatinternational joint-ventures have poor results in general. From 35% to 70% of research jointventures have negative results.
In the short term, Finnerty et al. (1986) reported that the average stock market reaction tointernational joint-venture formation announcements was not significantly different from zero. Lee
and Wyatt (1990) observed a negative average abnormal return of joint-ventures, which theyconjectured was a manifestation of agency problems. Hamel (1991), for instance, views alliances
as knowledge appropriation tools and suggest that venture stability can indicate organizational
learning dysfunctions rather than success.
4Hypothesis 4: joint ventures, and more formal alliances in general, have a negative impact on thefinancial performance of firms involved in the partnerships.
Mortehan (2003) examines a single industry, the Information Technology (I.T.) industry, andsuggests that the growth of collaborative agreements formed by industry leaders in the early 90’s
can be interpreted as an attempt to absorb a major industry shock characterized by verticaldisintegration and the emergence of “divided technical leadership”. Such incumbent firms haveadopted collaborative agreements to maintain their leadership by controlling new entrants in the
new industry segments resulting from the disintegration. Partnerships became a strategic
component of the new “divided technical leadership” market structure which emerged from thecompetitive crash of the early 90’s.
Based on a case study of the 3 leading firms of the Italian packaging industry, Lorenzoni andLipparini (1999) showed that the need to match their high standards, in terms of quality andtechnology, forced firms to concentrate on what they could do best. They studied the process ofvertical disintegration and focused on the ability to coordinate competencies and combine
knowledge across corporate boundaries arguing that the capability to interact with othercompanies accelerates the lead firm’s knowledge access and transfer with relevant effects oncompany growth and innovativeness. The ability to integrate knowledge residing both inside andoutside the firm’s boundaries emerges as a distinctive organizational capability.
Rothaermel (2001 and 2001a) analyzed the phenomenon of extensive cooperation between
incumbents and new entrants following radical technological change. Understanding how
incumbent firms may take advantage of technological discontinuities is becoming more and more
important as the new competitive landscape is characterized by an increasing speed andmagnitude of technological change (Bettis and Hitt, 1995). Incumbent survival in the face of radical
technological change has been explained by prior collaborative relationships (see Mitchell andSingh, 1996).
According to Rothaermel (2001), more research is needed to understand the phenomenon of interfirm cooperation and its subsequent impact on incumbent performance. Inter-firm networks canimprove an incumbent’s access to emerging technologies, increase opportunities fororganizational learning, and enable rapid adaptation to market and technology shifts [(Gulati(1998)] .
Mortehan (2003) provides an analysis of the financial evolution of the 14 large I.T. manufacturers
during the 1990-2000 decade showing that most of the characteristics of an industry reaching thematurity stage as described by Porter (1980) are met (slower growth of revenue, declining profit
margins, cost cuts impact resulting in SG&A reduction and lower internal R&D spending).
Bresnahan and Greenstein (1999) showed the structural changes which took place in thecomputer industry in the early 90’s, causing the industry disintegration.
Hypothesis 5: Firms involved in the agreements (large I.T. manufacturers and their partners) havedifferent outcomes from partnerships in function of their forms and types.
According to Narula (2000), firms in an industry where a dominant paradigm is establishedoutsource more technologies and make more alliances around niche technologies to be able tofollow the rapidity of change and lower costs of innovation. Hagedoorn and Duysters (1997) statethat the combination of rising R&D costs and shorter lifecycles cause firms to search foralternatives to internal development. Cooperation is often considered as a viable means to monitor
several technological developments at relatively low cost.
5Buckley and Casson (1998) observe that by establishing a network of joint-ventures covering
alternative technological trajectories, the firm can spread its costs whilst retaining a measure of
proprietary control on new technologies. The advantage of joint-ventures is further reinforced bytechnological convergence. Robertson and Gatignon (1998) suggest that the growth of R&D
externalization can be attributed, at least in part, in the United States to Congress’s passage of the
National Cooperative Research Act in 1984, which eased antitrust laws to permit collaborative
research.
Hypothesis 6: Collaborative agreements contribute to reduce the internal R&D efforts of large I.T.
manufacturers.
3. Empirical implementation
The frame of this analysis being the I.T. industry, the focus is put on the 14 largest firms of theindustry in order to keep a degree of homogeneity within the sample (Smaller firms are generally
also doing other activities than I.T. (semi conductors, communications, electronics). Matchingpartnerships made by the I.T. industry leaders during the transition period of the 90’s with theirfinancial performance can be very useful in order to test the six hypotheses. We further intend toevaluate the impact of collaborative agreements on smaller firms (most of the incumbents’partners).
Two panel datasets have been constructed. The first one is composed of the 14 large incumbents
in the industry. For most firms we have data on financial performances and the number ofcollaborative agreements over the period 1991-2000. The second panel is composed of smallerfirms and recent entrants in the industry. It is composed of 725 firms. For each of the two panels
we estimate two types of equations. The first one (see equation 1) attempts to explain financial
performances with the number of collaborative agreements (NCA). Since collaborative agreements
can take place with firms of different size, we use a second equation that intends to approximate
the “size” of the agreement, using the total sales of the partners as weight (see equation 2).

Yi ,t = ϕ t + λi + α NCAi ,t −θ + u it(1)

Yi ,t = ϕ t + λi + α WCAi ,t −θ + u it(2)

where :
N

NCAi = ∑ NCAij
j =1

N

WCAi = ∑ S j ⋅ NCAijj =1

Where Yi, t is an indicator of financial performance (total sales or total income) or R&D intensity offirm i during the year t. NCAij is the total number of collaborative agreements that a large (small)firm i has had with small (large) firm j. NCAi is the total number of agreements that firm i has
concluded with all other firms. WCAi is the weighted sum of the agreements performed by firm i.
6

The weights are the total sales value (Sj) of the partner firm. Various time lags (θ) have beentested with values of 0, 1 and 2.
The two equations include time dummies φt in order to correct for the cyclicality of the industry,
firm dummies λi (or within estimates) that allow to correct for firm-specific effects (like size), andan error term uit. All regressions are within panel data estimates performed with the STATA
software.
The database includes 1676 collaborative agreements formed by the largest firms of theInformation Technology (I.T.) industry between 1986 and 2000. Firms selected for the purpose ofthis study are the 14 I.T. largest hardware manufacturers (SIC code 357) as of their 1990-2000
rankings by revenue from both Fortune 500 and Gartner Annual Yardstick of top 100 I.T. firms
World-Wide. Firms acquired or merged during the period have also been included provided thatthe company resulting from the merger ranked among the 14 largest firms. The following firms
have been screened for their announcements of collaborative agreements: IBM, HP, Sun, Digital,
Compaq, Apple, Unisys, NEC, Xerox, Dell, Fujitsu, Siemens, Gateway, and NCR. The databasestructure is described in detail in Mortehan (2003).
Collaborative agreements have been identified via a Proquest® query searching for a broad rangeof terms (agreement, partnership, alliance, venture, merger, acquisition, relationship, collaboration)
on hundreds of newspapers and periodicals (the list is available on request, including more than1600 publications covering all regions of the World).
Considering the profile of the I.T. industry, most of the potential biases associated with thisapproach, and identified by Vonortas (1995), are less relevant. 1 Furthermore, contrary toalternative solutions such as interviewing IT firms management or getting the data from firm’s Web
sites, this approach also offers the advantage of selecting the agreements with the eyes ofneutrality which may increase the homogeneity of the database. From the viewpoint of I.T.
practitioners, to this neutrality advantage of the press one can add the positive impact of aconsiderable improvement of the I.T. press professionalism and technology expertise during thelast 20 years. Once the agreements identified with the query on press sources, their contents havebeen put into the database based on both the contents of press articles and additionally, for themost important and recent agreements, data published on Web sites of partners.
Consolidated statements of income of the 14 largest I.T. firms and 355 out of more than 700
partners have been entered in the database for the purpose of this study. They have beenobtained mainly from the US “Securities and Exchange Commission” (http://www.sec.gov/ ) orfrom “Investors relations” web sites of firms or, when not available, from Hoover’s online(http://www.hoovers.com). Table 1 here under provides a summary of the database contents usedin the paper. IBM is by far the company that performed the highest number of agreements. The
number of partners is always lower than the number of agreements. Large firms do performseveral agreements with similar partners and these agreements involve most of the time single
partners.
******** Insert Table 1 around here *********

1

small firms being underrepresented (the 14 firms of our target are all large enterprises) , too much English oriented (most of the
14 firms are US based) or announcement requirements varying per country (most I.T. press coming from US)

7

Table 2 clearly shows that collaborative agreements are differentiated by type, content andimportance (weights from 1 -less important agreements- to 6). It clearly shows that informal
alliances are the loin’s share of all collaborative agreements. In addition, a clear majority of these
agreements concern sales performances.
******** Insert Table 2 around here *********

4. Econometric results
The empirical results can be summarized in five sets of findings: (a) Collaborative agreements
have globally a positive impact on the financial performance of firms; (b) the outcome is different
for large manufacturers in function of the partnership content; (c) as well as for smaller partners;(d) partner industry is key for large I.T. manufacturers to get a positive return from theirpartnerships; (e) firms involved in the agreements (large I.T. manufacturers and their partners)
have different outcomes from partnerships in function of their forms or types (f) Collaborative
agreements contribute to reduce the internal R&D efforts of large I.T. manufacturers.
(a) There is strong empirical evidence that collaborative agreements have globally a positive
impact on the financial performance (both in terms of revenue and income) of large I.T.
manufacturers and their partners during the 24 months following the partnership signature.
******** Insert Table 3 around here *********
The first row of results in Table 3 shows that partnerships have generated a positive effect on theevolution of income and sales. This impact occurs during the first two years following theagreements. The larger the partner is, the larger the impact on firms’ economic performance.
Indeed, the estimates of equation (2), with the weighted sum of the partners’ sales as explanatory
variables, show a positive and significant impact of collaborative agreements on a large firm’ssales or income growth. In other words, the larger the partner, the larger the potential benefit of acollaborative agreement.
Regarding smaller partners, the results presented in the first row of Table 4 also show a positiveand significant impact of the number of agreements with large incumbents on their own sales orincome performances. The weighted sum model shows less significant results. From the viewpointof a small firm, all large firms are very large, and therefore the weighted sum of their sales does
not yield additional information in our model – what matters is to implement an alliance with a large
firm, not the size of the latter.
The fact that both the industry leaders and their partners do benefit from their collaborative
agreements confirms that partnerships became a strategic component of the new “divided
technical leadership” market structure which emerged during the I.T. industry transformation.
(b) When splitting agreements by content (sales, technology and mixed), their impact on large I.T.
manufacturers’ financial performance (revenue and income) differs notably. Row 3 of Table 3
shows that agreements that are exclusively oriented towards technology have no impact onrevenue and a negative impact on income, whereas both sales and mixed agreements have apositive impact on the two financial performance indicators.

8Such result questions the hypothesis formulated by Mortehan (2003) that collaborative
agreements are increasingly used by industry incumbents to improve their access to emerging
technologies while their partners get the most benefits from sales agreements to get market
coverage.
Meanwhile, the fact that mixed partnerships combining both technology and sales/marketing havea significantly positive impact on the revenue and profit of large firms strengthens the above
hypothesis. The fact that such mixed agreements have a higher average importance than either
technology or sales agreements also reinforces the argument. The poor results of technology
partnerships for large firms have three potential explanations. First, many of these agreements are
generally considered of minor importance, and would therefore not show up in our model.
Second, since these agreements are exclusively based on technology, it might take longer than
two years to reach the market. The short term impact on the financial performance of firms is notenough to evaluate the final outcome of technology alliances versus sales and marketing
partnerships. Anand and Khanna (2000) stress that learning effects should be stronger in R&D
partnerships than in other categories (production or marketing). Some explanation of thesecontradictory results can also be found in Hagedoorn and Schakeraad (1994) and Berg et al.
(1982) statements that short-term negative effects can be expected from knowledge acquisition
oriented joint-ventures (“technology partnerships”) because of the investments involved while
production and marketing ventures are expected to have short term positive effects.
Third, these poor results might also witness some kind of “innovator’s dilemma”, whereby adisruptive technology controlled by a smaller firm gradually reaps some market share of anincumbent firm. According to Christensen (2003), in the long run, the incumbent has either toacquire the small firm or adapt its own technology to survive. A related argument is suggested by
Mohanram and Nauda (1996) who provide evidence that joint-ventures are announced when theparent firms’ performance deteriorates. Firms entering in technology alliances might do so partly
because of bad financial performance causing a reversed effect between variables.
******** Insert Table 4 around here *********
It is difficult to identify which of these three potential explanations is more relevant to explain thenegative or insignificant impact of technology alliances on large firms’ economic performances.
The next results, which concern small firms, might shed some more light on this issue.
(c) When splitting agreements by content (sales, technology and mixed), their impact on smaller
I.T. manufacturers’ financial performance (revenue and income) differs to some extent:
agreements that are exclusively oriented towards technology have an overall positive impact on
economic performances and sales or mixed agreements have a positive impact if the size of thelarge firms is considered.
The third main row of Table 4 shows the impact of collaborative agreement by content on thesmaller partner firms. The results clearly show that only the number of technological alliances has
a positive impact on total revenue. The number of mixed agreements has a positive impact on thepartners’ profits whereas the impact of sales agreements shows up in the weighted sum model. In
other words, the size of the large incumbent matters more for sales agreements. The positive
impact of the number of technology agreements on the total revenue of partners suggests that thethird explanation, the disruptive technology effect, might be of importance when attempting tounderstand the poor results of these agreements on large firms’ revenues or profit.

9

(d) There is evidence that agreements with partners from the I.T. industry have a negative impact
on the financial performance (revenue and income) of large I.T. manufacturers, while agreementswith partners from the services industry have a positive impact.
Row 5 of Table 3 shows that the number of agreements with partners from the I.T. industry has anegative impact on the financial performance (revenue and income) of large I.T. manufacturers.
This result validates the results obtained by Chan et al. (1997) and Vanhaverbeke and Duysters(1997). These authors show that in sales partnerships the outcome is positive mainly when partner
industries differ.
The fact that the contribution of partnerships with services firms is significantly positive on thefinancial performance (revenue and income) of large I.T. manufacturers confirms the importancefor such market leaders to shift from low margin hardware segments to high-profitability
professional services. This has been a well-known strategy for most firms in the 90’s and our
results confirm its effectiveness.
(e) Firms involved in the agreements (large I.T. manufacturers and their partners) have different
outcomes from partnerships in function of their forms or types:
Row 2 of Table 3 and Table 4 show that large I.T. manufacturers do benefit from informal alliances(83% of all observed agreements) while joint ventures and alliances with participations have arather negative impact on their financial performance; on the opposite, their partners get most of
their return from more formal types of partnerships (consortia, acquisitions, alliances withparticipation and joint ventures,)
One explanation is that large firms do not need hierarchical forms of partnerships to benefit fromcollaborative agreements as they have a high negotiation power. They are able to control the
execution of the deal and therefore get most of the benefits. On the opposite, the fact that theirpartners, which are mostly small firms and new entrants, do need structured forms of partnerships
to get the most benefits from it is quite understandable: hierarchical forms such as joint-ventures
or alliances with equity participation protect them against the dominance of the large I.T. firms
involved in the partnerships.
The positive impact of consortia for partners sounds logic. By entering into consortia led by large
incumbents to promote industry standards, new entrants may get the necessary recognition for
their technology and products or services.
Strategic partnerships (weight 3 to 6) contribute to improve large I.T. manufacturers’ revenue andhave a negative impact on their partners’ revenue while less important agreements (weight 1 to 3)
have a positive contribution on partners’ sales.
Large firms do probably make more use of their power to influence the outcome and get most of
the results to them in strategic partnerships than in less important collaborative agreements (seerow 4 of Table 3 and Table 4). It must be noticed that in many cases the degree of importance of apartnership is directly linked with the contribution of the large firm involved. As a consequence,
small firms may only get control of less important partnerships which probably explains why they
benefit mainly from such minor agreements.
******** Insert Table 5 around here *********

10

(f) There is strong evidence that collaborative agreements contribute to reduce the internal R&D
efforts of large I.T. manufacturers: their negative impact on the R&D intensity (R&D
spending/revenue) is particularly visible in the case of acquisitions and/or mixed agreements.
Two complementary reasons can be put forward in order to explain these results (see Table 5):-

On the one hand, the standardization and emergence of a dominant technology in
the I.T. industry stimulates the formation of technology agreements (cooperation andexchange of information being easier and less risky). Therefore the aggregate –hence individual- innovative efforts can be reduced through the exploitation of otherfirms’ knowledge. Within the context of an acquisition, the drop of aggregate R&D
outlays is straightforward and clearly appears in our results.

-

On the other hand, firms confronted to declining profit margins need to cut costs by
all means and see partnerships as a way to reduce their internal R&D spending(R&D collaborative agreements allow to share costs and provide more flexibility toadjust R&D efforts in such an unstable technology environment)

7. Conclusions
This paper aimed at quantifying the impact of collaborative agreements on large I.T.
manufacturers and their partner’s financial performances (revenue and profit). The empiricalresults provide strong evidence that collaborative agreements played a key role during the I.T.
industry transformation phase of the 90’s. Firms involved in such partnerships got a positive returnfrom them at three levels:-

they contributed to maintain or improve revenue levels;
they had a positive impact on income levels which was vital in such a context ofnegative results;
they induced firms to reduce their internal R&D spending which was also critical insuch a transition period with heavy cost cuts and a clear trend towards
standardization.

However, a more in-depth analysis shows that all collaborative agreements are not automaticallybeneficial to large I.T. manufacturers or their partners. For instance, partnerships which are
exclusively technological (as opposed to sales/marketing or mixed agreements) show mainly aweak or negative impact on large I.T. incumbents’ revenue and a positive impact on their smaller
partners. These results might witness the emergence of some disruptive technologies leading toan innovator’s dilemma regarding large I.T. manufacturers.
In addition, collaborative agreements with partners from the services industry were much more
fruitful than collaborative agreements with partners from the I.T. industry, suggesting that alliancestargeting complementary competencies are more beneficial to large incumbents.
Relative negotiation power and information asymmetry also play an important role in setting up anagreement between a large incumbent and his partner. Formal agreements (alliance withparticipations, joint venture, acquisition and consortia) benefit more the partner firms, whereas
informal partnerships benefit more to the large incumbents.
In a nutshell, and on average, traditional I.T. leader firms successfully used collaborative
agreements to keep their leadership and control new entrants in the new segments resulting from11the industry disintegration. Results showing positive outcomes of partnerships for their partners
indicate that collaborative agreements are a win-win solution for both firms involved. Partners mostprobably used collaborative agreements to facilitate their market penetration and get recognition
for their technology. Nevertheless, paying a special attention on the type, form and content of a
new alliance would significantly improve its potential underlying benefits.

12

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15

TABLE 1. Number of collaborative agreements by the 14 largest I.T. firms
Agreements per firm
IBM
HP
Sun
DigitalCompaqApple
Unisys
Xerox
NEC
Dell
Fujitsu
Siemens
Gateway
NCR
Total
Average per firm

Nationality
USA
USA
USA
USA
USA
USA
USA
USA
Japan
USA
Japan
Germany
USA
USA

# of
partnerships
595
271
162
139
123
98
67
49
44
41
39
23
16
9
1676
120

# of partners
377
167
104
102
80
63
57
38
37
36
31
17
15
8
81

nr
Average for small firms
2.16
1.46
Sources: own computation, from 1676 collaborative agreements performed during the period (1986-2000)

16

TABLE 2. Collaborative agreements by type, content and weights (1986-1999)# of partnerships/ per
Sales
Technology
Mixed
Total
content :Agreements per type
Acquisitions
27
5
45
77
Alliances
827
390
191
1408
Alliances with participation
23
19
7
49
Consortia
2
77
79
Joint ventures
17
24
22
63
Agreements per weight
1
106
11
4
121
2
355
105
41
501
3
320
279
116
715
4
91
111
75
277
5
20
8
22
50
6
4
1
7
12
896
515
265
1676
Agreements per content
Sources: own computation, from 1676 collaborative agreements performed during the period (1986-2000)

17

TABLE 3: PARTNERSHIPS AND FINANCIAL EVOLUTION OF 14 I.T. LARGE FIRMS1

Dependent variable
1. All partnerships

R2
Agreements by type
Acquisitions
Alliances
Alliances with participation
Consortia
Joint ventures

R2
Agreements by content
Sales/marketing agreements

Dummies
Sales
Income
t+1
t+2
t+1
t+2
522.73*523.78*-6.22
48.77*(9.78)(9.79)(-0.36)(2.73)
0.33
0.33
0.18
0.29

Weighted sum
2
Sales
Income
t+1
t+2
t+1
t+2
0.28*
0.25*
2.02
8.55*
(4.57)(3.72)(0.79)(3.17)
0.21
0.18
0.15
0.36

260.31(0.36)
400.64*(5.56)
131.74(0.18)
176.16(0.39)-1090.49*(-2.02)0.27

243.35*(2.01)
Technological agreements-7.21(-0.04)Mixed agreements
1116.11*(4.24)
0.28
R2
Agreements by importance level
Weight 1
77.58

91.60
(0.12)
358.55*(4.46)
235.95(0.30)
270.37(0.56)a-1021.21(-1.64)0.23

91.88(0.33)
36.55(1.33)-1,236.6*(-4.36)-135.49(-0.79)-753.63*(-3.66)0.04-358.69(-1.14)97.06*(3.06)-1,258.0*(-4.00)-23.57(-0.12)-59.15(-0.24)0.32

0.30(0.38)
0.39*(4.82)-0.32(-0.48)0.23(0.62)-0.54*(-1.71)0.21

0.43(0.53)
0.33*(3.68)-0.63(-0.93)0.50(1.31)-0.37(-1.10)0.19

28.20(0.84)
2.31(0.67)-0.58(-0.02)18.60(1.18)-12.70(-0.93)0.16

44.10(1.34)
8.18*(2.19)-8.33(-0.30)12.90(0..82)10.40(0.75)
0.38

227.51*(1.70)-2.43(-0.01)985.57*(3.30)
0.25

105.01*(2.19)-399.89*(-6.22)239.73*(2.30)
0.03

122.20*(2.20)-174.22*(-2.40)284.52*(2.30)
0.31

0.28*(2.54)
0.12(0.90)
0.69*(3.95)
0.21

0.16(1.35)
0.23*(1.73)
0.86*(3.86)
0.19

13.70*(3.11)-19.6*(-3.88)20.50*(2.94)
0.18

9.30*(1.93)
3.98(0.74)
25.70*(2.82)
0.38

26.30-302.30a-182.93

0.20

0.01

-101*

(0.37)
a0.25(1.65)
a0.19(1.65)
0.60*(3.96)-0.74(-1.27)1.22(1.42)
0.21(0.02)
0.27(1.30)
0.18(1.43)
0.54*(3.48)-1.20*(-2.03)
1.78*(2.07)
0.19(-5.15)21.70*(3.94)
a-7.07(-1.65)10.10*(1.83)-77.2*(-3.63)32.30(1.03)
0.23

71.40*(-3.02)19.50*(2.34)
8.67*(1.77)
8.47(1.38)-28.00(-1.19)45.50(1.32)
0.40-1.25*(-2.74)
16.66(1.25)
0.06(0.33)
0.08(0.41)
0.41*(2.83)-1.09(-1.07)9.14

385.79(0.50)
11.34(0.74)
0.06(0.31)
0.09(0.41)
0.38*(2.38)-0.40(-0.35)26.50

35.70*(1.73)
632.70(1.05)-9.81(-1.27)
5.53(0.61)-6.39(-0.98)52.30(1.14)
702.7054,587(1.75)a-912(-1.47)2.43(0.31)
16.00*(1.76)
2.07(0.32)
35.00(0.76)
350.10(0.34)
2.07*(5.29)
0.20
173(0.83)
1.16*(2.27)
0.17
159(0.58)
10.70(0.61)
0.29
173(0.27)
45.70*(2.22)
0.45
159(0.17)(0.06)(-1.46)(-0.85)275.88*45.27-41.68-32.34(1.68)(0.24)(-0.58)(-0.38)Weight 3
257.05*237.67*-0.81
114.41*(2.04)(1.91)(-0.02)(2.03)
Weight 4
681.90*997.11*54.18
134.64(2.11)(3.10)(0.38)(0.93)
Weight 5-1380.01*-2,499.11* -263.39-307.45(-1.72)(-3.14)(-0.75)(-0.85)Weight 6
3382.76*
4,232.85*-209.47-193.72(1.99)(2.51)(-0.28)(-0.25)
0.29
0.24
0.00
0.32
R2
Agreements by partner industrya400.52432.93
1,150.35 1,967.55
Mining, oil extraction(0.13)(0.14)(1.01)(1.46)
Construction
10,532.75* 11,460.61* 211.11-865.25(2.51)(2.65)(0.14)(-0.48)Manufacturing
112.74389.42-157.09-67.10(0.37)(1.22)(-1.39)(-0.50)a-85.19-319.07*-185.81*
Computers-362.91(-1.47)(-0.33)(-3.46)(-1.70)Transport, comms oil & gas
520.84441.88240.0655.00(1.00)(0.83)(1.24)(0.24)
Wholesale & retail trade-305.17178.34-1,030.1* -649.96*(-0.42)(0.23)(-3.75)(-1.95)a801.00Finance, insurance & real 117.02
145.31-679.29
est.
(0.10)(0.11)(-1.54)(1.43)
Services
970.81*745.69*177.72*98.12(4.60)(3.30)(2.25)(1.02)
0.21
0.26
0.01
0.08
R2
Nobs
173
159
173
159
1. Within estimates, see equations (1) and (2), all equations time dummies.
6
2: original data x 10
Weight 2

18

TABLE 4: PARTNERSHIPS AND FINANCIAL EVOLUTION OF PARTNER FIRMS1
Weighted sum1

Dummies
Sales
t+1
t+2
559.16* 593.29*(4.59)(4.58)
0.02
0.02

Incomet+1
t+2
188.3*-77.0(1.97)(-0.96)
0.01
0.00

Sales
t+1
t+2
5.21*
5.05a
(1.92)(1.64)
0.01
0.01

Income2
t+1
t+2
0.13
3.05
(0.06)(1.28)
0.00
0.00

799.45(0.99)
157.94(1.28)
1939.7*(1.99)
2658.7*(3.80)
1302.3*(1.83)
0.02-1158.0(-1.33)180.67(1.32)
3196.2*(3.14)
1816.5*(2.39)-994.6(-1.3)
0.01

1101.6*(1.84)-7.98(-0.09)-31.50(-0.04)481.82(0.95)-141.03(-0.27)0.00

3644.8*(5.42)
116.86(1.11)
293.8(0.38)
390.4(0.67)-322.4(-0.54)0.02-17.42(-0.75)3.00(1.07)
8.38(0 .37)78.79*(5.48)
13.02(1.04)
0.02-22.20(-0.94)5.36*(1.68)
11.59(0.42)
11.05(0.55)
0 .64(0.05)
0.01

20.72(1.20)-0.23(-0.1)-16.02(-0.9)
19.69*(1.85)-0.57(-0.0)
0.00

42.35*(2.32)
2.76(1.12)
4.22(0.20)
6.44(0.41)
0.28(0.03)
0.00

11.57(0.07)
Technological agreements
837.11*(3.90)
Mixed agreements
34.29(0.99)
0.02
R2
3. Agreements by importance level
Weight 1
699.66(1.22)
Weight 2-110.36(-0.48)Weight 3
640.95*(3.28)
Weight 4-114.40(-0.35)Weight 5
1423.8*(1.78)
Weight 6-4014a(-1.56)

4.93(0.03)
456.62*(2.02)
530.83(1.33)
0.02-93.99(-0.73)7.59(0.05)
522.64*(2.04)
0.00-156.26(-1.05)210.93(1.21)
1409*(4.62)
0.01

11.61*(1.74)-4.17(-1.02)15.40*(3.17)
0.02

17.31*(2.40)-2.06(-0.42)
6.90(1.27)
0.01

9.69*(1.98)-3.85(-1.3)
0.52(0.15)
0.00

18.97*(3.41)-7.03*(-1.9)
6.67a(1.60)
0.01

148.86*(2.33)-326.11(-1.32)586.38*(2.73)-476.87(-1.33)1105.3(1.32)-2441(-0.95)203.95(0.48)
61.66(0.36)-0.12(0.00)-46.35(-0.19)60.86(0.10)
422.18(0.22)

167.48(0.34)
385.33*(2.01)-57.21(-0.35)376.44(1.36)
777.1(1.19)
1684.2(0.85)

12.00(0.84)-9.96*(-1.8)
16.25*(4.14)-4.98(-0.7)-58.8*(-1.8)-79.6*(-1.9)

25.72a(1.50)-10.8*(-1.7)
18.05*(4.06)-18.2*(-2.4)-10.24(-0.3)-62.2a(-1.5)

7.21(0.68)-4.30(-1.1)
2.19(0.75)-1.09(-0.2)
7.98(0.34)
18.66(0.60)

9.45(0.71)
1.56(0.32)
1.23(0.36)
9.83(1.67)
16.29(0.64)
33.74(1.03)

0.01
1742

0.00
1903

0.01
1764

0.01
1609

0.01
1268

0.00
1634

0.00
1293

Dependent variable: ($m)
1.All partnerships

R2
1. Agreements by type
Acquisitions
Alliances
Alliances with participation
Consortia
Joint ventures

R2
2. Agreements by content
Sales/marketing agreements

R2
nobs

0.01
1880

1. Within estimates, see equations (1) and (2), all equations include time dummies.
2. original data x 109

19

TABLE 5: PARTNERSHIPS AND INTERNAL R&D OF I.T. LARGE FIRMS1

2

Dependent variable: R&D intensity

Number

1. All partnerships-2.80*(-1.91)
0.11

Weighted sum-0.30*(-1.93)0.13-87.16*(-4.64)-1.70(-0.92)3.84(0.20)
6.42(0.55)
8.90(0.64)
0.09-7.03*(-3.49)-0.28(-1.37)-0.22(-0.13)0.20(0.21)-0.84(-1.02)0.12

0.40(0.12)-1.59(-0.36)-15.73*(-2.17)0.09-0.23(-0.77)-0.07(-0.21)-1.20*(-2.58)0.12

R2
2. Agreements by type
Acquisitions
Alliances
Alliances with participation
Consortia
Joint ventures

R2
3. Agreements by content
Sales/marketing agreements
Technological agreements
Mixed agreements

R2
4. Agreements by importance level
Weight 1

2.47(0.20)
Weight 2-2.34(-0.53)Weight 3-7.56*(-2.21)
Weight 4
8.59(0.98)
Weight 5-20.92(-0.98)Weight 6
51.28(1.15)
0.10
R2
nobs
195
1. Within estimates, all equations include time dummies.
4
2: original data x10

20

2

0.48
(0.33)-0.08(-0.20)a-0.50(-1.59)-0.32(-0.79)0.54(0.34)-1.05(-0.46)
0.12
195